An Empirical Study on Hazardous Chemicals Risk of Urban Residents in China: Analysis of Mediating Effect and Channel Preference of Response Action Decision Model
Abstract
:1. Introduction
1.1. Research Background
1.2. Literature Analysis
1.3. Theory and Hypothesis
2. Materials and Methods
2.1. Theoretical Model
2.2. Variable Measure
2.2.1. Channel Preference
2.2.2. Information Processing
2.2.3. Threat Perception
2.2.4. Mediator Variable
2.2.5. Response Action Decision
2.3. Sample Selection
2.3.1. Sample Area Selection
2.3.2. Sample Survey Method
2.3.3. Sample Size
2.4. Data Processing
2.4.1. Reliability Analysis
2.4.2. Nonparametric Test
2.4.3. Exploratory Factor Analysis
2.4.4. Homology Deviation Test
3. Results
3.1. Impact of Characteristics of the Small World on Information Dissemination
3.2. Analysis of Mediating Effects
3.2.1. Mediating Effect Analysis of Sub-Models 1–3
3.2.2. Mediating Effect Analysis of Sub-Models 4–6
Step 1: Interaction Analysis
Step 2: Analysis of Mediating Effect
3.2.3. Mediating Effect Analysis and Hypothesis Testing
3.3. Channel Preference Analysis
3.3.1. Channel Preference Analysis of Sub-Model 1
- Sex group: comparing critical ratios for differences between parameters showed that there was no significant difference between the sex groups for the path MM→IP→TP→RAD; male () were more preferred than female ().
- Marriage group: there were significant differences between the unmarried group () who are more inclined to MM→IP→TP→RAD than the married group (); the divorced group tends to MM→RAD (pa1 > 0.05); the married (β = 0.272, p < 0.001), unmarried (β = 0.417, p < 0.001) and divorced (β = 0.842, p > 0.05) groups have significant differences for the path MM→RAD.
- Age group: there were significant differences between the 15–29-year-olds and 45–59 age groups who were more likely to MM→IP→TP→RAD; the 30–44-year-olds (pa3 > 0.05) group and the 60–74-year-olds group (pa1, pa2, pa3 > 0.05) were more inclined to MM→RAD.
- Education group: there were significant differences between the junior high school and below group (), who were more inclined to MM→IP→TP→RAD than the university group (); the high school group and master’s degree group tend to MM→RAD.
- Income group: there were significant differences between the L1.5 group (), who were more inclined to MM→IP→TP→RAD than the 1.5–2.5 group (); 2.5–4.5, 4.5–6.5, m6.5 three groups (pa3 > 0.05) prefer MM→RAD.
- Experience group: There was no significant difference in path MM→IP→TP→RAD, and the no disaster training experience group () was more inclined to MM→IP→TP→RAD than the group which has the disaster training experience ().
- Geographical location group: There were significant differences in the regression coefficients between MM and RAD in the Chongqing area and Tianjin area; Changzhou area and Zibo area, and Tianjin residents prefer MM→IP→TP→RAD.
3.3.2. Channel Preference Analysis of Sub-Model 2
- Sex group: there were significant differences, where the male group () was more inclined to SM→IP→TP→RAD, while the female group tended to SM→RAD (pa1 > 0.05).
- Marriage group: there were significant differences, i.e., the unmarried group () was more inclined to SM→IP→TP→RAD than the married group (); the divorced group tended to SM→RAD (pa1 > 0.05).
- Age group: there were significant differences: the 15–29-year-old () group were more likely to SM→IP→TP→RAD; the 30–44 year old (pa1, pa3 > 0.05), 45–59-year-old (pa1 > 0.05), 60–74-year-old (pa1, pa2, pa3 > 0.05) groups were more inclined to SM→RAD.
- Education group: there were significant differences: junior high school and below groups () were more inclined towards SM→IP→TP→RAD; the high school group (pa1, pa3 > 0.05), university group (pa1, pa3 > 0.05), and master’s degree group (pa1, pa3 > 0.05) tend to SM→RAD.
- Income group: there were significant differences: the 1.5–2.5 group () was more inclined towards SM→IP→TP→RAD; the L1.5, 2.5–4.5, 4.5–6.5, m6.5 four groups (pa3 > 0.05) preferred SM→RAD.
- Experience group: there were significant differences: the group having disaster training experience () was more inclined to SM→IP→TP→RAD; the group with no disaster training experience group tended toward SM→RAD (pa1 > 0.05).
- Geographical location group: significant difference existed, Lanzhou residents () were more inclined to SM→IP→TP→RAD than Zhangzhou (); Chongqing (pa1 > 0.05), Tianjin (pa1, pa3 > 0.05), Changzhou (pa3 > 0.05), Zibo (pa1, pa3 > 0.05) residents tend to SM→RAD.
3.3.3. Channel Preference Analysis of Sub-Model 3
- Sex group: the differences are significant: the male group () was more inclined to FTF→IP→TP→RAD, and the female group tends to FTF→RAD (pa1 > 0.05).
- Marriage group: the differences were significant: the unmarried group () was more inclined to FTF→IP→TP→RAD than the married group (), the divorced group tend to FTF→RAD (pa1 > 0.05).
- Age group: the differences were significant: the 15–29-year-old group () liked FTF→IP→TP→RAD better; the 30–44 year old (pa1, pa3 > 0.05), 45–59-year-old (pa1 > 0.05), 60–74-year-old (pa1, pa2, pa3 > 0.05) groups were more inclined to FTF→RAD.
- Education group: the differences were significant: the junior high school and below group () was more inclined to FTF→IP→TP→RAD; the high school group (pa3 > 0.05), university group (pa3 > 0.05), and master’s degree group (pa3 > 0.05) preferred FTF→RAD.
- Income group: the differences were significant: the 1.5–2.5 group () was more inclined to FTF→IP→TP→RAD; the L1.5, 2.5–4.5, 4.5–6.5, m6.5 four groups (pa3 > 0.05) preferred FTF→RAD.
- Experience group: the differences were significant: the group having disaster training experience () was more inclined towards FTF→IP→TP→RAD; the group with no disaster training experience tends to FTF→RAD (pa3 > 0.05).
- Geographical location group: the differences were significant: Lanzhou residents () were more inclined to FTF→IP→TP→RAD than Zhangzhou () and than Chongqing (; Tianjin (pa3 > 0.05) and Changzhou (pa3 > 0.05), Zibo (pa3 > 0.05) residents favored FTF→RAD.
3.3.4. Channel Preference Comprehensive Analysis of Sub-Models 1–3
3.3.5. Channel Preference Analysis and Hypothesis Testing
4. Discussion
4.1. Mediating Effect
4.2. Channel Preference
5. Conclusions
- For the multi-stage model of channel preferences MM, SM, FTF, and both IP and TP have a significant mediating effect, which can promote RAD by channel preference→IP→TP.
- For the channel combination “MM↔SM”, the mediating effect of IP and TP is significant, and can have a certain inhibitory effect; IP and TP have no mediating effect in the channel combinations “MM↔FTF” and “SM↔FTF”, in other words, IP and TP have no positive effect on the decision model of the channel combination as independent variables.
- Hypothesis 1 is valid, Hypothesis 2 is valid, Hypothesis 3 is not valid, and Hypothesis 4 is not valid.
- 4.
- Overall effect of the channel MM is better than SM, and better than FTF;
- 5.
- Male, married, unmarried, 15–29-year-old, junior high school, 1.5–2.5 groups are inclined to three channels, that is, the mediating effect of IP and TP is significant for these groups;
- 6.
- Divorced, 30–44-year-old, 60–74-year-old, high school, master’s degree, 2.5–4.5,4.5–6.5, M6.5, Changzhou, and Zibo regional groups are inclined to three channels, that is, the mediating effect of IP and TP is not significant for these groups;
- 7.
- While female, 45–59-year-old, university, income less than 1.5, disaster training experience, Tianjin resident groups prefer MM→IP→TP→RAD, the group having disaster training experience, Zhangzhou, and Lanzhou groups favor SM→IP→TP→RAD; the group having no disaster training experience, Chongqing, Zhangzhou, and Lanzhou groups prefer FTF→IP→TP→RAD, that is, IP and TP produce a mediating effect on these groups which need to go through specific channels;
- 8.
- While Hypotheses 4—11 are partially valid, Hypothesis 12 is valid. While integrating the results of the mediating effect and channel preference analysis, RADM can be used effectively to study how Chinese urban residents can improve threat perception and make response action decisions.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Li, Z. Legislative Management of Major Chemical Hazards in the European Union; Modern Chemical Industry: Angkok, Thailand, 2001; Volume 21, pp. 45–48. [Google Scholar]
- State Economic and Trade Commission. Safety Production Bureau. In Workplace Chemical Safety Management; Chinese Petrochemical Press: Beijing, China, 1999; pp. 1–2. [Google Scholar]
- Zhang, L.; Tong, X. Social learning in “neighboring” conflict—A case study based on 7 PX Projects. Acad. Circ. 2016, 38–54, 325. [Google Scholar]
- Cui, W.; She, L.; Liu, S. Study on the effectiveness of chinese emergency plan—taking the special “11.22” Donghuang oil pipeline leakage and explosion as an important event. J. Natl. Inst. Adm. 2015, 4, 103–107. [Google Scholar]
- Lu, Z.; Fu, G.; Xue, Z.-Z. Study on the causes of fire and explosion accidents in “8.12” dangerous goods warehouse in Tianjin port. J. Disasters 2017, 32, 205–211. [Google Scholar]
- Lindell, M.K.; Perry, R.W. Warning mechanisms in emergency response systems. Int. J. Mass Emerg. Disasters 1987, 5, 137–153. [Google Scholar]
- Lindell, M.K.; Perry, R.W. Behavioral Foundations of Community Emergency Planning; Hemisphere Publishing Corp.: Washington, DC, USA, 1992. [Google Scholar]
- Hance, B.; Chess, C.; Sandman, P. Improving Dialogue with Communities; New Jersey Department of Environmental Protection: Trenton, NJ, USA, 1988.
- Mileti, D.S.; Sorensen, J.H.; O’Brien, P.W. Toward an explanation of mass care shelter use in evacuations. Int. J. Mass Emerg. Disasters 1992, 10, 25–42. [Google Scholar]
- Perry, R.W.; Nelson, L. Ethnicity and hazard information dissemination. Environ. Manag. 1991, 15, 581–587. [Google Scholar] [CrossRef]
- Conrads, J.; Reggiani, T. The effect of communication channels on promise-making and promise-keeping: Experimental evidence. J. Econ. Interact. Coord. 2016, 12, 595–611. [Google Scholar] [CrossRef]
- Xie, X.F.; Li, J.; Yu, Q.Y. How Can a Risk Be Increased? An analysis of risk communication channels. Acta Psychol. Sin. 2008, 40, 456–465. [Google Scholar] [CrossRef]
- Xue, K.; Wang, L.L.; Yu, M.Y. Study on the Impact of Social Media on Audience Risk Perception: A Case Study of PX Project Explosion in Gulei Peninsula. J. Bimon. 2015, 6, 101–107. [Google Scholar]
- Ren, J.H. Spread and reflection of micro-blogging information in emergencies—An empirical analysis based on the “PX Project Explosion in Zhangzhou, Fujian Province”. Soc. Sci. 2016, 8, 53–55. [Google Scholar]
- Skrehot, P.A.; Marek, J.; Skrehotova, M. Requirements to ensure the safety and health of pupils in the use of hazardous chemicals during the practical teaching chemistry. Chem. Listy 2016, 110, 947–952. [Google Scholar]
- Sun, O. Main pillars of legislation of hazardous chemicals in China. Chimica Oggi-Chem. Today 2013, 31, 33–36. [Google Scholar]
- Scruggs, C.E. Reducing hazardous chemicals in consumer products: Proactive company strategies. J. Clean. Prod. 2013, 44, 105–114. [Google Scholar] [CrossRef]
- Su, D.; Geng, J.; Geng, Z. The Dangerous chemicals safety management problems and countermeasures. J. Liaoning Tech. Univ. 2006, 25 (Suppl. S1), 13–15. [Google Scholar]
- Liu, H.; Luo, Y.; Tang, Y. Study on the integration of safety standardization and occupational health and safety management system in dangerous chemicals. Mod. Chem. Ind. 2008, 28, 81–84. [Google Scholar]
- Poechlauer, P.; Braune, S.; Dielemans, B. On-site-on demand production of hazardous chemicals by continuous flow processes. Chemica Oggi-Chem. Today 2012, 30, 51–54. [Google Scholar]
- Kunugi, M.; Fujimori, K.; Nakano, T. Development of marine environmental observation platform deployed on ferryboats, and observation of marine pollution with hazardous chemicals in the coastal region of Japan. Bunseki Kagaku 2004, 53, 1375–1387. [Google Scholar] [CrossRef] [Green Version]
- Goh, C.B.; Ching, C.B.; Tan, R. Risk analysis for the road transportation of hazardous chemicals in Singapore—a methodology. J. Loss Prev. Process. Ind. 1995, 8, 35–39. [Google Scholar] [CrossRef]
- Meng, S.; Meng, X.; Han, X. Study on temperature control system of new chemical vessel. J. Dalian Marit. Univ. 2006, 32, 58–60. [Google Scholar]
- Zhang, J.; Zhao, L. Risk analysis of dangerous chemicals transportation. Syst. Eng. Theory Prac. 2007, 27, 117–122. [Google Scholar] [CrossRef]
- Wang, J.; Chu, Y.-L. Evaluation of waterway hazardous chemicals transportation based on fuzzy comprehensive evaluation method. J. Dalian Marit. Univ. 2008, 34 (Suppl S1), 14–16. [Google Scholar]
- Kumar, D.; Ghai, D.P.; Soni, R. Ultrasonic photoacoustic spectroscopy of trace hazardous chemicals using quantum cascade laser. Opt. Commun. 2016, 381, 271–276. [Google Scholar] [CrossRef]
- Patricio Silva, A.L.; Amorim, M.J.B.; Holmstrup, M. Salinity changes impact of hazardous chemicals in Enchytraeus albidus. Environ. Toxicol. Chem. 2015, 34, 2159–2166. [Google Scholar] [CrossRef] [PubMed]
- Singh, R.; Lee, H.-J.; Singh, A.K.; Kim, D.-P. Recent advances for serial processes of hazardous chemicals in fully integrated microfluidic systems. Korean J. Chem. Eng. 2016, 33, 2253–2267. [Google Scholar] [CrossRef]
- Xu, J. Study on environmental risk assessment of sulfuric acid storage and transportation project. Environ. Prot. 2002, 11, 42–43. [Google Scholar]
- Qian, Y. Use of hazardous chemicals peracetic acid and waste disposal. Environ. Prot. 2004, 2, 22–24. [Google Scholar]
- Jacobs, M.M.; Malloy, T.F.; Tickner, J.A.; Edwards, S. Alternatives assessment frameworks: Research needs for the informed substitution of hazardous chemicals. Environ. Health Perspect. 2016, 124, 265–280. [Google Scholar] [CrossRef]
- Yuan, L.; Ren, L.; Tian, X.; Huang, Z.; Xiao, Y.; Wei, S.; Wang, Z. Investigation on polyvinyl-alcohol-based rapidly gelling hydrogels for containment of hazardous chemicals. RSC Adv. 2016, 6, 71425–71430. [Google Scholar] [CrossRef]
- Bao, C.; Bi, S.; Zhang, H.; Zhao, J.; Wang, P.; Yue, C.Y.; Yang, J. Graphene oxide beads for fast clean-up of hazardous chemicals. J. Mater. Chem. A 2016, 4, 9437–9446. [Google Scholar] [CrossRef]
- Di, J.; Liang, Z. Study on evaluation method of safety management of hazardous chemicals. J. Liaoning Tech. Univ. 2003, 22, 507–508. [Google Scholar]
- Zhang, W.; Chen, G.; Pan, Y.; Liang, Q.; Chen, Q. Study on safety management and quantitative evaluation of hazardous chemicals. Acta Chim. Sin. 2004, 55, 682–685. [Google Scholar]
- Zhang, C.; Chen, X.; Chen, J.; Liu, Y. Risk assessment of hazardous chemicals leakage accidents considering emergency rescue. J. Tsinghua Univ. (Nat. Sci. Edit.) 2009, 49, 621–624. [Google Scholar]
- Li, S.; Ma, Z.; Zou, K. Dangerous goods in the multi-target location-path problem. Oper. Manag. 2014, 23, 8–15. [Google Scholar]
- Ying, K.; Tang, L. Multi-stage contingency group decision-making method based on incomplete preference information. Oper. Res. Manag. 2014, 23, 73–80. [Google Scholar]
- Lindell, M.K.; Perry, R.W. The protective action decision model: Theoretical modifications and additional evidence. Risk Anal. 2011, 32, 616–632. [Google Scholar] [CrossRef]
- Wei, J.; Zhao, M.; Wang, F.; Cheng, P.; Zhao, D. An empirical study of the volkswagen crisis in china: Customers’ information processing and behavioral intentions. Risk Anal. 2015, 36, 114–129. [Google Scholar] [CrossRef]
- Huang, S.-K.; Lindell, M.K.; Prater, C.S.; Wu, H.-C.; Siebeneck, L.K. Household evacuation decision making in response to Hurricane Ike. Nat. Hazards Rev. 2012, 13, 283–296. [Google Scholar] [CrossRef]
- Huang, S.-K.; Lindell, M.K.; Prater, C.S. Multistage model of hurricane evacuation decision: Empirical study of Hurricanes Katrina and Rita. Nat. Hazards Rev. 2017, 18, 05016008. [Google Scholar] [CrossRef]
- Hwang, S.N.; Sanderson, W.; Lindell, M.K. State emergency management agencies’ hazard analysis information on the internet. Int. J. Mass Emerg. Disasters 2001, 19, 85–108. [Google Scholar]
- Liu, L.-Y. Subway Multimedia Information System Video Control Subsystem; Beijing Jiaotong University: Beijing, China, 2012. [Google Scholar]
- Zeng, H. Application of network digital broadcasting in metro station PIS system. TV Technol. 2011, 19, 123–125. [Google Scholar]
- Cui, L.-L. Analysis of the Current Situation and Development Strategy of Public Transport Mobile TV Media; Northeast Normal University: Jilin, China, 2009. [Google Scholar]
- Xie, L. Study on the Delay of College Students’ Social Media Dependence on Their Academic Background-Taking the Social Instant Social Platform as an Example; Nanjing University of Posts and Telecommunications: Jiangsu, China, 2014. [Google Scholar]
- Yuan, J.-H. The ideal and reality of micro-blogging—On the social media construction of three major factors of public space. J. Zhejiang Norm. Univ. (Soc. Sci. Edit.) 2010, 35, 20–25. [Google Scholar]
- Zhu, J.-L. Discussion on carrying out disaster prevention training in teachers. J. Disaster Prev. Sci. Technol. Inst. 2009, 11, 128–131. [Google Scholar]
- Michael, K.; Lindell, R.; Perry, W. Communicating Environment Risk in Multiethnic Communities; Sage Publications: New York, NY, USA, 2004. [Google Scholar]
- National Bureau of Statistics. Level of Urbanization Development (Urban Population/Resident Population). 2020. Available online: http://www.stats.gov.cn/ (accessed on 9 July 2021).
- State Council Security Committee. Hazardous Chemicals Production Safety Key Cities (Districts, Counties). 2020. Available online: http://www.chinasafety.gov.cn/newpage/Contents/Channel_21111/2014/0221/230257/content_230257.htm (accessed on 9 July 2021).
- State Administration of Safety Administration. Hazardous Chemicals Production Safety. 2020. Available online: http://www.chinasafety.gov.cn/newpage/ (accessed on 9 July 2021).
- Hazardous Chemicals Incident Information Network. Accidents of Hazardous Chemicals Production Safety. 2020. Available online: http://accident.nrcc.com.cn:9090 (accessed on 9 July 2021).
- Hazardous Chemicals Companies. 2020. Available online: http://www.chemicalsafety.org.cn (accessed on 9 July 2021).
- Wu, J.; Hong, G. Study on the Establishment of Sampling Survey Method in the Subject System of China’s Statistical Survey Method. North Econ. 1998, 12, 47–48. [Google Scholar]
- Wei, Z. Sampling survey method introduced (a). J. Stat. Educ. 2010, 8, 61–64. [Google Scholar]
- Wang, X.-X. Comparison of the effects of several major sampling methods. J. Stat. Inf. 1997, 1, 28–31. [Google Scholar]
- Ma, D. Many factors affecting sampling survey methods. Social 2004, 34, 41–43. [Google Scholar]
- Shen, H. Reconsideration of methods and practice of sampling survey. Stat. Res. 2002, 6, 35–37. [Google Scholar]
- Zhang, M.; Xu, L.; Lin, X.-W. New progress of sampling survey method in China. J. Stat. Decis. 2010, 8, 189. [Google Scholar]
- Yang, L. Talking about the status of the sample survey method in the statistical work. Business 2015, 20, 213–214. [Google Scholar]
- Cheng, D. Application of stratified sampling survey method in public security statistics. J. Police Technol. 2001, 6, 38–39. [Google Scholar]
- Qiu, S.; Tang, B. A random sampling survey method for multiple selection sensitive problems under stratified sampling and its application. J. Hunan Inst. Humanit. Sci. Technol. 2013, 2, 39–41. [Google Scholar]
- Su, J.; Guo, J.-K. Study on military sampling survey method based on stratified sampling and set pair analysis. Mil. Oper. Res. Syst. Eng. 2013, 27, 37–41. [Google Scholar]
- Rodriguez del Aguila, M.M.; Gonzalez-Ramirez, A.R. Sample size calculation. Allergol. Immunopathol. 2014, 42, 485–492. [Google Scholar] [CrossRef] [PubMed]
- Wu, M.-L. Structural Equation Model—Amos Theory and Application; Chongqing University Press: Chongqing, China, 2009. [Google Scholar]
- Terpstra, T. Emotions, trust, and perceived risk: Affective and cognitive routes to flood preparedness behavior. Risk Anal. 2011, 31, 1658–1675. [Google Scholar] [CrossRef] [PubMed]
- Hayes, A.F. Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Commun. Monogr. 2009, 76, 408–420. [Google Scholar] [CrossRef]
- Fang, J.; Wen, Z.; Zhang, M.; Sun, P. The analyses of multiple mediation effects based on structural equation modeling. Acta Psychol. Sin. 2014, 37, 735–741. [Google Scholar]
- Wen, Z.; Hau, K.-T.; Chang, L. A comparison of moderator and mediator and their applications. Acta Psychol. Sin. 2005, 37, 268–274. [Google Scholar]
- MacKinnon, D.P.; Lockwood, C.M.; Hoffman, J.M. A new method to test for mediation. In Proceedings of the Annual Meeting of the Society for Prevention Research, Park City, UT, USA, June 1998. [Google Scholar]
- Wen, Z.; Ye, B. Analyses of Mediating Effects: The Development of Methods and Models. Adv. Psychol. Sci. 2014, 22, 731–745. [Google Scholar] [CrossRef]
- Wen, Z.; Chang, L.; Hau, K.-T.; Liu, H. Testing and application of the mediating effects. Acta Psychol. Sin. 2004, 36, 614–620. [Google Scholar]
- Zhao, X.; Lynch, J.; Chen, Q. Reconsidering Baron and Kenny: Myths and Truths about Mediation Analysis. J. Consum. Res. 2010, 37, 197–206. [Google Scholar] [CrossRef]
- Wen, Z.; Wu, Y.; Hau, K.-T. Latent interaction in structural equation modeling: Distribution—Analytic approaches. Psychol. Explor. 2013, 33, 409–414. [Google Scholar]
- Wen, Z.; Hau, K.-T.; Herbert, W.M. Methods and recent research development in analysis of interaction effects between latent variables. Adv. Psychol. Sci. 2003, 11, 593–599. [Google Scholar]
- Wu, Y.; Wen, Z.-L.; Lin, G.-C. Structural Equation Modeling of Latent Interactions without Using the Mean Structure. Acta Psychol. Sin. 2009, 41, 1252–1259. [Google Scholar]
- Wen, Z.-L.; Wu, Y. Evolution and Simplification of the Approaches to Estimating Structural Equation Models with Latent Interaction. Adv. Psychol. Sci. 2010, 18, 1306–1313. [Google Scholar]
- Algina, J.; Moulder, B.C. A note on estimating the Joreskog -Yang Model for Latent Variable Interaction Using. LISREL8.3.Structural Equation Modeling. Multidiscip. J. 2001, 8, 40–52. [Google Scholar]
- Joreskog, K.G.; Yang, F. Nonlinear structural equation models: The Kenny-Judd Model with interaction effects. In Advanced Structural Equation Modeling: Issued and Techniques; Marcoulides, G.A., Schumacker, R.E., Eds.; Lawrence Erlbaum Associates, Inc.: Mahwah, NJ, USA, 1996; pp. 57–88. [Google Scholar]
- Marsh, H.W.; Wen, Z.L.; Hau, K.T. Structural equation models of latent interactions: Evaluation of alternative strategies. Paper presented as part the structural equation modeling paper session. In Proceedings of the American Educational Research Association Annual Meeting, New Orleans, LA, USA, 1–5 April 2002. [Google Scholar]
- Xiao, L.; Xu, S.H.; Wang, Q. Social media development and research review. Res. Libr. Sci. 2016, 14, 13–16. [Google Scholar]
- Ministry of Industry and Information Technology Data. 2020. Available online: http://www.techweb.com.cn/news/2020-07-22/2561937.shtml (accessed on 9 July 2021).
- Zhao, P.-P. Analysis of the spread of internet rumors in the social media era. Chin. Cult. Forum 2016, 9, 153–155. [Google Scholar]
- Zuo, Y.-Z. On the choice of contemporary Chinese youth under social pressure. J. Fujian Prov. Communist Party Sch. 2009, 3, 76–80. [Google Scholar]
- Peng, S.; Yang, A.; Cao, L.; Yu, S.; Xie, D. Social influence modeling using information theory in mobile social networks. Inf. Sci. 2017, 379, 146–159. [Google Scholar] [CrossRef]
Variable Type | Variable Name | Measurement Index | Index Explanation |
---|---|---|---|
Independent variable | Mass media (MM) Social media (SM) Face to face (FTF) | preference | Whether more likely to choose this channel to obtain hazard information |
permeability | The extent to which daily life is disturbed by hazard information | ||
accuracy | The authority of hazard information dissemination | ||
stability | The extent to which hazard information is understood | ||
Mediator variable | Information processing (IP) | exposure | Possibility of obtaining hazard information |
concern | Degree of concern around hazard information | ||
comprehension | Degree of understanding of hazard information. | ||
Mediator variable | Threat perception (TP) | possibility | The likelihood of accident to occur around hazardous chemicals |
seriousness | Threat to life, safety and property security. | ||
fear and unknown factors | Unknown and degree of fear of the characteristics of the hazards and evacuation routes etc. | ||
Dependent variable | Response Action Decision (RAD) | risk identification | Is there a real threat that I need to pay attention to? |
risk assessment | Do I need to take hazard response action? | ||
hazard response action search | What can be done to achieve hazard response? | ||
hazard response action assessment | What is the best method of hazard response? | ||
hazard response action implementation | Does hazard response action need to be taken now? | ||
information-seeking activities | What information do I need to answer my question? |
City | Resident Population | Sample Size |
---|---|---|
Chongqing | 30,170,000 | 246 |
Tianjin | 15,470,000 | 246 |
Zhangzhou | 5,000,000 | 246 |
Changzhou | 4,701,000 | 246 |
Zibo | 4,642,000 | 246 |
Lanzhou | 3,679,000 | 246 |
Variable | n | % | |
---|---|---|---|
Gender | Male | 826 | 48.6 |
Female | 874 | 51.4 | |
Marriage status | Married | 1135 | 66.8 |
Unmarried | 508 | 30.0 | |
Divorced | 50 | 2.8 | |
Widowed | 7 | 0.4 | |
Age | Less than 15 | 3 | 0.2 |
15–29 year old | 536 | 31.5 | |
30–44 | 560 | 32.9 | |
45–59 | 582 | 34.3 | |
60—74 | 17 | 1.0 | |
More than 75 | 2 | 0.1 | |
Education | Junior high school and below | 219 | 12.9 |
High school or secondary school | 451 | 26.5 | |
University | 948 | 55.8 | |
Master’s degree or above | 82 | 4.8 | |
Income | Less than 15,000 | 236 | 13.9 |
15,000–25,000 | 234 | 13.8 | |
25,000–45,000 | 337 | 19.7 | |
45,000–65,000 | 383 | 22.5 | |
More than 65,000 | 510 | 30.0 | |
Hazards Experience | Yes | 1116 | 65.6 |
No | 584 | 34.4 |
Variables | Items | Factor Loading | S.E. | C.R. | p | R2 | Composite Reliability | AVE |
---|---|---|---|---|---|---|---|---|
MM | MM1 | 0.646 | 0.033 | 26.661 | *** | 0.417 | 0.843 | 0.575 |
MM2 | 0.722 | 0.031 | 30.352 | *** | 0.521 | |||
MM3 | 0.832 | 0.030 | 35.275 | *** | 0.693 | |||
MM4 | 0.818 | / | / | a | 0.670 | |||
SM | SM1 | 0.742 | 0.034 | 26.079 | *** | 0.550 | 0.817 | 0.531 |
SM2 | 0.859 | 0.045 | 28.289 | *** | 0.737 | |||
SM3 | 0.594 | 0.043 | 26.551 | *** | 0.353 | |||
SM4 | 0.696 | / | / | a | 0.484 | |||
FTF | FTF1 | 0.640 | 0.032 | 33.808 | *** | 0.410 | 0.840 | 0.569 |
FTF2 | 0.714 | 0.035 | 29.441 | *** | 0.510 | |||
FTF3 | 0.842 | 0.037 | 26.017 | *** | 0.709 | |||
FTF4 | 0.805 | / | / | a | 0.648 | |||
IP | IP1 | 0.740 | 0.040 | 19.850 | *** | 0.548 | 0.809 | 0.586 |
IP2 | 0.759 | 0.042 | 19.129 | *** | 0.576 | |||
IP3 | 0.796 | / | / | a | 0.664 | |||
TP | TP1 | 0.738 | 0.053 | 11.273 | *** | 0.545 | 0.838 | 0.639 |
TP2 | 0.650 | 0.061 | 14.898 | *** | 0.423 | |||
TP3 | 0.975 | / | / | a | 0.951 | |||
RAD | RAD1 | 0.710 | / | / | a | 0.505 | 0.886 | 0.565 |
RAD2 | 0.762 | 0.031 | 36.181 | *** | 0.580 | |||
RAD3 | 0.811 | 0.042 | 29.903 | *** | 0.657 | |||
RAD4 | 0.800 | 0.039 | 29.583 | *** | 0.640 | |||
RAD5 | 0.705 | 0.037 | 26.308 | *** | 0.497 | |||
RAD6 | 0.714 | 0.037 | 26.642 | *** | 0.510 |
Sub-Models | Estimate | S.E. | C.R. | p | |
---|---|---|---|---|---|
Sub-model 1 | MM→IP | 0.206 | 0.030 | 6.794 | *** |
IP→TP | 0.505 | 0.026 | 19.186 | *** | |
MM→TP | −0.057 | 0.018 | −3.200 | 0.001 | |
TP→RAD | 0.112 | 0.044 | 2.574 | 0.010 | |
MM→RAD | 0.242 | 0.022 | 10.811 | *** | |
IP→RAD | 0.010 | 0.037 | 0.278 | 0.781 | |
Sub-model 2 | SM→IP | 0.144 | 0.035 | 4.081 | *** |
IP→TP | 0.492 | 0.025 | 19.453 | *** | |
SM→TP | −0.051 | 0.020 | −2.559 | 0.011 | |
TP→RAD | 0.186 | 0.048 | 3.872 | *** | |
SM→RAD | 0.215 | 0.025 | 8.498 | *** | |
IP→RAD | −0.056 | 0.040 | −1.374 | 0.169 | |
Sub-model 3 | FTF→IP | 0.385 | 0.037 | 10.308 | *** |
IP→TP | 0.501 | 0.027 | 18.403 | *** | |
FTF→TP | −0.035 | 0.022 | −1.572 | 0.116 | |
TP→RAD | 0.171 | 0.048 | 3.566 | *** | |
FTF→RAD | 0.161 | 0.028 | 5.754 | *** | |
IP→RAD | −0.073 | 0.043 | −1.671 | 0.095 |
Model | X | ||||||||
---|---|---|---|---|---|---|---|---|---|
Sub-model 1 | MM | 17.4% | 100% | 22.2% | |||||
Sub-model 2 | SM | .013 | 70.0% | 73.3% | 12.2% | ||||
Sub-model 3 | FTF | 0.033 | 0.061 | 100% | 54.1% | 27.5% |
Sub-Models | Estimate | S.E. | C.R. | p | |
---|---|---|---|---|---|
Sub-model 4 | MM*SM→IP | −0.055 | 0.026 | −2.103 | 0.035 |
IP→TP | 0.434 | 0.032 | 13.651 | *** | |
MM*SM→TP | 0.004 | 0.014 | 0.315 | 0.753 | |
TP→RAD | −0.103 | 0.018 | −5.825 | *** | |
MM*SM→RAD | 0.090 | 0.061 | 1.479 | 0.139 | |
IP→RAD | 0.065 | 0.039 | 1.660 | 0.097 | |
Sub-model 5 | MM*FTF→IP | −0.014 | 0.023 | −0.628 | 0.530 |
IP→TP | 0.495 | 0.026 | 19.384 | *** | |
MM*FTF→TP | 0.026 | 0.013 | 2.051 | 0.040 | |
TP→RAD | 0.076 | 0.043 | 1.776 | 0.076 | |
MM*FTF→RAD | −0.073 | 0.016 | −4.570 | *** | |
IP→RAD | 0.083 | 0.037 | 2.251 | 0.024 | |
Sub-model 6 | SM*FTF→IP | 0.013 | 0.032 | 0.418 | 0.676 |
IP→TP | 0.495 | 0.026 | 19.348 | *** | |
SM*FTF→TP | 0.024 | 0.018 | 1.329 | 0.184 | |
TP→RAD | 0.068 | 0.042 | 1.609 | 0.108 | |
SM*FTF→RAD | −0.069 | 0.022 | −3.092 | 0.002 | |
IP→RAD | 0.090 | 0.037 | 2.450 | 0.014 |
Demographic Characteristics | Sub-Model 1 | Sub-Model 2 | Sub-Model 3 | |||
---|---|---|---|---|---|---|
Tendency of MM→IP→TP→RAD | MM→RAD | Tendency of SM→IP→TP→RAD | SM→RAD | Tendency of FTF→IP→TP→RAD | FTF→RAD | |
Male | Obvious | β = 0.335 *** | Obvious | β = 0.263 *** | Obvious | β = 0.156 *** |
Female | Obvious | β = 0.329 *** | Not Obvious pa1 > 0.05 | β = 0.252 *** | Not Obvious pa3 > 0.05 | β = 0.214 *** |
Married | Obvious | β = 0.271 *** | Obvious | β = 0.197 *** | Obvious | β = 0.132 *** |
Unmarried | Obvious | β = 0.434 *** | Obvious | β = 0.356 *** | Obvious | β = 0.307 *** |
Divorced | Not Obvious pa1 > 0.05 | β = 0.295 *** | Not Obvious pa1 > 0.05 | β = 0.139 | Not Obvious pa1 > 0.05 | β = 0.012 |
15–29-year-old | Obvious | β = 0.367 *** | Obvious | β = 0.283 *** | Obvious | β = 0.215 *** |
30–44-year-old | Not Obvious pa3 > 0.05 | β = 0.304 *** | Not Obvious pa1, pa3 > 0.05 | β = 0.221 *** | Not Obvious pa1, pa3 > 0.05 | β = 0.205 *** |
45–59-year-old | Obvious | β = 0.295 *** | Not Obvious pa1 > 0.05 | β = 0.230 *** | Not Obvious pa1 > 0.05 | β = 0.132 ** |
60–74-year-old | Not Obvious pa1, pa2, pa3 > 0.05 | β = 0.208 *** | Not Obvious pa1, pa2, pa3 > 0.05 | β = 0.121 | Not Obvious pa1, pa2, pa3 > 0.05 | β = 0.011 |
Junior high school and below | Obvious | β = 0.431 *** | Obvious | β = 0.345 *** | Obvious | β = 0.263 *** |
High school or secondary school | Not Obvious pa3 > 0.05 | β = 0.312 *** | Not Obvious pa1, pa3 > 0.05 | β = 0.136 * | Not Obvious pa3 > 0.05 | β = 0.135 * |
University | Obvious | β = 0.271 *** | Not Obvious pa1, pa3 > 0.05 | β = 0.241 *** | Not Obvious pa3 > 0.05 | β = 0.171 *** |
Master’s degree or above | Not Obvious pa1, pa3 > 0.05 | β = 0.473 *** | Not Obvious pa1, pa3 > 0.05 | β = 0.464 *** | Not Obvious pa3 > 0.05 | β = 0.263 |
L15000 | Obvious | β = 0.375 *** | Not Obvious pa3 > 0.05 | β = 0.306 *** | Not Obvious pa3 > 0.05 | β = 0.213 ** |
15,000–25,000 | Obvious | β = 0.369 *** | Obvious | β = 0.259 *** | Obvious | β = 0.257 *** |
25,000–45,000 | Not Obvious pa3 > 0.05 | β = 0.370 *** | Not Obvious pa3 > 0.05 | β = 0.279 *** | Not Obvious pa3 > 0.05 | β = 0.148 * |
45,000–65,000 | Not Obvious pa3 > 0.05 | β = 0.327 *** | Not Obvious pa3 > 0.05 | β = 0.258 *** | Not Obvious pa3 > 0.05 | β = 0.138 * |
M65000 | Not Obvious pa3 > 0.05 | β = 0.248 *** | Not Obvious pa3 > 0.05 | β = 0.171 *** | Not Obvious pa3 > 0.05 | β = 0.175 *** |
Yes | Obvious | β = 0.305 *** | Obvious | β = 0.241 *** | Not Obvious pa3 > 0.05 | β = 0.192 *** |
No | Obvious | β = 0.361 *** | Not Obvious pa1 > 0.05 | β = 0.266 *** | Obvious | β = 0.155 ** |
Chongqing | Not Obvious pa1, pa3 > 0.05 | β = 0.330 *** | Not Obvious pa1 > 0.05 | β = 0.217 ** | Obvious | β = 0.075 |
Tianjin | Obvious | β = 0.176 ** | Not Obvious pa1, pa3 > 0.05 | β = 0.182 ** | Not Obvious pa3 > 0.05 | β = 0.077 |
Zhangzhou | Not Obvious pa3 > 0.05 | β = 0.318 *** | Obvious | β = 0.259 *** | Obvious | β = 0.180 ** |
Changzhou | Not Obvious pa3 > 0.05 | β = 0.341 *** | Not Obvious pa3 > 0.05 | β = 0.265 *** | Not Obvious pa3 > 0.05 | β = 0.311 *** |
Zibo | Not Obvious pa1, pa3 > 0.05 | β = 0.398 *** | Not Obvious pa1, pa3 > 0.05 | β = 0.230 *** | Not Obvious pa3 > 0.05 | β = 0.196 ** |
Lanzhou | Not Obvious pa3 > 0.05 | β = 0.438 *** | Obvious | β = 0.351 *** | Obvious | β = 0.267 *** |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Wang, Z.; Liu, Y.; Liu, T. An Empirical Study on Hazardous Chemicals Risk of Urban Residents in China: Analysis of Mediating Effect and Channel Preference of Response Action Decision Model. Int. J. Environ. Res. Public Health 2021, 18, 10932. https://doi.org/10.3390/ijerph182010932
Wang Z, Liu Y, Liu T. An Empirical Study on Hazardous Chemicals Risk of Urban Residents in China: Analysis of Mediating Effect and Channel Preference of Response Action Decision Model. International Journal of Environmental Research and Public Health. 2021; 18(20):10932. https://doi.org/10.3390/ijerph182010932
Chicago/Turabian StyleWang, Ziwei, Yongkui Liu, and Tiezhong Liu. 2021. "An Empirical Study on Hazardous Chemicals Risk of Urban Residents in China: Analysis of Mediating Effect and Channel Preference of Response Action Decision Model" International Journal of Environmental Research and Public Health 18, no. 20: 10932. https://doi.org/10.3390/ijerph182010932