Entries Tagged 'Working Papers' ↓

Novak, Thomas P., "Consumer Behavior Research in Second Life:  Issues and Approaches," (October 2007).

Presented at the Association for Consumer Research Pre-Conference, "Consumer Online:  Ten Years Later," Memphis, TN, October 25, 2007.

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Hoffman, Donna L. and Thomas P. Novak, “Flow Online:  Lessons Learned and Future Prospects” (September 14, 2007). 

Abstract.  Although the flow construct has been widely studied over the past decade in marketing and related fields, it has proven to be an elusive construct to measure and model.  In this paper, we first examine two of the most important themes in flow research in the last decade: the conceptualization and measurement of flow in online environments and the marketing outcomes of flow.  Second, while the unique characteristics of the Internet contributed to our belief that flow was an important construct for understanding consumer use of the Web in 1996, the environment of the Web itself has changed radically over the past decade.  Thus, we consider the current context of the Internet for the role and application of the flow construct, as well as important related constructs that will be useful for understanding compelling experiences in the contemporary online environment.   Download pdf.

White, Tiffany B., Thomas P. Novak and Donna L. Hoffman, “Evidence and Consequences of Egocentric Accounting Biases in Customer-Seller Relationships” (September 9, 2007).

We investigate egocentric biases in mental accounting within the context of information-driven consumer seller relationships. Our research suggests that consumers keep “loose mental accounts” of exchange benefits and costs that are balanced when resource exchanges are either contingent or temporally integrated (i.e., exchanged in the same transaction). However, when non-contingent resource exchange is temporally separated (specifically when exchange benefits precede exchange costs), or when bias correction is impeded in the same transaction, consumers keep two mental accounts, assigning differential value to marketers’ versus their own resources. Consequently, consumers egocentrically devalue- and therefore feeling less obligated to reciprocate- firms’ benefit offerings.   Download pdf.

Novak, Thomas P. and Donna L. Hoffman, “The Fit of Thinking Style and Situation:  New Measures of Situation-Specific Experiential and Rational Cognition” (August 27, 2007).

Abstract:  Decades of theoretical and empirical research in social and cognitive psychology provide strong evidence that consumers process information in two distinct and qualitatively different ways:   rational and experiential.  However, there has been surprisingly little research attention devoted to explicitly measuring how situational influences directly impact thinking style.  Further, attempts to simultaneously measure the two dimensions of thinking style as either situation-specific or as an enduring state are even fewer and lack validation in a broad context.  In five studies, we develop and validate a new instrument for measuring experiential and rational situation-specific thinking style in the context of a range of performance tasks designed to induce primarily rational or experiential cognition, as well as in the context of differing motivations toward the same task.  We establish congruence effects related to the fit of situation-specific thinking style and the nature of the task on performance outcomes; also, we show that the prediction of task performance and related outcomes from dispositional thinking style is completely mediated by situation-specific thinking style.  Download pdf

Neslin, Scott A., Thomas P. Novak, Kenneth R. Baker, and Donna L. Hoffman, “An Optimal Contact Model for Maximizing Online Panel Response Rates,” (August 2006).

Abstract.  We develop and test an optimization model for maximizing response rates for online marketing research survey panels.  The model consists of:  (1) a decision tree predictive model that classifies panelists into “states” and forecasts the response rate for panelists in each state, and (2) a linear program that derives a plan specifying how many panelists should be solicited from each state in order to maximize response rates.  The linear program is forward looking in that it optimizes over a finite horizon during which S studies are to be fielded.  It takes into account the desired number of responses for each study, the likely migration pattern of panelists between states as they are invited and respond or don’t respond, as well as demographic requirements.  The model is implemented using a rolling horizon, whereby the optimal solution for S successive studies is derived and implemented for the first study; then, as results are observed, an optimal solution is derived for the next S studies, and the solution is implemented for the first of these studies, etc.  The procedure is field tested and shown to increase response rates significantly, compared to random selection and the heuristic currently being used by panel management.  Implications are discussed for further model development, implementation issues for online panel managers, and for the broader area of optimal contact models in customer relationship management.  Download pdf.

Pavlou, Paul A. and Ting Lie, "What Drives Mobile Commerce? A Model of Mobile Commerce Adoption," (2006).

Abstract.  Mobile commerce - the consumer’s engagement in online transactions with sellers using wireless devices - differs from traditional or electronic commerce due to the potential for location-specific real-time transactions and the unique attributes of wireless devices. This paper aims to understand what drives consumers to engage in mobile transactions by viewing m-commerce adoption as a process consisting of three distinct behaviors: (a) ‘getting information’ (b) ‘giving information’, and (c) ‘purchasing’ products using wireless devices. These three inter-related behaviors are first integrated, following the theory of implementation intentions. Second, following the Theory of Planned Behavior (TPB), each behavioral intention is predicted through its attitude, subjective norm, and perceived behavioral control (self-efficacy and controllability). Third, a comprehensive set of salient beliefs for each behavior is elicited and tested by inductively identifying antecedents of the TPB constructs. These salient beliefs include technology acceptance variables (perceived usefulness and perceived ease of use), mobile device characteristics (download delay, screen quality, and device navigability), information characteristics (informativeness and information protection), product characteristics (product value and personalization), and consumer characteristics (monetary resources and consumer skills).

An empirical study with consumers in two countries (United States and Republic of China) supports the proposed m-commerce adoption model. The differences and similarities between these two countries are discussed, and implications for the adoption of m-commerce, TPB, and the IS literature are proposed.  Download pdf.

Dimoka, Angelika and Paul A. Pavlou, "Mitigating Adverse Product and Seller Selection in Online Auction Marketplaces," (2006)

Abstract.  To overcome a market of ‘lemons’, online auction marketplaces must differentiate among products and reward high-quality ones with price premiums. However, the literature has only focused on seller quality uncertainty (seller reputation), alas ignoring the role of product quality uncertainty. It is defined as the degree by which the outcome of a transaction cannot be accurately predicted due to fears that the product’s quality may differ from what is expected. Product quality uncertainty is particularly important in used and expensive experience products, such as used automobiles, whose quality cannot be conveyed via the web interface. To address adverse product selection, this study first introduces a set of product-related variables (warranty, inspection, posted price, standard value, year and mileage) that is proposed to influence price premiums by reducing product quality uncertainty. Moreover, it proposes their interaction effects with seller reputation. The proposed econometric model is tested with a multivariate regression model with secondary data from 350,000 auctions of used cars completed on eBay Motors using a custom data mining tool. Implications for mitigating adverse product selection and preventing a market of ‘lemons’ are discussed.  Download pdf.

Gefen, David and Paul A. Pavlou, "An Inverted-U Theory of Trust:  The Moderating Role of Perceived Regulatoary Effectiveness of Online Marketplaces," (2006).

Abstract.  Past research has widely regarded trust as unconditionally facilitating transaction behavior, and perceived risk as unconditionally detracting from it. This study advocates the necessity to examine trust and perceived risk within the broader perspective of the societal context. We propose the perceived regulatory effectiveness of online marketplaces to moderate the impact of trust on transaction behavior. More specifically, we hypothesize that the degree to which buyer’s trust affects transaction intentions will vary in an inverted U manner depending on the perception buyers have in the regulatory effectiveness of the online marketplace. The impact of trust on transaction intentions will increase as the buyer’s perceived regulatory effectiveness increases from low to medium levels, but it will decrease as the buyer’s perceived effectiveness increases from medium to high levels. Moreover, the perceived regulatory effectiveness of the online marketplace is also hypothesized to reduce the impact of perceived risk on transaction intentions. These moderating effects were examined and empirically supported in the context of eBay’s and Amazon’s online auction marketplaces. Implications for integrating the perceived regulatory effectiveness of online marketplaces into existing trust and risk models are discussed.  Download pdf.

Paton, David, Donald S. Siegel, and Leighton Vaughan Willians, "The Impact of Information Technology on Service Sector Productivity:  Plant-Level Evidence From Gambling Establishments in the United Kingdom," (May 2006) 

Abstract.  There have been numerous studies of the impact of investment in information technology (IT) on manufacturing productivity (e.g., Brynjolfsson and Hitt (1996)) and Morrison (1997)), but little evidence on this relationship in services. We attempt to fill this gap, by analyzing the impact of IT on the relative productivity of gambling establishments. Ours is also the first empirical study of productivity in gambling, one of the fastest growing industries in the service sector. The econometric analysis is based on data from the Annual Respondents Database (ARD) file, consisting of individual establishment records from the U.K. Annual Census of Production. The ARD file contains detailed data on output, materials, energy, employment, and numerous plant and firm characteristics and is quite similar to the U.S.-based Longitudinal Research Database (LRD). Production function models provide an excellent fit to the data, yielding plausible elasticity estimates and indicating constant returns to scale. More importantly, we find that gambling establishments investing more heavily in computers and the Internet are more productive than comparable establishments.  Download pdf.

Hu, Nan, Paul A. Pavlou, and Jennifer Zhang, "Can Online Word-of-Mouth Communication Reveal True Product Quality? Experimental Insights, Econometric Results, and Analytical Modeling," (April 2006).

Online word-of-mouth (WOM) communication in the form of online product reviews, such as those provided by consumers that have previously purchased a product, has become a major information source for consumers and marketers about a product’s quality. The literature has thus used online product reviews to predict a product’s sales and future success, assuming that the average (mean) score of these reviews represents true product quality. However, using experimental and econometric data, this study empirically shows that single point estimation (e.g., the mean) may not be a sufficient predictor of true product quality due to under-reporting by consumers with moderate product reviews.

To overcome this concern, this paper first tests the underlying distribution of online product reviews with secondary data from Amazon (www.amazon.com). The econometric results reveal that the reviews for the majority of the products have an asymmetric bimodal distribution. For these products, the mean of the online product reviews does not necessarily reveal the product’s true quality, resulting in misleading conclusions about the product’s future success. In contrast, results from a controlled experiment where all respondents reviewed a product show that their product reviews follow a unimodal (approximately normal) distribution. This confirms that consumers with extreme (positive or negative) views are more likely to review products than consumers with moderate views, resulting in a truncated sample.

To identify when single point estimation can be used as a sufficient predictor considering the consumers’ incentive of leaving reviews, this paper develops an analytical model to derive the conditions of when the mean can serve as a valid proxy of a product’s true quality. Finally, we introduce a dual point estimation model using the lower and upper ends of the modal interval of the DIP test to predict true product quality, which is shown to be a superior predictor of future product sales versus the simple and weighted average. The paper concludes by discussing the study’s theoretical and managerial implications.  Download pdf.