Trust beyond Technology Algorithms: A Theoretical Exploration of Consumer Trust and Behavior in Technological Consumption and AI Projects

In an era dominated by artificial intelligence (AI), establishing customer confidence is crucial for the integration and acceptance of AI technologies. This interdisciplinary study examines factors influencing customer trust in AI systems through a mixed-methods approach, blending quantitative analysis with qualitative insights to create a comprehensive conceptual framework. Quantitatively, the study analyzes responses from 1248 participants using structural equation modeling (SEM), exploring interactions between technological factors like perceived usefulness and transparency, psychological factors including perceived risk and domain expertise, and organizational factors such as leadership support and ethical accountability. The results confirm the model, showing significant impacts of these factors on consumer trust and AI adoption attitudes. Qualitatively, the study includes 35 semi-structured interviews and five case studies, providing deeper insight into the dynamics shaping trust. Key themes identified include the necessity of explainability, domain competence, corporate culture, and stakeholder engagement in fostering trust. The qualitative findings complement the quantitative data, highlighting the complex interplay between technology capabilities, human perceptions, and organizational practices in establishing trust in AI. By integrating these findings, the study proposes a novel conceptual model that elucidates how various elements collectively influence consumer trust in AI. This model not only advances theoretical understanding but also offers practical implications for businesses and policymakers. The research contributes to the discourse on trust creation and decision-making in technology, emphasizing the need for interdisciplinary efforts to address societal challenges associated with technological advancements. It lays the groundwork for future research, including longitudinal, cross-cultural, and industry-specific studies, to further explore consumer trust in AI.

Consumers’ Perception and Knowledge of Food Additives in Senegal: A Pilot Study

Food safety has become a major concern for consumers, as well as a priority for regulatory authorities. Faced with the growing industrial and domestic use of food additives, many questions are being asked and concerns are being felt by consumers around the world. Consumer perception defines the acceptability or rejection of food products, and has an impact on consumption patterns and behavior. To assess the level of knowledge and perception of food additives, a pilot study was carried out on a sample of 200 people in Dakar and Saint-Louis. A questionnaire was used to assess the acceptance or rejection, use and impact of food additives by consumers in Senegal. The results revealed several aspects. On the whole, the people surveyed expressed great mistrust and even rejection of these substances added to food products. This consumer perception is shared throughout the world, as indicated in numerous surveys. It also emerges from this study that, although most consumers are aware of the existence of these additives and their uses in the home, they feel that the use of these substances in industrial production is too excessive. What’s more, consumers associate food additives with numerous pathologies such as cancer, diabetes, hypertension, stroke and even sexual impotence. For some of these indexed pathologies, scientific studies have reached the same conclusions, although controversy still persists. On the other hand, for some of the other adverse effects mentioned, no cause-and-effect relationship has been scientifically demonstrated. In these latter cases, it seems that negative communication, misinformation and misconceptions have a major influence on consumer perception of food additives.

Study on the Influence of Food Packaging on the Psychology of Different Groups of Consumers

With the vigorous development of consumer culture in today’s society,various types of food packaging also appear in front of consumers in different forms.There are very big differences in food packaging in terms of shape,color,style and other aspects of information transmission,which have the most direct impact on the audience’s food consumption needs.Driven by the consumption-oriented society,food packaging has shown very obvious comprehensive characteristics,is significantly interdisciplinary,and has close connections with other disciplines.This article will analyze and sort out the impact of food packaging on consumer psychology from different perspectives.

Research on the Influence of Anchor Attributes on Consumers’Online Behaviors in Social E-Commerce Platforms:The Moderating Effect of Platform Contextual Factors

As e-commerce continues to mature,the advantages of live streaming within the industry have become increasingly apparent,offering significant growth opportunities.Social e-commerce platforms,which are user-centered,integrate social networks with e-commerce by leveraging social interactions to drive product sales and enhance the overall consumer shopping experience.This type of e-commerce fosters engagement and promotes products by merging online communities with shopping behavior,creating a more interactive and dynamic marketplace.It not only retains the traditional e-commerce trading and marketing functions but also adds a social dimension,making live stream anchors crucial figures connecting consumers with products.These anchors can attract consumers with their appearance and charm,and use their expertise on live streaming platforms to guide consumers by recommending live content.They can also interact with their audiences and potentially influence them to purchase the recommended goods.It is evident that the attributes of anchors in live streaming rooms significantly impact consumers’online behavior.Therefore,researching how platform contextual factors regulate consumers’online behavior is of great practical significance.This study employs multilevel regression analysis to support its hypotheses using data.The findings indicate that contextual factors of the platform significantly influence online behavior,enhancing the positive relationship between user attachment and online activities.

Consumers’experiences and preferences for plant-based meat food:Evidence from a choice experiment in four cities of China

This paper examined consumers’experiences in and preferences for plant-based meat(PBM)food and their respective correlates,based on data from an online survey of 579 consumers in four major cities in China in early 2021.We first described consumers’experiences in consuming and purchasing PBM food and their correlates,and then analyzed consumer preferences using hypothetical choice experiment.The experiment offered consumers various options to purchase burgers made from PBM or animal-based meat(ABM),combined with different countries of origin(COO),taste labels,and prices.Our data showed that respondents hold overall positive attitudes toward PBM food;85 and 82%of respondents reported experience in eating and purchasing PBM food,respectively.More than half of them ate PBM food because they wanted to try new food(58%),or were interested in healthy food(56%).Income,religion,and dietary restrictions were significantly correlated with consumers’experiences in PBM food consumption.Results from the Random Parameter Logit Model based on the hypothetical choice experiment data showed that 79%of respondents chose PBM burgers and were willing to pay an average of 88 CNY for a PBM burger.We also found that 99.8 and 83%of respondents are willing to buy burgers made in China and those with a taste label,with a willingness to pay(WTP)of 208 and 120 CNY,respectively.The heterogeneity test revealed that females and those with at least a bachelor’s degree,higher income,religious beliefs,and dietary restrictions are more likely to buy PBM burgers than their counterparts.

Modelling the Roles of Cewebrity Trust and Platform Trust in Consumers’Propensity of Live-Streaming:An Extended TAM Method

Live streaming is a booming industry in China,involving an increasing number of Internet users.Previous studies show that trust is a cornerstone to develop ecommerce.Trust in the streaming industry is different from that of other e-commerce areas.There are two major dimensions of trust in the live streaming context:platform trust and cewebrity trust,which are both important for customers to adopt and reuse a specific live streaming service.We collected questionnaire data from 520 participates who have used live streaming services in China.We model the collected data and identified factors that can influence users’propensity by an extended technology acceptance model(TAM)method.According to our analysis,both cewebrity trust and platform trust will greatly influence users’intention to reuse a certain platform.Moreover,results also indicate that cewebrity trust is far more important than platform trust.These findings can lead to several management strategies to improve the adherence of users to streaming platforms.

基于TrustZone硬件隔离技术的CLKScrew攻击防护研究

随着信息技术的迅猛发展,嵌入式系统中的安全性问题日益受到关注。本文针对嵌入式平台中动态电压频率调整技术存在的安全漏洞,基于高级精简指令集机器的TrustZone硬件隔离技术设计并实现了一种可信执行环境(TEE),利用TrustZone技术实现了TEE与普通执行环境操作系统间的切换。该环境能够有效抵御CLKScrew攻击,即通过多次调整中央处理器(CPU)线程工作频率来触发嵌入式平台的运行错误。实验结果表明,在该可信执行环境中,即使面临CLK⁃Screw攻击,CPU线程仍能正常工作,确保了系统的安全性。

A Study on The Factors Influencing the Facial Apparent Age of Chinese Cosmetic Consumers

To investigate the apparent age of Chinese cosmetic consumers and its influencing factors.The subjects’skin conditions in all dimensions were collected using professional instruments and clinical expert assessment,subjects’lifestyles,skin care and makeup habits were obtained through questionnaire.The apparent age of the subjects was obtained based on the visual perception of photos judged by observers and then averaged.The association between apparent age and skin characteristics,the association between the difference between apparent age and actual age and the subjects'lifestyle and its difference among cities were investigated.The results showed that apparent age had a high correlation with skin tone and severity of skin problems.The model of multiple regression analysis obtained a high resolution(R2=0.704).The use of skin care products may help to delay the apparent aging of the skin.The results of the study have some guiding significance for the development of anti-aging products and the evaluation of anti-aging efficacy and is informative for lifestyle choices to maintain youthfulness.

A Study on Emotional Design for Vitamin C Dietary Supplements(Chinese Market,Generation Z Consumers)

China,recognized as the world’s largest developing nation,displays considerably lower per capita consumption of dietary supplements in comparison to Asian nations such as Japan and South Korea.However,in recent years,there has been a substantial surge in health consciousness among the Chinese populace.This trend is not confined to the middle-aged and elderly;even younger consumer demographics are exhibiting increased health awareness.Consequently,the target demographic for dietary supplements is transitioning towards a younger demographic.Within the Chinese dietary supplement industry,vitamin C has consistently held the largest market share,commanding a broad consumer base.This underscores the substantial role of vitamin C in the dietary supplement sector.In response to the trend towards a younger target demographic in the dietary supplement industry,adjustments are required to accommodate the preferences of this younger consumer group.This research,guided by Norman’s emotional design framework,executed a survey of over 200 respondents to investigate the preferences of Generation Z consumers in China.The research encompassed packaging,product forms,and brand imagery,corresponding to the emotional design’s visceral,behavioral,and reflective layers,with a primary focus on optimally meeting the emotional needs of Generation Z.The findings indicated that consumers favor products in capsule form,packaged in zip-lock.The predominant color scheme is clean white,accented by vibrant orange elements,while emphasizing the product’s health and scientific attributes.This study offers valuable insights for the continued evolution of the vitamin C dietary supplement market in China.

A dynamic algorithm for trust inference based on double DQN in the internet of things

The development of the Internet of Things(IoT)has brought great convenience to people.However,some information security problems such as privacy leakage are caused by communicating with risky users.It is a challenge to choose reliable users with which to interact in the IoT.Therefore,trust plays a crucial role in the IoT because trust may avoid some risks.Agents usually choose reliable users with high trust to maximize their own interests based on reinforcement learning.However,trust propagation is time-consuming,and trust changes with the interaction process in social networks.To track the dynamic changes in trust values,a dynamic trust inference algorithm named Dynamic Double DQN Trust(Dy-DDQNTrust)is proposed to predict the indirect trust values of two users without direct contact with each other.The proposed algorithm simulates the interactions among users by double DQN.Firstly,CurrentNet and TargetNet networks are used to select users for interaction.The users with high trust are chosen to interact in future iterations.Secondly,the trust value is updated dynamically until a reliable trust path is found according to the result of the interaction.Finally,the trust value between indirect users is inferred by aggregating the opinions from multiple users through a Modified Collaborative Filtering Averagebased Similarity(SMCFAvg)aggregation strategy.Experiments are carried out on the FilmTrust and the Epinions datasets.Compared with TidalTrust,MoleTrust,DDQNTrust,DyTrust and Dynamic Weighted Heuristic trust path Search algorithm(DWHS),our dynamic trust inference algorithm has higher prediction accuracy and better scalability.

DTAIS:Distributed trusted active identity resolution systems for the Industrial Internet

In recent years,the Industrial Internet and Industry 4.0 came into being.With the development of modern industrial intelligent manufacturing technology,digital twins,Web3 and many other digital entity applications are also proposed.These applications apply architectures such as distributed learning,resource sharing,and arithmetic trading,which make high demands on identity authentication,asset authentication,resource addressing,and service location.Therefore,an efficient,secure,and trustworthy Industrial Internet identity resolution system is needed.However,most of the traditional identity resolution systems follow DNS architecture or tree structure,which has the risk of a single point of failure and DDoS attack.And they cannot guarantee the security and privacy of digital identity,personal assets,and device information.So we consider a decentralized approach for identity management,identity authentication,and asset verification.In this paper,we propose a distributed trusted active identity resolution system based on the inter-planetary file system(IPFS)and non-fungible token(NFT),which can provide distributed identity resolution services.And we have designed the system architecture,identity service process,load balancing strategy and smart contract service.In addition,we use Jmeter to verify the performance of the system,and the results show that the system has good high concurrent performance and robustness.

Enhancing Energy Efficiency with a Dynamic Trust Measurement Scheme in Power Distribution Network

The application of Intelligent Internet of Things(IIoT)in constructing distribution station areas strongly supports platform transformation,upgrade,and intelligent integration.The sensing layer of IIoT comprises the edge convergence layer and the end sensing layer,with the former using intelligent fusion terminals for real-time data collection and processing.However,the influx of multiple low-voltage in the smart grid raises higher demands for the performance,energy efficiency,and response speed of the substation fusion terminals.Simultaneously,it brings significant security risks to the entire distribution substation,posing a major challenge to the smart grid.In response to these challenges,a proposed dynamic and energy-efficient trust measurement scheme for smart grids aims to address these issues.The scheme begins by establishing a hierarchical trust measurement model,elucidating the trust relationships among smart IoT terminals.It then incorporates multidimensional measurement factors,encompassing static environmental factors,dynamic behaviors,and energy states.This comprehensive approach reduces the impact of subjective factors on trust measurements.Additionally,the scheme incorporates a detection process designed for identifying malicious low-voltage end sensing units,ensuring the prompt identification and elimination of any malicious terminals.This,in turn,enhances the security and reliability of the smart grid environment.The effectiveness of the proposed scheme in pinpointing malicious nodes has been demonstrated through simulation experiments.Notably,the scheme outperforms established trust metric models in terms of energy efficiency,showcasing its significant contribution to the field.

A Fuzzy Trust Management Mechanism with Dynamic Behavior Monitoring for Wireless Sensor Networks

Traditional wireless sensor networks(WSNs)are typically deployed in remote and hostile environments for information collection.The wireless communication methods adopted by sensor nodes may make the network highly vulnerable to various attacks.Traditional encryption and authentication mechanisms cannot prevent attacks launched by internal malicious nodes.The trust-based security mechanism is usually adopted to solve this problem in WSNs.However,the behavioral evidence used for trust estimation presents some uncertainties due to the open wireless medium and the inexpensive sensor nodes.Moreover,how to efficiently collect behavioral evidences are rarely discussed.To address these issues,in this paper,we present a trust management mechanism based on fuzzy logic and a cloud model.First,a type-II fuzzy logic system is used to preprocess the behavioral evidences and alleviate uncertainty.Then,the cloud model is introduced to estimate the trust values for sensor nodes.Finally,a dynamic behavior monitoring protocol is proposed to provide a balance between energy conservation and safety assurance.Simulation results demonstrate that our trust management mechanism can effectively protect the network from internal malicious attacks while enhancing the energy efficiency of behavior monitoring.

Trust Score-Based Malicious Vehicle Detection Scheme in Vehicular Network Environments

Advancements in the vehicular network technology enable real-time interconnection,data sharing,and intelligent cooperative driving among vehicles.However,malicious vehicles providing illegal and incorrect information can compromise the interests of vehicle users.Trust mechanisms serve as an effective solution to this issue.In recent years,many researchers have incorporated blockchain technology to manage and incentivize vehicle nodes,incurring significant overhead and storage requirements due to the frequent ingress and egress of vehicles within the area.In this paper,we propose a distributed vehicular network scheme based on trust scores.Specifically,the designed architecture partitions multiple vehicle regions into clusters.Then,cloud supervision systems(CSSs)verify the accuracy of the information transmitted by vehicles.Additionally,the trust scores for vehicles are calculated to reward or penalize them based on the trust evaluation model.Our proposed scheme demonstrates good scalability and effectively addresses the main cause of malicious information distribution among vehicles.Both theoretical and experimental analysis show that our scheme outperforms the compared schemes.

A Trust Evaluation Mechanism Based on Autoencoder Clustering Algorithm for Edge Device Access of IoT

First,we propose a cross-domain authentication architecture based on trust evaluation mechanism,including registration,certificate issuance,and cross-domain authentication processes.A direct trust evaluation mechanism based on the time decay factor is proposed,taking into account the influence of historical interaction records.We weight the time attenuation factor to each historical interaction record for updating and got the new historical record data.We refer to the beta distribution to enhance the flexibility and adaptability of the direct trust assessment model to better capture time trends in the historical record.Then we propose an autoencoder-based trust clustering algorithm.We perform feature extraction based on autoencoders.Kullback leibler(KL)divergence is used to calculate the reconstruction error.When constructing a convolutional autoencoder,we introduce convolutional neural networks to improve training efficiency and introduce sparse constraints into the hidden layer of the autoencoder.The sparse penalty term in the loss function measures the difference through the KL divergence.Trust clustering is performed based on the density based spatial clustering of applications with noise(DBSCAN)clustering algorithm.During the clustering process,edge nodes have a variety of trustworthy attribute characteristics.We assign different attribute weights according to the relative importance of each attribute in the clustering process,and a larger weight means that the attribute occupies a greater weight in the calculation of distance.Finally,we introduced adaptive weights to calculate comprehensive trust evaluation.Simulation experiments prove that our trust evaluation mechanism has excellent reliability and accuracy.

Machine learning and human‐machine trust in healthcare:A systematic survey

As human‐machine interaction(HMI)in healthcare continues to evolve,the issue of trust in HMI in healthcare has been raised and explored.It is critical for the development and safety of healthcare that humans have proper trust in medical machines.Intelligent machines that have applied machine learning(ML)technologies continue to penetrate deeper into the medical environment,which also places higher demands on intelligent healthcare.In order to make machines play a role in HMI in healthcare more effectively and make human‐machine cooperation more harmonious,the authors need to build good humanmachine trust(HMT)in healthcare.This article provides a systematic overview of the prominent research on ML and HMT in healthcare.In addition,this study explores and analyses ML and three important factors that influence HMT in healthcare,and then proposes a HMT model in healthcare.Finally,general trends are summarised and issues to consider addressing in future research on HMT in healthcare are identified.

Enhancing the Trustworthiness of 6G Based on Trusted Multi-Cloud Infrastructure:A Practice of Cryptography Approach

Due to the need for massive device connectivity,low communication latency,and various customizations in 6G architecture,a distributed cloud deployment approach will be more relevant to the space-air-ground-sea integrated network scenario.However,the openness and heterogeneity of the 6G network cause the problems of network security.To improve the trustworthiness of 6G networks,we propose a trusted computing-based approach for establishing trust relationships inmulti-cloud scenarios.The proposed method shows the relationship of trust based on dual-level verification.It separates the trustworthy states of multiple complex cloud units in 6G architecture into the state within and between cloud units.Firstly,SM3 algorithm establishes the chain of trust for the system’s trusted boot phase.Then,the remote attestation server(RAS)of distributed cloud units verifies the physical servers.Meanwhile,the physical servers use a ring approach to verify the cloud servers.Eventually,the centralized RAS takes one-time authentication to the critical evidence information of distributed cloud unit servers.Simultaneously,the centralized RAS also verifies the evidence of distributed RAS.We establish our proposed approach in a natural OpenStack-based cloud environment.The simulation results show that the proposed method achieves higher security with less than a 1%system performance loss.

Trust-based co-operative routing for secure communication in mobile ad hoc networks

The working of a Mobile Ad hoc NETwork(MANET)relies on the supportive cooperation among the network nodes.But due to its intrinsic features,a misbehaving node can easily lead to a routing disorder.This paper presents two trust-based routing schemes,namely Trust-based Self-Detection Routing(TSDR)and Trust-based Cooperative Routing(TCOR)designed with an Ad hoc On-demand Distance Vector(AODV)protocol.The proposed work covers a wide range of security challenges,including malicious node identification and prevention,accurate trust quantification,secure trust data sharing,and trusted route maintenance.This brings a prominent solution for mitigating misbehaving nodes and establishing efficient communication in MANET.It is empirically validated based on a performance comparison with the current Evolutionary Self-Cooperative Trust(ESCT)scheme,Generalized Trust Model(GTM),and the conventional AODV protocol.The extensive simulations are conducted against three different varying network scenarios.The results affirm the improved values of eight popular performance metrics overcoming the existing routing schemes.Among the two proposed works,TCOR is more suitable for highly scalable networks;TSDR suits,however,the MANET application better with its small size.This work thus makes a significant contribution to the research community,in contrast to many previous works focusing solely on specific security aspects,and results in a trade-off in the expected values of evaluation parameters and asserts their efficiency.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

Trusted Certified Auditor Using Cryptography for Secure Data Outsourcing and Privacy Preservation in Fog-Enabled VANETs

With the recent technological developments,massive vehicular ad hoc networks(VANETs)have been established,enabling numerous vehicles and their respective Road Side Unit(RSU)components to communicate with oneanother.The best way to enhance traffic flow for vehicles and traffic management departments is to share thedata they receive.There needs to be more protection for the VANET systems.An effective and safe methodof outsourcing is suggested,which reduces computation costs by achieving data security using a homomorphicmapping based on the conjugate operation of matrices.This research proposes a VANET-based data outsourcingsystem to fix the issues.To keep data outsourcing secure,the suggested model takes cryptography models intoaccount.Fog will keep the generated keys for the purpose of vehicle authentication.For controlling and overseeingthe outsourced data while preserving privacy,the suggested approach considers the Trusted Certified Auditor(TCA).Using the secret key,TCA can identify the genuine identity of VANETs when harmful messages aredetected.The proposed model develops a TCA-based unique static vehicle labeling system using cryptography(TCA-USVLC)for secure data outsourcing and privacy preservation in VANETs.The proposed model calculatesthe trust of vehicles in 16 ms for an average of 180 vehicles and achieves 98.6%accuracy for data encryption toprovide security.The proposedmodel achieved 98.5%accuracy in data outsourcing and 98.6%accuracy in privacypreservation in fog-enabled VANETs.Elliptical curve cryptography models can be applied in the future for betterencryption and decryption rates with lightweight cryptography operations.