Trust Recommendations Research Articles

Study of the influence of psychological factors on women’s purchasing decisions during e-commerce livestream

Apart from YouTube and Instagram, new e-commerce live streaming platforms have emerged (TikTok Shopping, Amazon Live), which opens new opportunities for engaging with the audience and driving sales. Women, looking for an emotional and interactive experience, tend to make impulse purchases during live broadcasts. That is why it is necessary to investigate this topic. The purpose of this study was to examine the psychological factors that influence women’s impulse purchases while watching live broadcasts and to determine the psychological characteristics of women who make impulse purchases during live broadcasts. The study included questionnaires and interviews with women living in the Philippines, East Timor, Thailand, Singapore, Myanmar, Malaysia, and Laos. The key findings are that the study revealed the psychological factors of influence and psychological characteristics of women as they make purchases during e-commerce live streams. Psychological characteristics include emotionality while watching (68.3% of respondents), fear of missing out on a good deal (58.3% of respondents) and being influenced by other viewers and streamers (74.2% trust recommendations). Psychological factors include a set of technological and social components. Technological factors include interactivity and real-time communication (56.7% consider it significant); visual appeal and product display (71.7% consider it significant); and the convenience of shopping while browsing. Social factors include parasocial interaction with the streamer, a sense of community with other viewers (63.3%). The study also identified the role of hosts, which plays a significant role in building trust in the product and online platform: building trust in the product and brand (74.2% trust recommendations); influence through charisma, expertise, and attractiveness (62.5% consider them attractive, 58.3% – competent). Live broadcasts effectively stimulate impulse purchases among women. Emotions, trust, and social influence play a key role in this process. The practical significance of this study lies in the possibility of helping women to better understand the factors that influence impulse purchases, to avoid the influence of emotional stimuli and social pressure. The findings of this study can be used to develop recommendations on how people can effectively shop online

Polio, public health memories and temporal dissonance of re-emerging infectious diseases in the global north

Social science research on polio has been centred in the global south, where countries that remain endemic or vulnerable to outbreaks are located. However, closely-related strains of poliovirus were detected in the sewage systems of several New York State counties and London boroughs in 2022. These detections constituted the first encounters with polio in the United States and United Kingdom for a generation – for both public health agencies and publics alike. This paper takes the transnational spread of poliovirus in 2022 as an opportunity to critique how public health memories of twentieth-century polio epidemics were mobilised to encourage vaccine uptake among groups considered vulnerable to transmission, notably Orthodox Jewish families. The study integrates data collected in London and New York as part of academic engagement with health protection responses to the spread of polio. Methods in both settings involved ethnographic research, and a total of 59 in-depth semi-structured interviews with public health professionals, healthcare providers, and Orthodox Jewish community partners and residents. Analysis of results demonstrate that narratives of epidemiological progress were deployed in public health responses in London and New York, often through references to sugar cubes, iron lungs, and timelines that narrate the impact of routine childhood immunisations. While memories of polio were deployed in both settings to provoke an urgency to vaccinate, vulnerable publics instead considered the more recent legacy of the COVID-19 pandemic when deciding whether to trust recommendations and responses. Critical attention to memory places analysis on the divergences between institutional (public health agencies) and peopled (publics) responses to disease events. Responses to re-emerging infectious disease outbreaks engender a temporal dissonance when historical narratives are evoked in ways that contrast with the contemporary dilemmas of people and parents.

On Trust Recommendations in the Social Internet of Things – A Survey

The novel paradigm Social Internet of Things (SIoT) improves the network navigability, identifies suitable service providers, and addresses scalability concerns. Ensuring trustworthy collaborations among devices is a key aspect in SIoT and can be realized through trust recommendations. However, the outcome of trust recommendations depends on multiple factors related to the context-dependent nature of SIoT and practical constraints brought by the devices and networks embedded in the SIoT. While the existing literature has proposed numerous trust recommendation models to assess the trustworthiness of devices in various scenarios, researchers have not sufficiently examined the required features for trust recommendations in the SIoT. Consequently, trust recommendation models may inaccurately assess the true risk of device interactions. In this literature survey, we investigate the context-dependent features and recommendation methods used for the SIoT using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. We propose a novel taxonomy to categorize trust recommendation models according to their input features and design. Our findings reveal limited attention is given to the context-dependent features, constraints of the information environment, and limited inference capabilities that impede more precise trust recommendations. Finally, we present the research gaps and outline future directions to enable trustworthy inter-domain operations within the SIoT.

Empathic voice assistants: Enhancing consumer responses in voice commerce

Artificial intelligence (AI)-enabled voice assistants (VAs) are transforming firm-customer interactions but often come across as lacking empathy. This challenge may cause business managers to question the overall effectiveness of VAs in shopping contexts. Recognizing empathy as a core design element in the next generation of VAs and the limits of scenario-based studies in voice commerce, this article investigates how empathy exhibited by an existing AI agent (Alexa) may alter consumer shopping responses. AI empathy moderates the original structural model bridging functional, relational, and social-emotional dimensions. Findings of an individual-session online experiment show higher intentions to delegate tasks, seek decision assistance, and trust recommendations from AI agents perceived as empathic. In contrast to individual shoppers, families respond better to functional VA attributes such as ease of use when AI empathy is present. The results contribute to the literature on AI empathy and conversational commerce while informing managerial AI design decisions.

LTrust: An Adaptive Trust Model Based on LSTM for Underwater Acoustic Sensor Networks

As an effective security mechanism, trust models have been proposed to estimate the reliability of the individual nodes in Underwater Acoustic Sensor Networks (UASNs) during adverse attacks. However, existing trust models neglect the relative importance of the different nodes within the network topology. Further, few trust models study the effects of defective recommendation trust filtering. In this work, we propose an adaptive trust model based on the Long Short-Term Memory (LSTM) network model for UASNs, which we term LTrust. The LTrust is composed of two stages: trust data collection and trust evaluation. In the first stage, the characteristics of the network topology are leveraged towards evaluating direct trust evidence, by aggregating the communication trust and environment trust metrics; a defective recommendation filtering method is designed for broadcasting accurate trust recommendations among the nodes. In the second stage, an adaptive trust model is designed based on the LSTM model, to identify anomalous nodes by evaluating their trust value. The LTrust model has been tested under both hybrid attack and single-mode attack scenarios. Simulation results demonstrate that the LTrust achieves effective performance, as compared to other approaches proposed in the literature, in terms of trust value, accuracy and error rate.

Experts, trust and competition

This paper provides a theoretical and experimental analysis of trust formation in markets with expert sellers and repeated interaction. Buyers are able to observe ex post whether the quality of a product was sufficient. This allows them to assess an expert’s trustworthiness by not following a high-quality product recommendation. We show that in the presence of a monopolistic seller, infinitely repeated interaction is not enough to induce buyer trust formation. However, with competing sellers, recommendations are partially informative and buyers trust recommendations with some probability. More intense competition in the form of second opinions further increases trustworthiness and trust. The experimental analysis supports the theoretical predictions regarding the effect of repeated interaction and competition on trust formation.

Filtering Dishonest Trust Recommendations in Trust Management Systems in Mobile Ad Hoc Networks

Trust recommendations, having a pivotal role in computation of trust and hence confidence in peer to peer (P2P) environment, if hampered, may entail in colossal attacks from dishonest recommenders such as bad mouthing, ballot stuffing, random opinion etc. Therefore, mitigation of dishonest trust recommendations is stipulated as a challenging research issue in P2P systems (esp in Mobile Ad Hoc Networks). In order to cater these challenges associated with dishonest trust recommendations, a technique named “intelligently Selection of Trust Recommendations based on Dissimilarity factor (iSTRD)” has been devised for Mobile Ad Hoc Networks. iSTRD exploits personal experience of an “evaluating node” in conjunction with majority vote of the recommenders. It successfully removes the recommendations of “low trustworthy recommenders” as well as dishonest recommendations of “highly trustworthy recommenders”. Efficacy of proposed approach is evident from enhanced accuracy of “recognition rate”, “false rejection” and “false acceptance”. Moreover, experiential results depict that iSTRD has unprecedented performance compared to contemporary techniques in presence of attacks asserted.

Recommendations Based QoS Trust Aggregation and Routing in Mobile Adhoc Networks

In mobile adhoc netwotk (MANET), a node’s quality of service (QoS) trust represents how much it is reliable in quality. QoS trust of a node is computed based on its multiple quality parameters and it is an interesting and challenging area in MANETs. In this work, QoS trust is evaluated by taking into consideration quality parameters like node residual energy, bandwidth and mobility. The proposed method “Recommendations Based QoS Trust Aggregation and Routing in Mobile Adhoc Networks-QTAR” is a frame work. Where the trust is established through four phases like QoS trust computation, aggregation, propagation and routing. The Dempster Shafer Theory (DST) is used for aggregation of trust recommendations. In the network, trust information is propagated through HELLO packets. Each node stores the QoS trust information of other nodes in the form of trust matrices. We applied matrix algebra operations on trust matrices for route establishment from source to destination. The time and space complexity of proposed method is discussed theoretically. The simulation is conducted for the varying of node velocity and network size, where the proposed method shown considerable improvement over existing protocols.

The impact of first COVID-19 peak on patient referrals to Liaison Psychiatry Service and staff perception about service provision in Birmingham and Solihull Mental Health Trust Birmingham - a service evaluation project

AimsBased on recommendations from the Royal College of Psychiatrists, this project aimed to evaluate the impact of the first peak of the COVID-19 pandemic on referral patterns to the Queen Elizabeth Hospital Birmingham (QEHB) Liaison Psychiatry (LP) service. Additionally, we aimed to explore staff experiences in LP services across Birmingham and Solihull Mental Health Trust (BSMHFT) in order to generate Trust recommendations promoting optimal healthcare provision amidst the on-going pandemic.MethodA mixed method service evaluation was conducted using quantitative and qualitative analysis. Quantitative methods involved reviewing referrals made to the QEHB LP service from March to June 2020, compared with the equivalent time period in 2019. Data were retrospectively extracted from the electronic clinical databases RIO and PICS, and subsequently analysed using Microsoft Office. The number of, and reasons for referrals to LP were identified, whilst focus groups were conducted to explore the subjective experiences of staff working across BSMHFT LP services.ResultBetween 1st March and 30th June 2020, 984 referrals were made to the QEHB LP service, compared to 1020 referrals in 2019, representing a 3.5% reduction. From 2019 to 2020, referrals due to psychotic symptoms and deliberate self-harm rose by 12.8% and 14.1% respectively, whilst referrals for drug and alcohol-related causes reduced by 28.3%. A significant increase (150%) in referrals for medication or management advice was seen. Focus groups indicated that staff perceived an initial reduction in number of referrals, but an increase in the acuity of patient presentations.Staff reported anxiety around contracting and transmitting SARS-Cov-2, exacerbated by uncertainty around patients’ COVID-19 status. In QEHB, sixty-five of the 984 referrals (7%) had a positive SARS-Cov-2 PCR swab, with the remaining 919 referrals being either negative (68%) or unknown (25%). Ninety-six percent of consultations were conducted face-to-face in QEHB. There were conflicting views amongst staff regarding whether more consultations could have been conducted remotely. Furthermore, varying perceptions of support and communication from both the physical and mental health trust were reported.ConclusionQuantitative data indicates that COVID-19 impacted LP healthcare provision in BSMHFT. Whilst referral numbers remained similar between the equivalent period in 2019 and 2020, a change in the nature of referrals to LP at QEHB was seen. This was corroborated by qualitative data which highlighted a perceived change in acuity of referrals. These findings have been disseminated across the Trust and subsequent recommendations are being implemented during the on-going pandemic.

An audit of high-dose and combination antipsychotic prescribing across the general adult inpatient wards in Mersey Care NHS Foundation Trust

AimsTo review the number of prescriptions of regular high-dose antipsychotics and combination antipsychotic therapy across the eight general adult inpatient wards in Mersey Care NHS Foundation Trust and examine whether these prescriptions followed Trust recommendations for high-dose antipsychotic therapy (HDAT).BackgroundThe two main rationales behind prescribing HDAT are pharmacokinetics differ in individuals and so insufficient amounts of antipsychotic may reach the effect site at maximum dose in some patients and variations in the effect site between patients may mean higher doses are required to achieve therapeutic effect.MethodThe electronic prescription records for all patients on the eight general adult inpatient wards were scrutinised. 121 patients were prescribed antipsychotic medication. Any patients on a combination of regular antipsychotic medication or on HDAT were identified. Any patient on combination therapy or HDAT was studied to determine if Clozapine had been considered. The electronic notes of HDAT patients were analysed to ascertain whether tests recommended by Trust guidelines – BMI, blood pressure (B.P), pulse rate, ECG, FBC, U and Es, LFTs, serum prolactin, serum cholesterol and HbA1c level had been performed prior to initiation and following any dose increase.Result21 of 121 patients prescribed antipsychotic medication were on combination therapy. 11 were subject to HDAT. 8 of the 11 HDAT patients were on combination therapy. Clozapine was considered before initiating HDAT in 9 of the HDAT patients. Clozapine was considered in 13 of the 21 patients on combination antipsychotic therapy, but only two were initiated on Clozapine (combined with Olanzapine or Risperidone).100% of HDAT patients had an ECG prior to initiation of HDAT; only 36% had one after dose increases above BNF maximum. 100% of HDAT patients had their BMI measured before initiation. 91% had baseline B.P and heart rate checked. Of the recommended blood tests, 100% of HDAT patients had baseline FBC, U and Es, LFTs and serum cholesterol. Fewer patients had a baseline HbA1c level (91%) or serum prolactin (46%) measured.ConclusionPrevalence of HDAT across the general adult inpatient wards in the Trust was 9%, much lower than the 28% reported in the HDAT audit completed by the Prescribing Observatory for Mental Health in 2012. Patients within Mersey Care are more likely to be prescribed combination therapy than HDAT. Not every HDAT patient has been considered for Clozapine. There is a need to ensure Trust monitoring guidelines for HDAT patients are being strictly adhered to.

Security Enhancements over AODV Using Multi-hop Acknowledgment

In most applications of Mobile Ad-hoc Networking and Wireless Sensor networks (WSNs), security and power consumption is a recurring problem which is very difficult to solve due to unpredictable behaviour, environmental changes and mobility of nodes. TERP is a Trust based Energy Routing Protocol that considers the energy conservation and security aspects of WSNs. It is an improved version of AODV and ensures low power consumption while adding a trust based system to the reactive nature of AODV. It also provides the solutions for the most basic attacks like node selfishness, spoofing, misbehaviour and packet sniffing. But black-hole attack, gray-hole attack, Sybil and wormhole attacks are yet to be addressed in the TERP. This paper provides a solution to detect and prevent the node misbehaviour and gray-hole attack. TERP identifies the node misbehaviour using direct and indirect trust recommendations received from neighbour nodes. The detection rate of misbehaving nodes is less during the initial life of the network. Therefore we propose a method that detects the node misbehaviour during the initial life by using a multi-hop acknowledgement system. The simulation results show that the proposed method performs well in detecting the gray-hole and node misbehaviour.

Trust Evaluation for Light Weight Security in Sensor Enabled Internet of Things: Game Theory Oriented Approach

In sensor-enabled Internet of Things (IoT), nodes are deployed in an open and remote environment, therefore, are vulnerable to a variety of attacks. Recently, trust-based schemes have played a pivotal role in addressing nodes’ misbehavior attacks in IoT. However, the existing trust-based schemes apply network wide dissemination of the control packets that consume excessive energy in the quest of trust evaluation, which ultimately weakens the network lifetime. In this context, this paper presents an energy efficient trust evaluation (EETE) scheme that makes use of hierarchical trust evaluation model to alleviate the malicious effects of illegitimate sensor nodes and restricts network wide dissemination of trust requests to reduce the energy consumption in clustered-sensor enabled IoT. The proposed EETE scheme incorporates three dilemma game models to reduce additional needless transmissions while balancing the trust throughout the network. Specially: 1) a cluster formation game that promotes the nodes to be cluster head (CH) or cluster member to avoid the extraneous cluster; 2) an optimal cluster formation dilemma game to affirm the minimum number of trust recommendations for maintaining the balance of the trust in a cluster; and 3) an activity-based trust dilemma game to compute the Nash equilibrium that represents the best strategy for a CH to launch its anomaly detection technique which helps in mitigation of malicious activity. Simulation results show that the proposed EETE scheme outperforms the current trust evaluation schemes in terms of detection rate, energy efficiency and trust evaluation time for clustered-sensor enabled IoT.

CTRUST: A Dynamic Trust Model for Collaborative Applications in the Internet of Things

Security through trust presents a viable solution for threat management in the Internet of Things (IoT). Currently, a well-defined trust management framework for collaborative applications on the IoT platform does not exist. In order to estimate reliably the trust values of nodes within a system, the trust should be measured by suitable parameters that are based on the nodes’ functional properties in the application context. Existing models do not clearly outline the parametrization of trust. Also, trust decay is inadequately modeled in most current models. In addition, trust recommendations are usually inaccurately weighted with respect to previous trust, thereby increasing the effect of bad recommendations. A new model, CTRUST, is proposed to resolve these shortcomings. In CTRUST, trust is accurately parametrized while recommendations are evaluated through belief functions. The effects of trust decay and maturity on the trust evaluation process were studied. Each trust component is neatly modeled by appropriate mathematical functions. CTRUST was implemented in a collaborative download application and its performance was evaluated based on the utility derived and its trust accuracy, convergence, and resiliency. The results indicate that IoT collaborative applications based on CTRUST gain a significant improvement in performance, in terms of efficiency and security.

Mitigate Lying and On-Off Attacks on Trust Based Group Key Management Frameworks in MANETs

Instant collaborative group communication can be achieved by deploying Mobile Ad Hoc Networks (or MANETs) without any pre-plan and pre-existing infrastructure setup. However, the curbs thrown by these networks, motivate the necessity of a group key management framework to secure data traffic. In this context, significant research work done in the last decade and proved that the trust based frameworks deliver better performance than others. In MANETs, trust quantified as the belief held by a node about another node. It is evaluated based on the assessing node direct experiences with assessed node itself, if they are neighbors and recommendations from remote nodes in the network with which assessed node has a direct experience, if they are remote. However, the framework based on recommendations by other nodes in the network might ultimately lead to system failure due to the menace of unfair recommendations by selfish and malicious nodes. Lying and on-off attacks on the system are such type of attacks. This motivates us to work on a framework which is immune to aforesaid attacks and should be lightweight due to node's has limited computing resources. We propose a framework which scrutinizes dishonest recommendations based on the trust threshold value of remote nodes, distance from mean of trust recommendations and by maintaining a history of recommendations. Our simulation outcomes prove that proposed framework can deliver better network performance and resistance to lying and on-off attacks.

G93 Aminoglycoside use and ototoxicity monitoring in UK CF units

BackgroundCystic fibrosis (CF) patients are at high risk of aminoglycoside ototoxicity, causing hearing loss, tinnitus or balance problems which can have significant social and psychological implications. The incidence of aminoglycoside...

AN EXPLORATORY STUDY ON PRODUCT, PRICE, DISTRIBUTION CHANNELS, AND PROMOTION OF SEAWEEDS IN SEMPORNA DISTRICT OF SABAH, MALAYSIA

This exploratory study aims to enhance understanding of marketing of seaweed in Semporna district of Sabah, Malaysia with concentration on product, price, distribution channels, and promotion of seaweeds. Qualitative research is conducted via preliminary observation and focus group interviews with nineteen subjects including small entrepreneurs as well as representatives of the companies involved in the seaweed industry in Semporna district of Sabah, Malaysia. This exploratory research found that seaweed farmers concentrated on producing only Kappaphycus Alvarezii (“cottonii”, of commerce) and Eucheuma Denticulatum (“spinosum”, of commerce) which are known by local people as “Agar-Agar”. Farmers sell their dried seaweed to the middlemen who normally travel among the farms to search for sellers and usually offer a good buying price. Promotion of seaweed products through word-of-mouth, besides consult and trust recommendations by friends and family members before making any buying decisions as they value them more highly than anything companies can say through various mediums of advertising.

Review of tobramycin levels in paediatric cystic fibrosis patients at Birmingham Children's Hospital

ObjectiveCystic fibrosis (CF) is a common recessive disorder with the lungs one of the primary organs affected. Many patients experience chronic pulmonary infection by Pseudomonas aeruginosa. Tobramycin, an aminoglycoside is...

P-289 - Physical health monitoring of young people on antipsychotic medication in the newcastle early interventions (EIP) service

Elevated rates of metabolic disturbances and shorter life expectancy due to cardiovascular complications are well known in individuals on antipsychotic medication. Recent literature highlights the use of prompts and educational interventions to improve physical health monitoring. This audit aimed to review whether young people in the EIP Service receiving antipsychotic medication had regular physical monitoring in accordance with the Trust's recommendations and suggested ways to improve it. The Physical Health Monitoring Record developed by the Medicines Management Committee recommends that patients on antipsychotic medication should have a full Physical Examination at Baseline and every 6 months thereafter. Blood tests, drug screens and ECGs should also be done at baseline and repeated on an annual basis. Electronic Patient Records of young people known to the Newcastle EIP Service over a 12-month period were reviewed to complete the audit tool captured data on type and duration of treatment, baseline and follow-up monitoring. Only 55% (N = 11) received baseline monitoring. During the treatment continuation phase, 82% adhered to the recommended annual blood tests, but physical examinations, drug screens and ECGs were not done. At the annual psychiatric reviews, only 64% of young people had physical monitoring requests sent to primary care. Young people receiving antipsychotic medication were not being monitored in accordance with Trust's recommendations. Wider circulation of the recommendations and improved communication with primary care physicians could improve monitoring. Incorporating a prompt within the Electronic Records with annual reminders would be a useful way forward.

307 Are the UK Cystic Fibrosis Trust recommendations for bone health achievable?

307 Are the UK Cystic Fibrosis Trust recommendations for bone health achievable?

Carbon Footprinting of Emergency Medical Services Systems: A Proof-of-Concept Study

Objective. In this proof-of-concept study, we evaluated the availability of emergency medical services (EMS) system energy consumption data required to calculate a carbon footprint. Methods. Two diverse North American EMS systems with more than 125,000 combined annual unit responses agreed to report their energy consumption for the last fiscal or calendar year using a data-collection tool based on Carbon Trust recommendations. They also identified the source of information (e.g., bills, logs, receipts), whether the amounts reported were directly measured or estimated, and whether any of the amounts were prorated from shared facilities (e.g., electricity for a shared office building). For this proof-of-concept study, we report only descriptive data about the availability of data and aggregate carbon emissions. Results. Both systems reported diesel fuel, gasoline, and electricity consumption. One system used natural gas; one system used aviation fuel. Direct measurement of consumption using utility bills and statements was possible for these energy types. One system prorated natural gas and electricity usage; one system was able to estimate commercial air travel. Annual carbon dioxide (CO2) emissions for these two systems totaled 11.1 million pounds of CO2. The largest source of CO2 emissions was diesel fuel (39%), followed by electricity (23%). Conclusion. These EMS systems were able to provide the data necessary to determine their carbon footprints. Future research could include broader study to establish EMS-specific norms for carbon emissions, benchmarking of these metrics between different EMS systems, and the assessment of programs designed to reduce EMS carbon emissions.