This paper presents the Interactive Behavior Change Model (IBCM 8.0), a system that integrates behavior change principles from neuroscience, psychology, and behavioral science into a behavioral meta-theory. With its broad, application-agnostic nature, the IBCM provides insight into behavior change, how it operates, and offers an alternative explanation for why various behavior change models work or do not work. It has applications as a behavioral system for education, research, analysis, intervention design, and implementation in various technologies, especially self-adaptive systems run by rule-based engines or artificial intelligence (AI). Due to space limits, this paper covers the model structure and theory with a limited high-level overview of its ontology.

Contrary to popular belief, social influence encompasses a much more complex area of behavioral science than the explanation offered by those who call all forms of social influence a social norm, peer pressure, or simply social proof. To help scholars and practitioners develop a deeper understanding of social influence, this study presents a measurement instrument for evaluating susceptibility to seven social influence principles, namely social learning, social comparison, social norms, social facilitation, social cooperation, social competition, and social recognition. Each principle is represented by a construct containing six theory-driven items, both positively and negatively framed. Further, the study introduces a social influence research model that describes how the seven social influence constructs are correlated and impact each other. This study extends previous scientific work on social influence by providing research tools that can be used to further study the role of social influence in designing tailored technologies for transformation.

Dark patterns are interactive design patterns that influence technology users through deception or trickery, and which represent unethical applications of persuasive technology. However, our ability to identify dark patterns is limited, creating a situation where it is difficult to manage abuses of persuasive psychology, because it is difficult to even identify them. Although there are numerous practitioner taxonomies of dark patterns, there is no scientifically-based taxonomy available. This workshop provides an introduction to dark patterns and an overview of the psychological mechanisms that drive them. Through participatory exercises, participants will help to identify the theoretical underpinnings that drive dark patterns, and contribute to the development of a taxonomy of dark patterns, based on consensus within the scientific community. In the workshops, we will form working teams who will review the dark pattern taxonomy, looking for alternative theoretical explanations. Each working team will participate in a group sorting exercise, designed to inform the development of a theoreticallyframed taxonomy of dark patterns. All outputs of the workshop will be captured, and used to advance this study towards validation of the taxonomy. After the workshops, the authors of this paper will incorporate all the advancements into the next stage of the research, which will feed into a subsequent paper on a taxonomy of dark patterns, addressing the identified research questions.

Many organizations use social media to attract supporters, disseminate information and advocate change. Services like Twitter can theoretically deliver messages to a huge audience that would be difficult to reach by other means. This article introduces a method to monitor an organization's Twitter strategy and applies it to tweets from United Nations Development Programme (UNDP) accounts. The Resonating Topic Method uses automatic analyses with free software to detect successful themes within the organization's tweets, categorizes the most successful tweets, and analyses a comparable organization to identify new successful strategies. In the case of UNDP tweets from November 2014 to March 2015, the results confirm the importance of official social media accounts as well as those of high profile individuals and general supporters. Official accounts seem to be more successful at encouraging action, which is a critical aspect of social media campaigning. An analysis of Oxfam found a successful social media approach that the UNDP had not adopted, showing the value of analyzing other organizations to find potential strategy gaps.

Numerous scholars study how to design evidence-based interventions that can improve the lives of individuals, in a way that also brings social benefits. However, within the behavioral sciences in general, and the persuasive technology field specifically, scholars rarely focus-on, or report the negative outcomes of behavior change interventions, and possibly fewer report a special type of negative outcome, a backfire. This paper has been authored to start a wider discussion within the scientific community on intervention backfiring. Within this paper, we provide tools to aid academics in the study of persuasive backfiring, present a taxonomy of backfiring causes, and provide an analytical framework containing the intention-outcome and likelihood-severity matrices. To increase knowledge on how to mitigate the negative impact of intervention backfiring, we discuss research and practitioner implications.

This editorial provides a behavioral science view on gamification and health behavior change, describes its principles and mechanisms, and reviews some of the evidence for its efficacy. Furthermore, this editorial explores the relation between gamification and behavior change frameworks used in the health sciences and shows how gamification principles are closely related to principles that have been proven to work in health behavior change technology. Finally, this editorial provides criteria that can be used to assess when gamification provides a potentially promising framework for digital health interventions.

This editorial provides a behavioral science view on gamification and health behavior change, describes its principles and mechanisms, and reviews some of the evidence for its efficacy. Furthermore, this editorial explores the relation between gamification and behavior change frameworks used in the health sciences and shows how gamification principles are closely related to principles that have been proven to work in health behavior change technology. Finally, this editorial provides criteria that can be used to assess when gamification provides a potentially promising framework for digital health interventions.

Background: Researchers and practitioners have developed numerous online interventions that encourage people to reduce their drinking, increase their exercise, and better manage their weight. Motivations to develop eHealth interventions may be driven by the Internet's reach, interactivity, cost-effectiveness, and studies that show online interventions work. However, when designing online interventions suitable for public campaigns, there are few evidence-based guidelines, taxonomies are difficult to apply, many studies lack impact data, and prior meta-analyses are not applicable to large-scale public campaigns targeting voluntary behavioral change.Objectives: This meta-analysis assessed online intervention design features in order to inform the development of online campaigns, such as those employed by social marketers, that seek to encourage voluntary health behavior change. A further objective was to increase understanding of the relationships between intervention adherence, study adherence, and behavioral outcomes.Methods: Drawing on systematic review methods, a combination of 84 query terms were used in 5 bibliographic databases with additional gray literature searches. This resulted in 1271 abstracts and papers; 31 met the inclusion criteria. In total, 29 papers describing 30 interventions were included in the primary meta-analysis, with the 2 additional studies qualifying for the adherence analysis. Using a random effects model, the first analysis estimated the overall effect size, including groupings by control conditions and time factors. The second analysis assessed the impacts of psychological design features that were coded with taxonomies from evidence-based behavioral medicine, persuasive technology, and other behavioral influence fields. These separate systems were integrated into a coding framework model called the communication-based influence components model. Finally, the third analysis assessed the relationships between intervention adherence and behavioral outcomes.Results: The overall impact of online interventions across all studies was small but statistically significant (standardized mean difference effect size d = 0.19, 95% confidence interval [CI] = 0.11-0.28, P<.001, number of interventions k = 30). The largest impact with a moderate level of efficacy was exerted from online interventions when compared with waitlists and placebos (d = 0.28, 95% CI = 0.17-0.39, P<.001, k = 18), followed by comparison with lower-tech online interventions (d = 0.16, 95% CI = 0.00-0.32, P=.04, k = 8); no significant difference was found when compared with sophisticated print interventions (d = -0.11, 95% CI = -0.34 to 0.12, P = .35, k = 4), though online interventions offer a small effect with the advantage of lower costs and larger reach. Time proved to be a critical factor, with shorter interventions generally achieving larger impacts and greater adherence. For psychological design, most interventions drew from the transtheoretical approach and were goal orientated, deploying numerous influence components aimed at showing users the consequences of their behavior, assisting them in reaching goals, and providing normative pressure. Inconclusive results suggest a relationship between the number of influence components and intervention efficacy. Despite one contradictory correlation, the evidence suggests that study adherence, intervention adherence, and behavioral outcomes are correlated.Conclusions: These findings demonstrate that online interventions have the capacity to influence voluntary behaviors, such as those routinely targeted by social marketing campaigns. Given the high reach and low cost of online technologies, the stage may be set for increased public health campaigns that blend interpersonal online systems with mass-media outreach. Such a combination of approaches could help individuals achieve personal goals that, at an individual level, help citizens improve the quality of their lives and at a state level, contribute to healthier societies.

This paper discusses two trends that threaten to undermine the effectiveness of online social marketing interventions: growing mistrust and competition. As a solution, this paper examines the relationships between Web site credibility, target audiences’ active trust and behaviour. Using structural equation modelling to evaluate two credibility models, this study concludes that Web site credibility is best considered a three-dimensional construct composed of expertise, trustworthiness and visual appeal, and that trust plays a partial mediating role between Web site credibility and behavioural impacts. The paper examines theoretical implications of conceptualizing Web sites according to a human credibility model, and factoring trust into Internet-based behavioural change interventions. Practical guidelines suggest ways to address these findings when planning online social marketing interventions.

This paper discusses problems faced by planners of real-world online behavioural change interventions who must select behavioural change frameworks from a variety of competing theories and taxonomies. As a solution, this paper examines approaches that isolate the components of behavioural influence and shows how these components can be placed within an adapted communication framework to aid the design and analysis of online behavioural change interventions. Finally, using this framework, a summary of behavioural change factors are presented from an analysis of 32 online interventions.

• In 2007, U.S. citizens lost $239 million to online crime (Internet Crime Complaint Center, 2007). • 90% of people could not differentiate between legitimate and criminal websites (Dhamija et al., 2006).• In 2006, roughly 80% of Americans searched for online health information and 55% acted on their findings. Only 75% verified sources sometimes, hardly ever, or never (Fox, 2006). • In 2005, the tobacco industry advertised online without restriction, developing interactive games and contests aimed at engaging youth (Lin and Hullman, 2005).

This paper evaluates data from an international anti-poverty campaign to assess if common principles from e-marketing and persuasive technology apply to online social marketing. It focuses on the relationships between website credibility, users’ active trust attitudes and behavioural intent. Using structural equation modelling, the evaluation found a significant relationship between these variables and suggests strategies for online behavioural change interventions.

Having engaged one billion users by early 2006, the Internet is the world's fastest-growing mass communications medium. As it permeates into countless lives across the planet, it offers social campaigners an opportunity to deploy interactive interventions that encourage populations to adopt healthy living, environmental protection and community development behaviours. Using a classic set of social campaigning criteria, this paper explores relationships between social campaign websites and behavioural change.