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      Understanding self-protective behaviors during COVID-19 Pandemic: Integrating the theory of planned behavior and O-S–O-R model

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          Abstract

          Adopting health preventive actions is one of the most effective ways to combat the COVID-19 pandemic. Based on the theory of planned behavior and the orientation–stimulus–orientation–response model, this study investigated the mechanisms by which health information exposure influenced individuals to adopt self-protective behaviors in the context of infectious disease. In this research, a convenience sampling was used and 2265 valid samples (Male = 843, 68.9% of participants aged range from 18 to 24) were collected in China. Structural equation modeling analysis was performed, and the analysis showed that health consciousness positively influenced the subsequent variables through interpersonal discussions and social media exposure to COVID-19-related information. The interaction between interpersonal discussion and social media exposure was found to be positively associated with the elements of the theory of planned behavior and risk perception. The findings also revealed that self-protective behavior was positively predicted by the components of the theory of planned behavior and risk perceptions, with subjective norms serving as the main predictor, followed by attitudes and self-efficacy.

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          Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives

          Li‐tze Hu, Peter M. Bentler (1999)
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            Efficacy of the Theory of Planned Behaviour: A meta-analytic review

            Christopher Armitage, Mark Conner (2001)
            The Theory of Planned Behaviour (TPB) has received considerable attention in the literature. The present study is a quantitative integration and review of that research. From a database of 185 independent studies published up to the end of 1997, the TPB accounted for 27% and 39% of the variance in behaviour and intention, respectively. The perceived behavioural control (PBC) construct accounted for significant amounts of variance in intention and behaviour, independent of theory of reasoned action variables. When behaviour measures were self-reports, the TPB accounted for 11% more of the variance in behaviour than when behaviour measures were objective or observed (R2s = .31 and .21, respectively). Attitude, subjective norm and PBC account for significantly more of the variance in individuals' desires than intentions or self-predictions, but intentions and self-predictions were better predictors of behaviour. The subjective norm construct is generally found to be a weak predictor of intentions. This is partly attributable to a combination of poor measurement and the need for expansion of the normative component. The discussion focuses on ways in which current TPB research can be taken forward in the light of the present review.
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              Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis

              Hae-Young Kim (2013)
              As discussed in the previous statistical notes, although many statistical methods have been proposed to test normality of data in various ways, there is no current gold standard method. The eyeball test may be useful for medium to large sized (e.g., n > 50) samples, however may not useful for small samples. The formal normality tests including Shapiro-Wilk test and Kolmogorov-Smirnov test may be used from small to medium sized samples (e.g., n 2.1 Kurtosis is a measure of the peakedness of a distribution. The original kurtosis value is sometimes called kurtosis (proper) and West et al. (1996) proposed a reference of substantial departure from normality as an absolute kurtosis (proper) value > 7.1 For some practical reasons, most statistical packages such as SPSS provide 'excess' kurtosis obtained by subtracting 3 from the kurtosis (proper). The excess kurtosis should be zero for a perfectly normal distribution. Distributions with positive excess kurtosis are called leptokurtic distribution meaning high peak, and distributions with negative excess kurtosis are called platykurtic distribution meaning flat-topped curve. 2) Normality test using skewness and kurtosis A z-test is applied for normality test using skewness and kurtosis. A z-score could be obtained by dividing the skew values or excess kurtosis by their standard errors. As the standard errors get smaller when the sample size increases, z-tests under null hypothesis of normal distribution tend to be easily rejected in large samples with distribution which may not substantially differ from normality, while in small samples null hypothesis of normality tends to be more easily accepted than necessary. Therefore, critical values for rejecting the null hypothesis need to be different according to the sample size as follows: For small samples (n < 50), if absolute z-scores for either skewness or kurtosis are larger than 1.96, which corresponds with a alpha level 0.05, then reject the null hypothesis and conclude the distribution of the sample is non-normal. For medium-sized samples (50 < n < 300), reject the null hypothesis at absolute z-value over 3.29, which corresponds with a alpha level 0.05, and conclude the distribution of the sample is non-normal. For sample sizes greater than 300, depend on the histograms and the absolute values of skewness and kurtosis without considering z-values. Either an absolute skew value larger than 2 or an absolute kurtosis (proper) larger than 7 may be used as reference values for determining substantial non-normality. Referring to Table 1 and Figure 1, we could conclude all the data seem to satisfy the assumption of normality despite that the histogram of the smallest-sized sample doesn't appear as a symmetrical bell shape and the formal normality tests for the largest-sized sample were rejected against the normality null hypothesis. 3) How strict is the assumption of normality? Though the humble t test (assuming equal variances) and analysis of variance (ANOVA) with balanced sample sizes are said to be 'robust' to moderate departure from normality, generally it is not preferable to rely on the feature and to omit data evaluation procedure. A combination of visual inspection, assessment using skewness and kurtosis, and formal normality tests can be used to assess whether assumption of normality is acceptable or not. When we consider the data show substantial departure from normality, we may either transform the data, e.g., transformation by taking logarithms, or select a nonparametric method such that normality assumption is not required.
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                Author and article information

                Contributors
                Yuhan Wang: yh_wang09@126.com
                Journal
                Journal ID (nlm-ta): Curr Psychol
                Journal ID (iso-abbrev): Curr Psychol
                Title: Current Psychology (New Brunswick, N.j.)
                Publisher: Springer US (New York )
                ISSN (Print): 1046-1310
                ISSN (Electronic): 1936-4733
                Publication date (Electronic): 16 February 2023
                Pages: 1-13
                Affiliations
                [1 ]GRID grid.259384.1, ISNI 0000 0000 8945 4455, Present Address: Faculty of Humanities and Arts, , Macau University of Science and Technology, ; Macau SAR, China
                [2 ]GRID grid.411851.8, ISNI 0000 0001 0040 0205, Convergence Media Center, , Guangdong University of Technology, ; Guangzhou, China
                Author information
                http://orcid.org/0000-0003-0091-6126
                http://orcid.org/0000-0003-0623-0985
                Article
                Publisher ID: 4352
                DOI: 10.1007/s12144-023-04352-3
                PMC ID: 9933017
                SO-VID: d96cab5b-fd92-4bd4-8486-19cd36653417
                Copyright © © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
                License:

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                Date accepted : 30 January 2023
                Funding
                Funded by: Macao Higher Education Institutions in the Area of Research in Humanities and Social Science
                Award ID: DSEDJ-23-005-FA
                Award Recipient :
                Categories
                Subject: Article

                ScienceOpen disciplines: Clinical Psychology & Psychiatry
                Keywords: preventive behaviors,covid-19,health information exposure,o-s–o-r model,the theory of planned behavior
                Data availability:
                ScienceOpen disciplines: Clinical Psychology & Psychiatry
                Keywords: preventive behaviors, covid-19, health information exposure, o-s–o-r model, the theory of planned behavior

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