Examining the generalizability of research findings from archival data

A large-scale test of whether 29 strategic management findings from archival data reproduce and generalize to new time periods and geographies. Published in PNAS (2022).
methodology
generalizability
reproducibility

Andrew Delios, Elena Giulia Clemente, Tao Wu, Hongbin Tan, Yong Wang, Michael Gordon, Domenico Viganola, Zhaowei Chen, Anna Dreber, Magnus Johannesson, Thomas Pfeiffer, Generalizability Tests Forecasting Collaboration, and Eric Luis Uhlmann, “Examining the generalizability of research findings from archival data,” PNAS (2022), doi: 10.1073/pnas.2120377119

Authors
Affiliations

Andrew Delios ORCID profile of Andrew Delios

National University of Singapore

Elena Giulia Clemente ORCID profile of Elena Giulia Clemente

Stockholm School of Economics

Tao Wu ORCID profile of Tao Wu

The Chinese University of Hong Kong

Hongbin Tan ORCID profile of Hongbin Tan

Tongji University

Yong Wang ORCID profile of Yong Wang

Xi’an Jiaotong University

Michael Gordon

Massey University

Domenico Viganola

World Bank

Zhaowei Chen ORCID profile of Zhaowei Chen

National University of Singapore

Anna Dreber ORCID profile of Anna Dreber

Stockholm School of Economics

Magnus Johannesson ORCID profile of Magnus Johannesson

Stockholm School of Economics

Thomas Pfeiffer ORCID profile of Thomas Pfeiffer

Massey University

Generalizability Tests Forecasting Collaboration (GTFC)

University College London

Eric Luis Uhlmann

INSEAD

Published

July 19, 2022

Doi

Abstract

This initiative examined systematically the extent to which a large set of archival research findings generalizes across contexts. We repeated the key analyses for 29 original strategic management effects in the same context (direct reproduction) as well as in 52 novel time periods and geographies; 45% of the reproductions returned results matching the original reports together with 55% of tests in different spans of years and 40% of tests in novel geographies. Some original findings were associated with multiple new tests. Reproducibility was the best predictor of generalizability—for the findings that proved directly reproducible, 84% emerged in other available time periods and 57% emerged in other geographies. Overall, only limited empirical evidence emerged for context sensitivity. In a forecasting survey, independent scientists were able to anticipate which effects would find support in tests in new samples.

Citation

 Add to Zotero

@article{deliosetal2022,
   author = {Delios, A. and Clemente, E. G. and Wu, T. and Tan, H. and Wang, Y. and Gordon, M. and Viganola, D. and Chen, Z. and Dreber, A. and Johannesson, M. and Pfeiffer, T. and {Generalizability Tests Forecasting Collaboration} and Uhlmann, E. L.},
   title = {Examining the generalizability of research findings from archival data},
   journal = {Proceedings of the National Academy of Sciences},
   volume = {119},
   number = {30},
   pages = {e2120377119},
   year = {2022},
   doi = {10.1073/pnas.2120377119}}