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European Journal of Business and Management www.iiste.org

ISSN 2222-1905 (Paper) ISSN 2222-2839 (Online)

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Effects of Self-leadership, Knowledge Management and Culture on Creativity

Kalyar Masood (Corresponding Author)

College of Management & Administrative Sciences, GC University

Faisalabad (38000), Pakistan.

Tel: 92-344-7933814 E-mail: [email protected]

Chaudhry Shahzad

National University of Modern Languages

Lahore (54000), Pakistan.

E-Mail: [email protected]

Rafi Nosheen

National University of Modern Languages



Kalyar Awais

Institute of Social & Cultural Studies, University of the Punjab



Received: October 22, 2011

Accepted: October 29, 2011

Published:November 4, 2011

Abstract

Creativity and innovation are two important factors that organizations adopt to make themselves successful

or to adapt change. The area of creativity and innovation has been attracting the attention of managers and

entrepreneurs since last decade. This area is still unexplored in Pakistan and needs research efforts to

develop better understanding on both individual and firm level creativity and innovation. This is an

empirical study analyzing the effects of self-leadership, knowledge management (KM) and organizational

culture on creativity. Data were collected from 227 manufacturing organizations in Pakistan. Multiple

regression analysis was used to test the hypotheses. Results indicated that creativity is predicted outcome of

self-leadership and knowledge management (KM). Moreover, self-leadership fully mediated the effect of

culture on creativity.

Keywords: Knowledge Management, Creativity, Self-leadership, Pakistan, Culture

1. Introduction

Creativity and innovation are two important factors that organizations adopt to make themselves successful

or to adapt change (Amabile, 1988; Woodman, Sawyer, and Griffin 1993). The concept of organizational

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creativity identifies a relatively unexplored area in organizational change and innovation (Woodman, et al.

1993). Although organizational change can include innovation but much of organizational change is not

innovation (DiLiello and Houghton 2008). Similarly, even though creativity may produce the new product,

service, idea, or process that is implemented through innovation (Amabile 1988), innovation can also

include the adaptation of pre-existing products or processes, or those created outside of the organization.

Creativity for individuals and organizations exemplifies a dramatic aspect of organizational change that

may provide a key to understanding change phenomena and, ultimately, organizational effectiveness and

long-term survival (Woodman et al. 1993). Research by Amabile and her associates (Amabile, Goldfarb,

and Brackfleld 1990) documented the value of examining the creativity of individuals and groups within

their relevant social settings. Understanding the concept of creativity is significant for the organizations that

are willing to bring change in processes and procedures, and to ensure innovation. Creativity and

innovation help an organization to improve its performance and to provide basis for sustainable competitive

advantage (Carmeli, Meitar, and Weisberg 2006; Schilling 2008). Creative theorists (Amabile, Barsade,

Mueller, and Staw, 2005; Heye 2006) suggested that creativity is an important predictor of innovation.

Creativity is an individual and cultural phenomenon that allows us to transform possibilities into reality

(Tan 2007). Creativity is also defined as the individual’s ability and capacity to create and develop new,

novel and useful ideas about firm’s products, practices, services or procedure (Mumford, 2003; Shalley and

Gilson 2004). When the ideas generated in creativity are successfully implemented, it becomes innovation.

An individual having higher ability to generate new, novel and useful ideas is more likely to create

innovation (Woodman, Sawyer, and Griffin 1993), which in turn contributes to group and organizational

innovation. On theoretical grounds, it is inferred that ability to create new and useful ideas increases the

likelihood of creating innovation.

Employee innovations enable an organization to choose from a broader array of products or procedures for

development and later implementation (Cummings and Oldham 1997). This innovation helps the

organization to achieve sustainable competitive advantage and long-term improved and successful

performance. Creativity requires absolute novelty of the idea whereas innovation only requires relative

novelty of the idea to the unit of adoption (Woodman, et al. 1993). Therefore, adopting a new policy from

another organization to the current organization would be innovative but not creative. The definition of

creativity also includes an essential requirement for the idea or product to be useful. When employees

produce novel and useful products or procedures, they are basically providing the organization with options.

Creativity is a complex process and often comes from several sources (Schilling 2008). Amabile (2000)

argued that creativity is something that does not come from external pressure rather from inner motivation,

enjoyment and satisfaction. Intrinsic motivation, fueling passion and self-determination are the elements

that do not only define the creativity but also paly vital role in developing and boosting the creativity.

Intrinsic motivation, self-determination, encouraging positive behavior and developments of constructive

thoughts come from self-leadership (Shalley and Gilson 2004). Pearce & Manz (2005) argued that

self-leadership is necessary in those organizations that need continuous innovation. According to Houghton

& Yoho (2005), self-leadership may mediate the influence of an organization’s leadership style on the

creativity of its members.

Creativity has been measured in a number of different ways, ranging from the assessment of the

characteristics and personality traits of highly creative individuals to the measurement of creative products

and achievements (DiLiello and Houghton 2008). Review of previous literature suggests that creativity is

more likely to occur when an individual has certain characteristics or distinctive skills (Simonton, 1992;

Tierney and Farmer 2002). This includes having specific knowledge embedded within social networks

(Kijkuit and Ende, 2007; Weisberg 1999), intrinsic motivation and self-leadership (Houghton and Yoho,

2005), and perceives a work environment that supports creativity (Amabile, Conti, Coon, Lazenby, and

Herron 1996). In short, creativity is a complex construct with various dimensions that must be carefully

assessed in order to create a true and accurate composite of an individual’s creative capacity (Feldhusen and

Goh 1995). This study is an effort to assess creativity based on three dimensions; self-leadership,

knowledge management and organizational constructive culture.




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2. Conceptual Framework and Hypotheses

2.1 Self-leadership

Self-leadership is defined as a process of influencing or leading oneself through the use of specific sets of

behavioral and cognitive strategies (Neck and Manz 2004). Self-leadership theorists have proposed that

creativity is anticipated outcome of individuals’ self-leadership (DiLiello and Houghton 2006; Neck and

Houghton 2006). However, research on the relationship between self-leadership and creativity is still at the

nascent stage. An additional research is needed to further clarify the relationship between self-leadership

and creativity (Neck and Houghton 2006; Pratoom and Savatsomboon 2010). Shalley & Gilson (2004)

advocated that individuals must have a definite level of internal force that pushes them to proceed in facing

the challenges in creative work. Self-leadership is necessary in those organizations that need continuous

innovation (Pearce and Manz 2005). According to Houghton and Yoho (2005), self-leadership may mediate

the influence of an organization’s leadership style on the creativity of its members. When employees are

encouraged to lead themselves in defining problems, solving problems, making decision, and identifying

opportunities and challenges both now and in the future, their creativity is encouraged. On the other hand, if

employees are not encouraged to lead themselves in critical situations, then creativity is not encouraged

(Pearce and Manz 2005). On the basis of discussion above it can be concluded that self-leadership is an

important predictor of creativity in an organization. It enables the workers and individuals to think

positively, enhancing self-determination and developing constructive thoughts. This leads the individuals to

handle stress and work environment pressure by enabling them to develop creative behavior that opens the

doors for creativity (Pearce and Manz 2005). Hence, self-leadership is expected to affect creativity,

significantly and positively.

H1: Self-leadership has a positive direct relationship with creativity.

2.2 Knowledge Management

Knowledge Management (KM) is the formal process that concerns access to experience, knowledge, and

expertise that creates new capabilities, enables superior performance, encourages innovation, and enhances

customer value (Beckman 1999). Knowledge management practices involve acquiring, capturing, sharing

and using knowledge and wisdom to enhance firm and individual’s performance, creativity and innovation.

Greater breadth of knowledge helps employees to explore and understand processes and procedures, and to

develop understanding about new products and phenomena. Knowledge management assists the conversion

of tacit knowledge into explicit knowledge, facilitating creative process and helping to identify gap in the

knowledge base (Pratoom and Savatsomboon 2010). A key outcome of managing knowledge effectively is

to have the right knowledge at the right time, so that proper values can be added, and workers can enact

creative actions (Muhammed et al. 2008). Organizational knowledge management affects individual’s

creativity by developing constructive controversy among organizational members to facilitate them in

taking risk, doing experiments and applying new techniques towards generating new products, procedures

or services. Teigland & Wasko’s (2003) study proposed a positive effect of knowledge management on

creativity.

H2: Knowledge management has a positive relationship with creativity.

2.3 Organizational culture

Organizational culture is defined as the way in which members in an organization are expected to think and

behave in relation both to their tasks and to other people (Cook and Rousseau 1988). Organizational culture

might boost creativity in employees through norms. Norms provide social information that individuals use

to understand and interpret what they experience at work. Norms that exist in an organization not only

shape specific behavior, but also influence much more general type of activities in which organizational

members engage (Caldwell and O'Reilly 2003; O'Reilly & Caldwell 1985). Evidences suggest that the




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culture that values creativity, innovation, active risk taking, and open debate might motivate and direct

individuals toward creative ideas, which in turn increases the likelihood of an innovation being generated

(Hurley 1995; Tesluk 1997). Managers can directly affect employee’s creativity by the way they construct

teams, assignments and work environment (Amabile 2000). The valuing of innovation and active risk

taking by constructive culture encourages individuals towards creativity and thus fosters innovation. The

culture that encourages risk taking and experimentations increases the likelihood of generation of new and

novel ideas, thus fostering the creativity.

H3: Organizational culture positively affects creativity.

Self-leadership theorists stated that contextual factors can boost self-leadership (Houghton and Yoho, 2005;

Manz 1986; Pearce and Manz 2005). Organizational reward, training and culture are the factors that shape

and encourage self-leadership (Manz 1986; Pearce and Manz 2005) and enable the employees to behave

positively, bear external pressure, develop constructive thoughts, promote self-determination and may

increase intrinsic motivation. Intrinsic motivation is central part of self-leadership (Neck and Manz 2004)

and it can be increased depending on contextual factors (Shalley, Zhou, and Oldham 2004). Contextual

factors have vital importance to encourage and promote self-leadership at workplace environment enabling

the individuals to lead themselves in predicting creativity and innovation. The mediating role of

self-leadership is expected for constructive culture to boost, promote and encourage creativity.

H4: Organizational culture positively affects creativity through self-leadership.

3. Methodology

3.1 Data Collection and Sample

The research study involved the analysis of a survey questionnaire consisting of statements relating to the

self-leadership, organizational culture, knowledge management and creativity. All the survey items were

measured on five-point likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Data were

collected from two hundred and twenty seven (227) participants from thirty five randomly selected

manufacturing organizations in Pakistan. Response rate was 68.7%. Initially, the names of producer group

organizations were drawn from the list of KSE 100-index listed companies and thirty five (14%)

organizations were selected using simple random sampling. Questionnaires were distributed postally and

electronically to the employees of these organizations. A total three hundred and thirty questionnaires were

distributed among the participants; eight to ten questionnaires per organization. Participants were assured

regarding privacy, confidentiality and independence of the researcher from their organization.

Overall, two hundred and twelve participants (93.4%) were men and only fifteen participants (6.6%) were

women. The age of the participant respondents ranged from 22 to 58 years (mean=33.4). Eighty six (37.9%)

had an education of graduation level, one hundred and thirty eight participants (60.8%) had an education at

master level or higher, however, only three participants had an education below graduation level . The

participants had experience ranging from 1 to 30 years (mean=8.25).

3.2 Research Measures

3.2.1 Creativity. A five-point likert scale survey instrument was used to measure groups’ knowledge

management ranging from 1 (strongly disagree) to 5 (strongly agree) and was adapted from the employee

creative behavior scale developed and validated by Rice (2006). All the dimensions were explored by using

exploratory factor analysis (EFA). Factor loading of the items ranged from 0.67 to 0.78. Alpha value for the

construct was 0.82; higher score indicated higher level of creativity. The KMO value was 0.793, chi-square

was 450 with degree of freedom 15, significant at p<0,001. These statistics showed goodness of fit for EFA.

3.2.2 Self-leadership. A five-point likert scale survey instrument was used to measure groups’ knowledge

management ranging from 1 (strongly disagree) to 5 (strongly agree) and was adapted from the

self-leadership questionnaire developed and validated by Houghton and Neck (2002). All the dimensions

were explored by using exploratory factor analysis. Factor loading of the items ranged from 0.37 to 0.83.




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Alpha value for the construct was 0.86; higher score on this construct indicated greater self-leadership. The

KMO value was 0.878, chi-square was 595 with degree of freedom 21, significant at p<0,001. These

statistics showed goodness of fit for EFA.

3.2.3 Constructive culture. A five-point likert scale survey instrument was used to measure groups’

constructive culture ranging from 1 (strongly disagree) to 5 (strongly agree). This seven items scale was

adapted from the constructive culture scale developed and validated by Cook and Rousseau (1988). All the

dimensions were explored by using exploratory factor analysis. Factor loading of the items ranged from

0.64 to 0.81. Alpha value for the construct was 0.79, higher score showed more constructive culture. The

KMO value was 0.75, chi-square was 431 with degree of freedom 21, significant at p<0,001. These

statistics showed goodness of fit for EFA.

3.2.4 Knowledge management. A five-point likert scale survey instrument was used to measure groups’

knowledge management ranging from 1 (strongly disagree) to 5 (strongly agree). This eight items scale was

adopted from operationalization of the variable and knowledge management scale of Darroch and

McNaughton (2002). All the dimensions were explored by using exploratory factor analysis. Factor loading

of the items ranged from 0.55 to 0.81. Alpha value for the construct was 0.81; higher score showed higher

level of knowledge management practices. The KMO value was 0.784, chi-square was 467 with degree of

freedom 28, significant at p<0,001. These statistics showed goodness of fit for EFA.

4. Data Analysis

The primary analysis was correlation and multiple regression statistics. Quantitative data analysis was

divided into two phases: Preliminary data analysis and hypothesis testing. In the preliminary phase raw data

was cleaned up and inputted to generate Descriptive statistics, which included central tendencies, frequency

distributions, correlations, mean, standard deviation, range and variance. For hypothesis testing Pearson’s

Correlation and multiple regressions were used to establish the degree of linear relationship between

creativity, knowledge management, self-leadership and culture. In order to test indirect effect of

organizational culture on creativity, Baron & Kenny’s (1986) preconditions for mediation were met.

According to Baron & Kenny (1986), a variable functions as a mediator when it meets the following

conditions: (a) variations in levels of the independent variable significantly account for variations in the

presumed mediator, (b) variations in the mediator significantly account for variations in the dependent

variable, and (c) when Paths “a” and “b” are controlled, a previously significant relation between the

independent and dependent variables is no longer significant.

To test the relationships between self-leadership, knowledge management, culture and creativity

Correlation and multiple regressions statistical analysis was done using IBM SPSS Statistics 19. The

statements regarding creativity was used as the dependent variable and self-leadership, knowledge

management and culture were used as the independent variables. Table 1 presents descriptive statistics:

means, standard deviations and Pearson’s Correlations. There were not too many correlations observed

within demographic variables except for creativity and education (r = 0.181, p< 0.01) and culture and

education (r = 0.181, p <0.01). Age had negative correlations with creativity (r = -0.350, p<0.01), Culture (r

= -0.144, p<0.05), knowledge management and (r = -0.389, p<0.01) self-leadership (r = -0.323, p<0.01).

For the correlation between dependent and independent variables, results indicate positive associations

between creativity, self-leadership, knowledge management and organizational culture. Organizational

culture was positively correlated with creativity (r = 0.529, p<0.01), knowledge management (r = 0.633,

p<0.01) and self-leadership (r = 0.618, p<0.01). Self-leadership had a strong positive Pearson’s correlation

coefficient with creativity (r = 0.712, p<0.01) and knowledge management (r = 0.779, p<0.01). Knowledge

management was also found having positive correlation with creativity (r = 0.722, p<0.01).

INSERT TABLE 1 HERE

In order to run multiple regressions, creativity was taken as dependent variable and self-leadership,

knowledge management and organizational culture were taken as independent variables. The results from

the analysis showed the coefficients for paths from independent variables to dependent variables. Following




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tables were generated from regression analysis using “Enter” method. Table 2 presents model summary,

ANOVA and coefficients of the variables. In model summary, R is the square root of R-Squared and

showing 76.1% correlation between the observed and predicted values of dependent variable. R-Square

indicates the proportion of variance in the dependent variable (creativity) which can be explained by the

independent variables (self-leadership, knowledge management and culture). This is an overall measure

of the strength of association. The value of R-Square shows that 57.4% variation in creativity is being

explained by the predicting variables. Remaining 42.6% variation in creativity would be because of some

other factors.

The ANOVA table reports a significant F statistic (F (2,223) = 102.305, p<0.001), indicating that using the

model is better than guessing the mean. Now considering the standardized regression coefficients, all of

independent variables have strong significant and positive path coefficients towards creativity except from

organizational culture. Standardized total effect of self-leadership for creativity is 0.368, p<0.001 which

means the increase in self-leadership by 1 is responsible for increase in creativity by 0.368 and vice versa.

This has proved the first hypothesis true that self-leadership has a positive relationship with creativity.

Results from regression analysis suggest that self-leadership skills enable organizational members to face

work stress and challenges and leading them towards creativity. Second hypothesis also has been supported

by data as standardized effect on creativity by knowledge management (KM) is 0.406, p<0.001. This shows

that knowledge management (KM) is responsible for 40.6% change in creativity. So far as hypothesis 3

(direct positive effect of culture on creativity) is concerned that data did not support the hypothesis. The

standardized path coefficient of culture for creativity is 0.045 at p = 0.439, which is a small and

insignificant positive relationship between the both. The proposition that work environment characterized

by risk taking, open debates and supporting culture encourages motivation has not been significantly

supported by the data.

INSERT TABLE 2 HERE

In order to test the indirect effect of organization culture on creativity another regression analysis was run.

According to Baron & Kenny (1986), one should estimate the three following regression

equations in order to test the mediation: first, regressing the mediator on the independent

variable; second, regressing the dependent variable on the independent variable; and third,

regressing the dependent variable on both the independent variable and on the mediator. Separate

coefficients for each equation should be estimated and tested. Moreover, a perfect mediation holds if

independent variable has no effect on dependent variable when mediator is controlled. Self-leadership has

been taken as dependent variable and organization culture as independent variable. “Enter” method of

regression has been used to get the analysis results. According to Wuensch (2008), the indirect effect is the

product of standardized coefficient of path “a” (path from independent variable to the proposed mediator)

with that of path “b” (path from the mediator to the dependent variable). The resultant value will be the

standardized mediated (indirect) effect. Sobel (1982) technique was used to measure significance of

indirect effect and t-statistics. Table 3 presents significant F statistics (F (1,225) = 138.845, p<0.001)

suggests goodness of fit model. R-Square indicates that the culture is 38.2% responsible for variation in

self-leadership. Standardized path coefficient of organizational culture is strong (0.618) and significant

(p<0.001).

INSERT TABLE 3 HERE

Standardized indirect effect of organizational culture of creativity is (0.368*0.618 = 0.227). Indirect effect

of culture was significant at the level of p<0.001 (t = 4.719, std. error = 0.0481).Hence, data fully supported

the hypothesis 4 providing the evidence that organizational culture is an important predictor of

self-leadership (R2 = 0.382, F (1,225) = 138.845, p<0.001) and has positive indirect effect on creativity.

Data of the present study fully supported the direct positive relationship between self-leadership and

creativity, and suggested that people with higher level of constructive thoughts and self-determination are

more likely to generate new and creative ideas. Similarly, knowledge management (KM) was also found

positively correlated with creativity. On the other hand, organizational culture had no significant positive

direct effect on creativity rather it had an indirect relationship with the creativity. In short, three hypotheses




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(H1, H2 and H3) were met and supported by the data whereas only one hypothesis (H3) was not supported

by the data.

5. Discussions and Conclusion

In order to respond quickly to dynamic customer needs, increased complexity of market mechanism and

rapidly changing technologies, the selection of the right technologies, products, services and procedures is

critical to a company’s long-term success. Review of previous research indicates that knowledge

management (KM), self-leadership and creativity are critical elements that help an individual and

organization to make innovation. Innovation, therefore, provides foundation to adapt change and acquire

new technologies to respond dynamic customer needs and market demand. In this research, creativity has

been analyzed as a joint function and outcome of self-leadership, knowledge management (KM) and

organizational culture. Creative theorists (e.g. Amabile, et al. 1996; Heye 2006) have argued that

individual’s creativity is important in itself and can be conceptualized as a necessary first step or

precondition required for innovation. An organizational member with high ability to generate new and

useful ideas is more likely to create their own innovation, which in turn contributes to organizational

innovation (Woodman et al. 1993). This area is still in darkness in Pakistan and previously no or limited

research efforts were made to explore this area. In the present study creativity has tested as predicted

outcome of self-leadership, knowledge management and culture. After conducting correlation and

regression analysis, self-leadership and knowledge management were found strong predictors of creativity

in Pakistani manufacturing organizations. A strong positive and significant direct effect of self-leadership

suggested that individuals with higher level of self-leadership skills such as positive self-talk, constructive

thoughts, intrinsic motivation and self-determination are more likely to make creativity. Similarly,

knowledge management also had a strong positive and significant direct influence on creativity supporting

the hypothesis that organizations emphasizing on creation, retention and dissemination of knowledge are

encouraging its individuals to do creative work. The knowledge on up-to-date processes and procedure,

products and services, and technologies helps organizations to deploy this knowledge in selection and

adaptation of new and useful procedures, products and/or services. Furthermore, managers having latest

knowledge on business and global market practices make themselves enable to lead towards defining and

responding customer and dynamic market needs.

On the other hand, data did not support the positive effect of organizational culture on creativity. It was

found that culture had a small positive relationship with creativity but the effect was not statistically

significant. In contrast, organizational culture had a positive indirect effect on creativity through

self-leadership. The results suggested that self-leadership skills can be improved when there is a supportive

culture that encourages organizational members to take risks, accept challenges and actively avail

opportunities. Mangers can uplift the level of creativity when they develop and maintain proper knowledge

management (KM) system and assisting employees to improve and make their self-leadership skills strong.

Contextual factors such as culture should also be made supportive and positive as it plays a significant role

in explaining self-leadership, which in turn leads towards creativity. As creative theorists have argued that

innovation is outcome of creativity, thus fostering the level of creativity enhances the likelihood of

extended innovation which ultimately helps an organization to develop and achieve sustainable competitive

advantage, better company performance and long-term profitability.

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Woodman, R., Sawyer, J., & Griffin, R. (1993a), "Toward a theory of organizational creativity", Academy

of Management Review, 18, 293-321.

Woodman, R. W., Sawyer, J. E., & Griffin, R. W. (1993b), "Toward a theory of organizational creativity",

The Academy of Management Review, 18, 293-321.

Wuensch, K. L. 2008. “Conducting Path Analysis using SPSS/AMOS”. Greenville, NC: East Carolina

University Press.




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Table 1 Means, standard deviations, and correlations

Mean S.D. 1 2 3 4 5 6 7

1- Gender 0.93 0.249

2- Age 33.40 6.44 .240**

3- Education level 1.59 0.518 -.071 .089

4- Experience 8.25 5.601 .209**

.807**

.121

5- Org. Culture 4.11 0.649 -.104 -.144* .181

** .008

6- Knowledge Management 3.86 0.689 -.140* -.389

** .095 -.207

** .633

**

7- Creativity 3.96 0.777 -.117 -.350**

.181**

-.178**

.529**

.722**

8- Self-Leadership 3.92 0.832 -.134* -.323

** .141

* -.118 .618

** .779

** .712

**

N= 227, *p<0.05, **p<0.01

Table 2 Regression analysis

Model

Unstandardized Coefficients Standardized Coefficients

t Sig. B Std. Error Beta 1 (Constant) .621 .227 2.732 .007

Self-Leadership .343 .067 .368 5.130 .000

Knowledge Management .458 .082 .406 5.583 .000

Culture .054 .069 .045 .776 .439

Model Summary

Model R R Square Adjusted R Square

Std. Error of the Estimate

1 0.761 0.579 0.574 0.50757

ANOVA Table

Model Sum of Squares

Df Mean Square F Sig.

1 Regression 79.069 3 26.356 102.305 0.000a

Residual 57.450 223 0.258 Total 136.519 226

a) Dependent Variable: Creativity

b) Independent Variable: Self-leadership, Culture and KM




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Table 3 Regression analysis

Model

Unstandardized Coefficients Standardized Coefficients

t Sig. B Std. Error Beta 1 (Constant) .673 .279 2.409 .017

Culture .791 .067 .618 11.783 .000

Model Summary

Model R R Square Adjusted R Square

Std. Error of the Estimate

1 0.618 0.382 0.379 0.65618

ANOVA Table

Model Sum of Squares

df Mean Square F Sig.

1 Regression 59.783 1 59.783 138.845 0.000a

Residual 96.879 225 0.431 Total 156.662 226

a. Dependent Variable: Self-Leadership

b. Independent variables: Organizational Culture




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Learning Organization and Intellectual Capital: An Empirical

Study of Jordanian Banks

Marwan M. Al-Nsour

Management Department, Faculty of Planning and Management

Al-Balqa Applied University, Assalt- Jordan

E-mail: [email protected].

Ghazi A. Al-Weshah

Management Department, Faculty of Planning and Management

Al-Balqa Applied University, Assalt- Jordan

E-mail: [email protected].




Abstract

The purpose of this paper is to investigate empirically the relation between the learning organization and

intellectual capita Jordanian banking industry. The intellectual capital is measured by three dimensions,

namely, human capital, structural capital, and customer capital. 86 Questionnaires are sent to managers and

executives in Jordanian banks headquarters using convenience sample, however, 66 questionnaires were

returned and the response rate is 77%. Quantitative approach is employed to test the proposed research

hypotheses; correlation analysis and regression analysis are conducted. The results support the hypothesis

that learning organization has a positive impact on banks intellectual capital. The results extend the

understanding of the role of organizational learning in creating intellectual capital and building sustainable

advantages for banks in emerging economies.

Keywords: Banking Industry, Intellectual Capital, Learning Organizations, Hypotheses Testing, Jordan.

1. Introduction

The increase of knowledge and its relationship with the learning process is a very important for all centuries

(Blumentritt and Johnston, 1999). Organizational learning occurs through a process of acquiring, sharing,

and integrating new knowledge from outside the firm as well as inside the firm (Crossan, Lane, & White,

1999; Argote & Ingram, 2000). Some investigators found that a focus on organizational learning has great

potential to build the collaboration and continuous improvement programs that promote organizational

performance (Levine, 2001, Holland, 2010).

Organizational intellectual capital represents technologies and other mechanisms that assist employees in






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creating revenues for organizations such as communication systems, data bases, policies, procedures,

technical systems, and other devices (Boisot, 2002; Ordo´n˜ez de Pablos, 2003). Recently, Intellectual

capital can include the skills and knowledge that a company has developed about how to make its goods or

services (Hernández & Noruzi, 2010). However, there is no clear direction as to how intellectual capital

develops or how to institute management planning and control processes to ensure that intellectual capital

inventories grow or are at least maintained (Issac et al. 2010). Nowadays, it is important to understand what

a learning organization is, what its characteristics are and how it relates to the emerging topic of intellectual

capital.

There is much evidence for active learning by banks. Retail banks were the outcome of much learning

(through crisis, failure, error, fraud, etc.) over at least 300 years. Banks have not historically been good at

learning and at exploiting prior lessons during periods of stability. Much learning has arisen during bank

crises and subsequent regulation based on best practice (Holland, 2010). Harris (2002) provides evidence

that learning from past mistakes, or even building upon past successes, continues to be the exception rather

than the rule. Banks had to learn about these problems and their solutions via direct actions and transactions

and via a strategy of active bank development. Learning arose via an iterative feedback process during

active internal change and external transacting and associated errors, failures and successes, rather than

through a rational ordered decision process (Holland, 2010).

In Jordan, the banking industry has been undergoing a tremendous change in the past few years. There are

many changes in the number and variety of products offered because of the branching or mergers and

acquisitions of banks. The intensity of competition and information technology growth within a harsh

environment has led to restructuring of some retail banks (Al-Weshah and Deacon, 2009). Banks which are

main player in the Jordanian business environment have a deal with intellectual capital and have all basics

of the learning organization which can support the intellectual capital. Therefore, banking industry has to

adopt the concept of learning organization as a solution to cope with this a problematic situation based on

its intellectual capital.

This highlights the importance of the creation of intellectual capital as a critical component of an

organization's ability to learn and adapt. This will be developed further in this paper as a focal point for

analysis of the synergies between the learning organization and intellectual capital. Therefore, this paper

provides an explanation of the relationship between the learning organization (LO) as an entity and

intellectual capital (IC) on the organizational level.

2. Learning organizations

Senge (1990) was one of those who early defined the learning organization as an entity within which people

continually expand their capacity to create the results they desire. Senge (1990) defined the Learning

Organization (LO) as the strategies and initiatives for improving organizational effectiveness through

emphases on developing the capabilities, capacities and qualities of the staff, and on approaches based on

behavioral and attitudinal, as well as skills enhancement.

The concept of organizational learning has taken its prominence in the past several decades as a way to




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achieve competitive advantage. That’s why companies are urged to become “learning organizations” to

develop their learning capability for survival and maintaining competitiveness (Hong, 1999).

Organizational learning occurs through a process of acquiring, sharing, and integrating new knowledge

from outside the firm as well as inside the firm (Crossan, Lane, & White, 1999; Argote & Ingram, 2000).

Al-Weshah et al (2011) confirmed that electronic networks can assist an organisation to discover and share

knowledge and learning within the organisation and from entities outside organisation.

Thurbin (1994) defined learning organization as one, which improves its knowledge and understanding of

itself and its environment over time, by facilitating and making use of the learning of its individual

members. The ‘‘learning organization’’ is the generic term given to strategies and initiatives for improving

organizational effectiveness through emphases on developing the capabilities, capacities and qualities of the

staff, and on approaches based on behavioural and attitudinal, as well as skills, enhancement (Pettinger,

2002). Chetley and Vincent (2003) defined the development of a learning organisation as an ongoing,

systematic process requiring trust and a recognition of the subtlety and complexity of human relations and

describe three stages in this process; firstly, individuals and teams are encouraged and supported to learn;

Secondly, these processes are socialised or institutionalised; and thirdly, learning is at the heart of an

organization, meaning that learning is used to transform and develop the organization. Revans (1998)

proposed a model of how learning should occur in organisations. He argued that learning should be greater

than or equal to the rate of change in the environment. If not, then the firm would be unable to achieve a

sustainable competitive advantage (SCA). This idea is clearly relevant to the core learning errors made by

failing banks during the 2007-2009 Crisis and to bank specific mistakes in previous periods (Holland,

2010). Uzzi and Lancaster (2003) investigated learning in (bank lending and debt) markets between banks

and firms. Bank-firm relationships formed networks and these shaped knowledge transfer and learning

processes by creating the opportunities for knowledge trade and reducing the learning risks.

Mansor (2010) investigated the extensiveness of Bank Islam Malaysia Limited (BIMB) in Terengganu and

Kelantan states in Malaysia as to the practice of OL. As been displayed by result on correlation analysis it

seems that the awareness of OL had continuously shaped the Islamic Banking activities. But still there are

rooms needed to be improved if the institutions are to be considered as one of the anchor bank in the future.

There are some mistakes and errors individual bank learning and knowledge use. Learning in individual

banks was not as systematic as the above multi case patterns suggested and arose via an iterative feedback

process during errors, failures and successes (Holland, 2010). Therefore, this study investigates the learning

and its relationship with intellectual capital in Jordanian banks

3. Intellectual capital

Historically, financial and built capitals have been critical assets in the wealth creating process for

organizations. More recently, these assets, which are recognized on the balance sheet, have taken a second

place to more intangible forms of capital, which are generally not found on the balance sheet (Issac et al.

2010). Financial statements are insufficient to measure progress toward competitive advantage. Instead,

intellectual capital assets, such as workforce knowledge and mechanisms, relationships, and organizational




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structures, often not recognized on the balance sheet are critical to developing competitive advantage

(Boulton et al., 2000; Lev, 2001; Low, 2000). Intellectual capital management processes must, therefore,

endeavour to get employees to share knowledge, to question why they perform certain procedures, and to

monitor the role that knowledge plays within the success of the organization (Issac et al. 2010).

Intangible assets have become more important to business success than the traditional factors of production

- land, labor and financial capital (Edvinson & Malone, 1997; Stewart, 1998). Furthermore, organizational

knowledge assets are a major component of these intangible assets. Intellectual capital is defined as the sum

of intangible assets related to knowledge of a company that have been formalized, captured, and leveraged

to produce a higher-valued asset and to create competitive advantage (Berry, 2004; Stewart, 1997;

Subramaniam & Youndt, 2005). The most widely used definition of intellectual capital is “knowledge that

is of value to an organization.” Its main elements are human capital, structural capital, and customer capital.

That definition suggests that the management of knowledge (the sum of what is known) creates intellectual

capital (Bassi, 1997). Most of literatures insure that components of intellectual capital consist of human

capital, structural capital and external (customer) capital. This problem was identified even earlier by

Nonaka and Takeuchi (1995) who stated that "organizational learning theories basically lack the view that

knowledge development constitutes learning and most OL theories concentrate on individual learning and

have not developed a comprehensive view of learning at an organizational level". According to Sandelands

(1999), organizations that are not able to embrace shared learning and knowledge generation at the

organizational level simply disappear. Brown and Woodland (1999) added further insight into the

learning/knowledge synergy by claiming that "it is impossible for an organization to sustain competitive

advantage without constantly learning and developing new knowledge". Intellectual capital includes many

issues such as data, information, intellectual property and experiences, which can be utilized to gain wealth

(Rivette, 2000)

Organizational intellectual capital captures knowledge that exists within the organization, and we suspect

that it arises from the human intellectual capital. Thus, its birth and evolution is highly dependent upon the

workforce (Isaac et al. 2010). The theory of intellectual capital has emerged in the past decade in response

to these advances within an organization. Although the theory is new and research is in the early formative

stages, theoretical foundations have been identified as anchors of intellectual capital.

Some studies classified intellectual capital into human capital, structural capital, and relational capital

(Johnson, 1999; Bontis, 1999; Bozbura, 2004). Ismail (2008) Classified intellectual capital into human

capital, customer capital, and structural capital. Moreover, Kiran (2008) classified intellectual capital into

human capital, customer capital, and social capital. However, the literature on intellectual capital has

deployed a variety of different classification schemes (i.e. Petrash 1996, Walsh et al, 2008, Hernández &

Noruzi, 2010). There are widely accepted, three-category classification, which divides intellectual capital

into codified knowledge about an organization’s systems and operations (systems capital); knowledge

about customers, markets, and distribution (customer capital); and knowledge acquired from people skills

and expertise (human capital) (Stewart 1997, Bontis and Fitz-enz 2002; Walsh et al, 2008).




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Regarding research on intellectual capital, early research brought awareness to the existence of intellectual

capital inventories within organizations and the need to manage, monitor, and measure them, but few

researchers have empirically studied what internal conditions lead to the development of these important

assets (Isaac et al, 2010). In developing countries, Seleim et al. (2007) test empirically a variety of

hypotheses related to human capital and organizational performance within software companies in Egypt.

The results provide evidence that certain types of human capital indicators show a positive statistically

significant relationship with company performance. Specifically In banking industries, El-Bannany (2008)

investigated the determinants of intellectual capital performance in the UK banks over the period

1999-2005. The results indicated that investment in information technology (IT) systems; bank efficiency,

barriers to entry and efficiency of investment in intellectual capital variables have a significant impact on

intellectual capital performance. Cabrita and Bontis (2008) examined the inter-relationships and

interactions among intellectual capital components and business performance in the Portuguese banking

industry. The findings confirmed that intellectual capital has a significant and substantive impact on

performance.

Goh (2005) measured the intellectual capital performance of commercial banks in Malaysia for the period

2001 to 2003, using efficiency coefficient called VAIC™ developed by Ante Pulic. The findings indicate

that all banks have relatively higher human capital efficiency than structural and capital efficiencies. In

Jordan, Bataineh & Al-Zoabi (2011) investigated the effect of intellectual capital on organizational

competitive advantage in Jordanian commercial banks. The findings indicated that there are strong

significant and positive influences between human and structural capital on competitive advantage, and

moderate significant and positive influences with relational capital.

Therefore, there is a growing awareness that intellectual capital is a key asset for development in today’s

environment. Intellectual capital is not only includes data or information in files and databases but It is also

comprises all useful knowledge in its all forms in the organization. Therefore, this study investigates the

relationships between and learning organization in Jordanian banks.

4. Learning organization and intellectual capital

Most literatures addressing IC have focused on the correlation between IC and organizational performances

(Chong and Lin, 2008; Ho, 2009). There are relatively few discussions on the relationship between LO and

IC, and even fewer studies on such a relationship in the banking industry. The core competitiveness of the

banking industry is highly reliant on the ability of management teams to systematically being a learning

organization Few studies were conducted to discover the relationship between the learning organization and

the intellectual capital in the banking industry.

today's organizations should try to use learning organizations paradigm to be competitive. Also because our

contemporary organizations may differ from the traditional organizations and so we should implement new

skills to be learning organization so that our staff can adjust themselves with new technologies (Hernández

& Noruzi, 2010). Learning enables a company to transfer information to valued knowledge, which in turn,




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enriches organizational capability of adapting to environmental changes and demand (Yang 2003).

The literature addressing the learning organization is largely descriptive and conceptual in nature. Although

many authors have described why a learning organization should work, there are few specific descriptions

about the mechanics of how the learning organization as a strategy works to improve performance (Kaiser,

2000; Bates & khasawneh, 2005). Artie (2006) adopted case studies of wireless technology companies

based in Canada to examine the interrelationship between intellectual capital components with a

resource-based view. The findings confirmed the interrelationship between components of intellectual

capital and business growth performance among the selected cases of wireless technology companies.

Building a learning organization is an important challenge for Jordanian banks. Learning organizations and

the Intellectual capital became one of the most important issues that affect all kind of business including

banking industry which faces a demand for better products and services has a triggered growing in the in

the managerial development, this development can be reach by enhancing in the intellectual capital that the

same organization can achieve it by the nature of being a learning organization. Al-Weshah et al (2010)

stated that banks in Jordan are one of the largest investors in the fields of knowledge and information

systems (IS). Therefore, this paper examines LO and IC empirically to generalize important factors

concerning LO and IC of banks. The major purpose of this study is to explore the relationship between LO

and IC through the construction of the correlation patterns between these two elements.

5. The study aim and objectives

The aim of this study is to measure how learning organization supports the intellectual capital. More

specifically, the intellectual capital is measure by three dimensions (human capital, structural capital, and

customer capital, therefore, the current study attempts to test some research hypotheses. the research main

hypothesis is, learning organization has positive impact on intellectual capital. In order to test this

hypothesis, three other sub- hypotheses:

Learning organization has positive impact on human capital.

Learning organization has positive impact on structural capital.

Learning organization has positive impact on customer capital.

6. The study methodology

The current study employs the quantitative approach of research. More specifically, hypotheses testing

approach are used to achieve the study aim and objectives. A self-administrated questionnaire has been

developed as data collection methods from managers and executives who work in headquarters of

Jordanian banks using convenience sample. 86 questionnaires were sent managers and executives in

Jordanian banks. A follow up procedure was employed by telephone or personally after two to three weeks.




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Only 66 questionnaires were returned with response rate is 77%. Table 1 show the respondents positions in

their banks.

Table 1: Respondents’ positions in their banks

Respondents’ position Respondents number Respondents percentages

Information systems managers 12 18%

Strategic planning managers 11 17%

Marketing mangers 13 19%

Financial managers 11 17%

Human resources managers 10 15%

Research and development managers 9 14%

Total 66 100%

For the questionnaire validity, the questionnaire was “pilot-examined” by interviewing 10 managers and

experts in the banking industry who agreed to fill in the questionnaire and also to comment on the scales

employed. Then, their suggestions were collected and considered to improve validity of questionnaire.

Moreover, the questionnaire was pretested by sending three questionnaires to different managers in

Jordanian banks to get their comments and feedback. Although the executives’ comments are considered in

the final version of the questionnaire, they are not selected later to fill the latter questionnaire.

For the questionnaire reliability, Cronbach’s alpha was used as a measure of internal consistency reliability.

A widely cited minimum threshold for the Crobanch Alpha is 0.70 (Malhotra, 2004). However, the

calculated Cronbach's Alpha for the questionnaire as whole was 0.89 percent. 89% indicates to high

internal consistency among the questions in the questionnaire instrument.

7. Hypotheses test and analysis

In order to test research hypotheses, SPSS software (Statistical Package for Social Sciences) has been used.

The results of the analysis have been discussed below.

Table 2: The first hypothesis: Learning organization has positive impact on human capital:

Hypothesis 1

Dependent

variable

Independent

Variable

R

square

Standard

β

T test Result

Intellectual

Capital

Learning

Organization

99200 0.280 .90.9 Confirmed

According to the results, Beta Standard ratio is calculated 0.280 which is significant. The slope of line

(0.280) indicates the expected change in intellectual capital (human capital) when learning in a bank is




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changed by one unit. R2

ratio is the proportion of variation (change) in intellectual capital (human capital)

that can be explained by learning organization. Therefore, this ratio (0.299) indicates to the relative

contribution of learning in supporting intellectual capital (human capital) in the Jordanian banks. Thus, this

hypothesis is confirmed and learning organization has positive impact on human capital.

Table 3: The second hypothesis: Learning organization has positive impact on structural capital:

Hypothesis 2

Dependent

Variable

Independent

Variable

R

square

Standard

β

T test Result

Intellectual

Capital

Learning

Organization

992.. 991.0 2960. Confirmed

According to the results, Beta Standard ratio is calculated 991.0 which is significant. The slope of line

(0.184) indicates the expected change in intellectual capital (structural capital) when learning in a bank is


ratio is the proportion of variation (change) in intellectual capital (structural

capital) that can be explained by learning organization. Therefore, this ratio (0.253) indicates to the relative

contribution of learning in supporting intellectual capital (structural capital) in the Jordanian banks. Thus,

this hypothesis is confirmed and learning organization has positive impact on structural capital.

Table 4: The third hypothesis 3: Learning organization has positive impact on customer capital

Hypothesis 3

Dependent

variable

Independent

Variable

R

square

Standard

β

T test Result

Intellectual

Capital

Learning

Organization

99290 9922. .9..0 Confirmed

According to the results, Beta Standard ratio is calculated 9922. which is significant. The slope of line

(0.255) indicates the expected change in intellectual capital (customer capital) when learning in a bank is


ratio is the proportion of variation (change) in intellectual capital (customer capital)

that can be explained by learning organization. Therefore, this ratio (0.225) indicates to the relative

contribution of learning in supporting intellectual capital (customer capital) in the Jordanian banks. Thus,

this hypothesis is confirmed and learning organization has positive impact on customer capital.




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8. Results and conclusions

The aim of this study is to measure how learning organization support the intellectual capital in Jordanian

banks. More specifically, the intellectual capital is measured by three dimensions (human capital, structural

capital, and customer capital). The concepts of the LO and IC have been shown to be closely related and

mutually supporting. The hypotheses testing approach show that learning in Jordanian banks has positive

impact on supporting intellectual capital by its three dimensions (human capital, structural capital, and

customer capital). The results support the hypothesis that learning organization has a positive impact on

banks intellectual capital. The results extend the understanding of the role of organizational learning in

creating intellectual capital and building sustainable advantages for banks in emerging economies.

Therefore, it is not sufficient that organizations learn something new, but the new knowledge needs to be

applied to a strategic context and needs to be relevant in that context (Bontis et al., 2001). Intellectual

capital is recognized as one of the most critical factors for the success of banks in a knowledge-based

economy. By ensuring better bank learning, knowledge creation and use, the banks policy makers can help

support intellectual capital components and reduce banks risks of future crisis.

The banking industry must invest to transform to the learning organization which in then will increase the

intellectual capital, consequently it will go forward in the competitive globalized environment. The study

can consider that skills of human resources in Jordanian banks are appropriate to transform their banks into

learning organizations. Building a learning organization is an important challenge for Jordanian banks.

Learning organizations and the Intellectual capital became one of the most important issues that affect all

kind of business including banking industry which faces a demand for better products and services has a

triggered growing in the in the managerial development, this development can be reach by enhancing

intellectual capital issues in Jordanian banks.

9. The study recommendations for practice

In the light of the study findings, the current study proposes some recommendation for banks managers and

executives. More specifically, LO and the IC are inextricably linked to the extent that they should taken in

mind of the banking industry planners and decision makers. Moreover, banks should focus on the total

inter-organization learning process (i.e. the creation of new corporate knowledge from the total

environment within which the bank operates) and the nurturing of the cultural environment that supports

and ensures its continuing development. In Jordanian banks, training approach can be employed to enhance

learning organization concepts in their staff minds. On the other hand, intellectual capital is not just data or

information in files and databases. It comprises all useful and available knowledge in several forms for

banks managements. Therefore, it is critically important that intellectual assets be well understood and

properly managed if banks are to compete successfully in today’s world environment.

10. The study recommendations for further research

In the light of the study limitations, the current study proposes some recommendation for future research.

More specifically, future studies can consider more variables concerned with learning organizations and




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intellectual capital such as knowledge management and information technologies. Management of

intellectual capital is in its infancy, but interest is growing. Therefore, models and measurements can be

developed by different research areas. Moreover, future studies can extend this analysis to different

industries such as insurance companies and manufacturing. Methodologically, future studies can employ

the qualitative approach of research to gain deep understanding for issues of learning organizations and

intellectual capital.

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Application of Queueing theory to port congestion problem in

Nigeria

Oyatoye E.O.

Department of Business Administration, University of Lagos. Nigeria

Tel: +234 805 281 1824 E-mail: [email protected]

Adebiyi, Sulaimon Olanrewaju (Corresponding author)

Department of Economics and Financial studies Fountain University Osogbo, Nigeria.

Tel: +234 803 368 2722 E-mail: [email protected]

Okoye John Chinweze

Torcelik International Company Ltd, Apapa, Lagos. Nigeria

Tel: +234 803 323 2666 E-mail:[email protected]

Amole Bilqis Bolanle

Department of Management and Accounting,

Obafemi Awolowo University, Ile-Ife, Nigeria

Tel: +234 803 472 1305 E-mail:[email protected]




Abstract

This paper stresses the importance of queueing theory to the problem of port congestion in order to enhance

sustainable development of Nigeria ports. Nigeria Ports are characterized with incessant congestion

problem in the recent past. This has resulted in diversion of ships scheduled for Nigeria Ports to other

neighbouring country ports which has caused the country to lose a lot of revenue. The effectiveness of a

Port is contingent upon loading and unloading of ships. The traffic movement through a port is a complex

phenomenon because of the random nature of the arrival and service time of the ships. This requires a

systematic approach in port planning and management. Queuing model was applied to the arrival and

services pattern which causes the problems of congestion and proffer solutions to the problem areas. It is

also used to predict the average arrival rate of ships to Tin Can Island Port and the average service rate per

ship in a month. The study to found out the number of berth in Nigeria port is adequate for the traffic

intensity of vessels but other factors leading to port congestion were identified through the content analysis

of the interview conducted with stakeholders at the port. Policy recommendations that could make Nigerian

ports to be cost effective, more attractive and enhance quick turnaround of vessels at the ports were made.

Key words: Queue theory, port congestion, berth, arrival time, service time.




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1. Introduction

The Nigerian Ports Authority (NPA) was established in 1955 and was saddled with the responsibility to

oversee the activities and operations of the Ports in Nigeria. Over the years, Tin Can Island Port has

experienced series of congestion which has resulted in the diversion of ships scheduled for Nigeria ports to

other neighbouring country ports. The first ports congestion was experienced in Nigeria during the

economy booming years of early 1970s. During that period, there were a lot of incoming goods into Nigeria

because of the discovery of crude oil and the reconstruction of the devastated effect of the civil war. The

period witnessed a lot of importation of cement into the country, so much that hundreds of vessels had to

queue for berthing space for months, (The Cement Armada). So much money was wasted on payment of

demurrage for these vessels, a development that created unpleasant consequences for the Nigeria economy.

The second Port congestion was witnessed in early 2000, when the Federal Government introduced a

policy requiring 100% physical examination of all containers that were imported into the country and the

contents unstuffed for the government agencies at the port to ascertain and establish that what was declared

was actually what the container contained. This resulted in many consignees abandoning their containers

thus creating backlog of un-cleared containers occupying the terminals and limiting the available space for

in-coming containers.

The most recent port congestion was between October 2008 and March 2009. This was as a result of two

factors. Firstly, the Cargo traffic in Nigeria tends to have a certain cycle. The peak period occurs between

October and March. This period records more volume of goods coming into the ports. The second factor is

that the Nigeria Customs Services (NCS) in an effort to fulfill its responsibility and ensure that its integrity

is maintained, introduced a circular known as “Circular – 02”. The circular stated that any importer who

makes a false declaration will have his goods seized and the importer persecuted in the law court. This

resulted in many of the consignees abandoning their cargos in the port and vicious cycle was created.

Meanwhile, the shipping companies and the terminal operators continued to charge demurrage, while

containers continue to come into the port without the owners clearing them out. This created a lot of

problems and it got to a stage when there was no space in the terminal to discharge incoming containers,

hence the ships has to queue for weeks, months before getting access to berthing space. The situation got to

a stage where the Nigerian Customs Service had to put aside the circular – 02 and even gave some waiver

to enable the importer clear out their goods, still the importers were not forthcoming because by then the

goods had accumulated so much demurrage.

However, the random arrival of the ships makes the predictability of the system and managerial decision

difficult. Queuing theory model could provide Managers/Port operators with a useful set of decision

making formulas and algorithms for designing Port systems and services (Kalavaty, 2007). An example of

this is the Classical Erlang blocking formula that was developed in 1917. This formula gives the probability

that “berths are busy” given the fixed number of ships that can berth at the same period of time. This model

will help the port managers decide what should be optimal Port size needed to effectively and efficiently

serve customers` need profitably. The measure used in the model is the arrival rate and service rate of the

system.

In order to respond adequately to this challenge, queueing theory was applied to arrival and service

pattern in Nigeria port in order to access the business behaviour and proffer a relief for problem of

congestion that seem to be reoccurring in the port even after the concessioning of the port to private sector

1.1 Statement of the Problem

Since 1977 Tin Can Island Port Nigeria, one of the Nigerians main Ports with shore facilities has been

playing a vital role in the economic development of Nigeria. It handles some general purpose Ships besides

having a few additional jetties to handle oil tankers and food grains etc. the Ports all over the world have

changed significantly with the advent of containerization and Tin Island Port is not an exception. Tin Can

Island Port despite many constraints has continued to cope with the changing mode of Maritime Trade.

However, it has been suffering from the problem of poor operational efficiency.

The traffic through the Port is increasing along with the economic development of Nigeria. It has been




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observed frequently that arriving ships has to form queue and sometimes ships has to wait longer than

necessary before berthing. In addition to that lack of adequate inland infrastructure to handle incoming

containers give rise to the instance of congestion in both Cargo and Ship and delays in final delivery of

goods to the importer’s premises with consequent increase in transportation and other costs.

Port congestion is inimical to economic growth and has become a regular occurrence at Nigeria ports.

This has been a source of concern to the Federal Government, who at various times had set up committees

to find solution to the persistence occurrence of congestion at the ports. The effectiveness of a port is

contingent upon the efficient loading and unloading of ship. When the traffic movement is hampered, it

creates delays in the system. This waiting line delays cause ships to queue for berthing space thus creating

congestion. This waiting time is calculated with the service time at the berths to form the turn-around time

of ships which is one of the ways of measuring the efficiency of a port.

The ever growing international trade has made demand on quick turnaround time of ships a paramount

problem in today’s shipping business. These ships waiting for berth space incurs extra cost of operation,

thus increasing cost of doing business for the importers. This study seeks to find ways to enhance quicker

turnaround time of ships and make the Nigeria Ports attractive and cost efficient.

Consequently, the following research questions are addressed by this study

i) Is the number of berths at Tin Can Island Port adequate for its traffic volume? ii) What are the factors responsible for port congestion in Nigeria? iii) How can queue theory be applied to port congestion? iv) What are the possible solutions to port congestion using queuing models?

1.2 Objective of the study

The broad objective of this study is to gain an understanding of the application of queueing theory to the

problem of port congestion in Nigeria Tin Can Island Port. To achieve this however, the research will

look at the following specific objectives:

i) To assess the adequacy of berths at the port ii) To examine the use queueing model for analyzing the queue behaviour of ships in Nigeria port. iii) To determine through the use of queue model the optimal number berth required for efficient

port operation in Nigeria. iv) To proffer solution to the problem of Port Congestion through the use of queueing model.

2. Literature Review

2.1 The Concept of Port Congestion

Over the years the traffic through the Nigeria Ports are increasing along with the economic development of

the country. It is frequently observed that a queue of arriving ships is formed and sometimes ships have to

wait for a longer time before berthing. This can be attributed firstly, to the mobility of the existing port

facilities to match the ever increasing global trade and secondly, some obnoxious government policies and

regulations. This incessant congestion in our ports has resulted in diversion of ships meant for Nigeria Ports

to other neighboring country ports. In the reforms and concessioning of 2006, Tin Can Island Port was

concessioned to four different private organizations to manage.

See Table 1

Maduka (2004) defined Port Congestion as massive un-cleared Cargo in the Port, resulting in

delay of ships in the seaport. According to him, this occurs when ships spend longer time at berth than

usual before being worked on or before berth. Onwumere (2008) refers to port congestion as a situation

where in a port; ships on arrival spend more time waiting to berth. In this context, more ships will queue at

the channels and the outside bar waiting to get space at the terminal for berth age. According to him, this

waiting time is calculated using the service time of vessels which is one of the ways of measuring port

efficiency. In his view, this is a situation where cargoes coming into the port are more than the storage




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facilities can handle.

2.2 The Incidence of Port Congestion in Nigeria

Port Congestion is a global phenomenon not limited to only Nigeria. In 2005 global map of congestion

around the world the entire Africa was there, the West Coast of Africa including Nigeria was there, the

Eastern part of Africa, around Kenya, Southern Africa even the West Coast of the United States of America

was there. This was as a result of so many factors (Zhang et al, 2008).

Maduaka, (2004) highlighted the factors responsible for port congestion in Nigeria and suggested ways to

control congestion at the Ports. According to him, there are advantages and disadvantages in port

congestion. He stated that Port congestion brought about realization for better planning, port expansion and

development. He cited loss of revenue, unemployment and bad image to the country as its major

disadvantages. Classic transport magazine, a logistic, shipping and multi-modal transport stated that Port

Congestion is inimical to the economic growth (volume 1 of 2009). According to the publication, port

congestion has a negative implication on the economic resources, wastage of time and space as well as

increase in the cost of operations and cost to the society.

Tom (2009) posited that Nigeria should be warned about reoccurrence of congestion in our port.

According to him in spite of the various waivers conceded by the government the dwell time of

consignment in the port is gradually jacking up against expected time. He cited the use of Manual Clearing

Process as one of the major factors responsible for the reoccurrence of the looming congestion.

2.3 The Concept of Queuing Theory

Adedayo et al. (2006) stressed that many situation in life requires one to line up or queue before being

attended to. This lines formed are referred to as waiting lines or queues. According to them queue occurs

when the capacity of service provided fall short of the demand for the service. Sanish (2007) in his article

on application of queuing to the traffic at New Mangalore Port refers to queuing theory as an analytical

techniques accepted as valuable tool for solving congestion problems. According to him the primary inputs

to the models are the arrival and service patterns. These patterns are generally described by suitable random

distribution. He observed that the arrival rate of ships follows exponential distribution while the service

time follows Erlang or Poisson distribution. He observed that queuing theory can be used to predict some

important parameters like average waiting time of ships, average queuing length, average number of ships

in the port and average berth utilization factor closer to the actual values.

2.4 Components of Queue Models

Queues are not an unfamiliar phenomenon and to define it requires specification of the characteristics

which describes the system such as the arrival pattern, the service pattern, the queue discipline and the

queue capacity Adedayo et al. (2006) observed that there are many queuing models that can be formulated.

According to them it is essential that the appropriate queuing model is used to analyze problems under

study.

The arrival pattern: This may be the arrival of an entity at a service point. This process involves a degree of

uncertainty concerning the exact arrival times and the number of entities arriving. And to describe this

process there are some important attributes such as the sources of the arrivals, the size of each arrivals, the

grouping of such an arrival and the inter-arrival times.

The service pattern: This may be any kind of service operation which processes the arriving entities. The

major features which must be specified are the number of servers and the duration of the service.

The queue discipline: This defines the rules of how the arrivals behave before service occurs.

The queue capacity: The queue capacity may be finite or infinite.

Sharma (2008) refers to the following as the components of queuing system.

Calling population (or input source)




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Queue process Queue discipline Service process (or mechanism)

2.5 Historical Perceptive of Queuing Theory

The ground work for many of the earliest techniques of analysis in queuing theory was laid by Erlang who

is referred to as the father of queuing theory between 1909 and 1929, Adedayo (2006). Erlang is given

credit for introducing the poison process to congestion theory for the method of creating balance state

equilibrium (Chapman-Kolmogorov equation) to mathematically represent the notion of statistical

equilibrium. Most of the pioneers of queuing theory were by engineers seeking solution to practical real

world problems.

2.6 Queuing Model of Tin Can Island Port

The system of operation at Tin Can Island Port can be model as a queuing process. Ships come to the port

as customers to get service and the facilities at the port render services to ships as servers. Here, services

refer to handling of cargoes and use of facilities at Tin Can Island Port for berthing of ships. A large portion

of the solution of waiting line problem encountered at the ports involves making decisions in one or a

combination of the following.

(i) Number of berths that are needed to serve the arriving ships.

(ii) Delay of loading/unloading of cargo/container

(iii) Future expansion of the port facilities considering the future expected port congestion at ports; an

attempt is made in this study to find solution to the incidence of port congestion in Nigeria with particular

reference to Tin Can Island Port.

The problem can be modeled as a multi-server queue problem with no system limit, arrival can be from a

theoretically infinite source and the service is on first-come-first-serve priority rule.

S = Number of berths

N = Average arrival rate

U = Average Service rate at each berth.

2.7 Port Performance Determination

Port performances are based on data recorded by port authorities who traditionally tend to focus on traffic

recordings and parameters used in measuring the Port services. Most available and reliable data are related

to the maritime interface where information is more easily collected than on the land interface. Port

authorities usually monitor berth occupancy and dwelling time of ships, characteristics of calls performance

of ships-to-ship cargo handling availability of the main handling equipment. Additional but often less

reliable data may be available as regards landward operations, dwelling time of cargo in the port,

characteristics of customs and other administrative procedures and rarely the performance of handling

equipment for delivery of goods.

Chen-Hsiu and Kuang-Che (2004) posited that port system efficiency may be measured by the average

time ship spends in a queue. Shippers and port users are interested in reducing the waiting time in the queue

system as much as possible. The ability of a port to load and unload cargo from the ship is critical to port

planning factor and port efficiency.

2.8 Berth Occupancy

Berth occupancy is defined and computed on the basis of the ratio of berth waiting time to berth serving

time. The acceptable levels of waiting times generally determine permissible berth occupancy ratios




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(Warwar, 1980). Longer service time causes longer ship’s waiting time where port facilities are inadequate

and the terminal’s space is over saturated.

The arrival of pattern ships of is usually a random process described by some type of probability

distribution (Fararoui, 1989). A negative exponential distribution of inter-arrival times (and hence a poisson

arrival rate) is the most commonly used approximation. Ships turnaround time involves the arrival of ships

expecting to use port facilities and the duration of occupancy at a berth (service time). The ports or more

precisely, the ship berth link are considered as queuing systems with bulk arrivals, single service and

unlimited queues at an anchorage (Radmilovich, 1992). The number of available berth is an obvious factor

in determining whether ships queues will be formed and what their length will be (Fararoui,1989). The time

between the arrival of ships and its departure are the main factors in port’s operations that influence the

capacity of ports.

3. Material and methods

This section examines the relevant variables to be considered in this study and provides a relationship that

exists between and among such variables, thus, leading to modeling of the possible relationship between the

variables. It also throws light into the sources and types of data to be involved in the course of analysis.

3.1 Instrument for Data Collection

The data used for this study was collected both from primary and secondary sources. The primary sources

are personal interview with the Port Managers and the terminal operators while the secondary sources

include past records of operational activities, policy papers on government aimed at proffering solution to

the problems of port congestion, papers presented by Maritime stakeholders. Also formal and informal

interviews at individual level of discussions were held with the employees to obtain adequate clarification

with regard to other variables that can influence the development of queuing models for Tin Can Island

terminal on ship congestion.

The interview conducted with the Terminal Operators and Port Managers by the researchers at Tin Can

Island Port revealed that an average of 120 vessels arrives at Tin Can Island Port in a month. It also

revealed that it takes an average of 3 days to unload and load empty containers on the vessels. From the

interview it was revealed that there are 10 berths at Tin Can Island Port numbered (1-10) and the vessels

berth on First Come First Serve (FCFS) basis.

See Table 2

3.2 Model Formulation

A logical extension of a single-server waiting line is to have multiple servers, similar to those we are

familiar with at many banks and ports. By having more than one server, the check-in process can be

drastically improved. In this situation, customers wait in a single line and move to the next available server.

Note that this is a different situation from one in which each server has a distinct queue, such as with

highway tollbooths, bank teller windows, or super market checkout lines. In such situation, customers

might “jockey” for position between servers (channels). Jockeying is the process of customers leaving one

waiting line to join another in a multi-server (channel) configuration. The model assumes that all servers

are fed from a single waiting line. In this section we discuss the various operating characteristics for a

multi-server waiting line. The model in use can be applied to situations that meet these assumptions:

(1)The waiting line has two or more identical servers

(2)The arrivals follow a poison probability distribution with a mean arrivals rate of λ

(3) The service times follow an exponential probability distribution

(4) The mean service rate, “μ” is the same for each server

(5)The arrivals wait in a single line and then move to the first open server for service in orderly manner

(6) The queue discipline is first-come-first serve (FCFS)

(7) No balking or reneging is allowed

Using these assumptions, operations researchers have developed formulas for determining the operating




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characteristics of the multi-server waiting line like the case of ships awaiting berth thereby causing

congestion which may not be desirable for all stakeholders. This paper relies on the laid foundation to

proffer solutions to port congestion in Tin can Island Port.

Relevant Performance measures in the analysis of the model are:

The distribution of the waiting time and the sojourn time of a ship or ships. The sojourn time is the

waiting time plus the service time.

The distribution of the number of ships in the system (including or excluding the one or those in

service.)

The distribution of the amount of work in the system (port). That is the sum of service times of the

waiting ship and the residual service time of the ships in service.

The distribution of the busy period of the server (berth). This is a period of time during which the

server is working continuously.

In particularly, we are interested in mean performance measures, such as the mean waiting time and the

mean sojourn time thereby leading to Port congestion.

3.3 Techniques Used For Model Solution

One of the advantages of application of queuing model for solving waiting line problem is the availability

of software packages to handle and manipulate large data to get solution for effective and efficient decision

making. The software packages also provide the user the opportunity for flexibility of the model in that

various additional constraint can be added to take into account other constraints that may be peculiar to

different situations in real life problem.

TORA menu driven optimization software package will be employed for the computation of result.

4. Results and Discussion

The congestion problem at Tin Can Island Port (TIP) was modeled as a Multi-Server queuing problem with

10 berths as currently operations at the Port. Theoretically, arrival is from infinite source and the service

pattern is on First=Come-First-Server (FCFS) priority rules. Twelve Calendar months 2008 field data of

arrivals and departures of vessels to and from Tin Can Island Port is considered.

See Table 3

Adedayo, et al (2008) stated that the closer the traffic intensity it to zero the more efficient the operations of

the service facilities. Then from the above analysis of the traffic intensity of each of the months under

consideration (January 2008 to December 2008) of port congestion problem at Tin Can Island Port Lagos

and this can be seen as the traffic intensity for January is 1.5, February 1.18, for March 1.20, April 1.11,

May 1.24, June 1.13, July 1.12, for August 1.13, September 1.10, October 1.18, for November 1.33 and

December 1.33. This indicates that the system facilities presently at Tin Can Island Port cannot favourably

cope with the flux of vessel arriving at Port and as a result there is bound to be queue.

In each of the month above, no steady state occurs because the assumption that traffic intensity (P) = λ <

1 is violated. According to Satty (1961), if λ > 1, the number of customers (Vessels) would be infinite. This

means that ships arrives at a faster rate than the berths can conveniently handle, since λ > μ. Then for each

of the months, to determine the average time arriving ships queuing in the system.

See table 4

The above table shows the mean time ship spent waiting to berth in the port. These figures calculated are

negative for all the months which mean that it has moved away from the positive sides and ships are forced

to wait for service at Tin Can Island Port. The extension of the queue results in the negative values as

shown in the above table. However, the queue does not benefit the system as well as its customers (ships).

See table 5

In the course of the analysis it is observed that the inter arrival is exponentially distributed




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with Parameter Lambda (λ) for each of the months as well as for the year under study.

From the above table output, the Po

column represents the probability that there are zero queues or no

ship in the queue for each of the months January 2008 to December 2008 is not possible with current arrival

and service rate at Tin Can Port.

It can be deduced that the probability that there is no ship in the queue for the month of January is 0.00585

which implies that the probability of ship being in the queue for the same month = 1 – Po, a simple

mathematical manipulation shows that 0.99414 which is 99.4 percent probability of ship joining the queue

on arrival while an insignificant 0.06 percent is the probability of having zero on arrival.

The same manipulation can be done for all the months and it could be established that there is queue for

ships on arrival at the Port is significant throughout the year under review.

The Ls column from TORA output shows the average number of ships in the system for each of the

months. The column indicates that an average of 7 ships has been in the system at a time and since the

service required by each ship varies significantly from one another and relative to the load carried. Thus,

there is delay in loading and off loading of ships and other human delay which will not make the system’s

recommendation of 6 to 7 ships in the system to work optimally. Thus, the operational inefficiency at the

Port may likely be the major determinant of queue thereby causing Port Congestion. And again from the

output of Tora windows version 2.00, the Lq column shows the average queuing length of ships. It was

revealed that for January 2008, an arriving ship will spend an average of 7 days in the queue before being

served, also an average of 5.6 days for the months of February, 6.87 days for March, 5.07 days for April,

5.97 days for May, 5.19 days for June, 5.13 days for July, 6.39 days for August, 4.89 days for September,

5.62 days for October, 6.50 days for November and 6.50 days for December.

However, the essence of the number of days and hours, minutes and seconds as shown in the analytical

queuing solution was to derive the accurate number of days spent on the queue by any of the ships. Thus,

this shows the number of days a ship is likely to say on the queue before berthing.

Next to be interpreted on the Tora Window Version 2.00 is the Ws column which shows the average time

a ship spends in the system before being served. This also varies with the number of arrivals and the extent

to which the Port could accommodate the berthing of arriving vessels. From the Ws column, the arriving

ship in the month of January will wait for about 6 hours which is almost 21/2 days before joining the queue.

Also the Tora output also suggested for other months under review. The last column on table 3 of Tora

output shows the average waiting time in the queue (Wq). This is the expected time that a ship may likely

wait on the queue for each of the months as presented in Table 2.

It was equally observed that the waiting time on the queue in Table 2 columns 4 are all negative for all

the months and by the forgoing analysis, the traffic intensity P > 1 for the various months. This means that

waiting exceeded the positive side and ships have to queue on arrival thereby leading to congestion at Tin

Can Island Port.

The result of this analysis as varied from one month to another in the above columns (Ls, Lq, Ws, Wq)

with the probability table from the Tora output in this research efforts will form the basis for summary of

findings, conclusion and recommendation in the next section.

5. Conclusion and Recommendations

From the study it was discovered that the problem of port congestion in Nigeria is not caused by only

inadequate berthing space but majorly by the operational inefficiency of the Port Managers and Operators

coupled with long years of infrastructural development neglect. Since the inception of port in Nigeria by the

colonial masters in the early twenties, no systematic process for their re-development has been put in place

until current concession programme.

Furthermore, It was also revealed from the study that traffic intensity (P) in the port is greater than one

(P>1) for all the months in the year under review, which in effect shows that the current port facilities

cannot adequately handle the influx of ships arriving at the port thereby causing delays.

The result of the analysis revealed that the mean time ships spend in the port exceeds positive values in

all the months which indicates that ships has to queue on arrival at the port. Thus, the Tora Software




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analysis shows that the probability of having Zero ship (Po) in the Queue in any of the months is

insignificant because of the present operational inefficiency, which shows that Queue exist in the Port thus

creating congestion.

Queue theory is a viable tool for solving congestion problems and its application in this study has helped

to identify the cause of congestion in the ports and has also provided the Port Managers with useful set of

decision making formulas with algorithm for designing Port systems and services.

Increased competition, booming in international maritime trade and the associated port congestion is

crucial in ports rethinking, how to bolster capacity and improve service quality, to maintain current and

attract new business. In response to these opportunities and challenges most ports have started to or plan to

redesign their operations and come up with long term investment plans and this has help to reduce the

turnaround time of vessels calling at the port. The managers of Nigeria Ports should embark on serious

infrastructural development to create an efficient competitive sector that will sustain economic growth in

the years ahead. This view corroborates the conclusion draw by Kazeem (2010) “Although, port reform

may not be a perfect policy as yet, especially in Nigeria, in our present circumstances it seems the best

option for now. In a situation where all our port infrastructural facilities had become so old and obsolete,

with NPA or Government having no fund to fix them, one could imaging what could have become our port

today without the port reform”.

The study will however, be incomplete without some recommendations on the possible means of

improving the quality of services provided in Nigerian Ports to make it effective and efficient. Based on the

findings, the problem of Port Congestion in Nigeria can be tackled the proper implementation of the

following recommendations.

The reduction of dwell time by introducing punitive measures to discourage improvers from using

Port as storage area.

The port managers should acquire modern and appropriate handling equipment to aid easy loading

and unloading of ships.

The operations at the ports should be properly designed, computerized for easy tracking of

containers at the terminals.

Ports customers/clearing agents should be educated on Cargo clearance procedures.

Introduction and use of punitive measures to discourage shipping lines from delaying submission

of ship manifest to customs.

24-hour operations must be encouraged in the ports.

Physical expansion of Port Capacity will lead to reduction in port congestion and make the port

more attractive to users.

Improvement of hinterland link roads to the Ports should be made passable to reduce traffic in and

around the port.

Motivating and training of staff on the use of modern equipments used in ports.

The extra containers must be taken to the customs approved of bonded terminals to ease the

pressure on the ports.

The customs clearing procedure which is the main reason for the backlog of containers in the Ports

should be simplified.

We strongly believe that; if all these measures are taken, the dwell time of ships will reduce considerably,

thus, eliminating congestion in the Ports. It is therefore, recommended that the concessionaires at Ports

should be mandated to embark on extensive infrastructural development and capacity expansion.

References

Abdulsalam M (July 19, 2009). Nigerian Ports Authority, The Nation Newspaper, pp 23

Adedayo O.A, Ojo O and Obamiro J.K (2006). Operations Research in Decision Analysis and Production

Management, Pumark Educational Publishers, Lagos




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Bojan Beškovnik (2008). “Measuring and Increasing: The Productivity Model on Maritime Container

Terminals”,

International Global Logistic Services, 22, 171-183

Chen-Hsiu Laih and Kuang-Che Hung (2004). “The Optimal Step Toll Scheme for Heavily Congested

Ports, Journal of Marine Science and Technology, Vol. 12, No. 1, 16-24

Erlang AK (1909): The theory of probabilities and telephone conversations. Nyt Tidsskrift Mat B 1909;

20:33–9

Fararoui F (1989). Queuing theory and berthing optimization, PhD thesis Priority Berthing in Congested

Ports

Maduka (2004). Port, Shipping, Safety and Environmental Management. Concept Publication Ltd., Lagos

Kalavaty S (2007) Operations research; second edition, Vikas Publishing house PVT Ltd, New Dephi. India

Kareem W.A (2010). An Assessment of Nigeria’s Port Reforms, The Fronteira Post Economic Policy &

Financial

Markets, Value Fronteira Limited; Occasional Paper Series, May 27, 2010

Onwumere, (2008). Handout on Maritime Transport, Operations and Management, Certified Institute of

Shipping,

Lagos.

Radmilovick Z.R (1992). Ship-berth link as bulk system in ports. Journal of Waterway, Port Coastal

Engineering Vol. 6. No.3. 23-31

Sharma J.K. (2008). Operations Research; Theory and Applications, third edition, Macmillan Indian Ltd,

Indian

Saaty T.L. (1961), Elements of queueing theory with applications, McGraw-Hill Book Co, New York.

Sanijb. A and Biswa S.K (2001). “Analysis of container Handing system of Chittagong Port” Mgt Science.

Vol. 34. No. 5, 124-129

Tom, K (2009). Reoccurence of congestion in Nigeria ports, Port News, Vol.15, No.6, 2-10

Tu-Cheng Kuo, Wen-Chih Huang, Sheng-Chieh Wu, and Pei-Lun Cheng (2006). A Case Study of




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Inter-Arrival Time Distributions of Container Ships, Journal of Marine Science and Technology, Vol. 14,

No. 3, 155-164

Warwar, P. (1980). Port Capacity Methodology, Journal of Transport Economics and Policy (9): 242-250.

Zhang, C., Liu, J., Wan, Y.W., Murty, K.G., and Linn, R.J. (2008). Storage Space Allocation in Containe

Terminals: Transportation Research Part B: 883-903.

Appendix

Table 1: The terminal structure after concessioning is as follows:

S/Nos Name of Company (Concessionaire) Berths

1 Joseph Dan Ports Services Ltd 1-2

2 Tin Can Island Port (TCIT) Ltd 3-5

3 Ports and Cargo Nig. Ltd 6-8

4 Five Star Logistics Ltd 9-10

Source: Authors compilation 2010

Table 2: SHIP CHART AT TIN CAN ISLAND PORT in year 2008

Month

under study

Total number of

vessels called at

TIP

Total number of

vessel called at Berths

Total number

vessels

awaiting Berth

Percentage number

of vessels at berth

Percentage of vessels

awaiting berth

January 150 100 50 66.67 33.33

February 130 110 20 84.62 15.38

March 120 100 20 83.33 16.67

April 100 90 10 90.00 10.00

May 105 85 25 80.95 19.05

June 90 80 10 88.89 11.11

July 95 85 10 98.47 10.53

August 105 80 25 76.19 23.81

September 110 100 10 90.91 9.09

October 130 110 20 84.62 15.38

November 160 120 30 75.00 25.00

December 200 150 50 75.00 25.00

TOTAL 1,495

Source: Field survey, 2010




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Table 3: Determining the Traffic Intensity for Tin Can Island Port

Months

under study

Total no of

Vessel

called at

TIP

Total no of Vessel

called at Berth

No of days in

each month

Hours of

operation

Per day

Mean of

Arrival rate

λ

Mean of

service rate

μ

Traffic

intensity

λ

p =

January 150 67 31 24hours 0.2016 0.1344 1.5

February 130 85 29 24hours 0.1868 0.1580 1.18

March 120 83 31 24hours 0.1613 0.1344 1.20

April 100 90 30 24hours 0.1388 0.1250 1.11

May 105 81 31 24hours 0.1411 0.1142 1.24

June 90 89 30 24hours 0.1250 0.1111 1.13

July 95 89 31 24hours 0.1277 0.1142 1.12

August 105 76 31 24hours 0.1411 0.1075 1.31

September 110 91 30 24hours 0.1528 0.1389 1.10

October 130 85 31 24hours 0.1747 0.1478 1.18

November 160 75 30 24hours 0.2222 0.1667 1.33

December 200 75 31 24hours 0.2688 0.2016 1.33

TOTAL 1495 81

Source: Computations from data obtained at field work

Table 4: Analysis of average time ships spent in the system

MONTHS λ for each months μ Service rate for each

month

Mean time ships spent in the port waiting to

berth μ - λ

January 0.2016 0.1344 1 1

0.1344-0.2016 = -0.0672 = -14.88

February 0.1868 0.1580 1

1

0.1580-0.1868 = -0.0288 =

-37.17

March 0.1613 0.1344 1 1

0.1344-0.1613 = -0.269 =

-37.17

April 0.1388 0.1250 1 1

0.1250-0.1388 = -0.0138 = -72.46

May 0.1411 0.1142 1 1

0.1142-0.1411 = -0.0269 = - 37.17

June 0.1250 0.1111 1 1

0.1111-0.1250 = -0.0139 =

μ

1




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-71.94

July 0.1277 0.1142 1 1

0.1142-0.1277 = -0.0135 =

-74.07

August 0.1411 0.1075 1

1

0.1025-0.1411 = -0.0336 =

-29.76

September 0.1528 0.1389 1 1

0.1389-0.1528 = -0.0139 = -71.94

October 0.1747 0.1478 1 1

0.1478-0.1747 = -0.269 = -37.17

November 0.2222 0.1667 1 1

0.1667-0.2222 = -0.0555 = -18.02

December 0.2688 0.2016 1 1

0.2016-0.2688 = -0.0672 = - 14.88

Table 5: Output of TORA Software analysis for Tin Can Island Port Congestion problem

Months Server λ μ L’daoff Po Ls Lq Ws Wq

Jan. 10 0.2015 0.1344 0.13361 0.00585 8.12866 7.13451 60.83687 53.3940

Feb. 10 0.1868 0.1580 0.15805 0.03450 6.57998 5.61448 41.63167 35.52293

March 10 0.1613 0.1344 0.11239 0.00895 7.86552 6.87446 69.98686 61.16851

April 10 0.1388 0.1250 0.11862 0.05101 6.02457 5.07558 50.78720 42.78720

May 10 0.1411 0.1142 0.11129 0.02548 6.94461 5.97009 62.40099 53.64442

June 10 0.1250 0.1111 0.10587 0.04708 6.14680 5.19388 58.06042 49.05952

July 10 0.1277 0.1142 0.10862 0.04889 6.08995 5.13883 56.06812 47.31156

August 10 0.1411 0.1075 0.10572 0.1652 7.38197 6.39849 69.82292 60.52059

Sept. 10 0.1528 0.1389 0.13141 0.05394 5.93657 4.99051 45.17679 37.97736

Oct. 10 0.1747 0.1478 0.14272 0.03439 6.58410 5.61849 46.13396 39.36806

Nov. 10 0.2222 0.1667 0.16424 0.01473 7.48314 6.49788 45.56110 39.56230

Dec. 10 0.2688 0.2016 0.19864 0.10470 7.48507 6.49977 37.68224 32.72192




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T-Y Granger Causality Between Stock Prices and

Macroeconomic Variables: Evidence From Dhaka Stock

Exchange (DSE)

Mohammad Bayezid Ali

Department of Finance, Faculty of Business Studies

Jagannath University, Dhaka, Bangladesh.

Tel: +88- 0171-5028151 E-mail: [email protected]




Abstract:

This study investigates the direction of the causal relationship between stock prices and macroeconomic

aggregates in Dhaka Stock Exchange (DSE). By applying the techniques of unit–root tests, cointegration

and the long–run Granger causality test proposed by Toda and Yamamoto (1995), we test the causal

relationships between the DSE Stock Index and the thirteen macroeconomic variables, viz., consumer price

index, deposit interest rate, foreign exchange rate, export payment, gross domestic product, investment,

industrial production index, lending interest rate, broad money supply, national income deflator, foreign

remittances and total domestic credit using monthly data for the period 1987 to 2010. The major findings

are that DSI is any way do not granger cause CPI, deposit interest rate, export receipt, GDP, investment,

industrial production index, lending interest rate and national income deflator. But unidirectional causality

is found from DSI to broad money supply and total domestic credit. In addition bi-directional causality is

also identified from DSI to exchange rate, import payment and foreign remittances.

Keywords: Macroeconomic Variables, Cointegration, T-Y Granger causality

1. Introduction:

In an emerging economy, it is generally agreed that stock market under general equilibrium must play a

very important role in collecting and allocating funds in an efficient manner. They are required to meet at

least two basic requirements of supporting industrialization through savings mobilization, investment fund

collections and maturity transformation and ensuring the environment of safe and efficient discharge the

aforesaid functions. In most of emerging markets economic reform programs including liberalization,

privatization ands restructuring have not yet been completed or in the process of completion. In this case

the knowledge of the prevailing relationship between stock prices and macroeconomic variables like

consumption, investment, industrial production, GDP and the like, is predominantly important in the view

of the fact that a stable relationship among these variables are likely to reform the important postulate in a

variety of economic models. The relationship between stock market and the economy can be seen in two

ways in general. The first relationship explains the stock market as the leading indicator of the economic





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activities of the country. Secondly, one may be seen through the possible impact of the stock markets in

aggregate demand particularly through aggregate consumption and investment. If the relationship holds

together, it results in some ambiguity concerning the direction of causality between fluctuations in stock

markets and economic activities (Ahmed M. F., 2000).

In the view economic developments, it has become imperative to study the movements of stock returns as

well as the numerous macroeconomic aggregates and indicators. This is because the stock market is the

most sensitive segment of any developing economy. The macroeconomic events working on the investors’

psychology affect their buy sell decision rules. This results in stock price volatility which in turn exert

influence on the macroeconomic aggregates. The crucial question here is how instantaneously this

information are transmitted to the investors and market analysts at large and reflected in the stock prices.

This brings us to the issue of stock market efficiency. The interactions taking place over time, analysis of

which necessitates a suitable time series analysis.

The purpose of the present paper is to investigate the interaction between the stock price and a set of

macroeconomic variables, in the context of the Bangladesh economy for the period from 1987 to 2010

which also witnessed commencement of economic liberalization. The analysis of the interrelationship runs

in terms of Efficient Market Hypothesis. The Efficient Market Hypothesis (semi-strong form), states that in

a semi strong efficient market, everyone has perfect knowledge of all publicly available information and

these are fully reflected in stock prices. Otherwise, the market participants are able to develop profitable

trading rules and the stock market will not channel financial resources to the most productive sectors.

The use of Granger Causality Test in examining market informational efficiency has recently been found

unable to capture many of the time series properties. This paper makes use of the most recently available

econometric technique, as proposed by Toda and Yamamoto (1995), which overcomes the technical

problems associated with the traditional Granger Causality test. The contribution of this paper lies first of

all, in focusing on stock market efficiency with respect to macroeconomic fundamentals rather than

identifying the determinants of equity returns as in most of the studies and secondly in applying the Toda

and Yamamoto causality technique which is superior to traditional Granger Causality Test.

A survey of the existing literature including empirical evidences on the nature of causal relationships

between macroeconomic aggregates and stock prices is conducted in Section 2.0 Section 3.0 discuses the

methodology employed and presents the variables and data descriptions. Section 4.0 analyses the empirical

results followed by concluding observation in Section 5.0.

2. Review of Literature:

After 1986, the relationship between macroeconomic variables and stock prices is extensively investigated.

A brief overview of the studies using macroeconomic factors models is presented in this section. The

findings of the literature suggest that a significant linkage exist macroeconomic variables and stock prices

in developed economies but such relationship doesn’t exist in developing economies.




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The first group of studies covers developed economies. Chen, Roll and Ross (1986) test the multifactor

model in the USA by employing seven macroeconomic variables. They found that consumption, oil prices,

and the market index are not priced by the financial market. However, industrial production, changes in risk

premium and twists in the yield curve are found to be significant in explaining stock return. Chen (1991)

performed the second study covering the USA. Finding suggest that future market stock return could be

forecasted by interpreting some macroeconomic variables such as default spread, term spread, one month

T-bill rate, industrial production growth rate, and the dividend-price ratio. Clare and Thomas (1994)

investigate the effect of 18 macroeconomic factors on stock returns in the UK. They find oil prices, retail

price index, bank lending and corporate default risk to be important risk factor for the UK stock returns.

Mukherjee and Naka (1995) use vector error correction approach to model the relationship between

Japanese stock returns and macroeconomic variables. Cointegration relation is detected among stock prices

and the six macroeconomic variables, namely exchange rate, inflation rate, money supply, real economic

activity, long term government bond rate and call money rate. Gjerde and Saettem (1999) examine the

causal relation between stock returns and the macroeconomic variables in Norway. Results show a positive

linkage between oil price and stock returns as well as real economic activity and stock returns. The study,

however fails to show a significant relation between stock returns and inflation. A recent study by Flannery

and Protopapadakis (2002) reevaluate the effect of some macro announcement series on US stock returns.

Among these series, six macro variables, namely, balance of trade, housing starts, employment, consumer

price index, M1 and producer price index seem to affect stock returns. On the other hand, two popular

measures of aggregate economic activity (real GNP and industrial production) do not appear to be related

with stock returns.

Second group of studies investigate the relationship between stock prices and macroeconomic variables for

some developing countries. Using cointegration techniques, Chowdhury A.R. (1995) explains the lack of

efficiency in the emerging stock markets by investigating the issue of informational efficiency in the Dhaka

Stock Exchange in Bangladesh. He argued that in an efficient market the prices of the securities fully reflect

all available information i.e. stock market participants incorporate the information contained in money

supply changes into stock prices. Initially he tested the bivariate relationship models between stock prices

and money supply changes. Results from bivariate models suggest independence between the stock price

and monetary aggregates. In other words Dhaka stock market is informationally inefficient. However, it is

well known that bivariate models fail to address the obvious possibility that the relationship may be driven

by another variable acting on both stock price and money supply. Hence multivariate models were

estimated which shows the presence of a unidirectional causality from the money (both narrow and broad)

to stock price. But the findings are insensitive to the functional form of the variables employed. Thus the

stock prices do not immediately reflect changes in monetary policy and the market in inefficient. One

important limitation of this study is that the cointegration test conducted only for bivariate model. In

addition, causality test result showed that money supply (both M1 and M2) do not help to predict stock

prices.

Ahmed M.F. (2000) examines the causal relation between DSE stock index and a couple of macroeconomic




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variables like consumption expenditure, investment expenditures, real economic activity measured by GDP

and industrial production index. The author employed Granger (1988) causality test and found a causal

relation from stock price to consumption expenditures. He also found a unidirectional causality from

investment to stock prices; weak relationship between stock price and GDP and no causal relation between

stock price and industrial production index. Finally he concluded in that study that stock market is not

informationally efficient in Bangladesh.

Bhattacharya B. and Mukherjee J. (2003), investigates the nature of the causal relationship between stock

returns and macroeconomic aggregates in India. By applying the techniques of unit–root tests, cointegration

and the long–run Granger non–causality test recently proposed by Toda and Yamamoto (1995), we test the

causal relationships between the BSE Sensitive Index and the seven macroeconomic variables, viz., money

supply, index of industrial production, national income, rate of inflation, real effective exchange rate,

foreign exchange reserves and trade balance using monthly data for the period 1992-93 to 2000-01. The

major findings are that here is no causal linkage between (i) stock returns and money supply, index of

industrial production and national income for the domestic sector and (ii) stock returns and real effective

exchange rate, foreign exchange reserves and trade balance for the external sector. However, a

bi-directional causality exists between stock return and rate of inflation.

Khan K.N. (2004) used the Theory of Cointegration and ECM to examine the relationship between inflation

and stock market development (through market capitalization and stock turnover index). At first the

existence of stationarity property is checked by applying ADF and PP test. When the variables are found to

be integrated of the same order then cointegration test is conducted by using Johansen Maximum likelihood

procedure to find out the presence of long run relationship between inflation and MCAP and inflation and

stock turnover index. The estimated test result implies that the series moves together in the long run,

negatively related and a potential link exist between them. This means inflation matters to stock market

performance. ECM test result showed that 65 percent of the adjustment towards equilibrium occurs within a

year.

Ahmed. M. N. and Imam M. Osman, (2007) examines the long run equilibrium and short tern dynamics

between DSE stock index and a set of macroeconomic variables. In the macroeconomic variables they use

money supply, 91 day T-bill rate, interest rate GDP and Industrial production index. They applied Johansen

and Juselius (1990) maximum likelihood Cointegration test, Vector Error Correction Model (VECM) and

also employed Granger Causality test. In the cointegration test, they found two cointegrating vectors but

between them one is statistically significant. In the VECM test, they found that the lagged stock index was

adjusted to long run equilibrium by percent by 43.82 percent by the combined lagged influence of all the

selected macroeconomic variables. Granger causality test provides a unidirectional causality from interest

rate change to stock market return.

Rahman J. , Iqbal A. and Siddiqi M., (2010) examines the nature and the direction of causality in Pakistan

between public expenditure and national income along with various selected components of public

expenditure by applying Toda-Yamamoto causality test to Pakistan for the period of 1971 to 2006. This




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study finds that there is a unidirectional causality running from GDP to government expenditure.

Asaolu T.O. and Ogunmuyiwa M.S. (2010) investigates the impacts of macroeconomic variables on

share price of Nigeria. He used average share price of the Nigerian Stock Exchange as dependent variables

and External Debt, Inflation rate, Fiscal Deficit, Exchange rate, Foreign Capital Inflow, Investment,

Industrial output as independent variables. The findings of Granger Causality test indicated that Average

Share Price (ASP) does not Granger cause any of the nine (9) macroeconomic variables in Nigeria in the

sample period. Only exchange rate granger causes average share price when considered in pairs. The

Johansen co-integration test and showed a long run relationship between share price and the

macroeconomic variables. Error correction method also showed a weak relationship between share price

and macroeconomic variables. That means stock price is not a leading indicator of macroeconomic

variables in Nigeria and R-square value indicated that about 60 percent of the variation in stock prices in

accounted for by macroeconomic variables in Nigeria.

Ali M. B. (2011) investigates the impact of changes in selected microeconomic and macroeconomic

variables on stock returns at Dhaka Stock Exchange (DSE). A Multivariate Regression Model computed on

Standard OLS Formula has been used to estimate the relationship. Regression coefficient reveals that

inflation and foreign remittance have negative influence and industrial production index; market P/Es and

monthly percent average growth in market capitalization have positive influence on stock returns. All the

independent variables can jointly explain 44.48 percent variation in DSE all share price index. No

unidirectional Granger Causality is found between stock prices and all the predictor variables except one

unidirectional causal relation from stock price and market P/Es. Finally, lack of Granger causality between

stock price and selected micro and macro variables ultimately reveals the evidence of informationally

inefficient market.

3. Methodology:

3.1 Research Methods:

This study basically tries to examine the causal relationship between Dhaka Stock Exchange (DSE) all

share price index (DSI) and a set of 13 macroeconomic variables like consumer price index (CPI), 3 months

weighted average deposit interest rate (DIR), exchange rate of BDT against USD (EXR), export payment

(EXRPT), gross domestic product at current market price (GDPMP), investment at market price (INVMP),

industrial production index (IPD), 3 months weighted average commercial lending interest rate (LIR),

broad money supply (M2), national income deflator (NID) foreign remittances (REMIT) and total domestic

credit (TDC). At the beginning, Granger causality (1988) test was thought to be the most appropriate test

for this study. To explain, in brief, a simple definition of Granger Causality, in the case of two time-series

variables, X and Y:

"X is said to Granger-cause Y if Y can be better predicted using the histories of both X and Y than it can by

using the history of Y alone."

We can test for the absence of Granger causality by estimating the following VAR model:




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Yt = a0 + a1Yt-1 + ..... + apYt-p + b1Xt-1 + ..... + bpXt-p + ut (1)

Xt = c0 + c1Xt-1 + ..... + cpXt-p + d1Yt-1 + ..... + dpYt-p + vt (2)

Then, testing H0: b1 = b2 = ..... = bp = 0, against HA: 'Not H0', is a test that X does not Granger-cause Y.

Similarly, testing H0: d1 = d2 = ..... = dp = 0, against HA: 'Not H0', is a test that Y does not Granger-cause X.

In each case, a rejection of the null implies there is Granger causality. But later on we identify two major

drawbacks of using Granger Causality test when more than two variables are considered:

Granger Causality test consider only two variables to examine the causal relation between them. But it does

not consider the effects of other associated variables which are subject to possible specification bias. As

pointed out by Gujarati (1995), causality is sensitive to model specification and the number of lags. It

would reveal different results if any variable (s) was relevant and was not included in the model. Therefore

the empirical evidence of a two variable Granger-Causality is fragile because of this problem.

Time series data are often non-stationary. This situation could exemplify the problem of spurious regression.

Gujarati (1995) also said that when variables are integrated, the F-test procedure is not valid, as the test

statistics don’t have a standard distribution. Although researchers can still test the significance of individual

coefficients with t-statistics, one may not able to use F-statistics to jointly test the Granger-Causality.

Enders (2004) proved that in some special cases, using F-statistics to jointly test first differential VAR is

permissible. First differential VAR also has its limitations, which can not be employed universally.

To sum up, because of the probable shortcomings of specification bias and spurious regression, this study

does not carry out traditional Granger-Causality procedure to test the relationship between more than two

variables. Later on we have decided to use Toda- Yamamoto (1995) procedure to examine the causal

relation for our selected variables. Toda and Yamamoto (1995) proposed a simple procedure requiring the

estimation of an ‘augmented’ VAR, even when there is cointegration, which guarantees the asymptotic

distribution of the MWald statistic. This method is applicable “whether the VAR’s may be stationary

(around a deterministic trend), integrated of an arbitrary order, or cointegrated of an arbitrary order” (Toda

and Yamamoto: Journal of Econometrics 66, 1995, pp. 227). This procedure has two important advantages

over the standard causality tests. First, it conducts Granger causality tests with allowance for the long-run

information often ignored in systems that requires first differencing and pre-whitening.2 Secondly, this

methodology is useful because it bypasses the need for potentially biased pre-tests for unit roots and

cointegration, common to other formulations such as the vector error correction model (VECM).

Toda and Yamamoto (1995) procedure involve a modified Wald (MWALD) test in an augmented VAR

model, and do not require pretesting for cointegration properties of the system. The idea underlying the

Toda–Yamamoto (TY) test is to artificially augment the true lag length (say, p) of the VAR model by the




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maximal order of integration (dmax) that might occur in the process. Then, one can estimate the VAR model

with a (p + dmax) order, ignoring the coefficients of the last dmax lagged vectors, and test the linear or

nonlinear restrictions on the first k coefficient matrices by the standard Wald test. Toda and Yamamoto

(1995) prove that the Wald statistic used in this setting converges in distribution to a 2 random variable,

no matter whether the process is stationary or nonstationary. The preliminary unit root and cointegration

tests are not necessary to implement the DL test, since the testing procedure is robust to the integration and

cointegration properties of the process.

Consider the following VAR( p) model:

ptptt YAYAY ............11

where, yt, c, and et ~(0, ) are n-dimensional vectors and Ak is an nxn matrix of parameters for lag k. To

implement the T-Y test the following augmented VAR( p +d) model to be utilized for the test of causality is

estimated,

dptpptptY YAYAAYt .............

1

where the circumflex above a variable denotes its ordinary least squares (OLS) estimate. The order p of the

process is assumed to be known, and d is the maximal order of integration of the variables. Since the true

lag length p is rarely known in practice, it can be estimated by some consistent lag selection criteria. Note

that if the maximal order of integration d =1, then the T-Y test becomes similar to the DL test. The jth

element of Yt does not Granger-cause the ith element of Yt, if the following null hypothesis is not

rejected:

H0 : the row i; column j element in Ak equals zero for k = 1,…….., p

The null hypothesis is tested by a Wald test which is termed as modified Wald (MWALD) test in case of the

augmented VAR outlined above.

3.2 Data and Data Sources:

This study concentrates on investigating the causal relation between Dhaka Stock Exchange (DSE) all share

price index and a set of 13 macroeconomic variables (i.e. consumer price index, 3 month weighted average

deposit interest rate, foreign exchange rate, export receipt, gross domestic product at current market price,

import payment, investment at market price, industrial production index, 3 months weighted average

commercial lending interest rate, broad money supply, national income deflator, foreign remittance, total

domestic credit). For the purpose of this study DSE all share price index data was collected for the period

from January 1987 to December 2010. The data source of DSE all share price index is the ‘Monthly Review’




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-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

Inverse Roots of AR Characteristic Polynomial

publication issued by Dhaka Stock Exchange. Monthly data for the set of selected macroeconomic variables

for the same period were collected from ‘Monthly Economic Trends’ published by Bangladesh Bank. But

due to abnormal stock price volatility was observed during the period from January 1996 to June 1997

(Total 18 months), so the stock price data along with monthly data for all other macroeconomic variables

for the same period is not incorporated in to our study.

4. Empirical Result:

4.1 Unit Root Test:

T-Y procedure of Granger Causality proposes to test the order of integration in all the variables under each

study. There have been a variety of unit root test that can be used for determining the order of integration

(for example Dickey and Fuller, 1979; Sargan and Bhargava, 1983; Phillips and Parron, 1988; Kwiatkowski,

Phillips, Schmidt, and Shin, 1992 among the others) and each has been widely used in the applied

economics literature. In this study Augmented Dickey-Fuller (ADF) Test and Kwiatkowski, Phillips,

Schmidt, and Shin (KPSS) test has been used to identify the order of integration in each and every variable.

ADF test assumes a null hypothesis of nonstationary data series against an alternative hypothesis of

stationary data series. On the other hand, KPSS assumes stationery data series as null hypothesis and

nonstationary data series in the alternative hypothesis. The basic reason for selecting this two method is to

ensure a cross check in estimating the order of integration in each variables. Table-1 presents the test result

of the order integration under ADF test and KPSS test. Test result shows that DIR, EXR are I(0) variables

and DSI, CPI, EXRPT, GDPMP, IMPMT, INVMP, IPD, LIR, REMIT, TDC are I(1) variables and m2 is

I(2) variable under both ADF and KPSS test. Only one variable i.e. NID shows I(2) in ADF test and I(1) is

KPSS test. Base on this test result we can say that out of 14 different variables, 10 of them are I(1), two of

them are I(2) and remaining one variable show different order of integration under ADF and KPSS test.

Similarity in the order of integration is significant in T-Y Granger Causality procedure because it is

expected that when variables are integrated of the same order, they must have a cointegration relation that

also contribute to have Granger Causal relation among the variables.

4.2 Selection of Lag length by Information Criteria:

When we know that 10 out of total 14 variables are I(1) then we set up a

VAR model using all the variables regardless of the order of integration

in the time series. In this case, the selection of appropriate VAR lag

order is estimated based on Schwarz criterion (SC) and Hannan-Quinn

criterion (HQ) information criteria which results VAR lag order one

(see Table-2). But at this lag order the VAR model is severely affected

by possible serial correlation and that’s why we have performed VAR

residual serial correlation LM test to identify the lag order at which serial correlation in the VAR model will

be removed. This serial correlation LM test identify lag order five (see Table-3)that removes the existence

of serial correlation in the VAR model. At this level, the VAR is found to be dynamically stable.

4.3 Johansen-Juselius Cointegration Test




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The Johansen method applies the maximum likelihood procedure to determine the presence of cointegrating

vectors in non-stationary time series as a vector autoregressive (VAR). Consider a VAR of order p

tptpttt BXYAYAYAY .............2211

where Yt is a k-vector of non-stationary l(1) variables, Xt is a d vector of deterministic variables, and e1 is

a vector of innovations. We can rewrite the VAR as:

tt

p

i

tt BXYZY 1

1

1

1

Where

11

p

i

tA and

p

ij

ji A1

Here Yt is a vector of nonstationary variables. The information on the coefficient matrix between the levels

of the series is decomposed as = where the relevant elements of the matrix are

adjustment coefficients and the matrix contains the cointegrating vectors. Johansen and Juselius (1990)

specify two likelihood ratio test statistics to test for the number of cointegrating vectors' The first likelihood

ratio statistics for the null of exactly r cointegrating vectors against the alternative of r+1 vectors is the

maximum eigen value statistic. The second statistic for the hypothesis of at most r cointegrating- vectors

against the alternative is the trace statistic. Critical values for both test statistics are tabulated in Johansen

and Juselius (1990). The number of lags applied in the cointegration tests is based on the information

provided by the SC and HQ information criteria.

The test of cointegration based on Johansen and Juselius (1990) has been performed assuming a linear

deterministic trend and an optimum lag length of 5. At first, the unrestricted cointegration rank among the

variables under study is examined through the use of trace statistics and eigenvalue statistics. Trace

statistics test the null hypothesis of r = 0 or r ≤ 1 against alternative hypothesis of r ≥1 or r = 2. On the

other hand maximum eigen value statistics test the null hypothesis of r = 0 or r = 1 against alternative

hypothesis of r = 1 or r = 2. Table -4 reveals that trace statistics reject the null hypothesis of no

cointegrating relationship among the variables. An examination of trace statistics with that of critical value

at 5 percent indicates that there are three cointegrating equation among the variables. Another important test

to identify the number of cointegrating vectors is examination of maximum eigen value. This test (see Table:

4) also reveals the identical result that there is three cointegrating equation among the variables.

However, 10 out of 14 variables are stationary at first difference. This means that dmax = 1. So, the study

estimate a system of VAR at levels with a total of k+dmax= 5+1 = 6 lags in the model. Now a VAR model

has been estimated with lag 5 for each and every endogenous variables and additional one lag (i.e. due to

dmax =1) is used for all the variables as exogenous variables.




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4.4 T-Y Granger Causality Test

The empirical results of Granger causality test based on Toda and Yamamoto (1995) methodology is

estimated through MWALD test and reported in Table: 5. this table presents that the test result follows the

chi-square distribution with 5 degrees of freedom in accordance with the appropriate lag length along with

their associated probability.

According to this estimates DSI has no causal relation with CPI: which is not consistent with Emrah Ozbay

(2009). This estimates also reveals that DSI has no causal relation with DIR. But DSI has bi-directional

causality with EXR. This result is consistent with Aydemir and Demirhan (2009) but Emrah Ozbay (2009)

found causal relation from stock price to exchange rate. On the other hand, Karamustafa and Kucukkale

(2003) indicate that the stock returns are neither the leading nor the lagging variable of exchange rate.DSI

also has no causal relation with EXRPT. DSI has no causal relation with GDPMP which is consistent with

Ahmed M .F. (2000), Ahmed. M. N. and Imam M. Osman, (2007). But Emrah Ozbay (2009) found

unidirectional causality from GDP to stock returns. DSI has bi-directional causality with IMPMT and no

causal relation with INVMP. DSI has no causal relation with IPD. This result is also supported by Errunza

and Hogan (1998), Karamustafa and Kucukkale (2003), Emrah Ozbay (2009), Ahmed M.F (2000), Ahmed.

M. N. and Imam M. Osman, (2007). On the other hand, Erbaykal et al. (2008) report that industrial

production index is effective on stock prices via inflation, indicating that industrial production can be

used as leading indicators in estimating the stock prices. Similarly, Errunza and Hogan (1998) report that

industrial growth rate volatility does Granger cause return volatility for Italy and Netherlands. Conversely,

Ozturk (2008) and Kaplan (2008) report that stock prices lead real economic activity in Turkey.

Furthermore, the direction of the causality between variables is only from stock market price to real

economic activity. However, Nishat and Shaheen (2004) infer bilateral Granger cause between industrial

production and stock prices.According to our estimates DSI has no causal relation with LIR. A less

significant unidirectional causality was found by Ahmed. M. N. and Imam M. Osman, (2007), but Emrah

Ozbay (2009) found bidirectional causal relation between stock price and interest rate. DSI has

unidirectional causality from DSI to M2. This result is consistent with Ahmed. M. N. and Imam M. Osman,

(2007) and Emrah Ozbay (2009). At the same time, DSI has no causal relation with NID. However, DSI has

bi-directional causality with REMIT. Finally, there exists unidirectional causality from DSI to TDC.

5. Conclusion:

The main purpose of this study is to identify the lead and lag relationship between Dhaka Stock Exchange

(DSE) all share price index (DSI) and a setoff thirteen selected macroeconomic variables. We have

employed Toda – Yamamoto Granger causality Procedure which establish multivariate VAR and perform

MWALD test to establish causal relation among different variables. This T-Y Granger Causality Procedure

is considered to be a successful extension of original Granger Causality Test which considers only two

variables to identify causal relation. According to various economic theories, macroeconomic variables

should have relationship with the stock market which is considered to be a sensitive area of economic

system. In the context of Bangladesh, using data set for the period from January 1987 to December 2010,




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our study found unidirectional causality from DSI to M2 and TDC and bi-directional causality between DSI

and EXR, IMPMT & REMIT. This study found no causal relation between DSI and CPI, DIR, EXRPT,

GDPMP, INVMP, IPD, LIR & NID. Our findings in this paper are partially supported by economic theories

and evidence from other study. The results of the study may not consistently stable with the results of the

previous studies due to differences between the macroeconomic factors used, the period covered, the

research methodology employed and the countries examined. For the future research, this paper certainly

assist other researchers to find clues about the lead and lag relationship between DSE all share price index

and different macroeconomic variables.

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With Application to the Demand for Money", Oxford Bulletin of Economics and Statistics 52, 169-210.

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http://ideas.repec.org/s/wpa/wuwpfi.html



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Table – 1 : Test of Order of Integration

Variables ADF Test KPSS Test

ADF Test Stat I(0)

I(1)

I(2) Critical

Value @

1 %

I(0) I(1) I(2)

t-stat prob t-stat prob t-stat prob LM Stat. LM Stat. LM

Stat.

DSI -1.855270 0.6748 -16.338 0.000 ---- ---- 0.347246 0.203774 ----- 0.216000

CPI 0.451360 0.9991 -3.5791 0.033 ----- ----- 0.410612 0.203774 ----- 0.216000

DIR -1.855270 0.6748 -16.338 0.000 ----- ----- 0.098738 ----- ----- 0.216000

EXR -6.223315 0.000 ---- ------- ----- ----- 0.149819 ----- ----- 0.216000

EXRPT 0.696686 0.9997 -3.8934 0.013 ----- ----- 0.477794 0.149495 ----- 0.216000

GDPMP -1.547180 0.8108 -16.301 0.000 ----- ----- 0.344127 0.070707 ----- 0.216000

IMPMT 0.892090 0.9998 -6.0157 0.000 ----- ----- 0.458712 0.152689 ----- 0.216000

INVMP -0.999552 0.9412 -14.594 0.000 ----- ----- 0.477901 0.051261 ----- 0.216000

IPD -0.542909 0.9810 -11.753 0.000 ----- ----- 0.527782 0.076509 ----- 0.216000

LIR -1.823144 0.6909 -16.681 0.000 ----- ----- 0.225868 0.060805 ----- 0.216000

M2 3.907506 1.000 0.93718 0.999 -15.96 0.00 0.487784 0.541332 0.0831 0.216000

NID 0.017701 0.9963 -3.1615 0.094 -29.13 0.00 0.371355 0.117194 ----- 0.216000

REMIT 0.153964 0.9976 -18.810 0.000 ----- ----- 0.483696 0.097956 ----- 0.216000

TDC 0.8110764 0.9998 -2.6113 0.275 ----- ----- 0.490458 0.167305 ----- 0.216000

Table-2 : VAR Lag Order Selection Criteria

Endogenous variables: DSI CPI DIR EXR EXRPT GDPMP IMPMT INVMP IPD LIR M2 NID REMIT TDC

Exogenous variables: C

Lag LogL LR FPE AIC SC HQ

0 -22364.99 NA 8.64e+56 170.8320 171.0227 170.9087

1 -18932.35 6472.240 1.61e+46 146.1248 148.9849* 147.2743*

2 -18661.09 482.4663 9.17e+45 145.5503 151.0799 147.7728

3 -18459.77 336.5557 9.07e+45 145.5097 153.7087 148.8051

4 -18235.94 350.2677 7.76e+45 145.2973 156.1657 149.6655

5 -18062.30 253.1669 1.01e+46 145.4680 159.0059 150.9092

6 -17732.82 445.1753 4.23e+45 144.4490 160.6564 150.9631

7 -17373.93 446.5631 1.51e+45 143.2056 162.0824 150.7926

8 -17109.55 300.7097* 1.20e+45* 142.6836* 164.2298 151.3435




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Table-3:VAR Residual Serial Correlation LM Tests

Null Hypothesis: no serial correlation at lag order h

Lags LM-Stat Prob

2 345.2810 0.0000

3 230.0568 0.0482

4 281.1994 0.0001

5 219.5171 0.1197

6 645.1005 0.0000

7 241.5213 0.0148

8 256.8226 0.0023

9 287.2645 0.0000

10 271.9245 0.0003

11 262.8853 0.0010

12 741.4978 0.0000

Table- 4 : Unrestricted Cointegration Rank Test (Trace) and (Maximum Eigenvalue)

Hypothesized No. of CE(s) Trace Statistic 0.05

Critical Value Prob.**

Max-Eigen

Statistic

0.05

Critical Value Prob.**

None * 420.4800 239.2354 0.0000 144.4319 64.50472 0.0000

At most 1 * 276.0481 197.3709 0.0000 109.3084 58.43354 0.0000

At most 2 * 166.7397 159.5297 0.0190 54.64502 52.36261 0.0287

At most 3 112.0946 125.6154 0.2469 37.12491 46.23142 0.3336

At most 4 74.96972 95.75366 0.5445 28.67151 40.07757 0.5143




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Table 5 :T-Y Granger Causality Test

Direction of Causality df. Chi-sq. Prob.

DSI

CPI

~

~

CPI

DSI 5

5.729393

1.801316

0.3334

0.8759

DSI

DIR

~

~

DIR

DSI 5

3.351448

7.076394

0.6460

0.2150

DSI

EXR

→

→

EXR

DSI 5

9.290916*

14.34270*

0.0980

0.0136

DSI

EXRPT

~

~

EXRPT

DSI 5

4.161131

7.648682

0.5265

0.1767

DSI

GDPMP

~

~

GDPMP

DSI 5

1.088010

0.707571

0.9552

0.9826

DSI

IMPMT

→

→

IMPMT

DSI 5

17.74316*

30.88673*

0.0033

0.0000

DSI

INVMP

~

~

INVMP

DSI 5

2.545285

0.746767

0.7697

0.9803

DSI

IPD

~

~

IPD

DSI 5

6.182735

8.049412

0.2888

0.1535

DSI

LIR

~

~

LIR

DSI 5

4.239602

0.440684

0.5155

0.9941

DSI

M2

→

~

M2

DSI 5

21.69861*

7.972382

0.0006

0.1586

DSI

NID

~

~

NID

DSI 5

3.715160

5.736329

0.5911

0.3327

DSI

REMIT

→

→

REMIT

DSI 5

22.83706*

16.04603*

0.0004

0.0067

DSI

TDC

→

~

TDC

DSI 5

9.679069*

3.844569

0.0849

0.5720

* sig. at 10 percent level




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Employee Engagement: A Driver of Organizational

Effectiveness

Bijaya Kumar Sundaray (Corresponding author)

Associate Professor, Regional College of Management, Autonomous,

Chakadola Vihar, Chandrasekharpur, Bhubaneswar-751023

Odisha, India

Tel: +91- 9437309410 E-mail: [email protected]




Abstract

Employee engagement has emerged as a popular organizational concept in recent years. It is the level of

commitment and involvement of an employee towards the organization and its values. An engaged

employee is aware of business context, and works with colleagues to improve performance within the job

for the benefit of the organization. Employee engagement develops positive attitude among the employees

towards the organization. This paper focuses on various factors which lead to employee engagement and

what should company do to make the employees engaged. Proper attention on engagement strategies will

increase the organizational effectiveness in terms of higher productivity, profits, quality, customer

satisfaction, employee retention and increased adaptability.

Key Words: Employee engagement, Organisational effectiveness, Productivity, Outcomes, Employee

retention

1. Introduction

Today, society and business are witnessing unprecedented change in terms of the global nature of work and

the diversity of the workforce. Organizations in the world are moving forward into a boundary-less

environment. Having the right talent in pivotal roles at the right time is of strategic importance, making a

difference to revenues, innovation and organisation effectiveness (Ashton and Morton, 2005). The ability to

attract, engage, develop and retain talent will become increasingly important for gaining competitive

advantage. Thus companies are competing for talent people who are having high performance and high

competence in workplace (Berger and Berger, 2004). Organisations need employees who are flexible,

innovative, willing to contribute and go ‘above and beyond the letter’ of their formal job descriptions or

contracts of employment (Hartley, et al., 1995). In the new economy, competition is global, capital is

abundant, ideas are developed quickly and cheaply, and people are willing to change jobs often. The

organisations, which are not able to provide a good treatment for their employees, will loose their talented

people. In this situation engaged employees may be a key to competitive advantage. Because, engaged

employees have high levels of energy, are enthusiastic about their work and they are often fully immersed

in their job so that time flies (Macey and Schneider, 2008; May et al., 2004). Organisations that understand

the conditions that enhance employee engagement will have accomplished something that competitors will

find very difficult to imitate. To the extent that employees are likely to be faced more frequently with

unanticipated and ambiguous decision-making situations, organizations must increasingly count on

employees to act in ways that are consistent with organizational objectives. In addition, many employees

are looking for environments where they can be engaged and feel that they are contributing in a positive

way to something larger than themselves.




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Employee engagement has emerged as a popular organizational concept in recent years, particularly among

practitioner audiences (Saks, 2006; Bakker and Schaufeli, 2008). This is seemingly as attractive for

organizations as it is for the professional societies and consulting groups. The outcomes of employee

engagement are advocated to be exactly what most organizations are seeking: employees who are more

productive in which they can work over the target within working time, profitable in which they spend the

financial usage of company efficiently, safer, healthier, less likely to turnover, less likely to be absent, and

more willing to engage in discretionary efforts (Buchanan, 2004; Fleming and Asplund, 2007; Wagner and

Harter, 2006). It is not surprising that corporate executives are consistently ranking the development of an

engaged workforce as an organizational priority (Ketter, 2008). Further, employee engagement can be a

deciding factor for organizational effectiveness. Not only does engagement have the potential to

significantly affect employee retention, productivity and loyalty, it is also a key link to customer

satisfaction, company reputation and overall stakeholder value. Thus, to gain a competitive edge,

organizations are turning to HR to set the agenda for employee engagement and commitment.

2. Employee Engagement: Literature Review

Employee engagement is a complex, broad construct that subsumes many well researched ideas such as

commitment, satisfaction, loyalty and extra role behavior. An engaged employee extends themselves to

meet the organization’s needs, takes initiative, reinforces and supports the organization’s culture and

values, stays focused and vigilant, and believes he/she can make a difference (Macey, 2006). In practice,

organizations typically define engagement as being a part of the organization, having pride and loyalty in

the company, being committed, and going “above and beyond the call of duty”. Kahn (1990) defined

employee engagement as ‘the harnessing of organization members’ selves to their work roles. In

engagement, people employ and express themselves physically, cognitively, and emotionally during role

performances. The cognitive aspect of employee engagement concerns employees’ beliefs about the

organisation, its leaders and working conditions. The emotional aspect concerns how employees feel about

each of those three factors and whether they have positive or negative attitudes toward the organisation and

its leaders. The physical aspect of employee engagement concerns the physical energies exerted by

individuals to accomplish their roles. Thus, according to Kahn (1990), engagement means to be

psychologically as well as physically present when occupying and performing an organisational role.

Engaged employees work with passion and feel a profound connection to their company. They drive

innovation and move the organization forward (Gallup, 2004). In contrast to this, not-engaged employees

are sleepwalking through their workday, putting time—but not energy or passion—into their work. They

don't have productive relationships with their managers or with their coworkers. Actively disengaged

employees aren’t just unhappy at work; they are busy acting out their unhappiness. Every day, these

workers undermine what their engaged coworkers accomplish.

Most often employee engagement has been defined as emotional and intellectual commitment to the

organisation (Baumruk, 2004; Richman, 2006; and Shaw, 2005) or the amount of discretionary effort

exhibited by employees in their job (Frank et al. 2004). Development Dimensions International (DDI, 2005)

defined engagement “The extent to which people value, enjoy, and believe in what they do”. It also states

that its measure is similar to employee satisfaction and loyalty. A leader, according to DDI, must do five

things to create a highly engaged workforce. They are: align efforts with strategy; empower people;

promote and encourage teamwork and collaboration; help people grow and develop; and provide support

and recognition where appropriate. Robinson et al. (2004) defined engagement similar to the established

constructs such as ‘organisational commitment’ and ‘organisational citizenship behaviour’ (OCB). It is a

positive attitude held by the employee towards the organization and its values. An engaged employee is

aware of the business context and works with colleagues to improve performance within the job for the

benefit of the organization. According to Maslach et al. (2001), six areas of work-life lead to either burnout

or engagement: workload, control, rewards and recognition, community and social support, perceived

fairness and values. They argue that job engagement is associated with a sustainable workload, feelings of

choice and control, appropriate recognition and reward, a supportive work community, fairness and justice,




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and meaningful and valued work. Like burnout, engagement is expected to mediate the link between these

six work-life factors and various work outcomes.

Corporate leadership Council (2004) defined employee engagement as “the extent to which employees

commit to something or someone in their organization, how hard they work and how long they stay as a

result of that commitment”. It is a desirable condition, where an organizational connotes involvement,

commitment, passion, enthusiasm, focused effort, and energy among employees. So it has both attitudinal

and behavioral components (Erickson, 2005). Engagement is the measure of an employee’s emotional and

intellectual commitment to their organization and its success. It is an outcome of employees’ organizational

experiences that are characterized by behaviors that are grouped in to three categories: say, stay and strive

(Hewitt, 2005). For Seijts and Crim (2006), employee engagement means a person who is fully involved in,

and enthusiastic about, his or her work. Engaged employees care about the future of the company and are

willing to invest the discretionary effort to see that the organization succeeds. Brown (2006) viewed

engagement as a progressive combination of satisfaction, motivation, commitment and advocacy resulting

from employees’ movement up the engagement pyramid.

Employee engagement can be considered as cognitive, emotional and behavioral. Cognitive engagement

refers to employees' beliefs about the company, its leaders and the workplace culture. The emotional aspect

is how employees feel about the company, the leaders and their colleagues. The behavioral factor is the

value added component reflected in the amount of effort employees put into their work (Lockwood, 2007).

Mone and London (2010) defined employee engagement is “a condition of employee who feels involved,

committed, passionate, and empowered and demonstrates those feelings in work behavior”. It is thus the

level of commitment and involvement an employee has towards their organization and its values. The

organization must work to develop and nurture engagement, which requires a two-way relationship

between employer and employee. Thus, employee engagement is a barometer that determines the

association of a person with the organization.

3. Objectives

The objectives of this study are:

To study the various factors influencing employee engagement.

To examine the impact of employee engagement on organizational effectiveness

To propose an engagement model based on exclusive literature review.

4. Factors Influencing Employee Engagement

There are some critical factors which lead to employee engagement. These factors are common to all

organisations, regardless of sector. These factors create a feeling of valued and involved among the

employees. But the components of feeling valued and involved, and the relative strength of each factor are

likely to vary depending on the organisation. The factors which influence employee engagement are;

Recruitment: The recruitment and selection process involves identifying potential employees, making

offers of employment to them and trying to persuade them to accept those offers. The messages

organization conveys while seeking to attract job applicants also can influence future employees’

engagement and commitment. While recruiting employees for desirable jobs, organisations enhance

their engagement (by maximizing the person-job fit) and commitment (by providing growth and

advancement opportunities to employees in return for their loyalty). To enhance engagement

organisations identify those candidates who are best-suited to the job and to organization’s culture.

Job Designing: Job characteristics encompassing challenge, variety and autonomy are more likely to

provide psychological meaningfulness, and a condition for employee engagement. Job becomes

meaningful and attractive to employee as it provides him variety and challenge, thereby affecting his

level of engagement.

Career Development Opportunities: Organizations with high levels of engagement provide employees

with opportunities to develop their abilities, learn new skills, acquire new knowledge and realize their

potential. When companies plan for the career paths of their employees and invest in them in this way




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their people invest in them. Career development influences engagement for employees and retaining

the most talented employees and providing opportunities for personal development.

Leadership: Employees need to feel that the core values for which their companies stand are

unambiguous and clear. Successful organizations show respect for each employee’s qualities and

contribution regardless of their job level. A company’s ethical standards also lead to engagement of an

individual.

Empowerment: Employees want to be involved in decisions that affect their work. The leaders of high

engagement workplaces create a trustful and challenging environment, in which employees are

encouraged to give input and innovative ideas to move the organization forward.

Equal Opportunities and Fair Treatment: The employee engagement levels would be high if their

superiors provide equal opportunities for growth and advancement to all the employees. Employees

feel that they are not discriminated in any aspects within the organisation.

Training and Development: Redundancy of skills has been cited as one of the reasons for employee

turnover, thereby indicating the necessity for training, re-training and multi-skill training. It is another

important area which contributes to employee engagement. Learning new skills may trigger renewed

interest in such aspects of the job which had not been meaningful earlier. Through training, you help

new and current employees acquire the knowledge and skills they need to perform their jobs. And

employees who enhance their skills through training are more likely to engage fully in their work,

because they derive satisfaction from mastering new tasks. Training also enhances employees’ value to

your company as well as their own employability in the job market.

Performance Management: Performance management processes provide conditions for employee

engagement. Performance management encourages managers to have a focus on roles and

responsibilities of employees and to include them in the goal-setting process. It promotes acceptance of

challenging objectives, and also recognizes and encourages contributions that exceed expectations. It

creates of a feeling of being valuable to the organisation which in turn helps in engaging the employee.

Compensation: Compensation is said to have a major influence on the employees’ conceptions of their

employment relationship. Compensation consists of financial elements (pay and benefits) but may also

include nonfinancial elements or perks, such as on-site day care, employee assistance programs,

subsidized cafeterias, travel discounts, company picnics and so on. The organisation should have a

proper compensation management system so that the employees are motivated to work in the

organization.

Health and Safety: Research indicates that the engagement levels are low if the employee does not feel

secure while working. Therefore every organization should adopt appropriate methods and systems for

the health and safety of their employees.

Job Satisfaction: Only a satisfied employee can become an engaged employee. Therefore it is very

essential for an organization to see to it that the job given to the employee matches his career goals

which will make him enjoy his work and he would ultimately be satisfied with his job.

Communication: The organisation should follow the open door policy. There should be both upward

and downward communication with the use of appropriate communication channels in the organization.

If the employee is given a say in the decision making and has the right to be heard by his boss than the

engagement levels are likely to be high.

Family Friendliness: A person’s family life influences his wok life. When an employee realizes that

the organization is considering his family’s benefits also, he will have an emotional attachment with

the organization which leads to engagement.

5. Outcomes of Employee Engagement

Employee engagement is a critical ingredient of individual and organizational success. There is a general

belief that there is a connection between employee engagement as an individual level construct and

business results. Employee engagement predicts employee outcomes, organizational success, and financial




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performance (Bates 2004; Baumruk 2004; Harter et al. 2002; Richman 2006). The impact of engagement

(or disengagement) can manifest itself through productivity and organisational performance, outcomes for

customers of the organisation, employee retention rates, organisational culture, and advocacy of the

organisation and its external image. A highly engaged employee will consistently deliver beyond

expectations (Harter, Schmidt and Hayes, 2002). Employee engagement is a key business driver for

organizational success. High levels of employee engagement with in a company promote retention of talent,

foster customer loyalty and improve organizational performance. It is also a key link to customer

satisfaction, company reputation and overall stakeholder value (Lockwood, 2007). It has a statistical

relationship with productivity, profitability, employee retention, safety, and customer satisfaction

(Buckingham & Coffman, 1999; Coffman & Gonzalez- Molina, 2002). Kahn (1992) proposed that high

levels of engagement lead to both positive outcomes for individuals, (e.g. quality of people’s work and their

own experiences of doing that work), as well as positive organisational-level outcomes (e.g. the growth and

productivity of organisations). Engagement has not only been found to impact important work outcomes,

but it has also been found to be more associated with health issues, such as depressive symptoms and

physical problems, which may affect employee well-being (Hallberg & Schaufeli, 2006). The Gallup

Organisation (2004) found critical links between employee engagement, customer loyalty, business growth

and profitability. It also acts as a catalyst towards the retention of staff. Greenberg (2004) says that

employee engagement is critical to any organization that seeks not only to retain valued employees, but also

increase its levels of performance. Hewitt Associates LLC, (2005) established a conclusive relationship

between engagement and profitability through higher productivity, sales, customer satisfaction, and

employee retention. Engaged employees not only contribute more but also are more loyal and therefore less

likely to voluntarily leave the organization. The various factors and outcomes of employee engagement

have clearly depicted in the model (Fig 1).

6. Conclusion

Employee Engagement is a positive attitude held by the employees towards the organization and its values.

It is rapidly gaining popularity and importance in the workplace and impacts organizations in many ways.

An organization should thus recognize employees, more than any other variable, as powerful contributors

to its competitive position. Engaged employees can help your organization achieve its mission, execute its

strategy and generate important business results. Therefore employee engagement should be a continuous

process of learning, improvement, measurement and action. This paper provides some noteworthy

implications for practitioners. It focuses on the various factors which influence employee engagement. It

has been observed that organisations with higher levels of employee engagement outperform their

competitors in terms of profitability. Engaged employees give their companies crucial competitive

advantages—including higher productivity, customer satisfaction and lower employee turnover. The

relationship between employee engagement and organizational outcomes would be stronger if better

measures were used. Thus, organisations need to better understand how different employees are affected by

different factors of engagement and focus on those in order to achieve the strategic outcomes as well as to

improve overall effectiveness.

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Brown, R. (2006). Doing your dissertation in business and management: the reality of researching and

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differently. New York, NY: Simon & Shuster.

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growth by unleashing human potential. New York, NY: Warner Books, Inc.

Corporate Leadership Council, (2004). Driving performance and retention through employee engagement.

Washington, DC: Corporate Executive Board.

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Retrieved October 10, 2011, from www.ddi world.com.

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and Pensions, May 26.

Fleming, J. H., and Asplund, J. (2007). Human sigma. New York: Gallup.

Frank, F.D., Finnegan, R.P. and Taylor, C.R., (2004). ‘The race for talent: retaining and engaging workers in

the 21st century’, Human Resource Planning, Vol 27, No 3, pp.12-25.

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Incorporated Publication, October, pp. 3.

Hallberg, U. & Schaufeli, W. B., (2006). "Same same" but different? Can work engagement be

discriminated from job involvement and organizational commitment? European Psychologist, 11, 119-127.

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satisfaction, employee engagement, and business outcomes: A meta-analysis. Journal of Applied

Psychology, 87, 268–279.

Hartley, J., Jackson, D., Klandermans, B., and Vuuren, T., (1995). Job Insecurity: Coping with Jobs at Risk.

Sage: London.

Hewitt Associates LLC. (2005). Employee engagement. Retrieved Agust 29, 2011, from

http://was4.hewitt.com/ hewitt/services/talent/subtalent/ee_engagement.htm

Kahn, W. A. (1990). Psychological conditions of personal engagement and disengagement at work.

Academy of Management Journal, Vol. 33, pp. 692-724.

Kahn, W.A. (1992). To be fully there: psychological presence at work. Human Relations, Vol. 45, pp.

321-50.

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44-49.

Lockwood, N. R. (2007). Leveraging Employee Engagements for Competitive Advantage: HRs Strategic

Role. HR Magazine, 52(3), pp. 1-11.

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Organizational Psychology 21st Annual Conference, May, Dallas, TX.

Macey, W., & Schneider, B. (2008). The meaning of employee engagement. Industrial and Organizational

Psychology, 1 (1), 3-30.

Maslach, C., Schaufelli, W.B. and Leiter, M.P., (2001). ‘Job burnout’, Annual Review of Psychology, Vol 52,

pp 397-422.

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Mone, Edward M., and London, M., (2010). Employee engagement; Through effective performance

management. A practical guide for managers. Routledge Press. NY.

Richman, A. (2006). ‘Everyone wants an engaged workforce how can you create it?’ Workspan, Vol. 49,

pp.36-39.

Robinson, D., Perryman, S. and Hayday, S. (2004) The Drivers of Employee Engagement. Brighton,

Institute for Employment Studies.

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Psychology, 21(7), pp. 600-619.

Seijts, G. H. & Crim, D. (2006). What engages employees the most or, the ten C’s of employee engagement.

Ivey Business Journal Online. Retrieved October 4, 2011, from http://find.galegroup.com.

Shaw, K. (2005). ‘An engagement strategy process for communicators’, Strategic Communication

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2011.

Wagner, R., & Harter, J. K. (2006). 12: The great elements of managing. Washington, DC: The Gallup

Organization.

Figure 1: Factors and Outcomes of Employee Engagement

Factors

Recruitment

Job Designing

Career Development

Opportunities

Leadership

Empowerment

Equal Opportunities and Fair

Treatment

Training and Development

Performance Management

Compensation

Health and Safety

Job Satisfaction

Communication

Family Friendliness

Organisational

Outcomes

Higher profits &

productivity

Improved quality

Satisfied/Loyal

customers

Increased retention

Revenue growth

Employee

Engagement

Individual

Outcomes

Motivation

Commitment

Satisfaction

Loyal

Secured job

Higher

performance




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How can behavioral finance help us in better understanding

the recent global financial crisis?

Thinley Tharchen

Academic Associate

AC1 courtyard, Indian School of Business,

Gachibowli, Hyderabad-500 032

Email: [email protected]




Abstract

The recent global financial crisis calls for a need to adopt a more interdisciplinary approach to the study of

economics and finance by focussing also on the individual and social psychology that drives the actions of

market participants. Behavioural finance offers such a perspective by drawing on the fields of psychology

and the other social sciences to explain how investors are led to make less than rational investment

decisions and how these could aggregate to less than rational market outcomes, like periods of excessive

investor euphoria preceding a financial crisis. This paper draws on the existing literature in behavioural

finance and particularly on the two models of “information cascade” by Bikchandani et al. (1992) and

“limits to arbitrage” by De Long et al. (1990) to provide a better understanding of the underlying reasons

behind the recent global financial crisis. The paper concludes with a view to inform policy of the ways it

can curb speculative excesses and prevent events like the recent global financial crisis.

Keywords: Investor psychology, information cascade, social contagion, limits to arbitrage, noise trader risk.

1. Introduction

The G-20 summit held on 15th

November 2008 at Washington, identified as the root cause of the recent

global financial crisis the following: “weak underwriting standards, unsound risk management practices,

increasingly complex and opaque financial products and consequent excessive leverage”. While it is true

that all of these factors played a major part in bringing about the recent financial crisis, these factors by

themselves could have been the result of less that rational decision making by managers and investors alike.

Behavioural finance could help us better understand the global financial crisis by informing us of the ways

in which people make less than rational investment decisions and how these aggregate to less than rational

outcomes in the markets leading to asset bubbles and a subsequent crisis.

1.1 From Efficient Market Hypothesis to Behavioural Finance

The efficient market hypothesis has been a key idea that has guided much of finance theory since the

1970’s. Fama (1970) defined market efficiency as “A market in which prices always ‘fully reflect’

available information is called ‘efficient’”. He further distinguishes three forms of market efficiency

namely weak form, semi-strong from and strong form depending on whether the information set contained

historical prices, all publicly available information or all private information respectively. The early tests of





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market efficiency were tests on whether prices and returns followed a random walk. If prices did follow a

random walk it meant they could not be forecast which meant that it was not possible to make risk adjusted

economic profits by trading on the basis of an information set thereby validating market efficiency. Fama

(1970) finds almost no evidence that dependence in returns could be used to generate profitable trading

strategies and also finds that prices on average reflected all available information during major information

generating events like earnings announcements and concluded that “...the evidence in support of the

efficient market model is extensive, and (somewhat uniquely in economics) contradictory evidence is

sparse”.

Over the years however a large number of studies have discovered anomalies that challenge the efficient

market hypothesis. Rozeff and Kinney (1976) found average returns on the equally weighted NYSE to be

about 4.5% in January, but only 0.5% for the other months thus demonstrating seasonality in returns.

Shiller (1981) shows that stock prices were far too much volatile to be attributable only to new information

on expected future dividends. De Bondt and Thaler (1985) show that dramatic fall (rise) in stock prices

were predictive of subsequent rise (fall) in earnings and attribute this as evidence of investor overreaction

to short term earnings movements. Jegadeesh and Titman (1993) find evidence of profitability in the

following 12 months of certain momentum strategies that buy stocks with high returns and sell stocks with

low returns over the previous three to twelve months. Jegadeesh and Titman (2001) find their earlier results

robust for the 12 month period. Besides these anomalies, further research has also led to new variables

being found to predict future returns. Fama and French (1992) find that size, book to market and market to

book leverage have significant effect on returns. Fama and French (1993) find that a three factor model

with market, size and book to market factors explains well stock returns.

These studies hence validate the claim that the market is less than fully efficient even in the weak sense and

call for an alternative explanation to market outcomes that deviate from the efficient market outcome.

Behavioural finance which is an approach to study financial decision making and the resulting market

outcomes by drawing on the fields of psychology and the other social sciences provides an explanation to

the causes of investor’s less than rational decisions and the resulting inefficient market outcomes.

1.2 Behavioural finance

Drawing on the discussion in Barberis and Thaler (2003), the two major “building blocks” on which

behavioural finance rests are “limits to arbitrage” and “psychology”. Limits to arbitrage is based on the

notion that in the real world rational arbitrageurs being faced by significant costs and risks to arbitrage will

limit arbitrage and thus let deviations from fundamental values persist. Psychology informs us on the

deviations from rationality we may observe among investors and their valuation of assets that results in less

than efficient asset prices and market outcomes. I present the key research findings on these two building

blocks.

1.2.1 Limits to Arbitrage

Proponents of the efficient market hypothesis would argue that in the event of asset prices going very far

away from fundamentals rational arbitrageurs would quickly bring these prices in line with fundamental

values by taking positions against these price movements. Barberis and Thaler (2003) point out that in

reality exploiting such a mispricing away from fundamentals may be risky due to fundamental risk and

noise trader risk. Fundamental risk is the risk that the fundamentals of the mispriced asset may worsen and

in the event of any close substitute securities be also impossible to hedge against. Noise trader risk is the

risk that a mispricing may worsen in the short run as deviations from fundamental values persist due to

continued positive or negative sentiment of the irrational investors.

Drawing on De Long et al. (1990) and Shleifer and Vishny (1997) they point out that rational arbitrageurs

foreseeing such a scenario of early liquidation in the event of a worsening mispricing tend to be more

cautious in taking positions against any such mispricing and may even also trade in the direction of the

mispricing. A celebrated example of the risks in arbitrage strategies detailed by Lowenstein (2000) is the




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fall of Long Term Capital Management, a leading US hedge fund in 1990’s which lost millions when its

arbitrage position in a dual listed company Royal Dutch Shell hoping that the prices of Royal Dutch and

Shell would converge had to be unwounded due to the unforeseen losses it had made on its other trades. I

later draw on the model by De Long et al. (1990) to explain why mispricing in real estate may have

persisted despite the presence of rational investors who may have seen a bubble in the housing markets.

1.2.2 Psychology

The broad research in psychology relevant to the financial markets is dealt in Shiller (2001). I follow the

discussion in Shiller and outline the most important literature in understanding the various deviations from

rationality. Kahneman and Travesky (1979) show through experimental evidence that people value losses

more than gains i.e. their value function is concave for gains and convex for losses around a reference point.

They also show that people distort probabilities in their minds and underweight events that are highly

probable vis-a-vis events that are extremely probable. This is also known as the “certainity effect.” Shefrin

and Statman (1985) show the “disposition effect” among investors to sell winning stocks too early and

continue holding on to losing stocks and attribute this to regret avoidance in making a bad investment.

Goetzmann and Peles (1993) attribute “cognitive dissonance” (which is a tendency among people to

continue with their old beliefs by disregarding new information or developing arguments to justify their

beliefs) to the tendency of mutual fund investors in losing funds to bias upwards their perceptions about the

fund resulting in lower outflow of funds from losing funds than inflow of funds into winning ones.

Northcraft and Neale (1987) show the phenomena of anchoring (which is a tendency among people to base

too strongly their responses around a reference point or an anchor) among real estate experts when giving

their valuations for a house. They show that experts who were provided with a higher “asking price” as an

anchor gave significantly higher “appraisal values” than those provided with a lower “asking price.”

Odean (1998) after analyzing 10,000 customer accounts from 1987 through 1993 at a large discount

brokerage house show investor’s persistent tendency to sell winner stocks and hold on to losing stocks

which may evidence “overconfidence” among investors in their beliefs that the losing stocks would bounce

back. Shiller (1987) collect responses from individuals and institutional investors after the stock market

crash on October 19th

, 1987, (the largest one day percentage decline in the Dow Jones Industrial Average)

and find a large number of investors who bought stocks on that day (around 47%) expect the stock market

to rebound and often attribute their buy decisions to “intuition” and “gut feeling” demonstrating

overconfidence in their intuitive feelings. Shiller attribute the crash to a feedback loop in which investors

constantly respond to price changes and to each other and thus generates a feedback loop that feeds further

price declines. Such a feedback loop in general could explain speculative behaviour in the financial markets

as well. Bikchandani et al. (1992) describe a model of herding by way of an “information cascade”. They

describe an information cascade as occurring when individuals reject their own private signals and adopt

the actions of those ahead of them. I later draw upon the argument by Shiller (1987) and the model by

Bikchandani et al. (1992) to explain why housing prices kept on increasing and why investors thought that

these prices would keep on increasing to even higher levels which was the psychology behind the housing

bubble that led to the recent financial crisis.

2.The Global Financial Crisis

I present below a brief account of the recent global financial crisis along with an account of the real estate

bubble in the US housing markets which was the single most important event that led to the crisis. The

recent global financial crisis has been the severest financial crisis we have seen since the Great Depression

of the 1930s. In its unwinding we witnessed the collapse of major investment banks Lehman Brothers and

Bear Stearns, the bailout of American International Group (AIG), America’s largest insurance company and




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that of Freddie Mac and Fannie Mac, America’s largest mortgage finance companies. The crisis quickly

spread to major financial institutions across the world with the British Bank Northern Rock witnessing a

run and banks like BNP Paribas, UBS, Citigroup, Merrill Lynch, Royal Bank of Scotland among others

with investments in sub-prime mortgage securities running losses into billions. The after effects of the crisis

have been a global credit crunch and a period of slower economic growth in the economies across the

world.

The primary event behind the financial crisis was the bursting of a speculative bubble in the housing

markets in the US. A speculative bubble is formed when the current market value of an asset is far greater

than the present value of its discounted payoffs into the future. A study of the housing bubble is presented

in Shiller (2008). Shiller constructs a price index of real home prices dating from 1890 and showed that real

home prices in the United States had increased 85% between 1997 and its peak in 2006. He found no

corresponding changes in building costs, population and interest rates to account for this sharp increase in

home prices. His studies show that home prices were clearly out of line with fundamentals and that there

was a resulting bubble. The price increases were also found to be the highest in the lowest price tier homes

which were in line with the growing expansion of subprime loans to lower income buyers to finance

purchase of low priced homes. This bubble in the housing market was fuelled further by access to easy

credit by the banks to anyone who wanted to buy a house with very little effort to assess the borrower’s

ability to repay their loans. Shiller brings out that the lending standards were so lax that the mortgage

lenders often failed to even verify the borrower’s incomes with the Internal Revenue Service in spite of

them having signed authorisation forms which gave them the right to.

These mortgages after they had been made did not remain on the books of the banks but were bundled into

tradable securities known as Collateral Debt Obligations (CDO’s) often backed by high credit ratings from

a ratings agency and sold to investors through a Special Purpose Vehicle (SPV). Such a model of banking is

known as the “originate and distribute model” of banking and the process of selling mortgage backed

securities known as securitisation. Keys et al. (2008) empirically show that portfolios that are more likely to

be securitised (by way of having a score higher than an ad-hoc credit score) defaults by 10%-25% more

than a portfolio of a similar risk profile but with a lower probability of securitisation and conclude that the

securitisation practises did adversely effect the screening incentives of lenders. All of the above factors

together with ineffective regulation to deal with the declining standards in lending and the proliferation of

complex mortgage backed securities that allowed banks to transfer the risk of holding these mortgages off

their balance sheet further fuelled the speculative fervour in the housing markets.

3.The Psychology of the Real Estate Bubble

Shiller (2007) argue that the boom in the housing markets from 2000 onwards was largely driven by

extravagant expectations of further price increases. Using data from questionnaires surveys for two major

US cities he finds that in times and places of high price changes, expectations of future price increases were

higher. Moreover he shows that as the rate of price increases changes, the expectations of future prices

increases are also altered in the direction of the change.

Further, he argues that the declining standards in lending and the proliferation of complex mortgage backed

securities were a result of the institutional changes that resulted during the boom and concludes that there is

a “coordination problem with psychological expectations” during periods of boom in that people find it

hard to alter their expectations of future price increases since they find it difficult to coordinate on a time to

alter their expectations inferring from the expectations of other investors.

In line with previous arguments Shiller (2008) attribute the boom in the housing market to a “social

contagion of boom thinking” and “new era stories” in the belief that home prices would continue to rise

forever, this belief being further strengthened by the media with its overly optimistic stories around the

price increases. He calls this a “price-story-price” feedback loop that takes place repeatedly during a

speculative bubble. Taking a cue from Shiller, I will draw on a model of “information cascades” by

Bikchandani et al. (1992) to provide a understanding of how such a feedback loop could be formed.




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4.The Models

In line with the arguments of behavioural finance, I draw on two models to elucidate further “limits to

arbitrage” and “investor psychology” in relation to how they can explain the underlying reasons for the

housing bubble and the financial crisis that followed. I will now draw on two models, one by De Long et

al. (1990) which shows arbitrage is risky and hence of limited use in bringing prices in line with

fundamental values and then a model of herding by Bikchandani et al. (1992) which shows how investor’s

paying little attention to their own private signals and basing their actions on the observed actions of those

before them can create a bubble. The two models together I feel can explain well how a bubble in the

housing market was formed and what allowed it to persist before its subsequent bursting leading to the

recent financial crisis.

While accounts of the crisis as discussed earlier place the cause of the crisis on declining lending standards

practised by the banks, proliferation of complex mortgage backed securities, improper assessment of the

risks of these securities by the credit rating agencies, incentive issues with the mortgage originators and the

ratings agencies and ineffective regulation to address all of these, I would like to take the view of Shiller

(2007) and think that these were not the driving forces behind the bubble but a result of the ensuing

circumstances that may have prevailed in a period of euphoria and confidence among investors in the

housing market founded in the optimism that the prices would always rise.

4.1.1 A model of herding by Bikchandani et al. (1992)

Bikchandani et al. (1992) describe a model of herding by way of an “information cascade”. They describe

an information cascade as occurring when individuals reject their own private signals and adopt the actions

of those ahead of them. Thus in the absence of any external disturbances all individuals adopt the same

action leading to conformity of behaviour among individuals. Bikchandani et al. (1992) however also

point out that the resulting conformity of behaviour can be “fragile and idiosyncratic” resulting in a shift in

action in the event people’s expectations shift. Without going into the formal details, Bikchandani et al.

(1992) thus essentially show that cascades prevents the useful aggregation of individual actions to inform

decisions of later individuals, as when a cascade starts individuals disregard their own private signals and

act on the actions of those before leaving behind no useful information available.

4.1.2 Application to the Financial Crisis

The model on information cascades can be applied to explain the real estate bubble. Consider an investor

with a private signal H (High) on the housing markets who adopts the signal at a cost equal to the

investment cost which maybe assumed to be the same for all investors. Considering a scenario of an

optimistic outlook towards real estate investments with the conviction that real estate prices would always

rise, it is plausible to assume that investors following would in the event of an H signal invest and in the

event of an L (Low) signal still choose to invest leading to a rush for investments in the housing market.

This created a scenario for banks to relax their lending standards and provide easy credit in the form of

subprime loans which further fuelled this rush for investments. Such a scenario of investors rushing to take

up mortgages could be seen as an UP cascade in which investors simply based their investment decisions

on the decisions of those before them ignoring their own private signal. Such a scenario also seems to agree

well with the argument presented earlier by Shiller (2007) on “coordination problem with psychological

expectations” during periods of boom in that people find it hard to alter their expectations of future price

increases. Moreover investors with a low private signal could have been led to ignore their signals due to

banks extending easy credit making these investors less risk averse than they would have been. One could

also attribute the disregard of a low private signal to “regret avoidance” in missing a good investment. The

model could also be extended to explain the behaviour of banks and institutional investors who invested in

the SIVs and the CDOs. The high credit ratings given by the rating agencies to most of these SIVs and

CDOs could also have an effect in the decision of the banks and institutional investors to ignore their




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private signals and invest in these securities which may have led to an UP cascade in subprime investments.

Danielsson (2008) argue that the ratings produced by the credit rating agencies on the CDO’s and other

Structures Investment Vehicles (SIVs) were incorrect assessments of their risks due to their underestimation

of default correlations which resulted in the risk of defaults being highly correlated in the event of a

downturn.

One of the key results of their model “that as the number of individuals increases, the probability of not

being in a cascade falls exponentially” could explain how the scale of investments in subprime mortgages

built up to the levels that were sufficient to trigger a global financial crisis. As more and more individuals

took up mortgages and more and more institutions invested in mortgage backed securities the probability of

another individual or institution making the same decision could have become more and more likely. These

arguments also fall in line with the arguments of Shiller (2008) that attribute the boom in the housing

market to a “social contagion of boom thinking” in the belief that home prices would continue to rise

forever and a “price-story-price” feedback loop that takes place repeatedly during a speculative bubble.

The characteristics of “fragility” and shifts in equilibrium in the event of changing expectations could serve

as an explanation to the sudden change in investor sentiment which led to the bursting of the housing

bubble followed by a period of a DOWN cascade during the ensuing financial crisis. Hence the model by

Bikchandani et al. (1992) on information cascades agrees well with the notions of “social contagion” and

“price-story-price” feedback loops founded in social/investor psychology and offers a plausible explanation

to formation and the bursting of the real estate bubble that preceded the crisis.

Danielsson (2009) highlight the problem of endogenous risk that financial markets are susceptible to.

Endogenous risk is defined as the risk arising from the actions of market participants in response to an

exogenous event. An exogenous shock may increase the risk aversion of market participants who may

chose to sell their holdings leading to decline in asset prices and further increase in risk aversion among

other investors resulting in further sales and price declines and thus result in a systemic crisis of the nature

we witnessed during the recent financial crisis.

4.2.1 A model on the limits to arbitrage by De Long et al. (1990)

I next draw on the theory of limits to arbitrage to understand why such a bubble persisted and why if prices

were going out of line with fundamentals were they not brought in line by rational arbitrageurs. I draw on a

model by De Long et al. (1990).

De Long et al. (1990) model the behaviour of two investor classes namely “noise traders”, who have false

beliefs that they know the future price of a risky asset and sophisticated investors who seek the take

advantage of the erroneous beliefs of the noise traders by following investment strategies that drive prices

towards fundamentals. Without going into the formal details, De Long et al. (1990) thus essentially show

that arbitrage to bring prices in line with fundamentals is limited due to presence of “noise trader risk”, in

that any arbitrage would come with the risk of the noise traders misperceptions being more extreme (and in

directions uncertain) thus driving prices further out of line with fundamentals tomorrow than today.

4.2.2 Application to the Financial Crisis

As detailed earlier, the precursor to the global financial crisis was a rapid boom in home prices in the US

which resulted in a bubble in a housing market and the resulting proliferation of easy credit, lax lending

standards and the use by banks of derivative products to transmit their credit risk to other financial

institutions and investors thereby exposing the entire financial system to a systemic risk in the event of a

downturn in home prices.

If however home prices were driven out of line with fundamentals why were these not brought in line with

fundamental values as efficient market theory would have us believe? Why did mispricing persist to the

lengths that led to a bubble and a major financial crisis? An answer to this may lie as argued earlier with

reference to De Long et al. (1990) in the limited ability of arbitrageurs to bring a mispricing in line with

fundamental values. Although the model of De Long et al. (1990) applied primarily to the stock markets,




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the same limitations of a mispricing worsening or being too unpredictable to take a position against, I think

applied to the housing market as well. I detail other factors that may have limited arbitrage in the housing

markets.

Farlow (2003) argue that arbitrage maybe limited by fundamental risk (which may also result due to an

inability to define a fundamental value leading to uncertainty in an arbitrage strategy and the resulting gains)

by citing the housing market as an example where there is imperfect information of the value of the

fundamental asset and also no close substitute to hedge thereby limiting arbitrage. Farlow also point to

“noise trader risk” in the housing market due to uncertainty in the length of the mispricing and argues that a

bank choosing not to lend (and thereby taking a position against the mispricing) in an upturn faces the risk

of profits eroding by way of losing market share to other banks which are lending. Farlow point out to

“horizon risk” faced by individual investors who may not be incentivised to put off a purchase or to sell out

of a housing investment due to uncertainty in the fundamental price and the length of the mispricing before

it corrects.

The model of De Long et al. (1990) and the arguments by Farlow (2003) hence show that arbitrage against

mispricing is costly and hence limited due to fundamental and noise trader risks which may explain why

banks kept on lending and other financial institutions kept on investing substantially in housing related

securities during the boom thereby feeding the subsequent bubble. The recent bubble in the housing

markets may thus be seen as such an example of the inability of arbitrageurs to correct a mispricing and the

mispricing persisting for a significant length of time to allow the formation of a bubble.

5. Conclusion and Policy Implications

The recent global financial crisis has been the worst financial crisis we have witnessed since the Great

Depression of the 1930’s. In its aftermath we have witnessed a global credit crunch and a prolonged period

of slow growth in economies across the world, the effects of which we are still under. While numerous

accounts of the crisis place the blame of the crisis (to cite a few) on the easy lending standards adopted by

the banks, the proliferation of complex mortgage securities underwritten by these banks and their improper

assessment by the credit rating agencies, the problems with the incentive structure of the mortgage

originators and the ratings agencies and the improper regulation of these practices by the regulators together

with an expansionary monetary policy followed by the major central banks in the US and UK among other

economies, these accounts by themselves only provide a partial and limited explanation to the crisis.

Behavioural finance could provide a more holistic understanding of the underlying factors that led to such a

scenario of events in the first place by focussing on the individual and social psychology that underlies any

period of excessive investor optimism or panic. A behavioural finance framework to explain the current

crisis must then focus on the underlying investor psychology that drove the real estate bubble and the

limitations faced by rational arbitrageurs to correct this bubble. Drawing on Shiller (2007) what seems to

have been the underlying cause of the bubble was a psychology of extravagant expectations of price

increases leading to a “coordination problem with psychological expectations” which limited people’s

ability to correct their overly optimistic expectations resulting in a “social contagion of boom thinking”.

What may have followed was an “information cascade” of the nature shown by Bikchandani et al. (1992)

resulting in investors copying the actions of others before leading to a huge rush of investments in the

housing markets and a “price-story-price” feedback loop that fed the resulting bubble. Further any attempts

to correct any mispricing may also have been limited as argued by Farlow (2003) due to inability to define

a fundamental value for housing and there being no close substitutes to hedge against and the risks to

arbitrage due to mispricing from fundamentals worsening as shown by De Long et al. (1990).

Policy Implications

Adopting a behavioural finance view taking into account individual and social psychology and the

limitations to arbitrage can help us design policy more in line with the underlying psychological forces that

drive the behaviour of market participants.




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Such policies could begin by having in place a monetary policy that takes a more proactive role in curbing

speculation. Shiller (2005) describe a novel way to do this by having “symbolic” increases in interest rates

accompanying a statement cautioning against speculation whenever markets are perceived to be overpriced.

Authorities could also help safeguard individual investors against excessive risk taking by way of educating

investors on effective hedging and diversification and cautioning them against “expert advice”

rampant in the finance news media. Effective regulation can also focus more on dealing with the

endogenous nature of market risk and its systemic effects. Lastly, financial technology and innovation can

expand into other new markets and products that incentivise analysts to concentrate on forecasting

fundamentals rather than simply forecasting the price levels. One such market proposed by Brennan (1998)

called “S&P 500 Strips” is a market for future annual total dividends of aggregate S&P 500 firms.

To conclude behavioural finance with its application of the insights on individual and social psychology to

finance can help increase our understanding of less than rational behaviour among investors and the less

than rational market outcomes in the form of speculative bubbles. This is however not to say that

behavioural finance is an alternative to the efficient market hypothesis which has its own merits, rather it is

a step towards a more integrated approach to finance by placing at the centre not a financial model in

abstraction but an individual and collective psychology that is the ultimate driver of all economic activity.

Such an integrated approach could inform policymakers and regulators to design policies and regulations

that check speculative excesses while not curbing financial innovation and market expansion that have the

potential to quickly and effectively correct excessive “irrational exuberance” and in doing so prevent events

like the financial crisis.

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Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and

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100(5):992-1026

Brennan, Michael. (1998), “Stripping the S&P 500”, Financial Analysts Journal, 54(1):12-22

Danielsson, J. (2009), “The myth of the riskometer”, voxeu.org

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De Long, J.B., A. Shleifer, L. Summers and R. Waldmann (1990), “Noise trader risk in financial markets”,

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Fama, E., and K. French (1993), “Common risk factors in the returns of bonds and stocks”, Journal of

Financial Economics 33:3−56.

Farlow, A. (2004), “The UK Housing Market: Bubbles and Buyers”, Credit Suisse First Boston, Jan 2004.

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market efficiency”, Journal of Finance 48:65−91.

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explanations,” Journal of Finance, 54, pp. 699-720.

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47:263−291.

Northcraft, G. B. and M. A. Neale (1987). “Experts, Amateurs, and Real Estate: An

Anchoring-and-Adjustment Perspective on Property Pricing Decisions,” Organizational Behavior and

HumanDecision Processes, 39: 84–97.

Odean, T. (1998), “Are investors reluctant to realize their losses?”, Journal of Finance 53:1775−1798.

Rozeff, M.S. and Kinney, W.R. (1976) Capital Market Seasonality: The Case of Stock Returns, Journal of

Financial Economics, 3, 379-402.

Shefrin, H. and M. Statman (1985). “The Disposition to Sell Winners Too Early and Ride Losers Too

Long,” Journal of Finance, 40: 777–790.

Shiller,R. (2008), The Subprime Solution, (Princeton University Press).

Shiller, R. (2007), "Understanding Recent Trends in House Prices and Home Ownership" Cowles

Foundation Discussion Paper No. 1630 [46pp]

Shiller,R. (2005), Irrational Exuberance, Second Edition (Princeton University Press).

Shleifer, A. (2000), Inefficient Markets: An Introduction to Behavioral Finance (Oxford University Press).

Shiller, R. (1998) "Human Behavior and the Efficiency of the Financial System" Cowles Foundation

Discussion Paper No. 1025 [34 pp]

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Cowles Foundation Discussion Paper No. 853 [43pp]

Shiller, R. (1981), “Do stock prices move too much to be justified by subsequent changes in

dividends?”,American Economic Review 71:421−436.

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Probing Study on Facilities of Competitive Sports in District

Jail, Lucknow (India)

S. Tariq Murtaza

Department of Physical Health and Sports Education,

Aligarh Muslim University, Aligarh, 202002, (U.P.), India.

E-mail: [email protected]

Riyaj Uddin



[email protected]

Mohd. Imran (Corresponding Author)




Mohd. Arshad Bari







The authors would like to acknowledge the cooperation of UGC-SAP (DRS-I) Programme, Department of

Physical Health and Sports Education, Aligarh Muslim University, Aligarh

Abstract:

The purpose of this study was to evaluate organizational structure, administrative frame-work and

facilities of Sports in District Jail of Lucknow in Uttar Pradesh, India. The sample of the present study was

drawn randomly from the jail administrators, prisoners and physical educator(s) of District Jail of Lucknow.

The size of the sample was 55 comprising 50 prisoners and 5 administrators. No physical educator(s) was

found in the jail. Questionnaire taken for the study was developed by the researchers in a pilot study. It

includes five sub-scales (a) Organizational Structure, (b) Administrative Frame-work, (c) Facilities of

Sports, (d) Preference of Sports, and (e) Achievements in Sports. The percentile method was used in the

analysis of the results. The data indicates that some of the inmate information’s intended to continue their

sports involvement following their release whereas some of the administrators recorded their responses to







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prescribe the Government of India for the proper sports facilities in their jail. Many of the inmates would

rather watch sports events on television rather than participate in one themselves.

Keywords: Organization; Facilities; Prisoners; Administrators; District Jail, Lucknow.

1.Introduction:

A century of literature documents the effects of sports and physical activity on antisocial behavior through

the targeting of underlying risk and protective factors and/or explicit behavior (Reid et al. 1994). Together

with the obvious physiological benefits, sports has been shown to improve emotional and cognitive skills

including self-esteem and problem-solving (Collis & Griffin 1993; Danish & Nellen 1997; Novick &

Glasgow 1993; Oman & Duncan 1995; Reid et al. 1994; Ryckman & Hamel 1995; Siegenthaler &

Gonzalez 1997; Svoboda 1995; Ykema 2002). These improvements can impact directly on behavioral risk

factors and, as such, sports may be a useful intervention strategy in reducing antisocial behavior. Two key

aspects of sports and physical activity are that they: Reduce boredom in youth; and Decrease the amount of

unsupervised leisure time. Preventing and reducing boredom is important due to its reported links to

depression, distractibility and loneliness (Coalter et al. 2000, Reid et al. 1994).

Diversion theories propose that participation in sport (and sport-based projects) can reduce the

opportunities for young people to commit crime and provide alternative ‘lifestyle choices’ (Drugs Strategy

Directorate, 2003). Sports programmes in this context aim at ‘the casual integration of youth at risk, in

order to reduce delinquency rates by encouraging the positive use of their leisure time’ (Robins, 1990).

Crime prevention is not the primary objective of sport and physical activity, but it might be an extremely

important by-product. This paper examines a variety of sporting activities that appear to have had beneficial

effect in helping young people steer away from trouble. It examines wilderness programs, programs in

which youths participate and learn skills, and programs in which the sense of belonging reduces vandalism

and develops pro-social behaviours. Of particular interest are sports carnivals in Aboriginal communities.

When the carnivals (organised and run by Aborigines for Aborigines) are held, they act as catalysts for

social and traditional cohesion. Harmful behaviours such as petrol sniffing, heavy drinking, and violence

are prohibited for the duration of the carnival, and the prohibitions hold in the short term. (Cameron and

MacDougall, 2000). Rehabilitation theories are generally based on the proposition that participation in

physical activity and sport can lead to improvements in mental health and pro-social behavior, which can

lead to a reduction in the propensity to commit crime (Taylor et al., 2000). Prisons can even be used to

improve the health of prisoners by subjecting them for a longer period to a regime that forces them to take

care and to improve their health (World Health Organization, 2004).

The success of recreational activities as a means of preventing violence and property crime has not been

evaluated in India so far. A few programs have been assessed internationally, and they show encouraging

effects on the reduction of offending and drug use (Sherman 1997). At present, theories of crime and

delinquency have not been fully integrated into the sports literature. However, there are a number of

studies that have linked sport to crime reduction. Three main channels have been identified. Firstly,

sport can help change behavior, increase self-esteem and self-control, instill discipline and help socialize

participants. Secondly, sport can provide a means of diverting people, especially young people, from

committing crime. Thirdly, sport can provide a means of rehabilitating offenders (Bailey, 2005; Coalter et

al., 2000). Arnie Caplan (1996) provides a very thorough historical review of Correctional Recreation and

Prior Correctional Recreation research in both the U.S. and Canada. Peter M. Carlson (2001) states that in

relation to the Zimmer Amendment which limited weightlifting in federal prisons, "Prison authorities have

long subscribed to the notion that positive activities for confined offenders keep them productively

occupied and less inclined to other nefarious interests” Accordingly, nearly all correctional agencies

throughout the country attempt to offer a recreation program among other activities that assists the personal

growth of inmates and facilitates the orderly management of institutions".

2. Procedure & Methodology:




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2.1 A Pilot Study:

A pilot study was taken by the researchers for the standardization of the questionnaire of the proposed study.

The research revived related literature, Uttar Pradesh Prison Manual, magazines, periodicals etc. and

interviewed and discuss related to the field. After thorough review literature & discussions, the researchers

formed a set of questions for the proposed study. Then the questionnaires were sent to as many as 50

persons of related field in order to determine the face validity of the questionnaires. Out of 50 experts 45

(90%) returned the questionnaires after ticking the appropriate questions. Common questions from all the

experts were included in the questionnaires.

After constructing the questionnaires, the researchers personally visited two district Jails i.e. Aligarh &

Moradabad in Uttar Pradesh for standardizing the questionnaires based on findings obtained in a pilot study.

The questionnaires were taken as valid by virtue of face validity. The questionnaires were constructed in

such a way that they cover important aspects of the study. The researcher formed questionnaires for jail

administrations, physical educator(s) and prisoners separately. The questionnaire for jail administrators and

physical educator(s) intend to check administrative feasibility and comprised 30 and 22 questions each.

Whereas the questionnaire for the prisoners intends to check the practical feasibility of available sports

facilities and comprised 22 questions. Under normal circumstances, administrators and prisoners will take

about 15 minutes and 25 minutes respectively.

2.2 Sample:

The sample of the present study was drawn randomly from the superintendent of jail and other

administrators, prisoners and physical educators of District Jail, Lucknow in Uttar Pradesh, India. The size

of the sample was 55 comprising 50 prisoners and 5 administrators. No physical educator was found in the

jail.

2.3 Procedure:

After obtaining permission from Director General of Prison, Uttar Pradesh, Lucknow, the researchers

contacted jail authorities of District Jail, Lucknow in Uttar Pradesh, India and discussed procedural matters

and other important points regarding the proposed study. The questionnaires were filled by the

administrative staff as well as prisoners of District Jail, Lucknow, Uttar Pradesh, India. It has the capacity

of 3300 inmates but at present it housed 3064 persons (U.P. Jail Administration, 2010). All the senior

inmates co-operated with the researcher and helped in filling-up required questionnaire from all the inmates.

The researcher, alongwith obtaining responses by the questionnaire, also interviewed the selected

population.

3. Analysis and interpretation of data:

The present study intends to explore the organizational structure, administrative frame-work and facilities

of sports in District Jail, Lucknow in Uttar Pradesh, India. The dependability and generalizability of the

findings of any research study, to a large extent, are determined by the techniques used for analysis and

interpretation of data. The data collected were subjected to percentile technique because of the nature of the

study. The analysis of the data is presented by using the variables of the study which are as follows: (a)

Organizational structure, (b) Administrative frame-work, (c) Facilities of

sports, (d) Preference of sports, and (e) Achievements in sports.

4. (1) Analysis of Data of Administrators of Jail:

4.1 Organizational Structure: 55 per cent of Jail administrators of District Jail Lucknow agreed that

there were sports facilities in their jail whereas 35 per cent did not agree with the questions asked related

with the organization of sports in jail. 10 per cent of administrators did not have any idea about the

organization of sports in jail.

4.2 Administrative Frame-work: 4.55 per cent administrators stated that they had sent some sort of

proposals related with sports to the Government of India whereas 50 per cent said that they had never

submitted any proposal regarding sports to Government of India. With same ratio of per cent of jail

administrators reported that Government relieved the punishment period for national level players. 45.45




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per cent administrators did not have any idea about the administrative frame-work of sports in jail.

4.3 Facilities of Sports: 20 per cent of the administrators informed about the facilities of sports in jail in

affirmation whereas 72.50 per cent told that there is no facility available for sports in jail. Remaining

administrators record their responses as they did not know about the existing facilities of sports.

4.4 Preference of Sports: 35 per cent of administrators preferred sports facilities and its governance in

their jails whereas 45 per cent recorded that they did not prefer facilities for sports in their jail. 20 per cent

of administrators recorded that they did not have any idea about the preferred facilities for sports in their

jail.

4.5 Achievement of Sports: 100 per cent of administrators responded that their inmates participated at

others tournaments.

The result is shown in the following graph no. 01

Graph-01

Analysis of Data for Administrators




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5535

10 4.55

50 45.45

20

72.5

7.5

3545

200 0

100

020

406080

100120

Yes

No

Don't K

now

Yes

No

Don't K

now

Yes

No

Don't K

now

Yes

No

Don't K

now

Inte

r-Jail

Sta

te L

evel

Oth

er

Tourn

am

ent

(a) Organizational

Structure

(b) Administrative

Frame-work

(c) Facilities of

Sports

(d) Preference of

Sports

(e) Achievements of

Sports

Pe

rce

nta

ge

5. Analysis of Data of Prisoners of Jail:

5.1 Organizational Structure: 44 per cent of prisoners of District Jail Lucknow agreed that there were

sports facilities in their jail whereas 56 per cent did not agree with the questions asked related with the

organization of sports in jail.

5.2 Facilities of sports: 15.60 per cent of the prisoners informed about the facilities of sports in jail in

affirmation whereas 76 per cent told that there is no facility available for sports in jail. Remaining prisoners

recorded their responses as they did not know about the existing facilities of sports.

5.3 Preference of Sports: 40 per cent of prisoners preferred sports facilities and its governance in their jails

whereas 56 per cent recorded that they did not prefer having facilities for sports. 04 per cent recorded that

they did not have any idea about the prefer facilities for sports in their jail.

5.4 Achievement of Sports: 100 per cent of prisoners responded that they had participated in other

tournaments.

The result is shown in the following graph no. 02




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Graph-02

Analysis of Data for Prisoners

4456

015.6

76

8.4

4056

4 0 0

100

020406080

100120

Ye

s

No

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(b) Facilities

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(c )

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(d)

Achievement

of Sports

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6. Discussion:

It is quite difficult to assess the accuracy of the responses especially of the prisoners because of the fact that

they are living under the jail administration. There is an extensive literature on other programmes which

have been introduced in to prisons to reform inmates. The general conclusion from the researcher’s survey

of a District Jail, Lucknow is that there is a dearth of sports facilities in the jail. Many inmate respondents

did not express the view that recreational sports programs in prison were beneficial for their long-term

rehabilitation. They all felt that sports facilities must be an integral part of their daily prison life for several

other reasons. Almost all the respondents were very adamant in their belief that the availability of sports

facilities & its related programs in prison are an absolute necessity. An over whelming majority of these

issues tend to have a major impact on social control within the correctional system.

Most respondents do not take part in any of the sports programs with a focus on their rehabilitation; they do

so because they don’t have to do anything else and the accessibility of these facilities, if any. Respondents

in the study claim that these programmes of sports or yoga camps help relieve stress, frustration and

alleviate boredom, they help develop and maintain self-esteem, and they help create friendships of common

interests inside the prison.

The data indicates that some of the inmate information’s intended to continue their sports involvement

following their release whereas some of the administrators recorded their responses to prescribe the

Government of India for the proper sports facilities in their jail. Many of the inmates would rather watch




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sports events on television rather than participate in one themselves. There was no physical educator in the

jail, even though Model Prison Manual (2003) of India suggests that every jail should have a general as

well as physical educator.

7. Conclusion:

The inmates of today continue to express their discontent regarding prison life. They argue that, just

because they have been courted in some cases, that doesn’t make them vulnerable of maltreatment in the

society. Living condition, food, & recreation are the main aspects which are often referred to by inmates as

their “rights” as human beings. Government & Non-Government Organizations alongwith other responsible

citizens of our country should extend their hands to ameliorate the conditions of sports facilities in our jails

throughout country, so that the inmates may be less stressful and more amicable towards society.

8. Recommendations:

There should be a post of physical Training Instructor to conduct program of demanding physical activities

for Jail inmates. Along with the unemployment and poverty, sports and recreation programs should be

viewed as the establishment of a general strategy to improve the opportunity of improvement in jail inmates.

Recommendations are made for the development of sports or recreation programs as a preventative role for

youth-at-risk; and the development of sporting and recreation facilities and opportunities for inmates

including financial assistance for sports facilities and development of any existing facilities. Government

should allocate separate budget for intensive sports programs aiming for the rehabilitation and to promote

wellness, a healthy life style and decrease medical treatment as envisaged by the World Health

Organization (2003).

References:

Arnie Caplan (1996) The Role of Recreation in the Federal Prison System. Thesis, Acadia University,

Canada.

Bailey, R (2005) Evaluating the Relationship between Physical Education, Sport and Social Inclusion,

Educational Review, Vol.57, no.1, pp71-90.

Cameron, M. and MacDougall, C. (2000) "Crime Prevention through Sport and Physical Activity",

Australian Institute of Criminology, Trends & Issues in Crime and Criminal Justice, No. 165.

Coalter, F, Allison, M and Taylor, J (2000) The Role of Sport in Regenerating Deprived Urban Areas,

Edinburgh, Scottish Office Central Research Unit

Collis, M. & Griffin, M. (1993) "Developing a course for young offenders", Youth Studies Australia ,

vol. 12, no. 3, pp. 25-8.

Danish, S.J. & Nellen, V.C. (1997) "New roles for sport psychologists: Teaching life skills through sport to

at-risk youth", Quest, vol. 49, pp. 100-13.

Drugs Strategy Directorate (2003) Cul-de-Sacs and Gateways: Understanding the Positive Futures

Approach, London, Home Office.

Model Prison Manual (2003). For the Superintendents and Management of Prisons In India, Formulated By

Bureau of Police Research and Development Ministry of Home Affairs Government of India New Delhi.

Chapter-IV.03.4(b), pp. 44




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Novick, M. & Glasgow, A. (1993) "Shaftesbury Youth Program: A model for early intervention", Youth

Studies Australia, vol. 12, no. 3, pp. 29-30.

Oman, R.F. & Duncan, T.E. (1995) "Women and exercise: An investigation of the roles of social support,

self-efficacy and hardiness", Medicine, Exercise, Nutrition and Health , pp. 306-15.

Peter M Carlson (2001) Unwanted Change: Legislation’s Impact on Adult Corrections Management. The

Public Manger Vol. 30. N0. 1. Spring Pgs. 49-54.

Reid, I., Tremblay, M., Pelletier, R. & MacKay, S. (1994) Canadian Youth: Does Activity Reduce

Risk? An Analysis of the Impact and Benefits of Physical Activity/Recreation on Canadian

Youth-at-risk, joint initiative of the Inter-Provincial Sport and Recreation Council, the Fitness

Directorate of Health Canada, and the Canadian Parks/Recreation Association.

Robins, D. (1990) Sport as Prevention: The Role of Sport in Crime Prevention Programmes Aimed at

Young People. Oxford: Occasional Paper, 12, Centre for Criminological Research, University of Oxford.

p. 19

Ryckman, R.M. & Hamel, J. (1995) "Male and female adolescents' motives related to involvement in

organised team sports", International Journal of Sports Psychology, vol. 26, pp. 383-97.

Sherman, L. (1997), “Communities and Crime Prevention”. Preventing Crime: What Works, What Doesn’t,

What’s Promising, National Institute of Justice, United States Department of Justice, Office of Justice

Programs, Research Report NCJ 165366, pp 3.1-3.49.

Siegenthaler, K.L. & Gonzalez, G.L. (1997) "Youth sports as serious leisure", Journal of Sport and

Social Issues, vol. 21, no. 3, pp. 298-314.

Svoboda, B. (1995) "Scientific review part 1", in I. Vuori, P. Fentem, B. Svoboda, G. Patriksson, W. Andreff

& W. Weber, The Significance of Sport for Society: Health, Socialization, Economy , Council of

Europe Press, Strasbourg.

Taylor P., Crow I., Irvine D., Nichols G., (2000). Demanding Physical Activity Programmes for Young

Offenders under Probation Supervision. London, Home Office;

U.P. Jail Administration (2010). Headquarter Prison, Pickup Bhawan, Lucknow. At

http://www.upprisons.up.in/prison_list.pdf Accessed on 21 September, 2010 at 11:27 a.m., I.S.T.

World Health Organization (2003). Health and development though physical activity and sport. Geneva,

Wito document production services.

World Health Organization (2004) PROCEEDINGS of the International Conference on Prison and Health.

De Leeuwenhorst, the Netherlands 21 October, Organized

Ykema, F. (2002) The Rock and Water Programme: A Psycho-physical Method that Does Boys

Justice: A Summary, Social Pedagogic Hulpverlening (Socio-Pedagogical Assistance), Rotterdam.


http://www./



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Relationship of Job involvement with Employee Performance:

Moderating role of Attitude

Muhammad Rizwan (Corresponding author)

MS Scholar, Department of Management Sciences, IQRA University

Islamabad, Pakistan

Tel: +92-300-9687985 Email: [email protected]

Dil Jan Khan

MS Scholar, Department of Management Sciences, IQRA University, Islamabad, Pakistan


Fawad Saboor

MS Scholar, Department of Management Sciences, IQRA University, Islamabad, Pakistan





Abstract

Job involvement was considered very important in the previous literature for increasing the performance of

the employees. It is hypothesized that involved employees exert considerable efforts for achieving the goals

and objectives of the organization. These kinds of employees are highly productive and produce better

results as compare to the employees who are not involved with their job. These employees work for the

organization with their hand, head and heart. Several studies examined the relationship of job involvement

with employee performance and claimed that there is a positive relationship between these two variables. In

this paper we try to further explain the construct of job involvement and combined the results of different

studies in this context. This paper also tries to explain the controversies between the results of different

studies and propose to make more construct valid measure for job involvement. Further we also

conceptualize the relationship of employee attitude in this connection.

Keywords: Job involvement, Employee performance, Attitude, Empowerment, Knowledge, Information

1. Introduction

In previous years, a lot of interest was developed in the term job involvement. Many researchers claimed

that the employee involvement clearly forecast employee outcomes, organizational performance and

organization success. (Bates, 2004; Baumruk, 2004; Harter et al., 2002; Richman, 2006). Besides this it is

also claimed that the employee involvement is going to decrease in the recent years and employee are very

much disinvolved with their jobs (Bates, 2004; Richman, 2006). In the American workforce, it has been

argued that the most of the workers are not fully engaged or disengaged with their jobs which cost US

business to $300 billion a year by decreasing productivity and this phenomenon is referred to “involvement

gap” in the employees (Bates, 2004; Johnson, 2004; Kowalski, 2003). Job involvement is described by

kahn in a way that developing a behavior in the employee that connect him to work and to other actively

with personal presence (physical, emotional and cognitive) leads to full performance (1990: 700). Involved

employees attach and connect their full selves in the job, putting physical, cognitive and emotional efforts







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to complete their job for the organization. These type of employees can be recognized by their

psychologically presence, paying special attention, conscientious, sentimental, associated, integrated, and

focused on their job. Involved employees are open to themselves, with other employees and as well as with

the organization to put their complete selves to work (Kahn, 1992). Kahn described that the involvement

can be noted by observing the behaviors of the employees whether he is putting personal, physical and

emotion energy to complete the tasks (Kahn, 1992). To be very simple involvement means investing the

hand, head and heart to the job (Ashforth & Humphrey, 1995: 110) while working in the organization. This

concept of involvement is very motivational because it describes the use of the personal resources to

complete the tasks of the job and it also describe that by how much these forces are applied by the

employee (Kanfer, 1990). It also subsumes the old concept of determining the physical and cognitive forces

represented by the employee and the depth of engagement of the employee towards the organization. In

simple word it defines the level of involvement of the employee to perform the work physically and

cognitively as well as maintaining the efforts in a connected way instead of dispersed manner (Kahn, 1992).

So, according to kahn job involvement can be explained the continuous efforts in the shape of physical,

cognitive and emotional for full work performance. Job involvement as described by Hall and Mansfield

(1971) is a non manipulated property of a person. Weber (1958) explains it as individuality and the virtue of

work as an end (Brown, 1996). That means for the employees the work is a virtue of an end itself and

possesses high job ethic level. Consequently, these kinds of employees perceive the work as an important

part of their lives (Dubin, 1956; Rabinowitz and Hall, 1977). In this way, these kinds of employees dedicate

a significant amount of time towards their job and highly involved in their job.

(Lodhal, 1964; Lodhal and Kejner, 1965). In a study, Brown (1996) observes a strong correlation between

the job involvement and work ethics.

This study tries to find the relationship between the job involvement and employee performance. Although

the relationship was prove in many previous researches but the main aim of the study to introduce a new

moderating variable of attitude. Attitude is the liking or disliking of the employee toward his/her job. We

hypothesize that if the job does not match with the liking of the employee the organization feels more

difficulty to involve the employee in the job. As we said previously that the job involved employee has

been described as one whose job is an integral part of his/her self-definition.

1.1 Objectives

Employee involvement has become a hot topic in recent years among consulting firms and in the popular

business press. However, employee involvement has rarely been studied in the academic literature and

relatively little is known about its antecedents and consequences. The purpose of this study was to propose

a model of the antecedents and consequences of job involvement. The main objectives of the study are as

under:

a. To define and understand the variable of Job Involvement

b. To explore the antecedents of Job Involvement

c. To conceptualize the consequences of Job Involvement and how they are beneficial for the

organization

d. To check the impact of the attitude of the employee on Job Involvement

2. Literature Review

The term of job involvement can be described as ‘‘the degree to which one is cognitively preoccupied with,

engaged in, and concerned with one’s present job’’ (Paullay et al., 1994, p. 225). These kinds of employees

can be recognized by the level they feel that the job is an important aspect of their self definition. This

statement and the construct is a popular term and widely used in the literature of employee performance

(Robinson et al., 2004). However, a lot of work has been done by the practitioners and it can be found in

the journals where it is recognized mostly as a theory rather than put it into practices and develops some

empirical results. Robinson et al. (2004) argued that the most of the work is surprisingly attract low




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attention from the organization and becomes popular. This results in a way that the concept is going to be

faddish or just present in the academic literature rather than in practice. The situation becomes worst as the

term job involvement was described by different researcher in a diverse pattern and these descriptions were

very different from each other. Most of the times, these descriptions are similar to the term of

organizational commitment or organizational citizenship behavior (Robinson et al., 2004). Some

researchers also describes the term as intellectual and emotional commitment towards the organization

(Baumruk, 2004; Richman, 2006; Shaw, 2005) or by the degree of discretionary efforts exercised by the

employees in the organization (Frank et al., 2004). Different researcher explain the term by their own

perception, Kahn (1990, p. 694) define it as “the harnessing of organization members’ selves to their work

roles; in involvement, people employ and express themselves physically, cognitively, and emotionally

during role performances”. Putting it simply, the term involvement refers to the physical and mental

presence of the employee while doing the work in the organization.

2.1 Consequences of Job Involvement

The point of interest in the term job involvement is the final consequences of this phenomenon that can be

explained that if the workers put forth considerable efforts for the achievement of the personal and

organizational objectives, this will lead to more productivity and the employee ultimately retain with the

organization (e.g. Kahn, 1990; Kanungo, 1979; Lawler, 1986; Pfeffer, 1994). On the opposite side, the

employees who are having low degree of involvement are more likely to experience low job satisfaction

and inclined to leave the organization. Besides this if they remain with the organization they put their

efforts towards non productive work or apply their energy in such activities that are not beneficial for the

organization and the productivity decreases (Kanungo, 1979, p. 133). These kinds of employees engage

themselves in different undesirable activities. There are some empirical researches showing a relationship

between different work outcomes and the degree of job involvement. For example, in a research job

involvement has been observed to be negatively related to the turnover intention, positively related to

organizational commitment and also related to the extra-role behavior and employee performance

(Schaufeli and Bakker, 2004; Sonnentag, 2003).

They also observed that job involvement mediate the relationship between turnover intention and job

resources. Job involvement has been observed to have a vital affect on different import outcomes. For job

performance Lawler (1986), Kahn (1990) and Brown (1996) explained that different work behaviors of the

employees are the consequences of job involvement and it is hypothesized that involvement ultimately

impacts on employees’ efforts and motivation, which in turn explained performance. Meta analysis by

Brown (1996) estimated that the population correlation is non significant between the job involvement and

overall performance but the population correlation to be significant between the job involvement and

different combination measures of performance, although the coefficient was relatively small. In this

situation, different researchers have tried to discover the reasons for the complex relationship between the

job involvement and employee performance. On the similar task, Diefendorff et al. (2002) described the

reason that major previous researches were using the scale developed by either Lodahl & Kejner (1965) or

Kanungo (1982). These scales were supposed to be contaminated by some extraneous constructs (Kanungo,

1982; Paullay et al., 1994). Diefendorff et al. (2002) argued that the positive association between the job

involvement and performance can be found if the researchers used a more valid measure of job

involvement. In a study by Diefendorff et al. (2002), when used a measure developed by paullay et al.

which differentiate job involvement from work centrality, a significant positive correlation has been found

between supervisor rated in-role performance and job involvement. While developing more valid and

accurate scale for job involvement, researchers also need to focus on performance criteria to understand the

relation of involvement and performance.

2.2 Attitude

An attitude is a measurement of the degree that represents that the level of liking or disliking of a

person towards any item that can be a person, object, place or any event. Attitude can be

determined by the negative or positive emotion or feeling of a person towards any item and this




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item is referred to attitude object. Sometimes peoples are confused about towards any item that

they the item attracts both positive and negative attitudes. In today era of globalization, when the

information level of a person has been increased by considerable level this happened more

frequently. In this situation, it creates a lot of problems and challenges for human resource

practitioners and creates an extensive need for extensive studies to be conducted to better

understand this phenomenon in cross culture organizations and provide guidance (Erez, 1994;

House, 1995; Triandis, 1994). The considerable work on employee attitude had been done by

Hofstede (1980, 1985). He expanded his research on employee attitude among 67 countries and

verified that this construct has been separated into four groups and the countries varied on these

aspects. These categories are Individualism-collectivism, Risk taking, Power distance and

Masculinity-Femininity which now called achievement orientation. But when describing the job

involvement the high situational influence was considered as the work itself or “intrinsic job

characteristics”. Different researches present the same results that when the employees are

asked to rate the different factors of job like coworkers, advancement opportunities, environment,

pay, promotion, supervision and so forth, the most important factor was found the work itself

(Judge & Church, 2000; Jurgensen, 1978). These results does not mean that the others factors

are not important for the employees but the conclusion is that to influence the job satisfaction the

most important thing is the work itself. The work should be interesting and challenging for the

employees. While mostly managers think that salary is the most important factor and exclusion the

other job attributes such as the nature of work itself.

2.3 Operational Definition of Job Involvement

It was argued by different researchers that job involvement is an important factor for the success

of the organization and also for retaining the employee by decreasing the intention to leave but the

question is how the organizations help their employees to be more satisfied with the work and

become involved? This question was answered by Marcus et al. (2009) by presenting some

practices that can enhance the level of involvement of the employee. They propose four elements

for making the employee more involved in the job and with the organization.

Empowerment

Empowerment refers to the degree of decision making that can be handled by the employee while working

in the organization. These employees feel more confident in their ability and presume latitude over their

work. Consequently, empowerment heightens employee autonomy in their work.

Information

Information means data about the quantity and quality of business operations such as unit outputs, costs,

revenues, profitability and customer reactions. This includes developing a system in the organization by

which the employees become more informed about different aspects of the organization. In case of

providing information it is also included that the feedback should be provided to the employee about his/her

work.

Knowledge

Knowledge is different from information. Information is the data about the company which knowledge

refer to the level that the employee can evaluate and generate different inferences on these information.

That can be done by providing the opportunities of training and development. The competency level of the

employees should be enhance and upgrade to perform their duties well.

Rewards

Rewards are the financial or non financial benefits provided to the employees against their services to

enhance organizational performance. These rewards are also considered as an important tool to motivate the

employees and consequently the employees involved with their work.




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3. Proposed Model and Prepositions

After reading and reviewing the available data we hypothesize that the job involvement has an impact on

employee performance. The employees who are more involved in their job can produce better results for

the organization. But the results cannot generalize for all the employees. One moderating factor can be the

attitude of the employee, which has contingent effect on the relationship between the independent and

dependent variable.

P1: Information has significant positive impact on Job Involvement.

P2: Knowledge has significant positive impact on Job Involvement.

P3: Empowerment has significant positive impact on Job Involvement.

P4: Rewards has significant positive impact on Job Involvement.

P5: Job Involvement has significant positive impact on Employee Performance.

P6: Attitude affects the relationship between the job involvement and Employee performance.

4. Discussion & Conclusion

The primary theoretical contribution of this paper is that we extend the theory of Kahn (1990) by

considering that job involvement is an important aspect by which organization can increase the productivity

of the employees. These results are checked and verified by different studies that the there is a positive

relationship between the construct of job involvement and employee satisfaction and performance (for ex.

Schaufeli and Bakker, 2004; Sonnentag, 2003; Diefendorff et al., 2002; Brown, 1996; paullay et al., 1994;

Kahn, 1990). By high degree of involvement the employee produce more favorable results for the

organization. This will not only beneficial for the organization but also the satisfaction of the employee

increase as the level of involvement increases. We argued that Kahn’s theory should be considered as an

important explanation how the organization can involved the employee and what are the consequences of

this job involvement. If the employees are more involved in their jobs they will put extra efforts for the

completion of the organizational objectives. These employees exert high level of efforts during their jobs

and these efforts are comprises of physical, cognitive and emotional efforts. More involved employees are

supposed to be actively present on their job and avoid the activities which are undesirable for the

organization. In this way not only the productivity of the organization will increase but the employees will

be more satisfied with their working in the organization. This satisfaction leads to the level as explained by

Kahn that these employees invest their hand, head and heart on the job. The organization need to focus on

the strategies by which they can involve their employees and become a successful organization. The

Job

Involvement

Information

Knowledge

Empowerment

Rewards

Employee

Performance

Attitude




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possible antecedents of job involvement are well explained by the Butt and his colleagues that can be

helpful for the organization to create this kind of environment. While developing this kind of culture one

important thing is the attitude of the employee. The work should be design in a way that reflects the

positive feeling of the employee. The nature of work is very important for the satisfaction of the employees

and ultimately satisfaction will lead toward the higher degree of performance. So according to our theory

while making the employee more involved a universal approach is not so much useful and work should be

design according to the attitude of the employees. Many researches prove that the nature of work is one of

the most important factors considered by the employee and normally management neglect this factor and

suppose that salary are most important factor for the motivation of the employee. Attitude restricts us to

develop a universal approach for the involvement of the employees. We have to consider the attitude of

employee before going to check the involvement of the employee. The employee can easily be involved in

his work if we design the job according to the attitude of the employee. This means that the employee

should consider the job challenging and enjoyable. In this way the employee will be more involved and put

extra efforts for the fulfillment of the organizational goals. In this way the employee will engaged in the job

with the hand, head and heart as described by kahn in his research. By hand we means that the employee

exert physical efforts to complete the job. By head we means that the employee will feel satisfaction and

proud of doing the work in the organization. By heart we wants that the employee is not only working

physically but also emotions are involved in his work. In this way the employee can say that this is my kind

of job. If the organization produces this kind of environment in the office they can achieve their objectives

easily. This thing not only increases the efficiency level of the employee but also decrease the intention of

turnover and absenteeism. The impact of stress was also discussed in different studies that the stress level

will be low if the employee are involved in his work. Attitude of the employee is very important in this

context because the employee can be involved in case the job is according to his perception. That is very

difficult for the organization to involve the employee in such a job that is not suited to the employee

attitude. In this case the employee will do the work only to pass the time and remain in the organization and

the objectives of the organization will remain unattained. These kind of non involved employees are not the

requirement of the organization and these employees can not worth for the human capital of the

organization. The organization needs the employees who are not only doing the work physically but also

present mentally. These kinds of employees work physically, cognitively and emotionally.

5. Managerial Implication

The purpose of the research is to help the management in producing the desired results for the

organization. If the employees are involved in their work they can work more effectively and efficiently.

The involved employees put extra efforts for the completion of their task. These employees become

involved in the work not only physically but also emotionally and cognitively. So the organization can best

achieve their objectives and targets by these kinds of employees. The organizations also try to recruit the

employees who produce best results but by involving the existing employees the organization can do the

same. But involving employees can be easier by designing the characteristics of the job according to the

attitude of the employee. If the nature of the job matches with the attitude of the employee, these employees

will be more productive and helpful for the organization for achieving their objectives and goals. Job

involvement is a tool for the management that can be used for enhancing the performance of the company

and attaining the desired objectives. The current study describes it that the job design is important in this

aspect. Employees should be considered in this respect and make the work challenging and interesting for

them. In this way the employee can be more involved in their job and work effectively.

6. Recommendations

According to our conceptual frame work the management should be aware of the benefits of involving the

employees. That is very simple and applicable work that the employee should be involved in their work in

the organization. But the level of involvement that enhances the performance of the employee can be

achieved by considering the attitude of the employees. It needs to review and redesign the job according to




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the perception of the employees.

7. Future Research

This research is qualitative and exploratory in nature. In which we try to hypothesize that performance of

employees can be increased by involving the employees in the job. Along with this, management has to

consider the factor of attitude while developing the strategies for job involvement. There is a need to

conduct an empirical study with a substantial sample size to prove the relationship between discussed

variables.

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0.whole paper

Business

creativity of individuals

area of creativity

concept of creativity

creativity andinnovation

innovation amabile

firm level creativity

change amabile

knowledge management