How Does Mobile App Failure Affect Purchases in Online and Offline Channels?

1 HOW DOES MOBILE APP FAILURE AFFECT PURCHASES IN ONLINE AND OFFLINE CHANNELS? Unnati Narang Venkatesh Shankar Sridhar Narayanan December 2020 * Unnati Narang ([email protected]) is Assistant Professor of Marketing, University of Illinois, Urbana Champaign, Venkatesh Shankar ([email protected]) is Professor of Marketing and Coleman Chair in Marketing and Director of Research, Center for Retailing Studies at the Mays Business School, Texas A&M University, and Sridhar Narayanan ([email protected]) is an Associate Professor of Marketing at the Graduate School of Business, Stanford University. We thank the participants at the ISMS Marketing Science conference, the UTDFORMS conference, and research seminar participants at the University of California, Davis, the University of Toronto, the University of Illinois, Urbana Champaign, and the University of Texas at Austin for valuable comments. 2 Abstract Mobile devices account for a majority of transactions between shoppers and marketers. Branded retailer mobile apps have been shown to significantly increase purchases across channels. However, app service failures can lead to decreases in app usage, making app failure prevention and recovery critical for retailers. Does an app failure influence purchases in general and within the online channel in particular? Does it have any spillover effects across other channels? What potential mechanisms explain and what factors moderate these effects? We examine these questions empirically, employing a unique dataset from an omnichannel retailer. We leverage a natural experiment of exogenous systemwide failure shocks in this retailer’s mobile app and related data to examine the causal impact of app failures on purchases in all channels using a difference-in-differences approach. We investigate two potential mechanisms behind these effects – channel substitution and brand preference dilution. We also analyze shopper heterogeneity in the effects using a theoretically-driven moderator approach as well as a data- driven machine learning method. Our analysis reveals that although an app failure has a significant overall negative effect on shoppers’ frequency, quantity, and monetary value of purchases across channels, the effects are heterogeneous across channels and shoppers. Interestingly, the decreases in purchases across channels are driven by purchase reductions in brick-and-mortar stores and not in the online channel. A significant decrease in app engagement post failure explains the overall drop in purchases. Brand preference dilution after app failure explains the fall in store purchases, while channel substitution post failure explains the preservation of purchases in the online channel. Surprisingly, purchases rise for a small group of shoppers who were close to the retailer’s store at the time of app failure. Furthermore, shoppers with a higher monetary value of past purchases, and less recent purchases are less sensitive to app failures. The results suggest that app failures lead to an annual revenue loss of about $2.4- $3.4 million for the retailer in our data. About 47% shoppers contribute to about 70% of the loss. We outline targeted failure prevention and service recovery strategies that retailers could employ. Keywords: service failure, mobile marketing, mobile app, retailing, omnichannel, difference-in- differences, natural experiment, causal effects 1 INTRODUCTION Mobile commerce has seen tremendous growth with mobile devices accounting for a majority of interactions between shoppers and marketers. This growth has accelerated through the rapid increase of smartphone penetration – about 3.2 billion people (41.5% of the global population) used smartphones in 2019.1 Mobile applications (henceforth, apps) have emerged as an important channel for retailers as they have been found to increase engagement and purchases across channels (e.g., Kim et al. 2015; Narang and Shankar 2019; Xu et al. 2016). While retailers have widely embraced mobile apps, there is little understanding about how service failures in this channel affect shopper behavior. This issue is important because unlike other channels, the mobile channel is highly vulnerable to failures. The diversity of mobile operating systems (e.g., iOS, Android), devices (e.g., mobile phone and tablet), and versions of hardware and software and their constant use across a variety of mobile networks often result in app failures. Failures in a retailer’s mobile app have the potential to negatively affect shoppers’ engagement with the app and their shopping outcomes within the mobile channel. In addition, app failures may have spillover effects across other channels due to both substitution of purchases across channels and dilution of preference for the retailer brand. Understanding how and why failures impact shoppers’ behavior across channels is important for retailers. Preventing and recovering from app failures is critical for managers because more than 60% of shoppers abandon an app after experiencing failure(s) (Dimensional Research 2015). In 2016, app crashes were the leading cause of system failures, contributing 65% to all iOS failures (Blancco 2016). About 2.6% of all app sessions result in a crash, suggesting about 1.5 billion app failures across 60 billion app sessions annually (Computerworld 2014). Given the extent of these 1 Source: Statista report on smartphone penetration (https://tinyurl.com/hy2skfk) last accessed 18 November 2020. 2 app failures and their potential damage to firms’ relationships with customers, determining the impact of app failures is important for formulating preventive and recovery strategies. Despite the importance of app failures, not much is known about their impact on purchases. While app crashes in a shopper’s mobile device have been shown to negatively influence app engagement (e.g., restart time, browsing duration, and activity level, Shi et al. 2017), the relationship between app failures and subsequent purchases has not been studied. Furthermore, a large proportion of shoppers use both online (desktop website, mobile website, and mobile app) and offline (brick-and-mortar) retail channels. However, we do not know much about the impact of app failures on shopping outcomes across channels (spillover effects). From a theoretical standpoint, the potential mechanisms behind such effects within and across channels are important. How much of these effects arise due to channel substitution post failure? What portion of the effects can be attributed to dilution of preference for the retailer’s brand? Prior research has not addressed these interesting questions. The effects of app failure may also differ across shoppers. Shoppers may be more or less negatively impacted by failures depending on factors such as shoppers’ relationship with the firm (Chandrashekaran et al. 2007; Goodman et al. 1995, Hess et al. 2003; Knox and van Oest 2014; Ma et al. 2015) and shoppers’ prior use of the firm’s digital channels (Cleeren et al. 2013; Liu and Shankar 2015; Shi et al. 2017). It is important for managers to better understand how the effects of failure vary across shoppers so that they can devise targeted preventive and recovery strategies. Yet not much is known about heterogeneity in the effects of app failure. Our study fills these crucial gaps in the literature. We quantify and explain the impact of app failures on managerially important outcomes, such as the frequency, quantity, and monetary value of purchases in online and offline channels. We address four research questions: 3 • What are the effects of a service failure in a retailer’s mobile app on the frequency, quantity, and monetary value of subsequent purchases by the shoppers? • What are the effects of a service failure in an app on purchases in the online and offline channels? • What potential mechanisms explain the effects of an app service failure on purchases? • How do these effects vary across shoppers or what factors moderate these effects? Estimation of the causal effects of app failures on shopping outcomes is challenging. It is typically hard to do this using observational data due to the potential endogeneity of app failures. This endogeneity may stem from an activity bias in that shoppers who use the app more frequently are also more likely to experience failures than other shoppers. Therefore, failure- experiencing shoppers may differ systematically from non-failure experiencers in their shopping behavior, leading to potentially spurious correlations between failures and shopping behavior. Panel data may not necessarily mitigate this issue because time-varying app usage/shopping activity is potentially correlated with time-varying app failures for the same reason. That is, shoppers are likely to engage more with the app when they are likely to purchase, potentially leading to more failures than in periods when shoppers engage less with the app. Additionally, the nature of activity on the app may be correlated with failures. For instance, a negative correlation between failures and purchases may result from a greater incidence of failures on the app’s purchase page than on other pages. Thus, it is hard to make the case that correlations between app failures and shopping outcomes in observational data have a causal interpretation. The gold standard among the methods available to uncover the causal impact of service failures is a randomized field experiment. However, such an experiment would be impractical in this context because a retailer will unlikely deliberately induce

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