Subjects: Psychology >> Applied Psychology submitted time 2024-04-28
Abstract： As an important extra-role behavior in the workplace, helping behavior refers to voluntarily caring for others and helping them deal with work-related affairs. To date, most studies focused on either the antecedents of helping behavior or its influences on helping providers and receivers. However, the discussion on the effects of helping behavior on observers is still limited. According to social comparison theory and the peer influence literature, this research argues that observed coworker helping behavior influences observers’ cognitive and emotional reactions and, ultimately, their behaviors. On the one hand, after witnessing coworker helping behavior, observers may experience higher indebtedness toward coworkers and shame emotions, which enhances their helping behavior. On the other hand, we propose that after witnessing coworker helping behavior, observers may experience lower organization-based self-esteem and pride emotions, which reduces their helping behavior. Based on social comparison theory, we further focus on observers’ helping behavior and consider that it can moderate the effects of observed coworker helping behavior.
Hypotheses were tested using a multi-wave survey and three experiments from the Eastern and Western samples. An original sample (Study 1) of participants in a food supply company in China were invited to participate in the survey. An experiment (Study 2) manipulated observed coworker helping behavior and observers’ helping behavior and then measured observers’ indebtedness toward coworkers and organization-based self-esteem. An experiment (Study 3) manipulated indebtedness toward coworkers and organization-based self-esteem, and then measured observers’ emotions and helping behavior. Another experiment (Study 4) manipulated observed coworker helping behavior and observers’ helping behavior and then measured the remaining variables of the model.
The above studies supported our hypotheses. Results demonstrated that observed coworker helping behavior is positively related to observers’ indebtedness toward coworkers and shame emotions, which, in turn, is positively related to observers’ helping behavior. At the same time, observed coworker helping behavior is negatively related to observers’ organization-based self-esteem and pride emotions, which, in turn, is negatively related to observers’ helping behavior. Observers’ helping behavior moderates the effects of coworker helping behavior. Specifically, when their helping behavior is higher (versus lower), the effects of coworker helping behavior on indebtedness toward coworkers and organization-based self-esteem are weaker.
This study contributes to the helping behavior literature in several ways. First, our study provides a new direction for existing research on helping behavior by introducing the third-party perspective. Different from the previous studies, this study examines the psychological and behavioral responses of observers to observed coworker helping behavior, which transfers the traditional research perspective to the observers in the process of helping. Second, this study considers both positive and negative ways of observed coworker helping behavior in influencing observers’ helping behavior, explaining why observed coworker helping behavior is a double-edged sword for observers. Thus, this study provides a more complete picture of the psychological mechanisms by which observed coworker helping behavior affects observers’ subsequential behavior. Third, this study focuses on observers’ initial helping behavior as the boundary condition and explains when observers are more likely to react positively or negatively to observed coworker helping behavior. This examination helps us understand the third party’s response to observed coworker helping behavior more comprehensively and accurately.
Subjects: Psychology >> Applied Psychology submitted time 2024-04-11
Abstract： Font emphasis has been demonstrated to attract attention and to facilitate language processing at all levels in young adults, and it may also help the elderly reduce the negative effects of declining working memory and visual ability. While there are considerable discrepancies in emotional information processing between the elderly and younger adults and the previous findings have been almost focused on neutral sentences, the present study aimed to examine the effects of font emphasis on emotional information processing and its aging changes. In the study, the author designed a sentence reading task in which sentences were embedded with keywords. The experimental sentences consist of 8-13 words, and the keywords which the emphasis status was controlled were all two-word verbs. In the emphasized condition, the keywords were presented in red font, and the other words in the sentence were presented in black font. In the control condition, all words of the sentence were presented in black font. In addition, the emotionality of the keywords was manipulated to be negative, positive, and neutral. We recruited 24 older adults (60-68 years old) and an equal number of younger adults (18-29 years old) to record prefrontal cortex activation while they read. In the experiment, sentences were presented word by word using a fixed-step paradigm. And the hemodynamic responses of the participants’ prefrontal cortex were recorded by using the LIGHTNIRS. Both HbO and HbR data were analyzed. The data were processed by using the NIRS_KIT software, and task-related β-values were calculated for the different conditions using general linear model. To obtain a clearer picture of the extent to which brain activation is enhanced during emotional processing, the β-values in the emotional condition were subtracted from those in the neutral condition. The HbR results showed that there was a significant interaction among age, emotion and font status in channels 8 and 16 (located at the right ventrolateral prefrontal lobe; rVLPFC). Further analyses revealed that in the control condition, compared to young adults, older adults showed a trend toward lower activation on the rVLPFC when reading positive words and higher activation when reading negative words. In the emphasized condition, the activation differences between older and younger adults disappeared. By observing the data, it can be found that when reading positive words, font emphasis leads to an upward trend in the activation of this brain region in older people and a decrease in activation in younger people; when reading negative words, font emphasis leads to a decrease in activation intensity in older people and an increase in activation intensity in younger people. These findings provide evidence that font emphasis, dependent on rVLPFC, captures attention in a bottom-up manner during emotional information processing, enhances readers’ appraisal and integration of emotional information, and facilitates controlled processing of emotional information. Furthermore, font emphasis has a different mechanism for older versus younger adults. Font emphasis produces positive affective effects in older adults, but stimulates negative affective preferences in younger adults.
Peer Review Status:
Awaiting Review
Subjects: Psychology >> Applied Psychology Subjects: Computer Science >> Computer Application Technology submitted time 2024-04-10
Abstract： Integrative complexity is a concept used in psychology to measure the structure of an individual’s thinking in two aspects: differentiation and integration. The measurement of integrative complexity relies primarily on manual analysis of textual content, which can be written materials, speeches, interview transcript large language models, or any other form of oral or written expression. To solve the problems of high cost of manual assessment methods, low accuracy of automated assessment methods, and the lack of Chinese text assessment scheme, this study designed an automated assessment scheme for integrative complexity on Chinese and English texts. We utilized text data enhancement technique of the large language model and the model migration technique for the assessment of integrative complexity, and explored the automated assessment methods for the two sub-structures of integrative complexity, namely, the fine integration complexity and the dialectical integration complexity. In this paper, two studies are designed and implemented. Firstly, a prediction model for the integration complexity of English text is implemented based on the text data enhancement technology of large language model; secondly, a prediction model for the integration complexity of Chinese text is implemented based on the model transfer technology. The results showed that: 1) We used GPT-3.5-Tubo for English text data enhancement, a pre-trained multilingual Roberta model for word vector extraction, and a text convolutional neural network model as a downstream model. The Spearman correlation coefficient between this model’s prediction of integration complexity and the manual scoring results was 0.62, with a dialectical integration complexity correlation coefficient of 0.51 and a fine integration complexity Spearman correlation coefficient of 0.60. It is superior to machine learning methods and neural network models without data enhancement. 2) In Study 2, a model with the same structure as the neural network in Study 1 was established, and the final model parameters in Study 1 were also transferred to the model in this study to train the integration complexity prediction model based on Chinese text. In the case of zero samples, the Spearman correlation coefficients of the transfer learning model for integrative complexity are 0.31, the Spearman correlation coefficient of dialectical integration complexity is 0.31, and the correlation coefficient of fine integration complexity is 0.33, all of which are better than the model in the case of random parameters (integrative complexity: 0.17, dialectical integrative complexity: 0.10, fine integrative complexity: 0.10). In the case of small samples, the Spearman correlation coefficient of the transfer learning model was 0.73, with a dialectical integration complexity correlation coefficient of 0.51 and a fine integration complexity correlation coefficient of 0.73.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology submitted time 2024-04-10
Abstract： Integrative complexity is a concept used in psychology to measure the structure of an individual’s thinking. It mainly involves two aspects: differentiation and integration. Differentiation refers to the ability of an individual to identify and understand different viewpoints or elements in the information. Integration refers to the ability of individuals to combine these different ideas or elements into a logical and coherent whole. The measurement of integrative complexity relies primarily on manual analysis of textual content, which can be written materials, speeches, interview transcripts, or any other form of oral or written expression. Integrative complexity has demonstrated its interdisciplinary value and extensive research potential in the fields of management psychology, political psychology and cultural psychology. In the field of management psychology, the level of integrated complexity of leaders affects how they approach complex management challenges, develop strategies, and promote team diversity. In political psychology, researchers use integrative complexity to analyze the thinking styles of political leaders, the foreign policy decision-making process, and the political attitudes and behaviors of the masses. Cultural psychology uses integrative complexity to explore the thinking patterns and information processing strategies of individuals in different cultural contexts. But in the field of health psychology, the integrative complexity has not been fully studied. Integrated complexity, as a measure of the structure of thought, can explain how individuals process information and deal with stress and negative emotions, which is very important for individual mental health. According to the suicide escape theory, individuals may escape unbearable self-consciousness and emotional pain through suicidal behavior. Under this theoretical framework, low integration complexity may be a risk factor for suicidal behavior, because low integration complexity may make it difficult for individuals to see multiple aspects of problems and possible solutions while facing stress and psychological pain, and thus leading to helpless and hopeless. This study explores the effect of integration complexity on suicidal ideation and suicidal behavior through social network media data. The results show that the complexity of dialectical integration negatively affects individual suicidal ideation, the complexity of fine integration positively affects individual suicidal ideation, and the complexity of dialectical integration negatively regulates the impact of negative emotions on suicidal ideation. Individuals with low dialectical integration complexity are more likely to be disturbed by negative emotions, and thus more likely to show suicidal ideation; Individuals with high dialectical integration complexity are less likely to be disturbed by negative emotions and thus less likely to exhibit suicidal ideation, but this pattern is not stable and may be disturbed by cultural background and other factors. On the eve of suicidal behavior, the integration complexity of the individual will continue to decrease.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology Subjects: Computer Science >> Computer Application Technology submitted time 2024-04-09
Abstract： In recent years, the Linguistic Inquiry and Word Count (LIWC) tool has garnered increasing attention, offering the promise of objective, automated, and transparent psychological text analysis. This resurgence has reignited enthusiasm among psychologists for language analysis research. The recent revision of the LIWC-22 dictionary has introduced numerous variables aimed at assessing various socio-psychological structures, thus expanding the application potential of the LIWC tool. To further promote the cultural adaptation of the LIWC tool, we have revised and validated the Simplified Chinese Linguistic Inquiry and Word Count Dictionary 2024 (SCLIWC2024) to better align with the features of LIWC-22. In Study One, building upon the SCLIWC dictionary, we revised SCLIWC2024 by comparing it with the LIWC-22 and CLIWC2015 dictionaries. In Study Two, we conducted two experiments to validate the efficacy of SCLIWC2024 in detecting different psychological semantics in online texts, addressing crucial questions regarding how to more effectively utilize SCLIWC2024 for detecting the psychological semantics of short texts on social networking platforms.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology submitted time 2024-03-28
Abstract： To investigate the current situation and relationship between teachers’ organizational support, psychological capital and job involvement in vocational colleges, and to provide reference for vocational colleges to carry out teacher team reform. Methods From September to December 2023, 282 teachers from 5 vocational colleges in our province were investigated by convenience sampling, general data questionnaire, organizational support scale, psychological capital scale and job involvement scale. Results The scores of organizational support were (3.36± 0.39), psychological capital (4.10±0.49) and job engagement (3.52±0.54). The three were positively correlated (r=0.943, r=0.884, r=0.898, P<0.01). Organizational support and psychological capital have a direct positive effect on teachers’ job involvement, and psychological capital has a partial mediating effect between organizational support and job involvement (18.05%). Conclusion The organizational support, psychological capital and job involvement of vocational college teachers are at the medium level, and psychological capital and organizational support have positive effects on teachers’ job involvement. Therefore, vocational colleges should further strengthen the organizational support and psychological capital investment level for teachers, enhance their sense of professional value and sense of mission, and promote the continuous improvement of vocational education and teaching quality
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology Subjects: Computer Science >> Computer Application Technology submitted time 2024-03-23
Abstract： In recent years, researchers have recognized the impact of Zhong-yong Thinking Style on mental health. However, it is not clear how Zhong-yong thinking style affects mental health through internal psychological mechanisms. Previous studies found that individuals with a better ability to coordinate agency (a motivation representing self-interest) and communion (a motivation representing altruism) tend to have a higher level of moral centrality. Moral centrality reflects the balance of internal motivation system, which can reduce the conflict between agency and communion, helping individuals reach a state that the opposing motivations support and energies each other. Moral centrality may play a potential mediating role in the impact of Zhong-yong thinking style on mental health. Although there are relatively mature methods for measuring individual moral centrality, it involves the complex task of coding values in personal strivings, making the measurement of moral centrality particularly complicated and labor-intensive. However, with the development of large language models(LLM) like ChatGPT, they have demonstrated excellent contextual comprehension skills and offered new possibilities for text analysis and coding work. Accordingly, this study intends to apply large language models to the coding work of psychological research, reduce the time and labor cost required in the process of measuring individual moral centrality, and explore how Zhong-yong thinking style affects individual mental health through moral centrality. Study 1 involves training GPT-3.5 Turbo to recognize values contained in personal strivings (achievement / power / universalism / benevolence) using differentiated prompts and evaluating its accuracy, precision, and recall rates, in order to obtain a model that meets the requirements for application. Study 2 applies above GPT-3.5 Turbo models in the process of measuring moral centrality, exploring how moral centrality mediates the impact of Zhong-yong thinking style on depression and anxiety. The findings are as follows: (1) The GPT-3.5 Turbo demonstrated an accuracy rate of not less than 0.80 in recognizing values of power, achievement, universlaism, and benevolence, showing the potential application of ChatGPT in psychological research; (2) Moral centrality played a mediating role in the impact of Zhong-yong thinking style on depression/anxiety. Specifically, individuals with a higher level of Zhong-yong thinking style could better integrate agency and communion, enhancing their moral centrality, and thereby reducing levels of depression/anxiety. In summary, this study utilized large language models to break through the technical limitations of traditional psychological research, exploring the mechanisms through which Zhong-yong thinking style affects mental health and verifying the mediating role of moral centrality. On the one hand, it proves the application potential of large language models in the field of psychological research. On the other hand, it deepens our understanding of the mechanisms through which Zhong-yong thinking style influence mental health, enriching the theoretical foundation of this field. It suggests that policymakers could use the advantages of Zhongyong thinking culture, advocating for values that emphasize individual development while also focusing on collective well-being, helping people improve moral centrality, thereby mitigating the negative impact of economic inequality on mental health.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology Subjects: Computer Science >> Computer Application Technology submitted time 2024-03-23
Abstract： In recent years, researchers have increasingly recognized the impact of unequal income distribution on individual mental health. However, it is not clear how it affects mental health through internal psychological mechanisms. As the macro environment in which individuals live, economy shape people’s different values and make individuals have different levels of motivation orientation. Previous studies have indicated that individuals with a better ability to coordinate agency and communion tend to have a relatively high level of moral centrality. Moral centrality reflects the balance of internal motivation system, which can reduce the conflict between agency and communion, helping individuals reach a state that the opposing motivations support and energies each other. Thus, individuals are not only able to efficiently realize their personal values but also more easily allow for the attainment of eudaimonic well-being, thereby reducing the risk of mental health problems. Therefore, moral centrality may play a potential mediating role in the impact of income distribution inequality on mental health. Overall, with income distribution inequality as independent variables, this study aims to explore the mechanisms through which it affects mental health, by examining how income distribution influences individual moral centrality and, in turn, affect mental health. Our research not only enriches the theoretical foundation of the mental health field, but also provides a theoretical basis for interventions, and helps to formulate targeted strategies to improve the psychological well-being of the public. With the help of social media big data and natural language processing technology, we use posts made by regional microblogs to extract word frequency features representing the group’s moral centrality and group’s mental health level through the psychosemantic lexicon, and use panel data analysis to examine how the inequality in income distribution affects the negative emotions and suicide risk of the regional group through moral centrality. The results confirm that moral centrality plays a mediating role in the effect of regional income distribution inequality on group negative emotions/suicide risk, and that regions with higher income distribution inequality tend to be accompanied by lower levels of group moral centrality, which in turn leads to an increase in negative emotions/suicide risk among groups in the region.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology submitted time 2024-03-18
Abstract： To assist young people infected with COVID-19 restore and develop a balanced state of mental health after the pandemic, we have developed an online self-help psychological intervention robot that can complement existing mental health resources. First, we utilized prompting engineering techniques to build a chatbot skilled in Problem-Solving Therapy (PST) based on the large language model GPT-4. Then, we conducted pre-testing and formal experiments to verify the effectiveness of the chatbot. The results of the pre-testing indicated that the chatbot followed the core work steps of PST during interactions with users. The results of the formal experiment showed that the PST chatbot performed better than the ordinary chatbot in terms of problem identification and problem-solving dimensions, indicating that the PST chatbot can help users quickly locate the problems that trouble them and develop feasible problem-solving plans. However, there was no difference between the PST chatbot and the ordinary chatbot in terms of relationship quality, and no differences were found in the evaluation of the two chatbots based on gender and post-COVID symptoms. This suggests that the PST chatbot did not significantly improve the quality of human-machine relationships, but the general acceptability and wide applicability of chatbots still have certain advantages in practical applications. The research results support the possibility of using large language models in innovative implementations of psychological self-help interventions.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology Subjects: Medicine, Pharmacy >> Preventive Medicine and Hygienics submitted time 2024-03-17
Abstract： HPV vaccination is an effective way to prevent and treat cervical cancer, but the vaccination situation in our country is not optimistic, and many young people hesitate to vaccinate HPV vaccine. Research has shown that information persuasion is an effective means to increase vaccination rates. This study will focus on the content of persuasion information and explore the relationship between influencing factors and individual personality characteristics. To this end, we recruited 284 subjects online to conduct a questionnaire survey and analyzed the data using ANOVA. The results show that there are significant differences in the persuasive effect of information containing different influencing factors. It is necessary to select more effective influencing factors to produce the persuasive effect of promoting vaccination, and the big five personality characteristics of individuals will have a significant impact on the persuasive effect of information. This study can provide scientific basis and guidance for the promotion of vaccination, and has important theoretical and practical value for promoting public health.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology submitted time 2024-03-13
Abstract： In the field of misinformation identification research, the motivated System 2 reasoning and classical reasoning accounts probe the influencing factors that shape individuals’ ability to identify misinformation from different perspectives, yet diverge in their interpretations of cognitive abilities’ roles. Building upon existing research, this study introduces factors such as emotions, information characteristics, individual stances, and their underlying motivations to further refine the hierarchical model of misinformation identification based on a signal detection theory. The objective is to enrich our comprehension of the multifaceted ways in which these diverse elements bear upon the process of misinformation identification. By differentiating the influence of various factors on both the discrimination sensitivity and the judgment criteria within the identification process, the model not only reconciles the contrasting perspectives on cognitive abilities posited by motivated System 2 reasoning and classical reasoning accounts but also furnishes a detailed and systematically organized analytical framework. This framework is instrumental in elucidating the intricate mechanisms that underpin the identification of misinformation.
Subjects: Psychology >> Applied Psychology submitted time 2024-02-29
Abstract： HPV vaccination can not only effectively prevent the development of cervical cancer and its precancerous lesions, but also prevent other parts of the disease caused by HPV infection. However, the vaccination situation in China is not optimistic, and many young people are hesitant to get the HPV vaccine. Based on the planning theory model, this study aims to explore the influencing factors of HPV vaccination intention, compile a questionnaire with good reliability and validity to evaluate the importance of influencing factors of HPV vaccination intention, and explore the importance degree of influencing factors of different vaccination intention. In experiment 1, this study explored the influencing factors of individual HPV vaccination intention through semi-structured interview method, and obtained 25 influencing factors such as vaccine safety, vaccine effectiveness, vaccination convenience, professionalism, conformity and data. In experiment 2, through exploratory factor analysis, confirmatory factor analysis and reliability and validity test, a 17-question, 4-dimensional questionnaire was constructed to evaluate the importance of factors influencing HPV vaccination intention. Among them, confirmatory factor analysis supported the 4-factor model (χ²/df<3, RMR=0.059, RMSEA=0.054, GFI=0.928, TLI=0.914, IFI=0.929), showing good model fit. The Cronbach’s α coefficient of the questionnaire was 0.853, and the retest reliability at a 4-week interval was 0.804. It shows that our questionnaire has good reliability and validity. In addition, there are significant differences in the evaluation of the importance of different influencing factors, and there are also significant differences in the evaluation of the importance of factors among individuals with or without a family history of cancer and different levels of education. This study will provide valuable insights into vaccination promotion strategies and provide scientific basis and reference for developing targeted approaches.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology Subjects: Computer Science >> Computer Application Technology submitted time 2024-02-29
Abstract： Objective With the development of network big data and machine learning, more and more studies starting to combine text analysis and machine learning algorithms to predict individual satisfaction. In the studies focused on building life satisfaction prediction models, it is often difficult to obtain large amounts of valid and labeled data. This study aims at solving this problem using data augmentation and optimizing the prediction model of life satisfaction. Method Using 357 life status descriptions annotated by self-rating life satisfaction scale scores as original text data. After preprocessing using DLUT-Emotionontology, EAD and back-translation method was applied and the prediction model was built using traditional machine learning algorithms. Results Results showed that (1) the prediction accuracy was largely enhanced after using the adapted version of DLUT-Emotionontology; (2) only linear regression model was enhanced after data augmentation; (3) rigid regression model showed the greatest prediction accuracy when trained by original data (r = 0.4131). Conclusion The improvement of feature extraction accuracy can optimize the current life satisfaction prediction model, but the text data augmentation methods, such as back translation and EDA may not be applicable for the life satisfaction prediction model based on word frequency.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology Subjects: Management Science >> Enterprise Management submitted time 2024-01-28
Abstract： The source of design effect is defined as the manner in which the source information of a firm’s product design affects consumer product preferences and corporate attitudes. Currently, there are three major sources: professional designers, users, and AI, each exerts either positive or negative influences on consumer preferences through different psychological mechanisms. The source of professional design influences consumer preferences through the perceived competence of expert designers, whereas the source of user design influences consumer preferences through the perceived capabilities of users, empowerment and the psychological distance between users and brands. Furthermore, the source of AI design influences consumer preferences by virtue of the value and information offered by products designed using AI. It is noteworthy that, the source of design effect is moderated by consumer individual differences, product characteristics and the openness of a firm’s design policy. Future research should delve deeper into consumer reactions to mixed design sources as well as the psychological mechanisms and boundary conditions of the source of design effect.
Subjects: Psychology >> Applied Psychology Subjects: Management Science >> Other Disciplines of Management Science submitted time 2024-01-18
Abstract： The debate over whether individual health behavior changes occur in stages is currently a controversial focal point. Medication adherence, as a crucial indicator influencing health care outcomes, significantly impacts an individual's physical and mental well-being. Previous reviews of medication adherence levels have predominantly adopted a medical perspective, focusing on adherence behavior related to specific diseases. However, within the context of the increasingly market-driven health care industry, there is a dearth of research exploring the influence of information processing methods and psychological processes on consumer medication adherence behavior from the consumer's perspective. Additionally, existing research lacks theoretical categorization and discourse on adherence behavior. Drawing upon the two-stage theory model, this review examines factors within the marketing domain that influence consumer medication adherence behaviors, elucidating intervention strategies, and proposing future research trends and prospects. Theoretically, this contributes to understanding individual medication adherence behavior within the stages of health behavior change, enriching the stage theories within the health domain. Practically, it aids in better comprehending consumer mental health and behavioral patterns, offering marketing insights for chronic disease management.
Subjects: Psychology >> Applied Psychology submitted time 2024-01-15
Abstract： As artificial intelligence continues to advance, algorithms find increasing applications across various domains, including education and transportation. In the realm of road traffic management, the escalating complexity of the traffic system poses challenges for traditional human monitoring, such as that carried out by traffic police. In light of this, there is a growing reliance on algorithmic monitoring, exemplified by electronic police systems. These systems offer extensive monitoring capabilities in terms of both time and space, providing an efficient means to uphold traffic order in the face of manual monitoring limitations. In order to enhance individuals’ inclination and adherence to traffic rules, algorithmic monitoring serves as a compelling alternative to address the shortcomings of manual monitoring, which often involves blind spots and high operational costs. Observations from daily life experiences suggest that the broad coverage of algorithmic monitoring has a mitigating effect on traffic rule violations. Despite this intuitive understanding, there exists a notable gap in empirical research to substantiate these observations. While numerous studies have delved into the impact of algorithms, yielding findings related to both algorithm appreciation and aversion, there remains a need for a focused investigation into the specific influence of algorithmic monitoring on compliance with traffic rules and an exploration of the underlying mechanisms.
This research aims to address this gap by providing a nuanced understanding of how algorithmic monitoring shapes individuals’ behavior in the context of obeying traffic rules. Drawing on construal-level theory, prior research has consistently shown that individuals tend to perceive humans at a high level of construal, while algorithms are typically construed at a low level. Moreover, traffic behaviors are also construed at different levels. Recognizing that the alignment of construal levels between the agent and behavior plays a pivotal role in influencing individuals, this paper posits a hypothesis: the effect of algorithmic monitoring on compliance with traffic rules hinges on the construal level of the traffic behavior, and the fit between the monitoring agent and monitored behavior acts as a mediating factor in this relationship. To address these considerations and test the proposed hypothesis, a preliminary investigation was conducted, selecting traffic behaviors with distinct construal levels (e.g., overspeed behavior as a low construal-level, and failure to give way to pedestrians as a high construal-level). In Study 1a, a situational test involving the de We found that for traffic behaviors with low construal levels, compared to human monitoring, people had a stronger sense of fit with algorithmic monitoring, thereby enhancing their intention to comply with traffic rules. Conversely, for traffic behaviors with high construal levels, there was a stronger sense of fit under human monitoring, leading to a greater compliance intention with traffic rules. In summary, the monitoring agent influences individuals’ intention to comply with traffic rules for behaviors at different construal levels, with the sense of fit playing an intermediate role. Further, after committing an error, the sense of fit induced by algorithmic monitoring decreased to a level comparable to human monitoring. Additionally, the positive effect on the intention to comply with traffic rules for behaviors with low construal levels disappeared. However, following an error in human monitoring, its monitoring effectiveness (i.e., compliance with traffic rules) for behaviors with high construal levels remained superior to that of algorithmic monitoring. Moreover, the mediating role of the sense of fit persisted. In essence, the monitoring effectiveness of algorithms is more significantly influenced by error information.
In summary, the enhancing effect of algorithmic monitoring on the intention to comply with traffic rules depends on the construal level of the observed traffic behavior, with the sense of fit playing a mediation role. Errors in algorithmic monitoring weaken its monitoring effectiveness for traffic behaviors with low construal levels. Hence, when the traffic management department chooses the monitoring agent, it should avoid indiscriminately using either algorithmic or human monitoring but consider the construal levels of traffic violations observed at the intersection.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology submitted time 2024-01-09
Abstract： This paper explores the frontiers of large language models (LLMs) in psychology applications. Psychology has undergone several theoretical changes, and the current use of Artificial Intelligence (AI) and Machine Learning, particularly LLMs, promises to open up new research directions. We provide a detailed exploration of how LLMs like ChatGPT are transforming psychological research. It discusses the impact of LLMs across various branches of psychology, including cognitive and behavioral, clinical and counseling, educational and developmental, and social and cultural psychology, highlighting their potential to simulate aspects of human cognition and behavior. The paper delves into the capabilities of these models to emulate human-like text generation, offering innovative tools for literature review, hypothesis generation, experimental design, experimental subjects, data analysis, academic writing, and peer review in psychology. While LLMs are essential in advancing research methodologies in psychology, the paper also cautions about their technical and ethical challenges. There are issues like data privacy, the ethical implications of using LLMs in psychological research, and the need for a deeper understanding of these models' limitations. Researchers should responsibly use LLMs in psychological studies, adhering to ethical standards and considering the potential consequences of deploying these technologies in sensitive areas. Overall, the article provides a comprehensive overview of the current state of LLMs in psychology, exploring potential benefits and challenges. It serves as a call to action for researchers to leverage LLMs' advantages responsibly while addressing associated risks.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology submitted time 2024-01-07
Abstract： Workplace cyberloafing has become increasingly common within organizations due to the intensification of work pressure and the development of a digital office. The phenomenon has attracted extensive interest among practitioners and academicians. This project discusses the outcome of workplace cyberloafing and its feedback effects by integrating actor and observer perspectives. The main contents include: (1) identifying core dimensions of cyberloafing, and categorizing different types of workplace cyberloafing; (2) testing the pros and cons of workplace cyberloafing from an actor-centered perspective; (3) unveiling the interpersonal effects of workplace cyberloafing on the leader and coworkers from an observer-centered perspective; (4) exploring the change trajectory of workplace cyberloafing from an interaction perspective, between the actor and observer. The four studies connect with each other and progress gradually, constituting a complete closed-loop system unveiling the whole process of workplace cyberloafing from its functions to its adjustment in response to feedback. The results of this study are expected to promote the development and innovation of the field of workplace cyberloafing research, and provide practical guidance for organizations to deal with workplace cyberloafing.
Subjects: Psychology >> Applied Psychology submitted time 2023-09-30
Abstract： There are many factors influencing prosocial behavior, and the research results of "who is more prosocial in high social class or low social class" are different, and the research conclusions are easy to generate group stigma, so further clarifying how social class affects prosocial tendency has become a research hotspot. Previous studies mainly discuss the effect of self-induced social status from the perspective of prosociety. However, by subdividing subjective social class into subjective family class and subjective individual class, the relationship between subjective social class and prosocial tendencies may be better explained. In addition, reciprocity beliefs, as the ideological and cognitive aspect of an individual's internal reciprocity norm, is a remote factor that determines the actual reciprocity behavior, and was also introduced into the prediction mechanism to better explain the relationship between subjective social class and prosocial tendencies.
Study 1 recruited college students through an online platform with 598 valid participants, and used the MacArthur Scale of Subjective, the Prosocial Tendencies Measures, the Personal Norms of Reciprocity Scale, and the Personal Relative Deprivation Scale to measure individuals' subjective family class, subjective individual class, prosocial tendency, reciprocity belief, and individual relative deprivation. SPSS process was used to conduct chain multiple mediation tests and moderating effect tests. Results found that subjective family class can positively predict subjective individual class and prosocial tendency, and subjective individual class can negatively predict balanced reciprocity belief. Balanced reciprocity belief positively predicted prosocial tendency, in which balanced reciprocity belief played an intermediary role between subjective family class and prosocial tendency, subjective individual class and balanced reciprocity belief had a masking effect between them, and individual relative deprivation played an enhanced moderating role between reciprocal belief and prosocial tendency; In study 2, CGSS2021 data was used to further verify the results of study 1 by selecting variables related items, 2312 valid data were obtained after deleting missing data, and the same model test was conducted by SPSS process. Results found that both subjective family class and subjective individual class can positively predict prosocial tendency, subjective family class can still positively predict subjective individual class, subjective individual class negatively predicted negative reciprocity belief, negative reciprocity belief negatively predicted prosocial tendency, and subjective individual class played a mediating role between subjective family class and prosocial tendency.Subjective individual class and negative reciprocity belief played an intermediary role between them, and there were boundary conditions on the direct effect of individual relative deprivation. Combining the results of the two studies, it can be stably found that subjective family class can positively predict prosocial tendencies, which provided new evidence for "higher subjective social class has higher prosocial tendencies".Subjective family class is the antecedent influencing factor of subjective individual class, which can predict reciprocity belief and then prosocial tendency through the latter. Subjective individual class and reciprocity belief are important mechanisms for subjective family class to predict prosocial tendency, but different reciprocity beliefs play different roles in predicting prosocial tendency.
Whether it is the relationship between subjective family class and subjective individual class, the different predictive effects of different reciprocity beliefs on prosocial tendency, or the chain mediating effects of subjective individual class and reciprocity beliefs on subjective family class and prosocial tendency. These stable findings can help to understand subjective social class and its prediction mechanism for prosocial tendency, provide a new perspective for understanding the relationship between subjective social class and prosocial tendency, and show that social cognitive theory and social exchange theory are not incompatible in explaining prosocial tendency, and can better understand individual prosocial tendency by combining them.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology submitted time 2023-09-14
Abstract： Objective: Nowadays, young adults are facing stressors from several aspects. They have already become the most anxious groups in Chinese society and in risk of developing a series of anxiety disorders. The theory of challenge-hindrance stress was proposed to explain the positive and negative outcomes of different stressors. It has been widely tested mostly in the field of organization and management. In the current study, we used the challenge-hindrance stress theory to clarify the association between stress in daily life and anxiety. We also examined the HPA-axis function buffering the influence of daily stress on anxiety.
Methods: we used the edited Chinese version of challenge-hindrance stress scales to measure challenge and hindrance stress over 6 months. The level of anxiety was measured by state-trait anxiety inventory. We also carried out a Trier Social Stress Test (TSST) in laboratory and recorded the change of cortisol level during the 60 minutes right after the acute stress.
Results: Results show that the recent level of hindrance stress positively predicts trait anxiety, but the level of challenge stress does not predict trait anxiety. It is also found that, the cortisol decline rate during the recovery of acute stress moderates the association between stress and anxiety. To be exact, individuals with low cortisol decline rate could not recover to baseline level even after rather long rest, and hindrance stress in their lives would lead to higher level of anxiety. But for individuals who has high cortisol decline rate after acute stress, they recover fast to baseline after the stressor disappear, and they become less anxious although facing the same level of hindrance stress
Limitations: Firstly, we only examined anxiety but left other distal outcomes of stress such as wellbeing to be further studied. Secondly, we choose the decline rate of cortisol to represent the HPA-axis function instead of taking different systems into consideration. Thirdly, stress appraisal could be further examined in the challenge-hindrance stress researches in addition to different stressors.
Conclusions: The current study checked the association between stress and anxiety under the framework of challenge-hindrance stress. We examined the moderating mechanism of HPA-axis function, and discussed the effect of physiological toughness from the respective of resources and demands.
Peer Review Status:Awaiting Review
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