JCMC 9 (4) July 2004
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The Impact of Customer Relationship Management on Customer Loyalty: The Moderating Role of Web Site Characteristics

Assion Lawson-Body
University of North Dakota

Moez Limayem
City University of Hong Kong


Abstract

The aim of this study is to explain the impact of Web site characteristics on the relation between customer relationship management (CRM) and customer loyalty. Data collected from 170 Canadian IT organizations showed that Web site characteristics (which include the levels of the organizations’ Internet presence and interactivity) have a significant impact on the link between CRM (in terms of partnerships, empowerment, relations with customers, and personalization), and customer loyalty. In other words, using the Internet to support CRM allows firms to increase their customer loyalty in the IT sector. However, the impact of Web site characteristics on the link between CRM, in terms of understanding customer expectations, customer prospecting, and interactive management, and customer loyalty has not been tested because the direct link between these three components of CRM and customer loyalty has not been significant in this study. The managerial and theoretical implications of these results are discussed.


Introduction

As the new millennium progresses, the business world is focusing more attention on issues concerning electronic commerce (Teo et al., 2003). The term 'electronic commerce' encompasses many activities carried out through computer networks and the Internet, including inter-organizational commerce, intra-organizational transactions, and transactions involving the individual consumer (Adelaar, 2000). The impact of the Internet has made a substantial difference in business-to-business (B2B) transactions (Teo et al., 2003; Venkatraman, 2000).

Many recent studies have predicted that revenues from B2B electronic commerce on the Internet will hit $6.8 trillion in 2004 (Gartner Group 2000). Such predictions indicate the importance of the Internet as a way of supporting business activities (Porter, 2001). Presently, the Internet seems to offer almost unlimited possibilities (Robbins & Stylianou, 2003). Indeed, numerous firms have already experienced its considerable benefits (Geyskens, Gielens & Dekimpe, 2002; McMillan, 2001). One of the consequences of the development of the Internet has been the emergence of the World Wide Web, an Internet service that organizes information according to hypermedia and hyperlink paradigms (Chiu, 2003; Joseph, Cook & Javalgi, 2001). Some organizations have invested in the Web – often with the objective of using it as a way to maximize resources (McMillan, 2001).

Today, the World Wide Web has great potential as a tool for conducting business and management activities (Bell & Tang, 1998). Customer relationship management (CRM) is a leading new approach to business, which has already become established in the literature (Szeinbach, Barnes, & Garner, 1997). Indeed, CRM refers to all business activities directed towards initiating, establishing, maintaining, and developing successful long-term relational exchanges (Heide, 1994; Reinartz & Kumar, 2003). One of the results of CRM is the promotion of customer loyalty (Evans & Laskin, 1994), which is considered to be a relational phenomenon (Chow & Holden, 1997; Jacoby & Kyner, 1973; Sheth & Parvatiyar, 1995; cited by Macintosh & Lockshin, 1997). The benefits of customer loyalty to a provider of either services or products are numerous, and thus organizations are eager to secure as significant a loyal customer base as possible (Gefen, 2002; Reinartz & Kumar, 2003; Rowley & Dawes, 2000). Recent developments in Internet technology have given the Internet a new role: to facilitate the link between CRM and customer loyalty.

Most of what is currently known about the impact of the Internet on business management is based on anecdotes, experiential evidence, and ad hoc descriptive studies (Avlonitis & Karayanni, 2000; Peterson, Balasubramanian, & Bronnenberg, 1997). There is a little existing research that has empirically tested the impact of the Internet on CRM which leads to customer loyalty. But the Internet still poses opportunities as well as threats (Geyskens, Gielens & Dekimpe, 2002). The degree to which the Internet is used by organizations and the considerable praise that it has received may be attributed to its enhanced informational and interactive communication capabilities. In this way, it can be used as a business channel and so lead to the development of more effective CRM as well as the emergence of new network cooperative opportunities (Avlonitis & Karayanni, 2000; Geyskens, Gielens & Dekimpe, 2002). However, the speculation that the Web presents CRM opportunities (Sheth & Parvatiyar, 1995) seldom has been empirically tested.

This article consists of two main parts. First, a conceptual model explaining the theoretical link between CRM and customer loyalty will be presented and explored. The impact of Web site characteristics on the link between CRM and customer loyalty will also be explained. The second part of this article will explain the results of an empirical study conducted to test the impact of Web site characteristics on the link between business-to-business relationship management and customer loyalty in the IT sector. Business-to-business relationship management or CRM has been chosen because business-to-business electronic commerce is more profitable for companies than business-to-consumer electronic commerce (Forrester Research, 2001). The impact of the Internet is well known in business-to-consumer transactions: witness the proliferation of Web sites for facilitating sales and services across a broad range of offerings. However, a revolution is occurring in business-to-business relationships as companies restructure their operations with trading partners (Venkatraman, 2000). The IT sector has been chosen because, according to Datamonitor (1999), more products (hardware and software) from this sector are sold online or through the Internet on a business-to-business basis than from any other sectors. Towards the end of the article, the managerial implications of the study will be considered.


Conceptual Model and Research Hypotheses

CRM Construct Components

There is a considerable body of literature concerning CRM, which covers a number of different components. CRM is organized as a series of events. These are clustered together according to types of action that constitute the extent of CRM in the context of this study. The extent of CRM comprises independent variables presented in the conceptual model. The seven major CRM components identified are: 1) customer prospecting, 2) relations with customers, 3) interactive management, 4) understanding customer expectations, 5) empowerment, 6) partnerships, and 7) personalization. These components are discussed below.

         Customer Prospecting

The term customer prospecting refers to all the various means employed in business to track, locate, and attract new customers (Reinartz & Kumar, 2003; Shultz, 1995). Many firms have developed databases that contain detailed interaction data on prospects as well as customers (Thomas, 2001). In the process described by Payne (1994), the concept of CRM is understood in terms of a loyalty scale leading from the customer prospect, through customer, client, and supporter, to partner. According to Payne (1994), customer prospecting plays a key role at the beginning of the CRM process. Thomas (2001) has examined a methodology for linking customer acquisition to customer retention. He found that customer acquisition and retention are not independent processes. Using the model described in his study, Thomas (2001) shows the financial impacts of not accounting for the effect of acquisitions on customer retention.

         Relations with Customers

The “relations with customers” component of CRM concerns the extent to which firms initiate, develop, maintain, and improve relationships with other firms (Berry et al., 1991; Gronroos, 1990; Heide, 1994; Jackson et al., 1985; Morgan et al., 1994; Nevin, 1995; Peterson, 1995; Reinartz & Kumar, 2003). Most definitions that can be found in the literature regard “relations with customers” as representing the keystone of CRM. The concept of relations with customers also relates, according to the literature, to customer loyalty. Chow and Holden (1997), for example, estimate that firms are oriented towards the benefits that can be reaped from the construction of customer loyalty. In addition, these authors specify that there has been a paradigmatic change so that the relationship with the customer is now seen as the unit of value.

         Interactive Management

Interactive management is a key aspect of CRM functions (Gronroos, 1994). It comprises all actions designed to transform the prospective client who enters into contact with the business representatives into an active and effective customer (Dufour & Maisonnas, 1997). It is conceptually based on reciprocity, which constitutes one important dimension of CRM (Bitner, 1995; Gummesson, 1994; Nevin, 1995), and feedback is an important part of the core of interactive management (Evans & Laskin, 1994). Indeed, Evans & Laskin (1994) consider customer feedback as a key step of the CRM process and define it as the best way for firms to keep in touch with their customers’ perceptions.

         Understanding Customer Expectations

This concept stresses the importance of identifying the customers’ desires and supplying to those customers products and services that meet their expectations (Power, 1988; cited by Evans & Laskin, 1994). Szeinbach, Barnes, & Garner (1997) describe understanding customer expectations as the strategy adopted by firms to generate more knowledge of customer expectations and needs and to provide customers with the best services in order to win their loyalty.

         Empowerment

Empowerment generally refers to the process a firm adopts to encourage and reward employees who exercise initiative, make valuable creative contributions, and do whatever is possible to help customers solve their problems (Evans & Laskin, 1994; Herzberg, 2003). Reichheld (2001) reports that he has yet to encounter a company that has achieved extremely high customer loyalty without fostering similarly high loyalty among employees. Most business representatives prefer to deal with regular customers because they are easy to serve, they understand the firm’s preoccupations, and make only a few requests (Bendapudi & Leone, 2002; Chow & Holden, 1997).

         Partnerships

Partnerships are created when suppliers work closely with customers and add desired services to their traditional product and service offerings (Evans & Laskin, 1994). Payne (1994) puts partnering at the extreme end of his loyalty scale, regarding it as an important step that usually leads to the development of a close and durable relationship between supplier and customer. Wilson (1995) has developed an integrated model devoted to the explanation of CRM process phases. In this model, partner selection is considered to be the first step in the CRM process.

         Personalization

Personalization refers to the extent to which a firm assigns one business representative to each customer and develops or prepares specific products for specific customers. Personalization is about selecting or filtering information for a company by using information about the customer profile (Schubert, 2003) A major component of personalization is the distribution of customized mail to a customer or customization of the relationship between firm and customer. This concept outlines a clear distinction, established by Gronroos (1994), between CRM and the management mix. The latter is a far more clinical approach in which the seller, or business representative, plays an active role, while the buyer, or customer, takes up a more passive position. In such a scenario, there is no personalized relationship between customer and business representative. Personalization, rather, is only included in CRM.

Loyalty

The development of loyalty involves building and sustaining a relationship with a customer, which leads to the repeated purchase of products or services over a given period of time. A loyal customer base allows companies to devote their energies to other business matters (Gefen, 2002; Rowley & Dawes, 2000). Customers can demonstrate their loyalty in several ways. They can choose to stay with a firm, whether this continuance is defined as a relationship or not, or they can increase the number of purchases, or they can do both (Reinartz & Kumar, 2003; Rowley & Dawes, 2000). For the purposes of this research, loyalty will be considered as the final result, or the key element, of effective CRM. Since many authors have suggested that loyalty is a relational phenomenon (Chow & Holden, 1997; Jacoby & Kyner, 1973; Sheth & Parvatiyar, 1995; cited by Macintosh & Lockshin, 1997), our purpose is to link loyalty to the emerging theory of CRM (Macintosh & Lockshin, 1997). Although some authors, such as Dick & Basu (1994), distinguish between brand loyalty, store loyalty, salespeople loyalty, product and service loyalty, and so on, in this study the concept of loyalty will be considered as the combination of all these types. Some authors, such as Evans & Laskin (1994), have also studied the impact of CRM on customer loyalty, but have not made any distinctions between different types of loyalty. They have merely specified that their concept of loyalty went beyond the idea of industrial loyalty. Therefore, the variable chosen to measure the effectiveness of CRM in this study is “customer loyalty.”

"Web Site Characteristic" Construct Components

One of the more widespread electronic commerce approaches is the digital storefront, i.e., the use of Web sites to advertise, display and purchase goods and services (Tagliavini, Ravarini & Antonelli, 2001). After using the theory of modular design to explain the concept of Web site characteristics, Iyer et al., (2003) have described how an emerging Web site’s framework can be used to support dynamic business networks. Based on previous published work, they defined a business network as a distinct system of participants that use the network to achieve customer satisfaction and profitability and where relationships evolve over time. An online feedback mechanism that encourages buyers and sellers to rate one another (Bakos & Dellarocas, 2002) seems to have succeeded in encouraging interactive behavior in an otherwise very risky trading environment. Domingo II & Hui (2003) have examined the high costs of attracting new customers on the Internet and found that aesthetic characteristics of the Web site have a positive effect on customer loyalty. Web technologies are complex and offer a variety of functionalities ranging from the static presentation of content to the dynamic capture of transactions with provisions for personalization [Chatterjee et al., 2002]. Hoffman, Novak, & Chatterjee (1995) have proposed a structural framework which classifies the commercialization efforts that characterize commercial Web sites into six distinct types: 1) online storefronts, 2) internet presence, 3) content, 4) malls, 5) incentive sites, and 6) search agents. The first three types represent the “Integrated Destination Site,” while the latter three represent forms of “Web Traffic Control” (Hoffman, Novak & Chatterjee, 1995). Angehrn (1997) describes an ICDT Model which takes its name from the segmentation of the space of new business opportunities created by the spread of the Internet into four “virtual spaces”: a virtual information space, a virtual communication space, a virtual distribution space, and a virtual transaction space. The Web site characteristics developed in this study have been adapted from the frameworks of Angehrn (1997), Hoffman, Novak & Chatterjee (1995) and Tagliavini, Ravarini & Antonelli (2001). There are essentially two variables that encompass the construct of Web site characteristics: the level of presence on the Internet, and the level of interactivity on the Internet.

         The Level of Presence on the Internet

A business can use an Internet presence to reach customers all around the world (Chiu, 2003; Jarvenpaa & Tractinski, 1999; Robbins & Stylianou, 2003). Lombard & Ditton (1997) explain that the concept of presence is central to the use, and therefore the usefulness and profitability, of the new technologies such as the World Wide Web. Internet presence has been selected from among six Web site characteristics outlined by Hoffman, Novak & Chatterjee (1995) because it is the only aspect of Web sites that dominates in business activities (Hoffman & Novak, 1996). In addition, presence on the Internet encompasses the concept of virtual information space as described by Angehrn (1997). This latter concept consists of the new Internet-based channels through which economic agents can display information about themselves and the products and services they offer. Essentially, the level of presence on the Internet refers to the virtual presence of firms and their offerings.

         The Level of Interactivity on the Internet

The concept of interactivity is complex and multi-dimensional (Lombard & Ditton, 1997). According to Rafaeli & Sudweeks (1997), as with face-to-face communication, computer-mediated communication has the capacity of enabling high interactivity. Two major aspects distinguishing the Internet from other communication media are the opportunities for two-way interaction, and the capacity for multimedia. A virtual communication space, as defined by Angehrn (1997), will be incorporated in our definition of the level of interactivity on the Internet, as it is an extension of the traditional spaces economic agents use to meet, interact, exchange valuable ideas and experiences, influence opinions, negotiate potential collaborations, engage in relationships, and create communities. Therefore, the level of interactivity on the Internet refers to the extent to which organizations engage in online communication without being affected by distance and time constraints.

The Research Model

Figure 1 represents the research model of this study.


Figure 1. Research model.


The choices that have been made regarding the content of each construct, and the arguments used to support them, have led to the redesigning and redefining of the research model above. In the research model, the extent of CRM variables has a direct impact on the customer loyalty variable. The use of Web site characteristics to strengthen existing CRM can increase businesses’ loyalty to each other. The Web site characteristics such as the level of presence on the Internet and the level of interactivity on the Internet can support the formation and maintenance of CRM because they facilitate the way organizations understand customer expectations, partner, build relations with customers, interact, empower and personalize to create loyal customers. In other words, a key goal of CRM is to improve customer loyalty (Evans & Laskin, 1994). Effective CRM will lead to greater customer loyalty (Evans & Laskin, 1994). Also, organizations can use Web site characteristics to build tight relationships with their trading customers who become loyal to them, rather than to select customers on a transaction-by-transaction basis from a large pool of non-loyal customers (Reinartz & Kumar, 2002). Therefore, we expect: 1) the extent of CRM variables to have a positive impact on customer loyalty; 2) the level of presence on the Internet to have a positive impact on the link between the extent of CRM variables and customer loyalty; 3) the level of interactivity on the Internet to have a positive impact on the link between the extent of CRM variables and customer loyalty. Our theoretical argument, explained in the previous literature review and model, enables us to posit the following hypotheses:

H1 (a, b, c, d, e, f, and g): The greater the extent of the CRM components (partnerships, relations with customers, customer prospecting, interactive management, empowerment, understanding customer expectations, and personalization) the greater the positive and direct effect on customer loyalty.

H2 (a, b, c, d, e, f, and g): The level of presence on the Internet will be positively associated with the link between the extent of the CRM components (partnerships, relations with customers, customer prospecting, interactive management, empowerment, understanding customer expectations, and personalization) and customer loyalty.

H3 (a, b, c, d, e, f, and g): The level of interactivity on the Internet will be positively associated with the link between the extent of the CRM components (partnerships, relations with customers, customer prospecting, interactive management, empowerment, understanding customer expectations, and personalization) and customer loyalty.


Methodology

Because scales were not available for all of the variables described in the conceptual model, certain measures had to be developed according to the guidelines suggested by Churchill (1979). An evaluation grid was used to measure Web sites characteristic variables, and was mounted according to the guidelines by Kassarjian (1977). In order to follow the guidelines suggested by Churchill (1979), an exploratory study based on multiple cases studies was conducted. The results of these multiple case studies enabled us to refine the measures previously described in the literature review. In total, eight companies, which belong to the IT sector in Quebec (Province of Canada), were asked to participate. The managers of these companies were interviewed for one hour, and recorded on tape. Content analysis was conducted to analyze and process the data from the interviews. To help conduct the interviews, an interview protocol was used.

Our refined questionnaires were extensively reviewed and evaluated, both by practitioners (the managers and directors of the IT companies) and by academics. Based on these evaluations, corrections and improvements were suggested which were included in the measurement instrument. The questionnaires were sent electronically to the 1000 executive directors of small, medium, and large IT companies in Canada. Of these, 170 electronic responses, or 17%, were returned. This response rate was similar to that obtained in other studies which used similar survey procedures. A Web page devoted to the questionnaire was set up and electronic cover letters were forwarded to respondents inviting them to visit the Web site in order to fill out the electronic questionnaire form. The responses were sent electronically to the researchers.

The measures used in this study were statistically validated. SPSS software was used to assess the reliability and the validity of the measures in the exploratory phase. The technique employed was Exploratory Factor Analysis based on Principal Components Method with Varimax Rotation. This technique allows the interpretation of the relevant factors and it is also the most used rotation technique in research (Norusis, 1993). The criterion used in the reliability assessment was Cronbach’s alpha. The results obtained from this first analysis are presented in Table 1a. It can be seen that this analysis helped to identify one factor for the construct “customer loyalty” and seven factors for the construct “the extent of CRM” (see Table 1a and Table 2a). The reliability assessment was followed by a Principal Components Analysis (PCA). This analysis was carried out by calculating Cronbach’s alpha coefficient using SPSS (see Table 1a and Table 2a). The items that were rejected due to poor loading, after the reliability assessment and PCA, are presented in Table 1b and Table 2b. Item definitions are provided in the Appendix.

  Items Loads Cronbach's alpha
  L1 0.762 0.79
Factor 1: Loyalty L2 0.798  
  L3 0.836  
  L4 0.731  

Table 1a. Exploratory factor analysis (customer loyalty)
(Principal Components method with Varimax rotation; Loading>=0.50).


  Construct: Loyalty
  Items
Factor 1: Loyalty L5
  L6
  L7

Table 1b. Exploratory factor analysis (customer loyalty)
(Items thrown out due to poor loading).


The extent of CRM Factor 1: Interactive Management Factor 2: Customer Prospecting Factor 3: Partnerships Factor 4: Personalization Factor 5: Empowerment Factor 6: Understanding Customer Expectations Factor 7: Relations with Customers
IM1 0.892 0.002 0.149 -0.003 0.005 -0.189 0.118
IM2 0.835 0.145 0.147 -0.076 0.052 0.137 0.042
IM3 0.796 0.201 0.077 0.088 0.043 0.129 -0.012
IM4 0.795 -0.087 0.115 -0.099 -0.046 -0.015 0.137
CP1 0.177 0.778 0.172 0.119 0.109 0.023 -0.068
CP2 -0.061 0.793 0.167 0.045 0.018 0.242 0.051
CP3 -0.033 0.623 -0.013 -0.242 0.027 0.324 0.007
PS1 -0.051 -0.105 0.788 -0.022 0.171 -0.112 -0.121
PS2 0.044 -0.017 0.763 0.091 0.100 -0.024 0.093
PS3 0.032 0.199 0.747 0.082 -0.267 0.012 0.023
PS4 -0.099 0.250 0.610 0.042 -0.144 0.191 0.223
P1 0.092 0.074 -0.119 0.858 0.156 0.175 0.041
P2 0.296 -0.054 0.281 0.702 -0.015 -0.228 0.295
ITL1 0.134 0.105 0.080 0.041 0.818 0.102 -0.031
ITL2 0.129 0.107 0.048 -0.043 0.811 0.087 0.037
ITL3 0.187 0.256 0.095 0.051 0.752 0.015 0.027
ITL4 0.056 0.266 0.025 0.291 0.569 -0.063 0.004
UCE1 0.024 0.045 -0.039 -0.021 0.014 0.806 0.063
UCE2 0.214 0.179 0.148 0.081 -0.021 0.778 0.037
UCE3 0.259 0.109 0.192 0.157 0.160 0.655 -0.124
RWC1 -0.040 0.082 0.083 0.080 0.280 -0.021 0.789
RWC2 0.341 0.079 0.220 0.035 -0.046 0.053 0.728
RWC3 0.028 -0.071 -0.028 -0.062 0.285 0.192 0.675
Cronbach’s alpha 0.86 0.70 0.73 0.56 0.72 0.74 0.72

Table 2a. Exploratory factor analysis (the extent of CRM)
(Principal Components method with Varimax rotation; loadings>=0.50).


Construct : The Extent of CRM
  Items
Factor 1 : Interactive Management IM5
Factor 2 : Customer Prospecting CP4
  CP5
  CP6
Factor 3 : Partnership P5
  P6
Factor 4 : Personalization P3
  P4
  P5
Factor 5 : Empowerment ITL5
Factor 6 : Understanding Customer Expectations UCE4
Factor 7 : Relations with Customers RWC4
  RWC5
  RWC6

Table 2b. Exploratory factor analysis (the extent of CRM)
(Items thrown out due to poor loading).


The rest of the methodology deals with confirmatory analysis. Confirmatory Factor Analysis, Construct Validity (Convergent and Discriminant validity), and a reliability assessment with PLS (Partial least squares) were used. To do this, results obtained from PCA (Principal Components Analysis) using SPSS were submitted to PLS. Results of this second analysis regarding the confirmatory phase identified one factor for the construct “customer loyalty” and seven factors for the construct “the extent of CRM.” All items with a coefficient of Student’s T (t value) more than 1.64 (p<=0.05) (see Table 3) were retained. The results of this analysis are presented in Table 3.

Rho/Items of factors Weight Loading Student’s T
(t value)		 Convergent validity (AVE)
Loyalty
Reliability coefficient Rho = 0.640		       0.50
L1 0.5080 0.4351 2.0165  
L2 -0.0885 0.1900 0.0869  
L3 0.1002 0.4901 2.3467  
L4 0.8895 0.8594 3.9825  
         
Interactive Management
Reliability coefficient Rho = 0.820		       0.61
IM1 0.0269 -0.6142 -2.2756  
IM2 -0.3362 -0.7773 -2.8419  
IM3 -0.9722 -0.9202 -3.3931  
IM4 0.3999 -0.3488 -1.3677  
         
Customer Prospecting
Reliability coefficient Rho = 0.822		       0.61
CP1 -0.1294 -0.6157 -2.1025  
CP2 -0.6755 -0.9438 -4.0174  
CP3 -0.3719 -0.7604 -2.6222  
         
Partnership
Reliability coefficient Rho = 0.718		       0.57
PS1 -0.0742 0.0272 0.1246  
PS2 -0.3011 -0.0780 -0.3509  
PS3 0.2369 0.5905 2.3464  
PS4 1.1191 0.8936 3.3875  
         
Personalization
Reliability coefficient Rho = 0.648		       0.56
P1 0.4498 0.5410 2.1389  
P2 1.1132 0.9149 3.4475  
         
Empowerment
Reliability coefficient Rho = 0.839		       0.64
ITL1 0.5292 0.8549 3.5055  
ITL2 0.3498 0.8089 3.5682  
ITL3 0.3740 0.7314 3.1552  
ITL4 -0.3072 0.0290 0.1299  
         
Understanding Customer Expectations
Reliability coefficient Rho = 0.783		       0.66
UCE1 -0.1660 0.2849 1.0729  
UCE2 0.1013 0.5810 2.1475  
UCE3 0.9989 0.9896 3.6410  
Relations with customers
Reliability coefficient Rho = 0.702		       0.57
RWC1 1.0952 0.9849 5.2322  
RWC2 -0.0279 0.4450 1.7677  
RWC3 -0.1950 0.3395 1.4349  
AVE = Σλi²/n Rho = (Σλi)²/ (Σλi)²+Var(ξ)
Var (ξ) = Σ(1-λi²)

Table 3. Confirmatory factor analysis and convergent validity.


The far right column on Table 3 shows the convergent validity assessment for each factor. To obtain these values, an averaged variance shared between each construct and its measure was used (Fornell & Larcker, 1981). According to Fornell & Larcker (1981), convergent validity coefficients should be higher than or equal to 0.50. We noticed that all convergent validity coefficients calculated for all factors in this study were higher than or equal to 0.50, as recommended by Fornell & Larcker (1981). The first column on Table 3 shows the reliability of each measure, which is the Rho coefficient. Aubert & Rivard (1994) report that the guidelines established by Nunnally (1978) for the interpretation of Cronbach’s alpha also apply to the Rho coefficient. These guidelines estimate that acceptable reliability coefficients must be higher than 0.6. All Rho coefficients range between 0.640 and 0.839. These values are considered very satisfactory.

The discriminant and convergent validities and Student’s T (t value) analysis are presented in Table 4. Discriminant validity is the extent to which a measure of a construct differs from measures of neighboring constructs (Fornell & Larcker, 1981). This is the evaluation of variance shared between different constructs. This shared variance is represented by a Covariance square (PHI square) between the constructs. To evaluate discriminant validity, Fornell & Larcker (1981) suggest a comparison between the average variance extracted (AVE) for each factor and the variance shared between the constructs. To complete this evaluation, we used the matrix of covariance of the constructs in which we replaced the diagonal with the square root of the AVE (underlined in Table 4). The numbers on the diagonal (underlined) are all much larger than the elements off the diagonal. Based on this analysis, the discriminant and convergent validity of the measures appeared to be satisfactory.

  Loyalty Partnership Prospecting Understanding Expectations Interactive Management Empowerment Relations with Customers Personalization
Loyalty 0.50              
Partnership 0.102 0.57            
Prospecting 0.003 0.178 0.61          
Understanding Expectations 0.122 0.000 0.018 0.66        
Interactive Management 0.151 0.144 0.007 0.006 0.61      
Empowerment 0.184 0.017 0.004 0.008 0.133 0.64    
Relations with Customers 0.007 0.002 0.070 0.000 0.054 0.005 0.57  
Personalization 0.000 0.191 0.039 0.112 0.162 0.009 0.065 0.56

Table 4. Matrix of covariance squared (PHI square)


The Web Site Evaluation Procedure

To measure the moderating variables (the levels of the organizations’ Internet presence and interactivity), the evaluation grid mounted according to the guidelines offered by Kassarjian (1977) was used. Web site characteristics (the levels of the organizations’ Internet presence and interactivity) were evaluated by two judges: the researcher himself and an MBA graduate student. The inter-judges reliability is the percentage of agreement amongst multiple judges who treat the same communication materials (Kassarjian, 1977). The reliability assessment currently used is the agreement ratio of codage out of the total number of codage decisions (Kassarjian, 1977). Therefore, each judge makes 51 decisions per company’s web site (170 company’s web sites in total), which adds up to a total of 8670 (51*170) decisions. The number 51 is equal to the number of criteria on the evaluation grid including the perceptions of the judges. Of these 8670 decisions, both judges agreed with 7613 decisions, which makes an average of 44 (7613/170) decisions per company. Both judges disagreed with 1057 decisions, which makes an average of 6 (1057/170) decisions per company’s web site. The reliability inter-judges coefficient is 86.27%. Berelson (1952; cited in Kassarjian, 1977) claimed a range located between 66% and 95% with a concentration at 90% for acceptable inter-judges reliability coefficients. The ratio of 87.80% appeared to be satisfactory.

Decisions made
Agreed Decisions
Disagreed Decisions
Inter-judges
reliability coefficient
Total
8670
7613
1057
86.27%
Average
8670/170= 51
7613/170=44
1057/170=6

Table 5. Results of the web site evaluation (measurement of the levels of the organizations’ Internet presence and interactivity)


Results

Characteristics of Participating Firms

The respondents were spread across 9 different IT sub-sectors. However, according to table 6, seventy-seven percent (77%) of the respondents were primarily involved in the manufacturing of computer software, or information technology related services, or information system consulting or high technology or others. In terms of annual sales volume, 80% of the sample had annual sales of less than $10 million, while 13% sold less than $50 million. Only 4% sold more than $50 million and 3% sold more than $100 million.

About 86% of the organizations had fewer than 100 employees, while about 10% had fewer than 500 employees. About 4% of the organizations possessed between 500 and 10000 employees.

About 52% of the participating organizations have been in business for more than 10 years. Of the organizations responding, 35% had between 4 and 9 years in business, while 13% have been in business for less than 3 years.

Twenty-seven percent of the respondents reported that their firm's Web site had been in existence between 4 and 6 years, while 64% of organizations claimed to have been online for more than nine years.

Seventy-four percent of the organizations reported an average duration of sales cycle between 1 and 8 months, while 8% have more than 13 months as an average duration of sales cycle. This information indicated that the responses were experience-based.


Industry sector Percentage
IT organisations 100%
Location of firms
Canada 95%
USA 4%
Other 1%
IT sub-sectors
Manufacturing of computer hardware 4%
Information Technology related services 13%
Information System consulting 11%
Wholesale, retail or distribution of computer hardware 5%
Wholesale, retail or distribution of computer software 6%
High technology 10%
Manufacturing of computer software 28%
Internet access and service provider 3%
Multimedia 5%
Other 15%
The number of years firms have been in business Percentage
Less than 3 years 13%
4-6 years 24%
7-9 years 11%
10 years or more 52%
The size of firm (persons) Percentage
1-100 employees 86%
101-500 employees 10%
501-1000 employees 2%
1001-10000 employees 2%
The average duration of sales cycle of firms Percentage
Less than one month 14%
Between 1 and 4 months 38%
Between 5 and 8 months 22%
Between 9 and 12 months 18%
13 months or more 8%
The revenue (sales) of firms Percentage
Less than $10 million 80%
$11-$50 million 13%
$51-$100 million 4%
$101-$500 million or more 3%
The age of the firms Web site Percentage
Less than one year 7%
1-3 years 1%
4-6 years 27%
7-9 years 1%
9 years or more 64%

Table 6. Characteristics of the companies sampled.


The Test of Hypothesis H1 with PLS

The test of Hypothesis H1 (a, b, c, d, e, f, and g) on the 170 questionnaire respondents was carried out with a statistical tool named PLS-GRAPH. Table 7 shows that Student’s T (t value) of links between the extent of the CRM components of partnerships (2.42), empowerment (1.64), relations with customers (2.65), and personalization (1.74) on the one hand, and customer loyalty on the other are higher than 1.65 (p<=0.05). This first hypothesis test shows that only these four variables among the seven CRM variables have a positive and direct impact on customer loyalty.

  Loyalty
  Path coefficient (Beta standardized) Student’s T
(t value)
Partnership 0.179 2.42
     
Empowerment 0.172 1.64
     
Understanding Customer Expectations -0.015 -0.2538
     
Customer Prospecting -0.076 -0.8846
     
Relations with Customers 0.332 2.65
     
Interactive Management 0.020 0.27
     
Personalization 0.136 1.74

Table 7. Path coefficient and Student’s T (t value).


The Tests of Hypotheses H2 and H3

To test the interaction effects, analysis was pursued only with the four variables that have a positive and direct impact on customer loyalty. The two variables of Web site characteristics that both play a moderating role are highly correlated. It is for this reason that the following analysis was conducted by separately taking into account each moderating variable in order to avoid the problem of multi-co-linearity.

The tests of Hypotheses H2 and H3 were carried out with covariance analysis (ANCOVA). This would satisfy the homogeneity criteria and an examination of the data would show that the distribution met the normality criterion required by ANCOVA.

         Normality Test

The normality criterion was examined using the Lilliefors test, which is based on a modification of the test of Kolmogorov-Smirnov. This test was the most appropriate one for this analysis. Table 8 shows the results of the Lilliefors test.

Statistic Degrees of freedom Probability (P)
Relations with customers 0.067 170 0.224
Partnerships 0.055 170 0.185
Empowerment 0.068 170 0.232
Personalization 0.071 170 0.304
Customer Loyalty 0.077 170 0.314
Level of Presence on the Internet 0.058 170 0.232
Level of Interactivity on the Internet 0.041 170 0.166

Table 8. A normality test based on a modification of the Kolmogorov-Smirnov test.


The results, presented in Table 8, show that the Lilliefors test was not significant as all probabilities are higher than 5% (P>=0.05). That means the data distribution met the normality criterion. The second test of homogeneity was made simultaneously with the test of the hypotheses.

         Homogeneity Testing of the Variance: The Case of Hypothesis H2

Before running an ANCOVA to enable a test of Hypothesis H2, which expresses the relation between the level of presence on the Internet and the link between the extent of CRM and customer loyalty, it is important to ensure regression coefficient equality. This can be done by using a variance homogeneity test (Conover, 1980). This test was carried out by adding to the model the effect of the extent of CRM in terms of partnerships, empowerment, relations with customers, and personalization, level of presence on the Internet and the interaction between the extent of CRM and the level of presence on the Internet. The term the extent of CRM * level of presence on the Internet allows us to test the regression coefficient equality.
 
Loyalty
 
Homogeneity
Hypothesis H2
  (F value) P (F value) P
Partnership 42.068 0.000 42.068 0.000
Level of presence on the Internet 0.037 0.848    
Partnership * level of presence 0.153 0.696 32.756 0.000
         
Empowerment 34.559 0.000 34.559 0.000
Level of presence on the Internet 2.175 0.142    
Empowerment * level of presence 1.849 0.176 13.542 0.000
         
Relations with customers 42.735 0.000 42.735 0.000
Level of presence on the Internet 2.981 0.086    
Relations with customers * level of presence 2.842 0.094 9.795 0.002
         
Personalization 41.429 0.000 41.429 0.000
Level of presence on the Internet 0.659 0.418    
Personalization * level of presence 0.931 0.336 24.389 0.000

Table 9. H2 hypothesis and homogeneity testing.


As can be seen, Table 9 includes a column entitled “Homogeneity” as well as terms {partnership * level of presence on the Internet (F=0.153; P >0.05), empowerment * level of presence on the Internet (F=1.849; P >0.05), relations with customers * level of presence on the Internet (F=2.842; P >0.05), and personalization * level of presence on the Internet (F=0.931; P >0.05)} which meet the criteria of regression coefficient equality for the construct loyalty. Covariance analysis was used to test Hypothesis H2 because the normality testing and the homogeneity testing were positively verified.

To carry out covariance analysis (ANCOVA) in order to test Hypothesis H2 (a, b, e, and g), the following terms were simultaneously introduced into the model: 1) the effect of the link between independent variables and dependent variables; 2) the effect of the link between the moderating variable (the level of presence on the Internet) and the relation between independent variables and the dependent variable. Table 9 (the column of Hypothesis H2 testing) presents the results of this test, including the statistical values of F and P. According to these results, the level of presence on the Internet positively influences the relation between the extent of CRM (in terms of partnerships, empowerment, relations with customers, and personalization) and customer loyalty.

         Homogenenity of Variance Testing: The Case of Hypothesis H3 (a, b, e, and g)

Before running ANCOVA to test Hypothesis H3, (which expresses the relationship between the level of interactivity on the Internet and the link between the extent of CRM and customer loyalty), it is important to ensure regression coefficient equality. This can also be done with the test of variance homogeneity (Conover, 1980). This test was carried out by adding to the model the effect of the extent of CRM in terms of partnerships, empowerment, relations with customers, and personalization, level of presence on the Internet and the interaction between the extent of CRM and the level of presence on the Internet. The term the extent of CRM * level of interactivity on the Internet allows us to test the regression coefficient equality.


 
Loyalty
  Homogeneity Hypothesis H3
  (F value) P (F value) P
Partnership 121.924 0.000 121.924 0.000
Level of interactivity on the Internet 0.176 0.675    
Partnership * level of interactivity on the Internet 0.122 0.727 58.801 0.000
         
Empowerment 148.854 0.000 148.854 0.000
Level of interactivity on the Internet 0.042 0.837    
Empowerment * level of interactivity 0.295 0.588 38.968 0.000
         
Relations with customers 146.235 0.000 146.235 0.000
Level of interactivity on the Internet 0.419 0.518    
Relations with customers * level of interactivity 1.160 0.283 26.331 0.000
         
Personalization 140.017 0.000 140.017 0.000
Level of interactivity on the Internet 0.203 0.653    
Personalization * level of interactivity 0.545 0.462 47.019 0.000

Table 10. Homogeneity testing and Hypothesis H3 testing.


Table 10 includes the column entitled “Homogeneity” and the terms {Partnership * level of Interactivity on the Internet (F=0.176; P> 0.05), Empowerment * level of Interactivity on the Internet (F=0.295; P>0.05), Relations with Customers * level of Interactivity on the Internet (F=1.160; P >0.05), and Personalization * level of Interactivity on the Internet (F=0.545; P >0.05)} which meet the criteria of regression coefficient equality for the construct loyalty. Covariance analysis was examined for Hypothesis H3 testing because the normality and homogeneity tests were satisfied.
To carry out covariance analysis (ANCOVA) to test Hypothesis H3 (a, b, e, and g), the following terms were simultaneously introduced into the model: 1) the effect of the link between independent variables and dependent variables; 2) the effect of the link between the moderating variable (the level of Interactivity on the Internet) and the relation between independent variables and the dependent variable. Table 10 (the column of Hypothesis H3 testing) shows the results of this test, such as the statistical values of F and P. According to these results, the level of interactivity on the Internet positively influences the relation between the extent of CRM (in terms of partnerships, empowerment, relations with customers, personalization) and customer loyalty.


Discussion

The Extent of CRM and Customer Loyalty

It is clear that Hypothesis H1 (a, b, e, and g) was accepted while hypothesis H1 (c, d, and f) was rejected. The findings of Hypothesis H1 (a, b, e and g) specify that the extent of CRM, in terms of partnerships, empowerment, relations with customers, and personalization, have a positive and direct impact on customer loyalty. On the other hand, according to the results of Hypothesis H1 (c, d, and f), the extent of CRM in terms of understanding customer expectations, customer prospecting, and interactive management, do not have a positive and direct effect on customer loyalty.

The positive impact of partnerships on customer loyalty was previously found in the study conducted by Evans & Laskin (1994). This finding also corroborated the speculations of Payne (1994). Indeed, as mentioned above, Payne placed partnering at the extreme end of his loyalty scale, considering it a stage where a durable and close relationship between supplier and customer can be developed. The positive effect of partnerships on customer loyalty also supports the arguments of Wilson (1995), who outlined an integrated model of the customer relationship process in which the selection of a partner was the first step of effective CRM. Obviously, effectiveness in the present study was examined by measuring customer loyalty.

Similar results concerning the test of the hypothesis that the extent of the CRM component of empowerment positively influences customer loyalty can be found in Evans & Laskin (1994). Other authors have also been interested in the link between empowerment and customer loyalty. Uncles & Laurent (1997) have both argued that customer loyalty is the tangible and measurable benefit of an effective relationship maintained with employees. There is clearly a strong link between our findings and the previous findings of these authors. In addition, our findings support the view of empowerment held by Berry (1983), Gronroos (1990), Morgan & Hunt (1994), and Bendapudi & Leone (2002), all of whom expanded the definition of CRM by incorporating the idea of practicing empowerment on employees so that they will serve customers more effectively. As a result, these authors contend that customer loyalty will improve. Finally, the results of this study support some of the conclusions reached by Chow & Holden (1997) who established a positive link between empowerment and customer loyalty.

The hypothesis positing that relations with customers will have a positive impact on customer loyalty was also confirmed. This finding is especially important, as a good relationship with the customer is the keystone of CRM philosophy. The presence of this variable was also significantly linked to the definition of CRM. Customer loyalty is considered to be the consequence of an effective relationship initiated and maintained with a particular customer. Indeed, customer loyalty is presented in the literature as a relationship phenomenon. All the speculations, suppositions, and research findings mentioned above are very much confirmed in this paper by the acceptance of this hypothesis.

The results of the hypothesis tests show that personalization has a positive impact on customer loyalty. This finding also confirms some of the arguments found in the literature. We have already discussed the idea that CRM supplanted the management mix because the latter was a far more clinical approach, rendering the business representative active and the customer passive, and so ensuring that there was no personalized relationship with the business representative. Given this, one of the outstanding contributions that CRM, which influences customer loyalty, has made to business in general, is the personalization of the relationship between business representatives and customers (Gronroos, 1994). Finally, the result discussed here corroborates the arguments of Fournier & Yao (1997) and Macintosh & Lockshin (1997) that effective interpersonal relations increase customer loyalty.

The Extent of CRM, Loyalty, and Web Site Characteristics

          Partnerships, Customer Loyalty, and the Level of Presence on the Internet

The hypothesis that the level of presence on the Internet has a positive impact on the link between the extent of the CRM component of partnerships, and customer loyalty is significant. The explanation that comes directly to mind concerns the arguments of Ghosh (1998) that presence on the Web is international by definition because Web sites allow a company to attract international consumers. To this argument it is added that business globalization is supported by such an opportunity. Partnerships between firms from different countries are a common occurrence. Another argument is that a Web site presented in many languages and available 24 hours a day will help to develop stronger links between companies and their customers. The arguments by nGhosh (1998) mentioned above enable us to understand the reasons why Internet presence greatly fosters the link between partnerships and customer loyalty. A further explanation of why this hypothesis is so significant is that a company’s Web site that presents its partners’ logos induces the partners to stay for a long time with this particular company. Companies also have the option of including connections to their partners’ Web sites, which can encourage loyalty.

          Empowerment, Customer Loyalty, and the Level of Presence on the Internet

Another significant hypothesis is that the level of presence on the Internet has a positive impact on the link between the extent of the CRM component of empowerment and customer loyalty. This is interesting because this hypothesis accords with the speculations of Elfrink et al. (1997) who show that the majority of firms list their expertise on their Web site, and often include such material as curriculum vitae, important references, and employees’ accomplishments. Companies that take such steps empower their employees when they have to negotiate with external customers. This empowerment process has repercussions on the relationships that employees initiate and maintain with their customers in order to get their loyalty. Finally when employees are well informed about their company’s Web site content, the customers will most likely be satisfied with their services, and so loyalty will develop.

          Relations with Customers, Loyalty, and the Level of Presence on the Internet

The level of presence on the Internet positively influences the link between the extent of the CRM component of relations with customers, and customer loyalty. This hypothesis corroborates much of the following information collected during our literature review. A company’s brand image has an impact on the relationships with customers (Elfrink et al., 1997). Thus a firm that presents a Web site using both graphics and text will improve its brand image, which can encourage its customers to stay in a lasting relationship with it. According to the literature examined above, the effectiveness of this relationship will positively influence a customer’s loyalty.

The presentation of key headings on a Web site will allow customers to peruse the most important parts of the site without getting lost. By proceeding in this way, customers will save time when exploring the Web site because they will not waste time on useless details and diversions. This can improve the relationship that the firms maintain with their customers, and as a result, the loyalty of the customers will be positively affected. Also, the first impression that a customer has of a company, or its first contact with the company, can also influence the development of the relationship. A well-presented home page encourages customers to continue exploring the Web site and so will help to develop loyalty. A firm which has, for example, a “What’s new” heading on its Web site will be able to communicate new developments to its customers frequently. This is a pretext for keeping in touch with loyal customers, since the Web creates “one-to-one” relationship opportunities for business representatives (Hofacker & Murphy, 1998). Other factors attracting customers to a Web site that are often mentioned include the newness of the home page as well as the entertainment and variety found there (Dholakia & Rego, 1998). The frequency of changes to the Web page is another factor (Dholakia & Rego, 1998). Launching new products into the market will favor the building of relationships with customers. Moreover, this way of doing business will constitute a pretext for the customers’ repeating their purchase of goods and/or services.

          Personalization, Loyalty, and the Level of Presence on the Internet

The hypothesis that the level of presence on the Internet favorably influences the link between personalization and customer loyalty was accepted. This confirms the assertion that Web sites allow a “one-to-one” relation between the business representative and the site visitor. Companies can, for example, deliver personalized messages to their customers (Sen et al., 1998). The acceptance of this hypothesis leads us to theorize that using password-protected personalized Web sites to offer products and services to customers helps the commercial relations between a firm and its customers.

The Extent of CRM, Customer Loyalty, and the Level of Interactivity on the Internet

         Partnerships, Customer Loyalty, and the Level of Interactivity on the Internet

The hypothesis that the level of interactivity on the Internet positively influences the link between the extent of the CRM component of partnerships, and customer loyalty was significant. The test of this hypothesis confirms that firm’s representatives share ideas, influence views, and establish partnership potential through news groups, forums, and e-mail with their customers. Such methods of doing business can allow firms to develop customer loyalty. Firms involved in virtual exchange, via interactivity on the Internet, will be motivated to become loyal to each other if the partnership is based on a “win-win” relationship. The positive impact that the use of interactivity on the Internet has on the link between partnership and customer loyalty can be explained by the fact that when firms are committed to a partnership, they will include a link to their partner’s site. Such a Web site will contain forms, e-mail, and so forth, which favor interactivity. It is to be expected that this kind of relationship will result in loyalty. In conclusion, a partnership supported by the interactivity of the Internet can increase the loyalty between partners.

          Empowerment, Loyalty, and the Level of Interactivity on the Internet

The hypothesis that the level of interactivity on the Internet acts positively on the relation between empowerment and customer loyalty has been confirmed. This is not surprising when taking into account information in the literature review. In fact, one study has claimed that companies often empower one or several employees to answer questions or to make comments online (Ghost, 1998). These actions are carried out by e-mail or through online discussion (online conferences, forums, or chat rooms) and allow companies and customers to make the most of the online contact time to share their views. Companies empower their employees who react promptly to customer complaints. The companies train their employees to offer various services, solutions, or products during interactive online contact with the customers and to modify on the screen the CRM statements that companies use to convince customers to buy, on the basis of customers’ reactions and views. The home page tries to convince customers to accept the firm’s position in regard to certain circumstances (Dholakia & Rego, 1998). The use of these different tools to create customer loyalty is confirmed by the results of this research.

          Relations with Customers, Loyalty, and the Level of Interactivity on the Internet

The hypothesis that the level of interactivity on the Internet has a positive impact on the link between relations with customers and customer loyalty was found to be significant. There are arguments that explain the positive impact of this hypothesis. Simply by participating in news groups, companies can initiate, develop, and improve relationships with customers. The literature showed in many ways that maintaining such relationships leads to loyalty. Customers who have made purchases, who take part in news groups, and who involve themselves in online discussions are motivated to repeat their purchases from these companies. Customers will be loyal to these companies because they share the same ideology and views. When companies take part in forums and news groups, they will be able to recognize the customers who want some information, and who ask specific questions about their industry. Customers will demonstrate loyalty to the company which has answered their specific questions by way of news groups. Firms often create virtual clubs with the features of a virtual community. Customers will be ready to commit themselves to a relationship maintained through such a virtual community.

          Personalization, Loyalty, and the Level of Interactivity on the Internet

The acceptance of Hypothesis H3(g), which states that the level of interactivity on the Internet reinforces the relationship between personalization and customer loyalty, seems to be justified. The personalization of the messages and of the relations between a firm and its customers simplifies and reduces the sales cycle, and allows a one-to-one relationship (Borg, 1997), which can lead to customer loyalty. The literature helps us to understand that the interactivity offered by the Internet is very favorable to one-to-one relationships, especially with e-mail and forums. Thus, the use of forums on the Internet to share information and to engage in a dialogue with customers allows personalization and can ultimately lead to loyalty.


Conclusion

Recommendations, and Managerial and Theoretical Implications

The results of this study will allow Internet and information systems experts to inform businesses about the impact of Internet network use on customer loyalty. These experts have to show businesses reliable methods of fixing customer loyalty that involve increasing the level of presence and interactivity provided by the Internet.

Regarding the theoretical contribution of this study, it is important to point out its originality because it facilitates the development of a new conceptual model, which will help future research. This study could, in fact, be considered as one of the first in the electronic commerce field to employ scientific literature and an evaluation grid in order to measure Web site variables. The use of the statistical PLS and SPSS tools is another distinctive element of this research. Finally, the combination of qualitative and quantitative approaches constitutes a further contribution of this study.
The findings of this research will help IT companies identify the CRM factors, which they should emphasize when Web site characteristics are used to augment customer loyalty:
There are several limitations to this study. First, the study focuses on only one industry (IT companies) and it remains to be seen if the results apply to other industries (e.g., banks and insurance). Second, there were limitations concerning the questionnaire used; in particular, the characteristics of respondents. From certain statements, we became aware that some respondents were managers or Webmasters who did not necessarily have a sound knowledge of their organizations. In addition, some responses were based on respondents’ opinions and subjective perceptions and not on objective data.

Further research is necessary as Internet technology evolves so rapidly. It is important for future studies to make use of longitudinal research. Future research should also expand the range of Web site characteristic variables and examine their effects on the link between CRM and customer loyalty. It may also be beneficial to test the impact of Web site characteristics on the strategic advantages of firms. Finally, further research could be to apply the conceptual model used in this study to other industries, or to make use of the model to compare industries and companies of different sizes.


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About the Authors

Assion Lawson-Body is an Assistant Professor at University of North Dakota. He obtained his Ph.D. and MBA in MIS from the Laval University, Quebec, Canada. He also received DESS-CTCI from IAE, University of Montpellier 2, France. His publications have appeared in Management Science. He has also published in several conference proceedings such as Association of Information Systems, International Conference on Electronic Business (ICEB), Wuhan International Conference on Electronic Business, and Association for Information and Management. He worked for companies in France and lectured at Laval University before joining University of North Dakota. His research interests include Electronic Commerce and E-business, Customer Relationship Management, E-loyalty, Management information technologies, and Database systems.
Address: Department of Information Systems, College of Business and Public Administration, University of North Dakota, P.O. Box 8363, Grand Forks, ND, 58202; Phone: (701) 777-3505; Fax: (701) 777-2518; Office: 365P.

Moez Limayem is an Associate Professor at City University of Honk Kong. He obtained his Ph.D. and MBA in MIS from the University of Minnesota. His publications have appeared or have been accepted for publication in Journal of Organizational Computing, Group Decision and Negotiation, Small Group Research, Management Science, Accounting Management and Information Technologies, Journal of Computer Information Systems, DSS Transactions, Communications of the ACM and more. He has also published in several conference proceedings such as International Conference on Information Systems, Association of Information Systems, IEEE International Conference on Systems Man and Cybernetics, Hawaiian International Conference on Systems Sciences. He was the chair of MIS department at Laval University, Quebec, Canada before joining City University of Hong Kong. His research interests include Decision support systems, Electronic Commerce and Business Process Reengineering, management of information technologies, and knowledge management.
Address: Department of Information Systems, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong; Phone: (852) 27888530; Fax: (852) 27888694; Room: P7817.

Appendix: Item Definition

Factors and their items Item definitions
Loyalty  
L1 Of the existing maintenance agreements, the percentage of these agreements that are being renewed is :
L2 The percentage of sales to regular customers (customers with whom your firm maintains business relationships)
out of your firm’s total sales is :
L3 The average yearly revenue per regular customer is :
L4 The number of your firm's regular customers is :
L5 The average number of years during which your firm maintains business relationships with its customers is:
L6 The percentage of revenues from regular customers out of your firm’s total revenues is :
L7 In general, your firm negotiates with its customers.
 
Interactive Management  
IM1 Your firm uses customer feedback to improve your products and services.
IM2 Your firm records customer feedback about your products and services.
IM3 In general, your firm actively solicits customers' opinions on your products and services.
IM4 Your firm discusses with its customers their feedback.
IM5 Your firm seeks formal customer product evaluations.
 
Customer Prospecting  
CP1 Your firm uses a database for tracking and prospecting new customers.
CP2 In general, your firm devotes resources to help with the prospecting of new customers.
CP3 Your firm develops and distributes promotional material (brochures, booklets, pamphlets, Web pages, etc…)
CP4 Salespeople from your firm use current customers' testimonials or references when prospecting new customers.
CP5 Your firm participates in trade shows, conferences, expositions, etc…
CP6 Your firm invests in media publicity (TV, radio, press, etc…)
 
Partnerships  
PS1 Resale of your firm’s products and services by your customers.
PS2 Reference to your firm’s products and services when customers sell their own products and services.
PS3 Creation of joint ventures with customers.
PS4 Joint development of products and services with customers.
PS5 Joint advertising programs with customers.
PS6 In general, your firm builds partnerships with its customers.
 
Personalization  
P1 Your firm sends customized mail to customers.
P2 Your firm assigns one salesperson to each customer.
P3 Your firm manages its customers technique problems
P4 Your firm assigns one salesperson to each customer
P5 Your firm develops or prepares specific products for specific customers.


Factors and their items Item’s definitions
Empowerment  
ITL1 Your firm rewards employees who do their very best to solve customer problems.
ITL2 Your firm has policies indicating to employees their degree of responsibility and authority in solving customer problems.
ITL3 In general, your firm empowers employees with regards to customer relations.
ITL4 Your firm has a management training program for technical service employees.
ITL5 Your firm has a technical training program for business representatives.
 
Understanding Customer Expectations  
UCE1 Your firm organises focus groups with customers
UCE2 Your firm conducts market studies on customer expectations.
UCE3 Your firm conducts satisfaction surveys among customers.
UCE4 In general, your firm makes an effort to determine customer expectations.
Factors and their items Item’s definitions
Relations with customers  
RWC1 Salespeople from your firm maintain contacts with customers.
RWC2 In general, your firm conducts customer follow-ups.
RWC3 Your firm actively initiates contacts with customers.
RWC4 Your firm devotes resources to support the installation of its products at customer locations.
RWC5 Your firm actively seeks to improve business relationships with its customers (promotions, discounts, etc...)
RWC6 Salespeople from your firm visit customers.




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