Multilevel Analysis of the Perceived Greenwashing Across Age Cohorts

Articles in Press

Document Type : Research Paper

Authors

1 Department of Marketing Management, Aras International Campus, University of Tehran, Tehran, Iran.

2 School of Business and Management, Christ University, Bangalore, India.

3 Operations and supply chain department. School of Management. Presidency University, Bengaluru, India

4 Financial Engineering Dept., Faculty of Management, University of Tehran, Tehran, Iran.

Abstract

Amid growing consumer demand for sustainability, corporate greenwashing—deceptive environmental marketing—has emerged as a significant threat to consumer trust. This study aims to develop a multilevel model to identify the key predictors of Perceived Greenwashing (PGW), with a specific focus on the role of generational cohorts in a cross-national context. A survey was conducted with 280 consumers from India and Iran. Multilevel Modelling (MLM) or Hierarchical Linear Modelling (HLM) was employed to analyse the data, with individuals (Level 1) nested within countries (Level 2), and predictors including generational cohort, awareness of greenwashing (AG), and exposure to greenwashing (EG). The analysis revealed that consumer awareness (β = 0.510) and exposure (β = 0.301) are the most powerful positive predictors of PGW. Critically, the widely assumed effect of generational cohort becomes non-significant after controlling for these experiential factors. Perceptions of industry type and regulations also emerged as significant, albeit smaller, predictors. The findings suggest that the perception of greenwashing is driven more by cognitive and experiential factors than by demographic identity. This study contributes to the literature by reframing the 'generational effect' as a function of information exposure and awareness. For practitioners, it underscores the importance of targeting consumer knowledge levels.

Keywords

International Journal of Business and Development Studies

Articles in Press, Accepted Manuscript
Available Online from 08 March 2026

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