About the theory
The goal of the FUTURE project, and this working group as well, is to develop a comprehensive integrated evidence-based theory depicting the complex impacts of technology usage on the physical, psychological, and social well-being of adolescents from both the short- and long-term perspectives.
We looked at existing theories that we aim to integrate and enhance. As the starting point, we use the Differential Susceptibility to Media Effects Model (DSMM) (Valkenburg & Peter, 2013), which aims to explain why some individuals are more susceptible to media effects and how and why media influences them. In essence, the DSMM distinguishes three types of differential susceptibility variables to media effects (e.g. dispositional, developmental, social) that affect the individuals’ media use. The use leads to immediate media responses (e.g. cognitive, emotional, excitable) that serve as moderators between the media use and the media effect (i.e. the main outcome). The DSMM acknowledges that susceptibility variables affect media use as predictors but also stimulate or reduce media effects as moderators. Finally, media effects can also reciprocally influence differential susceptibility variables, media use, and media responses. In our project, we adapt the DSMM model to focus on well-being as the main outcome and we enrich it with detailed consideration of both the direct and indirect impact of risky and beneficial activities, accounting for the diverse psycho-social and environmental influences in adolescence. Thus, we will also utilize other selected relevant theories that can provide more insights into adolescents’ well-being and media use and which can enhance various aspects of the DSMM model.
To conceptualize the differential susceptibility variables, we draw from Bronfenbrenner’s (1977) Ecological Systems Theory (EST) and Jessor’s (2014) Problem Behavior Theory (PBT). The EST provides a framework for understanding the influences on development by situating the adolescent within a system of relationships affected by multiple levels (i.e. systems) of the environment. Based on EST, we differentiate the susceptibility variables differently from DSMM and focus on the individual (i.e. microsystem), social (i.e. mesosystem), and country levels (i.e. macrosystem). The PBT provides a framework for adolescents’ problem behavior and specifies risk and protective factors that affect their development. It recognizes several domains within which we can identify these factors, including personality, perceived environment, and actual behavior. Other important factors in PBT include vulnerabilities and opportunities to engage in risks, controls against risks, and social support for positive behaviors. These factors thus appear among differential susceptibility variables, but also in media use, which can be conceptualized as either a risky or positive behavior. Finally, using the PBT allows us to overcome the dominant risk perspective because the PBT considers protective factors not only as protective for risks but also as promotive for positive development.
The positive view of technology usage for well-being improvement is also accentuated in the Health Belief Model (Ahadzadeh et al., 2015), which is widely used to explain health behavior change and how people use technology for change if they are aware of a threat from a specific health condition or behavior and perceive benefits from the considered actions. This model will be especially useful for the examination of the effects on physical well-being.
The conceptual model of the project
Based on the aforementioned theoretical inputs, we adapted the DSMM and developed our base conceptual model for the effects of online activities on adolescents’ well-being. The model is described below (Figure 1).
In our model, long-term well-being, which is conceptualized as physical, psychological, and social, corresponds to the media effect in DSMM. The same dimensions are also present in the short-term perspective (i.e. media response in DSMM), acknowledging that online activities can also lead to immediate and temporary changes in an adolescent’s well-being. We will thus understand both the short-term and long-term effects of technology on adolescents’ well-being as outcomes. The strength of this project is its comprehensive focus on all dimensions of well-being. However, it is not possible to focus on all of the possible indicators of well-being. We selected the following indicators to be captured in our research.
The indicators of physical well-being will be adolescents’ sleeping and physical in/activity (including sedentary behavior) habits. Both indicators can be affected in the short term (e.g. disrupted sleep after engaging in online gaming or higher physical activity after using mHealth applications) and in the long term (e.g. general sleeping and physical in/activity patterns over a longer period of time).
The indicators of psychological well-being in our project will be adolescents’ emotional state (e.g. distress, anxiety, happiness), self-esteem, and life satisfaction. The short-term aspects in this well-being dimension correspond to temporary emotional changes as a result of technology usage (e.g. being upset by comments on a social networking profile, feeling happy while communicating with a friend), and the long-term aspects correspond to more permanent and stable emotional levels (e.g. increased anxiety after facing repeated cyber aggression, enhanced self-esteem as a function of participation in online communities).
As for the indicators of adolescents’ social well-being, we will examine the short- and long-term aspects of relationship quality (with peers, family, school, community) including perceived social support and social skills. Short-term effects include increased perceived support, and long-term effects include increased connectedness among family members.
Differential susceptibility variables
Based on the EST, we categorize the susceptibility variables differently from the DSMM and specifically focus on those that can constitute risk or protective factors in line with the PBT. The selection of specific variables is also based on our prior research on children’s and adolescents’ risky activities online (see e.g. Smahel & Wright, 2014; Smahel et al., 2014). In our conceptual model, we differentiate:
- individual-level variables with factors such as age, gender, developmental stage, and skills
- social-level variables with factors such as peers, family, school, and community, and we examine and account for the inequalities reflected in media use and well-being by focusing on the role of socioeconomic status (SES)
- country-level variables such as culture, media, values, technology provision, and welfare.
Media use in our model is understood to be the extent and content of adolescents' online activities. Based on previous research (Castellacci & Tveito, 2018, Livingstone et al., 2011), we will focus predominantly on a set of activities that have shown to have a positive or negative impact on well-being or that raise substantial concern about their effect on well-being. As a basic differentiation, we distinguish between interaction activities (e.g. communication through technology with friends, family, and unknown people, participating in online groups) and consumption activities (e.g. viewing online content). In the project, we will also focus on the general pattern of technology usage (e.g. time and frequency, excessive use, usage of diverse devices). It is crucial to note that, although we will measure specific activities, our investigation will lead to the detection of more general patterns and the recognition of media affordances, such as interactivity, and textual or visual character. That is, we will focus on the meaning of the activity regardless of the specific device, platform, or application used. The focus on affordances rather than on how exactly they are achieved will allow us to provide insights and generate models that will be applicable in the future with technologies not yet developed.