The priority factor model for customer relationship management system success
Tae Hyup Roh
a,*, Cheol Kyung Ahn
b, Ingoo Han
caSeoul Information Technology University, 37-18 SamSung-Dong, GangNam-Gu, Seoul 135-090, South Korea
bKorea Insurance Development Institute, Seoul, South Korea
cGraduate School of Management, Korea Advanced Institute of Science and Technology, Seoul, South Korea
Abstract
As the market competition becomes keen, constructing a customer relationship management system is coming to the front for winning over new customers, developing service and products for customer satisfaction and retaining existing customers. However, decisions for CRM implementation have been hampered by inconsistency between information technology and marketing strategies, and the lack of conceptual bases necessary to develop the success measures. Using a structural equation analysis, this study explores the CRM system success model that consists of CRM initiatives:process fit, customer information quality, and system support; intrinsic success:efficiency and customer satisfaction; and extrinsic success:profitability. These constructs underlie much of the existing literature on information system success and customer satisfaction perspectives. We found the empirical support for CRM implementation decision-making from 253 respondents of 14 companies which have implemented the CRM system. These findings should be of great interest to both researchers and practitioners.
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Keywords:Customer relationship management; CRM success initiative; Information system success; Customer satisfaction; Profitability
1. Introduction
With an ever-increasing competition for marketing dominance, many firms have utilized the customer relation- ship management (CRM) system for improved business intelligence, better decision making, enhanced customer relations, and good quality of services and product offerings.
The underpinning of the customer-oriented managing concept is that identification and satisfaction of customer needs lead to improved customer retention, which is based on corporate profitability (Day, 1994; Sivadas & Baker- Prewitt, 2000). They recognize the CRM system could carry into the foreseeable future of hyper-competition, and try to implement off-the-shelf CRM solutions for CRM planning as is done for enterprise resource planning (ERP) systems, e-commerce systems, and advanced database systems (Holland & Light, 1999; Shao & Lin, 2002).
When a CRM project is started, many organizations may expect a substantial payback, increased revenue, reduced cost, loyal customers, real-time customer information, and satisfied CRM system users. The expenditures on CRM system equipment, a commitment of dedicated resources and services, have skyrocketed initially and thereafter. However, after implementing a CRM system, many organizations are left wondering enough return on investment. More in depth, many are asking the question, “Does CRM system lead to higher customer satisfaction and superior economic returns?
If so, which factors critically improve customer relationship and profitability?” Although the widespread acceptance of this relationship is evident in the growing popular literature on market-oriented and Information System (IS) success models, it is not yet clearly understood why and how CRM becomes successful while others fail.
In the realm of IS, the IS success model has been treated as a major issue of MIS research. The Davis’s (1986) technology acceptance model (TAM), an adaptation of the theory of reasoned action (TRA) (Fishbein & Ajzen, 1975) andDeLone and McLean’s (1992)IS success model provide the basic idea of user acceptance of IS and IS success
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doi:10.1016/j.eswa.2004.12.021
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* Corresponding author. Present address: Techno Management Research Center, Korea Advanced Institute of Science and Technology, Seoul, Korea. Tel.:C82 2 958 3685; fax:C82 2 958 3685.
E-mail address:[email protected] (T.H. Roh).
measures. In addition to these models, numerous researches have tried to find the underlying factor that may contribute to the relative success of dynamically changing IS (Cavaye
& Cragg, 1995; Johnston & Carrico, 1988; Reich &
Benbasat, 1990). The measurements of several dimensions of success factors have been used to assess IS success, such as process fit, customer information quality, and system support (Wixom, 2001). For many firms, the strong quality management of process, customer information, and system has become an essential ingredient for successful compe- tition (Fok, Fok, & Hartman, 2001). In the marketing and service management, the impacts of customer satisfaction and its profitability have been a major focus. The literature propose that there is a strong theoretical underpinning for an empirical exploration of the linkages between customer satisfaction and customer loyalty, which in turn affects profitability (Anderson, Fornell, & Lehmann, 1994; Day, 1994; Garbarino & Jonhnson, 1999; Hallowell, 1996;
Sivadas & Baker-Prewitt, 2000). This CRM issue should therefore be examined in light of both marketing and IS literatures. Customers have also increasingly become the end-user of information technology applications with the emergence of electronic commerce (Khalifa & Liu, 2002).
The specific research goals are to further develop the CRM success model based on empirically evident instru- ments that (1) measure factors that influence intrinsic CRM success and extrinsic CRM success, (2) identify the scales of these factors, (3) test the relative importance of various factors, and (4) are appropriate for use by academics and practitioners. In particular, we aim to examine the full range of variables that have been identified in prior studies and test the completeness of the model. This study intends to test many of the posited interrelationships by the sample of CRM system users. We discuss the causal relationships among CRM initiatives and intrinsic/extrinsic success instruments which contain the user-based measures and customer-based measures of CRM system for profitability.
The paper first outlines existing research findings concern- ing the factors which contribute to the successful implemen- tation of CRM and extends the previous work by bringing together empirically. In addition, the paper explores which success factors have the priority for CRM implementation and suggests managerial and technological implications.
2. Theoretical perspectives
DeLone and McLean (1992) formulated an IS success model using information and system quality to determine the effectiveness of an IS. Their comprehensive review of IS success measures makes two important contributions to understanding of IS success. First, it postulates a scheme for classifying a multitude of IS success measure into six aspects: system quality, information quality, system use, individual impact, organizational impact, and user satisfac- tion. Second, it suggests a model of ‘temporal and causal’
interdependencies between these categories. Based on their model, several IS success measures are proposed: system effectiveness, business profitability, improved decision quality and performance, perceived benefit of systems, level of system usage, and user satisfaction (Pitt, Watson, &
Kavan, 1995; Yoon, Guimaraes, & O’Neal, 1995).
Among the numerous dimensions that measure IS success factors and IS success itself, we formulate the CRM success model into the causal phases, which comprise CRM initiatives, intrinsic success and extrinsic success of CRM. These factors are the basis for our research model and hypotheses.
2.1. CRM initiatives
An enterprise-wide understanding of what factors lead to CRM success and where they start from is the vital starting block for effective CRM implementation and deployments.
Researchers studying IS success have focused the main determinant success factors of CRM on process fit, information quality and system support.
Process fit. To leverage the marketing and sales effort, the CRM system must be designed around an elaborate understanding of a CRM process. This will impede the CRM system initiatives and can be a key success factor. It is related to the description of structural contingency theory on technological fit, which identify the feasible set of process and technology (Drasin & Van de Ven, 1985). A review of the literature reveals that IS researchers offer a great diversity of views on the appropriate form for stating process theories (Markus & Robey, 1988; Orlikowski, 1993). The process fit, in this study, is viewed as having four important CRM processes: fitness level of customer interaction process, sales channel process, personalization process, and after-sales service process.
Customer information quality. A function of the output value produced by the CRM system as perceived by the system users. Making effective use of customer information resources is the critical issues facing IS executives. This reflects the high value of customer data resources and the importance of managing them effectively. Knowing custo- mers is critical to overall CRM success; however, just gathering customer data is not enough. With customer information analytics, these organizations can begin to realize the value from their CRM implementation. Custo- mer information analytics is more than just information about the facts. It builds insight into customer and market behaviors, enabling businesses to take the correct action necessary in ever-changing market environments.
Many different information characteristics, generated by an information system, are considered as important determinants of information quality perception including:
integrity, usefulness, currency, output timeliness, reliability, completeness, conciseness, format, and relevance (Bailey &
Pearson, 1983); understandability (Srinivasan, 1985); report usefulness (Mahmood & Medewitz, 1985). DeLone and
McLean (1992)point to the link by suggesting high quality of customer information will result in IS success and also suggest that to a large extent this relationship is intuitive.
Here, customer information quality is measured as follows:
integrity of customer information, usefulness of customer information, support of scoring and segmentation infor- mation, and forecasting the customer’s purchasing power.
System support. A measure of the CRM processing system itself (Negash, Ryan, & Igbaria, 2003). If the system has been implemented and adopted successfully, a firm is able to reap its benefits. The potential benefits to a firm are related to the impact dimension of system success. The determining criteria in assessment of system support are the performance characteristics of the systems under study.
These concern resource utilization (Kriebel & Raviv, 1980);
reliability, response time, and ease of terminal use (Swanson, 1974); and data accuracy, reliability, complete- ness, system flexibility, and ease of use (Hamilton &
Chervany, 1981); consistency of the user interface, quality of documentation, and sometimes, quality and maintain- ability of the program code (Seddon, 1997). The penetration of the system into the market, and the reaction of competitors are the factors discussed in the literature to impact on a firm’s ability to reap these benefits (Cavaye &
Cragg, 1995). If competitors react by implementing a similar system, the competitive edge gained by the first organization may only be temporary. Often the use of IT becomes a strategic necessity within the industry. This paper considers favorable system invest, implementation level, integration of CRM system with legacy MIS systems, and open networking system for sales force, which will reinforce the relationship between system users and customers.
2.2. Intrinsic CRM success
Efficiency. IS implementation success is frequently defined in terms of the achievement of some predetermined goals, which normally include multiple efficiency para- meters such as time, cost, and function (Hong & Kim, 2002;
Markus & Tanis, 2000). Efficiency is an important and useful measure of performance, which is closely related to, but different from, productivity. Unlike productivity, technical efficiency has been studied less frequently by IS researchers (Shao & Lin, 2002). When the purpose of IT investments is to improve operational efficiency, many traditional appraisal techniques may be considered appro- priate. Such investments are largely geared to the generation of tangible (financial) benefits, and are based on direct (financial) project costs. Such operational IT deployments have traditionally exploited the efficiency benefits of investing in IT. However, many managers are now appreciating the wider strategic implications of developing a robust and responsive IT infrastructure; yet this in turn presents businesses with the dilemma of how to assess, quantify and accommodate the implications of
infrastructural investments (Irani, 2002). Efficiency, in this study, is different from the traditional IS success measure in that it is comprehensive internal achievement of a firm’s CRM process. We measured internal efficiency as one of the intrinsic measures of CRM implementation success in terms of perceived improvements such as easiness of CRM, cost reduction, time saving, and alleviation of CRM load. We use efficiency to indicate internal success of a CRM system, determined by the process fit, customer information quality, and system support. Higher levels of internal efficiency are assumed to correspond to higher levels of CRM system.
Customer satisfaction. CRM is a customer-driven concept; that is, it allows customers to be in control of the system. Customer satisfaction is the collective outcome of the customer’s perception, evaluation, and psychological reaction to the consumption experience with product or service (Fornell, 1992; Yi, 1990). As customer satisfaction is commonly acknowledged as one of the most useful measurements of system success (Chen, Soliman, Mao, &
Frolick, 2000), we identify the underlying factors of customer satisfaction and develop an instrument to measure these factors. In the marketing studies focused on customer satisfaction with physical products and services delivered through channel (Khalifa & Liu, 2002). It is not clear whether the findings of these studies apply to CRM. This study demonstrates that customer satisfaction with customer relationship depends heavily on the roles and performance of organizational CRM activities. A customers’ relationship with a company is strengthened when that customer makes a favorable assessment about the company’s service quality and weakened when a customer makes negative assessments about the company’s service quality (Zeithaml, Berry, &
Parasuraman, 1996). Both the service management and the marketing literatures suggest that there is a strong theoretical underpinning for an empirical exploration of the linkages among customer satisfaction, customer loyalty, and profitability (Hallowell, 1996). We measure customer satisfaction as an intrinsic CRM success by perceived level of the shift after CRM system implementation: friendly interaction with customer, brand value, customer com- plains, and overall customer satisfaction.
2.3. Extrinsic CRM success
Profitability. The ultimate measure of CRM success is whether, if net benefit could be measured with precision, CRM success would equal net benefits logically. The issue of measuring IT returns has become even more pressing because the expenditures on IT equipment and service activities have risen. Several reasons are identified why management needs to scrutinize IT spending (Remenyi &
Twite, 1991). Firstly, the amounts of financial resources invested in IT are substantial and they are thus very likely to supplant other capital spending. Secondly, IT investments are seldom tied to the revenue-generating or profit-making aspects of the business and as a result, management may not
readily agree to IT’s value, contribution, or performance.
Thirdly, IT investments have frequently been perceived as high risk, compared with other traditional capital budgets.
However, CRM success has implicit and emotive areas of achievement which are not measurable by net benefits which is an idealized comprehensive measure of the monetary sum of all past and expected future benefits, less all past and expected future costs, attributed to the use of an information technology application. We use profitability as an alternative to net benefit. The operationalized scales of profitability are increase of new customers, reselling or up- selling, decrease of customers’ churn, and increase of overall profitability.
3. Theoretical development and research model
This paper focuses on the causal relationships among three CRM initiatives (process fit, customer information quality, and system support), intrinsic CRM successes (efficiency and customer satisfaction), and extrinsic CRM success (profitability). We examine the relationships among these constructs and develop the research hypotheses. The research model is presented inFig. 1.
First, we discuss the direct effects of CRM initiatives and profitability in H1. The literature on process fit, information quality, and system support has addressed the IS success that are associated with corporate profitability. The belief that a high level of these CRM initiatives will perform its obligation in the relation enables successful CRM. Process fit to the corporate operations is necessary in making CRM activities become familiar with the customer-oriented work process. In the customer information centric characteristic of CRM, companies should analyze customers’ experiences and problems, then respond and support their needs. CRM requires the perfect alignment with ever-changing custo- mers’ needs based on the integrated and reliable customer
information. In order to put the conceptual CRM into shape, systemic support leverages the every aspect of the CRM operation. It is therefore hypothesized that better process fit, customer information quality, and system support will positively influence CRM success. Thus, we develop H1 that are stated as follows.
H1a. Process fit of CRM is positively associated with Profitability.
H1b.Customer information qualityis positively associated withProfitability.
H1c.System supportof CRM is positively associated with Profitability.
Other important perspectives for achieving CRM success are efficiency and customer satisfaction. The relationships between CRM initiatives and intrinsic CRM success such as efficiency and customer satisfaction are dealt in H2. The underlying assumptions are that CRM initiatives will improve firm’s efficiency and enhance customer satisfaction and retention. It is hypothesized that making process, customer information, and system fitter to corporate CRM are positively associated with achieving efficiency and customer satisfaction. H2, therefore, is tested based on six sub-hypotheses.
H2a. Process fit of CRM is positively associated with Efficiency.
H2b. Customer information qualityof CRM is positively associated withEfficiency.
H2c.System supportof CRM is positively associated with Efficiency.
H2d. Process fit of CRM is positively associated with Customer satisfaction.
H2e. Customer information quality of CRM is positively associated withCustomer satisfaction.
Fig. 1. Hypothetical CRM success factor model.
H2f.System supportof CRM is positively associated with Customer satisfaction.
Customer satisfaction and efficiency are important areas of IS research because these are considered critical and useful factors in measuring its performance of IS, so there should be some relationship. We assume this as an intermediate path between efficiency and customer satis- faction, which is discussed in H3.
H3. There is a positive intermediate path fromEfficiencyto Customer satisfaction.
There is resurgent interest in understanding the links among efficiency, customer satisfaction, and profitability.
Finally, the links of intrinsic success to profitability in H4 will be tested. In a meta-analysis of strategy variables, several studies found a positive relationship between quality and economic returns (Anderson et al., 1994; Capon, Farley,
& Hoenig, 1990). In addition to this relationship, some theories are suggested that customer satisfaction is related to customer loyalty, which in return is related to profitability (Hallowell, 1996). Because customer satisfaction is critical for establishing long-term customer relationships (McKinney, Yoon, & Zahedi, 2002; Patterson, Johnson, & Spreng, 1997) and consequently significant in sustaining profit- ability, a fundamental understanding of factors impacting customer satisfaction is of great importance to CRM success. Given the increased emphasis on customer satisfaction, the question that begs our attention focuses on whether improvements in efficiency and customer satisfaction lead to improvements in the profitability of firms (Sivadas & Baker-Prewitt, 2000). Thus it is hypothesized in H4 that intrinsic CRM success has a direct effect on profitability.
H4a.Efficiencyis positively associated withProfitability.
H4b. Customer satisfaction is positively associated with Profitability.
4. Research methodology
4.1. Data collection and sample characteristics
The 253 survey questionnaires were gathered from 14 organizations. They involved 7 life insurance firms and 7 property and casualty insurance firms located in Korea, which have implemented and are operating the CRM system. The systems include the functions of e-mail response, call center management, data management (Data Warehouse), business intelligence, personalization, sales force automation, customer profiling/segmentation and so on. Target firms have been using the CRM system for about 2–5 years. The insurance industry in Korea is one of the first industries to implement the CRM system due to accelerating competitiveness attributed to open market policy and
globalization. The full survey was administered to persons who work in the divisions of marketing, sales, IT, system management, and operations. The selected interviewees have various amounts of experience in the insurance service domain. Among the 253 collected questionnaires, 234 cases were used for actual study. On an average, the respondents had 5.4 years of experience in their specialty areas.
4.2. Scale development
We first conducted literature reviews on related topics and carried out a series of in-depth interviews with CRM project managers and CRM operators to examine the external validity of our research model. We then developed the questionnaire items based on the literature and the field visits, as well as the comments gathered from the inter- views. The measures used to operationalize the constructs in the research model were mainly adopted from some of the related studies conducted in the past, with minor wording changes tailored to the interviewees. This resulted in the identification of 30 potential research items. These scales are presented in Appendix A and summarized in Table 1 with their related literature.
5. Results
5.1. Tests of the measuring scales
Internal consistency reliability is the accuracy or precision of a measuring instrument, which is the extent of uni-dimensionality, i.e. the detailed items (questions) measure the same thing (Hong & Kim, 2002; Straub, 1989).
The internal consistency reliability was assessed by calculating Cronbach’s alpha values. The reliability results of the constructs are summarized in Table 2. The internal consistency (Cronbach’s alpha) of the construct ranged from 0.7722 (for process fit) to 0.8591 (for efficiency), which were above the acceptable threshold (0.70) (Nunnally &
Bernstein, 1994).
Content validity of the survey instrument was established through the adoption of validated instruments by other researchers in the literature (Straub, 1989). Content validity means we measure what we are supposed to measure. In other words, if we aim at a good measure of CRM success we should be convinced that the measurement instrument includes the essential features of success (Saarinen, 1996).
With satisfactory content validity established, the measure- ment items were further tested for consistency, ease of understanding, and sequential appropriateness by a series of in-depth interviews with CRM project managers and CRM operators. Comments on or suggestions about the question sequence, wording choices, and measures were also solicited, leading to several minor modifications to the questionnaire. Subjects who had participated in the pretests were excluded from the subsequent main study.
Since each latent construct was measured by the multi- items, tests of construct validity were performed. Construct validity means that the underlying structure of the developed construct is found also in reality. Construct validity is established by relating a measuring instrument to a general theoretical framework in order to determine whether the instrument is tied to the concepts and theoretical assumption they are employing. This can be analyzed first, by correlating with the detailed items and scale. However, a more powerful method for analyzing the construct validity is factor analysis. In order to obtain evidence of the construct validity of an instrument, a researcher must make use of both convergent validity and discriminant validity. In this study, we followStraub’s (1989)processes of validating instruments to test construct validity in terms of convergent and discriminant validity.
Convergent validity, the degree to which multiple attempts to measure the same concept are in agreement, was evaluated by examining the item–total correlation, based on the correlation of each item to the sum of the remaining items. This approach assumes that the total score is valid and thus the extent to which the item correlates with the total score is indicative of convergent validity for the item.Table 2 shows the correlations for each of research variables whose item-to-total correlation score was greater than 0.5.
Discriminant validity was checked by factor analysis (Kerlinger, 1964). Because multi-item constructs measure each variable, factor analysis with varimax was employed to check unidimensionality among the items. We used
confirmatory factor analysis (CFA) shown in Fig. 2 with AMOS 4.0 to examine the convergent validity of each construct. The factor loadings are ranged from 0.5500 (SATI4) to 0.8010 (EFFI2), and these are greater than the recommended level of 0.35, which is based on 250 samples and 0.05 significance level (Hair, Anderson, Tatham, &
Black, 1998). In Table 2, discriminant validity was confirmed when items for each variables loaded onto single factors with loadings of greater than 0.5500. These results confirm that each of these construct is unidimensional and factorially distinct and that all items used to operationalize a particular construct is loaded onto a single factor.
The factor structure was not difficult to interpret, corresponding with process fit, customer information quality, system support, efficiency, customer satisfaction, and profitability, which is composed of four items each. The model explained 70.945% of the variance. The range for factor loadings was 0.6207–0.8662. Table 3 reports the results of factor analysis.
5.2. Test of the structural model
Structural equation modeling was performed to test the hypothesized model presented inFig. 1. We used the most recent software for this analysis, AMOS 4.0 developed by Arbuckle and Wothe (2000). The overall goodness-of-fit was assessed in terms of the following 7 common model fit measures: Chi-square (P-value), Chi-square/degree of free- dom, goodness-of-fit index (GFI), root mean square error (RMR), adjusted goodness-of-fit index (AFGI),
Table 1
Scale items for CRM success factors and success measures
Construct Factors Items Label
CRM initiatives Process fit Customer interaction process PROC1
Linkage to sales channels PROC2
Personalized marketing support process PROC3
After sales service process PROC4
Customer information quality Integrity of customer information sources INFO1
Usefulness of customer information INFO2
Support of customer scoring and segmentation information INFO3 Forecasting potential purchasing power INFO4
System support Invest in the system infrastructure for CRM SYS1
Implementation level of CRM system SYS2
Integration of CRM system with legacy MIS system SYS3 Open networking system for sales-force SYS4
Intrinsic CRM success Efficiency Making CRM easier EFFI1
Saving CRM time EFFI2
Saving CRM cost EFFI3
Making CRM load alleviated EFFI4
Customer satisfaction Increase of friendly interaction with customers SATI1
Enhancing brand value SATI2
Decrease of customer complaints SATI3
Increase of overall customer satisfaction level SATI4
Extrinsic CRM success Profitability Increase of new customers PROF1
Increase of reselling/upselling PROF2
Decrease of customers’ churn PROF3
Increase of overall profitability PROF4
comparative fit index (CFI), and parsimonious goodness-of- fit index (PGFI).
As presented inTable 4, the results of this hypothesized full CRM success model indicate a favorable fit of the model. All other indicators point to a good fit except the Chi-square statistics. As the Chi-square has an inherent
problem with sample size (Hartwick & Barki, 1994), discrepancy/degree of freedom was used as an alternative indicator of the Chi-square statistics. GFI is 0.903, AGFI is 0.851, RMR is 0.056, CFIZ0.912, and PGFIZ0.671. Thus overall the data indicate a favorable fit for our hypothesized model. The direct model shows an acceptable fit except
Fig. 2. Confirmatory factor analysis model.
Table 2
Summary of reliability and validity test
Construct Scale items Mean SD Reliability Convergent val-
idity, corrected item–total correlation
Discriminant validity, factor loading on single factor Alpha-coeffi-
cient
Alpha if item deleted
Process fit PROC1 3.4311 1.1075 0.7722 0.7174 0.5743 0.6350
PROC2 3.2511 1.0474 0.7094 0.5892 0.6730
PROC3 3.5398 1.0478 0.7228 0.5633 0.6510
PROC4 3.5733 1.0581 0.7202 0.5683 0.7350
Customer info.
quality
INFO1 3.8238 1.0109 0.7755 0.7149 0.5673 0.6870
INFO2 3.5487 0.9216 0.7001 0.5982 0.7140
INFO3 3.7699 0.8775 0.6889 0.6269 0.7210
INFO4 3.4890 1.1025 0.7353 0.5088 0.6110
System support SYS1 3.6211 0.9808 0.7895 0.7685 0.5333 0.5870
SYS2 3.6295 0.9460 0.7347 0.6056 0.6510
SYS3 3.3965 1.0566 0.7052 0.6584 0.7810
SYS4 3.2731 1.0540 0.7378 0.5979 0.7530
Efficiency EFFI1 3.7357 0.9268 0.8591 0.8320 0.6742 0.7460
EFFI2 3.6564 0.9246 0.8133 0.7209 0.8010
EFFI3 3.4978 0.9748 0.8146 0.7165 0.7950
EFFI4 3.4867 0.9966 0.8201 0.7045 0.7660
Customer satis- faction
SATI1 3.5198 0.9470 0.7853 0.7185 0.6220 0.7650
SATI2 3.1630 0.9523 0.7031 0.6509 0.7280
SATI3 3.2788 0.9943 0.7330 0.5957 0.7140
SATI4 3.0176 0.8569 0.7737 0.5078 0.5500
Profitability PROF1 3.7621 0.8497 0.8293 0.8142 0.5874 0.7190
PROF2 3.8282 0.8526 0.7802 0.6672 0.7490
PROF3 3.8502 0.9094 0.7551 0.7191 0.7790
PROF4 3.7566 0.9304 0.7856 0.6556 0.7240
Chi-square and CFI, but the full model appeared to be superior to the direct model in explaining profitability.
In general, it shows that three CRM initiatives (process fit, customer information quality, and system support) do not impact on the profitability directly although process fit has a statistically meaningful link to profitability. No significant impact of customer information quality and system support on profitability was observed as shown in Fig. 3.
The significance and the relative strength of individual links specified by the research model were also evaluated.
The results provide meaningful support for many of the posited research hypotheses. Seven out of the 12 postulated paths within this model were of statistical significance: one
at the 0.05 significance level and six at the 0.01 level. In general, it shows that a number of variables do impact on the success of the CRM; especially all CRM initiatives affect efficiency. However, the results indicate that three CRM initiatives (process fit, customer information quality, and system support) do not impact on customer satisfaction but internal efficiency directly. Neither process fit nor customer information quality influences the improvement of customer satisfaction. Only system support plays a part in influencing customer satisfaction. Efficiency appeared to be a mean- ingful link to customer satisfaction, but no significant impact of efficiency on profitability was observed.
The path coefficients, hypotheses tests, and effects of individual paths are shown in Table 5. As hypothesized,
Table 3 Factor analysis
Construct Scale items Factors
1 2 3 4 5 6
Process fit PROC1 0.7583 0.1013 0.1574 0.1455 0.0455 0.0667
PROC2 0.7445 0.0527 0.0974 0.3073 0.1156 0.0554
PROC3 0.6818 0.2720 0.828 0.0608 K0.0337 0.2733
PROC4 0.8562 0.1871 K0.0198 0.2025 0.3408 0.3508
Customer info.
quality
INFO1 0.1294 0.7501 0.0406 0.1302 0.0649 0.0990
INFO2 0.0698 0.7650 0.0919 0.0953 0.1134 0.1436
INFO3 0.0298 0.7903 K0.0242 0.1653 0.0720 0.0710
INFO4 0.3340 0.8244 0.0839 0.1399 0.0662 K0.1061
System support SYS1 0.1860 K0.1086 0.7307 0.0632 0.0811 0.1613
SYS2 K0.0156 0.1730 0.7951 0.0995 K0.0258 0.2000
SYS3 0.1028 0.0202 0.7501 0.1432 0.2971 0.0755
SYS4 0.1043 0.1917 0.8386 0.3258 0.3326 K0.0949
Efficiency EFFI1 0.2053 0.2379 0.1654 0.7542 K0.0200 0.1652
EFFI2 0.2321 0.1283 0.0600 0.7615 0.2598 0.1486
EFFI3 0.1495 0.1319 0.1429 0.7423 0.2187 0.2031
EFFI4 0.1181 0.1527 0.1936 0.7394 0.1345 0.1575
Customer satis- faction
SATI1 0.0202 0.2130 0.2537 0.2694 0.8060 0.2781
SATI2 0.1130 0.0585 0.1001 0.3079 0.7194 0.1332
SATI3 0.1633 0.0285 0.1930 0.2053 0.6207 0.3245
SATI4 0.0134 0.0972 0.1051 K0.0427 0.7698 0.1627
Profitability PROF1 0.3308 0.1584 0.0156 0.3793 0.3095 0.7759
PROF2 0.1905 0.2511 0.0939 0.2259 0.1535 0.7107
PROF3 0.1114 K0.0355 0.1457 0.2180 0.2314 0.7874
PROF4 0.1112 0.0376 0.1773 0.0972 0.2454 0.7677
Note. Extraction method: principal component analysis. Rotation method: Varimax with Kaiser normalization. Total variance explained (70.945%).
Table 4
Overall fit indexes of the investigated models
Fit measure Direct
model
Full model Recommended cut-off value
Reference
Chi-square 211.429 487.654 Near to degrees of
freedom
Degrees of freedom 98 237 The greater, the better
P-value 0 0 R0.05 Hair et al. (1998)
Chi-square /DF 2.157 2.058 %3.0;%5.0 Etezadi-Amoli and Farhoomand (1996) and McKinney et al. (2002)
GFI 0.849 0.903 R0.90 Etezadi-Amoli and Farhoomand (1996)
Adjusted GFI 0.809 0.851 R0.80 Chase (1978) and Etezadi-Amoli and Farhoomand (1996)
RMR 0.059 0.056 %0.08 Jo¨reskog and So¨rbom (1993)
CFI 0.893 0.912 R0.90 Hair et al. (1998)
PGFI 0.643 0.671 The higher, the better
process fit influences profitability [H1a] and three CRM initiatives was found to influence efficiency [H2a–c]. That system support influences customer satisfaction was also supported in hypothesis H2f. There was support for hypothesis H3 that efficiency will result in customer satisfaction.
However, neither customer information quality [H1b]
nor system support [H1c] was found to have a significant direct impact on profitability. It was not supported that process fit and customer information quality will influence customer satisfaction [H2d and e]. There was no support for H4a that efficiency significantly affects profitability.
Three CRM initiatives have no direct impact on profit- ability except process fit. The CRM initiatives influence profitability indirectly through efficiency and customer satisfaction path, which in turn fosters profitability.
Garbarino and Jonhnson (1999) showed that customer satisfaction can be the primary mediating construct between the component attitudes and future intentions. It thus appears that mediation effects can be assessed with traditional regression models or with path analytic tech- niques. Mediation is a hypothesized causal chain in which one variable affects a second variable that, in turn, affects a third variable. To clarify the mediation effects of extrinsic CRM success, operationalized concept ‘Profitability’, a procedure developed by Baron and Kenny (1986) was applied to the path analysis results. When the mediating model involves only measured variables, the basic analysis approach is multiple regression or OLS. Since a mediating model involves latent constructs in our model, we test the basic data analysis using structural equation modeling.
Complete mediation is the case in which the explanatory variable (X) no longer affects the dependent variable (Y)
after the mediating variable (M) has been controlled and so direct path is zero. Partial mediation is the case in which the path from X to Y is reduced in absolute size but is still different from zero when the mediator is controlled.
Since the coefficient of customer satisfaction in the link between customer satisfaction and profitability was signifi- cant, we could check the type of mediation effects on efficiency–profitability and CRM initiatives–profitability.
The results of the test of mediation effects are given inTable 6.
In the links of efficiency and profitability, complete mediation effects of customer satisfaction were found. No mediation effects of customer satisfaction were found in the relationship between process fit/customer information quality and profitability. Since there were complete mediation effects of customer satisfaction between effi- ciency–profitability, two-stage mediation effects of effi- ciency could be taken into consideration between CRM initiatives and profitability. Efficiency has indirect com- plete mediation effect of customer information quality–
profitability and system support–profitability.
6. Discussion
6.1. Managerial implications
The widespread belief in the intuitive relationship between CRM quality, customer satisfaction, and profit- ability, as well as the growing concentration on attempts to improve customer relationship, serves to underscore the importance of analytical and empirical work that increase our understanding of customer satisfaction and how it relates to profitability. As a business discipline, this research
Fig. 3. Results of structural equation model (regression coefficient/p-value!0.10; **p-value!0.05; ***p-value!0.01).
could be directed toward helping managers and practitioners decide CRM implementation priority, and improve both business processes and competitiveness through the deploy- ment of a CRM system. Managerial and technological implications can be drawn from this study.
First, investment in CRM processes, information, and system, in general, is expected to enhance an organization’s performance as measured by efficiency. This study sub- stantiates the positive correlation between IT investment
and a firm’s internal efficiency. Due to the connection among efficiency, customer satisfaction, and profitability, if management wishes to improve the firm’s profitability, one logical way of achieving this is to employ CRM in different aspects of the business and enhance its internal efficiency in the CRM process. However, management should not draw too hasty a conclusion from our findings. The positive relationship between CRM initiatives and profitability does not translate directly into reckless CRM investments, nor is
Table 5
Tests of hypothesized model
(a)Path coefficient and hypothesis test results
Hypothesis Estimate SE P-value Test results
H1a Process fit0profitability 0.262 0.071 0.000*** Supported
H1b Customer_info. quality0profitability K0.048 0.055 0.383 Rejected
H1c System_support0profitability K0.053 0.062 0.392 Rejected
H2a Process fit0efficiency 0.333 0.066 0.000*** Supported
H2b Customer_info. quality0efficiency 0.130 0.060 0.030** Supported
H2c System_support0efficiency 0.218 0.054 0.000*** Supported
H2d Process fit0customer_satisfaction 0.094 0.076 0.216 Rejected
H2e Customer_info. quality0customer_satisfaction 0.032 0.060 0.599 Rejected
H2f System_support0customer_satisfaction 0.266 0.061 0.002*** Supported
H3 Efficiency0customer_satisfaction 0.324 0.106 0.002*** Supported
H4a Efficiency0profitability 0.109 0.101 0.282 Rejected
H4b Customer_satisfaction0profitability 0.554 0.114 0.000*** Supported
(b)Effects of individual paths
Dependent variable Process fit Customer_info. quality System_support Efficiency Customer_satisfaction Total effects
Efficiency 0.333 0.130 0.218 – –
Customer_satisfaction 0.202 0.074 0.337 0.324 –
Profitability 0.410 0.007 0.158 0.288 0.554
Direct effects
Efficiency 0.333 0.130 0.218 – –
Customer_satisfaction 0.094 0.032 0.266 0.324 –
Profitability 0.262 K0.048 K0.053 0.109 0.554
Indirect effects
Efficiency – – – – –
Customer_satisfaction 0.108 0.042 0.071 – –
Profitability 0.148 0.055 0.210 0.180 –
*P!0.10; **P!0.05; ***P!0.01.
Table 6
Testing for mediation effects
Explanatory variable Mediating variable Dependent variable Mediation
Std. est. P-value Std. est. P-value
Customer_satisfaction Profitability
Efficiency 0.324 0.002 0.109 0.282 Complete
Efficiency Profitability
Process fit 0.333 0.000 0.262 0.000 Partial
Customer_info. quality 0.130 0.030 K0.048 0.383 Indirect complete
System_support 0.218 0.000 K0.053 0.392 Indirect complete
Customer_satisfaction Profitability
Process fit 0.094 0.216 0.262 0.000 No
Customer_info. quality 0.032 0.599 K0.048 0.383 No
System_support 0.266 0.000 K0.053 0.392 Complete
profitability from improving customer satisfaction immedi- ately realized. Because efforts to increase current custo- mers’ satisfaction primarily affect future purchasing behavior, the greater portion of any profitability from improving customer satisfaction also will be realized in subsequent periods. This implies that a long-run perspective is necessary for evaluating the efficacy of efforts to improve CRM initiatives. Firms that invest heavily in a CRM system and are highly efficient in the CRM process may differ inherently from inefficient firms in ways that are not rectifiable by merely increasing CRM expenditure. Strong support from top management, effective CRM strategies, innovative organizational culture, excellent IT personnel, and other resources must also be available to help exploit the promised benefit of a CRM system.
An important caveat must be made regarding the findings of this study. A CRM operator might infer from the conclusion that since customer satisfaction is related to profit, a company should endeavour to satisfy every customer. This could be an error in interpretation. A company’s population of customers undoubtedly contains individuals who either cannot be satisfied given the service levels and pricing the company is capable of offering, or will never be profitable given their marketing and sales activity (their use of resources relative to the revenue they supply).
Any company would be wise to target and serve only those customers whose needs it can meet better than its competitors in a profitable manner. These are the customers who are most likely to remain with that company for long periods, purchase multiple products and services, and recommend the company to their friends and relations, and who may be the source of superior returns to the company’s shareholders.
Based on our test, customer information quality is one of the key factors to realize value from any CRM implemen- tation. By utilizing it, businesses can take the correct action necessary in ever changing market environments. However, customer information itself has little value. It is merely a representation of actions and transactions. The real value of customer information lies in the insight it can offer and, ultimately, in the positive, customer-oriented action it triggers. The insight to measure effectiveness, cut costs, reduce churn, understand relationships, anticipate trends, predict demand, optimize promotions, or segment the market must be used as a catalyst for action.
From a causal perspective, the structural equation analysis suggests that efficiency may be an antecedent to customer satisfaction, rather than a parallel, or direct determinant of profitability. The insignificant correlation between efficiency and profitability all but vanishes when customer satisfaction is controlled for. This, coupled with a significant efficiency-customer satisfaction relationship is exactly the pattern one would expect if customer satisfac- tion mediated between ease of efficiency and profitability.
That is, the results are consistent with a chain of causality from efficiency to customer satisfaction to profitability. The causal influence of internal efficiency on external customer
satisfaction makes sense conceptually, too. This intriguing interpretation is preliminary and should be subjected to more wide-ranging experimentation.
6.2. Study limitations and further research
Although this study reports meaningful implications toward the development of multidimensional measures of factors that influence CRM success, the validity of an instrument cannot be firmly established on the basis of a single study. Especially, all cases used for tests were collected in insurances firms located in Korea. Korea is a relatively speedy and competitive arena for accepting new IT systems, but it has difference dependent on industry.
Especially, the financial and telecommunication industries have shown front-running features. It is appropriate for CRM practitioners and academicians to interpret our findings as a guide model, rather than generalizing our measures in all industries.
Another limitation is related to the exploratory nature of the study and what relates to framework development issues. Measurement instruments are not ‘set in stone’, because CRM implementation is an on-the-going project and its job processes are totally influenced by the industry.
Initial instrument development efforts contain some ambi- guity concerning the appropriate model for the underlying model framework. Further confirmatory studies are necess- ary to complete the instrument development process. These limitations notwithstanding, the results represent a promis- ing step toward the establishment of improved measures for creating CRM success model.
As has been implied, there is a need for further research efforts focused on accumulating further empirical evidence for the validation and assessment of measurement properties and data surmounting the limitations of the present study.
Such research is needed to address how other variables relate to efficiency and customer satisfaction. Effectiveness and customer loyalty, for example, has received inadequate attention in MIS and marketing theories. Further research could also investigate the relative importance of the factors impacting each stage of the CRM process. These efforts should involve studies identifying the organizational factors which affect such independent variables as process fit, customer information quality, and system support. Also, special attention should be focused on data collected in relatively various industries or specific context over an extended period. The analysis of such data may enable conclusions to be drawn about both more generalized relationships among variables and causality.
7. Conclusions
In this paper, we set out to investigate factors affecting the success of CRM implementation from three perspec- tives: efficiency literature in IS, customer satisfaction
literature in marketing, and firms’ aggregated profitability.
These were synthesized to identify CRM initiatives, intrinsic success, and extrinsic constructs for analyzing the CRM success model. Based on IS literature, we argued that measuring internal efficiency for process fit, customer information quality and system support provides the first insight for achieving CRM success. By synthesizing IS and marketing theories related to customer satisfaction and profitability, key constructs are identified for CRM implementation priority. The CRM success model provides strong support for the reliability and validity of the proposed metrics for measuring the key constructs of CRM success.
The findings of this study discovered multidimensional measures of factors that influence profitability through CRM that are intuitively appealing and reliable. The analysis of the measurement model indicates that the proposed metrics have a relatively high degree of validity and reliability. These measures can be used to evaluate what influences CRM success and to provide insight for making decisions about the priority of CRM investment. The results of the study provide reliable instruments for operationalizing the key constructs in the analysis of CRM success and have some important implications for implementing CRM systems.
One of the most significant findings is the relative strength of causal paths on the CRM initiatives–efficiency–
customer satisfaction–profitability compared to the CRM initiatives–profitability relationship. In the difference between the models, intrinsic success factors such as efficiency and customer satisfaction were strong mediating factors linking the CRM initiatives to profitability. This difference was even more pronounced in examining the indirect and mediation effect test of efficiency on profit- ability. We have obtained statistical evidence suggesting that CRM initiatives, in general, exert a significantly positive influence on internal efficiency. Due to the close relationship among CRM initiatives, efficiency, customer satisfaction, and profitability, this study offers a CRM success model and meaningful implications for CRM planning and implementation. Thus, a major conclusion of this study is that the three CRM initiatives such as process fit, customer information quality, and system support, while not impacting profitability directly, could impact profit- ability via impacting efficiency and should not be ignored by those attempting to plan successful CRM systems.
Appendix A. Survey
The different opinions are indicated by the numbers.
1: Strongly disagree, 2: disagree to some extent, 3:
uncertain, 4: agree to some extent, 5: strongly agree.
CRM success factors [Process fit]
1. The customer interaction processes built in CRM system are well equipped.
2. The linkages between sales channels are well controlled.
3. The personalized marketing support processes are well constructed.
4. The after sales service processes are well defined.
[Customer information quality]
1. Various customer information sources are well integrated.
2. The customer information provided by CRM system is useful.
3. The customer scoring and segmentation information are supported by CRM system.
4. The potential purchasing power of customers can be estimated.
[System support]
1. Our company is supportive of investing in the system infrastructure for CRM.
2. Our CRM system is well implemented.
3. The CRM system and legacy MIS system are well integrated.
4. Open networking system for sales-force is well supported.
CRM success measures [Efficiency]
1. The CRM system makes my CRM easier.
2. The time of CRM becomes lower by using the CRM system.
3. The cost of CRM becomes lower by using the CRM system.
4. Using the CRM system makes CRM job load alleviated.
[Customer satisfaction]
1. Friendly interactions with customers are increasing after implementation of the CRM system.
2. Implementation of the CRM system helps to enhance brand value.
3. Customer complaints are decreasing after implemen- tation of the CRM system.
4. Overall customer satisfaction level is increasing after implementation of the CRM system.
[Profitability]
1. New customers are increasing after implementation of the CRM system.
2. Reselling/up-selling is increasing after implementation of the CRM system.
3. Customers’ churn is decreasing after implementation of the CRM system.
4. Overall profitability is increasing after implementation of the CRM system.
References
Anderson, E. W., Fornell, C., & Lehmann, D. R. (1994). Customer satisfaction, market share, and profitability: Findings from Sweden.
Journal of Marketing,58(3), 53–67.
Arbuckle, J. L., & Wothe, W. (2000).Amos 4.0 user’s guide. SmallWaters Corporation.
Bailey, J. E., & Pearson, S. W. (1983). Development of a tool for measuring and analyzing computer user satisfaction.Management Science,29(5), 530–545.
Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations.Journal of Personality and Social Psychol- ogy,51(6), 1173–1182.
Capon, N., Farley, J. U., & Hoenig, S. (1990). Determinants of financial performance: A meta-analysis.Management Science,36, 1143–1159.
Cavaye, A. L. M., & Cragg, P. B. (1995). Factors contributing to the success of customer oriented interorganizational systems. The Journal of Strategic Information Systems,4(1), 13–30.
Chase, R. B. (1978). Where does the customer fit in a service operation?
Harvard Business Review,56(6), 137–142.
Chen, L., Soliman, K., Mao, E., & Frolick, M. N. (2000). Measuring user satisfaction with data warehouses: An exploratory study.Information and Management,37(3), 103–110.
Davis, F. D. (1986).A technology acceptance model for empirically testing new end-user information systems: Theory and results. Doctoral dissertation, Sloan School of Management, Massachusetts Institute of Technology.
Day, G. S. (1994). The capabilities of market-driven organizations.Journal of Marketing,58(4), 37–52.
DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable.Information Systems Research,3(1), 60–95.
Drasin, R., & Van de Ven, A. (1985). Alternative forms of fit in contingency theory.Administrative Science Quarterly,30(4), 514–539.
Etezadi-Amoli, J., & Farhoomand, A. F. (1996). A structural model of end user computing satisfaction and user performance.Information and Management,30(2), 65–73.
Fishbein, M., & Ajzen, I. (1975).Belief, attitude, intention and behavior:
An introduction to theory and research. Reading, MA: Addison- Wesley.
Fok, L. Y., Fok, W. M., & Hartman, S. J. (2001). Exploring the relationship between total quality management and information systems develop- ment.Information and Management,31(2), 355–371.
Fornell, C. (1992). A national customer satisfaction barometer: The Swedish experience.Journal of Marketing,55(1), 1–21.
Garbarino, E., & Jonhnson, M. S. (1999). The different roles of satisfaction, trust, and commitment in customer relationships.Journal of Marketing, 63(2), 70–87.
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998).
Multivariate data analysis with readings(5th ed). Englewood Cliffs, NJ: Prentice-Hall.
Hallowell, R. (1996). The relationships of customer satisfaction, customer loyalty, and profitability: An empirical study.International Journal of Service Industry Management,7(4), 27–42.
Hamilton, S., & Chervany, N. L. (1981). Evaluating information system effectiveness: Comparing evaluation approaches.MIS Quarterly,5(3), 55–69.
Hartwick, J., & Barki, H. (1994). Explaining the role of user participation in information system use.Management Science,40(4), 440–465.
Holland, C. R., & Light, B. (1999). A critical success factors model for ERP implementation.IEEE Software,16(3), 30–36.
Hong, K. K., & Kim, Y. G. (2002). The critical success factors for ERP implementation: An organizational fit perspective. Information and Management,40(1), 25–40.
Irani, Z. (2002). Information systems evaluation: Navigating through the problem domain.Information and Management,40(1), 11–24.
Johnston, H. R., & Carrico, S. R. (1988). Developing capabilities to use information strategically.MIS Quarterly,12(1), 36–48.
Jo¨reskog, K. G., & So¨rbom, D. (1993).LISRELw8: User’s reference guide.
Chicago, IL: Scientific Software International, Inc.
Kerlinger, F. (1964). Foundation of behavioral research. New York:
Rinehart and Winston.
Khalifa, M., & Liu, V. (2002). Satisfaction with internet-based services:
The role of expectations and desires. International Journal of Electronic Commerce,7(2), 31–49.
Kriebel, C., & Raviv, A. (1980). An economics approach to modeling the productivity of computer systems.Management Science,26(3), 297–
311.
Mahmood, M. A., & Medewitz, J. N. (1985). Impact of design methods on decision support system success: An empirical assessment.Information and Management,9(3), 137–151.
Markus, M. L., & Robey, D. (1988). Information technology and organizational change: Causal structure in theory and research.
Management Science,34(5), 583–598.
Markus, M. L., & Tanis, C. (2000).The enterprise systems experience from adoption to success. Cincinnati, OH: Pinnafiex Educational Resources Inc.
McKinney, V., Yoon, K., & Zahedi, F. M. (2002). The measurement of web-customer satisfaction: An expectation and disconfirmation approach.Information Systems Research,13(3), 296–315.
Negash, S., Ryan, T., & Igbaria, M. (2003). Quality and effectiveness in Web-based customer support systems.Information and Management, 40(8), 757–768.
Nunnally, J. C., & Bernstein, I. H. (1994).Psychometric theory(3rd ed).
New York: McGraw-Hill.
Orlikowski, W. J. (1993). CASE tools as organizational change:
Investigating incremental and radical changes in systems development.
MIS Quarterly,17(3), 309–340.
Patterson, P. G., Johnson, L. W., & Spreng, R. A. (1997). Modeling the determinants of customer satisfaction for business-to-business pro- fessional services.Academy of Marketing Science,25(1), 4–17.
Pitt, L. F., Watson, R. T., & Kavan, C. B. (1995). Service quality: A measure of information systems effectiveness.MIS Quarterly,19(2), 173–187.
Reich, B. H., & Benbasat, I. (1990). An empirical investigation of factors influencing the success of customer oriented information systems.
Information Systems Research,1(3), 325–347.
Remenyi, D. S. J. A., & Twite, M. A. (1991).A guide to measuring and managing IT benefits. Oxford, UK: Blackwell.
Saarinen, T. (1996). An expanded instrument for evaluating information system success.Information and Management,31(2), 103–118.
Seddon, P. B. (1997). A respecification and extension of the DeLone and McLean model of IS success. Information Systems Research, 8(3), 240–253.
Shao, B. B. M., & Lin, W. T. (2002). Technical efficiency analysis of information technology investments: A two-stage empirical investi- gation.Information and Management,39(5), 391–401.
Sivadas, E., & Baker-Prewitt, J. L. (2000). An examination of the relationship between service quality, customer satisfaction, and store loyalty.International Journal of Retail and Distribution Management, 28(2), 73–82.
Srinivasan (1985). Alternative measures of systems effectiveness: Associ- ations and implications.MIS Quarterly,9(3), 243–253.
Straub, D. W. (1989). Validating instruments in MIS research. MIS Quarterly,13(2), 147–169.
Swanson, E. B. (1974). Management information systems: Appreciation and involvement.Management Science,21(2), 178–188.
Wixom, B. H. (2001). An empirical investigation of the factors affecting data warehousing success.MIS Quarterly,25(1), 17–41.
Yi, Y. (1990). A critical review of consumer satisfaction. Review of Marketing,4, 68–123.
Yoon, Y., Guimaraes, T., & O’Neal, Q. (1995). Exploring the factors associated with expert systems success. MIS Quarterly, 19(1), 83–106.
Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(1), 31–46.
