HARNESSING THE PREDICTIVE VALUE OF ONLINE WORD-OF-MOUTH FOR IDENTIFYING MARKET SUCCESS OF NEW AUTOMOBILES: INPUT VERSUS OUTPUT WORD-OF-MOUTH PERSPECTIVES

Abstract

The automotive industry evaluates various success factors to achieve competitive advantage in selling products. Existing studies have predicted the success of newly launched automobiles based on an economic perspective. However, factors such as dynamic changes in consumer preferences and the emergence of numerous automobile brands pose difficulty in understanding product quality. This study proposes a method of understanding the automotive market using text mining techniques and online user opinions for newly launched cars. By analyzing customer experiences and expectations through their opinions, we can anticipate automobile demand in the market more easily. The proposed method is based on online reviews from an online portal for automobiles. Based on a literature review, this study presents a framework for analyzing input versus output word-of-mouth (WOM). It also integrates the success factors from existing automobile studies and derives functional categories and relevant keywords. The analysis identifies differences in consumer-interest factors that lead to short-term success or normal results in automobile sales. In addition, it confirms that the elements of WOM produces varying results depending on the timing these are employed in relation to the product launch (i.e., before or after a product’s launch). It revealed which dimensions of automobile characteristics are important factors in identifying sales volume and market share for specific types and brands of automobile models. The results of this study provide theoretical advantage in predicting market success in the automobile industry. In addition, the study derives practical insights into characteristics of classification information for market forecasts in the automotive industry. The paper provides empirical insights about how input WOM and output WOM which are analyzed differently can have predictive power in forecasting market share and sales volume for automobiles.