Strategies in Addressing Psychological Injuries at Work in Economically Transitioning Societies

Work-related mental health issues, also known as psychological injuries (such as burnout, stress, fatigue or depression as a result of long working hours, high work pressure and bullying or even violence), have become substantive workplace and social concerns due to the adverse effect on employees and peer workers. Consequentially, employers often face costs due to psychologically injured employee’s long periods of absence or productivity loss. People suffering from such injuries may also face challenging family and social relations and some of them would have to resort to public resources for treatment and support. Employers and governments in developed countries have put great efforts on addressing these issues. In contrast, in developing countries, the focus of work injuries mostly lays on physical injuries. Mental health problems resulting from employment have attracted far less attention. It is urgent to identify: how work-related mental health issues have evolved; the determinants of the issues; and, the key strategies/practices that mitigate them. More specifically, in economies undergoing rapid changes, such as radical structural transformation (e.g. industrialization, globalization, digitalization), economic recession or even crisis, or changing management culture (privatization, performance targets, casualization), mental health of the labour force can be seriously affected. However, how these macro-economic or work cultural changes have shocked labour force and resulted in new mental health issues, and how employers and policy makers should respond to such pressures, still remain to be resolved. This Research Topic calls for new empirical research on the strategies in addressing mental health issues at work. We aim to 1) provide new evidence on the impact of economic changes on labour force mental health; 2) identify innovative market or community solutions to mental health issues at work; 3) identify and evaluate employer interventions to address or prevent work-related mental health issues; 3) understand government policy responses and their effectiveness

Understanding affective trust in AI: The effects of perceived benevolence

The primary objective of this research was to gain understanding of affective trust in AI (how comfortable individuals feel with various AI applications). This dissertation tested a model for affective trust in AI grounded in interpersonal trust theories with a focus on the effects of perceived benevolence of AI—an overlooked factor in AI trust research. In Study 1a, online survey participants evaluated 20 AI applications with single-item measures. In Study 1b, four AI applications were evaluated with multi-item measures. Perceived benevolence was significantly, positively associated with affective trust over and above cognitive trust and familiarity in 21 of 24 AI tests. Confirmatory factor analysis suggested four factors, supporting the theory that cognitive trust and affective trust in AI are distinct factors. The secondary objective was to test the utility of manipulating perceived benevolence of AI. In Study 2, online survey participants were randomly assigned to one of two groups with 10 AI applications described as “augmented intelligence” that “collaborates with” a specific or exact same AI described as “artificial intelligence.” The augmentation manipulation did not matter; there were no significant direct or indirect effects to benevolence or affective trust. These results imply that “Augmented Intelligence” positioning has no significant effect on affective trust, counter to practitioners’ beliefs. In Study 3, online survey participants were randomly assigned to one of two groups—one that received benevolence messaging (a message informing the participant that the AI was intended for human welfare) for five AI applications and the other did not.Benevolence messaging was also tested to see if it moderated contexts expected to diminish affective trust (likelihood of worker replacement and likelihood of death from error). Benevolence was not influenced by the manipulation. Surprisingly, likelihood of worker replacement had no significant association with affective trust, and likelihood of death from error had only one significant association. People may be more ambivalent about these contexts than previously thought. Understanding affective trust in AI was expanded by identifying the importance of perceived benevolence. Until benevolence messaging can boost perceptions of benevolence, the success of that strategy remains unknown

Portland Daily Press: June 20,1865

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