Human Behavior and Emerging Technologies最新文献

What drives people to have trust in using artificial intelligence (AI)? How does the institutional environment shape social trust in AI? This study addresses these questions to explain the role of institutions in allowing AI-based technologies to be socially accepted. In this study, social trust in AI is situated in three institutional entities, namely, the government, tech companies, and the scientific community. It is posited that the level of social trust in AI is correlated to the level of trust in these institutions. The stronger the trust in the institutions, the deeper the social trust in the use of AI. To test this hypothesis, we conducted a cross-country survey involving a total of 4037 respondents in Singapore, Taiwan, Japan, and the Republic of Korea (ROK). The results show convincing evidence of how institutions shape social trust in AI and its acceptance. Our empirical findings reveal that trust in institutions is positively associated with trust in AI technologies. Trust in institutions is based on perceived competence, benevolence, and integrity. It can directly affect people’s trust in AI technologies. Also, our empirical findings confirm that trust in AI technologies is positively associated with the intention to use these technologies. This means that a higher level of trust in AI technologies leads to a higher level of intention to use these technologies. In conclusion, institutions greatly matter in the construction and production of social trust in AI-based technologies. Trust in AI is not a direct affair between the user and the product, but it is mediated by the whole institutional setting. This has profound implications on the governance of AI in society. By taking into account institutional factors in the planning and implementation of AI regulations, we can be assured that social trust in AI is sufficiently founded.

Programming is widely recognized as a fundamental and practical skill applicable across diverse fields through various applications. However, novices often face challenges in learning programming, primarily due to the absence of a structured instructional framework and the complexity of underlying concepts. This obstacle can diminish learners’ motivation to pursue further education. To address this, gamification is employed as a strategy to engage and inspire beginners in their educational journey. Consequently, the utilization of a gamified online programming education system is proposed to simplify the learning process. Nevertheless, designing and implementing educational courses that effectively integrate gaming elements requires expertise in the gaming field. In this study, a model-driven approach creates a gamification framework for teaching programming. The methodology develops a domain-specific modeling language for programming concepts and gamification, designs a graphical editor for course design, and implements a model-to-code transformation engine requiring minimal prior knowledge. Evaluation through usability testing, questionnaires, and the GQM approach shows enhanced usability, improved effectiveness, and high satisfaction compared to traditional methods. The framework offers a solution for simplifying gamified course development and supporting novice programmers.

Assistive technologies may have an important role in fulfilling unmet needs and increasing quality of life for people living with dementia. The type and design of user interfaces (e.g. touchscreen and voice activation) may impact how people with dementia use these technologies. We aimed to understand which types of user interfaces have been developed for this population, how interfaces are chosen, how their effectiveness is tested and what recommendations there are for optimizing interface design for people with dementia. This systematic scoping review summarized findings from 87 journal articles. Two-thirds of included studies incorporated codesign. Very few (14%) experimentally tested the effectiveness of a user interface, and many lacked ecological validity (52%). Common recommendations for user interface design included tailoring the interface to the user, providing multiple modalities, and incorporating principles of universal design. Training users on how to interface with the technology may not be beneficial for devices that are intended to be used entirely independently by a person living with dementia. Instead, designers should focus on harnessing retained or existing skills so that interaction is intuitive. More research is needed that directly compares different interface options to each other to gain evidence of what is most useful for people with dementia, as well as technology development that is deeply and meaningfully grounded in the lived experiences, values, preferences and priorities of people living with dementia.

Public safety remains a critical concern in Eswatini, as it prevents crime, reduces delayed response mechanisms, and optimizes police resources. This study applied machine learning techniques in predictive policing within the Kingdom of Eswatini (formerly Swaziland) to improve proactive law enforcement strategies and public safety. Crime has been a challenge in many societies and continues to threaten public safety, social cohesion, and economic development. Law enforcement agents often use reactive approaches to handle criminal incidents, which are generally associated with various impediments, such as delayed responses to crime incidents, resource-intensive operations, victimization, and insufficient proactive crime prevention measures. Integrating machine learning techniques for predictive policing emerges as a new panacea for effective policing and crime prevention. However, there is a dearth of literature advocating proactive policing through predictive policing. Therefore, this study proposes a proactive approach to crime prediction and prevention by using machine learning models such as XGBoost, random forest, multilayer perceptron (MLP), and K-nearest neighbors (KNN) models. These models were trained and tested using data from the Royal Eswatini Police Services (REPS). Our findings indicate that XGBoost provides the highest predictive accuracy at approximately 71.4%, with precision ranging from 0.65 to 0.81 and recall from 0.34 to 0.81, making it the preferred model for balanced performance across the metrics. Random forest recorded an accuracy of 66.2%, while MLP and KNN have 62.2% and 55.5% accuracy, respectively. The study recommends the integration of intelligence-based models to enhance proactive crime prediction and identify potential crime hotspots. This can assist in optimizing resource allocation to prevent crime. Additionally, collaboration among stakeholders, including national security agents, policymakers, and the community, is essential to effectively adopt and utilize predictive policing technologies to enhance security operations.

Given the contemporary landscape of social media interactions and their profound influence on consumer behavior, this study is aimed at exploring the intricate connections between subjective norms, social media stalking, peer influence, and their impact on internal cognitive and emotional processes. Specifically, we explore how these factors, including envy, the need to belong, and self-congruence, lead to transformative interactions that manifest as impulse buying, customer satisfaction, and emotional attachment. We utilized an online survey to collect data from 659 participants and subsequently employed SmartPLS to analyze the data collected via structural equation modeling. The findings showed the significant positive impact of subjective norms and social media stalking on peer influence, which enhances the chain relationship from peer influence to envy and then impulse buying. The mediating role of obsessive passion between peer influence and emotional attachment is supported in contrast to self-congruence. Contrary to earlier research findings indicating a direct link between customer satisfaction and emotional attachment in the field of impulse buying, the satisfaction resulting from impulse buying does not influence emotional attachment in this paper. Both theoretical and practical implications were discussed.

Artificial intelligence (AI) represents a transformative technology with the potential to profoundly influence organizational behavior (OB). It can enhance organizational performance and efficiency through mechanisms such as automation, resource optimization, and advanced data analysis. Nevertheless, the integration of AI within organizations presents various social and ethical dilemmas that could adversely impact fairness, privacy, and employee satisfaction. This research aims to develop a comprehensive framework that elucidates the role of AI in enhancing OB while also identifying the associated challenges and opportunities through a meta-synthesis approach. A systematic review of the literature was conducted, focusing on studies that explore the intersection of AI and OB, employing a qualitative meta-synthesis methodology. The data were sourced from scholarly articles published in esteemed scientific databases from 1995 to 2024. Ultimately, 18 articles specifically relevant to this subject were selected, and the data underwent analysis through open coding. This process yielded 231 distinct codes, which were subsequently organized and integrated based on their conceptual similarities into various dimensions and components. The findings showed that the impact of AI on OB includes five main dimensions: (1) automation, (2) innovation and organizational learning, (3) intelligent decision-making, (4) organizational culture and human interactions, and (5) ethics and leadership. These dimensions include components such as data analysis, improved decision-making, personalization, trust and information security, and adaptation to new technologies. Finally, a research model was presented focusing on these dimensions. In addition to the benefits related to productivity and improved decision-making, the implementation of AI in organizations requires ethical and cultural considerations to maintain satisfaction and human interactions. Paying attention to algorithmic fairness and transparency in decision-making can strengthen employee trust and facilitate the adoption of this technology. Therefore, organizations should manage the implementation of AI in a way that serves the development of OB and improved performance through training, developing ethical frameworks, and providing appropriate support.

The technological revolution of the last decades has revolutionized economic interactions, introducing new paradigms like e-banking and cryptocurrencies. Although the literature has questioned the antecedents associated with the use of cryptocurrencies and, in particular, the attitudes and beliefs underlying them, there is still a lack of a robust, multidimensional tool to measure beliefs about cryptocurrencies. Therefore, the aim of the study is to preliminarily validate a brand-new scale for a comprehensive assessment of beliefs related to cryptocurrencies: the scale of beliefs about cryptocurrencies (SBaC). The first version of the scale was tested on 395 Italian-speaking participants (53.1% were women, mean age 27.44 years, SD = 11.03). Thirteen percent of the sample also held cryptocurrencies at the time of completing the questionnaire. The results of the exploratory factor analysis (EFA) showed that the SBaC, with a total of 12 items, has four factors: (i) self-fulfillment, related to achieving independence and goals through cryptocurrencies; (ii) investment, indicating potential profitability; (iii) cryptocurrencies as a medium of exchange, as an alternative for transactions; and (iv) locus of control, related to individual attribution of success or failure in the crypto market. The results of the confirmatory factor analysis (CFA) on an independent sample (N = 133, mean age = 34.47, SD = 11.79) confirm the four-factor structure of the scale. The correlation analysis showed that positive beliefs toward cryptocurrencies as a medium of exchange and as investments are significantly correlated with willingness to engage and hold cryptocurrencies. Internal locus of control negatively correlates with willingness to engage with cryptocurrencies but does not significantly affect the amount held or investment willingness. Social influence plays a role in shaping perceptions of cryptocurrencies as a medium of exchange and investment but does not significantly impact locus of control or self-fulfillment. Self-fulfillment is positively correlated with willingness to engage with cryptocurrencies and investment willingness, albeit with weaker correlations. This study showed that the SBaC is a valuable tool for assessing cryptocurrencies’ beliefs, predicting behavioral intentions, and understanding cognitive processes driving engagement with digital currencies.

The way challenges are addressed across multiple areas of knowledge is currently being revolutionized by intelligent systems. These systems offer novel opportunities and viewpoints that deserve examination, particularly in the context of fiscal surveillance and control. However, although recent studies underscore a paradigm shift toward technology-driven audit research, the evidence on intelligent systems in fiscal oversight remains fragmented and has not been systematically organized. This article provides a systematic literature review that examines the potential of intelligent systems for efficiently managing public resources. To conduct the review, a search of documents from 2018 was conducted in databases such as Scopus, ScienceDirect, IEEE Xplore, DOAJ, and Google Scholar, following the PRISMA statement and the Kitchenham and Charters method. The objective was to select 48 documents for analysis, adhering to the inclusion and exclusion criteria, and to address the four research questions posed. Guided by these questions, the review (i) assesses the potential benefits of intelligent systems for fiscal surveillance and control, covering fraud detection, auditing, risk management, financial analysis, and automation; (ii) contrasts those advantages (greater transparency, efficiency, and efficacy) with the associated technical, organizational, legal, and social challenges; (iii) evaluates the current treatment of four core oversight areas; and (iv) identifies the prevailing technological trends, most notably blockchain, data mining, and artificial intelligence. Despite the limitations of the review, including its temporal scope, individual interpretations, and specific focus, these findings can provide valuable information for government agencies, enabling them to prioritize investments and enhance the management of public resources, thereby contributing to fairer and more equitable societies.

Digital transformation in education has become increasingly crucial in the 21^st century, particularly in multilingual contexts like Malaysia where Arabic language education faces persistent resource gaps and uneven technology implementation despite supportive policy frameworks. This mixed-methods study investigates primary Arabic language teachers’ anticipated acceptance of a proposed Arabic interactive module (AIM) in Malaysia using an extended technology acceptance model (TAM) framework. With 290 teachers participating in cross-sectional surveys and five teachers in semistructured interviews after AIM usage, the research examined expected usefulness (EU), expected ease of use (EEU), and perceived awareness (PA) through PLS-SEM analysis and thematic analysis. Results revealed that PA serves as a critical predictor of technology acceptance, showing exceptionally strong relationships with EU (β = 0.800, p < 0.001) and moderate influence on EEU (β = 0.288, p = 0.002), while demographic factors showed unexpected patterns, with male teachers perceiving lower ease of use (β = −0.225, p = 0.025) and experienced teachers showing reduced perceived usefulness (β = −0.072, p = 0.023). Qualitative findings identified three key themes: perceived effectiveness in achieving learning outcomes, enhanced student motivation and interest, and significant support for teaching processes, particularly in addressing Arabic language resource scarcity through multimedia integration and interactive elements. The study extends TAM theory by demonstrating awareness as a foundational antecedent to technology acceptance in educational contexts and suggests that successful digital transformation in Arabic education requires comprehensive awareness-building initiatives, differentiated training approaches, and pedagogically grounded interactive tools that thoughtfully integrate traditional and digital methods to inspire teachers and enhance learning outcomes.

A comprehensive measurement encompassing all photo-editing techniques to assess photo-editing behaviors remains absent. Based on 621 Chinese females, we developed and validated a self-report scale to measure photo-editing behaviors among Chinese females. In Study 1, experts classified photo-editing techniques from mainstream apps into categories based on their functionalities. Initial items for the Photo-Editing Scale (PES), comprising two subscales designed to measure the frequency and extent of participants’ photo-editing behaviors, were developed. The final items of PES were determined via factor analyses. In Study 2, the validity and reliability of both subscales were examined. Findings revealed that each subscale, containing eight items associated with one factor, exhibited satisfactory internal consistency (McDonald^’s omega = 0.91 for Photo-Editing Extent subscale; McDonald^’s omega = 0.85 for Photo-Editing Frequency subscale), test–retest reliability, as well as discriminant, predictive, and convergent validity. The newly developed PES may help us better understand the photo-editing behaviors and their impact on various mental health issues.