Studying compliance, in terms of the business responses to legal rules, is notoriously difficult. This paper focuses on the difficulty of capturing the behavioral response itself, rather than on difficulties in explaining compliance and isolating particular factors of influence on it. The paper argues that existing approaches to capture such compliance, using surveys and governmental data, run the risk of failing to capture compliance as it occurs in the reality of day-to-day business responses to the law. It does so by (...) means of a unique ethnographic approach to study compliance. Drawing from data of deep participant observation about responses to legal rules in two small businesses, the paper finds that in this context there is Compliance Dynamism. This means that compliance varies for different rules, it varies over time, and businesses learn from one response to the law to the next on a daily basis. Compliance is also situational, and there is an Indirect Observer Effect, where the way compliance is measured, especially when using data derived from inspections, shapes what compliance is observed and what is not. Therefore, compliance should be captured not as a singular state but as a string of reiterative processes that occur in their situational context. And this fundamentally challenges most existing methods to capture compliance and thus our understanding of what compliance occurs and what may shape it. In its conclusion, the paper draws out the implications this has for studies that seek to find simple and usable findings about compliance. (shrink)

Previous research indicates that the depletion of self-regulatory resources can promote unethical behavior that benefits the self. Extending this literature, we focus on norm-transgressing behavior that is intended to primarily benefit others. In particular, we predicted a differing effect of self-regulatory resource depletion on dishonesty that benefits one’s group, depending on the degree of identification with the group. Following a dual process approach, we argue that if identification with the group is strong, then people may have an automatic inclination to (...) benefit their group even perhaps by lying. In contrast, if identification with the group is weak, then the default, uncontrolled impulse may be to tell the truth. Accordingly, identification with the social group should interact with self-regulatory resource depletion in predicting group-benefiting dishonesty. Focusing on pro-organizational dishonesty, we tested our hypotheses in one field study with 1269 employees and in one experimental study with 71 university students. As predicted, the results revealed a highly significant interaction of organizational identification and self-control strength: Depletion of self-regulatory resources increased the level of pro-organizational dishonesty among those who identify highly with the organization, but decreased the level of such behavior among those who identify less. (shrink)

To understand how compliance develops both in everyday and corporate environments, it is crucial to understand how different mechanisms work together to shape individuals’ (non)compliant behavior. Existing compliance studies typically focus on a subset of theories (i.e., rational choice theories, social theories, legitimacy theories, capacity theories, and opportunity theories) to understand how key variables from one or several of these theories shape individual compliance. The present study provides a first integrated understanding of compliance, rooted in complexity science, in which key (...) elements from these theories are considered simultaneously, and their relations to compliance and each other are explored using network analysis. This approach is developed by analyzing online survey data (_N_ = 562) about compliance with COVID-19 mitigation measures. Traditional regression analysis shows that elements from nearly all major compliance theories (except for social theories) are associated with compliance. The network analysis revealed groupings and interconnections of variables that did not track the existing compliance theories and point to a complexity overlooked in existing compliance research. These findings demonstrate a fundamentally different perspective on compliance, which moves away from traditional narrow, non-network approaches. Instead, they showcase a complexity science understanding of compliance, in which compliance is understood as a network of interacting variables derived from different theories that interact with compliance. This points to a new research agenda that is oriented on mapping compliance networks, and testing and modelling how regulatory and management interventions interact with each other and compliance within such networks. (shrink)