The Intersection of Privacy by Design and Behavioral Economics: Nudging Users towards Privacy-Friendly Choices

This paper conducts a comprehensive review of existing research on Privacy by Design (PbD) and behavioral economics, explores the intersection of Privacy by Design (PbD) and behavioral economics, and how designers can leverage “nudges” to encourage users towards privacy-friendly choices. We analyze the limitations of rational choice in the context of privacy decision-making and identify key opportunities for integrating behavioral economics into PbD. We propose a user-centered design framework for integrating behavioral economics into PbD, which includes strategies for simplifying complex choices, making privacy visible, providing feedback and control, and testing and iterating. Our analysis highlights the need for a more nuanced understanding of user behavior and decision-making in the context of privacy, and demonstrates the potential of behavioral economics to inform the design of more effective PbD solutions.

Where Do I Go to Treat Me?Factors That Influence Users’Behavioral Intention

The aim of this paper is to analyze how some specific factors can impact on attitudes towards usage(ATU),and effect on behavioral intention(BI),when the potential medical tourist carries out the medical tourist destination information sourcing process.Specifically,we considered electronic word of mouth communication(eWOM),trust(TRT),perceived usefulness(PU),and perceived ease of use(PEOU).The data were collected from 698 experienced users participating in a tourism thematic Facebook group.The results state that being social media an effective channel to share contents among users,the more expert users are more influenced in the behavioural intention of choosing medical tourism destinations and trust is the variables with the strongest influence on attitude,which affects users’behavioural intention towards the use of Facebook to find information on medical tourism destination.These results contribute to the scientific debate on users’behavior in utilizing social media to find information for a medical tourism destination and provide support to the marketing and communication strategies of medical tourism practitioners.

AMachine Learning Approach to User Profiling for Data Annotation of Online Behavior

The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,criteria,and frequency.The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.

Behavioral Intention to Continue Using a Library Mobile App

To meet the needs of today’s library users,institutions are developing library mobile apps(LMAs),as their libraries are increasingly intelligent and rely on deep learning.This paper explores the influencing factors and differences in the perception of LMAs at different time points after a user has downloaded an LMA.A research model was constructed based on the technology acceptance model.A questionnaire was designed and distributed twice to LMA users with an interval of three months to collect dynamic data.The analysis was based on structural equation modeling.The empirical results show that the perceived ease of use,the perceived usefulness,the social influence,and the facilitating conditions affected the users’behavioral intention,but their impacts were different at different times.As the usage time increases,the technology acceptance model is still universal for understanding the user perception of LMA.In addition,two extended variables(social impact and convenience)also affect the user’s behavior intention.User behavior is dynamic and changed over time.This study is important both theoretically and practically,as the results could be used to improve the service quality of LMAs and reduce the loss rate of users.Its findings may help the designers and developers of LMAs to optimize them from the perspective of a user and improve the service experience by providing a deeper understanding of the adoption behavior of information systems by LMA users.

Extension of Goal-Directed Behavior Model for Post-Pandemic Korean Travel Intentions to Alternative Local Destinations: Perceived Risk and Knowledge

Since the outbreak of COVID-19,tourists have been increasingly concerned over various risks of international travel,while knowledge of the pandemic appears to vary significantly.In addition,as travel restrictions continue to impact adversely on international tourism,tourism efforts should be placed more on the domestic markets.Via structural equation modeling,this study unearthed different risk factors impacting Korean travelers’choices of alternative local destinations in the post-pandemic era.In addition,this study extended the goal-directed behavior framework with the acquisition of perceived risk and knowledge of COVID-19,which was proven to hold a sig-nificantly superior explanatory power of tourists’decisions of local alternatives over foreign countries during the COVID-19 pandemic.Furthermore,desire was found to play an imminent mediating role in the conceptual mod-el,maximizing the impact of perceived risk on travel intentions.Henceforth,this research offers meaningful the-oretical implication as thefirst empirical study to deepen the goal-directed behaviour framework with perceived risk and knowledge in the context of post-COVID-19 era.It also serves as insightful knowledge for Korean tour-ism authorities and practitioners to understand local tourists’decision-making processes and tailor effective recovery strategy for domestic tourism.

Effects of Negative Online Reviews on Consumers＇ Attitudes and Behavioral Intentionstoward Online Products

Online shopping has become an important new channel because of its rapid development and broad application of the Internettechnology. As consumer information search and release gradually shift from offline to online, online reviews of products havebecome more valuable. Research shows that most online shoppers view online reviews from product users before purchasing. Asone of the most important forms of spreading awareness, online product reviews has an increasing impact on customer purchasedecisions and has gradually become an urgent issue in network marketing research. This phenomenon impels businesses to realizethat online reviews significantly affect trading volume. Businesses have attempted to manipulate online reviews by providing asignificant number of positive comments that could lead to consumer confidence and purchase of products. Internet users inChina are more interested in reading negative comments compared with the rest of the global Internet users. Thus, the effects ofstructural characteristics of potential customers, negative attitudes, and behavioral intentions have not yet aroused global concernbecause this phenomenon has been limited to the local scale.Based on literature, the main objective of negative online reviews is the positioning of fashion products. The perception ofnegative online reviews, purchase attitudes, and behaviors are the factors considered in the present study. Negative online reviewsof clothing and accessories sold online and their influence on consumer purchase intention and attitudes are analyzed. The studyaims to confirm that negative online reviews have an effect on consumer purchase intention, attitudes, and behaviors. Moreover,buying attitudes influence behavior intention.

Cyberattack Detection Framework Using Machine Learning and User Behavior Analytics

This paper proposes a novel framework to detect cyber-attacks using Machine Learning coupled with User Behavior Analytics.The framework models the user behavior as sequences of events representing the user activities at such a network.The represented sequences are thenfitted into a recurrent neural network model to extract features that draw distinctive behavior for individual users.Thus,the model can recognize frequencies of regular behavior to profile the user manner in the network.The subsequent procedure is that the recurrent neural network would detect abnormal behavior by classifying unknown behavior to either regu-lar or irregular behavior.The importance of the proposed framework is due to the increase of cyber-attacks especially when the attack is triggered from such sources inside the network.Typically detecting inside attacks are much more challenging in that the security protocols can barely recognize attacks from trustful resources at the network,including users.Therefore,the user behavior can be extracted and ultimately learned to recognize insightful patterns in which the regular patterns reflect a normal network workflow.In contrast,the irregular patterns can trigger an alert for a potential cyber-attack.The framework has been fully described where the evaluation metrics have also been introduced.The experimental results show that the approach performed better compared to other approaches and AUC 0.97 was achieved using RNN-LSTM 1.The paper has been concluded with pro-viding the potential directions for future improvements.

The Research on E-mail Users' Behavior of Participating in Subjects Based on Social Network Analysis

The e-mail network is a type of social network. This study analyzes user behavior in e-mail subject participation in organizations by using social network analysis. First, the Enron dataset and the position-related information of an employee are introduced, and methods for deletion of false data are presented. Next, the three-layer model(User, Subject, Keyword) is proposed for analysis of user behavior. Then, the proposed keyword selection algorithm based on a greedy approach, and the influence and propagation of an e-mail subject are defined. Finally, the e-mail user behavior is analyzed for the Enron organization. This study has considerable significance in subject recommendation and character recognition.

Evaluation of Microblog Users’ Influence Based on PageRank and Users Behavior Analysis

This paper explores the uses’ influences on microblog. At first, according to the social network theory, we present an analysis of information transmitting network structure based on the relationship of following and followed phenomenon of microblog users. Informed by the microblog user behavior analysis, the paper also addresses a model for calculating weights of users’ influence. It proposes a U-R model, using which we can evaluate users’ influence based on PageRank algorithms and analyzes user behaviors. In the U-R model, the effect of user behaviors is explored and PageRank is applied to evaluate the importance and the influence of every user in a microblog network by repeatedly iterating their own U-R value. The users’ influences in a microblog network can be ranked by the U-R value. Finally, the validity of U-R model is proved with a real-life numerical example.

Impact of Tourists'Perceived Value on Behavioral Intention for Mega Events:Analysis of Inbound and Domestic Tourists at Shanghai World Expo

Based on the survey data for 2120 inbound and domestic tourists at the Shanghai World Expo,a structural equation model was constructed for the relationship among tourists' perceived value dimensions,behavioral intention and revisit intention.Additionally,the influence of tourists' perceived value dimensions on the behavioral intention and revisit intention was explored.The results show that the utilitarian value and enjoyment value significantly affect the inbound and domestic tourists' behavioral intention,while the convenience value and aesthetic value have no significant influence.The service value only significantly affects the domestic tourists' behavioral intention,and the aesthetic value only significantly affects the inbound tourists' behavioral intention.The utilitarian value,service value and enjoyment value significantly affect the inbound and domestic tourists' revisit intention,while the convenience value only significantly affects the domestic tourists' revisit intention.The utilitarian value is the primary factor affecting the inbound tourists' behavioral intention and revisit intention,and the perceived price has no significant effect on either inbound or domestic tourists' behavioral intention or revisit intention.The study explores the relationships between tourists' perceived value,behavioral intention and revisit intention,analyzes the divergence and causation,theoretically enriches the research field of tourism geography and behavioral geography,and has great practical significance to the sustainable development of mega events in China,including the further development of the Shanghai World Expo.

A Study of the Influence of Tourists’ Destination Image and Perceived Service Quality on Their Behavioral Intention

The purpose of this study is to understand the effect of tourists’ perception of destination image and service quality on their behavioral intention. A total of 1020 valid questionnaires were collected from tourists in Lukang Town, Taiwan, by means of convenient sampling. After descriptive statistics and PLS statistical analysis, the results show that: 1) Tourists’ image of Lukang town has a positive effect on their perceived service quality. In addition, it also has a positive effect on future travel behavior intention;2) The service quality of tourists in Lukang has a positive effect on their future travel behavior intention. 3) This study also finds that service quality has a mediating effect on tourism behavior intention. Finally, based on the results of the study, suggestions for future research and tourism planning are put forward.

Factors influencing academic library users' intention to use mobile systems: A comparison of current users and potential adopters

Purpose: The study intends to examine the factors influencing the behavioral intention to use academic libraries’ mobile systems from the perspective of current users and potential adopters, respectively. Design/methodology/approach: Our study investigates the mobile library system’s acceptance by using a context-specific extension of the theory of reasoned action(TRA) and the technology acceptance model(TAM), which includes such factors as mobile self-efficacy, personal innovativeness and perceived playfulness. Structural equation modeling was used to test the validity of the proposed model based on the empirical data which was collected from 210 questionnaire survey participants.Findings: The result shows that 1) for both current users and potential adopters, attitude toward use and subjective norm both have a significant and positive impact on behavioral intention to use; 2) perceived usefulness and perceived ease of use are significantly correlated to potential adopters’ attitude toward use whereas perceived usefulness and perceived playfulness are significantly related to current users’ attitude toward use; 3) as for the comparison between the two groups of users, personal innovativeness not only affects perceived usefulness of both current users and potential adopters, but also affects potential adopters’ perceived playfulness positively. Mobile self-efficacy has a significant effect on perceived ease of use for both types of users.Research limitations: Although the sample size met the basic statistics requirements for the social research, the participants were mainly college students, and other mobile system users like faculty members and researchers were not investigated. In addition, some influencing factors, such as information quality, system quality and service quality were not considered in the research model.Practical implications: This study reveals main factors which influence both current users and potential adopters’ intention to use the mobile system, providing academic libraries withinsights into management strategies to offer customized mobile services to different types of users. Originality/value: Previous studies did not distinguish current users from potential adopters, which is not conducive for academic libraries to provide customized services and attract potential users. We presented an exploratory study to address this issue.

User Profile & Attitude Analysis Based on Unstructured Social Media and Online Activity

As social media and online activity continue to pervade all age groups, it serves as a crucial platform for sharing personal experiences and opinions as well as information about attitudes and preferences for certain interests or purchases. This generates a wealth of behavioral data, which, while invaluable to businesses, researchers, policymakers, and the cybersecurity sector, presents significant challenges due to its unstructured nature. Existing tools for analyzing this data often lack the capability to effectively retrieve and process it comprehensively. This paper addresses the need for an advanced analytical tool that ethically and legally collects and analyzes social media data and online activity logs, constructing detailed and structured user profiles. It reviews current solutions, highlights their limitations, and introduces a new approach, the Advanced Social Analyzer (ASAN), that bridges these gaps. The proposed solutions technical aspects, implementation, and evaluation are discussed, with results compared to existing methodologies. The paper concludes by suggesting future research directions to further enhance the utility and effectiveness of social media data analysis.

Exploring users' within-site navigation behavior:A case study based on clickstream data

Purpose:The goal of our research is to suggest specific Web metrics that are useful for evaluating and improving user navigation experience on informational websites.Design/methodology/approach:We revised metrics in a Web forensic framework proposed in the literature and defined the metrics of footprint,track and movement.Data were obtained from user clickstreams provided by a real estate site’s administrators.There were two phases of data analysis with the first phase on navigation behavior based on user footprints and tracks,and the second phase on navigational transition patterns based on user movements.Findings:Preliminary results suggest that the apartment pages were heavily-trafficked while the agent pages and related information pages were underused to a great extent.Navigation within the same category of pages was prevalent,especially when users navigated among the regional apartment listings.However,navigation of these pages was found to be inefficient.Research limitations:The suggestions for navigation design optimization provided in the paper are specific to this website,and their applicability to other online environments needs to be verified.Preference predications or personal recommendations are not made during the current stage of research.Practical implications:Our clickstream data analysis results offer a base for future research.Meanwhile,website administrators and managers can make better use of the readily available clickstream data to evaluate the effectiveness and efficiency of their site navigation design.Originality/value:Our empirical study is valuable to those seeking analysis metrics for evaluating and improving user navigation experience on informational websites based on clickstream data.Our attempts to analyze the log file in terms of footprint,track and movement will enrich the utilization of such trace data to engender a deeper understanding of users’within-site navigation behavior.

Database Search Behaviors: Insight from a Survey of Information Retrieval Practices

This study examines the database search behaviors of individuals, focusing on gender differences and the impact of planning habits on information retrieval. Data were collected from a survey of 198 respondents, categorized by their discipline, schooling background, internet usage, and information retrieval preferences. Key findings indicate that females are more likely to plan their searches in advance and prefer structured methods of information retrieval, such as using library portals and leading university websites. Males, however, tend to use web search engines and self-archiving methods more frequently. This analysis provides valuable insights for educational institutions and libraries to optimize their resources and services based on user behavior patterns.

TdBrnn:An Approach to Learning Users’Intention to Legal Consultation with Normalized Tensor Decomposition and Bi-LSTM

With the development of Internet technology and the enhancement of people’s concept of the rule of law,online legal consultation has become an important means for the general public to conduct legal consultation.However,different people have different language expressions and legal professional backgrounds.This phenomenon may lead to the phenomenon of different descriptions of the same legal consultation.How to accurately understand the true intentions behind different users’legal consulting statements is an important issue that needs to be solved urgently in the field of legal consulting services.Traditional intent understanding algorithms rely heavily on the lexical and semantic information between the original data,and are not scalable,and often require taxing manual annotation work.This article proposes a new approach TdBrnn which is based on the normalized tensor decomposition method and Bi-LSTM to learn users’intention to legal consulting.First,we present the users’legal consulting statements as a tensor.And then we use the normalized tensor decomposition layer proposed by this article to extract the tensor elements and structural information of the original tensor which can best represent users’intention of legal consultation,namely the core tensor.The core tensor relies less on the lexical and semantic information of the original users’legal consulting statements data,it reduces the dimension of the original tensor,and greatly reduces the computational complexity of the subsequent Bi-LSTM algorithm.Furthermore,we use a large number of core tensors obtained by the tensor decomposition layer with users’legal consulting statements tensors as inputs to continuously train Bi-LSTM,and finally derive the users’legal consultation intention classification model which can comprehensively understand the user’s legal consultation intention.Experiments show that our method has faster convergence speed and higher accuracy than traditional recurrent neural networks.

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.

A Study on the Relationships Between Brand Experience,Perceived Value,and Behavioral Intention

Brand experience is essential in shaping the competitive advantage and sustainability of theme park attractions.Using Shanghai Disneyland as a reference case,this study examined the relationships among brand experience,perceived value,satisfaction,and behavioral intention.And their relationship is the key point for the sustainable development of theme park market.This study constituted the relationship path theory mode of hypothesis between brand experience,perceived value,satisfactory,behavior intention.The key findings of this study revealed that the brand experience of theme park has significant positive impact on perceived value and satisfaction.In that,thoughts have no significant impact on emotional value and societal value.Similarly,functional experience has no significant impact on recreational value.Subsequently,perceived value has significant positive impact on satisfaction.And lastly,satisfaction has significant positive impact on tourist behavioral intention.The findings of this study may offer constructive bases to the management of Shanghai Disneyland and other theme park attractions of similar nature in formulating policies and marketing strategies.

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.

A Study on the Path Mechanism of Para-social Interaction and Place Attachment on Film-Induced Tourists’Behavioral Intention

Under the influence of celebrity effect and para-social interaction,film is increasingly able to induce viewers’willingness to travel.Based on the theory of para-social interaction and the factors of place attachment,this study constructs a model of the influence mechanism of film-induced tourists’behavioral intention and puts relevant hypotheses.Taking the film A Little Red Flower as an example,the empirical test is carried out by using structural equation model(SEM).The results show that:(1)Film-induced tourists’emotional involvement has a significant positive impact on tourism behavioral involvement,but has no direct and significant impact on place dependence and place identity.(2)Film-induced tourists’behavioral involvement has a significant positive impact on place dependence and place identity.(3)Both place dependence and place identity of film-induced tourists have significant positive impact on tourism behavioral intention.Therefore,film tourism destinations should show the unique local conditions and customs according to the preference of fans and audiences,with the help of the popularity of film,so as to improve their tourism behavior intention.