The Behavioral Mechanisms Behind Feedback – A Preliminary Model for Quantifying Cause-Effect Relationships

The ubiquity of information technology makes it possible to provide individuals with personalized, context-specific, and timely feedback at low marginal cost. This allows for highly scalable behavioral interventions that can support behavior change even for frequent, habitual, and incidental behaviors. Despite the well-documented potential of feedback interventions in improving personal and societal outcomes, the associated theoretical insights – in particular for real-time feedback on everyday behaviors – are sparse. To this end, this research-in-progress paper proposes a conceptual model to formalize cause-effect relationships between feedback interventions, the antecedents of behavior, and resulting behavioral changes by integrating important concepts from feedback intervention theory and other behavioral theories. Moreover, this paper outlines the planned validation of the model and research hypotheses with an empirical field study. Ultimately, the results are expected to inform the development of (future) Information Systems for behavior change by holistically explaining behavioral mechanisms associated with feedback interventions

Overcoming Anchoring Bias: The Potential of AI and XAI-based Decision Support

Information systems (IS) are frequently designed to leverage the negative effect of anchoring bias to influence individuals’ decision-making (e.g., by manipulating purchase decisions). Recent advances in Artificial Intelligence (AI) and the explanations of its decisions through explainable AI (XAI) have opened new opportunities for mitigating biased decisions. So far, the potential of these technological advances to overcome anchoring bias remains widely unclear. To this end, we conducted two online experiments with a total of N=390 participants in the context of purchase decisions to examine the impact of AI and XAI-based decision support on anchoring bias. Our results show that AI alone and its combination with XAI help to mitigate the negative effect of anchoring bias. Ultimately, our findings have implications for the design of AI and XAI-based decision support and IS to overcome cognitive biases

No Longer Without a Reward: Do Digital Rewards Crowd Out Intrinsic Motivation of Young Children?

In the learning journey of young children, rewards are ubiquitous. Yet, psychologists and behavioral economists question the success of rewards and even claim that they displace intrinsic motivation, a phenomenon referred to as motivation crowding out. While information systems can help children learn everyday tasks, it is unclear if and when digital rewards produce motivation crowding out. Theoretically sound, empirical field studies on this topic are lacking and existing information system research on motivation crowding is limited to specific domains, not covering children’s behavior. Therefore, we aim to elicit how digital rewards influence an everyday health behavior that children learn in kindergarten – handwashing – and the underlying intrinsic motivation. We conduct a randomized controlled trial that is conceptualized in this paper. Our results will extend motivation crowding theory in the context of young children and inform the design of digital behavior change interventions

Pimecrolimus in atopic dermatitis: Consensus on safety and the need to allow use in infants

Atopic dermatitis (AD) is a distressing dermatological disease, which is highly prevalent during infancy, can persist into later life and requires long-term management with anti-inflammatory compounds. The introduction of the topical calcineurin inhibitors (TCIs), tacrolimus and pimecrolimus, more than 10 yr ago was a major breakthrough for the topical anti-inflammatory treatment of AD. Pimecrolimus 1% is approved for second-line use in children (≥2 yr old) and adults with mild-to-moderate AD. The age restriction was emphasized in a boxed warning added by the FDA in January 2006, which also highlights the lack of long-term safety data and the theoretical risk of skin malignancy and lymphoma. Since then, pimecrolimus has been extensively investigated in short- and long-term studies including over 4000 infants (<2 yr old). These studies showed that pimecrolimus effectively treats AD in infants, with sustained improvement with long-term intermittent use. Unlike topical corticosteroids, long-term TCI use does not carry the risks of skin atrophy, impaired epidermal barrier function or enhanced percutaneous absorption, and so is suitable for AD treatment especially in sensitive skin areas. Most importantly, the studies of pimecrolimus in infants provided no evidence for systemic immunosuppression, and a comprehensive body of evidence from clinical studies, post-marketing surveillance and epidemiological investigations does not support potential safety concerns. In conclusion, the authors consider that the labelling restrictions regarding the use of pimecrolimus in infants are no longer justified and recommend that the validity of the boxed warning for TCIs should be reconsidered

Using Digital Performance Feedback for Behavior Change: A Review

Feedback on task performance is a key component for improvements and learning for many behaviors. Although the IS literature has provided relevant individual contributions on the design, outcomes, and mechanisms of performance feedback, there exists no overarching conceptual framework or theory to integrate those results for a holistic explanation of behavior change through IS-enabled feedback. Further, an overview of the existing literature to facilitate theory development is still missing. This paper presents a systematic literature review on performance feedback in the IS discipline by reviewing 23 articles from high-quality journals. The review provides an overview on the research landscape by coding characteristics of feedback systems and outcomes of the research. It highlights four behavioral mechanisms that performance feedback influences to improve task performance (attention, behavioral control, personal norms, and perceived social norms) and concludes by presenting possible directions for future research

Explaining and predicting annual electricity demand of enterprises – a case study from Switzerland

Abstract In an attempt to channel sales activities, companies often focus on ‘high value targets’ that offer attractive prospective returns. In liberalized electricity markets, commercial customers with high electricity demand constitute such high value targets. The problem when acquiring new customers, however, is that the electricity consumption is not known to the sales organization in advance. This hinders the possibility to prioritize sales targets and thus increases the acquisition cost, reduces the competitiveness within the market and ultimately leads to higher cost for electricity customers. In this study, we investigate the annual electricity consumption of enterprises by means of a dataset with 1810 company addresses in a typical town in Switzerland. We use the industry branch of the enterprises together with open big data (geographic information, online-content, social media data and governmental statistical data) to explain and predict the electricity consumption of such. Our linear regression analysis shows that information on the economic branches of the enterprises, basal area of buildings, number of opening hours and social media data can explain up to 19% of variance in electricity consumption. Economic trends (e.g., in labor market and turnover statistics) reflect changes in the electricity consumption in the investigated years 2010–2014 for several economic branches. We show, that the electricity consumption can be predicted better than a random predictor, however with a high uncertainty. Nevertheless, the open data sources can be used to identify a relevant group of companies with high consumption (more than 100,000kWh per year) with good accuracy

Hand hygiene of kindergarten children-Understanding the effect of live feedback on handwashing behaviour, self-efficacy, and motivation of young children: Protocol for a multi-arm cluster randomized controlled trial.

Early implementation of interventions at a young age fosters behaviour changes and helps to adopt behaviours that promote health. Digital technologies may help to promote the hand hygiene behaviour of children. However, there is a lack of digital feedback interventions focusing on the hand hygiene behaviour of preschool children in childhood education and care settings. This study protocol aims to describe a study that evaluates the effectiveness of a gamified live feedback intervention and explores underlying behavioural theories in achieving better hand hygiene behaviour of preschool children in early childhood education and care settings. This study will be a four-arm cluster randomized controlled trial with three phases and a twelve-month follow-up by country stratification. The sample size is 106 children of which one cluster will have a minimum number of 40 children. During the baseline phase, all groups will have automated monitoring systems installed. In the intervention phase, the control group will have no screen activity. The intervention groups will have feedback displays during the handwashing activity. Intervention A will receive instructions, and intervention B and C groups will receive instructions and a reward. In the post-intervention phase, all the groups will have no screen activity except intervention C which will receive instructions from the screen but no reward. The outcome measures will be hand hygiene behaviour, self-efficacy, and intrinsic motivation. Outcome measures will be collected at baseline, intervention, and post-intervention phases and a 12-month follow-up. The data will be analysed with quantitative and qualitative methods. The findings of the planned study will provide whether this gamified live feedback intervention can be recommended to be used in educational settings to improve the hand hygiene behaviour of preschool children to promote health. The trial is registered with ClinicalTrials.gov (registration number NCT05395988 https://clinicaltrials.gov/ct2/show/NCT05395988?term=NCT05395988&draw=2&rank=1)