1,721,014 research outputs found
Can organizational resources improve well-being for employed caregivers of children with disabilities?
No full textEMBARGO NOTE: This item is embargoed until 2026-05-01Across the United States, an estimated 32.6 million employees in 2020 have informal caregiving responsibilities for those with health conditions. For such employees, they manage not only the demands from their job, but also the demands that come with caregiving, often resulting in burnout, as well as other deleterious mental and physical health conditions. However, as suggested by the Job Demands-Resources model (JD-R model; Demerouti et al., 2001), caregiving employees may also have resources that buffer these negative health effects, such as social support at home, and flexible time arrangements in the workplace. To better understand the underlying processes, the current study examines 327 employees: 89 of whom are also caregivers for children with Duchenne Muscular Dystrophy (DMD; a neuromuscular disease), and 238 are parents of children without disabilities, as a comparison group. Specifically, the study uses the JD-R model to integrate the demands and resources found in both the employment and caregiving roles and settings. My study generally did find support for demands leading to greater burnout. Yet, the impact of resources was relatively mixed, where many workplace policies did not reduce informal caregiving burnout and a rare few actually increased informal caregiving burnout. Furthermore, I did not find evidence of any resources (for the job or informal caregiving) acting as buffers for work-related and informal caregiving burnout. These results suggest that the resources received by the informal caregivers is largely inadequate, and that organizations and government should devote more effort in designing more effective and accessible work and informal caregiving resources to support working informal caregivers
Using the Job-Demands Resource Model to Predict Retention-Related Outcomes in Animal Welfare Volunteers
Get PDFRetaining an adequate number of volunteers is important to ensure animal welfare organizations can provide critical services to companion animals and members of their community. This study was conducted to examine whether the job demands-resource model (JD-R: Demerouti et al., 2001; Schaufeli & Bakker, 2004), a model commonly used in employment settings, is a useful framework to predict three retention-related variables in animal welfare volunteers: intention to quit, volunteer frequency, and commitment. The JD-R model posits that job demands and job resources are important predictors of burnout and engagement, which themselves are significant predictors of various work outcomes. The study tested this model using 11,430 volunteers across 148 animal welfare organizations. Utilizing structural equation modeling, the study found that organizational constraints, perception of voice, and recognition, were all predictive of burnout. Subsequently, burnout was identified as a predictor of intention to quit and volunteer frequency. Perception of voice and recognition were also identified as predictors of engagement, and subsequently predictive of commitment. Additional analyses revealed the relationship between organizational constraints and burnout was moderated by tenure, suggesting that volunteers with longer tenure were partially protected from the negative consequences of organizational constraints. Thus, results provided evidence that the JD-R model is indeed a useful framework to study the antecedents of retention-related outcomes in animal welfare volunteers. The theoretical and practical implications of these findings are discussed
Is Hiring Fair and Accurate? Perceptions of Statistical and Practical Significance of Adverse Impact Indices
No full textHistorically, the impact ratio and ZD test are widely used by organizations and the courts to assess a type of employment discrimination called adverse impact. However, previous court decisions have been inconsistent in their application of these measures. To understand this inconsistency, the present two-part study examines (1) how 31 personnel selection and legal experts select, apply, and communicate adverse impact measures in practice in a qualitative study, and (2) how 23 highly numerate experts make decisions about meaningful hiring differences when presented with adverse impact measures (the impact ratio and ZD test) in an experimental study. The qualitative study provides rich expert insights on the most generally recommended adverse impact measures, important situational and contextual factors, the measures most compelling when supporting adverse impact claims versus defending against claims, the easiest versus most difficult measures to communicate to stakeholders, and the measures viewed as ideal. Although many of the ideas discussed by the experts are not novel, some clear themes were identified in the qualitative study (i.e., statistical analyses, data, organization-centered factors, and contextual factors) as well as a host of subthemes (e.g., practical significance, sample size, data aggregation, communication, the goal of the adverse impact analysis). Turning to the experiment, all participants were highly numerate, creating range restriction that limited my original intent to analyze individual differences and decision-making quality through the lens of fuzzy-trace theory (a dual-process theory of memory and decision-making, e.g., Reyna & Brainerd, 1995). Nevertheless, I still found some preliminary support for Hypotheses 1-3, suggesting that participants extracted the meaning of the measures, and made calibrated ordinal judgments that were valid (distinguishing between no support to extreme support of meaningful hiring differences across conditions) and reliable (providing little variability in judgments within conditions). In regard to Hypothesis 4, both the impact ratio and ZD test were rated as similarly useful. Finally, an exploratory analysis suggested that experts relied more on the ZD test than the impact ratio and raw hiring rates when making judgments of meaningful hiring differences. I conclude with a two-part discussion that highlights integrative themes (i.e., history and legal precedent limiting the development and use of novel methods or improved practices, and communicating analyses to stakeholders) and elaborates on the experimental findings in light of fuzzy-trace theory
Making Decisions about Adverse Impact: The Influence of Individual and Situational Differences
No full textResearchers studying adverse impact have focused primarily on the statistical properties of various adverse impact tests, almost completely neglecting the human decision-making processes involved in evaluating the fairness of employee selection decisions. The purpose of this study is to (a) use signal detection theory (SDT) to explore the effect of hiring scenario characteristics (i.e., size of the applicant pool, overall selection ratio, and minority proportion of the applicant pool) on laypeople’s sensitivity in detecting adverse impact, (b) explore how several individual differences (i.e., social desirability bias, risk-taking, neuroticism, and ambivalent sexism) may influence their response bias, and (c) replicate my prior research findings surrounding their sensitivity and response bias in making adverse impact judgments (Alexander, 2021). In the current study, 97 working-age adults recruited from an online panel were shown 57 selection scenarios with varying degrees of difference in selection rates between men and women and asked to decide if each scenario was fair or unfair by the Equal Employment Opportunity Commission’s definition of adverse impact. Participants detected adverse impact beyond chance (d′ = 0.45) and exhibited a slightly conservative response bias (c = 0.18). Mixed-effects probit regression analyses were used to estimate SDT metrics reflecting relationships between various hiring scenario characteristics and individual differences in predicting decisions about adverse impact. Cognitive ability, overall selection ratio, and minority proportion of the applicant pool were all positively related to participant sensitivity. Hostile sexism related positively, and benevolent sexism related negatively, to response bias
Development of a new measure of helping at work
No full textIn this thesis, helping behavior is defined as extra role behaviors that an employee performs voluntarily and contributes to organizational effectiveness such as improved productivity and co-worker performance (Organ, 1988). People who help others at work tend to experience increased job satisfaction, increased organizational commitment and decreased intentions to leave the job. Taking into consideration the benefits of helping outcomes to both employees and organizations, I developed six scales that measure helping using a multi-stage item-development procedure. Based on a theoretical model distinguishing emotional- and instrumental helping, a multidimensional measure could not be developed. In this study, however, empirical support was found for two helping scales and criteria of interest. Future directions and implications of this study are discussed
Zooming in on Communities of writing students: The impact of COVID-19 on writing self-efficacy
No full textUnder the COVID-19 pandemic virtual learning environment, instructors have had to address various challenges such as integrating technologies with the curriculum, encouraging social interactions among students, and keeping students cognitively engaged and motivated. These challenges may be usefully organized into teaching, social, and cognitive presences within the Communities of Inquiry (CoI) framework as well as the learner characteristic of self-efficacy, which is necessary to keep students motivated. Specifically, the current study sought to investigate how these challenges are interrelated in the domain of writing and impact academic outcomes among 142 Rice University students enrolled in a First Year Writing Seminar. Because there was no variance in slopes among students and academic performance was range restricted, the main findings associated with the slope could not reject the null hypothesis. However, exploratory analyses reveal that cognitive presence and writing self-efficacy intercepts were more impactful on beliefs and affect about writing
Rater Sensitivity and Bias in Adverse Impact Decision-Making: A Signal Detection Theory Approach
No full textEMBARGO NOTE: This item is embargoed until 2027-12-01The concept of adverse impact (AI) was created to help identify employment discrimination and is defined as a meaningful difference in the selection rates (e.g., hiring, admissions, promotion) between two groups of employees or applicants (e.g., men and women). Researchers studying fairness in organizations have treated AI almost entirely as if it is only a quantitative problem to be addressed by quantitative tests (e.g., statistical testing) and have neglected to study the decision-making processes of individuals charged with the task of evaluating the fairness of employee selection outcomes (detecting AI). The purpose of this study is to understand how well people can detect AI and what factors influences people’s judgements of AI. Signal detection theory (SDT) is a framework that is well-suited to modelling individuals’ perceptions of the fairness of employee selection outcomes (AI) because it was developed to model human decision-making in the presence of uncertainty. In addition to global evaluations of accuracy (whether or not one can accurately determine whether a hiring decision is fair or unfair), SDT allows researchers to calculate people’s sensitivity to AI stimuli (d′; the ability to detect differences in the selection rates of two groups), as well as people’s response bias in doing so (c ;the difference in selection rates between two groups at which a rater tends to decide a selection decision is unfair). In this study, 234 participants (58% women) were asked to decide if 36 selection scenarios where men and women were hired at differing rates were fair (or unfair), based on the Equal Employment Opportunity Commission’s definition of AI. Participants also completed various individual difference measures hypothesized to influence sensitivity: i.e., cognitive ability, numeracy, and graph literacy. Similarly, participants completed various individual difference measures that were hypothesized to influence response bias: i.e., Procedural Justice Beliefs for Others, Distributive Justice Beliefs for Others, and the Ambivalent Sexism Inventory. Other person and environment characteristics were also recorded: i.e., gender and stimulus type (whether hiring decisions were presented as icon array graphs or numeric text). Participants’ sensitivity and response bias in AI decision-making were analyzed using both traditional and mixed-effects-regression-based SDT approaches. Cognitive ability, numeracy, and graph literacy all emerged as significant predictors of participant sensitivity in the analyses. Procedural Justice Beliefs for Others and gender emerged as significant predictors of response bias in the analyses. The implications of these results as well as the suitability of traditional and mixed-effects-regression-based SDT models as a framework for analyzing AI decision-making are discussed
What makes a good leader: Validation of a situational judgment test to assess leader behavioral knowledge
No full textLeadership behavior is a critical reflection of one’s leadership capability. Notably, having knowledge of leadership behaviors contributes to actually enacting these behaviors. Thus, the purpose of the current research is to validate a situational judgment test (SJT) measure to assess leadership behavioral knowledge. The first study aims to establish a multidimensional framework of leadership behaviors based on Campbell’s Model of Leader Performance (2012) and leadership inclusion behaviors toward diversity, equity, and inclusion (Shore et al., 2011; Silver et al., 2022). Using this framework, the second study proceeds to validate a SJT measure of leadership behavioral knowledge. Study findings suggest evidence supporting the multidimensional framework of leadership behaviors, in addition to the need for further refinement and validation of the SJT leadership measure. Altogether, the findings contribute to theoretically informing the construct space of effective leadership, in addition to providing practical guidance for developing a leadership assessment to be used for future leader selection, training, and development
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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