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Although mobile devices are ubiquitous among employees, their awareness and readiness to use mobile technologies for competence development is still not widespread and therefore requires further exploration. The purpose of this study is to propose a conceptual model based on the unified theory of acceptance and use of technology (UTAUT) to explain the determinants that affect employees’ intention to use mobile devices and software for knowledge transfer during the process of knowledge management.

A conceptual model based on the UTAUT with new variables concerning relative usability (RU) and user autonomy (UA) and new connections between the determinants was developed as a result of a subject matter literature review. A structural equation modelling approach was used to validate the model on the basis of data collected via a survey collected from 371 employees from 21 sectors, both public and private.

The UTAUT model extended by new variables like RU and UA explains employee acceptance of mobile technologies for knowledge transfer reasonably well. New proposed variables highlighted that the usability of technology compared to other solutions and user autonomy in the selection and the use of applications have the strongest impact on the employees’ intention to use mobile devices and software for knowledge transfer.

This model explains the 55 per cent behavioral intention of employees to use mobile technologies for knowledge transfer. Even though it is quite high in terms of acceptance theories, some new variables should be explored. Furthermore, study does not verify whether m-learning acceptance for knowledge transfer is sector-specific.

Mobile technologies used for knowledge transfer by employees should allow for high UA through their ability to select solutions that they find convenient, use of preferred platforms, personalize applications and utilize devices and software in various environments. They should not be simplified and should have the same functionality and efficiency of use as alternative solutions like web and desktop applications, even if additional effort to learn them would be required. Mobile technologies that take into account UA and RU support the process of employees capturing, distributing and effectively using knowledge.

The elaborated model provides a valuable solution with practical implications for increasing mobile technologies acceptance for knowledge transfer. The study results contribute both to knowledge management and technology acceptance research fields by introducing two new determinants for the acceptance of technologies in knowledge transfer, such as UA and RU with several additional connections between existing UTAUT variables.

The evolution of e-health technologies presents promising alternatives for health-care excellence. Despite the benefits arising from mobile e-health (m-health) and wearables technologies, the literature stands many contradictories signs regarding how users accept and engage in using these technologies. This study aims to synthesize the estimations about m-health user acceptance technologies.

A meta-analytic structural equation modeling was carried out using the 778 relationships estimated by 100 previous research. The estimations follow the relations and constructs proposed in the UTAUT2 technological acceptance model.

The results indicate the performance expectancy, effort expectancy, social influence and habit constructs are most important for predicting the behavioral intention of use of m-health technologies. The Latin American users of e-health technologies are still underestimated in the literature.

The study presents a guide to understanding the acceptance process of m-health technologies and delivers a general orientation for developing new m-health devices considering their acceptance by users.

The purpose of this paper is to focus on radio frequency identification (RFID) acceptance and examine three understudied drivers: a company's satisfaction with existing logistics technologies, its logistics technology readiness (technology optimism and technology innovativeness), and relationship hostage position. The proposed conceptual model also investigates the impacts of RFID acceptance and these three antecedents on the company's logistics performance.

Survey data were collected from the members of three professional associations in the USA. Structural equation modeling was used to test the proposed relationships.

According to the analysis results, a company's satisfaction with existing technology has negative impact on RFID acceptance, and technology readiness has positive impact on RFID acceptance. However, the relationship between a company's hostage position and RFID acceptance was found to be only partially significant. Also, the positive relationship between a company's RFID acceptance and its logistics performance is confirmed in this study. Furthermore, while a company's satisfaction with existing technology and technology innovativeness were found to be positively related to logistics performance, its technology optimism and hostage position were not significant related to its logistics performance.

This research confirms that a company's RFID acceptance is positively related to logistics performance. Therefore, even if a company is satisfied with its existing technologies, careful evaluation is warranted to determine if RFID as a new technology is needed to maintain the company's logistics performance in a dynamic environment. Also, this research suggests that supply chain members should be extremely cautious about the power usage toward its partners, because the authors' results show that forcing other partners to take cooperative actions may not yield the desired results.

This is first study examining RFID technology through a behavioral perspective. A new construct, RFID acceptance, was proposed, and related measurement scale was developed and tested along with its antecedents and outcomes.

Although the research promotes deploying renewable energy technologies (RETs) due to their inherently eco-friendly characteristics, consumer acceptance is relatively low, making RETs acceptance a subject of interest for most recent research. This systematic study aims to integrate and synthesize earlier research on the acceptance of RETs, assessing state-of-the-art and painting a complete picture of the consumer's adoption intention.

A systematic literature review and bibliometric analysis on the topic of RETs acceptance have been conducted to understand the spectrum of theoretical and methodological methods used in the past, as well as how the users perceive and react to the adoption of it.

The study of selected 70 research articles showed the role of technology, consumer acceptance and decision-making process as factors influencing the acceptance of RET. The findings suggest that it is crucial to comprehend these factors since users' opinions significantly affect the acceptance and use of renewable technologies.

This paper reviews the research on adopting RETs and identifies factors influencing it. The study further endorses that technology like digitalization can promote the acceptability of renewable energy sources and, inadvertently, assist managers and policymakers in managing business models and making better decisions for sustainable investments.

Research on accepting RETs by integrating the social and technological components is still in its infancy. This study is a novel attempt to identify broad patterns of RETs that can improve communication between decision-makers, technologists and users and aid in the switch to a sustainable energy transition.

With recent advances in internet technologies, internet of things (IoT) technology is having an increasing impact on our daily lives, and beginning to offer interesting and advantageous new services. The current research aims to develop and test an integrative model of factors determining consumers' acceptance of IoT technology.

Based on technology acceptance model (TAM), the authors proposed an IoT acceptance model that consists of three technology factors (perceived usefulness, perceived ease of use, and trust); one social context factor (social influence); and two individual user characteristics (perceived enjoyment and perceived behavioral control). Data from 368 Chinese consumers were used to test the research model through the use of structural equation modeling.

The results showed particularly strong support for the effects of perceived usefulness, perceived ease of use, social influence, perceived enjoyment, and perceived behavioral control. However, trust played an insignificant role in predicting the intention. In addition, perceived ease of use and trust were found to affect perceived usefulness. Compared with the individual TAM model, the integrated model provides more explanation on user behavioral intention toward IoT usage.

The integrated model explores the driving factors of individuals' willingness to use IoT technology from the perspectives of the technology itself, social context and individual user characteristic. It links the constructs of social influence, enjoyment, and perceived behavioral control to the TAM and successfully extends TAM in the IoT technology context.

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.

Drawing on the integrative perspective of the technology acceptance model (TAM) and theory of planned behaviour (TPB) and extending it further by examining the role of organisational facilitators and perceived HR effectiveness in this integrative perspective, we examine HR professionals’ AI-augmented HRM (HRM^(AI)) acceptance in this research.

The data (N=375) were collected from HR professionals working in different organisations in India. Structural equation modelling (SEM) was employed to analyse the data.

The results of the study suggest that along with organisational facilitator antecedents to the relevant components of both TAM and TPB, perceived HR effectiveness also enhanced the HRM^(AI) acceptance levels of HR professionals.

The research findings are expected to contribute to the understanding of the factors that influence the acceptance of AI-augmented HRM in organizations. The results may also help organisations to identify the facilitators that can enhance the adoption and implementation of AI-augmented HRM by HR professionals. Finally, the study provides a composite TAM-TPB theoretical framework that can guide future research on the acceptance of AI-augmented HRM.

To the best of our knowledge, this is one of the first attempts to factor in the effect of contextual factors (i.e. organisational facilitators and perceived HR effectiveness) in the TAM and TPB equations.

Many manufacturers are exploring adopting smart technologies in their operations, also referred to as the shift towards “Industry 4.0”. Employees' contribution to high-tech initiatives is key to successful Industry 4.0 technology adoption, but few studies have examined the determinants of employee acceptance. This study, therefore, aims to explore how managers affect employees' acceptance of Industry 4.0 technology, and, in turn, Industry 4.0 technology adoption.

Rooted in the unified theory of acceptance and use of technology model and social exchange theory, this inductive research follows an in-depth comparative case study approach. The two studied Dutch manufacturing firms engaged in the adoption of Industry 4.0 technologies in their primary processes, including cyber-physical systems and augmented reality. A mix of qualitative methods was used, consisting of field visits and 14 semi-structured interviews with managers and frontline employees engaged in Industry 4.0 technology adoption.

The cross-case comparison introduces the manager's need to adopt a transformational leadership style for employees to accept Industry 4.0 technology adoption as an organisational-level factor that extends existing Industry 4.0 technology user acceptance theorising. Secondly, manager's and employee's recognition and serving of their own and others' emotions through emotional intelligence are proposed as an additional individual-level factor impacting employees' acceptance and use of Industry 4.0 technologies.

Synthesising these insights with those from the domain of Organisational Behaviour, propositions were derived from theorising the social aspects of effective Industry 4.0 technology adoption.

Existing research suggests that the competitive advantage provided by technological development depends to a large extent on the speed and coordination of the technology’s implementation, and on how adoptable the technological applications are considered. While accepting this argument, the authors consider the explanatory model to be inadequate. This study aims to contribute to the theoretical discussion by analysing institutionalised industrial relations and other organisation-level factors, which are important for workplace restructuring and societal change.

The analysis is based on a representative nation-wide work and working conditions survey (N = 4,100) from Finland, which includes a variety of themes, including practices, changes and well-being at work. Changes are understood as organisational changes, focusing on modern technologies such as robotisation and digitalisation.

The results indicate that occupational division at workplace (low-skilled vs high-skilled occupations) affects job insecurity and acceptance of technologies at work. The characteristics of workplaces, such as the employees’ participation and involvement in the development of the organisation, play a significant part in both the acceptance and the implementation and outcomes of the technological transformations in the workplace.

The research provides new and interesting insights into working life practices. Furthermore, it reveals how technology acceptance and employment perspectives relate to working conditions and lessons learned from past reforms.

The authors consider current theories such as technology acceptance model at the micro level and that way rationalise the need for this study. This study shows the importance of individual, organisational and wider contextual factors in technology acceptance.

This study aims to investigate some individual factors that may positively/negatively impact upon the willingness to use AI-assisted hiring procedures (AI-WtU). Specifically, the authors contribute to the ongoing discussion by testing the specific role of individuals’ personality traits and their attitude toward technology acceptance.

Data have been collected from a cohort of workers (n = 157) to explore their individual level of AI-WtU, their personality traits and level of technology acceptance, along with a series of control variables including age, gender, education, employment status, knowledge and previous experience of AI-assisted hiring.

The results obtained show the significant role played by a specific personality trait –conscientiousness – and technology acceptance in shaping the level of AI-WtU. Importantly, technology acceptance also mediates the relationship between AI-WtU and conscientiousness, thus suggesting that conscientious people may be more willing to engage in AI-assisted practices, as they see technologies as means of improving reliability and efficiency. Further, the study also shows that previous experience with AI-assisted hiring in the role of job applicants has a negative effect on AI-WtU, suggesting a prevailing negative experience with such tools, and the consequent urge for their improvement.

This study, to the best of the authors’ knowledge, is the first to test the potential role of personality traits in shaping employees AI-WtU and to provide a comprehensive understanding of the issue by additionally testing the joint effect of technology acceptance, age, gender, education, employment status and knowledge and previous experience of AI-assisted hiring in shaping individual AI-WtU.