Advertisement

Tools and Measurements for Exploring the Consequences of Shopper Orientation

  • Daniele ScarpiEmail author
Chapter
  • 29 Downloads

Abstract

This chapter discusses the methodology that will be used for exploring the effects of hedonic and utilitarian shopping orientation. Specifically, it presents details about the data collection process, the tools, and the sample size. Then, it presents the scales used for measuring hedonism, utilitarianism, perceived value, purchase amount, store loyalty, price consciousness, and purchase frequency. It also addresses the concepts of measurement reliability and validity, discussing content validity, internal and external consistency, and convergent, discriminant, and nomological validity. Then, the chapter presents the final scale and discusses the specification of the structural equation model that will be estimated in Chaps.  5 to  7. Accordingly, this chapter discusses the tools and techniques for the analysis of the hypotheses in Chap.  3, and for the comparison of intensive distribution and selective distribution. Finally, the chapter provides details about model estimation and model comparison such as the analysis of residuals, and the chi-squared statistic.

Keywords

Conceptual model Measurement scales Sample collection Structural equation modeling Methods and tools 

References

  1. Afthanorhan, W. M. A. B., & Ahmad, S. (2014). Path analysis in covariance-based structural equation modeling with Amos 18.0. European Journal of Business and Social Sciences, 2(10).Google Scholar
  2. Ailawadi, K. L., Neslin, S. A., & Gedenk, K. (2001). Pursuing the value-conscious consumer: Store brands versus national brand promotions. Journal of Marketing, 65(1), 71–89.CrossRefGoogle Scholar
  3. Arbuckle, J. L. (2019). IBM SPSS Amos 26 User’s Guide.Google Scholar
  4. Armitage, C. J., & Christian, J. (2017). From attitudes to behavior: Basic and applied research on theory of planned behavior. In Planned behavior (pp. 1–12). Routledge.Google Scholar
  5. Armstrong, J. S., Morwitz, V. G., & Kumar, V. (2000). Sales forecasts for existing consumer products and services: Do purchase intentions contribute to accuracy? International Journal of Forecasting, 16(3), 383–397.CrossRefGoogle Scholar
  6. Babin, B. J., & Attaway, J. S. (2000). Atmospheric affect as a tool for creating value and gaining share of customer. Journal of Business Research, 49(2), 91–99.CrossRefGoogle Scholar
  7. Babin, B. J., Darden, W. R., & Griffin, M. (1994). Work and/or fun: Measuring hedonic and utilitarian shopping value. Journal of Consumer Research, 20, 644–657.CrossRefGoogle Scholar
  8. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94.CrossRefGoogle Scholar
  9. Bollen, K. A., & Jöreskog, K. G. (1985). Uniqueness does not imply identification. Sociological Methods and Research, 14, 155–163.CrossRefGoogle Scholar
  10. Bollen, K. A., & Long, J. S. (1993). Testing structural equation models. Sage Publications.Google Scholar
  11. Breckler, S. J. (1990). Applications of covariance structure modeling in psychology: Cause for concern? Psychological Bulletin, 107(2), 260.CrossRefGoogle Scholar
  12. Byrne, B. M. (2010). Multivariate applications series. Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). New York: Routledge/Taylor & Francis Group.Google Scholar
  13. Byrne, B. M. (2012). Choosing structural equation modeling computer software: Snapshots of LISREL, EQS, AMOS, and Mplus. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 307–324). New York: The Guilford Press.Google Scholar
  14. Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81.CrossRefGoogle Scholar
  15. Cangur, S., & Ercan, I. (2015). Comparison of model fit indices used in structural equation modeling under multivariate normality. Journal of Modern Applied Statistical Methods, 14(1), 14.CrossRefGoogle Scholar
  16. Clayton, M. F., & Pett, M. A. (2008). AMOS versus LISREL: One data set, two analyses. Nursing Research, 57(4), 283–292.CrossRefGoogle Scholar
  17. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.CrossRefGoogle Scholar
  18. Griffin, M., Babin, B. J., & Modianos, D. (2000). Shopping values of Russian consumers: The impact of habituation in a developing economy. Journal of Retailing, 76(1), 33–53.CrossRefGoogle Scholar
  19. Gunn, H. J., Grimm, K. J., & Edwards, M. C. (2019). Evaluation of six effect size measures of measurement non-invariance for continuous outcomes. Structural Equation Modeling: A Multidisciplinary Journal, 1–12.Google Scholar
  20. Hayduk, L. A. (1996). LISREL: Issues, debates, and strategies. Johns Hopkins University Press.Google Scholar
  21. Hayduk, L. A. (2016). Improving measurement-invariance assessments: Correcting entrenched testing deficiencies. BMC Medical Research Methodology, 16(1), 130.Google Scholar
  22. Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis second edition: A regression-based approach. New York: Ebook The Guilford Press. Google Scholar.Google Scholar
  23. Hayes, A. F., Montoya, A. K., & Rockwood, N. J. (2017). The analysis of mechanisms and their contingencies: PROCESS versus structural equation modeling. Australasian Marketing Journal (AMJ), 25(1), 76–81.CrossRefGoogle Scholar
  24. Hirschman, E. C., & Holbrook, M. B. (1982). Hedonic consumption: Emerging concepts, methods and propositions. Journal of Marketing, 46, 92–101.CrossRefGoogle Scholar
  25. Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53–60.Google Scholar
  26. Iacobucci, D. (2010). Structural equations modeling: Fit indices, sample size, and advanced topics. Journal of Consumer Psychology, 20(1), 90–98.CrossRefGoogle Scholar
  27. Jamieson, L. F., & Bass, F. M. (1989). Adjusting stated intention measures to predict trial purchase of new products: A comparison of models and methods. Journal of Marketing Research, 26, 336–345.CrossRefGoogle Scholar
  28. Jöreskog, K. G., & Sörbrom, D. (2003). LISREL 8.7 for MAC OS 9 and X. Lincolnwood: Scientific Software International.Google Scholar
  29. Kaplan, D. (2008). Structural equation modeling: Foundations and extensions (Vol. 10). Sage Publications.Google Scholar
  30. Keselman, R. C., & Zumbo, B. (1997). Specialized tests for detecting treatment effects in the two-sample problem. Journal of Experimental Education, 65, 355–366.CrossRefGoogle Scholar
  31. Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford Publications.Google Scholar
  32. Lim, N. (2016). Cultural differences in emotion: Differences in emotional arousal level between the East and the West. Integrative Medicine Research, 5(2), 105–109.CrossRefGoogle Scholar
  33. Matsumoto, D., & Hwang, H. S. C. (2019). Culture, emotion, and expression. Cross-Cultural Psychology: Contemporary Themes and Perspectives, 24, 501–515.Google Scholar
  34. McKinsey. (2019). The state of fashion. McKinsey&Company Eds.Google Scholar
  35. McMullan, R., & Gilmore, A. (2003). The conceptual development of customer loyalty measurement: A proposed scale. Journal of Targeting, Measurement and Analysis for Marketing, 11(3), 230–243.CrossRefGoogle Scholar
  36. Micceri, T. (1989). The unicorn, the normal curve, and other improbable creatures. Psychological Bulletin, 105, 156–166.CrossRefGoogle Scholar
  37. Moser, C. A., & Kalton, G. (1971). Survey methods in social investigation (2nd ed.). Heinemann: London.Google Scholar
  38. Nunnally, J. C. (1994). Psychometric theory 3E. Tata McGraw-Hill Education.Google Scholar
  39. Okely, J. (2019). Fieldwork emotions: Embedded across cultures, shared, repressed, or subconscious. In Affective dimensions of fieldwork and ethnography (pp. 325–345). Cham: Springer.CrossRefGoogle Scholar
  40. Pizzi, G., Scarpi, D., Pichierri, M., & Vannucci, V. (2019). Virtual reality, real reactions? Comparing consumers’ perceptions and shopping orientation across physical and virtual-reality retail stores. Computers in Human Behavior, 96, 1–12.CrossRefGoogle Scholar
  41. Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & 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.CrossRefGoogle Scholar
  42. Royo-Vela, M., & Voss, E. (2015). Downward price-based brand line extensions effects on luxury brands. Business and Economics Research Journal, 6(3), 145.Google Scholar
  43. Scarpi, D. (2012). Work and fun on the internet: The effects of utilitarianism and hedonism online. Journal of Interactive Marketing, 26(1), 53–67.CrossRefGoogle Scholar
  44. Scarpi, D., Pizzi, G., & Visentin, M. (2014). Shopping for fun or shopping to buy: Is it different online and offline? Journal of Retailing and Consumer Services, 21(3), 258–267.CrossRefGoogle Scholar
  45. Schumacker, R. E. (2017). Interaction and nonlinear effects in structural equation modeling. Routledge.Google Scholar
  46. Sheeran, P., Maki, A., Montanaro, E., Avishai-Yitshak, A., Bryan, A., Klein, W. M., & Rothman, A. J. (2016). The impact of changing attitudes, norms, and self-efficacy on health-related intentions and behavior: A meta-analysis. Health Psychology, 35(11), 1178.CrossRefGoogle Scholar
  47. Sirohi, N., McLaughlin, E. W., & Wittink, D. R. (1998). A model of consumer perceptions and store loyalty intentions for a supermarket retailer. Journal of Retailing, 74(2), 223–245.CrossRefGoogle Scholar
  48. Van Trijp, H. C. M., Hoyer, W. D., & Inman, J. J. (1996). Why switch ? Product category-level explanations for true variety-seeking behavior. Journal of Marketing Research, 33, 281–292.Google Scholar
  49. Wakefield, K. L., & Barnes, J. H. (1996). Retailing hedonic consumption: A model of sales promotion of a leisure service. Journal of Retailing, 72(4), 409–427.CrossRefGoogle Scholar
  50. Whitlark, D. B., Geurts, M. D., & Swenson, M. J. (1993). New product forecasting with a purchase intention survey. Journal of Business Forecasting, 12(3), 18–21.Google Scholar
  51. Widaman, K. F. (1985). Hierarchically nested covariance structure models for multitrait-multimethod data. Applied Psychological Measurement, 9, 1–26.CrossRefGoogle Scholar
  52. Wood, C., Conner, M., Miles, E., Sandberg, T., Taylor, N., Godin, G., & Sheeran, P. (2016). The impact of asking intention or self-prediction questions on subsequent behavior: A meta-analysis. Personality and Social Psychology Review, 20(3), 245–268.CrossRefGoogle Scholar
  53. Yuan, K. H., & Chan, W. (2016). Measurement invariance via multigroup SEM: Issues and solutions with chi-square-difference tests. Psychological Methods, 21(3), 405.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2020

Authors and Affiliations

  1. 1.University of BolognaBolognaItaly

Personalised recommendations