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FB15 4 - Price Pressure and Supplier Relations: Industry-Specific Findings R. Mohan Pisharodi, Associate Professor of Marketing, Oakland University, 414 Elliott Hall, School of Business Administration, Rochester, MI, 48309, United States of America, pisharod@oakland.edu, Ravi Parameswaran, John Henke, Jr. Original Equipment Manufacturers (OEMs) in a number of industries across the world are known to frequently follow the practice of using adversarial price reduction efforts to extract lower prices from their suppliers. Several other OEMs, while pursuing price reduction, have adopted more cooperative and less antagonistic approaches driven by the belief that adversarial price pressure on suppliers will be detrimental to good working relationships. This research probes the relationship between OEM price pressure on suppliers and the nature of OEM-supplier working relationships. A research model, founded on literature from multiple disciplines, was developed to examine the above relationship. The model has one outcome variable (Overall Relationship), two initial variables (Price Pressure and Other Pressures) and five mediating behavioral variables. Responses covering nine manufacturing industries were collected from a diverse sample of supplier respondents of global OEMs with procurement operations in North America, Asia, and Europe using an Internet-based survey questionnaire. The structural relations in the research model were analyzed through structural equation modeling using LISREL, with the complete data set as well as with industry-specific data sets. The results of statistical analysis reveal a consistent pattern of relationships with industry-specific variations. The overall pattern of relationships indicates that price pressure need not result in poor supplier-OEM relationships, and can exist along with good supplier-OEM relationships if the pressure is administered in a supportive manner. Inter-industry similarities and differences are assessed and their implications for research in marketing as well as for marketing management are discussed. ■ FB15 Champions Center V CRM III: Customer Loyalty Contributed Session Chair: Radu Dimitriu, Lecturer in Strategic Marketing, Cranfield School of Management, 1 Wynyard Court, Oldbrook, Milton Keynes, MK6 2SZ, United Kingdom, radu.dimitriu@cranfield.ac.uk 1 - Do Reward Programs Affect Consumer Behavior? Ricardo Montoya, University of Chile, Republica 701, Santiago, Chile, rmontoya@dii.uchile.cl, Oded Netzer, Ran Kivetz Reward programs have become ubiquitous in the marketplace and a key tool companies use in the hope of shaping the behaviors of customers, salespeople, and employees. For example, many retailer reward programs attempt to motivate customers to visit the store more often and spend more during each visit. In the present research, we test a series of existing and new hypotheses regarding the effects of reward programs on consumer behavior. We analyze a large transactional dataset from a major retailer’s reward program; the dataset includes individual-level purchases and reward redemptions. We augment our modeling of this secondary dataset with controlled laboratory experiments. Among other predictions, we examine the “goal gradient” hypothesis, the “post-reward pause” hypothesis, and the notion that customers “earn the right to indulge” (in luxury rewards) by exerting more effort in the program. To the best of our knowledge, our research is the first to model transactional data from a large retailer reward program in order to test a diverse set of behavioral hypotheses. For example, we find that as customers approach the program’s reward goals, they accelerate the rate at which they purchase in the chain’s stores (i.e., a goal gradient effect). We also observe that customers who need to exert greater effort to reach a reward threshold are more likely to redeem a luxury reward. Customers also exhibit a “post-reward pause,” whereby after redeeming a reward they temporarily reduce their purchase frequency. Our study of these and other phenomena leverages the richness of the secondary transactional dataset available from this retailer’s reward program. We empirically model these data to better understand the effects of rewards programs on consumer behavior. 2 - Shortcuts to Glory? Exploring When and Why Attribute Performance Can Directly Drive Loyalty Johannes Boegershausen, Grenoble Ecole de Management, 12 rue Pierre Sèmard, Grenoble, France, johannes.boegershausen@grenoble-em.com, Christophe Haon, Daniel Ray Providing customers with high satisfaction has been advocated as one of the primary means to enhance their loyalty intentions (Johnson et al. 2006; Gupta and Zeithaml 2006). In order to achieve high levels of overall customer satisfaction, many firms invest substantial resources into enhancing performance on the key service attributes. Over the last decade, there has been a substantial interest in chain frameworks such as the satisfaction-profit chain (Anderson and Mittal 2000), which in essence links attribute performance, customer satisfaction, customer retention, and profit. Surprisingly, despite numerous investigations and extensions of this and related chain frameworks, the occurrence and consequences of a direct effect of attribute performance on loyalty intentions has been largely neglected. Yet, several studies (e.g., Mittal et al. 1998; Kumar 2002; Lariviére 2008) report such unexpected direct effects. However, a critical re-assessment of the (full) mediating role of customer satisfaction in the attribute performance – loyalty intentions relationship is nonexistent. We address this void by drawing from the multi-attribute model, postpurchase thought, and service quality literature to provide a theoretical explanation MARKETING SCIENCE CONFERENCE – 2011 52 why certain attributes may directly impact attitudinal loyalty (i.e. repurchase and/or recommendation intentions). Our empirical analysis in an intercontinental aviation setting demonstrates that direct effects of attribute performance on loyalty intentions are the rule rather than the exception. Moreover, we highlight the detrimental effects for resource allocation of failing to control for these direct effects. Lastly, we open avenues for future research by exploring additional possibly omitted mediators (Zhao et al. 2010). 3 - How do E-Commerce Interfaces Affect Customer Satisfaction and Loyalty? Hsiu-Wen Liu, Assistant Professor, Soochow University, 56, Kueiyang St., Sec. 1, Taipei, Taiwan - ROC, hsiuwenliu@gmail.com, Yu-Li Lin This article presents an empirical test of user interfaces in the context of online retailer. The model posits that user interfaces lead to consumer satisfaction and loyalty. The sample includes 600 customer data. Results from the empirical test indicated that user interfaces affects customer satisfaction. Customer satisfaction affects customer loyalty. Further, customer satisfaction serves as a fully mediator of the effects of user interfaces and customer loyalty. Finally, theoretical, managerial and future research implications are included. 4 - Investigating Multipurpose Customers Radu Dimitriu, Lecturer in Strategic Marketing, Cranfield School of Management, 1 Wynyard Court, Oldbrook, Milton Keynes, MK6 2SZ, United Kingdom, radu.dimitriu@cranfield.ac.uk, Fred Selnes Whereas the bulk of the sales of many companies comes from product categories that are typical of their business model and activity, considerable sales opportunities arise from offerings in ancillary categories. For instance, the last decades have seen food retailers extending their product range to include non-food items, such as electrical appliances, clothing, banking solutions or even petrol. We define those customers buying across categories as “multipurpose customers” (e.g., customers buying both food and non-food items from the same retailer). We analyzed the customer purchase data for an online bookshop specializing in selling academic books. We computed repurchase likelihood scores for the customers in the database based on an RFM approach. Controlling for purchase frequency, recency and amount spent, we found that being or not a multipurpose customer (i.e., buying both academic and nonacademic books such as fiction) was a strong predictor of customers’ repurchase likelihood. Multipurpose customers seem therefore to be extremely attractive. Several important questions arise however about multipurpose customers. First, is their higher repurchase likelihood based on affective commitment, or rather on calculative commitment or simply inertia? Second, would a company benefit from initiating campaigns to migrate the other (usually largest) part of their customer portfolio toward becoming multipurpose, or would such a marketing effort lead to a poor return on investment? Overall, our study documents the importance of multipurpose customers and presents a series of propositions meant to guide further research on the topic. Friday, 1:30pm - 3:00pm ■ FC01 Legends Ballroom I Choice V: Empirical Results Contributed Session Chair: Christian Schlereth, Goethe University Frankfurt, Grueneburgplatz 1, Frankfurt, 60323, Germany, schlereth@wiwi.uni-frankfurt.de 1 - Measuring Scale Attraction Effects in Charitable Donations: An Application to Optimal ‘Laddering’ Kee Yeun Lee, Doctoral Student, University of Michigan, 620 Hidden Valley Club #201, Ann Arbor, MI, 48104, United States of America, keeyeun@umich.edu, Fred M. Feinberg When seeking donations, charities nearly universally use an appeals scale: a set of specific monetary values from which potential donors can choose. However, little is known about how to appropriately select the scale points themselves, which are intended to serve as referents or anchors. Choosing them well is crucial: if charities select very high scale points (e.g., to try to increase donation amounts), they may risk alienating donors and receiving nothing; low scale points may encourage more people to donate, but less overall from each. Using unique data from a 3.5 year quasiexperiment, we employ a Tobit-II type model to account for both donation incidence and frequency. The model allows for tests of several distinct (latent) reference-price operationalizations, as well as (heterogeneous) pulling-up and pulling-down scale point attraction effects. Results show that the appeals scale really matters: even though donors can give what they wish (or not at all), the scale impacts both the “whether” and the “how much?” of donations. We find a strong negative correlation between donation incidence and amount, suggesting that asking for too much might raise average donation, but at the cost of lowering the proportion who do donate. Intriguingly, although we did not find significant heterogeneity on when people give (seasonality), scale attraction effect strength does vary across donors. This variation provides tangible information to help charities with “laddering”: deciding how much to increase the amount requested of individual donors based on past history.
2 - Data or Structure? Using a Field Experiment to Assess the Determinants of Counterfactual Demand Predictive Performance Manuel Hermosilla, PhD Student, Kellogg School of Managment, Northwestern University, 2001 Sheridan Road, Room 474, Evanston, IL, 60208, United States of America, m-hermosilla@kellogg.northwestern.edu, Yi Qian, Eric Anderson We evaluate the roles of microeconomic/statistical structure and experimental data as determinants of counterfactual demand predictive performance. The specific structures under evaluation are those developed by Berry (1994) and Berry et al. (1995). Experimental demand data is obtained from a large-scale field experiment that specified several price conditions for various products of a retail category. Counterfactual demand scenarios arose in some of these conditions because experimental prices substantially departed from those observed in a subsample with non-experimental historical data. By re-estimating structural and structure-free econometric models with varying amounts of the experimental data, we are able to isolate the contribution of each source of identification on counterfactual demand predictive performance. Our results show that both experimental data and microeconomic/statistical assumptions (as given by the considered models), provide advantages in counterfactual demand prediction. These results are robust to different measures, and in general, suggest that the key driver of predictive performance in counterfactual demand scenarios is the quality of the data used for estimation. Results thus suggest that (i) if the goal is to generate predictions of counterfactual demand scenarios, it might not be worth the while to undergo the costly process of specification and estimation of a structural econometric model of demand, and (ii) if the goal is to understand the fundamentals of consumers’ choice, parameter estimates of a structural demand model can be reliably used for counterfactual prediction. 3 - Estimation of Willingness to Pay Intervals by Discrete Choice Experiments Christian Schlereth, Goethe University Frankfurt, Grueneburgplatz 1, Frankfurt, 60323, Germany, schlereth@wiwi.uni-frankfurt.de, Christine Eckert, Bernd Skiera Knowledge about consumers’ willingness to pay (WTP) is essential for a profitmaximizing price management. This willingness to pay has always been regarded as a point estimate, typically as the price that makes the consumer indifferent between buying and not buying the product. In contrast, this paper uses discrete choice experiments and a scale adjusted latent class model to estimate willingness-to-pay as an interval. The mid value of this interval corresponds to the traditional WTP point estimate and depends on the deterministic utility, while the range of the interval is influenced by the price sensitivity as well as the error variance (scale) that determines the random utility for a product. This error variance, i.e. the degree of uncertainty in consumers’ choices has strong implications for firms competing in the market. Those firms with more fa-vorable products should target consumers with low scale (high certainty), while the other firms should target those with high scale (low certainty). The results of our empirical study demonstrate that such knowledge about individual intervals of willingness to pay enables better segmenting customers. The results further show that the sizes of the will-ingness-to-pay intervals can be large and that ignoring these sizes may lead to non-optimal pricing decisions. ■ FC02 Legends Ballroom II UGC-III (Content and Impact) Cluster: Internet and Interactive Marketing Invited Session Chair: Raji Srinivasan, Associate Professor, University of Texas-Austin, 1 University Station, Austin, TX, 78712, United States of America, Raji.Srinivasan@mccombs.utexas.edu 1 - Bimodal Distribution of Emotional Content in Customer Reviews: Emotional Biases in Online Customer Reviews Wonjoon Kim, Associate Professor, KAIST, Guseong-dong, Yuseonggu, Daejeon, 305701, Korea, Republic of, wonjoon.kim@kaist.ac.kr Word-of-mouth (WOM), most visibly encountered in the form of online customer reviews in recent times, has received considerable attention of late by academics and practitioners alike. While a number of studies have examined the phenomenon’s frequency and distribution patterns to understand its characteristics and the motivation behind it, WOM contents across its distribution have remained underexplored. To fill this important gap in the literature, we analyzed WOM contents using Natural Language Processing (NLP) methods, which are used to study the intersection of computers and human language. Using this approach, we find that more extreme reviews have a greater proportion of emotional content than less extreme reviews, revealing a bimodal distribution of emotional content as in the case of WOM distribution; we refer to this as emotional bias. Second, we find that reviews have more positive emotional content toward positive extreme ratings than negative MARKETING SCIENCE CONFERENCE – 2011 FC02 53 emotional content toward negative extreme ratings, falling into a J-shaped distribution (self-selection bias). Lastly, we find that uncertainty related to product quality before consumption is associated with more emotional words across different product categories (uncertainty bias). However, in this context the amount of positive emotional content is higher than that of negative content, suggesting a J-shaped distribution of emotional content. Furthermore, we find that search goods are associated with the lowest amount of expressed emotion, followed by experience goods, and then credence goods. Our findings enhance our understanding of the motivation behind WOM and related consumer behavior in the context of product sales. 2 - Ad Revenue and Content Commercialization: Evidence from Blogs Monic Sun, Assistant Professor, Stanford University, 518 Memorial Way, Stanford, CA, 94305, United States of America, monic@stanford.edu, Feng Zhu Many scholars are concerned about the impact of ad-sponsored business models on content providers. They argue that content providers, when incentivized by ad revenue, are more likely to tailor their content to attract “eyeballs,” and as a result, popular content may be excessively supplied. We empirically test this prediction by taking advantage of the launch of an ad revenue-sharing program initiated by a major Chinese portal site in September 2007. Participating bloggers allow the site to run ads on their blogs and receive 50% of the revenue generated by these ads. After analyzing 4.4 million blog posts, we find that compared to nonparticipants, the percentage of popular content increases by about 13% on the participants’ blogs after the program takes effect. More than 50% of this increase can be attributed to topics shifting towards three domains: stock market, salacious content, and celebrities. We also find evidence that, relative to nonparticipants, the participants’ content quality increases after the program takes effect. 3 - Does Advertising Affect Chatter? - Assessing the Dynamics of Advertising on Online Word-of-mouth Seshadri Tirunillai, University of Southern California, Los Angeles, CA, United States of America, tirunill@usc.edu, Gerard J. Tellis Despite the increased importance of consumer media, the factors influencing the User-Generated Content (UGC) have seen limited research. In this study, we investigate the effect of corporate advertising on UGC using a natural experiment in a time series setting. We seek to answer the following questions: 1) If there does exist a relation, can we establish the direction of causality? 2) Among the various metrics of UGC, which metrics are influenced by advertising and how are they affected? 3) What are the dynamics of such a relationship in terms of growth, persistence and decays? We use big budget corporate advertising campaigns to assess the impact of the campaign on the different metrics of UGC (e.g. overall volume, negative and positive UGC) of the target firm and the competitors. We try to assess the influence of advertising on firms by assessing the differential impact on the UGC metrics. We also analyze the dynamics of advertisement on the metrics UGC using multivariate time series. We find that after the introduction of the advertising campaign, the chatter of the target firm increased by about 27% relative to the control firms that did not undertake any major brand campaign during this period. While we find that the positive chatter increases markedly as compared to the synthetic control, there was no systematic decrease in negative chatter during the time period. There is also a spillover of advertising across firms in a market. 4 - Social Influence in the Evolution of Online Ratings of Service Firms Raji Srinivasan, Associate Professor, University of Texas-Austin, 1 University Station, Austin, TX, 78712, United States of America, Raji.Srinivasan@mccombs.utexas.edu Online consumer review websites prominently display consumers’ online ratings of service firms, which influence other consumers’ purchase decisions. Yet there are few insights on the factors influencing online ratings of service firms. The authors develop hypotheses of how other consumers’ online ratings of the service firm moderate the effects of a reviewer’s service encounter characteristics – valence of the service encounter, occurrence of service failure, and service recovery effort – on the reviewer’s online rating of the service firm. They test the hypotheses using an ordered probit model with data from 7,499 online reviews of hotels in Boston and Honolulu between 2006 and 2010. The results support the hypotheses of the moderating effects of social influence on a reviewer’s online rating of the service firm. The authors decompose the effects of service encounter characteristics on online ratings of service firms into ‘service encounter’ and ‘social influence’ components, a novel contribution to the marketing literature in services. From a managerial perspective, when a service failure occurs in a service firm with a high online rating, the decrease in the firm’s long-term rating is more than when it has a low online rating. The opposite is true when a service recovery effort is attempted.
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