Formalizing the HRM and firm performance link: the S-curve hypothesis

Abstract

This study incorporates theory from economics to formalize the HRM–firm performance relationship. We propose and test a new theoretical model that predicts optimal points of investment in the HRM system where greater benefits are returned. The model also identifies investment levels that lead to negative and diminishing returns. In developing the intersection of HRM and economics we realize an opportunity to challenge the consistent adoption in the literature of what we call “linear logic”, or the assumption that continuous investment in HRM yields benefits at the same rate.

Hypotheses were tested using data collected over two years from subsidiary leaders of a large European multinational corporation (MNC) operating in 27 countries. Financial performance data were gathered over three years, as well as economic data pertaining to industry, country, and regional effects. The results reveal that the relationship between investing in the HRM system and firm marginal benefit is nonlinear in the shape of an S-curve. Our findings provide insights on investment levels where the HRM system can have a positive influence on firm performance. Implications for theory and practice are provided.

Keywords:

• SHRM
• MNC
• firm performance
• curvilinear
• diminishing returns

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 Academy of Management Journal (AMJ), International Journal of Human Resource Management (IJHRM), Journal of Management (JOM), Human Resource Management (HRM), Journal of International Business Studies (JIBS), Journal of Management Science (JMS), Personnel Psychology (PP), and Human Resource Management Journal (HRMJ). Although the list is not exhaustive of all studies that investigate the link between HRM and performance, we believe these journals capture the studies that are making the most significant contributions to the field due to each journal’s high impact factor.

2 We tested the linear model, but the relationship was not significant. This supports our assertions regarding the importance of investigating a non-linear relationship.

3 We estimated the data model with the generalized method of moments (GMM) primarily to address potential endogeneity in the model. The results show that the HRM cube coefficient was still significant.