The behavioral intention construct predicted 26% of the variance in high-risk drinking, which is similar to the results by other researchers (Armitage & Conner, 2001). However, the complexities surrounding intentions and actual behavior merit further examination, as intentions are not always predictive of behavior. For example, unique circumstances may result in different intentions or the need for individuals to change their original intention, some of which may not be captured with traditional survey data. How do intentions change when alcohol is free or when somebody is pursuing a "significant other" are just a couple of examples, which could influence the findings. In general, the more complicated the behavior or social dynamics the more challenging it is to assess intentions. The time between intentions and behavior is yet another variable to consider with this type of research. Nevertheless, behavioral intention within the TRA/TPB/IBM consistently predicts drinking behavior within the college population (Collins & Carey, 2007; O'Callaghan, Chant, Callan, & Baglioni, 1997).
Somewhat unexpectedly, neither instrumental attitude, descriptive norms nor perceived control predicted intentions to engage in high-risk drinking with statistical significance. Thus, to examine the efficacy of the IBM further a path analysis was performed using exclusively the three primary constructs within the IBM. The results showed that each of the primary constructs were statistically significant, with the model explaining approximately 44% of the intention to engage in high-risk drinking. Similar, to the first path analysis attitude was the strongest predictor followed by personal agency and then perceived norms.
The findings from this study indicate that the IBM provides utility in explaining high-risk drinking among college students. More specifically, researchers and practitioners should focus on experiential attitude, injunctive norms, and self-efficacy in designing interventions with this population and behavior. The precision the IBM provides in identifying which specific constructs to address when combating high-risk drinking demonstrates its usefulness beyond the theory's predecessors, the Theory of Reasoned Action and Theory of Planned Behavior.
The behavioral intention construct predicted 26% of the variance in high-risk drinking, which is similar to the results by other researchers (Armitage & Conner, 2001). However, the complexities surrounding intentions and actual behavior merit further examination, as intentions are not always predictive of behavior. For example, unique circumstances may result in different intentions or the need for individuals to change their original intention, some of which may not be captured with traditional survey data. How do intentions change when alcohol is free or when somebody is pursuing a "significant other" are just a couple of examples, which could influence the findings. In general, the more complicated the behavior or social dynamics the more challenging it is to assess intentions. The time between intentions and behavior is yet another variable to consider with this type of research. Nevertheless, behavioral intention within the TRA/TPB/IBM consistently predicts drinking behavior within the college population (Collins & Carey, 2007; O'Callaghan, Chant, Callan, & Baglioni, 1997). Somewhat unexpectedly, neither instrumental attitude, descriptive norms nor perceived control predicted intentions to engage in high-risk drinking with statistical significance. Thus, to examine the efficacy of the IBM further a path analysis was performed using exclusively the three primary constructs within the IBM. The results showed that each of the primary constructs were statistically significant, with the model explaining approximately 44% of the intention to engage in high-risk drinking. Similar, to the first path analysis attitude was the strongest predictor followed by personal agency and then perceived norms. The findings from this study indicate that the IBM provides utility in explaining high-risk drinking among college students. More specifically, researchers and practitioners should focus on experiential attitude, injunctive norms, and self-efficacy in designing interventions with this population and behavior. The precision the IBM provides in identifying which specific constructs to address when combating high-risk drinking demonstrates its usefulness beyond the theory's predecessors, the Theory of Reasoned Action and Theory of Planned Behavior.
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