An Empirical Study on the e-crm Performance Influence Model for Service Sectors in Taiwan

An Empirical Study on the e-crm Performance Influence Model for Service Sectors in Taiwan Te-Ming Chang Lin-Li Liao Wen-Feng Hsiao Department of Information Management, Department of Information Management, Department of Information Management, National Sun Yat-sen University National Sun Yat-sen University National PingTung Institute of Commerce temin@mail.nsysu.edu.tw m9142619@student.nsysu.edu.tw wfhsiao@mail.npic.edu.tw Abstract Electronic customer relationship management (e-crm) emerges from the Internet and web technology to facilitate the implementation of CRM. It focuses on internet- or web-based interaction between companies and their customers. Above all, e-crm enables service sectors to provide appropriate services and products to satisfy the customers so as to retain customer royalty and enhance customer profitability. This research is to explore the key research issues about e-crm performance influence for service sectors in Taiwan. A research model is proposed based on the widely applied technology-organization-environment (TOE) framework. Survey data from the questionnaire are collected to empirically assess our research model. With the structural equation modeling (SEM) analysis, the results show that e-crm technology and organizational support are positively related to the e-crm performance, while environmental pressure, though a drive to e-crm adoption, does not impact the performance significantly. More interestingly, it is observed that organizational support has a greater impact than e-crm technology adoption. It is always a good principle to care customers relationship more from human nature perspectives than from technologyoriented considerations. Furthermore, in the aspect of organizational support, skilled and knowledgeable e-crm personnel are the most important impacting factor among others. The results of this research can provide meaningful insights for service sectors practitioners in Taiwan. Keywords: e-crm, Performance influence, TOE framework, SEM analysis 1. Introduction Service sectors refer to the economic activities that provide time, place, or any forms of psychological efficacy for customers. Services activities rely on the interaction between service providers and customers on the spot. According to the Mark Twain Institute, world trade in services now approaches $l trillion per year and still growing. In Taiwan, the statistics based on economy and social measures from the Ministry of Auditing also showed that the service sectors possessed 67% of GDP in 2001. It seems that service sectors become the major economy sources in our society. Electronic customer relationship management (e-crm) is evolved recently with the emergence of information technology such as Internet and web technologies. It integrates and simplifies all customer-related processes through the Internet [20][10], and helps leverage integrated information on customers to improve customer acquisition, customer development, and customer retention by managing deep and long-lasting relationships. Firms can understand customer behavior and anticipate customer needs much more easily than before through online activities tracking and analyzing. According to Romano and Fjermestad [22], the recent e-crm researches can be classified into five major areas: e-crm markets, e-crm business models, e-crm knowledge Management, e-crm technology, and e-crm human factors. However, few studies are contributed to e-crm performance measures and to the factors that influence its It is essential for researchers and practitioners to build the principles on assessing e-crm investment and identify the impacting factors that shape e-crm Consequently, it is our research interest to explore those research issues as: (1) Extracting appropriate e-crm performance measures. (2) Identifying a feasible theoretical framework to develop the e-crm performance influence model. (3) Revealing impacting factors within this influence model. (4) Conducting empirical assessments on the model for service sectors in Taiwan. 2. Theoretical Foundations In this research, we develop a research model based on the framework of technology-organization-environment (TOE) [8], which studies a firm s contexts (technology, organization, and environment) that affect the process of its adopting and implementing technology innovation. Technological context describes both the internal and

external technologies relevant to the firm. Determining what technology to adopt depends on existing technologies within the firm and state-of-the-art technologies in the market. Organizational context refers to such measures as firm size and scope, the centralization and complexity of managerial structure, the quality of human resources, and the amount of resources available internally. Environmental context defines the area in which a firm conducts its business, such as the industry it belongs to, competitors, supplied resources it can access, and governments to deal with. The TOE framework describes the drive of a firm to adopt technology and change its process. Its theoretical foundation comes from the researches on the diffusion of innovation [21]. It streamlines the innovation with existing technology and strategy, and describes the resultant impact on organizational Since its introduction, TOE has been applied to many information systems (IS) researches, including MRP adoption [5], EDI adoption [14][17], IS adoption [28], open systems adoption [4][6], and e-commerce adoption [32]. TOE serves as an appropriate theoretical guideline for studying factors that impact new technology adoption. We adopt this framework and extend it to the e-crm area, because e-crm is enabled by the technology development of the Internet and web technologies, requires organizational enablers such as human resources, learning ability, and knowledge management, and may be shaped by the external competitive environments. TOE serves as a good framework that enables us to identify the impacting factors on e-crm 3. Research Model and Methodology In this section, we conceptualize a performance influence model that can be further empirically studied in our research based on the TOE framework. The details are described as follows. 3.1. Proposed Model Performance Measures. Traditional dimensions of performance measures are usually finance-based. When it comes to customer relationship, however, more measures should be related to customer perspectives. Customer loyalty is a critical measure under this consideration. It represents customers preferential, attitudinal and behavioral response toward one or more brands in a product category over a period of time. The commitment and loyalty between the customers and the companies lead to build the long-lasting relationship [2][26]. Business process improvement that assures efficiency and excellence of enterprise operations is also considered [15]. Although business operations are not directly related to customer relationship, its efficiency definitely can facilitate how customers interact with firms. Process efficiency consists of inventory and order management, and particularly the production of service support prompt to customers special request [11]. Degrees of channel integration can also be a good indication on CRM Channel management refers to coordinating and synchronizing communications across separate customer touch-point systems, and requires that all channels be fully integrated [23][16][24]. It produces an effortless sharing of knowledge about a customer s relationship with enterprises. Finally, service innovation is included. Kim et al. [16] pointed out that only through the ability to launch new products (and/or services) could firms create more customer values. Firms should provide innovative services to attract customers by integrating their internal resources [2][16][25]. These descriptions, therefore, lead to the following hypotheses: H 1 : Customer loyalty, internal process efficiency, channel management, and innovation are the reflective indicators of Technological context. Under e-crm consideration, the technology context in the TOE model refers to the e-crm related technology in a firm. By utilizing e-crm information technology, company can deal with customer relations more properly. We identify two factors in this context from literature, i.e., e-crm technology integration and customer information analysis. Technology integration refers to firms efficiently utilizing IT resources. We define e-crm technology integration as firms' ability to effectively convert the Internet technologies into capabilities for value creation. One of the major benefits of adopting CRM or e-crm systems is the easiness of customer data and/or information collection, which facilitates customer information analysis. Firms transform information to aid customer knowledge discovery and develop customer segments and profiles, which may further assist marketing decisions [20][27]. We define customer information analysis as the analytical capability of predicting and interpreting customer behaviors. These descriptions, therefore, lead to the following hypotheses: H 2 : e-crm technology is related to e-crm performance H 3 : e-crm technology integration and customer information analysis are reflective indicators of technological construct. Organizational context. Under e-crm consideration, the organizational context in the TOE model refers to the organizational support in a firm. The structure and contents of an organization are crucial to the adoption process of innovative technology. Successful adoption of e-crm

definitely relies on the organization s readiness and support. We identify three factors in this context from literature: e-crm personnel asset, learning and sensing customer market, and customer knowledge management. Personnel asset refers to well-trained, high-rewarding, and good-skilled employees with professional expertise [3][31]. Professional care of individualized attention and responsive complaint management can inspire customers trust and confidence by providing reliable and promised services to accelerate customers repurchasing willing. We define e-crm personnel asset as e-crm employees profession and expertise. Learning and sensing customer market capabilities refer to a firm s abilities to continuously perceive and act on events and trends in present and potential customer markets. Creating continuous learning opportunities are critical to the organization s long-term competitive advantage achievement in dynamic markets [7]. We define learning and sensing customer market as the ability to continuously sense and act on events and trends in dynamic customer markets. Furthermore, to compete effectively, firms must leverage their existing knowledge and create new knowledge that favorably positions them in their chosen markets. Knowledge management processes enable the organization to capture, reconcile and transfer knowledge in an effective way [9]. Customer knowledge management is to capture customer information, build customer relationships, and improve customer-related work practices and processes [2][29]. We define customer knowledge management as the mechanism to capture, store, and share customer-perspective knowledge to add organization values. These descriptions, therefore, lead to the following hypotheses: H 4 : Organizational support is related to e-crm H 5 : e-crm personnel resource, learning and sensing customer market, and customer knowledge management are reflective indicators of organizational construct. Environmental context. Under e-crm consideration, the environmental context in the TOE model refers to the environmental pressure in a firm. We identify two factors in this context from literature: competition intensity and customer power. Competition intensity refers to the market concentration that indicates the degree of market dominance by a few large firms in the industry [30]. High intensity of competition usually can be a drive to adopt e-crm. Competition can exist in price [18] and in new products introduction [19][13][18]. We define the competition intensity as the degree of market concentration on competition of price and new product introduction. With the CRM philosophy, customers have high negotiating and bargaining power to dictate the way of transactions or interaction with the firms. In extreme cases, customers could lead the technology adoption and business process changes. We define the customer power as the dominating power that affects customer-centric processes from customers. These descriptions, therefore, lead to the following hypotheses: H 6 : Environmental pressure is related to e-crm H 7 : Competition intensity and customer power are the reflective indicators of environmental construct. 3.2. Research Methodology To empirically examine the proposed research model and associated hypotheses, we designed a questionnaire with multiple questions per construct and carried out a survey to collect data from e-crm related personnel at firms of service sectors in Taiwan. The questionnaire was designed by a comprehensive literature review, and was refined via several pretests. The respondents are mainly the marketing and sales managers or customer relationship directors. A convenience sampling technique is employed due to the limitation of labor, resources, and time. We conducted the questionnaire survey in April 2004. The survey was administrated and monitored by the researcher on the spot. We received totally 162 data of the questionnaire results. After eliminating 26 invalid data, a sample of 136 data is obtained. Distribution of firm size measured by number of employees shows that most firms in survey are of smallor medium-size (less than 500), which is a typical feature for firms in Taiwan. Distribution of operating incomes shows that most firms (15.4%) receive operating incomes of 100-500 million N.T. dollars, followed by the firms (14.7%) with operating income of 1-5 million N.T. dollars. Finally, from the survey results, 66.2% of the firms have not yet installed their proprietary CRM department. It means that the CRM practices are done separately among all possible departments, which may result in difficulty and inefficiency. The empirical assessments of the research model are based on SEM analysis. Structural equation model (SEM) is a multivariate statistic method, which serves as an extension of path analysis, and also contains covariance structure analysis and confirmatory factor analysis. SEM can be used to explore the causal relationships in our proposed research model. 4. Data Analysis and Results To empirically assess the proposed research model, we first validate the model including construct validity,

construct reliability, and goodness-of-fit tests. The proposed hypotheses are then tested based on SEM analysis. Research findings are finally addressed. 4.1. Model Validation Table 1. Factor Loadings and Reliability Constructs Range of Average Number of Composite factor variance indicators reliability loadings* extracted e-crm technology 2 0.85-0.93 0.57 0.88 Organizational support 3 0.68-0.93 0.61 0.82 Environmental pressure 2 0.74-0.77 0.57 0.73 e-crm performance 4 0.75-0.81 0.60 0.86 * All factor loadings are significant at p < 0.01 level A confirmatory factor analysis that obtains the factor loadings of indicators can be used to assess construct convergent validity. It is, however, more efficient to obtain factor loadings by exploring the entire relationships in our proposed model simultaneously. Table 1 shows that all estimated factor loadings are significant, which suggests construct convergent validity. In addition, the correlation coefficients between any two constructs significantly differ from unity by at least two standard errors. This suggests construct discriminant validity as proposed by Hair et al. [12]. To evaluate construct reliability, we calculate the composite reliability and the average variance extracted (AVE). AVE reflects the variability amount the construct variability accounts for in the total variability. Table 1 shows that the composite reliabilities for all constructs exceed the suggested level of 0.7, and all AVE values are above the threshold of 0.5. The result suggests that the reliability of the measurement models is validated. Several goodness-of-fit tests are conducted to assess whether the theoretical model could explain the real observable data. The results are shown in Table 2 with suggested values listed. It shows that all fit measures are acceptable in our case. Table 2. Summary results of fit measures Measure Value Suggested value Chi-square of estimated Greater than 0.05 0.18 model (non-significance) Goodness of fit index (GFI) 0.95 Greater than 0.9 Root mean square residual (RMSR) 0.031 Below 0.05 Root mean square error of approximation (RMSEA) 0.035 Below 0.05 Adjusted Goodness of Fit Index (AGFI) 0.9 Greater than 0.9 Non-Normed Fit Index (NNFI) 0.99 Greater than 0.9 Normed Fit Index (NFI) 0.96 Greater than 0.9 Relative Fit Index (RFI) 0.93 Greater than 0.9 Incremental Fit Index (IFI) 0.99 Greater than 0.9 Comparative Fit Index (CFI) 0.99 Greater than 0.9 Normed chi-square 1.21 1-2 Parsimonious goodness of fit index (PGFI) 0.52 Greater than 0.5 4.2. SEM Analysis Once the model is validated, we perform the SEM analysis by using LISREL and obtain the estimation results for all parameters in the model. Figure 1 presents the estimated correlation coefficients in the model. Figure 1. Estimated correlation coefficients in the proposed model

The results show that e-crm technology integration contributes 93% to e-crm technology construct, and customer information analysis 85%. Similarly, e-crm personnel assets, learning and sensing customer market, and customer knowledge management contributes 93%, 68% and 70% to organization support construct, respectively. In environment pressure construct, competition intensity and customer power contributes 74% and 77%. Finally, customer loyalty, internal process efficiency, channel management, and innovation contribute 75%, 75%, 79%, and 81% to e-crm performance construct. These evidences indicate that all the selected indicators have sufficient interpretation toward their respective construct. Moreover, we inspect the correlations between technology, organization, environment constructs, and the e-crm performance construct, respectively. It shows that organizational construct has the most positively significant impact on e-crm Technology construct is also positively related to e-crm However, the environmental construct is not significantly related to the The intensity of competition is not critical for service sectors in Taiwan. All the above statements lead to the hypothesis-testing results as listed in Table 3. Hypot hesis H 1 H 2 H 3 H 4 H 5 H 6 H 7 Table 3. Results of hypothesis tests Content Customer loyalty, internal process efficiency, channel management, and innovation are the reflective indicators of e-crm technology is related to e-crm e-crm technology integration and customer information analysis are reflective indicators of technological construct. Organizational support is related to e-crm e-crm personnel resource, learning and sensing customer market, and customer knowledge management are reflective indicators of organizational construct. Environmental pressure is related to e-crm Competition intensity and customer power are the reflective indicators of environmental construct. 4.3. Research Findings Significa nce No From the empirical analysis we summarize seven findings as the following. (1) Customer loyalty, internal process efficiency, channel management, and innovation sufficiently account for the e-crm (2) The more adoption of e-crm technology, the better the e-crm (3) The higher support of e-crm practices from the organization, the better the e-crm (4) Environmental pressure in the industry does not impact the e-crm (5) e-crm technology integration and customer information analysis sufficiently account for the e-crm technology. (6) e-crm personnel assets, learning and sensing customer market, and customer knowledge management sufficiently account for the organizational support on e-crm. (7) Competition intensity and customer power sufficiently account for the environmental pressure on e-crm. 5. Conclusions 5.1. Concluding Remarks In this research, we investigate the e-crm performance influence at the firm level of service sectors in Taiwan. The results show that customer loyalty, internal process efficiency, channel management, and innovation sufficiently account for the e-crm Issues on e-crm technology and organizational support have a positive impact on the e-crm Environmental pressure that may contribute to the e-crm adoption, however, is not significantly related to the Within the technological context, it is sufficient to consider the e-crm technology integration and customer information analysis. As for the organizational context, e-crm personnel asset, learning and sensing customer market, and customer knowledge management sufficiently account for the organizational support on e-crm. Finally, it is adequate to consider the competition intensity and customer power in the environmental context. 5.2. Future Work The objective of this research is to investigate e-crm performance influence from the viewpoints of service sectors in Taiwan. However, it is found from the descriptive analysis that 66.2% of the firms at service sectors in Taiwan have not yet installed their proprietary CRM department, which indicates the CRM practices are done in a disperse manner. The future work can consider comparing the performance difference between firms with and without the proprietary CRM department. Firms may require an integrated organization unit to deal with customer relationship. In addition, it is clear that customer relationship involves both the firms and the customers. This research can be extended to consider impact factors of the e-crm performance from customers perspectives. The results can provide a good cross-validation with ours. Acknowledgement This research was supported by Taiwan s MOE Program for Promoting Academic Excellent of Universities under the grant number 91-H-FA08-1-4.

6. References [1] Bentler, P. M., and Chou, C. P., Practical issues in structural modeling, Sociological Methods and Research, 1987, 16, 78-117. [2] Brown, S. A., Customer Relationship Management-A strategic Imperative in the World of E-Business, PricewaterhouseCoopers, 2000. [3] Byrd, T. A. and Turner, D. E., Measuring the flexibility of information technology infrastructure, Journal of Management Information Systems, Vol.. 17, 2000, pp. 167 208. [4] Chau, P. Y. K., and Tam, K. Y., Factors affecting the adoption of open systems: an exploratory study, MIS Quarterly, Vol. 21, 1997, pp. 1-21. [5] Cooper R. B., and Zmud R. W., Information technology implementation research: a technological diffusion approach, Management Science, Vol. 36, 1990, pp. 123-140. [6] Dedrick, J., and West, J., Adoption of Open Source Platforms: An Exploratory Study, HBS - MIT Sloan Free/Open Source Software Conference: New Models of Software Development, 2003. [7] Ellinger, A. D., Ellinger, A. E., Yang, B., and Howton, S. W., The relationship between the learning organization concept and firms financial performance: an empirical assessment, Human resource development quarterly, Vol. 13, 2002. [8] Fleischer, M., Wiarda, E., and Rocco, D., The Context for Change: Organization, Technology and Environment, In The processes of technological innovation, Tornatzky, Louis G. and Fleischer, Mitchell (Eds.), Lexington, Mass.: Lexington Books, 1990, pp. 151-175. [9] Gold, A. H., Knowledge management: an organizational capabilities perspective, Journal of Management information systems, Vol. 18, 2001, pp.185-214. [10] Gordon, I., Best Practices: Customer Relationship Management, Ivey Business Journal, November/ December 2002. [11] Gunasekaran, A., Patel, C., and Tirtiroglu, E., Performance measures and metrics in a supply chain environment, International Journal of Operations & Production Management, Vol. 21, 2001, pp. 71-87. [12] Hair, J., Anderson, R., Tatham, R., and Black W., Multivariate Data Analysis (5 th ed.), Prentice Hall, New Jersey, 1998. [13] Hoque, Z., and Mia, L., Market competition, computer-aided manufacturing and use of multiple performance measures: an empirical study, British Accounting Review, Vol. 33, 2001, pp. 23 45. [14] Iacovou C. L., Benbasat I., and Dexter A. S., Electronic data interchange and small organization: adoption and impact of technology, MIS Quarterly, Vol. 19, 1995, pp.465-485. [15] Kaplan, R., and Norton, D., The balanced scorecard-measures that drive performance, Harvard Business Review, Jan-Feb 1992, pp. 71-79. [16] Kim, J., Suh, E., and Hwang, H., A model for evaluating the effectiveness of CRM using the balance scorecard, journal of interactive marketing, Vol. 17, 2003, pp.5-19. [17] Kuan, K. K. Y, and Chau, P. Y. K., A perception-based model for EDI adoption in small business using a technology-organization-environment framework, Information & Managemen, Vol. 38, 2001, pp.507-512. [18] Libby, T., and Waterhouse, J. H., Predicting change in management accounting systems, Journal of Management Accounting Research, Vol. 8, 1996, pp. 137 150. [19] Mia, L., and Clarke, B., Market competition, management accounting systems and business unit Performance, Management Accounting Research, Vol. 10, 1999, pp. 137-158. [20] Plakoyiannaki, E., and Tzokas, N., Customer relationship Management: A Capabilities Portfolio Perspective, Journal of Database Marketing, Vol. 9, 2002, pp.228-237. [21] Rogers, E. M., Diffusion of innovation, 3 rd ed., Free press, New York, 1983. [22] Romano, N. C., Jr., and Fjermestan, J., Electronic Commerce Customer Relationship Management: A Research Agenda, Information Technology and Management, Vol. 4, 2003, pp. 233-258. [23] Rosenbaum, H., and Huang, B.Y., A Framework for web-based e-commerce customer relationship management: research in progress, Proceedings of the America s Conference on Information Systems, 2002. [24] Sivakumar, S. C., A critical survey of protocols by the IETF as enablers for customer interaction in an electronic customer relationship management system-part I-eCRM metrics, IEEE Canadian Review, Spring 2002, pp5-9. [25] Stamoulis, D., Kanellis, P., and Martakos, D., An approach and model for assessing the business value of e-banking distribution channels: evaluation as communication, International Journal of Information Management, Vol. 22, 2002, 247 261. [26] Stone, M., Woodcock, N., and Wilson, M., Managing the change from marketing planning to customer relationship management, Long Range Planning, Vol. 29, 1996, pp. 675 683. [27] Storey, V. C., Straub, D. W., Stewart, K. A., and Welke, R. J., A conceptual investigation of the e-commerce industry, Communications of ACM, Vol. 43, 2000. [28] Thong, J. Y. L., An integrated model of information systems adoption in small business, Journal of Management Information Systems, Vol. 15, 1999, pp. 187-214. [29] Tiwana, A., The Essential Guide to Knowledge Management: E-Business and CRM Applications, Prentice Hall PTR, New Jersey, 2001. [30] Tornatzky, L. G. and Fleischer, M., The Processes of Technological Innovation, Lexington Books, Mass., 1990. [31] Wright, P. M., The impact of HR practices on the performance of business units, Human Resource Management Journal, Vol. 13, 2003, pp. 21. [32] Zhu, K., Xu, S., and Dedrick, J., assessing drivers of e-business value: results of a cross-country study, Twenty-Fourth International Conference on Information Systems, 2003.