Earticle

In construction, why practitioners resist agile methods remains far less examined than why they adopt them. This study tests how organizational uncertainty shapes perceived risk and how perceived risk drives agile adoption resistance. Survey data from 150 construction practitioners were analyzed using hierarchical regression with departmental controls, entering requirements uncertainty and collaboration uncertainty simultaneously. Mediation was tested with 5,000-resample bootstrap confidence intervals, using the percentile interval as the pre-declared inference criterion. The simultaneous-entry model revealed a near-complete double dissociation: requirements uncertainty raised performance risk only (its path to operational risk was nonsignificant at p = .071), collaboration uncertainty raised operational risk only, and operational risk affected resistance far more strongly than performance risk. Two of the four mediation paths were supported, both running through operational risk; neither path through performance risk reached significance. One supported path, from requirements uncertainty through operational risk, did not survive once collaboration uncertainty was controlled and is reported as conditional. Uncertainty type is therefore selectively associated with risk type, and role ambiguity and process disruption appear to be more critical barriers than cost concerns, implying that resistance is better managed by clarifying roles and decision procedures than by emphasizing efficiency gains.

As artificial intelligence (AI) is increasingly integrated into human resource management (HRM), organizations are adopting AI-supported evaluation systems to enhance the efficiency and objectivity of assessments. However, little is known about how different types of AI-supported performance evaluations affect employees’ perceptions of leader-member exchange (LMX). Drawing on LMX and attribution theory, this study analyzed the impact of AI-supported evaluation types on employees’ LMX perceptions and examined the mediating roles of “algorithmic reductionism” and “leader intervention.” To this end, a scenario-based experiment using a 2×2 factorial design was conducted, manipulating AI transparency (high vs. low) and responsibility attribution (leader vs. AI). Participants were assigned to one of four evaluation scenarios and subsequently responded to items regarding algorithmic reductionism, leader intervention, and LMX perceptions. The study results indicate that perceptions of LMX vary significantly depending on evaluation conditions. Members perceived the highest levels of LMX when leaders actively reviewed AI-generated evaluation results and took responsibility for the final decisions. Additionally, algorithmic reductionism was found to significantly influence LMX perceptions. These findings underscore the critical role of human responsibility in AI-driven HR decision-making processes. Academically, this study contributes to expanding research on AI-based HRM; practically, it suggests that organizations must ensure active leader involvement when implementing AI-driven performance evaluation systems.

As project environments become more complex and uncertain, the usefulness of deep learning-based schedule forecasting depends not only on algorithms but also on how project data are managed and processed. Drawing on organizational information processing theory, this study examines the associations among project data management competency, information processing quality, project characteristics, and schedule forecasting accuracy. PLS-SEM was applied to 283 survey responses from project practitioners and managers; model learning quality and project state representation were analyzed using a subsample of 127 respondents with experience in deep learning-based forecasting systems. The survey outcome represents respondents’ perceived forecasting accuracy, not error metrics calculated from their projects. The results show that data quality management, data operations management maturity, and data richness and diversity are positively associated with perceived forecasting accuracy, partly through information processing quality. Project complexity and uncertainty strengthen selected relationships, whereas the moderating role of project management methodology is limited. Separately, exploratory LSTM experiments using RCPLIB and DSLIB report objective performance through MAE and RMSE and provide supplementary evidence that forecasting errors vary with data conditions. The findings distinguish perceived from objective accuracy and suggest that data management competency and information processing capability are important conditions for AI-enabled project schedule forecasting.

Healthcare reform policies involve multiple stakeholders and complex decision-making processes, making them suitable for analysis from a governance perspective. This study examines recent healthcare reform initiatives in Korea, particularly medical workforce policy, as a policy implementation case and analyzes the governance structure underlying policy conflict. This research applies a governance framework focusing on stakeholder participation, allocation of decision-making authority, and institutionalization of consultation mechanisms. Using qualitative document analysis, the study investigates the policy formulation and implementation process, stakeholder interactions, and conflict management. The findings show that decision-making was centralized within the government, while prior consultation with key stakeholders was limited. Fragmented representation within the medical community weakened coordination among stakeholders, and participation by citizens and patient groups was restricted, undermining policy legitimacy. Furthermore, administrative control-oriented responses were prioritized after conflict emerged, limiting collaborative governance. These results indicate that healthcare reform policy conflict is closely associated with governance arrangements in policy implementation. The study highlights the need to strengthen stakeholder participation, institutionalize consultation mechanisms, and establish shared decision-making structures to improve governance in healthcare policy implementation.

In small and medium-sized enterprises (SMEs) where project-based work is expanding, employee innovative behavior is a critical outcome linked to coaching leadership both directly and through ESG management. A prior variable-centered study triangulating PLS-SEM, necessary condition analysis (NCA), and fuzzy-set qualitative comparative analysis (fsQCA) reported that competency development was the only necessary condition for innovative behavior despite its lowest average weight, and that two equifinal configurations could produce high innovative behavior. Whether this structure also describes distinct groups of employees remained untested, because variable-centered analyses assume a homogeneous population. This study reanalyzes the same dataset (N = 310) from a person-centered perspective, applying latent profile analysis to the four coaching leadership dimensions. Three profiles emerged: Deficient (53.9%), Development-Centered (34.8%), and Balanced High (11.3%). They differed substantially in innovative behavior and ESG management (both η² = .14). The Development-Centered profile, combining high competency development with low task-oriented dimensions, reported significantly higher innovative behavior than the Deficient profile (Δ = 0.51, p = .002), providing person-level evidence consistent with the original necessary-condition finding. Organizational position predicted Balanced High membership, with lower-ranked employees more likely to belong. The findings provide paradigm-level triangulation and a differentiated basis for coaching development.

This study develops a reliable and valid measurement scale for assessing the multidimensional value that suppliers provide to buying firms in B2B manufacturing contexts. Existing B2B research has largely focused on unidimensional value measurement centered on price and quality. Based on a review of prior literature, 40 B2B value elements were restructured for the B2B manufacturing context, and initial measurement items were generated. Through a pilot test (n=23) and expert review, content validity was established, resulting in a final set of 25 items. A survey was administered to N=300 purchasing decision-makers and practitioners in domestic B2B manufacturing firms. Exploratory factor analysis (EFA) and principal component analysis (PCA) yielded KMO=.962, Bartlett’s χ²=6317.617 (p<.001), and cumulative explained variance of 82.804%, confirming a nine-factor structure comprising: Basic Requirements, Performance, Economic, Strategic, Relational, Technology & Accessibility, Operational Efficiency, Personal & Career, and Future Vision & Purpose. Confirmatory factor analysis (CFA) verified convergent validity (AVE≥.633) and discriminant validity, with all nine scales demonstrating high internal consistency (Cronbach’s α≥.835). This study contributes to the literature by empirically validating a conceptual framework in the B2B manufacturing context and providing a standardized nine-dimension, 25-item measurement instrument for future B2B relationship marketing research.

This study examines whether the effect of Environmental, Social, and Governance (ESG) management on corporate financial performance, measured by return on assets (ROA), varies according to the duration of digital transformation (DX) projects. Using the top 100 KOSPI-listed firms by asset size in 2022, ESG performance is measured based on the comprehensive ESG ratings provided by the Korea Institute of Corporate Governance and Sustainability (KCGS), while DX project duration is measured as the number of years from each firm's initial DX project launch to 2022. Hierarchical multiple regression analysis is employed to test the proposed hypotheses. The results show that the independent direct effect of ESG management on ROA is not statistically significant, whereas DX project duration has a significant negative moderating effect on the relationship between ESG management and financial performance. Specifically, as DX projects run longer, the positive impact of ESG management on ROA becomes weaker. These findings suggest that long-running DX projects do not necessarily strengthen the financial value of ESG management and may instead reduce its effectiveness due to accumulated investment costs, resource competition, organizational fatigue, and the digital paradox. The study highlights the importance of integrating ESG and DX within a project portfolio management framework and provides practical implications for balancing these complementary strategic initiatives.

This study examines how management factors in information systems enhancement projects affect project success and whether system quality mediates these relationships. The model includes project management processes, stakeholder engagement, requirements management, project risk management, and information security management as independent variables, system quality as a mediator, and project success as the dependent variable. Survey data were collected from 324 practitioners who had directly participated in information systems enhancement projects completed within the previous three years. The data were analyzed using exploratory factor analysis, reliability analysis, correlation analysis, structural equation modeling, and bootstrapping. Project management processes, stakeholder engagement, and project risk management positively affected system quality. Requirements management and project risk management directly improved project success, while system quality also had a significant positive effect. System quality fully mediated the effects of project management processes and stakeholder engagement and partially mediated the effect of project risk management. No significant mediation was found for requirements management or information security management. System quality mediates the effects of project management, stakeholder engagement, and risk management on project success.

This study examines the structural transition of media discourse linking school violence to university admissions using news big data from 2000 to 2026. Analyzing 6,095 news articles through Semantic Network Analysis and Latent Dirichlet Allocation (LDA) topic modeling, the study traces shifts in core keywords and latent topics over time. The findings reveal that school violence discourse, which initially emphasized educational guidance and relationship restoration, has progressively moved toward mandatory student record entries, admission penalties, and judicial sanctions. Centrality analysis demonstrates that “admissions” functions as a critical mediating hub interconnecting diverse sub-discourses. Notably, aligned with major policy shifts-such as the mandatory inclusion of disciplinary records in college admissions starting in the 2026 academic year-media coverage of legal disputes, lawsuits, and procedural fairness expanded significantly. This research empirically illustrates how policy changes and media framing dynamically interact via policy feedback mechanisms, highlighting the systemic shift toward the judicialization of education. Ultimately, the study offers practical insights for anticipating legal risks and refining higher education admission guidelines.

This study examined the parallel mediating effects of social support and grit in the relationship between stress and college adjustment among college freshmen. Data were collected from 759 freshmen enrolled at W University in Daejeon, South Korea, and analyzed using correlation analysis and parallel mediation analysis with bootstrapping procedures. The results showed that stress was negatively correlated with social support, grit, and college adjustment, whereas social support and grit were positively correlated with college adjustment. Social support and grit each showed significant indirect effects in the relationship between stress and college adjustment, and the direct effect of stress remained significant, indicating partial mediation through both mediators. However, the difference between the two indirect effects was not statistically significant. These findings suggest that understanding college freshmen’s adjustment requires considering not only the direct effect of stress but also the indirect effects of social support and grit in the relationship between stress and college adjustment.