No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Abstract
This study explores how observational learning and social-network Word-of-Mouth (WOM) affect sales. Using a panel data set consisting of more than 500 deals from Groupon.com, we find that not only can both mechanisms help increase sales, they interact positively to generate additional sales. This complementarity result suggests observational learning and social-network WOM provide different types of informational signals to influence sales. While observational learning primarily updates consumer beliefs about a product, the predominant effect of social-network WOM is through advertising that complements the signaling effect of observational learning. We also compare these effects between search goods and experience goods. We find that observational learning and social-network WOM continue to be complements in both cases. However, compared to social-network WOM, observational learning has a stronger effect for experience goods than for search goods.
... The field's understanding of consumer GB behavior may be constrained if either phase is neglected, because both purchase and redemption are key components of GB business models. We know of only one study (Li and Wu 2013) that explores the aggregated level of GB deal purchase (without consumer-level data). Extending Li and Wu's (2013) work, we focus on the disaggregated level (with consumer-level data). ...
... We know of only one study (Li and Wu 2013) that explores the aggregated level of GB deal purchase (without consumer-level data). Extending Li and Wu's (2013) work, we focus on the disaggregated level (with consumer-level data). Using this extensive data set, we also identify how deal popularity and other factors (deal price, savings, and customer experience) may differentially affect purchases in relation to redemptions. ...
... Thus, consumers can access this information again when they are ready to redeem. 8 Although we paid a great deal of attention to issues of model identification, this research may not perfectly identify causal impact, similar to other studies on GB and social influences (Li and Wu 2013;Sridhar and Srinivasan 2012;Zhang and Liu 2012). Unless researchers use randomized field experiments with corporate partners (Hinz et al. 2001;Lambrecht and Tucker 2013;Luo et al. 2014), it is difficult to identify causal effects in general. ...
Group-buying (GB) deals entail a two-phase decision process. First, consumers decide whether to buy a deal or not. Second, consumers decide when to redeem a deal, conditional on purchase. Guided by theories of social influence and observational learning, the authors develop a framework predicting that (i) deal popularity increases consumers’ purchase likelihood and decreases redemption time conditional on purchase and (ii) these effects are amplified by the social influence-related factors of referral intensity and group consumption. This framework is tested and supported with a unique dataset of 30,272 customers of a GB website with several million data points. Substantially, these findings reveal a two-phase perspective of GB, longevity effects of deal popularity, and the amplifying role of customer referrals (influencing others) in the effects of deal popularity (others’ influence). In light of the criticism against GB industry practice, this study builds the case for GB websites and merchants to heed deal popularity information and the social influence-related contingencies.
... The number of "likes" can thus be considered as a social environmental stimulus on a C2C Facebook "buy and sell" group. The link between the number of "likes" and consumers' UBI can be explained by observational learning [75] and the herding effect [76,77]. Zhang, Hu and Zhao [35] mentioned that online social interaction may also play an important role in online impulse buying. ...
Although investors in financial markets have access to information from both mass media and social media, trading platforms that curate and provide this information have little to go by in terms of understanding the difference between these two types of media. This paper compares social media with mass media in the stock market, focusing on information coverage diversity and predictive value with respect to future stock absolute returns. Based on a study of nearly a million stock-related news articles from the Sina Finance news platform and 12.7 million stock-related social media messages from the popular Weibo platform in China, we find that social media covers less stocks than mass media, and this effect is amplified as the volume of media information increases. We find that there is some short-term predictive value from these sources, but they are different. Although mass media information coverage is more predictive than social media information coverage in a one-day horizon, it is the other way around in a two-to five-day horizon. These empirical results suggest that social media and mass media serve stock market investors differently. We draw connections to theories related to how crowds and experts differ and offer practical implications for the design of media-related IS systems.
This paper explores a research agenda based on the Jaworski and Patel (2020) interview.
A key feature of fixed-price group buying is the use of time-limited promotions, but few studies have examined the potential effect of different countdown formats. This study incorporates the unit effect and temporal focus theories to examine how different countdown time units affect consumers’ group-buying participation intentions, as well as the mechanisms and functional contexts of time units. The empirical results indicate that countdowns expressed with a contracted scale (vs. an expanded scale) reduce perceived time remaining until the end of the group-buying offer, shift consumers’ temporal focus to the present, and increase group-buying participation intentions. Temporal focus mediates the countdown time unit effect on group-buying participation intentions. Furthermore, hedonic (vs. utilitarian) products enhance the countdown time unit effect. The countdown time unit effect is stronger for consumers with high (vs. low) promotion skepticism.
We investigate the factors that affect the growth of Groupon, the leading online daily deals platform. We concentrate on the online-to-offline (O2O) aspect of the business that differentiates it from other e-commerce platforms—its strong connection to local markets. We focus on travel cost and store density, the key local characteristics that affect consumer deal demand and merchant deal offering. Using a comprehensive longitudinal data set on deal offerings and sales across local markets, and combining it with local market characteristics, we estimate a simultaneous equation model of the weekly number of deal offerings and deal sales characterizing the two-sided nature of the platform. We find that the word-of-mouth effect on the consumer side and the observational learning effect on the merchant side contribute to and reinforce the expansion of a two-sided platform. However, a larger number of deals intensifies the competition, which then lowers per deal sales and limits the number of deal offerings. We find that local characteristics have significant impact on both the deal demand and the supply side. We further use model simulation to show how differences in growth patterns across markets may be driven by local characteristics, and we decompose their relative impact on the demand and supply sides. The paper provides managerial implications for firms specializing in O2O commerce.
This paper was accepted by Matthew Shum, marketing.
Daily deal coupons have gained a prominent foothold on the web. The earliest and largest player is Groupon. Originally,Groupon dealswere a mix of deals with a minimum requirement (MR) of coupon sales before a deal became effective and of deals without a minimum requirement (NMR). Eventually, Groupon stopped using MR deals. For Groupon and its retailer customers, might this decision have actually resulted in negative impacts for both parties (fewer coupons sold and lower revenue)? The structure of Groupon deals (including a "Facebook like" option) together with electronic access to the necessary data offered the opportunity to empirically investigate these questions.We analyzed relationships among MR, Facebook likes (FL), quantity of coupons sold, and total revenue, performing the analysis across the four largest retail categories. Using timestamped empirical data, we completed a propensity score analysis of causal effects. We find that the presence of MR increases Facebook likes, quantity of coupons sold, and total revenue at the time point when the MR is met and at subsequent 2-hour intervals over the horizon of deals. A key finding is that the initial differences observed when MR is met not only continue but also actually increase over the life of the deals.
Group-buying (GB) deals entail a two-phase decision process. First, consumers decide whether to buy a deal or not. Second, consumers decide when to redeem a deal, conditional on purchase. Guided by theories of social influence and observational learning, the authors develop a framework predicting that (i) deal popularity increases consumers’ purchase likelihood and decreases redemption time conditional on purchase and (ii) these effects are amplified by the social influence-related factors of referral intensity and group consumption. This framework is tested and supported with a unique dataset of 30,272 customers of a GB website with several million data points. Substantially, these findings reveal a two-phase perspective of GB, longevity effects of deal popularity, and the amplifying role of customer referrals (influencing others) in the effects of deal popularity (others’ influence). In light of the criticism against GB industry practice, this study builds the case for GB websites and merchants to heed deal popularity information and the social influence-related contingencies.
Product fit uncertainty (defined as the degree to which a consumer cannot assess whether a product's attributes match her preference) is proposed to be a major impediment to online markets with costly product returns and lack of consumer satisfaction. We conceptualize the nature of product fit uncertainty as an information problem and theorize its distinct effect on product returns and consumer satisfaction (versus product quality uncertainty), particularly for experience (versus search) goods without product familiarity. To reduce product fit uncertainty, we propose two Internet-enabled systems—website media (visualization systems) and online product forums (collaborative shopping systems)—that are hypothesized to attenuate the effect of product type (experience versus search goods) on product fit uncertainty.
Hypotheses that link experience goods to product returns through the mediating role of product fit uncertainty are tested with analyses of a unique data set composed of secondary data matched with primary direct data from numerous consumers who had recently participated in buy-it-now auctions. The results show the distinction between product fit uncertainty and quality uncertainty as two distinct dimensions of product uncertainty and interestingly show that, relative to product quality uncertainty, product fit uncertainty has a significantly stronger effect on product returns. Notably, whereas product quality uncertainty is mainly driven by the experience attributes of a product, product fit uncertainty is mainly driven by both experience attributes and lack of product familiarity. The results also suggest that Internet-enabled systems are differentially used to reduce product (fit and quality) uncertainty. Notably, the use of online product forums is shown to moderate the effect of experience goods on product fit uncertainty, and website media are shown to attenuate the effect of experience goods on product quality uncertainty. The results are robust to econometric specifications and estimation methods. The paper concludes by stressing the importance of reducing the increasingly prevalent information problem of product fit uncertainty in online markets with the aid of Internet-enabled systems.
Follow the Leader?
The Internet has increased the likelihood that our decisions will be influenced by those being made around us. On the one hand, group decision-making can lead to better decisions, but it can also lead to “herding effects” that have resulted in financial disasters. Muchnik et al. (p. 647 ) examined the effect of collective information via a randomized experiment, which involved collaboration with a social news aggregation Web site on which readers could vote and comment on posted comments. Data were collected and analyzed after the Web site administrators arbitrarily voted positively or negatively (or not at all) as the first comment on more than 100,000 posts. False positive entries led to inflated subsequent scores, whereas false negative initial votes had small long-term effects. Both the topic being commented upon and the relationship between the poster and commenter were important. Future efforts will be needed to sort out how to correct for such effects in polls or other collective intelligence systems in order to counter social biases.
Online users often need to make adoption decisions without accurate information about the product values. An informa- tional cascade occurs when it is optimal for an online user, having observed others' actions, to follow the adoption deci- sion of the preceding individual without regard to his own information. Informational cascades are often rational for individual decision making; however, it may lead to adoption of inferior products. With easy availability of information about other users' choices, the Internet offers an ideal envi- ronment for informational cascades. In this paper, we empi- rically examine informational cascades in the context of online software adoption. We find user behavior in adopting software products is consistent with the predictions of the informational cascades literature. Our results demonstrate that online users' choices of software products exhibit distinct jumps and drops with changes in download ranking, as predicted by informational cascades theory. Furthermore, we find that user reviews have no impact on user adoption of the most popular product, while having an increasingly positive impact on the adoption of lower ranking products. The phenomenon persists after controlling for alternative explana- tions such as network effects, word-of-mouth effects, and product diffusion. Our results validate informational cas- cades as an important driver for decision making on the Internet. The finding also offers an explanation for the mixed results reported in prior studies with regard to the influence of online user reviews on product sales. We show that the mixed results could be due to the moderating effect of infor- mational cascades.
Recent research in information economics has focused on signals as mechanisms to solve problems that arise under asymmetric information. A firm or individual credibly communicates the level of some unobservable element in a transaction by providing an observable signal. When applied to conveying product quality information, this issue is of particular interest to the discipline of marketing. In this article, the authors focus on the ways a firm may signal the unobservable quality of its products through several marketing-mix variables. The authors develop a typoloqy that classifies signals and discuss the available empirical evidence on the signaling properties of several marketing variables. They consider managerial implications of signaling and outline an agenda for future empirical research.
While several sources tout the superiority of word-of-mouth over traditional marketing communication techniques, it still remains unclear how to measure word-of-mouth and how to compare its relative effectiveness in improving long-term performance. Internet social networking sites offer an attractive opportunity to study word-of-mouth due to their consistent and efficient tracking of electronic referrals. The authors test for and find endogeneity among WOM-referrals, signups, event marketing and media appearances. A Vector Autoregressive (VAR) modeling approach captures this dynamic feedback system and gives estimates for the short-term and long-term effects on signups. The authors find that word-of-mouth benefits carry-over much longer than traditional marketing actions do. The long-run elasticity of signups to WOM appears close to 0.5 – at least 2.5 times larger than average advertising elasticities reported in the literature. For the analyzed firm, the estimated WOM effect is about 20 times higher than the elasticity for marketing events, and 30 times larger than that of media appearances. Using the contribution of advertising income from a signup, the authors calculate the economic value for a referral, providing an upper bound for financial incentives to stimulate word-of-mouth.
Many online experiments exhibit dependence between users and items. For example, in online advertising, observations that have a user or an ad in common are likely to be associated. Because of this, even in experiments involving millions of subjects, the difference in mean outcomes between control and treatment conditions can have substantial variance. Previous theoretical and simulation results demonstrate that not accounting for this kind of dependence structure can result in confidence intervals that are too narrow, leading to inaccurate hypothesis tests.
We develop a framework for understanding how dependence affects uncertainty in user-item experiments and evaluate how bootstrap methods that account for differing levels of dependence perform in practice. We use three real datasets describing user behaviors on Facebook - user responses to ads, search results, and News Feed stories - to generate data for synthetic experiments in which there is no effect of the treatment on average by design. We then estimate empirical Type I error rates for each bootstrap method. Accounting for dependence within a single type of unit (i.e., within-user dependence) is often sufficient to get reasonable error rates. But when experiments have effects, as one might expect in the field, accounting for multiple units with a multiway bootstrap can be necessary to get close to the advertised Type I error rates. This work provides guidance to practitioners evaluating large-scale experiments, and highlights the importance of analysis of inferential methods for complex dependence structures common to online experiments.
Personalization is becoming ubiquitous on the World Wide Web. Such systems use statistical techniques to infer a customer's preferences and recommend content best suited to him (e.g., “Customers who liked this also liked…”). A debate has emerged as to whether personalization has drawbacks. By making the Web hyperspecific to our interests, does it fragment Internet users, reducing shared experiences and narrowing media consumption? We study whether personalization is in fact fragmenting the online population. Surprisingly, it does not appear to do so in our study. Personalization appears to be a tool that helps users widen their interests, which in turn creates commonality with others. This increase in commonality occurs for two reasons, which we term volume and product-mix effects. The volume effect is that consumers simply consume more after personalized recommendations, increasing the chance of having more items in common. The product-mix effect is that, conditional on volume, consumers buy a more similar mix of products after recommendations.
This paper was accepted by Sandra Slaughter, information systems.
In this paper we investigate the attributes and relative influence of 1.6M Twitter users by tracking 74 million diffusion events that took place on the Twitter follower graph over a two month interval in 2009. Unsurprisingly, we find that the largest cascades tend to be generated by users who have been influential in the past and who have a large number of followers. We also find that URLs that were rated more interesting and/or elicited more positive feelings by workers on Mechanical Turk were more likely to spread. In spite of these intuitive results, however, we find that predictions of which particular user or URL will generate large cascades are relatively unreliable. We conclude, therefore, that word-of-mouth diffusion can only be harnessed reliably by targeting large numbers of potential influencers, thereby capturing average effects. Finally, we consider a family of hypothetical marketing strategies, defined by the relative cost of identifying versus compensating potential "influencers." We find that although under some circumstances, the most influential users are also the most cost-effective, under a wide range of plausible assumptions the most cost-effective performance can be realized using "ordinary influencers"---individuals who exert average or even less-than-average influence.
Mere observation of others' choices can be informative about product quality. This paper develops an individual-level dynamic model of observational learning and applies it to a novel data set from the U.S. kidney market, where transplant candidates on a waiting list sequentially decide whether to accept a kidney offer. We find strong evidence of observational learning: patients draw negative quality inferences from earlier refusals in the queue, thus becoming more inclined towards refusal themselves. This self-reinforcing chain of inferences leads to poor kidney utilization despite the continual shortage in kidney supply. Counterfactual policy simulations show that patients would have made more efficient use of kidneys had the concerns behind earlier refusals been shared. This study yields a set of marketing implications. In particular, we show that observational learning and information sharing shape consumer choices in markedly different ways. Optimal marketing strategies should take into account how consumers learn from others.
Popularity information is usually thought to reinforce existing sales trends by encour-
aging customers to
ock to mainstream products with broad appeal. We suggest a
countervailing market force: popularity information may bene t niche products with
narrow appeal disproportionately, because the same level of popularity implies higher
quality for narrow-appeal products than for broad-appeal products. We examine this
hypothesis empirically using eld experiment data from a web site that lists wed-
ding service vendors. Our ndings are consistent with this hypothesis: narrow-appeal
vendors receive more visits than equally popular broad-appeal vendors after the intro-
duction of popularity information.
Recommendation agents (RAs) are software agents that elicit the interests or preferences of individual consumers for products, either explicitly or implicitly, and make recommendations accordingly. RAs have the potential to support and improve the quality of the decisions consumers make when searching for and selecting products online. They can reduce the information overload facing consumers, as well as the complexity of online searches. Prior research on RAs has focused mostly on developing and evaluating different underlying algorithms that generate recommendations. This paper instead identifies other important aspects of RAs, namely RA use, RA characteristics, provider credibility, and factors related to product, user, and user-RA interaction, which influence users' decision-making processes and outcomes, as well as their evaluation of RAs. It goes beyond generalized models, such as TAM, and identifies the RA-specific features, such as RA input, process, and output design characteristics, that affect users' evaluations, including their assessments of the usefulness and ease-of-use of RA applications. Based on a review of existing literature on e-commerce RAs, this paper develops a conceptual model with 28 propositions derived from five theoretical perspectives. The propositions help answer the two research questions: (1) How do RA use, RA characteristics, and other factors influence consumer decision making processes and outcomes? (2) How do RA use, RA characteristics, and other factors influence users' evaluations of RAs? By identifying the critical gaps between what we know and what we need to know, this paper identifies potential areas of future research for scholars. It also provides advice to information systems practitioners concerning the effective design and development of RAs.
We report results from a randomized natural field experiment conducted in a restaurant dining setting to distinguish the observational learning effect from the saliency effect. We find that, when customers are given ranking information of the five most popular dishes, the demand for those dishes increases by 13 to 20 percent. We do not find a significant saliency effect. We also find modest evidence that the observational learning effects are stronger among infrequent customers, and that dining satisfaction is increased when customers are presented with the information of the top five dishes, but not when presented with only names of some sample dishes. (JEL C93, D83)
Title from cover. "August 2000." Includes bibliographical references (leaf 15). Aykut Firat, Stuart Madnick, Michael Siegel.
An informational cascade occurs when it is optimal for an individual, having observed the actions of those ahead of him, to follow the behavior of the preceding individual with regard to his own information. The authors argue that localized conformity of behavior and the fragility of mass behaviors can be explained by informational cascades. Copyright 1992 by University of Chicago Press.
This paper considers how identity, a person's sense of self, affects economic outcomes. We incorporate the psychology and
sociology of identity into an economic model of behavior. In the utility function we propose, identity is associated with
different social categories and how people in these categories should behave. We then construct a simple game-theoretic model
showing how identity can affect individual interactions. The paper adapts these models to gender discrimination in the workplace,
the economics of poverty and social exclusion, and the household division of labor. In each case, the inclusion of identity
substantively changes conclusions of previous economic analysis.
Empirical research on industrial organization has failed to identify the empirical bases of product differentiation (that is, each seller facing an individual downward-sloping demand curve). Two sets of conditions are theoretically sufficient: one resting on complex attribute structures coupled with fixed costs, the other on costs of information to guide buyers' choices among brands. Factor analysis of observable decision variables and market patterns appears to reveal and confirm these underlying causes. The estimated factors perform successfully to test hypotheses about the effects of product differentiation on the mutual sensitivity of prices of Australian domestic manufactures and competing imports.
The economics of information offers a framework within which consumer responses to advertising may be examined. This research tests the propositions espoused by Nelson regarding differential skepticism for search and experience attribute claims. A 2 (search, experience claims) by 2 (objective, subjective) by 2 (low risk, high risk) between subjects analysis of covariance was performed. Results support Nelson's hypotheses. Consumers’ use of information is influenced by the nature of the advertised attribute, the semantic content of the claim, and the perception of risk associated with the product.
Economists have long believed that product differentiation is an important determinant of market behavior (e.g., Chamberlin, 1950; Bain, 1956), but economists have not been very successful in giving this concept operational meaning.1 One reason for this lack of success is that economists have not focused on one of the key elements of product differentiation: the consumer’s acquisition of information about product quality. I show in this paper that product differentiation will have quite different consequences on market behavior given different ways in which consumers obtain their information. These theoretical results are then supported by substantial empirical evidence.
The business case for investing in information technology (IT) has received increasing scrutiny in recent years. We propose that IT investments create additional business value through interactions with other business processes. In this paper, we formalize the interaction effect of IT by focusing on one core function, namely, research and development (R&D). We hypothesize that investments in IT can interact with and complement a firm's R&D investments, enhancing the firm's shareholder value creation potential. We test this by hypothesis by estimating the interaction impact of IT and R&D investments on Tobin's q, a forward-looking measure of firm performance using a recent multiyear, firm-level, archival data set. Our results suggest that the interaction effect of R&D and IT on Tobin's q is positive and significant after controlling for other firm- and industryspecific effects. Our findings provide rigorous empirical support for recent anecdotal evidence in the managerial literature with respect to the manner in which IT is enabling R&D-intensive innovation processes. Our analysis underscores the need for coordinated investments in IT and R&D, and permeating IT capabilities throughout other business processes such as R&D.
In a recent article in this journal, Lombard, Snyder-Duch, and Bracken (2002) surveyed 200 content analyses for their reporting of reliability tests, compared the virtues and drawbacks of five popular reliability measures, and proposed guidelines and standards for their use. Their discussion revealed that numerous misconceptions circulate in the content analysis literature regarding how these measures behave and can aid or deceive content analysts in their effort to ensure the reliability of their data. This article proposes three conditions for statistical measures to serve as indices of the reliability of data and examines the mathematical structure and the behavior of the five coefficients discussed by the authors, as well as two others. It compares common beliefs about these coefficients with what they actually do and concludes with alternative recommendations for testing reliability in content analysis and similar data-making efforts.
ABSTRACT Recently, the relative demand for skilled labor has increased dramatically. We investigate one of the causes, skill-biased technical change. Advances in information technology (IT) are amongthe most powerful forces bearing on the economy. Employers who use IT often make complementary,innovationsin their organizations and in the services they offer. Our hypothesis is that these co-inventions by IT users change the mix of skills that employers demand. Specifically, we test the hypothesis that it is a cluster of
Online reputation mechanisms harness the bi-directional communication capabilities of the Internet in order to engineer large-scale word-of-mouth networks. They are emerging as a promising alternative to more established assurance mechanisms, such as branding and formal contracting, in a variety of settings ranging from online marketplaces to Internet search engines. At the same time, they are transforming a concept that had traditionally fallen within the realm of the social sciences into an engineering design problem. This paper surveys our progress in understanding the new possibilities and challenges that these mechanisms represent. It discusses some important dimensions in which Internet-based reputation mechanisms differ from traditional word-of-mouth networks and surveys the most important issues related to designing, evaluating and using them. It provides an overview of relevant work in game theory and economics on the topic or reputation. It further discusses how this body of work is being extended and combined with insights from computer science, information systems, management science and psychology in order to take into consideration the special properties of online mechanisms such as their unprecedented scalability, the ability to precisely design the type of feedback information they solicit and distribute, and challenges associated with the volatility of identities and the absence of many familiar contextual cues in online environments.
Similarity breeds connection. This principle - the homophily principle - structures network ties of every type, including marriage, friendship, work, advice, support, information transfer, exchange, comembership, and other types of relationship. The result is that people's personal networks are homogeneous with regard to many sociodemographic, behavioral, and intrapersonal characteristics. Homophily limits people's social worlds in a way that has powerful implications for the information they receive, the attitudes they form, and the interactions they experience. Homophily in race and ethnicity creates the strongest divides in our personal environments, with age, religion, education, occupation, and gender following in roughly that order. Geographic propinquity, families, organizations, and isomorphic positions in social systems all create contexts in which homophilous relations form. Ties between nonsimilar individuals also dissolve at a higher rate, which sets the stage for the formation of niches (localized positions) within social space. We argue for more research on: (a) the basic ecological processes that link organizations, associations, cultural communities, social movements, and many other social forms; (b) the impact of multiplex ties on the patterns of homophily; and (c) the dynamics of network change over time through which networks and other social entities co-evolve.
This paper uses daily data on the ranking by sales of the top 100 apps sold through Apple’s App Store to provide evidence of the causal impact of today’s bestseller rank information on tomorrow’s demand. The estimates indicate that the willingness to pay of consumers is about $4.50 greater for a top ranked app than for the same unranked app. The results also indicate that the effects of bestseller status on willingness to pay decline steeply with rank at the top ranks, but remain economically significant for the apps in the first half of the top 100 list.
This document presents supporting materials for the following publication: Zhang, Juanjuan and Peng Liu (2012), "Rational Herding in Microloan Markets," Management Science, Vol. 58, No. 5, May, pp. 892-912. Microloan markets allow individual borrowers to raise funding from multiple individual lenders. We use a unique panel dataset which tracks the funding dynamics of borrower listings on Prosper.com, the largest microloan market in the United States. We find evidence of rational herding among lenders. Well-funded borrower listings tend to attract more funding after we control for unobserved listing heterogeneity and payoff externalities. Moreover, instead of passively mimicking their peers (irrational herding), lenders engage in active observational learning (rational herding); they infer the creditworthiness of borrowers by observing peer lending decisions, and use publicly observable borrower characteristics to moderate their inferences. Counterintuitively, obvious defects (e.g., poor credit grades) amplify a listing's herding momentum, as lenders infer superior creditworthiness to justify the herd. Similarly, favorable borrower characteristics (e.g., friend endorsements) weaken the herding effect, as lenders attribute herding to these observable merits. Follow-up analysis shows that rational herding beats irrational herding in predicting loan performance.
In this research we predict and demonstrate that consumers' propensities to generate word-of-mouth (WOM) is affected by their motivation to self-enhance, that is, to seek experiences that bolster the self-concept. Data from three experiments and an empirical analysis of real-world WOM indicate that self-described consumer experts demonstrate a positivity bias, generating more WOM regarding their satisfying versus their dissatisfying experiences. Importantly, this pattern intensifies when expertise is salient, and it only persists when the experience outcome is attributable to their expertise. In addition to demonstrating the effects and implications of self-enhancement in the WOM context, these insights may partially explain the inconsistencies in previous research regarding the ratio of positive versus negative WOM.
We examine performance of daily deals run through five major sites in 23 US markets. In a survey-based study of 324 businesses that conducted a daily deal promotion between August 2009 and March 2011, 55.5% of businesses reported making money, 26.6% lost money and 17.9% broke even on their promotions. Although close to 80% of deal users were new customers, significantly fewer users spent beyond the deal’s value or returned to purchase at full price. 48.1% of businesses indicated they would run another daily deal promotion, 19.8% said they would not, and 32.1% said they were uncertain. We also examined drivers of deal profitability, the loyalty of merchants to a daily deal site, and how spending on daily deals has affected spending of businesses on other marketing programs. Overall, our findings lead us to conclude that there are relatively few points of differentiation between the daily deal sites, making it harder for any one site to stand out from the others. Our findings also uncovered a number of red flags regarding the industry as a whole: (1) the relatively low percentages of deal users spending beyond the deal value (35.9%) and returning for a full-price purchase (19.9%) are symptomatic of a structural weakness in the daily deal business model, (2) less than half of the businesses indicated enthusiasm about running another daily deal in the future, (3) fully 72.8% indicated openness to considering a different daily deal site, and (4) only 35.9% of restaurants/ bars and 41.5% of salons and spas that had run a daily deal asserted they would run another such promotion in the future. All of these findings point to the same conclusion: Over the next few years, it is likely that daily deal sites will have to settle for lower shares of revenues from businesses compared to their current levels, and it will be harder and more expensive for them to find viable candidates to fill their pipelines of daily deals.
This paper uses actual WOM information to examine the dynamic patterns of WOM and how it helps explain box office revenue. The WOM data was collected from the website of Yahoo! Movies. The results show that WOM activities are the most active during pre-release and the opening week, and that movie audiences tend to hold relatively high expectation before release, but become more critical in the opening week. More importantly, WOM information offers significant explanatory power for both aggregate and weekly box office revenue, especially in the early weeks after opening. However, most of this explanatory power comes from the volume of WOM, not its valence as measured by the percentages of positive and negative messages.
The growing popularity of online product review forums invites the development of models and metrics that allow firms to harness these new sources of information for decision support. Our work contributes in this direction by proposing a novel family of diffusion models that capture some of the unique aspects of the entertainment industry and testing their performance in the context of very early postrelease motion picture revenue forecasting. We show that the addition of online product review metrics to a benchmark model that includes prerelease marketing, theater availability and professional critic reviews substantially increases its forecasting accuracy; the forecasting accuracy of our best model outperforms that of several previously published models. In addition to its contributions in diffusion theory, our study reconciles some inconsistencies among previous studies with respect to what online review metrics are statistically significant in forecasting entertainment good sales.
New ideas, products, and practices take time to diffuse, a fact that is often attributed to some form of heterogeneity among potential adopters. This paper examines three broad classes of diffusion models -- contagion, social influence, and social learning -- and shows how to incorporate heterogeneity into each at a high level of generality without losing analytical tractability. Each type of model leaves a characteristic "footprint" on the shape of the adoption curve which provides a basis for discriminating empirically between them. The approach is illustrated using the classic study of Ryan and Gross (1943) on the diffusion of hybrid corn. (JEL D83, O33, Q16, Z13)
The difference and system generalized method-of-moments estimators, developed by Holtz-Eakin, Newey, and Rosen (1988, Econometrica 56: 1371-1395); Arellano and Bond (1991, Review of Economic Studies 58: 277-297); Arellano and Bover (1995, Journal of Econometrics 68: 29-51); and Blundell and Bond (1998, Journal of Econometrics 87: 115-143), are increasingly popular. Both are general estimators designed for situations with "small T , large N" panels, meaning few time periods and many individuals; independent variables that are not strictly exogenous, meaning they are correlated with past and possibly current realizations of the error; fixed effects; and heteroskedasticity and autocorrelation within individuals. This pedagogic article first introduces linear generalized method of moments. Then it describes how limited time span and potential for fixed effects and endogenous regressors drive the design of the estimators of interest, offering Stata-based examples along the way. Next it describes how to apply these estimators with xtabond2. It also explains how to perform the Arellano-Bond test for autocorrelation in a panel after other Stata commands, using abar. The article concludes with some tips for proper use. Copyright 2009 by StataCorp LP.
A general formula (α) of which a special case is the Kuder-Richardson coefficient of equivalence is shown to be the mean of
all split-half coefficients resulting from different splittings of a test. α is therefore an estimate of the correlation between
two random samples of items from a universe of items like those in the test. α is found to be an appropriate index of equivalence
and, except for very short tests, of the first-factor concentration in the test. Tests divisible into distinct subtests should
be so divided before using the formula. The index
[`(r)]ij\bar r_{ij}
, derived from α, is shown to be an index of inter-item homogeneity. Comparison is made to the Guttman and Loevinger approaches.
Parallel split coefficients are shown to be unnecessary for tests of common types. In designing tests, maximum interpretability
of scores is obtained by increasing the first-factor concentration in any separately-scored subtest and avoiding substantial
group-factor clusters within a subtest. Scalability is not a requisite.
Social coupons are fast emerging as a popular marketing tool for businesses, an attractive shopping tool for consumers using
them and a profitable business model for the social coupon service providers. But does the popularity of social coupons extend
into providing short- and long-term profitability and new customers for the businesses? Through an analytical model, this
paper answers the following questions: (1) Are social coupons profitable for businesses? (2) Can businesses influence social
coupon profitability? (3) How can businesses recover the shortfall in profits from the coupon launch? (4) How long will it
take for businesses to recover the shortfall in profits from the coupon launch? Using data from three different businesses,
we find that social coupons in their current form are not ideally suited to ensure customer acquisition and yield profits
for the businesses. Additionally, we discuss possible areas where social coupons might work. Several research questions for
future testing are offered to explore this new area of marketing.
KeywordsSocial coupons–Short-term profitability–Long-term profitability–Customer acquisition–Customer retention
This article develops a framework for efficient IV estimators of random effects models with information in levels which can accommodate predetermined variables. Our formulation clarifies the relationship between the existing estimators and the role of transformations in panel data models. We characterize the valid transformations for relevant models and show that optimal estimators are invariant to the transformation used to remove individual effects. We present an alternative transformation for models with predetermined instruments which preserves the orthogonality among the errors. Finally, we consider models with predetermined variables that have constant correlation with the effects and illustrate their importance with simulations.
The “intangible” nature of e-commerce may cause shoppers to be uncertain about whether products ordered online will fit their needs or perform up to expectations. Such uncertainty is a dimension of consumer risk, or feelings that result because the actual outcome of a purchase decision can only be known in the future. However, the ability of e-retailers to offer a plethora of product-related information can ameliorate this uncertainty. This research assesses the influence of three online retailer communication practices—evoking vividness through pictures, allowing consumers to control information presentation, and presenting information from third-party sources. We demonstrate that these practices materially affect consumer perceptions of product performance uncertainty. Importantly, we also find that the influence of the practices differs by the search or experience orientation of the product, sometimes in counterintuitive ways. These results can serve to help enhance the effectiveness of e-retailers’ communications and, in the case of multichannel retailers, help determine which products are best suited for different channels.
Estimation of the dynamic error components model is considered using two alternative linear estimators that are designed to improve the properties of the standard first-differenced GMM estimator. Both estimators require restrictions on the initial conditions process. Asymptotic efficiency comparisons and Monte Carlo simulations for the simple AR(1) model demonstrate the dramatic improvement in performance of the proposed estimators compared to the usual first-differenced GMM estimator, and compared to non-linear GMM. The importance of these results is illustrated in an application to the estimation of a labour demand model using company panel data.
Crowd-funding markets have recently emerged as a new source of capital supporting entrepreneurial ideas and ventures. In these markets, any individual is capable of proposing an idea, and interested others can then contribute funds in support of it. Given the recency of crowd-funding's emergence, participants' behavior in these markets is not yet well understood. From a social influence standpoint, these markets are unique because the timing and amount of others' prior contribution decisions are publicly observable. This observable information about prior contributions is therefore likely to have an influence on later contribution decisions. We empirically examine this notion in a crowd-funded market for online journalism. Bearing in mind that prior literature has identified online journalism as a form of public good, we find that funders in this marketplace treat one another's contributions as substitutable, a behavior that may be indicative of free riding. Practical and theoretical implications are discussed.
Abstract In this paper, we investigate the effectiveness of the firm’s proactive management of customer-to-customer communication. We are particularly interested in understanding how, if at all, the firm should go about effecting meaningful word-of-mouth. To tackle this problem, we collect data from two sources: 1) We implemented,a large-scale field test in which a national firm created word of mouth,through,two populations: customers and non-customers, 2) We collected data from an online experiment. We break our theoretical problem into two subproblems. First, we ask, “What kind of WOM drives sales?” Motivated by previous research, we hypothesize that for a product with a low initial awareness level, the WOM that is most effective at driving sales is created by less-loyal (not highly-loyal) customers, and occurs between acquaintances (not friends). We find support for this in the field test as well as in an experimental,setting. Hence we demonstrate the potential usefulness of exogenously-created WOM: conversations,are created where,none would naturally have occured otherwise. Then, we ask, “Which agents are most effective at creating this kind of WOM?” In particular, we are interested in evaluating the effectiveness of the commonly-used opinion leader designation. We find that while opinion leadership is useful in identifying potentially effective spreaders of WOM among very loyal customers, it is less useful for the sample of less loyal customers. Keywords: Word of Mouth, Promotion, Advertising 1I ntroduction
I examine how information technology (IT) skills and use, communication network structures, and the distribution and flow of information in organizations impact individual information worker productivity. The work is divided into three essays based on the task level practices of information workers at a midsize executive recruiting firm: Essay 1: "Information, Technology and Information Worker Productivity: Task Level Evidence." I develop and econometrically test a multistage model of production and interaction activities at the firm, and analyze correlations among network structure, characteristics of information flow and real economic output. I find that (a) IT use is positively correlated with non-linear drivers of productivity; (b) the structure and size of workers' communication networks are highly correlated with performance; (c) an inverted-U shaped relationship exists between multitasking and productivity such that, beyond an optimum, more multitasking is associated with declining project completion rates and revenue generation; and (d) asynchronous information seeking such as email and database use promotes multitasking while synchronous information seeking over the phone shows a negative correlation. These data demonstrate a strong correspondence among technology use, social networks, and productivity for project-based information workers.
This paper tries to show how the major features of the behavior of advertising can be explained by advertising's information function. For search qualities advertising provides direct information about the characteristics of a brand. For experience qualities the most important information conveyed by advertising is simply that the brand advertises. This contrast in advertising by these qualities leads to significant differences in its behavior. How does advertising provide information to the consumer? The producer in his advertising is not interested directly in providing information for consumers. He is interested in selling more of his product. Subject to a few constraints, the advertising message says anything the seller of a brand wishes. A mechanism is required to make the selling job of advertising generate information to the consumer. [Авторский текст]
The author analyzes a sequential decision model in which each decisionmaker looks at the decisions made by previous decisionmakers in taking her own decision. This is rational for her because these other decisionmakers may have some information that is important for her. The author then shows that the decision rules that are chosen by optimizing individuals will be characterized by herd behavior; i.e., people will be doing what others are doing rather than using their information. The author then shows that the resulting equilibrium is inefficient. Copyright 1992, the President and Fellows of Harvard College and the Massachusetts Institute of Technology.
The difference and system generalized method of moments (GMM) estimators are growing in popularity. As implemented in popular software, the estimators easily generate instruments that are numerous and, in system GMM, potentially suspect. A large instrument collection overfits endogenous variables even as it weakens the Hansen test of the instruments' joint validity. This paper reviews the evidence on the effects of instrument proliferation, and describes and simulates simple ways to control it. It illustrates the dangers by replicating Forbes ["American Economic Review" (2000) Vol. 90, pp. 869-887] on income inequality and Levine "et al." ["Journal of Monetary Economics"] (2000) Vol. 46, pp. 31-77] on financial sector development. Results in both papers appear driven by previously undetected endogeneity. Copyright (c) Center for Global Development 2009.
The creation of online consumer communities to provide product reviews and advice has been touted as an important, albeit somewhat expensive component of Internet retail strategies. In this paper, we characterize reviewer behavior at two popular Internet sites and examine the effect of consumer reviews on firms' sales. We use publicly available data from the two leading online booksellers, Amazon.com and BarnesandNoble.com, to construct measures of each firm's sales of individual books. We also gather extensive consumer review data at the two sites. First, we characterize the reviewer behavior on the two sites such as the distribution of the number of ratings and the valence and length of ratings, as well as ratings across different subject categories. Second, we measure the effect of individual reviews on the relative shares of books across the two sites. We argue that our methodology of comparing the sales and reviews of a given book across Internet retailers allows us to improve on the existing literature by better capturing a causal relationship between word of mouth (reviews) and sales since we are able to difference out factors that affect the sales and word of mouth of both retailers, such as the book's quality. We examine the incremental sales effects of having reviews for a particular book versus not having reviews and also the differential sales effects of positive and negative reviews. Our large database of books also allows us to control for other important confounding factors such as differences across the sites in prices and shipping times.
We propose that consumers often make choices that diverge from those of others to ensure that they effectively communicate desired identities. Consistent with this identity-signaling perspective, four studies illustrate that consumers are more likely to diverge from majorities, or members of other social groups, in product domains that are seen as symbolic of identity (e.g., music or hairstyles, rather than backpacks or stereos). In identity domains, participants avoided options preferred by majorities and abandoned preferences shared with majorities. The social group associated with a product influenced choice more in identity domains and when a given product was framed as identity relevant. People diverge, in part, to avoid communicating undesired identities. (c) 2007 by JOURNAL OF CONSUMER RESEARCH, Inc..
Creating Social Contagion through Viral Product Design: A Randomized Trial of Peer Influence in Networks
- S Aral
Online Social Interactions: A Natural Experiment on Word of Mouth Versus Observational Learning
- Y Chen