PurposeAchieving sustainability and sustainable performance has emerged as a critical area of focus for both academic research and practice. However, this pursuit faces challenges, particularly concerning the inadequacy of supply chain information. To address this issue, our study employs the organizational information processing theory to explore how adopting blockchain technology enables firms to learn from and collaborate with their supply chain partners, ultimately facilitating their sustainable performance even in the presence of organizational inertia.Design/methodology/approachUnderpinned by the organizational information processing theory and drawing data from 220 manufacturing firms in China, we use structural equation modeling to test our conceptual model.FindingsOur results demonstrate that blockchain technology adoption can significantly enhance sustainable performance. Furthermore, supply chain learning acts as a mediator between blockchain technology adoption and sustainable performance, while organizational inertia plays a negative moderating role between blockchain technology adoption and supply chain learning.Originality/valueThese findings extend the existing literature on blockchain technology adoption and supply chain management, offering novel insights into the pivotal role of blockchain in fostering supply chain learning and achieving sustainable performance. Our study provides valuable practical implications for managers seeking to leverage blockchain technology to enhance sustainability and facilitate organizational learning.

PurposeThe launch of ChatGPT has brought the large language model (LLM)-based generative artificial intelligence (GAI) into the spotlight, triggering the interests of various stakeholders to seize the possible opportunities implicated by it. Nevertheless, there are also challenges that the stakeholders should observe when they are considering the potential of GAI. Given this backdrop, this study presents the viewpoints gathered from various subject experts on six identified areas.Design/methodology/approachThrough an expert-based approach, this paper gathers the viewpoints of various subject experts on the identified areas of tourism and hospitality, marketing, retailing, service operations, manufacturing and healthcare.FindingsThe subject experts first share an overview of the use of GAI, followed by the relevant opportunities and challenges in implementing GAI in each identified area. Afterwards, based on the opportunities and challenges, the subject experts propose several research agendas for the stakeholders to consider.Originality/valueThis paper serves as a frontier in exploring the opportunities and challenges implicated by the GAI in six identified areas that this emerging technology would considerably influence. It is believed that the viewpoints offered by the subject experts would enlighten the stakeholders in the identified areas.

PurposeHumanoid social robots (HSRs) are an innovative technology revitalizing various service sectors, such as the hospitality industry. However, limited research has explored how anthropomorphic features of HSRs influence user satisfaction with the services delivered by HSRs. To address this, a research model was proposed to evaluate how three distinct anthropomorphic features: appearance, voice and response, impact the perceived values (i.e. utilitarian, social and hedonic values) of HSRs, which, in turn, influence user satisfaction.Design/methodology/approachData from an online survey of hotel customers was utilized to test the research model (N = 509).FindingsThe results indicated that appearance, voice, and response affect perceived utilitarian, hedonic and social values differently. The response feature of HSRs demonstrated the strongest impact on perceived utilitarian, social and hedonic values. In addition, voice affected all three perceived values, while appearance only affected perceived utilitarian and social values. Furthermore, perceived utilitarian, hedonic and social values showed positive impacts on user satisfaction, with hedonic value being the most influential factor.Originality/valueThis study contributes to the literature on HSRs and anthropomorphism by explaining how different anthropomorphic features affect users’ value perceptions and user satisfaction with HSR services by utilizing the stimulus-organism-response (SOR) framework.

PurposeThis study aims to investigate the moderating effects of individual social engagement on the effectiveness of freemium strategies in digital content platforms.Design/methodology/approachThis study involved conducting a randomized field experiment with 74,758 consumers on a prominent e-book platform in China, comparing the effects of offering the first 50 chapters for free against no free content. Additionally, a causal random forest machine learning algorithm was applied to analyze data and optimize strategies based on individual social engagement levels.FindingsThis study indicates freemium strategies on digital content platforms can increase consumer willingness to pay but may reduce social community participation. These effects are moderated by consumers' prior social engagement, with excessive interaction leading to diminishing returns.Practical implicationsThe study offers actionable insights for digital content managers, showing how tailored freemium strategies can effectively balance consumer engagement and revenue generation. The findings suggest that platforms can significantly enhance profitability by moderating free content offerings based on detailed analysis of consumer engagement histories.Originality/valueThis study enhances the understanding of freemium strategies by showcasing their dual impact on consumer willingness to pay and social engagement, and detailing the complex, non-linear effects of individual social engagement, which challenges the traditional linear assumptions in existing literature. Additionally, it provides insights for implementing mixed marketing strategies on digital platforms, where multiple strategies often interact, guiding the effective management of these complexities.

PurposeUsers’ active participation in platform value co-creation is the key to the development of crowdsourcing logistics platforms, especially the active participation of the crowdsourcee who provide logistics services. However, driven by self-interest, coupled with loose links among subjects on the platform ecological chain, it is difficult for users to effectively form value co-creation intentions. This study aims to explore how platform governance affects crowdsourcees’ intention to participate in value co-creation on the platform through individual cognition.Design/methodology/approachBased on social cognitive theory (SCT), this study built a research model and took the crowdsourcee on China’s well-known crowdsourcing logistics platforms as the investigation object, using the collected 302 valid survey data to test the model.FindingsThe results showed that platform governance mechanisms have a significant influence on crowdsourcees’ intention to participate in value co-creation on the platform, either directly or through crowdsourcee cognition, and the impact is different. The crowdsourcee cognition plays a mediating role between platform governance mechanisms and crowdsourcees’ intention to participate in value co-creation on the platform, and the mediating effect of value acquisition perception (VAP) is significantly higher than self-efficacy (SE).Originality/valueThe results provided references for crowdsourcing logistics platforms to develop a targeted governance mechanism to promote the platform to achieve value co-creation.

PurposeThis study aimed to optimize the dyeing scheduling process with uncertain job completion time to reduce resource consumption and wastewater generation, and while reconciling the conflicting objectives of minimizing the makespan and the need to limit the production on specific machines to minimize rework.Design/methodology/approachWe employed a UNISON framework that integrates fuzzy decision tree (FDT) to optimize dyeing machine scheduling by minimizing the makespan and water consumption, in which the critical attributes such as machine capacity and processing time can be incorporated into the scheduling model for smart production.FindingsAn empirical study of a high-tech textile company has shown the validity and effectiveness of the proposed approach in reducing the makespan and water consumption by over 8% while high product quality and efficiency being maintained.Originality/valueHigh-tech textile industry is facing the challenges in reducing the environmental impact of the dyeing process while maintaining product quality and efficiency for smart production. Conventional scheduling approaches have not addressed the relationship between machine groups and reworking, resulting in difficulty in controlling the makespan and water consumption and increasing costs and environmental issues. The proposed approach has addressed uncertain job completion via integrating FDT into the scheduling process to effectively reduce makespan and wastewater. The results have shown practical viability of the developed solution in real settings.

PurposeWith burgeoning interest in the low-altitude economy, applications of long-endurance unmanned aerial vehicles (LE-UAVs) have increased in remote logistics distribution. Given LE-UAVs’ advantages of wide coverage, strong versatility and low cost, in addition to logistics distribution, they are widely used in military reconnaissance, communication relay, disaster monitoring and other activities. With limited autonomous intelligence, LE-UAVs require regular periodic and non-periodic control from ground control resources (GCRs) during flights and mission execution. However, the lack of GCRs significantly restricts the applications of LE-UAVs in parallel.Design/methodology/approachWe consider the constraints of GCRs, investigating an integrated optimization problem of multi-LE-UAV mission planning and GCR allocation (Multi-U&G IOP). The problem integrates GCR allocation into traditional multi-UAV cooperative mission planning. The coupling decision of mission planning and GCR allocation enlarges the decision space and adds complexities to the problem’s structure. Through characterizing the problem, this study establishes a mixed integer linear programming (MILP) model for the integrated optimization problem. To solve the problem, we develop a three-stage iterative optimization algorithm combining a hybrid genetic algorithm with local search-variable neighborhood decent, heuristic conflict elimination and post-optimization of GCR allocation.FindingsNumerical experimental results show that our developed algorithm can solve the problem efficiently and exceeds the solution performance of the solver CPLEX. For small-scale instances, our algorithm can obtain optimal solutions in less time than CPLEX. For large-scale instances, our algorithm produces better results in one hour than CPLEX does. Implementing our approach allows efficient coordination of multiple UAVs, enabling faster mission completion with a minimal number of GCRs.Originality/valueDrawing on the interplay between LE-UAVs and GCRs and considering the practical applications of LE-UAVs, we propose the Multi-U&G IOP problem. We formulate this problem as a MILP model aiming to minimize the maximum task completion time (makespan). Furthermore, we present a relaxation model for this problem. To efficiently address the MILP model, we develop a three-stage iterative optimization algorithm. Subsequently, we verify the efficacy of our algorithm through extensive experimentation across various scenarios.

PurposeThis paper aims to investigate how commercial influencers retain their followers and successfully persuade them to consider purchasing newly recommended products and services within the food industry. We explored the impact of followers’ purchase satisfaction upon their repurchase intention for newly promoted food products and services, directly and by the mediating roles of followers’ affective commitment and loyalty toward commercial food influencers.Design/methodology/approachOur conceptual model design was supported by the tricomponent attitude model, which helps explain followers’ emotional attachment to the influencers. We validated the proposed model using a sample of 200 followers of renowned commercial food influencers in Iran. We used partial least squares structural equation modeling for data analysis, with the assistance of Warp PLS (version 8.0) software.FindingsWe found that followers’ purchase satisfaction exerts a positive influence upon their repurchase intention, both directly and through the mediating roles of affective commitment and loyalty toward commercial food influencers.Practical implicationsThis study elucidates the role of followers’ satisfaction with their previous purchases in influencing their intention to buy newly recommended products. There is a multiplicity of important implications for restauranteur’s business models, as this marketing approach rewards a digital equivalent of a strong customer relationship and an honest, high-quality product. Our results also suggest that food influencers can operate effectively in the affiliate marketing sphere by operating and sustaining enduring relationships.Originality/valueThis work addresses how the influencer–follower relationship, followers’ purchase satisfaction and emotional attachment toward influencers, shape both follower retention and future repurchase intentions. This is from the perspective of the tricomponent attitude model within the food industry.

PurposeAs more firms adopted AI-related services in recent years, AI service failures have increased. However, the potential costs of AI implementation are not well understood, especially the effect of AI service failure events. This study examines the influences of AI service failure events, including their industry, size, timing, and type, on firm value.Design/methodology/approachThis study will conduct an event study of 120 AI service failure events in listed companies to evaluate the costs of such events.FindingsFirst, AI service failure events have a negative impact on the firm value. Second, small firms experience more share price declines due to AI service failure events than large firms. Third, AI service failure events in more recent years have a more intensively negative impact than those in more distant years. Finally, we identify different types of AI service failure and find that there are order effects on firm value across the service failure event types: accuracy > safety > privacy > fairness.Originality/valueFirst, this study is the initial effort to empirically examine market reactions to AI service failure events using the event study method. Second, this study comprehensively considers the effect of contextual influencing factors, including industry type, firm size and event year. Third, this study improves the understanding of AI service failure by proposing a novel classification and disclosing the detailed impacts of different event types, which provides valuable guidance for managers and developers.