Content Sampling, Household Informedness, and the Consumption of Digital Information Goods

References

• Ba, S., and Pavlou, P. A. Evidence of the effect of trust building technology in electronic markets: Price premiums and buyer behavior. MIS Quarterly, 26, 3 ( September 2002), 243–268.
   Web of Science ®Google Scholar
• Baumeister, R.F., and Bushman, B.J. Social Psychology and Human Nature, 2nd ed. Boston: Cengage Learning, 2010.
   Google Scholar
• Becker, G.S. A theory of the allocation of time. Economic Journal, 75, 299 ( September 1965), 493–517.
   Web of Science ®Google Scholar
• Bell, D. E. Regret in decision making under uncertainty. Operations Research, 30, 5 ( October 1982), 961–981.
   Web of Science ®Google Scholar
• Berger, B.; Matt, C.; Steininger, D.; and Hess, T. It is not just about competition with “free”: Differences between content formats in consumer preferences and willingness to pay. Journal of Management Information Systems, 32, 3, ( July 2015), 105–128.
   Web of Science ®Google Scholar
• Bhattacharjee, S.; Gopal, R.; Lertwachara, K.; and Marsden, J.R. Consumer search and retailer strategies in the presence of online music sharing. Journal of Management Information Systems, 23, 2 ( Summer 2006), 129–159.
   Google Scholar
• BI Intelligence. Illegal streaming is dominating online piracy. Business Insider. (August 2016). http://www.businessinsider.com/illegal-streaming-is-dominating-online-piracy-2016-8 (accessed on March 25, 2018).
   Google Scholar
• Brynjolfsson, E.; Hu, Y.J.; and Simester, D. Goodbye Pareto principle, hello long tail: The effect of search costs on the concentration of product sales. Management Science, 57, 8 ( June 2011), 1373–1386.
   Web of Science ®Google Scholar
• Cameron, A.C., and Trivedi, P.K. Regression Analysis of Count Data, 2nd ed. Econometric Society Monograph no. 53. Cambridge: Cambridge University Press, 1998.
   Google Scholar
• Carr, D. Giving viewers what they want. New York Times, February 24, 2013.
   Google Scholar
• Chang, M.R.; Kauffman, R.J.; and Kwon, Y. Understanding the paradigm shift to computational social science in the presence of big data. Decision Support Systems, 63 (2014), 67–80.
   Web of Science ®Google Scholar
• Chellappa, R., and Shivendu, S. Managing piracy: Pricing and sampling strategies for digital experience goods in vertically segmented markets. Information Systems Research, 16, 4 ( December 2005), 400–417.
   Web of Science ®Google Scholar
• Clemons, E.K.; Gao, G.; and Hitt, L. When online reviews meet hyper differentiation: A study of the craft beer industry. Journal of Management Information Systems, 23, 2 ( Fall 2006), 149–171.
   Web of Science ®Google Scholar
• Clemons, E.K.; Gu, B.; and Spitler, R. Hyper-differentiation strategies: Delivering value, retaining profits. In R.H. Sprague Jr. (ed.), Proceedings of the Thirty-Sixth Hawaii International Conference on System Science. Los Alamitos, CA: IEEE Computer Society Press, 2003. 225b.
   Google Scholar
• Clemons, E.K.; Spitler, R.; Gu, B.; and Markopoulos, P. Information, hyperdifferentiation, and delight: The value of being different. In S. Bradley and R. Austin (eds.), The Broadband Explosion: Leading Thinkers on the Promise of a Truly Interactive World. Boston: Harvard Business School Press, 2005, pp. 137–164.
   Google Scholar
• Cragg, J.G. Some statistical models for limited dependent variables with application to the demand for durable goods. Econometrica 39, 5 (September 1971), 829–844.
   Google Scholar
• David, F.N., and Johnson, N.L. Statistical treatment of censored data I: Fundamental formulae. Biometrika, 41, 1 ( June 1954), 228–240.
   Web of Science ®Google Scholar
• De Matos, M.; Ferreira, P.; Smith, M.D.; and Telang, R. Culling the herd: Using real-world randomized experiments to measure social bias with known costly goods. Management Science, 62, 9 ( February 2016), 2563–2580.
   Web of Science ®Google Scholar
• Dehejia, R.H., and Wahba, S. Propensity score-matching methods for non-experimental causal studies. Review of Economics and Statistics, 84, 1 ( February 2002), 151–161.
   Web of Science ®Google Scholar
• Demski, J. Information Analysis. Reading, MA: Addison-Wesley, 1980.
   Google Scholar
• Dey, D.; Lahiri, A.; and Liu, D. Consumer learning and time-locked trials of software products. Journal of Management Information Systems, 30, 2 ( Fall 2013), 239–268.
   Web of Science ®Google Scholar
• Dimoka, A.; Hong, Y.; and Pavlou, P.A. On product uncertainty in online markets: Theory and evidence. MIS Quarterly, 36, 2 ( June 2012), 395–426.
   Web of Science ®Google Scholar
• Ding, P.; VanderWeele, T.; and Robins, J. Instrumental variables as bias amplifiers with general outcome and confounding. Biometrika, 104, 2 ( June 2017), 291–302.
   PubMed Web of Science ®Google Scholar
• Dunn, J. The rise of music streaming services hasn’t killed music piracy. Business Insider (April 2017). http://www.businessinsider.sg/music-piracy-streaming-chart-2017-4/ (accessed on March 25, 2018).
   Google Scholar
• Dupont, W.D. Multiple Poisson regression. Chapter 9 in Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data. Cambridge: Cambridge University Press, 2002, pp. 295–318.
   Google Scholar
• Efron, B. Censored data and the bootstrap. Journal of American Statistical Association, 76, 374 ( June 1981), 312–319.
   Web of Science ®Google Scholar
• Efron, B., and Tibshirani, R. An Introduction to the Bootstrap. New York: Chapman and Hall, 1993.
   Google Scholar
• Faugère, C., and Kumar, G.T. Designing free software samples: A game theoretic approach. Information Technology and Management, 8, 4 ( September 2006), 263–278.
   Web of Science ®Google Scholar
• Freedman, A.M. Use of free product samples wins new favor as sales tool. Wall Street Journal, August 28, 1986.
   Google Scholar
• Gemici, S.; Rojewski, J.W.; and In-Heok, L. Use of propensity score matching for training research with observational data. International Journal of Training Research, 10, 3 ( June 2012), 219–232.
   Google Scholar
• Ghose, A. Internet exchanges for used goods: An empirical analysis of trade patterns and adverse selection. MIS Quarterly, 33, 2 ( June 2009), 163–291.
   Web of Science ®Google Scholar
• Gilovich, T., and Medvec, V.H. The experience of regret: What, when, and why. Psychology Review, 102, 2 ( April 1995), 379–395.
   Google Scholar
• Greene, W.H. Econometric Analysis. London: Pearson, 2012.
   Google Scholar
• Gurmu, S., and Trivedi, P.K. Excess zeros in count models for recreational trips. Journal of Business and Economic Statistics, 14, 4 ( October 1996), 469–477.
   Web of Science ®Google Scholar
• Halbheer, D.; Stahl, F.; Koenigsberg, O.; and Lehmann, D.R. Choosing a digital content strategy: How much should be free? International Journal of Research in Marketing, 31, 2 ( June 2014), 196–206.
   Web of Science ®Google Scholar
• Haubl, G., and Trifts, V. Consumer decision making in online shopping environments: The effects of interactive decision aids. Management Science, 19, 1 ( February 2000), 4–21.
   Google Scholar
• Heiman, A.; McWilliams, B.; Shen, Z.; and Zilberman, D. Learning and forgetting: Modeling optimal product sampling over time. Management Science, 47, 4 ( April 2001), 532–546.
   Web of Science ®Google Scholar
• Hilbe, J.M. Negative Binomial Regression, 2nd ed. Cambridge: Cambridge University Press, 2011.
   Google Scholar
• Holloway, K. The reasons you can’t stop binge watching. Alternet.com, December 30, 2015.
   Google Scholar
• Hong, Y., and Pavlou, P. A. Product fit uncertainty in online markets: Nature, effects, and antecedents. Information Systems Research, 25, 2 ( April 2014), 328–344.
   Web of Science ®Google Scholar
• Howard, J. Americans devote more than 10 hours a day to screen time, and growing. CNN.com, July 29, 2016.
   Google Scholar
• Johnson, E.J.; Moe, W.W.; Fader, P.S.; Bellman, S.; and Lohse, G.L. On the depth and dynamics of online search behavior. Management Science, 50, 3 ( March 2004), 299–308.
   Web of Science ®Google Scholar
• Jones, R., and Mendelson, H. Information goods vs. industrial goods: Cost structure and competition. Management Science, 57, 1 ( January 2011), 164–176.
   Web of Science ®Google Scholar
• Kamins, M.A.; Folkes, V.S.; and Fedorikhin, A. Promotional bundles and consumers’ price judgments: When the best things in life are not free. Journal of Consumer Research, 36, 4 ( November 2009), 660–670.
   Web of Science ®Google Scholar
• Kastranekes, J. Netflix knows the exact episode of a TV show that gets you hooked. TheVerge.com, September 23, 2015.
   Google Scholar
• Kwark, Y.; Chen, J.; and Raghunathan, S. Online product reviews: Implications for retailers and competing manufacturers. Information Systems Research, 25, 1 ( March 2014), 93–110.
   Web of Science ®Google Scholar
• Lafayette, J. Threat becomes profit center as TV leverages technology. BroadcastingCable.com, January 6, 2014.
   Google Scholar
• Lawal, B. Zero-inflated count regression models with applications to some examples. Quality and Quantity, 46, 1 ( January 2012), 19–38.
   Web of Science ®Google Scholar
• Li, T.; Kauffman, R.J.; Van Heck, E.; Vervest, P.; and Dellaert, B.G.C. Consumer informedness and firm information strategy. Information Systems Research, 25, 2 ( May 2014), 345–363.
   Web of Science ®Google Scholar
• Li, X. Could deal promotion improve merchants’ online reputations? The moderating role of prior reviews. Journal of Management Information Systems, 33, 1 ( June 2016), 171–201.
   Web of Science ®Google Scholar
• Liebeskind, J., and Rumelt, R.P. Markets for experience goods with performance uncertainty. RAND Journal of Economics, 20, 4 ( Winter 1989), 601–621.
   Web of Science ®Google Scholar
• Liebowitz, S.J., and Zentner, A. Clash of the titans: Does Internet use reduce television viewing? Review of Economics and Statistics, 94, 1 ( February 2012), 234–245.
   Web of Science ®Google Scholar
• Lin, C.A. Modeling the gratification-seeking process of TV viewing. Human Communication Research, 20, 2 ( December 1993), 224–244.
   Web of Science ®Google Scholar
• Markopoulos, P.M. Product information dissemination in Internet markets and markets for product information. Ph.D. diss., University of Pennsylvania, Philadelphia, 2004.
   Google Scholar
• Markopoulos, P.M., and Clemons, E.K. Reducing buyers’ uncertainty about taste-related product attributes. Journal of Management Information Systems, 30, 2 ( Fall 2013), 269–299.
   Web of Science ®Google Scholar
• Matt, C., and Hess, T. Product fit uncertainty and its effects on vendor choice: An experimental study. Electronic Markets, 26, 1 ( February 2016), 83–93.
   Web of Science ®Google Scholar
• McAlister, L., and Pessemier, E. Variety seeking behavior: an interdisciplinary review. Journal of Consumer Research, 9, 3 ( December 1982), 311–322.
   Web of Science ®Google Scholar
• McGuinness, D.; Gendall, P.; and Mathew, S. The effect of product sampling on product trial, purchase and conversion. International Journal of Advertising, 11, 1 ( January 1992), 83–92.
   Google Scholar
• Mehta, N.; Rajiv, S.; and Srinivasan, K. Price uncertainty and consumer search: A structural model of consideration set formation. Marketing Science, 22, 1 ( Winter 2003), 58–84.
   Web of Science ®Google Scholar
• Mordor Intelligence. Global video on demand market by revenue model, platform, applications, industry verticals, geography and vendors: market shares, forecasts and trends (2015 -2020). PR Newswire (September 2015). https://www.prnewswire.com/news-releases/global-video-on-demand-market-by-revenue-model-platform-applications-industry-verticals-geography-and-vendors---market-shares-forecasts-and-trends-2015—2020-300148230.html (accessed on March 25, 2018).
   Google Scholar
• Moretti, E. Social learning and peer effects in consumption: evidence from movie sales. Review of Economic Studies, 78, 1 ( January 2011), 356–393.
   Web of Science ®Google Scholar
• Nathanson, J. The economics of a hit TV show. Priceonomics.com, October 17, 2013.
   Google Scholar
• Nelson, P. Information and consumer behavior. Journal of Political Economy, 78, 2 ( March–April 1970), 311–329.
   Web of Science ®Google Scholar
• Newswire. Netflix declares binge watching is the new normal: Study finds 73% of TV streamers feel good about it. December 13, 2013.
   Google Scholar
• Niculescu, M., and Wu, D. J. Economics of free under perpetual licensing implications for the software industry. Information Systems Research, 25, 1, ( March 2014), 173–199.
   Web of Science ®Google Scholar
• New York Times. The best and worst moments of the 2017 Emmys. New York Times, September 18, 2017.
   Google Scholar
• O’Kane, S. Spotify unveils Touch Preview, a beautiful new music discovery tool. TheVerge.com, January 22, 2015.
   Google Scholar
• Oestreicher-Singer, G., and Zalmanson, L. Content or community? A digital business strategy for content providers in the social age. MIS Quarterly, 37, 2 ( June 2013), 591–616.
   Web of Science ®Google Scholar
• Pavlou, P.; Liang, H.; and Xue, Y. Understanding and mitigating uncertainty in online exchange relationships: A principal-agent perspective. MIS Quarterly, 31, 1 ( March 2007), 105–136.
   Web of Science ®Google Scholar
• Pinsker, J. The psychology behind CostCo’s free samples. Atlantic, October 2014, 1–6.
   Google Scholar
• Pirracchio, R.; Resche-Rigon, M.; and Chevret S. Evaluation of the propensity score methods for estimating marginal odds ratios in case of small sample size. BMC Medical Research Methodology, 12, 1 (May 2012), 70–79.
   PubMedGoogle Scholar
• Rao, A.R., and Monroe, K.B. Causes and consequences of price premiums. Journal of Business, 69, 4 ( October 1996), 511–535.
   Google Scholar
• Rode, A. Literature review: Non-unitary models of the household (theory and evidence). Working paper, University of California, Santa Barbara, 2011.
   Google Scholar
• Rosenbaum, P., and Rubin, D. Constructing a control group using multivariate matched sampling methods that incorporate the propensity. American Statistician, 39, 1 ( February 1985), 33–38.
   Web of Science ®Google Scholar
• Rosenbaum, P., and Rubin, D. The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 1 ( April 1983), 41–55.
   Web of Science ®Google Scholar
• Rubin, A.M. Television uses and gratifications: The interactions of viewing patterns and motivations. Journal of Broadcasting, 27, 1 ( January 1983), 37–51.
   Web of Science ®Google Scholar
• Rubin, D. Matching to remove bias in observational studies. Biometrics, 29, 1 ( March 1973), 159–183.
   Web of Science ®Google Scholar
• Russell, C.A.; Norman, A.T.; and Heckler, S.E. The consumption of television programming: Development and validation of the connectedness scale. Journal of Consumer Research, 31, 1 ( June 2004), 150–161.
   Web of Science ®Google Scholar
• Shapiro, C., and Varian, H.R. Information Rules: A Strategic Guide to the Network Economy. Boston: Harvard Business School Press, 1999.
   Google Scholar
• Vuong, Q. Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica, 57 2 (March 1989), 307–333.
   Web of Science ®Google Scholar