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Social Comparison and Reference Selection in Organizations

People

 

Martignoni D.

(Responsible)

External participants

Luger Johannes

(Third-party co-responsible)

Abstract

The notion that organizations compare themselves with others is central to many streams of research, including performance feedback theory (Greve and Gaba 2020, Greve 2003) and theories of imitation and vicarious learning (e.g., Baum, Li, and Usher, 2000; Posen and Martignoni 2018). While there is much consensus that such comparisons come with important consequences, for example, for acquisition decisions (Greve, 2011) or new product developments (Joseph and Gaba, 2015), we still lack an understanding of which peers or competitors are selected for comparison and the mechanisms and implications of these selections. Inter alia, this includes the question whether theoretical arguments developed about social comparison at an individual level (Festinger 1954) can be applied to an organizational level - an approach often applied in prior work (e.g., Audia & et al. 2015). In addition to such theoretical limitations, prior empirical work suffers from the problem that selection is not easy to observe and instead is typically “justified contextually or determined endogenously from the data” (Greve and Gaba, 2020: p. 31). As a consequence, theoretical arguments about social comparisons at an organizational level are underdeveloped, especially when comparing to an individual level. In our study, we seek to address this gap by directly observing these selection decisions and, in the case of our experiment, manipulating this decision. We will focus on different aspects of this selection process in four different sub-projects, using different research methods (e.g., survey data and simulation models) and relying on different data sources (survey data and simulated data). Such a multi-method approach allows us to address different aspects of the selection process: through our survey, we can collect direct evidence on firms’ reference selection choices; our simulation models allow us to determine the possible implications of these choices and extrapolate our findings to a variety of different contexts. For all subprojects, we have already started the data collection process (in total, we collected reference selection data from more than 1000 firms) and run some first analyses/computational experiments. These first steps have generated some interesting early results. For example, our survey data indicate that, different from prior assumptions, firms do not select references according to specific comparison rationales but rather compose ‘convenience samples’ of references that are able to potentially serve a variety of rationales in parallel. In our computational experiments, we find the optimal behavioral response to reference points may vary substantially across different decision contexts and that different types of reference points can be more valuable than others, depending on the decision contexts. Collectively, our findings provide important novel insights about how reference points are selected and how these selections affect subsequent choices and, ultimately, performance. Our empirical data also enables us to evaluate prior work’s application of individual level arguments to an organizational level. Thereby, our findings are important to many organizational theories, in particular theories relying on inter-organizational comparisons such as performance feedback theory (Greve and Gaba 2020, Greve 2003) and theories of imitation and vicarious learning (e.g., Baum, Li, and Usher, 2000; Posen and Martignoni, 2018). Building on our prior work on this topic (e.g., Brauer, Mammen and Luger, 2017; Posen and Martignoni, 2018), we aim to publish the outlined studies in the top management journals (Organization Science, Academy of Management Journal, and Strategic Management Journal).

Additional information

Start date
01.12.2022
End date
30.11.2025
Duration
36 Months
Funding sources
SNSF
Status
Active
Category
Swiss National Science Foundation / Co-Investigator Scheme