Computers in human behavior reports最新文献

The paper explores the phenomenon of home recording and the impact of democratization of recording technologies in the Czech independent music scene. The main goal of the paper is to clarify the links between home-recordings and the emergence of new music, describe current trends in the field of home recording, network studios, distribution, and self-education, as well as outline the ethical dilemma of using illegal software in music production. The research approach is based on conducting qualitative contextual interviews with musicians across the entire independent music scene and on the content analysis of a questionnaire survey among musicians and music producers. The results of the research provide an essential characterization of the impact of home recording on the emergence of new music in the Czech environment and chart trends in illegal software downloading.

This study investigated the motivations of Taiwanese consumers who choose to redistribute their possessions via Give Circle, a free collaborative redistribution platform. We aimed to understand why users prefer using a free platform to donate and solicit items over traditional second-hand exchange platforms. Additionally, we sought to identify the behavioral changes that occur from using this platform within the context of the sharing economy. A mixed-methods approach was adopted, starting with semi-structured interviews with frequent users of the platform to identify independent and dependent variables for the formal research framework. Based on this evidence, we selected the extended UTAUT2 model and gamification affordance theory to explore the factors of continued use intentions. We applied structural equation modeling to 691 valid questionnaires. Predictors of continued use intentions included performance expectations, convenience, time and effort investment, and autonomy and self-expression. Continued use intention positively influenced charitable donation behavior and consumers' minimalist tendencies. These findings represent a novel contribution to the literature on the sharing economy, filling gaps in research on non-monetary collaborative redistribution platforms, extending the application of the UTAUT2 model, and deepening our understanding of consumer psychology and behavioral patterns in Asian Chinese societies.

Digital transformation has led to the exponential advancement of e-learning platforms. Multimedia content plays a vital role in supporting knowledge dissemination for adaptive e-learning systems. However, online learners' cognitive preferences towards such multimedia contents have been less explored, and most of the e-learning adaptive systems models have not yet enrolled learners' cognitive processes. Thus, the digital divide between millennials and generation Z has evolved due to the lack of dynamic personalization of e-learning multimedia content with respect to learners' metacognitive styles. In order to address that challenge, this feasibility study approach is aimed at exploring the possibility of deploying information processing theory on adaptive e-learning platforms to support the enrolment of learners' metacognitive styles for e-learning multimedia content personalization, which in turn eradicates the existing digital divide. This study survey results suggest that the deployment of information processing theory on adaptive e-learning systems is feasible and optimal learning can be obtained when multimedia content personalization meets generation Z preferences.

Smartphones are ubiquitous and offer numerous benefits in daily life. However, the ongoing excessive use of smartphones has been associated with a range of adverse effects, capturing the attention of researchers worldwide. While higher smartphone use is often seen as potentially compulsive or addictive, it is essential to recognise that not all smartphone use is inherently problematic; practical reasons can also contribute to increased or excessive usage. Consequently, distinguishing between purposeful or productive use and excessive or potentially harmful smartphone behaviours is essential. Existing research recognises differences in smartphone usage but lacks depth in its exploration. There is a notable demand for in-depth studies that distinguish between productive and problematic use of smartphones and examine what drives the transition between these behaviours. Therefore, this review critically examines prior research to explain the distinctions among various types of smartphone use and explore the characteristics, reasons, causes, effects, and consequences associated with these behaviours. This article introduces an Integrative Pathways Model (IPM), a conceptual framework designed to explore the reasons behind individuals' active smartphone use. It delves into the specific gratifications users seek from their smartphone use and investigates the various factors that may influence these motivations and, thereby, affect their behaviours. It highlights three distinct yet not mutually exclusive smartphone use-related pathways: effectual use, ineffectual use, and problematic use. This research contributes to enhancing understanding of Problematic Smartphone Use and Dependence (PSUD) by probing into the multifaceted interplay of individual characteristics, social dynamics, and environmental factors. This article underscores the need for a multi-dimensional approach to better understand smartphone usage, acknowledging that increased usage does not always signify problematic behaviour. It also emphasises the increasing demand for practical strategies to effectively manage PSUD.

Workplace telepressure is a psychological experience related to work-related messages. Research on measurement invariance for telepressure is scarce, especially with respect to occupations. This paper used a moderated nonlinear factor analysis technique to examine how occupational characteristics predict telepressure experiences and differential ratings across two studies with full-time workers. Email-related behaviors and demands predicted factor means and variances of telepressure, but there were no consistent results for job control and time pressure. Invariance testing at the item-level showed that occupational characteristics did not moderate most item parameter estimates; that is, employees interpreted workplace telepressure items equivalently regardless of occupational context.

In today's times, knowledge management has emerged as a critical pursuit for organizations in preserving and enhancing their intellectual assets. Organizational culture stands out as a key determinant of success in this regard, underlining the need for organizations to enhance their knowledge management methods amidst rapid and unpredictable changes. This paper evaluates how various aspects of organizational culture such as Job dissatisfaction, Emphasis on rewards, lack of infrastructure, Performance Orientation, Inadequate technology, Support Defend, Silo mentality in knowledge, Innovation, and Stability affect organizational knowledge management success. The urgency of this research is underscored by the critical need for organizations to enhance their knowledge management strategies amidst rapid and unpredictable changes. What distinguishes this research is its innovative model, offering a quantitative and systematic approach to assessing the impact of organizational culture on knowledge management success through the TOPSIS to measure knowledge management success in ten government organizations in East Azerbaijan province, Iran, each with one hundred employees. In this study, questionnaires based on organizational culture characteristics were given to employees to collect quantitative data and also to assess the level of knowledge management, data were analyzed using the TOPSIS algorithm. The results of the study showed that Organization 3, where at least 50% of the employees responded positively to indicators related to the emphasis on rewards and performance orientation, had the highest level of success in knowledge management compared to other organizations.

Applications of learning analytics (LA) can raise concerns from students about their privacy in higher education contexts. Developing effective privacy-enhancing practices requires a systematic understanding of students’ privacy concerns and how they vary across national and cultural dimensions. We conducted a survey study with established instruments to measure privacy concerns and cultural values for university students in five countries (Germany, South Korea, Spain, Sweden, and the United States; N = 762). The results show that students generally trusted institutions with their data and disclosed information as they perceived the risks to be manageable even though they felt somewhat limited in their ability to control their privacy. Across the five countries, German and Swedish students stood out as the most trusting and least concerned, especially compared to US students who reported greater perceived risk and less control. Students in South Korea and Spain responded similarly on all five privacy dimensions (perceived privacy risk, perceived privacy control, privacy concerns, trusting beliefs, and non-self-disclosure behavior), despite their significant cultural differences. Culture measured at the individual level affected the antecedents and outcomes of privacy concerns. Perceived privacy risk and privacy control increase with power distance. Trusting beliefs increase with a desire for uncertainty avoidance and lower masculinity. Non-self-disclosure behaviors rise with power distance and masculinity and decrease with more uncertainty avoidance. Thus, cultural values related to trust in institutions, social equality and risk-taking should be considered when developing privacy-enhancing practices and policies in higher education.

Despite the emerging and rapid progress of esports, approaches for ensuring high-quality analytics and training among professional and amateur esports teams are lacking. In this paper, we demonstrate how the application of data science techniques and Machine Learning (ML) approaches in esports, particularly in sim racing science, can illuminate the most important in-game metrics that dictate performance. Thus, using a professional racing simulator and MoTec i2 Pro (v1.1.5, Australia), we gathered extensive telemetry data from 174 participants, who completed 1327 laps on the Brands-Hatch circuit in the Assetto Corsa Competizione (v1.9, KUNOS Simulazioni). We clustered the obtained laps based on performance (lap-time), and then identified driving behaviors within performance groups. We also analyzed the feature subset obtained from a hybrid feature selection approach using two correlation analyses and three ML models.

The best model achieved a prediction accuracy of 97.19%, demonstrating that the model effectively captured the critical factors that influenced driving performance during a lap. The results confirm that average speed is the most important metric, followed by lateral acceleration, steering angle, and lane deviation. Our analyses offer key metrics for refining training tools and techniques in sim racing performance improvement.

With the increasing importance of equipping young learners with computational thinking skills through learning to code, pair programming has emerged as a prevalent collaborative learning strategy in this context. Successful pair programming interventions necessitate mutual engagement between partners within a dyad. However, the measurement of mutual engagement in dyadic collaborative learning remains an under-researched area. This research represents a foundational stage in bridging this gap by developing a comprehensive 20-item Pair-Programming Mutual Engagement Questionnaire (PPME-Q) as a measure of mutual engagement in pair programming at the activity level. The questionnaire was validated through a sample of 86 eighth-grade students. Confirmatory factor analysis confirmed the existence of a four-factor structure comprising of the behavioral, cognitive, emotional, and social engagement factors. The findings demonstrate the validity (χ²/df = 1.32) and reliability (Cronbach's α = 0.888) of the PPME-Q, establishing it as an effective tool for assessing eighth graders' mutual engagement in pair programming activities. As this tool is in the nascent stages of development the measurement, we emphasize the need for further empirical studies to establish criterion validity. We also discuss the implications of these findings for future research and educational practices. This targeted instrument can then potentially be adapted or scaled to other age groups based on the insights gained.

With the advancement of technology, emerging technologies like Artificial Intelligence (AI) have also been growing rapidly and becoming more common than ever before. Kenya has taken tremendous steps in adopting the use of emerging technology in different sectors of the economy. In realization of the need to have a skilled digital workforce to develop solutions using these emerging technologies, Kenya has undertaken curriculum reforms and introduced the Competency-Based Curriculum (CBC) which has included digital literacy and coding in elementary school. Furthermore, computer science has been introduced in Junior Secondary School. In view of these changes, teachers should be adequately prepared with knowledge, skills, and attitudes to effectively teach these new technologies. However, in Kenya, AI was not and still is not part of the teacher training curriculum. Additionally, there are inadequate professional development opportunities in AI for both pre-service and in-service teachers since AI is not part of the CBC curriculum. That notwithstanding, it is inevitable for teachers in the current world to introduce AI to learners. Therefore, this study's objectives were to assess the confidence in AI, attitudes toward AI, AI ethics, subjective norms, perceived threats, and the readiness to teach AI among Kenyan K-12 in-service teachers and to assess how these factors influence their readiness to teach AI. To achieve these objectives, this study employed a quantitative research methodology by administering a survey using Google Forms to a random sample of 308 teachers from different grades from 37 out of 47 counties in Kenya. The findings showed that confidence in AI, AI ethics and subjective norms significantly influenced AI readiness while attitude towards AI and perceived threats did not significantly influence AI readiness. These results are significant in providing a basis for education policy change on AI education in Kenya, such as transforming the teacher training curriculum to include AI and designing AI professional development programs for in-service teachers to ensure they are well-equipped to teach AI.