A PANEL STUDY FOR THE INFLUENTIAL FACTORS OF THE ADOPTION OF CUSTOMER RELATIONSHIP MANAGEMENT SYSTEM

410 International Journal of Electronic Business Management, Vol. 4, No. 5, pp. 410-418 (2006) A PANEL STUDY FOR THE INFLUENTIAL FACTORS OF THE ADOPTION OF CUSTOMER RELATIONSHIP MANAGEMENT SYSTEM Jan-Yan Lin 1*, Zong-Lin Guo 1 and Cheng-Wen Lee 2 1 Department of Business Administration 2 Department of International Trade Chung Yuan Christian University Chungli (320), Taiwan ABSTRACT This study uses longitudinal study to compare the difference of the customer relationship management systems among the Taiwan s enterprises. The basis of the comparison of the longitudinal study depends on the 75 enterprises investigated in Lin s research. In regard to the analysis of present condition and the development situation for the customer relationship management (CRM) system among the Taiwan s enterprises, it reveals that some influential factors result in adoptive difference in terms of CRM system. According to the empirical results, we found that the adoption level of the CRM system and its performance exhibits an obviously positive growth trend. In addition, the different industrial characteristics will truly lead to significant difference of the adoption in CRM system. Moreover, there is mainly a significant positive impact of the external environment factors and internal enterprise factors with the change degree on the adoption of CRM system. Keywords: CRM, Adoption of IT, Longitudinal Study, Panel Study * 1. INTRODUCTION Customer Relationship Management (CRM) was first developed in the US. In early 1980s, contact management had been existent to collect all the information required for contacts between customers and companies. In early 1990s, it further evolved into the function of customer service and data analysis. In modern electronic industries, CRM has been extended and applied to information technologies to provide integrated planning, marketing, and customer service. In recent studies, Taiwan s business perspective for CRM has gradually shifted from telephone call center to customer-oriented applications. For instance, information technologies, such as database marketing and one-on-one marketing, have turned passive marketing strategies into active promotion and support of customer relationship. So far, the application of CRM system can be roughly divided into three main dimensions, including front-end interactions with customers, analyses of data warehousing and data mining, and operations related to sales and service activities. In terms of the adoption degree of CRM in * Corresponding author: rock@cycu.edu.tw Taiwan, Lin [9] pointed out in his research financially supported by National Science Council of Taiwan (NSC) that among 75 enterprises, 22 (29.3%) have introduced CRM, 7 (9.3%) are planning to introduce CRM, 27 (36%) are evaluating CRM, and 19 (25.3%) temporarily consider not to introduce it. Among the enterprises with CRM adopted, the average CRM adoption time is 2.95 years. As to the adoption level of the collaborative CRM, the application of web sites and e-mail is the highest, followed by the adoption of call center and direct marketing. The application of mobile wireless access and Short Message Service (SMS) is very low. In the operational CRM, the application of sales automation is the highest, followed by service automation, and marketing automation. In the analytical CRM, the application of Online Analytical Processing (OLAP) is relatively higher, compared with that of data warehousing and data mining. In general, only the application of web sites and e-mail in the collaborative CRM has a relatively higher adoption level among all the CRM items. This indicates that the many of CRM applications can be further expanded. According to Market Information Center (MIC), it was pointed in 2005 that CRM has become an important part for enterprises to implement digitalization. After the economic recession during

J. Y. Lin et al.: A Panel Study for the Influential Factors of the Adoption 411 2001-2003, the global CRM market has been gradually revived by economic situations and exhibited a steady growth in 2004. In the environment with rapid development in information technologies, the adoption level of CRM systems among modern enterprises is not the same as before. Thus, based on Lin [9], it will further investigate the evolution of the adoption level of CRM among Taiwan s enterprises and compare the differences between the past and the present, in an attempt to provide the linkage between CRM development and performance. This longitudinal study is expected to investigate the factors for the change degree in the adoption of CRM among Taiwan s enterprises and the evolution of CRM applications. The main factors for the difference in the adoption of CRM will be analyzed, in hope of providing a reference for enterprises to understand the current application status and focuses in adoption in advance, so as to save unnecessary cost derived from trial and error. In summary, the objectives of this study can be summarized as follows: 1. To investigate the general level of CRM applications and the evolution process among Taiwan s industries. 2. To investigate the difference in the adoption level of CRM systems among various Taiwan s industries. 3. To investigate the influential factors for the adoption of CRM systems. 4. To analyze the association between influential factors and adoption level of CRM systems. 2. LITERATURE REVIEW According to the concept about CRM applications proposed by MIC (2005), CRM is an interface system that integrates and supports services provided by an enterprise to its customers. It is not package software but a recreated business process management for enterprises. Through different channels between the enterprise and customers (salespersons, telephone, e-mail, fax, and web sites), this business process analyzes various variables affecting customer behaviors. Based on the quantified data, it expects to achieve the goals of marketing automation, sales automation, and service automation. In other words, no matter for the front-end contact channels where direct interactions with customers are involved, or for the rear-end analysis systems that provide valuable information, all the applications are presented on marketing, sales, and service. The CRM applications in practice mainly cover the above-mentioned application areas, including marketing, sales, and service [1,2,7]. In addition to the core CRM technologies, data warehousing and data mining, many other information technologies are also involved in these application systems. 2.1 Key Factors for the Success of CRM Adoption Foreign or Taiwan s literatures have discrepant dimensions for CRM systems, especially on the issues about the CRM adoption and how to promote and investigate customer satisfaction and customer loyalty from empirical studies. An overview of recent foreign and Taiwan s literatures on CRM is provided as follows. In Chen [3] of decision-making of the organization for CRM adoption, it was discovered that factors in the environment and organization dimensions simultaneously affect the motivation for CRM adoption and priorities of implementation methods. The additional factors in the business dimension have a more significant impact on the motivation for CRM adoption, priorities of implementation methods, and choice for self-implementation or outsourcing. As to the innovative characteristics of cognition, both project management and high-level participation may affect the properties of the CRM system in the organization. Huang [6] pointed out that the key success factors for CRM adoption among the case companies can be divided into necessary conditions and sufficient conditions. The necessary conditions include support from top managers, clear goal setting and process checking, client-focus concepts, participation of salespersons, and a real-time accessible database. Sufficient conditions involve in a high acceptance of the organization for innovation, professional ability of CRM system providers, top managers support from supervisors, and effective integration of existent systems. Lu [10] summarized the key factors for the successful adoption of information systems on small and medium enterprises (SMEs) and the factors for the composition of external technical experts. Advisors and software/hardware service providers are the major factors. Support from top supervisors, staff information knowledge, and user participation are factors ranked by its relative importance. Lin [9] applied AHP method to evaluate the key success factors for the introduction of CRM systems into the publishing industry. The research results indicated that successful introduction of CRM depends on not only the demand of the organization for either a portion of or the entire CRM system modules but also the adjustment of organizational culture, structure, and work process to adapt to the customer-focus management concepts. Huang [5] discovered: (1) Intellectual capital has a significant positive impact on the adoption level of CRM; (2) The adoption level of CRM has a significant positive impact on business performance; and (3) The adoption level of CRM plays a mediating role between intellectual capital and business

412 International Journal of Electronic Business Management, Vol. 4, No. 5 (2006) performance. Huang [5] discovered that in the dimensions of attitude and subjective norms, staff s perception and top-level managers perception are the most significant factors. However, comparative advantage, ease of e-crm use, and CRM solution provider s expertise are also factors that should be paid attention to. The effectiveness of CRM depends on planning and management of CRM. To sum up, this study will induce the factors for the adoption level of CRM into external environment factors, internal enterprise factors, and objective factors for this study. The external environment factors include maturity of information technologies, competition status for enterprises, and system provider s expertise. The internal enterprise factors consist of top managers support, innovation acceptance ability, and system management ability. The two sub-factors, Company size and industry listed in Lin [9] are chosen as objective factors in this study. 2.2 Literatures on Longitudinal Study Cross-sectional study involves the observation of selected samples of a specific population or a phenomenon on a certain time point. Investigative or descriptive studies are usually cross-sectional studies. In contrast to cross-sectional study, a longitudinal study focuses on the observation of a certain phenomenon over a long period of time to find out variables that change over time. Thus, a longitudinal study on the variation and change among variables will benefit the understanding of the effect on each other. So far, Taiwan s longitudinal studies are most adopted in the areas such as social science, psychology, education, medical health, and electronic computing. In business and management studies, cross-sectional studies on a single time point are the majority, and longitudinal studies are rare. This is mainly because the research objectives of business and management studies usually associated with highly dynamic environments and the unavailability of data. Longitudinal studies in the business and management area proposed in recent years include the studies [3,11,12]. In conclusion, longitudinal studies can be divided into trend study, cohort study, and panel study (Liu, 2004). And this study is a panel study, as the same samples are used in two investigations. The same samples are used to observe the development of the adoption of CRM in different time periods and investigate the current condition from dynamic dimensions, so as to manifest the stage evolution in the adoption of CRM among Taiwan s industries. 2.3 A Pilot Longitudinal Study (Lin [9]) The comparison of this longitudinal study is based on Lin [9], titled An Integrated Study on Innovation Adoption of CRM Systems on Relationship Marketing. A total of 75 valid questionnaires for enterprises were returned. A comparative study will be conducted on the same samples. The research results from the pilot study [9] are summarized as follows. Lin [9] conceived that knowledge about the use of CRM is not prevalent. The study is mostly provided on introductory articles, and systematic and academic investigations are very few. Thus, the pilot study only focused on the design and investigation of the questionnaire from an academic dimension, so as to understand problems encountered in the implementation and adoption processes. In addition to related factors, the pilot study also defined and measured the adoption level and further analyzed the correlations among different constructs to create an integrated structure and research hypotheses. Moreover, questionnaire survey was conducted on different types of organizations so as to statistically verify each hypothesis and the entire model. The pilot study investigated and verified each factor affecting the innovation adoption and implementation performance of CRM. Besides the related factors, it was discovered that adoption level has a significant impact on marketing performance. In other words, no matter the motivation comes from self-perceptions, customer or peer demands, or top managers support, if the enterprise is willing to make sufficient investment on CRM, satisfactory performance can be obtained. However, it was also discovered that the adoption level of the entire CRM system and average adoption level are still low. This implies that many functions of CRM can be expanded. Thus, Taiwan s enterprises in an urgent need for CRM should invest in the implementation level of CRM so as to improve business performance and competitiveness. 3. RESEARCH FRAMEWORK AND METHODS 3.1 Research Framework The theoretical framework of this study is mainly based on Lin [9]. This study aims to observe how Taiwan s industries analyze current adoption of CRM systems and the trend of evolution, and further investigate the impact of factors on the adoption level of CRM systems. This study is organized as shown in Figure 1. 3.2 Research Hypotheses According to the research framework and literature review, the following hypotheses are proposed: H1: There is difference in the adoption level of CRM systems between the previous and the current

J. Y. Lin et al.: A Panel Study for the Influential Factors of the Adoption 413 research periods. H2: There is a positive effect of influential factors on the adoption level of CRM systems in the current research period. H3: There is a positive effect of change degree on the adoption level of CRM systems in the current research period. Influential factors External Environment Factors 1. Maturity of Information Technologies 2. Competition status for Enterprises 3. System Provider s Expertise Adoption Level of CRM Systems (1) Collaborative CRM (2) Operational CRM (3) Analytic CRM Previous Period (2003) (75 samples) Internal Enterprise factors H1 1. Top managers support 2. Innovation Acceptance Ability 3. System Management Ability H2 Current Period (2006) (56 samples) Objective Factors 1. Company size 2. Types of Industries H3 Variation in Adoption Level of CRM Figure 1: Research framework 3.3 Questionnaire Design The survey subjects are 75 enterprises investigated in Lin [9]. In this longitudinal study, several interviews were conducted on the same samples, in order to observe the data distribution in this period and, more important of all, the variation in the adoption level and performance of CRM system in the progress of time. The questionnaire included four parts, including the influential factors for adoption of CRM systems, adoption level, adoption performance, and current status of adoptions and fundamental data of enterprises. And 5-point scale is used in the measurement level. Lin [9] served as a blue print of the questionnaire, so the longitudinal study on the previous and current periods could be conducted. 3.4 Reliability and Validity In the analysis of external environment factors, the reliability values of the three constructs - maturity of information technologies, competition status for enterprises, system provider s expertise -- are.843,.827, and.958, respectively. In the aspect of internal enterprise factors, the reliability values of top managers support, innovation acceptance ability, and system management ability are.950,.838, and.939, respectively. As Cronbach s alpha of each construct is above 0.8, indicated a high reliability of this questionnaire. In the analysis of validity, when this questionnaire was design, the influential factors in the first part were derived from theoretical basis and empirical results, so the constructs to be measured could be effectively reflected. And the adoption level of CRM in the second part was the result revised many times in the pre-test by CRM experts and scholars, who had approved that the questions in the survey could measure related variables. Thus, the design of this questionnaire has a high content validity. 4. DATA ANALYSIS 4.1 Descriptive Statistics Analysis In this study, the questionnaire was distributed the 75 enterprises investigated in Lin [9]. From the same samples, a total of 56 valid questionnaires were

414 International Journal of Electronic Business Management, Vol. 4, No. 5 (2006) returned, and the valid response rate was 74.67%. All the statistic analyses in this paper will be based on the data of the 56 enterprises in the previous and current research periods. The subjects were categorized by industries, where there were 13 (23.2%) enterprises from the electronic manufacturing industry, 6 enterprises (10.7%) from non-electronic manufacturing industry (1 firm from automobile industry, 2 firms from both textile industry and cement industry, and 1 firm from food industry), 13 enterprises (23.2%) from the financial industry (7 firms from banking industry, 4 firms from insurance industry, 1 firm from both security and bonds industries), 6 enterprises (10.7%) from the transportation industry (3 firms from telecommunication, 2 firms from aviation, and 1 firms from logistics industry), 15 enterprises (26.8%) from commercial industry (3 firms from distribution channel industry, 5 firms from automobile sales industry, 1 firm from direct marketing, 3 firms from trading industry, 1 firm from hotel industry, and 2 firms from other retail industry), and 3 enterprises (5.4%) from other service industries. As to the stages in the introduction of CRM systems, among the 56 interviewed enterprises, 20 of them (35.7%) have introduced CRM, 8 (14.3%) are planning, 18 (32.1%) are evaluating the feasibility of the introduction, and 10 (17.9%) have not started the evaluation. It could be inferred that the majority (82.1%) of enterprises value the implementation and adoption of CRM systems. Compared with the data provided in Lin (2003), the percentage of enterprises with CRM introduced has grown from 30.4% to 35.7%. Enterprises planning for the introduction have grown from 12.4% to 16.1%. As indicated by these figures, there is a growing trend of introducing CRM among enterprises. To understand the distribution of introduction stage of different industries, this study further divided the industries into manufacturing industries (n=19) and service industries (n=37). Electronic manufacturing industry and non-electronic manufacturing industry are included in the manufacturing industries, while financial industry, transportation industry, commercial industry, and other service industry are included in the service industries. Consequently, compared with manufacturing industries, service industries generally have a higher demand for CRM systems and a higher planning level. 4.2 Comparison Analysis of Paired Samples This study compared the differences in the adoption level of CRM among 56 enterprises between2006 with 2003. The differences were analyzed from three dimensions -- collaborative CRM, operational CRM, and analytical CRM. The analysis result is shown in Table 1. It could be seen that except direct marketing and mobile wireless in the collaborative CRM, a significant increase in other constructs was found in 2006. Table 1: Paired t-test analysis of adoption level of CRM systems Variables Mean SD t value p value 2003 2006 Web sites 3.12 3.36 0.72-2.45 0.02** E-mail system 2.99 3.38 1.14-2.55 0.01** Collaborative CRM Call center 2.48 2.63 0.60-1.85 0.07* Direct marketing 2.50 2.46 1.12 0.27 0.79 Mobil wireless 1.64 1.83 0.98-1.43 0.16 SMS service 1.72 2.04 0.79-3.03 0.00** Marketing automation 2.32 2.78 0.80-4.34 0.00** Operational CRM Sales automation 2.90 3.23 1.01-2.46 0.02** Service automation 2.67 3.06 0.94-3.10 0.00** Data warehousing 2.43 2.69 0.87-2.22 0.03** Analytical CRM Data mining 2.28 2.71 1.03-3.14 0.00** OLAP 2.72 2.93 0.88-1.77 0.08* NOTE: * indicates P 0.1; ** indicates P 0.05

J. Y. Lin et al.: A Panel Study for the Influential Factors of the Adoption 415 4.3 ANOVA on the Adoption Level of CRM Systems As the scales adopted by various industries are different and another influential factor is measurement scale, different statistic methods are required to verify H2 and H3. First of all, analysis of variance (ANOVA) was conducted on the adoption level of CRM systems. The dependent variables included the collaborative CRM, operational CRM, and analytical CRM, so MANOVA was applied to find out whether industry type and implementation stage of CRM cause a significant difference in the adoption level. If the significant level was reached, least-significant difference (LSD) was further used to compare the difference among different types of industries. The cross-sectional analysis on the adoption level of CRM among various industries showed that types of industries had a significant impact on the collaborative CRM, operational CRM, and analytical CRM of the adoption of CRM systems. In all the dimensions, the financial industry and transportation industry had a significant higher adoption level than other 4 industries, and the significant level was reached. This is mainly attributed to the direct and close contacts with customers in the financial and transportation industries. Besides, their customers are mostly general consumers, who may have a higher demand for applications in the collaborative CRM. So, the adoption level in the collaborative CRM is significantly higher than other that of other industries. However, in the electronic manufacturing industry, non-electronic manufacturing industry, commercial industry and other service industries, fluctuation in market and transaction models was minor. The analytical CRM had limited benefit for transactions in these industries, so the adoption level was relatively lower. The results of the longitudinal analysis on the change in adoption level among various industries are shown in Table 2. This study deducted the mean of the previous period from the mean of the current period derived from three dimensions to get a new variable for the variations in each dimension. The variations could reflect the degree of change in the adoption level from the previous period to the current period. Dependent Variables Number of Samples Variation in Collaborative CRM Variation in Operational CRM Variation in Analytical CRM Electronic Manufacturing Table 2: ANOVA on types of industries x variation in adoption level Non-Electronic Manufacturing Financial Transportation Commercial Others 13 6 13 6 15 3.17 -.25.71.25 -.05.28.39.01.58 -.20 0.61.51.11.03.77.08 0.22.45 NOTE: * indicates P 0.1; ** indicates P 0.05 F Value P Value 6.18 0.00** 1.36 0.26 1.99 0.1* LSD Post Hoc Test 3. & 1.2.4.5. 4. & 2. - 3. & 1.2.4.5. The analysis results showed that different types of industries had a significant impact on the variations in the collaborative CRM and analytical CRM but not on the variations in the operational CRM. In the variation difference of collaborative CRM, it could be discovered from LSD post-hoc test that significant variation differences existed in the collaborative CRM between financial industry and electronic manufacturing, non-electronic manufacturing, transportation, and commercial industries; another significant difference was also observed between transportation industry and non-electronic manufacturing industry. In the analytical CRM, the financial industry had a relatively higher improvement in adoption level of CRM from the previous period to the current period than electronic manufacturing, non-electronic manufacturing, transportation, and commercial industries. This is mainly because of the rapid changes in the financial environment. For general consumers, the demand for call center, Internet banking, feedback e-mail box, SMS notification is large. To meet this demand, the financial industry needs to make progress at all times in the collaborative CRM. As a result, the increase in the collaborative CRM is significantly greater in the financial industry than 4 other industries. In the comparison of variations, the financial industry has a significant higher improvement degree (0.77) than other industries. Thus, despite a higher cost of the introducing items in the analytical CRM, the wide distribution of clients

416 International Journal of Electronic Business Management, Vol. 4, No. 5 (2006) and large variation has increased the demand for customized financial services for clients, which has also induced the analytical CRM in the financial industry. Through the adoptions in the analytical CRM, customer s consumption habits can be effectively grasped and a large amount of customized services can be provided. 4.4 Correlation Analysis between Influential Factors and Adoption of CRM In addition to external influential factors, this section will investigate the correlation among each constructs of the adoption level of CRM. Pearson product-moment correlation will be applied to analyze whether a positive or a negative correlation exists among the various constructs of adoption level. The current status and the variation from the previous to the current period are included in the analysis. 4.4.1 Correlation Analysis between Influential Factors and Adoption of CRM The testing result of H2 is shown in Table 3. The cross-sectional analysis indicated that all the influential factors were highly correlated with the adoption level of CRM systems, indicating that both internal and external factors would affect the adoption of CRM. In other words, when the maturity of information technologies or top managers support is higher, or the Company size is larger, the adoption level is also higher. When the Company size is larger, it has a relatively better ability to undertake the implementation of CRM systems. Thus, the adoption level and Company size vary in the same direction and the same factors also the same. The testing result of H3 is shown in Table 4. 4.4.2 Correlation Analysis between Variance in Influential Factors and Adoption of CRM This study will further observe whether variation in influential factors is correlated with adoption of CRM. Due to different factors adopted in the previous and the current period, this study will only compare the same factors investigated in 2003 and 2006. It was discovered from the correlation analysis that except maturity of information technologies, the variance in external environment factors and internal enterprise factors is almost positively correlated with the adoption level of CRM. This indicates that when the variance of influential factors is increasing, enterprises will increase their adoption level of CRM systems. This also proves that change degree of factors has a positive effect on the adoption level of CRM systems. 5. CONCLUSIONS AND SUGGESTIONS This study mainly aims to understand the current status of adoption of CRM among Taiwan s enterprises and investigate the trend of variations, in hope of providing industries with a better understanding of the focuses in the adoption of CRM. Table 3: Correlation analysis between influential factors and adoption of CRM Variables Adoption Level of CRM Systems Collaborative CRM Operational CRM Analytical CRM Maturity of Information 0.51** 0.31** 0.46** Technologies 0.00 0.02 0.00 External Competition Status for 0.44** 0.34** 0.31** Environment Enterprises 0.00 0.01 0.02 Factors System Provider s 0.51** 0.41** 0.46** Expertise 0.00 0.00 0.00 0.55** 0.52** 0.44** Top Managers Support 0.00 0.00 0.00 Internal Innovation Acceptance 0.39** 0.40** 0.29** Enterprise Ability 0.00 0.00 0.03 Factors System Management 0.63** 0.49** 0.52** Ability 0.00 0.00 0.00 0.49** 0.26* 0.32** Company size 0.00 0.08 0.03 NOTE: * indicates P 0.1; ** indicates P 0.05 On each row, the upper value is correlation coefficient, while the bottom value is P value.

J. Y. Lin et al.: A Panel Study for the Influential Factors of the Adoption 417 Environment Factors Enterprise Factors Table 4: Correlation analysis between variance in influential factors and adoption of CRM Variables Adoption Level of CRM Systems Collaborative CRM Operational CRM Analytical CRM Maturity of Information Technologies Competition Status for 0.30** 0.33** 0.16 Enterprises 0.02 0.01 0.24 System Provider s 0.49** 0.28** 0.38** Expertise 0.00 0.03 0.00 Top Managers Support 0.61** 0.46** 0.46** 0.00 0.00 0.00 Innovation Acceptance 0.37** 0.41** 0.34** Ability 0.01 0.00 0.01 System Management 0.55** 0.43** 0.51** Ability 0.00 0.00 0.00 NOTE: * indicates P 0.1; ** indicates P 0.05 On each row, the upper value is correlation coefficient, while the bottom value is P value. 5.1 Conclusion Through statistic analyses on the adoption of CRM data derived from the previous and current periods, it was discovered that in the collaborative CRM, web site, Email system, call center, and SMS service showed a significant increase in adoption level. Marketing automation, sales automation, and service automation in the operational CRM; data warehousing, data mining, and OLAP system in the analytical CRM presented a significant increase too. Thus, H1 is supported. In this study, industries were categorized into six types including electronic manufacturing, non-electronic manufacturing, financial service, transportation, commercial company, and other service industries. The analysis results showed that types of industries caused a significant difference in all the three dimension of CRM system. In addition, types of industries also caused a significant difference in the variation of the collaborative CRM and analytical CRM. Further, the results showed that external environment factors and internal enterprise factors had a significant positive correlation with the adoption level of CRM system. Company size and the adoption level of CRM had a significant positive correlation too. Thus, H2 is also supported. The analyses indicated that if the change degree of external environment factors and internal enterprise factors was higher, the adoption level of CRM systems would be higher too. Thereupon, H3 is also supported. 5.2 Suggestions Based on the research conclusion, suggestions are proposed for system providers and adopters of CRM systems in the following sections. 5.2.1 Suggestions for System Providers Based on the observation of enterprises overall evaluation of CRM system providers, it was discovered that most of the enterprises conceived there is still a progress space for system providers, especially in the aspects of the provision of educational service and understanding of business properties. This means the services and support provided by system providers still have not satisfied the needs of enterprises. Thus, it is suggested that CRM system providers design discriminative function modules of various industry. The need for CRM applications of various industries should be satisfied first, before they bring forward comprehensive services and technique support, so that the demand for CRM systems can be promoted. 5.2.2 Suggestions for Adopters of CRM Systems It is conceived that the applications in the operational CRM play a key role in the operation of system functions. The research conclusion manifests that even an organization has not included applications in the analytical CRM due to limited abilities, the adoption of applications in the interaction and operational CRM will also contribute to the performance of the enterprise. The max effectiveness may not be reached, but the performance in marketing, sales, and service can be benefited. Thus, this study suggests that enterprises that have not completed the implementation of CRM systems, in addition to the more mature applications in the collaborative CRM in the previous period, they should devote more to the implementation of the

418 International Journal of Electronic Business Management, Vol. 4, No. 5 (2006) applications in the operational CRM so as to build a foundation that links the operations in the CRM system. It is known from the conclusion that the adoption levels of Taiwan s enterprises are mostly between planning and intermediate level of application. Generally, the use of CRM is not prevalent. This is probably attributed to the enterprise s insufficient confidence for CRM system providers or insufficient understanding of the benefits brought by CRM systems. Through the research analyses, it is proved that a higher adoption level in the collaborative, operational, and analytical CRM system will positively affect the performance in marketing, sales, and service. And the introduction of the CRM system will also enhance the adoption level. Thus, this study suggests enterprises select an appropriate CRM system according to the expected goals and budgets and further select an appropriate system provider. After the introduction is determined, positive top mangers support should be provided, so that business performance can be enhanced as expected. REFERENCES 1. Bhatia, A., 1999, A roadmap to implementation of customer relationship management, IT Toolbox Portal for CRM, http://crm.ittoolbox.com. 2. Chablo, E., 2000, The importance of marketing data intelligence in delivering successful CRM, http://www.crmforum.com. 3. Chen, C. P., 2001, Decision on adopting customer relationship management in enterprises, Graduate Institute of Business Administration, National Chengchi University, Master Thesis. 4. Chiang, H. H., 1998, The research of dynamic strategic group structure, competitive repositioning, and performance: A longitudinal analysis of the automobile industry in Taiwan, Department of Business Management, Chung Yuan Christian University, Master Thesis. 5. Huang, M. H., 2004, An empirical study of customer relationship management drivers in Taiwan s OPTO-electronics industry, Department of International Business, National Dong Hwa University, Master Thesis. 6. Huang, Y. Y., 2001, The study of key success factors of introducing customer relationship management for the financial industry, Graduate Institute of Business Administration, National Taipei University, Master Thesis. 7. Kalakota, R. and Robinson, M., 1999, E-Business Roadmap for Success, Addision-Wiley and Sons. 8. Lin, J. C., 2002, A study of critical factors on CRM system for the publishing industry by AHP, Graduate Institute of Publishing Organization Management, Nanhua University, Master Thesis. 9. Lin, J. Y., 2003, The integrated study on the impact of innovation adoption of CRM systems on relationship marketing, Research Project of National Science Council (NSC 91-2416-H-033-001). 10. Lu, Y. T., 2002, Key factors affecting the implementation of information systems among SME in Taiwan, Department of Industrial and Information Management, National Cheng Kung University, Master Thesis. 11. Tsai, C. W., 2005, A longitudinal study of relationship between quality management activities and hospital performance in Taiwan, Department of Health Care Administration, Taipei Medical University, Master Thesis. 12. Yang, T. M., 2004, A longitudinal study on applying discrepancy theory to training evaluation: an example of e-business training program, Department of Information Management, National Chung Cheng University, Master Thesis. ABOUT THE AUTHORS Jan-Yan Lin is currently a professor in the Department of Business Administration and the Dean at Research and Development, Chung Yuan Christian University, Taiwan. His research interests are mainly on the areas of e-business, e-commence, ERP and SCM. Tsung-Lin Kuo received his MBA degree in 2006 from Chung Yuan Christian University. He majored in information management and minored in marketing at the graduate school. Additionally, he is also interested in the applications of statistics software and information system. Cheng-Wen Lee holds an Associate Professor position and the head at the Department of International Trade, Chung Yuan Christian University, Taiwan. Her current research interests are on the areas of international marketing, network theory, global strategies, and knowledge management. (Received August 2006, revised November 2006, accepted December 2006)