Conference Sessions - Jesse H. Jones Graduate School of ...

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FD15 ■ FD15 Champions Center V CRM VI: Customer Satisfaction Contributed Session Chair: Jiana-Fu Wang, Assistant Professor, National Chung Hsing University, 250 Kuo Kuang Rd., Taichung, Taiwan - ROC, jfwang@dragon.nchu.edu.tw 1 - Modeling Determinants of the Satisfaction-loyalty Relationship: Theoretical and Empirical Evidence Young Han Bae, Doctoral Student, University of Iowa, Tippie College of Business, 108 PBB S252, Iowa City, IA, 52242-1994, United States of America, younghan-bae@uiowa.edu, Gary J. Russell, Lopo Rego Customer satisfaction and loyalty are central constructs to marketing research and practice since they reflect how effectively firms deliver value to their customers, and because they are important determinants of current and future product-marketplace and financial performance. In this study, we develop a comprehensive and flexible theoretical framework for analyzing the association between customer satisfaction and customer loyalty, which also incorporates competitive setting differences. This theoretical framework is grounded in more than 40 years of academic and practitioner research on the association between these two constructs and allows us to more precisely examine the true nature of the association between satisfaction and loyalty. Additionally, we test our theoretical framework by estimating an empirical hierarchical linear model, using American Customer Satisfaction Index (ACSI) data and several customer, firm and industry characteristics. Our findings indicate that the true nature of the association between satisfaction and loyalty is significantly influenced by context. Controlling for such differences allows firms and managers to significantly increase their ability to effectively convert satisfaction investments into loyalty. Additionally, we identify significant decreasing marginal returns for customer satisfaction investments, as well as important trade-offs between intercept and slope on the association between the two metrics. Our study provides important theoretical, managerial and regulatory insights, and broadens our understanding of the essential features of the satisfaction-loyalty association. 2 - Does the Variance in Customer Satisfaction Matter for Firm Performance? Eun Young Lee, Doctoral Student, Korea University Business School, Anam-dong Seongbuk-gu, Seoul, 136-701, Seoul, 136-701, Korea, Republic of, eyey@korea.ac.kr, Shijin Yoo, Dong Wook Lee, Sundar Bharadwaj Although much attention has been paid by both academics and corporate managers to customer satisfaction as a leading indicator of firm performance for many years, relatively little is known about the role of its variance. We investigate the relationship between the variance of customer satisfaction and three different dimensions of firm performance: accounting performance (i.e., revenue and profit), Tobin’s Q ratio, and stock return. Based on National Customer Satisfaction Index (NCSI) data collected by Korea Productivity Center in a last decade, we obtain three main findings. First, we confirm the findings of extant literature – mostly based on US data – that the average customer satisfaction is positively related to the firm performance. Second, we find that the variance of customer satisfaction negatively moderates the relationship between the mean of customer satisfaction and firm performance, i.e., the average customer satisfaction level is more strongly related to firm performance when the variance of customer satisfaction is low. Finally, the variance of customer satisfaction is found to directly affect firm performance as well. More specifically, the variance increases the sales and decreases the stock return. Academic and managerial implications are also discussed. 3 - The Impact of Online Railway Ticket Cancellation Policy on Revenue and Customer Satisfaction Jiana-Fu Wang, Assistant Professor, National Chung Hsing University, 250 Kuo Kuang Rd., Taichung, Taiwan - ROC, jfwang@dragon.nchu.edu.tw In some railway companies, booking limit is used to allocate the number of tickets among multiple legs in a train. Due to the ticket reservation feature of “first-comefirst-served” and the allowance of cancellations or no-shows, company revenue and customer loyalty might be jeopardized. This study examines a railway company’s online reservation system. When a reservation is made, the company holds it for at most two days before the reservationist makes the real purchase. The reservation might turn out to be a cancellation in the end, while during the above period, other customers might be rejected due to the policy of booking limit. In our June, 2010 database, 54% of the online reservations are either cancelled or give-up without notice. This incurs other customers’ unsatisfaction and a customer may take different strategies to tackle this situation: try again later, try an alternative train, order two or more sections to compose his/her original target section, and use other transport mode. In order to consider the above dynamic and nested customer behavior, a detailed simulation model is constructed according to the information extracted from the ticket reservation database. We use this model to estimate how much profit could have been lost, and how many “loyal” customers are lost due to denied bookings. We also suggest several strategies to improve revenues and reduce rejections for future research. MARKETING SCIENCE CONFERENCE – 2011 70 Saturday, 8:30am - 10:00am ■ SA01 Legends Ballroom I Conjoint Analysis: Improving the Process Contributed Session Chair: Dan Horsky, Simon Graduate School of Business, University of Rochester, Rochester, NY, 14627, United States of America, dan.horsky@simon.rochester.edu 1 - Best-worst Conjoint Analysis as a Remedy for Lexicographic Choosers Joseph White, Director, Marketing Sciences, Maritz Research, 1815 S. Meyers Rd., Suite 600, Oakbrook Terrace, IL, 60181, United States of America, joseph.white@maritz.com, Keith Chrzan Designed choice experiments often rely on strategies that seek to maximize efficiency by minimizing level overlap (Huber and Zwernia 1996). In the absence of overlapping levels, however, respondents making lexicographic choices or choices dominated by a single attribute may provide no information on attributes other than the most important one (Orme 2009). A large proportion of respondents make lexicographic or dominated choices (Killi, Nossum and Veisten 2007; Kohli and Jedidi 2007; Campbell, Hutchinson and Scarpa 2006), resulting in poor predictions in highly competitive holdout choices (Chrzan, Zepp and White 2010). Experimental designs that incorporate level overlap exist (Sawtooth Software 2007, Liu and Arora 2010, Chrzan, Zepp and White 2010). Plausibly, Best-Worst conjoint analysis (Swait, Louviere and Anderson 1995) as it does not rely on choice sets with or without level overlap, may provide another type of RUM experiment that avoids the problem lexicographic/dominant choosers pose to minimal overlap choice set designs. In addition to replicating tests of the comparability of Best-Worst conjoint analysis and discrete choice experiments (Swait, Louviere and Anderson 1995, Chrzan and Skrapits 1996), our presentation reports an experiment that compares the ability of a standard minimum overlap discrete choice experiment and a Best-Worst conjoint experiment to predict the choices of holdout respondents (some portion of whom hopefully make dominated choices). 2 - Using Additional Data Collection and Analysis Steps to Improve the Validity of Online-based Conjoint Sebastian Selka, Scientific Assistant, Brandenburg University of Technology, Erich-Weinert-Strafle 1, Cottbus, 03046, Germany, sebastian.selka@tu-cottbus.de, Daniel Baier Conjoint experiments are tending more and more to end up with low internal and external validity of the estimated part-worth function (see, e.g., Green et al. 2001), because of (missing) temporal stability and structural reliability of respondents’ partworth functions (see, McCullogh, Best 1979 or DeSarbo et al. 2005). Also respondents’ (missing) attentiveness during conjoint experiments is an important source of noisy data and more or less caused by uncontrolled data collection environments, e.g. many parallel web applications (e.g., social networks, electronic mail, newspapers or web site browsing, ) during CASI. Here, additional data collection and analysis steps have been proposed as solution (see, e.g., Netzer et al. 2008 for an overview). Examples of internal sources of data are response latencies, eye movements, or mouse movements, examples of external sources are sales and market data. The authors suggest alternative procedures for conjoint data collection that deal with these potential sources of internal and external validity by using additional calculations and analysis steps. A comparison in an adaptive conjoint analysis setting shows, that the new procedures lead to a higher internal and external validity. 3 - Estimation of Individual Level Multi-attribute Utility from Ordinal Paired Preference Comparisons Dan Horsky, Simon Graduate School of Business, University of Rochester, Rochester, NY, 14627, United States of America, dan.horsky@simon.rochester.edu, Paul Nelson, Sangwoo Shin Our work suggests that linear programming analysis of ordinal paired preference comparisons (or such comparisons inferred from interval-level ratings) may lead to better individual utility estimates than alternative methods which use interval-level ratings data directly. In this paper we: (a) outline a theoretical foundation for estimating a cardinal scaled utility function from ordinal preference data, in particular, pairs of pairs or ordered paired comparisons; (b) forward linear programming procedures and their HB versions, designed to estimate individual level attribute weights from such data; (c) evaluate the statistical properties of these estimators and develop statistical significance tests for them; and (d) evaluate the ability of these estimators to predict hold out sample preferences for two real world datasets. Simulations show that our ordinal preference-based weight estimates are more robust to data quality issues than either regression or ordered logit based estimates. Our real world results also show superior predictive performance. Our findings indicate that the higher potential for measurement error and scale usage heterogeneity that resides in cardinal scaled data is an issue. Correspondingly, ordinal preferences in conjunction with our linear programming estimation methodology provide individual level attribute weight estimates that are worthy of academic and managerial attention.
■ SA02 Legends Ballroom II Twitter and Social Media Cluster: Internet and Interactive Marketing Invited Session Chair: Abishek Borah, University of Southern California, Los Angeles, CA, United States of America, abhishek.borah.2012@marshall.usc.edu 1 - Methodology for Codifying Qualitative Twitter Content into Categorical Data Stephen Dann, Australian National University, MMIB, CBE, ANU, Acton, 00200, Australia, stephen.dann@anu.edu.au Twitter provides an immense wealth of qualitative data both in aggregate and at the individual account level. This paper proposes a method of classification of individual Twitter account content across a two level multiple domain framework. Whilst a range of prior studies have emphasized the quantification and classification of the Twitter public timeline, this method is purpose designed to operate within the account specific level for the purposes of benchmarking, analysis and classifying the use of Twitter at the individual level. This paper expands the existing application of Twitter as a data source for understanding marketing communications performance in social media by offering a split level classification scheme that can codify performance of an account holder in the primary domains of conversation, status reporting, information relay, news creation, and phatic communications. The method allows for a richer classification schema by expanding the five categories with further refinement to explore how the account is used for interaction in the social media space. The novelty of the method is the focal point of providing data to assess the performance of an account against the account author’s intended use of the service. The paper concludes with recommendations for the use of the methodology for market segmentation, social media communication objective setting, and as a rich social media metric. 2 - Gossip: Can It Kill a Giant? Liwu Hsu, PhD Candidate, Boston University, liwuhsu@bu.edu, Shuba Srinivasan, Susan Fournier Companies are increasingly confronted with new challenges in the era of Web 2.0. In this study, we focus on the role of brand equity (i.e., strong brand vs. weak brand) in an environment characterized by the proliferation of social media and the interactive interface between consumers and brands. Two powerful features of this environment are transparency and criticism (Fournier and Avery 2011). Once a crisis happens (e.g., product-harm recall), negative buzz is typically generated not only in traditional media but also in social media. It is difficult for a company to bury the mistake, and even worse, the social media environment makes consumers more critical of the company and its brand after a crisis. Conventional branding wisdom suggests strong brands can increase the firm’s profits and reduce the vulnerability of cash flows. Yet in the current environment, the bigger the brand the more vulnerable it is. Our objectives are to examine whether a strong brand alleviates the negative impact of a crisis event and to assess how the different metrics of social media moderate the impact of brand equity on the components of shareholder value: the levels of abnormal returns and stock risks. Using the event study method, we examine product recall announcements during a three-year period from 2008 to 2010 and incorporate daily user-generated content metrics such as volume and valence within the event window. We conclude with a discussion that the role of brand in Web 2.0 in risk management terms. 3 - Structural Dynamic Factor Analysis for Quantitative Trendspotting Rex Du, Associate Professor of Marketing, University of Houston, 375E Melcher Hall, Bauer College of Business, Houston, TX, 77204, United States of America, rexdu@bauer.uh.edu, Wagner Kamakura Trendspotting has been an important marketing intelligence tool for identifying and tracking major movements in consumer interest and behavior. Currently, trendspotting is done either qualitatively by “trend hunters” who comb through everyday life in search of signs indicating new trends in consumer needs and wants, or quantitatively by analysts who monitor individual indicators, such as how many times a keyword has been searched, blogged or “twitted” online. In this study, we demonstrate how the latter can be improved by uncovering common temporal patterns hidden behind the co-evolution of a large array of indicators. We propose a structural dynamic factor-analytic model that can be applied for simultaneously analyzing tens or even hundreds of time series, distilling them into a few latent dynamic factors that isolate seasonal cyclic movements from non-seasonal nonstationary trend lines. We demonstrate this novel multivariate approach to quantitative trendspotting in one application involving a promising new source of marketing intelligence – online keyword search data from Google Insights for Search, wherein search volume patterns across 38 major makes of light vehicles were analyzed over a 81-month period to identify key latent trends in consumer vehicle shopping interest. 4 - Is All That Twitters Gold? Market Value of Digital Conversations in Social Media Abishek Borah, University of Southern California, Los Angeles, CA, United States of America, abhishek.borah.2012@marshall.usc.edu, Gerard J. Tellis Social media are growing to be highly influential. Consumers and marketers are increasingly adopting social media such as Facebook, Twitter, YouTube. Even the president of the United States of America has a Twitter account. Some studies have shown that Tweets forecast movie revenues (Asur and Huberman 2010; Rui et al. MARKETING SCIENCE CONFERENCE – 2011 SA03 71 2010). However, research has not investigated the relationship between Tweets about brands and the stock market value of firms that own those brands. The authors collect more than 9 million tweets from Twitter for eight brands to address this issue. The authors use text mining techniques to create measures of volume, valence and word of mouth from the Twitter data. Using data at a daily level for a period of 6 months from October 2009, they evaluate whether the Twitter metrics have an impact on the stock market performance of firms using Vector AutoRegressive models. They control for several exogenous variables such as analyst coverage, advertising expenditures and key firm developments such as innovations, mergers, announcement of earnings, client contracts, strategic alliances, lawsuits and changes in key executives. The authors find that digital conversations in social media have a relation with stock market performance measures (7 of 8 brands have effects on abnormal returns). However, most of the effects are short term supporting the notion of market efficiency. Retweets (word of mouth) is the metric of Twitter with the most influence on abnormal returns. Volume of tweets is the metric with the most influence on trading volume. The authors discuss managerial implications of these results. ■ SA03 Legends Ballroom III Effects of Online Medium on Consumer Behavior Contributed Session Chair: Jie Zhang, Associate Professor, University of Maryland, 3311 Van Munching Hall, College Park, MD, 20742, United States of America, jiejie@rhsmith.umd.edu 1 - Disentangling the Effects of Online Shopping Decision Time on Website Conversion Dimitrios Tsekouras, PhD Candidate, Erasmus University Rotterdam, Burg. Oudlaan 50 (room H15-20), Rotterdam, 3062 PA, Netherlands, dtsekouras@ese.eur.nl, Benedict Dellaert Research on total time spent on pre-purchase information search on retail websites has shown contradictory effects on consumer purchase behavior. While some research has emphasized the positive effects of decision time online on conversion due to the fact that it increases the probability that the consumer selects an attractive product as well as decreases choice uncertainty, other studies showed that decision time represents higher consumer effort and an increased opportunity cost and, hence, has a negative effect on conversion. In order to explain these conflicting findings, we disentangle total decision time spent on a retail website into product comparison and product inspection time. We hypothesize that these two aspects have opposing effects on website conversion. First, we expect that spending more time comparing alternatives decreases the chances of conversion. The underlying reason is that comparison time is an indication of the difficulty of the choice set and of choice uncertainty which increases the risk associated with making a decision. Second, we expect that spending time inspecting each given alternative increases the chance of conversion, because it represents a higher expected utility of searching for information on the alternative. Using clickstream data from 16600 consumers that visited a mortgage recommendations website, we find support for the proposed relationships. This underlines the theoretical importance of disentangling total decision time. Online firms can also benefit from our findings to improve the composition of the offered product choice sets by taking into consideration the allocation of time spent by consumers on a given session and by balancing consumers’ time comparing alternatives with time spent inspecting alternatives’ details. 2 - Clicks to Conversion: The Impact of Product and Price Information Vandana Ramachandran, Assistant Professor, University of Utah, KDGB 319, Salt Lake City, UT, 84102, United States of America, vandana@business.utah.edu, Siva Viswanathan, Hank Lucas This study seeks to examine how different types of information – product-related and price-related information provided by retailers – impact purchase-related outcomes for consumers belonging to different states of shopping. Using mixture-modeling techniques on clickstream data obtained from a large online durable goods retailer, we find that a three-state model comprised of directed shoppers, deliberating researchers and browsers, best describes the latent differences across customers. In examining the impacts of information on purchase outcomes, we find that product and price-related information impacts consumers in these three shopping states differently. While product information highlighting features of product alternatives in a category has the strongest impact on deliberating researchers, specific price information related to category-level discounts increases the likelihood of purchase for both directed shoppers as well as browsers. Price information relating to site-wide free shipping has a positive impact on purchase for all consumers. Surprisingly, category-level discounts have a negative impact on deliberating researchers, while rich product information hampers the purchase process of directed shoppers. We discuss the managerial implications of our findings and the role of clickstream analytics in designing dynamic targeting and information provisioning strategies for online retailers.
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