the impact of electronic customer relationship...

European Journal of Economics, Finance and Administrative Sciences

ISSN 1450-2275 Issue 93 February, 2017

© FRDN Incorporated

http://www.europeanjournalofeconomicsfinanceandadministrativesciences.com

The Impact of Electronic Customer Relationship Management

(ECRM) Practices in Business Performance of

Jordanian Commercial Banks

Hani H. Al-Dmour

The University of Jorden

E-mail: [email protected]

Tel: 00962795666979

RawanKhawaja, R


Al-Dmour


Abstract

This study aims to develop an integrated framework to explore the influences

ofelectronic customer relationship management (appropriate process, customer data,

ECRM technology) upon business performance of Jordanian commercial banks in Amman

city. The study population was identified as branch managers, assistant branch managers

and head of departments in the branches in Jordanian commercial banks branches. The

study used the self-administrated questionnaire survey method in order to collect the

required data and to test the research hypotheses and to explore its implications. 130

questionnaires were disseminated to the targeted research sample. Multiple Regression

analysis, one way ANOVA and F. tests were applied to verify the research framework.

The results showed that the ECRM components (appropriate process, customer data

quality and ECRM system technology) would positively affect all the type of business

performance measures (financial, non-financial and combined). Furthermore, the research

results also showed that all these independent factorscan explain the variation of the two of

business performance measures together better than taken each one alone.

Keywords: ECRM, commercial banks, financial and non-financial performance

1. Introduction The service sector iswitnessing a significant expansion in today`s market place in terms of size and the

use of advance technology in both developed and emerging countries (Lovelock and Wirtz 2011). The

rapid development in information technology (IT) is permitting the service business to be extremely

developed and improved in service processes and operations. Banking sector is considered one of the

main service sectors that affect the market place. In every country a strong banking sector is very

important to stimulate economic growth and to maintain the financial stability of the country (AlSmadi

and AlWabel, 2011).

50 European Journal of Economics, Finance and Administrative Sciences Issue 93 (2017)

In Jordan the banking sector – as in many countries – is considered one of the main pillars of

the Jordanian economy. In Jordan the banking sector contributed around 18. 8% of gross domestic

product (GDP) at market prices in 2nd

quarter of 2015 (Awraq Investment, 2015). Hence the banking

sector motivated by the development of technology and information system (IS) revolution to increase

profitability by attracting more customers and retaining the existing by providing service quality and

meeting customers’ expectations (AlSmadi and AlWabel, 2011). In addition to IS revolution to

increase profitability banking few decades before has been changed to be customer centric oriented,

because customers considered as the most important asset in banks and should be retained and

increasing continuously (Sadek and colleges, 2012).

Today attracting new customers is more expensive than retaining the existing customer, for that

the banks oriented to grant their existing customers loyalty as it is critical for business continuity with

granted profit growing, therefore banks should meet their customers’ expectations and satisfaction “in

order to improve their loyalty” by seeking an effective and efficient management strategies. (Rootman

et al. , 2008). The most important key for any successful bank is “the customer”. Therefore, banks need

to meet their customer expectations by keeping a strong and long relationship with them and to manage

properly their data through an efficient and effective. Customer Relationship Management (CRM), will

help the banks to serve their clients in a better way by managing and categorizing the customer data

based on the valuable information related to him/her on the CRM system (Ahmad, 2009) Many researchers show the benefits of ECRM applications to the banks and their customers.

For example, Bezhovski and Hussain (2016) found that ECRM have decreased the work load, the

administrative cost, increase cross selling, increase bank revenue and to enable the bankers to

understand the customers’ future needs depending on past transactions. In addition, Scullin, et al.

(2002) posit that, well implemented ECRM system produces a winning customers and companies,

because over all improvements on customer experience leads to greater customer satisfaction which

will return on companies with positive effect “profitability”.

Bataineh (2015) also asserts that in order to cope with customers changing needs and to have

competitiveness advantages , banks should make all efforts to add value to their ECRM strategy to

create satisfied and loyal customer. Furthermore, he suggested that banks can enhance their ECRM

strategy from their customers’ perspective when determine customers’ needs, preferences and income

which classified as customer data quality. Abu Shanab and Anagreh(2015) indicated that that CRM is a

useful tool can improve banks profitability by retaining their customers and reducing cost ,in addition

to increase value of interaction with customers, also added that technology, customers and people are

main components for bank success while keeping the high security level in the banks.

This research has come to fill the gap in the literature review by examining practically the

influence of ECRM on the banks' business performance in Jordan as a developing country, since there

were no prior study was conducted in this context before . This study tried to answer the following

questions:

1. To which extent the CRMS applications are practiced or used by commercial banks in

Jordan?

2. What is the direction and strengths of the relationship between the ECRM and the type of

business performance (financial non-financial and combined)?

3. Which the main components of ECRM (Customer data quality, appropriate process, ECRM

technology service)that are highly associated with each type of bank's business

performance(financial non-financial and combined?.


2. Literature Review

2.1 Customer Relationship Management (CRM): Definition and Importance

The Customer Relationship Management (CRM) as a concept was introduced and developed in 1990s

in order to merge the market with the customer,as a result, the system was spread widely in all sizes of

companies that deal with customers (Xu, Yin, Lin and Chou 2002).

Galbreath and Roger (1999) defined CRM as: "Activities a business performs to identify,

qualify, acquire, develop and retain increasingly loyal and profitable customers by delivering the right

product, to the right customer, through the right time and the right cost. CRM integrates sales,

marketing, services, enterprise resources, planning and supply chain management functions through

business process automation, technology solutions, and information resources to maximize each

customer contact. CRM facilitates relationships among enterprises, their customers, business partners,

supplies and employees …. ”

Many studies defined the CRM as a system and a strategy used by companies to increase

customers value and loyalty in order to increase profitability and revenues (Kennedy, 2006), Christopher,

et al. , (1991) illustrated that the relationship between business philosophy of CRM with marketing is

improving the long term profitability by shifting the customer transaction into customer retention.

Shaw (1999) defined CRM as an interactive process that aims to achieve the optimum balance

between corporate and customer satisfaction in order to generate the maximum profit. To achieve such a

goal the author remarked that the activities of marketing, sales and services need to be integrated into the

CRM. TAhmed (2009) defined CRM as a software which provide the company with a valuable

information about customers such as: birthday date, place of birth, nationality, material status and names of

children. Also Yim, Anderson and Swaminathan (2004) showed that CRM affect customer satisfaction,

customer retention and sales growth. The author assert that CRM proves managers should think beyond

technologies and need to focus on CRM dimensions which significantly affect customer loyalty and

increase sales growth. CRM is more than atechnology, it is a strategic process (Hung and Lin 2008).

Focusing on CRM helps banks to understand current needs and to predictcustomer`s future plan

to do, in order to meet their goals (Xu 2002). Miremadi et al. ,(2012) stated that CRM overall goal is to

create trust, customer loyalty and longtime relationship in order to create maximum customer long life

relationship. Bouldinget al. (2005) noted that CRM has the potential to enhance both firm performance

and customer benefit, this means CRM enable firms to increase value extraction from customers in the

other hand customers gain greater value from firms because firms meet their needs and expectation

based on CRM.

Many previous studies showed the positive effect of CRM implementation in organizations in

general and in banks in specific. For example, Krasnikov et al. (2009) on selected banks in the US,

revealed that CRM program implementation has decreased the cost and increased the profitability of

the banks. Also many researchers analyzed the impact of CRM on relational variables such as loyalty

and satisfaction with positive significant effect (Feinberg at el. , 2002, Mithas at el. , 2005, Newell,

2001) and other research studies highlighted the positive significant impact of CRM on other variables

like profitability (Chang and Tsay 2004, Rahaman at el. , 2011). Woodcok and Stone (2012) were

discussing the simple strategies that customer relationship management (CRM) uses in wining,

keeping, developing customers and ensuring their profitability, in addition to the efficiency of the

customer management by reduce cost and increase yield. All above mentioned studies supported that

CRM program has a positive impact on banks and customers due to its benefits.

2.2 Electronic Customer Relationship Management (ECRM)

Nowadays internet and web services became the main core of any business, internet as an information

center helps facilitating information transferring and distribution (Navimippour 2015). Furthermore,

using the internet as a base for CRM functions and a channel for marketing, commerce and information

introducednew and great opportunities for businesses to be described as ECRM (Feinberg and Kadam,


2002), in addition Miremadi, et al. , (2012) defined Electronic Customer Relationship Management

(ECRM) “as the combination of traditional CRM with e-business market place application”.

Miremadi (2012) added that ECRM refers to the set of activities that enable the firm to utilize the

new technology of internet to implement CRM, for that banks all over the world realized the benefit of

CRM implementation with internet and have actively used ECRM strategies. Also Ledderer etal. , (2000)

described Electronic Customer Relationship Management (ECRM) as a combination of hardware,

software, applications and management commitment. But Javadi and Azmoon (2011) defined ECRM as

a strategy of marketing, selling and integration of online service which plays a role of identifying,

obtaining and maintaining the customers who they are the largest assets of the companies. Romano and

Fjermestad (2003) said that (ECRM) concerns about economically valuable customers by attracting and

keeping them in other hand eliminating the economically invaluable customers.

ECRM is one of the new technique used as a competitive advantage in order to achieve the

development and for the continues improvement and has many benefits on both sides (customer side and

business side). Effective implementation of ECRM has to increase customer satisfaction and loyalty

which will affect the market by increasing sales and repeated purchasing (Akhlagh, Daghbandan and

Yousefnejad, 2014) which support our study. ECRM system was found to serve customers in a better

way to meet their expectations and to achieve higher profit and revenue for the business.

Many studies were done to support the benefits of ECRM on business in banks in specific. For

example,Bezhovski and Hussain (2016) found that ECRM has benefits on banks like decreasing work

load on branches, decrease administrative cost, increase cross selling, bank revenue and enables the

bankers to analyze customers’ needs based on past transactions. Another qualitative supported study by

Miremadi, Ghalamakri and Ramezani (2012) found that ECRM implementation brings competitive

advantages to the banks, in keeping them updated with the technology, 'marketing and strategic factors'

and customer segmentation. Soltani and Navimipour (2016) stated that ECRM is a gathering of

concepts, tools and processes which helps the organizations to get the maximum value from their

businesses, it also helps companies to increase the effectiveness of their personal interaction with

customers especially through individualization. Chen and Chen (2004) reveal that some companies

using ECRM often increase their customer loyalty to leverage brand equity.

Another research has examined the outcomes of ECRM implementation in Thai banking sector

from their customer`s perspective. The results showed that success ECRM implementation increase the

relationship quality between the bank and customers, and increase the outcomes also, this comprises on

overall satisfaction, trust, loyalty, retention and willingness to recommend which will be reflected on

bank overall profitability (Sivaraks, Krairit and Tang, 2011).

Yazdanifard and Chenh long (2010) discussed the impact of ECRM in different scale of

companies on customer satisfaction. The authors asserted that most companies with proper ECRM and

success implementation had a positive impact. However, a negative impact was remarked in some

companies due to the failure in implementing an effective ECRM, the authors highlighted some of the

positive impacts as below:

• Increasing customers’ loyalty due to increase the communication with customers and

accessing the customers’ information.

• Predict customer expectation and purchasing power due to his transactions history.

• Classify the most profitable customers to be premium customers through time and

resources allocation

• Customer service efficiency and cost reduction due to improvement in ECRM system

software.

Dhingra and Dhingra (2013) in their study recognized the advantages of ECRM in banking

sector, such as customer interaction and satisfaction, high speed accurate transactions, customer

comfort and convenience, availability of the transaction history which considered the main benefit for

employees and banks and trust which is considered the most important advantage for customers.

Researchers studied many factors related to the ECRM, andthey mainly focused on the appropriate


process, customer data quality and ECRM system technology determinants (Soltani and Navimipour,

2016; Akhlagh, et al. , 2014; Roh, Ahn and Han, 2005; Wixom, 2001; Fok and Hartman, 2001). The

main components of ECRM are:

2.2.1 Appropriate Process

It is a part of technological topics that specified a set of processes and technologies that used in the

ECRM system (Akhlagh et al. , 2014) in addition Rohet al. (2005) defined the process fit that how the

system should be designed around for understanding, and it is one of the key success for ECRM

system.

Yi-Wen and Ku (2010) described the process fit in their study, as the association between

information system success and CRM profitability. Their study showed how the configuration of

technologies from the service provider and respond to the complex needs for customers using

information technology on a travel agent scenario, which means that companies should analyze

customers’ problems and needs and respond accordingly which called "process fit".

The appropriate process is to improve the performance of ECRM through an appropriate level

called "fitness level" of customer interaction process, sales channels process, personalization process

and after sales services process (Roh et al,2005).

• Customer interaction process: It t is a process that organizations should use to engage

with its customers in interactive communication that leads to one-to-one marketing which

considered as a successful key for the organizations when focusing on each customer

(Wells, et al. , , 1999).

• Sales channels process: it is a way that service provider delivers its products to customers

through these channels, single channel, multiple channels or online channels (Bilgicer, et

al. , 2015).

• Personalization process: it is to provide a personal service for each customer, and this

personal service should suit each customer's needs and tastes (Suprenant and Solomon

1987).

• After sales services process: it is a set of services provided to customers after they

purchase the product, this kind of services ensure a continuous and trouble free for the

purchased product during its life span with customer and to keep customer satisfied

(Egonsson and Bayarsaikhan, 2013).

2.2.2 Customer Data Quality

ECRM system was created to be related to the customers (Akhlagh et al. , 2014) therefore the

evaluation of the customer perception for the service provider is very necessary (Pitt and Watson

1995), Data quality is an important key of ECRM system success (Akoka et al 2007). Knowing the

customers that uses ECRM system is important for ECRM success as collecting data from customers

only will not help, data will be helpful when analyzing and segmenting the data in order to build

insight into customers and market behavior (Roh et al. , 2005). Data quality evaluation focuses in four

areas: data accuracy, timeliness, completeness and consistency (Ballou and Tayi, 1999; Wang and

Strong, 1996).

Hable and Aglassinger (2013) also classified data quality into, accuracy, completeness and

consistency, they evaluated the data quality problems that affect the success of the ECRM system, this

emphasizes that data quality is a key factor of successful application of ECRM system, at the end of

their research they suggested an improvement for a data quality problem that system facing. ECRM

system significant performance depends on the customer data quality (Mueller and Nyfeler, 2011) as

when employees entering customer data that not accurate, useful or reliable, neither the companies nor

the customers will get a benefit of the ECRM system (Chuang and Lin 2013).


According to Roh et al. (2005) quality of customer data can be measured by integrity of

customer information, usefulness of customer information, support of information segmentation and

forecasting the purchasing power of customers.

• Integrity of customer information: it concerns about protecting the information of ECRM

users from any manipulation that could be happened by companies or employees (Graves

and Wong, 2015)

• Usefulness of customer information: to have a rich information about ECRM users that

can be useful and valuable for the system, and the information to be clear, correct and

effective in order to be easy to analyze and understand (Berg 2012, Tervakari et al. ,

2014)

• Support of information segmentation: it describes how ECRM system support in customer

information segmentation by classifying the customer`s data which helps in determining

the valuable customers and the loyal customer (Qian and Xu, 2012).

• Forecasting the purchasing power of customers: the quality of the customer information in

ECRM system can help topredict the purchasing intention, to build trust and loyalty with

customers, and to predict which products the customers are more concern about (Lin, Tsai

et al. , 2015).

2.2.3 ECRM System Technology

The definition of system technology is to measure the processing of the system itself (Negash, Ryan

and Igbaria, 2003). To reap the benefits of ECRM system, it should be successfully implemented with

effective processing system, all this happened by organized IT infrastructure, then the system will

facilitate the segmentation, processing and responding time process (Rostami et al. , 2016). The system

technology in ECRM is to support and manage the organizational and business process for a

competitive advantage in the market (Turban et al. , 2008).

Many previous studies determined the main characteristics of the system technology, such as:

reliability, response time, ease of use, system flexibility, quality of documentation and consistency of

user interface (Swanson, 1974; Hamilton and Chervany, 1981;Seddon, 1997). According to Davis

(1989) stated thata good system technology should be

1) "ease of use" which means " the degree which users believe when using this technology

system that would be with free effort from his/her side to adapt this technology",

2) "usefulness" which means "the degree which users believe when using this technology

system that would be enhancing their jobs performance". In addition, many studies show

how the acceptance of system technology impacts positively ECRM, in Julta, Craig and

Bodorik (2001) research said that technology identified as a key of helper in ECRM system,

which has a positive impact and make ECRM system possible to use and effective.

Abu-Shanab and Anagreh (2015) recognized the advantages of ECRM in banks from system

technology are: (1) Customers are more convenience for the personalized service. (2) Accurate,

complete and up-to-date information. and (3)Ability to contact the bank anywhere and anytime through

various channels.

2.3 The Relationship between ECRM and Business Performance

Previous literatures defined business performance in many definitions, according to Ajanthan,

Balaputhiran and Nimalathashan (2013) define performance as the profitability and productivity in the

banking, also added that it is refer to the growth in share price or the present valuation of the company.

As Wheelen and Hunger (2002) said that performance is the outcome of all firm`s operation and

strategy. In other study business performance was defined as mainly derived from the degree that the

firm compete in the market place by choosing to operate and turn into function according to the

marketplace characteristics (Morgan, 2012). Measuring the firm`s performance is an important issue

and a concern for most firms (Al-Hawari, 2006).


Referring to the Key Performance Indicator (KPI) bank performance is influenced by

profitability of the company, as well as its customer retention ability (Asiedu, 2016). Many researchers

argued that measurement of performance should go beyond the financial performance measurement by

using market measurement such as customer satisfaction and customers loyalty (Ambler and

Kokkinaki, 1997; Doyle, 1995) . Uadiale and Fagbemi (2012) in their study to measure the bank

performance, they used both financial and nonfinancial performance as two subscales for the bank

performance. Financial performance can be defined as the results of a company`s operation in the

monetary terms. Also non-financial performance will be introduced with the financial performance

together to have the best measures of performance (Petersen and Schoeman, 2008).

To measure the financial performance of a firm Ismail and King (2005;2006) used profitability,

availability of financial resources and sales growth. To measure the non-financial performance, Sadek

(2012) used to determine the customer loyalty, satisfaction and customers willingness to purchase the

product. Many previous researches considered the business performance as financial performance and

non-financial performance (Abu-Assi, Haneen & Al-Dmour, H 2015, Bontis, 1998; Bontis et al. ,

2000). Bergendahl and Linbdlom (2006) evaluate the performance of Swedish banks by using the

financial and non-financial measures to be more accurate in his study. Other researchers like Dess and

Robinson (1984), Lyles and Salk (1996), Bart et al. (2001) emphasized the correlation and reliability

between financial performance and non-financial performance. Which means to consider the perceived

performance measure as a practical and appropriate to measure the business performance of the firm

(Bee Wah and Khong, 2006)?

There are many studies examine the ECRM effect with the bank performance like Abu-Shanab

and Anagreh (2015) studied the impact of ECRM in banking sector, as all banks concern about

enlarging their customers base and increase their customers’ satisfaction which will accordingly affect

the bank's profitability, and they proved that ECRM the most effective tool in the banks. Another study

in Egypt by Sadek et al. , (2011) examined the effect of ECRM on performance of commercial banks.

They concluded that the banks with highest level of ECRM has the highest level of customer

satisfaction which has a positive impact on loyalty and banks profitability "banks performance" in

general, in addition they added that the banks should invest in their ECRM system with successful

implementation. The banks performance can be improved by proper and success ECRM system with

good implementation and understood by the employees. As the effective ECRM system could be a

competitive advantage in banks, all this was improved by Coltman (2007) in his research how ECRM

can improve the bank performance, but he faced a poor understanding for the system, poor

implementation or partial implementation. A research study done by Akroush et al. (2011) emphasized

the CRM implementation scale in Jordanian banks is developed according to previous studies on CRM

components for implementation and has a positive relationship between CRM components and

business performance.

3. Research Theoretical Framework The Theoretical framework is the bedrock on which the entire research lays on. It is referred to by

Sekaran and Bougie (2013) as the “logically developed, described, and elaborated network of

associations among the variables deemed relevant to the problem” (p. 80). As such the theoretical

framework describes the relationships among the variables of the study, explains the theory underlying

these relations, and describes the nature and direction of these relationships. However, it can only do so

if a solid review of the literature is provided since the literature review acts as a foundation for the

development of a sound theoretical model (Sekaran and Bougie, 2013

This study's research framework is mainly based on the a study conducted by Akhlagh, et al. ,

(2014). The main purpose of conducting this study is to examine the relationship between ECRM and

business performance, where the dependent variable is the type of business performance, the

independent variable is the main constructs of ECRM (appropriate process, quality of customer data


and ECRM technology system). Thus,this section presents the theoretical framework developed for this

study and explain the role of each variable included in the diagram.

Figure 3.1: Research Theoretical Framework

The conceptual framework of this study has been designed to illustrate the main construct of

the ECRM and its relationship with each type of business performance. The operational definition of

these construct are

• Appropriate process (Independent variable) it is defined as a practical set of process,

strategy and technology as Drasin and Van de Ven(1985) described it in the structure of

the contingency theory, in addition it has the ability to improve ECRM performance

through customer interaction process, processing sales channels, personalization of

process and after-sales service process (Roh et al. , 2005).

• Customer data quality (Independent variable) is the collection of customers’ data to

understood the users’ satisfaction, needs and feedback (Hill, 2009) as the data should be

useful and accurate in order to be successful for CRM implementation (Abbott et al.

2001) .

• ECRM system technology (Independent variable) is to assess the ECRM system itself,

as it should have the ability to collect, analyze and process customers’ data, in addition

should be easy to use and friendly (butler, 2011)

• Business performance (Dependent variable) which measure bank performance from

branch managers’ perspective by comparing their business performance in relative to

bank performance average, as this comparison will allow controlling to different

economic activities in the study`s population, mainly will take into consideration

customer satisfaction and profitability (Kim, Hiskisson and Wan, 2004). For the purpose

of this study, there were two types of measures for business performance were used:

financial and non-financial performance.

4. Research Hypotheses Sekaran and Bougie (2013) defined hypotheses as “logically conjectured relationships between two or

more variables expressed in the form of testable statements” (p. 83). Therefore, in order to test the

relationship between ECRM and business performance, the following null hypotheses were developed:

• H01: There is no significant relationship between the independent variables (appropriate

process, customer data quality and ECRM system technology); and thefinancial business

performance of Jordanian commercial banks.

Independent Variables Dependent Variable

Appropriate Process

Customer Data Quality

ECRM Technology

System

Financial performance

Non-Financial

Performance


• H02:There is no significant relationship between the independent variables (appropriate

process, customer data quality and ECRM system technology); and the non-financial

business performance of Jordanian commercial banks.

• H03:There is no significant relationship between the independent variables (appropriate

process, customer data quality and ECRM system technology); and the combination of

financial and non-financial business performance of Jordanian commercial banks

5. Research Methodology 5.1 Research Type and Scale of Measurement

The study proposed to test the relationship between the Electronic Customer Relationship Management

(appropriate process, customer data quality and ECRM system technology) and the business

performance of Jordanian commercial banks. Hence, it is a relational and correlational study because it

searches in the existence of relations between the independent and the dependent variables. In addition,

it is considered as a quantitative research, as it produced quantitative data, and is concerned with the

hypothesis testingThe study used the questionnaire survey to verify the hypotheses and research

framework. The questionnaireswere distributed in the Jordanian commercial banks either by hand or

email and it contained27 questions. It also contained questions regarding the key characteristics of

respondents namely: age, gender, education level, position and type of ECRM system using in the

bank.

To measure the business performance “financial performance and non-financial performance”

of Jordanian commercial banks, the bank`s managers were asked to point out the degree of their

business performance in relative to the banking sector performance average. Comparing the bank to the

bank`s average will allow controlling for different economic activities in the study`s population. Thus,

in arriving at a measure for business performance, the degree of importance of each dimension were

used as a weight, with performance on each item being weighted by the relative importance of each

item. The study used a ‘five-point Likert scale from 1 to 5’ rating from strong disagree to strong agree

to measure the questionnaire items. It has become quite common to use the five-point Likert scale

measure in many studies.

5.2 Research Population and Sample Size

The studypopulationwas identified as the listed commercial Jordanian banks in Amman city, as per

Central Bank of Jordan (CBJ) there are 13 listed commercial Jordanian banks. Accordingly,

questionnaires were handed tobranch managers and head of departments manually in those locations or

to referrals or by email.

According to Sekaran and Bougie(2010), one of the rules for determining the sample size states

that the sample should be larger than 50 and less than 500 to be appropriate for most social studies.

Since the study was conducted on commercial Jordanian banks in Jordan, Amman city, as per central

bank of Jordan (CBJ), there are 13 listed commercial Jordanian banks, the survey was performed on

the 13 banks and10 questionnaires were distributed per bank without perspective of bank size or

number of branches as it is convenience sample. The sample size for this study was determined to be

130 respondents.

The sampling method pursued in this study was the non-probabilistic sampling in which each

element in the in the population does not have same probability to be a part of the sample (Sekaran and

Bougie, 2010). The non-probabilistic technique used was the convenience sampling. The subject of the

sample were the branch managers, assistant branch managers and head of departments in the branches,

who have the role to use the ECRM in all its features with authorization. According to Calderet al. ,

(1981) found that a maximally homogenous sample (such as branch managers, assistant branch

managers and head of departments in the branches) can be used for hypothesis testing. Also Pittaet al. ,

(1999) concluded that in order for studies to be fully focused, they can be narrowed into smaller


measurable group, i. e. a specific geographical area and a selective group of people (as our study in

Amman for a selective group in the branches).

Table 1: List of Jordanian Commercial Banks

No. Bank Name Number of branches

1 Housing Bank 58

2 Arab Bank 49

3 Jordan Bank 44

4 Jordan Kuwait Bank 39

5 Cairo Amman Bank 34

6 Ahli Bank 32

7 Etihad Bank 28

8 ABC Bank 19

9 Jordan Commercial Bank 18

10 Arab Jordan Invest Bank 16

11 SocieteGeneral Bank 11

12 Capital Bank 8

13 Invest Bank 7

Source: Association of Banks, 2014 (Thirty-six report)

5.3 Data Collection Method

The research data collection method used in the study was the questionnaire which was structured

based on prior research and the literature review. Most of the questions in the questionnaire were

adopted from the study conducted by Roh et al. (2005), For keeping the reliability of this questionnaire

an email was sent to Prof. Roh, to grant his permission and approval to use his questionnaire in this

study. However, some modifications on the questionnaire were done in order to match the objectives

of the research and the target respondents. The questionnaire has been developed through several

stages:

1. Review of previous research and study variables in order to benefit in determining the

independent and dependent variables.

2. The questionnaire has been distributed to experts and scholars where they were asked to check

the validity of the questionnaire to the study's objectives

3. Then the questionnaire was distributed to the keyrespondents.

5.4 Non Response Rate and Rate of Return

According to Chisnall(1997), the non-response rate is a critical limitation of a research. To reduce the

non-response rate in this research, the researcherswere in present with each respondent during filling

the questionnaire in banks locationsand tried to personally contact every person who filled the

questionnaire online in order to assist in any step or explain any ambiguous question. A total of 120

questionnaires were returned and only 119 were usable. One questionnaire was unusable because the

respondents have left a few questions unanswered. The response rate of the survey was 91. 5% which

was 119/130 respondents.

6. Data Analysis Techniques Questionnaires were sent directly to branch managers, assistant branch managers and head of

departments in the branches that selected for the study because they do have more authorities on banks

system, and in some banks questionnaires were sent to HR manager to be distributed through them to

the requested respondents.

After gathering all the completed questionnaires from the respondents, and before the data

analysis, coding has been executed in order to transform the research information from the


questionnairesinto computer files so that computer could analyze the data. The SPSS Version 21

program was used in order to analyze the data. Various descriptive and inferential statistical methods

have been used to analyze data such as:

• Cronbach’s Alpha Reliability measure

• Multiple regression analysis.

• Analysis of variance (ANOVA).

• Stepwise regression test.

6.1 Validity and Reliability of Scales

6.1.1 Validity of Scales

Validity refers to the fact that the findings are exactly what they should be (Saunderset al. , 2000) and

it is defined as the ability of the used instrument to measure the particular variable that it is supposed to

measure (Sekaran and Bougie, 2010). To examine the validity of the research instrument, the

questionnaire had been presented to a group of specialized people and experts in related subjects in

order to identify their views, and the appropriateness of all the questions to the main study objectives.

They proposed some adjustments and suggested some necessary corrections to ensure improvement of

the validity of the instruments. The questionnaire was subsequently amended in accordance with their

instructions and recommendations. Approval of the majority of the experts was considered indicative

of the questionnaire’s validity.

In addition, a pilot study was conducted by meeting three persons from the research sample. Those

persons are working in banks as branch managers and assistant branch manager. The research questionnaire

was disseminated in advance and then met with those individuals to discuss all its items, and asked them to

give their feedback after filling questionnaires to identify unnecessary, difficult or ambiguous questions.

The pilot study was followed by some revisions, before it was distributed to respondents.

6.1.2 Reliability of Scales

Reliability of a measure is established by testing both consistency and stability. Consistency indicates

how well the items measuring a concept hang together as a set (Sekaran and Bougie, 2010). It is the

extent to which research results would be stable or consistent if the same techniques were used

repeatedly. The role of reliability is to minimize the errors and biases in a study (Yin, 1994). Also

according to Chisnall (1997), reliability is concerned with consistency, accuracy and predictability of

specific research findings.

Cronbach's alpha is a reliability coefficient that indicates how well the items in a set are

positively correlated to one another. The closer Cronbach's alpha is to one, the higher the internal

consistency reliability and the greater the reliability of the instrument (Sekaran and Bougie, 2010). The

Cronbach's alpha was used in order to measure the reliability of the information and results obtained

through the questionnaire. Sekaran and Bougie (2010) stated that reliabilities that are less than 0. 60 are

considered poor, those in the 0. 70 rangeacceptable and those more than 0. 80 is excellent and it is the

scale that the researcher relied upon to determine the reliability of the factors.

Table (2) showed the results of Cronbach's alpha to measure the internal consistency reliability

for measuring the independent variables of the study, while Table (3. 5) showed the results of

Cronbach's alpha for the dependent variable. Cronbach’s alpha for all the variables ranged between .

800 and . 847 which is considered excellent.

Table 2: Cronbach's Alpha for the study variables

Independent Variables Cronbach's Alpha No. of Items

Appropriate process . 800 7

Customer data quality . 819 6

ECRM system . 847 5

Business performance . 843 9


6.2 Normality Test

Normality test is considered as an important test before analyzing the data, as the variables should be

normally distributed in a bell-shaped curve. The most common normality test is Shapiro-wilk (SW)

test, Kolmogorov-Smirnov (KS) test and Lilliefors (LF) test which is the modification of (KS) test

(Razali and Wah, 2011). If the values for (KS) test and (SW) test are greater than 0. 05 so the variables

are normally distributed (Ghasemi and Zahedisal, 2012).

Table 3: Test of normality

Kolmogorov-Smirnova Shapiro-Wilk

Statistic Df Sig. Statistic df Sig.

Business performance . 314 119 . 000 . 798 119 . 000

Appropriate process . 304 119 . 000 . 792 119 . 000

Customer data quality . 315 119 . 000 . 798 119 . 000

ECRM system technology . 306 119 . 000 . 808 119 . 000

Lilliefors Significance Correction

As shown in table (3) all results of variables are between . 304 and . 315 for (KS) test with (LF)

test correction and results for (SW) test between . 798 and . 808, which all above 0. 05 that indicate

that all variables are normally distributed as per to Ghasemi and Zahedisal (2012).

7. Testing Hypotheses The purpose of hypotheses testing is to determine accurately if the null hypotheses (denoted by H0)

can be rejected in order to support the alternate hypothesis (denoted by H1) (Sekaran and Bougie,

2010). The probability value (p-value) obtained from the statistical hypothesis test is considered the

decision rule for rejecting the null hypothesis (Creswell, 2003). If the p-value is greater than a

predetermined level of significance (α- level), then the null hypothesis cannot be rejected and no

support will be claimed for the alternative hypothesis. By contrast, if the p-value is less than or equal to

than the α-level, then the null hypothesis will be rejected and the alternative hypothesis will be

supported.

Multiple Regression Analysis, and Analysis of Variance (ANOVA were used to test the

research main hypotheses of the study. Multiple regression analysis and analysis of variance were

conducted in order to examine the effect of the independent variableson business performance in

Jordanian commercial banks and to test the three hypotheses of this study.

Also, in this study the severity or degree of multicollinearity is tested by examining the relative

size of the pairwise correlation coefficient between the explanatory independent factors. An

examination of the correlation matrix indicates that the correlation for each coefficient is less than

about (. 50). Therefore, it is possible to interpret the findings since the multicollinearity is not severe

(Hair et al. , 2010).

Hair et al. (2010) recommended assessing the tolerance and variance inflation factor (VIF).

Tolerance refers to the assumption of the variability in one independent variable that does not explain

the other independent variable. The VIF reveals much of the same information as the tolerance factor.

The common cut off threshold is a tolerance value of . 10, which corresponds to VIF value above 10.

Multicollinearity was indicated in a tolerance level of less than . 10 or a VIF value above 10. The

tolerance l value for each independent variable above the ceiling tolerance value of . 10, consistent

with the absences of serious level of multicollinearity. This judgment was further supported by a VIF

value for each independent variable above the threshold value of 1. 0. For more details as presented in

Table 4.


Table 4: Collinearity Statistics

Model Collinearity Statistics

Tolerance VIF

Appropriate process . 449 2. 229

Customer data quality . 427 2. 343

ECRM system technology . 571 1. 752

According to Table (4. ), the values of Tolerance ranges between . 427 and . 571 which is

acceptable, as for the VIF the values range between 1. 752 and 2. 343 which is also acceptable and this

indicates that there is no collinearity within the data. This implies that there is no problem in the model

regarding having interchangeable Beta values between independent variables.

The main objective of multiple regression analysis here is to understand the extent to which the

components of ECRM (appropriate process, Customer Data and ECRM Technology system) are being

implemented in enhancing the business performance measures (financial and non- financial

performance (taken separately or together). A summary of the results of multiple regression analysis,

with the F-ratio test, for the above hypotheses are s presented in Table (5).

The results indicate that there are significant and positive relationship between the extent of

ECRM being used and each type of business performance measures (financial and no-financial, and

combined) at . 000 level of significance, taken together or Separately. . Thus, it can be concluded that

there appears to be a relationship between the level of ECRM implementation and their impacts in

improving the business performance either financial or non-financial .

Table 5: A Summary Result of the Multiple Regressions:The Business Performance

Hypotheses Components (types) Multiple R R. Square Adjusted R Square DF F-Sign

Ho1a Financial . 694a . 481 . 468 118 . 000b

Ho2a Non-financial . 753a . 567 . 556 118 . 000b

Ho3a Taken together . 843a . 710 . 703 118 . 000b

Dependent Variable: financial Performance , non-financial Performance, combination

The result also shows that about . 70% of variance of the combination of business performance

measures (financial and non-financial) taken together can be explained by the availability ECRM

components, which is much higher than once each factor taken separately . According to the stepwise

multiple regression method, the factors which highly correlated with the dependent variable (i. e. , the

combined of business performance measures) is expected to enter into the regression equation. The F

value at . 00 level of significance is used to determine the “goodness of fit” for the regression equation.

The F value is the ratio of explained to unexplained variance accounted for by the regression equation,

when the total variance accounted is low, interpretation of the individual beta coefficient has little

meaning (SPSS, 2013). Therefore, when the adjusted R square is around . 10 or above and the F value

of the regression equation reaches to 0. 05 level of significance, the individual beta weight is

explained.

Prior to interpreting the results of the multiple regression analysis, several assumptions were

evaluated. First, stem-and-leaf plots and box plots indicated that each variable in the regression was

normally distributed and free univariate outliers. Second, inspection of the normal probability plot of

standardized residuals, as well as the scatter plot of standardized residuals against standardized

predicted value, indicated that the assumptions of normality, linearity and homoscedasticity of

residuals were achieved. The findings of the stepwise regression analysis are presented and discussed

here under the following subsections:


1. Stepwise Multiple Regressions: Financial Performance Factor: as a Dependent Variable;

taken alone.

The results of the stepwise regression analysis indicate that the ECRM (i. e. appropriate

process, customer data quality and ECRM technology system) is significantly related to the financial

performance factor.

Table 6: The Stepwise Regression Analysis: Financial Performance

Factors Step R R Square Adjusted R Square Beta Sig.

Customer Data Quality* 1 . 658a . 433 . 428 . 440 . 000

Appropriate Process 2 . 691b . 478 . 469 . 304 . 002

*Constant factor

The Stepwise regression analysis findings also indicate that out of those 3 explanatory

independent components, only two components included in the regression equation. These two

components in in terms of their order of importance are: "Customer Data Quality" and "Appropriate

Process". The adjusted square for these two components is . 469 s shown in table (6). This indicates

that about 47% of the variations of the financial performance of commercial bankscould be explained

byonly thesetwo components.

2. Stepwise Multiple Regressions: Non- Financial Performance Factor as an Dependent;

Taken alone.

The results of the regression analysis indicate that the ECRM (i. e. , all appropriate process,

customer data quality and ECRM technology system ) is significantly related to the non- financial

performance factor. The direction of this relationship is positive. . The Stepwise regression analysis

findings also indicate that out of those 3 explanatory independent components, only two components is

included in the regression equation. These two components in terms of their order of importance are

(1)"Appropriate Process", (2)" ECRM Technology System", see Table (7).

Table 7: the Stepwise Regression Analysis: Non- Financial Performance


Appropriate Process * 1 . 709a . 503 . 499 . 464 . 000

ECRM technology system 2 . 748b . 559 . 552 . 342 . 000

*Constant factor

To the best knowledge of the researchers, supporting empirical evidence for the effect of the

ECRM upon the non-financial performance measures might not be established in the previous studies.

The adjusted square for these two components is . 552 as shown in table (7) This indicates that about

55% of the variations of the non-financial performance can be explained by these components. In

comparing the results shown in Table (6) with those of the financial performance, it may be concluded

that the impact of the ECRM components s upon the non-financial performance (. 55) produce a much

higher explanation of the variance than upon the financial performance (. 47). This might indicate the

non- financial performance indicators could be improved much better than non-financial performance

by the implementation of ECRM. This might be due to the fact that ECRM system is more concern

with performance measures such as customer's satisfaction and loyalty than other measures.

3. Stepwise Multiple Regressions: (Financial and Non-financial Performance Factors as

aDependent, Taken Together.

This approach is expected to provide evidence to the influence of the ECRM components upon

business performance measures (i. e. , combination of financial and non-financial measures), taken

together when compared with their influence upon each one acts alone. More of the predictor

components are expected to enter in the regression equation. The findings of the multiple regression

indicate that the implementation of ECRM (i. e. all three components ) are associated with the level of


the financial and non-financial factors, taken together. The findings also indicate that all the

components of ECRM are included in the regression equation. The adjusted R square for those

components together is . 703, i. e. , about 70% of the variation of the combination of business

performance is explained by them Table (8). Those three most important factors included in the

regression equation are in terms of their order of importance"Customer Data Quality", "Appropriate

Process" and "ECRM Technology System".

Table Error! No text of specified style in document.8: The Stepwise Regression Analysis: Combined Factors

(Financial and Non-Financial)


Customer Data quality 1 . 795a . 632 . 629 . 411 . 000

Appropriate Process 2 . 835b . 697 . 692 . 306 . 000

ECRM Technology Process 3 . 843c . 710 . 703 . 204 . 000

*Constant factor

In comparing this solution with the other two solutions presented in the previous sections , it

may be concluded that the influence of ECRM components upon the combination of the two business

performance measures (i. e. , financial and non-financial) would give better explanation (predictive

power) than upon each factor acting alone. The rate of explanation, which they account for, is

increased from 55% (non-financial performance) and47% (financial performance) to about 70% as

presented in Table (8). The importance components ofECRM s that related to each type of business

performance (i. e. , financial , non-financial and combined) are summarized in Table (9) .

Table 9: A summary of the Stepwise Regression Analysis.: The Importance of Components of ECRM

Related to Business Performance

ECRM Components Financial Non- Financial Combined

Customer Data quality * *

Appropriate Process * * *

ECRM Technology Process * *

• Important Component

This conclusion implies that a better understanding of the impact of the implementation of

ECRM components on commercial banks upon their business performance requires that the two

combinations of financial and non-financial performance measures should be viewed and investigated

together rather than only viewing each of them alone. Furthermore, viewing non- financial

performance alone would also give better understanding than viewing -financial performance alone.

This result is supported by Akhlagh et al. (2014) and by Roh et al. (2005).

8. Conclusions and Recommendations

This research aimed mainly to investigate the relationship between the ECRM components and the

type of business performance measures (financial, non-financial and combination). The main focus of

this study was to gain an insight into the current status of ECRM implementation by commercial

banksin Jordan. In order to achieve the study objectives, and to conduct the research in a systematic

approach, a conceptual framework was developed. The conceptual framework ties together the major

of three major components proposed byAkhlagh et al. (2014). The main three components are:

Appropriate process, customer data qualityand ECRM technology system.

The analysis provides empirical evidence that the ECRM components better explains the

prediction of the business performance measures whether financial or non-financial or the combination.

This result supports the proposition that availabilityof ECRM is positively linked to business

performance. Therefore, a better understanding of the influence of ECRM components upon should be


viewed as a whole rather than isolated fragments. The application of thee multiple regression analysis

reveals that there is a moderate relationship between the availability of ECRM and each type

ofbusiness performance (financial, non-financial and combined) at the aggregate level.

In comparing this among the type of business performance (financial, non-financial and

combined), the result indicated that the influence of availability of ECRM principles upon the

combination of the two business performance measures (i. e. , financial and non-financial) would give

better explanation (predictive power) than upon each factor acting alone. The rate of explanation which

they account for is increased from 57% (non-financial performance) and47% (financial performance)

to about 70% as presented. Furthermore, the application of stepwise regression analysis also showed

that the relative importance of these factors isdiffering according to the type of business performance.

For example while the "Customer Data Quality" as a component of ECRM has shown importantly

related to enhance the financial and combined performance, it was not shown important to enhance

non-financial performances. Furthermore, the "Appropriate Process" has shown important to all types

of business performances measure. It can be concluded that the implementation of ERCM by

commercial bank plays an important role in enhancing the business performance whither financial or

non-financial performances . However, the results found that appropriate process had the greatest

impact on non-financial performance. This might indicate that if banks want to improve their

communication interaction with customers this will increase the customer satisfaction and loyalty and

it will be reflected on the bank overall performance and profitability.

This conclusion also implies that a better understanding of the impact of the availability

ofECRM on the commercial business banks' performance requires that the two combinations of

financial and non-financial performancemeasures should be viewed and investigated together rather

than only viewing each of them alone. Furthermore, viewing non- financial performance alone would

also give better understanding than viewing financial performance alone.

All these results were foundin line with the findings of Akhlagh et al. (2014) who supported the

positive relationship between appropriate process, customer data quality and ECRM system

technologywith business performance. Another study done by Roh et al. (2005) supported same results

of this study, that CRM process, customer information and system technology generally improve

firms’financial performance.

These findingsarealsoconsistent with what were demonstrated in the previous. As a result of

that, banks should take into consideration the appropriate process of ECRM system, customer data

quality and ECRM system technology in order to enhance their business performance whether

financial or non-financial performance. In addition, the banks should educate the employees about the

importance of customer data quality and its main effect on ECRM system and the business

performance of the banks.

The findings of the study are particularly important from managerial and marketing

perspectives more than purely building marketing strategies and campaigns focused on the increase of

the profitability and employees’performance. It is important from the customers’ perspective to be

cooperativewith banks by providing the needed data, feedback and comments that takes there concern

in order to make the requested upgrade.

9. Limitations and Future Researches This study has several limitations that should be considered when evaluating and generalizing its

conclusions. However, the limitations discussed below can provide a starting point for future research.

The study was conductedin one country, Jordan. Although Jordan is a valid indicator of prevalent

factors in the wider MENA region and developing countries, the lack of external validity of this

research means that any generalizations of the research findings should be taken with caution. Future

research can be orientated in other national and cultural settings and compared with the results of this

study. The data analysis was cross-sectional. As with all cross sectional studies, the parameters tended


to be static rather than dynamic. This drawback limits the generalization of the study’s findings to

further situations and beyond the specific population from which the data was gathered. Future

longitudinal studies could provide a better understanding of the implementation of ECRM over time.

The study proposes a framework for future research in measuring the electronic customer

relationship management (ECRM) andbusiness performance from customers’ perspective. This study

done only on Jordanian commercial banks in Amman city, for that in the future, same study could be

conduct on a wider range, foreigner banks, Islamic banks and to be compared with Jordanian

commercial banks and for the wider range, the sample could be spread all over Jordan.

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[111] In conclusion, this study has provided a theoretical model to show the firm relationship

between CRM dimensions, marketing capabilities and hotel performance. It contributes

enormously to the body of knowledge, as it provides a comprehensive framework that is used

for explaining the impact of the CRM four dimensions on hotel performance

the impact of electronic customer relationship...

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