Computers in Human Behavior最新文献

Universities are frequent targets of cyberattacks. This investigation seeks to explore common phishing techniques targeting institutions of higher education. This study analyzes the content and message features of a sample of 2300 emails from 2010 to 2023 collected from Cornell's Phish Bowl, including topics, persuasive appeals, emotional appeals, and spelling errors. Using analyses of association and text mining, the work maps out changes in phishing trends over time. One major finding is that security-focused phish have been replaced by those attempting to reflect routine university life, such as job offer scams. Additionally, this study identifies authority and scarcity as common persuasive appeals in phishing attempts and demonstrates a decrease in spelling errors over time. These findings have practical implications for cybersecurity training and awareness. They may also guide future work seeking to determine user susceptibility to phishing by providing insight into frequent attacks.

With the rising use of digital media, people, groups, and organizations are increasingly experiencing and responding to identity threats. We conducted an inductive, multiple-methods study to examine 50 viral stories on identity threat and response processes involving digital media (ITARP-DM). We uncovered three distinct ITARP-DM processes, which suggests that unique approaches are needed to deal with them. Although physical threats are more visible, threats to identity that are only words can still manifest in physiological consequences. When an ITARP-DM social justice cause affects a large group, it is more likely to spill over to offline protests and riots.

In fog computing, batch scheduling is an important and challenging task aiming at reducing the response latency. Response time includes the scheduling time, execution time and other factors such as network latency. However, the majority of the existing batch scheduling algorithms primarily focus on minimizing the execution time of tasks for IoT devices, ignoring the algorithms' scheduling time. This contributes to suboptimal overall response time, which is a critical quality-of-service (QoS) indicator and significantly impacts performance. Optimizing both execution and scheduling time is therefore crucial for achieving minimum response time, improving QoS, and alleviating load-balancing issues. This work introduces a novel approach for the load-balancing difference variable and uses dimensionality reduction techniques for dynamic task organization. The main contribution comprises two distinctive algorithms: the Pre-allocation Minimum Completion Time (PMT) algorithm and the Segmented Random Search Fog Computing Batch Scheduling Algorithm (SRS). These algorithms are designed to address the inherent characteristics of the problem. To evaluate the performance and applicability of batch scheduling algorithms, we establish a model incorporating compliance and application performance indicators. We introduce the Logical Recursive Indirect Comparison Analysis method to assess and evaluate batch scheduling algorithms. Simulation results demonstrate the exceptional effectiveness of the SRS algorithm. It exhibits a remarkable improvement in comprehensive performance indices, ranging from 139.10% to 261.18%, and optimization quality enhancement rates of 64.25%–154.13% compared to classical standard optimization algorithms. Compared with advanced algorithms, the SRS algorithm also outperforms, with optimization quality improvement rates of 22.74% and 71.11% compared to AEOSSA and CHMPAD.

Predicting suicide risk based on risk and protective factors is a critical and complex endeavor. In this study, we combined insights from comprehensive aetiological theories on suicide with the methodological strengths of machine learning techniques. Our primary objectives were twofold: a) to identify hazardous feature combinations that characterize a high risk of suicide, and b) to enhance our understanding of the potential interactions between risk and protective factors related to suicide. We established an interpretable decision tree model to classify young adults at high risk of suicide, utilizing fifty-five variables covering distal, developmental, proximal, and social context factors from a large-scale cross-sectional survey (N = 88,214). The results highlighted the significance of variables such as self-compassion and non-suicidal self-injury (NSSI), and the accumulation of depressive symptoms, medium-to-low self-compassion, and a history of NSSI as substantial indicators of heightened suicide risk. This study serves as a valuable reference for the clinical identification of individuals at risk of suicide.

Innovatively departing from conventional methodologies such as questionnaires, interviews, or text analysis, this study introduces a single-gender speech personality measurement approach to identify extraversion traits in executives from a speech perspective. Central to the research is how executive extraversion influences firm innovation performance. The research samples are derived from the audio recordings of performance conference communication calls held by Chinese listed companies on the Wind 3C conference platform. Through hierarchical regression analysis, empirical results demonstrate an inverted U-shaped relationship between executive extraversion and firm innovation performance (measured by R&D personnel input, R&D expenditure, and patent applications). The study also reveals that situational factors, including the proportion of shareholding by pressure-resistant institutional investors, incentive compensation, and equity concentration, significantly moderate the inverted U-shaped relationship. This research provides compelling evidence of the importance of executive extraversion in firm innovation performance and offers a new perspective on the mechanisms through which executive extraversion influences firm innovation performance. The single-gender speech personality recognition method allows researchers to effectively utilize executives’ speech information in the big data era. The research viewpoint and findings contribute beneficially to the Upper Echelons Theory and offer actionable insights for leveraging executive traits to bolster innovation.

Previous studies have clearly established the impact of culture on design creativity. For example, the presence of cultural values with low uncertainty avoidance (UA, the degree of anxiety and risk aversion that people feel during ambiguous situations) is linked to low workability of creative ideas. Currently, there is limited research on potential remedies to alleviate the impact of culture, specifically with respect to UA. Therefore, this study investigates the use of technology to prime UA cultural values and mitigate their potential negative impacts on design creativity. This was achieved using stimuli that had been generated by virtual reality (VR) technology and presented in an immersive environment. Participants in an experimental study were exposed to VR stimuli designed to either decrease or increase their UA. The results showed that the VR stimuli had successfully increased and decreased low and high UA values, respectively, which mitigated the influence of UA on design creativity. Furthermore, the VR stimuli influenced the emphasis of the participants on ideation, with lowered and enhanced UA values leading to them prioritizing novelty and usefulness, respectively. Overall, the findings provided evidence that VR could be leveraged from a psychological standpoint to reduce cultural influences on creativity through targeted priming. These findings indicated the essential implications of the study in terms of understanding the effect of immersive technologies in shaping human behaviors and mindsets.

Crowdsourcing is an essential data collection method for psychological research. Concerns about the validity and quality of crowdsourced data persist, however. A recent documented increase in the number of invalid responses within crowdsourced data has highlighted the need for quality control measures. Although a number of approaches are recommended, few have been empirically evaluated. The present study evaluated a Cyborg Method that used automated evaluation of participant meta-data and a review of short answer responses. Two samples were recruited – in the first, the Cyborg Method was applied after data collection to gauge the extent to which invalid responses were collected when a priori quality controls were absent. In the second, the Cyborg Method was applied during data collection to determine if the method would proactively screen invalid responses. Results suggested that Cyborg Method identified a substantial portion of invalid responses and both automated and human evaluation components w necessary. Furthermore, the Cyborg Method could be applied proactively to screen invalid responses and substantially reduced the per participant cost of data collection. These results suggest that the Cyborg Method is a promising means by which to collect high quality crowdsourced data.

There is an appreciable mental health treatment gap in the United States. Efforts to bridge this gap and improve resource accessibility have led to the provision of online, clinically-validated tools for mental health self-assessment. In theory, these screens serve as an invaluable component of information-seeking, representing the preparative and action-oriented stages of this process while altering or reinforcing the search content and language of individuals as they engage with information online. Accordingly, this work investigated the association of screen completion with mental health-related search behaviors. Three-year internet search histories from N = 7572 Microsoft Bing users were paired with their respective depression, anxiety, bipolar disorder, or psychosis online screen completion and sociodemographic data available through Mental Health America. Data was transformed into network representations to model queries as discrete steps with probabilities and times-to-transition from one search type to another. Search data subsequent to screen completion was also modeled using Markov chains to simulate likelihood trajectories of different search types through time. Differences in querying dynamics relative to screen completion were observed, with searches involving treatment, diagnosis, suicidal ideation, and suicidal intent commonly emerging as the highest probability behavioral information seeking endpoints. Moreover, results pointed to the association of low risk states of psychopathology with transitions to extreme clinical outcomes (i.e., active suicidal intent). Future research is required to draw definitive conclusions regarding causal relationships between screens and search behavior.

Perceiving empathy from healthcare professionals contributes to clinical benefits. Yet, for methodological and ethical reasons, the factors affecting perceived empathy, such as how the nonverbal behaviors of professionals interact, are less understood than those influencing the actual act of empathizing. Two online studies examined how the perception of empathy in a medical context of pain was influenced by factors related to digital healthcare professionals (DHPs) and participants acting as suffering patients. In Study 1 (n = 123), participants watched videos of DHPs showing variations in gaze direction, posture, and facial expression to rate perceived empathy from a visual patient perspective. They perceived more empathy from the face expressing pain, regardless of gaze, posture, and gender of the DHPs. The sex of participants also modulated perceived empathy. Study 2 (n = 116) expanded Study 1 by adding faces expressing pain and sadness of varying intensities, along with perspective-taking instructions, to determine whether higher perceived empathy for the face expressing pain stems from its congruence with the medical pain context. Participants perceived more empathy in faces expressing sadness than pain. Sadness and pain interacted differently with the effects of intensity, posture, and gaze direction. This work challenges the idea that exact congruence with the patient's affective state is necessary and further contributes to investigating nonverbal behaviors of empathy.