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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.

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.

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.

This paper aims to explore the effect of service robots on employees’ customer service performance and service-oriented organizational citizenship behavior through psychological need satisfaction and role stress. Moreover, this paper examines the moderating role of service robots’ autonomy.

Data collected from managers and employees at hotels in South Korea were used to test the aforementioned association. In this paper, partial least squares structural equation modeling was performed to test the hypotheses.

Service robots enhance service employee performance through employees’ psychological need satisfaction, which can decrease service employee performance through role stress. As hypothesized, service robots’ autonomy is the moderator on these associations.

This study shows that using service robots does not always lead to positive employee performance. Therefore, managers should find ways to mitigate the role stress and enhance perceived robot autonomy.

This study offers a balanced perspective of the personal benefits and costs of the use of service robots by developing a dual-path model that unpacks the energizing and draining mechanisms underlying the double-edged effects of working with service robots on employees’ psychological strain and employees’ psychological needs.

The present study investigates the customers' behavioural intention to use voice-based artificial intelligence (AI) to find the appropriate hotels and resorts in an emerging nation. This study determines the influences of information quality, system quality, privacy, and novelty value on attitude and behavioural intention to use voice-based artificial intelligence to obtain the appropriate information and find the location of the hotels and resorts.

This study used a purposive sampling method for collecting data from the respondents, who are customers of the hotels and resorts in Bangladesh. A self-administered survey questionnaire was used to obtain responses from 378 respondents. After collecting the data, the reliability and validity of the constructs and hypotheses were tested via partial least squares structural equation modelling (PLS-SEM).

The findings of the study indicate that information quality, system quality, privacy and novelty value have a positive and significant impact on attitude and behavioural intention to use voice-based AI assistant services in an emerging nation. However, system quality does not significantly influence behavioural intention to use voice-based AI assistant but it has an indirect significant influence on behavioural intention through the mediation effect of attitude.

The study’s findings provide essential guidelines for practitioners to understand the impacts of information quality, system quality, privacy, and novelty value on attitude and behavioural intention to use voice-based artificial intelligence to find the appropriate hotels and resorts to meet customers' needs and expectations.

This study contributes to the existing literature on technology adoption by highlighting the interconnectedness of various factors influencing users' behavioural intentions. The study’s focus on an emerging nation provides a valuable theoretical contribution. It highlights that user perceptions and attitudes towards technology adoption may differ from those in developed nations due to unique contextual factors.

With respect to severe working conditions such as heavy load and impact, this paper aims to investigate the friction reduction and anti-wear performance of kaolin and molybdenum dialkyl dithiophosphate (MoDDP) composite lubricant additives to improve the lubrication effect of a single additive.

A four-ball friction test was carried out to determine the optimal concentration of kaolin and organic molybdenum additives and the tribological properties of the kaolin/MoDDP composite lubricant additives. A ring block test of composite lubricant additives was designed to investigate its lubrication performance under the severe working conditions of low speed, heavy load and impact.

The results showed that the optimal addition mass fractions of kaolin and MoDDP were 4.0 and 1.5 Wt.%, respectively, when kaolin and MoDDP were used as single lubricant additives. Compared with the single additive, the 4.0 Wt.% kaolin/1.5 Wt.% MoDDP composite lubricant additive showed excellent friction reduction and anti-wear effects under heavy load and impact conditions. Physicochemical analysis of the wear surface revealed that the lamellar kaolin additive and MoDDP had excellent synergistic effects, and the friction process promoted the generation of lubricant films containing a chemically reactive layer of MoS₂, MoO₂, FeS₂ and Fe₂O₃ and a physically adsorbent layer containing SiO₂ and Al₂O₃, which play important roles in anti-wear and friction reduction.

The excellent friction reduction and anti-wear effects of lamellar silicate minerals and the excellent antioxidant properties and good synergistic effects of molybdenum were comprehensively used to develop the composite additives with great lubricating properties.

This study will conduct an in-depth investigation into the development paths and factors influencing the patterns of social-emotional competence enhancement and social self-concept construction among higher education students.

This study used a longitudinal research methodology to perform a one-year follow-up study with the Class of 2022 freshmen from College C of the University of Macau to investigate the causal mechanisms and mediating pathways involved.

This study revealed that students’ open-mindedness was a full mediator between self-cognition and engagement in Residential College system’s activities, that engagement was a complementary mediator between an open-mindedness and social-emotional competence, and that social-emotional competence was a competitive mediator between the engagement and social self-concept. The variety of activities offered in Residential College system fosters social behavior acquisition, which supports the growth of social-emotional competence and the development of social self-concept through emotional competence.

This study enriches the theoretical connotation of whole-person education in the Residential College system and emphasizes the significance of social emotional learning and social self-concept in higher education.

The purpose of this paper is to investigate user satisfaction with digital health solutions by identifying and prioritizing different service attributes on the basis of their impact on improving user satisfaction.

Through a literature review and interviews with health professionals and patients, 20 attributes of digital health services provided in Italy have been identified. User satisfaction with these attributes has been evaluated by adopting the Kano model’s continuous and discrete analyses.

The findings reveal the essential attributes of digital health services that meet users' expectations, identify the attributes that users appreciate or dislike having and highlight unexpected attributes that lead to a significant boost in satisfaction when provided.

This study demonstrates the efficacy of the Kano model in assessing the nonlinear correlation between user satisfaction and the quality of digital health services, thus contributing to fill a gap in the literature in this area. The main limitation of this work is the use of a non-probabilistic sampling method.

This research suggests healthcare institutions and organizations consider user preferences when designing digital health solutions to increase their satisfaction. The results indicate different effects on user satisfaction and dissatisfaction for different categories of attributes in the Italian context.

Previous works studied customer satisfaction with digital health, assuming a linear relationship with service quality, or investigated consumer adoption intentions focusing on the technological factors. This work advances available knowledge by analyzing the nonlinear relationship between digital health attributes and users’ satisfaction and dissatisfaction.