Validating a DEA-to-fsQCA Framework for the Configurational Antecedents of R&D Team Innovation Efficiency Using a Known Data-Generating Process

Hongyu Cao; Weixiang Gan

doi:10.62177/apemr.v3i5.1771

Authors

Hongyu Cao Langfang Zhenguigu Technology Co., Ltd. https://orcid.org/0009-0001-6114-5524
Weixiang Gan Langfang Zhenguigu Technology Co., Ltd.; Lincoln University College https://orcid.org/0009-0003-5879-5395

DOI:

https://doi.org/10.62177/apemr.v3i5.1771

Keywords:

Data Envelopment Analysis, Fuzzy-Set Qualitative Comparative Analysis, Methodological Validation, Simulated Data with Known Ground Truth, Calibration, Innovation Efficiency

Abstract

This study evaluates a two-stage analytical framework using controlled simulations with a known data-generating process. Studies of efficiency antecedents commonly use variable-oriented models to estimate net effects. These estimates do not directly represent conjunction, equifinality, or asymmetry. The framework separates efficiency measurement from configurational attribution. An input-oriented Banker-Charnes-Cooper data envelopment analysis (DEA-BCC) model provides efficiency scores for outcome calibration, and fuzzy-set qualitative comparative analysis (fsQCA) identifies combinations of psychological and situational conditions associated with the calibrated outcome. The simulation uses random seed 20260907 to generate 642 members across 160 R&D teams nested in 25 firms. The configurations underlying efficiency are therefore known. Mean raw BCC efficiency is 0.551, and 13 teams lie on the frontier. The strongest single condition accounts for 16.9% of the variation in efficiency, indicating limited explanatory power at the bivariate level. No condition or its negation meets the conventional threshold for necessity; the highest consistency is .757. Three sufficient configurations yield solution consistency of .887 and coverage of .571. Each of the 15 solution terms across six robustness variants contains knowledge sharing, work engagement, or both. Recovery of the generating structure is partial, with literal-level precision of .62 and recall of .86. One generating path is recovered exactly only after the efficiency model is changed. Knowledge sharing appears in all three configurations, including one whose generating counterpart does not contain it. The findings have two methodological implications. Simar and Wilson bias correction improves the point estimate but weakens the rank ordering used in calibration: the Spearman correlation with true efficiency falls from .521 to .337. Bounding the outcome at θ = 1 concentrates cases at the frontier and depresses the proportional reduction in inconsistency (PRI) near the crossover. For applications to field data, the findings favor calibrating raw efficiency scores, reporting corrected intervals to describe estimation uncertainty, and selecting the PRI threshold with reference to the calibrated distribution.

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References

Appelbaum, E., Bailey, T., Berg, P., & Kalleberg, A. L. (2000). Manufacturing advantage: Why high-performance work systems pay off. ILR Press.

Avey, J. B., Reichard, R. J., Luthans, F., & Mhatre, K. H. (2011). Meta-analysis of the impact of positive psychological capital on employee attitudes, behaviors, and performance. Human Resource Development Quarterly, 22(2), 127-152. https://doi.org/10.1002/hrdq.20070 DOI: https://doi.org/10.1002/hrdq.20070

Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092. https://doi.org/10.1287/mnsc.30.9.1078 DOI: https://doi.org/10.1287/mnsc.30.9.1078

Bliese, P. D. (2000). Within-group agreement, non-independence, and reliability: Implications for data aggregation and analysis. In K. J. Klein & S. W. J. Kozlowski (Eds.), Multilevel theory, research, and methods in organizations: Foundations, extensions, and new directions (pp. 349-381). Jossey-Bass.

Blumberg, M., & Pringle, C. D. (1982). The missing opportunity in organizational research: Some implications for a theory of work performance. Academy of Management Review, 7(4), 560-569. https://doi.org/10.5465/amr.1982.4285240 DOI: https://doi.org/10.2307/257222

Carmeli, A., Reiter-Palmon, R., & Ziv, E. (2010). Inclusive leadership and employee involvement in creative tasks in the workplace: The mediating role of psychological safety. Creativity Research Journal, 22(3), 250-260. https://doi.org/10.1080/10400419.2010.504654 DOI: https://doi.org/10.1080/10400419.2010.504654

Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444. https://doi.org/10.1016/0377-2217(78)90138-8 DOI: https://doi.org/10.1016/0377-2217(78)90138-8

Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-Solver software (2nd ed.). Springer. https://doi.org/10.1007/978-0-387-45283-8 DOI: https://doi.org/10.1007/978-0-387-45283-8

Du, Y., & Jia, L. (2017). Zutai shijiao yu dingxing bijiao fenxi (QCA): Guanlixue yanjiu de yi tiao xin daolu [A configurational perspective and qualitative comparative analysis (QCA): A new path for management research]. Guanli Shijie [Journal of Management World], 33(6), 155-167. https://doi.org/10.19744/j.cnki.11-1235/f.2017.06.012 (in Chinese)

Dușa, A. (2019). QCA with R: A comprehensive resource. Springer. https://doi.org/10.1007/978-3-319-75668-4 DOI: https://doi.org/10.1007/978-3-319-75668-4

Edmondson, A. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350-383. https://doi.org/10.2307/2666999 DOI: https://doi.org/10.2307/2666999

Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society: Series A (General), 120(3), 253-281. https://doi.org/10.2307/2343100 DOI: https://doi.org/10.2307/2343100

Fiss, P. C. (2011). Building better causal theories: A fuzzy set approach to typologies in organization research. Academy of Management Journal, 54(2), 393-420. https://doi.org/10.5465/amj.2011.60263120 DOI: https://doi.org/10.5465/amj.2011.60263120

Greckhamer, T., Furnari, S., Fiss, P. C., & Aguilera, R. V. (2018). Studying configurations with qualitative comparative analysis: Best practices in strategy and organization research. Strategic Organization, 16(4), 482-495. https://doi.org/10.1177/1476127018786487 DOI: https://doi.org/10.1177/1476127018786487

James, L. R., Demaree, R. G., & Wolf, G. (1984). Estimating within-group interrater reliability with and without response bias. Journal of Applied Psychology, 69(1), 85-98. https://doi.org/10.1037/0021-9010.69.1.85 DOI: https://doi.org/10.1037/0021-9010.69.1.85

Kahn, W. A. (1990). Psychological conditions of personal engagement and disengagement at work. Academy of Management Journal, 33(4), 692-724. https://doi.org/10.5465/256287 DOI: https://doi.org/10.2307/256287

Luthans, F., Avolio, B. J., Avey, J. B., & Norman, S. M. (2007). Positive psychological capital: Measurement and relationship with performance and satisfaction. Personnel Psychology, 60(3), 541-572. https://doi.org/10.1111/j.1744-6570.2007.00083.x DOI: https://doi.org/10.1111/j.1744-6570.2007.00083.x

Misangyi, V. F., & Acharya, A. G. (2014). Substitutes or complements? A configurational examination of corporate governance mechanisms. Academy of Management Journal, 57(6), 1681-1705. https://doi.org/10.5465/amj.2012.0728 DOI: https://doi.org/10.5465/amj.2012.0728

Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903. https://doi.org/10.1037/0021-9010.88.5.879 DOI: https://doi.org/10.1037/0021-9010.88.5.879

Ragin, C. C. (2000). Fuzzy-set social science. University of Chicago Press.

Ragin, C. C. (2008). Redesigning social inquiry: Fuzzy sets and beyond. University of Chicago Press. https://doi.org/10.7208/chicago/9780226702797.001.0001 DOI: https://doi.org/10.7208/chicago/9780226702797.001.0001

Schaufeli, W. B., Bakker, A. B., & Salanova, M. (2006). The measurement of work engagement with a short questionnaire: A cross-national study. Educational and Psychological Measurement, 66(4), 701-716. https://doi.org/10.1177/0013164405282471 DOI: https://doi.org/10.1177/0013164405282471

Schneider, C. Q., & Wagemann, C. (2012). Set-theoretic methods for the social sciences: A guide to qualitative comparative analysis. Cambridge University Press. https://doi.org/10.1017/CBO9781139004244 DOI: https://doi.org/10.1017/CBO9781139004244

Scott, S. G., & Bruce, R. A. (1994). Determinants of innovative behavior: A path model of individual innovation in the workplace. Academy of Management Journal, 37(3), 580-607. https://doi.org/10.5465/256701 DOI: https://doi.org/10.2307/256701

Siemsen, E., Roth, A. V., & Balasubramanian, S. (2008). How motivation, opportunity, and ability drive knowledge sharing: The constraining-factor model. Journal of Operations Management, 26(3), 426-445. https://doi.org/10.1016/j.jom.2007.09.001 DOI: https://doi.org/10.1016/j.jom.2007.09.001

Simar, L., & Wilson, P. W. (2000). A general methodology for bootstrapping in non-parametric frontier models. Journal of Applied Statistics, 27(6), 779-802. https://doi.org/10.1080/02664760050081951 DOI: https://doi.org/10.1080/02664760050081951

Simar, L., & Wilson, P. W. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136(1), 31-64. https://doi.org/10.1016/j.jeconom.2005.07.009 DOI: https://doi.org/10.1016/j.jeconom.2005.07.009

Thiem, A., & Dușa, A. (2013). QCA: A package for qualitative comparative analysis. The R Journal, 5(1), 87-97. https://doi.org/10.32614/RJ-2013-009 DOI: https://doi.org/10.32614/RJ-2013-009

Van den Hooff, B., & de Ridder, J. A. (2004). Knowledge sharing in context: The influence of organizational commitment, communication climate and CMC use on knowledge sharing. Journal of Knowledge Management, 8(6), 117-130. https://doi.org/10.1108/13673270410567675 DOI: https://doi.org/10.1108/13673270410567675