Trust In System Research Articles

Early years pharmacist retention – a listening exercise

Abstract Introduction Pressures on pharmacy workforces are increasing, with 88% of hospital pharmacists reporting high risk of burnout and 62% considering leaving their role due to the impact of work on wellbeing.1 Early years pharmacist (EYP) recruitment is increasingly challenging. Traditional pharmacist career pathways are in competition with increasingly preferred portfolio-based careers. Many trusts are introducing band 6 to 7 progressive schemes to improve retention, including at University Hospital Southampton (UHS). However, anecdotal departmental feedback suggests that EYPs feel this scheme is being applied unequally across different sections and there is discontent with differing workload and development opportunities. Aim To develop an understanding of concerns and expectations of Early Years Pharmacists at UHS. Methods All pharmacists in Agenda for Change band 7, and 6 to 7 progression posts, were invited to participate in an in-person forum. Attendance was voluntary but supported by the senior pharmacy management team. The group facilitator, a senior pharmacist known to participants, introduced the session topics, explained the rationale, then asked participants to provide written comments anonymously on a range of topics. Comments were gathered at the end of the session and thematically analysed with review from an experienced qualitative researcher. Whilst deemed a service evaluation, not requiring ethics approval, data was recorded and managed in accordance with best ethical practices. Results Twenty-Eight EYP attendees provided 102 separate comments. Themes identified related to departmental progressive post variations in; on-call expectations, mentoring, opportunities for formal credentialling, and feedback from line managers. The variations were perceived to be unjust. Commonly, points raised related to professional development. Issues included protected study time both for those undertaking post-graduate courses and those not, workload demands meaning that time for development was limited, and inequalities of access. Discussion/Conclusion As anticipated, there was discontent within the group particularly regarding perceived variations in workload, role opportunities and progression across the department. Additionally, there was some antipathy regarding whether things could be improved. However, the forum feedback suggested the opportunity to participate was well received, having provided participants an opportunity to raise their concerns in a confidential, collaborative, manner. Following initial headline feedback of EYP feeling unappreciated, all senior leaders were reminded of the importance of positive feedback and anecdotally the Trust systems for this have been used more frequently since this reminder. There are limitations with this evaluation, the group was too large to truly be considered a focus group and as such the facilitator was unable to interact with all members and probe for further information which may have been helpful. Furthermore, the facilitator was line manager for a small number of the participants, and they may have felt less able to make negative comments despite the anonymous comment collection. Several of the points raised have led to further questions and we now intend to conduct a formal qualitive study using semi-structured interviews with volunteers across the department to obtain more detailed insight into the challenges of this staff group. Meanwhile work will continue to make the EYP process and experience more equitable. Reference 1. Royal Pharmaceutical Society (2023). ‘Workforce and Wellbeing Survey 2023’. Available online. [Accessed 7th May 2024].

The challenges of media and information literacy in the artificial intelligence ecology: deepfakes and misinformation

In the ecosystem of artificial intelligence (AI), generative models enable the creation of hyper-realistic manipulations that are extremely plausible due to the precision of the audiovisual objects. These deepfakes are undetectable thanks to their components, which heightens concerns about the distortion of reality in the information ecosystem and how the ability to distinguish between real and fake audiovisual content affects public trust and democratic systems. This is a major challenge for media and information literacy if it is to combat misinfor­mation effectively. In this context, this study presents the results of a quasi-experiment conducted with 80 young people from the Community of Madrid (Spain) to assess their ability to detect deepfakes in immersive environments and to establish whether the context-identifying elements that enable detection of the reputation of the media source shape the credibility of the images. The results show that the images take precedence over the context identifiers, preventing a critical reading of the information that would make it possible to detect visual forgeries, something that is reinforced by their exceptional verisimilitude. It is concluded that the new post-humanist biome of virtual reality and artificial intelligence requires a reorien­tation of media and information literacy to raise the public’s awareness and educate them to make them less susceptible to disinformation based on deepfakes created with generative models.

Exploring the Effects of Low-Carbon Labels on Purchase Intentions for Green Agricultural Products

Expedited economic expansion and innovative technological advancement have precipitated considerable environmental predicaments, giving rise to extensive ecological conundrums worldwide. In response to the pressing situation, numerous countries have implemented policies aimed at reducing carbon emissions. One such initiative is the “low-carbon label”, which signifies a commitment to lowering carbon emissions. On one hand, it offers consumers an effective means to identify low-carbon products, thereby enhancing their willingness towards green consumption. On the other hand, it motivates businesses to transition towards greener practices, thereby guiding the market towards more environmentally friendly directions. This transition has had a significantly positive impact on reducing carbon emissions and promoting the development of a low-carbon economy, highlighting its critical value within the broader environmental movement. This study focuses on exploring whether the low-carbon label can influence consumers’ willingness to purchase green agricultural products. The theoretical framework was developed based on the theory of perceived value and SOR theory. The data were gathered through a randomized survey conducted in China. The research findings suggest that the perceived environmental value, functional value, and emotional value have significant impacts on consumers’ willingness to purchase green agricultural products. Trust in low-carbon labels plays a critical role in mediating the relationship between perceived value and consumers’ purchasing intentions for green agricultural products. Our research yielded the following conclusions. (1) Consumers are progressively cultivating an independent pro-environmental persona and increasingly aligning themselves with sustainable development, as indicated by their affinity towards carbon labels. (2) The selection of low-carbon agricultural products by consumers is contingent upon sensory perception, emphasizing environmental spillover value, functional enhancement, and emotional support. (3) Consumer confidence in green low-carbon agricultural products stems from an intuitive perception perspective, suggesting that low-carbon labels should accentuate functional and emotional values. The study suggests proactive measures to enhance the role of low-carbon labels in the consumption of green agricultural products. These measures include three parts: (1) Improving the content system of low-carbon labels and integrating digital technologies to reconstruct trust systems; (2) strengthening promotional efforts focusing on enhancing consumers’ intrinsic value perceptions; and (3) establishing a national-level low-carbon labels certification system, raising standards, and strengthening supervision.

Effect of learning culture and management control system on innovation performance: Evidence from startup companies in Indonesia

The startup must be able to innovate in response to the uncertainty of the business environment, which is changing rapidly along with advances in technology. This study aims to analyze the relationship between learning culture, belief, boundary control, dynamic capabilities, and innovation performance. Quantitative methods with AMOS and structural equation modeling were used to test 260 samples. Questionnaires were distributed among startup companies in Banten Province, Indonesia. The research results show that dynamic capabilities and the development of management control systems are influenced by learning culture. The findings inform that a strong learning culture attitude produces company confidence and a management boundary control system that can adapt to uncertain environmental changes. Startup companies should motivate their personnel to improve competence through a learning culture. The role of innovation performance is also indirectly influenced by beliefs and boundary control systems. Recognition of personnel abilities and contributions in collaboration with external sources opens up opportunities to compete in business. This finding is a key factor explaining that startup companies must face a rapidly changing environment by optimizing management’s ability to innovate, implementing management control systems routinely, and building a trust system to inspire and motivate employees.

The Impact of International Talent Inflow on Technological Innovation: The Moderating Role of Guanxi Culture and Social Trust

This paper studies the effect of international talent inflow on technological innovation and the influence of the Chinese cultural environment on their relationship. Based on provincial-level data from 2000 to 2012, we construct a static panel data model to explore the effects of international talents on technological innovation and the moderating role of Guanxi culture and social trust in the above relationship. We find that international talent inflow positively impacts technological innovation. Guanxi culture significantly negatively impacts technological innovation and negatively moderates the relationship between international talent inflow and technological innovation. Social trust significantly positively impacts technological innovation and positively moderates the relationship between international talent inflow and technological innovation. The interaction between social trust and Guanxi culture has a significant positive effect on technological innovation. The study shows that different cultural environments have an essential impact on developing international talent’s innovation ability. This paper provides evidence for the need to improve China’s cultural environment and provides important implications for the formulation of talent policies. Based on this, we suggest improving the talent introduction system, optimizing the relationship culture, and building a good social trust system to promote regional innovation.

Modal Sosial dalam Pengembangan Usaha

As a foreign company, Tiens Group has been successful and well accepted by the people of Banda Aceh, a city known for the implementation of Syariah Islam. This study analysed how the company uses its social capital resulting in its acceptance by the host society that is strongly associated with Syariah Islam. This research uses sosial capital theory by Robert D. Putnam. It is a qualitative reseach. Data were collected through interview and participant observation. This research found that the company has utilized sosial capital and succeeded in achieving its targets and well accepted by the host society. The interplay of the three aspects of sosial capital namely norms, trust and networks have been influential in achieving success of the company. Norms has been a focal point in the formation of group identity. It is the values that give rise to a strong trust among its members. Trust is formed through a strong believe towards values and norms. Network is needed to bridge and help expand the company to people outside the company through new recruits (bridging sosial capital), while at the same time, network within the group, would eventually strengthen the norms and the trust system as well as the sense of solidarity and work ethics among the members (bonding sociap capital).

BPS-V: A blockchain-based trust model for the Internet of Vehicles with privacy-preserving

The trust system is widely used to prevent malicious behaviors, and it is a key element for vehicles to establish interactions in the Internet of Vehicles (IoV). Nevertheless, trust and privacy remain unresolved concerns stemming from the distinctive features of the IoV. The IoV must thwart malicious attackers from spreading false data while ensuring that the vehicle’s evaluation data is not leaked, which is of utmost importance. In this paper, we propose a blockchain-based trust model (BPS-V), which supports ciphertext computation of trust evaluation data submitted by different vehicles. Design a cooperative update method for vehicle trust, which utilizes an improved distributed two-trapdoor public-key cryptography algorithm to achieve cooperative computing of trust and reduce the risk of privacy leakage of evaluation data. On this basis, BPS-V introduces blockchain sharding technology to realize cross-domain storage and sharing of the trust. Simulation results show that our scheme can effectively protect the privacy of evaluation data and maintain a high detection rate and low false alarm rate in different road environments. Compared with traditional schemes, BPS-V can improve the efficiency of trust updates and the detection of malicious vehicles by 9.5% and 32%.

A Survey on Reputation Systems for UAV Networks

The proliferation of unmanned aerial vehicle (UAV) networks is increasing, driven by their capacity to deliver automated services tailored to the varied demands of numerous smart city applications. Trust, security, and privacy remain paramount in the public domain. Traditional centralized network designs fall short of ensuring device authentication, data integrity, and privacy within the highly dynamic and adaptable environments of UAV networks. Decentralized reputation systems have emerged as a promising solution for enhancing the reliability and trustworthiness of data and communications within these networks while safeguarding UAV security. This paper presents an exhaustive survey of trust and reputation systems, exploring existing frameworks and proposed innovations alongside their inherent challenges. The crucial role of reputation systems is to strengthen trust, security, and privacy throughout these networks, and various strategies can be incorporated to mitigate existing vulnerabilities. As a useful resource for researchers and practitioners seeking to advance the state of the art in UAV network security, we hope this survey will spark further community discussion and stimulate innovative ideas in this burgeoning field.

A variational formulation for a trust and reputation system

A variational formulation for a trust and reputation system

Bagging Vs. Boosting in Ensemble Machine Learning? An Integrated Application to Fraud Risk Analysis in the Insurance Sector

ABSTRACT Addressing the pressing challenge of insurance fraud, which significantly impacts financial losses and trust within the insurance industry, this study introduces an innovative automated detection system utilizing ensemble machine learning (EML) algorithms. The approach encompasses four strategic phases: 1) Tackling data imbalance through diverse re-sampling methods (Over-sampling, Under-sampling, and Hybrid); 2) Optimizing feature selection (Filtering, Wrapping, and Embedding) to enhance model accuracy; 3) employing binary classification techniques (Bagging and Boosting) for effective fraud identification; and 4) applying explanatory model analysis (Shapley Additive Explanations, Break-down plot, and variable-importance Measure) to evaluate the influence of individual features on model performance. Our comprehensive analysis reveals that while not every re-sampling technique improves model performance, all feature selection methods markedly bolster predictive accuracy. Notably, the combination of the Gradient Boosting Machine (GBM) algorithm with NCR re-sampling and GBMVI feature selection emerges as the most effective configuration, offering superior fraud detection capabilities. This study not only advances the theoretical framework for combating insurance fraud through AI but also provides a practical blueprint for insurance companies aiming to incorporate advanced AI strategies into their fraud detection arsenals, thereby mitigating financial risks and fostering trust systems.

“People are Way too Obsessed with Rank”: Trust System in Social Virtual Reality

AbstractSocial Virtual Reality (VR) is growing in popularity and has drawn the attention of HCI academics. Social VR experiences harassment just like other online environments. The Trust System (TS) in VRChat, one of the most prominent social VR platforms, is designed to measure and indicate users’ trustworthiness in order to reduce toxicity in the platform. In this research, we analyzed data from “r/VRChat,” to understand how users perceive the system. We found that users interpret the system differently. Problems in its implementation cause distrust. The trust ranks, while intended to promote positive interactions, can actually lead to stereotyping and discourage communication between users of different ranks. The hierarchical structure within the ranks exacerbates discrimination and conflicts, particularly against the low-ranked users. We further discuss that trust ranks present challenges to newcomers and contribute to a competitive atmosphere that hinders the formation of less toxic norms. Finally, we provide implications for the future design of similar systems.

Towards novel blockchain decentralised autonomous organisation (DAO) led corporate governance framework

Decentralised autonomous organisation (DAO) powered by blockchain technologies has the potential to transform the organisational governance model with the foundations of algorithmic trust and democratic systems. DAO envisages a decentralised democratic organisation using software-based smart contracts to execute autonomous transactions with consensus among stakeholders. The study investigates the prospects of adopting DAO led corporate governance to manage agency (theory) problems in the light of institutional (theory) pressures. The paper depicts how algorithmic trust led corporate governance measures help any organisation follow policies, rules and regulations using DAO led corporate governance (DAOCG) framework. The authors mapped the constituents of agency theory, ethical decision-making, and institutional factors to propose the DAOCG framework. The DAOCG framework is assessed using a thematic analysis of 16 expert interviews with relevant experience from corporate governance and audit domains from varied industries across geographies. The research leveraged MAXQDA software to analyse interview transcripts and opinion codes. The framework helps to identify the core elements of DAO-led corporate governance (DAOCG) and provides the potential to study the theoretically sequenced practical constituents of corporate governance. The first-of-its-kind practical DAOCG framework can be used in the pursuits of digital transformation, business model innovation and social change.

Evaluating Trust Management Frameworks for Wireless Sensor Networks.

Wireless Sensor Networks (WSNs) are crucial in various fields including Health Care Monitoring, Battlefield Surveillance, and Smart Agriculture. However, WSNs are susceptible to malicious attacks due to the massive quantity of sensors within them. Hence, there is a demand for a trust evaluation framework within WSNs to function as a secure system, to identify and isolate malicious or faulty sensor nodes. This information can be leveraged by neighboring nodes, to prevent collaboration in tasks like data aggregation and forwarding. While numerous trust frameworks have been suggested in the literature to assess trust scores and examine the reliability of sensors through direct and indirect communications, implementing these trust evaluation criteria is challenging due to the intricate nature of the trust evaluation process and the limited availability of datasets. This research conducts a novel comparative analysis of three trust management models: "Lightweight Trust Management based on Bayesian and Entropy (LTMBE)", "Beta-based Trust and Reputation Evaluation System (BTRES)", and "Lightweight and Dependable Trust System (LDTS)". To assess the practicality of these trust management models, we compare and examine their performance in multiple scenarios. Additionally, we assess and compare how well the trust management approaches perform in response to two significant cyber-attacks. Based on the experimental comparative analysis, it can be inferred that the LTMBE model is optimal for WSN applications emphasizing high energy efficiency, while the BTRES model is most suitable for WSN applications prioritizing critical security measures. The conducted empirical comparative analysis can act as a benchmark for upcoming research on trust evaluation frameworks for WSNs.

DeFTA: A plug-and-play peer-to-peer decentralized federated learning framework

Federated learning (FL) is a pivotal catalyst for enabling large-scale privacy-preserving distributed machine learning (ML). By eliminating the need for local raw dataset sharing, FL substantially reduces privacy concerns and alleviates the isolated data problem. However, in reality, the success of FL is predominantly attributed to a centralized framework called FedAvg [1], in which workers are responsible for model training, and servers are in control of model aggregation. Nevertheless, FedAvg's centralized worker-server architecture has raised new concerns, including low scalability of the cluster, risk of data leakage, and central server failure or even defection. To overcome these challenges, we propose Decentralized Federated Trusted Averaging (DeFTA), a decentralized FL framework that serves as a plug-and-play replacement for FedAvg, bringing instant improvements to security, scalability, and fault-tolerance in the federated learning process. In essence, it primarily consists of a novel model aggregating formula with theoretical performance analysis, and a decentralized trust system (DTS) to significantly enhance system robustness. Extensive experiments conducted on six datasets and six basic models suggest that DeFTA not only exhibits comparable performance with FedAvg in a more realistic setting, but also achieves remarkable resilience even when 67% of workers are malicious.

Theoretical study and analysis of advanced wireless sensor network techniques in Internet of Things (IoT)

Wireless sensor networks those based on the Internet of Things (IoT) have evolved fast in numerous areas in recent years. Devices and sensors may interact with each other without human involvement in the IoT network. An IoT-based system where people and things are connected by wired and wireless technology including Wireless Sensor Networks (WSNs), ZigBee, NFC, RFID, GPRS, LTE, and Bluetooth is now one of the most popular technical ideas. The potential of the Internet of Things (IoT) has received enormous attention from the corporate and scientific communities during the past 10 years. The IoT can provide a number of significant benefits to application spaces. Researchers may be able to show crucial facts about IoT-based smart environmental monitoring systems. IoT has faced a variety of challenges, including authentication, differentiating evidence, accessibility and security and protection. Sociotechnical trust systems have also been examined in the past in various studies (STTS). Existing smart settings are now dealing with serious IoT security, protection, and STTS challenges. Trust-the-board will be highlighted and characterized, as well as how it should be utilized in the Internet of Things. As a result, the potential benefits of data aggregation solutions that use less energy while increasing the organization's lifespan are enormous. IoT-WSN data aggregation methods and computations have been reimagined. Specifically, this article examines the writing with reference to the views of sensor systems administrators in order to preserve energy and gather data.

Propensity to trust shapes perceptions of comforting touch between trustworthy human and robot partners

Touching a friend to comfort or be comforted is a common prosocial behaviour, firmly based in mutual trust. Emphasising the interactive nature of trust and touch, we suggest that vulnerability, reciprocity and individual differences shape trust and perceptions of touch. We further investigate whether these elements also apply to companion robots. Participants (n = 152) were exposed to four comics depicting human–human or human–robot exchanges. Across conditions, one character was sad, the other initiated touch to comfort them, and the touchee reciprocated the touch. Participants first rated trustworthiness of a certain character (human or robot in a vulnerable or comforting role), then evaluated the two touch phases (initiation and reciprocity) in terms of interaction realism, touch appropriateness and pleasantness, affective state (valence and arousal) attributed to the characters. Results support an interactive account of trust and touch, with humans being equally trustworthy when comforting or showing vulnerability, and reciprocity of touch buffering sadness. Although these phenomena seem unique to humans, propensity to trust technology reduces the gap between how humans and robots are perceived. Two distinct trust systems emerge: one for human interactions and another for social technologies, both necessitating trust as a fundamental prerequisite for meaningful physical contact.

Characterising trends in the initiation, timing, and completion of recommended summary plan for emergency care and treatment (ReSPECT) plans: Retrospective analysis of routine data from a large UK hospital trust

AimTo assess patient socio-demographic and disease characteristics associated with the initiation, timing, and completion of emergency care and treatment planning in a large UK-based hospital trust. MethodsSecondary retrospective analysis of data across 32 months extracted from digitally stored Recommended Summary Plan for Emergency Care and Treatment (ReSPECT) plans within the electronic health record system of an acute hospital trust in England, UK. ResultsData analysed from ReSPECT plans (n = 23,729), indicate an increase in the proportion of admissions having a plan created from 4.2% in January 2019 to 6.9% in August 2021 (mean = 8.1%). Forms were completed a median of 41 days before death (a median of 58 days for patients with capacity, and 21 days for patients without capacity). Do not attempt cardiopulmonary resuscitation was more likely to be recorded for patients lacking capacity, with increasing age (notably for patients aged over 74 years), being female and the presence of multiple disease groups. ‘Do not attempt cardiopulmonary resuscitation’ was less likely to be recorded for patients having ethnicity recorded as Asian or Asian British and Black or Black British compared to White. Having a preferred place of death recorded as ‘hospital’ led to a five-fold increase in the likelihood of dying in hospital. ConclusionVariation in the initiation, timing, and completion of ReSPECT plans was identified by applying an evaluation framework. Digital storage of ReSPECT plan data presents opportunities for assessing trends and completion of the ReSPECT planning process and benchmarking across sites. Further research is required to monitor and understand any inequity in the implementation of the ReSPECT process in routine care.

Artificial Intelligence in nursing: trustworthy or reliable?

Trustworthiness in Artificial Intelligence (AI) innovation is a priority for governments, researchers and clinicians; however, clinicians have highlighted trust and confidence as barriers to their acceptance of AI within a clinical application. While there is a call to design and develop AI that is considered trustworthy, AI still lacks the emotional capability to facilitate the reciprocal nature of trust. This paper aims to highlight and discuss the enigma of seeking or expecting trust attributes from a machine and, secondly, reframe the interpretation of trustworthiness for AI through evaluating its reliability and validity as consistent with the use of other clinical instruments. AI interventions should be described in terms of competence, reliability and validity as expected of other clinical tools where quality and safety are a priority. Nurses should be presented with treatment recommendations that describe the validity and confidence of prediction with the final decision for care made by nurses. Future research should be framed to better understand how AI is used to deliver care. Finally, there is a responsibility for developers and researchers to influence the conversation about AI and its power towards improving outcomes. The sole focus on demonstrating trust rather than the business-as-usual requirement for reliability and validity attributes during implementation phases may result in negative experiences for nurses and clinical users. This research will have significant implications for the way in which future nursing is practised. As AI-based systems become a part of routine practice, nurses will be faced with an increasing number of interventions that require complex trust systems to operate. For any AI researchers and developers, understanding the complexity of trust and creditability in the use of AI in nursing will be crucial for successful implementation. This research will contribute and assist in understanding nurses' role in this change.

An adaptive trust system for misbehavior detection in wireless sensor networks

An adaptive trust system for misbehavior detection in wireless sensor networks

A Study of The Fish Supply Chain at The Paotere Fishing Port of Makassar City

This study aims to determine (1) the flow of information and finance, (2) the main problems that occur in supply chain activities at PPI Paotere, and (3) supply chain models at PPI Paotere. This research was conducted in March 2021 at PPI Paotere, Makassar City. The type of research used is survey research using a quantitative approach. The sampling method used cluster sampling and the determination of the number of samples with the Slovin formula, so that the number of samples as many as 43 people. The data analysis used in this study is a quantitative and qualitative descriptive analysis. The results showed that in terms of information flow, pelagic and demersal fish sources of information mostly came from groups of fishermen, courtiers and even other traders. Financial flow with a system of mutual trust that has been built and maintained for a long time because the average supplier and trader have been subscribing for years. The flow of goods starts from the source of goods originating from fishermen who are obtained from nearby islands until they finally arrive to consumers and industry. There are various problems experienced by market players at the Paotere fishing port, including the timeliness of auctions, checking the number and types of fish and cooperative services at the Paotere fishing port. The supply chain model for pelagic and demersal fish in the Paotere fishing port is divided into two, namely for export needs and local consumers. The supply chain model for local consumer needs consists of groups of fishermen, courtiers, traders and consumers while for export needs consists of groups of fishermen, retainers, UPI, companies then exported.