Sustainability in footwear industry: a big data analysis

Authors

  • Francesco Polese
  • Maria Vincenza Ciasullo
  • Orlando Troisi
  • Gennaro Maione

DOI:

https://doi.org/10.7433/s108.2019.09

Keywords:

sustainability; footwear industry; big data; big data analysis; Twitter; web crawler; decision making

Abstract

Purpose of the paper: The work aims to achieve a twofold objective: highlight, through a big data analysis, the consumers’ perception about sustainability in the footwear context and provide useful considerations regarding the opportunities for companies to adequately manage the decision-making process in order to maximize the benefits and minimize the risks deriving from the definition and implementation of sustainability-oriented business strategies.

Methodology: A text analytics study has been performed to understand the main variables that people consider when adopting their shoe purchasing behavior. In particular, the collection of the people’s opinions has taken place on Twitter, a well-known and widespread social network community.

Findings: According to the big data analysis carried out, sustainability is ranked last among the factors capable of orienting the consumers’ purchasing choice, as demonstrated by the only 1311 posts on Twitter.

Research limits: The automated collection of people’s posts has prevented from going deeper in understanding their real ideas about variables more capable of orienting their shoe purchasing behavior.

Practical implications: The study suggests the importance to enhance managerial meta-capabilities for those footwear companies that are actively engaged in corporate sustainability. The systematic analysis of points of view, preferences and expectations would allow the development of innovative eco-value propositions in terms of both products (i.e. environmentally friendly shoes) and services (i.e. reverse logistics).

Originality of the paper: The paper is based on an innovative technique of data analysis, carried out by means of a web crawler, software for automatically identifying, collecting and classifying a big number of terms, capable of facilitating the understanding of the investigated phenomenon.

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Published

2019-04-30