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Government employees have various challenges of adopting e-government which include administrative problems, technological challenges, infrastructural problems, lack of trust on computer applications, security concerns and the digital divide. The purpose of this paper is to identify the most salient factors that influence the employee adoption of e-government in India as perceived by government employees involved in e-government service delivery.

The paper first identifies different factors influencing the employee adoption of e-government on the basis of literature review and then finds their relative importance by prioritizing them using the analytic hierarchy process (AHP). The AHP is a multi-criteria decision-making (MCDM) tool which combines all the factors into a hierarchical model and quantitatively measures their importance through pair-wise comparisons (Saaty, 1980). Eleven influencing factors of employee adoption of e-government have been identified, which are categorized under four main factors, namely, “employee’s personal characteristics”, “technical factors”, “organizational factors” and “trust”. The data pertaining to pair-wise comparisons of various factors and sub-factors related to the study is collected from ten senior government employees working with different departments and bodies of the Government of National Capital Territory of Delhi.

Based on the results obtained, the findings reveal that “organizational factors” and “technical factors” are the two most important factors which influence the intention of government employees to adopt e-government. Moreover, “training”, “technical infrastructure”, “access speed”, “technical support” and “trust” in infrastructure are the top five sub-factors which are considered to be important for the employee adoption of e-government.

One of the limitations regarding the methodology used in the study is that the rating scale used in the AHP is conceptual. There are chances of biasing while making pair-wise comparisons of different factors. Therefore, due care should be taken while deciding relative scores to different factors. Also, some factors and sub-factors selected, for the model may have interrelationships such as educational level and training; computer skills and trust; etc., and these interrelationships are not considered by the AHP, which is a limitation of the present study. In that case, the analytic network process (ANP) can be a better option. Therefore, this study can be further extended by considering some other factors responsible for e-government adoption by employees and applying the ANP in the revised model.

The results of the study may help government organizations, to evaluate critical factors of employee adoption of e-government. This may help them in achieving cost-effective implementation of e-government applications by efficiently managing their resources. Briefly, the findings of the study imply that government departments should provide sufficient training and support to their employees for enhancing their technical skills so that they can use the e-government applications comfortably. Moreover, the government departments should also ensure fast access speed of the e-government applications so that the employees can carry out their tasks efficiently.

Most of the existing literature on e-government is focused on citizens’ point of view, and very few studies have focused on employee adoption of e-government (Alshibly and Chiong, 2015). Moreover, these studies have majorly used generic technology adoption models which are generally applicable to situations where technology adoption is voluntary. As employee adoption of e-government is not voluntary, the present study proposes a hierarchy of influencing factors and sub-factors of employee adoption of e-government, which is more relevant to the situations where technology adoption is mandatory. Also, most of the previous studies have used statistical methods such as multiple regression analysis or structural equation modelling for examining the significant factors influencing the e-government adoption. The present study contributes to this area by formulating the problem as an MCDM problem and by using the AHP as the methodology to determine the weights of various factors influencing adoption of e-government by employees.

When considering whether to adopt a network technology, how does uncertainty about whom a potential adopter might interact with affect their adoption choice? On the one hand, uncertainty about potential network partners might enhance adoption incentives, as increased uncertainty induces the potential for economies of scope across the potential network. On the other hand, uncertainty may reduce the expected value of any particular connection, and reduce adoption incentives. Since this is a theoretical puzzle, this chapter presents empirical evidence to help illuminate it. It presents evidence the destabilizing of a social network may increase the scope of network externalities, using data on sales of a video-calling system made to an investment bank’s employees and subsequent usage by these customers. The terrorist attacks of 2001 led potential customers in New York to start communicating with a new and less predictable set of people when their work teams were reorganized as a result of the physical displacement that resulted from the attacks. This did not happen in other comparable cities. These destabilized communication patterns were associated with potential adopters in New York being more likely to take into account a wider spectrum of the user base when deciding whether to adopt relative to those in other cities. Empirical analysis suggests that the aggregate effect of network externalities on adoption was doubled by this instability, and that for those with diffuse networks, this more than compensated for the negative baseline effects of the instability.

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.

Despite much research on organizations’ adoption of innovation, little is currently known about individual employees have gone about it. The purpose of this paper is to empirically investigate the determinants that address individual employees’ decisions concerning innovation in the workplace.

Data were collected from 272 employees from a tertiary education institution in Australia using a structured instrument.

Results from the structural equation modeling analysis indicate that enjoyment and motivation impact significantly on attitudes to an innovation, which, in turn, affects how employees behave toward it.

Furthermore, organizational patronage, innovativeness and self-image have been found to influence the innovation adoption process. These findings have implications for the effective management and implementation of an innovation at the individual level.

Although innovation adoption has been studied extensively, drivers of adoption and research on individual innovation acceptance remain limited. Designing an effective approach for increasing end-user acceptance and subsequent use of innovation continues to be a fundamental challenge. The current literature indicates that we know relatively little about the ways in which individuals adopt and the factors that influence individual adoption of innovation. This study is designed to fill that gap. The identification of the factors is important to create a work environment that is conducive to individual adoption of innovation and thereby gain the expected benefits from the innovation.

AI-based chatbots are revamping employee communication in organizations. This paper examines the adoption of AI-based employee experience chatbots by employees.

The proposed model is developed using behavioral reasoning theory and empirically validated by surveying 1,130 employees and data was analyzed with PLS-SEM.

This research presents the “reasons for” and “reasons against” for the acceptance of AI-based employee experience chatbots. The “reasons for” are – personalization, interactivity, perceived intelligence and perceived anthropomorphism and the “reasons against” are perceived risk, language barrier and technological anxiety. It is found that “reasons for” have a positive association with attitude and adoption intention and “reasons against” have a negative association. Employees' values for openness to change are positively associated with “reasons for” and do not affect attitude and “reasons against”.

This is the first study exploring employees' attitude and adoption intention toward AI-based EEX chatbots using behavioral reasoning theory.

The use of artificial intelligence (AI) in the workplace is on the rise. To help advance research in this area, the authors synthesise the academic research and develop research propositions on the antecedents and consequences of AI adoption and application in the workplace to guide future research. The authors also present AI research in the socio-technical system context to provide a springboard for new research to fill the knowledge gap of the adoption and application of AI in the workplace.

This paper summarises the existing literature and builds a theoretically grounded conceptual framework on the socio-technical system theory that captures the essence of the impact of AI in the workplace.

The antecedents of AI adoption and application include personnel subsystem, technical subsystem, organisational structure subsystem and environmental factors. The consequences of AI adoption and application include individual, organisational and employment-related outcomes.

A research agenda is provided to identify and discuss future research that comprises not only insightful theoretical contributions but also practical implications. A greater understanding of AI adoption from socio-technical system perspective will enable managers and practitioners to develop effective AI adoption strategies, enhance employees' work experience and achieve competitive advantage for organisations.

Drawing on the socio-technical system theory, the proposed conceptual framework provides a comprehensive understanding of the antecedents and consequences of AI adoption and application in the work environment. The authors discuss the main contributions to theory and practice, along with potential future research directions of AI in the workplace related to three key themes at the individual, organisational and employment level.

This paper aims to explore the adoption of cashless, card‐based gaming machine payment systems from the organisational perspective, with an emphasis on the role of employees as change agents.

In a qualitative research approach, semi‐structured interviews with 14 registered club employees or change agents form the basis of analysis.

A typology of change agent involvement in the successful customer adoption of payment innovations is proposed. Agents' capacity to recognise and respond to customer needs is the first theme identified and discussed. An ability to reflect on the effect of customer adoption of cashless mechanisms on their role and responsibilities constitutes the second skill, and their perception of the consequences of use for the organisation and its customers represents the highest order change agent skill in the adoption process.

The exploratory nature of this qualitative study limits the generalisability of the findings to gaming machine venues that share similar contextual features.

The club employees interviewed are found to be potentially effective agents of change who readily observe and respond to the needs of gambling customers and can relate these to specific operational impacts. Opportunities for genuine employee participation in the diffusion process beyond the communication and adoption stages would, it is concluded, improve outcomes in the rate of adoption, range of customer use and the process of product development and enhancement.

This paper applies a typology of change agent roles in a service industry context, acknowledging the unique relationship that club employees have with customers. It highlights how this can be leveraged to improve new product development and customer adoption.

This research aims to use meta-analytical structural equation modeling to look into how hospitality employees use technology at work. It further investigates if the relationship between the constructs of the technology acceptance model (TAM) is moderated by job level (supervisory versus non-supervisory) and different cultures (eastern versus western).

In total, 140 relationships from 30 empirical studies (N = 6,728) were used in this study’s data analysis in accordance with the preferred reporting items for systematic reviews and meta-analysis.

The findings demonstrated that perceived usefulness had a greater influence on “user attitudes” and “acceptance intention” than perceived ease of use. This study also identified that the effect sizes of relationships among TAM constructs appeared to be greater for supervisory employees or in eastern cultures than for those in non-supervisory roles or western cultures.

The findings provide valuable information for practitioners to increase the adoption of employee technology. Practitioners need to focus on the identification of hospitality employee attitudes, social norms and perceived ease of use. Moreover, hospitality practitioners should be cautious when promoting the adoption of new technologies to employees, as those at different levels may respond differently.

This is the very first empirical investigation to meta-analyze the predictive power of the TAM in the context of hospitality staff technology adoption at the workplace. The findings also demonstrated differences in the predictive power of TAM constructs according to job level and cultural differences.

This study aims to use the novel approach of applying the behavioural reasoning theory (BRT) to understand the relative influence of reason for and reason against the adoption of mobile learning applications (M-learning apps) among information technology (IT) and information technology enabled services (ITeS) employees.

This study surveys 680 employees of IT and ITeS companies in India to examine the adoption of M-learning apps for learning using the BRT and the primary data analysis was done using the partial least squares-structural equation modelling technique.

It is found that the context-specific adoption factors for M-learning apps are hedonic motivation, self-efficacy, learning autonomy, ubiquitous and relative advantage, whereas the reasons against adoption of the M-learning apps are traditional barrier, usage barrier and image barrier. It is also found that values of openness to change positively affect the reasons for adoption and do not significantly affect reasons against adoption of M-learning. Values of openness to change affect the attitude towards M-learning apps and attitude affects the adoption intention of M-learning apps for learning.

This cross-sectional study was conducted only in the Indian IT/ITeS firms and future research can be conducted in other sectors and countries to generalize the results.

This research uniquely highlights the adoption factors both for and against, which should be considered while developing marketing strategies for M-learning apps’ adoption. It is imperative for training managers to consider these factors during the selection of M-learning apps and for designers while designing the M-learning apps.

This study provides new insights towards the use of mobile apps for learning with the employees’ perspective using the BRT theory and it highlights the reason for adoption and reason against adoption of M-learning apps.

Proactive firms recognize that environmental and social issues are sources of competitive advantages, but whatever the motivation, organizations face challenges when implementing sustainable practices. For small and medium-sized enterprises (SMEs), sustainable practices have stemmed from multinational corporations (MNC), but SMEs cannot adopt sustainable practices from the knowledge and experiences of large corporations because the two entities differ critically. This study introduces an integrated model of employee adoption of sustainable practices in SMEs. It is based on five behaviors to select practical areas to which SMEs can make internal changes to achieve sustainable practices and the benefits gained from them. The theory of planned behavior is used to extend employee adoption of sustainable practices to SMEs.