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1 – 10 of 253The application of artificial intelligence chatbots is an emerging trend in educational technology studies for its multi-faceted advantages. However, the existing studies rarely…
Abstract
Purpose
The application of artificial intelligence chatbots is an emerging trend in educational technology studies for its multi-faceted advantages. However, the existing studies rarely take a perspective of educational technology application to evaluate the application of chatbots to educational contexts. This study aims to bridge the research gap by taking an educational perspective to review the existing literature on artificial intelligence chatbots.
Design/methodology/approach
This study combines bibliometric analysis and citation network analysis: a bibliometric analysis through visualization of keyword, authors, organizations and countries and a citation network analysis based on literature clustering.
Findings
Educational applications of chatbots are still rising in post-COVID-19 learning environments. Popular research issues on this topic include technological advancements, students’ perception of chatbots and effectiveness of chatbots in different educational contexts. Originating from similar technological and theoretical foundations, chatbots are primarily applied to language education, educational services (such as information counseling and automated grading), health-care education and medical training. Diversifying application contexts demonstrate specific purposes for using chatbots in education but are confronted with some common challenges. Multi-faceted factors can influence the effectiveness and acceptance of chatbots in education. This study provides an extended framework to facilitate extending artificial intelligence chatbot applications in education.
Research limitations/implications
The authors have to acknowledge that this study is subjected to some limitations. First, the literature search was based on the core collection on Web of Science, which did not include some existing studies. Second, this bibliometric analysis only included studies published in English. Third, due to the limitation in technological expertise, the authors could not comprehensively interpret the implications of some studies reporting technological advancements. However, this study intended to establish its research significance by summarizing and evaluating the effectiveness of artificial intelligence chatbots from an educational perspective.
Originality/value
This study identifies the publication trends of artificial intelligence chatbots in educational contexts. It bridges the research gap caused by previous neglection of treating educational contexts as an interconnected whole which can demonstrate its characteristics. It identifies the major application contexts of artificial intelligence chatbots in education and encouraged further extending of applications. It also proposes an extended framework to consider that covers three critical components of technological integration in education when future researchers and instructors apply artificial intelligence chatbots to new educational contexts.
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Aleš Zebec and Mojca Indihar Štemberger
Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to…
Abstract
Purpose
Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.
Design/methodology/approach
The integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.
Findings
The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.
Research limitations/implications
In pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.
Practical implications
The provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.
Originality/value
While previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.
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Ahmad Honarjoo and Ehsan Darvishan
This study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of…
Abstract
Purpose
This study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of repairing and rehabilitating massive bridges and buildings is very high, highlighting the need to monitor the structures continuously. One way to track the structure's health is to check the cracks in the concrete. Meanwhile, the current methods of concrete crack detection have complex and heavy calculations.
Design/methodology/approach
This paper presents a new lightweight architecture based on deep learning for crack classification in concrete structures. The proposed architecture was identified and classified in less time and with higher accuracy than other traditional and valid architectures in crack detection. This paper used a standard dataset to detect two-class and multi-class cracks.
Findings
Results show that two images were recognized with 99.53% accuracy based on the proposed method, and multi-class images were classified with 91% accuracy. The low execution time of the proposed architecture compared to other valid architectures in deep learning on the same hardware platform. The use of Adam's optimizer in this research had better performance than other optimizers.
Originality/value
This paper presents a framework based on a lightweight convolutional neural network for nondestructive monitoring of structural health to optimize the calculation costs and reduce execution time in processing.
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Buddhini Ginigaddara, Srinath Perera, Yingbin Feng, Payam Rahnamayiezekavat and Mike Kagioglou
Industry 4.0 is exacerbating the need for offsite construction (OSC) adoption, and this rapid transformation is pushing the boundaries of construction skills towards extensive…
Abstract
Purpose
Industry 4.0 is exacerbating the need for offsite construction (OSC) adoption, and this rapid transformation is pushing the boundaries of construction skills towards extensive modernisation. The adoption of this modern production strategy by the construction industry would redefine the position of OSC. This study aims to examine whether the existing skills are capable of satisfying the needs of different OSC types.
Design/methodology/approach
A critical literature review evaluated the impact of transformative technology on OSC skills. An existing industry standard OSC skill classification was used as the basis to develop a master list that recognises emerging and diminishing OSC skills. The master list recognises 67 OSC skills under six skill categories: managers, professionals, technicians and trade workers, clerical and administrative workers, machinery operators and drivers and labourers. The skills data was extracted from a series of 13 case studies using document reviews and semi-structured interviews with project stakeholders.
Findings
The multiple case study evaluation recognised 13 redundant skills and 16 emerging OSC skills such as architects with building information modelling and design for manufacture and assembly knowledge, architects specialised in design and logistics integration, advanced OSC technical skills, factory operators, OSC estimators, technicians for three dimensional visualisation and computer numeric control operators. Interview findings assessed the current state and future directions for OSC skills development. Findings indicate that the prevailing skills are not adequate to readily relocate construction activities from onsite to offsite.
Originality/value
To the best of the authors’ knowledge, this research is one of the first studies that recognises the major differences in skill requirements for non-volumetric and volumetric OSC types.
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Rongrong Teng, Shuai Zhou, Wang Zheng and Chunhao Ma
This study aims to investigate whether and how artificial intelligence (AI) awareness affects work withdrawal.
Abstract
Purpose
This study aims to investigate whether and how artificial intelligence (AI) awareness affects work withdrawal.
Design/methodology/approach
This survey garners participation from a total of 305 hotel employees in China. The proposed hypotheses are examined using Hayes’s PROCESS macro.
Findings
The results indicate that AI awareness could positively affect work withdrawal. Negative work-related rumination and emotional exhaustion respectively mediate this relationship. Furthermore, negative work-related rumination and emotional exhaustion act as chain mediators between AI awareness and work withdrawal.
Practical implications
Given the growing adoption of AI technology in the hospitality industry, it is imperative that managers intensify their scrutiny of the psychological changes experienced by frontline service employees and allocate more resources to mitigating the impact of AI on their work withdrawal.
Originality/value
This study contributes to the burgeoning literature on AI by elucidating the chain mediating roles of negative work-related rumination and emotional exhaustion. It also makes a significant forward in examining mediating mechanisms, notably the chain-mediated mechanism, through which AI awareness impacts employee outcomes.
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Vishal Ashok Wankhede, Rohit Agrawal, Anil Kumar, Sunil Luthra, Dragan Pamucar and Željko Stević
Sustainable development goals (SDGs) are gaining significant importance in the current environment. Many businesses are keen to adopt SDGs to get a competitive edge. There are…
Abstract
Purpose
Sustainable development goals (SDGs) are gaining significant importance in the current environment. Many businesses are keen to adopt SDGs to get a competitive edge. There are certain challenges in realigning the present working scenario for sustainable development, which is a primary concern for society. Various firms are adopting sustainable engineering (SE) practices to tackle such issues. Artificial intelligence (AI) is an emerging technology that can help the ineffective adoption of sustainable practices in an uncertain environment. In this regard, there is a need to review the current research practices in the field of SE in AI. The purpose of the present study is to comprehensive review the research trend in the field of SE in AI.
Design/methodology/approach
This work presents a review of AI applications in SE for decision-making in an uncertain environment. SCOPUS database was considered for shortlisting the articles. Specific keywords on AI, SE and decision-making were given, and a total of 127 articles were shortlisted after implying inclusion and exclusion criteria.
Findings
Bibliometric study and network analyses were performed to analyse the current research trends and to see the research collaboration between researchers and countries. Emerging research themes were identified by using structural topic modelling (STM) and were discussed further.
Research limitations/implications
Research propositions corresponding to each research theme were presented for future research directions. Finally, the implications of the study were discussed.
Originality/value
This work presents a systematic review of articles in the field of AI applications in SE with the help of bibliometric study, network analyses and STM.
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Rob Law, Katsy Jiaxin Lin, Huiyue Ye and Davis Ka Chio Fong
The purpose of this study is to analyze state-of-the-art knowledge of artificial intelligence (AI) research in hospitality.
Abstract
Purpose
The purpose of this study is to analyze state-of-the-art knowledge of artificial intelligence (AI) research in hospitality.
Design/methodology/approach
This study adopts the theory-context-methods framework to systematically review 100 AI-related articles recently published (i.e. from 2021 to April 2023) in three top-tier hospitality journals, namely, the International Journal of Contemporary Hospitality Management, International Journal of Hospitality Management and Journal of Hospitality Marketing and Management.
Findings
Findings suggest that studies of AI applications in hospitality are mostly theory-driven, whereas most AI methods research adopts a data-driven approach. State-of-the-art AI applications research exhibits the most interest in service robots. In AI methods research, little attention was paid to the amid-service/experience.
Research limitations/implications
This study reveals inadequacies in theory, context and methods in contemporary AI research. More research from hospitality suppliers’ perspectives and research on generative AI applications are advocated in response to the unveiled research gaps and recent AI developments.
Originality/value
This study classifies the most recent AI research in hospitality into two main streams – AI applications research and AI methods research – and discusses the gaps in each research stream and latest AI developments. The paper then suggests future research directions to guide researchers in advancing AI research in hospitality.
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Anniek Brink, Louis-David Benyayer and Martin Kupp
Prior research has revealed that a large share of managers is reluctant towards the use of artificial intelligence (AI) in decision-making. This aversion can be caused by several…
Abstract
Purpose
Prior research has revealed that a large share of managers is reluctant towards the use of artificial intelligence (AI) in decision-making. This aversion can be caused by several factors, including individual drivers. The purpose of this paper is to better understand the extent to which individual factors influence managers’ attitudes towards the use of AI and, based on these findings, to propose solutions for increasing AI adoption.
Design/methodology/approach
The paper builds on prior research, especially on the factors driving the adoption of AI in companies. In addition, data was collected by means of 16 expert interviews using a semi-structured interview guideline.
Findings
The study concludes on four groups of individual factors ranked according to their importance: demographics, familiarity, psychology and personality. Moreover, the findings emphasized the importance of communication and training, explainability and transparency and participation in the process to foster the adoption of AI in decision-making.
Research limitations/implications
The paper identifies four ways to foster AI integration for organizational decision-making as areas for further empirical analysis by business researchers.
Practical implications
This paper offers four ways to foster AI adoption for organizational decision-making: explaining the benefits and training the more adverse categories, explaining how the algorithms work and being transparent about the shortcomings, striking a good balance between automated and human-made decisions, and involving users in the design process.
Social implications
The study concludes on four groups of individual factors ranked according to their importance: demographics, familiarity, psychology and personality. Moreover, the findings emphasized the importance of communication and training, explainability and transparency and participation in the process to foster the adoption of AI in decision-making.
Originality/value
This study is one of few to conduct qualitative research into the individual factors driving usage intention among managers; hence, providing more in-depth insights about managers’ attitudes towards algorithmic decision-making. This research could serve as guidance for developers developing algorithms and for managers implementing and using algorithms in organizational decision-making.
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Tiara Kusumaningtiyas, Prasetyo Adi Nugroho and Nurul Aida Noor Azizi
The purpose of this paper is to explore the use of artificial intelligence (AI) in libraries, especially university libraries, which are faced with users from various countries…
Abstract
Purpose
The purpose of this paper is to explore the use of artificial intelligence (AI) in libraries, especially university libraries, which are faced with users from various countries who have different languages and cultures. Seamless M4T, which is being developed, has great potential for helping university librarians maximize library services by providing ease of communication.
Design/methodology/approach
Analyzing the possibility of developing Seamless M4T using natural language processing techniques and how to train language models to be smarter AI tools and can be used to break down language barriers between librarians and users.
Findings
The implementation of AI-based application Seamless M4T can help university librarians provide maximum service to users who are hampered by language and culture with advanced communication skills. Seamless M4T has an automatic speech recognition feature for dozens of languages, so it can translate speech-to-text, text-to-speech or both text and speech. To convert written words into verbal forms, this AI can also translate and transcribe text and speech in real-time without significant delays.
Originality/value
This paper emphasizes the use of AI in university libraries to improve services, especially in communication due to language differences between librarians and users. Advantages in using AI in libraries can support the collaboration and scholarly communication process.
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This paper aims to present a lesson that showcases how artificial intelligence (AI) tools may be chiefly used in L2 language classrooms to design culture-focussed…
Abstract
Purpose
This paper aims to present a lesson that showcases how artificial intelligence (AI) tools may be chiefly used in L2 language classrooms to design culture-focussed telecollaboration tasks and aid their completion by students.
Design/methodology/approach
The paper begins by reviewing traditional approaches and guidance for developing telecollaboration tasks. It then models how tasks can be designed using the popular AI tool “Chat Generative Pre-training Transformer (ChatGPT)” and then simulates how tasks may be completed by learners using ChatGPT-generated information as a springboard for their own culturally appropriate outputs.
Findings
The simulated lesson illuminates the potential value of AI tools for teachers and students. However, it also highlights particular aspects of AI literacy that teachers and learners need to be aware of.
Practical implications
This paper has clear practical implications for teacher development by raising awareness of the importance of teachers upskilling in telecollaboration task design and in their understanding of how AI tools can collaborate with them in language classrooms.
Originality/value
The paper adds to the current body of literature on telecollaboration and more specifically adds weight to current discussions taking place around AI tools in language education. By the end of reading the paper, teachers will have a comprehensive grounding in how to use ChatGPT in their classrooms. In doing so, the author demystifies how teachers and students may start exploring these tools in ways that target developing intercultural communicative competence.
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