A predictive model for community adaptive behaviour towards air pollution

Air pollution emerges due to the inability to adapt to the waste produced due to the high population density and concentration in relatively small areas. Urban air pollution is a major health risk to millions of residents globally and is estimated to cause about 1.3 million deaths annually. More importantly, little is known about the factors that determine a community’s adaptive behaviour in response to air pollution, specifically in Malaysia. Therefore, the main purpose of this study is to develop a preliminary predictive model on factors determining the adaptative behaviour among urban Malaysians towards air quality by integrating three theories; Social Adaptation Theory, Protection Motivation Theory, and Psychometric Paradigm. This model development of the new ABR model applied using a deductive theory-generating research approach and a correlational research design. Seven variables were examined, namely values (VAL), attitude (ATT), perceived vulnerability (PVL), perceived severity (PSV), self-efficacy (SEF), response efficacy (REF), and risk perception (RPN). This study also determined the moderating role of education level and health status on the relationship between key predictors and adaptive behaviour. PLS-SEM was applied to capture the causal effect relationship model of these relationships. The study area was Klang Valley, and a multi-stage cluster sampling technique was employed. The respondents (n = 440) answered the face-to-face questionnaire survey. The findings of testing the model revealed that out of 7 path coefficients (β) in the structural model, 6 paths had statistically significant direct effects on the interrelationships, while one path did not have any significant effect. The paths that showed significant effects were: VAL, PVL, PSV, SEF, REF, and RPN on adaptive behaviour. The path with non-significant effects was the ATT. Furthermore, this study also examined the education level and health status as moderating variables for the model. In addition to the path coefficients (direct effect relationship), the structural model also revealed 3 coefficients with significant moderating effects out of 14 for the interrelationships among the key predictors and the adaptive behaviour investigated in the study. The moderating effect was observed for education level in the relationship between values and adaptive behaviour. As for health status, two significant relationships were found: the moderating role of health status in the relationship between perceived vulnerability and risk perception with adaptive behaviour. Overall, the structural model explained about 61.5% of the variance in the adaptive behaviour of the community towards urban air quality. In conclusion, this study verifies that values, self-efficacy, perceived severity, response efficacy, risk perception, and perceived vulnerability have a major impact on the adaptive behavioural responses of the community towards urban air pollution. The study contributed significantly to the literature by indicating PMT, SAT, and PPM as the ideal framework to capture the adaptive behavioural responses toward urban air pollution. Additionally, this study suggests that the authority could play a meaningful role in drafting effective guidelines to reduce the impact of air pollution on the public, especially in cities affected by air pollution.

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