The Performance of Customer Relationship Management System:

The Performance of Customer Relationship Management System: antecedents and consequences 1 Hyun Gi Hong, 2 Je Ran Chun 1, First Author Dept. Of Business Administration, Cheongju University, hghong@cju.ac.kr 2, Dept. Of Medical Administration, Daejeon Health Sciences College, jr@hit.ac.kr Abstract Recently, many companies have tried to implement the Customer Relationship Management (CRM) system to enhance the close relationship with their customers. The objective of this paper is to find out the influencing and influenced factors of the performance of the Customer Relationship Management (CRM) system. Furthermore, the relationships between these factors have been analyzed. A survey based on the 5-point Likert scale questionnaire survey was administered to 129 companies that have recently implemented CRM systems, or plan in the near future to implement the CRM system for the betterment of customer relationships. In order to analyze the performance of the CRM system, factor analysis with several Key Performance Indicators (KPIs) was conducted. And for the analysis of the factors affecting the performance of CRM system, multiple regression analysis and Chi-square test were executed. In this study, several hypotheses were derived to analyze the relationships between antecedents and consequences factors of performance of CRM system. These hypotheses were tested by using the Structural Equation Model (SEM). As the result of this study we discovered the CRM- Technology has positive effects on the CRM-Performance. And the CRM-Performance has also positive influences on the Business-Performance of companies. On the basis of the result of this research, we proposed some suggestions and guidelines for the successful implementation and improvement of CRM- Performance. Keywords: Customer Relationship Management, Exploratory Factor Analysis, Key Performance Indicator 1. Introduction Recently, companies are increasingly introducing strategic management strategies and information systems in order to improve efficiency of their operation and gain competitive advantages over other company [9]. The Customer Relationship Management (CRM) system has been also increasingly accepted as one of the core management strategies [8]. The CRM system is defined as a model for managing a company s interactions with current and future customers [12]. The CRM system is a post ERP application for enforcement of the close relationship with customers by providing information about the habit and tendency of individual customers [4]. The CRM system helps the management to provide better and customized customer services effectively. Although many companies have invested money, time and efforts to have the successful operation of CRM system, there are very few successful cases in the practice. Some studies concluded that the CRM projects fail to achieve the expected improvement in management performance [8]. Such result is caused because the companies failed to deploy the CRM resources they possessed, e.g. CRM system function and CRM person and management strategy, to build superior customer relationships and achieve competitive advantage [1]. Therefore, it is important to study which factors have positive influences on the performance of the CRM system, and to analyze the relationship between these factors and consequences of the performance of the HCRM system. The objective of this paper is to analyze the relationship among factors, antecedents and consequences, on the performance of the CRM system. A survey based on the 5-point Likert scale online questionnaire was administered to 129 companies that have recently implemented HCRM systems, or plan to implement HCRM system in the near future for the betterment of customer relationships. In order to analyze the performance of CRM, factor analysis with several Key Journal of Convergence Information Technology(JCIT) Volume8, Number12, July 2013 doi:10.4156/jcit.vol8.issue12.46 385

Performance Indicators (KPIs) was conducted. And for the analysis of the factors affecting the performance of CRM system, multiple regression analysis and Chi-square test were executed. Table 1. Result of EFA Factor Measured variables Factor loadings F1 F2 F3 F4 Cronbach- Alpha Company Performance CRM performance IT- Function CRM- Organization Revenue increase(p10).961 -.071.027 -.007 Impr. of buz. process(p11).936 -.034.047.032 Enlarg. of market share(p7).844.073 -.008 -.083 Impr. of crm process(p4).002.929 -.117.013 crm enhancement(p2).044.890 -.011.023 cust. maintenance(p1) -.062.770.087.046 erp function(f6).011 -.018.956 -.011 data-accuracy(f8).044 -.002.953 -.010 crm maint.(f10) -.052.029.034.883 crm edu.(f9).003.042 -.054.881 0.902 0.822 0.900 0.713 In this study several hypotheses were suggested to analyze the relationship between these factors. These hypotheses were tested by using the Structural Equation Model (SEM). As the result of this study we found that the CRM-Technology has positive effects on the performance of CRM- Performance. And the performance of the CRM system has positive influences on the Business- Performance in general. Based on the result of this research, some suggestions could be recommended so that company manager can improve their CRM project more effectively and efficiently. 2. Method and Materials 2.1. Survey and measurement This research paper consists of three steps. In the first step, we tried to define the measured variables (constructs) related to the performance of the CRM system, which will be later categorized into a certain number of factors. For this purpose, a survey based on the 5-point Likert scale questionnaire was administered to 129 companies, which have recently implemented and operated CRM systems. In the second stage, we carried out the Exploratory Factor Analysis (EFA) with these variables to define the factors that represented the group of certain measured variables. In this stage, we can explain what kind of measured variables belong to which factors. In the Last step, the research model and hypotheses about the relations between factors will be derived and tested. Based on the test about correlation coefficient between factors, the hypotheses could be accepted or rejected. 2.2. Operational definition of measured variable We determined several measured variables in 2 perspectives. For the first view we defined characteristics belonging to the characteristics of the IT system and CRM system in companies, like functions of the Enterprise Resource Planning (ERP) system, ERP-organization, ERP usage and CRM function. From the second viewpoint, we defined the items for the Business-Performance and 386

characteristics of CRM-Performance. The following are details of measured variables from each perspective. 2.2.1. IT-system view We defined 10 items as the measured variables from the viewpoint of IT-Technology in companies. The first 5 items are ease of use of the ERP system, the accuracy of the report from the ERP system, easy acceptance of CRM technology, user satisfaction and IT system extendibility. The second 5 variables are from the perspective of the IT-Organization. They are management strategy of companies, management strategy for customers, CRM experts in organizations and education of CRM systems and CRM maintenance. These variables were measured with the 5 point Likert scale questionnaires survey. 2.2.2. Performance view From Performance of the CRM system s viewpoint, we derived 6 measured variables: Retain customers, enhancement of the company image, customer acknowledgement of customer service, improvement of customer service processes, better interaction with customers and increase of customer win-back ratio. From the perspective of Business-Performance, we utilized overall performance items like enlargement of market share, growth of revenue, operation profitability, customer satisfaction and improvement of management process as the measured variables [12]. For the survey of these 11 variables, 5 point Likert scale questionnaires were conducted. After defining the measured variables, the Exploratory Factor Analysis (EFA) with Varimax rotation using SPSS 20.0 was executed. These 21 variables were converged to 4 factors: ERP-Function, Management & Organization, CRM- Performance and Business-Performance. 2.3. Research Model and Hypotheses The result of EFA execution with 21 variables is represented in Table 1. As shown in Table 1, the 21 variables are converged in 4 factors. Based on these factors, we suggested a research model as shown in Figure 1. The research model represents the relationship of the factors, IT-Function and CRM-Organization as independent variables with CRM- Performance as a dependent variable. And it shows also the influence of CRM-Performance as an independent variable on Company- Performance as a dependent variable. In this context, the CRM system performance has the role of mediate variable. IT-Function H1 H4 CRM Performance H3 Company Performance CRM Organization H2 H5 Fig. 1. Research Model Based on this research model, we derived 5 hypotheses. These hypotheses pertaining to the antecedents and consequences of the CRM- Performance can be described as below. Hypothesis 1: IT-Function (ERP-function, data accuracy of ERP report) has positive influence on the CRM-Performance (improvement of CRM-Process, customer satisfaction, customer maintenance). 387

Hypothesis 2: CRM-Organization (CRM expert in organization, CRM system education) has positive influence on the CRM-Performance (better customer-relationship, customer satisfaction). Hypothesis 3: CRM-Performance (improvement of CRM-Process, customer satisfaction, customer maintenance) has positive influence on the Company- Performance (revenue increase, improvement of business process, enlargement of market share). Hypothesis 4: IT-Function (ERP-function, data accuracy) has positive influences on the Company- Performance. Hypothesis 5: CRM-Organization (CRM expert in organization, CRM system education) has positive influence on the Company- Performance. For the acceptance or rejection of hypotheses, research model was analyzed with Structural Equation Model. 3. Result of research 3.1. Validity and reliability of measures As discussed, we administered an internet-based 21-item questionnaire survey to 129 companies. Then, item-to-total score correlation and deleting items based on Cronbach s alpha were applied together to determine measured variables for certain factors. Items with lower correlations were deleted, because they do not contain an additional domain of interest [6]. This resulted in the retaining of 10 measured variables, which converged to 4 factors in the Exploratory Factor Analysis (EFA) with Varimax rotation using SPSS 20.0. The result of this stage is represented in Table 1. As shown in Table 1, the Cronbach s alphas of the 4 factors ranged from 0.71 to 0.90, and the factor loadings were all above 0.5 as minimum level suggested by Fornell [2]. Table 2. Result of research model test Hypothesized Path Hypothesis Estimate S.E. C.R. P Finding CRM-Perf. IT-Function H1 0.254 0.127 2 0.046 Accept CRM-Perf. CRM-Orga. H2-0.063 0.073-0.862 0.389 Reject Com.-Perf. IT-Function H4-3.202 1.636-1.958 0.05 Accept Com.-Perf. CRM-Orga. H5 0.892 0.934 0.956 0.339 Reject Com.-Perf. CRM-Perf. H3 12.867 0.812 15.853 *** Accept Factor Average Standard Deviation. IT-Function 4.07 0.71 1.00 Table 3. Analysis of Inter-factors Correlation CRM-Org. 3.25 1.02 0.035 ** 1.00 Inter-Construct Correlation IT-Function CRM-Organ. CRM-Perf. Com.-Perf. CRM-Perf. 4.56 0.36 0.497 ** -.125 ** 1.00 Com.-Perf. 4.85 0.19 0.932 ** 0.025 ** 0.486 1.00 * correlation s coefficient 0.05 (bilateral significant) ** correlation coefficient 0.01(bilateral significant) 3.2. Result of Hypotheses test We evaluated measurement properties and reliability of the research model by conducting Confirmatory Factor Analysis (CFA). The models provided good levels of fit: x 2 (19)=119, p=0.000, GFI=0.857, RMSEA =0.203, CFI=0.706, TLI=0.566, AGFI=0.73, x 2 /df=6.3). Therefore, the 4 factor (10 items) correlated research model has acceptable reliability and validity. 388

The results showed that CRM-Performance were positively associated with Company- Performance. Furthermore, as shown in Table 2, it was found that CRM-Performance and Company- Performance had positive associations with IT-Function, too. Therefore, H1, H3 and H4 were strongly supported. Table 3 shows the average and standard deviation of measured variables of 4 factors and also correlation s coefficient between them. The correlation s coefficient for * are significant P<0.05 and for ** are P<0.02. 4. Discussion This study was conducted to identify the factors influencing the CRM performance and analyze the relationship among the antecedents and consequences factors of CRM system performance. With a competent CRM system, companies can maintain or upgrade the beneficial relationships with new and old customers. The core technology of the CRM system is the management function of customer relationships, like deploying customer-oriented management strategies and building a customer-centric organization. We demonstrated that CRM-Performance was influenced by IT-Function, like ERP- Function and Data-Accuracy from the ERP system. In order to achieve the expected CRM- Performance, companies should have a fully functional IT system, because the CRM system is regarded as one of the post-erp systems, e.g. the Supply Chain Management (SCM) system and Business Intelligence (BI) system. Besides, we demonstrated the important relationship of CRM- Performance with the consequence, Company-Performance. 5. Limitations and research direction The results of multiple regression on the hypotheses is shown in Table 2. This study clearly indicated that CRM- Performance was significantly and positively associated with Business- Performance. IT-Function contributed to CRM-Performance. However, as with all other research work, there were some limitations of this paper that should be noted. First, market differences in different industries might influence the types of strategies adopted by companies. Such differences influence a company s CRM strategy and have impacts on Business- Performance. So it is desirable to have the research, which covered the differences in each industry. Secondly, we have collected data only from a small size of sample companies. This small-sized sample data has limitations for the generalization of research result. At last, we assume that the effective management of customer relationships in practice was rather complex and complicated. There will be more moderate and mediate factors needed to examine the moderating effect of different factors such as competition intensity and market growth rate on the relationship between CRM-Performance and Business-Performance [10]. And we need more measured variables to examine the various effects of each component of antecedents and consequences of CRM-Performance. Nevertheless, despite these limitations, we made several positive findings on CRM-Performance. In summary, future study on CRM-Performance should monitor the progress of its performance in relation to the customer s behavioral changes in order to effectively examine how the customer s behavior affects CRM-Performance over time. 6. References [1] Day GS., and Van den Bulte, Superiority in customer relationship management: consequences for competitive advantage and performance, Marketing Science Institute, Working Paper, no. 02-123, 2002. [2] Fornell C., Larcker D.F., Evaluating structural equation model with unobserable variables and measurement error, Journal of marketing esearch, vol.18, Feb. Pp.39-50, 1992. [3] Hoots Mike, Customer relationship management for facility managers, Journal of Facility Management, vol3 no. 4, pp.346-361, 2012. [4] Kim HS, Kim YG, A Study on Developing CRM Scorecard, KMIS International Conference, p.135-141, 2005. [5] Kim Molan, Park JE., Dubinsky AJ., Chaaiy S., Frequency of CRM implementation activities: a customercentric view, Journal of Service Marketing, vol.26 no.2, pp.83-93, 2012,. 389

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