International Journal of Value Engineering

The role of circular human resource management to circular economic performance: A mediation and moderation model

This study examines the role of Circular Human Resource Management (CHRM) in enhancing Circular Economic Performance (CEP) through the mediating effect of Green Knowledge Management (GKM) and the moderating role of ethical leadership. Grounded in Self-Determination Theory, this research adopts a quantitative approach by collecting data from 250 employees working in manufacturing companies in Indonesia across multiple sectors. Data were analyzed using Partial Least Squares Structural Equation Modeling (SEM-PLS) with SmartPLS 4.0. The findings indicate that CHRM has a significant positive effect on CEP and GKM, while GKM also significantly influences CEP. Furthermore, GKM partially mediates the relationship between CHRM and CEP, confirming that knowledge-based processes are critical mechanisms in translating HRM practices into circular performance outcomes. The results also reveal that ethical leadership strengthens the relationship between CHRM and CEP, highlighting the importance of leadership as a boundary condition. This study contributes to the literature by addressing the conceptual ambiguity of CHRM and providing an integrated framework that links HRM, knowledge management, and circular performance through a motivational perspective. Practically, the findings suggest that organizations should align HR practices with knowledge management systems and leadership strategies to achieve sustainable and circular performance in manufacturing contexts.

Reinforcement mechanism and field performance of bamboo pile and bamboo raft foundations for ultra-soft ground in a shallow tidal flat

A sea dike expressway across thick marine clay must resist bearing failure during filling and limit the post-construction settlement of the pavement. The paper reports the reinforcement mechanism and the field behaviour of a bamboo pile and bamboo raft foundation below a 3.889 km sea dike expressway on the north coast of Central Java, Indonesia. Bamboo piles 8 m long, each formed from seven culms bound with nylon rope and driven in a 1 m square grid at both toes, act as anchor and anti-slide elements, and a 13-layer bamboo raft about 1.5 m thick carries the surcharge on clay 35-40 m thick. A numerical model gave a peak Mises stress of 5.22 MPa in the piles, an Asaoka analysis of 56 settlement plates gave an ultimate settlement of 2085-4123 mm, and ten inclinometers gave a lateral displacement of 20-230 mm.

AI-driven sustainable operations management in manufacturing supply chains: A systematic review, research gap analysis, and strategic framework for future directions

Artificial Intelligence (AI) is reshaping sustainable operations management by helping manufacturing supply chains become more intelligent, adaptive, resilient, and resource-efficient. As manufacturers pursue operational excellence while addressing environmental and social sustainability objectives, AI technologies are increasingly used to improve decision-making, process efficiency, resource utilization, and supply chain resilience. However, the existing literature remains fragmented across AI technologies, operational functions, sustainability dimensions, and industrial contexts, limiting theoretical integration and a comprehensive understanding of how AI capabilities enable sustainable operational transformation. This study addresses this gap through a systematic literature review and research gap analysis of AI-driven sustainable operations management in manufacturing supply chains, covering peer-reviewed studies published between 2016 and 1 June 2026. The review traces the intellectual development of the field, identifies dominant research themes, examines AI applications across manufacturing and supply chain operations, and evaluates their contributions to operational, environmental, economic, and social sustainability. The findings indicate a shift from isolated AI applications toward integrated intelligent operational ecosystems in which AI, the Internet of Things (IoT), digital twins, cyber-physical systems, and advanced analytics support predictive intelligence, autonomous decision-making, adaptive production, real-time operational visibility, resource optimization, and resilient supply chain coordination. Based on the identified research gaps, this study proposes an AI-Driven Sustainable Operations Management Strategic Framework integrating AI capabilities, organizational readiness, digital infrastructure, operational transformation mechanisms, sustainability enablers, and multidimensional performance outcomes. The framework provides a unified basis for understanding AI-enabled sustainable value creation and establishes a structured agenda for future research, while offering strategic insights for researchers, practitioners, and policymakers seeking to advance sustainable digital transformation and strengthen the long-term resilience, competitiveness, and sustainability of manufacturing supply chains.

Assessing the influence of proactiveness, environmental sustainable innovation, and entrepreneurial behaviors on SMEs performance in Surigao Del Sur

This study examines the influence of proactiveness, environmental sustainable innovation, and entrepreneurial marketing behaviors on the performance of small and medium enterprises (SMEs) in Surigao del Sur. Grounded in a behavioral and applied entrepreneurship perspective, the research conceptualizes entrepreneurial marketing as a set of observable behaviors reflecting entrepreneurs' cognitive, motivational, and decision-making orientations in dynamic and resource-constrained environments. A quantitative, descriptive-correlational research design was employed, drawing data from 250 SME owners and managers across multiple industries in the province. Entrepreneurial behaviors were measured in terms of proactiveness, sustainability-oriented innovativeness, and risk-taking orientation, while SME performance was assessed using multidimensional indicators including profitability, efficiency, reputation, and the attainment of owners' personal business goals. Descriptive statistics and Pearson correlation analysis were used to examine behavioral patterns and relational effects. Results indicate that SMEs demonstrate high levels of proactiveness and environmental sustainable innovation, alongside a moderately strong but cautious risk-taking orientation. Performance indicators reveal generally high satisfaction with financial outcomes and personal goal achievement among SME owners. Correlational analysis shows a statistically significant positive association between entrepreneurial marketing behaviors and SME performance, suggesting that behavior-driven and sustainability-oriented entrepreneurial practices are closely linked to favorable business outcomes. The findings highlight that SME performance in Surigao del Sur is shaped not merely by structural or economic factors but by proactive, innovative, and environmentally responsive entrepreneurial behavior. This study contributes empirical evidence to the entrepreneurial marketing literature by contextualizing SME performance as a behavioral outcome rooted in sustainability-oriented entrepreneurial action within a localized Philippine setting.

Analysis of factors and forecasting of the daily maximum electric load of an automobile factory

The study examines the factors influencing the daily maximum electrical load of an automobile plant and develops an approach to forecasting this indicator over daily, monthly, and nine-month horizons. The study is based on operational data from the Minsk Automobile Plant for 2024–2025, which includes 731 daily observations, meteorological data, and monthly production indicators, including data from the foundry production. The calculations were carried out using the author's comprehensive statistical analysis (ACSA) system, developed in Python. The system provides data loading, verification, and preliminary processing, as well as correlation and factor analysis, training of predictive models, and adaptive adjustment of the forecast. The initial data revealed seven days with discrepancies in the production schedule and twelve days with a violation of the permissible ratio between electricity consumption and the maximum load. Factor analysis of the daily data (n=485, KMO=0.653, p<0.001) made it possible to identify three factors that explain 62.6% of the variance. Eleven configurations of predictive models, including random forest and ridge regression, have been compared. A scheme with division by day type has been adopted: Ridge regression is used for working days, random forest for non-working days, and adaptive adjustment is applied to both. This combination was chosen due to the statistically indistinguishable accuracy of competing models, as it is more interpretable. The mean absolute percentage error for working days was 4.75% for a one-day forecast and 7.14% for a monthly forecast. Organizational measures to smooth out load peaks have been proposed, and their potential effect has been assessed.