Journals from our researchers
Journal
Journal
Artificial Intelligence and big data in sustainable entrepreneurship
Published: January 2024
Authors: Bickley, Steve J., Macintyre, Alison, & Torgler, Benno
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The study explores whether surgeons and biomaterial scientists and tissue engineers process information similarly or differently, affecting the translation of innovations from research to clinical practice. The hypothesis is that psychological differences between these groups hinder clinical application. Using an online survey with a behavioral economics cognitive framing experiment, data from 208 surgeons and 59 bio scientists and tissue engineers revealed no significant occupation-based framing bias. However, within-group differences showed surgeons' preferences varied with framing: positive framing led to higher rankings for autologous bone graft and Ilizarov bone transport methods compared to negative framing. This suggests that framing influences surgical decision-making.
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You are lying! How misinformation accusations spread on twitter
Published: September 2023
Authors: Galande, Ashish S., Mathmann, Frank, Ariza-Rojas, Cesar, Torgler, Benno, & Garbas, Janina
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Artificial intelligence-based video analytics for extracting opposing-through traffic conflict measures.
Non-stationary extreme value models improve the accuracy and precision of crash prediction.
Bivariate models with post encroachment time and gap time outperformed all the univariate models.
The expected post-collision velocity difference (Delta-V) predicted crash frequencies by severity levels.
Various stages of opposing-through conflicts within a bivariate extreme value model to predict crash risks.
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Patience predicts attitudes toward vaccination and uptake of vaccines
Published: September 2023
Authors: Chan, Ho Fai, Rizio, Stephanie M., Skali, Ahmed, & Torgler, Benno
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Resilience to disaster: evidence from American wellbeing data
Published: March 2023
Authors: Frijters, Paul, Johnston, David W., Knott, Rachel J., & Torgler, Benno
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The study aims to identify and quantify factors influencing dockless e-scooter demand using 2019 trip data from Austin, Texas. Demand data is developed at the Census Tract (CT) level for four daily time periods, separated by weekdays and weekends. A joint panel linear regression (JPLR) model is used to account for unobserved factors at multiple levels (CT, month, day, and time period). The JPLR models outperform independent linear regression models, showing significant associations between e-scooter demand and variables like sociodemographics, transportation infrastructure, land use, meteorology, and situational attributes. Significant panel-specific correlation effects emphasize the importance of considering common unobserved factors across different time-of-day dimensions. Model validation confirms the effectiveness of the JPLR models. Policy analysis highlights land use mix, commuter proportion, and season as key factors influencing e-scooter demand on weekdays and weekends.
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Modelling dockless shared e-scooter demand by time of day: A case study of Austin
Published: April 2023
Authors: Alsulami, Nami, Tirtha, Sudipta Dey, Yasmin Shamsunnahar, & Eluru, Naveen
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Commemorative stamps reflect a nation's culture, regime, and values, celebrating monuments, flora, fauna, historical events, and key figures. This study analyzes commemorative stamp data to understand the determinants shaping their provision. The political process and regime significantly influence stamp use as a recognition tool. More corrupt countries, controlling for regime, are likelier to use stamps to recognize personalities, while democratic regimes frequently use them for recognition. Cultural factors and ideologies show substantial influence, whereas fractionalization or income inequality do not. Personal and impersonal pro-sociality or creativity reduce stamp use for recognition, whereas market orientation, individualism, and traditionalism encourage it. Some results align with theoretical propositions, while others do not, necessitating further empirical evidence to understand how stamps differ from other forms of recognition.
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Behavioural economics: what have we missed? Exploring ‘classical’ behavioural economics roots in AI, cognitive psychology and complexity theory
Published: March 2023
Authors: Bickley, Steve J., & Torgler, Benno
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During the COVID-19 pandemic, several governments tried to contain the spread of SARS-CoV-2, the virus that causes COVID-19, with lockdowns that prohibited leaving one’s residence unless carrying out a few essential services. We investigate the relationship between limitations to mobility and mental health in the UK during the first year and a half of the pandemic using a unique combination of high-frequency mobility data from Google and monthly longitudinal data collected through the Understanding Society survey. We find a strong and statistically robust correlation between mobility data and mental health survey data and show that increased residential stationarity is associated with the deterioration of mental wellbeing even when regional COVID-19 prevalence and lockdown stringency are controlled for. The relationship is heterogeneous, as higher levels of distress are seen in young, healthy people living alone; and in women, especially if they have young children.
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Cash and the hidden economy” experimental evidence on fighting tax evasion in small business transactions
Published: July 2023
Authors: Chan, Ho Fai, Dulleck, Uwe, Fooken, Jonas, Moy, Naomi, & Torgler, Benno
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Vaccination is a pressing public health issue. We hypothesize that impatience (discounting future benefits of current actions) leads to lower vaccination rates and worse attitudes toward vaccines. In preregistered individual-level Study 1 (N = 2,614), we document a positive and quantitatively small association (standardized coefficient = 0.06) between patience and attitudes toward vaccines. In Study 2 (N = 76), national-level patience accounts for 21% of the global variation in COVID-19 vaccinations; patience’s effect is small-to-moderate (standardized coefficient = 0.19). In duration models (Study 3; 4,180 ≤N≤ 9,973), more patient countries more quickly reach high COVID-19 vaccination thresholds. The results generalize beyond COVID-19: Patience among European subnational regions predicts better attitudes toward vaccination against the 2009 swine influenza (Study 4: Nregions = 138; Ncountries = 17). Finally (Study 5, N = 75), our results are not specific to pandemics: National patience explains the global variation in infant vaccinations.
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Increasing tax compliance among self-employed business owners, especially in trades like construction and repair, is challenging due to the difficulty of auditing cash transactions. This study tests ten policy strategies based on enforcement, service, and trust/social paradigms in a setting allowing payment via tax-reporting transactions or cash. The sample includes both students and non-students active in service industries prone to cash transactions. Results indicate that increased enforcement by tax authorities has the greatest impact on compliance for both groups. However, cooperative approaches also effectively enhance compliance and should be considered if they can be implemented at low costs. Combining enforcement with cooperative elements ("carrot and stick" approach) is promising for increasing compliance in cash economies where implicit collusion facilitates tax evasion.
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The global shortage of sperm and oocyte donors is a significant concern in assisted reproductive medicine. While research often explores why current donors chose to donate, it rarely addresses why most people do not. This study analyzes open-form responses from 1,035 online survey participants to understand their reasons for not donating. Responses are categorized into four themes: conditional willingness, barriers, unconsidered, and conscientious objector, with eleven sub-themes. Women are generally more conditionally willing to donate (8.2% difference; p = 0.008) and more likely to cite reproductive history (21.3% difference; p = 0.000) or kin selection (5.7% difference; p = 0.008) as reasons for non-donation. Men, however, are more likely to cite sociocultural norms (6% difference; p = 0.000) or religion (1.7% difference; p = 0.030). The significant in-principle willingness for future donation highlights the need for more research on non-donor preferences, motivations, and behaviors.
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A bivariate, non-stationery extreme value model for estimating opposing through crash frequency by severity by applying artificial intelligence-based video analytics
Published: March 2024
Authors: Howlader Mohasin, Bhaskar Ashish, Yasmin Shamsunnahar, & Haque Mazharul
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Women undergoing cosmetic breast augmentation may not fully understand the risks and likelihood of revision surgeries pre-operatively, possibly due to inadequate information during consultations. To explore this, we conducted a recorded online experiment with 178 women (aged 18-40) who received varying amounts of risk-related information from two experienced breast surgeons in a hypothetical first consultation scenario. Factors such as age, self-rated health, income, education, and openness to experience significantly influenced initial risk preferences. Emotionally stable patients perceived greater risks, were less likely to recommend the procedure, and more likely to foresee revision surgery. Providing risk information increased risk assessment across all conditions and reduced willingness to recommend the procedure, but did not significantly affect perceptions of revision surgery likelihood. Individual differences like education, having children, conscientiousness, and emotional stability also impacted post-information risk assessment. Continuous improvement in informed consent consultations is crucial for optimizing patient outcomes and managing costs. Future research should focus on factors influencing women's understanding throughout the informed consent process for breast augmentation.
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Understanding the reasons why men and women do not donate gametes
Published: November 2022
Authors: Whyte, Stephen, Chan, Ho Fai, Ferguson, Nikita, Godwin, Megan, Hammarberg, Karin, & Torgler, Benno
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Transitioning to ecological sustainability requires radical changes in decision-making and resource use. Sustainable entrepreneurship (SE) is often seen as a solution to triple-bottom-line challenges, but its potential is limited. SE is beginning to adopt advanced technological tools for empirical decision-making guidance. Big Data (BD) enhances artificial intelligence (AI)'s ability to inform decisions and achieve desired outcomes, but the interaction between AI, BD, and SE is under-studied. This conceptual paper consolidates these literatures, suggesting that AI and BD can advance both weak and strong forms of sustainability. We propose two ways AI and BD can support SE and outline future research avenues.
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Factors impacting informed consent in cosmetic breast augmentation
Published: February 2023
Authors: Whyte, Stephen, Bray, Laura, Brumpton, Martin, Chan, Ho Fai, Peltz, Tim S, Tamar, Manisha, Dulleck, Uwe, & Hutmacher, Dietmar W
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Misinformation is hard to combat, and while social media firms focus on its publication, misinformation accusations are overlooked. This study examines factors contributing to the spread of these accusations using tweets about the 2020 US presidential election (234,556 tweets) and the 2022 US midterm elections (99,032 tweets). Findings show that lower locomotion orientation in writers, especially among liberals, explains the spread of misinformation accusations. Practitioners can develop algorithms to analyze post language to track and reduce misinformation accusations. Social media firms can identify potentially viral misinformation accusations by considering locomotion language and geographical data, enhancing political conversation quality and electoral decision-making. This study highlights the need for better strategies to manage misinformation accusations and retain trust.
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With climate change intensifying extreme weather events and increasing vulnerability in at-risk areas, understanding people’s resilience is crucial. This study quantifies resilience by examining how disasters in the US affected individual wellbeing in the weeks and months following these events, analyzing differences based on individual- and county-level factors. Comparing changes in wellbeing between affected and unaffected counties within the same state, we find a initial reduction in hedonic wellbeing equivalent to approximately 6% of a standard deviation within the first two weeks post-disaster, diminishing rapidly thereafter. Negative impacts are more pronounced among White, older, and economically advantaged populations, indicating lower resilience in these groups. However, existing indices of community resilience do not appear to moderate these effects. Overall, the findings suggest that most people in the US demonstrate resilience in response to extreme weather events.
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Commemorative stamps as a recognition tool: A cross-sectional analysis
Published: March 2024
Authors: Galliford, Patricia, Chan, Ho Fai, & Torgler, Benno
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As artificial intelligence (AI) becomes more integrated into modern life, it's crucial to include humans in its future development. Despite human involvement in AI production, it often retains "black box" characteristics, making its processes and decisions opaque. AI will face complex ethical dilemmas beyond common examples like trolley problems, and solutions to these cannot be hard-coded. Understanding AI's decision-making is essential, especially in areas affecting human livelihoods such as health, finance, and law. This requires making AI more transparent, explainable, and accountable. We advocate for using cognitive architectures to achieve this, enabling us to comprehend AI's motivations and values. By understanding AI on a deeper level, we can better harness its potential for positive outcomes and mitigate negative impacts, benefiting both AI and human understanding.
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Cognitive architectures for artificial intelligence ethics
Published: June 2022
Authors: Bickley, Steve J. & Torgler, Benno
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This chapter explores the essence and origins of behavioral economics, questioning its traditional focus on rationality and optimization. We advocate revisiting classical concepts and methods to gain deeper insights into economic behaviors at all levels (micro, meso, macro). We also examine related fields like cognitive psychology, complexity theory, and artificial intelligence. By engaging in detailed ontological and epistemological discussions, we aim for more profound scientific debates. Additionally, we highlight the value of mixed methods in behavioral economics research, policy, and practice.
Age-based Stereotype threat in the workplace: a daily diary study of antecedents and mechanisms
Published: July 2024
Authors: Authors: Coulon, S., von Hippel, C., & Peters, K.
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Residential mobility restrictions and adverse mental health outcomes during the COVID-19 pandemic in the UK
Published: January 2024
Authors: Chan, Ho Fai, Cheng, Zhiming, Mendolia, Silvia, Paloyo, Alfredo R., Tani, Massimiliano, Proulx, Damon, Savage, David A., & Torgler, Benno
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Older and younger employees face age-based stereotype threat, worrying about negative stereotypes due to their age. Limited understanding exists about the workplace events causing this threat and their mechanisms. A daily diary study examined how frequently these employees experience events like being overlooked for training or feeling excluded socially, and their link to stereotype threat. Findings showed that frequent experiences of such events increased feelings of stereotype threat, partially because they highlighted age. Additionally, those feeling more threatened were more likely to encounter these events. The impact was similar for both age groups, indicating stereotype threat is equally problematic for all employees.