INFORMATION AND COMMUNICATION TECHNOLOGY AND CRM METHODOLOGY

By | February 9, 2018

Data Collection

 

A survey was conducted of Taiwan’s 1,000 largest companies, as published by the Ministry of Economic Affairs for the year 2000. Letters were sent that briefly explained the purpose of this research project, funded by the National Science Council (NSC) of Taiwan, and provided general instructions on completing the enclosed questionnaire. Sales and marketing managers and cus- tomer service department heads were specifically targeted.

Initially, only 85 responses were received. Follow-up telephone calls urging recipients to com- plete the questionnaire brought the total to 120—a 12 percent response rate. Of these, 17 were incomplete and had to be discarded, reducing the sample to 103. Nonresponse may be attributed to companies’ either not practicing or not having sufficient experience with CRM. The sample covers various industries, including commerce and trade (4.5 percent), manufacturing (48.1), construction (6.4), financial services (18.2), transportation and logistics (2.7), real estate and general services (11.8), and others (8.2).

 

Measures

 

The standard psychometric scale development procedure of Gerbing and Anderson (1998) was followed to generate a series of multi-item scales. Items for each variable were either taken or patterned from previous studies (Table 8.1) and placed in the context of their abilities attributed to CRM (e.g., “with CRM, your company is able to . . .”). Others were based on interviews with IT and marketing professionals. Measures were formulated with single- and multiple-item for- mats; conceptualized multiple-item scales were developed as formative or reflective in nature. All items were operationalized on five-point Likert-type scales from strongly disagree (1) to strongly agree (5). The survey instrument was pretested on IT and marketing manager and later refined to improve clarity.

A factor analysis with a varimax rotation (using SAS 8.2) confirmed the existence of the seven hypothesized constructs: market orientation, IT investments, mass customization, CRM perfor- mance, partnership quality, customer network effect and information sharing. The factors, load- ings, and variables appear in Table 8.2. A Kaiser’s measure of sampling adequacy (MSA) of .798 supports the appropriateness of the factor analysis, given the 103 observations. Two variables, “valuable information shared with customers” and “customer satisfaction measured,” cross-loaded on the IT investment and information sharing, and CRM performance and partnership quality constructs, respectively. The higher loadings, though, properly place them on their constructs. Other items that describe the acquisition and retention of customers, customization of products and services, and other derived benefits from CRM performance did not load.

The Cronbach’s alphas for each of the constructs indicate an acceptable reliability of the mea-

 

 

 

sures. Since the items loading onto a construct form a linear combination, they were added to produce aggregate scores (for each construct). The scores were then used in multiple regression models to test the hypotheses. Measures reflecting business capital and the number of employees provide a means to observe their influence on the dependent variables. As control variables, they help ensure that neither biases the results and allow for greater generalizability of the findings (Pedhazur and Pedhazur, 1991).

ANALYSIS AND DISCUSSION

 

Generally, the results indicate that CRM performance plays a mediating role in the relationship between the CRM elements (marketing orientation, IT investment, mass customization) and cus- tomer lock-in (customer network effect, information sharing). However, the same does not hold true for partnership quality (as a mediator). Models I and II (Table 8.3) summarize the positive relation- ships between the CRM elements and CRM performance (H1), and between CRM elements and partnership quality (H2), respectively. The nonsignificance of the control variables suggests neither has an effect on the relationships. This is consistent with the results of Luneborg and Nielsen’s (2003) study. Also, the low variance inflation factors (VIF) reveal no collinearity problems.

The standardized coefficients in models I and II suggest that, of the three elements, market orientation has the greatest impact on CRM performance and partnership quality. Market orienta- tion represents the analytical aspect of CRM. Customer-centric strategies, developed and based on marketing intelligence involving the systematic collection of customer information deposited

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