Document Type

Article

Source of Publication

Australasian Journal of Information Systems

Publication Date

1-1-2021

Abstract

There is a growing academic interest on the dark side of engagement on social media and the role of user-generated content (UGC). The illicit trade of wildlife online is a major contributor to global species loss and, thus, strategies to reduce demand for wild species and consumer engagement in the market and are of paramount importance. We first conduct qualitative analyses on a large data set of UGC (n=14,729 words from 1060 comments from 12 Facebook groups) from a biodiversity hotspot, The Republic of Indonesia. We use automatic machine-learning lexical software to explore the discourse that occurs in comments of posts that promote behaviour change and demand reduction. Then, to examine the efficacy of these posts, we test an extended elaboration likelihood model to determine the nature of information processing that leads viewers to agree with wildlife conservation content. Our results show that motivation, opportunity and ability factors moderate the relationship between information processing and comment valence, as well as influencing whether comments indicate attitude change. This work extends the use of theory from information systems and marketing to conservation, and provides both conceptual and practical recommendations to encourage behaviour change and reduce the harmful effects of engagement.

ISSN

1449-8618

Publisher

Australasian Association for Information Systems (AAIS)

Volume

25

Last Page

35

Disciplines

Business

Keywords

consumer engagement, ELM, MOA, UGC, wildlife trade

Scopus ID

85108796129

Creative Commons License

Creative Commons Attribution-NonCommercial 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

Included in

Business Commons

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