Journal of the Academy of Marketing Science

, Volume 43, Issue 4, pp 528–544 | Cite as

Consumer resistance to innovation—a behavioral reasoning perspective

  • Marius C. ClaudyEmail author
  • Rosanna Garcia
  • Aidan O’Driscoll
Original Empirical Research


Behavioral research shows that reasons for and reasons against adopting innovations differ qualitatively, and they influence consumers’ decisions in dissimilar ways. This has important implications for theorists and managers, as overcoming barriers that cause resistance to innovation calls for marketing approaches other than promoting reasons for adoption of new products and services. Consumer behavior frameworks in diffusion of innovation (DOI) studies have largely failed to distinctly account for reasons against adoption. Indeed, no study to date has tested the relative influence of adoption and resistance factors in a single framework. This research aims to address this shortcoming by applying a novel consumer behavior model (i.e., behavioral reasoning theory) to test the relative influence of both reasons for and, importantly, reasons against adoption in consumers’ innovation adoption decisions. Based on two empirical studies, one with a product and a second with a service innovation, findings demonstrate that behavioral reasoning theory provides a suitable framework to model the mental processing of innovation adoption. Implications for managers and researchers are discussed.


Adoption of innovation Resistance to innovation Behavioral reasoning theory Consumer behavior 


  1. Agarwal, S., & Teas, R. K. (2001). Perceived value: mediating role of perceived risk. J Mark Theory Pract, 9(4), 1–14.Google Scholar
  2. Ajzen, I. (1991). The theory of planned behaviour. Organ Behav Hum Decis Process, 50, 179–211.CrossRefGoogle Scholar
  3. Antioco, M., & Kleijnen, M. (2010). Consumer adoption of technological innovations: effects of psychological and functional barriers in a lack of content versus a presence of content situation. Eur J Mark, 44(11/12), 1700–1724.CrossRefGoogle Scholar
  4. Arts, J. W. C., Frambach, R. T., & Bijmolt, T. H. (2011). Generalizations on consumer innovation adoption: a meta-analysis on drivers of intention and behavior. Int J Res Mark, 28(2), 134–144.CrossRefGoogle Scholar
  5. Bagozzi, R. P. (1992). The self-regulation of attitudes, intentions, and behavior. American Sociological Association: Social Psychology Quarterly.Google Scholar
  6. Bagozzi, R. P., & Yi, Y. (2012). Specification, evaluation, and interpretation of structural equation models. J Acad Mark Sci, 40(1), 8–34.CrossRefGoogle Scholar
  7. Bagozzi, R. P., Gürnao-Canli, Z., & Priester, J. (2002). The social psychology of consumer behavior. Buckingham: Open University Press.Google Scholar
  8. Bartl, M., Füller, J., Mühlbacher, H., & Ernst, H. (2012). A Manager’s perspective on virtual customer integration for new product development. J Prod Innov Manag, 29(6), 1031–1046.CrossRefGoogle Scholar
  9. Berchicci, L., & Bodewes, W. (2005). Bridging environmental issues with new product development. Bus Strateg Environ, 14(5), 272–85.CrossRefGoogle Scholar
  10. Chazidakis, A., & Lee, M. S. W. (2013). Anti-consumption as the study of reasons against. J Macromarketing, 33(3), 190–203.CrossRefGoogle Scholar
  11. Chen, F. F., Sousa, K. H., & West, S. G. (2005). Teacher’s corner: testing measurement invariance of second-order factor models. Struct Aequationes Model A Multidiscip J, 12(3), 37–41.Google Scholar
  12. Claudy, M. C., Peterson, M., & O’Driscoll, A. (2013). Understanding the attitude-behavior Gap for renewable energy systems using behavioral reasoning theory. J Macromarketing, 33(4), 273–287.CrossRefGoogle Scholar
  13. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Manag Inf Syst Q, 13, 319–339.CrossRefGoogle Scholar
  14. Day, R. L., & Herbig, P. A. (1992). Customer acceptance: the key to successful introductions of innovations. Mark Intell Plan, 10(1), 4–15.CrossRefGoogle Scholar
  15. Dholakia, U. M. (2001). A motivational process model of product involvement and consumer risk perception. Eur J Mark, 35(11/12), 1340–60.CrossRefGoogle Scholar
  16. Eagly, A. H., & Chaiken, S. (1998). Attitude structure and function. In D. T. Gilbert, S. T. Fiske, & G. Lindzey (Eds.), Handbook of social psychology (pp. 269–322). New York: McGraw-Hill.Google Scholar
  17. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: an introduction to theory and research. Philosophy rhetoric. Reading, MA: Addison-Wesley.Google Scholar
  18. Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error (pp. 39–50). XVI (February): Journal of Marketing Research.Google Scholar
  19. Garcia, R., Bardhi, F., & Friedrich, C. (2007). Overcoming consumer resistance to innovation. MIT Sloan Manag Rev, 48(4).Google Scholar
  20. Gatignon, H., & Robertson, T. S. (1989). Technology Diffusion: An Empirical Test of Competitive Effects, Journal of Marketing,53(January), 35–49Google Scholar
  21. Gerbing, D., & Anderson, J. (1988). An updated paradigm for scale development incorporating unidimensionality and its assessment. Journal of Marketing Research, XXV (May), 186–193.Google Scholar
  22. Gourville, J. T. (2006). Eager sellers and stony buyers: understanding the psychology of new-product adoption. Harv Bus Rev, 84(6).Google Scholar
  23. Gregan-Paxton, J., & John, D. R. (1997). Consumer learning by analogy: a model of internal knowledge transfer. J Consum Res, 24(3), 266–84.CrossRefGoogle Scholar
  24. Gregan-Paxton, J., & Moreau, P. (2003). How Do consumers transfer existing knowledge? a comparison of analogy and categorization effects. J Consum Psychol, 13(4), 422–430.CrossRefGoogle Scholar
  25. Hauser, J., Tellis, G. J., & Griffin, A. (2006). Research on innovation : a review and agenda for marketing science. Mark Sci, 25(6).Google Scholar
  26. Im, S., Mason, C. H., & Houston, M. B. (2007). Does innate consumer innovativeness relate to new product/service adoption behavior? the intervening role of social learning via vicarious innovativeness. J Acad Mark Sci, 35(1), 63–75.CrossRefGoogle Scholar
  27. Jackson, T. (2005). Motivating sustainable consumption. London: A Review of Evidence on Consumer Behavior and Behavioral Change. Report to the. Sustainable Development Research Network.Google Scholar
  28. Janis, I. L., & Mann, L. (1977). Decision making: a psychological analysis of conflict, choice and commitment. New York: Free Press.Google Scholar
  29. Janz, N. K., & Becker, M. H. (1984). The health belief model: a decade later. Health Educ Behav, 11(1), 1–47.CrossRefGoogle Scholar
  30. John, A., & Klein, J. (2003). The boycott puzzle: consumer motivations for purchase sacrifice. Manag Sci, 49(9), 1196–1209.CrossRefGoogle Scholar
  31. Karahanna, E. (2006). Reconceptualizing compatibility beliefs in technology acceptance research. MIS Q, 30(4), 781–804.Google Scholar
  32. Kleijnen, M., Lee, N., & Wetzels, M. (2009). An exploration of consumer resistance to innovation and its antecedents. J Econ Psychol, 30(3), 344–357.CrossRefGoogle Scholar
  33. Kulviwat, S., Bruner, G. C., & Al-Shuridah, O. (2009). The role of social influence on adoption of high tech innovations: The moderating effect of public/private consumption. J Bus Res, 62(7), 706–712.CrossRefGoogle Scholar
  34. Kunda, Z. (1990). The case for motivated reasoning. Psychol Bull, 108(3), 480–98.CrossRefGoogle Scholar
  35. Kvale, S. (1996). Interviews: An Introduction to Qualitative Research Interviewing. Sage PublicationsGoogle Scholar
  36. Langerak, F., & Verhoef, P. C. (2001). Possible determinants of consumers’ adoption of electronic grocery shopping in the Netherlands. J Retail Consum Serv, 8(5), 275–285.CrossRefGoogle Scholar
  37. Laukkanen, T., Sinkkonen, S., Kivijarvi, M., & Laukkanen, P. (2007). Innovation resistance among mature consumers. J Consum Mark, 24(7), 419–27.CrossRefGoogle Scholar
  38. Lee, T. W., Mitchell, T. R., Holtom, B. C., McDaniel, L. S., & Hill, J. W. (1999). The unfolding model of voluntary turnover: a replication and extension. Acad Manag J, 42(4), 450–462.CrossRefGoogle Scholar
  39. Lu, J.-L., Chou, H.-Y., & Ling, P.-C. (2009). Investigating passengers’ intentions to use technology-based self check-in services. Transp Res Part E Logist Transp Rev, 45(2), 345–356.CrossRefGoogle Scholar
  40. Marsh, H. W., & Hocevar, D. (1985). Application of confirmatory factor analysis to the study of self-concept: First- and higher order factor models and their invariance across groups. Psychol Bull, 97(3), 562–582.CrossRefGoogle Scholar
  41. McAlister, L., & Pessemier, E. (1982). Variety seeking behavior: an interdisciplinary review. J Consum Res, 9(3), 311–322.CrossRefGoogle Scholar
  42. Molesworth, M., & Suortti, J.-P. (2002). Buying cars online: the adoption of the Web for high-involvement, high-cost purchases. J Consum Behav, 2(2), 155–168.CrossRefGoogle Scholar
  43. Moore, G. A. (1999). Crossing the Chasm. Marketing and Selling High-Tech Products to Mainstream Customer (revised edition). New York: HarperCollinsGoogle Scholar
  44. Moreau, C., Lehmann, D., & Markman, A. (2001). Entrenched knowledge structures and consumer response to new products. Journal of Marketing Research, XXXVIII Google Scholar
  45. Nowak, A., Vallacher, R. R., Tesser, A., & Borkowski, W. (2000). Society of self: the emergence of collective properties in self-structure. Psychol Rev, 107(1), 39–61.CrossRefGoogle Scholar
  46. Nyrud, A. Q., Roos, A., & Sande, J. B. (2008). Residential bioenergy heating: a study of consumer perceptions of improved woodstoves. Energy Policy, 36(8), 3169–3176.CrossRefGoogle Scholar
  47. Oreg, S. (2003). Resistance to change: developing an individual differences measure. J Appl Psychol, 88(4), 680–693.CrossRefGoogle Scholar
  48. Parasuraman, A., & Grewal, D. (2000). The impact of technology on the quality-value-loyalty chain: a research agenda. J Acad Mark Sci, 28(1), 168–174.CrossRefGoogle Scholar
  49. Pennington, N., & Hastie, R. (1988). Explanation-based decision making: effects of memory structure on judgment. J Exp Psychol Learn Mem Cogn, 14, 521–533.CrossRefGoogle Scholar
  50. Porter, C. E., & Donthu, N. (2006). Using the technology acceptance model to explain how attitudes determine internet usage: the role of perceived access barriers and demographics. J Bus Res, 59(9), 999–1007.CrossRefGoogle Scholar
  51. Posavac, S. S., Brakus, J. J., & Herzenstein, M. (2007). Adoption of New and really New products: the effects of self-regulation systems and risk salience. J Mark Res, 44(2), 251–260.CrossRefGoogle Scholar
  52. Prochaska, J. O., Velicer, W. F., Rossi, J. S., Goldstein, M. G., Markus, B. H., & Rakowski, W. (1994). Stages of change and decisional balance model for 12 problem behaviors. Health Psychol, 13, 39–46.CrossRefGoogle Scholar
  53. Ram, S. (1987). A model of innovation resistance. Advances in Consumer Research, 14(1), 208–12.Google Scholar
  54. Ram, S., & Sheth, J. N. (1989). Consumer resistance to innovations: the marketing problem and its solution. J Consum Mark, 6(2), 5–14.CrossRefGoogle Scholar
  55. Reinders, M. J., Dabholkar, P. A., & Frambach, R. T. (2008). Consequences of forcing consumers to Use technology-based self-service. J Serv Res, 11(2), 107–123.CrossRefGoogle Scholar
  56. Rogers, E. M. (1962). Diffusion of Innovations. Free Press of GlencoeGoogle Scholar
  57. Schwartz, S. H. (2006). Basic human values : an overview basic human values : theory, methods, and applications introduction to the values theory. Appl Psychol, 48(1), 49–71.Google Scholar
  58. Schwarz, N., & Ernst, A. (2008). Agent-based modelling of the diffusion of environmental innovations - an empirical approach. Technol Forecast Soc Chang, 76(4), 497–511.CrossRefGoogle Scholar
  59. Sheth, J. N. (1981). Psychology of innovation resistance. Res Mark, 4, 273–282.Google Scholar
  60. Snyder, M. (1992). Motivational foundations of behavioral confirmation. In M. P. Zanna (Eds.), Advances in Experimental Social Psychology (pp. 67–114). San Dieg, CA.: Academic Press.Google Scholar
  61. Stone, R. N., & Grønhaug, K. (1993). Perceived risk: further considerations for the marketing discipline. Eur J Mark, 27(3), 39–50.CrossRefGoogle Scholar
  62. Sutton, S. (2004). Determinants of Health Related Behaviors: Theoretical and Methodological issues. In: The Sage Handbook of Health Psychology, Stephen R. Sutton, Andrew S. Baum and Marie Johnston, eds. London, UK: Sage, 94–126.Google Scholar
  63. Thaler, R. H. (1999). Mental accounting matters. J Behav Decis Mak, 12(3), 183–206.CrossRefGoogle Scholar
  64. Thomas, J. B., Clark, S. M., & Gioia, D. A. (1993). Strategic sensemaking and organizational performance. Linkages among scanning Manag, 36(2), 239–70.Google Scholar
  65. Tornatzky, L. G. & K. J. Klein (1982). Innovation characteristics and innovation adoption–implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, 29(1), 28–45.Google Scholar
  66. Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: heuristics and biases. Science, 185(4157), 1124–31.CrossRefGoogle Scholar
  67. Venkatesh, V., & Brown, S. (2001). A longitudinal investigation of personal computers in homes: adoption determinants and emerging challenges. MIS Q, 25(1), 71–102.CrossRefGoogle Scholar
  68. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology. Toward a Unified View MIS Quarterly, 27(3), 425–478.Google Scholar
  69. Westaby, J. D. (2005). Behavioral reasoning theory : identifying new linkages underlying intentions and behavior. Organ Behav Hum Decis Process, 98, 97–120.CrossRefGoogle Scholar
  70. Westaby, J. D., & Fishbein, M. (1996). Factors underlying behavioural choice: testing a new reasons theory approach. J Appl Soc Psychol, 13, 1307–1323.CrossRefGoogle Scholar
  71. Westaby, J. D., Probst, T. M., & Lee, B. C. (2010). Leadership decision-making: a behavioral reasoning theory analysis. Leadersh Q, 21(3), 481–495.CrossRefGoogle Scholar
  72. Wood, S. L., & Moreau, C. P. (2006). From fear to loathing ? How emotion Infiuences the evaluation and early use of innovations. J Mark, 70(July), 44–57.CrossRefGoogle Scholar
  73. Wu, J., & Wang, S. (2005). What drives mobile commerce ? an empirical evaluation of the revised technology acceptance model. Information Manag, 42, 719–729.CrossRefGoogle Scholar
  74. Zhang, T., Gensler, S., & Garcia, R. (2011). A study of the diffusion of alternative fuel vehicles: an agent-based modeling approach. J Prod Innov Manag, 28(2), 152.CrossRefGoogle Scholar

Copyright information

© Academy of Marketing Science 2014

Authors and Affiliations

  • Marius C. Claudy
    • 1
    Email author
  • Rosanna Garcia
    • 2
  • Aidan O’Driscoll
    • 3
  1. 1.School of BusinessUniversity College DublinDublinIreland
  2. 2.Poole College of ManagementNorth Carolina State UniversityRaleighUSA
  3. 3.College of BusinessDublin Institute of TechnologyDublin 2Ireland

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