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Using Multi-Informant Designs to Address Key Informant and Common Method Bias

In: Quantitative Marketing and Marketing Management

Author

Listed:
  • Christian Homburg

    (University of Mannheim)

  • Martin Klarmann

    (Karlsruhe Institute of Technology (KIT))

  • Dirk Totzek

    (University of Mannheim)

Abstract

The important key informant and common method problems in survey research are taken up in this article. The authors focus on the question how researchers can rely on multiinformant designs in order to limit the threats of key informant and common method bias on the validity and reliability of survey research. In particular, they show how researchers can effectively design studies that employ multiple informants and how multi-informant data can be aggregated in order to obtain more accurate results than can be obtained with single informant studies.

Suggested Citation

  • Christian Homburg & Martin Klarmann & Dirk Totzek, 2012. "Using Multi-Informant Designs to Address Key Informant and Common Method Bias," Springer Books, in: Adamantios Diamantopoulos & Wolfgang Fritz & Lutz Hildebrandt (ed.), Quantitative Marketing and Marketing Management, edition 127, chapter 4, pages 81-102, Springer.
    • Handle: RePEc:spr:sprchp:978-3-8349-3722-3_4
    DOI: 10.1007/978-3-8349-3722-3_4
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    Cited by:

    1. Mishra, Deepa Bhatt & Haider, Imran & Gunasekaran, Angappa & Sakib, Md. Nazmus & Malik, Nishtha & Rana, Nripendra P., 2023. "“Better together”: Right blend of business strategy and digital transformation strategies," International Journal of Production Economics, Elsevier, vol. 266(C).
    2. Christian Arnold & Daniel Kiel & Kai-Ingo Voigt, 2016. "How The Industrial Internet Of Things Changes Business Models In Different Manufacturing Industries," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 20(08), pages 1-25, December.

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