Exploring consumer participation in environment management: Findings from two‐staged structural equation modelling‐artificial neural network approach

2020 ◽  
Author(s):  
Arsalan Najmi ◽  
Kanagi Kanapathy ◽  
Azmin A. Aziz
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Structural Equation Modelling ◽  
Structural Equation ◽  
Consumer Participation ◽  
Network Approach ◽  
Neural Network Approach ◽  
Environment Management ◽  
Equation Modelling ◽  
Artificial Neural Network Approach

Artificial intelligent chatbots as brand promoters: a two-stage structural equation modeling-artificial neural network approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Crystal T. Lee ◽  
Ling-Yen Pan ◽  
Sara H. Hsieh
Keyword(s):  
Neural Network ◽  
Social Support ◽  
Artificial Neural Network ◽  
Purchase Intention ◽  
Structural Equation ◽  
Equation Modeling ◽  
Network Approach ◽  
Neural Network Approach ◽  
Content Type ◽  
Artificial Neural Network Approach

PurposeThis study investigates the determinants of effective human and artificial intelligence (AI) relationship-building strategies for brands. It explores the antecedents and consequences of consumers' interactant satisfaction with communication and identifies ways to enhance consumer purchase intention via AI chatbot promotion.Design/methodology/approachMicrosoft Xiaoice served as the focal AI chatbot, and 331 valid samples were obtained. A two-stage structural equation modeling-artificial neural network approach was adopted to verify the proposed theoretical model.FindingsRegarding the IQ (intelligence quotient) and EQ (emotional quotient) of AI chatbots, the multi-dimensional social support model helps explain consumers' interactant satisfaction with communication, which facilitates affective attachment and purchase intention. The results also show that chatbots should emphasize emotional and esteem social support more than informational support.Practical implicationsBrands should focus more on AI chatbots' emotional and empathetic responses than functional aspects when designing dialogue content for human–AI interactions. Well-designed AI chatbots can help marketers develop effective brand promotion strategies.Originality/valueThis research enriches the human–AI interaction literature by adopting a multi-dimensional social support theoretical lens that can enhance the interactant satisfaction with communication, affective attachment and purchase intention of AI chatbot users.

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