Set Of Policies Research Articles

Impact of Cybersecurity Disclosure Frequency on Stock Price Crash Risk

ABSTRACTBy exploring the relationship between stock price crash risk and the frequency of cybersecurity disclosures in financial reports, we present evidence that cybersecurity disclosure does matter because its frequency increases stock price crash risk. We analyze the association between cybersecurity disclosure and stock price crash risk from the perspective of two different theoretical lenses: information asymmetry and investor sentiment. We perform textual analysis on the Chinese A‐shares public companies’ cybersecurity disclosure in financial reports during 2017–2022. We find that the more cybersecurity disclosures in financial reports, the higher the stock price crash risk. Further findings reveal that the effect of the frequency of cybersecurity disclosure on stock price crash risk is more significant in non‐Big‐4 audit companies and companies with lower analyst coverage. These findings imply that the market presents the clear idea that cybersecurity disclosure's negative impact to stock price crashes would be greater than the positive impact under the current settings of the volunteer disclosure policies.

Donor Time to Death and Kidney Transplant Outcomes in the Setting of a 3-Hour Minimum Wait Policy

Lengthening waiting lists for organ transplant mandates the development of strategies to expand the deceased donor pool. Due to concerns regarding organ viability, most organ donation organizations internationally wait no longer than 1 to 2 hours for potential donation after circulatory death (DCD), possibly underutilizing an important organ source; UK policy mandates a minimum 3-hour wait time. To assess whether time to death (TTD) from withdrawal of life-sustaining treatment (WLST) is associated with kidney transplant outcomes. This population-based cohort study used data from the prospectively maintained UK Transplant Registry from all 23 UK kidney transplant centers from January 1, 2013, to December 31, 2021; follow-up was until the date of data extraction (October 2023). Participants comprised 7183 adult recipients of DCD kidney-alone transplants. Duration of TTD, defined as time from WLST to donor mechanical asystole. Primary outcome was 12-month estimated glomerular filtration rate (eGFR; for the main eGFR model, variables with significant right skew [histogram visual assessment] were analyzed on the log2 scale), with secondary outcomes of delayed graft function and graft survival (censored at death or 5 years). This study included 7183 kidney transplant recipients (median age, 56 years [IQR, 47-64 years]; 4666 men [65.0%]). Median donor age was 55 years (IQR, 44-63 years). Median TTD was 15 minutes (range, 0-407 minutes), with 885 kidneys transplanted from donors with TTD over 1 hour and 303 kidneys transplanted from donors with TTD over 2 hours. Donor TTD was not associated with recipient 12-month eGFR on adjusted linear regression (change per doubling of TTD, -0.25; 95% CI, -0.68 to 0.19; P = .27), nor with delayed graft function (adjusted odds ratio, 1.01; 95% CI, 0.97-1.06; P = .65) or graft survival (adjusted hazard ratio, 1.00; 95% CI, 0.95-1.07; P = .92). These findings were confirmed with restricted cubic spline models (assessing nonlinear associations) and tests of interaction (including normothermic regional perfusion). In contrast, donor asystolic time, cold ischemic time, and reperfusion time were independently associated with outcomes. Compared with a theoretical 1-hour maximum wait time, the UK policy (minimum 3-hour wait time) has been associated with 885 extra DCD transplants compared with 6298 transplants (14.1% increase). In this cohort study of DCD kidney recipients, donor TTD was not associated with posttransplant outcomes, in contrast to subsequent ischemic times. Altering international transplant practice to mandate minimum 3-hour donor wait times could substantially increase numbers of kidney transplants performed without prejudicing outcomes.

Artificial Intelligence/ChatGPT-Like: Perceptions and Use Among Library and Information Professionals from Italy, Bangladesh, Bulgaria, and Mexico

The aim of conducting a study on generative artificial Intelligence (GenAI), such as ChatGPT, was to identify international perceptions and use among library and information professionals from Italy, Bangladesh, Bulgaria, and Mexico. A questionnaire with 12 items was developed and distributed at conferences and by email at the convenience of researchers. The outcome was of 605 cases who responded to the instrument, whose results were analyzed with descriptive and some inferential statistics. The results showed that librarians and libraries, regardless of the socio-economic development of their countries, were slow in using AI. AI training and setting of AI library policies were also almost absent. Libraries and librarians can strengthen their social mission by urgently adopting AI technology.

Disparities in child protective services involvement in pediatric traumatic brain injury.

Traumatic brain injury (TBI) is a leading cause of pediatric death and disability. Abusive head trauma confers greater morbidity and mortality compared with accidental TBI. National trends reveal disproportionate involvement of minority children in the child welfare system. The study investigates socioeconomic disparities in child protective services (CPS) involvement in pediatric TBI. Retrospective chart review was conducted for TBI patients (n = 596) admitted to an academic pediatric level I trauma center from 2015 to 2022, where institutional policy dictates automatic CPS referral for TBI patients ≤ 2years. Analysis of variance, chi-squared, and logistic regressions compared racial and ethnic groups and calculated adjusted odds of CPS case acceptance. Rates of non-accidental trauma, CPS involvement, insurance, and marital status differed across racial and ethnic backgrounds (p < 0.05). Of patients ≤ 2years, Hispanic patients (OR: 0.38, 95%CI [0.16,0.91]) had decreased odds of CPS involvement compared to non-Hispanic White patients when adjusting for confounders including injury severity, injury type, and socioeconomic status. We highlight racial and ethnic differences in incidence of pediatric TBI and CPS involvement, even in the setting of an automatic CPS referral policy for pediatric TBI patients ≤ 2years.

The impact of long-term care insurance on the utilization of inpatient service: Evidence and mechanisms in China.

This paper empirically investigates the impact of public long-term care insurance (LTCI) on the utilization of inpatient services and associated expenditures among disabled Chinese individuals, using data from the China Health and Retirement Longitudinal Study from 2011 to 2018. Employing a staggered difference-in-difference approach within a propensity score matching framework (PSM-DID), the study finds that the introduction of LTCI significantly reduces the likelihood of inpatient service usage by 4.2%, the annual number of inpatient admissions by 10.2%, the annual inpatient cost by 16.2%, the out-of-pocket expenses by 20.7%, and the reimbursement expenditure by the public medical insurer by 9.9%. The study further explores the mechanisms underlying these effects and identifies that the Substitution Effect, where care services in community healthcare centers and nursing homes replace hospitalizations, outweighs the Income Effect generated by LTCI benefits. By leveraging the quasi-natural experimental setting of diverse LTCI policies across cities, the study also examines the heterogeneous impacts of LTCI based on household income, eligibility criteria, and reimbursement methods. The findings underscore the positive role of LTCI in controlling medical expenses and alleviating congestion in urban hospitals, offering valuable insights for promoting "Healthy Aging".

Fine-grained digital twin sharing framework for smart construction through an incentive mechanism

Prefabricated construction is increasingly applied in the construction industry. Recent academic and industrial efforts highlight that Digital Twin as a Service (DTaaS) is an innovative solution to strengthen the advantages of prefabricated construction by enabling continuous monitoring, precise control, and optimized maintenance of each unique module. However, the complexity of digital twins presents significant challenges for both platforms and subcontractors to apply and fully understand this technology. It is, therefore, crucial to develop a well-designed sharing environment where stakeholders volunteer to share their digital twin resources for synergistic benefits. This research proposes an innovative blockchain-based fine-grained digital twin sharing framework, with detailed architecture focused on two key stages of prefabricated construction: on-site construction and logistics involving warehousing and transport. An incentive mechanism is introduced to encourage all the stakeholders engaged in digital twin sharing framework to contribute and share more digital twin resources. Evolutionary and cooperative games explore the effective settings of the incentive policies, aiming to ensure the feasibility and sustainability of the proposed system by motivating users to share digital twins with fine granularity. Mathematical and numerical results demonstrate the optimal subsidy policies with/without budgetary constraints and further highlight the effectiveness of well-crafted incentives. Findings reveal that better performance in terms of profitability and digital twin sharing is achieved under the proposed incentives. Subsidies are primarily allocated to offset costs with a certain budget, whereas the emphasis shifts towards quality improvement under an ample budget. This study lays a solid foundation for digital twin reuse and development.

Promoting the participation of women: agenda setting in Swiss sports federations

ABSTRACT Following observations that efforts to promote female participation vary across sports federations, this study employs the multiple stream model to analyse agenda setting towards promotion programmes in Swiss sports federations. Through qualitative expert interviews in three case studies (football, ice hockey, and badminton), the study analyses why and when gender policies encouraging female participation at the grassroots level appear on a sports federation agenda. The findings show that the availability of external funding and the adaptation of external projects are essential opportunities for promotion activities. Engaged actors institutionalised within federations or emerging from personal commitment play a crucial role in seizing and exploiting opportunities. The study provides a conceptual contribution to the analysis of agenda setting of policies outside federations’ daily business and offers empirical insights to assist stakeholders in promoting women’s participation. Recommendations include involving committed individuals, especially in male-dominated sports, and securing (external) support to facilitate structural changes at the grassroots level.

Membangun Silogisme Pendekatan Naratif dalam Proses Pembuatan dan Analisis Kebijakan Publik William N. Dunn

This research explains how to build a narrative approach syllogism in policy making and analysis using William N. Dunn's framework. The data used in this research uses secondary data from internet searches and literature from reputable journals. This research uses a qualitative and descriptive approach. The conclusion of this research is the syllogism of the narrative approach in public policy studies, consisting of the narrative approach in qualitative research methods, the narrative policy framework (NPF), and the advocacy coalition framework (ACF). In public policy studies, the activities of the policy-making process are divided into two actors with different tasks: first, policymakers carry out agenda setting, formulation, adoption, implementation, and evaluation of policies. Meanwhile, the second actor is a policy analyst, whose job is to identify problems, forecast policies, recommend policies, and monitor and evaluate policies. Each stage can use a narrative approach syllogism, where policymakers can explain and understand whether a policy will impact individuals or communities, understand the experiences and perspectives of the community or other stakeholders, build community empathy and support, and identify unexpected consequences. For policy analysts, the narrative approach of the syllogism is useful in terms of understanding the impact of policies, identifying emerging problems and conflicts, organizing and presenting policy recommendations clearly and persuasively, assisting with efforts to identify emerging issues, identifying dominant and alternative narratives that shape policy debates, understanding the formation, maintenance, and role of coalitions in the policy process, and evaluating policy effectiveness and adjusting recommendations. Keywords: Public policy, syllogism, narrative approach, Narrative Policy Framework (NPF), Advocacy Coalition Framework (ACF)

The role of sectoral policies in the energy transition

The objective of this paper will be firstly to indicate the significance of sectoral policies, as well as to explain the impact of cross-sectoral policies on the achievement of their goals. Next, these considerations will be related to the role of energy sectoral policies in the energy transition process, taking into account the impact of the cross-sectoral climate policy. The article is directed at highlighting the need to distinguish between sectoral and cross-sectoral policies and to identify the relation between these policies, which has special significance in relation to the energy sector. In order to realise the adopted objective, an administrative-legal research method will be applied, taking into account the perspective of EU law in connection with the significant impact of EU policies on the setting of national sectoral policies.

Asymmetric Constrained Optimal Tracking Control With Critic Learning of Nonlinear Multiplayer Zero-Sum Games.

By utilizing a neural-network-based adaptive critic mechanism, the optimal tracking control problem is investigated for nonlinear continuous-time (CT) multiplayer zero-sum games (ZSGs) with asymmetric constraints. Initially, we build an augmented system with the tracking error system and the reference system. Moreover, a novel nonquadratic function is introduced to address asymmetric constraints. Then, we derive the tracking Hamilton-Jacobi-Isaacs (HJI) equation of the constrained nonlinear multiplayer ZSG. However, it is extremely hard to get the analytical solution to the HJI equation. Hence, an adaptive critic mechanism based on neural networks is established to estimate the optimal cost function, so as to obtain the near-optimal control policy set and the near worst disturbance policy set. In the process of neural critic learning, we only utilize one critic neural network and develop a new weight updating rule. After that, by using the Lyapunov approach, the uniform ultimate boundedness stability of the tracking error in the augmented system and the weight estimation error of the critic network is verified. Finally, two simulation examples are provided to demonstrate the efficacy of the established mechanism.

Estimating Objective Weights of Pareto-Optimal Policies for Multi-Objective Sequential Decision-Making

Sequential decision-making under multiple objective functions includes the problem of exhaustively searching for a Pareto-optimal policy and the problem of selecting a policy from the resulting set of Pareto-optimal policies based on the decision maker’s preferences. This paper focuses on the latter problem. In order to select a policy that reflects the decision maker’s preferences, it is necessary to order these policies, which is problematic because the decision-maker’s preferences are generally tacit knowledge. Furthermore, it is difficult to order them quantitatively. For this reason, conventional methods have mainly been used to elicit preferences through dialogue with decision-makers and through one-to-one comparisons. In contrast, this paper proposes a method based on inverse reinforcement learning to estimate the weight of each objective from the decision-making sequence. The estimated weights can be used to quantitatively evaluate the Pareto-optimal policies from the viewpoints of the decision-makers preferences. We applied the proposed method to the multi-objective reinforcement learning benchmark problem and verified its effectiveness as an elicitation method of weights for each objective function.

Codesign in Indigenous education policy and practice—A systematic literature review

AbstractCodesign is an increasingly common term in Indigenous education policy settings. However, it is unclear exactly what it means and how it is enacted. This systematic review examined 15 papers relevant to codesign in the context of Indigenous education, clearly distinguishing between codesign as a process and a method. These papers provide a snapshot of the various ways codesign is conceptualised, enacted as a process and evaluated in Indigenous education settings. In this paper, we respond to these three areas of codesign to inform a more nuanced framework to help policymakers and practitioners in the future.

A Policy Gradient Algorithm for the Risk-Sensitive Exponential Cost MDP

We study the risk-sensitive exponential cost Markov decision process (MDP) formulation and develop a trajectory-based gradient algorithm to find the stationary point of the cost associated with a set of parameterized policies. We derive a formula that can be used to compute the policy gradient from (state, action, cost) information collected from sample paths of the MDP for each fixed parameterized policy. Unlike the traditional average cost problem, standard stochastic approximation theory cannot be used to exploit this formula. To address the issue, we introduce a truncated and smooth version of the risk-sensitive cost and show that this new cost criterion can be used to approximate the risk-sensitive cost and its gradient uniformly under some mild assumptions. We then develop a trajectory-based gradient algorithm to minimize the smooth truncated estimation of the risk-sensitive cost and derive conditions under which a sequence of truncations can be used to solve the original, untruncated cost problem. Funding: This work was supported by the Office of Naval Research Global [Grant N0001419-1-2566], the Division of Computer and Network Systems [Grant 21-06801], the Army Research Office [Grant W911NF-19-1-0379], and the Division of Computing and Communication Foundations [Grants 17-04970 and 19-34986].

Effectiveness of rural internships for veterinary students to combat veterinary workforce shortages in rural areas.

Veterinarians are a pivotal force in addressing animal health and welfare surveillance, with a critical role in improving public health security and increasing the profits of livestock farmers. Yet, the veterinary profession is adversely affected by personnel shortages, particularly in rural areas. Since the health of people, animals and their shared environment are interconnected in a One Health perspective, a set of policies are required to ensure public health by attraction and retention of veterinarians in rural areas. In France, a tutored internship programme, financially subsiding students and mentors to execute a training period in remote rural areas, was promoted to better integrate and retain veterinary students ending their veterinary training. This paper aims to evaluate how veterinarians' tutored internships influences students' choices for rural practice, using three different statistical methods derived from causal inference theory. Using survey data for the period 2016-2020, we show that: (i) the average effect of the tutored internship on veterinarians' work in food animal sector is not significant; and that (ii) the tutored internship leads veterinarians with a low share of work in the food animal sector to have a rural practise after they graduated between 13 and 20% greater than those who did not participate in the tutored internship.

The influence of early life socio-environmental factors on executive performance in a healthy adult sample.

Attempts have been made (with research efforts encouraged) to deconstruct the "race" concept into language, cultural, and life experience variables that can help explain performance differences found between ethnic groups (Romero et al., 2009). The extant empirical literature reveals that early environmental factors and life experiences (e.g., socioeconomic status) are related to cognitive test performance in adulthood (Byrd et al., 2006). This study examined the explanatory value of early life childhood resources in the relationship between ethnicity and neuropsychological test performance in adulthood. Neurologically and psychologically healthy African American (n = 40), Caucasian (n = 14), and Hispanic (n = 107) college students ranging from 19-38 years of age. On average, participants had completed around 13 years of education, indicating that the majority were in the early stages of their undergraduate studies and mostly consisted of females (72%). Each participant completed a comprehensive neuropsychological battery that included tests of executive function and an extensive background questionnaire. A one-way analysis of variance (ANOVA) revealed that the CA group was significantly older (F (2, 160) = 18.38, p = .045) compared to the AA and H groups, but the groups did not differ in terms of number of years of educations or gender. Also, an ANOVA revealed significant group test performance differences on the Stroop-C [F (2, 160) = 1.53, p = .047], but not on the TMT-B and COWAT. Furthermore, a Tukey post hoc revealed that there were no significant differences in test performance on Stroop-C between the groups. Hierarchical multiple regression analyses revealed that group performance differences on executive function tests were medium or non-existent and only partially explained by years of education and early life financial resources. The results are discussed in light of the existing literature, study strengths and limitations, as well as directions for future research. This research can aid in pinpointing variables crucial for interpreting differences in neuropsychological assessments among diverse populations, holding potential implications for intervention research and policy settings. It is particularly relevant in the context of the continuously evolving social, political, and economic landscapes of societies.

Finding the optimal exploration-exploitation trade-off online through Bayesian risk estimation and minimization

We propose endogenous Bayesian risk minimization (EBRM) over policy sets as an approach to online learning across a wide range of settings. Many real-world online learning problems have complexities such as action- and belief-dependent rewards, time-discounting of reward, and heterogeneous costs for actions and feedback; we find that existing online learning heuristics cannot leverage most problem-specific information, to the detriment of their performance. We introduce a belief-space Markov decision process (BMDP) model that can capture these complexities, and further apply the concepts of aleatoric, epistemic, and process risks to online learning. These risk functions describe the risk inherent to the learning problem, the risk due to the agent's lack of knowledge, and the relative quality of its policy, respectively. We demonstrate how computing and minimizing these risk functions guides the online learning agent towards the optimal exploration-exploitation trade-off in any stochastic online learning problem, constituting the basis of the EBRM approach. We also show how Bayes' risk, the minimization objective in stochastic online learning problems, can be decomposed into the aforementioned aleatoric, epistemic, and process risks.In simulation experiments, EBRM algorithms achieve state-of-the-art performance across various classical online learning problems, including Gaussian and Bernoulli multi-armed bandits, best-arm identification, mixed objectives with action- and belief-dependent rewards, and dynamic pricing, a finite partial monitoring problem. To our knowledge, it is also the first computationally efficient online learning approach that can provide online bounds on an algorithm's Bayes' risk. Finally, because the EBRM approach is parameterized by a set of policy algorithms, it can be extended to incorporate new developments in online learning algorithms, and is thus well-suited as the foundation for developing real-world learning agents.

An innovative heterogeneous transfer learning framework to enhance the scalability of deep reinforcement learning controllers in buildings with integrated energy systems

AbstractDeep Reinforcement Learning (DRL)-based control shows enhanced performance in the management of integrated energy systems when compared with Rule-Based Controllers (RBCs), but it still lacks scalability and generalisation due to the necessity of using tailored models for the training process. Transfer Learning (TL) is a potential solution to address this limitation. However, existing TL applications in building control have been mostly tested among buildings with similar features, not addressing the need to scale up advanced control in real-world scenarios with diverse energy systems. This paper assesses the performance of an online heterogeneous TL strategy, comparing it with RBC and offline and online DRL controllers in a simulation setup using EnergyPlus and Python. The study tests the transfer in both transductive and inductive settings of a DRL policy designed to manage a chiller coupled with a Thermal Energy Storage (TES). The control policy is pre-trained on a source building and transferred to various target buildings characterised by an integrated energy system including photovoltaic and battery energy storage systems, different building envelope features, occupancy schedule and boundary conditions (e.g., weather and price signal). The TL approach incorporates model slicing, imitation learning and fine-tuning to handle diverse state spaces and reward functions between source and target buildings. Results show that the proposed methodology leads to a reduction of 10% in electricity cost and between 10% and 40% in the mean value of the daily average temperature violation rate compared to RBC and online DRL controllers. Moreover, online TL maximises self-sufficiency and self-consumption by 9% and 11% with respect to RBC. Conversely, online TL achieves worse performance compared to offline DRL in either transductive or inductive settings. However, offline Deep Reinforcement Learning (DRL) agents should be trained at least for 15 episodes to reach the same level of performance as the online TL. Therefore, the proposed online TL methodology is effective, completely model-free and it can be directly implemented in real buildings with satisfying performance.

A Comparative Research and Analysis of the Development of Sustainable Tourism in Georgian and other International Curricula

In recent years, sustainable development has become an important part of educational systems, beginning from primary schools to universities. Consequently, it has evolved into a societal priority. In 2019, the UNESCO adopted the ESD for 2030 framework with the overall objective of building a more just and sustainable world through the achievement of the 17 Sustainable Development Goals. Education for sustainable development for 2030 framework identifies 5 priority action areas: policy, education and training settings, educators, youth, and communities. Sustainable tourism development has been a commonly used term for many years and is, therefore, an important concept for consideration within tourism degree courses/programs. Based on qualitative research and case study method, this paper focuses on discussing the undergraduate and graduate programs in the context of teaching sustainable tourism in foreign countries, which are compared with the experience of the Georgian Technical University. The aim of the research was to study current educational programs related to sustainable tourism and highlight its importance. In conclusion, it can be asserted that sustainable tourism programs are experiencing a growing demand, with various types of policies being developed. However, the program at the Georgian Technical University stands out on an international scale, having no analogue and being truly unique. At the same time, the results indicate that in many cases, sustainability pedagogy/teaching methods, such as systems and holistic thinking, are not yet widely adopted and are in the process of implementation.

Assessing effects of pandemic-related policies on individual public transit travel patterns: A Bayesian online changepoint detection based framework

During a pandemic or natural disaster, people may alter transit usage behavior due to perception of changes in the environment. To effectively respond to these crises, it is important for governments and public transit agencies to understand when these changes occurred and how they were affected by relevant policies and responsive strategies. In this study, we develop a methodological framework based on Bayesian online changepoint detection (BOCD) to identify the occurrence time, direction, and persistency of changes in individual-level transit usage. We demonstrate the effectiveness of this framework in informing government decision-making in the context of COVID-19. Using Jeju Island, South Korea as a case study, we apply the framework over a nearly two-year smart card dataset collected from the beginning of 2019 till nine months into the pandemic. By focusing on frequent transit users, we detect when these users significantly changed their transit usage frequency during the pandemic and identify several types of users who experienced different behavior change patterns. Besides demonstrating the great heterogeneity in individual-level behavior changes, we perform a regression analysis to further understand how these changes were affected by key government policies (e.g., Risk alert, Social distancing, Public transit policy, and Eased social distancing). Our results suggest that only certain sets of policies appear to have significant effects. In particular, introducing Risk alert would cause a 277% to 317% increase in the number of users who reduced transit usage frequency. Policies that eased social distancing, though, would cause a 134% to 155% increase in the number of users with travel frequency increase. The proposed BOCD framework enables a scalable solution to identifying and understanding changes of individual transit behavior. The methodology and findings are beneficial for developing targeted policies and interventions to facilitate daily travel and public transit operations during public health crises.

Nudging plant-based alternatives to meat and dairy in a real-life online supermarket: A randomized controlled trial

A shift from predominantly animal-based to plant-based consumption can benefit both planetary and public health. Nudging may help to promote such a shift. This study investigated nudge effects on plant-based alternatives to meat and dairy in an online supermarket. We conducted a two-arm, parallel-group, randomized controlled real-life online supermarket trial. Each customer transaction was randomized to a control arm (regular online supermarket) or an intervention arm (addition of placement, hedonic property and dynamic social norm nudges promoting meat and dairy alternatives). Outcomes were the aggregate of meat and dairy alternative purchases (primary outcome), the number of meat purchases, dairy purchases, meat alternative purchases, and dairy alternative purchases (secondary), and retailer revenue (tertiary). Generalized linear mixed models with a Conway–Maxwell Poisson distribution were used to estimate incidence rate ratios (IRRs). Analyzed data included 8488 transactions by participants (n = 4,266 control arm, n = 4,222 intervention arm), out of which 2,411 (66%) were aged above 45 years, 5,660 (67%) were females, and 1,970 (23%) lived in socially disadvantaged neighborhoods. Intervention arm participants purchased 10% (IRR 1.10 (95% CI 0.99–1.23)) more meat and dairy alternatives and 16% (1.16 (0.99–1.36)) more meat alternatives than control arm participants, although these findings are not statistically significant. There was no difference in dairy alternative purchases (1.00 (0.90–1.10)). Intervention arm participants purchased 3% less meats (0.97 (0.93–1.02)) and 2% less dairy products (0.98 (0.95–1.02)) than control participants. Retailer revenue was not affected (0.98 (0.95–1.01)). Online nudging strategies alone did not lead to a statistically significant higher amount of plant-based purchases, but replication of this work is needed with increased study power. Future studies should also consider nudging strategies as part of a broader set of policies to promote plant-based purchases. Trial registrationProspectively registered on 14th of May 2022. ISRCTN16569242 (https://doi.org/10.1186/ISRCTN16569242).