Artificial intelligent chatbots as brand promoters: a two-stage structural equation modeling-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.

Parametric Predictions for Pure Electric Vehicles

Demand for pure electric vehicles has been found to be increasing over the years. This has necessitated the development of a model that would serve as a predicting machine for manufacturing different types of pure electric vehicles. Direct Artificial Neural Network approach was used for predictions of nine different parameters commonly found in pure electric cars. Predictions were found to be of high degree of accuracy while using unit and overall model errors as the basis of performance measurement. The mean absolute error, mean square error and root mean square error of the model were 0.109, 0.218 and 0.467, respectively, when the combined electric charge consumption was used for modeling. For the model formation, using the same variable, the losses for the training and testing were 3.9132 × 10−6 and 9.698 × 10−7, respectively. The model was also evaluated using redefined datasets. The developed model can be used by manufacturers and engineers to simulate future designs when certain parameters are given.