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Predicting Consumer Responses to a Chatbot on Facebook.
Brahim Zarouali1, Evert Van den Broeck1, Michel Walrave1
1Department of Communication Studies, University of Antwerp , Antwerp, Belgium .
Cyberpsychology, Behavior and Social Networking
|July 24, 2018
Summary
This study reveals that consumer perceptions of usefulness and helpfulness, along with emotional factors, significantly boost attitudes towards brands using Facebook chatbots, driving patronage intentions.
Area of Science:
- Marketing
- Consumer Behavior
- Human-Computer Interaction
Background:
- Chatbots are increasingly prevalent on social media platforms for commercial use.
- Understanding consumer acceptance of chatbots is crucial for brands.
- Facebook chatbots offer a significant avenue for brand engagement and commercialization.
Purpose of the Study:
- To examine the effectiveness of Facebook chatbots for brands.
- To test a model based on the Consumer Acceptance of Technology (CAT) model.
- To identify key determinants influencing consumers' attitude and patronage intention towards chatbot brands.
Main Methods:
- Proposed and tested a model integrating cognitive (perceived usefulness, ease-of-use, helpfulness) and affective (pleasure, arousal, dominance) determinants.
- Utilized structural equation modeling (SEM) for data analysis.
- Examined the influence of these determinants on consumers' attitude and patronage intention.
Main Results:
- Perceived usefulness and helpfulness (cognitive) and all affective dimensions positively influenced attitude toward the chatbot brand.
- Consumer attitude towards the brand significantly predicted patronage intention.
- All significant determinants indirectly affected patronage intention through attitude toward the brand.
Conclusions:
- Brands can enhance consumer acceptance of Facebook chatbots by focusing on perceived usefulness and helpfulness.
- Affective factors play a critical role in shaping positive consumer attitudes towards chatbot brands.
- Findings offer practical insights for brands leveraging chatbots and suggest future research directions.
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