Purpose: The rise of artificial intelligence (AI) applications has driven enterprises to provide many intelligent services to consumers. For instance, customers can use chatbots to make relevant inquiries and seek solutions to their problems. Despite the development of customer service chatbots years ago, they require significant improvements for market recognition. Many customers have reported negative experiences with customer service chatbots, contributing to resistance toward their use. Therefore, this study adopts the innovation resistance theory (IRT) perspective to understand customers’ resistance to using chatbots. It aims to integrate customers’ negative emotions into a predictive behavior model and examine users’ functional and psychological barriers. Design/methodology/approach: In this study, we collected data from 419 valid individuals and used structural equation modeling to analyze the relationships between resistance factors and negative emotions. Findings: The results confirmed that barrier factors affect negative emotions and amplify chatbot resistance influence. We discovered that value and risk barriers directly influence consumer use. Moreover, both functional and psychological barriers positively impact negative emotions. Originality/value: This study adopts the innovation resistance theory perspective to understand customer resistance to using chatbots, integrates customer negative emotions to construct a predictive behavior model and explores users’ functional and psychological barriers. It can help in developing online customer service chatbots for e-commerce.

Purpose: The logistics industry has benefited hugely from the growth of e-commerce. The logistics and transportation industry operators have realized that higher-quality service and logistics management provides the competitive edge as the primary channel of communication with consumers. Digital voice assistants (DVA) is an information system based on an artificial intelligence framework that can interact through voice, such that a deliveryman can query for or use information in a relatively short time. The purpose of this paper is to explore the value of DVA in logistic service. Design/methodology/approach: This study aims to develop a framework for innovation and logistics service capabilities of logistics and transportation services to structure a model based on the analysis hierarchy process method to discuss the factors considered when adopting DVA. Findings: The results of this study implied that common problem and expectations of current operators in the delivery of goods and their expectations of DVA. Practical implications: Innovative operations and planning are possible with information technology-enabled logistic services. It is important to identify relevant DVA development avenues. Originality/value: The purpose of this study is to show which factors are significant to the logistics and transportation industry using DVA to aid the deliverymen, and it provides guidance for manager evaluating adopted DVA and its object.

In recent years, cloud computing services have developed rapidly and are being widely used, but most applications focus on consumers. However, a few successful cases of enterprise application systems have been constructed and adopted by companies. To understand the factors that affect the adoption by a company of cloud Customer Relationship Management (CRM), which is an enterprise application system, this study used a Technology Organization Environment Framework (TOE) as a theoretical framework, reviewed related literature on the relevant factors affecting the adoption of a cloud service, and then constructed a three-level hierarchical table of the factors. A pairwise comparison questionnaire was then designed and issued to top managers of firms in the machinery industry in Taiwan that have adopted cloud CRM. A fuzzy analytical hierarchy process (FAHP) was then used to calculate the weights of the factors, based on the returned questionnaires. The results show that enterprises in the machinery industry gave priority to the factor, ‘support of senior managers’, followed by the factors ‘organizational policies’ and ‘system information security’. Some implications of the findings are discussed, which can serve as a guide for the successful adoption of cloud CRM in machinery industry if firms allocate their resources appropriately to these important factors. These results can also be used by the service providers of cloud CRM to devise effective marketing strategies.

Purpose: Artificial intelligence (AI) is the most progressive commodity among current information system applications. In-house development and sales of beneficial products are difficult for many software development and service companies (SDSCs). SDSCs have some implicit concerns about implementing AI software development due to the complexity of AI technology; they require an evaluation framework to avoid development failure. To fill the void, this study identified the factors influencing SDSCs when developing AI software development.Design/methodology/approach: Based on complex adaptive systems theory, three aspects were developed as the main factors of hierarchy, namely, employees' capabilities, environmental resources and team capabilities. Fuzzy analytic hierarchy process (FAHP) was used to assess the SDSCs' attitude. Based on SDSCs, attitudes toward implementing AI software projects were collected to calculate the hierarchy of factors.Findings: The outcome of FAHP is used as understanding the key factors of SDSCs for selecting an AI software project, toward the improvement of overall project planning. Employees' stress resistance was considered as a priority for the project, although professional AI skills and resources were also important.Originality/value: This study suggested three variables developed using complex adaptive systems. This study contributes to a better understanding of the critical aspects of developing AI software projects in SDSCs. The study's findings have practical and academic implications for SDSCs and subsequent academic development, broadening the scope of AI software development research.

Currently, the green procurement activities of private hospitals in Taiwan follow the self-built green electronic-procurement (e-procurement) system. This requires professional personnel to take the time to regularly update the green specification and software and hardware of the e-procurement system, and the information system maintenance cost is high. In the case of a green e-procurement system crash, the efficiency of green procurement activities for hospitals is affected. If the green e-procurement can be moved to a convenient and trusty cloud computing model, this will enhance the efficiency of procurement activities and reduce the information maintenance cost for private hospitals. However, implementing a cloud model is an issue of technology innovation application and the technology-organization-environment (TOE) framework has been widely applied as the theoretical framework in technology innovation application. In addition, finding the weight of factors is a multi-criteria decision-making (MCDM) issue. Therefore, the present study first collected factors influencing implementation of the cloud mode together with the TOE as the theoretical framework, by reviewing the literature. Therefore, an expert questionnaire was designed and distributed to top managers of 20 private hospitals in southern Taiwan. The fuzzy analysis hierarchical process (FAHP), which is a MCDM tool, finds the weights of the factors influencing private hospitals in southern Taiwan when they implement a cloud green e-procurement system. The research results can enable private hospitals to successfully implement a green e-procurement system through a cloud model by optimizing resource allocation according to the weight of each factor. In addition, the results of this research can help cloud service providers of green e-procurement understand users’ needs and develop relevant cloud solutions and marketing strategies.