A Qualitative Investigation of Gamification

Gamification ◽  
2015 ◽  
pp. 32-48
Author(s):  
Amon Rapp
Keyword(s):  
Behavior Change ◽  
Focus Groups ◽  
Interactive Systems ◽  
Change Processes ◽  
Diary Study ◽  
User Engagement ◽  
Qualitative Investigation ◽  
Subjective Experiences ◽  
Quantitative Studies ◽  
Intrinsic Motivations

Gamification is commonly employed in designing interactive systems to enhance user engagement and motivations, or to trigger behavior change processes. Although some quantitative studies have been recently conducted aiming at measuring the effects of gamification on users' behaviors and motivations, there is a shortage of qualitative studies able to capture the subjective experiences of users, when using gamified systems. The authors propose to investigate how users are engaged by the most common gamification techniques, by conducting a diary study followed by a series of six focus groups. From the findings gathered, they conclude the paper identifying some implications for the design of interactive systems that aim at supporting intrinsic motivations to engage their users.

A Qualitative Investigation of Gamification

2015 ◽  
Vol 11 (1) ◽  
pp. 67-82 ◽  
Author(s):  
Amon Rapp
Keyword(s):  
Behavior Change ◽  
Focus Groups ◽  
Interactive Systems ◽  
Change Processes ◽  
Diary Study ◽  
User Engagement ◽  
Qualitative Investigation ◽  
Subjective Experiences ◽  
Quantitative Studies ◽  
Intrinsic Motivations

Gamification is commonly employed in designing interactive systems to enhance user engagement and motivations, or to trigger behavior change processes. Although some quantitative studies have been recently conducted aiming at measuring the effects of gamification on users' behaviors and motivations, there is a shortage of qualitative studies able to capture the subjective experiences of users, when using gamified systems. The authors propose to investigate how users are engaged by the most common gamification techniques, by conducting a diary study followed by a series of six focus groups. From the findings gathered, they conclude the paper identifying some implications for the design of interactive systems that aim at supporting intrinsic motivations to engage their users.

scholarly journals Adults’ Preferences for Behavior Change Techniques and Engagement Features in a Mobile App to Promote 24-Hour Movement Behaviors: Cross-Sectional Survey Study (Preprint)

2019 ◽  
Author(s):  
Ann DeSmet ◽  
Ilse De Bourdeaudhuij ◽  
Sebastien Chastin ◽  
Geert Crombez ◽  
Ralph Maddison ◽  
...  
Keyword(s):  
Behavior Change ◽  
Repeated Measures ◽  
Self Regulation ◽  
Mobile App ◽  
User Preferences ◽  
Movement Behavior ◽  
Survey Study ◽  
User Engagement ◽  
Behavior Change Techniques ◽  
Self Monitoring

BACKGROUND There is a limited understanding of components that should be included in digital interventions for 24-hour movement behaviors (physical activity [PA], sleep, and sedentary behavior [SB]). For intervention effectiveness, user engagement is important. This can be enhanced by a user-centered design to, for example, explore and integrate user preferences for intervention techniques and features. OBJECTIVE This study aimed to examine adult users’ preferences for techniques and features in mobile apps for 24-hour movement behaviors. METHODS A total of 86 participants (mean age 37.4 years [SD 9.2]; 49/86, 57% female) completed a Web-based survey. Behavior change techniques (BCTs) were based on a validated taxonomy v2 by Abraham and Michie, and engagement features were based on a list extracted from the literature. Behavioral data were collected using Fitbit trackers. Correlations, (repeated measures) analysis of variance, and independent sample <italic>t</italic> tests were used to examine associations and differences between and within users by the type of health domain and users’ behavioral intention and adoption. RESULTS Preferences were generally the highest for information on the health consequences of movement behavior self-monitoring, behavioral feedback, insight into healthy lifestyles, and tips and instructions. Although the same ranking was found for techniques across behaviors, preferences were stronger for all but one BCT for PA in comparison to the other two health behaviors. Although techniques fit user preferences for addressing PA well, supplemental techniques may be able to address preferences for sleep and SB in a better manner. In addition to what is commonly included in apps, sleep apps should consider providing tips for sleep. SB apps may wish to include more self-regulation and goal-setting techniques. Few differences were found by users’ intentions or adoption to change a particular behavior. Apps should provide more self-monitoring (<italic>P</italic>=.03), information on behavior health outcome (<italic>P</italic>=.048), and feedback (<italic>P</italic>=.04) and incorporate social support (<italic>P</italic>=.048) to help those who are further removed from healthy sleep. A virtual coach (<italic>P</italic><.001) and video modeling (<italic>P</italic>=.004) may provide appreciated support to those who are physically less active. PA self-monitoring appealed more to those with an intention to change PA (<italic>P</italic>=.03). Social comparison and support features are not high on users’ agenda and may not be needed from an engagement point of view. Engagement features may not be very relevant for user engagement but should be examined in future research with a less reflective method. CONCLUSIONS The findings of this study provide guidance for the design of digital 24-hour movement behavior interventions. As 24-hour movement guidelines are increasingly being adopted in several countries, our study findings are timely to support the design of interventions to meet these guidelines.

Involving international postgraduate students in university initiatives – a qualitative investigation into ‘UCL ChangeMakers’

2018 ◽  
Vol 4 (1) ◽  
Author(s):  
Anastasia Vikhanova ◽  
Vanessa Wedi
Keyword(s):  
Focus Groups ◽  
Student Learning ◽  
Learning Experience ◽  
Student Population ◽  
Qualitative Investigation ◽  
Postgraduate Students ◽  
University College ◽  
University College London ◽  
Enhance Student Learning ◽  
Collaborative Initiative

UCL ChangeMakers is the collaborative initiative launched in 2014 to enhance student learning experience in University College London (UCL), UK. Its aim is to enable students and staff to work together to make changes in the UCL community. In 2016/17, the UCL ChangeMakers initiative struggled to recruit projects from the postgraduate (PG) student population; however, postgrads are believed to have brought exceptionally valuable ideas into the initiative. The current study aimed to investigate the general image of the UCL ChangeMakers initiative among the UCL PG population and identify potential areas of improvement for attracting more PG students into the scheme. Two focus groups were conducted with current international PGs participating in the UCL ChangeMakers programme and international PGs from the general UCL population. The results included a discussion on current UCL PG ChangeMakers’ experiences, the image of the initiative among the general PG UCL population and suggestions for promotion of and improvements to the initiative. Furthermore, recommendations for postgraduate involvement in university initiatives were made.

Non-Player Characters and Artificial Intelligence

Gamification ◽  
2015 ◽  
pp. 488-514
Author(s):  
Gonçalo Pereira ◽  
António Brisson ◽  
João Dias ◽  
André Carvalho ◽  
Joana Dimas ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computational Models ◽  
Research Topic ◽  
Interactive Systems ◽  
Learning Activity ◽  
Willingness To Accept ◽  
User Engagement ◽  
Efficient Communication ◽  
Game Environment ◽  
Computational Systems

Serious Games rely on interactive systems to provide an efficient communication medium between the tutor and the user. Designing and implementing such medium is a multi-disciplinary task that aims at an environment that engages the user in a learning activity. User engagement is significantly related to the users' sense of immersion or his willingness to accept the reality proposed by a game environment. This is a very relevant research topic for Artificial Intelligence (AI), since it requires computational systems to generate believable behaviors that can promote the users' willingness to enter and engage in the game environment. In order to do this, AI research has been relying on social sciences, in particular psychology and sociology models, to ground the creation of computational models for non-player characters that behave according to the users' expectations. In this chapter, the authors present some of the most relevant NPC research contributions following this approach.

scholarly journals “I Wish I was Wearing a Filter Right Now”: An Exploration of Identity Formation and Subjectivity of 10- and 11-Year Olds’ Social Media Use

2020 ◽  
Vol 6 (4) ◽  
pp. 205630512096515
Author(s):  
Claire Kathryn Pescott
Keyword(s):  
Social Media ◽  
Young Children ◽  
Focus Groups ◽  
Identity Formation ◽  
Primary Schools ◽  
Media Use ◽  
Subjective Experiences ◽  
Social Media Use ◽  
Emotional Resilience

Social media use is changing the experience of socialization for younger children, as they are heavy adopters of these platforms despite the terms of service being 13 years of age. This research recruited eight Year 6 focus groups in four primary schools and employed a range of activities to explore their views surrounding social media. Results indicate that young children are aware of overt dangers, such as catfishing, but may experience negative subjective experiences when interacting on social media. This was particularly apparent in the discussions around Snapchat filters (digital overlays placed over photographs). It is necessary to address emotional resilience in response to this.

scholarly journals Brand Coolness

2019 ◽  
Vol 83 (5) ◽  
pp. 36-56 ◽  
Author(s):  
Caleb Warren ◽  
Rajeev Batra ◽  
Sandra Maria Correia Loureiro ◽  
Richard P. Bagozzi
Keyword(s):  
Mixed Methods ◽  
Willingness To Pay ◽  
Focus Groups ◽  
High Status ◽  
Change Over Time ◽  
Mixed Methods Approach ◽  
Quantitative Studies ◽  
The Masses ◽  
Depth Interviews ◽  
Over Time

Marketers strive to create cool brands, but the literature does not offer a blueprint for what “brand coolness” means or what features characterize cool brands. This research uses a mixed-methods approach to conceptualize brand coolness and identify a set of characteristics typically associated with cool brands. Focus groups, depth interviews, and an essay study indicate that cool brands are perceived to be extraordinary, aesthetically appealing, energetic, high status, rebellious, original, authentic, subcultural, iconic, and popular. In nine quantitative studies (surveys and experiments), the authors develop scale items to reliably measure the component characteristics of brand coolness; show that brand coolness influences important outcome variables, including consumers’ attitudes toward, satisfaction with, intentions to talk about, and willingness to pay for the brand; and demonstrate how cool brands change over time. At first, most brands become cool to a small niche, at which point they are perceived to be more subcultural, rebellious, authentic, and original. Over time, some cool brands become adopted by the masses, at which point they are perceived to be more popular and iconic.

scholarly journals Understanding Health Behavior Technology Engagement: Pathway to Measuring Digital Behavior Change Interventions

10.2196/14052 ◽  
2019 ◽  
Vol 3 (4) ◽  
pp. e14052 ◽  
Author(s):  
Heather Cole-Lewis ◽  
Nnamdi Ezeanochie ◽  
Jennifer Turgiss
Keyword(s):  
Health Behavior ◽  
Behavior Change ◽  
User Experience ◽  
User Engagement ◽  
User Interactions ◽  
Behavior Change Intervention ◽  
Behavioral Determinants ◽  
Behavior Change Interventions ◽  
Frequency Of Use ◽  
Measurement And Evaluation

Researchers and practitioners of digital behavior change interventions (DBCI) use varying and, often, incongruent definitions of the term “engagement,” thus leading to a lack of precision in DBCI measurement and evaluation. The objective of this paper is to propose discrete definitions for various types of user engagement and to explain why precision in the measurement of these engagement types is integral to ensuring the intervention is effective for health behavior modulation. Additionally, this paper presents a framework and practical steps for how engagement can be measured in practice and used to inform DBCI design and evaluation. The key purpose of a DBCI is to influence change in a target health behavior of a user, which may ultimately improve a health outcome. Using available literature and practice-based knowledge of DBCI, the framework conceptualizes two primary categories of engagement that must be measured in DBCI. The categories are health behavior engagement, referred to as “Big E,” and DBCI engagement, referred to as “Little e.” DBCI engagement is further bifurcated into two subclasses: (1) user interactions with features of the intervention designed to encourage frequency of use (ie, simple login, games, and social interactions) and make the user experience appealing, and (2) user interactions with behavior change intervention components (ie, behavior change techniques), which influence determinants of health behavior and subsequently influence health behavior. Achievement of Big E in an intervention delivered via digital means is contingent upon Little e. If users do not interact with DBCI features and enjoy the user experience, exposure to behavior change intervention components will be limited and less likely to influence the behavioral determinants that lead to health behavior engagement (Big E). Big E is also dependent upon the quality and relevance of the behavior change intervention components within the solution. Therefore, the combination of user interactions and behavior change intervention components creates Little e, which is, in turn, designed to improve Big E. The proposed framework includes a model to support measurement of DBCI that describes categories of engagement and details how features of Little e produce Big E. This framework can be applied to DBCI to support various health behaviors and outcomes and can be utilized to identify gaps in intervention efficacy and effectiveness.

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