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In light of a growing trend towards mobile information management and a UK governmental drive for police forces to implement mobile technologies and realise significant benefits, it is important to examine the factors affecting officer acceptance. There appears to be little understanding of the key factors, yet this is critical to the success of the initiative. The purpose of this paper is to investigate the main factors that influence the usage of mobile technologies amongst police officers.

A qualitative, partially ethnographic design was followed to allow an in‐depth exploration of this issue. The study was based on a mixed‐methods longitudinal evaluation study of the implementation of mobile technologies within a UK police force over a nine‐month period. The technology acceptance model (TAM) and the subsequent TAM2 and TAM3, were then reengineered to provide a suitable theoretical model for a mobile policing context.

In total, four main categories of officer acceptance factors were identified: officer performance, security/reliability, management style and cognitive acceptance. Evidence from the study showed a key shortfall in all three versions of the TAM in that they focus on the user perspective and did not confirm the broader organisational factors within the implementation and social contexts of mobile policing.

Consequently, an adapted mobile‐TAM (m‐TAM) was produced that incorporated these factors into the existing TAM elements. The high‐level nature of the adapted model for mobile policing means it could be applied by other police forces and potentially other organisations, regardless of the type of mobile device implemented, to address the barriers to acceptance. The m‐TAM addresses the need for a more relevant and robust model to the mobile policing paradigm, which goes beyond the static technology environment in which the TAM2 and TAM3 were built.

This paper investigates effective human-robot collaboration (HRC) and presents implications for Human Resource Management (HRM). A brief review of current literature on HRM in the smart industry context showed that there is limited research on HRC in hybrid teams and even less on effective management of these teams. This book chapter addresses this issue by investigating factors affecting intention to collaborate with a robot by conducting a vignette study. We hypothesized that six technology acceptance factors, performance expectancy, trust, effort expectancy, social support, organizational support and computer anxiety would significantly affect a users' intention to collaborate with a robot. Furthermore, we hypothesized a moderating effect of a particular HR system, either productivity-based or collaborative. Using a sample of 96 participants, this study tested the effect of the aforementioned factors on a users' intention to collaborate with the robot. Findings show that performance expectancy, organizational support and computer anxiety significantly affect the intention to collaborate with a robot. A significant moderating effect of a particular HR system was not found. Our findings expand the current technology acceptance models in the context of HRC. HRM can support effective HRC by a combination of comprehensive training and education, empowerment and incentives supported by an appropriate HR system.

Notwithstanding the dynamics of digital transformation and its relevance for revenue generation in the entertainment industry, empirical research that focused on consumer behavior at the intersection of sport content and media technology acceptance is limited. Virtual reality (VR) is a re-emerging and nowadays commercially available technology that impacts sport consumed through media. The purpose of this paper is to investigate the consumer acceptance of VR technology and highlight the effects of content quality and flow experience as influencing factors of behavioral intention.

Based on a literature review, the authors constructed and empirically tested a model that extends the unified theory of acceptance and use of technology (UTAUT2) by considering additional antecedent factors. Participants (N=570) in the empirical study viewed sport content via VR technology (Sport VR) and completed a survey before and after viewing. The authors conducted factor analysis and structural equation modeling.

Three UTAUT2 influencing factors, i.e., performance expectancy, social influence and hedonic motivation, showed significant effects. Furthermore, flow and content quality had positive indirect effects. Thus, the quality of sport content and the state of flow that users experience when immersed in a VR environment are relevant factors that determine the performance expectations of consumers and their Sport VR usage intention.

This empirical study contributes to knowledge on consumer acceptance of a hedonic technology in a sport media context. Moreover, two factors extended the established UTAUT2 model.

This paper aims to examine the current technology acceptance model (TAM) in the field of mixed reality and digital twin (MRDT) and identify key factors affecting users' intentions to use MRDT. The factors are used as a set of key metrics for proposing a predictive model for virtual, augmented and mixed reality (MR) acceptance by users. This model is called the extended TAM for MRDT adoption in the architecture, engineering, construction and operations (AECO) industry.

An interpretivist philosophical lens was adopted to conduct an inductive systematic and bibliographical analysis of secondary data contained within published journal articles that focused upon MRDT acceptance modelling. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach to meta-analysis were adopted to ensure all key investigations were included in the final database set. Quantity indicators such as path coefficients, factor ranking, Cronbach’s alpha (a) and chi-square (b) test, coupled with content analysis, were used for examining the database constructed. The database included journal papers from 2010 to 2020.

The extant literature revealed that the most commonly used constructs of the MRDT–TAM included: subjective norm; social influence; perceived ease of use (PEOU); perceived security; perceived enjoyment; satisfaction; perceived usefulness (PU); attitude; and behavioural intention (BI). Using these identified constructs, the general extended TAM for MRDT in the AECO industry is developed. Other important factors such as “perceived immersion” could be added to the obtained model.

The decision to utilise a new technology is difficult and high risk in the construction project context, due to the complexity of MRDT technologies and dynamic construction environment. The outcome of the decision may affect employee performance, project productivity and on-site safety. The extended acceptance model offers a set of factors that assist managers or practitioners in making effective decisions for utilising any type of MRDT technology.

Several constraints are apparent due to the limited investigation of MRDT evaluation matrices and empirical studies. For example, the research only covers technologies which have been reported in the literature, relating to virtual reality (VR), augmented reality (AR), MR, DT and sensors, so newer technologies may not be included. Moreover, the review process could span a longer time period and thus embrace a fuller spectrum of technology development in these different areas.

The research provides a theoretical model for measuring and evaluating MRDT acceptance at the individual level in the AECO context and signposts future research related to MRDT adoption in the AECO industry, as well as providing managerial guidance for progressive AECO professionals who seek to expand their use of MRDT in the Fourth Industrial Revolution (4IR). A set of key factors affecting MRDT acceptance is identified which will help innovators to improve their technology to achieve a wider acceptance.

Integrating the Chat Generative Pre-Trained Transformer-type (ChatGPT-type) model with government services has great development prospects. Applying this model improves service efficiency but has certain risks, thus having a dual impact on the public. For a responsible and democratic government, it is necessary to fully understand the factors influencing public acceptance and their causal relationships to truly encourage the public to accept and use government ChatGPT-type services.

This study used the Latent Dirichlet allocation (LDA) model to analyze comment texts and summarize 15 factors that affect public acceptance. Multiple-related matrices were established using the grey decision-making trial and evaluation laboratory (grey-DEMATEL) method to reveal causal relationships among factors. From the two opposite extraction rules of result priority and cause priority, the authors obtained an antagonistic topological model with comprehensive influence values using the total adversarial interpretive structure model (TAISM).

Fifteen factors were categorized in terms of cause and effect, and the antagonistic topological model with comprehensive influence values was also analyzed. The analysis showed that perceived risk, trust and meeting demand were the three most critical factors of public acceptance. Meanwhile, perceived risk and trust directly affected public acceptance and were affected by other factors. Supervision and accountability had the highest driving power and acted as the causal factor to influence other factors.

This study identified the factors affecting public acceptance of integrating the ChatGPT-type model with government services. It analyzed the relationship between the factors to provide a reference for decision-makers. This study introduced TAISM to form the LDA-grey-DEMATEL-TAISM method to provide an analytical paradigm for studying similar influencing factors.

Introduction: Many organisations nowadays use artificial intelligence (AI) in human resource (HR) activities like talent acquisition, onboarding of new employees, learning and development, succession planning, retention of employees, and automation of administrative tasks. When AI is integrated with HR practices, it helps HR personnel to focus more on the strategic aspects of the HR function and relieve them from routine HR activities.

Purpose: The readiness of employees to accept any change depends on organisational facilitation to change, employee willingness to accept the change, the requirement for change, situational factors, etc. This research studies the factors influencing employees’ change readiness towards acceptance of AI in HR practices. The researchers also strive to develop a conceptual technology adoption model for AI in HR practices by studying the earlier models. Finally, the research explores the acceptance of AI by various service sector employees and identifies whether there is any difference in their acceptance of AI based on demographic variables.

Methodology: A conceptual framework was derived using a combination of previous models, including the Technology Readiness Index (TRI), Change Readiness Scale, Technology Acceptance Model (TAM), Technology, Organization, and Environment (TOE) model, and change readiness scale. A structured questionnaire was designed and distributed to 228 respondents from the service sector based on the conceptual framework. An exploratory factor analysis (EFA) was used to determine the elements that influence employees’ level of change readiness.

Findings: The exploratory results on data collected from 228 respondents show that the model can be used for further research if a confirmatory factor analysis and validity and reliability test are performed. Employees are aware of AI and how it is used in HR practices, based on the study results. Moreover, while most respondents favour using AI in their company’s HR practices, they are wary of some aspects of AI.

This study aims to provide an analysis of the attitude toward three banking services technologies in Sudan, namely, automated teller machines (ATMs), mobile banking and internet (online) banking. The study started by conducting an exploratory factor analysis, on the valid responses received from a random sample of bank customers in Sudan toward the three technologies.

The study used the “technology acceptance model” as a conceptual framework to investigate the factors that influence customers’ acceptance and intention to use bank technologies.

The study found that the customers’ attitude toward various bank technologies is not the same and is influenced by different factors. The results revealed that bank customers who are users of ATMs are influenced by its convenience, ease of use and service quality, whereas credibility was not seen as a significant driver. Mobile users were found to be influenced more by the benefits and ease of use and service quality, whereas internet customers were influenced by the benefits and ease of use and credibility of the systems. Under the three models, attitude emerged as a fully mediating factor for customers’ behavioral intentions.

The implications of this study are obvious for both regulators and bankers in Sudan for careful designing and implementation of their technology-based banking systems and focusing on the features of concern and desirable most by bank customers and necessary for secure and safe adoption of technology-based banking.

There are no studies to date that provide evidence of customer acceptance and the use of these services, the volume of bank business or profits derived from these services. This is especially important because security concerns are always associated and attached with technology-based services. The identification of technology acceptance factors is very important for bank regulators, bank marketers and banks’ customer base.

Mobile learning is a unique form of learning which uses the distinct features of mobile devices. The purpose of this paper is to investigate the present state of student and faculty perception towards m-learning at open and distance educational institutes in Pakistan.

The paper presents a conceptual model based on TAM, which explains factors influencing student and faculty perception towards m-learning acceptance. M-learning acceptance mainly depends on personal attitude, so this study focusses on individual context. Primary data from students and faculty including tutors (n=612, students =448, faculty/tutors=162) was collected through a properly designed questionnaire by using purposive convenient sampling technique during Autumn 2015 semester. Structural equation modelling was used to analyse the collected data.

The results indicate that student and faculty skill readiness and self-efficacy influence perceived ease of use and perceived usefulness, where these two factors along with prior experience positively influence behavioural intension (BI) to accept mobile learning. Furthermore study results specifically provide factors which positively influence BI either directly or indirectly.

The study was limited to AIOU.

The study specifically provides factors which influence BI either directly or indirectly.

This paper aims to examine the factors that might influence the acceptance of government-to-government (G2G) systems in the Government of Nepal (GoN), to enhance the communication for coordination among government agencies.

After reviewing the Unified Model for E-Government Acceptance (UMEGA), interviews, focus group discussions with government officials and interviews with the retired senior government officials, a conceptual model has been proposed. The model is empirically tested with 234 responses collected from the government officials working in the central ministries of GoN using the structural equation modeling technique.

The result showed that factors considered from UMEGA such as performance expectancy, effort expectancy, facilitating conditions and attitude have a significant influence on the behavioral intention to use the system in the GoN. Also, the identified factors such as a commitment from leadership, awareness among leadership and transparency have a significant influence on the behavioral intention of the users to accept the system.

The low sample size is one of the major limitations of this research.

The findings show that the identified factors have a significant influence on the acceptance model and provide useful insights to policymakers, government officials and system developers to achieve the successful implementation of the e-government system in Nepal. The findings can be used by the academicians and e-government practitioners to extend it to other developing nations.

This research work explores the factors affecting the acceptance of a G2G system in GoN through the modification of the UMEGA model. To the best of the authors’ knowledge, this is a novel research in the context of Nepal, where the implementation of e-government has been analyzed from the perspectives of acceptance models to support the better implementation of e-governance systems.

Conscious of the benefits building information modeling (BIM) has brought about to the architecture, engineering, construction and operations (AECO) industry, the Chinese government has been driving BIM adoption. Nonetheless, its acceptance and proliferation in China remain stagnant. Most relevant literature focuses on BIM diffusion at the industry and organizational levels, but the impact of non-managerial practitioners executing BIM or the traditional drafting approach in day-to-day work tends to be disregarded. This study aims to extend theoretical models pertaining to technology acceptance to understand non-managerial practitioners’ perceptions toward working with BIM in China.

A new BIM acceptance model was proposed based on previous technology acceptance theories. After a pilot study, a survey was conducted with 153 non-managerial practitioners in the Chinese AECO industry.

Among factors impacting non-managerial practitioners’ BIM acceptance in China, performance expectancy and task-technology fit significantly and positively influence behavioral intention to accept BIM, while the impacts from effort expectancy, social influence and facilitating conditions are not essential.

Management strategies, such as improving non-managerial staff’s benefits and sense of BIM usefulness, selecting suitable tools to match with the staff’s tasks and promoting a middle-out approach in parallel with top-down interventions, are proposed for Chinese AECO organizations to enhance BIM acceptance.

Few studies have explored BIM acceptance from the perspective of non-managerial users in the Chinese AECO industry, especially using the theories related to technology acceptance. The BIM acceptance model developed in this study is different from those used in previous global studies in terms of influencing factors.