Abstract
Objectives
It is important to understand what factors make some users of social media engage in risky activities. This under-researched area is the focus of the present study which applies the dual-process Prototype Willingness Model to demonstrate the potential role of reasoned and social reactive pathways in explaining risk behaviors in adolescents and adults in the online environment.
Design
Quantitative single time point study using online survey data from an international sample of social media users (N = 1220).
Methods
Two-step logistic regression analysis tested the predictive ability of the reactive pathway variables of the Prototype Willingness Model above and beyond reasoned pathway variables from expectancy-value models such as the Theory of Reasoned Action and Theory of Planned Behavior.
Results
The reactive pathway variables increased explained variance in willingness to engage in online risk behaviors (compared to reasoned pathway variables alone) by a mean improvement of 6.2% across both adolescent and adult age groups. Prototype favorability (how positively or negatively an individual judges their perception of the ‘typical person’ to engage in a risk behavior) emerged as a particularly strong predictor of willingness to engage in online risky behavior. The predictive ability of prototype similarity (an individuals perceived similarity to the ‘typical person’ to engage in risk behavior) differed according to the type of risk behavior involved, with similarity on conscientiousness and extraversion appearing to have the most influence upon willingness.
Conclusions
Reactive pathways significantly predict willingness to engage in risky behavior online across both age groups. The reactive pathway variables explained more additional variance in willingness for adolescents compared to adults suggesting that reactive processes may play a bigger part in adolescents’ online risk taking; with decision making potentially shifting towards a more reasoned, analytical pathway in adulthood.
It is important to understand what factors make some users of social media engage in risky activities. This under-researched area is the focus of the present study which applies the dual-process Prototype Willingness Model to demonstrate the potential role of reasoned and social reactive pathways in explaining risk behaviors in adolescents and adults in the online environment.
Design
Quantitative single time point study using online survey data from an international sample of social media users (N = 1220).
Methods
Two-step logistic regression analysis tested the predictive ability of the reactive pathway variables of the Prototype Willingness Model above and beyond reasoned pathway variables from expectancy-value models such as the Theory of Reasoned Action and Theory of Planned Behavior.
Results
The reactive pathway variables increased explained variance in willingness to engage in online risk behaviors (compared to reasoned pathway variables alone) by a mean improvement of 6.2% across both adolescent and adult age groups. Prototype favorability (how positively or negatively an individual judges their perception of the ‘typical person’ to engage in a risk behavior) emerged as a particularly strong predictor of willingness to engage in online risky behavior. The predictive ability of prototype similarity (an individuals perceived similarity to the ‘typical person’ to engage in risk behavior) differed according to the type of risk behavior involved, with similarity on conscientiousness and extraversion appearing to have the most influence upon willingness.
Conclusions
Reactive pathways significantly predict willingness to engage in risky behavior online across both age groups. The reactive pathway variables explained more additional variance in willingness for adolescents compared to adults suggesting that reactive processes may play a bigger part in adolescents’ online risk taking; with decision making potentially shifting towards a more reasoned, analytical pathway in adulthood.
| Original language | English |
|---|---|
| Pages (from-to) | 183-191 |
| Journal | Computers in Human Behavior |
| Volume | 78 |
| Early online date | 28 Sep 2017 |
| DOIs | |
| Publication status | Published - 1 Jan 2018 |