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The purpose of this paper is to present a conceptual model that defines the essential components shaping the new Digital Supply Chains (DSCs) through the implementation and acceleration of Industry 4.0.

The scope of the present work exposes a conceptual approach and review of the key literature from 1989 to 2019, concerning the evolution and transformation of the actors and constructs in logistics and Supply Chain Management (SCM) by means of examining different conceptual models and a state-of-the-art review of Industry 4.0’s concepts and elements, with a focus on digitization in supply chain (SC) processes. A detailed study of the constructs and components of SCM, as defined by their authors, resulted in the development of a referential and systematic model that fuses the inherent concepts and roles of SCM, with the new technological trends directed toward digitization, automation, and the increasing use of information and communication technologies across logistics global value chains.

Having achieved an exploration of the different conceptual frameworks, there is no compelling evidence of the existence of a conceptual SCM that incorporates the basic theoretical constructs and the new roles and elements of Industry 4.0. Therefore, the main components of Industry 4.0 and their impact on DSC Management are described, driving the proposal for a new conceptual model which addresses and accelerates a vision of the future of the interconnectivity between different DSCs, grouped in clusters in order to add value, through new forms of cooperation and digital integration.

This research explores the gap in the current SCM models leading into Industry 4.0. The proposed model provides a novel and comprehensive overview of the new concepts and components driving the nascent and current DSCs. This conceptual framework will further aid researchers in the exploration of knowledge regarding the variables and components presented, as well as the verification of the newly revealed roles and constructs to understand the new forms of cooperation and implementation of Industry 4.0 in digitalized SCs.

The purpose of this study is to identify research perspectives/clusters in the field of urban tourism (city tourism) in narrow sense and tourism cities (cities and tourism) in the broader sense to examine the complex relationship through the optics of science mapping. This paper believes that the existing qualitative assessments of this field can be experimentally verified and visualized.

First, the key conceptual dilemmas of research perspectives in urban tourism are highlighted. Based on the Web of Science (WOS) Core Collection and the VOSviewer (computer program for visualizing bibliometric networks), the data will be analyzed. Clustering is used to evaluate information retrieval (inclusivity or selectivity of the search query), publication patterns (journal articles), author keywords, terminology and to identify the respective cities and author collaborations between countries.

Terminological specificities and their contextuality (authors’ preferences) are elaborated, as the topic is studied by authors from different disciplinary fields. Compared to other specific tourisms, urban tourism includes geographic terms (variations of city names) and terms with different connotations (travelers, visitors). Recent Spanish (also Portuguese) linguistic/geographic contexts are noticeable and a strong presence of WOS Emerging Sources Citation Index papers. Research perspectives are represented in the network of clusters of connected terms. If the search is based on a narrower sense of strict urban tourism, then tourism-business topics predominate. If tourism and cities are less closely linked, socio-cultural and environmental-spatial perspectives emerge, as does tourism/cities vulnerability (climate change and health issues).

The construction of a search syntax for the purpose of retrieval is always marked by compromises, given different terminological usages. A narrow search query will miss many relevant documents. On the other hand, if the query is too general, it returns less relevant documents. To this end, this paper tested queries on three different levels of inclusivity or selectivity. More consistent use of terms would benefit authors in the field of urban tourism when searching for references (information retrieval) and, as a consequence, would allow better integration of the field.

This study provides a practical method for evaluating cities and tourism in combining the expertise of an information scientist and a sociologist. It points out numerous caveats in information retrieval. It offers an overview of publishing just prior to the outbreak of Covid-19, thus providing an opportunity for further comparative studies.

This study is the first to examine urban tourism using such a method and can serve as a complement to the existing systematization of qualitative approaches. The findings are consistent with numerous qualitative assessments of weak the research interconnection between the specifics of cities and tourism in terms of broader socio-spatial processes. However, the study suggests that such research linkage is increasing, which is noticeable in relation to issues of social sustainability (e.g. overtourism, Airbnb and touristification).

The literature on public procurement (PP) has increased significantly in recent years, and, to date, several reviews have been conducted to study this relevant subject. Nevertheless, a bibliometric analysis of the PP knowledge domain is still missing. To fill this knowledge gap, a bibliometric review is carried out to investigate the current state of PP research.

A total of 640 journal articles are selected from the Scopus database for the final analysis. The performance indicators of the literature are identified and explained through bibliometric analysis. Furthermore, the conceptual and intellectual structures are studied through a keyword co-occurrence network and bibliographic coupling.

The results of the review indicate that PP research has increased significantly in recent years. The top ten most productive journals, countries, authors and academic institutions are identified. The findings from the keyword co-occurrence network reveal six main research themes including innovation, corruption and green public procurement (GPP). By applying bibliographic coupling, the focus of PP research revolves around seven thematic areas: GPP, corruption, the role of small and medium-sized enterprises (SMEs) in PP, electronic PP, innovation, labour standards and service acquisition. The research potential of each thematic area is evaluated using a model based on maturity and recent attention (RA).

To the best of the authors' knowledge, this is the first study to successfully organise, synthesise and quantitatively analyse the development of the PP domain amongst a large number of publications on a large time scale.

This paper aims to explore and analyse stakeholders’ perceptions of the development priorities and suggests more effective strategies to assist sustainable economic growth in the United Arab Emirates (UAE).

The authors use the World Bank data set, which collects various stakeholders’ opinions on the UAE development. First, the exploratory factor analysis has been applied to detect the main groups of development priorities. Second, the fuzzy cluster analysis has been conducted to detect the groups of stakeholders with different attitudes towards the importance of extracted groups of priorities. Third, clusters have been compared according to demographics, media usage and shared prosperity goals.

The two main groups of development priorities have been extracted by the exploratory factor analysis: economic priorities and sustainability priorities. Four clusters have been detected according to the level of motivation when it comes to the economic and sustainability priorities: Cluster 1 (High economic – High sustainability), Cluster 2 (High economic – Medium sustainability), Cluster 3 (High economic – Low sustainability) and Cluster 4 (Low economic – Low sustainability). Members of the cluster that prefer a high level of economic and sustainability priorities (Cluster 1) also prefer more diversified economic growth providing better employment opportunities and better education and training for young people in the UAE.

Limitations stem from the survey being conducted on a relatively small sample using the data collected by the World Bank; however, this data set allowed a comparison of various stakeholders. Future research should consider a broader sample approach, e.g. exploring and comparing all of the Gulf Cooperation Council (GCC) countries; investigating the opinions of the expatriate managers living in the UAE that are not from GCC countries; and/or including other various groups that are lagging, such as female entrepreneurs.

Several practical implications were identified regarding education and media coverage. Since respondents prioritize the economic development factors over sustainability factors, a media campaign could be developed and executed to increase sustainability awareness. A campaign could target especially male citizens since the analysis indicates that males are more likely to affirm high economic and low sustainability priorities than females. There is no need for further diversification of media campaigns according to age since the analysis did not reveal relevant differences in age groups, implying there is no inter-generational gap between respondents.

This paper contributes to the literature by comparing the perceived importance of various development goals in the UAE, such as development priorities and shared prosperity indicators. The fuzzy cluster analysis has been used as a novel approach to detect the relevant groups of stakeholders in the UAE and their developmental priorities. The issue of media usage and demographic characteristics in this context has also been discussed.

The purpose of this paper is to present a quantitatively supported explanation of the intellectual development, the schools of thought and the sub-areas of the food cold chain (FCC) research to derive meaningful avenues for future research.

This study builds on bibliometric analysis and network analysis to systematically evaluate a sample of 1,189 FCC articles published over the past 25 years. The descriptive statistics and science mapping approaches using co-citation analysis were performed with VOSviewer software.

The findings reveal a state-of-the-art overview of the top contributing and influential countries, authors, institutions and articles in the area of FCC research. A co-citation analysis, coupled with content analysis of most co-cited articles, uncovered four underlying research streams including: application of RFID technologies; production and operation planning models; postharvest waste, causes of postharvest wastage and perishable inventory ordering polices and models; and critical issues in FCC. Current research streams, clusters and their sub-themes provided meaningful discussions and insights into key areas for future research in FCC.

This study might reshape practitioners’, researchers’ and policy-makers’ views on the multifaceted areas and themes in the FCC research field, to harness FCC’s benefits at both strategic and tactical level. Finally, the research findings offer a roadmap for additional research to yield more practical and modeling insights that are much needed to enrich the field.

We studied the relationship between job engagement and systematic problem solving (SPS) among shop-floor employees and how lean production (LP) and Internet of Things (IoT) systems moderate this relationship.

We collected data from a sample of 440 shop floor workers in 101 manufacturing work units across 33 plants. Because our data is nested, we employed a series of multilevel regression models to test the hypotheses. The application of IoT systems within work units was evaluated by our research team through direct observations from on-site visits.

Our findings indicate a positive association between job engagement and SPS. Additionally, we found that the adoption of lean bundles positively moderates this relationship, while, surprisingly, the adoption of IoT systems negatively moderates this relationship. Interestingly, we found that, when the adoption of IoT systems is complemented by a lean management system, workers tend to experience a higher effect on the SPS of their engagement.

One limitation of this research is the reliance on the self-reported data collected from both workers (job engagement, SPS and control variables) and supervisors (lean bundles). Furthermore, our study was conducted in a specific country, Italy, which might have limitations on the generalizability of the results since cross-cultural differences in job engagement and SPS have been documented.

Our findings highlight that employees’ strong engagement in SPS behaviors is shaped by the managerial and technological systems implemented on the shop floor. Specifically, we point out that implementing IoT systems without the appropriate managerial practices can pose challenges to fostering employee engagement and SPS.

This paper provides new insights on how lean and new technologies contribute to the development of learning-to-learn capabilities at the individual level by empirically analyzing the moderating effects of IoT systems and LP on the relationship between job engagement and SPS.

Integrated project delivery (IPD) and building information modeling (BIM) have been discussed as prominent collaborative concepts in recent architecture, engineering and construction (AEC) literature, thus recommended for more advanced value creation. However, they have been studied predominantly as discrete even though they are typically highly interrelated. This study aims to enhance collaboration in AEC projects by tracing recent trends in IPD and BIM literature by making sense of trends and by exploring how their interplay has been discussed and conceptualized.

This systematic literature review draws on Scopus and Web of Science as the primary databases. In total, 120 academic papers and review articles were sourced. Yet, the final sample includes 71 sources from the past decade (2011–2020), focusing on both IPD and BIM.

This study identifies 11 interrelated and overlapping themes that are indicative of trends in the recent IPD and BIM literature. This research found that among the identified themes, the clusters of sustainability, transformation and increasing the competence level of staff in the AEC industry, in addition to the concept of quality, require more extensive research in the context of IPD and BIM. Additionally, this study identifies four different approaches to the interplay of IPD and BIM, indicating an absence of scholarly consistency.

Based on the systematic analysis of the recent literature, this study indicates that IPD and BIM have several joint fundamental cornerstones. It is evident that both concepts support the implementation of each other. The success of implementing either one is strongly related to the other. Additionally, we have not found earlier systematic literature reviews that examine the interplay between IPD and BIM in the recent AEC literature.

This paper aims to provide an overview of the application of blockchain to agri-food supply chains, including key issues and trends. It examines the state of the art and conceptual structure of the field and proposes an agenda to guide future research.

This article performs a bibliometric analysis using VOSviewer software on a sample of 205 articles from the WoS database to identify research trend topics.

The number of publications in this area has increased since 2020, which shows a growing research interest. The research hotspots are related to the integration of blockchain technology in the agri-food supply chain for traceability, coordination between all actors involved, transparency of operations and improvement of food safety. Furthermore, this is linked to sustainability and the achievement of the sustainable development gtoals (SDGs), while addressing key challenges in the implementation of blockchain-based technologies in the agri-food supply chain.

The application of blockchain in the agri-food supply chain may consider four key aspects. Firstly, the implementation of blockchain can improve the traceability of food products. Secondly, this technology supports sustainability issues and could avoid disruptions in the agri-food supply chain. Third, blockchain improves food quality and safety control throughout the supply chain. Fourthly, the findings show that regulation is needed to improve trust between stakeholders.

The paper provides a comprehensive overview of the blockchain phenomenon in the agri-food supply chain by optimising the search criteria. Moreover, it serves to bridge to future research by identifying gaps in the field.

Industry 4.0 and Lean Production are a successful match in terms of performance improvement. While we understand the combined potential, there is still poor understanding of how companies should embrace digital transformation to make it successful and sustainable, and the role that lean plays in it. In this paper, we investigate how manufacturing companies embark upon digital transformation and how being lean might affect it.

We conducted multiple case studies with 19 manufacturing companies. We identified two clusters of companies according to their Lean maturity, and we assessed digital transformation patterns by analyzing insights coming both from cases and from the literature. Integrating cross-case analysis results, we developed a framework that shows two different digital transformation patterns according to companies’ commitment to Lean.

Our findings first and foremost show the significant role of lean in driving digital transformation. We identify two patterns, namely Sustaining digital transformation pattern, characterized by the pervasive role of lean culture with small and horizontal digital changes, involvement of people and willingness to maintain continuous process improvement, and Disruptive digital transformation pattern, characterized by few and large digital steps that imply a disruptive and radical change in the company system.

Empirical evidence supports the relevance of the proposed model and its practical usefulness. It can be used to design digital transformation, prepare properly the introduction of Industry 4.0 through a lean approach, and plan the future desired state, identifying the Industry 4.0 technologies that should be implemented.

It is widely recognized that the relationship between Industry 4.0 and lean is significant and positive, yet little evidence was presented to back that. We aim at bringing this debate forward by providing initial empirical evidence of the significant role that lean has on digital transformation, showing how lean drives the digital transformation pattern of companies.

Mouth segmentation is one of the challenging tasks of development in lip reading applications due to illumination, low chromatic contrast and complex mouth appearance. Recently, deep learning methods effectively solved mouth segmentation problems with state-of-the-art performances. This study presents a modified Mobile DeepLabV3 based technique with a comprehensive evaluation based on mouth datasets.

This paper presents a novel approach to mouth segmentation by Mobile DeepLabV3 technique with integrating decode and auxiliary heads. Extensive data augmentation, online hard example mining (OHEM) and transfer learning have been applied. CelebAMask-HQ and the mouth dataset from 15 healthy subjects in the department of rehabilitation medicine, Ramathibodi hospital, are used in validation for mouth segmentation performance.

Extensive data augmentation, OHEM and transfer learning had been performed in this study. This technique achieved better performance on CelebAMask-HQ than existing segmentation techniques with a mean Jaccard similarity coefficient (JSC), mean classification accuracy and mean Dice similarity coefficient (DSC) of 0.8640, 93.34% and 0.9267, respectively. This technique also achieved better performance on the mouth dataset with a mean JSC, mean classification accuracy and mean DSC of 0.8834, 94.87% and 0.9367, respectively. The proposed technique achieved inference time usage per image of 48.12 ms.

The modified Mobile DeepLabV3 technique was developed with extensive data augmentation, OHEM and transfer learning. This technique gained better mouth segmentation performance than existing techniques. This makes it suitable for implementation in further lip-reading applications.