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1 – 10 of over 23000
Article
Publication date: 25 January 2024

Lin Kang, Jie Wang, Junjie Chen and Di Yang

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…

Abstract

Purpose

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).

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

International Journal of Intelligent Computing and Cybernetics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 9 January 2023

Xie Hui and Zhang Kexin

Due to consumption changes in the post-pandemic era, the production safety of agricultural products is affecting global consumers. This paper constructs an evaluation index of the…

Abstract

Purpose

Due to consumption changes in the post-pandemic era, the production safety of agricultural products is affecting global consumers. This paper constructs an evaluation index of the agricultural Internet of things (IOT) traceability system and evaluates it using the dynamic hesitant-fuzzy linguistic term sets (HFLTS)-based DEMATEL method to improve agricultural supply-chain links and improve production quality.

Design/methodology/approach

The agricultural IOT traceability index system is constructed using the literature and expert interviews; it comprises 6 first-level indices and 20 second-level indices. The agricultural IOT traceability system is evaluated using the dynamic HFLTS-DEMATEL method.

Findings

Producers' awareness of agricultural-production safety (A11) has the most significant impact on production and processing links, while warehouse location and storage capacity (A31) have the largest impact on the circulation link. Inspection authenticity and transparency and quarantine information (A41) have the largest impact on the detection-consumption link. The extent to which the traceability-platform construction is complete (A62) has the largest impact on technical support.

Research limitations/implications

The present paper may be limited to the era of post-pandemic, and it is hard to consider all the indices. Further research can broaden the research context and establish a more comprehensive index system.

Practical implications

The index system constructed in this study will surely help relevant regulatory authorities in China to promote the construction of agricultural IOT traceability system and establish a unified standard, so as to provide a basis for future developers to enter the field. Accordingly, it also can help every subject to identify the key indices of each process in the agricultural-product supply chain and guide relevant departments to conduct targeted information tracking and management. The consumers could also understand the standards of traceable agricultural products and effectively protect their own rights and interests.

Originality/value

The existing literature does not provide an objective, unified standard for measuring a decentralized traceability system or identifying key processes. This study therefore proposes a new evaluation index system and uses a dynamic evaluation method to determine the importance of key indices. This study identifies the most important indices in each process, making it possible to discover, improve, and enhance the quality of agricultural products at a practical level.

Details

International Journal of Quality & Reliability Management, vol. 40 no. 8
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 6 November 2023

Miriam Eugenia Wolf, Agnes Emberger-Klein and Klaus Menrad

This paper aims to determine, which values guide consumers decision-making on natural health products for concentration and cognition (NHPCC) and how they link to choice-relevant…

Abstract

Purpose

This paper aims to determine, which values guide consumers decision-making on natural health products for concentration and cognition (NHPCC) and how they link to choice-relevant product attributes. The purpose is to contribute to a better understanding of NHPCC consumption choices, which can encourage more consumer-centric product development and positioning.

Design/methodology/approach

Based on the means-end chain approach, in-depth laddering interviews with 26 consumers of NHP were conducted in Germany from October to December 2020. Qualitative content analysis was applied and a hierarchical value map over the dominant association was built and analyzed.

Findings

Five terminal values were found to be relevant for NHPCC decision-making. The personal focused values security, self-direction and stimulation are via health mainly associated with trust and a conscious decision-making, which is linked to the product attributes of effectiveness, tolerance and declaration. Social focused values of universalism or benevolence guide attention on the attributes of sustainability and regionality.

Originality/value

The study contributes to close the knowledge gap concerning the linkages between abstract values and concrete product attributes of NHP through associated consequences. To the best of the authors’ knowledge, this is the first study that analyzed these links for NHPCC, although such products are gaining more interest among companies and consumers. Companies can benefit from the outcomes by developing more consumer-centric product concepts and marketing communication strategies for NHPCC. Due to higher attention on relevant information, consumers’ decision-making could become safer and more conscious.

Details

International Journal of Pharmaceutical and Healthcare Marketing, vol. 18 no. 1
Type: Research Article
ISSN: 1750-6123

Keywords

Article
Publication date: 21 February 2022

Lucia Gao, Shahbaz Sheikh and Hong Zhou

The purpose of this study is to empirically examine the relationship between executive compensation linked to corporate social responsibility (CSR) and firm risk. It also explores…

1209

Abstract

Purpose

The purpose of this study is to empirically examine the relationship between executive compensation linked to corporate social responsibility (CSR) and firm risk. It also explores the moderating role of CSR-linked compensation on the relationship between risk-taking incentives provided in executive compensation and firm risk.

Design/methodology/approach

This study uses Ordinary Least Squares (OLS) and firm-fixed effects regressions to estimate the association between CSR-linked compensation and firm risk. Furthermore, it employs instrumental variable, propensity score matching and first-order difference approaches to address concerns about endogeneity and sample selection.

Findings

Benchmark results show that CSR-linked compensation reduces both total and idiosyncratic measures of risk. Further results indicate that CSR-linked compensation reduces firm risk only when risk is above the optimal level and has no significant effect when risk is below the optimal level. Additionally, tests show that CSR-linked compensation also mitigates the positive effect of Vega of executive compensation on risk and this mitigation effect is significant only when risk is above the optimal level.

Practical implications

The empirical results of this study show that boards can use CSR-linked compensation not only to induce higher social performance but also as a risk management tool to manage risk, especially when risk is above value increasing optimal levels. Furthermore, boards can use CSR-linked compensation to mitigate excessive risk-taking induced by option compensation.

Originality/value

This study contributes to the emerging literature on CSR-linked compensation and firm risk. To our knowledge, this is the first study that documents the direct risk-reducing effect of CSR-linked compensation and its mitigating effect on the relation between Vega of executive compensation and firm risk.

Details

International Journal of Managerial Finance, vol. 19 no. 2
Type: Research Article
ISSN: 1743-9132

Keywords

Article
Publication date: 5 May 2021

Samrat Gupta and Swanand Deodhar

Communities representing groups of agents with similar interests or functions are one of the essential features of complex networks. Finding communities in real-world networks is…

Abstract

Purpose

Communities representing groups of agents with similar interests or functions are one of the essential features of complex networks. Finding communities in real-world networks is critical for analyzing complex systems in various areas ranging from collaborative information to political systems. Given the different characteristics of networks and the capability of community detection in handling a plethora of societal problems, community detection methods represent an emerging area of research. Contributing to this field, the authors propose a new community detection algorithm based on the hybridization of node and link granulation.

Design/methodology/approach

The proposed algorithm utilizes a rough set-theoretic concept called closure on networks. Initial sets are constructed by using neighborhood topology around the nodes as well as links and represented as two different categories of granules. Subsequently, the authors iteratively obtain the constrained closure of these sets. The authors use node mutuality and link mutuality as merging criteria for node and link granules, respectively, during the iterations. Finally, the constrained closure subsets of nodes and links are combined and refined using the Jaccard similarity coefficient and a local density function to obtain communities in a binary network.

Findings

Extensive experiments conducted on twelve real-world networks followed by a comparison with state-of-the-art methods demonstrate the viability and effectiveness of the proposed algorithm.

Research limitations/implications

The study also contributes to the ongoing effort related to the application of soft computing techniques to model complex systems. The extant literature has integrated a rough set-theoretic approach with a fuzzy granular model (Kundu and Pal, 2015) and spectral clustering (Huang and Xiao, 2012) for node-centric community detection in complex networks. In contributing to this stream of work, the proposed algorithm leverages the unexplored synergy between rough set theory, node granulation and link granulation in the context of complex networks. Combined with experiments of network datasets from various domains, the results indicate that the proposed algorithm can effectively reveal co-occurring disjoint, overlapping and nested communities without necessarily assigning each node to a community.

Practical implications

This study carries important practical implications for complex adaptive systems in business and management sciences, in which entities are increasingly getting organized into communities (Jacucci et al., 2006). The proposed community detection method can be used for network-based fraud detection by enabling experts to understand the formation and development of fraudulent setups with an active exchange of information and resources between the firms (Van Vlasselaer et al., 2017). Products and services are getting connected and mapped in every walk of life due to the emergence of a variety of interconnected devices, social networks and software applications.

Social implications

The proposed algorithm could be extended for community detection on customer trajectory patterns and design recommendation systems for online products and services (Ghose et al., 2019; Liu and Wang, 2017). In line with prior research, the proposed algorithm can aid companies in investigating the characteristics of implicit communities of bloggers or social media users for their services and products so as to identify peer influencers and conduct targeted marketing (Chau and Xu, 2012; De Matos et al., 2014; Zhang et al., 2016). The proposed algorithm can be used to understand the behavior of each group and the appropriate communication strategy for that group. For instance, a group using a specific language or following a specific account might benefit more from a particular piece of content than another group. The proposed algorithm can thus help in exploring the factors defining communities and confronting many real-life challenges.

Originality/value

This work is based on a theoretical argument that communities in networks are not only based on compatibility among nodes but also on the compatibility among links. Building up on the aforementioned argument, the authors propose a community detection method that considers the relationship among both the entities in a network (nodes and links) as opposed to traditional methods, which are predominantly based on relationships among nodes only.

Details

Information Technology & People, vol. 37 no. 2
Type: Research Article
ISSN: 0959-3845

Keywords

Open Access
Article
Publication date: 28 April 2022

Manuel Pedro Rodríguez Bolívar and Laura Alcaide Muñoz

This study aims to conduct performance and clustering analyses with the help of Digital Government Reference Library (DGRL) v16.6 database examining the role of emerging…

2024

Abstract

Purpose

This study aims to conduct performance and clustering analyses with the help of Digital Government Reference Library (DGRL) v16.6 database examining the role of emerging technologies (ETs) in public services delivery.

Design/methodology/approach

VOSviewer and SciMAT techniques were used for clustering and mapping the use of ETs in the public services delivery. Collecting documents from the DGRL v16.6 database, the paper uses text mining analysis for identifying key terms and trends in e-Government research regarding ETs and public services.

Findings

The analysis indicates that all ETs are strongly linked to each other, except for blockchain technologies (due to its disruptive nature), which indicate that ETs can be, therefore, seen as accumulative knowledge. In addition, on the whole, findings identify four stages in the evolution of ETs and their application to public services: the “electronic administration” stage, the “technological baseline” stage, the “managerial” stage and the “disruptive technological” stage.

Practical implications

The output of the present research will help to orient policymakers in the implementation and use of ETs, evaluating the influence of these technologies on public services.

Social implications

The research helps researchers to track research trends and uncover new paths on ETs and its implementation in public services.

Originality/value

Recent research has focused on the need of implementing ETs for improving public services, which could help cities to improve the citizens’ quality of life in urban areas. This paper contributes to expanding the knowledge about ETs and its implementation in public services, identifying trends and networks in the research about these issues.

Details

Information Technology & People, vol. 37 no. 8
Type: Research Article
ISSN: 0959-3845

Keywords

Article
Publication date: 29 March 2024

Anthony Frank Obeng, Samuel Awuni Azinga, John Bentil, Florence Y.A. Ellis and Rosemary Boateng Coffie

While much attention has been given to work-related factors influencing turnover intention through affective commitment, little focus has been directed to non-work factors…

Abstract

Purpose

While much attention has been given to work-related factors influencing turnover intention through affective commitment, little focus has been directed to non-work factors affecting the service industry. Hence, this study aims to investigate the impact of links, fit and sacrifice, representing off-the-job embeddedness in the community, on turnover intention in the hospitality industry of Ghana: Sub-Sahara Africa using the theory of conservation of resources (COR) and social exchange. The model has been extended to include affective commitment as the mediating mechanism.

Design/methodology/approach

A multi-wave technique was used to collect data through a questionnaire from 341 full-time frontline hospitality employees in Ghana. The responses were analysed using AMOS software structural equation modelling.

Findings

The findings show that links, fit and sacrifice significantly influence employees’ turnover intentions. Moreover, it has been observed that affective commitment decreased the negative relationship and partly mediated the main relationship between the dimensions of off-the-job embeddedness and turnover intention.

Research limitations/implications

The study’s results and academic, practical implications and limitations are discussed for future research.

Originality/value

This study emphasises the theory of COR to demystify community factors employees deem as valued resources, which lighten up their commitment to their organisation and decrease their intent to leave.

Details

International Journal of Organizational Analysis, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1934-8835

Keywords

Article
Publication date: 16 August 2023

Anish Khobragade, Shashikant Ghumbre and Vinod Pachghare

MITRE and the National Security Agency cooperatively developed and maintained a D3FEND knowledge graph (KG). It provides concepts as an entity from the cybersecurity…

Abstract

Purpose

MITRE and the National Security Agency cooperatively developed and maintained a D3FEND knowledge graph (KG). It provides concepts as an entity from the cybersecurity countermeasure domain, such as dynamic, emulated and file analysis. Those entities are linked by applying relationships such as analyze, may_contains and encrypt. A fundamental challenge for collaborative designers is to encode knowledge and efficiently interrelate the cyber-domain facts generated daily. However, the designers manually update the graph contents with new or missing facts to enrich the knowledge. This paper aims to propose an automated approach to predict the missing facts using the link prediction task, leveraging embedding as representation learning.

Design/methodology/approach

D3FEND is available in the resource description framework (RDF) format. In the preprocessing step, the facts in RDF format converted to subject–predicate–object triplet format contain 5,967 entities and 98 relationship types. Progressive distance-based, bilinear and convolutional embedding models are applied to learn the embeddings of entities and relations. This study presents a link prediction task to infer missing facts using learned embeddings.

Findings

Experimental results show that the translational model performs well on high-rank results, whereas the bilinear model is superior in capturing the latent semantics of complex relationship types. However, the convolutional model outperforms 44% of the true facts and achieves a 3% improvement in results compared to other models.

Research limitations/implications

Despite the success of embedding models to enrich D3FEND using link prediction under the supervised learning setup, it has some limitations, such as not capturing diversity and hierarchies of relations. The average node degree of D3FEND KG is 16.85, with 12% of entities having a node degree less than 2, especially there are many entities or relations with few or no observed links. This results in sparsity and data imbalance, which affect the model performance even after increasing the embedding vector size. Moreover, KG embedding models consider existing entities and relations and may not incorporate external or contextual information such as textual descriptions, temporal dynamics or domain knowledge, which can enhance the link prediction performance.

Practical implications

Link prediction in the D3FEND KG can benefit cybersecurity countermeasure strategies in several ways, such as it can help to identify gaps or weaknesses in the existing defensive methods and suggest possible ways to improve or augment them; it can help to compare and contrast different defensive methods and understand their trade-offs and synergies; it can help to discover novel or emerging defensive methods by inferring new relations from existing data or external sources; and it can help to generate recommendations or guidance for selecting or deploying appropriate defensive methods based on the characteristics and objectives of the system or network.

Originality/value

The representation learning approach helps to reduce incompleteness using a link prediction that infers possible missing facts by using the existing entities and relations of D3FEND.

Details

International Journal of Web Information Systems, vol. 19 no. 3/4
Type: Research Article
ISSN: 1744-0084

Keywords

Open Access
Article
Publication date: 13 February 2024

Luigi Nasta, Barbara Sveva Magnanelli and Mirella Ciaburri

Based on stakeholder, agency and institutional theory, this study aims to examine the role of institutional ownership in the relationship between environmental, social and…

Abstract

Purpose

Based on stakeholder, agency and institutional theory, this study aims to examine the role of institutional ownership in the relationship between environmental, social and governance practices and CEO compensation.

Design/methodology/approach

Utilizing a fixed-effect panel regression analysis, this research utilized a panel data approach, analyzing data spanning from 2014 to 2021, focusing on US companies listed on the S&P500 stock market index. The dataset encompassed 219 companies, leading to a total of 1,533 observations.

Findings

The analysis identified that environmental scores significantly impact CEO equity-linked compensation, unlike social and governance scores. Additionally, it was found that institutional ownership acts as a moderating factor in the relationship between the environmental score and CEO equity-linked compensation, as well as the association between the social score and CEO equity-linked compensation. Interestingly, the direction of these moderating effects varied between the two relationships, suggesting a nuanced role of institutional ownership.

Originality/value

This research makes a unique contribution to the field of corporate governance by exploring the relatively understudied area of institutional ownership's influence on the ESG practices–CEO compensation nexus.

Article
Publication date: 3 January 2024

Miao Ye, Lin Qiang Huang, Xiao Li Wang, Yong Wang, Qiu Xiang Jiang and Hong Bing Qiu

A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.

Abstract

Purpose

A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.

Design/methodology/approach

First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.

Findings

Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.

Originality/value

Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

Details

International Journal of Intelligent Computing and Cybernetics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1756-378X

Keywords

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