The social process of coping with work‐related stressors online: A machine learning and interpretive data science approach

Publication
Personnel Psychology

Abstract

People are increasingly turning to social media and online forums like Reddit to cope with work-related concerns. Previous research suggests that how others respond can be an important determinant of the sharer’s affective and well-being outcomes. However, less is known about whether and how cues embedded in the content of what is shared can shape the type of responses that one receives from others, obscuring the joint and interactive role that both the sharer and listener may play in influencing the sharer’s outcomes. In this study, we develop theory to advance our understanding of online coping with an explicitly social focus using computational grounded theorizing and machine learning (ML) techniques applied to a large corpus of work-related conversations on Reddit. Specifically, our theoretical model sheds light on the dynamics of the online social coping process related to the domain of work. We show that how sharers and listeners interact and react to one another depends on the content of stressors shared, the social coping behaviors used when sharing, and whether the sharer and listener belong to the same occupational context. We contribute to the social coping literature in three ways. First, we clarify how social actors respond to cues embedded in the social coping attempt. Second, we examine the moderating role that such responses play in shaping sharer outcomes. Finally, we extend theory on social coping with work-related stressors to the online domain. Taken together, this research highlights the importance of the dynamic interplay between sharer and listener in the context of online social coping.


I am the lead author of an article recently accepted for publication at the special issue of Personnel Psychology focusing on AI and Machine Learning in OBHR “The Social Process of Coping with Work-Related Stressors Online: A Machine Learning and Interpretive Data Science Approach” (Sajjadiani, Daniels, & Huang, 2022). My coauthors and I took a dynamic and systemic approach to the process of social coping and developed a three-phased theoretical model that illustrates the process of online social coping with work-related stressors and clarifies the role of the listener (i.e., respondent to a sharer’s social coping attempt) in this mutually-constitutive interaction. We empirically ascertained that listener responses are not exogenous inputs to the interaction, but are responsive to cues laden in the sharer’s social coping attempt, and further feedback into the sharer’s subsequent affective and behavioural reactions.

This paper, therefore, investigates the flip side of the coin with respect to my first research stream, showing how workers experience various HR/managerial practices (e.g., the inadequacy of compensation, HR policies, lack of work-life balance, socialization ambiguity, EDI mismanagement) and the processes by which they perceive, evaluate, and respond to these practices.

Sima Sajjadiani
Sima Sajjadiani
Assistant Professor at UBC Sauder School of Business-OBHR Division

I research the development of human capital resources through HR management strategies to achieve sustained competitive advantage for organizations.