The behavioral biases of Muslims in Islamic banking and finance: a second-order self-reported religiosity measurement invariance
This study examines a measurement invariance of a second-order factor model of the behavioral biases in Islamic banking and finance activities among Muslims in Malaysia. Data elicited via a survey questionnaire was subjected to structural equation modeling analysis using the Amos 23.0 software. The...
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Format: | Article |
Language: | English English English |
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IIUM Press, International Islamic University Malaysia
2016
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Online Access: | http://irep.iium.edu.my/54446/1/05%20Adewale%20%26%20Haron%20R.pdf http://irep.iium.edu.my/54446/7/54446_The%20behavioral%20biases%20of%20Muslims%20in%20Islamic%20banking%20and%20finance_WoS.pdf http://irep.iium.edu.my/54446/13/54446_the%20behavioral%20biases%20of%20Muslim_scopus.pdf http://irep.iium.edu.my/54446/ |
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Summary: | This study examines a measurement invariance of a second-order factor model of the behavioral biases in Islamic banking and finance activities among Muslims in Malaysia. Data elicited via a survey questionnaire was subjected to structural equation modeling analysis using the Amos 23.0 software. The study revealed that behavioral biases as a second-order factor is indicated by herding, cognitive
dissonance, anchoring, regret aversion and overconfidence of first order factors. Measurement invariance was tested based on self-reported religiosity across a number of hierarchically structured invariance analysis models, including configural at the first-order level, metric, intercepts, and residuals at both the first-order and
second-order levels. A significant variance was observed between the high and low self-reported religiosity groups. However, no difference was observed between the two groups and the ‘moderate’self-reported religiosity groups. Furthermore, based on the critical ratio score of statistical significance at alpha of 0.001, the latent
mean difference tests of the second-order model indicates statistical significance across groups based on a path-by-path analysis. |
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