Understanding the acceptance of carbon offset programs among hospitality consumers: an application of the extended parallel process model

Abstract

Carbon offset programs are effective for the hospitality industry to achieve carbon neutrality. Understanding the acceptance of carbon offset programs among hospitality consumers is a decisive starting point and fulcrum for hotels and other lodging establishments to implement such programs. Drawing on the extended parallel process model (EPPM), this study explores the roles of objective and subjective knowledge and perceived government efficacy in hospitality consumers’ acceptance of carbon offset programs. The findings of a questionnaire survey suggested that subjective and objective knowledge positively affect threat variables (perceived severity and perceived susceptibility) and efficacy variables (self-efficacy and response efficacy). Both threat and efficacy variables and perceived government efficacy exert positive and significant impacts on the acceptance of carbon offset programs. Moreover, perceived government efficacy positively moderates the relationships between efficacy variables and the acceptance of carbon offset programs but negatively moderates the relationships between threat variables and the acceptance of carbon offset programs. The findings provide practical implications for hospitality managers and policymakers to facilitate carbon offset programs and achieve carbon neutrality goals.

Keywords:

• Acceptance of carbon offset programs
• Subjective knowledge
• Objective knowledge
• Perceived government efficacy
• The extended parallel process model

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant number 71974177) and China Postdoctoral Science Foundation (Grant number 2022M713026).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (Grant number 71974177) and China Postdoctoral Science Foundation (Grant number 2022M713026).