The Impact of Perceptual and Situational Factors on Environmental Communication: A Study of Citizen Engagement in China

ABSTRACT

This study applied a situational approach to understanding an environmental problem: PM2.5 (its resulted haze and smog air pollution) in China. Based on a national sample of 374 citizens living in China, it tested a situational model of problem solving and extended it by adding citizens’ environmental engagement behaviour as an immediate consequence of their communicative action. Results of a structural equation modelling analysis supported all the causal links in the conceptual model. Moreover, communicative action significantly mediated the relationship between referent criterion and environmental engagement. Problem recognition, constraint recognition, and involvement recognition did not influence environmental engagement directly, but exerted their significant indirect impact via two key mediators in the model: situational motivation in problem solving and communicative action in problem solving. Theoretical and practical implications of the present study were discussed.

KEYWORDS:

• The perceptual and situational factors that influence environmental communication
• PM2.5 air pollution
• environmental engagement
• China

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 In the present study, the factor “environmental engagement” (see Figure 1) was examined by asking participants the following questions: “In the past 12 months, how often have you monitored the situation of environment in your city?”; “In the past 12 months, how often have you talked about environmental issues with members of local groups (such as campus groups, community groups, and work groups) you belong to?”; “In the past 12 months, how often have you actually took steps to improve the situation of environment (such as reduce, reuse, and recycle resources, pick up litter, trim bush, and so on)?”; “In the past 12 months, how often have you took part in campus- or community-based environmental education programs?”; and “In the past 12 months, how often have you donated money or time to groups or organizations that promote positive environmental action?.”

2 Information seeking.

3 Information attending.

4 Information forefending.

5 Information permitting.

6 Information forwarding.

7 Information sharing .

8 How do citizens talk about PM2.5 related environmental issues on their social networking sites? With their family and friends? With members of a local group, such as a campus group, a community group, and work groups they belong to?

9 How do they get PM2.5 updates? How do they monitor the environmental situation in their city?

10 How often do they take steps to improve the situation of environment (e.g. reduce, reuse, recycle resources, pick up litter, trim bush, and so on)? How often do they take part in some environmental education and advocacy programs? How often do they support (by donating money or time) an ENGO’s initiative or grassroots campaign against PM2.5?

11 Income varied from income varied from CNY (Chinese Yuan) 20,000 or less (n = 64, 17.6%), CNY 20,001–50,000 (n = 75, 20.6%), CNY 50,001–100,000 (n = 92, 25.3%), CNY 100,001–150,000 (n = 40, 11.0%), CNY 150,001–200,000 (n = 37, 10.2%), CNY 200,001–500,000 (n = 41, 11.3%), CNY 500,001–1,000,000 (n = 9, 2.5%), to CNY 1,000,001 and above (n = 6, 1.6%).

12 The second-order factor model was retained because of both its statistically tenable structure and the a priori theoretical justification that Kim and Grunig (2011a) and Kim, Shen, and Morgan (2011) specified.

13 In addition to the structural model (see Figure 2), the authors also ran an alternative model in which we added on Figure 2 with 5 direct paths from (1) situational motivation, (2) problem recognition, (3) constraint recognition, (4) involvement recognition, and (5) referent criterion to environmental engagement as the dependent variable (the new add-on to the STOPS model) (see Figure 3). The model also yielded acceptable data-model fit (CFI = .94; TLI = .93; RMSEA = .047 (C.I.: .042 to .053); SRMR = .083; χ² = 887.05 (p <.001); df = 503; n = 339). Five main effects were significant: (1) problem recognition→situational motivation (β =.53, p < .001); (2) constraint recognition→situational motivation (β = −.16, p < .05); (3) situational motivation→ communication action (β = .46, p < .001); (4) referent criterion→communication action (β = .51, p < .001); and (5) communication action→environmental engagement (β = .50, p < .01). Seven significant indirect effects included: (1) problem recognition→situational motivation→communicative action (β = .24, p < .001); (2) constraint recognition→situational motivation→communicative action (β = −.08, p < .05); (3) involvement recognition→situational motivation→communicative action (β = .09, p < .05); (4) referent criterion→communicative action→environmental engagement (β = .23, p < .01); (5) problem recognition→situational motivation→communicative action→environmental engagement (β = .11, p < .01); (6) constraint recognition→situational motivation→communicative action→environmental engagement (β = −.03, p < .05); and (7) involvement recognition→situational motivation→communicative action→environmental engagement (β = .04, p < .05). The authors finally rejected this alternative model due to the following reasons: (1) model parsimony and (2) the same significant paths for both direct and indirect effects as those in the structural model (Figure 2).

Additional information

Funding

This study was funded by Natural Science Foundation of China (No. 71403117) and the Fundamental Research Funds for the Central Universities (021114380062).