The authors investigate the construct validity of the organizational citizenship behavior (OCB)-task performance distinction by providing a quantitative review of the OCB literature. The authors extend previous meta-analytic reviews of the OCB literature by (a) using confirmatory factor analysis (CFA) to investigate the dimensionality of OCB, (b) using CFA to examine the distinction between OCB and task performance, and (c) examining the relationship between a latent OCB factor and task performance and attitudinal variables. Results support a single factor model of OCB that is distinct from, albeit strongly related to, task performance. In addition, results show that OCB consistently relates more strongly to attitudes than does task performance and shares a modest amount of variance with attitudinal correlates beyond task performance.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1037/0021-9010.92.2.555 | DOI Listing |
Comput Methods Programs Biomed
January 2025
Shanghai Maritime University, Shanghai 201306, China. Electronic address:
Background And Objective: Inferring large-scale brain networks from functional magnetic resonance imaging (fMRI) provides more detailed and richer connectivity information, which is critical for gaining insight into brain structure and function and for predicting clinical phenotypes. However, as the number of network nodes increases, most existing methods suffer from the following limitations: (1) Traditional shallow models often struggle to estimate large-scale brain networks. (2) Existing deep graph structure learning models rely on downstream tasks and labels.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan 430079, China.
Identifying phage-host interactions (PHIs) is a crucial step in developing phage therapy, which is the promising solution to addressing the issue of antibiotic resistance in superbugs. However, the lifestyle of phages, which strongly depends on their host for life activities, limits their cultivability, making the study of predicting PHIs time-consuming and labor-intensive for traditional wet lab experiments. Although many deep learning (DL) approaches have been applied to PHIs prediction, most DL methods are predominantly based on sequence information, failing to comprehensively model the intricate relationships within PHIs.
View Article and Find Full Text PDFSci Rep
January 2025
University of Ghana, P.O. Box 134, Legon-Accra, Ghana.
Sentiment analysis has become a difficult and important task in the current world. Because of several features of data, including abbreviations, length of tweet, and spelling error, there should be some other non-conventional methods to achieve the accurate results and overcome the current issue. In other words, because of those issues, conventional approaches cannot perform well and accomplish results with high efficiency.
View Article and Find Full Text PDFSci Rep
January 2025
Department of ECE, Kallam Haranadhareddy Institute of Technology, Guntur, Andhra Pradesh, India.
Cognitive load stimulates neural activity, essential for understanding the brain's response to stress-inducing stimuli or mental strain. This study examines the feasibility of evaluating cognitive load by extracting, selection, and classifying features from electroencephalogram (EEG) signals. We employed robust local mean decomposition (R-LMD) to decompose EEG data from each channel, recorded over a four-second period, into five modes.
View Article and Find Full Text PDFJ Hand Ther
January 2025
Research Service, James Haley VA, Tampa, FL, USA.
Background: The Activities Measure for Upper Limb Amputation (AM-ULA), an activity measure for prosthesis users, uses a complex grading rubric to assign a single score to task performance which may limit responsiveness.
Purpose: To enhance AM-ULA responsiveness by exploring a scoring that uses multiple grading elements.
Study Design: Cross-sectional study.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!