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Evaluating underlying factor structures using novel machine learning algorithms: An empirical and simulation study

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  • “Jimmy” Xu, Zhenning
  • Ramirez, Edward
  • Liu, Pan
  • Frankwick, Gary L.

Abstract

The scale development paradigm was created to improve the measurement of latent constructs. Although several statistical techniques have been successfully integrated into the overall process, identifying factor patterns and validating constructs using smaller datasets with different correlational structures remain a concern. This paper presents heatmapping and bootstrapping cluster analysis (HMBCA), a novel machine-learning based diagnostic workflow, as a new tool to aid in strengthening the process. A substantive example on the overall organizational knowledge acquisition behaviors demonstrates that the bootstrapping cluster simulation approach provided promising results regarding the factor structure as measured by the Approximately Unbiased (AU) p-values under the following conditions: when factor correlations are weaker or moderate, with simulated data containing smaller samples. The study suggests that researchers may leverage bootstrapping cluster simulations to validate constructs through both visual inspection and probability estimates when faced with constraints such as a small sample size.

Suggested Citation

  • “Jimmy” Xu, Zhenning & Ramirez, Edward & Liu, Pan & Frankwick, Gary L., 2024. "Evaluating underlying factor structures using novel machine learning algorithms: An empirical and simulation study," Journal of Business Research, Elsevier, vol. 173(C).
    • Handle: RePEc:eee:jbrese:v:173:y:2024:i:c:s0148296323008317
    DOI: 10.1016/j.jbusres.2023.114472
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    References listed on IDEAS

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    1. Haridimos Tsoukas & Efi Vladimirou, 2001. "What is Organizational Knowledge?," Journal of Management Studies, Wiley Blackwell, vol. 38(7), pages 973-993, November.
    2. Janssen, Marijn & van der Voort, Haiko & Wahyudi, Agung, 2017. "Factors influencing big data decision-making quality," Journal of Business Research, Elsevier, vol. 70(C), pages 338-345.
    3. Xu, Zhenning & Frankwick, Gary L. & Ramirez, Edward, 2016. "Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective," Journal of Business Research, Elsevier, vol. 69(5), pages 1562-1566.
    4. Susanne M. de Witt & Frauke Swieringa & Rachel Cavill & Moniek M. E. Lamers & Roger van Kruchten & Tom Mastenbroek & Constance Baaten & Susan Coort & Nicholas Pugh & Ansgar Schulz & Inge Scharrer & Ke, 2014. "Identification of platelet function defects by multi-parameter assessment of thrombus formation," Nature Communications, Nature, vol. 5(1), pages 1-13, September.
    5. Germann, Frank & Lilien, Gary L. & Rangaswamy, Arvind, 2013. "Performance implications of deploying marketing analytics," International Journal of Research in Marketing, Elsevier, vol. 30(2), pages 114-128.
    6. Klostermann, Jan & Plumeyer, Anja & Böger, Daniel & Decker, Reinhold, 2018. "Extracting brand information from social networks: Integrating image, text, and social tagging data," International Journal of Research in Marketing, Elsevier, vol. 35(4), pages 538-556.
    7. repec:ucp:bkecon:9780226316529 is not listed on IDEAS
    8. Evermann, Joerg & Tate, Mary, 2016. "Assessing the predictive performance of structural equation model estimators," Journal of Business Research, Elsevier, vol. 69(10), pages 4565-4582.
    9. Erevelles, Sunil & Fukawa, Nobuyuki & Swayne, Linda, 2016. "Big Data consumer analytics and the transformation of marketing," Journal of Business Research, Elsevier, vol. 69(2), pages 897-904.
    10. Makarius, Erin E. & Mukherjee, Debmalya & Fox, Joseph D. & Fox, Alexa K., 2020. "Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization," Journal of Business Research, Elsevier, vol. 120(C), pages 262-273.
    11. Dekimpe, Marnik G., 2020. "Retailing and retailing research in the age of big data analytics," International Journal of Research in Marketing, Elsevier, vol. 37(1), pages 3-14.
    12. Hudson F Golino & Sacha Epskamp, 2017. "Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-26, June.
    13. Wilkinson, Leland & Friendly, Michael, 2009. "The History of the Cluster Heat Map," The American Statistician, American Statistical Association, vol. 63(2), pages 179-184.
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