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This paper expects to analyze the connection between occupational stigma and job meaningfulness among Chinese takeaway riders, the mediating role of occupational identity and relative deprivation, and the moderating effect of job stress based on resource conservation theory.

The sample was derived from 371 takeaway riders across China. PLS-SEM was mainly utilized for the data analysis.

The findings of the study indicated a significant negative correlation between occupational stigma and job meaningfulness. Furthermore, it is worth noting that relative deprivation and occupational identity served as mediators and masks, respectively, in the relationship between occupational stigma and job meaningfulness. Furthermore, job stress amplifies the association between occupational stigma and occupational identity. Additionally, job stress diminishes the connection between occupational stigma and relative deprivation.

This study proposes a positive correlation between occupational stigma and occupational identity in the Chinese context. It also enriches the empirical research based on resource conservation theory. Furthermore, it holds practical implications for takeaway riders in China, offering insights to bolster their job meaningfulness.

There is a growing need for methodological plurality in advancing operations management (OM), especially with the emergence of machine learning (ML) techniques for analysing extensive textual data. To bridge this knowledge gap, this paper introduces a new methodology that combines ML techniques with traditional qualitative approaches, aiming to reconstruct knowledge from existing publications.

In this pragmatist-rooted abductive method where human-machine interactions analyse big data, the authors employ topic modelling (TM), an ML technique, to enable constructivist grounded theory (CGT). A four-step coding process (Raw coding, expert coding, focused coding and theory building) is deployed to strive for procedural and interpretive rigour. To demonstrate the approach, the authors collected data from an open-source professional project management (PM) website and illustrated their research design and data analysis leading to theory development.

The results show that TM significantly improves the ability of researchers to systematically investigate and interpret codes generated from large textual data, thus contributing to theory building.

This paper presents a novel approach that integrates an ML-based technique with human hermeneutic methods for empirical studies in OM. Using grounded theory, this method reconstructs latent knowledge from massive textual data and uncovers management phenomena hidden from published data, offering a new way for academics to develop potential theories for business and management studies.

This research empirically establishes that the interpersonal population diversity of executive board members partly explains the differences in financial misconduct across US banks. It advances the hypothesis that heterogeneity in the composition of an interpersonal population and diverse traits of board members, originating from the prehistoric course of the exodus of Homo sapiens from East Africa tens of thousands of years ago, is an important factor explaining the effectiveness of executive board monitoring with respect to a bank engaging in financial misconduct. The underlying intuition is that population-fragmented societies, characterized by mistrust, preference heterogeneity and corruption, find it difficult to sustain collective integrity action.

Employing a panel of US banks from 1998 to 2019 we find that adding directors from countries with different levels of interpersonal population diversity is positively associated with financial misconduct as measured by enforcement and class action litigation against banks by the main regulatory agencies. Furthermore, we document that the more population-diverse bank boards are more likely to commit misconduct, consistent with a mechanism of inter-generational transmission of cultural norms of mistrust and non-cooperation.

We find that adding directors from countries with different levels of interpersonal population diversity is positively associated with financial misconduct as measured by enforcement and class action litigation against banks by the main regulatory agencies. These results are robust to controlling for bank-specific variables, including other board characteristics, and to the use of instrumental variables.

The findings suggest that reducing financial misconduct by banks likely requires reducing the interpersonal population diversity of banks’ executive boards.

We show how bank boards with different interpersonal population diversity impact the likelihood of engaging in misconduct provides evidence of the microeconomic effects of interpersonal population diversity. We show the negative results of diversity that they can have on the management of a firm given that populated diverse boards are more likely to lead to higher levels of misconduct. Our evidence reveals that banks having interpersonal population fragmented boards are more likely to commit misconduct given the cultural norms of mistrust and the lack of societal cohesiveness.