Gene M. Alarcon

Student Burnout and Engagement: A Test of the Conservation of Resources Theory

ABSTRACT The current study explored predictors of burnout and engagement in 1st-year college students. The theory of conservation of resources was used to create a path model for burnout and engagement. Specifically, the theory suggests that perceptions of demands mediate the relationship between resources and coping strategies. In turn, coping mediates the relationship of demands on the outcomes of burnout and engagement. Results indicate demands partially mediated the relationship between resources and coping strategies. Similarly, coping partially mediated the relationship between demands and burnout and engagement. Results suggest that teaching students adaptive ways of coping and extinguishing maladaptive ways of coping with the academic environment can increase engagement and decrease burnout. Implications for research, theory, and practice are discussed.

The Relationship of Engagement and Job Satisfaction in Working Samples

ABSTRACT The present study explored the factor structure of engagement and its relationship with job satisfaction. The authors hypothesize that work engagement comprises 3 constructs: vigor, dedication, and absorption. Using structural equation modeling, the authors analyze data from 3 archival data sets to determine the factor structure of engagement. In addition, they examine the hypothesis that engagement and job satisfaction are separate but related constructs, using structural equation modeling and hierarchical regression. The authors test models in which engagement and job satisfaction items loaded onto a single latent variable and 1 in which they loaded onto 2 separate variables. Results from the confirmatory factor analysis indicate engagement has 3 factors. In addition, confirmatory factor analysis and hierarchical regressions indicate engagement and job satisfaction are separate constructs. Last, hierarchical regressions demonstrated the constructs have different relationships with the areas of work–life scale. Implications for theory and research are discussed.

A meta-analytic examination of the potential correlates and consequences of workload

Over the last four decades, occupational stress researchers have given considerable attention to the potential correlates and consequences of workload. In the current study, we use meta-analysis (overall k = 336) to quantitatively review the workload literature. In analyses of hypothesized correlates, we found that social support was negatively associated (ρ = −.20 for supervisor support; ρ = –.11 for co-worker support) and that trait negative affectivity (ρ = .22), role ambiguity (ρ = .28), role conflict (ρ = .44) and work-family conflict (ρ = .44 for work-to-family conflict; ρ = .20 for family-to-work conflict) were each positively associated with workload. Analyses examining hypothesized outcome variables suggest that workload is negatively associated with several indices of psychological and physical well-being (ρs were generally in the –.20s and –.30s), and affective organizational commitment (ρ = –.11), and is positively associated with turnover intention (ρ = .16) and absenteeism (ρ = .07).

Building better measures of role ambiguity and role conflict: The validation of new role stressor scales

ABSTRACT Occupational stress researchers have given considerable attention to role ambiguity and role conflict as predictors of employee health, job attitudes and behaviour. However, the validity of the Rizzo, House, and Lirtzman’s (1970) scales – the most popular role stressor measures – has been a source of disagreement among researchers. In response to the disputed validity of the Rizzo et al. scales, we developed new measures of role ambiguity and role conflict and conducted five studies to examine their psychometric qualities (Study 1 N = 101 U.S. workers; Study 2 N = 118 workers primarily employed in the U.S.; Study 3 N = 135 employed U.S. MBA students; Study 4 N = 973 members of the U.S. Air Force (USAF); Study 5 N = 234 workers primarily employed in the U.S.). Across these five studies, we found that the new role stressor scales have desirable psychometric qualities: they displayed high levels of substantive validity, high levels of internal consistency and test–retest reliability, they produced an interpretable factor structure, and we found evidence of their construct validity. We therefore recommend that these new scales be used in future research on role stress.

Developing a Mechanism to Study Code Trustworthiness

When software code is acquired from a third party or version control repository, programmers assign a level of trust to the code. This trust prompts them to use the code as-is, make minor changes, or rewrite it, which can increase costs and delay deployment. This paper discusses types of degradations to code based on readability and organization expectations and how to present that code as part of a study on programmer trust. Degradations were applied to sixteen of eighteen Java classes that were labeled as acquired from reputable or unknown sources. In a pilot study, participants were asked to determine a level of trustworthiness and whether they would use the code without changes. The results of the pilot study are presented to provide a baseline for the continuance of the study to a larger set of participants and to make adjustments to the presentation environment to improve user experience.

Application of the heuristic-systematic model to computer code trustworthiness: The influence of reputation and transparency

Abstract Computer programs (code) are integral to the functions of current society. Yet, little is known about why programmers trust code they did not create. The current paper applied the heuristic-systematic model (HSM) of information processing to perceptions of code trust for reuse. The studies explored transparency (readability and organization) and reputation (source) as factors that influenced trust perceptions and time spent reviewing code using professional programmers. Source and readability manipulations led to higher trustworthiness assessments in the first study. Organization had nonlinear effects on trustworthiness. A three-way interaction including time was also found. The second online study largely replicated the first study’s main and interaction effects for trustworthiness, but the main effects on time were not significant. Our findings suggest the relationships of transparency on trustworthiness are not as straightforward as previously thought. Additionally, the findings of the current study expand the HSM to trust in code.

Individual Differences in Trust in Code: The Moderating Effects of Personality on the Trustworthiness-Trust Relationship.

The daily use of technology has made people ever more reliant on software. It is important these software systems are produced in a manner that is both efficient and secure. In this context, psychological trust of software is a pertinent aspect of research. The present study explored the relationship of trustworthiness ratings, propensity to trust, and trait suspicion on software reuse. In addition, we explored personality as a moderator of the trustworthiness-reuse relationship, as hypothesized in the interpersonal trust literature [1]. We recruited participants from Amazon’s Mechanical Turk and requested they assess classes of Java code. Analyses revealed trait suspicion influenced decisions to reuse code and moderated the trustworthiness-trust relationship. A dual-process model of information processing was adopted for interpretation of these effects. Implications include contributions to research and theory on psychological trust, as well as practical implications for personnel selection with regard to software production.

Have a Heart: Predictability of Trust in an Autonomous Agent Teammate through Team-Level Measures of Heart Rate Synchrony and Arousal

Progression toward sophisticated machines with the capacity to act as partners in tactical and strategic situations means that human operators will increasingly rely on collaborative input from agent teammates (e.g., Masiello, 2013). However, plans to team autonomous agents with humans raise new questions regarding the effects that such teammates might have on important team psychological processes, such as team cognition and trust. Specifically, it is not known how modifications in team structure, such as changes in team size, influence team dynamics and psychological processes when the team includes an artificial agent, nor how trust established in such teams transfers to new environments, nor how measures that have been used to predict trust in humans generalize to agent teammates. Our current research examined these effects through the detection and analysis of an objective team phenomenon known as physio-behavioral coupling (PBC) using Multidimensional Recurrence Quantification Analysis (MdRQA; Wallot, Roepstorff, and Mønster, 2016) of shared physiological arousal during initial team formation and training. In particular, as shared physiological arousal measured in changing heart rhythms within human teams has been shown to be associated with measures of trust (Mitkidis, McGraw, Roepstorff, & Wallot, 2015) and concern for others (Konvalinka et al., 2011), we investigated how shared physiological arousal predicts willingness to trust an agent teammate in a novel task environment. We conducted an experiment consisting of collecting physio-behavioral data (i.e., heart rate) from teams of different sizes as they performed a series of collaborative, consensus building tasks. The independent variable was team size (teams of 2 or 3 human players, with an artificial agent teammate always present), and there were two separate team-oriented tasks: A first-round consensus-building wagering task, and a second-round task in which teams were able to make wagers on the expected performance of the agent teammate in a subsequent maze running task called Checkmate (Alarcon et al., 2017). We predicted that complimentary combinations of PBC (e.g., measures of overall similarity and stability in heart rate dynamics) obtained from MdRQA, along with self-reported measures of team and agent trust, would be positively related to future trusting behaviors in the agent teammate, and that increasing the number of teammates would result in higher order, more complex structure in the physio- behavioral data that would not be reducible to simpler patterns (e.g., Wallot et al., 2016). To this end, we predicted that measures of self-reported trust and multivariate PBC would be reducible to meaningful lower dimensional structures using principal components analysis (PCA), and that PBC calculated from the first task from the full team, but not from averages aggregated from subsets of the team, would significantly predict trusting behavior in the second task. Ninety-two participants (31 men and 61 women) recruited from the campus of a midwestern university in the U.S. took part in this study (19 dyads and 18 triads). Ages ranged from 18 to 42 (M = 22, SD = 5.48). The experiment was a univariate (team size; two or three human teammates with an agent teammate always present) between-subjects design. Self-reported measures were collected from each team member before each of the two tasks and included items that measured: Team ability, team benevolence, team integrity, and team trust (adapted from Mayer & Davis, 1999); trust in human teammates (adapted from Naquin & Paulson, 2003); agent competence, cognitive trust in the agent, emotional trust in the agent, intention to delegate to the agent, and intention to adopt the agent as an aid (adapted from Komiak & Benbasat, 2006); and collective efficacy (adapted from Riggs & Knight, 1994). Factor analysis of the composite scales from aggregated survey data indicated the data loaded well onto factors that corresponded to trust in the team and trust in the agent teammate. Factor analysis of MdRQA from the full team and from the averaged lower order analyses showed that each had one component with an eigenvalue greater than what would be expected by chance. Results from analyses using logistic regression to predict Checkmate betting showed that self-reported measures of trust in the agent and MdRQA of full team PBC in the initial task significantly predicted subsequent trusting behavior in an agent teammate in Checkmate, but lower-order PBC estimated from averages of team subgroups did not. These results suggest that multivariate team-level coupling has predictive power in subsequent team outcomes that cannot be fully captured using data aggregated from subgroup averages, and that measures of PBC measured from human teammates is related to trust in an agent teammate. We note two important contributions of the present study. First, that PBC and subjective measures of trust were significant predictors of observed trusting behavior regardless of team size suggests that important team processes and outcomes are at least partially invariant to changes in team size, a promising outcome for the prospect of meaningfully scaling measures of PBC beyond the typical dyadic context. Second, we have shown that shared team- level arousal is a significant predictor of subsequent trusting behavior in an agent teammate in a novel task, demonstrating that these objective measures are extensible to trust in non-human partners.

Trust of Learning Systems: Considerations for Code, Algorithms, and Affordances for Learning

This chapter provides a synthesis on the literature for Machine Learning (ML), trust in automation, trust in code, and transparency. The chapter introduces the concept of ML and discusses three drivers of trust in ML-based systems: code structure; algorithm performance, transparency, and error management – algorithm factors; and affordances for learning. Code structure offers a static affordance for trustworthiness evaluations that can be both deep and peripheral. The overall performance of the algorithms and the transparency of the inputs, process, and outputs provide an opportunity for dynamic and experiential trustworthiness evaluations. Predictability and understanding are the foundations of trust and must be considered in ML applications. Many ML paradigms neglect the notion of environmental affordances for learning, which from a trust perspective, may in fact be the most important differentiator between ML systems and traditional automation. The learning affordances provide contextualised pedigree for trust considerations. In combination, the trustworthiness aspects of the code, dynamic performance and transparency, and learning affordances offer structural, evidenced performance and understanding, as well as pedigree information from which ML approaches can be evaluated.

The effect of propensity to trust and perceptions of trustworthiness on trust behaviors in dyads

Research on trust has burgeoned in the last few decades. Despite the growing interest in trust, little is known about trusting behaviors in non-dichotomous trust games. The current study explored propensity to trust, trustworthiness, and trust behaviors in a new computer-mediated trust relevant task. We used multivariate multilevel survival analysis (MMSA) to analyze behaviors across time. Results indicated propensity to trust did not influence trust behaviors. However, trustworthiness perceptions influenced initial trust behaviors and trust behaviors influenced subsequent trustworthiness perceptions. Indeed, behaviors fully mediated the relationship of trustworthiness perceptions over time. The study demonstrated the utility of MMSA and the new trust game, Checkmate, as viable research methods and stimuli for assessing the loci of trust.