#BuyNothingDay: investigating consumer restraint using hybrid content analysis of Twitter data

Abstract

Purpose

This study aims to investigate motivations and human values of everyday consumers who participate in the annual day of consumption restraint known as Buy Nothing Day (BND). In addition, this study demonstrates a hybrid content analysis method in which artificial intelligence and human contributions are used in the data analysis.

Design/methodology/approach

This research uses a hybrid method of content analysis of a large Twitter data set spanning three years.

Findings

Consumer motivations are categorized as relating to consumerism, personal welfare, wastefulness, environment, inequality, anti-capitalism, financial responsibility, financial necessity, health, ethics and resistance to American culture. Of these, consumerism and personal welfare are the most common. Moreover, human values related to “openness to change” and “self-transcendence” were prominent in the BND tweets.

Research limitations/implications

This research demonstrates the effectiveness of a hybrid content analysis methodology and uncovers the motivations and human values that average consumers (as opposed to consumer activists) have to restrain their consumption. This research also provides insight for firms wishing to better understand and respond to consumption restraint.

Practical implications

This research provides insight for firms wishing to better understand and respond to consumption restraint.

Originality/value

The question of why everyday consumers engage in consumption restraint has received little attention in the scholarly discourse; this research provides insight into “everyday” consumer motivations for engaging in restraint using a hybrid content analysis of a large data set spanning over three years.

Citation

Paschen, J., Wilson, M. and Robson, K. (2020), "#BuyNothingDay: investigating consumer restraint using hybrid content analysis of Twitter data", European Journal of Marketing, Vol. 54 No. 2, pp. 327-350. https://doi.org/10.1108/EJM-01-2019-0063