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This study, a theoretical article, aims to introduce new institutionalism as a framework through which business and management researchers can explore the significance of artificial intelligence (AI) in organizations. Although the new institutional theory is a fully established research program, the neo-institutional literature on AI is almost non-existent. There is, therefore, a need to develop a deeper understanding of AI as both the product of institutional forces and as an institutional force in its own right.

The authors follow the top-down approach. Accordingly, the authors first briefly describe the new institutionalism, trace its historical development and introduce its fundamental concepts: institutional legitimacy, environment and isomorphism. Then, the authors use those as the basis for the queries to perform a scoping review on the institutional role of AI in organizations.

The findings reveal that a comprehensive theory on AI is largely absent from business and management literature. The new institutionalism is only one of many possible theoretical perspectives (both contextually novel and insightful) from which researchers can study AI in organizational settings.

The authors use the insights from new institutionalism to illustrate how a particular social theory can fit into the larger theoretical framework for AI in organizations. The authors also formulate four broad research questions to guide researchers interested in studying the institutional significance of AI. Finally, the authors include a section providing concrete examples of how to study AI-related institutional dynamics in business and management.

Studies showed that a proactive delivery system employing innovative artificial intelligence (AI) in urban services might perform better. This has become an important national policy for many countries. Thus, this study aims to explore the influence of AI in urban services in Malaysia.

Official documents such as Structure Plan and Government Transformation Programme Policy Document covering various levels of cities in Malaysia, articles related to urban studies mostly written by researchers regarding urban growth in Malaysia and the Urban Development Bulletin from the Federal Department of Town and Country Planning from 1957 to date were reviewed and analysed.

The findings show that the influence of AI in urban services has long existed and been carefully planned by local authorities since colonial times. The development of global digital technology influences the upgrading of AI in urban services in Malaysia. Also, the success of AI in these municipal services is influenced by the rate of information technology literacy among the urban population. These developments have led to the definition of a conceptual city.

This paper's findings and conclusion were based on reviewed literature but did not compromise the strength of this paper. Thus, as part of the implications for future research, mixed-methods research design has been suggested.

As part of the implications, this article intends to promote AI in urban services in Malaysia and other developing countries with similar urban services challenges.

This paper encourages AI applications in urban services because it enhances service delivery performance. This intends to key stakeholders to promote AI via policies across Malaysia's urban services as part of the study implications.

This study used an integrated framework that incorporates the technology acceptance model (TAM) (Davis, 1989), the theory of planned behavior (TPB) (Ajzen, 1991) and trust to examine factors that mainly influence consumers' intention towards the use of smart technologies in tourism and hospitality (T&H) industry. The Internet of things (IoT), artificial intelligence (AI), virtual reality systems, augmented reality systems, etc. are the Smart 4.0 technologies generally used in T&H industry these days.

Convenience sampling approach was employed in this study. Data were collected over the Internet using a survey instrument by posting the questionnaire link on social network web pages of travel agencies from November 10, 2021, to December 30, 2021. In the opening statement of the questionnaire, we have explained about the Smart 4.0 technologies so that every respondent could understand what we mean by Smart 4.0 technologies.

The findings show that conjoining the TAM and the TPB with trust resulted in a robust model for explaining customers' intention toward using smart technologies in the T&H industry.

Smart technologies have become one of the most profitable e-commerce applications. This study examines and integrates the various advantages of smart technologies for the consumers in T&H industry, as well as providing insight into the intentions of Indian consumers. Hence, this study gives significant information to IT companies, online travel agencies, tour operators, travel agents, T&H planners and other stakeholders on Indian consumers' behavioral intentions (BIs).

This study tested the utility of the extended model in predicting consumers' intention towards the use of smart technologies in T&H industry. As far as the authors' knowledge is concerned, this is the first study that predicted intention of Indian consumers towards the use of smart technologies in T&H industry by integrating TAM, TPB and trust.

Researchers explore the impact of an intelligent assistant in virtual teams by applying the theoretical lens of a transactive memory system (TMS) to understand the relationships between trust in a specific technology, knowledge sharing and knowledge application.

An online survey was administered to a Qualtrics-curated panel of individual, US-based virtual team members utilizing an intelligent assistant with team collaboration software. Partial least squares structural equation modeling (PLS-SEM) was utilized to examine the hypothesized relationships of interest.

Results suggest that knowledge application is strongly influenced by trust in a specific technology and knowledge sharing. Additionally, a transactive memory system positively increases trust in the intelligent assistant, and similarly, trust in the intelligent assistant has a significant positive relationship with knowledge sharing.

The research model contributes to our understanding of the impact of an intelligent assistant in virtual teams. Although the transactive memory system construct has been explored in various contexts and models, few have explored the impact of an intelligent assistant and trust in a specific technology.

This study explored the different artificial intelligence (AI) applications used in academic libraries and the key factors and impediments related to their implementation.

The author applied quantitative research methods in the form of a questionnaire, using both open and closed questions. A total of 472 valid questionnaires were received from academic librarians.

The author sought responses from librarians who had implemented AI applications and those who had not, identifying the types of AI applications implemented, key factors relating to their implementation, and impediments to promoting AI. Gaps were identified between the level of support for AI applications and the negative effect of the impediments. Furthermore, the more extensive the individual and organizational knowledge activities performed by the librarians and libraries held, the more positive the attitude was librarians' attitude toward AI applications in their libraries. However, librarians recognized that AI applications are inevitable, but indicated that the difficulties of in execution have hampered the adoption of AI.

The sample data were collected in Taiwan; therefore, the data may only represent the views of Taiwanese academic librarians on AI applications. The results of this study may not apply to librarians worldwide; however, they may provide a useful reference.

The results revealed the top four AI applications that libraries would most likely implement in the near future. Therefore, AI application developers and suppliers can prioritize the promotion of these products for to academic libraries. This study revealed that funding and costs related to AI implementation were discovered to be key factors relating to implementing AI applications. Some impediments to the implementation of AI applications relate to technological problems. Several librarians suggested that managers should invest more resources at an early stage rather than reducing cutting back on human resources initially. Although worries regarding privacy and ethics were mentioned expressed by some respondents, most academic librarians did not regard these to be major concerns.

This study provides the perspectives of librarians who have implemented AI applications and of those who have not. In addition, it explores the advantages and disadvantages of AI applications, and the level of support for and impact of AI applications and promotions. This study also included a gap analysis. Moreover, individual and organizational knowledge activity scales were adopted to examine AI awareness and the perceptions of academic librarians.

This study explores features that impact consumers' purchase intention through artificial intelligence (AI), because it is believed that through artificial intelligence, consumers' intention to purchase grows significantly, especially in the retail sector, whereby retailers provide lucrative offers to motivate consumers. The study develops a theoretical framework based on media-richness theory to investigate the role of perceived anthropomorphism toward an intention to purchase products using AI.

The study is based on cross-sectional data through an online survey. The data have been analyzed using PLS-SEM and SPSS PROCESS macro.

The results show that consumers tend to demand anthropomorphized products to gain a better shopping experience and, therefore, demand features that attract and motivate them to purchase through artificial intelligence via mediating variables, such as perceived animacy and perceived intelligence. Moreover, trust in artificial intelligence moderates the relationship between perceived anthropomorphism and perceived animacy.

The study investigates and concludes with managerial and academic insights into consumer purchase intention through artificial intelligence in the retail and marketing sector.

Given the regional heterogeneity of economic development, electricity consumption in various regions exhibits a discrepant growth pattern. The purpose of this study is to construct a grey system forecasting model with intelligent parameters for predicting provincial electricity consumption in China.

First, parameter optimization and structural expansion are simultaneously integrated into a unified grey system prediction framework, enhancing its adaptive capabilities. Second, by setting the minimum simulation percentage error as the optimization goal, the authors apply the particle swarm optimization (PSO) algorithm to search for the optimal grey generation order and background value coefficient. Third, to assess the performance across diverse power consumption systems, the authors use two electricity consumption cases and select eight other benchmark models to analyze the simulation and prediction errors. Further, the authors conduct simulations and trend predictions using data from all 31 provinces in China, analyzing and predicting the development trends in electricity consumption for each province from 2021 to 2026.

The study identifies significant heterogeneity in the development trends of electricity consumption systems among diverse provinces in China. The grey prediction model, optimized with multiple intelligent parameters, demonstrates superior adaptability and dynamic adjustment capabilities compared to traditional fixed-parameter models. Outperforming benchmark models across various evaluation indicators such as root mean square error (RMSE), average percentage error and Theil’s index, the new model establishes its robustness in predicting electricity system behavior.

Acknowledging the limitations of traditional grey prediction models in capturing diverse growth patterns under fixed-generation orders, single structures and unadjustable background values, this study proposes a fractional grey intelligent prediction model with multiple parameter optimization. By incorporating multiple parameter optimizations and structure expansion, it substantiates the model’s superiority in forecasting provincial electricity consumption.

The study aims to examine the impact of an artificial intelligent service agent (AISA) on customer services to the rural population provided by KAYA, Kotak Life's AI-enabled insurance chatbot avatar that offers quality insurance services.

Multi-stage cluster sampling method was adopted to collect the responses from the 707 customers across the rural population of southern states of India. SPSS V.2 and Smart PLS 4 were used to apply simple percentage analysis, multiple linear regression analysis, and structural equation modeling (SEM) to validate the hypothesis. The dependent variables are economic performance and market performance based on the independent variables: efficiency, security, availability, enjoyment and contact.

The study revealed that efficiency and security are the highest predictors and the most influencing variables in predicting the economic and market performance of the insurance companies in determining the quality of service when rendered through AISA among the customers. Efficiency, security, availability, contact and enjoyment are the critical dimensions of AISA. It has a more significant impact on quality service (claim processing) to the rural population. It improves the economic and market performance among the insurance companies and the rural population.

Customers need convenience when making claims. Even little challenges might lead to stress and unhappiness, depending on the situation. Restrictions on where customers can file claims may not be the most outstanding service insurance firms can offer, given rising travel and commuting costs and widening geographical borders. Customers value proactive communication from service providers about the status of their insurance claims.

This study aims to find a dynamic model in an effort to optimize tourism performance in ecotourism destinations. The model structure is built based on competitive performance in geographic areas and the application of ecotourism elements that are integrated with big data innovation through artificial intelligence technology.

Data analysis is performed through dynamic system modeling. Simulations are carried out in three models: First, existing simulation models. Second, Scenario 1 is carried out by utilizing a causal loop through innovation of big data-based artificial intelligence technology to ecotourism elements. Third, Scenario 2 is carried out by utilizing a causal loop through big data-based artificial intelligence technology on aspects of ecotourism elements and destination competitiveness.

This study provides empirical insight into the competitiveness performance of destinations and the performance of implementing ecotourism elements if integrated with big data innovations that will be able to massively demonstrate the growth of sustainable tourism performance.

This study does not use a primary database, but uses secondary data from official sources that can be accessed by the public.

The paper includes implications for the development of intelligent technology based on big data and also requires policy innovation.

Sustainable tourism development.

This study finds the expansion of new theory competitiveness of ecotourism destinations.

With the great changes brought by information technology, there is also a challenge for the elderly's acceptance. This study aimed to determine the antecedents of elderly people's usage intention of financial artificial intelligent customer service (FAICS) and to examine the relationships between various factors and thus to help them better adapt to the digital age.

A mixed method, including the qualitative and quantitative study, was utilized to explore answers of the research questions. As the qualitative study, the authors used semi-structured interviews and data coding to uncover the influencing factors. As the quantitative study, the authors collected data through questionnaires and tested hypotheses using structural equation modeling.

The results of data analysis from interviews and questionnaires suggested that perceived anthropomorphism and virtual identity of elderly users have a positive impact on their perceived ease of use, and the perceived intelligence of elderly users positively influences their perceived ease of use, satisfaction and perceived usefulness. Additionally, the elderly's cognition age can moderate the effects of perceived usefulness and satisfaction on their usage intention of FAICS.

This study contributes to the literature by taking the elderly group as the research participants and combining those influencing factors with technology acceptance model and information systems success model. The findings provide a basis for accelerating the promotion of FAICS and help address the problem that the elderly have difficulty adapting to a new technology.