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Combined importance–performance map analysis (cIPMA) in partial least squares structural equation modeling (PLS–SEM): a SmartPLS 4 tutorial

Author

Listed:
  • Marko Sarstedt

    (Ludwig-Maximilians-University Munich
    Babeş-Bolyai University)

  • Nicole F. Richter

    (University of Southern Denmark)

  • Sven Hauff

    (Helmut Schmidt University)

  • Christian M. Ringle

    (Hamburg University of Technology
    James Cook University)

Abstract

Recent research on partial least squares structural equation modeling (PLS–SEM) extended the classic importance–performance map analysis (IPMA) by taking the results of a necessary condition analysis (NCA) into consideration. By also highlighting necessary conditions, the combined importance–performance map analysis (cIPMA) offers a tool that enables better prioritization of management actions to improve a key target construct. In this article, we showcase a cIPMA’s main steps when using the SmartPLS 4 software. Our illustration draws on the technology acceptance model (TAM) used in the cIPMA’s original publication, which features prominently in business research.

Suggested Citation

  • Marko Sarstedt & Nicole F. Richter & Sven Hauff & Christian M. Ringle, 2024. "Combined importance–performance map analysis (cIPMA) in partial least squares structural equation modeling (PLS–SEM): a SmartPLS 4 tutorial," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(4), pages 746-760, December.
    • Handle: RePEc:pal:jmarka:v:12:y:2024:i:4:d:10.1057_s41270-024-00325-y
    DOI: 10.1057/s41270-024-00325-y
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    References listed on IDEAS

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    1. Richter, Nicole Franziska & Hauff, Sven, 2022. "Necessary conditions in international business research–Advancing the field with a new perspective on causality and data analysis," Journal of World Business, Elsevier, vol. 57(5).
    2. Dul, Jan, 2024. "A different causal perspective with Necessary Condition Analysis," Journal of Business Research, Elsevier, vol. 177(C).
    3. Joseph F. Hair & G. Tomas M. Hult & Christian M. Ringle & Marko Sarstedt & Kai Oliver Thiele, 2017. "Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods," Journal of the Academy of Marketing Science, Springer, vol. 45(5), pages 616-632, September.
    4. Svenja Damberg & Manfred Schwaiger & Christian M. Ringle, 2022. "What’s important for relationship management? The mediating roles of relational trust and satisfaction for loyalty of cooperative banks’ customers," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(1), pages 3-18, March.
    5. Saari, Ulla A. & Damberg, Svenja & Frömbling, Lena & Ringle, Christian M., 2021. "Sustainable consumption behavior of Europeans: The influence of environmental knowledge and risk perception on environmental concern and behavioral intention," Ecological Economics, Elsevier, vol. 189(C).
    6. Pinaz Tiwari & Rahul Pratap Singh Kaurav & Kian Yeik Koay, 2024. "Understanding travel apps usage intention: findings from PLS and NCA," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(1), pages 25-41, March.
    7. Marko Sarstedt & Yide Liu, 2024. "Advanced marketing analytics using partial least squares structural equation modeling (PLS-SEM)," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(1), pages 1-5, March.
    8. Sever, Ivan, 2015. "Importance-performance analysis: A valid management tool?," Tourism Management, Elsevier, vol. 48(C), pages 43-53.
    9. Marko Sarstedt & Jun-Hwa Cheah, 2019. "Partial least squares structural equation modeling using SmartPLS: a software review," Journal of Marketing Analytics, Palgrave Macmillan, vol. 7(3), pages 196-202, September.
    10. Nicole F. Richter & Sven Hauff & Christian M. Ringle & Siegfried P. Gudergan, 2022. "The Use of Partial Least Squares Structural Equation Modeling and Complementary Methods in International Management Research," Management International Review, Springer, vol. 62(4), pages 449-470, August.
    11. Richter, Nicole Franziska & Tudoran, Ana Alina, 2024. "Elevating theoretical insight and predictive accuracy in business research: Combining PLS-SEM and selected machine learning algorithms," Journal of Business Research, Elsevier, vol. 173(C).
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