Computers in Human Behavior最新文献

The privacy paradox describes a scenario in which individuals express privacy concerns but still share private data online. We explore how the paradox can be understood following the introduction of the European Union's landmark GDPR (General Data Protection Regulation) legislation. Through qualitative interviews with online platform users, we find that individuals are concerned about personal data but remain constrained in their privacy self-management. In this context of limited perceived control, users' privacy attitudes are guided by anticipated value from using the platform and the convenience of privacy protection measures. Our study also highlights the role of peer influence on users' privacy choices, specifically through micro- and macro-network effects. We identify that (1) users move to privacy-protecting platforms to align with their social network, or because of information disseminated within their networks; and (2) users remain on platforms offering minimal privacy protection despite privacy concerns due to presence of their entire peer network. These findings provide a unified view on Privacy Paradox post-GDPR, bringing together a more comprehensive range of influences on individual-level privacy dynamics. Our research underscores the need for policymakers to streamline and standardize data protection measures lest the intentions of GDPR be undermined. We also highlight the need to go beyond a reliance on privacy self-management by better regulating the architecture of data management and enforcing principles of privacy by design and default.

The present paper introduces a novel approach to studying social media habits through predictive modeling of sequential smartphone user behaviors. While much of the literature on media and technology habits has relied on self-report questionnaires and simple behavioral frequency measures, we examine an important yet understudied aspect of media and technology habits: their embeddedness in repetitive behavioral sequences. Leveraging Long Short-Term Memory (LSTM) and transformer neural networks, we show that (i) social media use is predictable at the within and between-person level and that (ii) there are robust individual differences in the predictability of social media use. We examine the performance of several modeling approaches, including (i) global models trained on the pooled data from all participants, (ii) idiographic person-specific models, and (iii) global models fine-tuned on person-specific data. Neither person-specific modeling nor fine-tuning on person-specific data substantially outperformed the global models, indicating that the global models were able to represent a variety of idiosyncratic behavioral patterns. Additionally, our analyses reveal that individual differences in the predictability of social media use were not substantially related to differences in the frequency of smartphone use in general or the frequency of social media use, indicating that our approach captures an aspect of habits that is distinct from behavioral frequency. Implications for habit modeling and theoretical development are discussed.

Grounded in literature on attribution theory, framing, and information sharing, this study addresses the process and behavioral consequences of celebrity advocacy for climate change mitigation on social media. An experimental design (N = 693) examined how parasocial relationship (PSR) with celebrities was related to information virality of celebrity climate change messages on social media, as well as whether perceived motivations of celebrities' climate advocacy functioned as a mediator. The motivations examined were labeled affective (i.e., motivated by genuine environmental concern) and calculative (i.e., the ulterior motive of image promotion). In addition, the study explored how level of fear in message framing impacted information virality of celebrity climate messages. Social media users with higher PSR attributed higher levels of affective motivations for celebrities' advocacy, and stronger affective motivations were subsequently related to a greater likelihood of retweeting and replying to celebrities’ climate messages. Affective motivation attribution was a significant mediator in the relationship between PSR and message virality. Those with higher PSR attributed lower levels of calculative motivations for celebrity advocacy. However, perceived levels of calculative motivations were not related to likelihood of retweeting and replying behaviors. In addition, high fear messages were more effective than low fear messages in promoting the likelihood of retweeting and replying. These findings suggest that celebrity climate change tweets were more effective when the celebrities had a stronger bond with audiences and were seen as affective in their motivations, and when a higher level of fear was used to frame environmental topics.

Addressing the rising obesity prevalence, a Virtual Reality full-body illusion as a novel approach to obesity prevention is examined, allowing individuals to experience ownership of a virtual body (VBO). Although never tested, an immersive experience of a virtual self that suffers from the consequences of an unhealthy lifestyle, like an overweight body, may motivate health behaviors to avoid these consequences. Hence, the study aimed to investigate 1) whether experiencing the consequences of unhealthy snacking could increase healthy snacking intentions and -intake; 2) perceived threat as potential mechanisms of this effect; and 3) potential facilitators and inhibitors of VBO (i.e., avatar body size, visuotactile stimulation/VTS and facial similarity), and whether these factors could enhance effects on perceived threat, healthy snacking intentions and -intake.

Employing a 2 (Experiencing consequences: yes vs. no) ×2 (VTS: congruent vs. incongruent) ×2 (Facial similarity: similar vs. generic) between-subjects design (N = 229), findings revealed that experiencing consequences increased individuals’ healthy snacking intentions and healthy snack intake, but only through perceived severity. Furthermore, embodying an overweight avatar inhibited VBO, while congruent VTS facilitated VBO. The study provides valuable insights into the Virtual Reality experiences, paving the way for innovative research avenues in health communication and behavior change tools.

Considerable efforts have focused on predicting sports event outcomes to enhance spectating, coaching, and betting. However, accurately predicting match results across various competitive scenes is challenging. Many researchers have achieved reasonable prediction accuracy using machine learning (ML) models and static data, such as player statistics up to the last match, to perform binary win/loss classification. However, the inherent uncertainty of sports often leads to unexpected outcomes. For example, narrowing the skill-level gap between players may negatively impact prediction accuracy. Moreover, accurately predicting “upsets,” where lower-skilled players defeat higher-skilled opponents, remains challenging. Conventional win/loss prediction techniques rely on static attributes derived from past match statistics, which fail to capture the player's condition immediately before the match. To address this limitation, this study focused on dynamic information reflecting the pre-match player's condition, particularly electroencephalography (EEG) data, recognized as a potent mental conditioning biomarker. We collected pre-match EEG data from esports experts and trained ML models on these data, aiming to assess the ability of ML techniques trained on EEG data to predict match outcomes, particularly in cases subject to high unpredictability. The findings revealed that the light gradient boosting machine (LightGBM) algorithm trained on EEG data achieved the highest prediction accuracy (80%), with parietal beta activity being the most relevant feature. Furthermore, predictive performance remained consistent even in match scenarios involving similar-level players and upset situations. Hence, this approach extends the applicability of win/loss prediction to traditionally unpredictable sports scenes, enhancing the quality of spectator experiences.

Using the theory of consumption value as the theoretical framework and online game subscription as the subject, this study considered the antecedents (game motivations, fear of missing out [FoMO], and variety preference) and consequences (use and continual use intention) of the values (playing games, social, enjoyment, price, and quality) offered by subscription services. Using the partial least squares method, an empirical analysis of 412 subscribers' and 265 non-subscribers’ views at the online game subscription service level revealed the limited consequences of game motivations for the use (continual use) of online game subscription services. Perceiving FoMO as a bidimensional construct (losing-out and interactive aspects) could provide more insights into how online game players perceive subscription services. FoMO's (interactive aspect) contribution to value is more evident among non-subscribers than subscribers. For both subscribers and non-subscribers, variety preference positively contributes to numerous value types. Three value types (playing games, enjoyment, and price) contribute to the use (continual use) intention. This study augments the rather limited subscription retail literature by focusing on online game subscription services. By viewing FoMO as bidimensional instead of the usual unidimensional construct, this study found that these two FoMO types behaved differently. Moreover, this study showed that studies at the game level cannot be applied to the subscription level in a lock, stock, and barrel manner.

Immersive media is becoming more mainstream but its role in challenging social norms and individuals’ behaviours is less understood. We implemented a survey at The Green Planet Augmented Reality Experience, one of the largest immersive experiences in the United Kingdom, investigating the potential of nature-themed augmented reality (AR) experiences to encourage sustainability beliefs and pro-environmental behaviours, with a one-month follow-up. A video condition group was also recruited to watch a 2D screen-capture video of the AR experience. Results suggest that the AR group had increased in sustainability attitudes and pro-environmental behaviours after (vs. before) the experience, and this persisted at follow-up one month later. When compared to the video group, the AR group scored higher in sustainability beliefs immediately after engaging with the media (but did not differ in pro-environmental behavioural intentions). At a one-month follow-up, both groups appeared to have declined in sustainability beliefs and behaviours in comparison to immediately after media exposure (but were still greater when compared to before exposure). While the two groups did not differ in sustainability beliefs, AR visitors reported to have performed more pro-environmental behaviours in the month following the experience and even increased significantly more since before attending the experience when compared to those in the video condition. Finally, we built a model of cognitive mechanisms of longitudinal pro-environmental behaviour change by media, identifying the sense of presence and connection with nature as mechanisms through which immersive media can promote greater behaviour change when compared to traditional video media. Limitations and industry implications are discussed.

Increasing scholarly interest in phubbing has helped researchers accrue knowledge regarding its psychological outcomes; however, how phubbing affects person perceptions and interpersonal behaviors in general interpersonal contexts, which is critical to a thorough knowledge of the implications of phubbing, remains inadequately researched. To bridge the gaps, we investigated whether and how phubbing affects dehumanization and the downstream consequence of this effect. Four studies (one preregistered), using authentic behavioral data (69,399 posts) from social media (Study 1), correlational design (Study 2), and experimental methods (Studies 3–4), concurred in supporting the fact that phubbing increases dehumanization. Furthermore, subjectivity uncertainty mediated the effect of phubbing on dehumanization (Studies 2–4), while the alternative mediating role of metadehumanization was ruled out (Studies 2–3). In addition, dehumanization elicited by phubbing further increased aggressive behaviors (Study 4). Our research provides new insights into the consequences of phubbing and the factors contributing to dehumanization. It reminds people to note the possible severe consequences of phubbing, an unwitting act, in interpersonal interactions.

Previous studies have forwarded a self-fulfilling prophecy explanation for how algorithmic beliefs may shape affective and relational outcomes of online dating (Hu & Rui, 2023; Sharabi, 2021). The current work examined this self-fulfilling prophecy explanation by investigating relevant online dating correlates of algorithmic beliefs. Secondary analyses were conducted on data from a nationally representative sample of U.S. adults (N = 6034) with model comparison procedures. Collectively, results showed that, among currently single relationship-seeking online daters, algorithmic beliefs were negatively related to online dating disappointment; However, findings for online dating excitement were mixed. Additionally, among past online daters, algorithmic beliefs were positively related to being in a relationship with someone they met in online dating. Consistencies and differences between the current findings and previous findings were discussed to offer theoretical and practical implications.

Advanced technologies offer a promising answer, especially integrating personalized context-aware systems through ubiquitous devices and adaptive learning. This paper explores the ability of these technologies to transform agricultural practices, improving performance and sustainability. This study aims to analyze the effect of integrating context-aware structures in agriculture using ubiquitous gadgets and adaptive learning fashions. It specializes in assessing the upgrades in helpful resource control, crop yield, and environmental sustainability and explores the farming community's economic, social, and academic advantages. Utilizing a combined-methods approach, the studies combine intensive literature with empirical statistics series, including discipline experiments, surveys, and interviews with key agricultural stakeholders. It examines contemporary farming practices, the capabilities of rising technology, and the conditions for enforcing robust context-aware systems in farming. Implementing context-aware systems improves agricultural practices by optimizing water and chemical usage, enhancing soil health, and increasing crop yields. Ubiquitous devices and adaptive learning models facilitate specific, real-time selection-making, leading to extra sustainable and green farming operations. Feedback from the rural community similarly underscores the positive effect of technology on improving accessibility to facts and collaborative learning. The study demonstrated that integrating personalized, context-aware systems with IoT and adaptive learning significantly improves agricultural efficiency and sustainability, evidenced by enhanced resource management and increased crop yields. This study contributes to the discourse on leveraging advanced technology to reap agricultural sustainability and units the groundwork for destiny research and coverage development in era-better farming.