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By focusing on Sustainable Development Goal 12 (SDG 12) and tourism automation, this perspective paper aims to investigate how tourism and automation will work to create a world in which tourism has more sustainable production and consumption patterns.

This perspective paper reviews the past developments of automation in tourism in the context of sustainable production and consumption patterns, the lessons learned from the COVID-19 pandemic and looks at the future of tourism and how automation will help it be more sustainable in terms of consumption and production patterns.

The insights from this analysis suggest that automation technologies will play a major role in both the supply and demand sides of the tourism and hospitality industry, encouraging increased tourism sustainability. While automation technologies will have the greatest impact on the supply side in the near future, as such technologies will be used to minimise waste and energy usage, creating large gains for environmental protection, the technologies will also benefit responsible consumption. Big data and analytical technologies will work in ways to ensure that consumers are nudged into consumer practices that are increasingly sustainable.

This perspective paper synthesises the literature on the subjects, namely, automation and SDG 12 in tourism, and points to important new future research agenda. This is one of the first papers in tourism to blend automation and SDG 12 literature to shed light on the use of automation in sustainable consumption and production in tourism.

通过聚焦于可持续发展目标12和旅游自动化, 本前瞻性文章旨在探讨旅游业和自动化如何共同创造一个让旅游产业拥有更可持续的生产和消费模式的世界。

本文回顾了旅游自动化在可持续生产和消费模式背景下的发展, 从COVID-19大流行中学到的教训, 并展望旅游业的未来以及自动化如何帮助其在消费和生产模式方面变得更加可持续。

根据分析, 自动化技术将在旅游和酒店业的供求两侧发挥重要作用, 促进旅游业的可持续性发展。虽然自动化技术在近期内将对供应侧产生最大影响, 因为这些技术将被用来最小化废物和能源使用, 为环境保护创造巨大收益, 但这些技术也将惠及负责任消费。大数据和分析技术将以确保消费者被引导向越来越可持续的消费实践。

本前瞻性论文综合了关于旅游中的自动化和可持续发展目标12的文献, 并指出了重要的新的未来研究议程。这是旅游业中第一批结合自动化和可持终发展目标12文献以阐明旅游中可持续消费和生产的自动化使用的论文之一。

Al centrarse en el ODS12 y la automatización del turismo, este artículo de perspectiva pretende investigar cómo el turismo y la automatización trabajarán para crear un mundo en el que el turismo tenga unos patrones de producción y consumo más sostenibles.

Este artículo de perspectiva revisa los desarrollos pasados de la automatización en el turismo en el contexto de los patrones de producción y consumo sostenibles, las lecciones aprendidas de la pandemia COVID-19, y examina el futuro del turismo y cómo la automatización le ayudará a ser más sostenible en términos de patrones de consumo y producción.

Las conclusiones de este análisis sugieren que las tecnologías de automatización desempeñarán un papel importante tanto en la oferta como en la demanda de la industria del turismo y la hotelería, fomentando una mayor sostenibilidad del turismo. Mientras que las tecnologías de automatización tendrán el mayor impacto en el lado de la oferta en un futuro próximo, ya que dichas tecnologías se utilizarán para minimizar los residuos y el uso de energía, creando grandes ganancias para la protección del medio ambiente, las tecnologías también beneficiarán al consumo responsable. Los macrodatos y las tecnologías analíticas funcionarán de manera que se incite a los consumidores a adoptar prácticas de consumo cada vez más sostenibles.

Este documento de perspectiva sintetiza la bibliografía sobre los temas, a saber, la automatización y el ODS12 en el turismo, y apunta a una nueva e importante agenda de investigación futura. Se trata de uno de los primeros trabajos sobre turismo que combina la literatura sobre automatización y ODS12 para arrojar luz sobre el uso de la automatización en el consumo y la producción sostenibles en el turismo.

This study aims to focus on publications that jointly address robots in the tourism field and the technology acceptance model (TAM).

This study adopts bibliometric analysis. Publications listed in the Web of Science database constitute the scope of this research. 51 publications were analyzed within the scope of the research.

Between the years 2017 and 2023, an upward trend in the number and citations of publications was identified. It has been observed that article studies are more prevalent compared to other types of publications. When considering the indexes of the publications, a significant majority were found to be in Social Sciences Citation Index (SSCI) and Science Citation Index (SCI)-EXPANDED. The status of the keywords identified within the scope of the research in the abstracts of the publications has been presented. The keyword “robot” was found to be the most frequently occurring in the abstracts. The abstracts were also analyzed, and the publications were accordingly clustered into five distinct themes.

This study offers a comprehensive evaluation of publications concerning the use of robots in the tourism sector, framed within the context of the TAM. Within the scope of the study, the findings were interpreted using bibliometric analysis. The publications have been categorized into themes. The results presented provide insights into the necessity for further publications in this field.

The purpose of this study is to conduct a systematic literature review of the adoption of immersive marketing technology (IMT) in terms of strategic planning of its adoption, resource requirements and its implications and challenges.

This study categorizes and contextualizes qualitative approaches to evaluate the literature, with Scopus databases serving as the primary source of 90 selected articles in the areas of information technology, business and marketing strands. Theme analysis was carried out using thematic techniques and grounded approach principles to facilitate thematic coding and generate theme analysis.

The analysis was supported by the three concepts of business flexibility, agility and adaptability, which were drawn as a strategy for IMT adoption. The findings presented three main themes: proactive flexibility, responsive agility and reactive adaptability that enable business owner–managers to craft a strategy for IMT adoption.

The novel contribution of this study is the inclusion of key implications related to IMT as a starting point of the next level of innovative marketing for all academics, practitioners and business owner–managers.

The compliance checking of Building Information Modeling (BIM) models is crucial throughout the lifecycle of construction. The increasing amount and complexity of information carried by BIM models have made compliance checking more challenging, and manual methods are prone to errors. Therefore, this study aims to propose an integrative conceptual framework for automated compliance checking of BIM models, allowing for the identification of errors within BIM models.

This study first analyzed the typical building standards in the field of architecture and fire protection, and then the ontology of these elements is developed. Based on this, a building standard corpus is built, and deep learning models are trained to automatically label the building standard texts. The Neo4j is utilized for knowledge graph construction and storage, and a data extraction method based on the Dynamo is designed to obtain checking data files. After that, a matching algorithm is devised to express the logical rules of knowledge graph triples, resulting in automated compliance checking for BIM models.

Case validation results showed that this theoretical framework can achieve the automatic construction of domain knowledge graphs and automatic checking of BIM model compliance. Compared with traditional methods, this method has a higher degree of automation and portability.

This study introduces knowledge graphs and natural language processing technology into the field of BIM model checking and completes the automated process of constructing domain knowledge graphs and checking BIM model data. The validation of its functionality and usability through two case studies on a self-developed BIM checking platform.

This study aims to adopt a systematic review approach to examine the existing literature on law and LLMs.It involves analyzing and synthesizing relevant research papers, reports and scholarly articles that discuss the use of LLMs in the legal domain. The review encompasses various aspects, including an analysis of LLMs, legal natural language processing (NLP), model tuning techniques, data processing strategies and frameworks for addressing the challenges associated with legal question-and-answer (Q&A) systems. Additionally, the study explores potential applications and services that can benefit from the integration of LLMs in the field of intelligent justice.

This paper surveys the state-of-the-art research on law LLMs and their application in the field of intelligent justice. The study aims to identify the challenges associated with developing Q&A systems based on LLMs and explores potential directions for future research and development. The ultimate goal is to contribute to the advancement of intelligent justice by effectively leveraging LLMs.

To effectively apply a law LLM, systematic research on LLM, legal NLP and model adjustment technology is required.

This study contributes to the field of intelligent justice by providing a comprehensive review of the current state of research on law LLMs.

Rebar is the prime component of reinforced concrete structures, and rebar monitoring is a time-consuming and technical job. With the emergence of the fourth industrial revolution, the construction industry practices have evolved toward digitalization. Still, hesitation remains among stakeholders toward the adoption of advanced technologies and one of the significant reasons is the unavailability of knowledge frameworks and implementation guidelines. This study aims to investigate technical factors impacting automated monitoring of rebar for the understanding, confidence gain and effective implementation by construction industry stakeholders.

A structured study pipeline has been adopted, which includes a systematic literature collection, semistructured interviews, pilot survey, questionnaire survey and statistical analyses via merging two techniques, i.e. structural equation modeling and relative importance index.

The achieved model highlights “digital images” and “scanning” as two main categories being adopted for automated rebar monitoring. Moreover, “external influence”, “data-capturing”, “image quality”, and “environment” have been identified as the main factors under “digital images”. On the other hand, “object distance”, “rebar shape”, “occlusion” and “rebar spacing” have been highlighted as the main contributing factors under “scanning”.

The study provides a base guideline for the construction industry stakeholders to gain confidence in automated monitoring of rebar via vision-based technologies and effective implementation of the progress-monitoring processes. This study, via structured data collection, performed qualitative and quantitative analyses to investigate technical factors for effective rebar monitoring via vision-based technologies in the form of a mathematical model.

In order to estimate the value of semi-automated subject indexing in operative library catalogues, the study aimed to investigate five different automated implementations of an open source software package on a large set of Swedish union catalogue metadata records, with Dewey Decimal Classification (DDC) as the target classification system. It also aimed to contribute to the body of research on aboutness and related challenges in automated subject indexing and evaluation.

On a sample of over 230,000 records with close to 12,000 distinct DDC classes, an open source tool Annif, developed by the National Library of Finland, was applied in the following implementations: lexical algorithm, support vector classifier, fastText, Omikuji Bonsai and an ensemble approach combing the former four. A qualitative study involving two senior catalogue librarians and three students of library and information studies was also conducted to investigate the value and inter-rater agreement of automatically assigned classes, on a sample of 60 records.

The best results were achieved using the ensemble approach that achieved 66.82% accuracy on the three-digit DDC classification task. The qualitative study confirmed earlier studies reporting low inter-rater agreement but also pointed to the potential value of automatically assigned classes as additional access points in information retrieval.

The paper presents an extensive study of automated classification in an operative library catalogue, accompanied by a qualitative study of automated classes. It demonstrates the value of applying semi-automated indexing in operative information retrieval systems.

The purpose of this paper is to report on the results of a study carried out to identify and analyse which potential subject areas may have impact on developments in the field of building maintenance (BM). That is, it is intended to contribute to the integration of new approaches so that building maintenance management (BMM) becomes as automated, digital and intelligent or smartness as possible in the near future.

The research approach has resulted in a theory that is essentially based on a qualitative design. The route followed was a literature review, involving the collection, analysis and interpretation of carefully selected information, mostly from recently published records. The data assembled and the empirical experience itself made it possible to present a comprehensive viewpoint and some future outlooks.

Five thematic areas considered as potentially impactful for BM developments have been highlighted, analysed and generically labelled as thematic base words, which are monitoring, automation, digitalisation, intelligence and smart. It is believed that these may be aspects that will lay the groundwork for a much more advanced and integrated agenda, featured by a high-tech vision.

This is thought to be a different way of looking at the problem, as it addresses five current issues together. Trendy technological aspects are quite innovative and advantageous for BMM, providing opportunities not yet widely explored and boosting the paradigm shift.

The applications of Artificial Intelligence (AI) in various areas of professional and knowledge work are growing. Emotions play an important role in how users incorporate a technology into their work practices. The current study draws on work in the areas of AI-powered technologies adaptation, emotions, and the future of work, to investigate how knowledge workers feel about adopting AI in their work.

We gathered 107,111 tweets about the new AI programmer, GitHub Copilot, launched by GitHub and analysed the data in three stages. First, after cleaning and filtering the data, we applied the topic modelling method to analyse 16,130 tweets posted by 10,301 software programmers to identify the emotions they expressed. Then, we analysed the outcome topics qualitatively to understand the stimulus characteristics driving those emotions. Finally, we analysed a sample of tweets to explore how emotional responses changed over time.

We found six categories of emotions among software programmers: challenge, achievement, loss, deterrence, scepticism, and apathy. In addition, we found these emotions were driven by four stimulus characteristics: AI development, AI functionality, identity work, and AI engagement. We also examined the change in emotions over time. The results indicate that negative emotions changed to more positive emotions once software programmers redirected their attention to the AI programmer's capabilities and functionalities, and related that to their identity work.

Overall, as organisations start adopting AI-powered technologies in their software development practices, our research offers practical guidance to managers by identifying factors that can change negative emotions to positive emotions.

Our study makes a timely contribution to the discussions on AI and the future of work through the lens of emotions. In contrast to nascent discussions on the role of AI in high-skilled jobs that show knowledge workers' general ambivalence towards AI, we find knowledge workers show more positive emotions over time and as they engage more with AI. In addition, this study unveils the role of professional identity in leading to more positive emotions towards AI, as knowledge workers view such technology as a means of expanding their identity rather than as a threat to it.

Industry 4.0 has an inimitable potential to create competitive advantages for the apparel industry by enhancing productivity, production, profitability, efficiency and effectiveness. This study, thus, aims to assess the digitalisation level of the Tanzanian apparel industry through the Industry 4.0 perspectives.

A mixed-methods-based approach was deployed. This study deployed semi-structured interviews, document review and observation methods for the qualitative approach. For the quantitative approach, closed-ended questionnaires were used to ascertain the digitalisation levels and maturity level of the textiles and apparel (T&A) factories and small and medium-sized textile enterprises in Tanzania. The sample size was 110, with participants engaged through the purposive sampling technique.

Industry 4.0 frameworks evolved into practices mainly since 2011 in several service and manufacturing industries globally. For Tanzania, the findings indicate that the overall maturity level of the T&A industries is 2.5 out of 5.0, demonstrating a medium level of adoption. Thus, the apparel industries are not operating under the industry 4.0 framework; they are operating within the third industrial revolution – Industry 3.0 – framework. For such industries to operate within the fourth industrial revolution – Industry 4.0 – that is only possible if there is significantly well-developed industrial infrastructure, availability of engineering talent, stable commercial partnerships, demand from the marketplace and transactional relationship with customers.

This study’s limitations include: firstly, Industry 4.0 is an emerging area; this resulted in limited theoretical underpinnings in the Tanzanian perspectives. Secondly, the studied industries may not suffice the need to generalise the findings for the entire country, thus needing another study.

Although Industry 4.0 conceptual frameworks have been on trial in several industries since 2011, this is amongst the first empirical research on Industry 4.0 in the Tanzanian apparel industry that assesses the digitalisation levels.