Human Behavior and Emerging Technologies最新文献

Conversational agents (CAs) are effective tools for health behavior change, yet little research investigates the mechanisms through which they work. Following the Computer as Social Actors (CASA) paradigm, we suggest that agents are perceived as human-like actors and hence influence behavior much as human coaches might. As such, agents should be designed to resemble ideal interaction patterns, for example, by resembling their users. In this registered report, we evaluated this paradigm by testing the impact of customization on similarity and reciprocity, which in turn were hypothesized to improve perceptions of the agent and compliance with the agent’s recommendations to complete a cognitive training exercise. In an online study, 2437 participants were randomly assigned to one of two surface-level CA customization conditions (present/absent) and to one of two deep-level CA customization conditions (present/absent) in a between-subject experimental design. As part of a conversation flow with a CA, participants assigned to the present surface- and/or deep-level customization conditions were able to choose their preferred CA based on the four personality summaries and/or choose their CA’s gender (male/female/agender robotic), avatar (choice between seven avatars corresponding to the chosen gender), and name. While the ability to customize increased similarity to the user and the perceptions of customizability, our findings show that customization did not impact experience or compliance. However, the perceived customizability of the agent was linked to increases in the likeability and usefulness of the agent. We conclude that our work finds no negative effects of customization; yet, its impact on the relationship between the agent and its user is complex and can benefit from more research as merited by its applicability to public health. As aging and ill populations increase the burden on health systems worldwide, CAs have the potential to transform the landscape of accessible care.

Consumers’ willingness to pay a price premium is pivotal for assessing brand value and competitive advantage. Yet, limited and scattered research has focused on how combining brand emotion, strength, and brand loyalty can influence consumers’ willingness to accept a price premium. The present study examines the role of brand attachment, brand strength, and brand loyalty in determining consumers’ willingness to pay a price premium and explores their interplay using a serial mediation model within a unified framework, specifically focusing on home appliance brands. Data from 323 valid questionnaires collected from Algerian households were analyzed using PLS-SEM. Results demonstrate that consumers’ willingness to pay a price premium is significantly and positively influenced by brand strength, brand attachment, and brand loyalty. Furthermore, the relationship between brand strength and consumers’ willingness to pay a price premium is mediated positively by brand attachment and brand loyalty. Grounded on various theories and addressing gaps captured in previous studies, this research is considered pioneering in this field. This study significantly advances our understanding of how brand emotional bonds, brand relationships, and brand strength interplay to influence consumers’ willingness to pay a premium. The findings highlight the importance for brand managers to sustain robust brands to stimulate consumers’ opening to pay extra, thereby achieving and maintaining long-term success in a competitive market.

The integration of big data analytics in digital marketing has fundamentally transformed how organizations understand and influence consumer behavior. This systematic review explores the potential of big data to offer deep insights into consumer preferences and behaviors. The current literature on big data and consumer behavior showcases its potential in enhancing marketing and customer experiences. However, significant gaps exist, such as longitudinal studies on how continuous big data–driven personalization affects consumer trust and loyalty over time. Addressing these gaps will advance academic understanding and offer practical insights for optimizing marketing strategies and improving customer experiences ethically. Focusing on these areas will contribute to a holistic view of big data’s responsible use in digital marketing. By utilizing large datasets, businesses can now tailor their marketing strategies to individual consumers, enhancing customer satisfaction and engagement. Adopting the PRISMA methodology, this review synthesizes recent literature to evaluate the benefits of big data in digital marketing. The research was conducted through a rigorous five-stage process, encompassing the identification of key research questions, database searches, and the critical analysis of selected articles: (1) defining the initial topic, (2) developing the appropriate research questions, (3) identifying the necessary keywords, (4) identifying and searching databases, and finally (5) accessing and reading the articles. The databases that were searched were as follows: Scopus, Web of Science, Emerald Insight, Springer Link, and ScienceDirect. The articles that were selected were 19, in a total sum of 265 identified articles. The findings consolidate current knowledge on how big data analytics can optimize marketing strategies and consumer experiences. Ultimately, this review underscores the transformative potential of big data in digital marketing, highlighting its role in enhancing customer insights and driving more effective marketing strategies.

Mobile applications positively influence the practice of physical activity in adolescents, but the effect of this improvement on the psychological state and the addictive use of technologies in this population is unknown. For this reason, the aims of the present investigation were to establish the differences in psychological variables and problematic mobile phone use by adolescents after a 10-week intervention with mobile apps, according to gender and the different mobile applications used. A randomized controlled trial was conducted in Spain with 400 adolescents aged 12–16 years (mean age: 13.96 ± 1.21 years old) whose physical activity level, satisfaction of competence, autonomy, and relatedness, life satisfaction, and addictive use of the mobile phone were measured. Two measurements were taken, with a 10-week intervention period in between. During the intervention, adolescents in the experimental group (EG) must use one of the selected mobile applications (Strava, Pacer, MapMyWalk, or Pokémon-Go) a minimum of 3 times per week, covering the distance indicated for each week. The use of the mobile applications was randomized for each class group, and an explanation was given to the adolescents prior to the start of the intervention. The results showed that EG showed a significant improvement in the psychological variables (p = 0.003 − 0.036) compared to the control group and also a decreased problematic mobile phone use (p = 0.004). Specifically, females in the EG increased autonomy (p = 0.010), relatedness (p = 0.019), and life satisfaction (p = 0.020), while males improved relatedness (p = 0.021) and competence (p = 0.018). In addition, the different applications used could influence autonomy, relatedness, and problematic mobile phone use. To conclude, the use of mobile step trackers could be useful to maintain an adequate psychological state of the adolescent population without increasing the addictive or problematic use of these technologies.

Trial Registration: ClinicalTrials.gov identifier: NCT04860128.

In the contemporary digital era, the extensive integration of information and communication technologies (ICT) has significantly changed offline activities, including communication, shopping, and media consumption. This integration has been accelerated by the COVID-19 pandemic, leading to increased reliance on ICT for work, education, socializing, and accessing essential services. Consequently, there is growing concern about the impact of ICT on well-being, particularly regarding psychological and financial health, as well as the association with psychiatric disorders. This study is aimed at exploring the psychometric properties of two scales adapted for the Brazilian context: the Digital Life Balance (DLB) scale and the Need for Online Social Feedback (NfOSF) scale. These scales measure individuals’ perceived balance between online and offline activities and their need for social validation online, respectively. Using a sample of 220 Brazilian individuals (50.9% female, 43.6% male, mean age = 34.96 years, SD = 11.32), we conducted confirmatory factor analysis (CFA) to assess the scales’ factor structures and test the reliability and validity of the two measures. The results demonstrated good fit indices and reliable internal consistency for both scales. Additionally, metric invariance between Brazilian and Italian samples was established, supporting cross-cultural applicability. External validity was examined through correlations with time spent on social media and the perceived importance of followers. Findings indicate that higher DLB is associated with less time spent online, while greater NfOSF correlates with higher importance placed on social media followers. These insights highlight the importance of understanding digital balance and the role of social feedback in ICT use, contributing to the effective screening of potential dysfunctional ICT use in Brazil. As a result of this study, validated Brazilian versions of the NfOSF and DLB scales were successfully obtained, offering valuable tools for assessing DLB and the NfOSF in the Brazilian context.

The spread of misinformation on social media is a critical issue. One potential solution to mitigate the spread is user corrections; however, users often refrain due to various concerns. Leveraging the established influence of user interface design (UID) on how user interact with and respond to misinformation, this study investigates how user interface features can be designed to motivate users to challenge misinformation. It is aimed at gaining insights into users’ needs and UID requirements that encourage this behaviour. We conducted four codesign sessions with 18 social media users (age range 20–60 years M = 39.1; 10 female and 8 male). We applied the unified theory of acceptance and use of technology (UTAUT) as a theoretical framework and analysed our data based on the core constructs of this framework: performance expectancy, effort expectancy, social influence, and facilitating conditions. Our findings reveal four design considerations: creating secure and supportive environments, facilitating informed discussions through easy confrontation and access to reliable resources, leveraging recognition and social proof, and user support infrastructure. We also identified specific design elements with users, including indirection, semianonymity and privacy, simplicity, one-click challenging, easy access to reliable sources, recognition, displaying social proof, and platform support. These elements are aimed at reducing social discomfort and making the process of correcting misinformation more approachable for users. Our findings offer actionable insights for social media platform designers to reduce the spread of misinformation by creating environments that encourage constructive dialogues and allow users to challenge misinformation without fear of conflict.

Context: In high-stakes assessments, such as court cases or managerial evaluations, decision-makers heavily rely on psychological testing. These assessments often play a crucial role in determining important decisions that affect a person’s life and have a significant impact on society.

Problem Statement: Research indicates that many psychological assessments are compromised by respondents’ deliberate distortions and inaccurate self-presentations. Among these sources of bias, socially desirable responding (SDR) describes the tendency to provide overly positive self-descriptions. This positive response bias can invalidate test results and lead to inaccurate assessments.

Objectives: The present study is aimed at investigating the utility of mouse- and eye-tracking technologies for detecting SDR in psychological assessments. By integrating these technologies, the study sought to develop more effective methods for identifying when respondents are presenting themselves in a favorable light.

Methods: Eighty-five participants completed the Lie (L) and Correction (K) scales of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) twice: once answering honestly and once presenting themselves in a favorable light, with the order of conditions balanced. Repeated measures univariate analyses were conducted on L and K scale T-scores, as well as on mouse- and eye-tracking features, to compare the honest and instructed SDR conditions. Additionally, machine learning models were developed to integrate T-scores, kinematic indicators, and eye movements for predicting SDR.

Results: The results showed that participants in the SDR condition recorded significantly higher T-scores, longer response times, wider mouse trajectories, and avoided looking at the answers they intended to fake, compared to participants in the honest condition. Machine learning algorithms predicted SDR with 70%–78% accuracy.

Conclusion: New assessment strategies using mouse- and eye-tracking can help practitioners identify whether data is genuine or fabricated, potentially enhancing decision-making accuracy.

Implications: Combining self-report measures with implicit data can improve SDR detection, particularly in managerial, organizational, and forensic contexts where precise assessments are crucial.

Humanity now lives and works in two worlds—the physical world and the cyber world. Tech workers are employees, not necessarily information technology (IT) professionals, who work in both worlds and seamlessly harness accessible resources in the worlds to meet organizational goals. This category of employees has unique job experiences and is more complicated to manage than the traditional workforce. Job satisfaction—a measurable outcome of employee wellbeing—remains a crucial indicator of an employee’s job experience. This psychological health of employees is usually measured using a job satisfaction scale. However, existing job satisfaction scales for tech workers lack specificity of measurement or cultural inclusivity. This study is, therefore, aimed at developing and validating a job satisfaction scale for tech workers in the global context. The systematic and scoping literature review methods were adopted for initial factors and item extraction. Two separate online surveys were conducted across the globe to randomly solicit tech workers’ acceptance rating of extracted factors and, after the factor selection, the rating of extracted associated items. The accepted numbers of respondents’ responses were 261 and 223, respectively. The Statistical Package for Social Sciences version 22 was used for data adequacy analysis, factor analysis, and Cronbach’s alpha coefficient test. A seven-factor model with 25 items was realized. Confirmatory factor analysis (CFA) using the Analysis of Moment Structure software has been performed on the seven-factor model. The model was further analyzed for organizational effectiveness. A notable finding was that successful validation is not enough to ship psychometric scales to the market—a sound social effectiveness analysis outcome is required. A practical seven-factor job satisfaction scale for tech workers has been developed and validated for the tech industry.

Background: Artificial intelligence is an innovative technology that mimics human cognitive capacities and has gathered the world’s attention through its vast applications in various fields.

Aim: This study is aimed at assessing the effects of ChatGPT 3.5 and ChatGPT 4 on the validity, reliability, and authenticity of standard assessment techniques used in undergraduate dentistry education.

Methodology: Twenty questions, each requiring a single best answer, were selected from two domains: 10 from operative dentistry and 10 from endodontics. These questions were divided equally, with half presented with multiple choice options and the other half without. Two investigators used different ChatGPT accounts to generate answers, repeating each question three times. The answers were scored between 0% and 100% based on their accuracy. The mean score of the three attempts was recorded, and statistical analysis was conducted.

Results: No statistically significant differences were found between ChatGPT 3.5 and ChatGPT 4 in the accuracy of their responses. Additionally, the analysis showed high consistency between the two reviewers, with no significant difference in their assessments.

Conclusion: This study evaluated the performance of ChatGPT 3.5 and ChatGPT 4 in answering questions related to endodontics and operative dentistry. The results showed no statistically significant differences between the two versions, indicating comparable response accuracy. The consistency between reviewers further validated the reliability of the assessment process.

Incorporating AI tools like ChatGPT into higher education has been beneficial, yet the extent of user satisfaction with the quality of information provided by these tools, known as user information satisfaction (UIS) and information quality (IQ) theory, remains underexplored. This study introduces a UIS model specifically designed for ChatGPT’s application in the educational sector based on multidimensions of IQ theory. Drawing from established UIS and IQ theory, we crafted a model centered around seven essential factors that influence the effective use of ChatGPT, aiming to guide educators and learners in overcoming common challenges such as plagiarism and ensuring the ethical use of AI. Data was collected from Indonesian university participants (N = 508) and analyzed using structural equation modeling with Smart-PLS 4.0. The results reveal that completeness, precision, timeliness, convenience, and information format are the most influential factors driving user satisfaction with ChatGPT. Interestingly, our research indicated that the accuracy and reliability of the information, typically deemed paramount, were not the primary concerns in the academic use of ChatGPT. Our findings recommend a cautious approach to integrating ChatGPT in higher education. We advocate for strategic use that recognizes its innovative potential while acknowledging its limitations, ensuring responsible and effective application in educational contexts. This balanced perspective is crucial for integrating AI tools into the academic fabric without compromising educational integrity or quality.