vol 2 no 1
TRANSCRIPT
Contents
RESEARCH Page
Automated Service Quality and Its Behavioural Consequences in CRM Environment: A Structural Equation Modeling and Causal Loop Diagramming Approach Arup Kumar Baksi, Bivraj Bhusan Parida
Towards Robust Forecasting of Demand for Water in Crop Production Swagato “Ban” Banerjee, Babatunde A. Odembe
Financial Inclusion and Its Determinants in India: An Inter-State Analysis Arindam Laha, Amar Nath Das, Pravat Kumar Kuri
Exploring the Significance of Physical Resources in B-School Selection Decision in West Bengal Subrata Chattopadhyay, Saumya Singh
Manufacture Owned Brand Vs Private Label Brand: Where Does the Buying Wind Blow? Isita Lahiri
Implication of Single and Multiple Reasons for the Preference of Tourists: Empirical Evidence from Sikkim Debasish Batabyal
Performance Analysis of Select Mutual Fund Schemes: A Study in Context to the Role and Effect of Mutual Funds in the Recent Global Economic Meltdown Soheli Ghose Banerjee
INTERVIEW
Apparel Companies have to move from their focus on making and selling huge quantities of cheap, throw-away products to making and selling fewer products from new materials that provide long-term value to consumers Marsha A. Dickson
BOOK REVIEW
POOR ECONOMICS rethinking poverty & the ways to end it
Abhijit Roy
Automated Service Quality and Its Behavioural Consequences in CRM
Environment: A Structural Equation Modeling and Causal Loop Diagramming
Approach
Arup Kumar Baksi Assistant Professor Department of Management Science, Bengal Institute of Technology & Management, Santiniketan, West Bengal, India Bivraj Bhusan Parida Reader & Head Department of Tourism Management, The University Of Burdwan
Abstract
Information technology induced communications (ICTs) have revolutionized the operational aspects of service sector and have triggered a perceptual shift in service quality as rapid dis-intermediation has changed the access-mode of services. ICT-enabled services modified the perception of service quality with renewed dimensions. This perceptual shift in service quality proved to be a point of academic interest towards analyzing its effect on behavioural outcomes of customers. Customer Relationship Management (CRM) has emerged as an offshoot to automation breakthrough as it ensured service-encapsulation by integrating people, process and technology. This paper attempts to explore the relationship between automated service quality and its behavioural consequences in a relatively novel business-philosophy – CRM with an empirical introduction to the concept of CRM indexing based on CRM component performance. The study used structural equation modeling (SEM) to justify the proposed model construct and causal loop diagramming (CLD) to depict the negative and positive linkages between the variables.
Key words: automated service quality, CRM, structural equation model, bank, ICT, behavioural consequence
Introduction
The banking operation in India has undergone a total transformation with the introduction of technology. The
conventional unidimensional service market trinity got converted to a three dimensional interactive model with
service providers (banks), service employees (bankers) and customers interacting with each other through
technology. The knowledge, skill and behaviour of service employees, considered as internal customers, remained
critical while perceiving service quality; although automated banking services ensured disintermediation to a large
extent (Khan and Mahapatra, 2009). Conventional service quality concept has also metamorphosed with
operational efficiency, security and confidentiality of information stored, reliability, accuracy and speed of
transactions, virtual interfaces, IVR etc. being considered as major quality dimensions. Customers are demanding
new level of convenience and flexibility in addition to powerful and easy-to-use financial management tools,
products and services that conventional banking operations could not offer (Hanzaee and Sadeghi, 2010). Studies
conducted by Ravi et al (2007) revealed that automated banking transactions in India is still at its nascent stage
with private sector banking responding and adapting earlier to these changes (Malhotra and Singh, 2007). It was
only in the extreme later half of 1990s that the nationalized public sector banks in India decided to shade-off its
silos-based operational legacy and upgrade themselves to the digital platform. This shift of paradigm was further
stimulated by the recommendations of Rangarajan committee to initiate automation in banking operations. The IT
Act of 2000 of Govt. of India provided a legal recognition to electronic banking transactions with RBI establishing a
work-group to supervise and monitor issues such as security and technology, legal and control and supervision.
Automated banking, for a considerable period of time, was an activity constrained to the metros and big cities in
India. Phenomenal penetration of technologies and its convergence paved the path for banking service automation
in semi-urban and rural areas of India also. The probable two behavioural consequences of service quality which
are factor-prime for service organizations like banks are customer loyalty and propensity to switch because both
these phenomenon are linked to profitability. With the competition becoming fierce, customer loyalty and
favourable behavioural consequences have emerged as two potential defensive tools for the banks. The recent
adoption of Customer Relationship Management (CRM) as a business philosophy saw the banks developing better
proactive strategies to ensure better personalization and customization of service delivery.
This paper attempts to explore the probable impacts of automated service quality on behavioural intentions of
customers in a CRM dominated environment of a bank. The rationale behind choosing SBI has been the
completion of their decade long modernization and up-scaling of their operation from a legacy dominated silos-
based customer transaction to a electronic banking format and being the largest nationalized bank in India its
geographical penetration and bank branch networking (availability of services). The organisation of this study
following the ‘Introduction’ has been done as: review of literature, research model and formulation of hypotheses,
methodology, data analysis and interpretation and conclusion with limitations of the study and future research
prospect.
Review of literature
Service quality was one of the most critical issues in maintaining sustainable relationship with the customers (Peng
and Wang, 2006). Researchers, over the years, explored and conducted a number of empirical works to
understand the nature of service quality, its dimensions and dynamics and probable ways to enhance the
perceived service quality (Cronin and Taylor, 1992, 1994; Rust and Zahorick, 1993; Avkiran, 1994, Kearns and
Nadler, 1992; Parasuraman et al, 1985, 1988, Julian and Ramaseshan, 1994, Llosa et al, 1998, Crosby and Stephens,
1987). Contemporary research works have also highlighted the independent effect of perception on service quality
evaluations and have questioned the use of disconfirmation paradigm (Parasuraman et al, 1994, Oliver, 1981) as
the basis for the assessment of service quality (Carman, 1990; Bolton and Drew, 1991a; Babakus and Boller, 1992,
Cronin and Taylor, 1992). Grönroos (1982) described service quality as a customer’s perception of difference
between the expected service and the perceived service. The study of service quality was pioneered by
Parasuraman, Zeithaml and Berry (PZB), who developed the gaps framework in 1985 and its related SERVQUAL
instrument (Parasuraman, Zeithaml and Berry 1985, 1988, 1991) whereby five dimensions of service quality were
proposed namely tangibles, reliability, responsiveness, assurance and empathy. The transition of service delivery
system from employee-customer interaction to employee-technology and technology-customer interactions
included a new dimension in service delivery mechanism and vis-à-vis perceived service quality (Alkibsi and Lind,
2011). Technology integration in services has empowered the customers to enjoy a degree of autonomy and has
reduced the burden of non-monetary cost, mainly psychological in nature, to a great extent. Henderson et al
(2003) was of the opinion that automated service provides organisation to introduce new models for service
design and development. Ruyter et al (2001) defined automated service as interactive, content-centered and
internet-based customer service driven by the customer and integrated with the related organisation customer
support process and technologies with the goal of strengthening the customer-service provider relationship.
Parasuraman et al (2005) viewed automated services as web-based services while Buckley (2003) conceptualized
automated services as electronic provision of services to a customer. Automated service quality has been
identified by Santos (2003) as consumers’ evaluation of e-service quality in a virtual market place.
The banking system adopted the automated service delivery process and went one step further to focus on
convergence of technologies to provide a customer more than one channel to access in service delivery process. It
was argued by Joseph and Stone (2003) that service delivery quality is a critical element in the success of service
transactions and to a great extent influence customer satisfaction and retention. Introduction of automated
banking services triggered changes in consumer behaviour, consumer perception towards banking service quality,
innovation in service delivery system, channel integration, communication and relationship marketing which
received adequate emphasis on behalf of the academic researchers (Laforet and Li, 2005; Gerard and Cunningham,
2003; Hernandez and Mazzon, 2007; Wolfinbarger and Gilly, 2002; Yang et al, 2004, Mukherjee and Nath, 2003).
Banking, which was conventionally a high contact service, the disintermediation with the introduction on
technology, was considered to be critical towards establishing quality perception in the minds of the customers
(Broderick and Vachirapornpuk, 2002). Dhabolkar ((1994) argued that the automated channels made customer
participation in service delivery process more intense. A number of researchers considered ATM, internet banking,
telephone/mobile banking as the principal automated service delivery channels (Dabholkar, 1994; Meuter et al,
2000; Szymanski and Hsiech, 2006; Radecki et al, 1997). Dabholkar (1996) concluded that these three major
electronic/automated service channels were frequently accessed by the bank customers in combination with each
other which was further considered to be a relationship-building platform (Lans and Colgate, 2003; Patricio et al,
2003; Ramsay and Smith, 1999). In a comparatively recent study Lin and Hsiech (2006) investigated factors that
affect customers’ perception of service quality within the domain of self-service technologies and identified seven
dimensions of automated service quality – functionality, enjoyment, security, assurance, design, convenience and
customization. Quite a few researchers explored automated service quality dimensions and subsequently
developed models to assess service quality such as SITEQUAL (Yu and Donthu, 2001), WEBQUAL (Loiacono, Watson
and Goodhue, 2002), eTailQ (Wolfinbarger and Gilly, 2002), E-SERVQUAL (Zeithaml, Parasuraman and Malhotra,
2005) SSTQUAL (Lin and Hsiech, 2006). Al Hawari, Hartley and Ward (2005) developed the concept of Automated
Service Quality Index (ASQI) by highlighting five factors – ATM service quality, telephone banking, internet banking
services, core service quality and customer perception of service quality. In a study conducted by Al Hawari and
Ward (2006), it was concluded that the three major automated service channels used by the banks to deliver
services are significantly related to customer retention thereby providing the researcher cues to conclude a
possible behavioural intention link to automated service quality. In a study conducted by Hanzaee and Sadeghi
(2010) it was observed that accuracy, reliability, image, impression of the bank management and website design
were significantly correlated to customer satisfaction.
Superior service quality leads to favorable behavioral intentions, leading to retention and subsequent generation
of revenue, increased spending, payment of price premiums, and generation of referred customers (Zeithaml et
al., 1996). Excellent service is a profit strategy because the results include new customers, increased business with
existing customers, fewer lost customers, more cushioning from price competition and fewer mistakes requiring
the services to be repeated (Berry et al., 1994). Listening to the customer is a part of providing excellent service.
Inferior service quality leads to unfavorable behavioral intentions which lead to customer defection from the
organization which leads to decreased spending, lost customers, and increasing costs associated with attracting
new customers (Zeithaml et al., 1996). Customer switching behavior can damage market share and profitability.
Switching can cost an organization the customer’s future revenue stream (Keaveney, 1995). Evidence that
customer loyalty makes an organization more profitable makes it imperative that complaints and other
unfavorable behavioral intentions are handled effectively to ensure the stability of these relationships (Tax &
Brown 1998a). Managers of service firms should know that some customers would switch services even when they
are satisfied with a former provider (Keaveney, 1995). Zeithaml et al. (1996) highlighted the behavioural
consequences of service quality and proposed a comprehensive, multi-dimensional framework of customer
behavioural intentions, nomenclated as Behavioural Intentions Battery (BIB), to be used in the service industry.
The framework consists of 13-items across five dimensions namely loyalty to organisation, propensity to switch,
willingness to pay more, external responses to a problem and internal responses to a problem.
The automation of bank’s operational aspects was not restricted to technological upgradation alone as it paved
way for a novel business philosophy – Customer Relationship Management (CRM). Customer Relationship
Management (CRM), defined by Nguyen et al (2007), is an information system that enables organizations to track
customers’ interactions with their firms and allows employees to extract customer-based information namely
history of sales, unresolved problems, payment records, service records etc. Customer Relationship Management
(CRM) has been argued to replace the traditional 4Ps of marketing (product, price, place and promotion) concept
as a dominant logic in marketing process (Guraˇu, 2003) and refers to all business activities directed towards
initiating, establishing, maintaining, and developing successful long-term relational exchanges (Heide, 1994;
Reinartz & Kumar, 2003). Gradual polarization of marketing process towards a relationship base was found to be
dyadically more effective in establishing mutually profit-benefit transactions between sellers and buyers
respectively. The scholastic debate sprung a number of views about the domain of CRM – some researchers view
CRM as a mere software based application, therefore emphasizing on the process part; while others consider CRM
as a philosophy which aims to translate customer intimacy into profit (Yueh et al, 2010, Soon, 2007; Nguyen et al,
2007 & Eric et al, 2006). Subsequent research works have highlighted CRM as an integration of people, process and
technology, targeted to bring firms closer to customers. Reynolds (2002) identified three key processes which
brought companies closer to customers and vice-versa:
(a) Data-enabling product-centric processes
(b) Customer-centric processes
(c) One-to-one philosophy
Empirical research works pointed out, time and again, towards the mutual and symbiotic benefits both for the
sellers and customers (Dekimpe, Steenkamp, Mellens & Abeele, 1997). In a study Paul Gray and Jongbok Byun
(2001) viewed CRM as a continuous flow of corporate changes in culture and processes that combines three focal
areas: (i) Customer (ii) Relationship and (iii) Management. Richard Barrington (2008) viewed that CRM systems
evolved as a system to track customer interactions with an objective to offer customized products and services to
the customer. With this introduction of hyper-customized products and services, particularly in the cross-selling
and up-selling domains of a financial service organization, the customer needs and desires have undergone a sea
change. CRM Guru (2006) conducted a study which was subsequently reported by Judith Sandall (2007), with
regard to this growing complexity in customer need identification. Grabner-Kraeuter and Moedritscher (2002)
point to the lack of an adequate CRM strategic framework from which to define success as being a reason for the
disappointing results of many CRM initiatives. One of the major reasons for CRM failing to deliver goods is
overemphasis on technological aspect by ignoring the ‘people’ and the process part. Buttle (2001) provides a CRM
value chain. One of the results of CRM is the promotion of customer loyalty (Evans & Laskin, 1994), which is
considered to be a relational phenomenon (Chow & Holden, 1997; Jacoby & Kyner, 1973; Sheth & Parvatiyar, 1995;
cited by Macintosh & Lockshin, 1997). The benefits of customer loyalty to a provider of either services or products
are numerous, and thus organizations are eager to secure as significant a loyal customer base as possible (Gefen,
2002; Reinartz & Kumar, 2003; Rowley & Dawes, 2000). The idea that one cannot have a profitable relationship
with all customers and the practice of targeting customers with a differentiated product or service is already
widespread in many financial services, e.g. banking, insurance, credit cards etc.
Review of literature revealed that while academic research works were carried out substantially to identify the
dimensions of automated service quality, not much of emphasis was given to explore the probable linkage
between perceived automated service quality and behavioural consequences of customers in a CRM dominated
business environment.
Constructs Development of Customer Relationship Management Index (CRMI)
Peffers and Dos Santos (1996) developed a process for measuring the impact of information technology, more
specifically ATM services, on market share and overall performance of a bank using an S-shaped logistic model:
btae
my
1
where y is the benefit of the technology application at time t, m is the upper bound on the benefits of the
application, and a and b are constants that determine the shape of the curve. Similar kind of logic can be used in
computing Customer Relationship Management Index (CRMI) whereby it is assumed that CRMI will improve
with the improved performance of CRM components (CRMCP). The impact of CRMCP performance at time ‘t’ is
proportional to the CRMI gained at time t-1 (CRMIt-1) relative to maximum possible gains from the CRMCP
performance (i.e. 1) and the remaining CRMI is yet to be gained (i.e 1 - CRMIt-1). It can be represented as (over
time t):
)1( 1tCRMICRMCPdt
dCRMI --------- 1
where CRMCP is a term denoting efficiency of performance in delivering services for a service provider. Solving
equation-1 for CRMI:
tCRMCPae
CRMI1
1 ----------- 2
Equation-2 represents a S-shaped logistic model where 1 is the upper-bound on the CRMI from the CRMCP
performance. It is assumed that the constant ‘a’ is zero because each service provider is supposed to initiate CRM
induced services with a negligible CRMI. Therefore equation for CRMI is developed as:
tCRMCPe
CRMI1
1 ----------- 3
The term CRMCP is a function of the relative weight of the eigenvalue (RWE) of each CRM components multiplied
by the average factor value (AVF) of the corresponding CRM component.
332211 CRMCPCRMCPCRMCPCRMCPCRMCPCRMCP AVFRWEAVFRWEAVFRWECRMCP
Where, CRMCP1 = People dimension
CRMCP2 = Process dimension
CRMCP3 = Technology dimension
Research Model and Formulation of Hypotheses
Based on the review of literature this paper attempts empirically to explore possible linkages between perceived
automated service quality (PASQ) and behavioural intentions (BI) for bank customers in a Customer Relationship
Management (CRM) environment. The proposed research model is depicted in Fig.1 below:
Fig.1: The research model
Accordingly it is hypothesized that:
H1 : Behavioural intention (BI) is dependent on perceived automated service quality (PASQ).
H01: Behavioural intention (BI) is independent of perceived automated service quality (PASQ).
H2: Perceived automated service quality (PASQ) is influenced by CRM components
H02: Perceived automated service quality (PASQ) is uninfluenced by CRM components
H3: Customer loyalty is influenced by perceived automated service quality (PASQ).
H03: Customer loyalty is uninfluenced by perceived automated service quality (PASQ).
EFFI
SYST.
AVAIL FULFILL
PRIVACY
RESPON
COMPEN
CONTAC
PASQ
BI (+)
INTR
LOY
W2PM
EXTR
P2S
BI (-)
PEOPLE PROCESS TECHNO.
H4: Propensity to switch is influenced by perceived automated service quality (PASQ).
H04: Propensity to switch is uninfluenced by perceived automated service quality (PASQ).
H5: Aggregate perceived automated service quality (∑PASQ) is influenced by CRM components’ performance
(CRMI).
H05: Aggregate perceived automated service quality (∑PASQ) is uninfluenced by CRM components’ performance
(CRMI).
Methodology
The objectives of this study were to investigate the impact of automated service delivery channels (perceived
automated service quality) on behavioural intentions (BI) of customers, to suggest a model to fit the relationship
using SEM approach and to identify the nature of relationship between the variables using Causal Loop
Diagramming (CLD). The study was conducted in two phases. To carry out this study, State Bank of India (SBI), the
largest nationalized public sector bank in India was selected primarily because of its intensive branch network
(availability of services) , its upgradation to digitized platform towards service delivery and its adoption of CRM
philosophy. A structured questionnaire was developed to obtain the primary data. The questionnaire had four
sections. Section-I asked questions about customers’ perception of automated service quality, section-II dealt with
placing questions with regard to behavioural intentions of the customers, section-III targeted customer response in
context with CRM components and their performance and section-IV attempted to collect the demographic profile
of the customers. E-SERVQUAL scale developed by Zeithaml, Parasuraman and Malhotra (2005) was used to
generate response about customers’ perception of automated service quality across both the core and recovery
dimensions. To obtain response with regard to behavioural intentions of customers as an output to customer
satisfaction, the Behavioural Intention Battery (BIB) developed by Zeithaml et al (1996) was used. The respondents
were asked to rate the statements related to automated banking service channels over a 7 point Likert scale
(Alkibisi and Lind, 2011). The study was carried out in two phases. Phase-I involved a pilot study to refine the test
instrument with rectification of question ambiguity, refinement of research protocol and confirmation of scale
reliability was given special emphasis (Teijlingen and Hundley, 2001). 20 respondents representing bank
customers, bank employees and academic were included to conduct the pilot study. FGI was administered.
Cronbach’s α coefficient (>0.7) established scale reliability (Nunnally and Bernstein, 1994). The second phase of the
study was conducted by using a structured questionnaire which was distributed amongst 1000 SBI bank-customers
at Kolkata, West Bengal, randomly selected with every 5th customer leaving the bank premise was selected as
sample. ‘Usage-of-automated-banking-service’ was used as critical-fit criteria while selecting samples. A total
number of 712 usable responses were generated with a response rate of 71.20%. Exploratory factor analysis (EFA)
was employed using principal axis factoring procedure with orthogonal rotation through VARIMAX process with an
objective to understand the factor loadings/cross loadings across components. Cronbach’s α was obtained to test
the reliability of the data, Kaiser-Meyer-Olkin (KMO) was done for sample adequacy and Barlett’s sphericity test
was conducted. Structural equation modeling approach using Lisrel 8.80 was used to test the research model.
Data analysis and interpretation
The demographic data obtained were tabulated in Table-1:
Table-1: Demographic Data of the Respondents
Demographic Variables Factors Frequency %
Gender Male 497 69.80%
Female 215 30.20%
Age
≤ 21 years 32 4.49%
22-32 years 321 45.08%
33-43 years 216 30.34%
44-54 years 68 9.55%
≥ 55 years 75 10.54%
Income
≤ Rs. 14999.00 10 1.40%
Rs. 15000-Rs. 24999.00 247 34.69%
Rs. 25000-Rs. 44999.00 367 51.54%
≥ Rs. 45000.00 88 12.37%
Occupation
Service [govt./prv] 399 56.03%
Self employed 132 18.54%
Professionals 65 9.13%
Student 23 3.23%
Housewives 57 8.00%
Others [retd., VRS etc] 36 5.07%
Educational qualification
High school 3 0.43%
Graduate 472 66.29%
Postgraduate 205 28.79%
Doctorate & others (CA, fellow etc) 32 4.49%
Table-2 represents the rotated component matrix following the exploratory factor analysis. The Cronbach’s α value
for all the measures (except three items of core E-SQUAL namely ‘the site enables me to get on to it quickly’, ‘the
site makes items available for delivery within a suitable time frame, ‘it has in-stock the items the company claims
to have’ and for the five items of recovery E-SQUAL namely ‘the site compensates me for problems it creates’, ‘it
compensates me when what I ordered does not arrive on time’, ‘it picks up items I want to return from my home
or business’, ‘the site offers a meaningful guarantee’ and ‘it offers the ability to speak to alive person if there is a
problem’) exceeded the minimum standard of .7 (Nunnally and Bernstein, 1994) suggesting and confirming about
the reliability of the measures. The items which were loaded with a lesser value to .7 were subsequently deleted.
Table 2: Rotated component matrix and Reliability statistics
Variable Variable statement Factors
F1 F2 F3 F4 F5 F6
V1 SBI’s websites makes it easy to search what is required .821
V2 Navigation is smooth in the SBI’s websites .867
V3 Page download is fast .768
V4 Transaction takes place in real-time and does not
freeze before completion .712
V5 Information are well displayed in Banks’ websites .855
V6 SBI’s web-services are simple to use .871
V7 SBI’s websites are always available for transaction .823
V8 SBI’s websites launch and run right away .811
V9 SBI’s website does not crash .798
V10 Pages in SBI’s websites do not freeze while transaction
is on .875
V11 SBI’s website deliver services when promised .841
V12 SBI’s websites promptly delivers services .824
V13 SBI’s websites are truthful about their offerings .819
V14 SBI website’s make accurate promises about
transactions .809
V15 SBI’s provides financial security and confidentiality .921
V16 Web-interface is secured with virtual keyboard set-up
for logging in .911
V17 SBI’s websites can be trusted against misuse of
information of transaction details .807
V18 SBI’s websites can be trusted against mishandling of
personal information stored .739
V19 SBI’s websites provide convenient options for
cancelling transactions .768
V20 SBI’s websites deals well with cancelation of
transactions .717
V21
SBI’s websites guide me in case of transactions not
being processed .784
V22 SBI’s web-service takes care of problems promptly .754
V23 SBI’s web-service has customer representative who
shows willingness to support/help .789
V24 SBI’s websites provide a valid telephone number to
contact the bank when required .694
V25 SBI’s website offers the facility to speak live to an
authorized service if there is a problem .712
Cronbach’s α
.926 .891 .889 .871 .859 .912
The initial 33 variables (including both core and recovery items of E-SERVQUAL) were reduced to 25 variables with
variables having factor loading scores of <0.7 were discarded. The variables were grouped into six dimensions
according to the factor loading scores and were nomenclated as in Table-3.
Table 3: Dimensions
Variables Dimension Dimension types
V1-V6 Efficiency
Core dimensions V7-V10 Web-System
V11-V14 Commitment
V15-V18 Security
V19-V22 Responsiveness Recovery dimensions V23-V25 Contact
To test the relationship between perceived automated service quality (PASQ) and the core & recovery dimensions
of modified E-SERVQUAL bivariate correlation was applied to understand the correlation between the variables.
The results of correlation analysis have been displayed in Table-4. The PASQ score was obtained by calculating the
mean of response for an individual respondent over a 7 point Likert scale across all the items of E-SERVQUAL scale.
Table-4: Bivariate correlation between perceived automated service quality and dimensions of E-SERVQUAL
PASQ Efficiency
Commitment
Security Responsiveness
Contact Websystem
PASQ Pearson Correlation 1.000 .205** .924** .125** .220** .209** .506**
Sig. (2-tailed) .000 .000 .004 .000 .000 .000
N 528 528 528 528 528 528 528
Efficiency Pearson Correlation .205
** 1.000 .241
** -.032 .500
** .461
** .166
**
Sig. (2-tailed) .000 .000 .467 .000 .000 .000
N 528 528 528 528 528 528 528
Commitment
Pearson Correlation .924** .241** 1.000 .088* .195** .190** .513**
Sig. (2-tailed) .000 .000 .043 .000 .000 .000
N 528 528 528 528 528 528 528
Security Pearson Correlation .125** -.032 .088* 1.000 .162** -.055 .557**
Sig. (2-tailed) .004 .467 .043 .000 .209 .000
N 528 528 528 528 528 528 528
Responsiveness
Pearson Correlation .220** .500** .195** .162** 1.000 .353** .247**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000
N 528 528 528 528 528 528 528
Contact Pearson Correlation .209** .461** .190** -.055 .353** 1.000 .168**
Sig. (2-tailed) .000 .000 .000 .209 .000 .000
N 528 528 528 528 528 528 528
Websystem
Pearson Correlation .506** .166** .513** .557** .247** .168** 1.000
Sig. (2-tailed) .000 .000 .000 .000 .000 .000
N 528 528 528 528 528 528 528
**Correlation is significant at 0.01 level (2-tailed), *Correlation is significant at 0.05 level (2-tailed)
The results of correlation analysis (Table-4) exhibited a strong and positive correlation between perceived
automated service quality (PASQ) and the core E-SERVQUAL dimensions namely efficiency (r=.205**, p<.001),
commitment (r=.924**, p<.001), security (r=.125**, p<.005) and web-system (r=.506**, p<.001) suggesting
significance of the dimensions in perceiving the automated service quality. It was further established that a strong
and positive relationship between the recovery dimensions of automated service quality and responsiveness
(r=.220**, p<.001) and contact (r=.209**, p<.001) exist which is indicative of significance of recovery dimensions
towards perceiving automated service quality.
The Behavioural Intention Battery (Zeithaml et al, 1996) was used to obtain the behavioural intention scores of the
respondents across five dimensions (13 items) of the same namely loyalty, will-to-pay-more, internal response
(positive behavioural intention indicators) and propensity-to-switch and external response (negative behavioural
intention indicators). Correlation matrix (Table-5) revealed that perceived automated service quality (PASQ) had a
strong and positive relationship with loyalty (r=.634**, p<.001), will-to-pay-more (r=.509**, p<.001) and internal
response (r=.491**, p<.001) while PASQ revealed a negative relationship with propensity-to-switch (r=-.141*,
p<.005) indicating that customers with higher and better perceived automated service quality with regard to their
bank (SBI) tend to exhibit positive behavioural intentions. Perceived automated service quality did not exhibit a
significant relationship with external response.
Table-5: Correlation matrix between perceived automated service quality (PASQ) and behavioural intention (BI)
dimensions
**Correlation is significant at 0.01 level (2-tailed), *Correlation is significant at 0.05 level (2-tailed)
The Pearson ‘r’ correlation coefficient suggested that the PASQ level of customers about State Bank of India is
indicating customers’ likelihood to remain associate with the bank in future, on the basis of significant
correlationship with ‘loyalty’ and ‘willing to pay more’ dimensions of BIB. Further to this the respondents
demonstrated confidence in the bankers (internal response) when faced with a problem.
PASQ Loyalty Will2paymore
Propensity2switch
Externalresponse
Internalresponse
PASQ Pearson Correlation 1.000 .634** .509** -.141* .065 .491**
Sig. (2-tailed) .000 .000 .003 .448 .000
N 528 528 528 528 528 528
Loyalty Pearson Correlation .634** 1.000 -.045 .079 .020 .744**
Sig. (2-tailed) .000 .304 .069 .653 .000
N 528 528 528 528 528 528
Will2paymore Pearson Correlation .509** -.045 1.000 -.111* .062 .010
Sig. (2-tailed) .000 .304 .011 .158 .812
N 528 528 528 528 528 528
Propensity2switch Pearson Correlation -.141* .079 -.111* 1.000 -.105* .109*
Sig. (2-tailed) .003 .069 .011 .016 .012
N 528 528 528 528 528 528
Externalresponse Pearson Correlation .065 .020 .062 -.105* 1.000 .057
Sig. (2-tailed) .448 .653 .158 .016 .188
N 528 528 528 528 528 528
Internalresponse Pearson Correlation .491** .744** .010 .109* .057 1.000
Sig. (2-tailed) .000 .000 .812 .012 .188
N 528 528 528 528 528 528
To have a better understanding of relationship of loyalty and propensity to switch with perceived automated
service quality, regression analysis was applied. The results of the same were represented in Table-6 and Table-7.
The model summary of regression between PASQ and loyalty exhibited R2 and adjusted R2 (Table-6) to be as .357
and .356 indicating that perceived automated service quality (PASQ-independent variable) measures 35.70% of the
variation in loyalty (dependent variable) which is considered to be significant enough for predictability of the
model. The regression results between PASQ and propensity-to-switch displayed R2 (Table-7) and adjusted R
2 as
.190 and .188 respectively affirming 19% measure of variation. ANOVA (Table-6 and Table-7) established that the
variation showed by the perceived automated service quality was significant at 1% level (f=31.874, p<.001 and
f=19.611, p<.001). Regression coefficients (Table-6) confirmed a strong and positive associationship between
perceived automated service quality and loyalty (β=.597, t=9.082, p<.001). Regression coefficients (Table-7)
exhibited a significant but negative relationship between perceived automated service quality and propensity-to-
switch (β=.143, t=3.616, p<.001). Hypotheses 1,3 and 4 were accepted.
Table-6: Regression results between PASQ and Loyalty
Model Summary ANOVA Regression coefficients
R R2 adjusted R2 F Sig β t sig.
.598 .357 .356 31.874 .000 .597 9.082 .000
a. Predictor: Perceived automated service quality (PASQ), b. Dependent variable: Loyalty
Table-7: Regression results between PASQ and Propensity-to-switch
Model Summary ANOVA Regression coefficients
R R2 adjusted R
2 F Sig β t sig.
.436 .190 .188 19.611 .000 .143 3.616 .000
a. Predictor: Perceived automated service quality (PASQ), b. Dependent variable: Propensity-to-switch
Successful implementation of CRM requires the proper implementation of people, process and technology mix.
These are the three key areas that touch the customer. The CRM Score is taken on the three touch-points, the
CRM-components: People, Process & Technology (Table-8). A 7 point Likert scale was used to obtain the response
from the respondents about the performance of the three CRM components.
Table-8: CRM components
People
Empathy
1. Individual attention to customers
2. Understands specific need of customers
3. Employees have customers' best interest at heart
Responsiveness 4. Employees instill confidence in customers
5. Employees deal with public situations carefully
Process
Single Window (SWO) Service 6. Ease of in-premise transaction
7. Assorted service range
Know Your Customer (KYC) policy
8. Comprehensive information about customer
9. Better segmentation of customers
10. Better understanding of customers' specific need
Multi-Channel Integration (MCI) 11. Seamless and disintermediated delivery process
12. Access to multiple channels for transaction
Technology
Unified integrator 13. Core Banking platform (CBS)
Mobility enhancement 14. Mobile computing/Mobile commerce
Information Communication Technology (ICT)
15. Internet
Automated ancillary process 16. Automated Vending Machines (in-premise)
Security 17. Digital vigilance system (in-premise)
Multiple regression analysis was performed to assess the strength of associationship between perceived
automated service quality (PASQ) and CRM components and predictability of CRM components to predict and
determine PASQ. ANOVA (Table-9) result was significant for the model (f=42.890, p<.001). Regression coefficient
(Table-10) exhibited a strong and positive relationship between PASQ and the CRM components namely people
(β=.344, t=9.258, p<.001), process (β=.356, t=9.979, p<.001) and technology (β=.392, t=10.567, p<.001). To
determine the degree of multi-collinearity, the variance inflation factor (VIF) was computed for each independent
variable in regression equation. The results (Table-10) suggested that the ‘Structural Model for Path Analysis’ is
worth pursuing as the ‘tolerance’ value is over 0.200 for each of the independent variable suggesting absence of
correlation. The VIF values also did not reveal a considerably high value to 1 confirming non-collinearity as VIF
values considerably greater than 1 are indicative of multi-collinearity (Netter et al, 1996) and greater than 2.5 are
cause of concern (Allison, 1999) (VIF=1/tolerance). Hypothesis 2 was accepted.
Table-9: ANOVA results
Model Sum of Squares Df Mean Square F Sig.
1
Regression 16.965 3 5.655 42.890 .000a
Residual 93.350 708 .132
Total 110.315 711
a. Predictor: People, Process and Technology
b. Dependent variable: PASQ
Table-10: Regression coefficients
Model
Unstandardized Coefficients
Standardized Coefficients T Sig.
95% Confidence Interval
Collinearity statistics
Tolerance
VIF
B Std. Error
Beta Lower Bound
Upper Bound
1
(Constant) 3.353 .147 22.870 .000 3.066 3.641
PEOPLE .216 .020 .344 9.258 .000 -.009 .040 .870 1.150
PROCESS .217 .022 .356 9.979 .000 .174 .259 .967 1.035
TECHNOLOGY .155 .022 .392 10.567 .000 .013 .097 .924 1.082
a. Dependent variable: PASQ
Factor analysis validated the measures used for Customer Relationship Management Index (CRMI) namely its three
components people, process and technology. Exploratory factor analysis was deployed using orthogonal rotation.
The reliability index was obtained as >0.70. The convergent validity was found to be >0.60 for all the items. Factor
loading <.500 were discarded. Table-11 displayed the results of factor analysis
Table-11: Factor structure of variables (N=712)
Factor Eigenvalues
Cronbach’s α
Items Factor loadings
Convergent validity
People
4.27
0.93
1. Individual attention to customers
2. Understands specific needs of customers
3. Employees have customers’ best interest at heart
4. Employees instill confidence in customers
5. Employees deal with public situation carefully
0.862
0.793
0.871
0.809
0.798
0.875
0.811
0.889
0.811
0.812
Process
4.41
0.91
6. Ease of in-premise transactions
7. Assorted service range
8. Comprehensive information about customers
9. Better segmentation of customers
10. Better understanding of customers’ demand
11. Seamless delivery process
12. More than one channel to enter into transaction
0.788
0.826
0.751
0.851
0.741
0.829
0.729
0.801
0.839
0.766
0.869
0.745
0.847
0.746
Technology
4.11
0.96
13. CBS efficiency
14. Mobile-technology/mobile commerce applications
15. Internet enabled banking efficiency
16. Auto-vending machine (in-premise) facility available
17. Digital surveillance (in-premise) facility available
0.832
0.798
0.807
0.783
0.812
0.842
0.805
0.814
0.799
0.817
Table-12 and Table-13 displayed the relative weight of eigenvalue (RWE) and average factor value (AFV)
respectively, which were considered for calculating the CRMI.
Table-12: Relative weight of eigenvalue (RWE)
Factor Eigenvalue RWE
People 4.27 0.33
Process 4.41 0.38
Technology 4.11 0.29
Total 12.79 1.00
Table-13: Average factor value (AVF)
Organization People (CRMCP1) Process (CRMCP2) Technology (CRMCP3)
SBI 0.51 0.79 0.42
Calculating for Customer Relationship Management Components’ performance (CRMCP) as per the following
equation, we get
332211 CRMCPCRMCPCRMCPCRMCPCRMCPCRMCP AVFRWEAVFRWEAVFRWECRMCP
CRMCP = (0.33 * 0.51) + (0.38*0.79) + (0.29 * 0.42)
= 0.1683 + 0.3002 + 0.1218
= 0.5903
Therefore, calculating for CRMI as per equation-3:
5903.01
1
eCRMI
CRMI = 0.62881
To have an understanding about the possible linkage between performance of CRM components and aggregate
perceived automated service quality (∑PASQ) correlation analysis was performed between CRM –index (CRMI) and
(∑PASQ) (r=.461**, p<.001). Table-14 revealed that aggregate perceived automated service quality is significantly
and positively correlated with CRM-index suggesting that an improvement in CRM-components’ efficiency
performance will enhance the perceived automated service quality of customers.
Table-14: Correlation between ∑PASQ and CRMI
∑PASQ CRMI
∑PASQ Pearson Correlation 1.000 .461**
Sig. (2-tailed) .000
N 712 712
CRMI Pearson Correlation .461** 1.000
Sig. (2-tailed) .000
N 712 712
** Correlation is significant at 0.01 level (2-tailed)
Regression analysis was performed to examine the predictability and strength of associationship between CRMI
(independent variable) and ∑PASQ (dependent variable) and the results were displayed in Table-15. The model
summary showed R2 and adjusted R2 to be as .438 and .436 indicating that CRM index (CRMI) measures 43.80% of
the variation in aggregate perceived automated service quality (∑PASQ-dependent variable) which is considered to
be significant enough for predictability of the model. ANOVA established that the variation showed by the
perceived automated service quality was significant at 1% level (f=527.389, p<.001). Regression coefficients
confirmed a strong associationship between CRMI and ∑PASQ (β=.671, t=23.096, p<.001) and that CRMI could be
an effective predictor to ∑PASQ thereby suggesting dependency of ∑PASQ on CRMI. Hypothesis 5 was accepted.
Table-15: Summary of regression results
Model Summary ANOVA Regression coefficients
R R2 adjusted R2 F sig β t sig.
.662 .438 .436 527.389 .000 .671 23.096 .000
a. Dependent variable: ∑PASQ, b. Predictor: CRM index (CRMI)
Structural equation modeling (SEM) was used to test the nomological validity of the proposed model. E-SERVQUAL,
BIB and CRM computation of the scores for the individual dimensions were done by summating the ratings on their
individual scale items which were used as indicators of the latent E-SERVQUAL, BIB and CRM items. Confirmatory
factor analysis was used to understand the dimensionality, convergence and discriminant validity for each
construct to determine whether all the 42 indicators (including E-SERVQUAL, BIB and CRM component
performance) measure the construct adequately as they had been assigned for. LISREL 8.80 programme was used
to conduct the Structural Equation Modeling (SEM) and Maximum Likelihood Estimation (MLE) was applied to
estimate the CFA models. A number of fit-statistics (Table-16) were obtained. The GFI, AGFI and NFI scores for all
the constructs were found to be consistently >.900 indicating that a significant proportion of the variance in the
sample variance-covariance matrix is accounted for by the model and a good fit has been achieved (Baumgartner
and Homburg, 1996; Hair et al, 1998; Hulland, Chow and Lam, 1996; Kline, 1998; Holmes-Smith, 2002, Byrne,
2001). The CFI value for all the constructs were obtained as > .900 which indicated an acceptable fit to the data
(Bentler, 1992). The RMSEA values obtained are < 0.08 for an adequate model fit (Hu and Bentler, 1999). The
probability value of Chi-square is more than the conventional 0.05 level (P=0.20) indicating an absolute fit of the
models to the data. The Cronbach’s α values were consistently >.7 and hence the scale is reliable (Nunnally and
Bernstein, 1994). The factor loadings for the items were also significant (>.500).
Table-16: Summary representation of Confirmatory Factor Analysis (CFA)
Factor indicators χ2
df P-
value GFI AGFI CFI NFI RMSEA Factor
loadings
α –
value
Efficiency 8.916 5 0.081 0.971 0.961 0.981 0.979 0.062 0.979
EF1 0.841
EF2 0.854
EF3 0.876
EF4 0.864
EF5 0.802
EF6 0.771
EF7 0.787
Web-System 8.541 3 0.027 0.918 0.909 0.989 0.967 0.032 0.936
WS1 0.819
WS2 0.797
WS3 0.801
WS4 0.779
Commitment 9.195 4 0.139 0.977 0.943 0.987 0.971 0.076 0.941
COM1 0.818
COM2 0.794
COM3 0.819
COM4 0.766
COM5 0.838
Security 3.998 2 0.049 0.916 0.901 0.971 0.965 0.048 0.832
SEC1 0.807
SEC2 0.791
SEC3 0.770
Responsiveness 8.197 3 0.116 0.980 0.974 0.951 0.952 0.020 0.891
RES1 0.861
RES2 0.865
RES3 0.708
RES4 0.798
Contact 6.375 2 0.028 0.966 0.905 0.979 0.959 0.080 0.901
CON1 0.771
CON2 0.779
Loyalty 9.219 4 0.031 0.919 0.917 0.921 0.923 0.073 0.929
LOY1 0.881
LOY2 0.781
LOY3 0.709
LOY4 0.817
LOY4 0.811
Will-to-pay-more 7.891 2 0.041 0.946 0.941 0.978 0.938 0.049 0.911
WPM1 0.791
WPM2 0.715
Internal response 4.129 1 0.027 0.918 0.916 0.954 0.931 0.071 0.891
INTR1 0.708
Propensity to
switch
6.871 2 0.045 0.971 0.963 0.970 0.961 0.064 0.917
PTS1 0.866
PTS2 0.837
External response 8.752 3 0.069 0.955 0.943 0.959 0.967 0.049 0.978
EXTR1 0.792
EXTR2 0.811
EXTR3 0.781
CRM 9.693 4 0.091 0.967 0.981 0.991 0.987 0.051 0.997
CRM1 0.873
CRM2 0.859
CRM3 0.786
Structural Equation Modeling (SEM) was used to test the relationship among the constructs. A number of fit-
indices namely Chi-square/df = 1123/158, GFI = 0.997, AGFI = 0.969, CFI = 0.981, NFI=0.979, RMSEA=0.037,
expected cross validation index (ECVI)=0.911 were found to be significant. All the 24 paths drawn were found to be
significant at p<0.05. The research model holds well (Fig.2) as the fit-indices supported adequately the model fit to
the data. The double-curved arrows indicate co-variability of the latent variables. The residual variables (error
variances) are indicated by Є1, Є2, Є3, etc. The regression weights are represented by λ. The co-variances are
represented by β. To provide the latent factors an interpretable scale; one factor loading is fixed to 1 (Hox &
Bechger).
Fig.2: Structural model showing the path analysis using SEM
PEOPLE PROCESS TECH
Є14=1.16 Є15=1.41 Є16=1.19
λ14=1.04 λ15=1.11 λ16=1.26
Є12=1.56
Є13=1.49
PASQ
BI (+)
BI (-)
LOY
W2P
INTR
E
P2S
EXTR
Є7
Є8
Є9
Є10
Є11
λ5=0.79
λ6=0.81
λ7=1.00
λ8=0.95
λ9=1.00
λ10=1.00
λ11=0.92
λ12=1.00
λ13=0.83
β7=0.92
β8=0.88
β9=0.94
β10=0.93
1.79
1.57
1.42
1.04
1.42
β11=0.91 β12=0.89
β13=0.96
EFFICIENCY
WEBSYS
COMMIT
SECURITY
RESPONSE
CONTACT
Є1
Є2
Є4
Є5
Є6
λ1=1.00
λ2=0.91
λ3=0.97
λ4=0.88
β1=0.89
β2=0.81
β3=0.91
β4=0.83
β5=0.81
β6=0.87
1.61
1.47
1.18
1.37
1.19
1.09
A causal loop diagram (CLD) is a diagram that aids in visualizing how interrelated variables affect one another. The
diagram consists of a set of nodes representing the variables connected together. The relationships between these
variables, represented by arrows, can be labeled as positive or negative. The dynamic causal loop diagramming for
the current study may be represented as follows:
Fig.3- Causal loop diagramming showing relationship between perceived automated service quality (PASQ),
customer relationship management and behavioural intentions (both positive and negative) with its micro-level
outputs.
PASQ
BI(+)
BI(-)
LO
Y
W2P
INR
P2S
EXR
TECH
PRO
PEO
+
+
+
+ +
+ +
+
+
+
+
-
-
- -
-
-
+
+
+
Conclusion
The modernization and automation of State Bank of India (SBI) had been a significant event in the banking industry
in India as, being the largest nationalized public sector bank in India, SBI has become the face of Indian electronic
banking. The reach and penetration of SBI has been phenomenal and at present due to rapid proliferation of
internet services across the length and breadth of the country, the automated (electronic) banking services
penetrated the rural geo-demographic domain of India. The core-bank-system of SBI has changed the perception
of banking and vis-à-vis quality perception. The study revealed that the automated service quality dimensions
which proved to be significant in perceiving quality are efficiency, web-system, commitment, security,
responsiveness and contact. The study also confirmed that the customers of SBI had gradually become habituated
with automated banking services and are satisfied with the same as it established a strong and positive
behavioural intention depicting intentions for loyalty, willing to pay more for services and addressing problems to
internal customers only. Behavioural intentions reflected negative attitude towards propensity to switch and
lodging external negative canvassing hinting towards customer satisfaction with the automated service quality
actually delivered by their bank. The Customer Relationship Management (CRM) practice initiated by SBI seemed
to have properly integrated with their automated operational procedures as the CRM components were found to
influence the perceived automated service quality of customers in a positive way. The proposed research model
also came through as the model constructs fit the data thereby establishing a cause and effect relationship
between the variables and the causal loop diagram effectively exhibited the positive and negative causal
relationships between the variables. The study was indicative of the shift and subsequent adoption of automated
banking services in a semi-urban/rural set up.
The study had geographical limitations as it has been restricted to Kolkata, West Bengal, which in future, can be
widened to obtain a more generalized conclusion. In future the study can be comparative in nature as competition
is increasing and there is a strong requirement of service differentiation and customization, whereby service
quality between more than one service providers can be chosen effectively. Further to this other intermediating or
conclusive variables may be included also for much more elaborative perspectives.
References
1. Alkibsi, S. and Lind, M., (2011), “Customer perceptions of technology-based banking service quality
provided by banks operating in Yemen”, European, Mediterranean & Middle Eastern Conference on
Information Systems 2011 (EMCIS2011) May 30-31 2011, Athens, Greece.
2. Allison, P. D., (1999), “Event History Analysis: Regression for Longitudinal Event Data”, Newbury Park, CA:
Sage Publications. (Pp. 9-42). [EHA]
3. Al-hawari, M., Hartley, N. and Ward, T., (2005), “Measuring Banks’ Automated Service Quality: A
Confirmatory Factor Analysis Approach”, Marketing Bulletin, 16(1).
4. Al-Hawari, M. and Ward, T., (2006), “The effect of Automated Service Quality on Australian banks’
Financial Performance and the Mediating role of Customer Satisfaction”, Marketing Intelligence &
Planning, 24(2), pp. 127-47.
5. Avkiran, N. K., (1994), “Developing an Instrument to Measure Customer Service Quality in Branch
Banking”, International Journal of Bank Marketing, 12(6), pp. 10-18.
6. Babakus, E. and Boller, G.W., (1992), “An Empirical Assessment of the SERVQUAL scale”, Journal of
Business Research, 24, pp. 253-268.
7. Baumgartner, H. and Homburg C., (1996), “Applications of Structural Equation Modeling in Marketing and
Consumer Research: A review”, International Journal of Research in Marketing, 13, pp.139-161.
8. Bentler, P.M., (1992), “On the Fit of Models to Co variances and Methodology to the Bulletin”,
Psychological Bulletin, 112(3), pp.400-4.
9. Bolton, R. and Drew, J., (1991a), “A Longitudinal Analysis of the Impacts of Service Changes on Customer
Attitudes,” Journal of Marketing, 55(1), pp. 1-9.
10. Broderick, A. J. and Vachirapornpuk, S., (2002), “Service Quality in Internet Banking: The Importance of
Customer Role”, Marketing Intelligence & Planning, 20(6), pp.327 - 335.
11. Buckley J., (2003), “E-service and the public sector”, Managing Service Quality, 13(6), pp. 453- 462.
12. Buttle, F., (2001), “The CRM Value Chain”, available at: http://www.crm-forum.com (accessed on 19-07-
2008).
13. Byrne, B. M., (2001), Structural Equation Modeling with AMOS - Basic Concepts, Applications, and
Programming.LEA, ISBN 0-8058-4104-0.
14. Carman, J. M., (1990),
“Consumers’ Perceptions of Service quality: an Assessment of the SERVQUAL dimensions”, Journal of
Retailing, 66(1), pp. 33-55.
15. CRM Guru, (2006), The top five tips for CRM Strategy, available at: http://www.crm-guru/the-top-five-
tips-for-crm-strategy.php, accessed on 16-07-2008.
16. Cronin, J. and Taylor, S.A., (1992), “Measuring Service Quality: A Re-examination and Extension”, Journal
of Marketing, 56, July, pp.55-67.
17. Cronin, J. and Taylor, S. A., (1994), “SERVPERF versus SERVQUAL: Reconciling Performance-Based and
Perceptions-Minus-Expectations Measurement of Service Quality”, Journal of Marketing, 58, (January) pp.
125-131.
18. Crosby, L. A. and Stephens, N., (1987), “Effects of Relationship Marketing on Satisfaction, Retention, and
Prices in the Life Insurance Industry”, Journal of Marketing Research, November, 14, pp. 404-11.
19. Dabholkar, P., (1994), “Technology based service delivery”, Advances in Service Marketing and
Management, 3(1), pp.241-271.
20. Dabholkar, P., (1996), “Consumer Evaluations of New Technology-based Self-service Options: An
Investigation of Alternative Modes of Service Quality”, International Journal of Research in Marketing.
13(1), pp. 29-51.
21. Dekimpe, M.G., Steenkamp, J.E.M., Mellens, M. and Abeele, P.V., (1997), “Decline and variability in brand
loyalty”, International Journal of Research in Marketing, 5(14), pp. 405-20.
22. Eric, P. J., Tom A. B. and Charles, E. M., (2006), “Operational challenges in call center Industry; a case
study and resource based framework”, Managing Service Quality Journal, pp. 477-500.
23. Evans, J. R. and Laskin, R. L., (1994), “The relationship marketing process: A conceptualization and
application”, Industrial Marketing Management, 23(5), pp. 439-452.
24. Gefen, D., (2002), “Customer Loyalty in e-commerce”, Journal of the Association for Information Systems,
3, pp. 27-51.
25. Gerrard, P. and Cunningham J.B., (2003), “The Diffusion of Internet Banking among Singapore
Consumers”, International Journal of Bank Marketing, 21(1), pp.16-28.
26. Gurau, C., (2003), “Tailoring e-service quality through CRM”, Journal Managing Service Quality, 13(6), pp.
520-531.
27. Grabner-Kraeuter, Sonja and Gernot Moedritscher, (2002), “Alternative Approaches Toward Measuring
CRM Performance”. Paper presented at the Sixth Research Conference on Relationship Marketing and
Customer Relationship Management, Atlanta, USA, 2002.
28. Gray, P. and Byun, J., (2001), Customer Relationship Management, Centre for Research on Information
Technology and Organizations, University of California.
29. Grönroos, C., (1982), Strategic Management and Marketing in the Services Sector, Marketing Science,
Cambridge, MA.
30. Grönroos, C., (1983), Strategic Management and Marketing in the Service Sector, Marketing Science
Institute, Boston, MA.
31. Grönroos, C., (1984), “A service quality model and its marketing implications”, European Journal of
Marketing, 18(4), pp.36-44.
32. Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C., (1998), Multivariate data analysis, 5th edition,
Prentice Hall, Upper Saddle, New jersey.
33. Hanzaee, K.H. and Sadeghi, T., (2010), “Customer Satisfaction (CSFs) with Online Banking Services in an
Islamic country: I.R. Iran”, Journal of Islamic Marketing, 1(3), pp.249-267.
34. Heide, J. B., (1994), “Interorganizational governance in marketing channels”, Journal of Marketing, 58(1),
pp. 71-85.
35. Henderson J., McGoldrick E. and McAdam, R., (2003). “A Critical Review of e-service in Northern Ireland
Electricity”, Managing Service Quality, 13(6), pp.463-470.
36. Hernandez, José Mauro C, Mazzon José Afonso, (2007), “Adoption of Internet Banking: Proposition
and Implementation of an Integrated Methodology Approach”, International Journal of Bank
Management, 25(2), pp.72-88.
37. Holmes-Smith, P., (2002), Applied Structural Equation Modeling, Canberra.
38. Hu, L., Bentler, P.M., (1999), “Cutoff Criteria for Fit Indexes in Covariance Structure Analysis:
Conventional Criteria Versus New Alternatives”, Structural Equation Modeling, 6(1), pp.1-55.
39. Hulland J., Chow, Y. H. and Lam S., (1996), “Use of Causal Models in Marketing Research: A review”,
International Journal of Research in Management, 13(2), pp.181-197.
40. Hox and Bechger, “An Introduction to Structural Equation Modeling”, Family Science Review, 11, pp. 354-
373.
41. Jacoby, J. and Kyner, D.B., (1973), “Brand loyalty versus repeat purchase behavior”, Journal of Marketing
Research, 10(1), pp. 1-9.
42. Joseph, M. and Stone, G., (2003), “An Empirical Evaluation of US Bank Customer Perceptions of the Impact
of Technology in Service delivery in the Banking sector”, International Journal of Retail & Distribution
Management, 31(4), pp.190-202.
43. Julian, C. C. and Ramaseshan, B., (1994), “The Role of Customer-contact Personnel in the Marketing of a
Retail Bank's Service”, International Journal of Retail & Distribution Management, 5, pp. 29-34.
44. Kearns, D. and Nadler, D., (1992), Prophets in the Dark: How Xerox Reinvented Itself and Beat Back
Japanese, New York: Harper Collins Publishers.
45. Keaveney, Susan M., (1995), “Customer Switching Behavior in Service Industries: an Exploratory Study”,
Journal of Marketing, 59, pp. 71-82.
46. Khan, M.S. and Mahapatra, S.S., (2009), “Service quality evaluation in internet banking: an empirical
study in India”, International Journal Indian Culture and Business Management, 2(1), pp.30-46.
47. Kline, R. B., (1998), Principles and practices of structural equation modeling. New York: Guilford.
48. Laforet Sylvie, Li Xiaoyan, (2005), “Consumers’ Attitude Towards Online and Mobile Banking in Chin”,
International Journal of Bank Management, 23(5), pp.362-380.
49. Lans, B. and Colgate, M., (2003), “Relationship quality, on-line banking and the information technology
gap”, International Journal of Bank Marketing, 21(1), pp. 29-37.
50. Lehtinen, J.R and Lehtinen, U., (1982), “Service Quality: a study of Quality Dimensions”, Unpublished
working paper, Service Management Institute, Helsinki.
51. Lin, J.S.C. and Hsieh, P., (2006), “The role of technology readiness in customer perception and adaptation
of self-service technologies”, International Journal of Service Industry Management, 17(5), pp.497-517.
52. Llosa, S., Chandon, J.L. and Orsingher, C., (1998) “An Empirical Study of SERVQUAL’s dimensionality”, The
Service Industries Journal, 18(2), pp.16-44.
53. Loiacono, E.T., Watson, R.T. and Hoodhue, D.L., (2002), ‘WEBQUAL: Measure of website quality’, 2002
Marketing Educators Conference: Marketing theory and applications, 13, pp.432-437.
54. Macintosh, G. and Lockshin, L. S., (1997), “Retail relationships and loyalty: A multi-level perspective”,
International Journal of Research in Marketing, 14(5), pp. 487-497.
55. Malhotra, P. and Singh, B., (2007) “Determinants of internet banking adoption by banks in India”, Internet
Research, 17(3), pp.323–339.
56. Meuter, M., Ostrom, A., Roundtree, R. and Bitner, M., (2000), “Self-service technology: Understand
customer satisfaction with technology-based service encounters”, Journal of Marketing, 64(3), pp. 50-64.
57. Mukherjee A. and Nath P., (2003). “A Model of Trust in Online Relationship Banking”, International
Journal of Bank Management, 21(1), pp.5-15.
58. Nguyen T, Sherif, J. and Newby, M., (2007), “Strategies for Successful CRM Implementation”, Information
management and Computer security, 15(2), pp. 76-95.
59. Nunnally, J.C. and Bernstein, H., (1994), “Psychometric Theory”, New York, McGraw-Hill.
60. Oliver, Richard L., (1981), “A Cognitive Model of the Antecedents and Consequences of Satisfaction
Decisions”, Journal of Marketing Research, 17 (September), pp.460-469.
61. Parasuraman, A., Berry, L. L. and Zeithaml, V. A., (1985), “A conceptual model of service quality and its
implications for future research”, Journal of Marketing, 49, pp. 41-50.
62. Parasuraman, A., Zeithaml, V. A. and Berry, L. L., (1988), “A Multiple-item Scale for Measuring Consumer
Perceptions of Service Quality”, Journal of Retailing, 64 (Spring), pp.12-37.
63. Parasuraman, A., Berry, L. L. and Zeithaml, V. A., (1991), “Refinement and reassessment of the SERVQUAL
scale”, Journal of Retailing, 67(4), pp. 420-450.
64. Parasuraman, A., Zeithaml, V. and Berry, L.L., (1994), “Reassessment of expectations as a comparison
standard in measuring service quality: implications for future research”, Journal of Marketing, 58, January,
pp. 111-24.
65. Parasuraman, A., Zeithaml, V. and Malhotra, A., (2005), “E-S-QUAL: A Multiple-Item Scale for Assessing
Electronic Service quality”, Journal of Service Research, 7(3), pp.213-234.
66. Patricio, L., Fisk, R. and Cunha, J., (2003), “Improving Satisfaction with Bank Service Offerings: Measuring
the Contribution of Each Delivery Channel”, Managing Service Quality, 13(6), pp.471-482.
67. Peng, L. Y. and Wang, Q., (2006), “Impact of Relationship Marketing Tactics (RMTs) on Switchers and
Stayers in a Competitive Service Industry”, Journal of Marketing Management, 22, pp.25-59.
68. Radecki, L., Wenninger, J. and Orlow, D., (1997), “Industry Structure: Electronic Delivery's Potential Effects
on Retail Banking”, Journal of Retail banking Service, 19(4), pp.57-63.
69. Ramsay, J. and Smith, M., (1999), “Managing Customer Channel Usage in the Australian Banking Sector’,
Managerial Auditing Journal, 14(7), pp.329-338.
70. Reinartz, W. J. and Kumar, V., (2003), “The impact of customer relationship characteristics on profitable
lifetime duration”, Journal of Marketing, 67(1), pp. 77-99.
71. Ravi, V., Mahil, C. and Vidya Sagar, N., (2007) “Profiling of Internet Banking Users in India Using Intelligent
Technique”’, Journal of Services Research, 6(2), pp.61–73.
72. Rowley, J. and Dawes, J., (2000), “Disloyalty: A closer Look at Non-loyals”, Journal of Consumer
Marketing. 17(6), pp. 538-549.
73. Rust, R.T. and Zahorik, A.J., (1993), “Customer Satisfaction, Customer Retention and Market Share”,
Journal of Retailing, 69(2), pp.193-215.
74. Ruyte,R. K., Wetzels, M. and Kleijnen, M., (2001), “Customer adoption of e-service: an experimental
study”, International Journal of Service Industry Management, 12(2), pp.184-207.
75. Santos, F., (2003), “E-service Quality: A Model of Virtual Service Quality Dimensions,” Managing Service
Quality, 13(3), pp. 233-246.
76. Sheth, N. J. and Parvatiyar, A., (1995), “Relationships marketing in consumer markets: Antecedents and
consequences”, Journal of Academy of Marketing Science, 23(4), pp. 255-271.
77. Soon Hoo So, (2007), An Empirical Analysis on the operational Efficiency of CRM call centers in Korea, Call
center Industry Research Center.
78. Szymanski, D. and Hise, R., (2000), “E-satisfaction: an initial Examination”, Journal of Retailing, 76(3), pp.
309-322.
79. Tax, Stephen, S. and Brown, Stephen W., (1998a). “Recovering and Learning from Service Failures.”, Sloan
Management Review, 40, pp. 75-88.
80. Teijlingen, E. R; V. Hundley (2001), *“The Importance of Pilot Studies”, Social research UPDATE,.35,
available at: http://sru.soc.surrey.ac.uk/SRU35.html ](accessed on 16-12-2009)
81. Weinstein, Art. and Johnson, William C., (1999c), Designing and delivering superior customer value:
concepts, cases, and applications. Boca Raton: CRC Press LLC. p.119a,119b,117c,119d,124-
126e,126f,127g.
82. Wolfinbarger, M.F. and Gilly, M.C., (2002), “ComQ: Dimensionalizing, Measuring and Predicting Quality of
the E-tail Experience”,Working paper, Marketing Science Institute, Cambridge, M.A., pp.02-100.
83. Yang, Z. and Fang, Z., (2004), “Online Services Quality Dimension and their Relationships with Satisfaction:
a Content Analysis of Customer Reviews of Securities Brokerage Services”, The International Journal of
Bank marketing, 15(3), pp. 189-206.
84. Yoo, B. and Donthu, N., (2001), “Developing a scale to measure perceived quality of an Internet shopping
site (SITEQUAL)”, Quarterly Journal of Electronic Commerce, 2(1), pp.31-46.
85. Zeithaml, Berry and Parasuraman, (1996), “The Behavioral Consequences of Service Quality”, Journal of
Marketing, April 1996, 6(2), pp. 31-46.
Towards Robust Forecasting of Demand for Water in Crop Production Swagata “Ban” Banerjee Assistant Professor Department of Finance, Agribusiness and Economics College of Business and Public Affairs, Alabama A&M University, USA Babatunde A. Obembe Graduate Assistant Department of Finance, Agribusiness and Economics College of Business and Public Affairs, Alabama A&M University, USA Abstract Limited water supply (due to natural causes such droughts, and non-natural causes such as competing uses and government policies) in the southern and southeastern United States has been posing a serious problem in agriculture in recent years. Under such reduced supply scenarios, existing physical models reduce irrigation proportionally among crops in the farmer’s portfolio, disregarding temporal changes in economic and/or institutional conditions. Hence, changes in crop mix due to expectations about risks and returns are ignored. A method (combining acreage forecasts with inherent water-requiring potential of crops) is developed that considers those changes by accounting for substitution and expansion effects. Forecasting studies based on this method in Georgia and Mississippi demonstrates the relative strength of econometric modeling vis-à-vis physical methods. Comparing policy-induced simulation scenarios, water savings of 19%-25% are indicated using the proposed method. Results from a pilot study launched for Alabama will verify the robustness of those findings. 1
Keywords: agriculture, acreage response, allocation, crop distribution, expansion, econometric, farmer, forecasting, institutional changes, irrigation, irrigated acreage, land, policies, portfolio, production, slippage, substitution, time series, water demand, water saving, water supply
Introduction
Agriculture in many parts of the United States, including the South and Southeast, has been plagued with the
nagging problem of limited water supply in recent years (USGS, 2011a; USGS, 2011b; Figures1-3). For example, the
aquifer level under the alluvial soil to the immediate east of the Mississippi River (Mississippi River Alluvial Aquifer)
has been declining (YMD 2011; Figures 1-3).
A declining water supply has possibly been made worse due to the recent droughts. Additionally, policies in the
future may further restrict usage of water for irrigating crops. For example, the long-standing issue of ‘equitably’
allocating water in the tri-state area of Alabama, Florida, and Georgia prompted auctioning of water among
farmers in Georgia about a decade ago (FRDPA, 2001).2
1 This study was made possible in part by the U.S. Department of Agriculture – National Institute of Food and Agriculture Project No. ALAX 013-1008, and by Mississippi Agricultural and Forestry Experiment Station – Special Research Initiative Project No.171450. Gratitude also goes to Drs. Michael Wetzstein, Lewell Gunter, and Steven Martin. 2 Attempting to move towards an efficient water management program within the tri-state area, the Georgia legislature in 2001
passed the Flint River Drought Protection Act (FRDPA, 2001). A component of this act was to hold an auction among southwest Georgia agricultural producers with water permits for the withdrawal of acreage from irrigation using perennial surface water sources. The objective of this auction was to increase the Flint River water flow, which was adversely affected by the drought in
Last but not least, the recent interest in alternative fuels may create different crop mixes creating different water
demands. Therefore, a method of evaluating the water needs of different crops and the value of water to each
crop similar to Banerjee et al. (2007) would provide agricultural producers with valuable information.
From a policymaking perspective, decision-makers also need better tools to devise programs and policies to deal
with such water shortages. However, demand for water is unobservable; hence it is best studied via crop acreage
estimates/forecasts mated with water used/required by the relevant crops. A model that combines a land
allocation model with the crop- and region-specific water use coefficients (proxy for net irrigation water
requirements by crop) is proposed to estimate irrigation water demand and hence estimate water value through
crop acreage. This land allocation model is based on a portfolio type analysis that not only incorporates measures
of risks and returns, but also allows for agronomic and other influences.
Objectives
The overall objective of this ongoing pilot study is to develop a robust method of precisely predicting agricultural
water demand for irrigating major crops grown in the South/Southeast such as corn, cotton, peanuts, rice, and
soybeans. Results from the representative state of Mississippi are shown as an illustration.
In particular, the following steps let us fulfill the basic objective of developing such a method of prediction:
1. Develop an econometric model of crop irrigated acreage allocation based on expected prices,
expected yields, expected crop returns, variances and covariances of crop returns, and total
irrigated acres by crop.
2. Employ the acreage forecasts from the estimated econometric model to the relevant actual water use
data in Mississippi to estimate water demand by crop, and compare and contrast the predicted results
from this econometric approach against those from the traditional physical (“engineering”)3 approach
that uses the initial crop distribution to predict water demand.
the southeastern United States. On March 17, 2001, bids by producers to suspend irrigation were submitted. If a bid was accepted by the Environmental Protection Division (EPD), a producer would then agree not to use irrigation on the land for the 2001 growing season. After five rounds of auction, EPD declared the auction closed and accepted offers on 209 of the 347 water permits registered at an average offer price of $135.70 per acre. This auction withdrew slightly more than 33,000 acres of farmland from irrigation. The EPD estimated removing 33,000 acres from direct surface water irrigation would result in approximately a 399 acre-foot daily increase in the Flint River water flow and its tributaries (Georgia EPD). Such an increase would aid in mitigating the drought conditions (Banerjee et al., 2007, pp. 641-642). 3 The words “physical” and “engineering” are used synonymously and interchangeably in this paper and related literature to indicate the naïve, simplistic, “traditional” way of proportionally reducing water allocation across crops over time in the wake of a reduced supply.
Steps 1 and 2 allowed a precise estimation of crop irrigated acreage a year in advance, thus enabling us to
calculate the value of water saved in terms of irrigated acreage.
3. From the above water demand estimates for the econometric and engineering approaches, use simulated
prediction scenarios to determine responsiveness of the econometric approach vis-à-vis the engineering
approach to certain economic and institutional variables, and calculate “slippage” – a measure to
distinguish between the two approaches. The value of water saved by differing the crop mix allows the
calculation of the value per acre-inch of water on a crop-by-crop basis. Calculation of “slippage” (one
minus the ratio of the econometric change to the physical change in total water demand) enables us to
visualize this difference as in related literature (Tareen, 2001; Banerjee, 2007).
Data and Research Methods
Data for this study were primarily obtained from U.S. Department of Agriculture – National Agricultural Statistics
Service (USDA-NASS) (2011) (data on state planted and irrigated acres by crop, and yields by crop through 2007),
Commodity Research Bureau (data on futures prices by crop through 2007), U.S. Department of Agriculture –
Economic Research Service (USDA-ERS) (2011) (data on variable costs by crop through 2007), and Yazoo Mississippi
Delta (YMD) Joint Water Management District (2011) (data on water use by crop through 2008). A time series for
Mississippi starting in 1984 and ending in 2003 was chosen for the sample. Years 2004 and 2005 were chosen for
out-of-sample forecasts as the latest irrigated acreage data available for all crops for comparison were until 2005.
Theoretical Modeling
The representative farmer is assumed to maximize his/her utility of profits (Πi) and come up with an optimal choice
of irrigated acreage (Ai) for each crop:
A*i = Ai(Πj , σjj, σjk, A, T , G ), i, j, k = 1, . . . , n, j > k, (1)
where Πj is u the expected profit accruing from the jth crop,
σjj denotes the variance in profit for the jth crop,
σjk the covariance of profit between the jth
and kth
crops,
A is total irrigated acres,
T is technology, and
G represents governmental programs.
The vector of covariances accounts for the mechanism of risk spreading by farmers via the portfolio effect.
Technology and government programs were considered fixed in estimating the model.
Empirical Modeling
Step 1: Expected profits and the variances and covariances of expected profits were calculated using futures prices,
past yields (Holt, 1999) and covariances between those prices and yields (Bohrnstedt and Goldberger, 1969). The
irrigated acres of each of the four crops (corn, cotton, rice, and soybeans) were then linearly regressed on
expected profits, variances and covariances of profits from all four crops, and total irrigated acres (Figure 4). This
yielded a set of crop acreage predictions, Banerjee et al. (2007).
Step 2: Irrigated acreage forecasts obtained from the acreage allocation equations were employed to the actual
water use data (Figure 5) available from (YMD, 2011) for obtaining the current and future water demand
estimates. Specifically, predicted acreage times the relevant water use coefficient (2002-2007 annual average
water used by each crop) equaled the average annual water demand in acre-inches for each crop.
Step 3: By varying some of the economic and institutional parameters, the responsiveness of irrigated acres was
determined. Specifically, once the base simulation was created at the end-point within the sample, 2003, several
types of simulations were conducted out of the sample to determine how our model compared with the physical
model. This was done by altering prices, yields, costs, and total irrigated acres to reflect out-of-sample data for two
consecutive years (2004 and 2005). One such simulation assumed an institutionally forced reduction of total
available irrigated acreage by 50,000 acres. The resulting water demand estimates obtained by our econometric
approach were compared and contrasted with the conventional alternative, (physical/engineering) approach
through the calculation of “slippage.” This provided insights into the appropriate model for forecasting crop
acreage, and hence for forecasting agricultural water demands (Table 1).
In addition, as an out-of-sample forecast, prices projected by Food and Agricultural Policy Research Institute
(FAPRI, 2011) for 2016 were used to forecast water demand through irrigated acreage predictions. Based on this
simulation, the conventional engineering approach was compared with the econometric approach proposed.
Results and Discussion
Respective R2 values for the corn, cotton, rice, and soybean equations were 0.92, 0.95, 0.96, and 0.91. About 50%
of the variables were significant with their expected signs, and about 80% of the significant variables had their
expected signs. Perhaps the most interesting result emerging from the irrigated acreage model was that the
expected profit of cotton in its own equation was negative and significant, indicating that cotton producers tended
to shift cotton acres out of irrigation and into dry land, reducing the percentage of irrigated cotton, when expected
profit from cotton production went up and vice versa.
Reduction-in-Irrigation-Capacity Scenario
Assuming there was a 50,000 acres policy-induced decrease in irrigation in 2006 over 2005, the differences
between the physical and econometric models would result in an increase of water savings of around 25%, as
measured by “slippage,” by shifting water out of irrigation from rice and soybeans into corn and cotton (Table 1).
[The same for a policy-induced 33,006-acre reduction, (FRDPA, 2001) in a study on the Flint River Basin in Georgia
was between 19% and 24%, depending on if acres were reduced simultaneously with prices or sequentially as in
the current illustrative study (Banerjee et al., 2007).]
Using 2016 FAPRI price projections ($2.99/bushel for corn, $0.60/lb for cotton, $8.87/cwt for rice, and
$6.37/bushel for soybeans), the slippage was also approximately 25%, with all the directional impacts (shifts in
water demand) of relevant crops as shown in Table 1, and hence not reported. The FAPRI projections use ending
stock prices, and the projections for all the crops under study were not different enough to illustrate a greater
change in the difference between the two approaches than already illustrated using 2006 prices. However, with
higher prices resulting in a major shift in acres from cotton and other crops to corn in 2007 and 2008, this
percentage savings of water could be presumed to be more pronounced for a study using updated commodity
prices.
Conclusions
Dependable and predictable water supply is vitally important for agricultural producers and hence for the general
well-being and economic development of a state/region. A physical/engineering model would not consider any
changes in economic or institutional conditions. Hence it would not account for changes in crop mix over time due
to economic or institutional changes. Our model takes into account such changes and reallocates irrigated acres in
the new economic or institutional regime by accounting for substitution and expansion effects.
The proposed method is based on water requirements of individual crops, thus capturing the intrinsic value of
each crop relative to its water-requiring potential. Thus, with the successful introduction and implementation of
the proposed model, farmers will have a better and more scientific method of anticipating water demand and
value for their crops not only in the wake of a short supply due to natural causes, but also due to government
policy that restricts water use. Policymakers will have a more precise method to calibrate acreage reduction
programs to meet targeted levels for reductions in agricultural water use. Results for Georgia (with surface water)
and Mississippi (with ground water) are well-comparable. The Alabama study results will hopefully confirm the
claim even further and provide robustness to this type of analysis. Future research could focus on a county-level
study for the states of Mississippi and Alabama with otherwise similar irrigation data. In fact, an ongoing study has
been looking at 34 Alabama counties (see Appendix). The robustness of this pilot study will indicate its scalability
and validity for use in other regions of the United States and even other countries such as India.
References
1. Alabama Cooperative Extension System (ACES), Alabama AG. Irrigation Info Network, available at:
http://www.aces.edu/anr/irrigation/ANR-545.html. Accessed on Jan 11, 2012.
2. Alabama River Basin Map, Accessed on Feb 01, 2012.
3. http://www.alabamamaps.ua.edu/contemporarymaps/Alabama/physical/basemaps.pdf.
4. Alabama Agricultural Statistics (1980 – 2005), http://www.nass.usda.gov/al. Accessed on Feb 01, 2012.
5. Banerjee, S.B., Tareen, I.Y., Gunter, L.F., Bramblett, J. and Wetzstein, M.E., (2007), “Forecasting Irrigation
Water Demand: A Case Study on the Flint River Basin in Georgia”, Journal of Agricultural and Applied
Economics, 39(3), pp. 641-655.
6. Bohrnstedt, G.W. and Goldberger, A.S., (1969), “On the Exact Covariance of Products of Random
Variables”, Journal of the American Statistical Association, 64(328), pp. 1439-1442.
7. Holt, M.T., (1999), “A Linear Approximate Acreage Allocation Model”, Journal of Agricultural and Resource
Economics, 24(2), pp. 383-397.
8. Commodity Research Bureau (CRB) (2007), CRB InfoTech CD – Futures.
9. Flint River Drought Protection Act (FRDPA) (2001), House Resolution 17, Georgia General Assembly.
10. Food and Agricultural Policy Research Institute (FAPRI), Commodity Price Projections, Center for
Agricultural and Rural Development (CARD), Iowa State University,
http://www.fapri.org/Outlook2001/Tables/CPrices.xls. Accessed October 01, 2011.
11. Georgia Environmental Protection Division (2001), Atlanta, GA: Geological Survey Branch.
12. Tareen, I.Y., (2001), “Forecasting Irrigation Water Demand: An Application to the Flint River Basin”,
Unpublished Ph.D. dissertation, Department of Agricultural and Applied Economics, The University of
Georgia, Athens, Georgia, USA.
13. United States Department of Agriculture – Economic Research Service (USDA-ERS), Briefing Room Farm
Income and Costs: Commodity Costs and Returns,
http://www.ers.usda.gov/briefing/farmincome/costsandreturns.htm. Accessed on October 01, 2011.
14. United States Department of Agriculture – National Agricultural Statistics Service (USDA-NASS),
http://www.usda.gov/nass/. Accessed on October 01, 2011.
15. United States Geological Survey (USGS) (2011a), http://al.water.usgs.gov/. Accessed on October 01, 2011.
16. United States Geological Survey (USGS) (2011b), http://ms.water.usgs.gov/ms_proj/eric/delta/index.html.
Accessed October 01, 2011.
17. Yazoo Mississippi Delta (YMD) Joint Water Management District, available at: http://www.ymd.org/.
Accessed October 01, 2011.
Table 1. Slippage in Measuring Change in Water Demand,a Mississippi, 2005 - 2006
_______________________________________________________ Crop Water Use
b Change in Water Demand
c
Physical Econometric _______________________________________________________ Corn 9.70 -33,012 118,116d Cotton 6.40 -112,188 -27,104 Rice 35.34 -289,225 -485,154 Soybeans 8.18 -170,825 -361,668_______________________________________________________ Total -605,250 -755,810 Slippagee -0.2488f _______________________________________________________ a Change in water demand is measured in acre-inches (1 acre-inch = 27,150 gallons).
b Measured in acre-inches per acre, based on 2002-2007 annual average (YMD, 2011).
c Change in physical (econometric) water demand = physical (econometric) crop distribution times the change in
crop irrigated acreage times the relevant water use coefficient. Crop distribution assumes no other major users of
water. The only other major water user in the state of Mississippi is catfish, but it has not used groundwater every
year in the period 2002-2007 (YMD, 2011).
d A positive (negative) change indicates an increase (decrease) in water demand.
e Slippage = 1 – (econometric change in water demand ÷ physical change in water demand).
f Slippage using FAPRI 2016 price projections ($2.99/bushel for corn, $0.60/lb for cotton, $8.87/cwt for rice, and
$6.37/bushel for soybeans) turned out be a very close 0.2483.
Source: relation between confined and unconfined aquifers; from USGS circular 1186, February 05, 2012,
http://en.wikipedia.org/wiki/File:Schematic_aquifer_xsection_usgs_cir1186.png
Figure 1. Schematic of an Aquifer
Source: Yazoo Mississippi Delta Joint Water Management District’s Water Use and Aquifer Trends in the Mississippi
Delta 2006 Report
Figure 2. Water Level Change, 2005-2006
Source: Yazoo Mississippi Delta Joint Water Management District’s Water Use and Aquifer Trends in the Mississippi
Delta 2007 Report
Figure 3. Water Level Change, 1997-2007
Source: http://www.nass.usda.gov/Statistics_by_State/Mississippi/Search/index.asp
Figure 4. Total Irrigated Acres, Census Years, MS, 1982-2002
430,901
630,901
830,901
1,030,901
1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002
Year
Acres
Source: http://www.ymd.org/pdfs/wateruse/Water%20Use%20Report%202007.pdf
Figure 5. Water Use, Acre-Inches, MS Delta, 2002-2007 Annual Average
0.00
10.00
20.00
30.00
40.00
Water Use
2002 2003 2004 2005 2006 2007
Year
Corn
Cotton
Rice
Soybean
Appendix
A Case Study on the Alabama-Coosa-Tallapoosa (ACT) and Apalachicola-Chattahoochee-Flint (ACF) River Basins
in Alabama
A competing variety of demands for water (e.g., for agriculture, domestic, and industrial purposes) drawn from the
ACT and ACF River Basins in Alabama prompt the need for more efficient management and better use of existing
supplies. A recent study by the Water Investigation Program of the Geological Survey of Alabama shows that
government policies in the future may further restrict usage of water for irrigating crops due to increased drought
and competing uses in the region.
Hence, we develop a model that will precisely predict and allot water demand for irrigating major row crops
specifically, corn and soybeans, on county-level basis grown within the ACT/ACF River Basin counties in Alabama.
This study is similar to, and buttresses, forecasting studies conducted by Tareen (2001) in Georgia and Banerjee
(2004, 2007) in Georgia and Mississippi, respectively, to demonstrate the strength of econometric modeling vis-à-
vis physical methods.
Data and Methods
Majority of the studies on irrigated acreage and forecasting water demand are based on states or multi-state
aggregate data, with few examinations in metropolitan areas and counties. The area chosen for our study consists
of 34 Alabama counties served jointly by the ACT and ACF River Basins (Alabama ACT/ACF River Basin, 2012a;
Alabama State County Map, 2012b; Figures 6 & 7).
Data for this ongoing pilot study are primarily obtained from U.S. Department of Agriculture – National
Agricultural Statistics Service (USDA-NASS) (2011) (data on state planted and irrigated acres by crop, and yields by
crop through 2007), Commodity Research Bureau (data on futures prices by crop through 2007), U.S. Department
of Agriculture – Economic Research Service (USDA-ERS) (2011) (data on variable costs by crop through 2007), and
Alabama Cooperative Extension System (ACES) Ag Irrigation Info Network Water Management and Scheduling
(2011) (data on water use by crop through 2008).
A time series for Alabama starting in 1980 and ending in 2005 has been chosen for the sample. Years 2006 and
2007 have been chosen for out-of-sample forecasts. The underlying theoretical foundation of the ongoing study is
the Expected Utility Theory (from a producer’s perspective) as delineated in equation (1) in the main section of the
paper.
Source: Alabama River Basin Map.
http://www.alabamamaps.ua.edu/contemporarymaps/Alabama/physical/basemaps.pdf
Figure 6. Alabama ACT/ACF River Basin, 2012
Source: National Atlas of the United States, United States Department of the Interior.
http://www.washingtonstatesearch.com/United_States_maps/Alabama/Alabama_county_map.html
Figure 7. Alabama State County Map, 2012
Financial Inclusion and Its Determinants in India: An Inter-State Analysis Arindam Laha
Assistant Professor in Economics
Department of Commerce, the University of Burdwan
Amar Nath Das
Ph. D Research Scholar (External)
Department of Commerce, the University of Burdwan
Pravat Kumar Kuri
Associate Professor in Economics
Department of Economics, the University of Burdwan
Abstract
Financial inclusion is a process to bring about the weaker and vulnerable sections of society within the ambit of the
organized financial system. This paper attempts to explore the level of access to the formal banking network and to
examine the extent of inter-state variations in financial inclusion at the disaggregated level. The level of financial
inclusion is measured by a composite index using the penetration, availability and usage indicators of banking
access. Empirical results confirm a wide inter-state variation in the level of financial inclusion in India. This paper
also identifies the underlying factors that are responsible for explaining the process of financial inclusion across
states of India. Analysis based on Regression Analysis establishes that the per capita net state domestic product,
extent of urbanization, energy consumption and outreach of surface communication are crucial factors which have
significant bearings in promoting the process of financial inclusion in India.
Introduction
Access to financial service to a given population can be measured in terms of access to certain institutions, such as
banks, other formal and informal financial institutions. The residual sections of the population, who do not have
access to these financial institutions, whether voluntary or involuntary, are considered as financially excluded. In
this context, there is a growing recognition that increasing access to formal financial access can cause economic
growth and thereby improve income distributioni (World Bank, 2005). In fact, Levine (1997) established a close
linkage and transmission mechanism through which finance and economic growth are linked. Access of financial
services can promote the channels to growth by way of capital accumulation and technological innovation. In the
context of India economy, empirical evidences suggest that financial intermediaries in India have played a crucial
role in influencing its economic performance (Bell and Rousseau, 2001). Even the expansion of banking outreach in
rural areas was associated with non-agricultural growth and thereby ultimately helped in reducing rural poverty in
India (Burgess, Pande and Wong, 2004). Thus adoption of supply lending strategies would promote financial
inclusion. However, the Committee on Financial Inclusion (NABARD, 2008) is of the view that financial exclusion is
also caused by demand side issues. Thus, both supply side and demand side barriers are responsible for lower
achievement in financial inclusion. The Committee has identified some supply side constraints as poor banking
infrastructure, low resource base of credit purveying institutions, security based lending procedures, lengthy and
cumbersome procedure, low level of financial literacy etc. From the perspectives of demand side or ‘real sector’,
some important constraints are inadequate human capital, skewed distribution of land, presence of large section
of landless labourers, poor state of physical infrastructure, underdeveloped social capital, low productivity, poor
risk management mechanisms etc. Truly, providing access to financial services by addressing both demand and
supply side constraints is a challenging task before the policy makers for the obvious reason that it has far reaching
economic and social implications (Mehrotra et al, 2009).
Identification of financially excluded section of population is therefore urgently called for effective implementation
of such inclusive government policies targeting vulnerable groups. In a narrow sense the extent of non-
indebtedness both in formal and informal sources can be considered as a crude measure of financial exclusion
(NSSO, 2005). The National Sample Survey data reveals that. 51.4 per cent of the total farm households do not
have any access to credit either from institutional or non-institutional sources. More importantly, despite the vast
network of rural branches, only 27 per cent of the total farm households are indebted to formal sources; of them
one-third also borrow from informal sources. However, apart from the fact that exclusion itself is large, it also
varies widely across regions, social groups and asset holdings. The proportion of households having no access to
formal credit is alarmingly high at 95.91 per cent, 81.26 per cent and 77.59 per cent in the North Eastern, Eastern
and Central Regions respectively. The Southern region, on the other hand, exhibits relatively better levels of access
to formal or informal sources (72.7 per cent) mainly on account of spread of banking habits and a more robust
infrastructure. Under the circumstances, attempts have been made in this paper to examine the inter-state
variations in the level of financial inclusion on the basis of three separate dimensions, i.e., penetration, availability
and usage of banking services. These three dimensions are then combined to construct a comprehensive index of
financial inclusion. The index is used to measure the extent of financial inclusion across states of India both at
aggregative and dis-aggregative level. Moreover, the paper identifies important socio-economic and infrastructural
factors affecting the level of financial inclusion across states of India.
Data Sources and Methodology
Construction of the Index of Financial Inclusion
The level of financial inclusion is measured by several dimensions like amount of bank credit and amount of bank
deposit, number of bank branches per million people or number bank accounts per 1000 adult people etc. For the
first time Sarma (2008)ii introduced a comprehensive measure of financial inclusion incorporating several aspects
of inclusion- banking penetration, availability of banking services and uses of financial services. The IFI incorporates
information on these dimensions in one single number lying between 0 and 1, where 0 denotes complete financial
exclusion and 1 indicates complete financial inclusion in an economy. IFI has been computed by the following
formula:
IFIi = n
ddd n
222
2 )1(....)1()1(1
1
where Dimension Index=ii
ii
mM
mAd ( ),...2,1 ni
Here Ai = Actual value of dimension i
mi = lower limit for dimension i, given by the observed minimum for dimension i
Mi = upper limit for dimension i, given by the observed maximum for dimension i
Higher the value of di, higher would be the states’ achievement in dimension i and thereby ultimately on higher IFI.
Three important dimensions of financial inclusion that have been identified in the study are: banking penetration,
availability of banking services and uses of financial services.
Banking penetration (dimension 1): An inclusive financial system should entail as many users as possible in its
network. A number of people having a bank account (banked population) is a measure of the banking penetration
of the system. In the absence of the data on “banked” population number of bank accounts is used as a proportion
of the total population as an indicator of this dimension. Data on number of bank accounts for different states of
India as on March 2010 are collected from Basic Statistical Returns (RBI), 2010. Data on number of adult
population are considered by taking into account the Census of 2011.
Availability of banking services (dimension 2): Easy availability of banking services is an indicator of inclusive
financial system. Availability of services can be indicated by the number of bank outlets (per 100 populations). We
use the number of bank branches per 100 populations to measure the availability dimension. Data on number of
bank branches for different states of India as on March 2010 were collected from Basic Statistical Returns (RBI),
2010.
Usage (dimension 3): Highly banked rural/urban areas does not ensure that all the account holder use the services
provided by banks. This dimension has been selected by the notion of “under banked” or “marginally banked”
people, thus, merely having a bank account is not enough for an inclusive financial system; it is also imperative that
the banking services are adequately utilized. In incorporating the usage dimension in our index, we consider two
basic services of the banking system – credit and deposit. Accordingly, the volume of credit and deposit as
proportion of a state’s net GDP has been used to measure this dimension. Data on volume of credit and deposit
(Rs. Lakh) as on March 2010 were collected from Basic Statistical Returns (RBI), 2010. Data on Net State Domestic
Product (NSDP) at current prices for 2009-10 are collected from Central Statistical Organization (2011).
Multiple Regression Analysis
In the analysis, several socio-economic and infrastructural variables are identified which have significant impact on
the process of financial inclusion. The estimated value of index of financial inclusion is considered as dependent
variable. To determine the variables relevant for financial inclusion in a state, we have considered several socio-
economic and infrastructural variables obtained from Census, CSO, Ministry of Human Resource Development,
Ministry of Road Transport and Highways. The explanatory variables and hypothesis (as specified by expected sign)
are presented in table I below.
Table I
Expected sign of the determinants to determine Financial Exclusion
Independent
Variables
Notation
Description
Financial
Inclusion
Literacy Rate
Sex Ratio
Urbanization
Per Capita NSDP
Area of the state
Electricity
Road length
Educational
infrastructure
LITERACY
SEX_RATIO
URBAN
NSDP
AREA
ELECTRIC
ROAD
EDUDEV
7+ literate population as a
proportion of total 7+ adult
population
Proportion of female
population as a ration of total
population (in thousand)
Percentage of urban
population as a proportion of
total population
NSDP as a proportion of total
population of the state
Parentage of area (km square)
of the state
Per capita consumption of
gross energy generation
Road length per100 sq.km. of
area
Composite Education
Development Index(primary
and upper primary)
+
–
+
+
+/ –
+
+
+
Result of Regression Analysis is obtained by using sophisticated statistical package like E-Views.
Dimensions of Financial Inclusion in India: An Inter-State Analysis
Banking Penetration across States of India:
One of the common measure of financial inclusion is the number of bank accounts (i.e., savings, current and term
accounts) as a proportion of total adult population. The available secondary data (as shown in Table II) suggest that
in the states like Delhi, Chandigarh, Goa, Punjab, Pondicherry, Himachal Pradesh, Kerala, Uttaranchal, Haryana,
Karnataka, Andhra Pradesh, Tamil Nadu, Jammu & Kashmir and Gujarat, an average individual has more than one
bank account. A significant inter-state variation in the number of bank account is noticeable, as an average
individual in Delhi, Chandigarh and Goa has more than three bank accounts, while more than 40 percent of the
adult population in Mizoram, Bihar and Manipur do not have any bank account. Such unbanked population is
higher in north-eastern and eastern regions.
Table II
Index for Penetration across States of India
States/UTs
No of
accounts per
100 of adult
population
Index for
Penetration Rank
Delhi 355.86 1.00 1
Chandigarh 332.95 0.93 2
Goa 326.77 0.91 3
Punjab 155.49 0.37 4
Pondicherry 152.41 0.36 5
Himachal Pradesh 137.12 0.31 6
Kerala 130.87 0.29 7
Uttaranchal 129.03 0.28 8
Haryana 127.37 0.28 9
Karnataka 124.81 0.27 10
Andhra Pradesh 122.18 0.26 11
Tamil Nadu 119.41 0.25 12
Jammu & Kashmir 117.06 0.24 13
Andaman & Nicobar 111.06 0.22 14
Gujarat 104.54 0.20 15
Maharashtra 99.15 0.19 16
Sikkim 93.69 0.17 17
Uttar Pradesh 93.24 0.17 18
West Bengal 88.11 0.15 19
Tripura 84.85 0.14 20
Arunachal Pradesh 76.91 0.12 21
Jharkhand 75.80 0.11 22
Rajasthan 75.25 0.11 23
Orissa 74.54 0.11 24
Madhya Pradesh 72.08 0.10 25
Assam 71.86 0.10 26
Meghalaya 66.67 0.08 27
Chhattisgarh 63.85 0.07 28
Mizoram 58.05 0.06 29
Bihar 52.63 0.04 30
Manipur 40.23 0.00 31
All India 100.24 0.19
Sources: Authors calculation based on Basic Statistical Return of SCBs (Reserve Bank of India) March 2010, Census
of India, 2011.
Figure I: Index for Penetration across states of India
00.20.40.60.8
11.2
Index for Penetration Across States of India
Index for Penetration
Financial Services Availability across States of India:
Availability of banking services can be considered an important indicator of financial inclusion from the supply side
point of view of financial institutions. Table III suggest that states like Chandigarh, Goa, Himachal Pradesh, Delhi,
Punjab, Uttaranchal, Sikkim, Kerala, Pondicherry, Haryana, Karnataka, Mizoram, Andaman & Nicobar, Jammu &
Kashmir, Meghalaya, Tamil Nadu, Andhra Pradesh, and Gujarat have more bank branches in comparison to all India
average of 1.19 branches per 10000 adult populations. A significant inter-state disparity in the availability of
banking services in India is evident as more than four banks are found in operation for more than 10000 adult
population in states like Chandigarh, Goa and Himachal Pradesh, while only one bank is serving for more than
10000 adult population in states like Uttar Pradesh, West Bengal, Chhattisgarh, Assam, Bihar and Manipur.
Table III
Index for Availability across States of India
States/UTs
No of bank
branches
per 100
adult pop
Index for
Availability Rank
Chandigarh 0.047 1.000 1
Goa 0.043 0.913 2
Himachal Pradesh 0.023 0.436 3
Delhi 0.023 0.434 4
Punjab 0.020 0.366 5
Uttaranchal 0.020 0.365 6
Sikkim 0.020 0.360 7
Kerala 0.019 0.329 8
Pondicherry 0.017 0.286 9
Haryana 0.016 0.268 10
Karnataka 0.016 0.263 11
Mizoram 0.015 0.245 12
Andaman & Nicobar 0.015 0.235 13
Jammu & Kashmir 0.014 0.224 14
Meghalaya 0.014 0.210 15
Tamil Nadu 0.013 0.193 16
Andhra Pradesh 0.013 0.192 17
Gujarat 0.012 0.178 18
Maharashtra 0.012 0.156 19
Rajasthan 0.011 0.143 20
Orissa 0.011 0.140 21
Arunachal Pradesh 0.010 0.125 22
Tripura 0.010 0.124 23
Madhya Pradesh 0.010 0.122 24
Jharkhand 0.010 0.119 25
Uttar Pradesh 0.009 0.104 26
West Bengal 0.009 0.101 27
Chhattisgarh 0.009 0.089 28
Assam 0.008 0.073 29
Bihar 0.007 0.054 30
Manipur 0.005 0.000 31
All India 0.012 0.163
Sources: Authors calculation based on Basic Statistical Return of SCBs (Reserve Bank of India) March 2010, Census
of India, 2011.
Figure II: Index for Availability across states of India
Usage of Financial Services across States of India:
Credit and deposit as a proportion of NSDP can be considered as one of the important indicator of actual utilisation
of fiancial services by the population. Usage of banking habit is crucial as it indicates the development of banking
habit subject to the constraint of the penetration and availability of banking services. The volume of credit and
deposit is found to be more than double than NSDP in the states like Delhi, Chandigarh and Maharashtra. The
inequality in the usage of financial services is widespread in India, out of 31 States 24 states are lagging behind the
all India average. A poor rating of banking habit is observed in Mizoram, Andaman & Nicobar and Manipur.
Table IV
Index for Usage across States of India
States/UTs
Credit and
Deposit as a
proportion of
NSDP
Index for
Usage Rank
Delhi 4.762 1.000 1
Chandigarh 4.593 0.960 2
Maharashtra 2.688 0.512 3
Karnataka 1.687 0.276 4
00.20.40.60.8
11.2
Index for Availability Across States of India
Index for Availability
Goa 1.657 0.269 5
Tamil Nadu 1.428 0.215 6
Jammu & Kashmir 1.414 0.212 7
Punjab 1.319 0.189 8
Kerala 1.225 0.167 9
West Bengal 1.225 0.167 10
Andhra Pradesh 1.198 0.161 11
Himachal Pradesh 1.074 0.132 12
Sikkim 1.038 0.123 13
Jharkhand 1.026 0.120 14
Madhya Pradesh 0.987 0.111 15
Uttar Pradesh 0.976 0.109 16
Uttaranchal 0.967 0.106 17
Gujarat 0.960 0.105 18
Pondicherry 0.953 0.103 19
Orissa 0.948 0.102 20
Haryana 0.911 0.093 21
Rajasthan 0.891 0.088 22
Meghalaya 0.855 0.080 23
Arunachal Pradesh 0.839 0.076 24
Assam 0.820 0.072 25
Bihar 0.808 0.069 26
Chhattisgarh 0.780 0.062 27
Tripura 0.766 0.059 28
Mizoram 0.676 0.038 29
Andaman & Nicobar 0.625 0.026 30
Manipur 0.516 0.000 31
All India 1.443 0.218
Sources: Authors calculation based on Basic Statistical Return of SCBs (Reserve Bank of India) March 2010, Central
Statistical Organization, 2011.
Figure II: Index for Usage across states of India
Financial Inclusion in India: An Inter-State Analysis
Segregated analysis of all three dimensions suggests that the performances of the states are not uniform in all the
dimensions of financial inclusion. In some states like Himachal Pradesh, Punjab and Pondicherry, a highly banked
areas do not ensure that all the account holder use the services provided by banks. Banking services are
adequately utilized in these states in comparison to Maharashtra and Karnataka where banking penetration or
availability of banking services is not so well developed. Thus a composite analysis based on all three dimensions is
desirable to provide an overall picture of the level of financial inclusion in Indian States. To measure the inter-state
variations in the level of financial inclusion in a broader sense we have considered three dimensions of financial
inclusion namely banking penetration (the number of bank accounts as a proportion of the total adult population),
availability of banking services (the number of bank branches per 1000 adult population), usages of banking
services (the volume of credit and deposit as proportion of the state’s Net State Domestic Product). Accordingly,
the inter-state variations in the level of financial inclusion as estimated are shown in table below. Table V
illustrates the values of IFI for 31 states or UTs of India.
00.20.40.60.8
11.2
Index for Usage Across States of India
Index for Usage
Table V
Index for Financial Inclusion across States of India
States/UTs Index for
Penetration
Index for
Availability
Index for
usage IFI Rank
Chandigarh 0.927 1.000 0.960 0.952 1
Delhi 1.000 0.434 1.000 0.673 2
Goa 0.908 0.913 0.269 0.572 3
Punjab 0.365 0.366 0.189 0.302 4
Himachal Pradesh 0.307 0.436 0.132 0.281 5
Karnataka 0.268 0.263 0.276 0.269 6
Maharashtra 0.187 0.156 0.512 0.267 7
Kerala 0.287 0.329 0.167 0.258 8
Uttaranchal 0.281 0.365 0.106 0.243 9
Pondicherry 0.355 0.286 0.103 0.241 10
Jammu & Kashmir 0.243 0.224 0.212 0.226 11
Tamil Nadu 0.251 0.193 0.215 0.219 12
Sikkim 0.169 0.360 0.123 0.211 13
Haryana 0.276 0.268 0.093 0.208 14
Andhra Pradesh 0.260 0.192 0.161 0.203 15
Gujarat 0.204 0.178 0.105 0.161 16
Andaman & Nicobar 0.224 0.235 0.026 0.156 17
West Bengal 0.152 0.101 0.167 0.139 18
Uttar Pradesh 0.168 0.104 0.109 0.126 19
Meghalaya 0.084 0.210 0.080 0.123 20
Jharkhand 0.113 0.119 0.120 0.117 21
Orissa 0.109 0.140 0.102 0.117 22
Rajasthan 0.111 0.143 0.088 0.114 23
Madhya Pradesh 0.101 0.122 0.111 0.111 24
Mizoram 0.056 0.245 0.038 0.108 25
Tripura 0.141 0.124 0.059 0.107 26
Arunachal Pradesh 0.116 0.125 0.076 0.106 27
Assam 0.100 0.073 0.072 0.081 28
Chhattisgarh 0.075 0.089 0.062 0.075 29
Bihar 0.039 0.054 0.069 0.054 30
Manipur 0.000 0.000 0.000 0.000 31
All India 0.190 0.163 0.218 0.190
Sources: Authors calculation based on Basic Statistical Return of SCBs (Reserve Bank of India) March 2010, Census
of India, 2011, Central Statistical Organization, 2011.
Table V indicates that Chandigarh occupies the highest ranking in the IFI with a value of 0.952. It is followed by
Delhi and Goa, which belong to the high IFI group with IFI values of 0.5 or more. Another eleven states, viz. Punjab,
Himachal Pradesh, Karnataka, Maharashtra, Kerala, Uttaranchal, Pondicherry, Jammu and Kashmir, Tamil Nadu,
Sikkim, Haryana and Andhra Pradesh form the group of medium IFI states with IFI values between 0.2 and 0.5. All
the other states bear a low IFI values, lying between 0.0000 and 0.161. These include states like Gujarat (16th
),
Andaman and Nicobar island (17th), West Bengal (18th), Uttar Pradesh (19th), Meghalaya (20th), Jharkhand (21st),
Orissa (22nd), Rajasthan (23rd), Madhya Pradesh (24th), Mizoram (25th), Tripura (26th), Arunachal Pradesh (27th),
Assam (28th
), Chhattisgarh (29th
), and Bihar (30th
). At the lowest rank of IFI values is Manipur (31th
rank) with a low
IFI value of 0.000. It needs to be pointed out that most of the states with high IFI values are belong to northern and
western region. Overall, the empirical result seems to suggest that northern (Haryana, Himachal Pradesh, Punjab.
Rajasthan, Chandigarh, Delhi), western (Gujarat, Maharashtra, Goa) and southern (Andhra Pradesh, Kerala,
Karnataka, Tamil Nadu, Pondicherry) regions are better performers in including the excluded in the financial
network than north-eastern (Assam, Manipur, Meghalaya, Tripura, Arunachal Pradesh, Mizoram), eastern (Bihar,
Orissa, West Bengal, Andaman and Nicobar island, Sikkim, Jharkhand) and central (Madhya Pradesh, Uttar
Pradesh, Chhattisgarh, Uttaranchal) region.
Indices of rural and urban Financial Inclusion have been constructed separately for 27 states of India for the year
2010iii and are presented in Table VI.
Table VI: Index for Financial Inclusion across States of India
(Rural, Urban and Combined)
State
IFI
(Rural)
Rank
(Rural)
IFI
(Urban)
Rank
(Urban)
IFI
(Comb.)
Rank
(Comb.)
Rank
(Urban)
–
Rank
(Rural)
Andhra
Pradesh 0.060 12 0.330 7 0.203 13 -5
Assam 0.039 20 0.234 16 0.081 24 -4
Bihar 0.026 24 0.281 11 0.054 26 -13
Chandigarh 1.000 1 0.853 1 0.952 1 0
Chhattisgarh 0.023 25 0.184 20 0.075 25 -5
Delhi 0.348 2 0.684 2 0.673 2 0
Gujarat 0.050 16 0.212 18 0.161 14 2
Haryana 0.053 15 0.356 5 0.208 12 -10
Himachal
Pradesh 0.135 5 0.251 14 0.281 4 9
Jammu &
Kashmir 0.102 8 0.280 12 0.226 10 4
Jharkhand 0.056 13 0.174 22 0.117 18 9
Karnataka 0.068 11 0.412 3 0.269 5 -8
Kerala 0.183 3 0.098 25 0.258 7 22
Madhya
Pradesh 0.038 21 0.220 17 0.111 21 -4
Maharashtra 0.021 26 0.405 4 0.267 6 -22
Manipur 0.002 27 0.025 27 0.000 27 0
Meghalaya 0.040 19 0.283 10 0.123 17 -9
Mizoram 0.078 10 0.031 26 0.108 22 16
Orissa 0.048 17 0.266 13 0.117 19 -4
Pondicherry 0.164 4 0.155 23 0.241 9 19
Punjab 0.131 6 0.332 6 0.302 3 0
Rajasthan 0.037 22 0.240 15 0.114 20 -7
Tamil Nadu 0.094 9 0.208 19 0.219 11 10
Tripura 0.053 14 0.113 24 0.107 23 10
Uttar
Pradesh 0.041 18 0.296 8 0.126 16 -10
Uttaranchal 0.122 7 0.180 21 0.243 8 14
West Bengal 0.026 23 0.289 9 0.139 15 -14
All India 0.047 -- 0.277 -- 0.190 -- --
Sources: Authors calculation based on Basic Statistical Return of SCBs (Reserve Bank of India) March 2010, Census
of India, 2011, Central Statistical Organization, 2011.
Overall, it can be argued that the extent of financial inclusion in urban areas (0.277) is comparatively higher than
rural India (0.047)iv. A positive value in the rank difference indicates rural areas are performing better than urban
areas, while a negative value indicates the general pattern, i.e., urban areas are financially more included than
rural areas. Chandigarh maintains a consistently first position irrespective of urban and rural financial inclusion and
thus the rank difference is found to be zero. In most of the States rank differences are found to be negative.
However, in some states like, Gujarat, Himachal Pradesh, Jammu & Kashmir, Jharkhand, Kerala, Mizoram,
Pondicherry, Tamil Nadu, Tripura and Uttaranchal a noticeable exception arises in the sense that rural areas are, in
fact, more included than urban areas. This exception is mainly because of the fact that size and the degree of
urbanization of the States are not uniform. All these states are smaller in size except Jammu & Kashmir, Gujarat
and Tamil Nadu. In small sized states with lower degree of urbanization, rural areas are found to be financially
more included than the urban areas.
Determinants of Financial Inclusion
The process of financial inclusion is conditioned upon a numbers of factors; some are social, some are economic,
some are demographic, some are infrastructural and some are institutional. In this section an attempt has been
made to examine the effect of various socio-economic and infrastructural determinants in explaining financial
inclusion in India. For this purpose major Indian states have been selected for these two sets of regression analysis.
In the first set of regression analysis, Index of Financial Inclusion is considered as a dependent variable whereas
literacy rate, urbanization, sex ratio, per capita NSDP, percentage of area (km square) of the state are considered
as independent variables. The result of the Regression Analysis for 22 statesv is presented in the table VII.
Table VII
Socio-economic Determinants of Financial Inclusion
Dependent Variable: IFI
Method: Least Squares
Included observations: 22
Variable Coefficient Std. Error t-Statistic Prob.
C 0.207545 0.278360 0.745601 0.4667
LITERACY 0.001580 0.003258 0.484989 0.6343
SEX_RATIO -0.000329 0.000340 -0.967212 0.3478
NSDP 2.51E-06 8.70E-07 2.886537 0.0107
URBAN 0.002479 0.001316 1.884021 0.0779
AREA -0.369079 0.511317 -0.721821 0.4808
R-squared 0.870996 Adjusted R-squared 0.830682
Note: The statistical analysis has been made using E-Views statistical package
It is undeniable fact that the attainment of a higher level of per capita NSDP fulfills the basic needs and economic
wellbeing of the weaker section of the society. This, in turn, has far reaching consequences in the process of
financial inclusion. The co-efficient of the variable indicating NSDP is found to be positive and statistically
significant. It supports the contention that the states with higher per capita NSDP augment the banking habits
among the people and thereby intensify the process of financial inclusion. The degree of urbanization positively
influences the level of financial inclusion through the development of the secondary and tertiary sectors of the
economy. In fact, banking penetration is inevitable in the process of urbanization. This hypothesis is supported by
the positive and significant coefficient of urbanization and our result is consistent with the study of Sharma and
Pais (2008) and Singh and Kodan (2011). Formation of human capital plays an important role in the process of
financial inclusion. Empirical evidence suggests that states with higher literacy rates have achieved higher financial
inclusion but it is found to be statistically insignificant. To examine the role of gender in the process of financial
inclusion sex ratio has been chosen as an independent variable. The sex ratio is found to be inversely associated
with the achievement of financial inclusion. This implies the gender disparity prevails in the process of financial
inclusion, women are observed to be financially less included than men. However, the result is again not
statistically significant. Regression result established that small states are financially more included than larger
ones.
In the second set of regression analysis, Index of Financial Inclusionvi is considered as a dependent variable
whereas availability of electricity, road and educational development of the state are considered as independent
variables. The result of the Regression Analysis for 31 states is presented in the table VII.
Table VII
Infrastructure related Determinants of Financial Inclusion
Dependent Variable: IFI
Method: Least Squares
Included observations: 31
Variable Coefficient Std. Error t-Statistic Prob.
C -0.073757 0.099952 -0.737927 0.4669
ELECTRIC 8.18E-05 3.50E-05 2.337695 0.0271
ROAD 0.000245 3.24E-05 7.556986 0.0000
EDUDEV 0.211584 0.179640 1.177824 0.2491
R-squared 0.781105 Adjusted R-squared 0.756783
Note: The statistical analysis has been made using E-Views statistical package
To examine the role of energy consumption as an infrastructural factor in the process of financial inclusion
electricity has been chosen as an independent variable. It is found to be statistically significant and positively
associated with the achievement of financial inclusion. This implies that higher energy consumption tends to lower
financial exclusion. The co-efficient of the variable indicating road length is found to be positive and statistically
significant. It supports the contention that the road as an infrastructure could intensify the process of financial
inclusion. Education plays an important role in formation of human capital which is likely to influence the process
of financial inclusion. Empirical evidence suggests that states with development of primary and upper-primary
level has a positive impact on the process of financial inclusion but the result is found to be statistically
insignificant.
Conclusions
Financial inclusion is a process to include the people who lack formal financial services. There is observed to be a
wide inter-state variation in the level of financial inclusion in India. An analysis of the components of financial
inclusion suggest that the performance of the states on the basis of three dimensions are not the same, i.e., some
states are performing better in respect of some dimensions but their positions are not found uniform across all
dimensions of financial inclusion. Thus a composite analysis based on all three dimensions is desirable to provide a
composite picture of the financial inclusion. The composite indicator of financial inclusion suggests that
Chandigarh is at the top and Manipur is at the bottom in terms of the level of financial inclusion. Further, a
disaggregated analysis reveals that there exists a wide disparity in the access to financial services in rural and
urban areas of the country. Urban India is financially more included than rural India. It is thus desirable to enhance
the outreach of banking services in the rural area. In this context, to address the problem the study dealt with an
inter-state analysis of the socio-economic and infrastructural determinants of financial inclusion. Empirical results
using Multiple Regression Analysis suggest that the net state domestic product and extent of urbanization among
the socio-economic variables and energy consumption and outreach of surface communication among the
infrastructural variables significantly enhances the degree of financial inclusion. The coefficients of the level of
literacy rate, sex ratio and area of the state are not found to be statistically significant.
Endnotes: i Economists have divergent opinions regarding the importance of financial system for economic growth. For an excellent survey of the various issues in this area, see Levine (1997). ii Sarma (2008) has, in fact, computed the values of IFI for 54 countries using the three basic dimensions of financial
inclusion– accessibility, availability and usage of banking services. iii The existing data from Basic Statistical Return (2010) on three dimensions are partitioned into four areas such as
rural, semi-urban, urban and metropolitan areas. We have broadly defined a rural area as composed of both rural and semi-urban, whereas urban area is composed of both urban and metropolitan areas. It, thus help us to examine the variations of financial inclusion in rural/urban areas across the states of India. The states like Arunachal Pradesh, Nagaland, Sikkim, Andaman & Nicobar, Goa, Dadra & Nagar Haveli, Daman & Diu, and Lakshadweep do not have any urban or metropolitan area. iv It is to be noted that all of the three dimensions of financial inclusion, such as, penetration (d1), accessibility (d2) and usage (d3) of formal banking services are significantly lower in rural areas in comparison to urban areas. v Due to non-availability of data for some socio-economic variables for all the states of India, the study is restricted to the 22 Indian states. vi Index of financial inclusion is computed for the year 2008-09 in order to make it compatible with the infrastructural determinants which are available for the year 2008-09.
References
1. Bell, C. and Rousseau, P. L. (2001), “Post-independence India: A case of finance-led industrialization?”,
Journal of Development Economics, 65(1), pp. 153-175.
2. Burgess, R., Pande, R. and Wong, G. (2004), Banking for the Poor: Evidence from India, available at:
http://econ.lse.ac.uk/staff/rburgess/eea/jeeabankindia.pdf. Accessed on October 2008.
3. Mehrotra, N., Nair, G. and Sahoo, B. B. (2009), Financial Inclusion-An Overview, NABARD Occasional
Paper-48.
4. NABARD (2008), Report of the Committee on Financial Inclusion, January, available at:
www.nabard.org/report_comfinancial.asp. Accessed on April 2009.
5. NSSO (2005), Situational Assessment of Farmers: Indebtedness of Farmer Households, NSS 59th round.
6. Levin, R. (1997), “Financial Development and Economic Growth: Views and Agenda”, Journal of Economic
Literature, 35(June).
7. Reserve Bank of India (2010), Basic Statistical Return of Scheduled Commercial Banks in India, 39 (March).
8. Sarma, M. (2008), Index of Financial Inclusion, Working paper no. 215, Indian Council for Research on
International Economic Relations, June.
9. ------------- and Pais, J. (2008), Financial Inclusion and Development: A Cross Country Analysis, available at:
www.icrier.org/pdf/6nov08/Mandira%20Sarma-Paper.pdf. Accessed on June 2009.
10. Singh, K. and Kodan, A. S. (2011), “Financial Inclusion, Development and Its Determinants: An Empirical
Evidence of Indian States”, The Asian Economic Review, 53(1), 115-134, April 2011.
11. World Bank (2005), Indicators of Financial Access: Household Level Surveys, Financial Sector Vice-
Presidency, The World Bank.
Exploring the Significance of Physical Resources in B-School Selection Decision in West Bengal Subrata Chattopadhyay Assistant Professor and Head-Corporate Relations Future Institute of Engineering and Management Saumya Singh Associate Professor ISM University, Dhanbad Abstract
Physical infrastructure and facilities do have a lot of bearing in the selection of B-School for Management Program- facilities like A.C online smart classroom with laptop ports and acoustics, 24*7 centralised library, state of the art infrastructure, lush green campus in an Industrial township, Wi-fi Campus with Hi tech facilities, 24*7 cyberlabs, A.C Auditorium, study rooms, clubs, conferencing and research facilities, cafeteria and leisure etc. do carry a lot of importance and the regulatory bodies like U.G.C, AICTE,NAAC etc. have prescribed the basic norms for the same which go a long way towards promotion of excellence. The development, maintenance, up-gradation and extension of the physical infrastructure and facilities with emphasis overall development of the management studies is the need of the hour, hence a primary research study on the same was carried out using stratified random sampling to understand the perception of the aspirers and the pursuers regards physical resources and infrastructural facilities and how they create a lasting impression in their decision for management studies.
Keywords: physical infrastructure, intellectual capital, physical capital perception
Introduction: Impact of Physical resources in selection of MBA by the respondents
IIM-A, unquestionably provides the benchmark, but there is very little difference between the other B-Schools at
the top 25 (Sinha, 2007). Du Plessis and Rousseau (2005:111) state that it is imperative that organisations realise
that a consumer’s perception is a reality for the consumer and determine how they act towards the organisation
and its service products. Mabote (2001:62) is of the opinion that perception is equal to the truth, which, if not
managed, can destroy an organisation.
Dworkin (2002:33) suggests that students should consider the following factors before choosing where and what
to study:
• Students should decide whether recreation and sporting facilities are important or not
• Awards, scholarships, bursaries and loans awarded by the institutions should be investigated;
• Students should decide what size of institution they should study at. Larger institutions may be more
impersonal while smaller institutions may have fewer students in class and students may thus receive
more individual attention;
• Students should check the entry requirements and see whether they qualify; and students should
investigate whether the institution will equip them with skills required by industry and whether the
institution offer practical training and assist in job placements.
Haviland (2005:62) expresses the opinion that the “feel” of a higher education institution can also influence the
attitude of prospective students and thus influence their selection process.
In the beginning of the year 2007, while many good schools have initiated new plans, the quality of majority of
Institutions still lies below the desired level. At the most basic level, in a very traditional manner, structural
deficiencies like education infrastructure, lax standards, absence of strict norms etc. make majority of the 1800
management Institutions vulnerable to changing times.
The Ishwar Dayal Committee emphasized that most of the committees set up during the 90’s did not follow the
conditions prescribed by the AICTE in respect of library, faculty, computer facilities and the like. Due to rapid
expansion of the teaching institutions AICTE was unable to develop adequate mechanism to enforce standards.
AICTE committee set up in 2003 put increased focus on infrastructure. It also emphasized that to keep our
country’s globalization initiatives open, the presence of an Indian institution abroad with quality infrastructure and
facilities was required. All the four committees focused on little attention provided in the preparation of course
materials specific to the Indian context. Library and computer infrastructure is poor barring top management
institutions.
Till March 2007, the National Accreditation and Assessment Council (NAAC), an autonomous body set up by the
government to ensure quality, could accredit only 3942 colleges and 140 Universities across the country; which
was based on parameters like educational qualifications of the faculty, availability of books, sports facilities, and
infrastructure and so on. This implies that nearly 75% of the colleges and 56% of the Universities have never been
assessed for the quality standards by the body set up for the purpose. Hence working towards accreditation, both
National and International will be an important tool in ensuring quality standards (Chandra, 2003).
Literature Review:
The infrastructure in universities has undergone large scale obsolescence without adequate replacement – a
factor partially responsible for poor quality of education and low levels of satisfaction among students. The lead
surrendered to the rest of the world during this period has not been recovered in later periods though the
proportion of GDP spent on higher education has recovered to 0.6 percent. Budgeted plan expenditure is less than
5 percent of non-maintenance expenditure and totally inadequate for developmental purposes. Many facilities
which are taken for granted in universities in the developed world such as broadband facilities for students and
teachers and computerization of admission processes and administration of exams are lacking.(Discussion paper
Mitra and Singh, CUTS International, 2008.)
There exist socio-economic, cultural and geographical barriers for people who wish pursue higher education
(Bhattacharya and Sharma, 2007). Innovative use of Information and Communication technology (ICT) can
potentially solve the problem.
Education surveys to study student (customer evaluation of program quality and satisfaction (Zammuto, Keavney,
O’Conner, 1996, Browne etal, 1998), student/ customer expectations and attitudes (Winza and Morley, 1994) were
done.
Research Methodology
The study uses stratified random sampling with the sample area being the state of West Bengal and the
government, government aided and the Private B-Schools as the strata on one end and the different B-School
entry coaching centres on the other. Survey based research with a structured questionnaire was conducted over a
sample size of 184 with equal weight to the aspirers and the pursuers i.e the sample size taken from the B-Schools
was the same as that obtained from the different coaching centres.. The respondents educational, financial,
economic background and the related demographics, psychographics were studied to understand their preferred
choice of the Intellectual Capital desirable and the corresponding degree of emphasis. The age group were
considered in equi-spaced intervals of five from 19 to 38 years.The income level considered was in intervals of INR
10000, with the higher limit open as >INR 55000.Occupational and experience parameters like service,
government, public, private, fresher, business and the level of experience if so, was given due weightage. All
possible forms of undergraduate education were taken. The respondents had varied preferences regards the
physical resources. On a scale of 1-10 the respondents had to render their preference, choice and satisfaction level
as regards A.C online smart classrooms with laptops/super-acoustics, centralized library, state of the art
infrastructure, Wi-fi Campus, A.C Auditorium, facilities like on campus bank/ATM, gymnasium, caféteria, study
rooms, clubs, access to conference papers, language laboratories, indoor/outdoor games facility etal. The purpose
of investigation was what the expectation of the students as customers from the Institutions so that excellent
educational ambience can be provided, study the stipulations of the regulatory bodies and the accrediting
organizations and understand the gap/ lacunae of the infrastructure which is a bare necessity and how that can be
developed towards providing customer delight. SPSS -17 was used to identify the related impact levels and the
KMO and Bartlett test of sphericity, communality method of principal component extraction along with the scree
plot was done to identify the key factors contributing to the enhancement of intellectual capital base. The
questionnaire was expert opinionated and Cronbach Alpha values of 0.84 indicated the reliability and validity.
Discussion of Research findings- Ranking the Physical Resources by aspirers and pursuant:
AC online smart classroom with laptop ports
It was found that this was not considered to be an urgent/ immediate requirement as perceived by the aspirants.
Though 35% of the aspirants ranked it in the first three on their priority index, 30.4% ranked it as the 6th.
On the other hand whereas 47.8% of the pursuers ranked it among the first three, 28.3% ranked it sixth indicating
that as aspirants near to the selection of an MBA institution it gains importance as pursuers are higher in their
preference level of the same as compared to the aspirants – emphasizing further that it is not a basic need but a
secondary need, which could be a value added advantage for the pursuing candidates. The last two decades have
witnessed a revolution caused by the rapid development of Information and Communication Technology (ICT). ICT
has changed the dynamics of various industries as well as influenced the way people interact and work in the
society (UNESCO, 2002; Bhattacharya and Sharma, 2007; Chandra and Patkar, 2007). Internet usage in home and
work place has grown exponentially (McGorry, 2002).
The challenges before the education system in India can be said to be of the following nature:
Access to education- There exist infrastructure, socio- economic, linguistic and physical barriers in India for people
who wish to access education (Bhattacharya and Sharma, 2007).
Quality of education- This includes infrastructure, teacher and the processes quality.
Resources allocated- Central and State Governments reserve about 3.5% of GDP for education as compared to the
6% that has been aimed (Ministry of Human Resource Development, 2007).
People have to access knowledge via ICT to keep pace with the latest developments (Plomp, Pelgrum & Law,
2007).
The four main rationales for introducing ICT in education:
Rationale Basis:
Social Perceived role that technology now plays in society and the need for familiarizing
students with technology.
Vocational Preparing students for jobs that require skills in technology.
Catalytic Utility of technology to improve performance and effectiveness in teaching,
management and many other social activities.
Pedagogical To utilize technology in enhancing learning, flexibility and efficiency in curriculum
delivery
(Source: Cross and Adam (2007).)
ICTs also allow for the creation of digital resources like digital libraries where the students, teachers and
professionals can access research material and course material from any place at any time (Bhattacharya and
Sharma, 2007; Cholin, 2005). Such facilities allow the networking of academics and researchers and hence sharing
of scholarly material. This avoids duplication of work (Cholin, 2005). Plomp et al (2007) state that the experience of
many teachers, who are early innovators, is that the use of ICT is motivating for the students as well as for the
teachers themselves. Bottino (2003) and Sharma (2003) mention that the use of ICT can improve performance,
teaching, administration, and develop relevant skills in the disadvantaged communities. The possibility of real time
interaction in all the different aspects of the education system like teaching, collaboration, debates etc hold great
promise for the future (Mason, 2000). It helps to individualize the teaching or guidance method as per the
student’s need (Mooij, 2007; Ozdemir and Abrevaya, 2007). It also boosts the confidence level and the self-esteem
of the students who acquire the ICT skills through the process of being exposed to such kind of learning (Casal,
2007). The main goals of ICT adoption in the education field are reducing costs per student, making education
more affordable and accessible, increasing enrollments, improving course quality, and meeting the needs of local
employers (Ozdemir and Abrevaya, 2007). Low overheads and cost efficiency are attracting many private players in
the field of Internet enabled education. This is also being driven by technological advances, competitive pressures
and the positive experiences of many early adopters (McGorry, 2002).
Centralised Library with 24*7 access
Education is the driving force of economic and social development in any country (Cholin, 2005;Mehta and Kalra,
2006).AICTE handbook states that if B is the number of divisions in the library, minimum number of titles should be
100, with the volumes 500*B, number of national and international journals as 6 and 3 times the number of
divisions. E-Journals are required-Eliminating time barriers in education for learners as well as teachers (Sanyal,
2001; Mooij, 2007; Cross and Adam, 2007; UNESCO, 2002; Bhattacharya and Sharma, 2007); Eliminating
geographical barriers as learners can log on from any place (Sanyal, 2001;Mooij, 2007; Cross and Adam, 2007;
UNESCO, 2002; Bhattacharya and Sharma, 2007);
The library should be equipped with a reading room with a seating capacity of 15% of the total number of students
having a maximum capacity of 150, and PC’s should be 1% of the total number of students with a ceiling of ten
(Source AICTE Handbook 2010-’11). It further emphasizes that multimedia digital library, reprography, document
scanning and printing facility is essential.
Almost echoing the importance of a centralized library with 24*7 access, the respondents both the aspirers and
the pursuant, a good 73.9% and 78.7% of them respectively, ranked the same in the top three of their priority. The
Kurien Committee, the second review committee set up by the G.O.I in 1991 stressed “on the interrelatedness of
teaching, research and consultancy needs to be better emphasized for greater synergy. There should be greater
focus on development of relevant teaching materials and research”. Martin (1994:36) found that first year
students at the University of South Australia ranked library resources as having a strong influence on their choice
of university. All the four committees were unanimous in their conclusion that “library and computer facilities have
been poor except in the top management institutes”. The resource view holds that the quality of an institution of
Higher education can be determined by assessing its internal resources –the number of books in the library,
number of faculty with terminal degrees, size of endowment, reputation etc.
Infrastructural Development and Its Relevance
A good 54.3%of the aspirants and 56.5% of the pursuants indicated that quality infrastructure was what they
sought/seek when they choose a B-School_ thus making it one of the most vital parameters in B-School selection
as the interviewers ranked it in the first three as the priority list. AICTE stipulates the norms required for the
minimum carp of area, classroom, tutorials, computer centre, library and reading room seminar halls (appendix).
It also states the requirements for Principal/Directors office, Board room, room requirements for offices, HODs,
faculty, central stores, placement office etc (appendix). Circulation area of 25% of sum of Instructional
Administration amenities are desired cover area, common walkways, staircase, entrance lobby.Boys and Girls
common room, cafeteria, canteen, Guest house etc was also required, the specification of which are given by
AICTE(pp99,AICTE Hand book, 2010-11).
The serious resource crunch in universities implies that there is little financial flexibility, given that certain
expenditures are unavoidable. According to NKC (2006), 75 percent of maintenance expenditure is on salaries and
pensions on an average. Much of what is left is absorbed by infrastructure costs such as rents, electricity,
telephones and examinations. Thus, there is very little left for development of infrastructure from these funds with
the consequence that laboratories, libraries and buildings are dilapidated and deteriorating rapidly. Budgeted plan
expenditure is less than 5 percent of non-maintenance expenditure and totally inadequate for developmental
purposes. Many facilities which are taken for granted in universities in the developed world such as broadband
facilities for students and teachers and computerization of admission processes and administration of exams are
lacking.(Discussion paper Mitra and Singh, CUTS International, 2008.)
Lush green Campus in Industrial Township
An Industrial Township has its natural advantages of well connectivity and availability of practical exposure so that
holistic learning could be achieved. Thus 41.3% of the aspirants and 39.1% of the pursuant do prefer the same. An
AACSB and EMFD (Dymsza 1982) recommanded that “Management education should be holistic in character. It
needs to be integrated – in corporating a number of functional, qualitative and analytical fields.” To educate the
“whole” manager to meet the responsibilities and challeanges of the future. As “knowledge and action” are
inseparable, we have to educate wisely and well those who will manage the critical institutions of the world
(Dysuza ). Two studies (Werlay and Baldmin 1986,Keys and Wolfe 1988) on comprehensive approach to
management development stated a) Enhanced institutional accountability for quality. b) Increased use of
experiential techniques. c) Intensive use of educational technology and d) A recognition of the need for life long
learning.
Wi-Fi Hi - Tech Campus and Cyber Lab facility
Though the aspirants felt that Wi-Fi Hi Tech campus would definitely aid their learning on a real time basis as they
can surf through the materials the pursants (67% ranked 1,2,3) felt that a holistic mix was required and it was one
of the essential ingredients of the same. The regulatory body, AICTE States minimum number of PC’s are required
with the PC: student ratio as 1:2. Application and system software should be legal, LAN and internet connectivity
should be there for all the labs, mail server and client vs desirable with 5%of the total PC connected to a printer
(colour preferable). AICTE further states that open source software may be encouraged and as per the student
needs also the regulatory recommends a secure Wi-Fi facility. It also desires that the management institutions
purchase recent hardware and has adequate number of software licenses. Further, “library, administration offices
and faculty members are to be provided with requirement meant for the students. ICT can be used as a tool to
overcome the issues of cost len number of teachers and poor quality of education as well as to overcome time and
distance barriers (McLorry, 2002). ICT allow the creation of digital resources like digital libraries where students,
teachers and professionals can place at any time (Bhattacharya and Sharma 2007, Cholin 2005). They improve the
perception and understanding of the students and can be used to prepare the workforce for the information
society and the new globle economy (Kozna 2005). Thus new educational approaches can be used. (Sanyal 2001).
Premier Institution like IIM-C have entered into a strategic alliance with NIIT for providing programs through virtual
classrooms. JU is using a mobile learning center (Bhattacharya and Sharma 2007). The strong presence of a
leadership is evident when ICT integration is initiated successfully (Mason 2000) echoing the perception of both
the aspirants and pursuants rightly.
On Campus Bank/AIM/Bookshop / Cafeteria /Gym /Swimming pool etc.
AICTE advices that such facilities would be a feather in the cap for a B-School and brighten up the perception as
regards. The aspirants would be eager to have such as an added attraction and to spend their leisure and hang out
after a tedious day’s session. They would give them a lease to life and energize them. Thus a good 39.1% of the
aspirants and 34.8% of the pursants ranked them in their first two of the priority. It emphasis Ho etal,(2007) that
an integrated multiple criteria decision making approach is made towards resource allocation in higher education.
Perception of B-School students towards accreditation and quality
Working towards National and International Accreditation is an important and effective tool in ensuring quality
(Chandra 2003). The literature on quality in higher education is more voluminous that one would expect, partly
because terms like quality, accountability and assessment are used somewhat interchangeably (Ewell 1991, 1993).
“It has become evident that students, the primary customers of the institution need and want more then library
books and an impressive set of faculty degrees enumerated a the end of the college catalog”. (Seymour,1999,p7).
A total of 18,818colleges and 317 Universities exist in India Tile March 2007, National Assessment and Accredition
Council (NAAC), an autonomous body set up by Govt. in 1994to ensure quality, could accredit only 3942 colleges
and 140 Universities in the country. The assessment is based on several parameters like qualification of faculty,
availability of books, sports, and infrastructure and so on. This implies that 75% of the colleges and 56% of the
Universities have never been assessed for quality standards by the body set up for the purpose.(pp10,Salvay &
Thakur, Vol.8, 2007). Among the 3942 NAAC accredited colleges only 245 are in the A category, 1745 are in the B-
range and 668 in the C-range graded as high, medium or low. In a survey conducted by the UGC, quality standards
of 111 Universities and 1473 colleges were checked which included parameters like qualification of teacher,
student-teacher ratio, number of books available, a host of basic facilities like hostels, sports, auditoriums,
common room etc. Only 8% of the colleges got A grade, 37% and 36% fall in the B and C-range respectively. Quality
can be reviewed in the resource and performance perspective. After a long entrenchment of old ways of thinking
(resource view of quality) some institutions have embraced the performance view of excellence in education;
motivated by competition, costs, accountability (Seymour, 1992). This means quality of a Higher Educational
Institution is determined by outputs- efficient use of resources, producing uniquely educated, highly satisfied and
employable graduates. This view is popularly termed as value added (Astin 1991) approach to higher education. It
stresses on teaching, student competencies measurement etc. gained through a baccalaureate education (Bennet,
2001). Of the 4000 B-Schools in India, not even a single B-School happens to be in the top 50 in any of the
international rankings. Quality distinctly is something where schools have to focus and the International bodies like
AACSB, AMBA,EQUIS,IACBE focus on different parameters of quality, excellence, innovation and continuous
improvement (Sahay and Thakur, 2007, pp8).
The survey conducted also shows 32% of the respondents’ preferred NAAC/AACSB/EFMB/ISO certification as
proof of quality standard in selection of B-School of their choice.
Study rooms for debates/ conference and discussions
This was very much required as felt by the aspirants especially, with 41% giving it as a first preference. A recent
study by Lafose and Zinter (2002), highlights the use of case/conference approach in psychology on learning new
paradigm related information and came to the conclusion that as groups the students’ knowledge about
paradigms actually improved.
Access to conference journals, research journals and emphasis on research was more desired by the aspirants as
research by faculty and focus on the same would enhance the knowledge and upgrade them with the latest
happenings in the business world.
Domain knowledge is the general semantic and episodic knowledge is the insight regards the product. This affects
the comprehension and the specific meanings produced during elaboration. Both develop concurrently in the long
term memory as consumer experiences with the product accumulate. (Celci and Olsen, 1998), Sujan (1985) proved
that an individual’s prior knowledge or “expertise” affects their evaluation process. He also found that knowledge
impacts the evaluation process independent of involvement and increases the amount of processing. Hence both
felt involvement and domain knowledge independently impact behavior and cognition. This was reflected in the
fact that 77.4% of the aspirants and 45.4% of the pursuers felt that research conferences and its emphasis, debate
clubs, language laboratories, soft skills development hubs (60% of the aspirants) and audiometric labs as the prime
necessity.
Conclusions and Recommendations
Need for the basic physical infrastructure as per the recommendations of the UGC and the AICTE was stressed
upon by the respondents. The pursuers were more eager in their preference for A.C online smart classrooms as
they were exposed to the same. Need for a centralized library with the latest software and acess to research and
conference journals and papers were necessitated.
An Industrial Township has its natural advantages of well connectivity and availability of practical exposure so that
holistic learning could be achieved. Wi-Fi Hi Tech campus would definitely aid their learning on a real time basis as
they can surf through the materials the pursers especially, felt that a holistic mix was required and it was one of
the essential ingredients of the same. The aspirers would be eager to have such as an added attraction and to
spend their leisure and hang out after a tedious days’ session hence an on Campus cafeteria, shopping mall,
swimming pool, gymnasium, ATM facility etal would be an added advantage.
All the categories of respondents felt that research conferences and its emphasis, debate clubs, language
laboratories, soft skills development hubs and audiometric labs as the prime necessity.
Quality standards of the institution and the respondents preference for NAAC/AACSB/EFMB/ISO certification as
proof of quality standard in selection of B-School of their choice indicates the paramount importance that they lay
in all round development and maintenance of quality standards as a brand differentiator .
Appendix-List of Tables and Figures
Figure 1. Higher Education in MBA
Frequency Percent Valid Percent Cumulative Percent
Valid MBA Aspirant 92 49.7 50.0 50.0
MBA Pursuing 92 49.7 50.0 100.0
Total 184 99.5 100.0
Missing System 1 .5
Total 185 100.0
Figure 2. Qualification of the prospect
Frequency Percent Valid Percent Cumulative Percent
Valid 1 0.5 0.5 0.5
UG-B.Tech/B.E 96 51.9 51.9 52.4
UG-B.Sc 8 4.3 4.3 56.8
UG-B.Com 36 19.5 19.5 76.2
UG-B.A 4 2.2 2.2 78.4
UG-BCA 4 2.2 2.2 80.5
UG-BBA 34 18.4 18.4 98.9
UG-B.Pharma 2 1.1 1.1 100.0
185 100.0 100 Total.0
Figure3.
1st =Max,10th =Min Higher Education in MBA
MBA Aspirant MBA Pursuing
A.C online, smart classroom with laptop ports with super acoustics
10th Rank 6.5% .0%
9th Rank 8.7% 2.2%
8th Rank 2.2% .0%
7th Rank 2.2% 4.3%
6th Rank 30.4% 28.3%
5th Rank 8.7% 6.5%
4th Rank 6.5% 10.9%
3rd Rank 13.0% 15.2%
2nd Rank 8.7% 15.2%
1st Rank 13.0% 17.4%
Centralized library with 24*7 facility 10th Rank 2.2% 4.3%
9th Rank 2.2% .0%
8th Rank 6.5% 4.3%
7th Rank 4.3% .0%
6th Rank .0% 4.3%
5th Rank 2.2% 6.5%
4th Rank 8.7% 2.2%
3rd Rank 23.9% 19.6%
2nd Rank 15.2% 19.6%
1st Rank 34.8% 39.1%
State of the art infrastructure 10th Rank 4.3% 8.7%
9th Rank 6.5% 2.2%
8th Rank 2.2% .0%
7th Rank 2.2% .0%
6th Rank 13.0% 10.9%
5th Rank 8.7% 8.7%
4th Rank 8.7% 13.0%
3rd Rank 15.2% 21.7%
2nd Rank 17.4% 17.4%
1st Rank 21.7% 17.4%
Figure 4.
1st =Max,10th =Min Higher Education in MBA
MBA Aspirant MBA Pursuing
Lush green Campus in an Industrial Township
10th Rank 2.2% 2.2%
9th Rank 2.2% .0%
8th Rank 4.3% 2.2%
7th Rank .0% .0%
6th Rank 19.6% 13.0%
5th Rank 6.5% 6.5%
4th Rank 8.7% 15.2%
3rd Rank 15.2% 21.7%
2nd Rank 15.2% 13.0%
1st Rank 26.1% 26.1%
Wi-Fi Campus with Hi-tech facilities
10th Rank 2.2% .0%
9th Rank 6.5% 6.5%
8th Rank 6.5% .0%
7th Rank 4.3% .0%
6th Rank 6.5% 6.5%
5th Rank 2.2% 8.7%
4th Rank 8.7% 10.9%
3rd Rank 15.2% 17.4%
2nd Rank 21.7% 17.4%
1st Rank 26.1% 32.6%
Cyber Lab facility available 24*7
10th Rank .0% 2.2%
9th Rank 2.2% 2.2%
8th Rank .0% 6.5%
7th Rank 4.3% 4.3%
6th Rank 8.7% 6.5%
5th Rank 10.9% 4.3%
4th Rank 10.9% 10.9%
3rd Rank 15.2% 17.4%
2nd Rank 21.7% 10.9%
1st Rank 26.1% 34.8%
Figure 5.
1st =Max,10th =Min Higher Education in MBA
MBA Aspirant MBA Pursuing
A.C auditorium 10th Rank 2.2% .0%
9th Rank 4.3% .0%
8th Rank .0% 2.2%
7th Rank 4.3% 6.5%
6th Rank 15.2% 26.1%
5th Rank 8.7% 17.4%
4th Rank 15.2% 6.5%
3rd Rank 17.4% 13.0%
2nd Rank 15.2% 6.5%
1st Rank 17.4% 21.7%
On campus Bank/ ATM/ Bookshop/ Cafeteria/ Gym/ Swimming pool etc
10th Rank 4.3% .0%
9th Rank 2.2% 2.2%
8th Rank 2.2% .0%
7th Rank .0% 2.2%
6th Rank 4.3% 10.9%
5th Rank 15.2% 21.7%
4th Rank 13.0% 6.5%
3rd Rank 19.6% 21.7%
2nd Rank 13.0% 6.5%
1st Rank 26.1% 28.3%
NAAC/AACSB International/EFMD/ISO Certification
10th Rank 4.3% 2.2%
9th Rank .0% 4.3%
8th Rank .0% 6.5%
7th Rank 2.2% 2.2%
6th Rank 8.7% 10.9%
5th Rank 4.3% 8.7%
4th Rank 6.5% 13.0%
3rd Rank 23.9% 15.2%
2nd Rank 17.4% 15.2%
1st Rank 32.6% 21.7%
Figure 6.
1st =Max,10th =Min Higher Education in MBA
MBA Aspirant MBA Pursuing
Study room/Clubs for discussion debates/ conferencing facilities
10th Rank 2.2% 2.2%
9th Rank .0% .0%
8th Rank 2.2% 6.5%
7th Rank 4.3% 6.5%
6th Rank .0% 6.5%
5th Rank 2.2% 4.3%
4th Rank 8.7% 6.5%
3rd Rank 17.4% 21.7%
2nd Rank 21.7% 17.4%
1st Rank 41.3% 28.3%
Access to conference papers, journals and emphasis on research
10th Rank .0% 2.2%
9th Rank 4.3% 4.3%
8th Rank .0% 4.3%
7th Rank .0% 2.2%
6th Rank 2.2% 8.7%
5th Rank 6.5% 4.3%
4th Rank 2.2% 10.9%
3rd Rank 17.4% 17.4%
2nd Rank 23.9% 17.4%
1st Rank 43.5% 28.3%
Language laboratory, soft skills development audiometric labs
10th Rank .0% 4.3%
9th Rank 6.5% 2.2%
8th Rank .0% .0%
7th Rank 2.2% 4.3%
6th Rank 4.3% 2.2%
5th Rank .0% 4.3%
4th Rank 8.7% 6.5%
3rd Rank 17.4% 23.9%
2nd Rank 21.7% 26.1%
1st Rank 39.1% 26.1%
Figure 7.
1st =Max,10th =Min Higher Education in MBA
MBA Aspirant MBA Pursuing
Indoor/Outdoor Games facilities
10th Rank .0% 2.2%
9th Rank .0% 2.2%
8th Rank 2.2% 4.3%
7th Rank .0% .0%
6th Rank 21.7% 19.6%
5th Rank 6.5% 13.0%
4th Rank 19.6% 13.0%
3rd Rank 26.1% 13.0%
2nd Rank 8.7% 8.7%
1st Rank 15.2% 23.9%
Gymnasium/Swimming pool/Joggers Park/Horse riding/Recreation facilities
10th Rank .0% 6.5%
9th Rank 10.9% 2.2%
8th Rank .0% 2.2%
7th Rank 4.3% 4.3%
6th Rank 15.2% 23.9%
5th Rank 13.0% 10.9%
4th Rank 17.4% 4.3%
3rd Rank 10.9% 8.7%
2nd Rank 8.7% 15.2%
1st Rank 19.6% 21.7%
Figure-8: Distribution of answers of Disagree/Agree questions
Strongly Agree Agree
Neither Agree / Nor Disagree Disagree
Strongly Disagree
Quantitative Curriculum 15.2% 52.2% 22.8% 7.6% 2.2%
Prestige Pursuit 12.0% 39.1% 37.0% 9.8% 2.2%
Expensive/High Course Fees 31.5% 30.4% 17.4% 16.3% 4.3%
High Return on Investment/worth studying whatever the fees
15.2% 27.2% 20.7% 29.3% 7.6%
Average run of the Mill Institutes
12.0% 40.2% 34.8% 12.0% 1.1%
Student's Care 14.1% 15.2% 21.7% 42.4% 6.5%
Infrastructure 13.0% 19.6% 21.7% 37.0% 8.7%
Placement Services 19.6% 23.9% 10.9% 31.5% 14.1%
Syllabus 3.3% 15.2% 29.3% 38.0% 14.1%
Faculty Base 8.7% 7.6% 21.7% 44.6% 17.4%
Figure-9 KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .754
Bartlett's Test of Sphericity Approx. Chi-Square 716.293
Df 45
Sig. .000
KMO Test
Since the KMO test value comes 0.754 which is more than 0.5 so we can go for factor analysis.
Bartlett’s test
Here the chi-square value is much higher i.e., 716.293and the sig level is 0.000, means we can confidently apply
factor analysis.
Figure-10. Communalities
Initial Extraction
Quantitative Curriculum 1.000 .526
Prestige Pursuit 1.000 .644
Expensive/High Course Fees 1.000 .728
High Return on Investment/worth studying whatever the fees 1.000 .676
Average run of the Mill Institutes 1.000 .596
Student's Care 1.000 .716
Infrastructure 1.000 .687
Placement Services 1.000 .769
Syllabus 1.000 .720
Faculty Base 1.000 .744
Extraction Method: Principal Component Analysis.
Figure- 11. Total Variance
Component
Initial Eigenvalues Extraction Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %
1 3.543 35.427 35.427 3.543 35.427 35.427
2 2.068 20.675 56.103 2.068 20.675 56.103
3 1.195 11.950 68.053 1.195 11.950 68.053
4 .786 7.860 75.912
5 .631 6.314 82.226
6 .555 5.549 87.776
7 .405 4.051 91.827
8 .342 3.423 95.250
9 .281 2.812 98.062
10 .194 1.938 100.000
Extraction Method: Principal Component Analysis.
Total Variance Explained
The above table shows all the factors extractable from the analysis along with their Eigen values, the percent of
variance attributable to each factor, and the cumulative variance of the factor and the previous factors. Notice that
the first factor accounts for 35.427% of the variance, the second 20.675% and the third factor 11.950% of variance
is explained by all the three factors i.e. total 68.053% of the variance is explained by all the three factors.
Screen Plot:
The screen plot flattens between factors 3 and 4. Note also that factor 4 has an Eigen value of less than 1, so only
three factors can be retained namely- “Serving Students perception”, “Costly Education perception” , “Bright
Future perception”
Figure- 12. Rotated Component Matrixa
Component
Serving
Students
Costly
Education Bright Future
Placement Services .850
Faculty Base .850
Infrastructure .828
Syllabus .827
Student's Care .825
Expensive/High Course Fees .794
Prestige Pursuit .703
Average run of the Mill Institutes .657
High Return on Investment/worth studying whatever the
fees
.803
Quantitative Curriculum .725
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 5 iterations.
References:
1. AACSB International – The Association to Advance Collegiate Schools of Business International. (April,
2003, Revised January, 2004). Eligibility Procedures and Standards for Business Accreditation. Hanna
Ashar, Maureen Lane..
2. Agarwal, P., (2006), 'Higher education in India : the need for a change', Indian Council For Research On
International Economic Relations.
3. Baldrige National Quality Programme, (2006), Education Criteria for Performance Excellence, available at:
http://www.baldrige.nist.gov.
4. Baxi, C.V. and Sahay B. S., (2005), Board Governance in Business Schools, Study Sponsored by All India
Council for Technical Education (AICTE) and Conducted by Management Development Institute (MDI).
5. Bennett, D.C., (2001), Assessing Quality in Higher Education, Liberal Education, 87(2), available at:
zwwaacu.org/liberaleducation/lesp01/lesp01bennett2.cfm. Accessed 20 January 2004.
6. Beverly, A. Browne, Dennis O Kaldenberg, William G. Browne, Daniel J. Brown, (1998) “Student as
Customer: Factors Affecting Satisfaction and Assessments of Institutional Quality” Journal of Marketing
for Higher Education, 8 (3).
7. Carnegie Foundation for the Advancement of Teaching (2006), Classification descriptions, available at:
http://carnegiefoundation.org/classifications.
8. Chandra, Pankaj, (2003), Elements of a World Class Management School, Indian Institute of Management,
Ahmedabad, Working Paper No. 2003-09-03.
9. Clotfelter, C. T., (2003), “Alumni Giving to Elite Private Colleges and Universities”, Economics of Education
Review, 22 (2), pp.109 – 120 .
10. Couturier, L .K., (2003), “ Balancing state control with society’s needs”, Chronicle of Higher Education, 49 ,
p. B20 .
11. Dantnow , A., ( 1998 ), The Gender Politics of Educational Change , Falmer, London.
12. Dickson, A., Gallacher , J., Longden , B. and Bartlett , P., (1985), “ Higher education and the community ,”
Higher Education Review, 17 (3 ), pp. 49 – 58.
13. Duderstadt, J. J., (2003), A University for the 21st
Century, University of Michigan Press, Ann Arbor, MI.
14. Ehrenberg, R.G., (2006), What’s Happening to Public Higher Education? Greenwood Press, Portsmouth,
NH.
15. Franzak, F. J, Cowles D. L., (1993), Viewing the Curriculum as a Product: Implications from a Marketing
Research Study, Journal of Marketing for Higher Education, 1(2).
16. Focus Groups Help to Focus Marketing Strategy, Journal of Marketing for Higher Education, 7 (2), 1996.
17. Glassick, D.C. Huber M. T. and Maeroff, G. I., (1997), Scholarship Assessed: Evaluation of theProfessorate ,
Jossey-Bass, San Francisco, CA.
18. Gray, D., (2005), “Development for deans Annual training program” sponsored by the Council for the
Advancement and Support of Education (CASE), Washington, DC.
19. Higher Education Learning Commission (2006), Institutional Accreditation: An Overview. Available at:
http://www.ncahl.org.
20. Joseph G. Glynn, (2004), “Expectations of Incoming MBA Students: Implications for Curriculum
Development and Program Promotion”, Journal of Business & Economics Research, 2(2), February.
21. Joseph G. Glynn, (2005), “MBA Student Pre-Program and Post-Program Assessment of Critical Skills:
Implications for Outcomes Assessment and Curriculum Design”, Journal of College Teaching & Learning,
2(2).
22. Lafosse, J. M. and Zinster, M. C., (2002), “A Case-conference Exercise to Facilitate Understanding of
paradigms in abnormal psychology”, Teaching of Psychology, 29 pp. 220-222.
23. Lindgren Jr. H. John, and Konopa, L.J., (1980), “A Comparative Analysis of Multiattribute Attitude Models”,
Academy of Marketing Science Journal, 8(4), pp. 374–389.
24. M.Joseph, B.Joseph, (1997), “Employer Perceptions of Service Quality in Higher Education”, Journal of
Marketing for Higher Education: 8, (2).
25. Mannivannan, D.G., (2006), “A New Model for B-School Grading, Indian Management”, The Journal of The
All India Management Association, pp. 74-80.
26. Parasuraman A., (2006), “Modeling Opportunities in Service Recovery and Customer-managed
Interactions”, Mark Sci, 26(6), pp. 590–593.
27. Parasuraman, A., Berry, L.L. and Zeithaml, V.A., (1990), “An Empirical Examination of
28. Relationships in an Extended Service Quality Model”, Marketing Science Institute, Cambridge,MA.
29. Parasuraman, A., Berry, L.L. and Zeithaml, V.A., (1991a), “Perceived Service Quality as a Customerbased
Performance Measure: an Empirical Examination of Organizational Barriers using an extended service
quality model”, Human Resource Management, 30 (3), pp. 335-64.
30. Parasuraman, A., Zeithaml, V. and Berry, L.L., (1985), “A Conceptual Model of Service Quality and its
Implications For Future Research”, Journal of Marketing, 49, pp. 41-50.
31. Parasuraman, A., Zeithaml, V. and Berry, L.L., (1986), “SERVQUAL: a Multiple-item scale for Measuring
Customer Perceptions of Service Quality”, Report No. 86-108, Marketing Science Institute, Cambridge,
MA.
32. Seymour, D.T., (1992), On Q: Causing Quality in Higher Education, Macmillan, New York, NY.
33. Seymour, D.T. (1993), TQM in Higher Education: Clearing the Hurdles (A Survey on Strategies for
Implementing Quality Management in Higher Education), GOAL/QPC, Methuen, MA.
34. Sanyal B.C., (2011),’New Functions of Higher Education and ICT to Achieve Education for all’, The Internet
and Higher Education,6(2)pp.109-124,
35. The Paper prepared for the Expert Roundtable on University and Technology-for- Literacy and Education
Partnership in Developing Countries, International Institute for Educational Planning, UNESCO, September
10 to 12, Paris.
Manufacture Owned Brand Vs Private Label Brand : Where Does the Buying
Wind Blow?
Isita Lahiri,
Associate Professor
Dept. of Business Administration, University of Kalyani
Abstract
Looking at the tempting growth of organized retail in India an increasing number of private label brands are
competing to win over local and manufacturer owned brands for a larger share of the retail pie. Apparel retailers
like Shoppers Stop, Westside, Pantaloon, and so on could popularize their private labels among bargain-conscious
consumers of urban centers appealing with lower price than comparable manufacturers' brands. The present paper
examines the factors influencing customer behavior to buy manufacturer owned brand versus private label brands
in the organized retail apparel market in West Bengal. Based on a customer survey the paper delves into the effect
of demographics, product related attributes and non-product related attributes on customers behaviour to buy
manufacturers owned brand and private label brands in the retail apparel market using various statistical tools
according to the just and requirement appeared in the study.
Introduction
The evolution of retailing in India in an organized format has made private labels familiar among Indians. The
emergence of major retail brands is a significant new phenomenon in the Indian economic scenario. Some of the
emerging retail brands include Pantaloons, Shoppers' Stop, Westside, Crossroads, Culture Shop, Big Bazaar and In
Orbit. Considerable curiosity on private labels is mainly because even India has seen a surge of private labels in
apparel and groceries and is catching up fast with the rest of the world ( Roy, 2005). Yet at the same time the
manufacturers’ brands holds a significant position in the consumer apparel market. Today, private label brands in
the organized retail segment in India have evolved into strong brands in their own right, in many cases challenging
the once-dominant manufacturers brand in the premium space.
Private label can increase profit for retailers and improve value for their customers. The momentum from that
winning proposition will cause profits to continue to drain away from manufacturers into the coffers of
consolidating retailers. Manufacturers must take immediate action: assess the threat customer by customer, invest
in innovation, and develop skills in pricing and economic optimization (Mike, 2004), (Hultman, Magnus and
Ljungros, 2003). Currently, India is experiencing a revolution in private label branding and the consumers are the
ones who are enjoying the benefits. If the end 1990s saw the emergence of private labels in apparels, the 2000s
watched the boom in food retailing with private labels coming in daily needs like rice and pulses (Roy, 2005, Gupta,
2004).
Table 1: Evolution of Organised Indian Retailers and their Private Label Brands
Outlet Year of Origin Private Label Offered
Shopper’s Stop 1991 Stop
Pantaloons 1997-1998 John Miller
West side 1998 Westside
Life Style 1998 Splash
Globus 1998 Globus, F21
Source: Bolta (2006)
Objective of the Research
Organized retailing in India has huge potential and the future of private labels seems to be very bright. Under these
circumstances the paper tries to explore the consumer buying pattern in urban India for manufacturer owned
brand vs. retail brand in the retail apparel sector. The research was limited to the malls in Kolkata. In this paper a
detail study on private label and manufacturer brand was conducted. Besides the researcher also tried to study the
factors influencing consumer brand selection pattern in Urban India for manufacturer owned brand versus retail
brand in apparels. This study is very crucial in present day context as competition in apparel retailing is getting
stepper. Particularly after the economic slow down in a highly volatile economic environment, retailers must
analyze the challenges and opportunity ahead of them in this segment. Rethinking and restructuring the marketing
strategies for private label vis a vis manufacturing brands will be one of the most challenging issues for any retail
manager. As observed globally, a critical area of interest to retailers is the consumer attitudes towards store
brands and its relationship with customer satisfaction and store loyalty(Mittal and Mittal, 2009).
Contemporary Studies on Private Label and Manufacturer Brands
Store brands or Private labels –or simply PLs are defined as the ‘products owned and branded by the organizations
whose primary objective is distribution rather than production’ ( Schutte, 1969). PLs, also called own- labels, can
be defined as ‘any products over which a retailer (has) exercised total sourcing and market control’ (Mintel, 2005a,
b).
According to research scholar, Baltas (1997), a private label is “A consumer product produced by, or on behalf of,
retailers and sold under retailers’ own name or trademark through their own outlets”. Thus the onus of the
development of a private label depends solely on the retailer. Private labels are also known as store brands , own
label brands, retailer’s brands etc( Roy, 2005).
With the rise of national advertising, manufacturers brands or national brands (NBs) became widely recognized by
consumers who elected their preferred brands and became loyal to them. Over time, manufacturers could exercise
greater influence over the final demand for their products and secured a better bargaining position when dealing
with retailers (Grant, 1987). Retailers saw their margins drastically reduced, and their power to determine the
prices and their power to determine the prices to consumers depreciated (Borden, 1967). Way retailers found to
beat competition was through the establishment of PL (Chernatony, 1989). The two main advantages derived from
adoption of PLs by retailers are: bigger margins, and increased store loyalty (Fontenelle, 1996).
In Indian context Private Labels are in the danger of facing the ‘Double Jeopardy’ effect. Double Jeopardy is an
empirical generalization (Goodhardt, Erenberg and Chatfield 1984) that explains that small brands suffer twice –
they have fewer customers and these customers buy the brand less often (Ehrenberg, Goodhardt and Barwise,
1990). This pattern has been observed in a variety of markets, in a variety of markets, in a variety of conditions
(different lengths of time , different points in time) and in various contexts ( Pare, Dawes and Driesener, 2006). The
advantages of having successful private labels is strategic. They provide the same benefits to retailers in India that
provide, or are supposed to, in other international markets( Mittal and Mittal, 2009) . Given that the apparel
segment has a substantial share in the organized retail sector in India, there is a requirement for differentiation in
terms of style, fabric, cut design, etc becomes essential. It is for this reason that one sees a proliferation of private
labels in the apparel segment in India.
Manufacturer brands generally sell for more than house brands (private labels), or generics. Presumably
consumers believe that the manufacturers’ name brands are of higher quality. Whether this belief is true or not is
irrelevant to the market outcome so long as consumers believe it. A Gallup Poll found that nearly 80 per cent of
people who try a product with a store-brand label become repeat buyers. Typically the store-brand buyer is a
better-educated, affluent person who reads and understands the labels. Many consumers do study labels and
prices. The Gallup Poll indicates that 40 per cent of shoppers shop selectively: They do not just choose the
manufacturers’ brand, but compare products on a variety of dimensions (quality, price, and special offers).
Nonetheless, Nonetheless, nationally, only 2 or 3 per cent of store-brand sales are generics. (Morch,
1984).Consumer purchasing habits are changing in today’s economy. One such shift is that consumers are moving
away from manufacturers’ brands to private label brands. It appears that consumers will stay loyal to store brands
even when the economy improves. Private Label Manufacturers Association (PLMA) and GfK Custom Research
North America have been conducting ongoing research to find that "91% of respondents will keep buying store
brand products after the recession ends, while only 8% say they will stop buying these products once the economy
turns around." This is due to consumer perception that private label brands are just as good, or even better than,
manufacturers’ brand products.
Private labels in India are poised to grow in near future. Store-brand labels in apparel industry in India are on a
complete upswing. With more and more retailers offering products under their own private labels, consumers
have not had it so good as far as shopping for apparels is concerned. Marketing managers struggle between cost-
saving standardization for a mass market and high-cost customization for a specific niche to improve consumer-
acceptance. Though private labels have attracted attention of channel researchers about forty years ago (Stern,
1966; Boyd and Frank, 1966), in India, private brands have attracted attention primarily only in the last decade.
However, research work in this area appears to leave a void. For Indian conditions, the current era symbolizes the
wake up call that manufacturers’ brand manufacturers should take note of, to effectively combat the threat of
private labels. This paper assesses the recent trends in the changing scenario of the customer buying pattern in
India with special reference to manufacturer and retailer brands.. This paper explores deeper into the factors
influencing the purchase of apparels from organized retail outlets for private labels and manufacturers’ brands.
Research Methodology
Drawing from the past studies, various attributes have been identified to assess the consumers evaluations of
Private Labels (retailers brands) or manufacturer brands. The product related and non product related attributes
that have been used in the questionnaire to understand the customers buying pattern of retailer and
manufacturer brands are as follows: Price, Quality, Socioeconomic class, Variety, Discounts and offers, Influence of
Family usage, Design, Brand Name, Advertisements, User and Usage Imagery, Uniqueness, Goodwill of the store
and Exchange policy.
The following table (Table 2) gives a review of the studies from which the Retailers Brand or Manufacturer brand
attributes have been included:
Table 2: Factors identified by Earlier Studies
Factors Study
Quality, Price, trust, availability of alternatives, frequent
advertising, Sales Promotions, Well Known, availability, brand
image, prestige
Dolekogolu et al.( 2008)
Price Consciousness, Price Quality Association Batra and Sinha, 2000
Advertising Pricing Karray and Martin – Herran ( 2008)
Price and Quality Ailawadi, Pauwels and SteenKamp ( 2008)
Store Personnel, Tim , M ( 2002)
Design Dolekogolu et.al(2008),Brill, M ( 2006)
User & Usage Imagery Bolta ( 2006), Keller ( 2006)
Goodwill of the Store Daw, S.(1983)
Customer Purchase Habituation Brady, Brown , Hullit, www.bcg.com
Personnel, Convenience, Ease of Shopping, Merchandise
Assortment
Hyllegard, Eckman, Descals & Bojra ( 2005)
In this case of research, the sampling was designed very carefully as the sample size is less, being cut down to 309.
Random sampling technique was followed during the research. The researcher tried to target both, both male and
female respondents in the age group 20-65yrs who are mostly involved in buying from the organized retail
formats.
Hypothesis Formulation
Hypothesis building directs the researcher to the concepts that should be studied in order to get answers to the
research questions (Ghauri and Gronhaug, 2002). According to the research questions listed above the hypotheses
will be formulated as follows:
H01: There is a relationship between customer demographics (age, income, etc) and customers buying pattern
of retailers and manufacturer brands
Ha1: There is no relationship between Customer Demographics (age, income, etc) and customers buying
pattern of retailers and manufacturer brands
In the first hypothesis, the independent variable consists of customer demographics and the dependent variable is
customers buying pattern of retailers and manufacturer brands.
H02: There is a relationship between the brand attributes (product related and non product related ) and the
customers buying pattern of retailers and manufacturers brands.
Ha2: There is no relationship between the brand attributes (product related and non product related ) and the
customers buying pattern of retailers and manufacturers brands.
In the second hypothesis, the independent variables are brand attributes (product related and non product
related) and the dependent variable is customers buying pattern of retailers and manufacturer brands.
The Research technique used for this research
The type of research technique used in this case is that of survey. The survey questionnaire had primarily two
sections. The first section was designed primarily to collect the demographic data of the respondents. The second
part of the questionnaire contains a number of statements. It aims to identify the factors influencing the buying
behaviour towards buying of retailers or manufacturer brands. Before administering the final questionnaire, the
questionnaire was tested in a pilot study.
Empirical Result and Interpretation
After getting considerable data it will be analyzed through different statistical techniques in order to reach
conclusion. We try to see the effect of different demographic and behavioral variables on choice of purchase of
retail and manufacturer brands. The variables are analyzed by chi-square test for independence of attributes. This
statistic is used to test the hypothesis of no association of columns and rows in tabular data. This test works best
when adequate cell size assumption holds i.e. not more than 20% cells have expected cell count less than 5 and no
cells have zero count. If this is not satisfied we have used the Likelihood ratio chi-square test is based on maximum
likelihood estimation.
We have taken the level of significance as 5%. If the p-value for any attribute against the purchase behaviour is
lower than that we reject the null hypothesis there is no association between the corresponding attribute and
purchase behaviour. Hence, the corresponding attribute significantly affect purchase behaviour. Otherwise we fail
to reject the null hypothesis and conclude that the corresponding attribute does not significantly affect purchase
behaviour.
A summary of the tests conducted for all the attributes is given in the following table. Attributes which are
significant are marked with bold in the significant column.
Tab Table 3: P value of the Factors for Own label and Manufacturer brands
Factors influencing Buying
Pearson
Chi-
Square
Likelihood
Ratio
Linear -by-linear
Association P value Significance
Gender 4.947 5.058 0.015 0.084 Significant
Age 17.694 14.094 5.406 0.024 Significant
Marital Status 1.081 1.1 1.069 0.583
Not
Significant
Educational Qualification 12.82 8.118 5.405 0.046 Significant
Financial Dependence 1.533 1.561 1.525 0.465
Not
Significant
Income 10.5 13.636 2.126 0.398
Not
Significant
Residential Area 12.772 10.078 3.954 0.047 Significant
Regularity of Buyers 2.558 3.013 2.291 0.278
Not
Significant
Type of Apparel Purchased 17.348 15.056 13.822 0.008 Significant
Frequency of visit to retail outlets 17.178 17.076 4.802 0.071 Significant
Retailer own Brand is less Expensive 31.169 26.686 4.418 0 Significant
Exchange Policy of Retailer Brands 16.945 17.076 0.028 0.031 Significant
Socio economic class 13.251 12.484 0.915 0.104
Not
Significant
Options available for Retailers Brands 7.298 7.411 2.729 0.505
Not
Significant
Discounts and offers of Retailer
Brands 6.002 5.891 1.013 0.647
Not
Significant
Impact of friends and family buying
retailer brands 10.298 8.99 2.205 0.245
Not
Significant
Latest Design of Retailers brand 6.926 7.612 0.648 0.545
Not
Significant
More incentives of Sales people for
selling retailer brands 4.142 5.252 0.397 0.844
Not
Significant
Goodwill of the store 9.12 11.25 2.179 0.332
Not
Significant
Quality of Retailer Brands 7.115 7.383 0.736 0.524
Not
Significant
Social Recognition 7.991 8.654 0.015 0.434
Not
Significant
Brand Name 5.711 7.654 0.23 0.68
Not
Significant
Impact of Advertisement 9.773 9.431 1.455 0.281
Not
Significant
Influence of Brand Ambassador 13.353 15.97 3.733 0.1
Not
Significant
Buying on a Special Occasion 21.379 15.97 5.632 0 Significant
Buiyng a gift for some one 10.175 9.407 0.86 0.038 Significant
Buying a gift for someone special 22.292 20.099 7.919 0 Significant
Buying Better Quality 7.377 6.879 1.484 0.117
Not
Significant
Price and Quality Consciousness 2.395 2.397 0.628 0.664
Not
Significant
Uniqueness 2.587 2.511 1.207 0.629
Not
Significant
From the above analysis we may accept the first hypotheses: that there is a relationship between customer
demographics (age, income, etc) and customers buying pattern of retailers manufacturer brands. Factors like
gender, age, educational qualification, marital status and frequency of buying are significant factors that influence
customers buying of retailers or manufacturer brands.
Similarly we also accept the second hypothesis that There is a relationship between the brand attributes (product
related and non product related) and the customers buying pattern of retailers and manufacturers brands. Factors
like type of apparels to be purchased, price, exchange policy, usage imagery (buying on special occasion), user
imagery (buying a gift for someone and buying a gift for someone special) are significant factors that influence the
purchase of manufacturer or retailer brands.
To further understand the factors that that influence buying of private label or manufacturer brands, we conduct a
factor analysis. Here, variable reduction is the prime objective. To make analysis become easier we reduce the
number of variables. We apply factor analysis to group the statement under different heads. We use principal
component analysis with rotation varimax to generate the factors. Here already there are three clusters. (Refer to
question no 13 of questionnaire):
Only retailer own label brand (1)
Only manufacturer brand (2)
Both Manufacturer or /and Retailer (3)
Table 4: Factor Analysis
Rotated Component Matrix(a)
Component
1 2 3 4 5
S1 0.076 0.173 -0.034 0.69 0.019
S2 0.057 -0.082 0.158 0.678 -0.072
S3 -0.028 0.023 0.034 0.684 0.261
S4 0.419 -0.206 0.209 0.095 0.513
S5 0.154 0.202 -0.157 0.152 0.773
S6 0.526 0.151 0.273 -0.026 0.324
S7 0.687 -0.13 0.089 -0.061 0.252
S8 -0.346 -0.104 0.602 -0.176 0.356
S9 0.589 0.324 0.083 0.114 0.01
S10 0.748 0.052 0.015 0.086 -0.02
S11 0.06 0.839 0.09 0.098 0.103
S12 0.064 0.825 0.165 -0.008 -0.009
S13 0.226 0.23 0.746 0.169 -0.082
S14 0.322 0.206 0.712 0.159 -0.098
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
Rotation converged in 12 iterations.
The statements are grouped in the following factors:
• Factor 1 : 6.7.9,10 --- Quality and Goodwill
• Factor 2: 11,12 ---- Social Recognition
• Factor 3 : 8,13,14----- Communication / Promotional mix
• Factor 4 : 1,2,3,------- Price sensitivity
• Factor 5 : 4,5,…….. Options and Discounts
Next the researcher tried to conduct a multinomial logistic regression. Here the factors were used as independent
variable and the three clusters (Refer to question no 13 in questionnaire) as a dependent variable and multinomial
logistic regression was carried out.
We take the customer s buying both manufacturer and/or retailer brand as the reference category and try to see
the factors which facilitate movement of a customer from this category to the other two categories.1.Only retailer
own label brand 2.Only manufacturer brand 3. Both manufacturer or/and retailer
From which we are going to see which factors influence customers to move customers from 3 to 1 and also from 3
to 2.
Table 5: Parameter Estimates
Parameter Estimates
Brand_combo_finala B
Std.
Error Wald df Sig. Exp(B)
95% Confidence Interval for
Exp(B)
Lower
Bound
Upper
Bound
Only
Retailer
Intercept-2.801 .277 101.951 1 .000
FAC1_1-.521 .271 3.697 1 .055 .594 .349 1.010
FAC2_1.043 .251 .030 1 .863 1.044 .638 1.709
FAC3_1-.056 .240 .055 1 .815 .945 .591 1.512
FAC4_1-.069 .267 .068 1 .795 .933 .553 1.574
FAC5_1-.233 .257 .824 1 .364 .792 .478 1.311
Only
Manufact
urer
Intercept-2.426 .242 100.743 1 .000
FAC1_1-.046 .187 .060 1 .807 .955 .662 1.378
FAC2_1-.021 .212 .010 1 .922 .980 .647 1.484
FAC3_1-.621 .204 9.237 1 .002 .537 .360 .802
FAC4_1.732 .199 13.524 1 .000 2.079 1.407 3.071
FAC5_1-.114 .194 .346 1 .557 .892 .610 1.305
a. The reference category is: Combination of Manufacturer and Retailer own Brand.
If we consider buying of manufacturer and retailers brand as the base, and try to move customers from that to
only retailers brand, then we find that Factor 1 (comprising of statements – 6,7,9,10) is significant and is a very
important influencing factor influencing customers to move from buying of both manufacturer brands and retailer
brands to buying of Retailer brands only. Therefore we may say that quality and goodwill are the two most
important factors that influence customers to buy only retailers brand.
Again taking the case of buying of both manufacturer and/or retailer brand as base if we need to understand that
what factor influences customers to buy manufacturer brands only, then we find that Factor 3 (Comprising of
Statements 8,13,14 ) and Factor 4 (Comprising of Statements 1,2,3) is significant and can be very mush important
in influencing customers to move from buying of both manufacturer and retailer brand to buying of
manufacturer brands only. Therefore we may say that the impact of the elements of the promotional mix and price
sensitivity are the most important factors that influence customers to buy only manufacturer brands.
Conclusion
Therefore we may conclude that in the changing retail environment, retailers must give special emphasis on the
quality of their products and also ensure goodwill in the market that will facilitate the sale of their Private label
brands. On the other hand manufacturer brands apart from maintaining their quality must also plan the right
promotional mix for their brands and suitably price their products to ensure that customers get good value for
their money.
The findings of this research will help retailers and manufactures have a better understanding of what factors are
significant in influencing customers buying of retailers own label or manufacturer brands. Accordingly
manufacturers can develop strategies to overcome the growing competition from retailers’ brands. Besides, this
research will also help apparel retailers to design their merchandise that they offer for sale, in a way that will
enable them to maximize their sales.
References
1. Avlonitis, G. J and Papastathopoulou, P., (2006), Product and Services Management, SAGE publication,
New Delhi.
2. Bolta, L., (2006), Private Labels: An Introduction, ICFAI University Press,
3. Boyd, H. W, Jr. and Ronald, E. F., (1966), “The Importance of Private Labels in Food Retailing”, Business
Horizons, Summer, pp.81-90.
4. Brady, L., Brown, A. and Hulit, B, Private Label: Threat to Manufacturers, Opportunity for Retailers,
www.bcg.com, Accessed on 10.08.2011.
5. Bucklin, L. P., (1965), “Postponement, Speculation and the Structure of Distribution Channels”, Journal of
Marketing Research, 1(3), pp. 26-31.
6. Dhar, S. K and Stephen, J. H., (1997), “Why Store Brand Penetration Varies by Retailer”, Marketing
Science, 16(3), pp 72-84.
7. Dunne, D and Chakravarthi, N., (1999), “The New Appeal of Private Labels”, Harvard Business Review,
77(3), pp.41-52.
8. HSIL: From Homes to Malls, Images Retail, Sep.30, 2004.
9. Into Our Own, Retail Biz, September 2004, pp.17-18
10. “Invading Private Labels”, Retail Biz, September 2004, pp. 19-20
11. Kerin, R. A, Hartley, S. W and Rudelius, W., (2003), Marketing: The Core, McGraw Hill, London.
12. Madaan , K.V.S., (2009), Fundamental of Retailing, Tata McGraw Hill, New Delhi.
13. retail.about.com accessed on March 11.
14. Valery, R., (2006), Retail Product Management, Routledge, London.
15. Vedmani, G. G., (2003), Retail Management, Jaico Publishing House, Mumbai.
Annexure 1: Questionnaire
1. Name of the Organised Retail Outlet
2. Name of the Respondent
3 Sex i) Male ii) Female
4 Age i) Less than 18
years ii) 18- 23 years iii) 23- 28 years iv) 29- 40 years
v) Above 40 years
5. Marital Status i) Single ii) Married
6 Educational Qualification i) Secondary ii) Higher Secondary iii) Graduate
iv) Post Graduate & above
7 Are you financially independent? i) Yes ii) No
8 Family Income per month (Rs.) i) Up to 8,000 ii) 8,000 to 14,000
iii) 14,000 to 22,000 iv) 22,000 – 28,000
v) 28000 – 34000 vi) 35000& above
9 Residential Area i) Village ii) Town iii) City iv) Metro
10 Do you buy Apparel from organized retail outlet regularly? i) Yes ii) No
(If No, we may stop here)
11 Type of Apparel for which retail outlets are considered i) Casual ii) Formal
iii) Party wear iv) For all types
12 How frequently you visit the retail Apparel outlets? i) More than once in a week
ii) Once in a week
iii) Once in a fortnight
iv) Once in a month
v) only before occasions
vi) Very rarely
vii) 1st time
13 When I buy dress from retail outlet I buy i) Only Retailer own Brand
ii) Only Manufacturer Brand
iii) Both Retailer own & Manufacturer Brand
iv) Either Manufacturer or Retailer own brand
Factors influencing Purchase of Apparel Brands: Retailer own Brands //Manufacturers Brands
This part of the questionnaire contains a number of statements. Please indicate your preference according to
the factors influencing purchase of Apparel from organized retail outlet by putting a mark against the
statements that have options ranging from a ‘Strongly Agree’ to a ‘Strongly Disagree’ type.
Sl.
No
Strongly
Agree Agree
Not
known Disagree
Strongly
Disagree
1 Buying a retailer own brand
Apparel is less expensive
2 For a retailer own brand the
exchange policy is easy
3 Retailer own brands are meant for
middle class people
4
Selection of Retailer own Brands
becomes easy since too many
options are available
5
I buy retailer own brands because
stores give more discounts and
offers for Retail Brands
6
I buy retailer own brands because
my friends and family members
buy them
7
I buy retailer own brands because
the Latest design
(More stylish and trendy) is
available
8
Salespeople in the store get more
incentive in selling retailer own
brands
9 I buy retailer own brands because
of the goodwill of the store
10 I buy retailer own brands because
of their quality
11
I buy a manufacturer brand
because I get better social
recognition of it.
12 I buy a manufacturer brand
because of the Brand Name
13
I buy manufacturer brands
because I was attracted by the
advertisements
14
I buy manufacturer brands
because I was attracted by the
Brand Ambassador
15 To wear in a special occasion or a party I buy i) Retailer own Brand
ii) Manufacturer Brand
iii) Both
16 If I am buying a gift for someone I buy i) Retailer own Brand
ii) Manufacturer Brand
iii) Both
17 If I am buying a gift for someone special I buy a i) Retailer own Brand
ii) Manufacturer Brand
iii) Both
18 When I am looking for better quality I buy i) Retailer own Brand
ii) Manufacturer Brand
iii) Both
19 Customer ‘s who are both price and quality
conscious buy i) Retailer own Brand
ii) Manufacturer Brand
iii) Both
20 When I am looking at the uniqueness of the dress,
I buy i) Retailer own Brand
ii) Manufacturer Brand
iii) Both
Any contact number /address /email of the respondent (optional)
Implication of Single and Multiple Reasons for the Preference of Tourists: Empirical Evidence from Sikkim
Debasish Batabyal
Assistant Professor
Durgapur Society of Management Science, Durgapur
Abstract
There is a frequently asked question in tourism marketing i.e. why do people travel? Traditionally, academicians tried to search for and list out the reasons for which people travel with their changing taste and preferences from time to time. Sikkim is one of the mature Himalayan States offering tourism for more than three decades. Already there are many reasons for which tourists are visiting Sikkim. But, there is relatively a new identified question in tourism marketing i.e. at the time of choosing a destination, is there any single (pull) factor (i.e. leisure and recreation) predominating or multiple factors jointly putting forward a combined effect and thereby stimulating people to go? Again, what are the effects of other factors preceded by one another? After entering into the deep root of searching for the reasons of travel to a destination in tourism literature, the availability of information and its access to tourists have been discussed as it contributes to the selection of a destination for the purposes of tourism. Here, the preference study for the destination is based on the structure of the market broadly divided into the number of domestic and international tourists and their respective contributions. In doing so, relevant non-parametric tests e.g. Kendall’s Coefficient of Concordance, Kolmogorov Smirnov Test are taken into use for the analysis of the data collected from the field survey in four different places of its four respective districts in Sikkim i.e. Gangtok in East Sikkim, Namchi in South Sikkim, Managan in North Sikkim and Pelling in West Sikkim.
Introduction
Any destination is widely acknowledged to be one of the most difficult products to manage and market. Over the
coming decade, the challenges facing destination marketers are likely to be even greater with a whole host of
issues likely to impact on the future marketing of destinations ( Kozak et.al., 2006). Destination consumer behavior
is made up of various study subjects: image, attitude, perception, satisfaction, choice, motivations, decision
making and so on. As a result of its proposed close relationship with the repeat purchase or the repeat visit
behavior, customer satisfaction plays a vital role in the structure of consumer behavior models (e.g. Mountinho,
1987; Yi, 1990). Past empirical studies in the fields of both marketing and tourism confirmed the existence of a
strong relationship between overall customer satisfaction and the intention to return and between overall
customer satisfaction and the intention to recommend (Zeithaml, Berry, & Parasuraman, 1996). Thus, greater
customer satisfaction might result in a greater intention to repurchase or return and recommend. For destinations
to be a successful, marketers will therefore need to engage the customers as never before, as well as to be able to
provide them with the types of information and experience they are increasingly able to demand. It is now ‘the
customer who can decide how and when they access their travel and tourism information, and how and through
what process they access and purchase their travel and tourism arrangements’ (King, 2002,p. 106). According to
Camprubi R. et. al. (2008) it is assumed that there are tourism agents that use the tourism image as a pull factor to
influence the buying behavior of potential visitors. Basically, these agents are: internal actors, located within a
particular tourism destination, and external agents – i.e. tour-operators – which are not normally associated with
any particular destination, but have stakes in the travel decision process of potential visitors. In parallel, it is
assumed that the tourism destination is a web of relational networks where the agents are connected by means of
collaborative links that facilitate the supply of a tourist product or experience to the visitors. Based on the
cognitive and affective theories, some authors (e.g. Hsu et.al., 2008) assumed that the affective or emotional
commitment for product/ destination loyalty is mediated by the customers’ emotional states toward the
destination atmosphere, of which in turn foster the functioning changes of the service area or destination. This
article is a new approach in that it considered prior purchase intention and decision to get involved in the process
of consumption of tourism products.
Sikkim is a small hilly state, bounded by vast stretches of Tibetan plateau in the North, the Chumbi Valley, and the
kingdom Bhutan in the East, the kingdom of Nepal in the west and Darjeeling (West Bengal) in the South. Sikkim is
famous for scenic valleys forest, snow clad mountains, magnificent Buddhist culture and heritage and peace-loving
people. Though small, the environmental, social and cultural diversities are not so. Some scholars believe that the
word Sikkim involves Nepalese dialect and it refers to a ‘new place’ or the term has been derived from a Sanskrit
word which means a ‘mountain crest’. The people of Sikkim have ethnic diversity. The Bhutias came from Tibet,
the Lepchas were the aboriginal community, and the Nepalese came from Nepal. When Sikkim was an
independent state and faced many invasions by its neighboring countries and the king took the help of the British
India and, later, gifted some of its region including Darjeeling to the British Government. Now this 22nd Indian State
(joined Indian Union in 1975) has Over 81% of the total geographical area under the administrative managerial
control of the Ministry of Environment and Forest, Government of India. Over 45% of the total geographical area
of the state is under tree cover and nearly 34% of the geographical area is set aside as protected area network in
the form of national park and wildlife sanctuary. The basic statistics of flora and fauna are given here under.
Besides, the State is having a wide range of species ranging from one hundred forty four mammals, five hundred
fifty birds, six hundred fifty butterflies and moths, five hundred fifty orchids to over four thousand species of
flowering plants and three hundred ferns and allies (Economic Survey 2006-07, Govt. of Sikkim) . Sikkim is
ecologically a fragile region. The state has the responsibility to conserve its rich biological diversity that includes
coexistence and protection of over 5000 species of angiosperm (one third of the total national angiosperms). Again
this place has multi-ethnic communities. After becoming 25th
state of the Union Government of India in the year
1975 the rapid development activities ushered in a new era of tourism in Sikkim. Increased accessibility by
roadways and air transport, rapid socio-economic development, competitive advantage both from the side of the
destination and geographical proximity to tourist generating states contributed to the development of tourism in
Sikkim. Recognizing the increased tourist arrivals, accommodation units were set up in Gangtok and a few towns
mostly by outsiders without proper land use planning and architectural design. Ever increasing tourist arrival and
increasing dependence on tourism as a powerful industry and employment generating source, restoration of peace
and harmony, hospitable people, plenty of diversified natural and cultural resources, a typical interest of the
people of Indian to discover the unknown hidden Sikkim brought about a new dimension for the development and
marketing of tourism in Sikkim. Development and adoption of marketing approach extensively include a demand –
supply equilibrium and as such takes into account tourism system. Destination marketing is unlike a marketing of
FMCG products as it considers and more towards services. Again, in many cases the adoption and control is not
fully devolved upon corporate sectors. As a service marketing it includes customer relationship management,
internal marketing (with respect to a destination where all stakeholders are part of the product and contribute to
the image and identity), increased importance of strategic alliances/ linkage etc. Destination marketing should be
a part of destination management but in Indian destinations the concept is not in vogue and very often
management and marketing are wrongly conglomerated with one another. There is a very few destinations well
managed by scientific research and background analysis. The recently adopted policy to position the state as an
Ultimate Ecotourism Destination is really a committed responsibility towards sustainable development. Next to
this is rural tourism and adventure tourism. All these types of tourism will contribute to the alternative tourism
development in the state and the changes in types and forms of tourism will automatically have an impact on the
activities of the tourists, duration of stay, the number of tourist arrivals and the tourism industry as a whole. So a
proactive environment friendly approach has already been adopted in the Unique Selling Proportions (USP) of
Sikkim though there are many things remaining.
Objectives of the Study
The objectives of the study are-
To identify whether tourists have a single reason (preference) or a set of reasons for which they choose
Sikkim as a destination.
To measure the differences between domestic tourists and their foreign counterparts with regard to the
reasons (preference) for the selection of Sikkim.
To identify and measure the degree of preference for selecting various sources of information in the same
way as in the case of reasons for traveling.
Data, Hypotheses and Methodology
To understand the reasons for travel to Sikkim and selection of information for the same purpose, , the first and
foremost question is to know whether a preference exists or not. Again the impacts of other factors are also
measured. The primary data are collected from the four different places of Gangtak, Namchi, Mangan and Pelling.
Stratified random sampling method is used to represent all districts properly.
Following hypotheses are considered here for the testing.
H01 : There is no preference of tourists when selecting Sikkim as a tourist destination. Because, tourists are
traveling not only for a single reason, rather many other factors may have a combined effect while choosing Sikkim
as a tourist destination.
H02 : There is no preference for selecting a source of information before visiting Sikkim and everything is decided
subject to the availability or access to it.
H03 : Individual wise ranks put by domestic and foreign tourists are consistent with regard to the selection of
causes for which they travel and sources of information beforehand.
The non-parametric Kolmogorov-Smirnov test and Kendall’s Coefficient of concordance are used to measure
relevant data.
Kolmogorov Smirnov Test
Kolmogorov-Smirnov test statistic, (D) = max)()(0 xSxF n .
)(0 xF, indicates the cumulative frequency
distribution under null hypothesis (H0) and )(xSn is the observed cumulative frequency distribution, with a
random sample of n observations. Here we determine the cumulative theoretical distribution under the null
hypothesis and arrange the observed frequencies as a cumulative distribution by pairing each value of )(xSn with
comparable)(0 xF
.For each pair of the cumulative distributions, determine )()(0 xSxF n . For given level of
significance, determine the critical value for D and reject the null hypothesis if the calculated value of D is greater
than the critical value.
Kendall’s Coefficient of Concordance
Kendall’s Coefficient of Concordance, a non-parametric test, determines the degree of association among several
(k) sets of ranking of N objects or individuals. It is considered an appropriate measure of studying the degree of
association among three or more sets of rankings. This descriptive measure of the agreement has special
applications in providing a standard method of ordering objects according to consensus when we do not have an
objective order of the objects. The basis of this test is to imagine how the given data would look if there were no
agreement among the several sets of rankings, and then to imagine how it would look if there were perfect
agreement among all the sets. In our study, the consistency of ranks was tested with the appropriate P value.
While computing Kendall’s Coefficient of Concordance (W), the mean of rank (Rj) was taken into note of with the
appropriate consistency.
Here, 2( )s Rj Rj
2 31 ( )
12
sW
k N N
k=Number of sets of rankings i.e. the number of respondents
N=number of objects ranked
2 3
1
12 ( )k N N = maximum possible sum of square deviations i.e. the sum s which would occur with perfect
agreement among k rankings.
Results and Discussion
Tourism and Activities of Tourists in Sikkim
When tourists were asked to put their opinion on the basis of a simple rank to know the reasons for traveling to
Sikkim, the majority of tourists unanimously accepted the leisure and recreation as the first and foremost reason
behind. It is further strengthen empirically with a non-parametric (Kolmogorov- Smirnov) test to verify whether
there is any preference of reason for which they are choosing Sikkim. The result was positive as null hypothesis
was rejected and ‘preference for the reasons to travel’ was found (with Dvi=0.096 i.e. Greater than the tabulated
value). So, null hypothesis contradicts the alternative Hypothesis and it implies that there is a preference among
tourists for selecting only one reason being the most important while visiting Sikkim instead of several reasons
held responsible for each respondent. But, the different tourists considered different reason as they found to be
the most important to them. So, the measurement of consistency of the opinion was important along with the
study of other reasons for each respondent. Along with the first preference of leisure and recreation , the second
priority was found to the same reason to provide an accompany to friends and relatives during the trip. This is
because of a significant trend among the domestic tourists who travel to provide accompany to their relatives and
family members or friends jointly sharing the expenditure. Besides, a significant number of tourists is traveling to
Sikkim for ecotourism ( 11.44%) and adventure tourism ( 9.95%) with third and forth positions respectively.
Another significant noticeable trend came to light with the Kendall’s Coefficient of Concordance for testing
consistency with the null hypothesis that ‘Individual wise ranks are consistent’. The p value of ranks given by all
201 respondents was highly insignificant. The same was true for all such tests conducted separately for domestic
tourists and international tourists respectively but the purposes of travel considered by foreign tourists were
different. The mean of ranks are showing that the foreign tourists are visiting Sikkim for ecotourism, adventure
tourism and special interest tourism and Buddhist heritages and pilgrimages. The opinion survey also indicated
that the most of the foreign tourists consider Sikkim as one of the cheap destinations but at the same time costs of
services and facilities are inflated and not at per with their expectation. Following is the table showing the mean of
ranks for the purpose of travel while considering the reasons for visiting Sikkim.
Table 1: Kendall’s W Rank Showing the Purposes of Travel to Sikkim
Factor All tourists Domestic Tourists Foreign Tourists Mean of Ranks
Ranks Mean of Ranks
Ranks Mean of Ranks
Ranks
Holidaying, leisure and recreation
1.64 1 1.49 1 3.09 2
Social (visiting friends and relatives, marriage etc.)
5.89 8 5.71 7 7.28 8
Religious and pilgrimage
5.82 7 5.79 8 5.84 6
Adventure and sports
4.17 4 4.35 4 3.13 3
Providing holiday opportunity to spouse/ family/attendant
4.11 3 3.76 2 6.30 7
Eco tourism 3.89 2 4.06 3 3.19 4
Rural tourism 5.47 6 5.54 6 4.28 5
Special interest 5 5 5.29 5 2.89 1
Source: Primary Data, 2008-09 Note: The statistical analysis has been made using SPSS statistical package
Availability of Information Pertaining to the Tourism and Activities of Tourists in Sikkim
As a majority among all destination stakeholders, tourists were asked ‘how do they collect information regarding
their trip beforehand’. An interesting result was brought to notice that the maximum number of tourists of
33.83%, 29.85% and 34.83% depend on internet and use-net facility as a source of updated , reliable information
for accommodation, transportation and attraction respectively ( see table 2). Again, informal destination
information or what do we understand by information through friends, relatives and other visitors was given the
second importance with 26.86% ( 21.89%+ 4.97%), 31.84% ( 20.9%+ 10.94%) and 22.88% ( 12.93% + 9.95%) for,
accommodation, transportation and attraction respectively. These figures clearly show that tourists rely more on
informal information for their accommodation than any other sources. Internet service is found to be the single
largest source of information for attraction with 34.83% followed by guidebook, friends, relatives and other visitors
and the offices of the department of tourism with 28.86%, 22.88% and 10.94% respectively. Keeping in view the
importance of each source of information the strategy for sustainable marketing and promotion shall revolve
around the weight introduced by per cent ages. Tourists were also asked to rank the source of information
separately for each principal/ service provider to know their preference and access to the source. This was
important for identifying the appropriate mean to use and accordingly to give correct weight. Following is the table
showing the number of respondents for each principal supplier of tourism in Sikkim.
Table 2: Table Showing Use of Various Sources of Information before Visiting Sikkim
Source of Information Transportation Accommodation Attraction
Guide Book 56 (27.86)
54 (26.87)
58 (28.86)
Print Media 2 (.99)
5 (2.49)
2 (.99)
Television/Radio 5 (2.49)
2 (.99)
3 (1.49)
Internet 68 (33.83)
60 (29.85)
70 (34.83)
Relatives/ Friends 44 ( 21.89)
42 ( 20.9)
26 (12.93)
People Visited (other than Friends/Relatives)
10 (4.97)
22 (10.94)
20 (9.95)
Tourism Department( Offices)
16 (7.96)
16 (7.96)
22 (10.94)
Source: Primary Data, 2008-09
Note: Figures in the parentheses indicate percentage of respondents marking the concerned problem of booking as 1 (being the most important).
Apparently, it appears that the maximum number of people use internet as the appropriate source of information
even for all major principals of tourism products in Sikkim. But another suitable non-parametric Kolmogorov-
Smirnov test was done by setting the null hypothesis that ‘there is no preference for selecting a source of
information before visiting Sikkim’. Here )(0 xF indicate the cumulative frequency distribution under null
hypothesis and )(xSn is the observed cumulative frequency distribution, with a random sample of n
observations. Here we used the same test statistic separately for accommodation, transportation and attraction.
The calculated value of mode )()(0 xSxF n for each principal is given here under.
Table 3: Table Showing the Computed Values of the Test Statistic D for Attraction, Accommodation and Transportation
Difference Guide Book
Print Media
T.V./Radio Internet Relatives/ Friends
People already Visited
Tourism Department (Offices)
)()(0 xSxF n
for Accommodation
0.136 0.002 0.115. 0.081 0.157 0.064 0
)()(0 xSxF n
for Transportation
0.126 0.008 0.125 0.031 0.097 0.063 0
)()(0 xSxF n
for Attraction
0.146 0.013 0.115 0.091 0.077 0.034 0
Source: Primary Data, 2008-09
Considering the maximum value of D at each case we can conclude that there is a priority for the selection of an
appropriate source of information when planning to visit Sikkim. But which one or two or more are given priority
for each case can not be computed by the K-Smirnov test and as such Mean of Ranks (jR ) was used to compute
further ranks for each component of travel. A table is given here under showing the Mean of Ranks (jR ) derived
from the non-parametric Kendall’s Coefficient of Concordance rank test. Here the Null Hypothesis ( H0) was ‘
Individual wise Ranks are Consistent’. As the p value was greater than 0.10 we accepted the Null Hypothesis and
considered the mean of ranks as a suitable rank test for showing the ranks of priority considered in each case.
Table 4: Mean of Ranks for Various Sources of Information Collected by Tourists Visiting Sikkim
Source of Information
Attraction Accommodation Transportation
Mean of ranks
Ranks Mean of ranks f Ranks Mean of ranks Ranks
Guide book 2.5 2 2.89 2 2.64 2
Print media 4.56 5 4.86 6 4.84 5.5
TV/ Radio 5.52 7 6.13 7 5.81 7
Internet 2.36 1 2.56 1 2.50 1
Relatives and Friends
4.35 4 3.19 3 3.71 4
People already Visited
4.92 6 4.05 5 4.84 5.5
Offices of Tourism Department
3.78 3 4.33 4 3.66 3
Source: Primary Data, 2008-09 Note: Statistical analysis has been made using SPSS statistical Package
Though the number of internet users in our country is significantly low, yet the same is not true for tourism
industry and as such the use of internet is the most widely used and preferable source of firsthand information
before visiting Sikkim. The second most user friendly source of prior information is guidebook for all cases of
attraction, accommodation and transportation. Interestingly, third rank ( 3.19) for the source of information of
accommodation was ‘Relatives and Friends’ while the same rank is given to the ‘Offices of tourism Department(s)’
for attraction and transportation respectively. For transport and accommodation related information in or around
Sikkim tourists depends on Relatives and Friends ( 3.71) and experienced people ( 4.05) with the rank four in either
case. But the same rank is given to the Guide book as a source of attraction. Fifth rank is given to print media,
People already visited and both ( 5.5) for attraction, accommodation and transportation respectively.
Conclusion
The preference for leisure and recreation clearly advocates a leisure-centric, institutionalized form of development
that may result in a radiation of mass tourismvi into other parts of the State. So, there is a high possibility of the
same development-replica in other areas considered for tourism development. It is further strengthen with the
second most important rank which is ‘providing accompany to relatives and friends participating in leisure and
recreation’. When consistency of the opinion of the respondents was tested for all tourists, an inconsistency was
found between the opinion of domestic tourists and their international counterparts. This is really contradictory to
the recently adopted policy of the government to alternative tourism as most of the foreign tourists are visiting
Sikkim for special interest, ecotourism, adventure tourism etc. It is noticed that the foreign tourists are visiting
Sikkim for ecotourism, adventure tourism and special interest tourism and Buddhist heritages and pilgrimages
instead of the leisure and recreation. It is found in tourism literature that tourists travel the places disseminating
relatively more information regarding attraction, accessibility and hosting of people. So, the choice of sources of
information was tested and analyzed in the same way. This comparative measurement of choice for attraction,
accommodation and transportation ( see table 3) exhibited an increasing tendency to use informal sources as
being the most important for accommodation. Instead of formal sources of information from a travel agent, tour
operator, hotel or other formal proponent, tourists use depend on the friends and relatives with a high degree of
trust and reliability. When asked for, tourists are primarily indicated discrepancy in price of tourism products and
services, including the price for accommodation. It is found that Sikkim has a conventional problem of determining
its pricing strategy as the same products are priced differently to different segments of tourists and the price
ranges vary largely (Batabyal, 2010). Yet, Sikkim is one of the cheapest destinations to many foreign tourists.
Destination product/ package pricing strategy needs to be formulated by the Destination Management
Organization or the tourism department as the apex decision making authority. Regional disparity among tourists,
seasonal fluctuations, difference among demographic and psychographic profiles of tourists, control and co-
ordination between private and public sectors, extent of adoption of sustainable development principles are found
to be the important parameters influencing destination product/package relationship in Sikkim.
References
1. Batabyal, D., (2010), “Significant Changes in Consumer Behaviour, A Case Study of Sikkim”, Tourism
Theory and Practice: Consumer Behavior Issue, Kolkata, 8(2), pp.50-57.
2. Batabyal, D., (2010), Implication of a Scientific Destination Study in Tourism Product Management: A Case
Study in Sikkim. Article Presented and Published in a National Conference on Science and Technology
Applications in Tourism Sector focusing on Uttarakhand Opportunities, September 27-28, HNB Garhwal
University, Srinagar, Uttarakhand, India.
3. Batabyal, D. and Parida, B.B., (2011), “Review of Tourism Literature in a Destination Perspective: A Case
Study of Sikkim”, Tourism Theory and Practice: Tourism Literature Issue, Kolkata. 9(1), pp. 86-98.
4. Butler, R., (2005), Modeling Tourism Development: Evolution, Growth and Decline in Wahab S. and
Pigram J.J. (2005), eds. Tourism Development and Growth, London & New York, Routledge.
5. Butler, R.W., (2005), Problems and Issues of Integrating Tourism Development in Pearce D.G. and Butler
R., eds. Contemporary Issues in Tourism Development, Routledge, Taylor and Francis e-Library, pp. 65-79.
6. Campruni, R., Guia, J. and Comas, J., (2008), Destination Networks and Induced Tourism Image. In Kozak,
M., Gnoth J and Luisa A. Tourism Review, Vol.63, No.2, pp.47-58.
7. Clarke, J., (2005), Marketing Management for Tourism, In Pender, L. Sharpley, R. ( 2005). Ed. The
Management of Tourism, pp. 102-118. London, Sage.
8. King, J., (2002), “Destination marketing organisations: Connecting the Experience Rather than Promoting
the Place”, Journal of Vacation Marketing, 8(2), pp105–108.
9. Kozak, M. and Andrew, L., (2006), “Destination Marketing and Competitiveness”, In Kozak, M. and
Andrew L. Progress in Tourism Marketing. pp. 71-74.
10. Laws, E., (1995), Tourist Destination Management: Issues, Analysis and Policies, Routledge, London.
11. Moutinho, L., (1987), “Consumer Behaviour in Tourism”, European Journal of Marketing, 21(1), pp. 5–44.
12. Pike, S., (2004), Destination Marketing Organizations, pp.125-154. London, Elsevier.
13. Rahman, S.A., (2006), The Beautiful India- Sikkim, Reference Press, New Delhi.
14. Sharpley, R. and Telfer, D.J., (2008), Tourism and Development in the Developing World, Routledge, Taylor
and Francis Group, London and New York.
15. Sharpley, R., (2009), Tourism, Development and the Environment: Beyond Sustainability, Earthscan, UK.
16. Wahab, S. and Pigram, J.J., (2005), eds Tourism Development and Growth: The Challenge of Sustainability,
Routledge, London & New York.
17. Zeithaml, V. A., Berry, L. L. and Parasuraman, A., (1996), “The Behavioral Consequences of Service
Quality”, Journal of Marketing, 60(April), pp. 31–46.
Performance Analysis of Select Mutual Fund Schemes : A Study in Context to the Role and Effect of Mutual Funds in the Recent Global Economic Meltdown
Soheli Ghose Banerjee
Assistant Professor
Department of Commerce, J. D. Birla Institute Kolkata.
Abstract
Recently we witnessed a global economic meltdown which affected the worldwide financial markets in varying
degrees. The so called Recovery Phase of the economic recession is being slowed down due to the deepening debt
crisis in the USA, political corruption which has gripped our nation and inflation to name a few. In this context I
have studied the effect and role of Mutual Funds in the fluctuating financial markets analysing 1 public sector (UTI
Opportunities) and 4 private sector mutual fund schemes ( ICICI Prudential Discovery, DSPBR Small and Mid Cap
Reg, HDFC Equity, Tata Equity PE ) between January 2010 and September 2011 covering the turbulent period of this
economic crisis.
Key words: Equity Diversified Open Ended Mutual Fund Schemes, Sector/ Company Holdings.
Introduction
According to the Association of Indian Mutual Fund Industry, “A Mutual Fund is a trust that pools the savings of a
number of investors who share a common financial goal. The money thus collected is then invested in capital
market instruments such as shares, debentures and other securities. The income earned through these
investments and the capital appreciation realized is shared by the unit holders in the proportion to the number of
units owned by them. Thus a mutual fund is the most suitable investment for the common man as it offers an
opportunity to invest in a diversified professionally managed basket of securities at a relatively low cost.” Mutual
funds act as a financial intermediary in activities of fund mobilization and investment (Singh D., 2003). The essence
of a Mutual Fund is the diversified portfolio of investment, which diversifies the risk by spreading out the investor’s
money across available or different types of investments. Thus, Indian Mutual Funds are playing a very crucial
developmental role in allocating resources in the emerging market economy (Sadhak H., 2003). Of all investing
institutions, Mutual Funds have grown at the fastest rate due to their operational flexibility and because they
provide better returns to the investors and serve as a sophisticated market-clearing agent (Sadhak H., 2003).
Rationale of Mutual Fund
Mutual Funds diversify in a predetermined category of investments. Thus without spending considerable time and
money small investors can enjoy the facility of diversification reducing and spreading the risk factor of investors
(Singh H., 2001). Through mutual fund, investors can purchase stocks or bonds with much lower trading costs.
Mutual funds provide investors with various schemes with different investment objectives. Investors can switch
their holdings from a debt scheme to an equity scheme and vice-versa. Option of systematic investment and
withdrawal is offered to the investors in most open-end schemes. The minimum investment in a Mutual Fund is
relatively smaller than that of shares. Mutual Fund industry is part of a well-regulated investment environment
where the interests of the investors are protected by the regulator. All funds are registered with SEBI. Mutual
Funds provide the benefit of skilled portfolio management by professional managers, to small investors who
otherwise cannot afford such expertise or knowledge (Singh H., 2001). An investor may not be able to sell some of
the shares held by him very easily and quickly, whereas units of a mutual fund offer sufficient liquidity (Sadhak H.,
2003). Thus, a risk averse investor can expect the market return at a lower cost and lower risk (Sadhak H., 2003).
The stock market fluctuations have less impact over Mutual Funds (Singh H., 2001). Mutual Funds play a dynamic
role in mobilizing savings by issuing units and channelling funds in the capital market into productive investment.
In this way, they further the process of financial intermediation and provide depth to the market (Sadhak H.,
2003). The growth of Mutual Funds has helped accelerate the development of the capital market, by channeling a
growing part of savings of the household sector into equity invetments and also by increasing awareness about
capital markets among many investors (Dave S., 1996).
History of Mutual Fund Industry in India
The formation of Unit Trust of India marked the evolution of the Indian mutual fund industry in the year 1963 with
the primary objective of attracting the small investors. The history of mutual fund industry in India can be divided
into following phases:
Phase 1: Establishment and Growth of UTI - 1964-87: UTI was established by an act of Parliament by the Reserve
Bank of India and operated under RBI until the two were delinked in 1978 and the entire control was transferred to
IDBI. UTI launched Unit Scheme in 1964. It launched ULIP in 1971. By the end of 1987, UTI's AUM was Rs 6700
crores.
Phase II. Entry of Public Sector Funds - 1987-1993: In November 1987, SBI Mutual Fund became the first non-UTI
mutual fund in India. It was followed by Canbank Mutual Fund (1987), LIC Mutual Fund (1989), Indian Bank Mutual
Fund (1989), Bank of India Mutual Fund (1990), GIC Mutual Fund (1990) and PNB Mutual Fund (1989), Bank of
Baroda Mutual Fund (1992). By 1993, the AUM of the industry increased to Rs. 47,004 crores. However, UTI
remained to be the leader with about 80% market share.
Phase III. Emergence of Private Sector Funds - 1993-96: By 1994-95, about 11 private sector funds had launched
their schemes. In 1993, the first Mutual Fund Regulations came into being, under which all mutual funds, except
UTI were to be registered and governed. The erstwhile Kothari Pioneer (now merged with Franklin Templeton) was
the first private sector mutual fund registered in July 1993. Mutual Funds from Domestic and Foreign Private
Sectors had taken away a significant proportion of the market share of UTI and Public Sector Mutual Funds (Sadhak
H., 2003).
Phase IV. SEBI Regulation - 1996-2004: The mutual fund industry witnessed growth and stricter regulation from
the SEBI after 1996 as uniform standards were set for all mutual funds in India. Various Investor Awareness
Programmes were launched by SEBI and AMFI, with an objective to educate investors about the mutual fund
industry. In February 2003, the UTI Act was repealed and UTI was stripped of its Special legal status bringing all
mutual fund players on the same level. UTI was reorganized into two parts (The Specified Undertaking and The UTI
Mutual Fund). By the end of September 2004, there were 29 funds, with AUM of Rs.153108 crores under 421
schemes. But, UTI Mutual Fund was still the largest player in the industry.
Phase V. Growth and Consolidation - 2004 Onwards: The industry has also witnessed several mergers and
acquisitions recently (Alliance Mutual Fund by Birla Sun Life, Sun F&C Mutual Fund and PNB Mutual Fund by
Principal Mutual Fund). More international mutual fund players entered India like Fidelity, Franklin Templeton
Mutual Fund etc. This is a continuing phase of growth of the industry through consolidation and entry of new
international and private sector players. The growth and success of Mutual Fund industry depends upon sound
financial management of policies and investment practices. It pursues to bring about value addition to the corpus
of Mutual Funds (Sondhi., 2004).
The structure of Mutual Funds in India is as follows
Sponsors: It is the agency, which of its own or in collaboration with other body corporates comply the formalities
of establishing a mutual fund (Bansal L., 1997).
Trust: A company is appointed as a Trustee to manage Mutual Fund. It is their responsibility to supervise collection
of any income due to be paid to the scheme (Bansal L., 1997).
Asset Management Company (AMC): The Trustees appoint the AMC, which is established as a legal entity, to
manage the investor’s money for a fee. The AMC floats new schemes and manages these schemes by buying and
selling securities.
Custodian: The Mutual Fund appoints a custodian to hold the funds securities in safekeeping, settle securities
transactions for the fund, collect interest and dividend paid on securities and record information on stock splits
and other corporate action (Baid R., 2007).
Transfer Agents: Registrar and Transfer Agents (RTAs) maintain the investor’s (unit holder’s) records, reducing the
burden on the AMCs. Regulatory accountability is of prime importance to the regulated and the investor (Sen B.,
1996). More importantly, the goal of regulatory agency is not just to regulate but also to inculcate the culture of
self-regulation supported by less external regulation yielding better results (Sahadevan and Thiripalraju., 1997).
Literature Review
Research work on Mutual Funds began in 1960s in the U.S.A. and European region. The pioneering work on the
mutual funds in U.S.A. was done by Friend (1962) in Wharton School of Finance and Commerce for the period 1953
to 1958. He made an extensive and systematic study of 152 mutual funds found that mutual fund schemes earned
an average annual return of 12.4 percent, while their composite benchmark earned a return of 12.6 percent. Their
alpha was negative with 20 basis points. Overall results did not suggest widespread inefficiency in the industry.
Irwin, Brown, et.al., (1965) analyzed issues relating to investment policy, portfolio turnover rate, performance of
mutual funds and its impact on the stock markets. He concluded that, on an average, funds did not perform better
than the composite markets and there was no persistent relationship between portfolio turnover and fund
performance. The most prominent study by Sharpe, William F. (1966) developed a composite measure of return
and risk. He evaluated 34 open-end mutual funds for the period 1944-63. Reward to variability ratio for each
scheme was significantly less than DJIA and ranged from 0.43 to 0.78. Expense ratio was inversely related with the
fund performance, as correlation coefficient was 0.0505. The results depicted that good performance was
associated with low expense ratio and not with the size. Sample schemes showed consistency in risk measure. He
was one of the first to introduce a measure for the performance of Mutual Funds, popularly known as the Sharpe
Ratio: R(x) = (Rx - Rf) /σx, (x is some investment; Rx is the average annual rate of return of x; Rf is the best available
rate of return of a "risk-free" security (i.e. cash); σx is the standard deviation of Rx). Treynor developed the Treynor
Ratio measuring returns earned in excess of that which could have been earned on a risk less investment per each
unit of market risk. The ratio: (rp - rf ) / βp ; (rp = Average return of the portfolio; rf = Average return of the risk-free
proxy; βp= Beta of the portfolio). Treynor and Mazuy (1966) evaluated the performance of 57 fund managers in
terms of their market timing abilities and found that, fund managers had not successfully outguessed the market,
suggesting that, investors were completely dependent on fluctuations in the market. Jensen (1968) derived a risk-
adjusted measure of portfolio performance (Jensen’s alpha) that estimates how much a manager’s forecasting
ability contributes to fund’s returns. Sarkar A (1991) critically examined mutual fund evaluation methodology and
pointed out that Sharpe and Treynor performance measures ranked mutual funds a like in spite of their differences
in terms of risk.. Shashikant U (1993) pointed out that money market mutual funds with low-risk and low return
offered conservative investors a reliable investment avenue for short-term investment. Gupta (2003) examined
the performance of select Mutual Funds by using five performance measures: (a) Rate of Return Measure, (b)
Sharpe Ratio, (c) Treynor Ratio, (d) Jensen Differential Return Measure, and (e) Fama's Components of Investment
Performance. Though the performance of some private sector funds was superior there is no conclusive evidence
to suggest that the performance of Mutual Funds was better than the relevant benchmark. Sarkar and Majumdar
(1995) evaluated financial performance of five close-ended growth funds for the period February 1991 to August
1993, concluding that the performance was below average in terms of alpha values (all negative and statistically
not significant) and funds possessed high risk. Ferson and Schadt (1996) measured the performance with both
unconditional and conditional form of - CAPM, Treynor-Mazuy model and Henriksson-Merton model suggesting
that the use of conditioning lagged information variables improves the performance of the Mutual Fund schemes,
causing the alphas to shift towards the right and reducing the number of negative timing coefficients. This was
further tested in the works of Ferson and Warther (1996), concluding that conditional approach is better in
identifying the true market timing and stock selection ability of the fund managers than the unconditional models.
Jayadev (1996) studied the performance of UTI Mastergain 1991 and SBI Magnum Express from 1992-94 with 13
percent return offered by Post Office Monthly Income Deposits as risk-free return. Mastergain earned an average
return of 2.89 percent as against market earnings of 2.84 percent. Volatility of Magnum Express was high
compared to Mastergain. Both the funds did not earn superior returns because of lack of selectivity on the part of
the fund managers indicating that, the funds did not offer the advantages of professionalism to the investors.
Tripathy (1996) suggested that, mutual funds should build investors confidence through schemes meeting the
diversified needs of investors, speedy disposal of information, improved transparency in operation, better
customer service and assured benefits of professionalism. Yadav and Mishra (1996) evaluated 14 close end
schemes over the period of April 1992 to March 1995 with BSE National Index as benchmark. Their analysis
indicated that, 57 percent of sample schemes had a mean return higher than that of the market, higher Sharpe
Index and lower Treynor index. Schemes performed well in terms of diversification and total variability of returns
but failed to provide adequate risk-premium per unit of systematic risk. Fund managers of growth schemes
adopted a conservative investment policy and maintained a low portfolio beta to restrict losses in a rapidly falling
stock market. Sahadevan and Thiripalraju (1997) stated that, mutual funds provided opportunity for the middle
and lower income groups to acquire shares. The savings of household sector constituted more than 75 percent of
the GDS along with a shift in the preference from physical assets to financial assets and identified that, savings
pattern of households shifted from bank deposits to shares, debentures, and mutual funds. Mishra (2001)
evaluated the performance of 24 public sector mutual funds over a period, April 1992 to December 1996 in terms
of rate of return, Treynor, Sharpe and Jensen’s measures of performance also addressing beta’s instability issues.
The study concluded dismal performance of PSU mutual funds in general. Gupta A (2001) evaluated the
performance of 73 selected schemes with different investment objectives, both from the public and private sector
using Market Index and Fundex. NAV of both close-end and open-end schemes from April 1994 to March 1999
were tested. The sample schemes were not adequately diversified, risk and return of schemes were not in
conformity with their objectives, and there was no evidence of market timing abilities of mutual fund industry in
India. Narasimhan and Vijayalakshmi (2001) analysed the top holdings of 76 mutual fund schemes from January
1998 to March 1999 showing 62 stocks were held in portfolio of several schemes, of which only 26 companies
provided positive gains. The top holdings represented more than 90 percent of the total corpus in the case of 11
funds. The top holdings showed higher risk levels compared to the return. The correlation between portfolio stocks
and diversification benefits was significant at one percent level for 30 pairs and at five percent level for 53 pairs.
Roy and Deb (2003) used the Treynor-Mazuy model and Henriksson-Merton model to measure the Conditional
Performance of Indian Mutual Funds. They observed that, the traditional techniques use the unconditional
moments of the returns not capturing the time-varying element of return. Thus, recent studies have empirically
tested the persistence in fund performance. Saha (2003) identified that Prudential ICICI Balanced Fund, Zurich (I)
Equity Fund were the best among the equity funds while Pioneer ITI Treasury scheme was the best among debt
schemes. He concluded that, the efficiency of the fund managers was the key in the success of mutual funds and
so the AMCs had to ensure more professional outlook for better results. Elango’s (2004) analytical results indicate
that, private funds had a high positive association between the past and current year NAV compared to public
sector. The private sector schemes outperformed public sector in terms of NAV range value, innovative products
and in deployment of funds. Public sector funds showed low volatility as against greater variability for private
sector indicating low consistency. Student ‘t’ test indicated the existence of a high significant difference between
the mean NAV of private sector funds and public sector with a high statistical significance of (-) 5.95. Satish D
(2004) opined that investors from seven major cities in India had a preference for mutual funds compared to
banking and insurance products expecting moderate return risk. 60 percent of investors preferred growth
schemes. The image of AMC acted as a major factor in the choice of schemes. Investors had the same level of
confidence towards shares and mutual funds. Sharath Jutur (2004) studied 58 schemes during the bear period
(September 1998 to April 2002). He identified that the risk was low for 37, below average risk for 11 and of
average risk for 10 schemes revealing average mutual funds were found to be with low unsystematic and high total
risk. The return was positive for 46 schemes, with 30 yielding above 5 percent, 32 had positive Treynor ratio, 30
had positive Sharpe ratio and 35 had positive Jensen measure due to the bearish market with low CAPM returns.
Sondhi and Jain (2005) examined 17 public and 19 private sector mutual fund equity schemes. The mean and
median returns for the aggregate period (1993-2002) were lower than the returns on 364 days treasury bills, and
higher than the BSE 100 index. Private equity schemes had superior performance. More than three-fourth of public
sector schemes were unable to achieve better returns in spite of higher investor confidence associated with high
safety. The funds did not show consistency in performance. Guha Deb and Banerjee (2009) used Value at Risk
approach (VaR) as a single risk measure summarizing all sources of downward risk. They attempted to highlight the
importance of VaR as a measure of ‘downside risk’ for Indian equity Mutual Funds, an aspect that is completely
ignored for performance reporting in Indian Mutual Fund industry.
Objective and Methodology of the Study:
I have analysed 1 public sector and 4 private sector mutual fund schemes which are ICICI Prudential Discovery (2),
DSPBR Small and Mid Cap Reg (7), HDFC Equity (27), Tata Equity PE (36), UTI Opportunities (47) between January
2010 and September 2011 covering the turbulent period of this economic crisis. It seems that Mutual Fund
Industry is a shock absorber to market fluctuations and if it fails to do so, it cannot be differentiated from other
types of investments. In these trying times all sectors are getting affected adversely, the performance analysis of
Mutual Funds is very pertinent in that context. The study is based on secondary data collected from a sample of
top 50 Equity Diversified funds based on rankings provided by Valueresearchonline (A popular and authentic
mutual fund research organization) as on 1st January 2010. The second stage sample schemes were taken from
these and were analysed for performance and fluctuations, changes in their sector holdings to establish a relation
between sector holdings and fund performance during business cycle fluctuations and Correlate various factors
with return generated.
Results and Interpretation
ICICI Pru Discovery: In the beginning of the study period January 2010 it garnered very high return (9.17) which
had dropped a little in March 2010 (6.89) and June 2010 (5.56) but again picked up very well in September 2010
(12.17). However the downfall began in December 2010 onwards when the return was 0.9 and continued
plummeting further in March 2011 (-6.64), June 2011 (1.14) and September 2011 (-12) (GRAPH 1). This negative
turn can be explained as:
a) The economy as a whole had taken a downward turn from the beginning of 2011 and we later witnessed
difficult times both politically and socially which adversely affected the Indian Financial Markets including the
Mutual Fund Industry.
b) The major sector holdings were Financial, Energy, Healthcare, Communication, Chemicals, Metals and Services.
The stockholdings in Energy and Healthcare Sector did not change much. The energy (especially crude oil) crisis
deepened from early 2011 with oil prices skyrocketing. This may have affected the returns generated towards the
later quarters of 2011. The stockholdings in the Financial sector fell from 19.68 in January 2010 to 10.4 in
September 2011. However, with the Financial Sector performing well overall this reduction in percentage
stockholdings may have affected the performance of the Fund. The holdings in the Chemicals sector had also
reduced considerably similar to the Communication sector where the holdings were completely given up in June
and September 2011. This was triggered by the severe crisis the Telecommunication Industry is going through.
Though there has been a sharp rise in the Technology sector (3.25 to 10.53), it did not help the returns much
(GRAPH 2).
c) The major changes in the company holdings were Bharti Airtel (6.11 to 0) and ONGC (4.15 to 0), which is
obviously reflected in the returns generated (GRAPH 3).
DSPBR Small & Midcap Reg: In January 2010 it garnered very high return (13.47) dropping badly in March 2010
(3.97) but again picking up very well in September 2010 (15). However the downfall began in December 2010
onwards when the return was -0.44 and continued plummeting further in March 2011 (-9.75), June 2011 (1.99)
and September 2011 (-6) (GRAPH 4). This can be explained as: a) Same as 1.
b) The major sector holdings were Financial, Energy, Healthcare, Communication, Chemicals, Construction and
Services. The energy (especially crude oil) crisis deepened from early 2011 with oil prices skyrocketing affecting the
returns generated towards the later quarters of 2011. In addition, the holdings in the energy sector increased
from 5.73 to 7.39, further affecting the returns. The stockholdings in the Healthcare sector fell from 13.34 in
January 2010 to 6.71 in September 2011. However, with the Sector performing well overall this reduction may
have further affected the performance. The Communication sector holdings were completely given up in June and
September 2011 probably triggered by the severe Telecommunication Industry crisis. However, there has been a
steady rise in the Technology, Construction and FMCG sectors and in spite of these sectors performing well overall,
the increase in percentage stock holdings have not been that high to counter the adverse effect of the other sector
holding (GRAPH 5).
c) The major changes in the company holdings were Gujarat State Petronet (1.84 to 0) and Thermax (2.02 to 0),
which is obviously reflected in the returns generated (GRAPH 6).
HDFC Equity: In January 2010, it garnered very high return (9.06) dropping badly in March 2010 (2.28) but again
picking up very well in September 2010 (17.12). However the downfall began in December 2010 onwards when the
return was 0.62 and continued plummeting further in March 2011 (-5.1), June 2011 (-0.73) and September 2011 (-
12.73) (GRAPH 7). This negative turn can be explained as: a) Same as 1.
b) The major sector holdings were Financial, Energy, Healthcare, FMCG, and Services. The stockholdings in Energy
Sector increased from 11.26 to 17.1. The energy crisis deepened from early 2011 with oil prices skyrocketing
affecting the returns generated towards the later quarters of 2011. The stockholdings in the Healthcare and
Financial sector fell from 9.56 and 26.33 in January 2010 to 3.35 and 22.48 in September 2011 respectively.
However, with the Sectors performing well overall this reduction may have further affected the performance of
the Fund. The holdings in the Communications sector had also increased considerably from 0 to 4.1 This, along
with the severe Telecommunication Industry crisis may have further reduced the returns. However, there was a
rise in the Technology sector, FMCG sector has seen a sharp decrease and with these sectors performing well, the
increase in percentage stock holdings in one and decrease in another has counteracted leading further to the
adverse effect of the other sector holdings (GRAPH 8).
c) The major changes in the company holdings were Bharti Airtel (0 to 4.1), Oil India (0 to 2.13), Coal India (0 to 2.4)
and ONGC (5.95 to 0), which showed in the returns generated (GRAPH 9).
Tata Equity Pe: In January 2010 it garnered very high return (9.64) dropping badly in March 2010 (0.67) but picking
up in September 2010 (10.41). However the downfall began in December 2010 onwards when the return was 0.63
and continued plummeting further in March 2011 (-6.49), June 2011 (-1.71) and September 2011 (-10.26) (GRAPH
10). This negative turn can be explained as: a) Same as 1.
b) The major sector holdings were Financial, Energy, Metals, FMCG, Technology and Services. The stockholdings in
Energy Sector jumped from 8.82 to 21.38. The energy crisis deepened from early 2011 with oil prices skyrocketing
affecting the returns generated towards the later quarters of 2011. The stockholdings in the Services and Metal
sector fell from 11.05 and 7.46 in January 2010 to 7.11 and 5.82 in September 2011 respectively. However, with
the Sectors performing well overall this reduction may have further affected the performance. The holdings in the
Communications sector had not changed much further reducing the returns. Though there has been stability in the
Technology sector, FMCG sector has seen a sharp increase and as these sectors are performing well overall, but
the increase in percentage stock holdings in them have not been that high to counter the adverse effect of the
other sector holding, thus not helping the returns much (GRAPH 11).
c) The major changes in the company holdings happened in Bharti Airtel (2.25 to 3.2), Bank of Baroda (0 to 2.47),
BPCL (0-2.49), Oil India (0 to 1.57), and Reliance Industries (0 to 2.49) which is obviously reflected in the returns
generated (GRAPH 12).
UTI Opportunities: In January 2010 it garnered very high return (5.62, though not as high as the private funds
studied here) which had dropped a badly in March 2010 (-0.75) but again picked up very well in September 2010
(15.32). However the downfall began in December 2010 onwards when the return was 1.84 and continued
plummeting further in March 2011 (-5.05), June 2011 (0.29) and September 2011 (-4.6). However, the negative
return in September 2011 is not as low as compared to the private funds studied here (GRAPH 13). This can be
explained as: a) Same as 1.
b) The major sector holdings were Financial, Energy, Construction, FMCG, Metals and Technology. The
stockholdings in Energy Sector was stable at around 13%, which is quite high. The energy crisis deepened from
early 2011 with oil prices skyrocketing affecting the returns generated towards the later quarters of 2011. The
stockholdings in the Metals and Technology sector fell from 11.3 and 9.48 in January 2010 to 1.35 and 6.54 in
September 2011 respectively. However, with the Sectors performing well overall this reduction may have further
affected the performance. The holdings in the Communications sector increased from 0 to 2.56 further reducing
the returns. Though there has been a sharp rise in the FMCG, Construction and Cons Durable sector and in spite of
these sectors performing well overall, the increase in percentage stock holdings have not been high enough to
counter the adverse effect of the other sector holdings (GRAPH 14).
c) The major changes in the company holdings were in Bharti Airtel (0 to 1.88), Cairn India (0 to 3.87), ITC (2.5 to
6.96) and Titan Industries (0 to 4.54), which is obviously reflected in the returns generated (GRAPH 15).
Comparing the returns of the Private and Public Sector Funds, the conclusions were drawn (GRAPH 16):
a) The economy performed well in the 1st
quarter of 2010 then dipping a little and picking up momentum in
September quarter of 2010. This was clearly reflected in the returns of all the chosen funds. The recovering
economy again faced a down ward turn from December 2010 due to international debt crisis, political, social and
economic problems in India, which is still continuing. This has also clearly affected the Financial markets and
thereby the Mutual Fund Industry as a whole. Thus the better performing funds fluctuate along with the
fluctuating economy. Thus there is a correlation between fund performance and financial market fluctuations
brought on by the Global Economic Crisis.
b) The returns are also linked with the sector performance. All the funds had majorly invested in the Energy and
Communications sector, which met with a crisis thereby reducing returns. It was also seen that few funds had
reduced their percentage holdings in the better performing sectors like Financial, Technology, Services and FMCG,
thereby not being able to benefit from the sector returns. Thus there is a correlation between Sector Holdings,
performance of those sectors in the economy and returns generated by the Mutual Fund Schemes.
c) The returns generated by the Public Sector Funds are not as high compared to the Private Sector Funds and the
negative returns are also more controlled in the Public Sector Funds as compared to the Private Sector Funds. This
is partly due to the difference in sector holdings and also due to the conservative nature of investment followed by
the public sector funds.
Thus from the above study one can interpret that the Global Meltdown has adversely affected the entire Indian
Financial market including the Mutual Fund Industry.
Graphs, Tables and Figures
Graph 1: Return Generated By ICICI Pru Discovery
9.17 6.89 5.5612.17
0.9
-6.64
1.14
-12-20
0
20
Dec 09/Jan 10
Mar-10 Jun-10 Sep-10 Dec-10 Mar-11 Jun-11 Sep-11Re
turn
Quater
ICICI PRU DISCOVERY
Graph 2: Sector Holdings of ICICI Pru Discovery
Table 1: Sector Holdings of ICICI Pru Discovery
Sector ICICI Pru Discovery (2)
Jan-10 Mar-10 Jun-10 Sep-10 Dec-10 Mar-11 Jun-11 Sep-11
Automobile 2.81 3.4 2.42 1.4 3.74 3.48 4.74 4.78
Chemicals 6.53 6.85 7.92 6.04 5.08 6.82 3.89 3.71
Communication 6.11 6.13 5.73 4.19 4.27 4.24 0 0
Cons Durables 0 0 0 0 0 0 0 0
Construction 2.06 2.17 4.82 4.47 4.92 6.11 5.74 4.29
Diversified 2.76 2.72 4.77 1.97 1.89 4.15 2.32 2.46
Energy 12.81 14.53 8.51 10.44 9.4 9.56 14.59 13.48
Engineering 1.92 2.47 3.62 5.3 4.87 3.84 5.73 5.27
Financial 19.68 15.23 15.32 13.83 13.22 8.89 10.65 10.4
Fmcg 5.35 7.09 7.55 6.84 6.34 3.79 4.33 4.43
Healthcare 10.37 10.41 9.71 10.07 11.36 10.83 12.82 12.66
Metals 5.34 6.76 7.94 8.75 10.02 10.83 8.74 8.7
Services 6.05 5.89 4.72 8.88 7.79 7.65 8.51 8.86
Technology 3.25 3 5.61 5.45 5.6 6.28 10.33 10.53
Textiles 3.94 4.03 2.92 3.11 2.56 2.19 2.66 2.69
0
10
20
30ICICI PRU DISCOVERY SECTOR HOLDINGS
Jan-10Mar-10Jun-10Sep-10Dec-10Mar-11Jun-11Sep-11
Graph 3: Company Holdings of ICICI Pru Discovery
Table 2: Company Holdings of ICICI Pru Discovery:
Company ICICI Pru Discovery (2)
Company Jan-
10
Mar-
10
Jun-
10
Sep-
10
Dec-
10
Mar-
11
Jun-
11
Sep-
11
Amara Raja Batteries 1.69 1.96 2.49 3.31 2.97 2.93 3.71 3.25
Balkrishna Inds 0 0 0 1.48 1.62 1.66 2.12 2.17
Bharti Airtel 6.11 6.13 5.73 4.19 4.27 4.24 0 0
Cadila Healthcare 5.11 4.97 4.38 3.71 3.82 2.73 0 0
Cesc 2.84 3.25 2.6 3.11 3.91 3.31 3.27 3.03
Cipla 0 0 0 0 0 0 3.7 3.74
Eclerx Services 1.97 2.04 1.65 2.31 2.41 2.15 1.58 1.6
Fdc 3.51 3.51 3.32 2.63 1.88 1.59 0 1.4
Federal Bank 2.51 2.33 2.2 1.55 1.42 1.49 0 0
Great Eastern Shipping Co 3.84 3.85 3.07 5.31 3.76 3.93 3.16 3.35
Hcl Technologies 0 0 0 0 0 0 1.39 0
India Cements 0 0 0 0 0 1.61 0 0
Infotech Enterprises 1.77 1.69 0 1.58 2.18 2.03 1.61 1.54
Ing Vyasa Bank 2.17 2.11 0 1.83 1.75 1.9 1.94 2
Mindtree 0 0 0 0 0 1.18 1.71 1.79
Ongc 4.15 5.39 3.05 5.87 3.83 4.14 2.05 0
Oracle Fin Ser Software 0 0 1.8 0 1.39 1.23 2.24 2.36
Power Finance Corp 0 0 0 0 0 0 1.51 1.57
0
5
10A
MA
R…
BA
LKR
I…B
HA
RTI
…C
AD
ILA
…C
ESC
CIP
LAeC
LER
X …
FDC
FED
ER…
GR
EAT …
HC
L …IN
DIA
…IN
FOTE
…IN
G …
MIN
DT…
ON
GC
OR
AC
L…P
OW
E…
PU
NJA
…R
AIN
…R
ELIA
N…
STER
LI…
TATA
…TA
TA …
TOR
RE…
UN
ION
…U
NIT
E…
VA
RD
H…
VO
LTA
SW
IPR
O
ICICI PRU DISCOVERY COMPANY HOLDINGS Dec 09/Jan 10
Mar-10
Jun-10
Punjab National Bank 0 0 0 1.63 2.18 2.16 1.65 1.7
Rain Commodities 0 0 3.01 3.5 3.78 3.08 3.03 2.78
Reliance Industries 0 0 0 0 0 0 6.01 6.25
Sterlite Industries 1.9 3.43 3.5 3.63 5.55 6.18 2.85 2.54
Tata Motors 0 0 0 1.63 1.62 1.43 1.52 2.39
Tata Steel 0 0 0 0 0 2.21 2.35 1.83
Torrent Pharmaceuticals 0 0 0 2.35 3.14 3.38 2.18 1.85
Union Bank Of India 4.67 2.51 2.3 3.33 3.1 2.75 2.83 2.88
United Phosphorus 2.76 3.25 4.29 3.46 2.78 3.38 2.92 2.78
Vardhman Textiles 3.47 3.76 2.92 3.11 2.56 2.19 2.4 2.45
Voltas 0 0 0 0 0 0 1.77 1.88
Wipro 0 0 0 0 0 0 1.48 1.52
Graph 4: Return Generated By DSPBR Small & Midcap Reg:
Graph 5: Sector Holdings Of DSPBR Small & Midcap Reg:
13.47 3.97 8.88 15
-0.44 -9.75
1.99
-6-50
0
50
Dec 09/Jan 10 Mar-10 Jun-10 Sep-10 Dec-10 Mar-11 Jun-11 Sep-11Ret
urn
Quater
DSPBR SMALL & MIDCAP REG
0
5
10
15
20DSPBR SMALL & MIDCAP REG SECTOR HOLDINGS Jan-10
Mar-10
Jun-10
Sep-10
Dec-10
Table 3: Sector Holdings of DSPBR Small & Midcap Reg:
Sector DSPBR Small & Midcap Reg (7)
Jan-10 Mar-10 Jun-10 Sep-10 Dec-10 Mar-11 Jun-11 Sep-11
Automobile 4.93 5.76 7.88 6.09 6.38 5.37 6.1
Chemicals 6.88 6.21 8.26 10.81 7.19 6.91 8.65 8.1
Communication 1.5 0 0.75 0.69 1.24 0 0
Cons Durables 0.58 0 0 0 1.61 0.26 0.27
Construction 5.12 5.23 7.45 5.51 5.65 6.04 7.95
Diversified 7.16 8.7 6.08 6.09 5.84 7.01 6.28
Energy 5.73 5.51 3.94 2.5 2.95 8.16 7.93
Engineering 6.4 6.48 7.8 6.63 11.2 8.76 7.65 7.39
Financial 6.72 6.35 9.34 7.92 11.71 7.27 7.96
Fmcg 8.11 10.68 11.87 11.42 10.02 8.93 10.86 11.32
Healthcare 13.34 13 10.58 6.98 8.36 7.03 7.24 6.71
Metals 4.06 5.23 2.22 0.74 0.72 0.53 0.53
Services 15.91 16.64 13.75 10.73 14.14 14.73 10.48 11.26
Technology 4.03 3.52 4.86 4.93 4.91 5.99 5.68
Textiles 3.38 2.34 5.31 5.72 6.81 6.72 8.04
Graph 6: Company Holdings of DSPBR Small & Midcap Reg:
0
5DSPBR SMALL & MIDCAP REG COMPANY HOLDINGS Dec 09/Jan 10
Mar-10Jun-10Sep-10Dec-10Mar-11Jun-11Sep-11
Table 4: Company Holdings of DSPBR Small & Midcap Reg:
DSPBR Small & Midcap Reg (7)
Company Jan-
10
Mar-
10
Jun-
10
Sep-
10
Dec-
10
Mar-
11
Jun-
11
Sep-
11
Alstom Projects 1.74 2.23 2.44 2.6 1.97 1.64 0 0
Apollo Tyres 0 0 0 0 0 0 1.77 1.67
Areva T&D 0 0 0 0 2.8 1.89 1.69 1.72
Arvind 0 0 0 0 1.74 2.01 2.36 2.96
Bajaj Finance Services 0 0 0 0 0 0 1.93 2.04
Bata India 1.85 2.27 2.81 1.76 0 0 0 2.41
Bayer Crop Science 2.86 3.07 2.88 2.96 2.52 2.93 2.51 2.21
Bharat Forge 0 0 0 2.8 2.71 2.64 1.53 0
Biocon 0 0 0 0 0 0 2.32 2.65
Bombay Dyeing & Mfg. Co. 0 0 0 0 0 2.09 1.79 2.41
Chambal Fertilizers & Chem 0 0 0 0 0 2.1 3.82 3.43
CMC 1.56 1.81 1.74 1.95 2.42 1.94 1.82 1.6
E I D Parry (I) 2.66 2.61 2.73 2.75 3.57 2.91 3.43 3.13
Eicher Motors 1.97 2.13 2.54 2.46 2.03 2.34 2.07 2.95
GMDC 0 0 0 1.55 2.13 2.19 3.9 3.8
Godrej Industries 0 0 0 0 1.93 1.94 2.66 2.74
Godrej Properties 0 0 0 0 1.81 2.51 2.46 2.38
Gruh Finance 0 0 1.74 0 1.57 0 2.08 2.13
Gujarat Pipavav Port 0 0 0 0 0 0 2.12 2.06
Gujarat State Petronet 1.84 0 0 1.65 2.23 2.36 0 0
HPCL 0 0 0 0 0 0 3.21 3.16
IDBI Bank 1.57 0 0 0 0 1.9 0 0
IDBI Certificate Deposit 0 0 0 0 0 1.72 0 0
Indian Hotels 0 2.49 2.33 0 0 2.55 0 0
Info Edge (India) 0 0 0 2.64 2.22 2.39 2.36 2.36
Kajaria Ceramics 0 0 0 0 0 0 0 1.63
KEC International 1.73 1.73 0 1.63 0 0 1.51 0
KPIT Cummins Infosystems 0 0 0 0 0 0 1.55 1.57
Lakshmi Machine Works 0 0 0 0 1.89 1.81 0 0
SRF 0 0 0 1.66 1.98 1.89 1.72 1.78
Thermax 2.02 2.27 2.34 2.74 2.66 1.95 0 0
Torrent Pharmaceuticals 1.65 2.09 1.97 2.3 2.11 2.22 0 0
Trent 2.39 2.34 2.14 2.2 3.11 3.35 4.24 4.2
Union Bank Of India 0 0 0 0 0 3.44 0 0
Graph 7: Return Generated By HDFC Equity:
Graph 8: Sector Holdings of HDFC Equity:
9.06 2.28 7.2 17.120.62
-5.1 -0.73 -12.73-50
0
50
Dec 09/Jan 10Mar-10 Jun-10 Sep-10 Dec-10 Mar-11 Jun-11 Sep-11Ret
urn
Quarter
HDFC EQUITY
0102030 HDFC EQUITY SECTOR HOLDINGS
Jan-10
Mar-10
Jun-10
Sep-10
Table 5: Sector Holdings of HDFC Equity:
Sector HDFC Equity (27)
Jan-10 Mar-10 Jun-10 Sep-10 Dec-10 Mar-11 Jun-11 Sep-11
Automobile 6.49 6.32 6 6.14 5.59 5.4 3.84 3.83
Chemicals 0.86 1.44 1.12 0 0 0.06 0 0
Communication 0 0 2.79 4.11 1.99 2.3 4.12 4.1
Cons Durables 3 3.38 3.77 4.4 4.45 4.57 2 1.74
Construction 5.27 4.7 3.82 1.95 2.15 1.14 0 0
Diversified 2.56 2.71 2.5 1.54 2.9 0.75 1.2 1.76
Energy 11.26 13.47 16.06 18.82 16.69 20.64 17.6 17.1
Engineering 3.32 2.32 1.97 2.03 3.43 3.01 4.08 3.98
Financial 26.33 25.69 23.31 24.08 22.7 23.83 22.81 22.48
FMCG 8.28 9.24 7.65 5.75 4.56 4.96 1.21 2.14
Healthcare 9.56 10.12 9.32 8.07 8.3 7.26 3.38 3.35
Metals 3.43 3.47 2.81 2.74 3.21 5.48 4.95 4.37
Services 8.4 8.27 8.08 7.14 6.18 4.61 5.07 5.06
Technology 6.3 6.2 6.52 8.15 11.31 11.09 10.31 10.73
Textiles 0.7 0.64 0.56 1.5 1.48 1.21 0 0
Graph 9: Company Holdings of HDFC Equity:
0
10
20 HDFC EQUITYCOMPANY HOLDINGSDec 09/Jan 10Mar-10
Jun-10
Sep-10
Table 6: Company Holdings of HDFC Equity:
Company HDFC Equity (27)
Company Jan- 10 Mar-10 Jun-10 Sep-10 Dec-10 Mar-11 Jun-11 Sep-11
Axis Bank 2.8 2.16 1.94 1.65 0 0 1.6 1.47
Bank Of Baroda 3.73 3.92 4.02 4.29 4.16 4.34 3.3 3.31
Bharti Airtel 0 0 2.79 4.11 1.99 2.3 4.12 4.1
BPCL 1.9 1.68 1.92 2.67 2.16 2.04 2.05 1.67
Cipla 2.08 2.14 1.86 1.69 2.04 1.73 2.23 2.17
CMC 1.91 2.19 2.12 2.33 2.96 2.23 2.02 1.71
Coal India 0 0 0 0 2.93 3.43 3.59 2.4
Crompton Greaves 2.2 2.32 1.97 2.03 1.89 1.52 2.65 2.6
GAIL 2.55 2.52 3.35 2.4 2.63 2.04 1.79 0
ICICI Bank 6.5 6.34 3.43 4.09 5.89 6.76 6.05 5.88
Infosys Technologies 2.08 2.01 2.3 2.85 4.64 4.47 3.82 4.71
ITC 0 0 2.03 0 0 1.18 0 0
LIC Housing Fin 2.4 2.68 2.66 0 0 0 2.15 2.08
Lupin 1.73 1.8 1.9 0 1.6 1.34 0 0
Motherson Sumi Systems 0 0 1.48 1.52 1.4 1.61 0 0
Mundra Port & Sez 0 0 0 0 0 0 1.61 1.69
Oil India 0 0 1.96 2.4 1.76 1.45 2.12 2.13
ONGC 5.95 5.62 5.85 4.33 2.89 2.32 1.77 1.74
Punjab National Bank 0 0 0 2.3 2 2.03 1.76 1.75
Reliance Industries 0 0 0 0 0 4.24 0 3.16
State Bank Of India 7.01 7.09 7.5 9.69 7.75 7.99 7.95 7.99
Sterlite Industries 0 0 0 0 0 1.57 1.96 1.68
TCS 2.03 1.77 2.1 2.97 3.71 4.59 4.47 4.31
Tata Motors Dvr 0 0 0 2.59 2.5 2.31 2.72 2.8
Tata Steel 1.67 1.77 0 0 1.67 2.6 2.99 2.69
Titan Industries 3.34 3.38 3.77 4.4 4.45 4.57 2 1.74
Zee Entertainment Ent 1.82 1.72 2.72 1.5 0 1.27 2.24 2.21
Graph 10: Return Generated By Tata Equity Pe:
Graph 11: Sector Holdings of Tata Equity Pe:
Table 7: Sector Holdings of Tata Equity Pe:
Sector Tata Equity Pe (36)
Jan-10 Mar-10 Jun-10 Sep-10 Dec-10 Mar-11 Jun-11 Sep-11
Automobile 3.53 3.13 9.1 8.13 10.35 10.64 8.37
Chemicals 4.33 4.28 3.14 3.52 4.01 2.82 2.74
Communication 3.82 3.95 3.49 3.06 3 3.75 3.51
Cons Durables 0 0 0 0 0 0 0.46
Construction 2.7 4.11 2.98 2.47 3.47 3.5 3.32
Diversified 5.16 4.73 6.14 5.51 5.97 5.74 6.09
Energy 8.82 12.43 16.93 13.27 14.31 15.73 18.65 21.38
Engineering 4.55 3.71 3.73 3.73 3.47 3.99 3.86
Financial 11.74 14.36 12.37 14.1 13.12 14.97 12.71 12.65
9.64 0.67 4.46 10.41 0.63
-6.49 -1.71 -10.26-50
0
50
Dec 09/Jan 10 Mar-10 Jun-10 Sep-10 Dec-10 Mar-11 Jun-11 Sep-11Re
turn
Quarter
TATA EQUITY PE
0102030
TATA EQUITY PE SECTOR HOLDINGS Jan-10
Mar-10
Jun-10
Sep-10
Dec-10
FMCG 7.38 6.51 6.41 8.3 9.27 8.81 10.53
Healthcare 6.24 6.87 4.65 5.29 5.08 5.67 3.72
Metals 7.46 7.07 6.38 8.22 9.39 6.91 6.45 5.82
Services 11.05 9.95 7.3 5.87 5.75 5.16 7.3 7.11
Technology 10.38 9.59 11.73 11.16 11.71 10.43 9.49 9.96
Textiles 0 0 0 0 0 0.12 0 0
Graph 12: Company Holdings of Tata Equity Pe:
Table 8: Company Holdings of Tata Equity Pe:
Company Tata Equity Pe (36)
Company Jan-
10
Mar-
10
Jun-
10
Sep-
10
Dec-
10
Mar-
11
Jun-
11
Sep-
11
Axis Bank 6.03 5.1 4.38 3.2 2.65 2.83 2.8 2.36
Balrampur Chini Mills 3.53 2.92 2.77 2.97 3.46 3.02 2.66 2.27
Bank Of Baroda 0 0 0 0 0 2.4 2.58 2.47
Bharti Airtel 2.25 2.72 1.94 2.73 2.51 2.57 3.37 3.2
BPCL 0 0 0 0 1.29 2.06 2.3 2.49
Cadila Healthcare 3.94 3.39 3.07 2.58 2.88 3 3.5 3.28
Cairn India 0 0 0 0 0 0 3.02 4.4
Exide Inds 2.3 1.89 1.49 1.82 1.75 0 1.74 1.59
Federal Bank 1.5 0 0 0 1.82 1.97 2.08 1.98
Glaxo Consumer Healthcare 0 0 0 1.47 1.82 1.81 2.03 2.15
GMDC 2.28 0 0 0 0 0 0 1.66
0
5
10
AX
IS …
BA
LRA
…
BH
AR
TI …
CA
DIL
A …
CEN
TR…
FED
ER…
GA
IL
GM
DC
GU
JAR
…
HIN
DA
…
HIN
DU
…
IND
IA …
LUP
IN
MA
RU
…
MP
HA
S…
OIL
…
OR
AC
L…
PO
LAR
I…
RA
IN …
SHR
EE …
TATA
…
TATA
…
TV …
TATA EQUITY PE COMPANY HOLDINGS Dec 09/Jan 10Mar-10Jun-10Sep-10Dec-10Mar-11Jun-11
Grasim Industries 0 0 0 2.32 2.3 3.31 3.49 4.09
Hindalco Inds 0 2.06 3.21 6.03 7.09 4.49 3.77 3.23
Hindustan Unilever 2.15 2.24 1.81 1.97 1.88 1.76 2.09 3.98
Hindustan Zinc 2.94 2.36 1.56 1.36 1.61 1.66 1.79 1.65
HPCL 0 2.5 2.31 2.55 1.85 1.73 0 0
ITC 0 0 0 0 0 2.68 2.04 2.13
Lupin 2.3 1.95 1.82 1.49 1.73 1.54 1.75 0
Mahindra & Mahindra 0 0 0 5.12 5.91 5.45 5.48 3.32
Maruti Suzuki India 0 0 0 0 0 2.72 2.71 2.66
Oil India 0 0 1.49 0 0 0 0 1.57
ONGC 0 3.35 4.26 4 3.47 3.2 3.09 3.35
Oracle Fin Ser Software 0 0 1.54 0 0 0 0 2.14
Patni Computer Systems 2.36 2.01 2.31 1.51 1.62 1.67 0 0
Polaris Software Lab 2.36 3.23 4.6 4.09 4.57 5.08 4.85 4.07
Rain Commodities 0 1.66 1.34 0 0 0 1.78 0
Reliance Industries 0 0 0 1.92 1.94 2.4 2.32 2.49
Tata Chemicals 4.33 4.28 3.44 3.14 2.91 2.57 2.82 2.74
Tata Motors 0 0 0 0 0 2.18 2.45 2.22
Tata Power 0 1.65 0 0 0 1.61 1.62 0
Voltas 3.01 2.9 2.41 2.33 1.99 1.7 0 0
Graph 13: Return Generated By UTI Opportunities:
5.62
-0.75
2.43
15.32
1.84
-5.05
0.29
-4.6-20
0
20
Dec 09/Jan 10
Mar-10 Jun-10 Sep-10 Dec-10 Mar-11 Jun-11 Sep-11Ret
urn
Quarter
UTI OPPORTUNITIES
Graph 14: Sector Holdings of UTI Opportunities:
Table 9: Sector Holdings of UTI Opportunities:
Sector UTI Opportunities (47)
Jan-10 Mar-10 Jun-10 Sep-10 Dec-10 Mar-11 Jun-11 Sep-11
Automobile 1.85 3.06 9.86 8.64 9.11 6.52 4.1 3.87
Chemicals 0 0 1.13 1.13 0.97 0.85 0.78 0.67
Communication 0 0 0 0 1.05 0.82 2.68 2.56
Cons Durables 1.27 1.98 2.79 3.84 4.1 4.46 4.58 4.54
Construction 3.5 3.24 3.45 6.83 7.31 8.32 7.22 7.48
Diversified 3.01 4.83 2.54 2.82 2.89 3.11 2.63 4.04
Energy 13.89 13.72 17.67 19.26 15.89 13.78 14.3 13.37
Engineering 3.19 3.68 4.38 7.61 8.34 7.51 6.36 5.31
Financial 20.19 24.02 19.18 16.95 15.81 16.13 18.8 18.42
FMCG 6.7 10.62 10.11 14.06 14.15 14.47 15.4 15.13
Healthcare 2.42 2.32 3.36 4.53 5.3 4.6 4.62 4.34
Metals 11.3 11.63 7.09 1.8 1.76 1.76 1.42 1.35
Services 0.47 0.43 0 0 0 0.38 1.72 1.76
Technology 9.48 8.7 7.83 8.29 8.14 8.19 6.49 6.54
Textiles 0 0 0 0 0 0 0 0
0102030 UTI OPPORTUNITIES SECTOR HOLDINGS
Jan-10
Mar-10
Jun-10
Sep-10
Dec-10
Graph 15: Company Holdings of UTI Opportunities:
Table 10: Company Holdings of UTI Opportunities:
Company UTI Opportunities (47)
Company Jan-
10
Mar-
10
Jun-
10
Sep-
10
Dec-
10
Mar-
11
Jun-
11
Sep-
11
ACC 0 0 0 0 0 2.04 1.99 2.13
Ambuja Cements 0 0 0 2.57 2.83 3.3 3.2 3.5
Ashok Leyland 0 1.77 2.11 3.07 2.65 2.41 1.94 0
Bharti Airtel 0 0 0 0 0 0 1.97 1.88
BHEL 2.03 1.91 2.05 3.54 4.3 3.94 3.21 2.91
Cairn India 0 0 0 2.86 2.76 4.23 4.06 3.87
Colgate Palmolive (I) 0 0 1.77 2.21 2.15 2.06 2.18 2.2
Crisil 0 0 2.45 2.8 2.69 2.95 3.54 3.66
Exide Inds 0 0 2.33 3.63 3.61 3.17 3.15 2.4
GAIL 3.2 3.25 3.48 4.24 4.43 3.23 2.52 2.46
Glaxo Consumer Healthcare 0 0 0 0 1.8 1.77 0 1.74
Glenmark Pharma 2.42 2.32 2.27 2.28 2.68 2.15 1.96 1.9
Grasim Industries 1.64 3.54 2.54 2.82 2.89 3.11 2.63 2.75
HDFC Bank 0 0 0 1.9 0 1.78 2.81 2.72
HDFC 2.58 3.32 3.31 4.65 4.5 4.43 4.14 3.91
ICICI Bank 4.15 4.45 2.73 0 1.77 1.76 0 3.95
Infosys Technologies 4.54 4.13 4.08 4.16 3.82 3.69 2.71 2.86
0
5
10 UTI OPPORTUNITIES COMPANY HOLDINGSDec 09/Jan 10Mar-10
Jun-10
Sep-10
ITC 2.5 3.78 5.93 7.13 6.79 7.25 7.58 6.96
Kotak Mahindra Bank 1.69 1.88 1.85 2.24 2.07 2.14 1.97 2
Nestle India 0 2.11 2.42 3.11 3.41 3.39 3.83 3.62
Petronet Lng 0 0 2.41 4.03 4.63 4.62 6.13 5.5
State Bank Of India 4.29 2.36 4.75 3.6 3.04 3.06 2.31 2.19
Sun Pharmaceutical Inds 0 0 0 2.24 2.62 2.45 2.66 2.45
Tata Consultancy Services 2.76 2.52 3.73 4.12 4.32 4.49 3.76 3.66
Tata Motors 1.69 4.22 5.57 6.46 4.12 2.16 2.22
Titan Industries 0 1.98 2.79 3.84 4.1 4.46 4.58 4.54
Graph 16: Comparison between Mutual Fund Schemes:
Figure 1: Assets under Management:
Source: amfi.com
-15
-10
-5
0
5
10
15
20
Dec 09/Jan 10
Mar-10 Jun-10 Sep-10 Dec-10 Mar-11 Jun-11 Sep-11
Ret
urn
s
COMPARISON BETWEEN MUTUAL FUND SCHEMES
ICICI PRU
DISCOVERY
DSPBR SMAL l &
MIDCAP REG
HDFC EQUITY
TATA EQUITY PE
UTI
OPPORTUNITIES
Figure 2: Structure of Mutual Fund Industry in India:
Source: personalfn.com
Reference
1. Elango, (2004), “Which fund yields more returns?” The Management Accountant, 39(4), pp. 283-290.
2. Ferson and Schadt, (1996), “Measuring Fund Strategy and Performance in changing Economic Conditions”,
The Journal of Finance, 51(2), pp. 425-61.
3. Ferson, Warther (1996), “Evaluating Fund Performance in a Dynamic Market”, Financial Analysts Journal,
52(6), pp. 20-28.
4. Friend, (1962), A Study of Mutual Funds, U.S. Securities and Exchange Commission, USA.
5. Guha Deb, Banerjee and Chakrabarti (2008), “Persistence in Performance of Indian Equity Mutual Funds:
An Empirical Investigation”, IIMB Management Review, 20(2).
6. Gupta, (June 2001), “Mutual Funds in India: A Study of Investment Management”, Finance India, 15(2),
pp. 631-637.
7. Irwin, Brown, et al., (1965), “A Study of Mutual Funds: Investment Policy and Investment Company
Performance”, Elements of Investments, pp. 371-385.
8. Jayadev, (March 1996), “Mutual Fund Performance: An Analysis of Monthly Returns”, Finance India, 10(1),
pp. 73-84.
9. Jensen, (1968), “The performance of Mutual Funds in the period 1945 – 1964”, Journal of Finance, 23(2),
pp. 389-416.
10. Mishra, (2001), Measuring mutual fund performance using lower partial moment, Global Business Trends,
Contemporary Readings, 2001 Edition.
11. Narasimhan and Vijayalakshmi, (March 2001), “Performance Analysis of Mutual Funds in India”, Finance
India, 15(1), pp. 155-174.
12. Saha, (October 2003), “Indian Mutual Fund Management”, Management Accountant, 38(10), pp. 765-771.
13. Sarkar, A., (March 1991), “Mutual Funds in India - Emerging Trends”, Management Accountant, 26(3), pp.
171-174.
14. Sarkar and Mazumdar, (1995), “Weak Form of Efficient Market Hypothesis, A Special Analytical
Investigation”, Vikalpa, (April-June), pp. 25-30.
15. Sathis, D., (July 2004), “Investors Perceptions: A Survey by MARCH Marketing Consultancy & Research”,
Chartered Financial Analyst, 10(7), pp. 35-36.
16. Sharath, J., (July 2004), “Evaluating Indian Mutual Funds”, Chartered Financial Analyst, 83.
17. Sharpe, W. F., (1966), “Mutual Fund Performance”, Journal of Business, 39, pp. 119-138.
18. Shashikant, U., (1993), “Accounting Policy and Practices of Mutual Funds: The Need for Standardization”,
Prajan, 24(2), pp. 91-102.
19. Sondhi and Jain, (July 2005), “Financial Management of Private and Public Equity Mutual Funds In India:
An Analysis of Profitability”, The ICFAI Journal of Applied Finance, pp. 14-27.
20. Tripathy, (March 1996), “Mutual Fund in India: A Financial Service in Capital Market”, Finance India, 10(1),
pp. 85-91.
21. Treynor and Mazuy, (1966), “Can Mutual Funds Outguess The Markets”, Harvard Business Review, 44, pp.
131-136.
22. Yadav and Mishra, (July 1996), “Performance Evaluation of Mutual Funds: An empirical analysis”, MDI
Management Journal, 9(2), pp. 117-125.
23. Roy and Deb, (2003), Conditional performance of Indian Mutual Funds, ICFAI University Press, Working
paper. Available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=593723.
24. Baid, R., (2007), Mutual Funds Products and Services, Taxman Publications, New Delhi, p.42.
25. Banerjee, (2007), Mutual Funds in India, ICFAI University Press, Hyderabad.
26. Bansal, L., (1997), Mutual Funds Management and Working, Deep and Deep Publications, New Delhi,
p.70.
27. Chandra, (2002), Performance Appraisal of Mutual Funds in India, Excel Books, New Delhi.
28. Datta Chaudhuri and Seal, (2008), Mutual Fund Industry: Issues and Experiences, ICFAI University Press,
Hyderabad.
29. Dave, S., (1996), Mutual Funds in India: Challenges, Opportunities and Strategic Perspectives, UTI Institute
of Capital Markets, Mumbai, p.19.
30. Krishnamurthi, (2004), Mutual Funds in India, PHI, New Delhi.
31. Sadhak, H., (2003), Mutual Funds in India, Response Books, New Delhi, pp.33-106.
32. Sahadevan and Thiripalraju, (1997), Mutual Funds: Data Interpretation and Analysis, PHI, New Delhi, p.21.
33. Sharpe, William F. and Alexander, Gorden J., (1994) Investment, PHI, New Delhi.
34. Singh, D., (2003), Mutual Funds in India, Rajat Publications, New Delhi, p.6.
35. Singh H. (2001), Mutual Funds and the Indian Capital Market-Performance and Profitability, Kanishka
Publisher, Distributors, New Delhi, p.26.
36. Sen B. (1996), Mutual Funds in India: Challenges, opportunities and Strategic Perspectives, UTI Institute of
Capital Markets, Mumbai, p.233.
37. Sondhi, (2004), Financial Performance of Equity Mutual Funds in India, ICFAI University Press, Hyderabad,
pp.3.
38. Tripathi, (2007), Mutual Funds in India, Excel Books, New Delhi.
Websites: www.amfindia.com; www.valueresearchonline.com.(both sites last visited on 31/12/2011).
The JMREE Interview
Apparel Companies have to move from their focus
on making and selling huge quantities of cheap,
throw-away products to making and selling fewer
products from new materials that provide long-term
value to consumers
Marsha A. Dickson
Dr. Marsha A. Dickson is professor and chairperson in the Department of Fashion and Apparel Studies and co-
director of the Sustainable Apparel Initiative at the University of Delaware. She has masters and Ph.D. degrees in
textiles and clothing from Iowa State University and a bachelor’s degree in fashion design from Kansas State
University. Dr. Dickson is internationally known for her research and teaching on social responsibility in the
apparel industry. She is lead author of the book Social Responsibility in the Global Apparel Industry and has
published in peer-reviewed journals such as Business & Society, Journal of Business Ethics, Clothing and Textiles
Research Journal, and Journal of Fashion Marketing & Management. She has conducted research on social
responsibility in the apparel industry in China, Guatemala, Hong Kong, India, Thailand, and Vietnam. Dickson is
also a member of the board of directors of the Fair Labor Association (FLA), a non-governmental organization
originally formed by President Clinton to improve working conditions in factories around the world. She serves the
FLA as executive committee member and chairs the monitoring committee. Dr. Dickson has received several
awards for her academic and industry contributions in social responsibility, including the All Star Award from
Apparel Magazine and the International Textile and Apparel Association (ITAA) in 2009 and ITAA Distinguished
Scholar in 2011.
In the paper titled “Consumer Likelihood of Purchasing Organic Cotton Apparel: Influence of Attitude and Self-
identity”, you provided a fresh insight into the attitudes and motivation of environmentally concerned US
consumers of organic apparel. In India, by surveying consumers’ opinion on environment friendly products, we
generally receive a very positive response towards the products. However, sales figures of this type of products
do not reflect these positive opinions of the consumers. How to tackle this issue and know their actual
behavioral pattern?
MAD : There is definitely a difference between attitudes or opinions and behaviors. I’ve recently reviewed the
literature from over 15 years of consumer studies on socially responsible behavior (Dickson, forthcoming). Most
simple surveys that use simple rating scales to directly ask consumers what they would do regarding purchase of
socially or environmentally friendly products over-estimate what consumers will actually do in the market.
According to a seminal study by a social psychologist in 1934 (LaPiere) people do what they say they will do only
about 30% of the time. The sales figures you note demonstrate the choice consumers have made. We can do one
of a couple of things to tackle the issue One might be to more carefully design experimental type studies that
place consumers in scenarios that more accurately capture market realities. My preference would be, however, to
assume that consumers just aren’t that interested in socially or environmentally responsible purchasing and
instead focus on changing their behaviors. I’m advocating action research that attempts to influence consumers to
purchase behaviors.
It is said that education can empower people with adequate knowledge and thus help them to derive benefit
from a superior product or service. However, people in developed countries consume huge quantities of junk
food knowing fully well that it is bad for them. How then can we expect consumers in poor and developing
countries to act somewhat differently? In India, people with less than $2-3 daily income develop habit of having
potato chips or soft drinks quite often. Being one of the eminent scholars on sustainability, throw some light on
this consumption pattern.
MAD : When people in developing countries first gain some discretionary income, it is in small amounts and it goes
only so far in purchasing. Food choices are often what expand first since these purchases do not take much money
as compared to other products (e.g., cell phones, clothing, and even cars). What worries me most about this from
a sustainability angle is whether these consumers are being socialized to crave cheap, low quality products for
immediate satisfaction of their desires. Imagine how this might play out in future years as their earnings rise and
discretionary income increases. Will they continue the behavior by buying lots of cheap, poor quality fast-fashion
products that are priced so low as to not cover their full social and environmental impact? And will they go on to
discard great amounts of product into the waste stream? It might be valuable for researchers and educators to try
to address this early before developing countries follow the same, unsustainable path as those in developed
countries have.
In apparel industry, some corporations are implementing a long term and sustainable strategy. They have
followed the sustainability principle for over more than a decade now in their business strategy. These principles
include right to the employees, safe working conditions in the entire supply chain, proper remunerations etc. Can
these progressive producers come out with low cost products targeting ‘bottom of the pyramid’ market, which is
considerably huge market in emerging economies?
MAD : While it is possible to find cost-savings and greater efficiencies with more sustainable production practices,
there are certain things that may increase costs more than the savings achieved. If global brands consi9der their
full product lines and balance profits across their whole portfolios, it would seem likely they could offer lower-
priced and lower margin products in emerging economies. If their profit and loss is measured more by division,
which it often is, this may be problematic since the division offering products for emerging markets may appear to
be “unprofitable.” Hopefully companies can keep a broader perspective, however.
In your paper in 2001 with D Shen, you argued the CSR literature has been concerned with favoring social and
ecological sustainability. Social sustainability encompasses providing workplace health and safety, proper
remuneration and other related aspects. However, there is another dimension of social responsibility of fashion
industry which may be taken care of. In a research on “CSR in the Fashion Industry: A Humanizing Functional
Perspective”, M.H.Barroso Flores stated that fashion advertising, fashion shows and events may diminish the
dignity of the person by presenting him/ her as an object. This objectification of a person happens when, i) the
parts of the body that are related to sensuality are accentuated. ii) The fashion is focused on the body to such
an extent that it makes it tough to perceive the psychological dimensions of the persons. iii) Advertisement
generates a narrow image of both women and men, obscuring the deeper aspects or truth of their daily life. For
example, the idea of size zero models may threaten the well-being of the people by promoting the value of
extreme thinness.
Please comment on this aspect of social responsibility of fashion industry.
MAD : While I am currently focusing my research on social responsibility, especially human rights and labor
standards and how businesses address those, there is a much broader range of impacts the apparel industry has
on people and the environment that can be considered by researchers. At the University of Delaware, we research
and educate about environmental and social issues, but also consumer issues including ones related to body image
and disordered eating. Companies are increasingly mapping the social and environmental risks in their supply
chains. Perhaps fashion brands and retailers should also map the risks to people and the environment that are
present throughout the value chain that includes when products are sold and used?
In another research, you identified a three dimensional concept of socially responsible apparel business by
studying the opinions of the apparel and textile scholars. The three dimensions include environment and its
people, philosophy balancing ethics/ morality with profit and emphasis on the business strategies and actions
resulting in significant positive changes for people and the environment. We would like to ask you a) Has
apparel and textile students accepted this idea with enthusiasm? b) How industry leaders, appreciated your
initiative of Sustainable Apparel in University of Delaware?
MAD : Our students at the University of Delaware have been enthusiastic about tackling the issues in the industry.
Moe and more often, I am meeting new students who have chosen to attend school here because of our emphasis
on sustainability. Others are exposed opt the issues through class and our distinguished lecture series focused on
Fashioning Social Responsibility when they arrive here and decide to pursue research with faculty on related topics
or enroll in our graduate certificate in socially responsible and sustainable apparel business in order to expand
their opportunities in sustainability.
The industry also appreciates what we are doing in our Sustainable Apparel Initiative. My colleagues and I are
frequently invited to speak at various industry events both in the United States and globally and we participate in
various industry committees and initiatives such as the American Apparel and Footwear Association’s
environmental and social responsibility committees and the Fair Labor Association board of directors. We were
required to have industry support to join the Sustainable Apparel Collation and we are actively participating in the
work of that association alongside industry colleagues.
In a fashion supply chain, there are service providers like the fashion bureau, the fashion media, designers,
software providers, etc. They contribute significantly in the whole supply chain. In the Sustainable Apparel
Initiative, taken by the University of Delaware, how are these service providers motivated and how do they get
involved?
MAD : We have representatives from a number of service producers on our apparel industry advisory board and
they are very interested in the sustainability topics we address. There are opportunities for service providers to
take part in a more sustainable apparel industry, whether it is through the testing and auditing they do, the data
management systems they create, or other ways.
You have shown your interest in India in your research work “Measuring Quality of Life of Apparel Workers in
Mumbai”. Any future research plan on India?
MAD : I have a graduate student (Archana) who is currently writing her master’s thesis on data she recently
collected in New Delhi from a group I learned about a year ago while in the country. Archana’s research builds on
the research I did in Mumbai by looking at how one organization is working to make homework more acceptable
to the mainstream fashion industry. You know a lot of brands and retailers simply try to ban homework rom their
supply chains but under the right conditions, this work is essential and beneficial to women in India;. We’ll see
what her research finds and then plan additional study. I very much like the people I’ve met there and hope to
extend my research there in the future.
Our journal primarily focuses on emerging economies which include India and China. You have researched in
Indian and Chinese apparel industry. Any significant differences or similarities in the industries of India and
China that you would like to highlight on? Are you interested in any future project comparing Indian and Chinese
apparel industry?
MAD : I have more experience with large factories in China where my experience with the apparel industry in India
is more with small-scale enterprises, which makes comparison pretty difficult. Your question certainly gives me
ideas about where I could focus research in the future.
Please share your views on the scope of multivariate analysis like conjoint analysis, cluster analysis, factor
analysis, etc in works on apparel and textile design and fashion research.
MAD : I really enjoy all kinds of research, whether qualitative or quantitative. The neat thing about multivariate
analysis techniques is the insight they provide on patterns in the data and relationships between variables. They
allow you to really dive into the complexity of factors that influence behaviors and other end results and point to
areas where you can inform business decisions and other outcomes. My forthcoming article reviewing 15 years of
consumer research on socially responsible apparel consumption points out the value multivariate studies for
modeling the relationships between values, attitudes, and other factors that influence ethical purchasing behavior
or at least consumers’ intentions to purchase ethically. It is from this type of study that we can begin to think
about how to change consumer behavior!
As a very senior researcher, please comment on the future of apparel, textile and fashion industry.
MAD : That’s a big question! This is an ever-changing industry with big opportunities and challenges looming.
With limitations on a wide variety of resources, from cheap labor to energy and water, the industry is desperately
in need of a paradigm shift. Companies have to move from their focus on making and selling huge quantities of
cheap, throw-away products to making and selling fewer products from new materials that provide long-term
value to consumers. Successful companies will be the ones that are able to make this shift.
References
Dickson, M.A. (forthcoming). Identifying and understanding ethical consumer behavior: Reflections on 15 years of
research.
LaPiere, R.T. (1934). Attitudes vs. actions. Social Forces, 13, 230-237.
BOOK REVIEW POOR ECONOMICS
rethinking poverty & the ways to end it
Abhijit V Banerjee & Esther Duflo
ISBN: 8184001819 Random House India, pp 320, Rs. 499.00
The fight against poverty is littered with the fancies of intellectually debated policies and recommendations, well
intended projects and above all millions of dollars of western aid to poor countries to find the solutions. We need
no argument to prove that most of these interventions have failed to find solutions and could give us nothing but
some uplifting anecdotes. This obviously leads to an uncanny debate on what aid can do to the poor. Is it a bliss or
it does more bad than good? Economists like William Easterly and Dambisa Moyo, needless to mention that there
are others in this league, strongly argue against the aid interventions to fight poverty and believes that it prevents
people from searching for their own solutions and end up helping to create self perpetuating lobby for aid
agencies.
So in spite of various interventions millions of people are struggling with this acute problem of poverty. What is
going wrong? Why various aid or non-aid interventions have failed to deliver as expected? Is it the problem of
improper theoretical understanding of poverty? Or models are wrong? Or it is the problem of so called elitist
argument of poor economics that has differentiated poor as a different class (may be aliens) and suffers from basic
lack of understanding about them. Is the way poor people view the world different from the rest? Are their
rationality in dealing with income and managing lives different from the people who are little more blessed?
With the true spirit of scholarship, in their recently published book ‘Poor Economics: rethinking poverty and the
ways to end it’, two developmental economists from MIT Abhijit Banerjee and Esther Duflo elucidate the ways to
fight global poverty by radically challenging the stereotypes. The book is the extract of their findings and
understanding about the people who live on less than 99 cents per day from 18 countries in Asia, Africa and Latin
America. The authors have presented the problem of global poverty in a completely different way – in fact
repetitive attempts are made with the help of cases and numbers to understand the ways how poor people look at
their problems and manage it. It is dynamic in nature as the problems and their solutions vary with changing
situations and strategies. There is no well formulated antibiotic that can solve the problem of global poverty in
general. The authors suggest that the key is to look into the tiny detail to understand the lives of the poor with all
their problems and richness, get into the things that affect the decision that poor people make and finding out the
best possible ways of intervention. Even to do nothing could have been a better option sometimes.
In drawing their conclusions Banerjee and Duflo make great use of Randomised Control Trials (RCTs). RCTs are
reliable scientific tools for social science experiments. Using this format two groups of poor villages are randomly
chosen to eliminate the possible bias in the sample. Then these groups are tested for the same set of results (for
example percentages of mothers turn up with their child for vaccination in the nearest health centre) but one
group has one added variable which is not used in the other. Now if the added variable in one group makes a
significant difference in the result then that variable is identified to design the strategic interventions. These RCTs
are really helpful in finding solutions where the problems are same but the means to end it are different.
The content revolves around the key issues like health, education, access to financial services and the behaviour
towards various things that comes in the way of the lives of the poor. A poverty trap can be poor health which may
snap the money with them and restrict future savings. The trap can be due to lack of education which limits the
future earning potential. A poverty trap can be there due to lack of access to finance as it limits the productive
investment of money. The authors Banerjee and Duflo look at the problem with different argument. They believe,
if the poverty traps are truly responsible for poverty then a big intervention or push could do enough to solve the
problem of poverty.
Take example of food and nutrition. The authors put a valid question; are there really a billion hungry people? The
people who are surviving with less than 99 cents per day do not seem to act that they are starving. The eighteen
country data set shows food represents from 36% to 79% of the consumption of the rural extremely poor and 53%
to 74% consumption among their urban counterparts. It does not mean the rest is spent on other necessities. In
Udaipur of Rajasthan it is found that a poor can spend up to 30% more on food if they curtail their consumption on
tobacco, alcohol and festival. So even the people who live on less than 99 cents a day seem to have other choices
and they are not choosing to spend as much as they can on food. So are there really a billion hungry people?
Poor people are more vulnerable to health problems. In fact health itself can be the source of different traps like,
workers living in unhealthy environment may miss several workdays which leads to the loss of earnings; a seek
child often unable to do well in the school and in long run fails to take the benefit of education. “Each of these
channels is potentially a mechanism for current misfortunes to turn into the future poverty”. This mechanism of
health trap essentially means that even in small health problems poor need money to visit a doctor or a free
primary health centre which is not nearby or non operational and so not accessible from where they live. This
causes the small illness to turn as a big health problem which snaps their money and ability to work and eventually
pushes them into the poverty trap. Lack of money and access to facilities are the real problems but the authors
show that that is not the end of the story. A Sachs-ian ‘push’ may not be enough to solve the problem here.
There are technologies in the health those are so cheap that even the poorest of the poor should be able to afford
it. Breast feeding does not require any costs at all but only less than 40% of the infants all over the world are
breast fed for six months. Chlorinated water costs 800 kwachas which is equivalent to $0.18 US PPP for a month.
This can help in reducing diarrhea among children up to 48%. But the reality is only 10% of the population treat
their water with chlorine. Similarly the demand for bed nets is also very low. Poor pregnant mothers from Kenya
happily accepts free bed nets and use them as desired whereas a mother in India needs a great push to get her
child vaccinated.
Contradictions about the means to solutions arise across all the development sectors. Like aid, education policy
has also been the subject of intense policy debates. There is a demand supply war in education policies. An UN
based approach deals with the problems to get more number of the children into the classroom and to teach them
by a well trained teacher. This is a supply based approach. The critic of this approach, typically called “demand
wallahs”, believe that there is no point in supplying education unless there is a demand for it. Too many children
are often viewed as poverty trap but families with fewer children do not seem to be better off from their
productive counterparts. “For many parents children are there economic futures; an insurance policy; a savings
product and some lottery tickets” and sending them to school means loosing whatever they could earn.
The strength of the study is that the approach of the authors is not rigid. They don’t merely rely on well designed
RCTs but anecdotes from the experiences of various NGOs are also used extensively. Take the example of Gram
Vikas, an Indian NGO. While working on the sanitation they took the consent of the whole village to handle caste
prejudices, as all of them have to share the same water. This has made the implementation smooth and reduced
the cost per household as much as 80%. The authors have presented a large number of cases from the ‘people on
the ground’, sometimes it is Shantarama, a forty year old widow and mother of six from the village of Naganadgi in
Karnataka or Pak Sudarno, a scrap collector from the Cica Das slum in Indonesia. In a lucid story telling format
readers will come to know about their concerns, their ways to fight against the odds and choices they make.
There is no shortage of entrepreneurs among the poor – the capitalists without capital. In the eighteen country
data 50% of the urban extremely poor run a non agricultural business. Mohammad Yunus often describes poor as
‘natural entrepreneurs’. The richness of the poor economics is accredited by the late legendary consultant C. K.
Prahlad as he asked businesses to focus on what he called the “bottom of the pyramid”. Microcredit plays a role in
true sense, to give poor people a chance for entrepreneurship. The authors have covered the topic of microfinance
in depth and found it to be an useful tool to fight poverty but its impact should not be exaggerated. By feature,
microcredits are very small and of short term in nature which goes against the inherent financial and operational
risk of the business. Limited capital is the main hindrance to growth. From the eighteen country data authors show
that majority of the businesses run by poor do not have a paid staff. “The average number of paid employees
ranging from essentially zero in rural Morocco to 0.57 in urban Mexico”. Their ventures, in most cases, are just able
to make enough to survive only or find no option than to close it down. The result of the RCTs with the Spandana,
a reputed microfinance institution (MFI), indicates the inability of the microcredit to a radical transformation in the
lives of the poor.
All through the pages, Poor Economics is ultimately about how to fight against global poverty. There is so much in
this book that is hard to fit in one review but the implanted philosophy throughout the literature is that, the fight
against poverty is not impossible but what is needed is to know the right lever to pull. The contribution of Abhijit
Banerjee and Esther Duflo in the shape of Poor Economics is simply outstanding and the kind of material they have
pulled for this book is formidable. They conclude, “poverty has been with us for many thousands of years; if we
have to wait another fifty or hundred years for the end of poverty, so be it. At least we can stop pretending that
there is some solution at hand and instead join hands with millions of well-intentioned people across the world ……
in the quest for many ideas, big and small, that will eventually take us to the world where no one has to live on 99
cents per day”.
Readers are thankful to the authors for sharing their wisdom.
Abhijit Roy Faculty of Management Studies Dr. B. C. Roy Engineering College