Ethical Perceptions of AI in Hiring and Organizational Trust: The Role of Performance Expectancy and Social Influence

The use of artificial intelligence (AI) in hiring entails vast ethical challenges. As such, using an ethical lens to study this phenomenon is to better understand whether and how AI matters in hiring. In this paper, we examine whether ethical perceptions of using AI in the hiring process influence individuals’ trust in the organizations that use it. Building on the organizational trust model and the unified theory of acceptance and use of technology, we explore whether ethical perceptions are shaped by individual differences in performance expectancy and social influence and how they, in turn, impact organizational trust. We collected primary data from over 300 individuals who were either active job seekers or who had recent hiring experience to capture perceptions across the full range of hiring methods. Our findings indicate that performance expectancy, but not social influence, impacts the ethical perceptions of AI in hiring, which in turn influence organizational trust. Additional analyses indicate that these findings vary depending on the type of hiring methods AI is used for, as well as on whether participants are job seekers or individuals with hiring experience. Our study offers theoretical and practical implications for ethics in HRM and informs policy implementation about when and how to use AI in hiring methods, especially as it pertains to acting ethically and trustworthily

Research productivity of management faculty: job demands-resources approach

Purpose The main purpose of this study was to examine which job resources are most valuable for research productivity, depending on varying teaching demands. Design/methodology/approach Data was collected from 324 management faculty at research, balanced and teaching (i.e. respectively low-, moderate- and high-teaching demands) public universities in the United States. Findings Results showed that no single job resource predicted research productivity across all three types of schools. At research schools (i.e. low-teaching demands), productivity was positively associated with job resources including summer compensation, level of protection for untenured faculty and number of research assistant hours, while negatively associated with travel funding. At balanced schools (i.e. moderate-teaching demands), research output was positively associated with time allocated to research, grant money, travel funding and conference attendance, while negatively associated with amount of consulting hours. At teaching schools (i.e. high-teaching demands), the only significant resource was time allocated to research. Practical implications This paper can help management faculty and business school leaders understand what resources are most appropriate given the teaching demands associated with the specific institution, and by further helping these institutions attract and retain the best possible faculty. Originality/value This study extends prior work on academic research performance by identifying resources that can help faculty publish given different levels of teaching demands. This is important as teaching demands tend to be relatively stable within an institution, while they can vary greatly across types of institutions

Influence of Job-Dedicated Social Media on Employer Reputation

The popularity and value of social media sites has stretched beyond its initial social connection purposes; today, they represent critical tools for individual and firm visibility. This paper compares and contrasts institutional theory and signaling theory to investigate (1) whether having a job-dedicated page on social media sites (i.e. Facebook, LinkedIn, and Twitter) is related to an organization’s employer reputation, and (2) whether it is merely the fact of having a job-dedicated social media page, or actually communicating (i.e. posting, tweeting, etc.) on that page that is related to an organization’s employer reputation. We used data collected from three major social media sites and found that having a job-dedicated LinkedIn page was positively related to employer reputation, whereas having a job-dedicated Facebook or Twitter page was not related to employer reputation. Furthermore, we did not find social media activity to be related to employer reputation

Seeming Ethical Makes You Attractive: Unraveling How Ethical Perceptions of AI in Hiring Impacts Organizational Innovativeness and Attractiveness

More organizations use AI in the hiring process than ever before, yet the perceived ethicality of such processes seems to be mixed. With such variation in our views of AI in hiring, we need to understand how these perceptions impact the organizations that use it. In two studies, we investigate how ethical perceptions of using AI in hiring are related to perceptions of organizational attractiveness and innovativeness. Our findings indicate that ethical perceptions of using AI in hiring are positively related to perceptions of organizational attractiveness, both directly and indirectly via perceptions of organizational innovativeness, with variations depending on the type of hiring method used. For instance, we find that individuals who consider it ethical for organizations to use AI in ways often considered to be intrusive to privacy, such as analyzing social media content for traits and characteristics, view such organizations as both more innovative and attractive. Our findings trigger a timely discussion about the critical role of ethical perceptions of AI in hiring on organizational attractiveness and innovativeness

Career transitions across the lifespan:A review and research agenda

Career transitions are becoming increasingly prevalent across the lifespan, and research on the topic has proliferated in recent years. However, the literature is fragmented across disciplines and has primarily focused on specific one-off transitions (e.g., school-to-work, unemployment-to-work, work-to-work, work-to-retirement). To reconcile these different perspectives, we conducted a review of processual career transition research, analyzing 93 quantitative longitudinal studies in this area. We problematize and synthesize the existing literature focusing on four main challenges: (1) an overemphasis on normative and predictable transitions, (2) a fragmented use of theories, (3) a lack of focus on behavioral antecedents and outcomes, and (4) a lack of attention to boundary conditions. Building on these literature critiques, we formulate a future research agenda across five directions by integrating the existing studies into a self-regulation framework of career transitions. This review thereby contributes to creating a more consistent and integrative understanding of career transitions across the lifespan.</p