Abstract
Purpose: The purpose of this paper is to propose a research model to identify the key determinants and examine their impact towards online pro-brand and anti-brand community citizenship behaviours (CCBs). Design/methodology/approach: A survey based on the research model is used to collect empirical data from 260 and 200 members of online pro-brand communities (OBCs) and online anti-brand communities (OABCs), respectively. A two-stage approach employing fuzzy-set qualitative comparative analysis and artificial neural network (ANN) is first applied to uncover new observations. Findings: Moral identity and positive brand emotion (BE) are the two most influential factors driving both online pro-brand and anti-brand CCBs. A higher level of internalisation might be required to exhibit online anti-brand CCB as opposed to online pro-brand CCB. This contradicts the current understanding that anti-brand behaviours are less morally restricted given the virtuality and anonymity of online communities. OABC members may need to better justify themselves internally to overcome positive BE when exercising anti-brand action. Also, brand identification, brand dis-identification and BE would be used to identify two types of OABC members. Research limitations/implications: The effect of motives other than pro-social remains unclear on online pro-brand and anti-brand CCBs. Originality/value: This is the first paper to develop two new dimensions which provide a more complete definition of CCB. Also, some new observations are uncovered by comparing the effect of different key determinants on online pro-brand CCB against that of online anti-brand CCB. The research model can be used to define and improve member (or brand) engagement which would enhance the management of OBCs and OABCs.
| Original language | English |
|---|---|
| Pages (from-to) | 850-872 |
| Number of pages | 23 |
| Journal | Industrial Management and Data Systems |
| Volume | 118 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2018 |
Keywords
- Artificial neural network
- Community citizenship behaviour
- Fuzzy-set qualitative comparative analysis
- Online community participation
- Social identity theory
- Social support
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