Communicating With Consumers in Environmental Sustainability Advertising: An Analysis of Award-Winning Ads Through Text Mining and Sentiment Analysis

Introduction

As the world's population grows, the corresponding increase in consumption highlights the urgent need for more efficient use of natural resources. Once only related to production, sustainability has become a crucial factor in consumer behavior today. Despite the awareness of the limited nature of natural resources, profit-oriented output, and company sales have slowed down somewhat due to changing consumer habits. However, more than this slowdown is needed, and we need more concerted efforts towards sustainability. We require more organized endeavors to transform products and services into a trend suitable for recycling, environmentally friendly, and sensitive to nature preservation. In this context, brands that promote green consumption through their activities and emphasize environmental sensitivity are expected to significantly impact brand image and reputation (Kayıkçı et al., 2019).

Worldwide, consumers expect companies to go beyond their economic interests and create positive social impacts (LaVoi & Haley, 2021). The expectation that companies exist for a broader social purpose can manifest in various ways. In this context, companies are turning to pro-social ventures, programs, and products to generate a positive social impact. Sustainability, aimed at balancing economic growth, environmental protection, and social welfare, has become a fundamental paradigm for creating a positive social impact for new and existing companies (Brundtland, 1987).

Almost one out of every five packaged products is marketed sustainably, and these products are growing seven times faster than their traditional counterparts (Crawford, 2020). More than 85% of individuals worldwide are willing to change their purchasing habits to include sustainable products (Business Wire, 2021; Xu et al., 2021). As a result, sustainability has become pervasive in branding and advertising, offering a strategic resource for competitive advantage (Kong et al., 2021; Simpson & Radford, 2012). These macro trends underscore companies' need to communicate their sustainability initiatives effectively to consumers (Yang et al., 2015).

In this context, it is a fact that companies are increasingly focusing on environmentally sustainable advertising campaigns that prioritize society's values and concerns over commercial interests. Various brands from different sectors run advertising campaigns focusing on environmental and climate issues. By examining advertising campaigns from multiple industries that address climate change, recycling, water scarcity, and drought, it is known that they convey the idea that a livable world can be achieved if people unite and correct past mistakes. To overcome this process more quickly, advertising campaigns that emphasize the need for people to use resources more carefully and efficiently and serve as a source of hope for people have been descriptively analyzed.

This study aims to uncover consumers' perceptions of environmentally sustainable-themed advertisements featured by brands. Fundamentally, the research seeks to explore the sensitivities and reactions of consumers towards advertisements containing environmental sustainability initiatives prepared by brands. Cai et al. (2010) emphasized monitoring emotion to reveal consumer, brand, and market insights. In this context, the study examines how consumers/users on social media approach environmental sustainability-themed advertisements and how they express their emotions and utilize sentiment analysis. This method has yet to be used in advertising research.

This study consists of two layers. First, we use opinion-mining techniques to explore consumers' spontaneous expressions of emotions toward various brands' environmentally sustainable-themed advertising videos. Second, we use a contemporary approach, sentiment analysis, and a realistically generated user dataset to provide insight into the relationship between brand perception and performance metrics.