This research examines how inter-price relationships among base and add-on products affect the sales of a new add-on product in the mobile gaming context. Based on theories of price fairness, product familiarity and price-quality relationship, a research model was drawn and tested on a sample of 6,766 weekly observations derived from 74 base games and 514 add-on virtual goods. Our empirical results show that the relative price of a new add-on product, when compared to the base game price, is negatively related to the add-on product sales, but positively related to its sales, when compared to prices of existing add-on products. Furthermore, the inter-price relationships among base and add-on gaming products are moderated by visual richness and game genre. This study provides the implications for IS research and practice.

This paper investigates how pricing actions of base and multiple add-on products sequentially offered in the marketplace affect their sales with a consideration of the interactive price relationships between base and add-on products and among multiple add-on products, as well as a moderating role of product characteristics.

We study the differential effects of a variety of WOM and mass communication activities on the means and on the variances of individual preference parameters. Analyzing the effects of communication on the means of individual preference parameters provides useful information regarding how much WOM and mass communication activities increase or decrease consumers’ preference parameters. Such a study helps us better understand how communication activities influence consumer heterogeneity. This raises a new question: “Do WOM and mass communication activities make consumers homogeneous or heterogeneous in terms of their product preferences?” This important question has not been previously addressed in the marketing literature. We propose a two-level choice model using a hierarchical Bayesian probit to incorporate the differential effects of mass and WOM communication activities, and the proper interaction between communication activities and product attributes. Using actual movie choice data, we analyze the effects of WOM communication activities compared to those of mass communication activities. Furthermore, we measure the differential effects of communication activities and the interaction effects between communication and product attributes. Based on the empirical analysis, we provide relevant managerial guidelines about communication activities.

Customers’ final purchase decisions for electronic products are understandably influenced by previous experiences, marketing messages such as price and promotion, and opinions from other consumers (Simonson and Rosen 2014). In particular, millions of product reviews are posted daily on online review boards or social media represent aggregate consumer preference data (Decker and Trusov 2010). Past studies analyzing online reviews or word-of-mouth (WOM) have focused more on the quantitative dimension of volume of WOM (or “how much people say”), but less on qualitative dimension of valence of WOM (or “what people say”) (Gopinath, Thomas, and Krishnamurthi 2014). However, recent studies have analyzed disaggregate-level UGC by performing text mining in addition to a general analysis of volume and valence of OUGC. Onishi and Manchanda (2012) investigate the relationship between movie sales and both TV advertising and blogs. Although the authors find that the volume and the valence of OUGC (i.e., blogs) are predictive of market outcomes, they retain only certain words (i.e., advertising, award, interesting, and viewed) that consumers would find useful, therefore having general predictive power for market outcomes. Gopinath, Thomas, and Krishnamurthi (2014) address the relationship between the content of online WOM, advertising, and brand performance of cell phones and find that the volume of OUGC does not have significant impact on sales, but only the valence of recommendation UGC has a direct impact. Liu, Singh, and Srinivasan (2015) find that both the volume and sentiments of Tweets do not outperform the information content of Tweets in predicting TV series ratings. Although these three papers have investigated the importance of qualitative UGC through text mining techniques, such studies have not accounted for the detailed dimensions of specific contents. For example, Onishi and Manchanda (2012) use only 4 words out of top 30 frequently cited words for their analysis, and Gopinath, Thomas, and Krishnamurthi (2014) classify the OUGC into three disaggregated dimensions (i.e., attribute, emotion, and recommendation) without further classifications of subcategories and valence of positivity and negativity. Liu, Singh, and Srinivasan (2015) mainly focus on positive and negative Tweet contents about TV shows, lacking further classification of functional and emotional dimensions. In contrast to these studies, this study aims to examine in-depth multidimensional aspects of the content of online reviews, i.e., qualitative UGC, and their impacts on product sales. In this process, we develop defensible measurements of UGC by executing a comprehensive empirical text analysis and evaluate the impact of measures of qualitative UGC relative to volume measure of quantitative UGC. Specifically, we analyze a large data set of UGC on the 350 most talked-about smartphone games from seven different genres (e.g., action, arcade, shooting, puzzle, role playing, simulation, and sports) over a 30 month period, August 2010 to February 2013. We utilize a theoretical framework that classifies qualitative UGC into two major perceptions of functional and emotional dimensions. Prior studies show that perceptions of both functional (cognitive) and emotional (affective) dimensions should be considered to investigate their effects on perceived user satisfaction (Coursaris and van Osch 2015) and online shopping behavior (Van der Heijden 2004). It is evident that both functional and emotional UGC influence consumers to purchase a focal product (Lovett, Peres, and Shachar 2013). The functional UGC relates to the positive and negative attributes and beliefs about a product, and the emotional UGC pertains to the feelings and emotions in response to product experience. As an example, consider one innovative car-racing mobile game which, although expensive, has 3D graphics and high level of complexity. After playing this game, consumers may express their feedback on this game online by describing it as well-made, unique, but sometimes fearful (because a high bill charge is expected from excessive playing time), and addictive (because they like the game too much to stop playing it). This type of online reviews contains different types of UGC: functional (e.g., quality, innovativeness) and emotional (e.g., fear). Another layer of our analysis involves the heterogeneity of impact on product sales across different qualitative UGCs. Specifically, we consider the effects of functional UGC on product sales across emotional contexts such as anger and happiness, in other words, a simultaneous association between functional UGC and emotional UGC. For example, although a consumer may be attracted by some reviews on the high quality graphics of a mobile game (functional UGC), she may hesitate to purchase this product because other reviews express their fear about high cost of purchasing virtual goods (emotional UGC). Accordingly, we expect the functional UGC’s effects on sales to be moderated (amplified or reduced) by emotional UGC. We accommodate such interaction effects in both aggregate and disaggregate models. To the best of our knowledge, this study is the first to empirically identify two dimensions of qualitative UGC (functional and emotional), and shed light on the effects of multidimensional UGC categories on sales. Our findings on the influence of qualitative UGC on product sales are quite different from the prevailing view that firms should pay attention more to the volume of UGC (Chevalier and Mayzlin 2006; Liu 2006) but little to the valence of UGC (Duan, Gu, and Whinston 2008; Godes and Mayzlin 2004; Liu 2006). Rather, our research is in line with recent three papers (Gopinath, Thomas, and Krishnamurthi 2014; Liu, Singh, and Srinivasan 2015; Onishi and Manchanda 2012) in terms of the importance of considering specific contents from a vast amount of text data. However, our paper provides two key contributions. First, we show that specific categories of qualitative online UGC such as functional and emotional variables can be used to predict product sales; this result will be of a high managerial relevance. Especially, traditional methods that use simple metrics such as volume and valence of UGC are less accurate than our method that employs a sophisticated, multidimensional content analysis. Second, the results offer guidance to firms in determining which specific UGC (quantitative or qualitative; functional or emotional; under what contexts) they should focus on for increasing the efficiency of their online marketing activities. Utilizing a large dataset of online reviews on 350 mobile games consisting of four million postings generated for thirty months, the authors identified 76 representative words to describe the functional and emotional UGC using text analysis and word classification. We combined the resulting UGC volumes with weekly sales, resulting in 1,835 observations for analysis with hierarchical Bayesian methods. We find that functional UGC includes 54 representative words to describe various levels of product quality, product innovativeness, price acceptability, and product simplicity, and emotional UGC includes 22 words to express anger, fear, shame, love, contentment, and happiness. The results show that the volume and valence of aggregated functional UGC and the share of aggregated emotional UGC have the positive effects on sales. The volume and valence of functional UGC subcategories have mixed effects on sales and the link is moderated by the share of emotional UGC subcategories. These results are in contrast to those in the literature. Further, a sales forecasting model which includes 13 variables of UGC subcategories shows the best predictive validity. The authors discuss the implications of these results for online marketers.

Product naming is one of the most important communication decisions for any firm to deliver product information to consumers. Product names are highly likely to have critical impacts on the market performances of products, in particular, consisting of unobservable attributes such as motion pictures, music, books, and games. The purpose of this study is to analyze the effects of product naming strategies on their market performances and provide managerial implications on how to name products, especially for experience goods such as movies. We, firstly, suggest a conceptual framework to describe naming decisions as two-stage strategic decisions. The first stage decision is about what type of product information should be provided to consumers via a product name, which we call information choice strategy, and the second stage decision is about how to express the product information in the name, which we call expression strategy. We develop a two-level hierarchical Bayesian log-linear model to consider the main effects of product information strategy with the 1st level of the model, and the mediate effects of expression strategies with the 2nd level of the model. We applied the model to a data set consisting of viewership, names, and release dates of 634 movies released in eight countries where English is an official language. We have decided the types of naming strategies with the help of three industry practitioners, who have been working in the film industry for over 10 years as a producer, marketer, and investor, respectively. The country characteristics, obtained from Hofsted's Homepage (National culture scores of each country) and the World Bank (GDP and population of each country at the released t year), were considered for country heterogeneity. The empirical results show that information choice for movie titles have significant impacts on the movie viewership. Especially, movie titles including story or negative words have more positive impact on their market performances. One of the important findings is that the effects of “what to choose” depends on “how to express”. For example, not frequently used words, nonwords, proper nouns, foreign words in movie titles have positive impacts on the viewership. Popular words used for movie titles have a positive influence directly on the viewership. Interestingly, while sentences for movie titles have negative impacts on the viewership, storyline and theme expressed in sentences show positive impacts on the viewership. Another important finding is the mediating effects of country and product heterogeneities. The effects of naming strategy differs across movie genres and country characteristics. This study makes at least three contributions to the literature. Firstly, this study suggest a conceptual framework an empirical model for naming strategies in the literature. Secondly, this study provides information on what type of naming strategies are more effective on market performance, which has never been addressed in the literature. Lastly, the study provides some managerial implications which are useful for researchers and industry practitioners who are interested in product naming.