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With its potential to significantly improve the tourism sector, augmented reality (AR), an emerging technology, is a growing force in the field. It has attracted significant academic attention and expanded publishing options over the past 20 years. However, so far, no review has provided a comprehensive overview of AR-related research in tourism. This work uses the VOS Viewer and Bibliometrix R-package software to present a bibliometric review of the scientific literature in the area of AR-related tourism research in order to fill this gap.

The theme of AR in tourism was searched in the Scopus database. The original search yielded 327 papers in total, which were then filtered based on the parameters. A total of 190 relevant documents were generated by the refining process. For the publications pertaining to AR in tourism, the most cited papers, prominent authors, productive journals and countries, co-authorship among countries, trending topics, thematic map, co-occurrence analysis of keywords and bibliographic coupling of documents were all looked into.

This report shows a recent rise in research on AR in the tourism industry. The top 10 studies in the topic had a combined total of 1749 citations, while the authors found three papers with more than 200 Scopus citations. Till the date of extraction, 555 writers have contributed to the field of AR in tourism, with Tom Dieck M.C., Chung N. and Jung T. receiving the most citations. According to co-authorship analysis, the UK collaborates with other countries the most. Thematic map has thrown insight into the niche themes and emerging themes in the field. Coupling analysis has revealed four major research streams in the field of AR in tourism.

This study can be considered as a first study to utilize a bibliometric method to address this research gap, identifying the top documents, journals and, most importantly, the future research areas identified through the thematic map analysis of keywords and bibliographic coupling.

Historical growth within this discipline was made clear by an in-depth analysis of the studied themes in this area. Hence, the discovery of relatively new subjects offers scholars the chance to plan their research in this area and thereby enhance the literature. The study is also beneficial for practitioners in the tourism industry.

This work examined previously published publications on AR in tourism using a bibliometric analytic technique. Additionally, it contributes to the current knowledge accumulation in learning about the applicability of AR in tourism and enlightens tourism scholars on the structure of the field.

It is well known in the mining industry that the increase in failures and breakdowns is due mainly to a poor maintenance policy for the equipment, in addition to the difficult access that specialized personnel have to combat the breakdown, which translates into more machine downtime. For this reason, this study aims to propose a remote assistance model for diagnosing and repairing critical breakdowns in mining industry trucks using augmented reality techniques and data analytics with a quality approach that considerably reduces response times, thus optimizing human resources.

In this work, the six-phase CRIPS-DM methodology is used. Initially, the problem of fault diagnosis in trucks used in the extraction of material in the mining industry is addressed. The authors then propose a model under study that seeks a real-time connection between a service technician attending the truck at the mine site and a specialist located at a remote location, considering the data transmission requirements and the machine's characterization.

It is considered that the theoretical results obtained in the development of this study are satisfactory from the business point of view since, in the first instance, it fulfills specific objectives related to the telecare process. On the other hand, from the data mining point of view, the results manage to comply with the theoretical aspects of the establishment of failure prediction models through the application of the CRISP-DM methodology. All of the above opens the possibility of developing prediction models through machine learning and establishing the best model for the objective of failure prediction.

The original contribution of this work is the proposal of the design of a remote assistance model for diagnosing and repairing critical failures in the mining industry, considering augmented reality and data analytics. Furthermore, the integration of remote assistance, the characterization of the CAEX, their maintenance information and the failure prediction models allow the establishment of a quality-based model since the database with which the learning machine will work is constantly updated.

Digital technologies emerged as innovative avenues for launching new products, advertising brands, increasing customer awareness and thus leaving a remarkable impact on the online marketplace. The present study analyzed the effects of crucial antecedents of AR interactive technology on customers' behavior toward AR-based e-commerce websites.

Convenience sampling was used to collect primary data from 357 iGen respondents aged 16–22 years; residing in New Delhi and the NCR region of India and examined using the structural equation modeling technique.

Results revealed that technology anxiety and virtuality significantly influence customers' attitudes and behavioral intentions toward AR-based e-commerce websites. However, interactivity and innovativeness remain non-significant. Additionally, non-significant moderating effects were identified for the moderators, i.e. trust and need for touch. At the same time, gender has a significant moderating effect only for the association between technology anxiety and attitude toward AR-based e-commerce websites.

The study summarizes numerous theoretical and managerial implications for AR-based website designers and policymakers, followed by the crucial limitations and directions for future research.

The present research provides a significant understanding of the e-commerce industry by providing valuable insights about young iGen consumers' perceptions of AR-based e-commerce websites.

With the emergence of digital twins, the construction industry is looking toward improving the inspection and maintenance of all kinds of assets, such as bridges, roads and utilities. The purpose of this paper is to provide insights into how the development of an interactive digital twin creates a variety of interactions between users of this technology and assets to be monitored.

The development of a digital twin inspection model, focusing on the specific case of a sewage pumping station, is chosen as the subject of a case study. Through the development of this model, this study explores the various user–technology interactions that can be designed in a digital twin context.

Users interact with digital twins by following virtual instructions in a certain way, which creates a “quasi-other” relationship. A digital twin based on virtual reality (VR) also make users feel as if they are within the created VR of an inspection site, thereby immersing them in the VR environment. The design of a VR-based digital twin, which is determined by decisions made during the development process, shapes the context in which users interact with the technology and assets.

This study shows that a digital twin in construction practice may play different “actant” roles having different types of influences. Analyzing these actant roles and influences in terms of force and visibility adds a new perspective on the interaction between users and digital twins in construction and asset monitoring practice.

This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.

Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).

Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.

Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.

The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.

By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.

Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.

In the prior literature, the motivation to adopt wearable fitness technology (WFT) has been linked with either intrinsic or extrinsic. However, how the subcategories of extrinsic motivations (identified, introjected and external) affect the consumers’ WFT adoption decision remains sparse. Furthermore, do regulatory focus (prevention vs promotion) and gender differences the effects of different motivations on WFT adoption is almost unknown in the health-care marketing literature. This study aims to fill the above-mentioned gap and to unfold the WFT adoption beyond the traditional motivation by incorporating the organismic integration theory (part of self-determined theory) and regulatory focus theory.

This study used a questionnaire-based survey. Using the “AMOS” survey, questionnaire responses of 641 respondents were analyzed and validated by using structural equation modeling. All the variables were adopted from the literature.

The results show that intrinsic, identified and external motivations have the greatest impact on consumers’ decisions, while introjected motivation was not significant directly. The moderation effects of regulatory focus are significant in such a way that prevention focus influences the introjected motivation and promotion focus affects the external motivation and WFT adoption decision. Furthermore, the findings on gender moderation suggest that women are more intrinsically motivated, and men are more externally motivated for WFT adoption.

The new insights and contributions of this study provide a better understanding of WFT adoption and help sellers develop more effective marketing strategies.

This study incorporates subcategories of extrinsic motivations to provide a deeper understanding of consumers’ behavior. Furthermore, this study applies a unique framework of organismic integration theory to consumers’ WFT adoption. It is also among very few research that investigate regulatory focus and gender impact on consumers’ WFT adoption.

A positive outlook on the impact of Industry 4.0 (I4.0) on sustainability prevails in the literature. However, some studies have highlighted potential areas of concern that have not yet been systematically addressed. The goal of this study is to challenge the assumption of a sustainable Fourth Industrial Revolution by (1) identifying the possible unintended negative impacts of I4.0 technologies on sustainability; (2) highlighting the underlying motivations and potential actions to mitigate such impacts; and (3) developing and evaluating alternative assumptions on the impacts of I4.0 technologies on sustainability.

Building on a problematization approach, a systematic literature review was conducted to develop potential alternative assumptions about the negative impacts of I4.0 on sustainability. Then, a Delphi study was carried out with 43 experts from academia and practice to evaluate the alternative assumptions. Two rounds of data collection were performed until reaching the convergence or stability of the responses.

The results highlight various unintended negative effects on environmental and social aspects that challenge the literature. The reasons behind the high/low probability of occurrence, the severity of each impact in the next five years and corrective actions are also identified. Unintended negative environmental effects are less controversial than social effects and are therefore more likely to generate widely accepted theoretical propositions. Finally, the alternative hypothesis ground is partially accepted by the panel, indicating that the problematization process has effectively opened up new perspectives for analysis.

This study is one of the few to systematically problematize the assumptions of the I4.0 and sustainability literature, generating research propositions that reveal several avenues for future research.

We have witnessed an evolution in the use of smartphones in recent years. We have been aware for some time of the potentially deleterious impact of smartphones on users' lives and their propensity for user addiction, as reflected in the large and growing body of work on this topic. One modern phenomenon – the distracted mobile phone user in public, or “smartphone zombie” – has received limited research attention. The purpose of the present study is to develop a robust measure of smartphone zombie behaviour.

The research deign comprises three studies: A round of focus groups (n = 5) and two online surveys (survey one n = 373, survey two n = 386), in order to develop and validate a three-factor, 15-item measure named the Smartphone Zombie Scale (SZS).

Following the round of focus groups conducted, Exploratory Factor Analysis and a Confirmatory Factor Analysis, the SZS measure (Cronbach's α = .932) is demonstrated to be robust and comprises three factors: Attention Deficit (Cronbach's α = .922), Jeopardy (Cronbach's α = .817) and Preoccupation (Cronbach's α = .835), that is shown to be distinct to existing closely related measures (Smartphone Addiction scale and Obsessive Compulsive Use).

The present study represents the first extant attempt to produce a measure of smartphone zombie behaviour, and provides us with a reliable and valid measure with which we can study this growing phenomenon.