Business and Society Review, Volume 127, Issue 1, Page 97-125, Spring 2022.

This paper examines whether firms strategically legitimize large labor dismissals by performing ex-ante downward earnings management. We further assess whether the effect is larger under stakeholder pressure and whether these practices influence the external perception of firms’ behavior. As laying off employees without an economic reason is perceived as a breach of the social contract, stakeholders pressure firms to provide economic justification for LLDs. We argue that firms strategically legitimize LLDs by artificially worsening their financial performance through downward earnings management. (...) From a sample of European listed firms for the period 1998–2014, using a propensity score matching model to control for differences in observable characteristics between LLD and non-LLD firms, we find that firms manage their earnings downward in the year prior to LLDs. These results are more pronounced in the presence of high stakeholder pressure. As such, firms with stronger trade unions, higher visibility, stricter employment protection legislation, and more stakeholder-oriented legal environments have stronger incentives to manage earnings downward before LLDs. Finally, we document that downward earnings management prior to LLDs is effective at legitimizing LLDs, offsetting the negative external perceptions of firms in the aftermath of LLDs. This study contributes to the business ethics literature by identifying unintended consequences of stakeholder pressure around LLDs. It also brings important empirical insights into how ethically questionable decisions are strategically legitimized through further unethical practices. (shrink)

This article offers a comprehensive overview of Business Sustainability (BuS), and directly addresses the lack of consensus around this important concept. Through a mixed-methods approach, we conduct the first systematic literature review of BuS employing Latent Dirichlet Allocation (LDA) topic modeling to uncover hidden thematic structures, Narrative Synthesis to refine and extend BuS definitions within different contexts, and the LDA-HSIM method to classify topics and design a new framework. We analyzed an extensive dataset comprising 92,311 articles sourced from 11,579 journal (...) outlets. From this dataset, we identified 9,561 articles suitable for LDA topic modeling by applying funnel criteria, focusing on articles with clear theoretical underpinnings. A text extraction technique enabled us to identify and analyze theories used in BuS studies. This analysis revealed 150 underlying theories that advance the BuS concept across different research topics. The study contributes to BuS theory development with great potential to improve ethical decision-making by establishing meaningful, context-specific definitions and providing clear guidance for future researchers in selecting appropriate theoretical perspectives for their work. We identify research gaps, propose a prioritized research agenda focused on theory development, and formulate key implications for practitioners and policymakers. This study demonstrates the effectiveness of machine learning methods in conducting large-scale literature reviews to accelerate theoretical advancements and generate research agendas. (shrink)