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Artificial intelligence (AI) is a growing paradigm and has made considerable changes in many fields of study, including medical education. However, more investigations are needed to successfully adopt AI in medical education. The purpose of this study was identify the determinant factors in adopting AI-driven technology in medical education.

This was a descriptive-analytical study in which 163 faculty members from Tabriz University of Medical Sciences were randomly selected by nonprobability sampling technique method. The faculty members’ intention concerning the adoption of AI was assessed by the conceptual path model of task-technology fit (TTF).

According to the findings, “technology characteristics,” “task characteristics” and “TTF” showed direct and significant effects on AI adoption in medical education. Moreover, the results showed that the TTF was an appropriate model to explain faculty members’ intentions for adopting AI. The valid proposed model explained 37% of the variance in faulty members’ intentions to adopt AI.

By presenting a conceptual model, the authors were able to examine faculty members’ intentions and identify the key determining factors in adopting AI in education. The model can help the authorities and policymakers facilitate the adoption of AI in medical education. The findings contribute to the design and implementation of AI-driven technology in education.

The finding of this study should be considered when successful implementation of AI in education is in progress.

The purpose of this paper is to describe the first National Technology Roadmap (NTRM) of China, compiled by the Ministry of Science and Technology in 2007, in accordance with the analytical framework of “national needs‐strategic tasks‐key technologies‐development priorities,” and to comment upon its progress.

The technology foresight in 2003‐2006 was organized by the Chinese Ministry of Science and Technology. Based on two large rounds of Delphi investigations involving 1,400 experts and a SWOT analysis of every candidate technology, the key technologies were selected and the characteristics of them acquired. A large number of panels were organized to discuss and affirm the national needs and strategic tasks, so the logic relationships among the key items of NTRM could be clarified.

Five national needs, 30 strategic tasks, and 90 key technologies were promoted, and their relationships constructed. The characteristics of all the 90 technologies, including their R&D basis, gap between their level and the levels of advanced countries, and the possible time needed to realize these technologies were evaluated. The NTRM of China was successfully formulated.

Only nine technology fields were considered in the Delphi investigations, because of the limited study conditions. This might become the limitation to the application of this NTRM. In further study, more technology fields should be involved and all the processes finished as soon as possible.

The results of the NTRM were partly adopted by the 11th science and technology plan of China. Through the progress of NTRM, agreements concerning the key technologies and their development roadmaps were made. Both the NTRM and its compiling process provide important reference points for scientific and technological managers, scientists, and entrepreneurs, because they will help to make better decisions and contribute greatly to the research and development of the technologies.

This is the first attempt in China to make a NTRM. Results from the large‐scale Delphi investigations were taken as the basis for the roadmapping. The model of “national needs‐strategic tasks‐key technologies‐development priorities” was promoted and the technology roadmapping at a national level was conducted successfully.

Smart card-based E-payment systems are receiving increasing attention as the number of implementations is witnessed on the rise globally. Understanding of user adoption behavior of E-payment systems that employ smart card technology becomes a research area that is of particular value and interest to both IS researchers and professionals. However, research interest focuses mostly on why a smart card-based E-payment system results in a failure or how the system could have grown into a success. This signals the fact that researchers have not had much opportunity to critically review a smart card-based E-payment system that has gained wide support and overcome the hurdle of critical mass adoption. The Octopus in Hong Kong has provided a rare opportunity for investigating smart card-based E-payment system because of its unprecedented success. This research seeks to thoroughly analyze the Octopus from technology adoption behavior perspectives.

Cultural impacts on adoption behavior are one of the key areas that this research posits to investigate. Since the present research is conducted in Hong Kong where a majority of population is Chinese ethnicity and yet is westernized in a number of aspects, assuming that users in Hong Kong are characterized by eastern or western culture is less useful. Explicit cultural characteristics at individual level are tapped into here instead of applying generalization of cultural beliefs to users to more accurately reflect cultural bias. In this vein, the technology acceptance model (TAM) is adapted, extended, and tested for its applicability cross-culturally in Hong Kong on the Octopus. Four cultural dimensions developed by Hofstede are included in this study, namely uncertainty avoidance, masculinity, individualism, and Confucian Dynamism (long-term orientation), to explore their influence on usage behavior through the mediation of perceived usefulness.

TAM is also integrated with the innovation diffusion theory (IDT) to borrow two constructs in relation to innovative characteristics, namely relative advantage and compatibility, in order to enhance the explanatory power of the proposed research model. Besides, the normative accountability of the research model is strengthened by embracing two social influences, namely subjective norm and image. As the last antecedent to perceived usefulness, prior experience serves to bring in the time variation factor to allow level of prior experience to exert both direct and moderating effects on perceived usefulness.

The resulting research model is analyzed by partial least squares (PLS)-based Structural Equation Modeling (SEM) approach. The research findings reveal that all cultural dimensions demonstrate direct effect on perceived usefulness though the influence of uncertainty avoidance is found marginally significant. Other constructs on innovative characteristics and social influences are validated to be significant as hypothesized. Prior experience does indeed significantly moderate the two influences that perceived usefulness receives from relative advantage and compatibility, respectively. The research model has demonstrated convincing explanatory power and so may be employed for further studies in other contexts. In particular, cultural effects play a key role in contributing to the uniqueness of the model, enabling it to be an effective tool to help critically understand increasingly internationalized IS system development and implementation efforts. This research also suggests several practical implications in view of the findings that could better inform managerial decisions for designing, implementing, or promoting smart card-based E-payment system.

Adopting an interdisciplinary perspective, the purpose of this paper was to posit an industry-wide technological intervention for hotel housekeeper safety and health through the advancement of wearable technology.

Using the task-technology fit (TTF) model and examples of successful safety and health applications of wearable technologies in the health-care and construction management industries, interventions and future research directions are presented to address workplace hazards experienced by hotel housekeepers.

The fit between a variety of hotel housekeeper user requirements, task demands and wearable functions are explored with justification for the use of wearable devices to improve safety and health-related outcomes.

A research agenda is proposed for the adoption and use of wearables in the hospitality industry with the intention to generate meaningful interventions beyond corporate wellness, and the mitigation of employee privacy concerns to enhance wearable adoption.

Given the importance of consumer safety and health assurance in a post-pandemic business environment, hospitality and tourism organizations should place greater emphasis on protecting front line employees who will be essential in regaining economic viability.

Theoretical and practical foci should move beyond a simplistic view of hospitality and tourism worker safety and health that generally centers on wellness initiatives and other baseline strategies, toward a more holistic view benefitting the hospitality industry.

Extant concerns about hotel housekeeper safety and health, in addition to new concerns and threats in a post-pandemic work environment, are largely understudied and worthy of investigation.

The purpose of this paper is to analyze the determinants of the performance of regional industrial technology development programs among the regional strategic industrial development program that the central government and Daegu metropolitan city jointly promoted between 2004 and 2012. Specifically, in this research, the authors are trying to identify the effects of R&D capabilities and technical development tasks on technological and managerial performance.

The dependent variables of this study are technical and economic performance. Technical performance, product and process innovation, economic performance, sales and export increases were measured using five-point Likert scales. The authors added the contribution of sales through technology development to economic performance. The independent variable is the company’s R&D capability, measured by the number of R&D staff compared to the average total number of employees from 2004 to 2012. The characteristics of the technology development tasks were measured by technical characteristics, market characteristics and collaborative research types. The technological characteristics were measured by seven factors, including technological change, technical difficulty, potential in commercialization, competition between domestic and foreign competitors, difficulty in introducing overseas technology and the technological gap. Market characteristics were largely divided into complexity, dynamics and competitiveness. The types of collaborative research were divided into whether or not there were collaborative research with the participation of large corporations. The control variables are firm size (number of employees) and firm age. Regression analysis was used to analyze the determinants of performance, and a difference analysis was conducted to determine the effect of collaborative research on performance.

The main determinants of the regional industrial technology development program performance are the characteristics of the technology development task rather than the internal R&D capability; moreover, the technical characteristics, complexity of the developed product market and participation of large corporations had significant effects on R&D capability. The R&D capacity of firms in internal R&D capacity had a significant effect only on the improvement of technology development ability. Therefore, R&D capacity, which is the main determinant of technology innovation, did not have a significant effect on the performance of short-term technology development tasks. Technological change, technological difficulty, competition between domestic and foreign competitors and the technological gap had positive effects on performance, excluding sales contributions. In addition, the complexity of the developed product market such as the diversification of demand, competitive product and sales distribution channels had positive influences on the performance of technology development programs, unlike dynamics and competitiveness. In this study, the authors cannot confirm the effect of collaborative research on the performance of the technology development programs, but they confirmed that collaborative research involving large corporations had a positive influence on performance.

The results of the analysis of the determinants of regional industrial technology development programs suggest some implications in the future evaluation of these regional industrial technology development programs. It is necessary to review the application qualification and merit, advance review of the business plans and confirmation, an examination of the research results and performance of the applicants and a review of the technology and market situation of the project. For this, the authors suggest that the written review from the relevant technical experts be submitted to the evaluation committees. Also, when establishing regional industrial development programs, they should be evaluated thoroughly, including detailed information and contents about the technical and market characteristics of the local industry.

This research is one of the first to investigate the achievements of R&D support programs among regional industrial development programs in Korea. The results of this study can substantially contribute to the development and implementation of the R&D support policies of the central and local governments. Furthermore, the findings suggest guidelines for improving the performance of R&D support programs in the future. A theoretical model for enhancing the efficiency of government R&D support programs may be established, and an empirical analysis may be conducted to provide practical and academic implications for further research.

Global virtual teams (GVTs) – composed of members in two or more countries who work together primarily using information and communication technologies – are increasingly prevalent in organizations today. There has been a burgeoning of research on this relatively new organizational unit, spanning various academic disciplines. In this chapter, we review and discuss the major developments in this area of research. Based on our review, we identify areas in need of future research, suggest research directions that have the potential to enhance theory development, and provide practical guidelines on managing and working in GVTs.

Online labour markets (OLMs) have recently become a widespread phenomenon of digital work. While the implications of OLMs on worker well-being are hotly debated, little empirical research examines the impact of such work on individuals. The highly competitive and fast-paced nature of OLMs compels workers to multitask and to perform intense technology-enabled work, which can potentially enhance technostress. This paper examines the antecedents and well-being consequences of technostress arising from work in OLMs.

The authors draw from person–environment fit theory and job characteristics theory and test a research model of the antecedents and consequences of worker technostress in OLMs. Data were gathered from 366 workers in a popular OLM through a large-scale online survey. Structural equation modelling was used to evaluate the research model.

The findings extend existing research by validating the relationships between specific OLM characteristics and strain. Contrary to previous literature, the results indicate a link between technology complexity and work overload in OLMs. Furthermore, in OLMs, feedback is positively associated with work overload and job insecurity, while strain directly influences workers' negative affective well-being and discontinuous intention.

This study contributes to technostress literature by developing and testing a research model relevant to a new form of work conducted through OLMs. The authors expand the current research on technostress by integrating job characteristics as new antecedents to technostress and demonstrating its impact on different types of subjective well-being and discontinuous intention. In addition, while examining the impact of technostressors on outcomes, the authors consider their impact at the individual level (disaggregated approach) to capture the subtlety involved in understanding technostressors' unique relationships with outcomes.

Traditionally, Task–Technology Fit (TTF) theory has been applied to examine the usefulness of technology in the work environment. Can the same approach (based on experience rather than tasks) be applied to non-work, cultural heritage environments? This is the question the authors ask in this study. This study proposes a new variation of TTF based on the experience economy model, namely Experience–Technology Fit (ETF), for the non-work environment, in particular, in the context of cultural heritage, where visitor experience is enhanced by extended reality technology, which combines immersive technologies and artificial intelligence.

Employing a quantitative survey method, the empirical analysis seeks to determine the influence of Mixed Reality (MR) characteristics (interactivity, vividness), Voice User Interface (VUI) characteristics (speech recognition, speech synthesis) and experience economy factors (education, entertainment, esthetic, escape) on satisfaction, revisit intention and actual purchase to propose a new ETF model.

VUI, MR, and experience factors were significantly associated with ETF; when combined with MR-based experience, ETF was significantly associated with satisfaction. This study’s findings further demonstrate the relationship between users' satisfaction when engaging with MR-based experience and revisit intention, while purchase intention was significantly associated with the actual purchase.

The novel contribution of this study is the proposal of the EFT model, a new variation of TTF based on the experience economy model. Overall, this study expands the applications of TTF to an experience-oriented business, thereby broadening the authors’ understanding of technological success with a specific focus on the technology fit of Extended Reality (XR) in the context of cultural heritage.

The purpose of this paper is to investigate the effect of human-computer interaction (HCI) on customers’ perceived electronic service (e-service) value and the mediating role of task-technology fit (TTF) in that effect.

This paper develops a model based on service-dominant logic (SDL) and TTF theory, and validates it using a hierarchical regression with the data collected from 634 online banking customers in Guangdong Province and Guangxi Zhuang Autonomous Region in China.

The findings reveal that HCI in e-service contexts comprises five components. Three fundamental components (i.e. technology functionality, customer technology readiness and task routine) contribute to value co-creation. Two core components (i.e. interaction between customer technology readiness and technology functionality, and interaction between task routine and technology functionality) are inhibitors, but the inhibitory effect of the former is only significant in the Guangdong sample. TTF takes a mediating role in these relationships, but the mediating effect of the former core component is only significant in the Guangdong sample.

This paper explains two basic questions about the trigger points of value co-creation in e-service contexts (i.e. what their operational definitions are and how to measure them) and unlocks the “black box” of value co-creation by taking TTF as a mediator. SDL and TTF theory are extended. The paper provides suggestions for how practitioners can efficiently advance value co-creation with customers.

Human resource managers are adopting AI technology for conducting various tasks of human resource management, starting from manpower planning till employee exit. AI technology is prominently used for talent acquisition in organizations. This research investigates the adoption of AI technology for talent acquisition.

This study employs Technology-Organization-Environment (TOE) and Task-Technology-Fit (TTF) framework and proposes a model to explore the adoption of AI technology for talent acquisition. The survey was conducted among the 562 human resource managers and talent acquisition managers with a structured questionnaire. The analysis of data was completed using PLS-SEM.

This research reveals that cost-effectiveness, relative advantage, top management support, HR readiness, competitive pressure and support from AI vendors positively affect AI technology adoption for talent acquisition. Security and privacy issues negatively influence the adoption of AI technology. It is found that task and technology characteristics influence the task technology fit of AI technology for talent acquisition. Adoption and task technology fit of AI technology influence the actual usage of AI technology for talent acquisition. It is revealed that stickiness to traditional talent acquisition methods negatively moderates the association between adoption and actual usage of AI technology for talent acquisition. The proposed model was empirically validated and revealed the predictors of adoption and actual usage of AI technology for talent acquisition.

This paper provides the predictors of the adoption of AI technology for talent acquisition, which is emerging extensively in the human resource domain. It provides vital insights to the human resource managers to benchmark AI technology required for talent acquisition. Marketers can develop their marketing plan considering the factors of adoption. It would help designers to understand the factors of adoption and design the AI technology algorithms and applications for talent acquisition. It contributes to advance the literature of technology adoption by interweaving it with the human resource domain literature on talent acquisition.

This research uniquely validates the model for the adoption of AI technology for talent acquisition using the TOE and TTF framework. It reveals the factors influencing the adoption and actual usage of AI technology for talent acquisition.