Trust Network Research Articles

Generalized opinion dynamics model for social trust networks

Generalized opinion dynamics model for social trust networks

Green Business Model Innovation in Symbiosis Business Value Networks: Bridging Green Business Model Innovation to Different Green Symbiosis Business Value Networks with Future Wireless Technologies

Societies all over the globe push to greening businesses and their business models on all Green Business Model Parameters. However, they are challenged to find efficiency in green business models innovation and to jump to the next step of “the Green Wall” – greening their businesses. There is therefore a growing interest to investigate Green Business Model Innovation (GBMI)in Symbiosis Business Value Networks. The profitability and achievement of other values seems in Green Symbiosis Business Value Networks (GSBVN) to be much higher than classical GBMI isolated to single business model innovation. In this context the GSBVN seems to be a promising initiative and new strategic way to go – to increase transformation of business models into green business models. The GSBVNs seems by nature to be built for greening our society and businesses operations. Our hypothesis is that GSBVN could potentially achieve better results and effects on “the triple bottom line”combined with future wireless technologies and advanced sensor technologies. As 5G and beyond technologies offer better networks solutions “the Green Wall”challenges can be overcome because sensoring, measurement, calculations and monitoring the green business models and GBMI in the terms of monetary and nonmonetary values can be performed much more detailed, in real-time and for the entire lifetime of all the GSBVN’s business models. Fundamentally security, strong network constructions and trust are core to GSBVN’s growth. Embedding Future Wireless Networks (FWN)s and advanced sensor technologies seems to be able to make this become even easier, and a promising way to achieve the green business model goals of the GSBVNs and the surrounding society. There is a lack of studies regarding the potential and initiatives of GBMI in GSBVN. This paper tries to fill some part of this gap by presenting some initial studies on (i) How can green business model innovation in symbiosis be defined? and (ii) How can Future Wireless Technologies support and value the innovation and evolvement of Green Business Models in Symbiosis Business Value Networks. The paper seeks to unwrap different GSBVNs’ approaches, origins, and views. The paper ends up with discussing the role future wireless technologies can play in designing, reconfiguring, developing and operating GSBVNs.

Correction to: An incomplete probabilistic linguistic multi-attribute group decision making method based on a three-dimensional trust network

Correction to: An incomplete probabilistic linguistic multi-attribute group decision making method based on a three-dimensional trust network

Designing a Pattern of Relationship between the Functions of the Intermediary Institutions of Open Innovation in Health System

Background. The health system generally comprises a variety of players. In the health system, there are intermediary institutions of innovation, playing a key role in reducing the distance between the members of the network and building trust among them. Therefore, this study aimed at developing pattern of relationship between the function of the intermediary institutions of open innovation in the health system. Methods. This study was conducted in two main phases. The first phase was carried out to identify and approve the principal components of the function of the intermediary institutions in the health. The data were collected based on the researcher-made questionnaire in accordance with the existing research literature. It was distributed among 140 managers and experts of the research and quality improvement divisions of the clinical centers, laboratories, and pharmaceutical companies. The data were analyzed, using confirmatory factor analysis. In the second phase, interpretive structural modeling (ISM) was employed to design the pattern of relationship. Experts’ view of 12 members were used for designing the pattern. Interpretive structural modeling was used for developing various parts of the questionnaire. Results. The findings of the confirmatory factor analysis are an indication of the proper relationship between the principal components and the respective indicators. Accordingly, all principal components of the pattern of the functions of the intermediary institutions were confirmed in the statistical population of the research. Based on the interpretive structural modeling, the findings revealed that two components, i.e. organizing and supplying resources and strengthening the network and participation, were the two main and most effective functions of the intermediary institutions. The component of commercialization was the most susceptible function of the intermediary institutions and ranked first in this regard. Conclusion. The intermediary institutions can reduce the deficits in each stage of the process of innovation in the field of health according to their various functions, serving as an accelerator increasing the knowledge and innovation in the field of health.

Connecting for success: Egocentric network types among underrepresented minority students at college

This study investigates the egocentric networks of underrepresented minority students. Adjustment to college for underrepresented minority students often means balancing between the origin (i.e., low-status family) and host (i.e., academic) environments, requiring students to develop different coping strategies. The analysis included 124 Roma university students. We employed the contact diary method and the EgoNet program for mapping students’ egocentric networks. We explored classification among egocentric networks by cluster analysis based on the network proportion of the origin, fellow, and host groups. Three clusters were distinguished. The Host-based cluster proved to be the most advantageous, while the Balancing cluster appeared to be the most disadvantageous in terms of network resources, well-being, and trust. Partners’ ethnicity and educational attainment showed a strong connection with cluster membership. Our results highlight that small fellow communities can successfully interconnect the origin and host groups in students’ egocentric networks.

Security of Zero Trust Networks in Cloud Computing: A Comparative Review

Recently, networks have shifted from traditional in-house servers to third-party-managed cloud platforms due to its cost-effectiveness and increased accessibility toward its management. However, the network remains reactive, with less accountability and oversight of its overall security. Several emerging technologies have restructured our approach to the security of cloud networks; one such approach is the zero-trust network architecture (ZTNA), where no entity is implicitly trusted in the network, regardless of its origin or scope of access. The network rewards trusted behaviour and proactively predicts threats based on its users’ behaviour. The zero-trust network architecture is still at a nascent stage, and there are many frameworks and models to follow. The primary focus of this survey is to compare the novel requirement-specific features used by state-of-the-art research models for zero-trust cloud networks. In this manner, the features are categorized across nine parameters into three main types: zero-trust-based cloud network models, frameworks and proofs-of-concept. ZTNA, when wholly realized, enables network administrators to tackle critical issues such as how to inhibit internal and external cyber threats, enhance the visibility of the network, automate the calculation of trust for network entities and orchestrate security for users. The paper further focuses on domain-specific issues plaguing modern cloud computing networks, which leverage choosing and implementing features necessary for future networks and incorporate intelligent security orchestration, automation and response. The paper also discusses challenges associated with cloud platforms and requirements for migrating to zero-trust architecture. Finally, possible future research directions are discussed, wherein new technologies can be incorporated into the ZTA to build robust trust-based enterprise networks deployed in the cloud.

Networks of trust: accessing informal work online in Indonesia during COVID-19.

Although studies on digital labour platforms demonstrated how the internet has opened up access to income opportunities in the developing world, an exploration of how informal workers use the internet to access work without an intermediary is missing. Using data from digital ethnography and interviews with workers in Indonesia, this article examines how platform‐based motorcycle taxi drivers and domestic workers accessed work through social media in the time of COVID‐19 when the platforms were not allowed to operate. The evidence suggests that while social media can offer increased opportunities for workers, their success was largely dependent on their social networks and bounded by the algorithms designed by platform owners.

The Role and Trust Toward World Health Organization (WHO) in Managing Covid-19 Pandemic: Network and Text Analysis in @Who Twitter

The purpose of this study is to analysis the role and trust toward World Health Organization (WHO) in managing Covid-19 pandemic, through social media twitter. This research is to compare the network and public trust on a three (3) different time period. This study employs social network analysis method and text analysis on @WHO twitter account. Researcher used Netlytic and Gephi Software to retrieve data on social media and to calculate network statistic. Data used is based on 3 different time period, they are, 27 February, 20 March, and 9 April 2020. More than 7500 node and 15.000 edges were collected for this research. The result of this study suggests that @WHO account network is very large, but is not fully utilized. The lack of information disclosed by WHO through their Twitter account and one-way communication, have resulted in many negative accusations toward WHO in managing the pandemic. The most frequent accusation directed is the inability of WHO and Dr. Tedros in managing Pandemic, WHO and China are collaborating in hiding the data on Pandemic, and accusation that the pandemic is a pretence and politic of China. WHO Twitter account has become public attention for seeking information on the Pandemic, however this organization has yet to carry out two-way communication on answering public question directed to them. This research sought to see the role and trust of WHO as an organization responsible in managing the Pandemic through social media twitter by comparing three result on a different time period and to analyse the content of the message directed towards WHO.

Evaluating households’ community participation: Does community trust play any role in sustainable development?

Traditional community support, prevention from disasters, and mitigation depend on the top-down mode of management regulated by the government. This study aims to analyze the association between social networks and its structure on the decision of households’ participation in community. A households survey was conducted in Yanliang district of Shaanxi province of China and 393 households were targeted. An ordered multi classification logistic regression model was utilized to empirically analyze the association between the variables. The results showed that social networks have a significant positive impact on the households’ participation in cooperatives/communities. Among other variables, network interaction, network learning, network trust, and households’ participation in green energy technologies and cooperatives/community showed significant and positive correlation, but the variable network reciprocity was found to be negatively correlated to the adoption of green energy technologies. Study findings further revealed that, there is an interaction effect in the participation of households’ behavioral decision-making and joining cooperatives. Furthermore, the social experience of the head of the household and the source of income of the family showed a significant positive association with households’ participation in cooperatives/communities. Based on the study findings, it is suggested that the utilization and cultivation of households’ social network, service level of cooperatives need to be improved.

Population based training and federated learning frameworks for hyperparameter optimisation and ML unfairness using Ulimisana Optimisation Algorithm

This paper introduces the Ulimisana Optimisation Algorithm enabled Population Based Training (PBT-UOA) framework which allows for hyperparameters to be fine-tuned using a population based meta-heuristic algorithm at the same time as parameters are being optimised. Models are trained until near-convergence on the updated hyperparameters and the parameters of the best performing model are shared to warm start the other models in the next hyperparameter tuning iteration. In the PBT-UOA, all models are trained using the same dataset. This framework performed better than the Bayesian Optimisation algorithm. This paper also introduces the Ulimisana Optimisation Algorithm enabled Federated Learning (FL-UOA) framework which is an extension of the PBT-UOA. This framework is introduced to address the challenges of scattered datasets and privacy that is presented by the increase in connected end-devices. The FL-UOA learns on local data in scattered end-devices without sending datasets to a central server. The training datasets in local end-devices are used to evaluate models trained in other end-devices. The performance metrics are used to update the Social Trust Network (STN) of the FL-UOA framework. The FL-UOA outperformed the classic Federated Learning framework. This STN updating technique was tested in Machine Learning (ML) Unfairness to see how well it functioned as a regularisation term. This was achieved by training different models on subsets that contained datasets representing only specific sensitive groups. Results showed that by updating the hyperparameters while learning the parameters on the dataset scattered across different devices, the FL-UOA, takes advantage of diversified learning and reduces the ML Unfairness for models trained on group specific datasets.

NetDAO: Toward Trustful and Secure IoT Networks without Central Gateways

The Internet of Things (IoT) suffers from a profound lack of trust between central gateways and sensors, e.g., gateways suspect sensors of flooding malicious packets, and vice versa, sensors suspect gateways of manipulating traffic data. One important reason for the mistrust is the asymmetry of a centralized network organization. A Decentralized Autonomous Organization (DAO) can establish a trustful and symmetric network with the blockchain. However, it is a vacant area for IoT networks to build trust between gateways and sensors within the DAO. In this paper, we firstly propose a trustful and secure IoT Network DAO solution (NetDAO) to mitigate the data manipulation and the malicious flooding packets. In particular, the NetDAO has a security rating algorithm to assign a reputation value for each entity in the network. Based on this, each entity can mitigate the malicious flooding packets using a proof-of-reputation packet-forwarding mechanism. In addition, the NetDAO stores traffic data using the blockchain to mitigate the data manipulation. The experimental results show that the NetDAO effectively mitigates malicious flooding packets and costs 1 s for ∼480 entities to complete the rating algorithm.

Recommendation system using a deep learning and graph analysis approach

AbstractWhen a user connects to the Internet to fulfill his needs, he often encounters a huge amount of related information. Recommender systems are the techniques for massively filtering information and offering the items that users find them satisfying and interesting. The advances in machine learning methods, especially deep learning, have led to great achievements in recommender systems, although these systems still suffer from challenges such as cold‐start and sparsity problems. To solve these problems, context information such as user communication network is usually used. In this article, we have proposed a novel recommendation method based on matrix factorization and graph analysis methods, namely Louvain for community detection and HITS for finding the most important node within the trust network. In addition, we leverage deep autoencoders to initialize users and items latent factors, and the Node2vec deep embedding method gathers users' latent factors from the user trust graph. The proposed method is implemented on Ciao and Epinions standard datasets. The experimental results and comparisons demonstrate that the proposed approach is superior to the existing state‐of‐the‐art recommendation methods. Our approach outperforms other comparative methods and achieves great improvements, that is, 15.56% RMSE improvement for Epinions and 18.41% RMSE improvement for Ciao.

Knowledge sharing in private social solidarity institutions during pandemics

The COVID-19 pandemic period resulted in a global crisis, whether in the economy, personal or professional life. Because of the pandemic, people and institutions had to change the way they did things.
 Even though people are becoming more aware of the value of knowledge and it is becoming more common in some institutions, knowledge management methods are still not well known in the social sector and as a key tool for institutions in crisis.
 Considering the beneficial role that knowledge sharing (KS) practices play in organizations, the current study aims to investigate the impact of KS practices in Portuguese private social solidarity institutions in adapting to the COVID-19 pandemic period. To achieve the purpose and considering the exploratory nature of the research, semi-structured interviews were conducted with fifteen professionals from four private social solidarity institutions in northern Portugal. Nvivo processed the interviews. Because COVID-19 is new, there is no research on knowledge sharing in these institutions, so the study can be considered as original.
 Before and during pandemics, the presence of knowledge sharing practises, such as the integration of new employees, the proactivity of learning, the sharing of new ideas and mistakes, and the sharing relationship between peers and superiors and other institutions, was observed through the interviews. In this study, we discovered that trust, communication, technology, and social networks, as well as the role of leadership in creating an environment conducive to formal and informal sharing, were elements that facilitated knowledge sharing practises, even throughout the pandemics. During the interviews, both technical directors and employees acknowledged the following: the relationship between superiors and employees in decision-making processes; recognition, feedback and incentives from leaders and the presence of formal and informal communication networks. When it came to sharing, which could happen in a formal or informal setting, employees seemed to prefer informal interactions.
 To summarise, the institutions were able to adjust to the limits imposed by the pandemic, and the basic practises of KS are part of the daily routine of the organisations analysed.

SP-TRUST: a trust management model for speed trust in vehicular networks

ABSTRACT Information security mechanisms are crucial for Vehicular Ad-hoc NETworks (VANET) applications in order to preserve their robustness. The efficiency of these applications relies on the reliability of the used information, especially the vehicle-related information such as location and speed. Trust management models are crucial to evaluate the quality of the used information. Accordingly, we focus in this paper on the assessment of speed information to detect the malicious vehicles using a fuzzy-based model (SP-TRUST). The proposed model relies on two fuzzy inference systems. The first one evaluates the speed trust based on traffic rules (inter-vehicle distance) while considering the road topology (angle deviation) and the traffic state (density). The correlation between these parameters and the speed value is assessed. The second inference system assesses the speed trust based on the behavior of neighbor vehicles using the median speed of vehicles. Simulations were carried out to evaluate the robustness and the scalability of SP-TRUST model. The results of the experimental studies proved that the model performs well in detecting different behaviors of malicious vehicles in different scenarios, especially when the percentage of malicious vehicles is lower than 50%.

At the Heart of Trust: Understanding the Integral Relationship Between Emotion and Trust

Trust and emotion have been among the most influential and frequently studied constructs in organizational behavior over the last two decades. Although emotion would seem to be integral to interpersonal trust, it has played a minor part in the literature. This article is intended to advance understanding of emotion’s role in shaping trust judgments to help bring this concept into the central conversation on trust within organizations. Our paper begins with a review of existing research to help understand how emotion fits within the nomological network of trust and to identify some of the key insights from empirical research. The review suggests emotion impacts trust in two fundamentally different ways, based on independent streams of research, by how it is experienced as a trustor and expressed by the trustee. We provide a conceptual model that integrates these different perspectives as well as brings in key ideas which are absent from research on organizational trust: (a) using a validated and more sophisticated model of trust and trustworthiness will add greater insight into how emotion impacts trust, (b) experienced and expressed emotions jointly and interactively affect trust, (c) trust and emotion operate at multiple levels of analysis, including dyadic and network, and (d) trust and emotion change over time as a function of each other. We conclude that there is much opportunity for future research on trust and emotion, and suggest future directions.

Blockchain-Assisted Adaptive Reconfiguration Method for Trusted UAV Network

Due to the high mobility of nodes and the complexity of the mission environment, mission-oriented UAV networks are not only subject to frequent topology changes, but also to the risk of being compromised, hijacked and corrupted. As a result, an operating UAV network is essentially a Byzantine distributed system whose physical structure and node trustworthiness change over time. How to implement the global management of UAV networks to achieve a rational allocation of UAV network resources and reconfiguration of trusted networks is a problem worthy of in-depth study. The method proposed in this paper introduces a lightweight storage blockchain in the UAV network through two-stage consensus, firstly performing data consensus on the local state records of the nodes, then performing decision consensus on the data consensus results using algorithms such as fuzzy K-Modes clustering and global trustworthiness assessment, and finally recording the decision consensus results into a new block as the new configuration information of the UAV network. A lightweight storage blockchain-assisted trusted zone routing protocol (BC_TZRP) is designed to dynamically and adaptively build configurable trusted networks in a way that the blockchain continuously adds new blocks. Using QualNet simulation experimental software, an experimental comparison between the classical routing protocol for mobile self-organizing networks and the traditional consensus algorithm for blockchains is conducted. The results show that the approach has significant advantages in terms of packet delivery rate, routing overhead and average end-to-end delay, and can effectively improve the overall working life and fault tolerance of the UAV network.

Assessing the Level of Trust in the Humanitarian Supply Chain: The Cases of Study: the Fire-induced Collapse of Plasco Building, Shahran Gas Explosion, and Flash Flood in Tehran Subway

INTRODUCTION: The present study aimed to investigate communication, information exchange, cooperation, and trust networks in Iran's Humanitarian Supply Chain at both inter-organizational and interpersonal levels. METHODS: This applied research was conducted based on a descriptive survey design. The statistical population of this research included the organizations and people involved in relief operations performed in three recent disasters: the fire-induced collapse of the Plasco building, Shahran gas explosion, and the flash flood in the Tehran subway. In order to analyze networks, a questionnaire containing network analysis questions was designed. Upon the completion of the questionnaires, the data of each network were entered systematically into a matrix on an excel spreadsheet. Such indexes as degree centrality, betweenness centrality, intersection points, isolation points, and network density were calculated by Ucinet and NetDraw software packages; therefore, the networks were analyzed. FINDINGS: Based on the results obtained from the analysis of communication, information exchange, and cooperation networks, the network density was 21%, indicating a low level of organizational relationship. At this level of communication, information exchange and cooperation were obtained at 11.5% and 16.5%, respectively. The analysis of the trust network also illustrated that the density of this network was 26.6%, suggesting that out of 21% of connections, there was about a quarter of possible trust. This is suggestive of low levels of inter-organizational Trust. CONCLUSION: Building trust in Iran's humanitarian service supply chain can be very effective in the acceleration of service supply, organization, and success of relief operations

Reputation Effect on Contract Choice and Self-Enforcement: A Case Study of Farmland Transfer in China

The prevailing informal contracts of farmland transfer in China are facing frequent disputes and defaults, which call for effective self-enforcement mechanisms operating through transactors’ reputations and social networks. However, the effects of reputation on contract choice and self-enforcement have not been thoroughly considered and examined by existing research in the case of farmland transfer. This study explores the reputation’s ex-ante signaling effect on farmers’ contract choices and the ex-post penalty effect on farmers’ performance in informal contracts. Based on 403 transfer contracts obtained from a field survey conducted in the Hebei province of China, we apply the multinomial logit model and Heckman probit model to perform empirical analysis. The results show that, affected by the penalty effect, farmers with good reputations are more likely to fulfill informal contracts to avoid reputation damage and the resulting loss of future trading opportunities. However, in the ex-ante stage of contract choice, a farmer’s reputation has no significant signaling effect on the formation of informal contracts. The informal contracts are chosen due to farmers’ trust in the close social network and the demand for reduced transaction costs. These findings highlight the importance of personal reputation serving as a form of relational governance in the self-enforcement of informal contracts, which provides a means of enhancing the informal contract’s effectiveness in terms of farmland transfer in the rural acquaintance society. It also provides insights into the necessity of creating a supportive environment for informal rules. Policies should encourage the building of personal reputation and establishment of good social norms to form a long-term, stable and reasonable contractual relationship for farmland transfer.

Can Neo-Rural Initiatives Bolster Community Resilience in Depopulated Coupled Human and Natural System?: Insights From Stakeholder Perceptions in Central Spain

Preindustrial era agro-sylvopastoral land uses have influenced structure, function and disturbance in Mediterranean type mountainous landscapes for millennia. In this study we analyze through semi-structured interviews, stakeholder perceptions of coupled human and natural system (CHANS) community resilience in one such landscape; the municipality of Puebla de la Sierra, Madrid. The municipality is part of the Biosphere Reserve of the Sierra del Rincon and the Natura 2000 network and as such is subject to various conservationist regulations emanating from multiple levels of governance. In the preindustrial past most municipal lands formed an oak “dehesa” or open forest CHANS that made biomass extraction through pollarding compatible with pastoralism and shifting agriculture. After a period of rapid land-use change in the early 20th century—marked by the state led plantation of coniferous forests, the final decades of the last century were characterized by rural abandonment and the collapse of traditional forms of land use as well as the gradual transformation of the municipality into an eco-touristic, exurban destination for Madrid residents. More recently, the municipality has experienced an influx of neo-rural settlers in the area wishing to connect traditional knowledge and management with modern agro-environmental practices. In our study, we identify two limiting factors to community resilience in Puebla de la Sierra; governance and financing. The current governance model is perceived by respondents to be contrary to their reality and needs, which translates into environmental, urban and health regulations that, in their views, penalizes agroecological and small-scale economic activities. In addition, respondents believe there is a dearth of material and financial resources to initiate these transformative local actions which further weakens community resilience. Stakeholders however also identified other factors that reinforce community resilience such as a collective willingness to revive key traditional ecosystem management practices such as pollarding, the networks of trust existing between the people participating in these new initiatives and the capacity for deliberating between different visions of future development pathways amongst local stakeholders.

Psychological trust in social networks

The article analyzes the influence of the social network on humanity. For many years, humanity has realized even in social networks the need for psychological security. Social media confrontation is psychological warfare on a virtual level. Expecting negative information on a social network, people communicate only with friends, like-minded people. Communication on the social network has intensified over the past three years, due to the pandemic in the world, minimal contact has entered the way of life of mankind. Nevertheless, any positive and negative information can affect the psychological state of a person. Therefore, security at the psychological level is necessary and requires new approaches of non-violence.