Human Resource Management最新文献

In our study, we examine implementation of strength-based practice (SBP) that invokes a new way of working for social workers in England. We note two antecedent conditions to support new ways of working. First, hybrid managers, who combine professional and organizational perspectives, act as a conduit for implementation of new ways of working through supporting implementation of high performance work practices (HPWPs). Second, the financial context faced by social care providers influences whether new ways of working, and HPWPs associated with this, aim to improve productivity, or enhance capability and commitment of social workers. We identify three HPWPs that support the new way of working because they align with professional identity of social workers. First, hybrid manager jobs were designed to afford opportunity for recruitment of appropriately able social workers to enact strategic influence over SBP implementation. Second, intervention to support peer-to-peer learning enhanced the ability of social workers to deliver SBP, and also motivated social workers toward SBP implementation because they retained professional autonomy in developing their practice. Third, performance management intervention was developmental rather than judgmental, designed to enhance ability of social workers to deliver SBP. Similar to the peer learning intervention, it remained within control of social workers, hence motivated them to engage with SBP.

Corporate social responsibility (CSR) is purely an asset when it comes to talent acquisition: that is the dominant narrative among Human Resource Management (HRM) practitioners and scholars alike. Growing evidence, however, gives reason to question this assumption. Accordingly, in this conceptual paper, we develop a process model to articulate both the pros and cons of CSR for recruitment. Using fairness heuristic theory as a central organizing framework, we integrate three theoretical perspectives into the HRM and micro-CSR literatures. First, we leverage dual-processing attribution theory to propose that job seekers process information about CSR through both heuristic and deliberative processes, leading them to attribute employer CSR to substantive or symbolic motives. We explain how CSR attributions represent a fairness heuristic, meaning a proxy for how trustworthy job seekers appraise an employer to be. Second, invoking expectancy violation theory, we propose that the more job seekers attribute employer CSR to substantive (symbolic) motives, the higher (lower) their justice expectations will be, thereby increasing (decreasing) the consequences to employers for violating those expectations. Third, expanding scholarship on the dynamic nature of organizational fairness perceptions, we propose that job seekers update their attributions of employer CSR in a recursive cycle that can improve, but tends to degrade, as the recruitment process unfolds—particularly if they have high expectations to begin with. In so doing, we nudge the talent acquisition literature beyond static, fixed-in-time accounts to a more representative description of the dynamic and dual-sided role of CSR in recruitment over time.

Although Assessment Center (AC) role-play assessments have received ample attention in past research, their reliance on actual behavioral information is still unclear. Uncovering the behavioral basis of AC role-play assessments is, however, a prerequisite for the optimization of existing and the development of novel automated AC procedures. This work provides a first data-driven benchmark for the behavioral prediction and explanation of AC performance judgments. We used machine learning models trained on behavioral cues (C = 36) to predict performance judgments in three interpersonal AC exercises from a real-life high-stakes AC (selection of medical students, N = 199). Three main findings emerged: First, behavioral prediction models showed substantial predictive performance and outperformed prediction models representing potential judgment biases. Comparisons with in-sample results revealed overfitting of traditional approaches, highlighting the importance of out-of-sample evaluations. Second, we demonstrate that linear combinations of behavioral cues can be strong predictors of assessors' judgments. Third, we identified consistent exercise-specific patterns of individual cues and cross-exercise consistent behavioral patterns of behavioral dimensions and interpersonal strategies that were especially predictive of the assessors' judgments. We discuss implications for future research and practice.

This article presents a contemporary review of human resource management (HRM) research on algorithmic technologies, including artificial intelligence, machine learning, and natural language processing. By connecting these recent advancements to the long-standing scholarly tradition of HRM-technology relations, this review examines current knowledge on how algorithmic technologies are reshaping three key areas: (1) work structures and design, (2) HR delivery activities, and (3) the management of technology workers. Using a threefold conceptualization of technology—the tool view, proxy view, and ensemble view—this review explores how organizations employ algorithmic systems to enhance productivity, how the human agency interacts with and resists these technologies, and how broader social, cultural, and institutional contexts shape the use of algorithms in HRM. Additionally, this article offers suggestions for future research, highlighting the unique opportunities algorithmic technologies provide to HR scholars for making enduring contributions to the broader conversations on HRM and technology.

We examine differences in jobs held by men and women based on a measure not used before, the standard human resources measures of “knowledge, skills, and abilities” generated by job analyses. While there is an abundance of evidence on gender disparities in pay, we know much less in detail about differences in the work men and women perform and especially how these differences have changed over time. We use nationally representative data for two cohorts of college graduates, one entering the post-college workforce in 1994 and another in 2009, and we follow them for the first 10 years of their careers. We find that women generally held jobs with lower requirements relative to men. The gender gap in job requirements grew over the course of individuals' careers for the first cohort (1994–2003) but declined sharply for the second (2009–2018). We also find that among the second cohort, women received a wage premium for social skills that was greater than what men received.

Drawing on self-determination theory, this study advances our understanding of employees' experiences working from home (WFH). Specifically, we examine the effects of two social-contextual characteristics of WFH arrangements: whether employees voluntarily initiate their arrangement (WFH initiation) and the proportion of WFH employees in a unit (WFH density). We conducted multilevel analyses on a multisource dataset drawn from organizational HR records and two surveys of 2115 WFH employees in a Fortune 500 organization. Employees who voluntarily initiated WFH, rather than at their employer's direction, experienced higher job autonomy and lower isolation. WFH employees in units with a lower proportion of other WFH employees experienced higher job autonomy. WFH initiation and WFH density also had effects on several distal employee outcomes, including job satisfaction, organizational knowledge, and turnover intentions, through their effects on job autonomy and isolation. Our findings provide valuable insight into the experiences of WFH employees and call attention to two important, yet understudied, factors that shape these experiences.