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Organisations and industries are often looking for technologies that can accomplish multiple tasks, providing economic benefits and an edge over their competitors. In this context, drones have the potential to change many industries by making operations more efficient, safer and more economic. Therefore, this study investigates the use of drones as the next step in smart/digital warehouse management to determine their socio-economic benefits.

The study identifies various enablers impacting drone applications to improve inventory management, intra-logistics, inspections and surveillance in smart warehouses through a literature review, a test of concordance and the fuzzy Delphi method. Further, the graph theory matrix approach (GTMA) method was applied to ranking the enablers of drone application in smart/digital warehouses. In the subsequent phase, researchers investigated the relation between the drone application's performance and the enablers of drone adoption using logistic regression analysis under the TOE framework.

This study identifies inventory man agement, intra-logistics, inspections and surveillance are three major applications of drones in the smart warehousing. Further, nine enablers are identified for the adoption of drone in warehouse management. The findings suggest that operational effectiveness, compatibility of drone integration and quality/value offered are the most impactful enablers of drone adoption in warehouses. The logistic regression findings are useful for warehouse managers who are planning to adopt drones in a warehouse for efficient operations.

This study identifies the enablers of drone adoption in the smart and digital warehouse through the literature review and fuzzy Delphi. Therefore, some enablers may be overlooked during the identification process. In addition to this, the analysis is based on the opinion of the expert which might be influenced by their field of expertise.

By considering technology-organisation-environment (TOE) framework warehousing companies identify the opportunities and challenges associated with using drones in a smart warehouse and develop strategies to integrate drones into their operations effectively.

This study proposes a TOE-based framework for the adoption of drones in warehouse management to improve the three prominent warehouse functions inventory management, intra-logistics, inspections and surveillance using the mixed-method.

The imperative to conserve resources and minimize operational expenses has spurred a notable increase in the adoption of lean manufacturing within the context of the circular economy across diverse industries in recent years. However, a notable gap exists in the research landscape, particularly concerning the implementation of lean practices within the pharmaceutical industry to enhance circular economy performance. Addressing this void, this study endeavors to identify and prioritize the pivotal drivers influencing lean manufacturing within the pharmaceutical sector.

The outcome of this rigorous examination highlights that “Continuous Monitoring Process for Sustainable Lean Implementation,” “Management Involvement for Sustainable Implementation” and “Training and Education” emerge as the most consequential drivers. These factors are deemed crucial for augmenting circular economy performance, underscoring the significance of management engagement, training initiatives and a continuous monitoring process in fostering a closed-loop practice within the pharmaceutical industry.

The findings contribute valuable insights for decision-makers aiming to adopt lean practices within a circular economy framework. Specifically, by streamlining the process of developing a robust action plan tailored to the unique needs of the pharmaceutical sector, our study provides actionable guidance for enhancing overall sustainability in the manufacturing processes.

This study represents one of the initial efforts to systematically identify and assess the drivers to LM implementation within the pharmaceutical industry, contributing to the emerging body of knowledge in this area.

The aim of this Real Estate Insight is to comment upon commercial real estate research. Much of the current research on commercial real estate sits in academic silos, constrained by disciplinary boundaries and rejecting insights from other areas. This can lead to an impoverished understanding of the processes and practices that drive market behaviour.

This Real Estate Insight, through the lens of history, draws on insights from a century earlier and, in particular, from the work of Frank Ramsey; the paper argues that market behaviour is shaped by the role of key actors and persistent beliefs which need to be accounted for in our models of market practice.

The paper argues that current research paradigms need to accommodate agency explicitly into existing models and that real estate research will benefit immensely if researcher were more open in seeking ideas from outside the real estate field and to be more open to external ideas and concepts.

The paper suggests that property research needs to be more embracing of other academic disciplines to develop a full understanding of the numerous and various drivers within commercial real estate markets.

This is a review of how beliefs impact upon commercial real estate markets. As with many things, history can help researchers today get a broader and more appropriate perspective on market drivers and how they affect decision-making.

The traditional failure mode and effect analysis (FMEA) has some limitations, such as the neglect of relevant historical data, subjective use of rating numbering and the less rationality and accuracy of the Risk Priority Number. The current study proposes a machine learning–enhanced FMEA (ML-FMEA) method based on a popular machine learning tool, Waikato environment for knowledge analysis (WEKA).

This work uses the collected FMEA historical data to predict the probability of component/product failure risk by machine learning based on different commonly used classifiers. To compare the correct classification rate of ML-FMEA based on different classifiers, the 10-fold cross-validation is employed. Moreover, the prediction error is estimated by repeated experiments with different random seeds under varying initialization settings. Finally, the case of the submersible pump in Bhattacharjee et al. (2020) is utilized to test the performance of the proposed method.

The results show that ML-FMEA, based on most of the commonly used classifiers, outperforms the Bhattacharjee model. For example, the ML-FMEA based on Random Committee improves the correct classification rate from 77.47 to 90.09 per cent and area under the curve of receiver operating characteristic curve (ROC) from 80.9 to 91.8 per cent, respectively.

The proposed method not only enables the decision-maker to use the historical failure data and predict the probability of the risk of failure but also may pave a new way for the application of machine learning techniques in FMEA.

By adopting network analytic techniques, this paper aims to examine interlocking directorates among firms operating in the hospitality services sector in seven major Italian tourism destinations.

The authors collected information for all the hotel corporations whose headquarters are located in the seven top Italian destinations: Florence, Milan, Naples, Rimini, Rome, Turin and Venice. Data come from the Analisi Informatizzata delle Aziende Italiane database by Bureau Van Dijk and were used to build a network where the nodes are board members (people) and corporations (hotels) and the links represent the membership of individuals in the boards. From this, with a one-mode projection, the authors obtain two networks: people and corporations. The overall networks’ structures are analysed by assessing their connectivity characteristics.

The findings indicate a relatively low number of interlocks that signals a high degree of fragmentation, showing that the interconnections (both within and between destinations) are scarce. This suggests that in absence of formalized cooperation arrangements, corporations might collaborate informally.

This work extends previous research on complexity in business settings, focusing specifically on service companies whose output depends on multiple interactions and helps clarifying coopetition practices of hospitality service firms. Policymaking perspectives are discussed as well as managerial viewpoints.

Not many studies of the interlocking directorates in the hospitality domain exist. This paper uses network analysis for a better understanding of the cooperative practices and the formal social structures of the Italian hospitality industry and derives a series of implications important for both researchers and practitioners while also looking at potential future studies.

The study aims to explore the digital fashion trend within the Metaverse, characterized by non-fungible tokens (NFTs), across Twitter networks. Integrating theories of diffusion of innovation, two-step flow of communication and self-efficacy, the authors aimed to uncover the diffusion structure and the influencer's social roles undertaken by social entities in fostering communication and collaboration for the advancement of Metaverse fashion.

Social network analysis examined the critical graph metrics to profile, visualize, and cluster the unstructured network data. The authors used the NodeXL program to analyze two hashtag keyword networks, “#metaverse fashion” and “#metawear,” using Twitter API data. Cluster, semantic, and time series analyses were performed to visualize the contents and contexts of communication and collaboration in the diffusion of Metaverse fashion.

The results unraveled the “broadcast network” structure and the influencers' social roles of opinion leaders and market mavens within Twitter's “#metaverse fashion” diffusion. The roles of innovators and early adopters among influencers were comparable in collaborating within the competition venues, promoting awareness and participation in digital fashion diffusion during specific “fad” periods, particularly when digital fashion NFTs and cryptocurrencies became intertwined with the competition in the Metaverse.

The study contributed to theory building by integrating three theories, emphasizing effective communication and collaboration among influencers, organizations, and competition venues in broadcasting digital fashion within shared networks. The validation of multi-faceted Social Network Analysis was crucial for timely insights, highlighting the critical digital fashion equity in capturing consumers' attention and driving engagement and ownership of Metaverse fashion.

With the rapid development of Internet technology, cybersecurity threats such as security loopholes, data leaks, network fraud, and ransomware have become increasingly prominent, and organized and purposeful cyberattacks have increased, posing more challenges to cybersecurity protection. Therefore, reliable network risk assessment methods and effective network security protection schemes are urgently needed.

Based on the dynamic behavior patterns of attackers and defenders, a Bayesian network attack graph is constructed, and a multitarget risk dynamic assessment model is proposed based on network availability, network utilization impact and vulnerability attack possibility. Then, the self-organizing multiobjective evolutionary algorithm based on grey wolf optimization is proposed. And the authors use this algorithm to solve the multiobjective risk assessment model, and a variety of different attack strategies are obtained.

The experimental results demonstrate that the method yields 29 distinct attack strategies, and then attacker's preferences can be obtained according to these attack strategies. Furthermore, the method efficiently addresses the security assessment problem involving multiple decision variables, thereby providing constructive guidance for the construction of security network, security reinforcement and active defense.

A method for network risk assessment methods is given. And this study proposed a multiobjective risk dynamic assessment model based on network availability, network utilization impact and the possibility of vulnerability attacks. The example demonstrates the effectiveness of the method in addressing network security risks.

Despite the fact that hygiene and sanitation are becoming more critical for improving the present situation in developing nations, the factors that affect them are not well covered in the present research. This paper investigates the quality of the hygiene and sanitization factors and identifies the interrelations between the identified factors.

A graph theory-based approach is proposed to assess the factors influencing the practice, and a critical service index (CSI) is used to quantify the same.

Two Indian villages are used to illustrate the implementation of the suggested approach. This represents the validation of the suggested method, as well as assisting in the development of essential suggestions for increasing the quality of hygiene and sanitization in the Indian context. In spite of the increasing importance of hygiene and sanitation for improving the current situation in developing countries, the factors that influence them are not well-researched.

This study contributes in two ways. First, it provides an organized methodology for quantifying hygiene and sanitation factors and a critical service index that incorporates the findings. The suggested approach may also be used to evaluate and classify other sectors. Second, it shows how the methodology was used to create key recommendations for two Indian villages, which may be considered the first effort in India’s hygiene and sanitation initiatives.

This research discussed improvements in sanitation and hygiene habits among Indian households, which have not been achieved as expected under the Swachh Bharat Mission.

Since the performance of vehicular users and cellular users (CUE) in Vehicular networks is highly affected by the allocated resources to them. The purpose of this paper is to investigate the resource allocation for vehicular communications when multiple V2V links and a V2I link share spectrum with CUE in uplink communication under different Quality of Service (QoS).

An optimization model to maximize the V2I capacity is established based on slowly varying large-scale fading channel information. Multiple V2V links are clustered based on sparrow search algorithm (SSA) to reduce interference. Then, a weighted tripartite graph is constructed by jointly optimizing the power of CUE, V2I and V2V clusters. Finally, spectrum resources are allocated based on a weighted 3D matching algorithm.

The performance of the proposed algorithm is tested. Simulation results show that the proposed algorithm can maximize the channel capacity of V2I while ensuring the reliability of V2V and the quality of service of CUE.

There is a lack of research on resource allocation algorithms of CUE, V2I and multiple V2V in different QoS. To solve the problem, one new resource allocation algorithm is proposed in this paper. Firstly, multiple V2V links are clustered using SSA to reduce interference. Secondly, the power allocation of CUE, V2I and V2V is jointly optimized. Finally, the weighted 3D matching algorithm is used to allocate spectrum resources.

This study aims to determine the barriers hindering the incorporation of blockchain technology (BCT) in two key service industries – hotel and health care – as well as to assess their readiness for implementing BCT after overcoming the barriers.

The barriers of this study are determined through two phases: a review of prior literature and obtaining expert opinions, which are then analyzed to identify specific barriers that are impeding the incorporation of BCT. Moreover, to generate a blockchain implementation reluctance index (BIRI), this study presents an interval-valued intuitionistic fuzzy set (IVIFS) that uses graph theory and matrix approach (GTMA). The permanent function in the GTMA approach is computed using the PERMAN algorithm. Finally, to compare the readiness of the hotel and health-care industries to adopt BCT, the BIRI values are plotted and evaluated.

The barriers identified by this study are listed under five major headings, namely, financial, operational, behavioral, technical and legal. This study revealed that the operational and technical barriers of BCT are critically hindering its widespread integration in hotel and health-care industries. Furthermore, on comparing the BIRI values of both industries, the result suggested that the hotel industry needs to work more on these barriers to effectively incorporate BCT. Besides the comparison, the BIRI values clearly indicate that both industries have to put a lot of effort into the mitigation of the barriers found by this study to successfully integrate BCT.

The experts’ opinions are used to evaluate the identified barriers, which raises the chance that the opinions are prejudiced based on the experts’ perspectives and ideologies. The sensitivity of decision-maker loads toward preference outcomes is not analyzed in this manuscript. Therefore, any recent sensitivity analysis may be considered a prospective field for future research. This study applies a multicriteria decision-making (MCDM) approach, IVIFS–GTMA, which limits the evaluation of the influence caused by individual barriers on the integration of BCT in the hotel and health-care industries. Henceforth, in future investigations, alternative MCDM methods may be used to analyze individual barriers.

According to the findings, if the hotel or health-care industry aims to incorporate BCT in its supply chain operations, it is recommended to emphasize more on the operational barriers along with the technical and behavioral barriers. The barriers mentioned in this manuscript can be used as guidance for developers in their development activities, such as scalability concerns, establishment costs, the 51% attack and the inefficient nature of BCT. Furthermore, they may address the potential users’ negative perceptions about security, privacy, trust and risk avoidance through creatively developed blockchain solutions to promote BCT implementation.

To the best of the author’s knowledge, this is the first study that identifies barriers toward BCT incorporation in the major service industries, i.e. hotel and health care. Moreover, this is the first study that compares the preparedness of the hotel and health-care industries to determine the industry that requires more work to implement BCT.