Consumer Search Behavior on the Mobile Internet: An Empirical Analysis

The increasing diffusion of smartphones and tablet computers has facilitated access to product information by providing Internet access anywhere and at any time. As a result, consumers are increasingly using the mobile Internet to search for product information to help them in their purchase decisions. However, there is very little documentation of how, where and when consumers actually carry out such search. Using location-based data from a leading European product information and barcode-scanning app that contains more than 80 million observations, this study provides insights using actual consumer search behavior. The results show that consumer search on the mobile Internet is not bound to store opening hours and is likely to happen to a large extent as ongoing search during consumption. Furthermore, consumers’ geographic mobility is positively correlated and previous search experience is negatively correlated with their search intensity. Finally, access to more types of information via search results, especially product related information, reduces further search on price information, suggesting that product information content can lower price sensitivity.http://deepblue.lib.umich.edu/bitstream/2027.42/111728/1/1275_Manchanda.pd

Gamification Design for Mobile Marketing Effectiveness

Retailing and other business sectors have been buffeted by the diffusion of mobile technology. Millennials, in particular, consider such technology indispensable and have lately been using it for gaming applications. In order to thrive in the new mobile and game centric world, retailers will need to adapt and leverage mobile game-like applications using the process known as gamification. Our own sense is that the gamified interfaces currently offered by firms mostly miss the mark. We provide a systematic overview of game design and point out how principles derived from that field are highly applicable to gamified retail apps as well as to other firm offerings with game like elements. We are aided in our systematic approach by the work of Schell (2008) whose Elemental Game Tetrad Model allows us to offer a coherent look at the gamification antecedents of its psychological and marketing outcomes.Retailing and other business sectors have been buffeted by the diffusion of mobile technology. Millennials, in particular, consider such technology indispensable and have lately been using it for gaming applications. In order to thrive in the new mobile and game centric world, retailers will need to adapt and leverage mobile game-like applications using the process known as gamification. Our own sense is that the gamified interfaces currently offered by firms mostly miss the mark. We provide a systematic overview of game design and point out how principles derived from that field are highly applicable to gamified retail apps as well as to other firm offerings with game like elements. We are aided in our systematic approach by the work of Schell (2008) whose Elemental Game Tetrad Model allows us to offer a coherent look at the gamification antecedents of its psychological and marketing outcomes.http://deepblue.lib.umich.edu/bitstream/2027.42/111727/1/1274_Manchanda.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/111727/3/1274_Manchanda.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/111727/5/1274_Manchanda_Aug15.pdfDescription of 1274_Manchanda.pdf : April 2015 - proper coverDescription of 1274_Manchanda_Aug15.pdf : August 2015 revisio

Service Quality Variability and Termination Behavior

We investigate the roles of the level and variability in quality in driving customer retention for a new service. We present model-free evidence that while high average quality helps in retaining customers, high variability leads to higher termination rates. Apart from these main effects, we use model-free evidence to document the presence of (a) an interaction effect between average service quality and its variability on termination rates, (b) customer learning about service quality over time, and (c) slower rate of learning among households that experience high variability. We postulate a mechanism involving risk aversion and learning, which can induce this interaction effect and test this against several alternative explanations. We show that it is important to consider variability in quality while inferring the impact of improvements to average quality - ignoring the interaction effect between average quality and variability leads 18% to 64% (5% to 31%) overestimation (underestimation) of quality improvement elasticities among high-variability (low-variability) households. Given that responsiveness to quality decreases with variability, it is better for the firm to focus quality improvement efforts on customers experiencing low variability; increasing average quality by 1% lowers termination by 1.1% for low-variability households, but only by 0.41% for high-variability households.http://deepblue.lib.umich.edu/bitstream/2027.42/106392/1/1224_Siriam.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/106392/4/1224_Sriram_Sept14.pdfDescription of 1224_Sriram_Sept14.pdf : Sept 2014 revisio

The Role of "Live" in Livestreaming Markets: Evidence Using Orthogonal Random Forest

The common belief about the growing medium of livestreaming is that its value lies in its "live" component. In this paper, we leverage data from a large livestreaming platform to examine this belief. We are able to do this as this platform also allows viewers to purchase the recorded version of the livestream. We summarize the value of livestreaming content by estimating how demand responds to price before, on the day of, and after the livestream. We do this by proposing a generalized Orthogonal Random Forest framework. This framework allows us to estimate heterogeneous treatment effects in the presence of high-dimensional confounders whose relationships with the treatment policy (i.e., price) are complex but partially known. We find significant dynamics in the price elasticity of demand over the temporal distance to the scheduled livestreaming day and after. Specifically, demand gradually becomes less price sensitive over time to the livestreaming day and is inelastic on the livestreaming day. Over the post-livestream period, demand is still sensitive to price, but much less than the pre-livestream period. This indicates that the vlaue of livestreaming persists beyond the live component. Finally, we provide suggestive evidence for the likely mechanisms driving our results. These are quality uncertainty reduction for the patterns pre- and post-livestream and the potential of real-time interaction with the creator on the day of the livestream

The Value of Measuring Customer Satisfaction

A growing number of service firms now collect customer satisfaction ratings, along with objective service performance measures, for each service transaction. However, little is known about whether these two types of data are substitutes or complements, from both a conceptual and an applied point of view. This paper answers this question via the use of unique data consisting of individual-level cross-sectional and time-series measures of objective service performance, customer satisfaction, and purchase behavior. Using theory from the customer satisfaction literature, the data are applied to a two stage model of customer satisfaction and interpurchase time. The results suggest that the two sources of data provide complementary insights. In other words, customer satisfaction data provide information on business outcomes over and above that obtained from objective service performance data. The benefit of using and the cost of collecting these data are also quantified. The results are consistent across two different - quick service restaurant and auto rental - service industries, suggesting that they may be generalizable.http://deepblue.lib.umich.edu/bitstream/2027.42/113095/1/1283_Manchanda.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/113095/4/1283_Manchanda_Oct2015.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/113095/6/1283_Cho_Jan2017.pdfDescription of 1283_Manchanda_Oct2015.pdf : October 2015 RevisionDescription of 1283_Cho_Jan2017.pdf : January 2017 revisio

Gamification and Mobile Marketing Effectiveness

A variety of business sectors have been buffeted by the diffusion of mobile technology, a trend that presents a variety of difficult challenges but interesting opportunities to marketers. One such opportunity is gamification, which, one hopes, will enhance appeal to mobile consumers. Our sense from both personal experience and the literature is that the gamified mobile apps currently offered by firms mostly miss the mark. We provide a systematic overview of game design and note how principles derived from that field are highly applicable to gamification in mobile marketing settings. We are aided by the work of Schell (2008), whose Elemental Game Tetrad Model allows us to offer a coherent look at how gamification should affect mobile marketing outcomes

Quantifying Cross and Direct Network Effects in Online C2C Platforms

Consumer-to-Consumer (C2C) platforms have become a major engine of growth in Internet commerce. This is especially true in countries such as China, which are experiencing a big rush towards electronic commerce. The emergence of such platforms gives researchers the unique opportunity to investigate the evolution of such platforms by focusing on the growth of both buyers and sellers. In this research, we build a utility-based model to quantify both cross and direct network effects on Alibaba Group’s Taobao.com, the world’s largest online C2C platform (based in China). Specifically, we investigate the relative contributions of different factors that affect the growth of buyers and sellers on the platform. Our results suggest that the direct network effects do not play a big role in the platform’s growth (we detect a small positive direct network effect on buyer growth and no direct network effect on seller growth). More importantly, we find a significant, large and positive cross-network effect on both sides of the platform. In other words, the installed base of either side of the platform has propelled the growth of the other side (and thus the overall growth). Interestingly, this cross-network effect is asymmetric with the installed base of sellers having a much larger effect on the growth of buyers than vice versa. The growth in the number of buyers is driven primarily by the seller’s installed base and product variety with increasing importance of product variety. The growth in the number of sellers is driven by buyer’s installed base, buyer quality, and product price with increasing importance of buyer quality. We also investigate the nature of these cross-network effects over time. We find that the cross-network effect of sellers on buyers increases and then decreases to reach a stable level. In contrast, the cross-network effect of buyers on sellers is relatively stable. We discuss the policy implications of these findings for C2C platforms in general and Taobao in particular.http://deepblue.lib.umich.edu/bitstream/2027.42/108494/1/1248_Manchanda.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/108494/4/1248_Manchanda_Mar2015.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/108494/6/1248_Manchanda_Sept2015.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/108494/8/1248_Manchanda_Nov2015.pdfDescription of 1248_Manchanda_Sept2015.pdf : September 2015 revisionDescription of 1248_Manchanda_Mar2015.pdf : March 2015 RevisionDescription of 1248_Manchanda_Nov2015.pdf : November 2015 revisio

Platforms: A Multiplicity of Research Opportunities

Platforms refer to intermediaries that facilitate economic interaction between two sets of agents wherein the decisions of one set of agents is likely to have an effect on the other via direct and/or indirect externalities. Given their nature, platforms need to find the appropriate balance between the competing objectives of agents and act as catalysts by facilitating the beneficial effects of externalities. In this paper, we discuss the current theoretical and empirical literature on two-sided platforms. We then identify three dimensions that offer opportunities to advance the empirical literature: (a) unanswered theoretical and conceptual questions, (b) data-related opportunities, and (c) methodological challenges.http://deepblue.lib.umich.edu/bitstream/2027.42/110978/1/1271_Sriram.pd