Consumer Resistance to Innovation in the Fashion Industry

Puiu Ionela-Andreea 1
  • 1 The Bucharest University of Economic Studies, , Romania

Abstract

Innovation is considered an essential point of market competitivity and industrial dynamics. However, there is one main reason that delays or inhibits the innovation spread, the consumer resistance to innovation. Innovation resistance of the consumers has undergone less consideration to describe and forecast adoption-related behaviour. The actual paper develops and empirically validates a scale that intends to measure consumers’ inclination to resist innovation that emerge from people’s tendency to resist changes and manifest status-quo satisfaction when it’s related to fashion items. The scale describes a measure of the inclination to the status-quo option, even if people are satisfied with existing fashion items or with the degree of innovation in the fashion industry.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • 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. European Journal of Marketing, Vol. 44, no. 11/12, pp. 1700-24.

  • Antón, C., Camarero, C., Rodríguez, J., (2013), Usefulness, enjoyment, and self-image congruence: The adoption of e-book readers. Psychology and Marketing, Vol. 30, no. 4, pp. 372-84.

  • Balachandra, R., John, F.H., (1997), Factors for success in R&D projects and new product innovation: A contextual framework, IEEE Transactions on Engineering Management, pp. 276-287.

  • Castellion, G., Markham, S. K., (2013), Perspective: new product failure rates: influence of argumentum ad populum and self-interest. Journal of Product Innovation Management, Vol. 30, no. 5, pp. 976-979.

  • Claudy, M., (2011), An empirical investigation of consumer resistance to green product innovation. Doctoral thesis, Dublin Institute of Technology, Dublin.

  • Cornescu, V., Adam, R., (2013), The Consumer Resistance Behaviour towards Innovation. Procedia Economics and Finance, no. 6, pp. 457-465.

  • Cortina, J.M., (1993), What is Coefficient Alpha: An Examination of Theory and Applications? Journal of Applied Psychology, no.78, pp. 98-104.

  • Druică, E., (2018), Research Methods in Behavioural and Experimental Economics. Exploratory Factor Analysis [Lecture].

  • Druică, E., Druică, I., Ianole, R., Sandu, M., (2011), Statistică pe înțelesul tuturor. Editura C.H. Beck: Bucureşti.

  • Fuller, B.K., Blackwell, S.C., (1992), Wardrobe consultant clientele: Identifying and describing three market segments. Clothing and Textiles Research Journal, Vol. 10, no. 2, pp. 11-17.

  • Garcia, R., Bardhi, F., Friedrich, C., (2007), Overcoming consumer resistance to innovation. MIT Sloan Management Review, Vol. 48, no. 4, pp. 82-88.

  • Hapenciuc, V., (2015), Procedee de eşantionare aplica în cercetarea pieței şi administrarea eficientă a afacerilor. Retrieved from Universitatea Ștefan cel Mare din Suceava: http://www.seap.usv.ro/~valentinh/capitol%203.pdf.

  • Henderson, R., Clark, K., (1990), Architectural Innovation: The Reconfiguration of Existing Product Technologies and the Failure of Established Firms. Administrative Science Quarterly, Vol. 35, no. 1, pp. 9-30.

  • Hox, J., Bechger, T., (1999), An Introduction to Structural Equation Modelling. Family Science Review, no. 11, pp. 354-373.

  • Kahneman, D., Tversky, A., (1979), Prospect theory: An analysis of decision under risk. Econometrica, no. 47, pp. 263-91.

  • Kleijen, M., Lee, N.J., Wetzels, M., (2009), An exploration of consumer resistance to innovation and its antecedents. Journal of Economic Psychology, Vol. 30, no. 3, pp. 344-357.

  • Kline, R.B., (2016), Principles and Practice of Structural Equation Modelling. Fourth Edition. The Guilford Press: New York.

  • Kline, S.J., Rosenberg, N., (1986), An Overview of Innovation.

  • MacCallum, R.C., Widaman, K.F., Zhang, S.B., Hong, S.H., (1999), Sample size in factor analysis. Psychological Methods, Vol. 4, no. 1, pp. 84-99.

  • Marsh, H. W., Hau, K.T., Wen, Z., (2004), In Search of Golden Rules: Comment on Hypothesis-Testing Approaches to Setting Cutoff Values for Fit Indexes and Dangers in Overgeneralizing. Hu and Bentler's Findings (1999). Structural Equation Modelling, Vol. 11, no. 3, pp. 320-341.

  • Nabih, M.I., Bloem, J.G., Poiesz, T.B.C., (1997), Conceptual issues in the study of innovation adoption behaviour. Advances in Consumer Research, Vol. 24, no.1, pp. 190-96.

  • Osborne, W.J., (2014), Best Practices in Exploratory Factor Analysis. Scotts Valley, CA: CreateSpace Independent Publishing.

  • Patsiotis, A. G., Hughes, T., Webber, D.J., (2013), An examination of consumers’ resistance to computer-based technologies. Journal of Services Marketing, Vol. 27, no. 4, pp. 294-311.

  • Ram, S., (1987), A model of innovation resistance. Advances in Consumer Research, Vol. 14, no.1, pp. 208-12.

  • Ram, S., Sheth, N.J., (1989), Consumer Resistance to Innovations: The Marketing Problem and its solutions. Journal of Consumer Marketing, Vol. 6, no. 2, pp. 5-14.

  • Revelle, W., (2018), psych: Procedures for Personality and Psychological Research, Northwestern University, Evanston, Illinois, USA, https://cran.r-project.org/web/packages/psych/ Version = 1.8.12.

  • Rogers, E. M., (2003), Diffusion of innovations. NewYork: The Free Press.

  • Schumpeter, J., (1934), The Theory of Economic Development, Harvard University Press, Cambridge, Massachusetts.

  • Sheth, J.N., (1981), Innovation resistance. The less developed concept (LDC) in diffusion research. Research Vol. 4, pp. 273-82.

  • Stryja, C., Satzger, G., (2018), Digital nudging to overcome cognitive resistance in innovation adoption decisions. The Service Industries Journal.

  • Talke, K., Heidenreich, S., (2014), How to Overcome Pro-Change Bias. The Journal of Product Innovation Management, Vol. 31, no. 4, pp. 894-907.

  • Tomarken, A., Waller, G.N., (2005), Structural Equation Modelling: Strengths, Limitations, and Misconceptions. Annual review of clinical psychology. Vol. 1, pp. 31-65.

  • Yves, R., (2012), lavaan: An R Package for Structural Equation Modelling. Journal of Statistical Software, Vol. 48, no. 2, pp. 1-36. URL: https://www.jstatsoft.org/article/view/v048i02.

OPEN ACCESS

Journal + Issues

Search