Literature DB >> 28623608

Gambling and the Reasoned Action Model: Predicting Past Behavior, Intentions, and Future Behavior.

Ethan Dahl1, Michael J Tagler2, Zachary P Hohman3.   

Abstract

Gambling is a serious concern for society because it is highly addictive and is associated with a myriad of negative outcomes. The current study applied the Reasoned Action Model (RAM) to understand and predict gambling intentions and behavior. Although prior studies have taken a reasoned action approach to understand gambling, no prior study has fully applied the RAM or used the RAM to predict future gambling. Across two studies the RAM was used to predict intentions to gamble, past gambling behavior, and future gambling behavior. In study 1 the model significantly predicted intentions and past behavior in both a college student and Amazon Mechanical Turk sample. In study 2 the model predicted future gambling behavior, measured 2 weeks after initial measurement of the RAM constructs. This study stands as the first to show the utility of the RAM in predicting future gambling behavior. Across both studies, attitudes and perceived normative pressure were the strongest predictors of intentions to gamble. These findings provide increased understanding of gambling and inform the development of gambling interventions based on the RAM.

Keywords:  Behavioral intention; Gambling; Perceived behavioral control; Predicting behavior; Reasoned Action Model

Mesh:

Year:  2018        PMID: 28623608     DOI: 10.1007/s10899-017-9702-6

Source DB:  PubMed          Journal:  J Gambl Stud        ISSN: 1050-5350


  26 in total

1.  Theories of reasoned action and planned behavior as models of condom use: a meta-analysis.

Authors:  D Albarracín; B T Johnson; M Fishbein; P A Muellerleile
Journal:  Psychol Bull       Date:  2001-01       Impact factor: 17.737

2.  Gambling activities of young Australians: developing a model of behaviour.

Authors:  S M Moore; K Ohtsuka
Journal:  J Gambl Stud       Date:  1997

3.  Injunctive norms and problem gambling among college students.

Authors:  Clayton Neighbors; Ty W Lostutter; Ursula Whiteside; Nicole Fossos; Denise D Walker; Mary E Larimer
Journal:  J Gambl Stud       Date:  2007-03-30

4.  Delinquency among pathological gamblers: A causal approach.

Authors:  G Meyer; T Fabian
Journal:  J Gambl Stud       Date:  1992-03

5.  Problem gambling of Chinese college students: application of the theory of planned behavior.

Authors:  Anise M S Wu; Catherine So-kum Tang
Journal:  J Gambl Stud       Date:  2012-06

Review 6.  Prevalence of comorbid disorders in problem and pathological gambling: systematic review and meta-analysis of population surveys.

Authors:  Felicity K Lorains; Sean Cowlishaw; Shane A Thomas
Journal:  Addiction       Date:  2011-03       Impact factor: 6.526

7.  Pathological gambling in treatment-seeking alcoholics: a national survey in France.

Authors:  Bertrand Nalpas; Jacques Yguel; Benoît Fleury; Sandrine Martin; Delphine Jarraud; Michel Craplet
Journal:  Alcohol Alcohol       Date:  2011-01-17       Impact factor: 2.826

8.  Is pathological gambling moderated by age?

Authors:  Roser Granero; Eva Penelo; Randy Stinchfield; Fernando Fernandez-Aranda; Lamprini G Savvidou; Frida Fröberg; Neus Aymamí; Mónica Gómez-Peña; Miriam Pérez-Serrano; Amparo del Pino-Gutiérrez; José M Menchón; Susana Jiménez-Murcia
Journal:  J Gambl Stud       Date:  2014-06

Review 9.  Disordered gambling among college students: a meta-analytic synthesis.

Authors:  Lynn Blinn-Pike; Sheri Lokken Worthy; Jeffrey N Jonkman
Journal:  J Gambl Stud       Date:  2007-06

10.  How the Internet is changing gambling: findings from an Australian Prevalence Survey.

Authors:  Sally M Gainsbury; Alex Russell; Nerilee Hing; Robert Wood; Dan Lubman; Alex Blaszczynski
Journal:  J Gambl Stud       Date:  2015-03
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  2 in total

1.  Predictors of Sports Gambling among College Students: The Role of the Theory of Planned Behavior and Problem Gambling Severity.

Authors:  Xin Wang; Doyeon Won; Hyung Sang Jeon
Journal:  Int J Environ Res Public Health       Date:  2021-02-12       Impact factor: 3.390

2.  Are direct messages (texts and emails) from wagering operators associated with betting intention and behavior? An ecological momentary assessment study.

Authors:  Alex M T Russell; Nerilee Hing; Matthew Browne; Vijay Rawat
Journal:  J Behav Addict       Date:  2018-10-24       Impact factor: 6.756

  2 in total

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