A smart city is developed through the Internet of Things (IoT), cloud computing, big data, mobile Internet, and other new generation technologies regarding information and communication, and data resources in various fields are integrated and applied. The issue of information security in the network era is the strategic focus, as well as the focus of people’s attention, during Taiwan’s smart city construction. Information security policies are the information security guidelines for organizations, and are key to the organization’s information security performance; moreover, such policies show the organization’s support and commitment to the information security of smart cities. This paper discusses the model of information security policy in Taiwan’s smart cities, uses Path Analysis to explore the characteristics of information security policy in smart cities, and examines the relationship between the formulation, implementation, maintenance, and effectiveness of information security policies. Furthermore, this study examines the impact on the effectiveness of organizational information security policies and information security performance from the following aspects: The length of information security policy publication time, policy review, policy advocacy, employee compliance, fair law enforcement, etc., which are all concrete manifestations of the formulation, implementation, and maintenance of information security policy models. Through a questionnaire survey, the correlation between various assumptions, as well as the relationship between organizational information security characteristics, information security policies, and the effectiveness of information security, are verified one by one during the implementation of information security policies. Finally, conclusions and implications are put forward.

Inconsistency between attitude and behavior is a major obstacle to research on the predictive power of attitudes on behavior. To clarify the mechanism underlying such inconsistency, we combined event-related potential (ERP) and questionnaires to explore the relationship between contextualized attitudes and retrospective attitudes in the context of illusion of privacy empowerment (IPE). When the participants read the IPE events (including platform empowerment intention, technique, result, etc.) on slides, we measured retrospective attitudes with questionnaires and recorded contextualized attitudes with ERPs. We found that individuals’ retrospective attitudes were different from contextualized attitudes: retrospective attitudes were mainly affected by the individual’s analytic system, while contextualized attitudes were mainly affected by the direct stimulus-response (i.e., heuristic system). Therefore, retrospective attitudes may not accurately reflect individual cognition in the immediate context, and inconsistency between attitudes and behavior may be caused by the mismatch between retrospective attitudes and immediate behavior. Our findings provide a more reasonable account of the relationship between attitudes and behavior.

Background: Online labor platforms rely on algorithmic control to manage gig work, but its impact on work engagement remains contested. Existing research predominantly adopts technological determinism perspectives, neglecting gig workers’ agency, and lacking systematic exploration of motivational mechanisms and emotional resources. Based on Self-Determination Theory, this study examines how perceived algorithmic control influences work engagement through psychological empowerment, with deep acting as a moderator. Methods: Data were collected from Chinese gig workers (delivery riders/ride-hailing drivers, N = 392) through snowball and convenience sampling. Established scales measured core variables. Common method bias was tested using SPSS and AMOS, while PLS-SEM analyzed reliability, validity, and hypothesized pathways. Results: Perceived algorithmic control positively affects work engagement. Three psychological empowerment sub-dimensions—meaning, influence, and competence—partially mediate relationships between perceived algorithmic control sub-dimensions and work engagement respectively. Deep acting strengthens the positive effect of perceptual algorithm tracking evaluation on influence, and shows highest importance for work engagement but suboptimal performance. Among psychological empowerment sub-dimensions, meaning exhibits the most prominent importance and requires priority optimization. Conclusions: This study transcends technological determinism and validates the positive pathway through which algorithmic control enhances work engagement via psychological empowerment. It reveals meaning construction’s central role and deep acting’s differentiated moderating effects. Online labor platforms should optimize algorithm design, strengthen meaning perception, reduce ineffective monitoring, implement psychological empowerment incentive mechanisms, provide emotional resource support, and guide deep acting strategies.

Targeted advertising equipped with a recommendation algorithm can achieve accurate matching between users and recommended content, but overly precise recommendations may exacerbate negative audience reactions or behaviors. Improving the transparency of algorithm recommendation is one of the ways to address audience concerns or skepticism, and transparency guarantees the audience's right to know and thus brings more trust, which will reduce the audience's negative behavior. But increased transparency may also make the audience feel pressured or threatened, and requiring more cognitive and behavioral effort, which was called coping behavior. In order to clarify the relationship between the transparency of the algorithm recommendation and the audience's coping behavior, based on the persuasion theory, this study discusses the mechanism of the influence of the characteristics of the algorithm recommendation information flow on the audience's coping behavior of targeted advertising from the perspective of the flow mode and transmission principle of information. Based on the data of 120 online pretests and 297 formal tests, the results show that the perceived trust and perceived threat caused by the information flow characteristics of the algorithm recommendation jointly determine the possible coping behaviors of targeted advertising audiences. Additionally, users' self-efficacy regulates the relationship between mental process and coping behavior. Different from previous studies on audience coping behaviors of targeted ads, which mainly start from the perspective of participants and advertising content, this research tries to start from the perspective of information flow. The research results demystify the relationship between recommendation algorithm information flow and the audience's coping behavior, and enrich the algorithmic persuasion framework. The research results have reference value for the improvement of personalized recommendation effect, and provide a new way to further study the transparency of algorithm recommendation in the field of consumer behavior. Meanwhile, it also provides suggestions for the practices of platforms and advertisers in practice.

As the prevalence of generative artificial intelligence (GenAI) in the service sector continues to grow, the impact of the language style and recovery strategies utilized during service failures remains insufficiently explored. This study, grounded in the theory of social presence and dual-process theory, employed a mixed-method approach combining questionnaire surveys and event-related potential (ERP) experiments to investigate the effect of different language styles (rational vs. humorous) and recovery strategies (gratitude vs. apology) on users’ willingness to forgive during the GenAI service recovery process. It further delves into the chained mediating role of perceived sincerity and social presence in this process. The findings revealed that a humorous language style was more effective in enhancing users’ willingness to forgive compared to a rational style, primarily through the enhancement of users’ perceived sincerity and sense of social presence; recovery strategies played a moderating role in this process, with the positive impact of perceived sincerity on social presence being significantly amplified when the GenAI service adopted an apology strategy. ERP results indicated that a rational language style significantly induced a larger N2 component (cognitive conflict) in apology scenarios, while a humorous style exhibited higher amplitude in the LPP component (positive emotional evaluation). This research unveils the intricate relationships between language style, recovery strategies, and users’ willingness to forgive in the GenAI service recovery process, providing important theoretical foundations and practical guidance for designing more effective GenAI service recovery strategies, and offering new insights into developing more efficacious GenAI service recovery tactics.