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Intent detection (ID) and slot filling (SF) are two important tasks in natural language understanding. ID is to identify the main intent of a paragraph of text. The goal of SF is to extract the information that is important to the intent from the input sentence. However, most of the existing methods use sentence-level intention recognition, which has the risk of error propagation, and the relationship between intention recognition and SF is not explicitly modeled. Aiming at this problem, this paper proposes a collaborative model of ID and SF for intelligent spoken language understanding called ID-SF-Fusion.

ID-SF-Fusion uses Bidirectional Encoder Representation from Transformers (BERT) and Bidirectional Long Short-Term Memory (BiLSTM) to extract effective word embedding and context vectors containing the whole sentence information respectively. Fusion layer is used to provide intent–slot fusion information for SF task. In this way, the relationship between ID and SF task is fully explicitly modeled. This layer takes the result of ID and slot context vectors as input to obtain the fusion information which contains both ID result and slot information. Meanwhile, to further reduce error propagation, we use word-level ID for the ID-SF-Fusion model. Finally, two tasks of ID and SF are realized by joint optimization training.

We conducted experiments on two public datasets, Airline Travel Information Systems (ATIS) and Snips. The results show that the Intent ACC score and Slot F1 score of ID-SF-Fusion on ATIS and Snips are 98.0 per cent and 95.8 per cent, respectively, and the two indicators on Snips dataset are 98.6 per cent and 96.7 per cent, respectively. These models are superior to slot-gated, SF-ID NetWork, stack-Prop and other models. In addition, ablation experiments were performed to further analyze and discuss the proposed model.

This paper uses word-level intent recognition and introduces intent information into the SF process, which is a significant improvement on both data sets.

This study aims to investigate the consumer behavior toward telemedicine services in India during the COVID-19 pandemic onset. With lockdown restrictions and safety concerns in visiting brick-and-mortar clinics or hospitals during the pandemic, Telemedicine had emerged as a potent alternative for seeking redressal to health issues. Based on theory and focus interviews with the telemedicine users, the researchers proposed a model to understand the intent and actual usage of telemedicine in India.

The cross-sectional study undertaken used a questionnaire designed on a seven-point Likert scale and administered to respondents with the objective of identifying the determinants of intent and actual usage of telemedicine services. Simple random sampling was used to collect primary data. The data was cleaned and finally a sample of 405 responses complete in all respects was considered for analysis. The questionnaire comprised of 34 items and following the recommendation of Hair et al. (2016), which says the minimum sample size in structural equation modeling should be ten times the number of indicator variables, a sample size of 405 was deemed adequate.

The research paper finds that performance expectancy, attitude, credibility and self-efficacy positively impact the intention of consumers to use telemedicine services. As the effort expectancy or risk perception toward telemedicine increases the intent and actual usage of telemedicine decreases. The intention to use telemedicine emerged as a strong predictor of the actual usage of telemedicine. Intent to use telemedicine was explained 81.4% by its predictors of performance expectancy, effort expectancy, attitude, risk, credibility and self-efficacy, and actual usage was explained 79.9% by its predictors. This study also reports that telemedicine was found to be popular among chronic as well as episodic patients though the preference was skewed in favor of the episodic patients. One of the advantages of telemedicine is its availability round the clock, and the study found that 8 a. m. to 12 noon time slot as the most preferred slot for seeking telemedicine services.

Chang (2004) opined that telemedicine can fulfill the needs of all stakeholders: citizens, health-care consumers, medical doctors and health-care professionals, policymakers, and so on. Considering the promise telemedicine holds, this realm must be studied and leveraged to the full potential. The study found that patients were using telemedicine even for their day-to-day aliments. This indicates a growing popularity of telemedicine and as such an opportunity for telemedicine companies to leverage it. In India, pharmaceutical companies cannot give commercial advertisements for medicines, and the same can only be sold through a registered medical practitioner’s prescription. As such there is total dependency on the medical practitioner for the sale of medicines. Telemedicine companies offer services of home delivering medicines clubbed with medical consultation thus giving them forward integration in their business models. Using telemedicine the patients had control over the timings of the services offered, and as such the waiting time to get a consultation and subsequent treatment was reduced considerably. Best medical advice from across the globe is available to the patient at less cost. Medical practitioners also stand to benefit as they can treat a variety of cases, collaborate among the medical fraternity and give consultation safely in case of fatal contagious diseases.

This study points to a definite growing popularity of telemedicine services not only in episodic patients but also chronic patients. Telemedicine with its unique advantages holds the promise to grow exponentially in the future and is a compelling health-care segment to focus on for delivering health-care solution to the geographically distant consumers.

International assignments are not just opportunities for career advancement but also for personal growth and exploration. This paper, by capturing the changing expectations and success parameters across the assignments, is a timely and relevant resource for individuals navigating the complexities of international careers. In addition, the paper aims to help organizations build policies for enabling successful assignments for international assignees and managers.

Our research took a unique two-pronged approach to understanding the success parameters of international assignments. We first interviewed 16 managers to understand the success parameters they set for expatriates. Then, we interviewed 19 international assignees to explore their choices and how they defined success in their international experiences.

Our comprehensive research, based on 35 interviews with international assignees and their supervisors, reveals that an assignment's success evolves with different stages of the assignment, over the assignments that the assignees choose, and varies according to the intent of the mobility.

This paper makes a significant contribution to the field of cross-cultural management and international assignments. By discussing the evolving definition of success across various international assignments, it not only provides valuable insights for expatriates in their international careers but also to managers to build aligned policies and practices.

This study aims to analyze the implications of the recent European Union Regulation 2023/1542 on the circular economy and stakeholder strategies within the electric vehicle lithium-ion battery (EV-LIB) sector. It aims to explain the policy intentions, recommend practical strategies for stakeholders and examine how the new regulation exerts pressure on stakeholders to transition from older directives to more sustainable practices and operational standards, while also highlighting policy gaps.

The research employs a dual-method approach, combining text analysis of EU legislation with semi-structured interviews of industry stakeholders. This methodology allows for a comprehensive understanding of the regulatory impacts by integrating legislative intent with practical, on-the-ground insights from key players in the EV-LIB sector.

Our findings show that the three aggregated dimensions of operational sustainability, R&D and new technologies and collaborative dynamics are the key dynamics underlying the intended outcomes. The findings also highlight the policy’s historical development, the stakeholder categories, the implications for each and practical recommendations in responding to the policy requirements. Additionally, the findings identify policy gaps, such as weak incentives and broad economic operator classifications, with examples from international markets. The regulation creates proactive stakeholders driving innovation and collaboration and reactive ones adapting to changes, where static implicit implications may affect their viability by imposing unequal burdens.

To the best of the authors’ knowledge, this paper is the first to analyze the new EU Regulation 2023/1542, offering novel insights into the strategic responses required by stakeholders to adapt to the regulatory pressures. By focusing on the latest regulatory framework and its practical implications, the study bridges the gap between policy and practice, providing valuable guidance for industry players navigating the evolving regulatory environment.

• (1)

  EU’s policy shift from Directive to Regulation (EU) 2023/1542 has extended implications on the Electric Vehicles battery sector.

• (2)

  Duel qualitative methods of text analysis and semi-structured interviews validated three aggregate dimensions and policy gaps.

• (3)

  R&D with advancing technology, Operational sustainability and safety and Collaboration dynamics are dominating the scene.

• (4)

  Emergence of Proactive vs Reactive stakeholder dynamics.

• (5)

  The broad classification of “economic operators” and insufficiently detailed incentives, hinting at potential competitive imbalances and underexplored roles of end-users in achieving circular economy goals are appearing policy’ gaps.

EU’s policy shift from Directive to Regulation (EU) 2023/1542 has extended implications on the Electric Vehicles battery sector.

Duel qualitative methods of text analysis and semi-structured interviews validated three aggregate dimensions and policy gaps.

R&D with advancing technology, Operational sustainability and safety and Collaboration dynamics are dominating the scene.

Emergence of Proactive vs Reactive stakeholder dynamics.

The broad classification of “economic operators” and insufficiently detailed incentives, hinting at potential competitive imbalances and underexplored roles of end-users in achieving circular economy goals are appearing policy’ gaps.

The purpose of this paper is to identify the factors determining students’ attitude toward using newly emerged artificial intelligence (AI) tool, Chat Generative Pre-Trained Transformer (ChatGPT), for educational and learning purpose based on technology acceptance model.

The recommended model was empirically tested with partial least squares structural equation modeling using 375 student survey responses.

The study revealed that students have a favorable view of the instructional use of ChatGPT. Usefulness, social presence and legitimacy of the tool, as well as enjoyment and motivation, contribute to a favorable attitude toward using this tool in a learning environment. However, perceived ease of use was not found to be a significant determinant in the adoption and utilization of ChatGPT by the students.

This research is intended to benefit enterprises, academic institutions and the global community by offering light on how students perceive the ChatGPT service in an educational setting. Furthermore, the application enhances confidence and interest among learners, leading to improved literacy and general awareness. Eventually, the outcome of this research will help AI developers to improve their product and service delivery, as well as benefit regulators in regulating the usage of AI-based bots.

Due to its novelty, the current research on AI-based ChatGPT usage in the education sector is rather restricted. This study provides the adoption aspects of ChatGPT, a new AI-based technology for students, thereby contributing significantly to the existing research on the adoption of advanced education technologies. In addition, the literature lacks research on the adoption of ChatGPT by students for educational purposes; this study addresses this gap by identifying adoption determinants of ChatGPT in education.

The purpose of this article is to discuss the “learning nation” concept and examine the characteristics and implications of using the “learning” premodifier in this nation-building program.

This article reviews how the “learning” aspect is inter-related to a series of national information and communication technology masterplans and includes a comparative analysis of the related premodifier “smart” as Singapore sets forth its ambition to become a “smart nation” as part of the digitalization megatrend. A print media indicator and Google Trends form part of the methodology to ascertain the rise of digital technology over a certain period. The former technique involves identifying relevant bibliographic databases and analyzing the volume of publications. The latter technique is a real time index of the volume of queries that users input into Google.

It is suggested that using the term “learning nation” previously and more recently “smart nation” is a consequence of the rise of the digitalization megatrend. The “smart-ness” involves learning about digital technologies, developing digital competencies and harnessing the benefits of these digital capabilities. From a public policy perspective, the article showcases how a city-state can transform itself through technology by riding on megatrends. Also, there is a need to be selective in developing specific areas for the application of digital technologies.

This article contributes to a better understanding on the frequent usage of the word “learning” as a premodifier and Singapore’s nation-building journey through human capital development and digitalization.

The purpose of this study is to explore whether five factors drawn from the unified theory of acceptance and use of technology (UTAUT) and UTAUT2 significantly influence customers' intention to use hotel in-room voice assistants (VAs). It further examined culture as a moderator of the relationships between the five factors and customers' intention to use.

The authors collected data from US and Singapore to examine cultural differences in customer acceptance of in-room VAs. All hypotheses were tested via structural equation modeling and multi-group analysis.

The results showed that performance expectancy, social influence and hedonic motivation significantly affected customers' intentions to use in-room VAs, while effort expectancy and facilitating conditions did not. The results confirmed that culture did not play a substantial role in moderating the relationships between these factors and intentions to use.

This study established that the instrument and structural paths in the research model were equivalent across two samples from different countries. The findings may not generalize to other countries as the data arises from customers in the US and Singapore.

The findings provide important implications for hotel operators and vendors seeking to enhance customer acceptance of in-room voice technology.

This study addresses the gaps of extant research by developing and testing a research model to better understand the influential factors of in-room VA adoption within the hotel domain.

The purpose of this study is to provide a detailed demonstration of how artificial intelligence (AI) can be used to potentially generate valuable insights and recommendations regarding the role of accounting in addressing key sustainability-related issues.

The study offers a novel method for leveraging AI tools to augment traditional scoping study techniques. The method was used to show how the authors can produce recommendations for potentially enhancing organisational accountability pertaining to seasonal workers.

Through the use of AI and informed by the knowledge base that the authors created, the authors have developed prescriptions that have the potential to advance the interests of seasonal workers. In doing so, the authors have focussed on developing a useful and detailed guide to assist their colleagues to apply AI to various research questions.

This study demonstrates the ability of AI to assist researchers in efficiently finding solutions to social problems. By augmenting traditional scoping study techniques with AI tools, the authors present a framework to assist future research in such areas as accounting and accountability.

This study addresses how algorithmic recommendations and their affordances shape everyday information practices among young people.

Thematic interviews were conducted with 20 Finnish young people aged 15–16 years. The material was analysed using qualitative content analysis, with a focus on everyday information practices involving online platforms.

The key finding of the study is that the current affordances of algorithmic recommendations enable users to engage in more passive practices instead of active search and evaluation practices. Two major themes emerged from the analysis: enabling not searching, inviting high trust, which highlights the how the affordances of algorithmic recommendations enable the delegation of search to a recommender system and, at the same time, invite trust in the system, and constraining finding, discouraging diversity, which focuses on the constraining degree of affordances and breakdowns associated with algorithmic recommendations.

This study contributes new knowledge regarding the ways in which algorithmic recommendations shape the information practices in young people's everyday lives specifically addressing the constraining nature of affordances.

The world today is heavily controlled by the content available on the internet, where a one-star rating gain may work wonders for a company and a one-star rating decline can cause huge damage. Online booking platforms provide more freedom, privacy and contact with experienced travelers than physical hotel booking. The study identifies the factors shaping travelers' online hotel booking intention (OHBI).

We utilized structural equation modeling (SEM) to expand the horizons of the technology acceptance model (TAM) and stimulus-organism-response (SOR) framework in the hospitality sector. The results are based on the data collected from 705 travelers who made online hotel reservations.

The findings demonstrate that online reviews, hotel website quality and hotel website convenience quotient favorably shape prospective tourists' perceived trust, magnifying their inclination to book a hotel online. Website convenience quotient and trust partially mediate the association between the constructs. In addition, the linkage between perceived trust and OHBI is strengthened by promotional offers but weakened by perceived risk.

Our findings provide several important implications for hotel managers, prospective travelers, hotel owners, website developers, policymakers, hotel employees, the local community and competitors to expedite the growth of the Indian hotel industry.

The literature reveals that website convenience quotient, perceived trust and promotional offers have not received enough attention in the hospitality industry and warrant attention. Our study strives to broaden the scope of the TAM and SOR models to better understand these constructs in the backdrop of the Indian hospitality sector. The study also examines how promotional offers and perceived risk influence the linkages between the underlying constructs.