Social Trust Relationships Research Articles

The Influence of Corporate Social Responsibility and Customer Retention on Customer Relationship Management Performance with the Mediating Effect of Customer Trust in SMEs of Ningxia Province, China

In recent years, e-commerce has experienced rapid expansion and developed as the mainstream business practice in small and medium-sized enterprises (SMEs) in developing areas of China. There are a variety of channels for consumers to purchase from. Therefore, maintaining customers is increasingly important. In the meantime, companies expect to gain more significant insights into their consumers’ demands to earn customer trust, thus achieving customer relationship management (CRM) performance. This study develops based on trust theory and consists of four latent variables: corporate social responsibility (CSR), customer retention, customer trust, and customer relationship management performance. The current study used probability and random sampling techniques. It conducted the questionnaire survey to collect data form managers of SMEs in Ningxia province of China. The Partial least square structural equation modeling (PLS-SEM) technique was used for this study. As a result, a total of 150 respondents were usable for these questionnaires. The study revealed that corporate social responsibility and customer retention have a positive effect on customer relationship management performance. Moreover, customer trust has been examined that it had a positive effect on customer relationship management performance. In addition, customer trust was discovered as a partial mediation between the relationship of corporate social responsibility, customer retention, and customer relationship management performance. Furthermore, based on the samples analyzed, the proposed model seems to be fitted considerably well. Therefore, this study intends to contribute to a better understanding of factors influencing customer relationship management performance in Chinese SMEs, thereby providing companies with practical ideas to improve overall performance.

The trust incentive mechanism by trust propagation to optimize consensus in social network group decision making

Trust relationship in social network group decision making (SN-GDM) is one of the key factors to promote group consensus. This trust relationship reflects the importance of experts, and the consensus feedback opinion induced by the trust relationship is easier to be accepted by the group. But this trust relationship is often assumed to be static, and therefore the dynamic effect of incentive behavior among experts on trust relationship is not considered. To do that, the purpose of this paper is to develop a trust incentive mechanism to effectively promote group consensus. First, the new discount based Uninorm trust propagation operator with 2-tuple linguistic is presented, which transforms an incomplete trust relationship into a complete trust relationship. And then, the trust incentive mechanism with opinions similarity between experts is constructed, which reflects not only the incentive process among experts, but also the dynamic change of the social trust relationship. Further, the trust incentive mechanism driven consensus optimization model is established to generate experts’ recommendation advices, which not only effectively reduce the total adjustment cost of experts to promote consensus, but also makes the experts continue to motivate the discussion after revising their opinions, leading to dynamic changes in the social trust relationship in the next consensus round. Finally, an example of agricultural UAV brand selection in “Metaverse” intelligent shopping is provided to illustrate the proposed method, and the results show that with the increase of incentive factor, the total adjustment cost decreases, and the best UAV brand also changes alternately and tends to be stable. In addition, comparing with the method without trust incentive, the interaction cost of the proposed method is 13.46% lower, and then our proposed method has certain flexibility and advantages.

A multi-period intuitionistic fuzzy consensus reaching model for group decision making problem in social network

A new intuitionistic fuzzy consensus reaching model is developed with multi-period public opinions and expert evaluation values in social network environment. First, the public opinions are obtained by using the crawler software and sentiment analysis technology is used to transform public opinions into intuitionistic fuzzy decision matrix in each period. Attribute weights are calculated by using the time attenuation factor and changes in public opinion. Second, the social trust relationship is modeled and incomplete social trust relationships are completed by using Archimedean t-norm. The expert weights are calculated by using the dynamic trust degree and similarity degree. Third, a consensus framework is proposed for multiple-period decision making problem, which coordinates conflicts between experts through dual feedback paths. The collective opinion scores are calculated by using weights of periods and attribute weights obtained from the word frequency of public opinions. The tourism attraction recommendation method is used to illustrate the proposed method.

TPoison: Data-Poisoning Attack against GNN-Based Social Trust Model

In online social networks, users can vote on different trust levels for each other to indicate how much they trust their friends. Researchers have improved their ability to predict social trust relationships through a variety of methods, one of which is the graph neural network (GNN) method, but they have also brought the vulnerability of the GNN method into the social trust network model. We propose a data-poisoning attack method for GNN-based social trust models based on the characteristics of social trust networks. We used a two-sample test for power-law distributions of discrete data to avoid changes in the dataset being detected and used an enhanced surrogate model to generate poisoned samples. We further tested the effectiveness of our approach on three real-world datasets and compared it with two other methods. The experimental results using three datasets show that our method can effectively avoid detection. We also used three metrics to illustrate the effectiveness of our attack, and the experimental results show that our attack stayed ahead of the other two methods in all three datasets. In terms of one of our metrics, our attack method decreased the accuracies of the attacked models by 12.6%, 22.8%, and 13.8%.

A qualitative analysis of post-hoc interviews with multilevel participants of a randomized controlled trial of a community-based intervention.

Community-based health interventions often demonstrate efficacy in clinical trial settings but fail to be implemented in the real-world. We sought to identify the key operational and contextual elements of the Los Angeles Barbershop Blood Pressure Study (LABBPS), an objectively successful community-based health intervention primed for real-world implementation. LABBPS was a cluster randomized control trial that paired the barbers of Black-owned barbershops with clinical pharmacists to manage uncontrolled hypertension in Black male patrons, demonstrating a substantial 21.6 mmHg reduction in systolic blood pressure. Despite this success, the LABBPS intervention has not expanded beyond the original clinical trial setting. The aim of this study was to determine the facilitating and limiting factors to expansion of the LABBPS intervention. We undertook a qualitative assessment of semi-structured interviews with study participants performed after trial completion. Interviews included a total of 31 participants including 20 (6%) of the 319 LABBPS program participants ("patrons"), 10 (19%) barbers, and one (50%) clinical pharmacist. The semi-structured interviews were focused on perceptions of the medical system, study intervention, and influence of social factors on health. Several common themes emerged from thematic analysis of interview responses including: importance of care provided in a convenient and safe environment, individual responsibility for health and health-related behaviors, and engagement of trusted community members. In particular, patrons reported that receiving the intervention from their barber in a familiar environment positively influenced the formation of relationships with clinical pharmacists around shared efforts to improve medication adherence and healthy habits. All interviewee groups identified the trust diad, comprising the familiar environment and respected community member, as instrumental in increasing health-related behaviors to a degree not usually achieved by traditional healthcare providers. In conclusion, participants of an objectively successful community-based intervention trial consistently identified key features that could facilitate wider implementation and efficacy: social trust relationships, soliciting insights of trust bearers, and consistent engagement in a familiar community setting. These findings can help to inform the design and operations of future community-based studies and programs aiming to achieve a broad and sustainable impact.

Spiritual Resilience in the Sands: Exploring Religion and Subjective Well-being in Abu Dhabi's Community

The practice of religion is often strongly linked to various factors influencing well-being. Utilizing data from the 4th cycle of the Abu Dhabi Quality-of-Life survey conducted in 2023, this research aims to uncover the intricate associations between religious practice and various determinants of well-being among the residents of Abu Dhabi. Path analysis was employed to explore the direct and indirect connections between the frequency of religious practice and different well-being factors. A total of 22 paths revealed the overwhelming interrelations between each pair considered in the model, indicating the nuanced ways in which religious beliefs and behaviors contribute to individuals' sense of fulfillment, satisfaction, and resilience, as well as the diverse facets of well-being within the context of religious practices. Notably, results recorded the highest total effects of practicing religion on social trust and social relationship was the only variable that affects religious practice. Additionally, the significance of age, gender, education, nationality, and marital status was observed in this context. The findings are discussed in the light of relevant literature and potential avenues for future research concerning the interplay between well-being indicators and religious practices are suggested.

Social network group decision-making model considering interactions between trust relationships and opinion evolution

PurposeSocial network group decision-making (SNGDM) has rapidly developed because of the impact of social relationships on decision-making behavior. However, not only do social relationships affect decision-making behavior, but decision-making behavior also affects social relationships. Such complicated interactions are rarely considered in current research. To bridge this gap, this study proposes an SNGDM model that considers the interaction between social trust relationships and opinion evolution.Design/methodology/approachFirst, the trust propagation and aggregation operators are improved to obtain a complete social trust relationship among decision-makers (DMs). Second, the evolution of preference information under the influence of trust relationships is measured, and the development of trust relationships during consensus interactions is predicted. Finally, the iteration of consensus interactions is simulated using an opinion dynamics model. A case study is used to verify the feasibility of the proposed model.FindingsThe proposed model can predict consensus achievement based on a group’s initial trust relationship and preference information and effectively captures the dynamic characteristics of opinion evolution in social networks.Originality/valueThis study proposes an SNGDM model that considers the interaction of trust and opinion. The proposed model improves trust propagation and aggregation operators, determines improved preference information based on the existing trust relationships and predicts the evolution of trust relationships in the consensus process. The dynamic interaction between the two accelerates DMs to reach a consensus.

Social network trust relationship environment based advanced ovarian cancer treatment decision-making model: An approach based on linguistic information with experts’ multiple confidence levels

Advanced ovarian cancer is the deadliest gynecologic cancer in World. The survival rate is not significant, and it depends on how well cancer responds to treatment. Generally, the treatment decision depends on Gynecologic oncology experts, which treatment is best for the patient. However, a Gynecologic oncology expert’s limited rationality and personality traits strongly impact a patient’s treatment decision. In addition, according to the American Cancer Society, a patient’s treatment decision may change if Gynecologic oncology experts change. To solve this kind of medical problem, we design a group decision-making model (GDM) based on linguistic preference relations (LPRs) with multiple confidence levels like confidence and doubting levels of experts to analyse experts’ treatment decision opinions digitally for improving the survival rate of patients. In a GDM, consistency analysis and obtaining expert weights are two keys. Hence, this paper design an LPRs with confidence and doubting levels (LPRs-CDLs) based GDM model under a unique consistency improvement algorithm and Pythagorean linguistic trust relations social network analysis driven expert weight determination. First, we define the additive consistency of LPRs-CDLs based on minimum confidence and maximum doubting levels. Second, a consistency reaching process (CRP) algorithm with convergence theorem is offered to improve the consistency of experts’ opinion decisions. Third, develop a trust propagation model based on Pythagorean linguistic trust relations under a social network environment for expert’s weights determination. Then, a social network trust relationship-based GDM model with LPRs-CDLs information is presented to the advanced ovarian cancer patient’s treatment final decision. Finally, we show the rationality of this study with some comparative analysis.

The Overlapping Community-Driven Feedback Mechanism to Support Consensus in Social Network Group Decision Making

Social network group decision-making (SN-GDM) provides valuable support for obtaining agreed decision results by effectively utilizing the connected social trust relationships among individuals. However, the impact of intricately overlapped social trust relationships within overlapping communities on evaluation modifications in the SN-GDM consensus reaching process is seldom considered. To alleviate this issue, this study attempts to construct an overlapping community driven feedback mechanism for improving consensus in SN-GDM. The Lancichinetti-Fortunato method (LFM) is used to detect the overlapping community structures under social trust networks. Subsequently, the trusted recommendation advice is conducted within overlapping communities, which guides the inconsistent subgroups to make an interaction with each other to reach higher consensus level. Then, an associated feedback mechanism for SN-GDM with overlapping communities is proposed, which enables the inconsistent subgroups to minimize the consensus cost by selecting personalized feedback parameters. Moreover, it shows that the overlapping communities based feedback mechanism is superior to the feedback mechanisms with non-overlapping communities. Finally, an illustrative example is included, which is also used to testify the efficacy of proposal by comparing the consensus cost under different representative recommendation advice in overlapping social trust networks.

Improving the recommendation accuracy of TrustSVD via trustworthy analysis in the social network environment

Recommender systems help Internet users quickly find information they may be interested in from an extremely large amount of resources. Recent studies have shown that incorporating auxiliary social trust relationship information into the recommender system improves the accuracy of recommendations. Most existing research only considers explicit trust relationships, which result in sub-optimal recommendation performance. In this research, we present a trust model which analyses user trustworthiness based on user’s behaviours on the social networks. The proposed trust model increases the density of trust relationships by considering explicit and implicit social trust relationships and also reflects a more fine-grained and realistic trust level between users. This improved social trust information is then incorporated into TrustSVD, a matrix factorisation–based social recommendation method. By analysing the prediction result using a real-world data set, Douban-600k from the Douban Movie website, we found that our proposed method provides more accurate predictions compared with SVD++ and traditional TrustSVD, improving users’ experiences.

From collective opinion dependence to personalization: The impacts of social trust relationship in consensus formation

Social network group decision-making (SNGDM) has become popular recently in the area of decision analysis as the social network can effectively model the relationship among decision makers. In the SNGDM, decision makers will present conflicting assessments due to individual differences in education backgrounds and cognitive levels. This study aims to develop a personalized consensus reaching framework considering the impact of social trust network on assessments-modifications willingness. A personalized feedback is designed for guiding assessments-modifications, which is based on the assumption that a decision maker will accept his\\her trusted decision makers’ assessments when modifying assessment in a social trust network. Then, a dual optimization-based personalized feedback is proposed for supporting consensus formation, where the first personalized feedback seeks to minimize the assessments-modifications and the second aims to minimize network-modifications. Particularly, two ways are used to measure network-modifications, referred to as trust values modifications with fixed network structure and network structure modifications, respectively. Further, an interactive dual optimization-based personalized consensus reaching process is constructed. With the aim of justifying the effectiveness of the proposed model, it is applied to solve the green supplier evaluation problem. Finally, it is illustrated that our proposal can improve consensus efficiency compared with collective opinion dependence feedback.

A Reliable and Lightweight Trust Inference Model for Service Recommendation in SIoT

In the era of Internet of Things (IoT), millions of heterogeneous IoT devices generate an explosion of data and services waiting to be discovered. The convergence of IoT with social networks (SIoT) interconnects multiple IoT applications and alleviates the common data sparsity and cold start problems in traditional recommendation systems. However, the social trust relationships may also be very sparse, which affects the accuracy of trust-based recommendation systems. Meanwhile, mobile devices have limited resources and are more vulnerable to malicious attacks in the IoT environment. In order to complete the trust relationship and further improve the trust-based recommendation performance, we propose a reliable and lightweight trust inference model for service recommendation in SIoT, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TIRec</i> . First, we obtain a comprehensive weighted centrality metric (LGWC) considering both local and global contexts. Based on this, we propose a corresponding lightweight trust path selection algorithm. Then, we present a reliable trust inference calculation algorithm consist of trust propagation and aggregation strategy, which can efficiently resist two common malicious attacks. Finally, we incorporate the rating, direct trust, and indirect trust together into the matrix factorization model, and integrate the influence of truster and trustee to obtain the synthetic model for rating predication. To the best of our knowledge, this article is the first to integrate trust inference algorithm into the trust-based recommendation systems. The extensive experiments are conducted on three real-world data sets, and the results show that our <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TIRec</i> model performs better than other advanced recommendation models in both “all users” view and “cold start users” view.

A survey for trust-aware recommender systems: A deep learning perspective

A significant remaining challenge for existing recommender systems is that users may not trust recommender systems for either inaccurate recommendation or lack of explanation. Thus, it becomes critical to embrace a trustworthy recommender system. This survey provides a systematic summary of three categories of trust issues in recommender systems: social-aware recommender systems, which leverage users’ social trust relationships; robust recommender systems, which filter untruthful information, noises and enhance attack resistance; and explainable recommender systems, which provide explanations of the recommended items. We focus on the work based on deep learning techniques, which is an emerging area in the recommendation research.

Variational cold-start resistant recommendation

Conventionally, cold-start limitations are managed by leveraging side information such as social-trust relationships. However, the relationships between users in social networks are complex, uncertain, and sparse. Therefore, it is necessary to extract beneficial social connections to make the recommendation models cold-start resistant. Towards this end, we propose a novel recommendation model called Variational Cold-start Resistant Recommendation (CORE-VAE). More concretely, we employ a social-aware similarity function and a graph convolutional network (GCN) to generate robust social-aware user representations that account for the complexities, uncertainties, and sparse nature of the social-trust network. Subsequently, these powerful social-aware representations aid us in producing cold-start resistant rating vectors for all users. To explore the rich user rating information, we propose an expressive variational autoencoder (VAE) model. Unlike earlier VAE-based CF models, CORE-VAE utilizes a novel prior distribution and a well-designed skip-generative network to alleviate the posterior collapse issue considerably. Besides, CORE-VAE can also capture the latent space’s uncertainty and ensure that observations and their accompanying latent variables have high mutual information. Overall, these novel techniques dramatically help produce better latent representations for generating more accurate recommendations. We show that CORE-VAE outperforms numerous competitive baseline models on real-world datasets through comprehensive empirical evaluation and analysis.

Different Davids vs One Goliath: A Critical Affirmation of Individuals’ and Peoples’ Self-Advocacy for the Preservation of Human Rights as Entrenched in Judicial Decisions

It often occurs that South Africans suffer human rights violations at the hands of other South Africans. In these cases, the adjudication is generally straightforward and remedied between the two parties in a court of law. This case note, however, explores the complex human rights transgressions perpetuated by the State which are accompanied by peculiar reprehension due to the constitutional standards of appropriate conduct that the State is under in its role as protector and provider of its people. To illustrate this, the judgments of Johannes Moko v Acting Principal Malusi Secondary School, Tlou Mokgonyana and Others (CCT 297/20) [2020] ZACC 30; 2021 (4) BCLR 420 (CC); 2021 (3) SA 323 (CC) (28 December 2020) and Minister of Health and Others v Treatment Action Campaign and Others (No 2) (CCT8/02) [2002] ZACC 15; 2002 (5) SA 721; 2002 (10) BCLR 1033 (5 July 200236). The note focuses on the realisation that people have had to resort to self-advocacy against the State to garner enjoyment from their guaranteed human rights. This, in turn, manifests itself in a crack in the trust relationship between the State and the people. Despite self-advocacy lacking novelty in South African judicial culture, what does stand out is the State’s seeming ignorance of the damage to the sacred social-trust relationship upon which the credibility of the State’s government rests. Without this trust component, the author argues, the credibility of the government evaporates and, more importantly, the fabric of constitutionalism is tarnished.

GATrust: A Multi-Aspect Graph Attention Network Model for Trust Assessment in OSNs

Social trust assessment that characterizes a pairwise trustworthiness relationship can spur diversified applications. Extensive efforts have been put in exploration, but mainly focusing on applying graph convolutional network to establish a social trust evaluation model, overlooking user feature factors related to context-aware information on social trust prediction. In this article, we aim to design a new trust assessment framework GATrust which integrates multi-aspect properties of users, including user context-specific information, network topological structure information, and locally-generated social trust relationships. GATrust can assigns different attention coefficients to multi-aspect properties of users in online social networks, for improving the prediction accuracy of social trust evaluation. The framework can then learn multiple latent factors of each trustor-trustee pair to establish a social trust evaluation model, by fusing graph attention network and graph convolution network. We conduct extensive experiments on two popular real-world datasets and the results exhibit that our proposed framework can improve the precision of social trust prediction, outperforming the state-of-the-art in the literature by 4.3% and 5.5% on both two datasets, respectively.

Consensus reaching with non-cooperative behavior management for personalized individual semantics-based social network group decision making

Leveraging social network trust relationships among experts to reach consensus has become a popular topic in linguistic group decision making (GDM). However, in linguistic contexts, it is commonly accepted that words mean different things for different people, which indicates the necessity of modeling experts’ personalized individual semantics (PISs). Moreover, experts sometimes may show non-cooperative behaviors during the consensus reaching process (CRP) due to their own interests. As a result, this paper focuses on developing a consensus reaching algorithm with non-cooperative behavior management for PIS-based social network GDM problems. First, linguistic preference relations are transformed into fuzzy preference relations by the PIS model, and then social network analysis techniques are used to obtain experts’ weight vector. Afterwards, we propose a feedback adjustment mechanism to improve experts’ adjustment willingness in CPRs, in which the trust relationships and the PISs of experts are utilized to generate adjustment advice for experts. Furthermore, a non-cooperative behavior management mechanism which dynamically adjusts the trust degrees in social network is devised. Followed by this, a numerical example is provided to demonstrate the proposed algorithm. Finally, detailed simulation results are presented to analyze the influence of different parameters on CRPs and illustrate the validity of the proposed algorithm.

Comparing Latino Community Members' and Clinical Staff's Perspectives on Barriers and Facilitators to Colorectal Cancer Screening.

Latinos in the United States have low rates of colorectal cancer (CRC) screening even though CRC is the third leading cause of cancer death among Latinos. This qualitative study aimed to understand and compare the perspectives of clinical staff (CS) and Latino community members (LCMs) in an urban Southern California community regarding barriers and facilitators of CRC screening. Through purposive sampling, 39 LCMs (mean age: 59.4years, 79.5% female) were recruited to participate in one of five focus groups, and 17 CS (mean age: 38.8years, 64.7% female) were recruited to participate in semi-structured in-depth interviews, along with a demographic survey. Interviews and focus group recordings were transcribed verbatim, translated, and analyzed using direct content analysis. Demographic data were summarized using descriptive statistics. Findings suggest that CS and LCMs have both similar and opposing perspectives with regard to barriers and facilitators of CRC screening. Themes discussed included attitudes towards CRC screening, CRC knowledge, access to resources, commitments and responsibilities, social support, vicarious learning, patient-provider communication, trust, and social relationships. Study findings can be used to guide interventions and policies to improve access to CRC screening among LCMs.

Social Trust Driven Consensus Reaching Model With a Minimum Adjustment Feedback Mechanism Considering Assessments-Modifications Willingness

Social network group decision making (SNGDM) has emerged as a new decision tool to effectively model the social trust relationships among decision makers. The impact of the social trust relationships on assessments-modifications in the consensus reaching in the SNGDM is seldom considered. This study aims at addressing this issue. The main starting point is the assumption that a decision maker will not be willing to accept the assessments-modifications suggestions that significantly differ from his/her trusted decision makers’ assessments in a social trust network. Thus, this study proposes a social trust driven minimum adjustments consensus model (STDMACM) for SNGDM. Simultaneously, a social trust driven consensus maximum optimization model (STDCMOM) is proposed for maximizing the consensus level among decision makers under the above assumption. Based on both STDCMOM and STDMACM, an interactive consensus reaching process is presented, in which the assessments-modifications suggestions generated from the STDMACM are used, when the maximum consensus level obtained from STDCMOM is acceptable, as the references for guiding the consensus reaching; otherwise, assessments-modifications suggestions are generated from the designed STDCMOM. The validity of the social trust driven consensus reaching process with respect to its consensus convergence rate and consensus success ratio is verified with a simulation and comparison analysis.

Trust-aware recommendation based on heterogeneous multi-relational graphs fusion

Users’ trust relations have a significant influence on their choice towards different products. However, few recommendation or prediction algorithms both consider users’ social trust relations and item-related knowledge, which makes them difficult to cope with cold start and the data sparsity problems. In this paper, we propose a novel trust-ware recommendation method based on heterogeneous multi-relational graphs fusion, termed as T-MRGF. In contrast with other traditional methods, it fuses the user-related and item-related graphs with the user–item interaction graph and fully utilizes the high-level connections existing in heterogeneous graphs. Specifically, we first establish the user–user trust relation graph, user–item interaction graph and item–item knowledge graph, and the user feature and item feature, which have been obtained from the user–item graph, are used as the input of the user-related graph and the item-related graph respectively. The fusion is achieved through the cascade of feature vectors before and after feature propagation. In this way, the heterogeneous multi-relational graphs are fused for the feature propagation, which largely refines the user and item representation for model prediction. Simulation results show that the proposed method significantly improve the recommendation performance compared to the state-of-the-art KG-based algorithms both in accuracy and training efficiency.