PurposeThe issue of counterfeiting has exerted a significant impact on the level of trust and collaboration within the Pu-erh tea supply chain. While digital identification methods have been implemented to mitigate counterfeiting, they offer only temporary remedies rather than comprehensive solutions. The emergence of blockchain technology presents an opportunity to address the traceability problem in the Pu-erh tea industry. However, the precise impact of blockchain on interfirm cooperation within Pu-erh tea supply chains remains unclear.Design/methodology/approachThis research gap incentivizes our study, where we use game theory to propose stylized models for Pu-erh tea supply chains that capitalize on the trust-enhancing capabilities of blockchain technology. In our study, a factor called “trust degree” is introduced and its influence on the pricing of Pu-erh tea and the performance of the entire supply chain following the adoption of blockchain is examined.FindingsThe blockchain-driven Pu-erh tea supply chain could not reach Pareto improvement without intervention. A simple cost-sharing contract is not sufficient to enable coordination in the supply chain, while a hybrid contract with benefit-sharing and cost-sharing can be used to achieve this goal.Practical implicationsBy comparing centralized and decentralized decision-making scenarios, we illustrate how blockchain can impact the performance along the entire Pu-erh tea supply chain. Finally, through numerical examples, we evaluate the value of cost-sharing contracts and benefit-sharing contracts and then propose a hybrid coordination mechanism to incentivize firm behaviour in the Pu-erh tea supply chain.Originality/valueThe framework uses a coordination mechanism that incentivizes the adoption of blockchain technology in the Pu-erh tea supply chain and offers valuable insights for the development of blockchain applications in food supply chain management.

PurposeMulti-goal setting has complex effects on individual behavior, which may be due to the changes in self-efficacy in the process of multi-goal pursuit. Most research focuses on the pursuit of a single goal, and a few discuss the static effects of multi-goal setting on behavior change. However, self-efficacy dynamically changes when individuals pursue diverse goals, which is a dynamic process. This study takes an online fitness platform as an example to explore how multi-goal pursuit dynamically influences users’ self-efficacy and then changes their fitness behavior.Design/methodology/approachWe collect detailed goal pursuit and fitness information of 1,387 unique users from a popular online fitness platform. Hidden Markov model is used to reveal the real-time self-efficacy state of users and capture the varying effects of multiple goals across users and over time.FindingsWe demonstrate that multiple goals have a positive effect on the transition to higher self-efficacy. Furthermore, the difficulty of goals and the tenure of users play different roles in the effect of physical and mental goals. Higher-difficulty goals only boost the relationship between physical goals and maintaining high self-efficacy. However, the moderation effect of user tenure is completely opposite.Originality/valueThis is the first study using Hidden Markov model to explain the underlying mechanism between multiple goals and fitness behavior in online context. We also find heterogeneous effects of multiple goals for different goal difficulties, which extends the research on goal difficulty in goal-setting theory. Our study provides insights to managers on gearing interventions toward users in specific states.

PurposeHow to accurately identify the realization path of realizing the value of enterprise data asset is the key to improving enterprise data management ability and building an efficient data governance mechanism.Design/methodology/approachFrom the perspective of information ecosystem theory, an empirical study was conducted using necessity condition analysis (NCA) and fuzzy set qualitative comparison method (fsQCA) based on 285 questionnaire data. Analyze the impact of multi factor linkage in the dimensions of subject, object, and environment on the realization of enterprise data asset value.FindingsThe results show that a single factor is not the necessary condition for the formation of enterprise data asset value. There are three configuration paths of Enterprise Data Asset Value Formation: subject-object-environment joint driving type, subject-environmental dual drive type and environmentally dominant supported by big data capabilities. In addition, absorption capacity, technology maturity and data resource base are particularly prominent in the process of enterprise data asset value formation.Originality/valueThe research clarifies the path for realizing the value of enterprise data assets and provides insights for data asset management for enterprises.

PurposeFinancial technologies, specifically robo-advisors, are becoming indispensable in the digital transformation of financial institutions. This study focuses on the effects of user characteristics and the human-like attributes of robo-advisors on downstream outcomes such as trust, financial well-being and loyalty.Design/methodology/approachPartial least squares structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) was applied.FindingsThe findings indicate that (1) human-like characteristics of robo-advisors are essential for fostering users’ trust in robo-advisors; (2) while not necessary conditions, financial self-efficacy and financial socialization positively influence trust; (3) trust, particularly its congruence, communication and commitment dimensions, is key to enhancing financial well-being; (4) the frequency of robo-advisor usage moderates the trust–financial well-being relationship and (5) financial well-being positively impacts customer loyalty.Originality/valueThis research advances robo-advisory service studies by exploring how user traits and humanoid robot features affect trust and loyalty, extending prior research focused on adoption and continuous use. Additionally, the study introduces NCA to pinpoint trust-building factors in robo-advisors, providing practical insights for developing desired consumer behavior (i.e. loyalty) in robo-advisory services.

PurposeSome restaurant customers who contract foodborne illnesses do not contact public health authorities but instead post online reviews to social media. By monitoring social media discourse, health authorities can gather information updates about restaurants’ hygiene deficiencies and thereby identify potential venues for outbreaks of foodborne illness. This study proposes a social media analytics framework to analyze the associations among negative hygiene aspects mentioned in customers’ reviews and use those associations to predict restaurants’ food safety.Design/methodology/approachThis study analyzes customer reviews of restaurants and identifies the co-occurrence patterns of hygiene-related keywords. To assess the extent to which the word co-occurrences are effective in preventing foodborne illnesses, classification models were constructed to use those co-occurrences as inputs to predict restaurants’ food safety risk.FindingsThis study obtains 20 association rules that reveal the co-occurrences of hygiene-related keywords. Using those co-occurrences as inputs, our best-performing model can detect 87.58% of high-risk restaurants.Practical implicationsWhen monitoring social media, health authorities can focus on a group of keywords and deploy our model to identify restaurants that are likely to contribute to foodborne illnesses.Originality/valueThrough the lens of signaling theory, this study is a pioneering work to reduce the dimensionality of social media data to a few meaningful hygiene-related keywords, filtering out irrelevant signals that disturb the signaling process. Social media data, after being processed by appropriate machine learning algorithms, become credible signals for risk prediction.

PurposeIn this paper, we present a case study to explore both how end-of-life electric vehicle batteries (EVBs) might be remanufactured efficiently and the data sharing required to support such remanufacturing. Specifically, the paper explores how blockchain technology can enhance transparency, traceability and efficiency in the lifecycle of EVBs.Design/methodology/approachThe research employs a case study methodology to study the remanufacturing processes of EVBs at Tesla, China. This approach is chosen for its effectiveness in examining complex phenomena within their real contexts and is particularly suitable for exploring the interplay between technology and industry practices.FindingsOur findings suggest that implementing blockchain technology allows stakeholders to manage data regarding battery quality and battery history. Although the adoption of blockchain in remanufacturing faces challenges, the research suggests that integrating blockchain could revolutionize the EVBs remanufacturing process by ensuring more effective and secure data management across the supply chain.Research limitations/implicationsThe study’s focus on Tesla in China as a unique case limits its generalizability, as Tesla’s advanced technology and China’s specific regulatory environment may not reflect the conditions faced by other EV manufacturers or in different regions, necessitating adaptations for broader applicability.Practical implicationsBased on the insights from participants at a leading EV manufacturer, this study proposes a blockchain-based closed-loop supply chain (CLSC) to monitor battery production, usage, remanufacturing and reuse, ensuring real-time data sharing among key stakeholders.Originality/valueThe research highlights blockchain technology’s pivotal role in enhancing the traceability, security and efficiency of remanufacturing EVBs within CLSCs. It enriches the existing theoretical frameworks by integrating regulatory influences and advanced data-sharing mechanisms, addressing consumer trust issues and proposing sustainable practices that could be adopted across the industry.

PurposeThis study aims to investigate the impact of eco-innovation such as eco-product innovation and eco-process innovation on sustainable manufacturing firm performance (SMFP) and further investigates green innovation as a mediator and digital innovation as a moderator among these relationship.Design/methodology/approachThe hypotheses were tested using the partial least squares structural equation modeling (PLS-SEM-4) technique. Data were gathered through convenience sampling from 149 managers within the manufacturing industry.FindingsThe results reveal a significant positive relationship between eco-innovation and SMFP. Furthermore, green innovation fully mediates the relationship between eco-innovation and SMFP. Also, findings suggest that digital innovation plays a moderating role in the relationship between green innovation and SMFP, which implies that green innovation is more effective in determining SMFP under high environmental dynamism.Practical implicationsThis study offers valuable insights for business owners and policymakers in the manufacturing industry.Originality/valueThis study contributes important insights to the literature on eco-innovation, green innovation and the manufacturing industry, enhancing the understanding of the drivers behind different types of eco-innovation. Additionally, the study’s exploration of the role of eco-innovation types in SMFP represents a significant contribution to the eco-innovation literature.

PurposeDespite rapid advancements in information and communication technology (ICT) service innovations, many peer-to-peer accommodation (PPA) platforms struggle to quickly translate these innovations into effective use (EU). Drawing on engagement theory, we examine the role of ICT service innovation and consumer engagement in the EU of the PPA platform.Design/methodology/approachWe empirically validate the model using a multimethod approach, including an instrument development study and an online field survey.FindingsIncremental and radical ICT service innovations positively influence consumers’ emotional, cognitive and behavioral engagement, which further facilitates their EU of the PPA platform.Originality/valueThese empirical findings offer theoretical insights into EU and provide practical guidance for optimizing the use of PPA platforms under rapid ICT service innovation.

PurposeSupply chain resilience (SCR) is crucial for organizations to navigate disruptions and sustain operations. This study investigates the role of big data analytics (BDA) capability and organizational ambidexterity in enhancing SCR. Specifically, we explore how the three dimensions of BDA capability—technical skills, managerial skills, and a data-driven decision-making culture—contribute to SCR through the mediating role of organizational ambidexterity, which encompasses both alignment and adaptability.Design/methodology/approachDrawing on data from 277 manufacturers in China, this study employs structural equation modeling (SEM) to test the hypothesized relationships.FindingsThe results indicate that all three dimensions of BDA capability uniquely contribute to SCR. Technical skills and a data-driven decision-making culture enhance organizational ambidexterity, while managerial skills do not significantly affect it. Furthermore, organizational ambidexterity mediates the relationship between technical skills and SCR, as well as between a data-driven culture and SCR, highlighting the importance of balancing alignment and adaptability in resilience-building. However, the mediating effect of organizational ambidexterity in the relationship between managerial skills and SCR is nonsignificant.Originality/valueThis study offers a detailed understanding of how distinct dimensions of BDA capability interact with organizational ambidexterity to enhance SCR. By integrating dynamic capability theory and organizational information processing theory, it provides practical insights for supply chain managers and contributes novel theoretical perspectives on resilience-building mechanisms.