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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.

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.

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.

The existing technology acceptance models have not yet investigated functional and motivational factors impacting trust in and use of conversational artificial intelligence (AI) by integrating the feedback and sequential updating mechanisms. This study challenged the existing models and constructed an integrated longitudinal model. Using a territory-wide two-wave survey of a representative sample, this new model examined the effects of hedonic motivation, social motivation, perceived ease of use, and perceived usefulness on continued trust, intended use, and actual use of conversational AI.

An autoregressive cross-lagged model was adopted to test the structural associations of the seven repeatedly measured constructs.

The results revealed that trust in conversational AI positively affected continued actual use, hedonic motivation increased continued intended use, and social motivation and perceived ease of use enhanced continued trust in conversational AI. While the original technology acceptance model was unable to explain the continued acceptance of conversational AI, the findings showed positive feedback effects of actual use on continued intended use. Except for trust, the sequential updating effects of all the measured factors were significant.

This study intended to contribute to the technology acceptance and human–AI interaction paradigms by developing a longitudinal model of continued acceptance of conversational AI. This new model adds to the literature by considering the feedback and sequential updating mechanisms in understanding continued conversational AI acceptance.

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 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.

Sandbag building technologies (SBTs) have been offered as a cost-effective and sustainable alternative building technology (ABT) capable of accelerating house construction in South Africa, but its acceptance remains low. However, knowledge about how to effectively improve SBT social acceptance is limited. This study aims to develop and prioritise SBT social acceptability strategies towards providing a comprehensive framework for the successful deployment and widespread adoption of sandbag technology.

This study used a quantitative research strategy that included a literature review and a structured questionnaire survey of 228 ABT professionals and stakeholders in the South African housing industry. The study statistically analysed 13 strategies for the social acceptance of SBT.

The analysis showed that the top three strategies include the availability of sandbag demonstration projects in all provinces, the approval of a sandbag building code and the availability of standard design methods for earthbags. A factor analysis clustered the 13 strategies into Stakeholders integration and policy formation, Effective education and knowledge sharing and Grassroots advocacy and incentives.

The current study’s findings provide a broad framework for the effective implementation and wide acceptance of sandbag technology in housing projects. It offered certain best practices that policymakers and practitioners might use to promote ABT and SBT societal acceptability.

To the best of the authors’ knowledge, the study represents the first and only attempt to investigate the viewpoints of experts and housing market stakeholders in South Africa regarding sandbag technology social acceptance strategies and contributes to the social acceptance body of knowledge in ABT and SBT.