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This study aims to examine how the theory of planned behaviour and technology acceptance theory can be used to understand the adoption of non-pharmaceutical interventions (NPIs). The relationships between attitudes, behavioural intentions towards using NPIs, actual use of NPIs and word-of-mouth (WOM) were examined and compared between early and late adopters.

A survey was conducted to test the hypotheses with partial least squares structural equation modelling (n = 278).

The results indicate that relationships between attitudes, intentions and behavioural intentions were positive and significant in the whole data set – and that there were differences between the early and late adopters. WOM had no substantial relationship with actual usage and early adopters’ behavioural intentions.

This research gives a better sense of how WOM impacts attitudes, behavioural intentions and actual usage among early and late adopters of NPIs and highlights the effectiveness of WOM, especially among late adopters of NPIs. Furthermore, using the TAM allows us to make specific recommendations regarding encouraging the use of NPIs. A new three-stage communications model is introduced that uses early adopters as influencers to reduce the NPI adoption time by late adopters.

The study aims to explore the digital fashion trend within the Metaverse, characterized by non-fungible tokens (NFTs), across Twitter networks. Integrating theories of diffusion of innovation, two-step flow of communication and self-efficacy, the authors aimed to uncover the diffusion structure and the influencer's social roles undertaken by social entities in fostering communication and collaboration for the advancement of Metaverse fashion.

Social network analysis examined the critical graph metrics to profile, visualize, and cluster the unstructured network data. The authors used the NodeXL program to analyze two hashtag keyword networks, “#metaverse fashion” and “#metawear,” using Twitter API data. Cluster, semantic, and time series analyses were performed to visualize the contents and contexts of communication and collaboration in the diffusion of Metaverse fashion.

The results unraveled the “broadcast network” structure and the influencers' social roles of opinion leaders and market mavens within Twitter's “#metaverse fashion” diffusion. The roles of innovators and early adopters among influencers were comparable in collaborating within the competition venues, promoting awareness and participation in digital fashion diffusion during specific “fad” periods, particularly when digital fashion NFTs and cryptocurrencies became intertwined with the competition in the Metaverse.

The study contributed to theory building by integrating three theories, emphasizing effective communication and collaboration among influencers, organizations, and competition venues in broadcasting digital fashion within shared networks. The validation of multi-faceted Social Network Analysis was crucial for timely insights, highlighting the critical digital fashion equity in capturing consumers' attention and driving engagement and ownership of Metaverse fashion.

Diffusion of innovations, defined as the adoption and implementation of new ideas, processes, products, or services in health care, is both particularly important and especially challenging. One known problem with adoption and implementation of new technologies is that, while organizations often make innovations immediately available, organizational actors are more wary about adopting new technologies because these may impact not only patients and practices but also reimbursement. As a result, innovations may remain underutilized, and organizations may miss opportunities to improve and advance. As innovation adoption is vital to achieving success and remaining competitive, it is important to measure and understand factors that impact innovation diffusion. Building on a survey of a national sample of 654 clinicians, our study measures the extent of diffusion of value-enhancing care delivery innovations (i.e., technologies that not only improve quality of care but has potential to reduce care cost by diminishing waste, Faems et al., 2010) for 13 clinical specialties and identifies healthcare-specific individual characteristics such as: professional purview, supervisory responsibility, financial incentive, and clinical tenure associated with innovation diffusion. We also examine the association of innovation diffusion with perceived value of one type of care delivery innovation – artificial intelligence (AI) – for assisting clinicians in their clinical work. Responses indicate that less than two-thirds of clinicians were knowledgeable about and aware of relevant value-enhancing care delivery innovations. Clinicians with broader professional purview, more supervisory responsibility, and stronger financial incentives had higher innovation diffusion scores, indicating greater knowledge and awareness of value-enhancing, care delivery innovations. Higher levels of knowledge of the innovations and awareness of their implementation were associated with higher perceptions of the value of AI-based technology. Our study contributes to our knowledge of diffusion of innovation in healthcare delivery and highlights potential mechanisms for speeding innovation diffusion.

Although social media has gained prominence as a communication and marketing tool in various sectors, its adoption and utilization within the construction industry remain relatively underexplored. Therefore, this study fills this gap by evaluating the level of awareness and the extent of adoption of social media within the Nigerian construction industry, shedding light on its current status and potential impact.

This objective was attained via a quantitative research approach that utilized a structured questionnaire to obtain responses from construction professionals such as architects, builders, engineers, quantity surveyors and estate managers. Frequencies and percentages and the mean item score (MIS) were used to analyze the questionnaire responses and assess the overall awareness and adoption of social media among construction professionals. Additionally, the Kruskal–Wallis H-test provided valuable insights into the variations in social media adoption levels among different professional categories within the construction industry.

The results indicate that construction professionals possess a generally high level of awareness regarding various social media platforms. However, despite this awareness, the extent of adoption does not align with the level of awareness, suggesting that adoption rates are not as widespread as anticipated.

The findings of this study underscore the importance of not just awareness but also effective adoption and utilization of social media platforms. While awareness is a crucial first step, construction firms should focus on implementing strategies to encourage greater adoption and integration of these platforms into their daily operations. This can go a long way in bridging the awareness – adoption gap which was revealed in this study.

While the limited existing research on social media in the construction industry has predominantly concentrated on areas such as marketing, addressing the root causes of fatalities, data environment tools and business branding, none have undertaken a thorough evaluation of social media awareness and adoption within the sector. This study fills a critical gap by narrowing its focus to the adoption dynamics and the technology’s potential impact on communication, collaboration and knowledge sharing among construction professionals.

The purpose of this paper is examining how financial inclusion can be enhanced in rural and remote areas in emerging economies through the adoption of Financial technologies. The study examines the direct influence of technological characteristics, facilitating conditions, trust and perceived risk on adoption of FinTech. Furthermore, the study examines the mediating effects of facilitating conditions and moderating effects of education level in adoption of FinTech.

The study employed quantitative research design. Stratified and simple random sampling were employed to identify areas in which respondents will be collected. Drop and Pick method was further employed to collect 275 valid and reliable responses. Structural equation modeling was used to analyze the data and assess the reliability and validity of the measurement model. Structural model analysis was employed to examine the hypothesized relationship.

The results show that compatibility, complexity agent trust, perceived risk and facilitating have significant influence on adoption of FinTech. Furthermore, the findings show that facilitating conditions mediate the relationship between all technological characteristics and FinTech adoption except the relationship between compatibility and FinTech. The study also has found that education level moderates the relationship between observability and FinTech adoption as well as trialability and FinTech adoption.

This study employed cross-sectional research-based techniques in collecting data for analyzing the hypothesized relationship. However, the drawback of cross-sectional may impact the findings of this study over a long time. Therefore, future studies must consider conducting longitudinal studies which may collect data over some time for analysis of the FinTech adoption. Furthermore, the generalization of these findings should be considered carefully by other countries which are not economically similar to Tanzania and developed countries. This also provides room for future studies to examine the same area in countries with different economic development.

This study contributes to knowledge into two folds: First there is limited knowledge on adoption of FinTech in emerging economies; the study addresses the knowledge gap by providing empirical findings which will help scholar. Furthermore, less studies have examined the moderating effects of facilitating condition in adoption of technology. This study addresses the knowledge gap by examining the moderating influence of moderating influence of facilitating conditions. Also, this study provides empirical findings which will help policymakers to implement strategy that will speed-up financial inclusion in rural and isolated areas in emerging economies.

This study develops a comprehensive research model and investigates the significant factors affecting positive marketing outcomes in the Metaverse through perceived social benefits and trust.

The authors propose a new research model based on social exchange theory (SET) and examine the impact of cost and reward factors. Using 327 survey samples collected from current Metaverse users in South Korea, dual-stage analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM) and an artificial neural network (ANN) were employed to test the study’s hypotheses.

The results showed that perceived social benefit and trust had significant mediating effects on marketing outcomes, such as loyalty to the seller, product/service attitude, and purchase intention. All antecedents, except perceived performance risk, had a crucial impact on the two mediators. The most interesting finding of this study is the positive influence of knowledge-seeking efforts on perceived social benefits.

This study is the first empirical research to examine the effectiveness of marketing in the Metaverse. It also proposes a new theoretical model based on SET to investigate users’ behavioral intentions regarding marketing in the Metaverse, and confirms its explanatory power. Moreover, the results of this study also offer suggestions to brands on how to market to consumers in the Metaverse.

The study utilized the consumption value theory to explore the motivational factors that define and differentiate the users and nonusers of fashion rental services

A focus group was conducted to generate an initial list of measurement items. These items were refined through a pretest and then used in a self-administered online questionnaire to collect data from a total of 300 users and 300 nonusers. The collected data were analyzed using factor analysis to identify the factors that define users and nonusers. A MANOVA was then conducted to explore the differences in the identified factors between users and nonusers.

Using factor analysis, nine factors were extracted across the five consumption values (functional, social, emotional, conditional and epistemic). MANOVA revealed a significant difference between users and nonusers across all factors. Further analyses suggested that the most differentiating factors are two emotional value factors and one social value factor.

Despite existing studies of fashion rental services, it is debatable whether the phenomenon is fully understood since previous studies primarily focus on consumers who engage in fashion renting services – there is a lack of focus on nonusers. This study provides unique contributions by exploring the phenomenon from both the user's and the nonuser's perspective.

The aim of this paper is to summarise the state-of-the-art debate on impact of artificial intelligence on unemployment and reporting up-to-date academic findings.

The paper is designed as a review of the labour vs capital conundrum, the differences between industrial automation and artificial intelligence, threat to employment, the difficulty of substituting, role of soft skills and whether technology leads to the deskilling of human workers or favors increasing human capabilities.

Some authors praise the bright future developments of artificial intelligence while others warn about mass unemployment. Therefore, it is paramount to present an up-to-date overview of the problem, compare and contrast its features with what happened in past innovation waves and contribute to academic discussion about the pros/cons of current trends.

The main value of this paper is presenting a balanced view of 100+ different studies, the vast majority from the last five years. Reading this paper will allow to quickly grasp the main issues around the thorny topic of artificial intelligence and unemployment.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2023-0338

This study delves into the psychological processes underlying sport fans' post-purchase innovativeness behaviour. This exploratory research aims to establish a theoretical framework that elucidates the formation of sport fans' word-of-mouth (WOM) behaviours, particularly emphasising the structural relationship between motivated consumer innovativeness and satisfaction in using AR live-streaming services.

Utilising an online survey and convenience sampling, the study garnered a total of 243 usable responses from three online baseball fan communities in South Korea. Confirmatory factor analysis was employed to assess the psychometric properties of the constructs. Subsequently, a structural equation model was used to probe the influence of motivated consumer innovativeness on WOM, with a particular focus on the mediating role of satisfaction.

Three dimensions of motivated sport fans innovativeness – functional, hedonic and cognitive – showed a positive association with WOM, partly mediated by satisfaction. In contrast, socially motivated sport fans innovativeness did not directly lead to WOM but influenced it solely through satisfaction. The full mediating role of satisfaction in the relationship between socially motivated fans innovativeness and WOM was found.

This research stands out as one of the scant studies exploring motivated sport fans innovativeness in the context of AR live sport streaming. The findings not only corroborate but also augment the extant literature by empirically confirming that three dimensions of motivated fans innovativeness, coupled with satisfaction, are pivotal antecedents to WOM intention.

Significant developments in the service sector have been brought about by Industry 4.0. Automated digital technologies make it possible to upgrade existing services and develop modern industrial services. This study prioritizes critical factors for adopting Industry 4.0 in the Indian service industries.

The author identified four criteria and fifteen significant factors from the relevant literature that have been corroborated by industry experts. Models are then developed by the analytical hierarchy process (AHP) and analytical network process (ANP) approach to ascertain the significant factors for adopting Industry 4.0 in service industries. Further, sensitivity analysis has been conducted to determine the sensitivities of the rank of criteria and sub-factors to corroborate the results.

The outcome reveals the top significant criteria as organizational criteria (0.5019) and innovation criteria (0.3081). This study prioritizes six significant factors information technology (IT) specialization, digital decentralization of all departments, organizational size, smart services through customer data, top management support and Industry 4.0 infrastructure in the transition toward Industry 4.0 in the service industries.

The potential factors identified in this study will assist managers in determining strategies to effectively manage the Industry 4.0 transition by concentrating on top priorities when leveraging Industry 4.0. The significance of organizational and innovation criteria given more weight will lay the groundwork for future Industry 4.0 implementation guidelines in service industries.

Our research is novel since, to our knowledge, no previous study has investigated the potential critical factors from organizational, environmental, innovation and cost dimensions. Thus, the potential critical factors identified are the contributions of this study.