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Knowledge payment is a new method of electronic learning that has developed in the era of social media. With the impact of the COVID-19 pandemic, the market for knowledge payment is rapidly expanding. Exploring the factors that influence users' sustained willingness is beneficial for better communication between knowledge payment platforms and users, and for achieving a healthier and more sustainable development of the knowledge payment industry. The model of unsustainable usage behavior of knowledge payment users was constructed on the basis of expectation inconsistency theory, price equilibrium theory, and perceived value theory, using the 'cognitive-emotional-behavioral' model framework of cognitive emotion theory. The data were collected from 348 users through a web-based questionnaire and analyzed using structural equation modeling. Findings show that expectation inconsistency, price equilibrium, and quality value, emotional value, and social value have significant effects on discontinuous use intentions. Discontinuous use intentions also significantly affect discontinuous use behavior. © 2013 IEEE.
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Online shopping has revolutionized our daily lives in the modern era. We can purchase needed goods on mobile shopping applications (apps) anytime and anywhere without leaving home. Especially during the COVID-19 pandemic, we have become increasingly dependent on various mobile shopping activities. However, the visual design of the shopping app interface often affects the user's interactive experience and the efficiency of browsing product information. In addition, gender differences are also worth being considered in the shopping interface design process. To achieve the goal, the research conducted a user study (N=40) of a 2× 2 x 2 mixed factorial design (i.e., information layout x display mode x gender difference). Each participant performed four tasks during the experiment. The authors measured the task completion time, collected the subjective responses from the SUS and the 7-point Likert scale questionnaire, and interviewed participants. The results revealed that: (1) females perform faster in lighter mode when searching for information location, while males perform faster in darker mode. (2) The information layout affects the user's visual search performance and subjective evaluation;females prefer the list style, but men prefer the matrix style. (3) Participants (both males and females) perceived matrix style as more popular than list style in dark mode;however, the result was reversed in light mode. The findings generated from the research can serve as a good reference for the development of user experience in the user interface design of mobile shopping apps. © 2013 IEEE.
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This talk considers the role of social and behavioral science at every stage of the clinical trial process from design to enrolment, participation, retention and outcomes. Based on a review of the literature and three decades' experience as a social scientist conducting leading HIV and COVID studies, it argues that understanding human behavior and decision-making alongside the context in which these decisions are made are key to effective, efficient and quality clinical trials.
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OBJECTIVE: The main objectives of the current study were to find out the frequencies of Psychiatric disorders in the general population during COVID-19 and to compare the gender-based association between newly diagnosed patients during COVID-19 with already existing psychiatric patients in Peshawar to provide patient care on priority bases. METHODOLOGY: This Cross-sectional design study was carried out in the Department of Psychiatry and Behavioral Sciences, HMC/MTI, from May to August 2020. Those patients who approached psychiatry OPD through video/audio online calls and could understand and respond to suggestions were included. The bio-data was collected, and DSM-5 criteria were used for diagnosis. Descriptive statistics were used for statistical significance, and the statistical package of social sciences (SPSS-21) was used for analysis and results. RESULTS: The results findings of the current study revealed that 59.3% of the patients approached for telepsychiatry consultation were from the district of Peshawar. Among them, 54% were female, and most patients were young married females (50.7%) with no job outside the home. The finding further revealed that most of the sample affected by psychiatric illness were uneducated (31.3%) and unemployed (28%). Furthermore, in the present findings, 46% of patients were diagnosed with depression, and 12% had Dissociative disorders. CONCLUSION: It is concluded from the present study that in the Covid-19 Pandemic, primarily females who were married with no job description are more vulnerable to psychiatric illness. Furthermore, during Covid-19 mostly cases were reported with depression and dissociative disorders. © 2023, Liaquat University of Medical and Health Sciences.
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Contract violations are frequent due to the high uncertainty and complexity of construction projects. However, enforcement after a violation has received limited attention. This study distinguishes between three types of violations, i.e., letter violations, mutually agreed spirit violations, and unilaterally assumed spirit violations, based on the documentation and mutuality dimensions. By using the data collected from Chinese general contractors, this study concludes that compared with unilaterally assumed spirit violations, violations of high mutuality of obligations (the first two violations) will lead to more severe contractual and reputational enforcement while with high mutuality, whether the violated obligations are written in the contract or not (corresponding to the first two violations, respectively) does not significantly affect the severity of enforcement. The mediating effects of relational risk perception on the aforementioned effects are empirically supported. This study contributes to the enforcement literature by exploring the effects of the characteristics of violations, especially violations of undocumented elements of contracts, on enforcement and fills the gaps in the scarce literature on reputational enforcement and its antecedents. Project managers can benefit from this study by recognizing the application of reputational enforcement and making better alignment between different types of violations and enforcement. IEEE
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Understanding the residents’routine and repetitive behavior patterns is important for city planners and strategic partners to enact appropriate city management policies. However, the existing approaches reported in smart city management areas often rely on clustering or machine learning, which are ineffective in capturing such behavioral patterns. Aiming to address this research gap, this article proposes an analytical framework, adopting sequential and periodic pattern mining techniques, to effectively discover residents’routine behavior patterns. The effectiveness of the proposed framework is demonstrated in a case study of American public behavior based on a large-scale venue check-in dataset. The dataset was collected in 2020 (during the global pandemic due to COVID-19) and contains 257 561 check-in data of 3995 residents. The findings uncovered interesting behavioral patterns and venue visit information of residents in the United States during the pandemic, which could help the public and crisis management in cities. IEEE
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Traditional approaches to system management are not suited to highly uncertain conditions. Hard system approaches with a top-down management approach are often used to manage well-defined systems that are not easily able to cope with uncertainty. Soft system approaches of the with bottom-up or participative style may cause a lack of conformance to industry standards. Few studies have investigated these approaches within the context of COVID-19 pandemic. Therefore, this paper aims to use the philosophy of Total Systems Intervention to investigate the applicability of an integrated management approach to cope with the uncertainty of COVID-19. Three different countries from Europe, Oceania and Asia are selected as typical case studies to clarify the strengths and weaknesses of differing management approaches. The case studies demonstrate that using an integrated management approach can potentially assist decision-makers to deal with crises and conclusively reveal the superiority of the integrated approach, independent of cultural milieu. © 2023 The Authors. Systems Research and Behavioral Science published by International Federation for Systems Research and John Wiley & Sons Ltd.
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With the coronavirus outbreak, new and strengthened norms of plastic dependency emerged in the Middle East and North Africa region through the desperate demand for products like face masks and other personal protective equipment (PPE), highlighting the tradeoffs between health and the environment. While the rise in demand has been considered as temporary, behavioral barriers and misperceptions might make these norms particularly sticky and hinder society's ability to transition to a circular economy. Fortunately, behavioral science offers valuable insights about why the current pandemic can actually be a catalyst to create new eco-conscious behaviors. As some behaviors are often strenuous to change and require enforcement through traditional policy solutions (e.g. regulations), behavioral science offers complementary tools that will make policies more effective. We have an opportunity to start thinking about ways to leverage behavioral insights to create new norms that promote a circular economy while ultimately ensuring proper adherence to hygiene practices to curb the spread of the virus. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
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On Twitter, COVID-19 is a highly discussed topic. People worldwide have used Twitter to express their viewpoints and feelings during the pandemic. Previous research has focused on particular topics such as the public's sentiment during the lockdown, their opinion on governmental measures, or their stance towards COVID-19 vaccines. However, until today, there is no comprehensive overview that presents possible areas of application for sentiment analysis of COVID-19 Twitter data. Therefore, this study reveals how sentiment analysis can provide relevant insights for managing the pandemic by applying a behavioral and social science lens. In this context, our systematic literature review focuses on machine learning-based sentiment analysis techniques and compares the best-performing classification algorithms for COVID-19-related Twitter data. We performed a search in five databases, which are: IEEE Xplore DL, ScienceDirect, SpringerLink, ACM DL, and AIS Electronic Library. This search resulted in 40 papers published between October 2019 and January 2022 that used sentiment analysis to evaluate the public opinion on COVID-19-related topics, which we further investigated. Our research indicates that the best performing models in terms of accuracy are ensemble models that comprise various machine learning classifiers. Especially BERT and RoBERTa models provide the most promising results when fine-tuned on Twitter data. Our study aims to combine machine learning-based sentiment analysis and insights from social and behavioral science to provide decision-makers and public health experts with guidance on the application of sentiment analysis in the fight against the spread of COVID-19. © 2013 IEEE.
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The recent COVID-19 pandemic has created an unprecedentedly complex situation and wicked problem in the education domain. This has forced educators and learners to study from home using unfamiliar pedagogical typologies and technologies in order to adapt to the new work routine. This research contributes to theory and practice by adopting a sociotechnical approach (STS) to understand the technical and social implications of learning management systems (LMS) to inform pedagogical development. A qualitative approach is adopted, and semi-structured interviews are conducted across two university cases with 40 academics and students to capture their perceptions of LMS usage. We found that technical paradoxes present a barrier to pedagogical development in the transition from blended learning environments to remote ones, where many wicked and unprecedented challenges emerge from learning remotely during a pandemic, while social paradoxes arise from cultural issues such as user resistance that impede the university's pedagogical goals and visions. © 2023 The Authors. Systems Research and Behavioral Science published by International Federation for Systems Research and John Wiley & Sons Ltd.
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Thousands of colonoscopies were canceled during the initial surge of the COVID-19 pandemic. As facilities resumed services, some patients were hesitant to reschedule. The purpose of this study was to determine whether a decision aid plus telephone coaching would increase colorectal cancer (CRC) screening and improve patient reports of shared decision making (SDM). A randomized controlled trial assigned adults aged 45-75 without prior history of CRC who had a colonoscopy canceled from March to May 2020 to intervention (n = 400) or usual care control (n = 400) arms. The intervention arm received three-page decision aid and call from decision coach from September 2020 through November 2020. Screening rates were collected at 6 months. A subset (n = 250) in each arm was surveyed 8 weeks after randomization to assess SDM (scores range 0-4, higher scores indicating more SDM), decisional conflict, and screening preference. The sample was on average, 60 years old, 53% female, 74% White, non-Hispanic, and 11% Spanish speaking. More intervention arm patients were screened within 6 months (35% intervention vs 23% control, p < 0.001). The intervention respondents reported higher SDM scores (mean difference 0.7 [0.4, 0.9], p < 0.001) and less decisional conflict than controls (-21% [-35%, -7%], p = 0.003). The majority in both arms preferred screening versus delaying (68% intervention vs. 65% control, p = 0.75). An SDM approach that offered alternatives and incorporated patients' preferences resulted in higher screening rates. Patients who are overdue for CRC screening may benefit from proactive outreach with SDM support.
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Until the approval of vaccines at the end of 2020, societies relied on non-pharmaceutical interventions (NPIs) in order to control the COVID-19 pandemic. Spontaneous changes in individual behavior might have contributed to or counteracted epidemic control due to NPIs. For example, the population compliance to NPIs may have varied over time as people developed 'epidemic fatigue' or altered their perception of the risk and severity of COVID-19. Whereas official measures are well documented, the behavioral response of the citizens is harder to capture. We propose a mathematical model of the societal response, taking into account three main effects: the citizen response dynamics, the authorities' NPIs, and the occurrence of unpreventable events that significantly alter the virus transmission rate. A key assumption is that a society has a waning memory of the epidemic effects, which reflects on both the severity of the authorities' NPIs and on the citizens' compliance to the prescribed rules. This, in turn, feeds back onto the transmission rate of the disease, such that a higher number of hospitalizations decreases the probability of transmission. We show that the model is able to reproduce the COVID-19 dynamics in terms of hospital admissions for several European countries during 2020 over surprisingly long time scales. Also, it is capable of capturing the effects of disturbances (for example the emergence of new virus variants) and can be exploited for implementing control actions to limit such effects. A possible application, illustrated in this letter, consists of exploiting the estimations based on the data of one country, to predict and control the evolution in another country, where the virus spreading is still in an earlier phase. © 2017 IEEE.
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The provision of physical healthcare services during the isolation phase is one of the major challenges associated with the current COVID-19 pandemic. Smart healthcare services face a major challenge in the form of human behavior, which is based on human activities, complex patterns, and subjective nature. Although the advancement in portable sensors and artificial intelligence has led to unobtrusive activity recognition systems, very few studies deal with behavior tracking for addressing the problem of variability and behavior dynamics. In this regard, we propose the fusion of PRocess mining and Paravector Tensor (PROMPT)-based physical health monitoring framework that not only tracks subjective human behavior, but also deals with the intensity variations associated with inertial measurement units. Our experimental analysis of a publicly available dataset shows that the proposed method achieves 14.56% better accuracy in comparison to existing works. We also propose a generalized framework for healthcare applications using wearable sensors and the PROMPT method for its triage with physical health monitoring systems in the real world. © 2001-2012 IEEE.
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Society is facing a global pandemic, causing millions of deaths and hundreds of millions of infections. The importance of vaccination to face COVID-19 is decisive in the fight against the virus. However, many people have decided not to get vaccinated, ruining public health policy. The objective of this article is to apply knowledge about behavioral economics in the explanation of the behavior of those who decide not to be vaccinated, as well as the use of decision architecture and nudges for the design of behavioral interventions. The methodology used was qualitative, supported by bibliographic search and content analysis, with theoretical methods such as historical-logical analysis and deductive and hypothetical analysis. Behavioral economics has been used to modify behaviors associated with chronic non communicable diseases, so it can provide a solution to increase the number of people who are inoculated against the virus. The perception of risk and uncertainty, the amount of information and social pressure are identified as factors that influence the decision, as well as various heuristics and cognitive biases. The design of behavioral interventions should employ nudges in the decision architecture, starting from the "simple, attractive, social and timely" methodology as an opportunity to increase the number of people who are vaccinated. Copyright © 2022, Editorial Ciencias Medicas. All rights reserved.
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This perspectives article considers the challenges posed by anti-science and how we can use research to respond more effectively. In public health, the challenges were more visible and the impact more serious during the COVID-19 pandemic. In part, this was due to a more organized anti-science and effective use of narrative methods. Regarding climate change, the role of anti-science represents a critical issue, but perhaps more recognized in environmental research and practice. The article draws on a narrative review to show some of the research into the nature of anti-science and the challenges it poses. It proposes that, as researchers, practitioners, and educationalists, we can be more effective if we make more use of recent research in the sciences of communications, behavior, and implementation, and shows some of the resources we can use to help our work be more relevant in the new era in which we are living.
Subject(s)
COVID-19 , Public Health , Humans , Pandemics , Climate Change , CommunicationABSTRACT
The outbreak of the Covid-19 Pandemic has been for more than two years around the world, as well as there were substantial regional differences in the infection cases and deaths per million. Plenty of studies in psychology and behavioral science in the past two years showed that cultural factors might influence individual cognitions and behaviors, and then change collective responses to the Covid-19 Pandemic. However, few studies are conducted to discern the effects of these cultural factors on the prevention and control of the Covid-19 epidemic situation simultaneously and identify what cultural variables are valid indeed for us preventing Covid-19. Thus, the present study aimed to examine the effects of the major cultural variables together, e.g., individualism-collectivism, tightness-looseness, authoritarianism, emancipative moral judgments, and national identity, on the epidemic situations during the several stages of the Pandemic. The results suggested that, in the early stages of the epidemic, the countries with higher parochialism had fewer cases and deaths per million;meanwhile the countries with higher uncertainty avoidance had more cases and deaths per million. Across the whole epidemic periods so far, the countries with tighter cultures had fewer cases and deaths per million, as well as more individual autonomy countries, had higher deaths per million. The integrative analysis of the multiple cultural factors in the present study provided theoretical insights and empirical evidence for a better understanding of how culture affects individual and collective responses to the Pandemic, making efficient policies for Covid-19 control, and coping with potential epidemics in the future. © 2022 IEEE.
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Objectives: The One Health Approach (OHA) involves a collaborative, multisectoral, multidisciplinary framework to address public health challenges and achieve optimal health outcomes. OHA recognizes the interconnection between people, animals, plants, and their shared environment. This M-CERSI project amplifies the OHA by amplifying and synergizing different disciplines (e.g., social, and behavioral sciences, machine learning, and artificial intelligence options) with expertise from various FDA centers, offices, and academia to harness narrative COVID-19 unstructured publicly available data. Method(s): Human curation and machine learning techniques are augmented with social and behavioral science methods and input by subject matter experts, across four sequential components. First, the collection of publicly available data from various FDA input and output sources. Second, the systematic narrowing of scope of inclusion to public comments submitted to Regulations.gov in response to COVID-19 related meetings and dockets. Third, the extraction of approximately 140,000 comments using computing methods and the newly available OpenGSA Application Programming Interface (API). Fourth, preprocessing and analysis to generate insights using a machine learning technique, topic modeling, combined with human curation techniques. Result(s): Results included the determination of a structure whereby public comment groupings can be parsed into meaningful subsets. Integrative analysis via human curation and computing methods yielded insights into public opinion as well as producing machine learning models that may be applied to future datasets. These results highlight the value of building a multidisciplinary OHA framework. Conclusion(s): This multidisciplinary research collaboration supports FDA's regulatory public health mission and the OHA, effectively reducing silos and leveraging expertise across the scientific spectrum. This approach can be implemented to provide ongoing, timely and accurate information across stakeholder groups. The next phase of research will apply discovered insights to design focus group sample populations, contrast emerging themes, and develop clear messaging that is responsive to public interests and concerns. Copyright © 2022
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Society is facing a global pandemic, causing millions of deaths and hundreds of millions of infections. The importance of vaccination to face COVID-19 is decisive in the fight against the virus. However, many people have decided not to get vaccinated, ruining public health policy. The objective of this article is to apply knowledge about behavioral economics in the explanation of the behavior of those who decide not to be vaccinated, as well as the use of decision architecture and nudges for the design of behavioral interventions. The methodology used was qualitative, supported by bibliographic search and content analysis, with theoretical methods such as historical-logical analysis and deductive and hypothetical analysis. Behavioral economics has been used to modify behaviors associated with chronic non communicable diseases, so it can provide a solution to increase the number of people who are inoculated against the virus. The perception of risk and uncertainty, the amount of information and social pressure are identified as factors that influence the decision, as well as various heuristics and cognitive biases. The design of behavioral interventions should employ nudges in the decision architecture, starting from the "simple, attractive, social and timely" methodology as an opportunity to increase the number of people who are vaccinated. Copyright © 2022, Editorial Ciencias Medicas. All rights reserved.
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Background: Covid-19 affects health behaviors in terms of less physical activity and increased sedentary behavior of office employees, which is a cause of non-communicable diseases. Objective(s): To investigate the effectiveness of a motivational enhancement program in work exercise movement (MEP in WEM) based on the Capability Opportunity Motivation Behavior (COM-B) model. Material(s) and Method(s): The cluster randomized controlled trials (RCTs) were designed to collect data and test the program's effectiveness. Fifty-eight officers, divided into two groups, with 28 officers in the experimental group and 30 officers in the control group, were included in this study. This was calculated by statistical power analysis for the behavioral sciences with an effect size of 0.80, p-value 0.05. The data were collected from the Likert rating scale with a Cronbach's reliability score of 0.776 to 0.911. The MEP ran for 11 weeks with 12 intervention activities between April and July 2021. Descriptive statistics and t-tests were used for data analyses. Result(s): After participation, employees' motivation (mean 3.12, SD 0.520) and sedentary behavior (mean 2.65, SD 0.691) were at a high level, and the MEP in WEM was significantly effective in enhancing motivation. Additionally, the experimental group had the higher motivation and less sedentary behavior than the control group before participating in the intervention at a significance level of 0.05. Conclusion(s): This ME program should be used to initiate policies in promoting physical activity of office employees. Copyright © 2022 JOURNAL OF THE MEDICAL ASSOCIATION OF THAILAND.