Advances in Human Resource Management Research

Unpacking the Impact of Family Communication Patterns on Gen Z Professional Performance, Mediated by Work Ethic and Moderated by Organizational Culture

2026-05-09T20:27:43+07:00

Purpose: This study investigates the relationships between family communication patterns, Organizational culture, work ethic, and professional performance within the frameworks of social cognitive theory and theory of planned behavior.

Research Method: Employing a quantitative cross-sectional approach, with 311 respondents. The research analyzes data from participants to test six hypotheses by using SEM-PLS and SMART PLS software.

Results and Discussion: The results prove that it significantly enhances both work ethic and professional performance, with work ethic as a key mediator. These findings extend prior literature by focusing on subjective performance perceptions, highlighting cultural influences, particularly in collectivist contexts, and challenging traditional models of work-family facilitation. The study's novelty lies in integrating those theories to explain these dynamics, offering fresh insights into how personal and environmental factors shape workplace outcomes.

Implications: The results suggest integrating family support and work ethic development into organizational policies to boost employee performance, alongside conflict resolution strategies to manage dual commitments.

Originality: The originality of this research lies in the integration of three domains that have previously been studied largely separately, namely Family Communication Patterns (Koerner & Fitzpatrick, 2002; Schrodt et al., 2008), Work Ethic (Miller & Smith, 2019; Weber & Seron, 2019), and Employee Performance (Borman & Motowidlo, 1997) into a single model mediated by organizational culture among Generation Z employees in Indonesia.

Comparative Analysis of Machine Learning Models for Emotion Prediction in Reviews of Large Language Model-Powered Applications

2026-08-01T05:26:51+07:00

Purpose: This study compares the performance of conventional machine learning algorithms for multiclass emotion prediction in reviews of Large Language Model (LLM)-powered applications using a unified TF-IDF representation.

Research Method: User reviews were collected from the Google Play Store for eight popular LLM applications through web scraping. After text preprocessing, reviews were labeled into five emotion categories (Happy, Sadness, Anger, Fear, and Surprise). Ten machine learning algorithms, namely Perceptron, KNN, Naïve Bayes, Logistic Regression, MLP, SVM (Linear and RBF), Random Forest, XGBoost, and LightGBM, were evaluated using Accuracy, Balanced Accuracy, Precision, F1-score, MCC, and ROC-AUC.

Result and Discussion: XGBoost achieved the best performance, with 87.30% Accuracy, 77.84% Balanced Accuracy, 64.28% F1-score, and 0.5626 MCC. ROC-AUC values between 0.91 and 0.96 demonstrate excellent discrimination across all emotion classes. The superior performance of XGBoost indicates that gradient boosting effectively captures informative patterns from sparse TF-IDF features.

Implication: The findings provide practical guidance for selecting efficient machine learning models for emotion analysis of LLM application reviews and support AI application improvement through user emotion understanding.

Originality: This study presents a comprehensive benchmark of ten conventional machine learning algorithms using a large-scale multi-application LLM review dataset and a unified five-emotion classification framework.

Clinical Credibility in Nurse Influencer Marketing: A Scopus-Based Scoping Review and Evidence-Gap Analysis

2026-08-01T04:29:17+07:00

Purpose: This study maps the development of Scopus-indexed nurse influencer research and examines whether the literature has progressed from professional visibility toward consumer trust, marketing effectiveness, and preventive health service conversion.

Research Method: A Scopus-based scoping review was conducted following the PRISMA-ScR guidelines. English-language nursing articles published between 2016 and 2026 were retrieved, screened, and synthesized using a marketing-funnel framework encompassing credibility, visibility, engagement, trust, intention, service adoption, and retention. Methodological quality was assessed using Joanna Briggs Institute (JBI) appraisal tools.

Results and Discussion: Four studies met the inclusion criteria. The evidence primarily addresses conceptual characteristics, professional identity, digital advocacy, privacy, misinformation, and ethical issues. Although clinical credibility is consistently identified as a strategic communication asset, no study empirically measures validated consumer trust, appointment booking, willingness to pay, preventive service utilization, retention, referral behavior, or financial outcomes. The literature therefore remains largely conceptual and communication-oriented rather than marketing-oriented.

Implications: Future research should integrate nursing, marketing, and health service perspectives by employing validated marketing constructs and behavioral outcomes to evaluate the effectiveness of nurse influencers in promoting preventive healthcare.

Originality: This is the first Scopus-based scoping review to evaluate nurse influencer research through a marketing-funnel perspective, revealing critical evidence gaps linking clinical credibility with consumer trust and measurable healthcare service conversion.

Keywords: nurse influencers; influencer marketing; clinical credibility; consumer trust; preventive health services; scoping review