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International Journal of Technology Marketing (2 papers in press)
Special Issue on: Role of Digital Sources and Technological Advancement in Marketing Practices and Problems
Effect of eWOM valence on purchase intention: the moderating role of product by Gobinda Roy, Biplab Datta, Srabanti Mukherjee, Rituparna Basu, Avinash K. Shrivastava Abstract: This study investigates the effects of electronic word of mouth (eWOM) valence on online purchase intention. The study also examines the moderating effect of the products nature on the relationship between valence and purchase intention. The study empirically tested the proposed model with survey-based data using the analysis of variance method with a randomised block design. The result indicated a significant effect of eWOM valence on purchase intention that is moderated by the nature of products. Practical and theoretical implications of research along with future research directions are also discussed. Keywords: electronic word of mouth; eWOM; mixed neutral eWOM; search products; experience products; purchase intention; eWOM valence. DOI: 10.1504/IJTMKT.2021.10035724
Artificial neural network to diagnose the consumer behaviour towards non-fuel products and services at filling stations by Gautam Srivastava, Surajit Bag Abstract: The petroleum companies are transforming their business model from fuel retailing to non-fuel retailing to increase their revenues. However, predicting the complex buying behaviour of the consumer is a major challenge faced by petroleum retailers. Therefore, there is a need for research to develop models, which can predict consumer behaviour pertaining to non-fuel retailing at filling stations. The study intended to predict the buying behaviour of the consumers of non-fuel products and services at filling stations. The study proposed a predictor model by using an artificial neural network (ANN). The literature reveals that an artificial neural network is a better predictor than traditional predictors, such as logistic regression and discriminant analysis. This research can help develop the supervised machine learning model and further classify the consumers visiting the filling stations. This model can analyse consumer behaviour with an automation system, which reduces the cost of marketing with more accurate results. This paper extends the applications of ANN in the domain of marketing and the precise analysis of the consumer behaviour. Keywords: artificial neural network; ANN; consumer behaviour; predictor; non-fuel retailing; filling stations; India. DOI: 10.1504/IJTMKT.2021.10036874