PurposeThe hypothesized relationship between the attitude of job involvement and performance has received limited support. In 2002, Diefendorff et al. proposed that previous attempts to confirm this relationship were flawed, and subsequently found support for job involvement's criterion‐related validity. The present study seeks to provide another test of job involvement's association with performance.Design/methodology/approachData were gathered using a field sample combined within a longitudinal design. Hypotheses were tested using correlation and hierarchical regression.FindingsEmployees’ self‐reported job involvement significantly predicted certain supervisor performance ratings above and beyond work centrality.Research limitations/implicationsThe psychological environment may have been disrupted by the public announcement that the focal organization had been acquired by an international firm shortly before data collection began.Practical implicationsEncouraging greater job involvement may positively influence work‐related behaviors, especially individually directed citizenship behaviors.Originality/valueThe present study tested the long‐term relationship of employee attitudes to workplace behaviors with an applied sample, while providing a theoretical context to describe the effects.
PurposeThe purpose of his paper is to present a teaching methodology for improving the understanding of ethical decision making. This pedagogical approach is applicable in college courses and in corporate training programs.Design/methodology/approachParticipants are asked to analyze a set of eight ethical dilemmas with differing situational contingencies and to choose from among alternatives for handling the dilemma. Group discussion then focuses on a comparison of participants' choices relative to their personal ethical orientations as measured by a standardized self‐report instrument.FindingsThe experiences of the authors with this methodology indicates that participants are able to gain a better understanding of the factors, both individual and situational, that frame an ethical dilemma. This methodology can also show how individuals can be influenced to make unethical choices based on the presence of certain contextual factors.Originality/valueThis paper describes a novel instructional approach for improving the understanding of the factors that frame and influence ethical decision making. This approach is innovative in that it uses vignettes describing real‐life ethical dilemmas in conjunction with an assessment of individual differences in ethical orientation.
In‐baskets are high‐fidelity simulations often used to predict performance in a variety of jobs including law enforcement, clerical, and managerial occupations. They measure constructs not typically assessed by other simulations (e.g., administrative and managerial skills, and procedural and declarative job knowledge). We compiled the largest known database (k = 31; N = 3,958) to address the criterion‐related validity of in‐baskets and possible moderators. Moderators included features of the in‐basket: content (generic vs. job specific) and scoring approach (objective vs. subjective) and features of the validity studies: design (concurrent vs. predictive) and source (published vs. unpublished). Sensitivity analyses assessed how robust the results were to the influence of various biases. Results showed that the operational criterion‐related validity of in‐baskets was sufficiently high to justify their use in high‐stakes settings. Moderator analyses provided useful guidance for developers and users regarding content and scoring.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.