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Precision Retailing a Behaviorally-Informed and AI-Enabled Translational Science Hub for 21St Century Individual and Collective Health, Wealth, and Wellbeing

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Precision Retailing a Behaviorally-Informed and AI-Enabled Translational Science Hub for 21St Century Individual and Collective Health, Wealth, and Wellbeing 1 Precision Retailing A Behaviorally-Informed and AI-Enabled Translational Science Hub for 21st Century Individual and Collective Health, Wealth, and Wellbeing Summer 2018 Two reading lists are provided: 1. Core articles brought together as foundations for the PR translational research live case discussion. Students have to review the 3 most relevant to enrich their disciplinary work in each session with the class briefing and translational live cases providing an actionable synthesis of all papers for on-going integration into the student’s term project disciplinary enrichment journey. They will be expected to contribute actively to the discussion with a focus on the articles they have chosen to read (P.1-11). 2. Complementary disciplinary and transdisciplinary articles from which each student picks 1 in 5 sessions to produce written brief on the paper contribution and what angle of this research provide insights in the student’s disciplinary enrichment journey. Each student presents 3 of these for class discussion. This comprehensive list will also serve more generally for the term project and longer-term knowledge building (P.11-48). 1. PR Core Articles Session 1 Simon, H. A. (1992). What is an “explanation” of behavior? Psychological science, 3(3), 150-161 Introduction: Precision http://journals.sagepub.com/doi/pdf/10.1111/j.1467-9280.1992.tb00017.x retailing as an AI-enabled translational hub for Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the behaviorally-informed future of cognitive science. Behavioral and Brain Sciences, 36(3), 181-204 disciplinary science, https://www.cambridge.org/core/journals/behavioral-and-brain- innovation, process, and, sciences/article/whatever-next-predictive-brains-situated-agents-and-the- practice at professional, future-of-cognitive-science/33542C736E17E3D1D44E8D03BE5F4CD9 organizational, systems and policy levels Cacioppo, J. T. (2013). Psychological science in the 21st century. Teaching of Psychology, 40(4), 304-309. http://journals.sagepub.com/doi/abs/10.1177/0098628313501041 Kotler, P. (2011). Reinventing marketing to manage the environmental imperative. Journal of Marketing, 75(4), 132-135. http://www.dyane.net/linked/2.1.%20Reinventing%20Marketing%20to%20Ma nage%20the%20Environmental%20Imperative.pdf Banks, G. C., Pollack, J. M., Bochantin, J. E., Kirkman, B. L., Whelpley, C. E., & O’Boyle, E. H. (2016). Management’s science–practice gap: A grand challenge for all stakeholders. Academy of Management Journal, 59(6), 2205-2231. https://www.researchgate.net/publication/305460988_Management's_scienc e-practice_gap_A_grand_challenge_for_all_stakeholders 2 Fox, C.R., & Sitkin, B. M (2015). Bridging the Divide Between Behavioral Science and Policy. Behavioral Science & Policy, 1, 1-12. https://behavioralpolicy.org/wp- content/uploads/2017/05/BSP_vol1is1_Fox.pdf Agrawal, A. K., Gans, J. S., & Goldfarb, A. (2017). What to expect from artificial intelligence. MIT Sloan Management Review, 58(3), 23. http://ilp.mit.edu/media/news_articles/smr/2017/58311.pdf Faraj, S. Pachedi, S., Sayegh, K. (in press), Working and organizing in the age of the learning algorithm. Information and organization. https://www.sciencedirect.com/science/article/pii/S1471772718300277 Part 1: Challenges, possibilities and methods of PR Session 2 Duggirala, M., Malik, M., Kumar, S., Hayatnagarkar, H. G., & Balaraman, V. (2017). Evolving a grounded approach to behavioral composition. AI-enabled composition IEEE Simulation Conference (WSC) 2017 Winter, 4336-4347. methods for scientific http://ieeexplore.ieee.org/iel7/8232982/8247314/08248139.pdf study of behavior in context and behavioral Singh, M., Duggirala, M., Hayatnagarkar, H., Patel, S., & Balaraman, V. economics approach to (2016). Towards fine grained human behavior simulation models. behavior change Proceedings of the 2016 IEEE Winter Simulation Conference, 3452-3463. https://pdfs.semanticscholar.org/49a8/60959c5b608ee7dbabf567c9bc79ce67 4b04.pdf Griffin, D. W., Gonzalez, R., Koehler, D. and Gilovich, T. (2012). Judgmental heuristics: a historical overview. The Oxford Handbook of Thinking and Reasoning, 322-345. http://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780199734689.00 1.0001/oxfordhb-9780199734689-e-17 Oppong-Tawiah, D., & Bassellier, G. (2015). IS continuance in experiential computing contexts: linking rational and non-rational behaviors through technology associability. Association for Information Systems, DIGIT 2015 Proceedings, 1 – 18. https://pdfs.semanticscholar.org/2257/750ba94ab2b44c3b7797c369a566563 28194.pdf Nair, H. S., Misra, S., Hornbuckle IV, W. J., Mishra, R., & Acharya, A. (2017). Big data and marketing analytics in gaming: Combining empirical models and field experimentation. Marketing Science, 36(5), 699-725. https://marketing.wharton.upenn.edu/wp-content/uploads/2016/10/Paper- Nair-Harikesh-03-06-2014.pdf Session 3 Cacioppo, J. T., Berntson, G. G., Lorig, T. S., Norris, C. J., Rickett, E., & Nusbaum, H. (2003). Just because you're imaging the brain doesn't mean Integrative real-time you can stop using your head: a primer and set of first principles. Journal of biological-behavioral- Personality and Social Psychology, 85(4), 650. contextual laboratory measures and methods https://pdfs.semanticscholar.org/5d32/c77777aee73240b4409a35888693ee6 and linkages with real- aed0a.pdf world data Telpaz, A., Webb, R., & Levy, D. J. (2015). Using EEG to predict consumers' future choices. Journal of Marketing Research, 52(4) 511-529. https://www.stanford.edu/~knutson/nfc/telpaz15.pdf 3 Vincent, B. T., & Rainforth, T. (2017). The DARC Toolbox: automated, flexible, and efficient delayed and risky choice experiments using Bayesian adaptive design. Retrieved from psyarxiv.com. https://psyarxiv.com/yehjb/download Milosavljevic, M., Navalpakkam, V., Koch, C., & Rangel, A. (2012). Relative visual saliency differences induce sizable bias in consumer choice. Journal of Consumer Psychology, 22(1), 67-74. https://www.sciencedirect.com/science/article/pii/S1057740811001033 Venkatraman, V., Dimoka, A., Pavlou, P. A., Vo, K., Hampton, W., Bollinger, B., ... & Winer, R. S. (2015). Predicting advertising success beyond traditional measures: New insights from neurophysiological methods and market response modeling. Journal of Marketing Research, 52(4), 436-452. http://web-docs.stern.nyu.edu/marketing/RWinerPaper2015.pdf Trusov, M., Ma, L., & Jamal, Z. (2016). Crumbs of the cookie: User profiling in customer-base analysis and behavioral targeting. Marketing Science, 35(3), 405-426. https://pubsonline.informs.org/doi/abs/10.1287/mksc.2015.0956 Serrano, K. J., Yu, M., Coa, K. I., Collins, L. M., & Atienza, A. A. (2016). Mining health app data to find more and less successful weight loss subgroups. Journal of Medical Internet Research, 18(6): e154. http://www.jmir.org/2016/6/e154/ Knudsen, E. I., Heckman, J. J., Cameron, J. L., & Shonkoff, J. P. (2006). Economic, neurobiological, and behavioral perspectives on building America’s future workforce. Proceedings of the National Academy of Sciences, 103(27), 10155-10162 http://fhs.mcmaster.ca/ceb/community_medicine_page/docs/Knudsen%20et %20al%202006%20(rev).pdf Session 4 Cachon, G. P., & Kök, A. G. (2007). Category management and coordination in retail assortment planning in the presence of basket shopping Embedding behavioral consumers. Management Science, 53(6), 934-951. knowledge within and https://repository.upenn.edu/cgi/viewcontent.cgi?article=1095&context=oid_p across the disciplinary apers sciences that guide innovation and practice Hanssens, D. M., Pauwels, K. H., Srinivasan, S., Vanhuele, M., & Yildirim, G. (2014). Consumer attitude metrics for guiding marketing mix decisions. Marketing Science, 33(4), 534-550 https://pdfs.semanticscholar.org/5a8d/3936fe70920b7894a54dc907be73702 34a49.pdf Bodur, H. O., Klein, N. M., & Arora, N. (2015). Online price search: Impact of price comparison sites on offline price evaluations. Journal of Retailing, 91(1), 125-139. https://www.sciencedirect.com/science/article/pii/S0022435914000645 Ailawadi , K., Ma, Y., Grewal, D. (in press). The Club Store Effect: Impact of Shopping in Warehouse Club Stores on Consumers’ Packaged Food Purchases. Journal of Marketing Research https://doi.org/10.1509/jmr.16.0235 Dahl, D. W. (2016). The argument for consumer-based strategy papers. Journal of the Academy of Marketing Science, 44(3), 286-287. https://link.springer.com/content/pdf/10.1007%2Fs11747-016-0474-9.pdf 4 Ben-Ner, A. (2013). Preferences and organization structure: Toward behavioral economics micro- foundations of organizational analysis. The Journal of Socio-Economics, 46, 87-96 https://pdfs.semanticscholar.org/f76a/94895108e0ddce97e4f1071e7d451d20 c337.pdf Han, K., Oh, W., Im, K. S., Oh, H., Pinsonneault, A., & Chang, R. M. (2012). Value cocreation and wealth spillover in open innovation alliances. MIS Quarterly, 36(1). https://ybri.yonsei.ac.kr/downloadfile.asp?wpid=8&mid=m02_01&cmid=m02_ 01&sYear=&sGubun= Gold, E. R. (2016). Accelerating translational research through open science: The neuro experiment. PLoS biology, 14(12), e2001259. http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2001259 Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The increasing dominance of teams in production of knowledge. Science, 316(5827), 1036-1039. http://www.kellogg.northwestern.edu/faculty/jones- ben/htm/Teams.ScienceExpress.pdf Session 5 Michie, S., Thomas, J., Johnston, M., Mac Aonghusa, P., Shawe-Taylor, J., Kelly, M. P., ... & O’Mara-Eves, A.
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