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Data equivalence in cross-cultural international business research: assessment and guidelines

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Abstract

Data equivalence refers to the extent to which the elements of a research design have the same meaning, and can be applied in the same way, in different cultural contexts. Failure to establish data equivalence in cross-cultural studies may bias empirical results and theoretical inferences. Although several authors have encouraged researchers to ensure high levels of data equivalence, no study has assessed the status of the field in relation to compliance with data equivalence standards. Accordingly, this study examines three aspects of data equivalence (construct equivalence, measurement equivalence, and data collection equivalence) within 167 studies that involve cross-cultural data published in the Journal of International Business Studies, Management International Review, Journal of World Business, Strategic Management Journal and the Academy of Management Journal from 1995 to 2005. The findings indicate that international business researchers report insufficient information in relation to data equivalence issues, thus limiting confidence in the findings of many cross-cultural studies. To enhance future research, a guideline for procedures for researchers to follow and report in establishing data equivalence is offered.

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Notes

  1. We have included 20 studies that were conducted in a single country, but surveyed companies or managers from different countries.

  2. The percentages do not total to 100% owing to rounding.

  3. The Tamhane test was used for post hoc comparisons. Further, it is important to note that there were significant differences in sample sizes across journals: thus the post hoc analysis should be viewed with caution.

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Acknowledgements

We appreciate the guidance provided by Editor-in-Chief Arie Lewin and the anonymous reviewers, as well as research funding from the Center for International Business Education and Research at Michigan State University (MSU-CIBER).

Author information

Authors and Affiliations

  1. Eli Broad Graduate School of Management, Michigan State University, East Lansing, USA

    G Tomas M Hult, David A Griffith, Tracy Gonzalez-Padron & S Tamer Cavusgil

  2. Auburn University, Auburn, USA

    David J Ketchen Jr

  3. University of Colorado, Colorado Springs, USA

    Carol A Finnegan

  4. Bilkent University, Ankara, Turkey

    Nukhet Harmancioglu

  5. University of Arizona, Tucson, USA

    Ying Huang

  6. HEC Montréal, Quebec, Canada

    M Berk Talay

Authors
  1. G Tomas M Hult
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  2. David J Ketchen Jr
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  3. David A Griffith
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  4. Carol A Finnegan
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  5. Tracy Gonzalez-Padron
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  6. Nukhet Harmancioglu
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  7. Ying Huang
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  8. M Berk Talay
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  9. S Tamer Cavusgil
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Correspondence to G Tomas M Hult.

Additional information

Accepted by Arie Y Lewin, Editor-in-Chief, 21 August 2007. This paper has been with the authors for two revisions.

APPENDIX

APPENDIX

Coding Form

Construct equivalence: Are we studying the same phenomena in countries X, Y, and Z?

Functional equivalence: Checked whether a given concept or behavior serves the same function from country to country (including literature review).

Conceptual equivalence: Checked whether the same concepts/behaviors occur in different countries – the way in which they are expressed is similar.

Category equivalence: Checked same product attributes/characteristics considered.

Post data collection: Checked for tests for unidimensionaliy, reliability, convergent validity, discriminant validity

Any specifically national or cultural constructs?

Measurement equivalence: Are the phenomena in countries X, Y, and Z measured in the same way?

Calibration equivalence: For example, monetary units, measures of weights, distance and volume and perceptual cues; compared factor loadings (λ's) (via multigroup SEM); checked for the comparability of standards and units.

Translation equivalence: Checked whether the concept can be measured by using the same or similar questions in every country.

Method of translation/back-translation reported.

Metric equivalence: Checked for scoring consistency: compared reliabilities or compared measurement error variances (δ's).

Checked for scaling equivalence: Multimethod of measurement, profile analysis, optimal scaling or compared measurement error variances (δ's) and factor loadings (λ's) (via multigroup SEM).

Decentered or adapted scoring/scaling for that country?

Any other cultural biases (e.g., exaggerated or mean responses) accounted for?

Data collection equivalence: Are the data collection procedures in countries X, Y, and Z the same?

Criterion for country/culture selection (convenience, theoretical justification); sufficient variance between countries/cultures.

Sample size for each country/culture studied.

Relevant or same respondent for each country (manager, decision-maker, executive, etc.).

Sampling frame techniques match between countries?

Sampling frame comparability.

Coverage comparability.

Countries where the survey was developed.

Sampling procedure equivalence (telephone interviews, surveys, etc.) (do procedures match?).

Any procedure for non-sampling/non-response bias?

Sampling method in each country

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Hult, G., Ketchen, D., Griffith, D. et al. Data equivalence in cross-cultural international business research: assessment and guidelines. J Int Bus Stud 39, 1027–1044 (2008). https://doi.org/10.1057/palgrave.jibs.8400396

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  • DOI: https://doi.org/10.1057/palgrave.jibs.8400396

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