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Putting market-facing technology to work: Organizational drivers of CRM performance

Abstract

A large proportion of firms that adopt customer relationship management (CRM) technology find it challenging to integrate CRM technology into their core marketing processes and utilize CRM strategically to appreciably improve their performance. The authors conceptualize a model to understand the drivers of superior CRM performance after CRM technology has been adopted by a firm and examine strategic utilization of CRM technology as driven by user acceptance and proficiency in the form of employee buy-in and expertise. Top management championship practices, employee information technology (IT) skills, and CRM knowledge are identified and examined as key building blocks toward strategic utilization. The empirical test of the conceptual model is based on a mail survey of North American firms that have adopted information technology-based CRM systems. The results, based on random effects model, show that strategic utilization of CRM technology leads to higher performance when there is an emphasis on using it to manage business-to-business rather than business-to-consumer relationships, user expertise (but not buy-in) impacts CRM performance through strategic utilization, and top management championship practices, CRM knowledge, and employee IT skills impact strategic utilization through buy-in and expertise.

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Fig. 1

Notes

  1. For example, see Liker et al. (1992) and Cooper and Zmud (1990) for evidence of gap between adoption and assimilation of computer-aided design and material requirements planning technologies, respectively.

  2. That is, (1) relational management knowledge, (2) collaboration management knowledge, (3) market environment knowledge, (4) customer profitability knowledge, (5) employee IT skills, (6) top management championship practices, (7) buy-in, (8) strategic utilization, (9) expertise, and (10) CRM performance.

  3. Specifically, goodness of fit index ranged from 0.92 to 0.94, comparative fit index ranged from 0.97 to 0.98, nonnormed fit index was 0.97 in both cases, and root mean square error of approximation ranged from 0.060 to 0.066 (statistically equivalent to 0.05 in both cases). Although the χ 2 statistic was statistically significant in the models, the ratios of χ 2 to degrees of freedom were 1.89 and 2.32 for the two models. An Appendix with a comprehensive list of items for all constructs is available upon request.

  4. Out of these 151 firms, 17 used custom-made in-house CRM software, five used Peoplesoft, four used Oracle, and three firms each used SAP and Epicor.

References

  • Aiken, L. S. & West, S. G. (1996). Multiple regression: testing and interpreting interactions. Newbury Park: Sage.

    Google Scholar 

  • Anderson, J. C. & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423.

    Article  Google Scholar 

  • Armstrong, S. & Overton, T. S. (1977). Estimating non-response bias in mail surveys. Journal of Marketing Research, 14(3), 396–402.

    Article  Google Scholar 

  • Attewell, P. (1992). Technology diffusion and organizational learning: The case of business computing. Organization Science, 3(1), 1–19.

    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(6), 1173–1182.

    Article  Google Scholar 

  • Bharadwaj, A. S. (2000). A resource-based perspective on information technology capability and firm performance: An empirical investigation. MIS Quarterly, 24(1), 169–196.

    Article  Google Scholar 

  • Bolton, R., Lemon, K. N., & Verhoef, P. C. (2008). Expanding business-to-business customer relationships: Modeling the customer’s upgrade decision. Journal of Marketing, 72(1), 46–64.

    Article  Google Scholar 

  • Boulding, W. (1990). Unobservable effects and business performance—comments. Marketing Science, 9(1), 88–91.

    Article  Google Scholar 

  • Campbell, D. T. (1955). The informant in quantitative research. American Journal of Sociology, 60(4), 339–342.

    Article  Google Scholar 

  • Chatterjee, D., Grewal, R., & Sambamurthy, V. (2002). Shaping up for e-commerce: Institutional enablers of the organizational assimilation of web technologies. MIS Quarterly, 26(2), 65–89.

    Article  Google Scholar 

  • Cooper, R. B. & Zmud, R. W. (1990). Information technology implementation research: A technological diffusion approach. Management Science, 36(2), 123–139.

    Article  Google Scholar 

  • Diamantopoulos, A. & Winklhofer, H. M. (2001). Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research, 38(2), 269–277.

    Article  Google Scholar 

  • Dyer, J. H. & Singh, H. (1998). The relational view: Cooperative strategy and sources of interorganizational competitive advantage. Academy of Management Review, 23(4), 660–679.

    Article  Google Scholar 

  • Ebner, M., Hu A., Levitt D., & Mccrory. J. (2002). How to rescue CRM. The Mckinsey Quarterly, 2002 Number 4.

  • Fichman, R. G. & Kemerer, C. F. (1997). The assimilation of software process innovations: An organizational learning perspective. Management Science, 43(10), 1345–1363.

    Article  Google Scholar 

  • Fichman, R. G. & Kemerer, C. F. (1999). The illusory diffusion of innovation: An examination of assimilation gaps. Information Systems Research, 10(3), 255–275.

    Article  Google Scholar 

  • Fornell, C. D. & Larcker, F. (1981). Evaluating structural equation models with unobserved variables and measurement errors. Journal of Marketing Research, 18(1), 39–50.

    Article  Google Scholar 

  • Geyskens, I., Gielens, K., & Dekimpe, M. G. (2002). The market valuation of internet channel additions. Journal of Marketing, 66(2), 102–119.

    Article  Google Scholar 

  • Goodman, L. A. (1960). On the exact variance of products. Journal of the American Statistical Association, 55, 708–713.

    Article  Google Scholar 

  • Grewal, R., Comer, J. M., & Mehta, R. (2001). An investigation into the antecedents of organizational participation in business-to-business electronic markets. Journal of Marketing, 65, 17–33.

    Article  Google Scholar 

  • Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251–1271.

    Article  Google Scholar 

  • Hunter, G. K. & Perreault, W. D. (2007). Making sales technology effective. Journal of Marketing, 71(1), 16–34.

    Article  Google Scholar 

  • Jacobson, R. (1990). Unobservable effects and business performance. Marketing Science, 9(1), 74–85.

    Article  Google Scholar 

  • Jayachandran, S., Sharma, S., Kaufman, P., & Raman, P. (2005). The role of relational information processes and technology use in customer relationship management. Journal of Marketing, 69(4), 77–192.

    Article  Google Scholar 

  • Johnson, J. L. & Sohi, R. (2001). The influence of firm predispositions on interfirm relationship formation in business markets. International Journal of Research in Marketing, 18(4), 299–318.

    Article  Google Scholar 

  • Johnson, J. L., Sohi, R. S., & Grewal, R. (2004). The role of relational knowledge stores in interfirm partnering. Journal of Marketing, 68(3), 21–36.

    Article  Google Scholar 

  • Klein, K. J. & Sorra, J. P. (1996). The challenge of innovation implementation. Academy of Management Review, 21(4), 1055–1080.

    Article  Google Scholar 

  • Kline, R. B. (1998). Principles and practice of structural equation modeling. New York: Guilford.

    Google Scholar 

  • Liker, J. K., Fleischer, M., & Arnsdorf, D. (1992). Fulfilling the promises of CAD. Sloan Management Review, 33(3), 74–86.

    Google Scholar 

  • MacKinnon, D. P., Warsi, G., & Dwyer, J. H. (1995). A simulation study of mediated effect measures. Multivariate Behavioral Res, 30(1), 41–62.

    Article  Google Scholar 

  • Mankoff, S. (2002). Ten critical success factors for CRM: Lessons learned from successful implementations. Siebel Systems White Paper.

  • Moshe, F. (1994). Beyond industry boundaries: Human expertise, diversification and resource-related industry groups. Organization Science, 5(2), 185–199.

    Article  Google Scholar 

  • Noble, C. H. & Mokwa, M. P. (1999). Implementing marketing strategies: Developing and testing a managerial theory. Journal of Marketing, 63(4), 57–73.

    Article  Google Scholar 

  • Palmatier, R. W., Scheer, L. K., Evans, K. R., & Arnold, T. J. (2008). Achieving relationship marketing effectiveness in business-to-business exchanges. Journal of the Academy of Marketing Science, 36(2), 174–190.

    Article  Google Scholar 

  • Payne, A. & Frow, P. (2005). A strategic framework for customer relationship management. Journal of Marketing, 69(4), 167–176.

    Article  Google Scholar 

  • Podsakoff, P. M., MacKenzie, S. B., Lee, J., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.

    Article  Google Scholar 

  • Purvis, R. L., Samabamurthy, V., & Zmud, R. W. (2001). The assimilation of knowledge platforms in organizations: An empirical investigation. Organization Science, 12(2), 117–135.

    Article  Google Scholar 

  • Reinartz, W., Krafft, M., & Hoyer, W. D. (2004). The customer relationship management process: Its measurement and impact on performance. Journal of Marketing Research, 41(3), 293–305.

    Article  Google Scholar 

  • Rigby, D. K. & Ledingham, D. (2004). CRM done right. Harvard Business Review, 82(11), 118–129.

    Google Scholar 

  • Rigby, D. K., Reichheld, F. F., & Schefter, P. (2002). Avoid the four perils of CRM. Harvard Business Review, 80(2), 5–11.

    Google Scholar 

  • Sobel, M. E. (1982). Asymptotic intervals for indirect effects in structural equations models. In S. Leinhart (Ed.), Sociological methodology. San Francisco: Jossey-Bass.

    Google Scholar 

  • Srivastava, R. K., Shervani, T. A., & Fahey, L. (1998). Market-based assets and shareholder value: A framework for analysis. Journal of Marketing, 62(1), 2–18.

    Article  Google Scholar 

  • Thong, J. Y., Yap, L. C., & Raman, K. S. (1996). Top management support, external expertise and information systems implementation in small businesses. Information Systems Research, 7(2), 248–267.

    Article  Google Scholar 

  • White, C. E., Jr. & Christy, D. P. (1987). The information center concept: A normative model and a study of six installations. MIS Quarterly, 11(4), 451–458.

    Article  Google Scholar 

  • Winer, R. S. (2001). A framework for customer relationship management. California Management Review, 43(4), 89–105.

    Google Scholar 

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Correspondence to Amit Saini.

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The authors thank the Marketing Science Institute (Grant #4-1204) and the Institute for the Study of Business Markets (ISBM) at Pennsylvania State University for their support for this research.

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Saini, A., Grewal, R. & Johnson, J.L. Putting market-facing technology to work: Organizational drivers of CRM performance. Mark Lett 21, 365–383 (2010). https://doi.org/10.1007/s11002-009-9096-z

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Keywords

  • CRM
  • CRM knowledge
  • Employee IT skills
  • Top management championship
  • CRM performance
  • Buy-in
  • Expertise
  • Strategic utilization
  • Customer relationship management