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In this study, supervision and control of the possible problems among people over a large area with a limited number of drone cameras and security staff is established.

These drones, namely unmanned aerial vehicles (UAVs) will be adaptively and automatically distributed over the crowds to control and track the communities by the proposed system. Since crowds are mobile, the design of the drone clusters will be simultaneously re-organized according to densities and distributions of people. An adaptive and dynamic distribution and routing mechanism of UAV fleets for crowds is implemented to control a specific given region. The nine popular clustering algorithms have been used and tested in the presented mechanism to gain better performance.

The nine popular clustering algorithms have been used and tested in the presented mechanism to gain better performance. An outperformed clustering performance from the aggregated model has been received when compared with a singular clustering method over five different test cases about crowds of human distributions. This study has three basic components. The first one is to divide the human crowds into clusters. The second one is to determine an optimum route of UAVs over clusters. The last one is to direct the most appropriate security personnel to the events that occurred.

This study has three basic components. The first one is to divide the human crowds into clusters. The second one is to determine an optimum route of UAVs over clusters. The last one is to direct the most appropriate security personnel to the events that occurred.

Cross-layer approach in media access control (MAC) layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in wireless sensor network (WSN) and Internet of Things (IoT) applications. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes. Game theory optimization for distributed may increase the network performance. The purpose of this study is to survey the various operations that can be carried out using distributive and adaptive MAC protocol. Hill climbing distributed MAC does not need a central coordination system and location-based transmission with neighbor awareness reduces transmission power.

Distributed MAC in wireless networks is used to address the challenges like network lifetime, reduced energy consumption and for improving delay performance. In this paper, a survey is made on various cooperative communications in MAC protocols, optimization techniques used to improve MAC performance in various applications and mathematical approaches involved in game theory optimization for MAC protocol.

Spatial reuse of channel improved by 3%–29%, and multichannel improves throughput by 8% using distributed MAC protocol. Nash equilibrium is found to perform well, which focuses on energy utility in the network by individual players. Fuzzy logic improves channel selection by 17% and secondary users’ involvement by 8%. Cross-layer approach in MAC layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in WSN and IoT applications. Cross-layer and cooperative communication give energy savings of 27% and reduces hop distance by 4.7%. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes.

Other optimization techniques can be applied for WSN to analyze the performance.

Game theory optimization for distributed may increase the network performance. Optimal cuckoo search improves throughput by 90% and reduces delay by 91%. Stochastic approaches detect 80% attacks even in 90% malicious nodes.

Channel allocations in centralized or static manner must be based on traffic demands whether dynamic traffic or fluctuated traffic. Usage of multimedia devices also increased which in turn increased the demand for high throughput. Cochannel interference keep on changing or mitigations occur which can be handled by proper resource allocations. Network survival is by efficient usage of valid patis in the network by avoiding transmission failures and time slots’ effective usage.

Literature survey is carried out to find the methods which give better performance.

With the rapid low-carbon transformation in China, the industrial approach and labor structure of mining enterprises are undergoing constant changes, leading to an increasing psychological dilemma faced by coal miners. This study aims to reveal the relationship and mechanism of factors influencing the psychological dilemma of miners, and to provide optimal intervention strategies for the safety and sustainable development of employees and enterprises.

To effectively address the complex issue of the psychological dilemma faced by miners, this study identifies and constructs five-dimensional elements, comprising 20 indicators, that influence psychological dilemmas. The relational mechanism of action of factors influencing psychological dilemma was then elucidated using an integration of interpretive structural modeling and cross-impact matrix multiplication.

Industry dilemma perception is a “direct” factor with dependent attributes. The perceptions of management response and relationship dilemmas are “root” factors with driving attributes. Change adaptation dilemma perception is a “susceptibility” factor with linkage attributes. Work dilemma perception is a “blunt” factor with both dependent and autonomous attributes.

The aforementioned findings offer a critical theoretical and practical foundation for developing systematic and cascading intervention strategies to address the psychological dilemma mining enterprises face, which contributes to advancing a high-quality coal industry and efficient energy development.

This study aims to develop the first Theory of Technological Response and Progress in Chaos (TRPC) and examine the case of technological development during the COVID-19 pandemic. The research objectives of this study were to: identify the key technologies that act as a response mechanism during the chaos event, specifically in the case of COVID-19; examine how technologies evolve, develop and diffuse in an immediate crisis and a chaotic environment; theorise various types and periods of technological response and progress during the emergence of chaos and the stages that unfold; and develop policy-oriented recommendations and establish technological foundations to address subsequent chaos events.

This study used the grounded theory as a methodology with a mixed-method approach that included quantitative and qualitative methods. The authors used the quantitative method to assist with the qualitative step to build the TRPC theory. Accordingly, this study integrated machine learning and text mining approaches to the qualitative data analysis following the steps of the grounded theory approach.

As a result of the TRPC theory development process, the authors identified three types of technologies (survival, essential and enhancement technologies) and five types of periods (stable, initial, survival-dominant, essential-dominant and enhancement-dominant periods) that are specific to chaos-technology interactions. The policy implications of this study demonstrate that a required technological base and know-how must be established before a chaotic event emerges.

Concerning the limitations of this study, social media data has advantages over other data sources, such as the examination of dynamic areas and analyses of immediate responses to chaos. However, other researchers can examine publications and patent sources to augment the findings concerning scientific approaches and new inventions in relation to COVID-19 and other chaos-specific developments. The authors developed the TRPC theory by studying the COVID-19 pandemic, however, other researchers can utilise it to study other chaos-related conditions, such as chaotic events that are caused by natural disasters. Other scholars can investigate the technological response and progress pattern in other rapidly emerging chaotic events of an uncertain and complex nature to augment these findings.

Following the indications of the OECD (2021a) and considering the study conducted by the European Parliamentary Research Service (Kritikos, 2020), the authors identified the key technologies that are significant for chaos and COVID-19 response using machine learning and text intelligence approach. Accordingly, the authors mapped all technological developments using clustering approaches, and examined the technological progress within the immediate chaos period using social media data.

The key policy implication of this study concerns the need for policymakers to develop policies that will help to establish the required technological base and know-how before chaos emerges. As a result, a rapid response can be implemented to mitigate the chaos and transform it into a competitive advantage. The authors also revealed that this recommendation overlaps with the model of dynamic capabilities in the literature (Teece and Pisano, 2003). Furthermore, this study recommends that nations and organisations establish a technological base that specifically includes technologies that bear 3A characteristics. These are the most crucial technologies for the survival- and essential-dominant stages. Moreover, the results of this study demonstrate that chaos accelerates technological progress through the rapid adoption and diffusion of technologies into different fields. Hence, nations and organisations should regard this rapid progress as an opportunity and establish the prior knowledge base and technologies before chaos emerges.

The authors have contributed to the chaos studies and the relationship between chaos and technological development by establishing the first theoretical foundation using the grounded theory approach, hereafter referred to as the TRPC theory. As part of the TRPC theory, the authors present three periods of technological response in the following sequence: survival technology, essential technology and enhancement technology. Moreover, this study illustrates the evolving technological importance and priorities as the periods of technological progress proceed under rapidly developing chaos.

This study aims to evaluate the issue of household food waste in Greece, with an emphasis on assessing the level of awareness and key behaviours among consumers. Moreover, the study focuses on examining consumer behaviours related to food waste and identifying distinct consumer profiles that can provide valuable insights into the issue in order to uncover unique behavioural factors and offer targeted interventions to curb food waste in the country.

A nationwide survey was conducted in Greece using a structured online questionnaire, which was sent to 1,270 participants, through the snowball technique. However, due to some incomplete responses, only 1,238 of the responses were considered suitable for analysis. Common descriptive statistics were used to sketch the respondents' profiles, and a non-hierarchical K-means cluster analysis was performed to identify distinct subgroups in the sample.

The study revealed a significant level of food waste awareness among Greek consumers. The cluster analysis identified four distinct consumer groups and substantial differences among them. Notably, sociodemographic analysis underscored a pronounced inclination towards food wastage among younger individuals. Additionally, each cluster's attributes, including their environmental awareness, shopping behaviours meal-planning tendencies and propensity for excess purchases, were examined. Consequently, this study underscored the imperative for targeted informational campaigns tailored for consumer segmentation, offering a pathway to identify prospective interventions conducive to the promotion of sustainable food-consumption practices.

The originality and value of this work lie in its unique focus on addressing the significant issue of household food waste within the context of Greece. What sets this study apart is the application of non-hierarchical K-means cluster analysis (which allowed the authors to identify distinct consumer profiles), a method not widely utilised in the Greek context. By filling this knowledge gap, this study offers crucial insights that can inform targeted interventions aimed at reducing food waste, in alignment with global sustainability initiatives such as the United Nations Agenda 2030 and the European Union's “Farm to Fork” strategy. Additionally, this study contributes to the efforts to provide innovative solutions to prevent household food waste and foster a sustainable future in an ever-changing international environment marked by various crises

This study is exploratory in nature and designed to address poorly documented issues in the literature. The dimensions of regional distribution or spatial organisation of Industry 4.0 (I4.0), including the potential role of clusters, have only recently been addressed, with most available studies focusing on advanced, mainly Western European countries. Although developing fast, the literature on I4.0 in other countries, such as the Central and Eastern European or post-transition economies like Poland, needs to pay more attention to the spatial distribution or geographical and organisational aspects. In response to the identified knowledge gap, this paper aims to identify the role of clusters in the transformation towards I4.0. This explains why clusters may matter for advancing the fourth digital transformation, how advanced in implementing I4.0 solutions are the residents of Polish clusters and how they perceive the advantages of cluster membership for such implementation. Finally, it seeks to formulate policy recommendations based on the evidence gathered.

The methodology used in this study combines quantitative analysis of secondary data from a cluster benchmarking survey with a case study approach. The benchmarking survey, conducted by the polish agency for enterprise development in 2021, gathered responses from 435 cluster members and 41 cluster managers, representing an estimated 57% of the current clusters in Poland. In addition to quantitative analysis, a case study approach was used, incorporating primary sources such as interview with cluster managers and surveys of cluster members, as well as secondary sources like company documents and information from cluster organisation websites. Statistical analysis involved assessing the relationship between technology implementation and the adoption of management systems, as well as exploring potential correlations between technology use and company characteristics such as revenue, export revenue share and number of employees using Pearson correlation coefficient.

In Poland, implementing I4.0 technologies by cluster companies is still modest. The cluster has influenced the use of I4.0 technologies in 23% of surveyed companies. Every second surveyed company declared a positive impact of a cluster on technological advancement. The use of I4.0 technologies is not correlated with the revenue of clustered companies. A rather bleak picture emerges from the results, revealing a need for more interest among cluster members in advancing I4.0 technologies. This may be due to a comfortable situation in which firms still enjoy alternative competitive advantages that do not force them to seek new advanced advantages brought about by I4.0. It also reflects the sober approach and awareness of associated high costs and necessary investments, which are paramount and prevent successful I4.0 implementation.

The limitations inherent in this study reflect the scarcity of the available data. This paper draws on the elementary survey administered centrally and is confined by the type of questions asked. The empirical section focuses on an important, though only one selected sector of the economy – the automotive industry. Nevertheless, the diagnosis of the Polish cluster’s role in advancing I4.0 should complement the existing literature.

The exploratory study concludes with policy recommendations and sets the stage for more detailed studies. Amidst the research’s limitations, this study pioneers a path for future comprehensive investigations, enabling a deeper understanding of Polish clusters’ maturity in I4.0 adoption. By comparing the authors’ analysis of the Polish Automotive Group (PGM) cluster with existing literature, the authors uncover a distinct disparity between the theoretical prominence of cluster catalysis and the current Polish reality. Future detailed dedicated enquiries will address these constraints and provide a more comprehensive map of Polish clusters’ I4.0 maturity.

This study identifies patterns of I4.0 implementation and diagnoses the role of clusters in the transformation towards I4.0. It investigates how advanced is the adoption of I4.0 solutions among the residents of Polish clusters and how they perceive the advantages of cluster membership for such transformation. Special attention was paid to the analysis of the automotive sector. Comparing the conclusions drawn from the analysis of the Polish PGM cluster in this case study to those from the literature on the subject, it becomes clear that the catalytic role of clusters in the implementation of I4.0 technologies by enterprises, as emphasised in the literature, is not yet fully reflected in the Polish reality.

Brand reputation (BR) is one of the most important factors that affect the consumer–brand relationship and give businesses a competitive advantage. Businesses with a strong BR can increase their market shares and product market prices, in addition to gaining a competitive advantage. In order for businesses to have these advantages, they need to know and analyze their consumers. This study aimed to develop an alternative analysis method by using classification algorithms and regression analysis to measure and evaluate the effect of consumers' BR perceptions on their willingness to pay premium prices (WPP).

The research data were collected from 483 participants by the online survey method due to the COVID-19 pandemic. The data were first analyzed with regression analysis, and the effect of BR on WPP was found to be significant. Then, using artificial intelligence (AI) methods that were not used in previous studies, consumers' perceptions of BR and WPP were clustered and classified.

The results revealed the highest and lowest customer groups with BR and WPP and empirically demonstrated that highly accurate practical classification models can be applied to determine strategies in line with these findings.

The model proposed in this study offers an integrated approach by using AI and regression analysis together and tries to fill the gap in the literature in this field. Therefore, the novelty of this study is to quantitatively reveal and evaluate the relationship between BR and WPP by using AI classification algorithms and regression analysis together.

The purpose of this research is to extend the results of previous studies regarding corporate reputation scales and identify new and specific items relevant for studying global corporate reputation from a customer’s point of view.

This research was based on the qualitative projective “Album on Line” (AOL) technique. The authors used a sample of 12 French consumers distributed equally between affective and cognitive scenarios. An individual-difference multidimensional scaling approach (INDSCAL) was applied to display the overall semantic space among generated items.

The exploratory AOL approach generated 62 items related to both cognitive and affective orientations characterizing online and offline corporate reputation. The results uncovered six semantic clusters for each scenario. All in all, seven new items could be added in the process of building a new global corporate reputation measurement scale by adding: avant-garde, singularity, exclusivity, savings, return policy, freeness and speed.

This research makes it possible to propose a new global corporate reputation measurement scale with sound psychometric properties. This scale will be adapted for click and mortars and pure players. This paper unlocks future perspectives by suggesting a causal model that integrates online corporate reputation and its main antecedents and consequences.

From a managerial perspective, this research offers insights to managers with the main orientations surrounding the components of global corporate reputation. Moreover, the AOL mappings delineate which quadrants the managers would like to be fitted into or avoid, and hence define more precisely which key elements should be stressed or discarded.

This research outlines AOL, an original qualitative projective technique that can be used to understand customers’ thoughts, which are stocked and collected as images. Moreover, this research intends to analyze the gathered data using both INDSCAL and fuzzy k-means cluster analysis to reduce conventional biases related to subjectivity.

Generation Z (Gen Z) is particularly influenced by digital technologies as this cohort is found to have grown up with technology forming the pivot of most of their routine activities. Owing to the huge potential of this market, online retailers are keen to build and sustain their loyalty. Shopper’s loyalty varies across age, gender, income, service quality perceptions, etc. of customers. This study aims to show that it is necessary to identify distinct consumer segments of these shoppers which can enable online retailers to fine tune their marketing programs and increase program effectiveness.

Using a sample of 700 students pursuing Masters in Business Administration (553 usable responses) from two state universities in North India, data have been collected with reference to accessories, clothes, books and electronic goods. SPSS and AMOS have been used to analyse data using cluster analysis and multinomial logit (MNL) regression analysis.

The results of cluster analysis reveal that these shoppers can be clustered into three segments, namely disloyal shoppers (DS), staunch loyals (SL) and vacillating shoppers (VS) on the basis of their online retail loyalty. The odds ratio reveals that less frequent online shoppers are less likely to be VS or DS than being SL shoppers. People who experience flow while surfing online shopping websites are 3.260 times more likely to be VS than being SL. Further, service quality decreases the odds of a shopper acting as a VS in comparison to SL shopper by 0.113.

These findings would help marketers identify strategies that can transform the VS or the disloyal ones into loyal and profitable segments. The present study is limited to Gen Z shoppers and so results may vary for customers belonging to other age groups.

The study contributes to existing literature by understanding the antecedents which contribute to online retail loyalty of distinct segments of young shoppers.

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.