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This study aims to investigate how the employees' attributes, capabilities, behavioral control and behavioral intentions affect their intention to become a supply chain manager (SCM). The study also investigates how employees' capabilities mediate the relationship between attributes and intention to become SCM.

The purposive sampling frame is employed to collect the data, and multiple statistical tools and techniques are used for analyzing the data.

The findings reveal that attributes are the most crucial factor for the capabilities of employees to become successful SCM. Employees' behavioral intention and capabilities have a significant impact on intention, whereas perceived behavioral control has a significant negative impact on it. Also, the result finds that capabilities partially mediate the relationship between attributes and intention to become an SCM.

The study extends the application of employees' intention to become a supply chain manager. The idea collected is based on Malaysia; thus, further study should be extended to assess the impact of employees' attributes, capabilities and behavioral control to become SCM as well as economic performance in other countries.

To the best of the authors' knowledge, this is the first empirical analysis on the relationship between employees' attributes, capabilities, behavioral control and intention to become SCM in the context of Malaysia. The findings will help the top management to select the right people as SCM and improve their attributes, capabilities and behavior so that they become an effective SCM.

The study aims to identify the extent to which industry 4.0 (IR4.0) adoption impacts the sustainable manufacturing (SM) performance of the manufacturing industry, focusing on the comparative analysis between developed and developing economies amid coronavirus disease 2019 (COVID-19) pandemic.

The study proposes a conceptual model formed on seminal theories and literature using the cross-sectional design. For data collection, a purposive sampling method is used where 154 Malaysian (developing) and Australian (developed) manufacturing firms' data were collected. Partial least square-based structural equation modeling is employed to test the hypothesis and proposed research model.

This study finds that adoption of IR4.0 technologies does not directly influence the sustainability performance of the manufacturing industry, but rather the trajectories of SM (efficiency, flexibility, automation and big data and granularity) fully mediate the relationship between IR4.0 adoption and sustainability manufacturing performance. The comparative analysis between Australia and Malaysia shows no significant difference in the relationships or the framework; hence, the differences between developed and developing countries are not significant in this mechanism.

The study contributes to the insights of the managers regarding COVID-19 and the implementation of IR4.0 in the SM domain. The policymakers would further get better insights since the study pays attention to sustainable development goal, industry, innovation, infrastructure and responsible production.

The purpose of this study is to explore the private general practice (GP) clinics' service environment, patients' satisfaction and their impact on word of mouth (WoM) for others for future treatment in GP clinics.

Data are collected from 367 respondents using a paper-based survey questionnaire. Partial least square (PLS) is used to evaluate the proposed model and hypotheses relationships.

The findings reveal that ambience and service delivery have a high significant influence on patients' emotional satisfaction (β = 0.27, t = 4.31, p = 0.00) and (β = 0.26, t = 4.81, p = 0.00), respectively, while interior décor has a positive and significant influence on satisfaction (β = 0.13, t = 1.98, p = 0.04). The results indicate that exterior design and cleanliness are not associated with satisfaction. Patients' emotional satisfaction is highly related to WoM (β = 0.55, t = 13.44, p = 0.00). The results also show that emotional satisfaction has a significant mediating effect on the relationship between clinic service environments (ambience, interior décor, service delivery) and WoM (β = 0.15, t = 3.94, p = 0.00), (β = 0.073, t = 3.94, p = 0.04), (β = 0. 0.143, t = 4.13, p = 0.00), respectively.

The study will provide insights regarding Malaysian health consumers' perceptions toward GP clinics' service environment, whether they remain utilitarian or have evolved to entail hedonic appreciations. The contribution to the service environment could be adopted by future health-care studies, particularly those intended to examine GP clinics and other clinic-based institutions.

Due to the fierce market competition, many organizations seek global suppliers because of lower procurement costs and better product quality. However, selecting suitable global suppliers is one of the complicated decision-making tasks for decision-makers due to the involvement of various qualitative and quantitative factors. The primary purpose of this research is to design an integrated approach for global supplier selection and order allocation in the context of developing an environment-friendly supply chain under data uncertainty.

Initially, the fuzzy analytical hierarchy process (FAHP) is used to calculate the selected criteria weights. After that, the weights obtained from FAHP are inserted into the fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) to examine the performance of selected suppliers and determine their final ranks. Finally, the obtained results from FTOPSIS are incorporated into the multi-choice goal programming (MCGP) model, which involves multi-aspiration levels to allocate the optimal order quantity to the selected global suppliers.

A real-time case study of the automotive industry is presented to demonstrate the efficiency and practicality of the suggested approach. The case study and sensitivity analysis results show that the proposed model effectively tackles suppliers' evaluation and order allocation data uncertainty.

Incorporation of risks, environmental management and economic factors during global supplier selection in the automotive sector has not been given much attention in the past literature. So, this research aims to fulfill the gap by developing an integrated approach that can tackle data uncertainty effectively.

Selecting and scheduling optimal project portfolio simultaneously is a complex decision-making problem faced by organizations to realize the strategy. However, dynamic synergy relationships among projects complicate this problem. This study aims at constructing a project portfolio selection and scheduling (PPSS) model while quantifying the dynamic synergetic effects to provide decision support for managing PPSS problems.

This study develops a mathematical model for PPSS with the objective of maximal project portfolio benefits (PPBs). To make the results align with the strategy, comprehensive PPBs are divided into financial and non-financial aspects based on the balanced scorecard. Then, synergy benefits evolve dynamically in the time horizon, and system dynamics is employed to quantify them. Lastly, a case example is conducted to verify the applicability of the proposed model.

The proposed model is an applicable model for PPSS while incorporating dynamic synergy. It can help project managers obtain the results that which project should be selected and when it should start while achieving optimal PPBs.

This study complements prior PPSS research in two aspects. First, financial and non-financial PPBs are designed as new criteria for PPSS, making the results follow the strategy. Second, this study illuminates the dynamic characteristic of synergy and quantifies the synergetic effect. The proposed model provides insights into managing a PPSS effectively.

The purpose of this study is to develop a holistic optimization model for an integrated sustainable fleet planning and closed-loop supply chain (CLSC) network design problem under uncertainty.

A novel mixed-integer programming model that is able to consider interactions between vehicle fleet planning and CLSC network design problems is first developed. Uncertainties of the product demand and return fractions of the end-of-life products are handled by a chance-constrained stochastic program. Several Pareto optimal solutions are generated for the conflicting sustainability objectives via compromise and fuzzy goal programming (FGP) approaches.

The proposed model is tested on a real-life lead/acid battery recovery system. By using the proposed model, sustainable fleet plans that provide a smaller fleet size, fewer empty vehicle repositions, minimal CO₂ emissions, maximal vehicle safety ratings and minimal injury/illness incidence rate of transport accidents are generated. Furthermore, an environmentally and socially conscious CLSC network with maximal job creation in the less developed regions, minimal lost days resulting from the work's damages during manufacturing/recycling operations and maximal collection/recovery of end-of-life products is also designed.

Unlike the classical network design models, vehicle fleet planning decisions such as fleet sizing/composition, fleet assignment, vehicle inventory control, empty repositioning, etc. are also considered while designing a sustainable CLSC network. In addition to sustainability indicators in the network design, sustainability factors in fleet management are also handled. To the best of the authors' knowledge, there is no similar paper in the literature that proposes such a holistic optimization model for integrated sustainable fleet planning and CLSC network design.