Modelling the community adaptive behaviour towards air pollution: a confirmatory factor analysis with PLS-SEM
Air pollution has become a serious threat to public health due to the rapid economic development globally, and urban air pollution is thought to cause 1.3 million deaths annually. Urban areas have a huge potential for human exposure to the severity of air pollution and health concerns. Therefore, it...
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| Main Authors: | , , |
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| Format: | Article |
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Malaysian Institute of Planners
2022
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| Online Access: | http://psasir.upm.edu.my/id/eprint/102209/ https://www.planningmalaysia.org/index.php/pmj/article/view/1139 |
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| Summary: | Air pollution has become a serious threat to public health due to the rapid economic development globally, and urban air pollution is thought to cause 1.3 million deaths annually. Urban areas have a huge potential for human exposure to the severity of air pollution and health concerns. Therefore, it is essential to advance our understanding of the factors influencing behaviour to provide compelling evidence for successful behavioural interventions and guidelines. Doing so will increase the practicality of public adaptation to the guidelines. Yet, little is known about the adaptive behaviour toward air pollution. This study aims to establish a predictive model of factors impacting the adaptative behaviour of urban Malaysians toward air quality. A deductive theory-generating research approach and a correlational research design were used in the development of a new ABR model. The following seven factors were tested: values (VAL), attitude (ATT), perceived vulnerability (PVL), perceived severity (PSV), self-efficacy (SEF), response efficacy (REF), and risk perception (RPN). Klang Valley served as the study area, and a multi-stage cluster sampling technique was used to select the respondents (n = 440) of a face-to-face questionnaire survey. In conjunction with PLS-SEM analyses, confirmatory factor analysis (CFA) was used to evaluate the structural models. The results demonstrated that PLS-SEM CFA is suitable for building a reliable structural model to examine community adaptive behaviour. |
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