in St. Louis for their helpful comments and suggestions. They also thank Jeremie Berrebi, Ilan Abehassera, and David Levy for their collaboration. This paper is based on Andrew Stephen's dissertation.

We empirically study the motivations of users to contribute content to social media in the context of the popular microblogging site Twitter. We focus on noncommercial users who do not benefit financially from their contributions. Previous literature suggests that there are two main types of utility that motivate these users to post content: intrinsic utility and image-related utility. We leverage the fact that these two types of utility give rise to different predictions as to whether users should increase their contributions when their number of followers increases. To address the issue that the number of followers is endogenous, we conducted a field experiment in which we exogenously added followers (or follow requests, in the case of protected accounts) to a set of users over a period of time and compared their posting activities to those of a control group. We estimated each treated user's utility function using a dynamic discrete choice model. Although our results are consistent with both types of utility being at play, our model suggests that image-related utility is larger for most users. We discuss the implications of our findings for the evolution of Twitter and the type of value firms may derive from such platforms in the future.

We gratefully acknowledge the contribution of Robert M. Freund who proposed the use of the analytic center and approximating ellipsoids and gave us detailed advice on the application of these methods.This research was supported by the Sloan School of Management and the Center for Innovation in Product Development at M.I.T. This paper may be downloaded from http://mitsloan.mit.edu/vc. That website also contains (1) open source code to implement the methods described in this paper, (2) open source code for the simulations described in this paper, (3) demonstrations of web-based questionnaires based on the methods in this paper, and (4) related papers on web-based interviewing methods. All authors contributed fully and synergistically to this paper. We wish to thank Ray Faith, Aleksas Hauser, Janine Sisk, Limor Weisberg, Toby Woll for the visual design, programming, and project management on the Executive Education Study. This paper has benefited from presentations at the CIPD Spring Research Review, the Epoch Foundation Workshop, the Marketing Science Conferences in Wiesbaden Germany and Alberta Canada, the MIT ILP Symposium on "Managing Corporate Innovation," the MIT Marketing Workshop, the MIT Operations Research Seminar Series, the MSI Young Scholars Conference, the New England Marketing Conference, and Stanford Marketing Workshop, and the UCLA Marketing Seminar Series. Polyhedral Methods for Adaptive Choice-Based Conjoint Analysis AbstractChoice-based conjoint analysis (CBC) is used widely in marketing for product design, segmentation, and marketing strategy. We propose and test a new "polyhedral" question-design method that adapts each respondent's choice sets based on previous answers by that respondent. Individual adaptation appears promising because, as demonstrated in the aggregate customization literature, question design can be improved based on prior estimates of the respondent's partworths -information that is revealed by respondents' answers to prior questions. The otherwise impractical computational problems of individual CBC adaptation become feasible based on recent polyhedral "interior-point" algorithms, which provide the rapid solutions necessary for real-time computation.To identify domains where individual adaptation is promising (and domains where it is not), we evaluate the performance of polyhedral CBC methods with Monte Carlo experiments. We vary magnitude (response accuracy), respondent heterogeneity, estimation method, and question-design method in a We close by describing an empirical application to the design of executive education programs in which 354 web-based respondents answered stated-choice tasks with four service profiles each. The profiles varied on eight multi-level features. With the help of this study a major university is revising its executive education programs with new formats and a new focus.

We test methods, based on cognitively-simple decision rules, that predict which products consumers select for their consideration sets. Drawing on qualitative research we propose disjunctions-of-conjunctions (DOC) decision rules that generalize well-studied decision models such as disjunctive, conjunctive, lexicographic, and subset conjunctive rules. We propose two machine-learning methods to estimate cognitively-simple DOC rules. We observe consumers' consideration sets for global positioning systems for both calibration and validation data. We compare the proposed methods to both machine-learning and hierarchical-Bayes methods each based on five extant compensatory and non-compensatory rules. On validation data the cognitively-simple DOC-based methods predict better than the ten benchmark methods on an information theoretic measure and on hit rates; significantly so in all but one test. An additive machinelearning model comes close on hit rate. Our results are robust with respect to format by which consideration is measured (four formats tested), sample (German representative vs. US student), and presentation of profiles (pictures vs. text). We close by illustrating how DOC-based rules can affect managerial decisions. Keywords:Consideration sets, non-compensatory decisions, consumer heuristics, statistical learning, machine learning, revealed preference, conjoint analysis, cognitive complexity, cognitive simplicity, environmental regularity, lexicography, logical analysis of data, decision trees, combinatorial optimization. Two-stage, consider-then-choose decision rules are particularly relevant in the automobile market, but modeling and forecasting such decision rules is of general interest. When consumers face a large number of alternative products, as is increasingly common in today's retail and web-based shopping environments, they typically screen the full set of products down to a smaller, more-manageable consideration set which they evaluate further (e.g., Bronnenberg and Vanhonacker 1996;DeSarbo et al., 1996; Hauser and Wernerfelt 1990; Jedidi, Kohli and DeSarbo, 1996;Mehta, Rajiv, and Srinivasan, 2003;Montgomery and Svenson 1976;Payne 1976;Roberts and Lattin, 1991;Shocker et al., 1991;Wu and Rangaswamy 2003). Consideration sets for packaged goods are typically 3-4 products rather than the 30-40 products on the market (Hauser and Wernerfelt 1990;Urban and Hauser 2004). Forecasting consideration sets can explain roughly 80% of the explainable uncertainty in consumer decision making (assuming equally likely choice within the consideration set, Hauser 1978). In complex product categories research suggests that at least some consumers use non-compensatory decision processes when evaluating 3 many products and/or products with many features (e.g., Payne, Johnson 1988, 1993). 2 CONSIDERATION SETS AND DECISION RULESIn this paper we explore machine-learning algorithms based on non-compensatory decision rules that model decisions by consumers in the consideration stage of a consider-thenchoo...

Idea generation (ideation) is critical to the design and marketing of new products, to marketing strategy, and to the creation of effective advertising copy. However, there has been relatively little formal research on the underlying incentives with which to encourage participants to focus their energies on relevant and novel ideas. Several problems have been identified with traditional ideation methods. For example, participants often free ride on other participants' efforts because rewards are typically based on the group-level output of ideation sessions. This paper examines whether carefully tailored ideation incentives can improve creative output. I begin by studying the influence of incentives on idea generation using a formal model of the ideation process. This model illustrates the effect of rewarding participants for their impact on the group and identifies a parameter that mediates this effect. I then develop a practical, web-based asynchronous “ideation game,” which allows the implementation and test of various incentive schemes. Using this system, I run two experiments that demonstrate that incentives do have the capability to improve idea generation, confirm the predictions from the theoretical analysis, and provide additional insight on the mechanisms of ideation.idea generation, new product research, product development, marketing research, agency theory, experimental economics, game theory

We gratefully acknowledge the contribution of Robert M. Freund who proposed the use of the analytic center and approximating ellipsoids and gave us detailed advice on the application of these methods. This research was supported by the Sloan School of Management and the Center for Innovation in Product Development at M.I.T. This paper may be downloaded from http://mitsloan.mit.edu/vc. That website also contains (1) open source code to implement the methods described in this paper, (2) open source code for the simulations described in this paper, (3) demonstrations of web-based questionnaires based on the methods in this paper, and (4) related papers on web-based interviewing methods. All authors contributed fully and synergistically to this paper. We wish to thank Limor Weisberg for creating the graphics in this paper. This paper has benefited from presentations at the CIPD Spring Research Review, the Epoch Foundation Workshop, the MIT ILP Symposium on "Managing Corporate Innovation," the MIT Marketing Workshop, the MSI Young Scholars Conference, and the Stanford Marketing Workshop. Fast Polyhedral Adaptive Conjoint Estimation AbstractWeb-based customer panels and web-based multimedia capabilities offer the potential to get information from customers rapidly and iteratively based on virtual product profiles. However, web-based respondents are impatient and wear out more quickly. At the same time, in commercial applications, conjoint analysis is being used to screen large numbers of product features. Both of these trends are leading to a demand for conjoint analysis methods that provide reasonable estimates with fewer questions in problems involving many parameters.In this paper we propose and test new adaptive conjoint analysis methods that attempt to reduce respondent burden while simultaneously improving accuracy. We draw on recent "interior-point" developments in mathematical programming which enable us to quickly select those questions that narrow the range of feasible partworths as fast as possible. We then use recent centrality concepts (the analytic center) to estimate partworths. These methods are efficient, run with no noticeable delay in web-based questionnaires, and have the potential to provide estimates of the partworths with fewer questions than extant methods.After introducing these "polyhedral" algorithms we implement one such algorithm and test it with Monte Carlo simulation against benchmarks such as efficient (fixed) designs and Adaptive Conjoint Analysis (ACA). While no method dominates in all situations, the polyhedral algorithm appears to hold significant potential when (a) profile comparisons are more accurate than the self-explicated importance measures used in ACA, (b) when respondent wear out is a concern, and (c) when the product development and marketing teams wish to screen many features quickly. We also test a hybrid method that combines polyhedral question selection with ACA estimation and show that it, too, has the potential to improve predictions in many contexts. The algorithm w...

We gratefully acknowledge the contribution of Robert M. Freund who proposed the use of the analytic center and approximating ellipsoids and gave us detailed advice on the application of these methods.This research was supported by the Sloan School of Management and the Center for Innovation in Product Development at M.I.T. This paper may be downloaded from http://mitsloan.mit.edu/vc. That website also contains (1) open source code to implement the methods described in this paper, (2) open source code for the simulations described in this paper, (3) demonstrations of web-based questionnaires based on the methods in this paper, and (4) related papers on web-based interviewing methods. All authors contributed fully and synergistically to this paper. We wish to thank Ray Faith, Aleksas Hauser, Janine Sisk, Limor Weisberg, Toby Woll for the visual design, programming, and project management on the Executive Education Study. This paper has benefited from presentations at the CIPD Spring Research Review, the Epoch Foundation Workshop, the Marketing Science Conferences in Wiesbaden Germany and Alberta Canada, the MIT ILP Symposium on "Managing Corporate Innovation," the MIT Marketing Workshop, the MIT Operations Research Seminar Series, the MSI Young Scholars Conference, the New England Marketing Conference, and Stanford Marketing Workshop, and the UCLA Marketing Seminar Series. Polyhedral Methods for Adaptive Choice-Based Conjoint Analysis AbstractChoice-based conjoint analysis (CBC) is used widely in marketing for product design, segmentation, and marketing strategy. We propose and test a new "polyhedral" question-design method that adapts each respondent's choice sets based on previous answers by that respondent. Individual adaptation appears promising because, as demonstrated in the aggregate customization literature, question design can be improved based on prior estimates of the respondent's partworths -information that is revealed by respondents' answers to prior questions. The otherwise impractical computational problems of individual CBC adaptation become feasible based on recent polyhedral "interior-point" algorithms, which provide the rapid solutions necessary for real-time computation.To identify domains where individual adaptation is promising (and domains where it is not), we evaluate the performance of polyhedral CBC methods with Monte Carlo experiments. We vary magnitude (response accuracy), respondent heterogeneity, estimation method, and question-design method in a We close by describing an empirical application to the design of executive education programs in which 354 web-based respondents answered stated-choice tasks with four service profiles each. The profiles varied on eight multi-level features. With the help of this study a major university is revising its executive education programs with new formats and a new focus.

in St. Louis for their helpful comments and suggestions. They also thank Jeremie Berrebi, Ilan Abehassera, and David Levy for their collaboration. This paper is based on Andrew Stephen's dissertation.