A green manufacturing supply chain can improve ecological environments and resource utilization in enterprises. The objective of this paper is to assess green supply chain (GSC) risks in manufacturing to help enterprises increase value. Specifically, based on theoretical analysis and literature review, the risk assessment model of green SC in manufacturing is put forward through questionnaire survey data and Principal Component Analysis. Furthermore, to improve the effectiveness of the risk assessment model obtained from Principal Component Analysis, principal component analysis-based Fuzzy Mature-Element (FME) method is used to form the risk assessment model for green SC in manufacturing. Finally, the FME model on manufacturing green SC risk assessment is tested through the simulation of BP neural network. Some findings present that the proposed risk evaluation system of manufacturing green SC is appropriate and the FME based comprehensive evaluation method is reasonable.

Despite a large number of retailers have embraced the voice shopping channels, the factors that determine consumers' voice shopping value co-creation intentions are still unclear. This study explores the impacts of two social cognition dimensions (competence and warmth) on voice shopping value co-creation intention from the perspectives of confidence in consumers themselves and in AI. Based on the social exchange theory and stimulus—organism—response, a research model was developed and empirically examined by data collected from 363 students at a university in China. The results of structural equation modeling (SEM) analysis indicate that perceived competence and perceived warmth positively affect confidence in consumers themselves and in AI and consequently determine voice shopping value co-creation intention. In addition, the impacts of confidence in consumers themselves and in AI on voice shopping value co-creation intention varies. The results of the fuzzy set qualitative comparative analysis (fsQCA) method further validate these findings.

This paper identifies risk factors in the smart supply chain and develops a risk assessment index system for reducing potential losses in intelligent manufacturing. Specifically, based on the supply chain operation reference theory, we investigate the performance indicators and characteristics of the smart supply chain in intelligent manufacturing in China. A conceptual model for identifying risks of the smart supply chain is developed, and a questionnaire is designed to measure the risks of the smart supply chain in intelligent manufacturing. Using the hierarchical clustering analysis, an improved risk assessment model is derived with 22 risk factors based on 814 valid sample data. Moreover, the information entropy weight method is used to compute the risk weights for the smart supply chain in intelligent manufacturing. The risk weights are further verified by simulation and proved that these risk factors and risk weights have high practical efficiency. Theoretical and practical implications are also presented.

This paper aims at investigating influencing factors of block chain on collaborative product innovation (CPI) performance in the manufacturing supply chain. First, according to the application value of block chain in CPI and the CPI characteristics in manufacturing SC, this paper proposes a research model based on the innovation diffusion theory. Second, a set of hypotheses were derived from the research mode and a questionnaire was deployed to collect data in Chinese manufacturing enterprises. Data analysis was performed by using the AMOS modeling method based on a total of 373 valid surveys. Findings show a positive linear chain relationship between block chain, supply chain’s collaborative innovation capability, and supply chain’s CPI performance. Moreover, block chain capability is diffused into CPI capability for indirectly promoting the supply chain’s CPI performance. This study can put forward theoretical guidelines for manufacturing enterprises to grasp the strategic significance of block chain in product innovation activities.

Product characteristics of wearable devices have large impacts on users’ initial trust as users intend to adopt wearable devices, which further impacts wearable devices' use intention. This paper aims at investigating the impacts of wearable device product characteristics on consumers’ initial trust based on four factors: wearability, functionality, personalization, and integration. Through a questionnaire survey method, 328 valid questionnaires were obtained. The empirical analysis results from the structural equation model analysis show that wearability, functionality, and integration factors have significant impacts on the initial trust of wearable devices. The conclusions of the study can provide references for the future research on the initial trust of wearable devices, and point out the direction of product development for wearable device manufacturers.

This article investigates how perceived enjoyment and mobility drive user continuance intentions toward using mobile applications. A second-stage continuance model was developed after a thorough literature review. A survey instrument was deployed to collect data from 584 smartphone users. The model was empirically tested using structural equation modeling procedures. Data analyses show that the salience of disconfirmation and beliefs in enjoyment and mobility serve as the primary driver of the changes in satisfaction and attitude toward continuance intentions. Furthermore, perceived enjoyment, mobility and satisfaction jointly explained over 60% of the variance in post-usage attitude. As an initial effort to revise and test the expanded IS continuance model, this study deepens our understanding of enjoyment and mobility at post-usage stage of mobile experience. It urges mobile application providers to forge continuance by devoting more resources and efforts toward creating a truly enjoyable as well as mobile experience.

This paper focuses on the factors influencing consumers' initial trust in wearable commerce. Specifically, a conceptual model for wearable commerce is created and hypotheses are developed to study the initial trust. Through a survey of 266 valid questionnaires, we carry out an empirical analysis via Structure Equation Modeling method. The results show that five proposed factors (privacy concern, trust propensity, performance expectancy, facilitating conditions, hedonic motivation) all have significant effects on initial trust in wearable commerce. The conclusion is valuable to future research on wearable commerce. Theoretical and practical implications are also presented.

This paper aims to identify factors that affect consumers' web-mobile shopping extension behaviors. Drawing on the trust transfer theory, the present study investigates the trust transfer process (including trust in source, trust in target, and relationship between source and target) and its effect on consumers' benefit-sacrifice evaluations and behaviors toward mobile shopping extension. The research model was empirically tested on the data collected from 309 web-shopping users of a famous business-to-consumer retailer in China. The SEM analysis results indicate that trust in web shopping services and perceived integration between web and mobile shopping services positively affect trust in mobile shopping services. The results also demonstrate that the trust transfer process positively affects behaviors toward mobile shopping extension directly and indirectly via a dual mechanism: by enhancing perceived benefit and by reducing perceived risk of mobile shopping services. Theoretical and practical implications of these findings are discussed.

This paper developed a method to assess information security risks in e-healthcare. Specifically, it first developed a static E-Healthcare Information Security Risk (EHISR) model to present thirty-three security risk factors by identifying information security threats and their sources in e-healthcare. Second, a dynamic E-Healthcare Information Flow (EHIF) model was developed to logically link these information risk factors in the EHISR model. Pattern analysis showed that information security risks could be classified into two levels, and versatility analysis showed that the overall security risks for eight information flows were close with a range from 55% to 86%. Third, one quantifiable approach based on a relative-weighted assessment model was developed to demonstrate how to assess the information security risks in e-healthcare. This quantitative security risk measurement establishes a reference point for assessing e-healthcare security risks and assists managers in selecting a reliable information flow infrastructure with a lower security risk level.