Extant studies assessing determinants of peer-to-peer (P2P) accommodation prices have mainly focused on individual listing-level analysis, with a less attention to inter-regional dynamics between cooperation and competition. While Airbnb has announced how to set a pricing strategy tool called “smart pricing” to help hosts gauge rental rates, many hosts find that its suggesting prices are too low, failing to offer a robust picture of their destination market (Jiang et al., 2022). This calls for a research on how P2P accommodation hosts can benefit from coopetitive pricing strategy – a hybrid behavior of cooperation and competition that occurs among hosts or regions (Chim-Miki & Batista-Canino, 2018).

We study retailer app use and shopping behavior by investigating whether and how app adopters’ preferred store registration (PSR), a voluntary enrollment of store loyalty program, can lead to changes in purchase behaviors among the adopters in offline stores. A panel dataset from a large bakery franchise chain was studied using difference-in-differences with propensity score matching for causal inference. The results show that customers who participated in PSR increase their spending, compared to customers who did not. In addition, the results confirm that the PSR-spending relationship is moderated by customer and store characteristics; customers who visited infrequently and spent less prior to PSR participation increase their purchases, and they spend more at stores with better service quality.

Adding new stores close to incumbent franchisees, or franchise encroachment, has been a contentious issue in the marketplace. Prior studies on the encroachment effect focus mainly on the store-level cannibalization, lacking the understanding of customer-level post-encroachment outcomes. The authors investigate individual-level transaction data from a cluster of bakery franchise stores to quantify the franchise encroachment effect at store- and individual-levels with store and customer heterogeneity. Our results show that incumbents experience a decrease in sales, and the extent of the lost sales is attenuated by the distance between incumbent and new stores and the size of incumbents’ product assortment. The individual-level analysis reveals that three customer segments exhibit different post-encroachment shopping behaviors: (1) patronizing incumbents only, (2) patronizing both incumbent and new stores, and (3) completely switching to new stores. While the second segment increases the post-encroachment overall spending, the third segment contributes to both cannibalization to incumbents and higher sales to the franchisor. Our findings offer managerial implications of how to manage franchise encroachment with context-dependency and customer segmentation in terms of maximizing overall franchise performance.

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.

Despite a growing interest of value capture in the phenomenon of open innovation (OI), empirical evidence documenting the link between new product development (NPD), OI practices, and market performance is scarce. Drawing on organizational learning, NPD, and OI literature stream, this paper conceptualizes a framework in which open product innovation (OPI) practices are disentangled into two types: pre-launch OPI (which occurs before a new product is launched) and post-launch OPI (which occurs after a new product is launched). Specific types of OPI practices – technology in-licensing (i.e., pre-launch OPI) and product upgrades (i.e., post-launch OPI) – during the NPD process are expected to influence market performance of new products independently and interactively. This paper empirically analyzes the secondary data related to product innovation and market performance of 536 mobile games that were developed and launched by 265 local and global firms in South Korea. The results support hypotheses and indicate that NPD projects that engage in technology in-licensing by both local and global firms lead to better market performance than NPD projects that do not engage in. Furthermore, the more product upgrades that NPD projects employ during product life cycle, the better market performance. Finally, the involvement of active product upgrades strengthens market performance of global NPD projects that develop new products internally. The results regarding the role of pre-launch and post-launch OPI mechanisms contribute to research on OI and NPD, and also inform managers as to what product innovation practices are recommended to improve market performance of NPD projects.

This paper presents a way of classifying qualitative online consumer reviews (OCRs) in terms of functional and emotional dimensions and measures the direct and indirect impact of both volume and valence of OCRs on product sales. Utilizing four million online postings across 342 mobile games for thirty months, the authors use text analysis and word classification and identify 74 representative words to describe the various levels of functional OCRs consisting of product quality, product innovativeness, price acceptability, and product simplicity, and emotional OCRs including anger, fear, shame, love, contentment, and happiness. They combine the resulting OCR volumes with weekly sales, resulting in 1,835 observations for analysis with hierarchical Bayesian methods. Results suggest that the volume and valence of aggregated functional OCRs and the valence of aggregated emotional OCRs have the positive effects on sales. The volume and valence of functional OCR subcategories have mixed effects on sales and the link is moderated by the share of emotional OCR subcategories. Further, a sales forecasting model which includes 13 variables of OCR subcategories shows the best predictive validity.

Due to the explosive growth of peer-to-peer transportation sharing services offered by micro-entrepreneurial travelers, investigating how their quality cues affect demand is of paramount importance. The goal of this study was to identify what types of intrinsic and extrinsic quality cues affect the demand of peer-to-peer transportation sharing services. Based on the rich data set of 414,292 transactions from a leading peer-to-peer ridesharing platform, it was identified that intrinsic quality cues – car status and driver status – and extrinsic quality cues – price and offering duration – significantly affect the peer-to-peer transportation demand independently and interactively. Although the lower price and longer offering duration of a transportation sharing service positively affect its demand, the higher price or shorter offering duration can increase its sales when combined with better intrinsic cues. Implications for vulnerable segments such as female and minority drivers are provided

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.

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.

This study assessed the degree of marketplace inclusion inherent in the distribution of grocery stores in the Detroit Metropolitan Area; to account for spatial effects such as spatial heterogeneity, phenomena rarely considered in prior marketing literature, geographically weighted regression and geographic information systems were employed.

This study investigates (1) how promotion activities in intra-firm and inter-firm levels influence customers’ total spending amount (revenue) and (2) how customer- and firm-generated online information influences revenue directly and indirectly in luxury hotel industry.