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The main purpose of the study is to reveal the hotel managers' perspectives strategies, and predictions on smart technologies, and their expectations for current staff and potential staff to be employed in the future.

A qualitative research approach was used to reveal the internal perspectives of managers to determine their Smart Tourism (ST) perspectives. With the snowball sampling method, face-to-face interviews were conducted with 16 senior hotel managers working in a 5-star hotel in Antalya. The data obtained were carried out with inductive content analysis and descriptive analysis from qualitative research analysis methods.

In this study, hotel managers' broad perspectives on ST were discussed in depth and presented comprehensively. Managers' perceptions of smart technologies are classified in terms of aggregate dimensions and themes. Among the most important findings, rapidity/quickness, managing/holistic assessment, standardization, harmony/integration-coordination and experience-memory, defined as the crossroads of total dimensions or the heart of smart technologies, were identified as the most important themes. In addition, Stylos et al. (2021), another important finding is the classification of technologies used in top-quality hotels, based on the conceptual framework for smart technologies presented in the literature.

The effective and efficient use of technology, its internalization and openness to technology provides important advantages in hotels such as increasing revisits and satisfaction, providing loyalty and reducing costs. Thus, the perceptions, practices, strategies and prediction of senior managers working in high-level hotels about ST provide valuable data to other hotels that want to survive in the competition.

From a theoretical point of view, this study is valuable in that it deals with the ST perceptions of senior managers with an exploratory research approach. Managerially, the research findings offer valuable contribution about the attitudes, trends, forecasts and expectations of senior managers working in high-level hotels towards ST.

Despite its potential, warehouse managers still struggle to successfully assimilate autonomous mobile robots (AMRs) in their operations. This paper means to identify the moderating factors of AMR assimilation for production warehouses that influence the digital transformation of their intralogistics via AMRs.

Drawing on innovation of assimilation theory (IAT), this study followed an explorative approach using the principles of the case study method in business research. The cases comprised of four AMR end users and six AMR service providers. Data were collected through semi-structured interviews.

Four clusters of moderators that affect each stage of AMR assimilation were identified. These clusters include organizational attributes of end users (i.e. production warehouses), service attributes of service providers, technology attributes of AMRs and relational attributes between the AMR service providers and the AMR end users.

The authors extend the IAT framework by identifying various moderating factors between different stages of the AMR assimilation process. To the authors' knowledge, this is the first study to introduce the perspective of AMR end users in conjunction with AMR service providers to the “Industry 4.0” technology assimilation literature. The study propositions regarding these factors guide future intralogistics and AMR research.

Increasing personnel costs and labour shortages have pushed retailers to give increasing attention to their intralogistics operations. We study hybrid order picking systems, in which humans and robots share work time, workspace and objectives and are in permanent contact. This necessitates a collaboration of humans and their mechanical coworkers (cobots).

Through a longitudinal case study on individual-level technology adaption, we accompanied a pilot testing of an industrial truck that automatically follows order pickers in their travel direction. Grounded on empirical field research and a unique large-scale data set comprising N = 2,086,260 storage location visits, where N = 57,239 storage location visits were performed in a hybrid setting and N = 2,029,021 in a manual setting, we applied a multilevel model to estimate the impact of this cobot settings on task performance.

We show that cobot settings can reduce the time required for picking tasks by as much as 33.57%. Furthermore, practical factors such as product weight, pick density and travel distance mitigate this effect, suggesting that cobots are especially beneficial for short-distance orders.

Given that the literature on hybrid order picking systems has primarily applied simulation approaches, the study is among the first to provide empirical evidence from a real-world setting. The results are discussed from the perspective of Industry 5.0 and can prevent managers from making investment decisions into ineffective robotic technology.

This study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into the logistics management field.

Rooting their analytical categories in the LMD literature, the authors performed a deductive, theory refinement SLR on 307 interdisciplinary journal articles published during 2015–2022 to integrate this emergent phenomenon into the field.

The authors derived the potentials, challenges and solutions of drone deliveries in relation to 12 LMD criteria dispersed across four stakeholder groups: senders, receivers, regulators and societies. Relationships between these criteria were also identified.

This review contributes to logistics management by offering a current, nuanced and multifaceted discussion of drones' potential to improve the LMD process together with the challenges and solutions involved.

The authors provide logistics managers with a holistic roadmap to help them make informed decisions about adopting drones in their delivery systems. Regulators and society members also gain insights into the prospects, requirements and repercussions of drone deliveries.

This is one of the first SLRs on drone applications in LMD from a logistics management perspective.

The purpose of this study is to understand how manufacturers (both discrete and process) are managing disruptions amid the COVID-19 pandemic outbreak, using UAE as an empirical context.

This research uses a multiple case study approach and undertakes 36 semi-structured interviews with senior management of four discrete and four process manufacturing firms that outsource products/components from overseas and domestic suppliers.

Results reveal that manufacturing firms are using six distinct actions to mitigate the effects of the COVID-19 pandemic. For instance, they are relying on the automation process, transferring new and updated knowledge to the current and new suppliers, managing workforce diversity, understanding the impact of demand’s disruption, managing the ecosystem and finally using digital technologies to mitigate the impact of the COVID-19 pandemic.

This study has some limitations. Firstly, the results of this study cannot be generalized to a broader population as it attempts to build an initial theory in manufacturing supply chains within the context of a pandemic outbreak. Second, the study uses a cross-sectional approach to explore the actions used by manufacturing firms to mitigate the effects of the COVID-19 pandemic.

Manufacturing firms can replicate the actions proposed in this study to lessen the effect of the COVID-19 pandemic and emerge stronger in the post-COVID-era.

This study contributes to the manufacturing supply chain literature within the context of pandemic outbreaks by exploring the steps taken by manufacturing firms to minimize the effects of the COVID-19 pandemic. Particularly, it explores such steps by considering both the discrete and process manufacturing industries within the United Arab Emirates.

The relevance of analytics to the healthcare supply chain is increasing with emerging trends and technologies. This study examines how analytics are used in the healthcare supply chain in the “new normal” environment.

A systematic literature review was conducted by extracting research articles related to analytics in the healthcare supply chain from Scopus. The author used a hybrid review approach that combines bibliometric analysis with a theories, contexts, characteristics, and methodology (TCCM) framework-based review to identify various themes of analytics in the healthcare supply chain.

The hybrid review strategy yielded results that focus on prevalent theories, contexts, characteristics, and methodologies in the field of healthcare supply chain analytics. Future research should explore the resulting antecedents, decision-making processes and outcomes (ADO) framework, which integrates technological, economic, and societal concerns and outcomes. Future research agendas could also seek to apply theoretical perspectives in the field of analytics in the healthcare supply chain.

The result of a review of selected studies adds to the current body of work and contributes to the growth of research in the field of analytics in the healthcare supply chain. It also provides new directions to healthcare supply chain managers and academic scholars.

The purpose of the present research is to address the issue by conceptualizing artificial intelligence (AI) experience quality and its dimensions, and furthermore, to empirically test the relationships among AI experience quality, positive affective reactions, AI experience satisfaction and AI-seeking intention.

The data were collected from an AI community canteen in Shanghai. They were also analyzed using exploratory and confirmatory factor analyses (EFA and CFA) and structural equation modeling (SEM).

Four primary dimensions and 15 sub-dimensions of AI experience quality for community canteens were identified. The hypothesized paths between the higher-order constructs – AI experience quality, positive affective reactions, AI experience satisfaction and AI-seeking intention – were confirmed as well.

This is the first study to synthesize AI experience quality, positive affective reactions, AI experience satisfaction and AI-seeking intention in an AI restaurant setting.

This study aims to provide a bibliometric analysis and systematic literature review of Industry 4.0 (I4.0) research in the supply chain (SC) area and to understand related contemporary research trends. I4.0 has the potential to change the way goods are manufactured, distributed and made available to customers through the digitalisation of SC. Although I4.0 originated in 2011 in Germany, its application in managing the SC has only recently started gaining momentum. Therefore, it is essential to understand the research progress and identify the current trends of I4.0 application in the SC field.

A bibliometric analysis was conducted to empirically analyse the literature related to I4.0 implementation in the SC. This study retrieved papers from the Scopus database, reviewing 1,155 articles from the period 2016 to 2023 (November) for bibliometric analysis. Bibliometrix, using R software, was used for the bibliometric analysis, and VOSviewer was used for network analysis.

The findings provide an overview of the most relevant journals, most productive scholars, top academic institutions and top countries contributing to I4.0 research in the SC context. The results show that the most recent research contributions are related to the topics of SC performance, sustainability, digitalisation and digital transformation. Furthermore, a detailed review of articles published in the three and above-rated journals in the Chartered Association of Business Schools list is presented.

The novelty of this study lies in identifying the current research trends and themes of I4.0 research in the SC area. This research benefits researchers by identifying potential research areas for I4.0 implementation in the SC and providing directions for future research.

Drawing on the work of Niklas Luhmann, the paper argues that technology can be viewed as a self-referential system which is autonomous from both human beings and other function systems of society. The paper aims to develop a philosophy of technology from the work of Niklas Luhmann. To achieve this aim, it draws upon the systems-theory work of Jacques Ellul, a philosopher of technology who focuses on the autonomous potential of technological evolution.

The paper draws on the work of Niklas Luhmann and Jacques Ellul to explore the theme of autonomous technology and what this means for our thinking about technological issues in the twenty-first century. Insights from these two thinkers and researchers working in the Luhmannian sociological tradition are applied to remote work.

The sociological approach of Luhmann, coupled with Ellul's insights into the autonomous nature of technology, can help us develop a systems theory of technology which takes seriously its irreducibility to human functions.

The paper contributes to the growing sociological literature that thematizes the Luhmannian approach to technology, helping us better understand this phenomenon and think in new ways about what technological autonomy means.

The paper brings together the work of Luhmann, Ellul and contemporary researchers to advance a new understanding of technology and technological communication.

Future service interactions are anticipated to use humanoid robots in a society that is shifting to a digitalized era. Currently, it is evident that many businesses are replacing service interactions with self-service technologies (SSTs). This movement creates substantial societal changes that researchers have not paid sufficient attention to comprehend. In this setting, the purpose of this study is to examine the social drivers that influence customer mobility toward co-creating value via SSTs. The study also seeks to discover variations in customers' willingness and capacity to adopt SSTs.

To fulfill the research aims, a qualitative technique was adopted, with semistructured interviews conducted with 25 SST users from varied demographic backgrounds. To recruit individuals for the study, a nonprobabilistic purposeful sampling technique was adopted, with the goal of employing information-rich instances. The data were analyzed using thematic analysis.

The study identified eight social drivers that are important in the customer transition toward co-creating value with SSTs. According to the study, SSTs are characterized as a social trend in which adoption is accepted (social norm) and modifies social connections in a new direction. Using SSTs has evolved into a socializing tool that gives people social acknowledgment. Some people see SSTs as social pressure, putting them at a disadvantage if they do not adopt. People, on the other hand, acquire sufficient social support and independence to use SSTs. Customers were categorized into four groups depending on their willingness and ability to embrace SSTs: trendsetters, dreamers, old-fashioned and stragglers.

In practice, service providers can use this knowledge to successfully promote their SSTs and create enhanced client experiences through technological interfaces.

The study adds new knowledge by identifying social determinants in customer shifts toward SSTs, a phenomenon that has not been studied previously, and it adds to marketing theory by proposing a typology to group customers based on their ability and willingness to embrace SSTs.