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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.

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.

The results of this study implied that common problem and expectations of current operators in the delivery of goods and their expectations of DVA.

Innovative operations and planning are possible with information technology-enabled logistic services. It is important to identify relevant DVA development avenues.

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.

Hypermarkets face a very competitive environment, as their rivals also include traditional markets, convenient stores and supermarkets. In addition to offering innovative services, hypermarket managers must also pay attention to customer shopping patterns and behaviors in order to gain market share. The purpose of this paper is to posit that there will be significant differences between male and female hypermarket customers in terms of both shopping time and categories of goods purchased.

To investigate actual shopping behaviors, this study observed customers in hypermarkets and recorded how long they spent shopping and what they purchased.

The results of this study implied that gender is a key factor that affects both shopping time and the goods purchased in Taiwanese hypermarkets. The empirical findings also indicated that customers favor certain goods categories.

The results of this study provide details of the differences between male and female hypermarket customers, with which managers can use to develop better marketing strategies.

The purpose of this study is to show hypermarket retailers how gender differences affect customer behavior in this context. It also presents a consumer product categories list for hypermarkets, and this can be used as a reference for follow-up studies.

People have utilized artificial intelligence (AI) reading assistants for study. This tool assists readers in summarizing the content of a book. However, the crucial factor in summarizing book content lies in the quality of the content by generative AI, as this quality affects readers’ willingness to use AI tools as reading aids. This study expands the acceptance architecture for artificially intelligent device use (AIDUA), integrates the concept of generative AI quality and proposes a new model for users’ continuous use of generative AI reading assistants.

This study employed a quantitative approach. A total of 362 respondents were from Taiwan. This study used partial least squares structural equation modeling (PLS-SEM) to validate, aiming to identify factors influencing users’ continued adoption of AI reading assistants.

The results show that the quality of AI-generated content and readability significantly influence users’ performance expectations and effort expectancy. However, credibility and representationalness have different effects, impacting effort expectancy but not performance expectancy. These findings underscore the critical role of generative AI quality in shaping user expectations and their continued use of AI reading assistants.

This research is of great significance in examining the quality of generative AI. It establishes a theoretical framework applicable to future research, enabling industry players to understand better the pivotal role of generative AI quality in the operation of information services. And focus on using AI reading assistants, describing the specific use of AI for specific tasks.

eXplainable artificial intelligence (XAI) is an evaluation framework that allows users to understand artificial intelligence (AI) processes and increases the reliability of AI-produced results. XAI assists managers in making better decisions by providing transparency and interpretability in AI systems. This study explores the development of XAI in business management research.

This study collects and analyzes business management research related to XAI using common management keywords as the basis. We used the success/failure system to explore its research guidelines XAI in business management.

The study found significant growth in XAI research within business management. This research will be discussed from various management disciplinary perspectives to help scholars understand the current research directions. Additionally, we utilize a success/failure system to explore how this theory can be applied to artificial intelligence and business management research.

The success/failure system offers a comprehensive framework encompassing the evolution of the cosmos, nature, and ecology. This theory can offer valuable insights for business management in XAI and competitive societies, governments, and enterprises, enabling them to formulate effective strategies for the future.

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.

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.

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.

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.

Digital games have not only surged in popularity but also have sparked a renaissance, catapulting virtual adventures into the realm of cultural phenomena. It has spilled over as countless individuals find themselves drawn to the tangible pieces of the worlds they love, mulling over the procurement of game merchandise and memorabilia. These items are more than mere collectibles; they are emblems of unwavering enthusiasm for game culture. This study employs the consumer culture theory (CCT) to investigate game players’ propensity to purchase merchandise products.

In this study, social media and online forums were used to collect samples from Taiwan, obtaining 311 valid responses. Partial least squares (PLS) was employed to analyze the research model.

The findings underscore the significance of loyalty as a critical factor affecting individuals’ ethnocentrism and cosmopolitanism attitudes, which also significantly impact the likelihood of players purchasing game merchandise products.

Based on CCT, this study explores game players’ willingness to purchase game merchandise. In Asia, transforming digital game content into peripheral products is a marketing strategy. This study holds practical and academic implications, contributing to the advancement of research in this field.

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.

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.

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.

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.

The purposes of this study were to formulate a hierarchical table of factors that influence adoption of an inter-organization system (IOS) by enterprises and to apply multi-criteria decision-making (MCDM) tools to find the weights of these factors and to objectively identify the critical success factors (CSFs) for the adoption of IOSs by small- and medium-sized enterprises (SMEs).

This study first used a literature review to collect the factors that affect an enterprise’s adoption of an IOS and then constructed a three-level hierarchical table of these factors, based on a technology – organization – environment framework. Fuzzy analytic hierarchy processing was used, based on the returned questionnaires, to determine the weights of the factors. The concept of VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) acceptable advantage was used to objectively identify the CSFs of SMEs that have adopted an IOS.

This study identifies six CSFs of SMEs that have adopted an IOS: industry knowledge and experience, the degree of application of information technology within the industry, system safety, the organizational infrastructure, customer relationships and ease of use. In addition, four findings are proposed.

The work has studied, in depth, the factors that influence the adoption of an IOS by SMEs and identified four practice implications that provide a useful guideline for SMEs when they plan to adopt an IOS.

The identification of CSFs is also an MCDM problem. However, very few previous articles have used MCDM tools to identify the CSFs. This study adopted MCDM tools to objectively identify these CSFs and determine their appropriate weights. The results can help the managers of SMEs allocate their resources, according to the weighting of these CSFs, when they are making plans to adopt an IOS.

The purpose of this paper is to propose an implementation model for enterprise resource planning (ERP) based on resource-based view, and using the dynamic capability theory as its theoretical foundation. This model includes: the establishment of the objectives of the implementation, an assessment of the available resources and the scope of the implementation, the redesign/integration and organizational learning during the process, the implementation of the system, and the measurement and evaluation of its performance.

An integrated circuits design company in Taiwan was used in a case study to examine the validity of the proposed model.

When the proposed ERP implementation model was applied in this study, the results show that organizational coordination, system-process redesign and integration, and organizational learning are the critical strategies for enterprises, in order to reduce the risks during the implementation of ERP projects.

This model can help enterprises recognize the resources needed when implementing an ERP. In addition, they need to consider the reliability of these resources, as this will increase the efficiency of the implementation, and thus the probability of success.

Studies of past models in the implementation of ERP have been conducted in various industries. There is a need for further studies that evaluate the different concepts in terms of the effectiveness of specific methods, in order to enhance the probability of successfully implementing a dynamic system. This paper is one of the first to explain how an enterprise can implement an ERP that is based on the theory of dynamic capabilities. The case study illustrates the important, critical success factors.