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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.

This study aims to develop a comprehensive adoption model that combines constructs from various theories and tests these theories against each other. The study combines a technology acceptance model, innovation diffusion theory and risk theory. It develops this model in a smart home applications context.

The study is based on an online survey consisting of 409 participants, and the data are analyzed using structural equation modeling.

Each theory provides unique insights into technology acceptance and numerous constructs are interrelated. Predictors from innovation diffusion and risk theory often display indirect effects through technology acceptance variables. The study identifies risk perception as a major inhibitor of use intention, mediated through perceived usefulness. Results reveal that the most important determinants of use intention are compatibility and usefulness of the application.

Studies which do not examine different theories together may not be able to detect the indirect effects of some predictors and could falsely conclude that these predictors do no matter. The findings emphasize the crucial role of compatibility, perceived usefulness and various risk facets associated with smart homes.

This study broadens the understanding about the necessity of combining acceptance and adoption drivers from several theories to better understand the usage of complex technological systems such as smart home applications.

This study identifies key facets leading to consumers' Internet of Things (IoT) adoption intention.

Applying four technology acceptance theories (theory of planned behavior, technology acceptance model, pleasure-arousal-dominance theory and technology readiness index), the author uses deductive quantitative research to develop a model, explaining IoT adoption intentions. Administrated questionnaires are distributed in Egypt among generation-Z and millennials in malls. A total of 400 questionnaires are used for hypotheses testing, applying structural equation modeling (SEM) path coefficient analysis.

Results of this study show that attitude, dominance, perceived usefulness, innovativeness and insecurity impact consumers' IoT adoption intentions; subjective norms, perceived behavior control, pleasure, arousal, perceived ease, optimism and discomfort hold insignificant impact on consumers' IoT adoption intentions.

Exploring IoT facets and how these facets impact consumers' adoption intentions, this study helps grasp technology acceptance in theory and practice, guiding scholar and practitioners (e.g. IoT developers, retailers, marketers and other field experts) to consider consumers' mindset when developing, improving and marketing IoT.

The contribution stems from the incorporation of various frameworks used to explain technology acceptance. By studying several theories jointly, the research extracts and identifies a significant set of facets (technical and psychological) to build a comprehensive theory of IoT acceptance, showing consumers' IoT adoption is not entirely similar to adoption of other past innovations. This understanding allows marketers to focus on content that needs to be promoted to boost consumers' IoT purchase plans. Future researchers could replicate the results to IoT categories (e.g. home appliances, cars, healthcare, education, sportswear, etc.) to improve external validity of the findings, among other future research opportunities.

The study aims to validate a mobile learning readiness scale through the technology readiness and acceptance model (TRAM), thereby assessing students' readiness to adopt m-learning in teaching and learning, including its acceptance.

A structured questionnaire was administered to open and distance learning (ODL) students in Odisha, India, to assess their readiness and acceptance of m-learning. 665 valid responses were collected, and collected data was analysed using statistical packages for social sciences (SPSS) and SmartPLS.

The findings of the study reveal that optimism contributes positively to perceived ease of use (PEOU) and perceived usefulness (PU) of m-learning (β = 7.921, p < 0.001; β = 2.123, p < 0.05), whereas innovativeness positively contributes to PEOU of m-learning (β = 2.227, p < 0.05), but not PU of m-learning. ODL student's optimism improves his/her PEOU and PU of m-learning, but innovativeness improves only his/her PEOU. Further, the impact of innovativeness is higher than that of optimism in the TRAM and innovativeness is the strong predictor to adopt m-learning. It also shows that the PU of m-learning positively influences behavioural intention to use m-learning (β = 4.757, p < 0.001). Integrating technology readiness (TR) with technology acceptance model (TAM) to predict students' acceptance of m-learning is very useful.

The paper will help decision-makers to adopt and use m-learning in higher educational institutions.

This paper is the first to explore the readiness and acceptance of m-learning in higher education in India.

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.

This study aims to investigate the predictive technology acceptance models and their evolution in the tourism context. These predictive models make a knowledgeable decision about the possibility of future outcomes by analysing data. As futurists are interested in making a prediction about the likelihood of different behaviours over time, researchers of these predictive models have focussed on behaviour and predicting the intentions of users. This study proposes to demonstrate the revolution of these models and how are changed overtime. It also indicates the role of them in future studies.

By reviewing the predictive models and literature, this study looks in-depth in the process of alteration of these models.

This study explores the reasons of the evolution of predictive models and how they are changed. It shed light on the role of predictive models in future research and will suggest new directions for forthcoming studies.

One of the main limitations of this study is that as the world is currently struggling with COVID-19 and predictability of these models will be changed. As the future is disruptive, it cannot be concluded that how these models will be altered in future.

Role of predictive behavioural models of tourists is fundamentally crucial in assessing the performance of planners and marketers of tourism services in the future. It will also vastly helps the successful development of tourism sectors, and it has practical value for all tourism stakeholders.

Few studies have focussed on the evaluation of these models and their role in future research.

A number of studies have shown that internet banking (IB) implementation is not only determined by banks or government support, but also by perceptions and experience of IB users. IB studies have showed encouraging results from academics in developed countries. Yet little is known about the user adoption of IB in Lebanon. The purpose of this paper is to investigate the factors that may hinder or facilitate the acceptance and usage of IB in Lebanon.

A conceptual framework was developed through extending the unified theory of acceptance and use of technology (UTAUT) by incorporating two additional factors namely; perceived credibility (PC) and task-technology fit (TTF). A quantitative approach based on cross-sectional survey was used to collect data from 408 IB consumers. Data were analysed using structural equation modelling based on AMOS 20.0.

The results of the structural path revealed that performance expectancy (PE), social influence, PC and TTF to be significant predictors in influencing customers’ behavioural intention (BI) to use IB and explained 61 per cent of its variance, with PE was found the strongest antecedent of BI. Contrary to the UTAUT, the effect of effort expectancy on BI was insignificant. In addition, both BI and facilitating conditions were found to affect the actual usage behaviour and explained 64 per cent of its variance

This study would be helpful for bank managers and policy makers to explain the currently relatively low penetration rate of IB in formulating strategies to encourage the adoption and acceptance of IB by Lebanese customers, where IB is still considered an innovation.

This study is the first research that extend the UTAUT by incorporating two additional factors namely; PC and TTF to study the IB in the Lebanese context. This study contributes to the research on computer technology usage by looking at IB adoption and incorporation into the lives of customers via the BI to use and actual usage of IB in Lebanon.

This study investigates the acceptance of mobile phone technology in Tanzanian small- and medium-sized enterprises (SMEs) using the Technology Acceptance Model (TAM) with a special focus on service quality.

The conceptual framework was designed by extending the TAM with an additional construct, service quality, before testing a model in a survey of 155 respondents and analysing using Smart PLS 4.

Service quality was found to be among the significant factors in the acceptance of mobile phone technology among SME employees.

This implies that the higher the quality of service offered, the more employees accept and use mobile phone technology in their duties and improve the productivity of SMEs.

The aspects of quality of mobile phone technology usage such as call dropouts, network quality, speed, etc., must be improved significantly.

The Mobile Network Operators and Regulators must understand that employees are offered the most accurate and reliable mobile phone services for its usefulness to be realised.

The originality is a modified version of a TAM that accommodates service quality that has been tested in the Tanzanian context.

In this paper, the authors explore the reasons for reluctance to use mobile banking with the help of the technology acceptance model (TAM) and modifications proposed by the literature that is particularly adequate for developing countries and mobile banking: the theory of trying (TT) and the concept of attitude strengths.

This study intends to shed more light on the acceptance of mobile banking in Ethiopia from the banking industry's perspective. To this end, the authors identify models used in the literature for explaining acceptance and fit them to a sample of Ethiopian bank customers. The authors' sample of 394 mobile banking subscribers does not include non-banked individuals, because the authors' main intention is to help banks understanding why banks' platforms are not used as desired.

The authors' findings suggest that attitude is the most significant factor determining acceptance, a multi-dimensional approach to attitude is more recommendable to understanding how mobile banking users shape users' attitude and combining TAM and TT is impactful and better explains the factors influencing attitude.

The authors' analysis suggests focusing on improving the attitude of users toward mobile banking since users' attitude is the integral component for acceptance decisions. In doing so, banks shall concentrate on making mobile banking services/platforms easy to use than promoting the usefulness of mobile banking, because the perception of ease of use has a more significant influence on attitude than usefulness.

Practical application of the authors' findings may guide future marketing decisions while individuals can elucidate where individuals stand on mobile banking.

This article aims to study the determinants of e-learning acceptability by university students based on their experiences with distance learning during the coronavirus disease 2019 (COVID-19) pandemic.

A questionnaire was used to collect data from 448 students enrolled in a Moroccan business school's fourth and fifth years. The technology acceptance model (TAM) was the primary framework used for this analysis, into which variables from the expectation confirmation model were injected, namely facilitating conditions, social influence, expectation confirmation and satisfaction. The proposed conceptual model was tested and evaluated using the partial least squares structural equation modeling (PLS-SEM) technique. Then the authors have offered an in-depth analysis by employing the importance-performance map analysis (IPMA) approach.

The investigation suggested that the proposed measurement scale effectively assesses the factors impacting students' decision to continue using e-learning in the future. This study’s results show that e-learning acceptance depends significantly on the students' satisfaction, perceived ease of use (PEU) and perceived usefulness (PU). In contrast, the facilitating conditions are not a valid measurement scale to determine students' attitudes toward e-learning.

This is one of the first studies in the Moroccan context to evaluate e-learning acceptability by management students after COVID-19 using a unique research model.