Skip to main content

Testing the Moderation Effects on Gartner’s Customer Relationship Management Practices and Customer Acquisition

  • Chapter
  • First Online:
Innovation, Technology, and Market Ecosystems

Abstract

The prime objective of the study is to understand the moderating effect of job satisfaction and gender on the relationship between customer relationship management (CRM) practices and customer acquisition. The study first investigates the relationship between the four best CRM practices; CRM vision, CRM strategy, valued customer experience and organizational collaboration, suggested by Gartner’s competency model with customer acquisition and then tries to test the moderation effect of employee’s job satisfaction and gender. The findings of the study are based upon the responses from 196 employees of a selected retail store, through a self-administered questionnaire. The study finds a significant moderation effect of job satisfaction on the relationship between Gartner’s CRM practices and customer acquisition.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Arbuckle, J., & Wothke, W. (1999). AMOS 4.0 User’s guide. Chicago: Smallwaters Corporation, Inc.

    Google Scholar 

  • Bagozzi, R., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of Academy of Marketing Sciences, 16, 74–94. https://doi.org/10.1007/BF02723327

    Article  Google Scholar 

  • 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 Psychology, 51, 1173–1182.

    Article  Google Scholar 

  • Becker, U. J., Greve, G., & Albers, S. (2009). The impact of technological and organizational implementation of CRM on customer acquisition, maintenance and retention. International Journal of Research in Marketing, 26(3), 207–215.

    Article  Google Scholar 

  • Bollen, K. A. (1989). Structural equations with latent variables. New York: John Wiley & Sons.

    Book  Google Scholar 

  • Bose, R. (2002). Customer relationship management: Key components for IT success. Industrial Management & Data Systems, 102(2), 89–97.

    Article  Google Scholar 

  • Buttle, F. (2009). Customer relationship management: Concepts and technology. Burlington, USA: Elsevier.

    Google Scholar 

  • Byrne, B. M. (1998). Structural equation Modeling with LISREL, PRELIS and SIMPLIS: Basic concepts, applications and programming. Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Campbell, D. T. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56, 81–105. https://doi.org/10.1037/h0046016

    Article  Google Scholar 

  • Chandler, G., Detienne, D., McKelvie, A., & Mumford, T. V. (2011). Causation and effectuation processes: A validation study. Journal of Business Venturing, 26, 375–390. https://doi.org/10.1016/j.jbusvent.2009.10.006

    Article  Google Scholar 

  • Chen, I. J., & Popovich, K. (2003). Understanding customer relationship management (CRM) – People, process and technology. Business Process Management, 9(5), 672–688.

    Article  Google Scholar 

  • Chou, C. D., Lin, B., Xu, Y., & Yen, C. D. (2002). Adopting customer relationship management technology. Journal of Industrial Management and Data Systems, 102(8), 442–452. https://doi.org/10.1108/02635570210445871

    Article  Google Scholar 

  • Crowley, S. L., & Fan, X. (1997). Structural equation Modeling: Basic concepts and applications in personality assessment research. Journal of Personality Assessment, 68(3), 508–531.

    Article  Google Scholar 

  • Diamantopoulos, A., & Siguaw, J. A. (2000). Introducing LISREL. London: Sage Publications.

    Book  Google Scholar 

  • Eisenfeld, B., & Nelson, S. (2003). CRM best practices: From vision to collaboration (COM-21-1015). Retrieved from http://www.gartner.com/Id-117541

  • Fornell, C., & Lacker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50.

    Article  Google Scholar 

  • Gerpott, T. J., Rams, W., & Schindler, A. (2001). Customer retention, loyalty and satisfaction in the German cellular telecommunications market. Telecommunications Policy, 25(4), 249–269.

    Article  Google Scholar 

  • Greenberg, P. (2004). CRM at the speed of light (3rd ed.). McGraw-Hill: Osborne Media.

    Google Scholar 

  • Hair Jr., J. F., Anderson, R. E., Tatham, R. L., Babin, B. J., & Black, W. C. (2007). Multivariate data analysis (6th international ed.). Delhi: Dorling Kindersley (India) Pvt. Ltd.

    Google Scholar 

  • Hair Jr., J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariant data analysis. Upper Saddle River, NJ: Pearson International Edition.

    Google Scholar 

  • Homburg, C., & Baumgartner, H. (1995). Beurteilung von Kausalmodelen. Marketing, 17(3), 162–176.

    Google Scholar 

  • Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modeling: Guidelines for determining model fit. The Electronic Journal of Business Research Methods, 6(1), 53–60.

    Google Scholar 

  • Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.

    Article  Google Scholar 

  • Jaccard, J., & Wan, C. K. (1996). LISREL approaches to interaction effects in multiple regression. Thousand Oaks, CA: Sage Publications.

    Book  Google Scholar 

  • Joreskog, K. G., & Sorbom, D. (1993). LISREL 8 user’s reference guide. Chicago: Scientific Software International, Inc.

    Google Scholar 

  • Judd, C. M., Kenny, D. A., & McClelland, G. H. (2001). Estimating and testing mediation and moderation in within-participant designs. Psychological Methods, 6, 115–134.

    Article  Google Scholar 

  • Kirkby, J. (2001a). Creating a CRM vision (TG-14-9470). Retrieved from http://www.gartner.com/Id-103196

  • Kirkby, J. (2001b). Developing a CRM strategy (TU-14-9475). Retrieved from http://www.gartner.com/Id-350469

  • Kirkby, J., Thompson, E., & Wecksell, J. (2001). Customer experience: The voice of the customer (TG-14-9567). Retrieved from http://www.gartner.com/Id-350573

  • Kotler, P. (1997). Marketing management: Analysis, planning, implementation and control. Englewood-Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  • Peelen, E., Montfort, K., Beltman, R., & klerkx, A. (2009). An empirical study into the foundations of CRM success. Journal of Strategic Marketing, 17, 453–471.

    Article  Google Scholar 

  • Radcliffe, J., Thompson, E., & Eisenfeld, B. (2001). True CRM requires organizational collaboration (DF-14-6658). Retrieved from http://www.gartner.com/Id-350571

  • Ranjit Bose, (2002). Customer relationship management: key components for IT success. Industrial Management & Data Systems 102 (2):89–97

    Google Scholar 

  • Reinartz, W., Krafft, M., & Hoyer, W. D. (2018). The customer relationship management process: Its measurement and impact on performance. Journal of Marketing Research, 41(3), 293–305.

    Article  Google Scholar 

  • Seeman, E. D., & O’Hara, M. (2006). Customer relationship management in higher education: Using information systems to improve the student-school relationship. Campus-Wide Information Systems, 23(1), 24–34.

    Article  Google Scholar 

  • Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). New York: Allyn and Bacon.

    Google Scholar 

  • Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432–2440.

    Article  Google Scholar 

  • Wheaton, B., Muthen, B., Alwin, D. F., & Summers, G. F. (1977). Assessing reliability and stability in panel models. Sociological Methodology, 8, 84.

    Article  Google Scholar 

  • Yiing, L. H., & Ahmad, K. Z. (2009). The moderating effects of organizational culture on the relationship between leadership behavior and organizational commitment and between organizational commitment and job satisfaction and performance. Leadership & Organizational Development Journal, 30(1), 53–86.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 The Author(s)

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Das, S., Mishra, M., Mohanty, P.K. (2020). Testing the Moderation Effects on Gartner’s Customer Relationship Management Practices and Customer Acquisition. In: Rajagopal, Behl, R. (eds) Innovation, Technology, and Market Ecosystems. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-23010-4_16

Download citation

Publish with us

Policies and ethics