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This paper considers an e-tailer planning to distribute a product under one direct sales channel and multiple asymmetric agency platforms. Based on the multinomial logit (MNL) choice model, this study optimizes the pricing strategy and channel selection strategy to maximize the e-tailer’s profit.

A two-stage channel selection and pricing problem is formulated, where the profit-maximizing e-tailer first optimally selects a specified number of agency platforms from a set of alternatives to distribute the product and then determines the optimal prices in those channels.

An optimal pricing strategy is proposed to maximize the e-tailer’s total profit on multiple asymmetric channels. The results show that the e-tailer can obtain a higher profit by selling products on more asymmetric agency platforms. Moreover, an effective channel selection algorithm is provided to help the e-tailer optimally select the M agency platforms from N alternatives.

This study enriches the relevant research on multichannel selection and pricing by proposing an optimal pricing strategy and an effective channel selection algorithm. Evaluation results based on real-world industrial data show that the proposed optimal multichannel pricing strategy in this paper can significantly improve the profit of a real-world e-tailer compared to the e-tailer’s actual profit.

The purpose of this study is to contribute to empirical research on individual ambidexterity drivers. This paper analyzes the relationships between inclusive leadership, team knowledge acquisition, team knowledge sharing, digital tools usage and individual ambidexterity.

This study conducted a questionnaire survey of high-tech and manufacturing enterprises in China and obtained 75 leader questionnaires and 365 employee questionnaires. The hypotheses were tested using hierarchical and cross-level regressions.

The research indicates that inclusive leadership improves team knowledge acquisition and sharing. However, only team knowledge sharing significantly boosts individual ambidexterity, and not team knowledge acquisition. Thus, inclusive leadership fosters individual ambidexterity primarily through team knowledge sharing. Digital tools usage strengthens the impact of inclusive leadership on team knowledge sharing, thereby intensifying its effect on individual ambidexterity. However, digital tools usage weakens the effect of inclusive leadership on team knowledge acquisition.

First, this study addresses the call for research on ambidexterity at different levels, revealing the heterogeneous impact of team knowledge acquisition and sharing on individual ambidexterity. Second, this study developed a theoretical model to explore how leadership affects individual ambidexterity. Third, this study responds to the question that digitalization has won, but has leadership lost by investigating the role of digital tools usage in the relationship between inclusive leadership and team knowledge integration.

This study explores the factors influencing artificial intelligence (AI)-driven decision-making proficiency (AIDP) among management students, focusing on foundational AI knowledge, data literacy, problem-solving, ethical considerations and collaboration skills. The research examines how these competencies enhance self-efficacy and engagement, with curriculum design, industry exposure and faculty support as moderating factors. This study aims to provide actionable insights for educational strategies that prepare students for AI-driven business environments.

The research adopts a hybrid methodology, integrating partial least squares structural equation modeling (PLS-SEM) with artificial neural networks (ANNs), using quantitative data collected from 526 management students across five Indian universities. The PLS-SEM model validates linear relationships, while ANN captures nonlinear complexities, complemented by sensitivity analyses for deeper insights.

The results highlight the pivotal roles of foundational AI knowledge, data literacy and problem-solving in fostering self-efficacy. Behavioral, cognitive, emotional and social engagement significantly influence AIDP. Moderation analysis underscores the importance of curriculum design and faculty support in enhancing the efficacy of these constructs. ANN sensitivity analysis identifies problem-solving and social engagement as the most critical predictors of self-efficacy and AIDP, respectively.

The study is limited to Indian central universities and may require contextual adaptation for global applications. Future research could explore longitudinal impacts of AIDP development in diverse educational and cultural settings.

The findings provide actionable insights for curriculum designers, policymakers and educators to integrate AI competencies into management education. Emphasis on experiential learning, ethical frameworks and interdisciplinary collaboration is critical for preparing students for AI-centric business landscapes.

By equipping future leaders with AI proficiency, this study contributes to societal readiness for technological disruptions, promoting sustainable and ethical decision-making in diverse business contexts.

To the author’s best knowledge, this study uniquely integrates PLS-SEM and ANN to analyze the interplay of competencies and engagement in shaping AIDP. It advances theoretical models by linking foundational learning theories with practical AI education strategies, offering a comprehensive framework for developing AI competencies in management students.

This study aimed to investigate the impact of benevolent leadership on proactive customer service performance by creating a moderated mediation model. The model focuses on the role of harmonious passion as a mediator in the relationship between benevolent leadership and proactive customer service performance as well as the moderating influence of proactive personality on this mediation.

The model was tested using data from 339 immediate supervisor-subordinate pairs in eight five-star hotels in Egypt. Frontline service employees and their immediate supervisors completed separate questionnaires, and the responses were matched using identification numbers.

The results indicate that harmonious passion fully mediates the positive relationship between benevolent leadership and proactive customer service performance. Additionally, proactive personality was found to moderate the mediated relationship between benevolent leadership and proactive customer service performance through harmonious passion, such that the mediation was stronger for employees with higher proactive personalities.

By testing the moderated mediation model, this study contributes to our theoretical understanding of the motivational mechanism through which benevolent leadership influences proactive customer service performance.

This research offers initial evidence of the mediating role of harmonious passion in the positive relationship between benevolent leadership and proactive customer service performance. The moderated mediation model extends existing findings by incorporating proactive personality as a significant moderator in explaining the impact of benevolent leadership on proactive customer service performance.

Managers in China prioritize the cultivation of loyal employees, resulting in positive effects associated with leader-member exchange (LMX). However, fragmented evidence suggests that LMX also can trigger deviant behavior. LMX provides employees with access to resources, while it also harbors potential risks for deviant behaviors. Based on the cognitive-affective system theory of personality and resource-related theories, this study aims to explore the double-edged sword effects of LMX by examining how LMX influences interpersonal deviant behaviors through emotional and cognitive pathways, respectively.

This study involved three waves of paired data surveys that were conducted in China over one month, and a total of 117 leaders and 235 subordinates participated in this study.

Even though LMX as a job resource reduces workplace anxiety, LMX also generates work overload for employees. Workplace anxiety and work overload further result in interpersonal deviant behavior. Narcissistic admiration, as a personality trait, can weaken the mediating role of work overload but not that of workplace anxiety.

The finding can help managers pay attention to negative effect of LMX and provide suggestions for preventing employees’ workplace deviant behavior.

The findings revealed how LMX leads to negative outcomes in the workplace. In addition, the results demonstrated the buffering effect of narcissistic admiration on the negative effect of LMX.

This study examines the effectiveness of enterprise resource planning (ERP) system implementation in Russian companies, considering the vendor’s country of origin and the context of sanctions.

To estimate the impact of implementation, the event study method is employed, with a control group selected using propensity scores matching to address the issue of systematic self-selection. Operational efficiencies are calculated using the Stochastic Frontier Estimation method. The impact of the vendor’s country and the sanctions context is evaluated using regression analysis. The dataset comprises large manufacturing companies in Russia that adopted ERP systems between 2007 and 2021.

The results show that Russian companies benefit in operational efficiency even during the preparation phase for ERP implementation, and this effect persists up to a year post-implementation, after which it diminishes. Additionally, companies implementing ERP systems of foreign vendors gain more from the implementation. Moreover, the sanctions context drives an increase in operational efficiency. Furthermore, initially less efficient firms receive greater benefits from the implementation of ERP systems, while efficient firms receive smaller gains in operational efficiency.

This study offers new insights into how the choice of domestic or foreign vendor influences the impact of ERP adoption on operational efficiency in emerging economies. Our methodological contribution lies in extending the framework for analyzing operational efficiency to include firms with negative operating income, which is important in the context of emerging markets and turbulent environments.

Green manufacturing (GM) has emerged as a vital strategy to minimize environmental impacts and maximize resource efficiency in industrial production. The main aim of this work is to identify and validate essential criteria for GM and prioritize drivers of successful GM implementation frameworks for the manufacturing industry based on Best–Worst Methodology (BWM).

This work explores the essential factors required to achieve long-term success in GM, followed by their comparison using the BWM to determine the most and least important indicators. The study conducted purposive sampling to gather data from 15 experts representing diverse industries in the manufacturing sector. The research methodology consists of three main steps: criteria identification through literature review, criteria validation using the content validity ratio (CVR) method and the BWM application to rank the indicators.

The main success factors identified included top management commitment, organizational culture, employee training, cost saving, investment in innovation and technology, environmental regulation, zero-emission and waste management. The results obtained through BWM indicated top management commitment, investment in innovation and technology and organizational culture as the most critical factors for successful GM implementation. Other factors, such as zero-emission, waste management and cost savings, were also significant but ranked lower in significance. In conclusion, this study highlights the importance of top management commitment to successfully adopting GM initiatives.

This research provides insights into the key success factors, through which decision-makers are assisted in prioritizing efforts and implementing sustainable and eco-friendly practices in manufacturing processes. However, further research is recommended to address existing gaps and foster a deeper understanding of crucial success factors for successful GM implementation.

Prefabricated building projects (PBPs) require multiple stakeholder collaboration due to the strong linkages between the design, production and installment of the precast components. Current contractual governance fails to foster a trusting environment for stakeholder collaboration, leading to conflicts of interest, cost overrun and delays in project schedules. Previous studies revealed that terminating shortcomings in contractual governance can be mitigated by implementing relational governance mechanisms. This study aims to explore the configurational effects of contractual and relational governance mechanisms on stakeholder collaboration in PBPs and identifies the practical configurational conditions to achieve high stakeholder collaboration.

Data were collected from 39 experts through semi-structured interviews and analyzed to explore the configuration effects on stakeholder collaboration in PBPs through fuzzy-set qualitative comparative analysis (fsQCA).

Our study reveals that stakeholder collaboration requires configurational conditions of multiple governance elements (i.e. historical working experience and risk-sharing, reward, selection, authorization, trust, commitment and communication mechanisms). Four equivalent configurational strategies were identified for achieving high stakeholder collaboration in PBPs. These included three configurational strategies dominated by relational governance mechanisms and one configurational strategy complementary to contractual and relational governance mechanisms. The configurations dominated by relational governance mechanisms were developed with communication mechanisms and historical working experiences as core conditions; the configurational strategy with complementary contractual and relational governance mechanisms was centered on risk-sharing, reward and trust mechanisms as core conditions.

Our study enriches the literature on the antecedents of stakeholder collaboration in the PBPs by testing the significance of the relational governance mechanism, which expands the implications of relational contract theory. It also expands the implications of stakeholder theory in PBPs by exploring the asymmetric causal relationship between project contractual governance and stakeholder collaboration. Meanwhile, this study recommends countermeasures for managers to improve stakeholder collaboration by providing four practical configurational conditions in PBPs.

This study aims to provide a practical and novel solution for the complex multi-criteria decision-making (MCDM) problem of airline route selection, which is characterized by conflicting criteria, alternative routes, and complex judgments.

This study proposes a hybrid MCDM approach using Interval-valued Pythagorean Fuzzy AHP and Interval-valued Pythagorean Fuzzy weighted aggregated sum product assessment (WASPAS) methods. Decision analysis is applied to select a new route between different alternatives through selection criteria. Pythagorean Fuzzy AHP is used for weighting criteria, and Pythagorean Fuzzy WASPAS is used for assessing alternatives. The pair-wise linguistic comparisons of selection criteria are transferred into Pythagorean fuzzy numbers (PFNs) to weigh each criterion’s importance.

The pair-wise linguistic comparisons of selection criteria are transferred into PFNs to weigh each criterion’s importance. The results of these comparisons show that the main criteria, cost (43% weight) and demand (33% weight), impact route selection decisions more than social/economic conditions (15% weight) and competitiveness (9% weight). Regarding the criteria, the five routes alternative were evaluated by the route development experts, and the best route was selected with Pythagorean Fuzzy WASPAS.

The proposed model is used for a route selection problem of Turkish Airlines, the airline that flies to the most countries in the world.

To the best of the authors’ knowledge, this study is the first to use the Interval-valued Pythagorean Fuzzy AHP combined with Interval-valued Pythagorean Fuzzy WASPAS to solve the route selection problem. This hybrid MCDM methodology presents a novel and feasible solution for selecting the new route for airlines.

This study investigates the nonlinear relationship between inventory leanness and ESG (environmental, social and governance) performance, exploring how market concentration and digital transformation moderate this connection in manufacturing enterprises.

Using a large panel data collected from Chinese listed manufacturing enterprises over the period from 2012 to 2021, this research employs the instrumental variable method combined with two-stage least squares estimators to explore the U-shaped relationship between inventory leanness and ESG performance. Furthermore, the moderating roles of market concentration and digital transformation are demonstrated.

The results reveal an inverted U-shaped relationship between inventory leanness and ESG performance, indicating that moderate lean inventory management enhances ESG outcomes, but excessive reduction hinders them. Additionally, market concentration and digital transformation positively moderate this relationship, suggesting that competitive market conditions and technological advancement mitigate the adverse effects of overly lean inventories.

Managers are advised to balance inventory management with market dynamics and embrace digital transformation to optimize ESG performance. Policymakers can leverage these insights to guide firms in sustainable inventory practices.

This study contributes to the literature by unveiling the nonlinear dynamics between inventory leanness and ESG performance and highlighting the moderating roles of market concentration and digital transformation.