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

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

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

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

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. (2017). The Human Behavior-Change Embedding behaviorally- Project: harnessing the power of artificial intelligence and machine learning informed disciplinary and for evidence synthesis and interpretation. Implementation Science, 12(1), translational sciences 121. within innovation and https://implementationscience.biomedcentral.com/articles/10.1186/s13012- practice at professional, 017-0641-5 organizational, system and policy level involved Lu, J., Lajoie, S. P., & Wiseman, J. (2010). Scaffolding problem-based in micro-processes learning with CSCL tools. International Journal of Computer-Supported shaping macro-level Collaborative Learning, 5(3), 283-298. outcomes http://digitool.library.mcgill.ca/webclient/DeliveryManager?pid=149108&custo m_att_2=direct

Tricco, A. C., Ashoor, H. M., Cardoso, R., MacDonald, H., Cogo, E., Kastner, M., ... & Straus, S. E. (2015). Sustainability of knowledge translation interventions in healthcare decision-making: a scoping review. Implementation Science, 11(1), 55. https://implementationscience.biomedcentral.com/articles/10.1186/s13012- 016-0421-7

Luo, L., Kannan, P. K., & Ratchford, B. T. (2008). Incorporating subjective characteristics in product design and evaluations. Journal of Marketing Research, 45(2), 182-194. http://www-bcf.usc.edu/~lluo/SubjectiveCharacteristics.pdf

Qian, Y., & Xie, H. (2015). Drive more effective data-based innovations: Enhancing the utility of secure databases. Management Science, 61(3), 520- 541. https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2014.2026 Gans, J., & Ryall, M. D. (2017). Value capture theory: A strategic management review. Strategic Management Journal, 38(1), 17-41. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2549003

Jha, V.,Gold, E.R., & Dube, L. (2018) Modular governance of convergent innovation. Manuscript Under Review.

https://www.dropbox.com/s/i7nlnql2y50h9gu/SUB_FIRST_CI%20Modular%2 0Governance_JhaGoldDube_JBE.pdf?dl=0

Murmann, J. P. (2013). The coevolution of industries and important features of their environments. Organization Science, 24(1), 58-78. https://pubsonline.informs.org/doi/pdf/10.1287/orsc.1110.0718

Shaban‐Nejad, A., Lavigne, M., Okhmatovskaia, A., & Buckeridge, D. L. (2017). PopHR: a knowledge‐based platform to support integration, analysis, and visualization of population health data. Annals of the New York Academy of Sciences, 1387(1), 44-53. https://nyaspubs.onlinelibrary.wiley.com/doi/abs/10.1111/nyas.13271

Session 6 Yoshua Bengio (2016). Machines Who Learn. Scientific American 314, 46 - 51 (2016) Published online: 17 May 2016. doi:10.1038/scientificamerican0616-46 Machine and deep http://engl105041.web.unc.edu/files/2017/02/2-Machines-Who-Learn.pdf learning as complementary tools for LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), bridging theory, data and 436. analytics for high- http://pages.cs.wisc.edu/~dyer/cs540/handouts/deep-learning- dimensional and nature2015.pdf multiscale understanding and modeling of Liu, X., Singh, P. V., & Srinivasan, K. (2016). A structured analysis of behaviorally-informed unstructured big data by leveraging cloud computing. Marketing Science, science, innovation, and 35(3), 363-388. practice: A primer taking https://pubsonline.informs.org/doi/abs/10.1287/mksc.2015.0972 social media as context Villarroel Ordenes, F., Ludwig, S., De Ruyter, K., Grewal, D., & Wetzels, M. (2017). Unveiling what is written in the stars: analyzing explicit, implicit, and discourse patterns of sentiment in social media. Journal of Consumer Research, 43(6), 875-894 https://academic.oup.com/jcr/article/43/6/875/2801804

Choi, J., Bell, D. R., & Lodish, L. M. (2012). Traditional and IS-enabled customer acquisition on the internet. Management Science, 58(4), 754-769. https://pubsonline.informs.org/doi/pdf/10.1287/mnsc.1110.1447

Lamberton, C., & Stephen, A. T. (2016). A thematic exploration of digital, social media, and mobile marketing: research evolution from 2000 to 2015 and an agenda for future inquiry. Journal of Marketing, 80(6), 146-172. https://tst.ama.org/globalassets/articles/jm/jm.15.0415-thematic-exploration- of-digital-social-media-and-mobile-marketing.pdf

Kane, G. C. (2015). Enterprise Social Media: Current Capabilities and Future Possibilities. MIS Quarterly Executive, 14(1). https://pdfs.semanticscholar.org/047b/a78b9b3e5d254f3c73a5748c00a9676 0f92b.pdf

Huang, D., & Luo, L. (2016). Consumer preference elicitation of complex products using fuzzy support vector machine active learning. Marketing Science, 35(3), 445-464. http://www-bcf.usc.edu/~lluo/ActiveLearningFinal.pdf

Bartunov, S., Santoro, A., Richards, B. A., Hinton, G. E., & Lillicrap, T. (2018). Assessing the scalability of biologically-motivated deep learning algorithms and architectures. Under review, ICLR 2018. https://openreview.net/pdf?id=BypdvewVM

Part 2: Seeds for Behavior-Specific PR research

Session 7 Schonberg, T., Fox, C. R., & Poldrack, R. A. (2011). Mind the gap: bridging economic and naturalistic risk-taking with cognitive neuroscience. Trends in Decision making and cognitive sciences, 15(1), 11-19. behavior under risk and https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3014440/ uncertainty: A portfolio of disciplinary translation Smith, K., Dickhaut, J., McCabe, K., & Pardo, J. V. (2002). Neuronal substrates for choice under ambiguity, risk, gains, and losses. Management Science, 48(6), 711-718. http://web.mit.edu/writing/2009/July/Neuronal%20Substrates%20for%20choi ce%20under%20risk.pdf

Lo, A. W. and Repin, D. V. (2002). “The psychophysiology of real-time: financial risk processing,” Journal of cognitive neuroscience, 14:3, pp. 323 339. http://alo.mit.edu/wp- content/uploads/2015/06/PsychophysFinRiskProcessing2 002.pdf

Shultz, T. R. (2013). Managing Uncertainty in Rule-Based Cognitive Models. arXiv preprint arXiv:1304.1083. https://arxiv.org/abs/1304.1083

Agrawal, A. K., Gans, J., & Goldfarb, A. (2017). Prediction, Judgment, and Uncertainty. In Economics of Artificial Intelligence. University of Chicago Press. http://www.nber.org/papers/w24243

Nalca, A., Boyaci, T. & Ray, S. (2017). Consumer taste uncertainty in the context of store brand and national brand competition. ESMT Working Paper, No. 17-01. https://www.econstor.eu/bitstream/10419/149866/1/878203672.pdf

Augustin, P., & Izhakian, Y. Y. (2017). Ambiguity, Volatility, and Credit Risk. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2776377

Jiang, B., Narasimhan, C., & Turut, Ö. (2016). Anticipated Regret and Product Innovation. Management Science, 63(12), 4308-4323. https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2016.2555

Holm, H. J., Opper, S., & Nee, V. (2013). Entrepreneurs under uncertainty: An economic experiment in China. Management Science, 59(7), 1671-1687 https://pubsonline.informs.org/doi/abs/10.1287/mnsc.1120.1670

Gonzalez, C., Fakhari, P., & Busemeyer, J. (2017). Dynamic Decision Making: Learning Processes and New Research Directions. Human factors, 59(5), 713-721. http://journals.sagepub.com/doi/abs/10.1177/0018720817710347

Session 8 Kamenica, E. (2012). Behavioral economics and psychology of incentives. Annual Review Economics, 4(1), 427-452. Motivated and Goal- http://web.worldbank.org/archive/website01542/WEB/IMAGES/EMIR_KAM.P Directed Decision Making DF and Behavior: Rewards, incentives, and self- Decker, J. H., Otto, A. R., Daw, N. D., & Hartley, C. A. (2016). From control/regulation/ creatures of habit to goal-directed learners: Tracking the developmental determination emergence of model-based reinforcement learning. Psychological science, 27(6), 848-858.

http://www.princeton.edu/~ndaw/dodh16.pdf

Schonberg, T., Bakkour, A., Hover, A. M., Mumford, J. A., Nagar, L., Perez, J., & Poldrack, R. A. (2014). Changing value through cued approach: an automatic mechanism of behavior change. Nature neuroscience, 17(4), 625. https://www.nature.com/articles/nn.3673

Berkman, E. T., Hutcherson, C. A., Livingston, J. L., Kahn, L. E., & Inzlicht, M. (2017). Self-control as value-based choice. Current directions in psychological science, 26(5), 422-428. http://michaelinzlicht.com/publications/articles-chapters/2017/3/21/self- control-as-value-based-choice-pdf

Van Osselaer, S. M., & Janiszewski, C. (2011). A goal-based model of product evaluation and choice. Journal of Consumer Research, 39(2), 260- 292. https://pdfs.semanticscholar.org/c97b/a083844cc28fd78f02026426e4d79495 bdfb.pdf

Wang, Y., Lewis, M., Cryder, C., & Sprigg, J. (2016). Enduring effects of goal achievement and failure within customer loyalty programs: A large-scale field experiment. Marketing Science, 35(4), 565-575. https://pubsonline.informs.org/doi/abs/10.1287/mksc.2015.0966

Markman, G. D., Russo, M., Lumpkin, G. T., Jennings, P., & Mair, J. (2016). Entrepreneurship as a platform for pursuing multiple goals: A special issue on sustainability, ethics, and entrepreneurship. Journal of Management Studies, 53(5), 673-694. https://onlinelibrary.wiley.com/doi/epdf/10.1111/joms.12214

Session 9 Cisek, P., & Pastor-Bernier, A. (2014). On the challenges and mechanisms of embodied decisions. Philosophical Transactions of the Royal Society B, Sensory-motor, 369(1655), 20130479. perceptual and embodied https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4186232/pdf/rstb20130479.pd decision making and f behavior: Salience, Embodied Pezzulo, G. and Cisek, P. (2016). Navigating the affordance landscape. Cognition/Decision and Trends in cognitive sciences, 20(6), 414-424. Affordance https://www.sciencedirect.com/science/article/pii/S1364661316300067?via% 3Dihub

Pezzulo, G., Barsalou, L. W., Cangelosi, A., Fischer, M. H., McRae, K., & Spivey, M. (2013). Computational grounded cognition: a new alliance between grounded cognition and computational modeling. Frontiers in psychology, 3, 612. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3551279/

Ramstead, M. J. D., Veissiere, S. P. L., and Kirmayer, L. J. (2016). Cultural affordances: Schaffolding local worlds through shared intentionality and regimes of attention. Frontiers in Psychology, 7, 1090. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4960915/

Busse, M. R., Lacetera, N., Pope, D. G., Silva-Risso, J., & Sydnor, J. R. (2013). Estimating the effect of salience in wholesale and retail car markets. The American Economic Review, 103(3), 575-579. http://www.nber.org/papers/w18820

Leonardi, P., & Vaast, E. (2016). Social media and their affordances for organizing: A review and agenda for research. Academy of Management Annals. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2993824

Gylfe, P., Frank, H., Lebaron, C., & Mantere, S. (2016). Video Methods in strategy research: focusing on embodied cognition. Strategic Management Journal, 37, 133-148. https://onlinelibrary.wiley.com/doi/epdf/10.1002/smj.2456

Lepora, N. F., Martinez-Hernandez, U., Pezzulo, G., & Prescott, T. J. (2013). Active Bayesian perception and reinforcement learning. In Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on (pp. 4735- 4740). IEEE. https://pdfs.semanticscholar.org/ac66/df94ee595ec800a4413340c151af719c 1a94.pdf

Session 10 Lupien, S. J., Maheu, F., Tu, M., Fiocco, A., & Schramek, T. E. (2007). The effects of stress and stress hormones on human cognition : Implications for Emotion- and the field of brain and cognition. Brain and Cognition, 65, 209-237. experience-based https://www.sciencedirect.com/science/article/pii/S0278262607000322/pdfft? decision making and md5=4e8cd27c8f5d734f93555ea05f183a69&pid=1-s2.0- behavior: stress, fun, and S0278262607000322-main.pdf emotional intelligence Blascovich, J. (2014). Challenge, threat, and social influence in digital immersive virtual environments. Social Emotions in Nature and Artefact. Oxford University Press, New York, 44-54 http://www.oxfordscholarship.com/oxford/downloaddoclightbox/$002f10.1093 $002facprof:oso$002f9780195387643.001.0001$002facprof- 9780195387643-chapter- 4/Challenge$002c$0020Threat$002c$0020and$0020Social$0020Influence$ 0020in$0020Digital$0020Immersive$0020Virtual$0020Environments?nojs=tr ue

Tams, S., Legoux, R., & Léger, P. M. (2018). Smartphone withdrawal creates stress: A moderated mediation model of nomophobia, social threat, and phone withdrawal context. Computers in Human Behavior, 81, 1-9. https://www.sciencedirect.com/science/article/pii/S0747563217306647

Reeck, C., Ames, D. R., & Ochsner, K. N. (2016). The social regulation of emotion: An integrative, cross-disciplinary model. Trends in cognitive sciences, 20(1), 47-63. http://www.cell.com/trends/cognitive-sciences/pdf/S1364-6613(15)00227- 2.pdf

Cote, S. , and Miners, C. T. (2006). Emotional Intelligence, Cognitive Intelligence, and Job Performance. Administrative Science Quarterly, 51, 1 – 28. http://journals.sagepub.com/doi/abs/10.2189/asqu.51.1.1

Côté, S., DeCelles, K. A., McCarthy, J. M., Van Kleef, G. A., & Hideg, I. (2011). The Jekyll and Hyde of emotional intelligence: Emotion-regulation knowledge facilitates both prosocial and interpersonally deviant behavior. Psychological Science, 22(8), 1073-1080. http://www-2.rotman.utoronto.ca/facbios/file/PSJekyllAHydeOfEI.pdf

Nie, JY et al. (2018). Emotion Aware Neural Response Generation. Association for the Advancement of Artificial Intelligence. Submitted. https://arxiv.org/abs/1606.08340

Gneezy, U., Imas, A., & Madarász, K. (2014). Conscience accounting: Emotion dynamics and social behavior. Management Science, 60(11), 2645- 2658. http://sticerd.lse.ac.uk/dps/te/te563.pdf

Kahou, S. E., Bouthillier, X., Lamblin, P., Gulcehre, C., Michalski, V., Konda, K., ... & Ferrari, R. C. (2016). Emonets: Multimodal deep learning approaches for emotion recognition in video. Journal on Multimodal User Interfaces, 10(2), 99-111. https://link.springer.com/article/10.1007/s12193-015-0195-2

Session 11 Hutcherson, C. A. , Seppala, E. M., and Goss,, J.J. (2017), The neural correlates of social connection. Cognitive, Affective, & Behavioral Social decision making Neuroscience, 15(1), 1-14 and behavior: Loneliness, https://link.springer.com/article/10.3758%2Fs13415-014-0304-9 social dis/connectedness, trust, empathy, and Gao, J., Davis, L. K., Hart, A. B., Sanchez-Roige, S., Han, L., Cacioppo, J. communication T., & Palmer, A. A. (2017). Genome-wide association study of loneliness demonstrates a role for common variation. Neuropsychopharmacology, 42(4), 811. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5312064/

Nowland, R., Necka, E. A., & Cacioppo, J. T. (2017). Loneliness and Social Internet Use: Pathways to Reconnection in a Digital World?. Perspectives on Psychological Science, 1745691617713052. http://journals.sagepub.com/doi/abs/10.1177/1745691617713052?url_ver=Z 39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed

Goldfarb, A., McDevitt, R. C., Samila, S., & Silverman, B. S. (2015). The effect of social interaction on economic transactions: Evidence from changes in two retail formats. Management Science, 61(12), 2963-2981. https://www.aeaweb.org/conference/2013/retrieve.php?pdfid=445

Bapna, R., Gupta, A., Rice, S., & Sundararajan, A. (2017). MIS Quarterly, 41(1). Trust and the Strength of Ties in Online Social Networks: An Exploratory Field Experiment. https://pdfs.semanticscholar.org/1c95/c998f168dea235bef5e30672e83ef5d4 a088.pdf

Mourey, J. A., Olson, J. G., and Yoon, C. (2017). Products as pals: engaging with anthropomorphic product mitigates the effect of social exclusion, Journal of Consumer Research, 44, 414. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2907436

Herzenstein, M., Sonenshein, S., & Dholakia, U. M. (2011). Tell me a good story and I may lend you money: The role of narratives in peer-to-peer lending decisions. Journal of Marketing Research, 48(SPL), S138-S149. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1840668

Einav, L., Farronato, C., & Levin, J. (2016). Peer-to-peer markets. Annual Review of Economics, 8, 615-635. http://www.nber.org/papers/w21496

Farronato, C., & Fradkin, A. (2016, July). Market structure with the entry of peer-to-peer platforms: The case of hotels and Airbnb. In Unpublished. Geraadpleegd https://editorialexpress.com/cgi- bin/conference/download.cgi?db_name=IIOC2016&paper_id=285

Xiong, L., & Liu, L. (2003, June). A reputation-based trust model for peer-to- peer e-commerce communities. In E-Commerce, 2003. CEC 2003. IEEE International Conference on (pp. 275-284). IEEE.

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.14.2641&rep=rep1 &type=pdf

Session 12 Buckner, R. L. & Carroll, D. C. (2006). Self-projection and the brain. Trends in cognitive Sciences, 11:2. Prospective decision http://people.hss.caltech.edu/~steve/buckner.pdf making and behavior: Identity, self, other, and Stillman, P.E., Lee, H., Deng, X., Unnava, H.R., Cunningham, W.A. & Fujita, interaction K. (2017). Neurological evidence for the role of construal level future-directed thought. Social Cognitive and Affective Neuroscience, 12(6), 937-947. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5472149/

Escalas, J., White, K., Argo, J. J., Sengupta, J., Townsend, C., Sood, S., ... & Van Boven, L. (2013). Self-identity and consumer behavior. Journal of Consumer Research, 39(5), xv-xviii. http://www.jstor.org/stable/10.1086/669165

Dacin, Peter A. and Tom J. Brown (2006), “Corporate Branding, Identity and Customer Response” Journal of the Academy of Marketing Science. http://journals.sagepub.com/doi/pdf/10.1177/0092070305284967

Delmestri, G., & Greenwood, R. (2016). How Cinderella became a queen: Theorizing radical status change. Administrative Science Quarterly, 61(4), 507-550. http://journals.sagepub.com/doi/abs/10.1177/0001839216644253

Belk, R. W. (2013). Extended self in a digital world. Journal of Consumer Research, 40(3), 477-500. https://www.sciencedirect.com/science/article/pii/S2352250X15003000

Goel, S., & Goldstein, D. G. (2013). Predicting individual behavior with social networks. Marketing Science, 33(1), 82-93. https://5harad.com/papers/birds.pdf

Dahl, D. W., Argo, J. J., & Morales, A. C. (2011). Social information in the retail environment: The importance of consumption alignment, referent identity, and self-esteem. Journal of Consumer Research, 38(5), 860-871. http://www.jstor.org/stable/10.1086/660918

Kodeih, F., & Greenwood, R. (2014). Responding to institutional complexity: The role of identity. Organization Studies, 35(1), 7-39. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2274848

Zhang, K., Bhattacharyya, S., & Ram, S. (2016). Large-Scale Network Analysis for Online Social Brand Advertising. MIS Quarterly, 40(4). https://www.semanticscholar.org/paper/Large-Scale-Network-Analysis-for- Online-Social-Zhang- Bhattacharyya/6e07c7740c6b84f3bec016ee54726b3728378968

Session 13 Szpunar, K. K., Spreng, R. N., and Schacter, D. L. (2014). A taxonomy of prospection: Introducing an organizational framework for future oriented Prospective decision cognition. Processing of the National Academy of Sciences, 111(52), 18414- making and 18421. behavior: Future http://www.pnas.org/content/111/52/18414

cognition, mind wandering, creativity, Saunders, B., Rodrigo, A. H., & Inzlicht, M. (2016). Mindful awareness of foresight, and prediction feelings increases neural performance monitoring. Cognitive, Affective, & for science and practice Behavioral Neuroscience, 16(1), 93-105. shaping a future of https://link.springer.com/article/10.3758%2Fs13415-015-0375-2 individual and collective health, wealth and Baird, B., Smallwood, J., Mrazek, M. D., Kam, J. W., Franklin, M. S., & wellbeing Schooler, J. W. (2012). Inspired by distraction: mind wandering facilitates creative incubation. Psychological Science, 23(10), 1117-1122. http://journals.sagepub.com/doi/abs/10.1177/0956797612446024?url_ver=Z 39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed

Smallwood, J., & Schooler, J. W. (2015). The science of mind wandering: empirically navigating the stream of consciousness. Annual review of psychology, 66, 487-518 https://www.annualreviews.org/doi/full/10.1146/annurev-psych-010814- 015331?url_ver=Z39.88- 2003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub%3Dpubmed

Burroughs, J. E., Dahl, D. W., Moreau, C. P., Chattopadhyay, A., & Gorn, G. J. (2011). Facilitating and rewarding creativity during new product development. Journal of Marketing, 75(4), 53-67 https://www.researchgate.net/publication/261942923_Facilitating_and_Rewa rding_Creativity_During_New_Product_Development

Ketoviki, M,, Mantere, S. & Cornelissen, J. (2018) Reasoning by analogy and progress of theory, Academy of management review. http://amr.aom.org/content/42/4/637.abstract

Wu, C. (2015). Matching value and market design in online advertising networks: An empirical analysis. Marketing Science, 34(6), 906-921. https://pubsonline.informs.org/doi/abs/10.1287/mksc.2015.0944

Session 14 Ketokivi, M., Mantere, S., & Cornelissen, J. (2017). Reasoning By Analogy And The Progress Of Theory. Academy of Management Review, 42(4). Project presentations https://www.dropbox.com/s/3kdhnq8w5ocvqz2/ketokivi-mantere-cornelissen- amr.pdf?dl=0

2. Complementary Disciplinary Articles

Session 1 PR partial background for embedding both non-rational and rational sides of human behavior fully into science, innovation, process, and Introduction: Precision practice: insights from 5 Nobel Laureates in Economics retailing as an AI- enabled translational hub Thaler, R. H. (2017). From cashews to nudges: The evolution of behavioral for behaviorally-informed economics [lecture]. disciplinary science, https://www.nobelprize.org/nobel_prizes/economic- innovation, process, and, sciences/laureates/2017/thaler-lecture.html practice at professional, organizational, systems Deaton, A. (2015). Consumption, poverty, and welfare. Retrieved from and policy levels https://www.nobelprize.org/nobel_prizes/economicsciences/laureates/2015/advanced-economicsciences2015.pdf

Shiller, R. J. Speculative asset prices. American Economic Review, 104(6), 1486 – 1517 https://www.nobelprize.org/nobel_prizes/economicsciences/laureates/2013/shiller-lecture.pdf

Kahneman, D. (2003). Maps of bounded rationality: Psychology for behavioral economics. The American Economic Review, 93(5), 1449-1475. http://www.econ.tuwien.ac.at/lotto/papers/Kahneman2.pdf

Simon, H. A. (1979). Rational decision making in business organizations. American Economic Review, 69(4), 493-513. https://www.jstor.org/stable/1808698?seq=1#page_scan_tab_contents

Retail and Marketing

Kumar, V. (2015). Evolution of marketing as a discipline: What has happened and what to look out for. Journal of Marketing, 79(1), 1-9. https://www.dropbox.com/s/87sn28ctkt1u1q8/Evolution%20of%20marketing %20as%20a%20discipline- %20What%20has%20happened%20and%20what%20to%20look%20out%20 for..pdf?dl=0

Management – Others and Economics

Brynjolfsson, E., & McElheran, K. (2016). The rapid adoption of data-driven decision-making. American Economic Review, 106(5), 133-39 https://dspace.mit.edu/openaccess-disseminate/1721.1/108650

Durand, R., Grant, R. M., & Madsen, T. L. (2017). The expanding domain of strategic management research and the quest for integration. Strategic Management Journal, 38(1), 4-16. http://rodolphedurand.com/wp- content/uploads/2017/07/introDurandGrantMAdsenSMJ2017.pdf

Bendoly, E., Croson, R., Goncalves, P., & Schultz, K. (2010). Bodies of knowledge for research in behavioral operations. Production and Operations Management, 19(4), 434-452. https://doi-org.proxy3.library.mcgill.ca/10.1111/j.1937-5956.2009.01108.x

Gino, F., & Pisano, G. (2008). Toward a theory of behavioral operations. Manufacturing & Service Operations Management, 10(4), 676- 691. https://pubsonline-informs- org.proxy3.library.mcgill.ca/doi/abs/10.1287/msom.1070.0205

Katsikopoulos, K. V., & Gigerenzer, G. (2013). Behavioral operations management: A blind spot and a research program. Journal of Supply Chain Management, 49(1), 3-7. https://www.researchgate.net/publication/263557284_Behavioral_Operations _Management_A_Blind_Spot_and_a_Research_Program

Medicine, Health and Other Social Sectors

Geruso, M., Jena, A.B., & Layton, T.J. (2018). Will Personalized Medicine Mean Higher Costs for Consumers? Harvard Business Review https://hbr.org/2018/03/will-personalized-medicine-mean-higher-costs-for- consumers

Karczewski, K. J., & Snyder, M. P. (2018). Integrative omics for health and disease. Nature Reviews Genetics. doi:10.1038/nrg.2018.4 https://mcgill.worldcat.org/title/integrative-omics-for-health-and- disease/oclc/7338780824&referer=brief_results

Vineis, P., & Wild, C. P. (2017). The science of precision prevention of cancer. The Lancet Oncology, 18(8), 997-998. https://mcgill.worldcat.org/title/the-science-of-precision-prevention-of- cancer/oclc/7093116086&referer=brief_results

Neuroscience and Psychology

Arkowitz, H., & Lilienfeld, S. O. (2007). Why don’t people change? Scientific American Mind, 18(3), 82-83. http://faculty.fortlewis.edu/burke_b/counseling/cs%20readings/sciam- why%20people%20change.pdf

Nilsen, W. J., & Pavel, M. (2013). Moving behavioral theories into the 21st century: technological advancements for improving quality of life. IEEE pulse, 4(5), 25-28. http://lifesciences.embs.org/wp- content/uploads/sites/53/2013/10/06603417.pdf

Karmarkar, U. R., & Plassmann, H. (2017). Consumer Neuroscience: Past, Present, and Future. Organizational Research Methods, 1094428117730598. https://faculty.insead.edu/hilke-plassmann/documents/2015-98.pdf

Laureiro-Martínez, D., Venkatraman, V., Cappa, S., Zollo, M., & Brusoni, S. (2015). Cognitive Neurosciences and Strategic Management: Challenges and Opportunities in Tying the Knot The first and second authors contributed equally. In Cognition and Strategy (pp. 351-370). Emerald Group Publishing Limited. goo.gl/5Gdm93

Cacioppo, J. T., Cacioppo, S., & Petty, R. E. (2018). The neuroscience of persuasion: A review with an emphasis on issues and opportunities. Social neuroscience, 13(2), 129-172. https://www.tandfonline.com/doi/full/10.1080/17470919.2016.1273851

Kaplan, R. M., Riley, W. T., & Mabry, P. L. (2014). News from the NIH: leveraging big data in the behavioral sciences. Translational Behavioral Medicine, 4(3), 229 – 31. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4167902/

Simon, H. A. (1959). Theories of decision-making in economics and behavioral science. The American economic review, 49(3), 253-283. goo.gl/CFVTUp

Part 1: Challenges, possibilities and methods of PR

Session 2 Retail and Marketing

AI-enabled composition Colicev, A., Malshe, A., Pauwels, K., & O'Connor, P. (2017). Improving methods for scientific consumer mind-set metrics and shareholder value through social media: The study of behavior in different roles of owned and earned. Journal of Marketing, 82(1), 37 – 56. context and behavioral https://goo.gl/34ryPF economics approach to behavior change Hossain, T., & List, J. A. (2012). The behavioralist visits the factory: Increasing productivity using simple framing manipulations. Management Science, 58(12), 2151-2167 http://www.nber.org/papers/w15623

Medicine, Health and Other Social Sectors

Marshall, T., Champagne-Langabeer, T., Castelli, D., & Hoelscher, D. (2017). Cognitive computing and eScience in health and life science research: artificial intelligence and obesity intervention programs. Health information science and systems, 5(1), 13. https://mcgill.worldcat.org/title/cognitive-computing-and-escience-in-health- and-life-science-research-artificial-intelligence-and-obesity-intervention- programs/oclc/7174656862&referer=brief_results

Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017). Neuroscience-inspired artificial intelligence. Neuron, 95(2), 245-258. https://mcgill.worldcat.org/title/neuroscience-inspired-artificial- intelligence/oclc/7085827608&referer=brief_results

Ammerman, A. S., Hartman, T., & DeMarco, M. M. (2017). Behavioral Economics and the Supplemental Nutrition Assistance Program. American journal of preventive medicine, 52(2), S145-S150. https://mcgill.worldcat.org/title/behavioral-economics-and-the-supplemental- nutrition-assistance-program/oclc/6930274211&referer=brief_results

Insel, T. R. (2017). Digital phenotyping: technology for a new science of behavior. Jama, 318(13), 1215-1216. https://mcgill.worldcat.org/title/digital-phenotyping-technology-for-a-new- science-of-behavior/oclc/7150296921&referer=brief_results

Neuroscience and Psychology

Singh, M., Duggirala, M., Hayatnagarkar, H., & Balaraman, V. (2016). Multi- agent model of workgroup behavior in an enterprise using a compositional approach. ISEC 2nd Modeling Symposium (ModSym 2016), 10 – 16. https://www.researchgate.net/publication/292985464_Multi- Agent_Model_of_Workgroup_Behaviour_in_an_Enterprise_using_a_Compos itional_Approach

Duggirala, M., Malik, M., Kumar, S., Hayatnagarkar, H. G., & Balaraman, V. (2017, December). Evolving a grounded approach to behavioral composition. In Simulation Conference (WSC), 2017 Winter (pp. 4336-4347). IEEE. http://ieeexplore.ieee.org/abstract/document/8248139/

Spruijt-Metz, D., Hekler, E., Saranummi, N., Intille, S., Korhonen, I., Nilsen, W., ... & Sanna, A. (2015). Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research. Translational behavioral medicine, 5(3), 335-346. https://doi-org.proxy3.library.mcgill.ca/10.1007/s13142-015-0324-1

Session 3 Retail and Marketing

Integrative real-time Arora, N., & Huber, J. (2001). Improving parameter estimates and model biological-behavioral- prediction by aggregate customization in choice experiments. Journal of contextual laboratory Consumer Research, 28(2), 273-283. measures and methods http://www.jstor.org/stable/10.1086/322902 and linkages with real- world data Management – Others and Economics

Barth, D. J., Papageorge, N. W., & Thom, K. (2017). Genetic ability, wealth, and financial decision-making. IZA Institute of Labor Economics Discussion Paper Series (10567), 1 – 55. https://wpcarey.asu.edu/sites/default/files/papageorge_- _genetic_ability_wealth_and_financial_decision-making.pdf

Nadler, A., Jiao, P., Johnson, C. J., Alexander, V., & Zak, P. J. (2017). The bull of Wall Street: Experimental analysis of testosterone and asset trading. Management Science. https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2017.2836

Betermier, S., Calvet, L. E., & Sodini, P. (2017). Who are the value and growth investors? The Journal of Finance, 72(1), 5-46. http://onlinelibrary.wiley.com/doi/10.1111/jofi.12473/pdf

White, R. E., Thornhill, S., & Hampson, E. (2007). A biosocial model of entrepreneurship: The combined effects of nurture and nature. Journal of Organizational Behavior, 28(4), 451-466. https://onlinelibrary.wiley.com/doi/epdf/10.1002/job.432

Medicine, Health and Other Social Sectors

Romdhani, H., Hwang, H., Paradis, G., Roy‐Gagnon, M. H., & Labbe, A. (2015). Pathway‐based association study of multiple candidate genes and multiple traits using structural equation models. Genetic Epidemiology, 39(2), 101-113. https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.21872

Gallion, J., Koire, A., Katsonis, P., Schoenegge, A. M., Bouvier, M., & Lichtarge, O. (2017). Predicting phenotype from genotype: Improving accuracy through more robust experimental and computational modeling. Human Mutation, 38(5), 569-580. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5516182/

Chen, M., Ma, Y., Li, Y., Wu, D., Zhang, Y., & Youn, C. H. (2017). Wearable 2.0: Enabling human-cloud integration in next generation healthcare systems. IEEE Communications Magazine, 55(1), 54-61. https://mcgill.worldcat.org/title/wearable-20-enabling-human-cloud- integration-in-next-generation-healthcare- systems/oclc/6941365863&referer=brief_results

Engelhardt, M. A. (2017). Hitching Healthcare to the Chain: An Introduction to Blockchain Technology in the Healthcare Sector. Technology Innovation Management Review, 7(10). http://timreview.ca/article/1111

Young, S. D., Rivers, C., & Lewis, B. (2014). Methods of using real-time social media technologies for detection and remote monitoring of HIV outcomes. Preventive medicine, 63, 112-115. https://mcgill.worldcat.org/title/methods-of-using-real-time-social-media- technologies-for-detection-and-remote-monitoring-of-hiv- outcomes/oclc/5903481565&referer=brief_results

Chiauzzi, E., Rodarte, C., & DasMahapatra, P. (2015). Patient-centered activity monitoring in the self-management of chronic health conditions. BMC medicine, 13(1), 77. https://mcgill.worldcat.org/title/patient-centered-activity-monitoring-in-the-self- management-of-chronic-health- conditions/oclc/5808511877&referer=brief_results

Scott-Boyer, M. P., Lacroix, S., Scotti, M., Morine, M. J., Kaput, J., & Priami, C. (2016). A network analysis of cofactor-protein interactions for analyzing associations between human nutrition and diseases. Scientific reports, 6, 19633.

https://www.nature.com/articles/srep19633

Neuroscience and Psychology

Fellows, L. K. (2012). Current concepts in decision-making research from bench to bedside. Journal of the International Neuropsychological Society, 18(6), 937-941. https://www.mcgill.ca/decisionlab/files/decisionlab/fellows_2012_current_con cepts_in_decision-making_research_from_bench_to_bedside.pdf

Taschereau-Dumouchel, V., Hétu, S., Chagnon, Y. C., & Jackson, P. L. (2015). Measuring how genetic and epigenetic variants can filter emotion perception. Psychiatric Genetics, 25(5), 216-222 https://mcgill.worldcat.org/title/measuring-how-genetic-and-epigenetic- variants-can-filter-emotion-perception/oclc/7007892621&referer=brief_results

Trail, J. B., Collins, L. M., Rivera, D. E., Li, R., Piper, M. E., & Baker, T. B. (2014). Functional data analysis for dynamical system identification of behavioral processes. Psychological Methods, 19(2), 175. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4098896/

White, C. N., Congdon, E., & Poldrack, R. A. (2014). Decomposing decision components in the stop-signal task: a model-based approach to individual differences in inhibitory control. Journal of cognitive neuroscience, 26(8), 1601-1614. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4119005/

Vincent, B. T. (2016). Hierarchical Bayesian estimation and hypothesis testing for delay discounting tasks. Behavior Research Methods, 48(4), 1608- 1620. https://link.springer.com/article/10.3758/s13428-015-0672-2

Lin, H., Saunders, B., Hutcherson, C. and Inzlicht, M (2016). Neurometric variation in decision conflict: Neurophysiological signals during intertemporal choice. Psychophysiology, 53, S66 – S66. https://tspace.library.utoronto.ca/bitstream/1807/75172/3/Lin_Hause_201611 _MA_thesis.pdf

Columbus, G., Sheikh, N. A., Côté-Lecaldare, M., Häuser, K., Baum, S. R., & Titone, D. (2015). Individual differences in executive control relate to metaphor processing: an eye movement study of sentence reading. Frontiers in Human Neuroscience, 8, 1057. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4292575/

Dedovic, K., Giebl, S., Duchesne, A., Lue, S. D., Andrews, J., Efanov, S., ... & Pruessner, J. C. (2016). Psychological, endocrine, and neural correlates of attentional bias in subclinical depression. Anxiety, Stress, & Coping, 29(5), 479-496. https://www.tandfonline.com/doi/full/10.1080/10615806.2015.1101457

Collins, L. M. (2018). Optimization of behavioral, biobehavioral, and biomedical interventions: The multiphase optimization strategy (MOST). Springer. DOI: 10.1007/978-3-319-72206-1 https://www.dropbox.com/s/2fe8bn7nvs2nx9z/Optimization%20of%20behavio ral%2C%20biobehavioral%2C%20and%20biomedical%20interventions- The%20multiphase%20optimization%20strategy.pdf?dl=0

Louie, K., Glimcher, P. W., & Webb, R. (2015). Adaptive neural coding: from biological to behavioral decision-making. Current Opinion in Behavioral Sciences, 5(40) 91-99. https://www.sciencedirect.com/science/article/pii/S235215461500114X

Liu, L., & Dzyabura, D. (2017). Capturing heterogeneity among consumers with multi-taste preferences. SSRN. http://people.stern.nyu.edu/lliu/Multitaste_LiuDzyabura_latest.pdf

Graham, J. W., Taylor, B. J., Olchowski, A. E., & Cumsille, P. E. (2006). Planned missing data designs in psychological research. Psychological Methods, 11(4), 323. https://pdfs.semanticscholar.org/cd38/1d94835eb2ab464dcc843ff8e29e55be eb34.pdf

Pezzulo, G., Rigoli, F., & Friston, K. (2015). Active Inference, homeostatic regulation and adaptive behavioral control. Progress in Neurobiology, 134, 17-35. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4779150/

Towal, R. B., Mormann, M., & Koch, C. (2013). Simultaneous modeling of visual saliency and value computation improves predictions of economic choice. Proceedings of the National Academy of Sciences, 110(40), 3858 - 3867. http://www.pnas.org/content/110/40/E3858

Busemeyer, J., & Wang, Z. (2017). Is there a problem with quantum models of psychological measurements?. PloS one, 12(11), e0187733. http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0187733& type=printable

AI and Advanced Analytics

Vicol, P., Tapaswi, M., Castrejon, L., & Fidler, S. (2017). Moviegraphs: Towards understanding human-centric situations from videos. arXiv preprint arXiv:1712.06761. https://arxiv.org/abs/1712.06761

Session 4 Retail and Marketing

Embedding behavioral Dawar, Niraj (2013) When marketing is strategy. Harvard Business Review, knowledge within and 100 – 110. across the disciplinary sciences that guide https://www.dropbox.com/s/4760bowhqseruvw/When%20marketing%20is%2 innovation and practice 0strategy.pdf?dl=0

Bernstein, F., & Martínez-de-Albéniz, V. (2016). Dynamic product rotation in the presence of strategic customers. Management Science, 63(7), 2092- 2107. https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2016.2448

Cachon, G. P., & Kök, A. G. (2007). Category management and coordination in retail assortment planning in the presence of basket shopping consumers. Management Science, 53(6), 934-951. https://doi.org/10.1287/mnsc.1060.0661

Eggers, J. P. (2012). All experience is not created equal: learning, adapting, and focusing in product portfolio management. Strategic Management Journal, 33(3), 315-335.

goo.gl/jxHb2a

Huang, T., Liang, C., & Wang, J. (2017). The value of ‘bespoke’: Demand learning, preference learning, and customer behavior. Management Science. https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2017.2771

Arora, N., & Henderson, T. (2007). Embedded premium promotion: Why it works and how to make it more effective. Marketing Science, 26(4), 514-531. https://pubsonline.informs.org/doi/abs/10.1287/mksc.1060.0247

Grewal, D., Puccinelli, N., Monroe, K.B. (in press). Meta-Analysis: Integrating Accumulating Knowledge. Journal of the Academy of Marketing Science https://link.springer.com/article/10.1007/s11747-017-0570-5

Kumar, V., & Christodoulopoulou, A. (2014). Sustainability and branding: An integrated perspective. Industrial Marketing Management, 43(1), 6-15. https://www.sciencedirect.com/science/article/pii/S0019850113001260

Management – Others and Economics

Bryan, K. A., Tilcsik, A., & Zhu, B. (2017). Which entrepreneurs are coachable and why? American Economic Review, 107(5), 312-16. http://www- 2.rotman.utoronto.ca/facbios/file/WhichEntrepreneursAreCoachableAndWhy. pdf

Daniels, Michael A., and Greguras, Gary J. (2014). Exploring the nature of power distance: Implications for micro- and macro-level theories, processes, and outcomes. Journal of Management, 1 – 29. http://journals.sagepub.com/doi/pdf/10.1177/0149206314527131

Banerjee, A. V., & Duflo, E. (2009). The experimental approach to development economics. Annual Review of Economics, 1(1), 151-178. https://economics.mit.edu/files/3158

Casciaro, T., Barsade, S. G., Edmondson, A. C., Gibson, C. B., Krackhardt, D., & Labianca, G. (2015). The integration of psychological and network perspectives in organizational scholarship. Organization Science, 26(4), 1162-1176. https://pubsonline.informs.org/doi/abs/10.1287/orsc.2015.0988

Allen, C. R., & Holling, C. S. (2002). Cross-scale structure and scale breaks in ecosystems and other complex systems. Ecosystems, 5(4), 315-318. https://link.springer.com/content/pdf/10.1007/s10021-001-0075-3.pdf

Majchrzak, A., More, P. H., & Faraj, S. (2012). Transcending knowledge differences in cross-functional teams. Organization Science, 23(4), 951-970. goo.gl/ECSWkT

Medicine, Health and Other Social Sectors

Hizel, C., Tremblay, J., Bartlett, G., & Hamet, P. (2017). Every individual is different and precision medicine offers options for disease control and treatment. Progress and Challenges in Precision Medicine, 1 – 34. https://www.sciencedirect.com/science/article/pii/B9780128094112000015

Gambhir, S. S., Ge, T. J., Vermesh, O., & Spitler, R. (2018). Toward achieving precision health. Science translational medicine, 10(430), eaao3612.

https://mcgill.worldcat.org/title/toward-achieving-precision- health/oclc/7338888657&referer=brief_results

Spruijt-Metz, D., Hekler, E., Saranummi, N., Intille, S., Korhonen, I., Nilsen, W., ... & Sanna, A. (2015). Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research. Translation behavioral medicine, 5(3), 335-346 https://mcgill.worldcat.org/title/building-new-computational-models-to-support- health-behavior-change-and-maintenance-new-opportunities-in-behavioral- research/oclc/5869190323&referer=brief_results

Asarnow, J. R., Rozenman, M., Wiblin, J., & Zeltzer, L. (2015). Integrated medical-behavioral care compared with usual primary care for child and adolescent behavioral health: a meta-analysis. JAMA pediatrics, 169(10), 929-937. https://mcgill.worldcat.org/title/integrated-medical-behavioral-care-compared- with-usual-primary-care-for-child-and-adolescent-behavioral-health-a-meta- analysis/oclc/5890782122&referer=brief_results

Neuroscience and Psychology

Poldrack, R. A., & Gorgolewski, K. J. (2014). Making big data open: data sharing in neuroimaging. Nature Neuroscience, 17(11), 1510. https://www.nature.com/articles/nn.3818

AI and Advanced Analytics

McCall, C., Bunyan, D. P., Bailenson, J. N., Blascovich, J., & Beall, A. C. (2009). Leveraging collaborative virtual environment technology for inter- population research on persuasion in a classroom setting. Presence, 18(5), 361-369. http://vhil.stanford.edu/mm/2009/mccall-pres-leveraging.pdf

Session 5 Retail and Marketing

Embedding behaviorally- Gershoff, A. D., & Frels, J. K. (2015). What makes it green? The role of informed disciplinary and centrality of green attributes in evaluations of the greenness of translational sciences products. Journal of Marketing, 79(1), 97-110. within innovation and http://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=sit practice at professional, e&authtype=crawler&jrnl=00222429&AN=100279009&h=2iPHhzlxTdiqAvYG organizational, system dcqI1tea8YzOEz%2F%2Bp49PKOaOix3Ka3IO88CvgpZyttPVWaOy9buvAk8 and policy level involved SRcNpAsQsI3s6uA%3D%3D&crl=f in micro-processes shaping macro-level Archak, N., Ghose, A., & Ipeirotis, P. G. (2011). Deriving the pricing power of outcomes product features by mining consumer reviews. Management Science, 57(8), 1485–1509. http://pages.stern.nyu.edu/~aghose/pricingpower_print.pdf

Luo, L., Kannan, P. K., & Ratchford, B. T. (2007). New product development under channel acceptance. Marketing Science, 26(2), 149-163. https://pubsonline.informs.org/doi/pdf/10.1287/mksc.1060.0240

Kolsarici, C., & Vakratsas, D. (2011). The complexity of multi-media effects. Marketing Science Institute Working Paper Series. No. 11-100. http://www.mcgill.ca/files/_nea/211583_MSIWP.pdf

Mehta, N., Chen, X., & Narasimhan, O. (2008). Informing, transforming, and persuading: Disentangling the multiple effects of advertising on brand choice decisions. Marketing Science, 27(3), 334-355.

https://pubsonline.informs.org/doi/abs/10.1287/mksc.1070.0310

Mallapragada, G., Grewal, R., & Lilien, G. (2012) User-generated open source products: Founder’s social capital and time to product release. Marketing Science 31(3), 474 – 492. https://pubsonline.informs.org/doi/pdf/10.1287/mksc.1110.0690

Bolton, L. E., & Alba, J. W. (2006). Price fairness: Good and service differences and the role of vendor costs. Journal of Consumer Research, 33(2), 258-265. https://academic.oup.com/jcr/article-pdf/33/2/258/11426094/33-2-258.pdf

Kumar, V., & Reinartz, W. (2016). Creating enduring customer value. Journal of Marketing, 80(6), 36-68. http://web.a.ebscohost.com/ehost/pdfviewer/pdfviewer?vid=0&sid=dfc4e068- cc11-4c02-9525-645898c854fc%40sessionmgr4006

Kumar, V., Anand, A., & Song, H. (2017). Future of retailer profitability: an organizing framework. Journal of Retailing, 93(1), 96-119. https://www.sciencedirect.com/science/article/pii/S0022435916300781

Press, M., Arnould, E. J., Murray, J. B., & Strand, K. (2014). Ideological challenges to changing strategic orientation in commodity agriculture. Journal of Marketing, 78(6), 103-119. https://www.researchgate.net/publication/273230761_Ideological_Challenges _to_Changing_Strategic_Orientation_in_Commodity_Agriculture

Ter Braak, A., Dekimpe, M. G., & Geyskens, I. (2013). Retailer private-label margins: The role of supplier and quality-tier differentiation. Journal of Marketing, 77(4), 86-103. http://journals.ama.org/doi/pdf/10.1509/jm.11.0566

Wang, W., Keh, H. T., & Bolton, L. E. (2009). Lay theories of medicine and a healthy lifestyle. Journal of Consumer Research, 37(1), 80-97. http://www.jstor.org/stable/10.1086/649772

Lee, S., Bolton, L. E., & Winterich, K. P. (2017). To Profit or Not to Profit? The Role of Greed Perceptions in Consumer Support for Social Ventures. Journal of Consumer Research, 44(4), 853-876. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2980968

McAlister, L., Srinivasan, R., Jindal, N., & Cannella, A. A. (2016). Advertising effectiveness: the moderating effect of firm strategy. Journal of Marketing Research, 53(2), 207-224. http://web.a.ebscohost.com/ehost/pdfviewer/pdfviewer?vid=0&sid=1a0dae37- de72-44fe-983c-e0a9db422180%40sessionmgr4009

Management – Others and Economics

Kudaravalli, S., Faraj, S., & Johnson, S. L. (2017). A configural approach to coordinating expertise in software development teams. MIS Quarterly, 41(1). http://web.b.ebscohost.com/ehost/pdfviewer/pdfviewer?vid=0&sid=806f28e6- e9c8-486b-a915-c68690d72126%40sessionmgr102

Gans, J. S. (2016). Keep calm and manage disruption. MIT Sloan Management Review, 57(3), 83. https://sloanreview.mit.edu/article/keep-calm-and-manage-disruption/

Marx, M., Gans, J. S., & Hsu, D. H. (2014). Dynamic commercialization strategies for disruptive technologies: Evidence from the speech recognition industry. Management Science, 60(12), 3103-3123. http://pubsonline.informs.org/doi/pdf/10.1287/mnsc.2014.2035

Galasso, A., & Tombak, M. (2014). Switching to green: The timing of socially responsible innovation. Journal of Economics & Management Strategy, 23(3), 669-691. http://www- 2.rotman.utoronto.ca/facbios/file/GT_JEMSaccepted_and_appendix.pdf

Burgelman, R., Floyd, S., Laamanen, T., Mantere, S., Vaara, E., & Whittington, R. (2017). Strategy processes and practices: Dialogues and intersections. Strategic Management Journal, 39(3), 531 – 558. https://onlinelibrary.wiley.com/doi/epdf/10.1002/smj.2741

Gehman, J., Lounsbury, M., & Greenwood, R. (2016). How Institutions Matter: From the Micro Foundations of Institutional Impacts to the Macro Consequences of Institutional Arrangements. In How Institutions Matter! (pp. 1-34). Emerald Group Publishing Limited. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2811210

Mair, J., Wolf, M., & Seelos, C. (2016). Scaffolding: A process of transforming patterns of inequality in small-scale societies. Academy of Management Journal, 59(6), 2021-2044. http://web.b.ebscohost.com/ehost/pdfviewer/pdfviewer?vid=0&sid=f13073ea- 422e-47f7-a4e6-b3f089292927%40sessionmgr104

Schakel, J. K., van Fenema, P. C., & Faraj, S. (2016). Shots fired! Switching between practices in police work. Organization Science, 27(2), 391-410. https://pubsonline.informs.org/doi/abs/10.1287/orsc.2016.1048

Smets, M., Morris, T. I. M., & Greenwood, R. (2012). From practice to field: A multilevel model of practice-driven institutional change. Academy of Management Journal, 55(4), 877-904. http://www.jstor.org/stable/23317618?seq=1#page_scan_tab_contents

Vaghefi, I., Lapointe, L., & Shahbaznezhad, H. (2018). A multilevel process view of organizational knowledge transfer: enablers versus barriers. Journal of Management Analytics, 1-17. https://www.researchgate.net/publication/322605167_A_multilevel_process_v iew_of_organizational_knowledge_transfer_enablers_versus_barriers

Vermeulen, P., Zietsma, C., Greenwood, R., & Langley, A. (2016). Strategic responses to institutional complexity. Strategic Organization, 14(4), 277-286. http://journals.sagepub.com/doi/full/10.1177/1476127016675997

Kouame, S. and Langley, A. (2017). Relating microprocesses to macro- outcome in qualitative strategy process and practice research, Strategic Management Journal, 1-23 https://onlinelibrary.wiley.com/doi/epdf/10.1002/smj.2726

Acemoglu, D., Ozdaglar, A., & Tahbaz-Salehi, A. (2017). Microeconomic origins of macroeconomic tail risks. American Economic Review, 107(1), 54- 108. http://www.nber.org/papers/w20865

Mayer-Schoenberger, V. (2007) Schumpeterian Policy Makers: Pro-Active Policies for Innovative Entrepreneurship. Hudson Institute Research Paper, 07. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1031385

Kwark, Y., Chen, J., & Raghunathan, S. (2017). User-generated content and competing firms’ product design. Management Science, 1 – 43. https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2017.2839

Fjeldstad, Ø. D., Snow, C. C., Miles, R. E., & Lettl, C. (2012). The architecture of collaboration. Strategic management journal, 33(6), 734-750. goo.gl/Kw9q5y

Medicine, Health and Other Social Sectors

Verghese, A., Shah, N. H., & Harrington, R. A. (2018). What This Computer Needs Is a Physician: Humanism and Artificial Intelligence. Jama. https://mcgill.worldcat.org/title/what-this-computer-needs-is-a-physician- humanism-and-artificial-intelligence/oclc/7296066234&referer=brief_results

Guthrie, J., Mancino, L., & Lin, C. T. J. (2015). Nudging consumers toward better food choices: policy approaches to changing food consumption behaviors. Psychology & Marketing, 32(5), 501-511. https://mcgill.worldcat.org/title/nudging-consumers-toward-better-food- choices-policy-approaches-to-changing-food-consumption- behaviors/oclc/5814018847&referer=brief_results

Karamitri, I., Talias, M. A., & Bellali, T. (2017). Knowledge management practices in healthcare settings: a systematic review. The International journal of health planning and management, 32(1), 4-18. https://mcgill.worldcat.org/title/knowledge-management-practices-in- healthcare-settings-a-systematic- review/oclc/6998019792&referer=brief_results

Arrow, K., Auerbach, A., Bertko, J., Brownlee, S., Casalino, L. P., Cooper, J., ... & Fuchs, V. R. (2009). Toward a 21st-century health care system: recommendations for health care reform. Annals of Internal Medicine, 150(7), 493-495 http://annals.org/aim/fullarticle/744430/toward-21st-century-health-care- system-recommendations-health-care-reform

Berry, L. & Awdish, R. (2017). Taking Time to Really Listen to Your Patients. Harvard Business Review. https://hbr.org/2017/10/making-time-to-really-listen-to-your-patients

Dai, H., Mao, D., Riis, J., Volpp, K. G., Relish, M. J., Lawnicki, V. F., & Milkman, K. L. (2017). Effectiveness of Medication Adherence Reminders Tied to “Fresh Start” Dates: A Randomized Clinical Trial. Jama cardiology, 2(4), 453-455. https://jamanetwork.com/journals/jamacardiology/fullarticle/2601070

Dai, H., Mao, D., Volpp, K. G., Pearce, H. E., Relish, M. J., Lawnicki, V. F., & Milkman, K. L. (2017). The effect of interactive reminders on medication adherence: A randomized trial. Preventive medicine, 103, 98-102. https://www.sciencedirect.com/science/article/pii/S0091743517302669?via% 3Dihub

Emanuel, E. J., Ubel, P. A., Kessler, J. B., Meyer, G., Muller, R. W., Navathe, A. S., ... & Sen, A. P. (2016). Using behavioral economics to design physician

incentives that deliver high-value care. Annals of internal medicine, 164(2), 114-119. http://assets.wharton.upenn.edu/~juddk/papers/Emanuel_etal_2016.pdf

Kremer, M., Levin, J., & Snyder, C. (2008). Designing advanced market commitments for new vaccines. Photocopy, Economics Department, Harvard University. https://editorialexpress.com/cgi- bin/conference/download.cgi?db_name=IIOC2009&paper_id=229

Collins, L. M., Murphy, S. A., & Strecher, V. (2007). The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): new methods for more potent eHealth interventions. American Journal of Preventive Medicine, 32(5), S112-S118. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2062525/

Tamblyn, R., Winslade, N., Lee, T. C., Motulsky, A., Meguerditchian, A., Bustillo, M., ... & Moraga, T. (2017). Improving patient safety and efficiency of medication reconciliation through the development and adoption of a computer-assisted tool with automated electronic integration of population- based community drug data: the RightRx project. Journal of the American Medical Informatics Association. https://academic.oup.com/jamia/advance- article/doi/10.1093/jamia/ocx107/4443115

Froomkin, A. M., Kerr, I. R., & Pineau, J. (2018). When AIs outperform doctors: The dangers of a tort-induced over-reliance on machine learning and what (not) to do about it. Retrieved from University of Miami Legal Studies, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3114347.

Verboom, B., Montgomery, P., & Bennett, S. (2016). What factors affect evidence-informed policymaking in public health? Protocol for a systematic review of qualitative evidence using thematic synthesis. Systematic reviews, 5(1), 61. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4831125/

Tonsaker, T., Law, S., Ormel, I., Nease, C., & Bartlett, G. (2016). Engaging caregivers: exploring perspectives on web-based health information. Family practice, 34(4), 479-484. https://academic.oup.com/fampra/article/34/4/479/2503163

Nahum-Shani, I., Smith, S. N., Spring, B. J., Collins, L. M., Witkiewitz, K., Tewari, A., & Murphy, S. A. (2016). Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Annals of Behavioral Medicine. https://academic.oup.com/abm/advance-article/doi/10.1007/s12160-016- 9830-8/4733473

Session 6 Retail and Marketing

Machine and deep Gopaldas, A. (2014). Marketplace sentiments. Journal of Consumer Research, learning as 41(4), 995-1014. complementary tools for

bridging theory, data and https://www.researchgate.net/publication/272594079_Marketplace_Sentiment analytics for high- s dimensional and multiscale understanding Culotta, A., & Cutler, J. (2016). Mining brand perceptions from twitter social and modeling of networks. Marketing Science, 35(3), 343-362. behaviorally-informed https://pubsonline.informs.org/doi/pdf/10.1287/mksc.2015.0968 science, innovation, and practice: A primer taking Ghose, A., Ipeirotis, P.G., and Li, B. (2012) Designing Ranking Systems for social media as context Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content. Marketing Science 31(3), 493-520 https://pubsonline.informs.org/doi/pdf/10.1287/mksc.1110.0700

Du, R. Y., & Kamakura, W. A. (2012). Quantitative trends spotting. Journal of Marketing Research, 49(4), 514-536. https://www.bauer.uh.edu/rexdu/quantitative%20trendspotting.pdf

Ahn, D. Y., Duan, J. A., & Mela, C. F. (2015). Managing user-generated content: A dynamic rational expectations equilibrium approach. Marketing Science, 35(2), 284-303. https://pubsonline.informs.org/doi/pdf/10.1287/mksc.2015.0937

Netzer, O, Feldman, R. Goldenberg, R. and Fresko, M. (2012). Mine Your Own Business: Market-Structure Surveillance Through Text Mining, Marketing Science, 31(3) , 521-543 https://pubsonline.informs.org/doi/abs/10.1287/mksc.1120.0713

Bradlow, Eric, Manish Gangwar, Praveen K. Kopalle, Sudhir Voleti (2017). The Role of Big Data and Predictive Analytics in Retailing. Journal of Retailing, 93 (March) 79-95. https://www.sciencedirect.com/science/article/pii/S0022435916300835

Shah, D., Kumar, V., & Zhao, Y. (2015). Diagnosing brand performance: Accounting for the dynamic impact of product availability with aggregate data. Journal of Marketing Research, 52(2), 147-165. http://web.a.ebscohost.com/ehost/pdfviewer/pdfviewer?vid=1&sid=d31c476e- 041c-4cd6-8eff-990c015a99e0%40sessionmgr4010

Management – Others and Economics

Shiller, R. J. (2017). Narrative economics. American Economic Review, 107(4), 967-1004. https://cowles.yale.edu/sites/default/files/files/pub/d20/d2069.pdf

Wu, C., Che, H., Chan, T. Y., & Lu, X. (2015). The economic value of online reviews. Marketing Science, 34(5), 739-754. https://pubsonline.informs.org/doi/pdf/10.1287/mksc.2015.0926

Deans, P. C. (2011). The impact of social media on C-level roles. MIS Quarterly Executive, 10(4), 187-200. http://misqe.org/ojs2/index.php/misqe/article/view/400

Rivard, S. (2014). Editor's comments: the ions of theory construction. MIS Quarterly, 38(2), iii-xiv. https://misq.org/misq/downloads/download/editorial/600/

Einav, L., & Levin, J. (2014). Economics in the age of big data. Science, 346(6210), 1243089. https://web.stanford.edu/~leinav/pubs/Science2014.pdf

Einav, L., & Levin, J. (2014). The data revolution and economic analysis. Innovation Policy and the Economy, 14(1), 1-24. http://www.nber.org/papers/w19035

Kolinjivadi, V., Grant, A., Adamowski, J., & Kosoy, N. (2015). Juggling multiple dimensions in a complex socio-ecosystem: The issue of targeting in payments for ecosystem services. GeoForum, 58, 1-13. https://www.mcgill.ca/bioeng/files/bioeng/juggling_multiple_dimensions_in_a_ complex_socio-ecosystem.pdf

Cukier, K.N. and Mayer-Schoenberger, V. (2013). The rise of big data. Foreign Affairs. 92(3). https://www.foreignaffairs.com/system/files/pdf/articles/2013/92305.pdf

Zhu, F., & Iansiti, M. (2012). Entry into platform‐based markets. Strategic Management Journal, 33(1), 88-106. goo.gl/RjezwX

Medicine, Health and Other Social Sectors

Mayer‐Schönberger, V., & Ingelsson, E. (2017). Big Data and medicine: a big deal?. Journal of internal medicine. https://onlinelibrary.wiley.com/doi/epdf/10.1111/joim.12721

Mayer-Schönberger, V. (2015). Big data for cardiology: novel discovery?. European heart journal, 37(12), 996-1001. https://academic.oup.com/eurheartj/article/37/12/996/2466107

Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2017). Deep learning for healthcare: review, opportunities and challenges. Briefings in bioinformatics. https://mcgill.worldcat.org/title/deep-learning-for-healthcare-review- opportunities-and-challenges/oclc/7034211419&referer=brief_results

Cook, D. J., Schmitter-Edgecombe, M., & Dawadi, P. (2015). Analyzing activity behavior and movement in a naturalistic environment using smart home techniques. IEEE journal of biomedical and health informatics, 19(6), 1882- 1892. https://mcgill.worldcat.org/title/analyzing-activity-behavior-and-movement-in- a-naturalistic-environment-using-smart-home- techniques/oclc/5871011667&referer=brief_results

Nikfarjam, A., Sarker, A., O’Connor, K., Ginn, R., & Gonzalez, G. (2015). Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. Journal of the American Medical Informatics Association, 22(3), 671-681. https://mcgill.worldcat.org/title/pharmacovigilance-from-social-media-mining- adverse-drug-reaction-mentions-using-sequence-labeling-with-word- embedding-cluster-features/oclc/6911215204&referer=brief_results

Neuroscience and Psychology

Richards, B. A., Xia, F., Santoro, A., Husse, J., Woodin, M. A., Josselyn, S. A., & Frankland, P. W. (2014). Patterns across multiple memories are identified over time. Nature Neuroscience, 17(7), 981. https://www.nature.com/articles/nn.3736

Rueger, A., & McGivern, P. (2010). Hierarchies and levels of reality. Synthese, 176(3), 379-397.

https://pdfs.semanticscholar.org/5e81/4f0e024b8f9226dd74c76785fd3d59fde 235.pdf

Santoro, A., Frankland, P. W., & Richards, B. A. (2016). Memory transformation enhances reinforcement learning in dynamic environments. Journal of Neuroscience, 36(48), 12228-12242. http://www.jneurosci.org/content/36/48/12228

Bengio, E. (2016). On reinforcement learning for deep neural architectures: conditional computation with stochastic computation policies (master’s thesis). McGill University, Montreal, Quebec. http://folinoid.com/files/masters-thesis.pdf

AI and Advanced Analytics

Judd, P., Albericio, J., Hetherington, T., Aamodt, T. M., Jerger, N. E., & Moshovos, A. (2016, June). Proteus: Exploiting numerical precision variability in deep neural networks. In Proceedings of the 2016 International Conference on Supercomputing (p. 23). ACM. http://dl.acm.org/ft_gateway.cfm?id=2926294&type=pdf

Pesteie, M., Abolmaesumi, P., & Rohling, R. (2018). Deep Neural Maps. Open Review. https://openreview.net/pdf?id=HyG76D1wf

Fox, M. S., Barbuceanu, M., & Teigen, R. (2001). Agent-oriented supply- chain management. In Information-Based Manufacturing (pp. 81-104). Springer, Boston, MA. http://www.eil.utoronto.ca/wp-content/uploads/aac/papers/Fox- FlexMfg00v12n2.pdf

Zhu, S., Fidler, S., Urtasun, R., Lin, D., & Loy, C. C. (2017). Be your own Prada: Fashion synthesis with structural coherence. arXiv preprint arXiv:1710.07346. https://arxiv.org/abs/1710.07346

Vaast, E., & Urquhart, C. (2017). Building Grounded Theory with Social Media Data. In R. Mir & S. Jain (Eds.), The Routledge Companion to Qualitative Research in Organization Studies. London: Taylor & Francis. https://www.taylorfrancis.com/books/e/9781317414148/chapters/10.4324%2F 9781315686103-40

Ruths, D., & Pfeffer, J. (2014). Social media for large studies of behavior. Science, 346(6213), 1063-1064. http://science.sciencemag.org/content/346/6213/1063/tab-pdf

Bengio, Y. (2016). Machines who learn. Scientific American, 314, 46 - 51 https://www.nature.com/scientificamerican/journal/v314/n6/full/scientificameri can0616-46.html

Part 2: Seeds for Behavior-Specific PR research

Session 7 Retail and Marketing

Decision making and Mazar, N., Shampanier, K., & Ariely, D. (2016). When Retailing and Las behavior under risk and Vegas Meet: Probabilistic Free Price Promotions. Management Science, uncertainty: A portfolio of 63(1), 250-266. disciplinary translation https://pubsonline.informs.org/doi/pdf/10.1287/mnsc.2015.2328

Ray, S., Wood, C. A., & Messinger, P. R. (2012). Multicomponent Systems Pricing: Rational Inattention and Downward Rigidities. Journal of Marketing, 76(5), 1-17. https://doi.org/10.1509/jm.09.0094

Bolton, L. E., Keh, H. T., & Alba, J. W. (2010). How do price fairness perceptions differ across culture?. Journal of Marketing Research, 47(3), 564- 576. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2280856

Bolton, L. E., Warlop, L., & Alba, J. W. (2003). Consumer perceptions of price (un) fairness. Journal of consumer research, 29(4), 474-491. https://academic.oup.com/jcr/article-pdf/29/4/474/17927713/29-4-474.pdf

Tereyağoğlu, N., Fader, PS, Veeraraghavan, S. (2017). Multiattribute Loss Aversion and Reference Dependence: Evidence from the Performing Arts Industry. Management Science http://opim.wharton.upenn.edu/~senthilv/papers/RefDepLossAversion.pdf

Management – Others and Economics

Hardisty, D. J., & Pfeffer, J. (2016). Intertemporal uncertainty avoidance: When the future is uncertain, people prefer the present, and when the present is uncertain, people prefer the future. Management Science, 63(2), 519-527. http://davidhardisty.info/downloads/Intertemporal-Uncertainty-Avoidance- manuscript-V24.pdf

Pope, D. G., & Schweitzer, M. E. (2011). Is Tiger Woods loss averse? Persistent bias in the face of experience, competition, and high stakes. The American Economic Review, 101(1), 129-157. https://pdfs.semanticscholar.org/0fbc/29a8e84c677557696e4344802eddf15a ae5f.pdf

Milliken, F. J. (1987). Three types of perceived uncertainty about the environment: State, effect, and response uncertainty. Academy of Management Review, 12(1), 133-143. https://www.jstor.org/stable/257999?seq=1#page_scan_tab_contents

Coates, J. M., & Herbert, J. (2008). Endogenous steroids and financial risk taking on a London trading floor. Proceedings of the national academy of sciences, 105(16), 6167-6172 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2329689/

Fehr, E., Hart, O., & Zehndera, C. A. (2011). Contracts as reference points— experimental evidence. The American Economic Review, 101(2), 493-525. . https://scholar.harvard.edu/files/hart/files/contractsasreferencepoints- experimentalevidenceaer.pdf

Brenner, L. A., Griffin, D. W., & Koehler, D. J. (2012). A case-based model of probability and pricing judgments: Biases in buying and selling uncertainty. Management Science, 58(1), 159-178. https://pubsonline.informs.org/doi/abs/10.1287/mnsc.1110.1429

Galasso, A. (2010). Over-confidence may reduce negotiation delay. Journal of Economic Behavior & Organization, 76(3), 716-733. http://www-2.rotman.utoronto.ca/facbios/file/jebo_accepted.pdf

Huang, X., Boyacı, T., Gümüş, M., Ray, S., & Zhang, D. (2015). United we stand or divided we stand? Strategic supplier alliances under order default risk. Management Science, 62(5), 1297-1315. http://www.danzhang.com/papers/Alliance_Rev3.pdf

Koudstaal, M., Sloof, R., & Van Praag, M. (2015). Risk, uncertainty, and entrepreneurship: Evidence from a lab-in-the-field experiment. Management Science, 62(10), 2897-2915 https://pubsonline.informs.org/doi/pdf/10.1287/mnsc.2015.2249

Nagarajan, M., & Shechter, S. (2013). Prospect theory and the newsvendor problem. Management Science, 60(4), 1057-1062. https://pubsonline.informs.org/doi/pdf/10.1287/mnsc.2013.1804

Simchi-Levi, D., Schmidt, W., Wei, Y., Zhang, P. Y., Combs, K., Ge, Y., ... & Zhang, D. (2015). Identifying risks and mitigating disruptions in the automotive supply chain. Interfaces, 45(5), 375-390. https://pubsonline.informs.org/doi/abs/10.1287/inte.2015.0804

Simon, M., Houghton, S. M., & Aquino, K. (2000). Cognitive biases, risk perception, and venture formation: How individuals decide to start companies. Journal of business venturing, 15(2), 113-134. https://www.sciencedirect.com/science/article/pii/S0883902698000032

Bundorf, M. K., Levin, J., & Mahoney, N. (2012). Pricing and welfare in health plan choice. American Economic Review, 102(7), 3214-48. https://web.stanford.edu/~jdlevin/Papers/HealthPlans.pdf

El-Masri, M., & Rivard, S. (2012). Towards a Design Theory for Software Project Risk Management Systems. https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1203&context=icis2012

Eckhardt, J. T. (2016). Welcome contributor or no price competitor? The competitive interaction of free and priced technologies. Strategic Management Journal, 37(4), 742-762. https://onlinelibrary-wiley- com.proxy3.library.mcgill.ca/doi/abs/10.1002/smj.2365

Medicine, Health and Other Social Sectors

Mittal, C., & Griskevicius, V. (2016). Silver spoons and platinum plans: How childhood environment affects adult health care decisions. Journal of Consumer Research, 43(4), 636-656. https://mcgill.worldcat.org/title/silver-spoons-and-platinum-plans-how- childhood-environment-affects-adult-health-care- decisions/oclc/6995914363&referer=brief_results

Hunter, D. J. (2016). Uncertainty in the era of precision medicine. New England Journal of Medicine, 375(8), 711-713. https://mcgill.worldcat.org/title/uncertainty-in-the-era-of-precision- medicine/oclc/6785078090&referer=brief_results

Neuroscience and Psychology

Helfinstein, S. M., Schonberg, T., Congdon, E., Karlsgodt, K. H., Mumford, J. A., Sabb, F. W., ... & Poldrack, R. A. (2014). Predicting risky choices from brain activity patterns. Proceedings of the National Academy of Sciences, 111(7), 2470-2475. http://www.pnas.org/content/111/7/2470

Hsu, M., Bhatt, M., Adolphs, R., Tranel, D., & Camerer, C. F. (2005). Neural systems responding to degrees of uncertainty in human decision- making. Science, 310(5754), 1680-1683. https://people.hss.caltech.edu/~camerer/Ec101/Hsu05.pdf

Behrens, T. E., Woolrich, M. W., Walton, M. E., & Rushworth, M. F. (2007). Learning the value of information in an uncertain world. Nature Neuroscience, 10(9), 1214. https://www.nature.com/articles/nn1954

Bruhn, P., Huette, S., & Spivey, M. (2014). Degree of certainty modulates anticipatory processes in real time. Journal of Experimental Psychology: Human Perception and Performance, 40(2), 525. http://psycnet.apa.org/record/2013-32708-001

Penning, J.M. E., Garcia, P., Hendrix, E. (2005) Towards a theory of revealed economic behavior: The economic- neurosciences interface, Journal of Bioeconomics,. https://link.springer.com/content/pdf/10.1007/s10818-005- 6417-z.pdf

Nieder, A. (2005). “Counting on neurons: the neurobiology of numerical competence,” Nature Reviews Neuroscience, 6, 177–190 https://www.nature.com/articles/nrn1626

Michaels, J., Chaumillon, R., Nguyen-Tri, D., Watanabe, D., Hirsch, P., Bellavance, F., ... & Faubert, J. (2017). Driving simulator scenarios and measures to faithfully evaluate risky driving behavior: A comparative study of different driver age groups. PLoS one, 12(10), e0185909. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0185909

Johnsen Haas, I. & Cunningham, W.A. (2014). The Uncertainty Paradox: Perceived Threat Moderates the Effect of Uncertainty on Political Tolerance. Political Psychology 35(2), 291 – 302. http://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1061&context=polis cifacpub

Johnson, E. J., Häubl, G., & Keinan, A. (2007). Aspects of endowment: a query theory of value construction. Journal of experimental psychology: Learning, memory, and cognition, 33(3), 461. http://psycnet.apa.org/record/2007-06096-001

Bertsekas, D. P., & Tsitsiklis, J. N. (1995, December). Neuro-dynamic programming: an overview. In Decision and Control, 1995., Proceedings of the 34th IEEE Conference on (Vol. 1, pp. 560-564). IEEE. http://web.mit.edu/people/dimitrib/NDP_Encycl.pdf

St-Amand, D., Sheldon, S., & Otto, A. R. (2018). Modulating Episodic Memory Alters Risk Preference during Decision-making. Journal of cognitive neuroscience, (Early Access), 1-9. http://otto.lab.mcgill.ca/papers/stamand_et_al_in_press.pdf

AI and Advanced Analytics

Agrawal, A., Gans, J., & Goldfarb, A. (2017). Exploring the Impact of Artificial Intelligence: Prediction versus Judgment. NBER Working Paper No. w24243.

https://www.aeaweb.org/conference/2017/preliminary/paper/BHKkz394

Session 8 Retail and Marketing

Motivated and Goal- Hardisty, D. J., & Weber, E. U. (2009). Discounting future green: money Directed Decision versus the environment. Journal of Experimental Psychology: General, Making and Behavior: 138(3), 329. Rewards, incentives, and http://psycnet.apa.org/record/2009-11328-001 self-control/regulation/ determination Carlson, Kurt A., Jared Wolfe, Simon J. Blanchard, Joel C. Huber and Dan Ariely (2015). The Budget Contraction Effect: How Contracting Budgets Lead to Less Varied Choice. Journal of Marketing Research, 52 (3), 337-348.

https://pdfs.semanticscholar.org/852d/e5f64069cfe15cc0d4654af8495cdd23e 363.pdf

Cheema, A., & Patrick, V. M. (2012). Influence of warm versus cool temperatures on consumer choice: A resource depletion account. Journal of Marketing Research, 49(6), 984-995. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2088973

Garvey, A. M., & Bolton, L. E. (2017). Eco-Product Choice Cuts Both Ways: How Proenvironmental Licensing Versus Reinforcement Is Contingent on Environmental Consciousness. Journal of Public Policy & Marketing, 36(2), 284-298. https://mcgill.worldcat.org/title/eco-product-choice-cuts-both-ways-how- proenvironmental-licensing-versus-reinforcement-is-contingent-on- environmental-consciousness/oclc/7285300299&referer=brief_results

Petersen, J. A., & Kumar, V. (2015). Perceived risk, product returns, and optimal resource allocation: evidence from a field experiment. Journal of Marketing Research, 52(2), 268-285. https://pdfs.semanticscholar.org/a2eb/9c9821bd000e6250874eba18b3a484a 058e6.pdf

Wang, T., Mukhopadhyay, A., & Patrick, V. M. (2017). Getting Consumers to Recycle NOW! When and Why Cuteness Appeals Influence Prosocial and Sustainable Behavior. Journal of Public Policy & Marketing, 36(2), 269-283. http://web.b.ebscohost.com/ehost/pdfviewer/pdfviewer?vid=1&sid=0cd8dc98- 04b7-4c11-9fc8-09998464f742%40pdc-v-sessmgr01

Ovchinnikov, A. S., Boulu-Reshef, B. and Pfeifer, P. (2014). Balancing Customer Acquisition and Retention Spending for Firms with Limited Capacity” Management Science, 60 (8), 2002–2019 https://pubsonline.informs.org/doi/pdf/10.1287/mnsc.2013.1842

Management – Others and Economics

Calvo, E., & Martínez-de-Albéniz, V. (2015). Sourcing strategies and supplier incentives for short-life-cycle goods. Management Science, 62(2), 436-455. https://pubsonline.informs.org/doi/pdf/10.1287/mnsc.2014.2138

Cappelen, A. W., Reme, B. A., Sørensen, E. Ø., & Tungodden, B. (2015). Leadership and incentives. Management Science, 62(7), 1944-1953. https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2015.2225

DellaVigna, S., & Malmendier, U. (2004). Contract design and self-control: Theory and evidence. The Quarterly Journal of Economics, 119(2), 353-402 https://eml.berkeley.edu/~ulrike/Papers/qjec_119_2_353_0.pdf

Nicolaou, N., Shane, S., Cherkas, L., & Spector, T. D. (2008). The influence of sensation seeking in the heritability of entrepreneurship. Strategic Entrepreneurship Journal, 2(1), 7-21. http://onlinelibrary.wiley.com/doi/10.1002/sej.37/pdf

Augustin, P., & Tédongap, R. (2014). Disappointment Aversion, Term Structure, and Predictability Puzzles in Bond Markets. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2789273

Kopalle, Praveen K., Yacheng Sun, Scott A. Neslin, Baohong Sun, and Vanitha Swaminathan (2012). The Joint Sales Impact of Frequency Reward and Customer Tier Components of Loyalty Programs. Marketing Science, 31 (2): 216-35. http://cn.ckgsb.com/userfiles/doc/ck_faculty_bhsun_loyalty.pdf

Medicine, Health and Other Social Sectors

Miller, A. S., Cafazzo, J. A., & Seto, E. (2016). A game plan: Gamification design principles in mHealth applications for chronic disease management. Health informatics journal, 22(2), 184-193. https://mcgill.worldcat.org/title/a-game-plan-gamification-design-principles-in- mhealth-applications-for-chronic-disease- management/oclc/6101819762&referer=brief_results

Asch, D. A., Troxel, A. B., Stewart, W. F., Sequist, T. D., Jones, J. B., Hirsch, A. G., ... & Frasch, A. B. (2015). Effect of financial incentives to physicians, patients, or both on lipid levels: a randomized clinical trial. Jama, 314(18), 1926-1935. https://mcgill.worldcat.org/title/effect-of-financial-incentives-to-physicians- patients-or-both-on-lipid-levels-a-randomized-clinical- trial/oclc/5913294263&referer=brief_results

Neuroscience and Psychology

Loewenstein, G., Price, J., & Volpp, K. (2016). Habit formation in children: Evidence from incentives for healthy eating. Journal of health economics, 45, 47-54. https://www.sciencedirect.com/science/article/pii/S0167629615001368

Volpp, K. G., & Galvin, R. (2014). Reward-based incentives for smoking cessation: how a carrot became a stick. Jama, 311(9), 909-910. https://repository.upenn.edu/cgi/viewcontent.cgi?article=1120&context=hcmg _papers

Koscik, T. R., Man, V., Jahn, A., Lee, C. H., & Cunningham, W. A. (2017). Decomposing the neural pathways mediating value-based choice. bioRxiv, 171744. https://www.biorxiv.org/content/biorxiv/early/2017/08/02/171744.full.pdf

Rangel, A., Camerer, C., & Montague, P. R. (2008). A framework for studying the neurobiology of value-based decision making. Nature Reviews Neuroscience, 9(7), 545-556. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4332708/

Courville, A. C., Daw, N. D., & Touretzky, D. S. (2006). Bayesian theories of conditioning in a changing world. Trends in cognitive sciences, 10(7), 294- 300.

https://pdfs.semanticscholar.org/6b9f/656c49d912dd60389fad08584d051391 762b.pdf

Cunningham, W. A., & Brosch, T. (2012). Motivational salience: Amygdala tuning from traits, needs, values, and goals. Current Directions in Psychological Science, 21(1), 54-59. http://journals.sagepub.com/doi/abs/10.1177/0963721411430832

Kruglanski, A., Fishbach, A., Woolley, K., Belanger, J., Chernikova, M., Molinario, E. & Pierro, A. (2017). A structural model of intrinsic motivation: On the psychology of means-ends fusion. PsyArXv preprints. DOI: 10.17605/OSF.IO/KDCT8 https://psyarxiv.com/kdct8/

Cunningham, W. A., Raye, C. L., & Johnson, M. K. (2005). Neural correlates of evaluation associated with promotion and prevention regulatory focus. Cognitive, Affective, & Behavioral Neuroscience, 5(2), 202-211. https://link.springer.com/article/10.3758/CABN.5.2.202

Cole, S. W., Yoo, D. J., & Knutson, B. (2012). Interactivity and reward-related neural activation during a serious videogame. PLoS One,7(3), e33909. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0033909

McCall, C., Tipper, C. M., Blascovich, J., & Grafton, S. T. (2011). Attitudes trigger motor behavior through conditioned associations: neural and behavioral evidence. Social cognitive and affective neuroscience, 7(7), 841- 849. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3475357/

Luttrell, A., Stillman, P. E., Hasinski, A. E., & Cunningham, W. A. (2016). Neural dissociations in attitude strength: Distinct regions of cingulate cortex track ambivalence and certainty. Journal of Experimental Psychology: General, 145(4), 419. http://psycnet.apa.org/record/2016-06214-001

Inzlicht, M., Berkman, E., & Elkins-Brown, N. (2016). The neuroscience of “ego depletion”. Social neuroscience: Biological approaches to social psychology, 101-123. https://goo.gl/gbmxnJ

Inzlicht, M., & Gutsell, J. N. (2007). Running on empty: Neural signals for self- control failure. Psychological science, 18(11), 933-937. http://journals.sagepub.com/doi/abs/10.1111/j.1467-9280.2007.02004.x

Teper, R., & Inzlicht, M. (2014). Mindful acceptance dampens neuroaffective reactions to external and rewarding performance feedback. Emotion, 14(1), 105. http://psycnet.apa.org/record/2013-34867-001

Randles, D., Harlow, I., & Inzlicht, M. (2017). A pre-registered naturalistic observation of within domain mental fatigue and domain-general depletion of self-control. PLoS One, 12(9), e0182980. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0182980

Hagger, M. S., Chatzisarantis, N. L., Alberts, H., Anggono, C. O., Batailler, C., Birt, A. R., ... & Calvillo, D. P. (2016). A multilab preregistered replication of the ego-depletion effect. Perspectives on Psychological Science, 11(4), 546-573. http://journals.sagepub.com/doi/pdf/10.1177/1745691616652873

Legault, L., & Inzlicht, M. (2013). Self-determination, self-regulation, and the brain: Autonomy improves performance by enhancing neuroaffective responsiveness to self-regulation failure. Journal of Personality and Social Psychology, 105(1), 123. http://psycnet.apa.org/record/2012-29188-001

Milyavskaya, M., Inzlicht, M., Hope, N., & Koestner, R. (2015). Saying “no” to temptation: Want-to motivation improves self-regulation by reducing temptation rather than by increasing self-control. Journal of Personality and Social Psychology, 109(4), 677. http://psycnet.apa.org/record/2015-21603-001

Zhang, A., Satija, H., & Pineau, J. (2018). Decoupling Dynamics and Reward for Transfer Learning. Open Review. https://openreview.net/pdf?id=H1aoddyvM

Maisto, D., Donnarumma, F., & Pezzulo, G. (2013). Using subgoals to reduce the descriptive complexity of probabilistic inference and control programs. RLDM 2013, 53. https://www.researchgate.net/publication/260115432_Using_subgoals_to_red uce_the_descriptive_complexity_of_probabilistic_inference_and_control_pro grams

Rivera, D. E., Pew, M. D., & Collins, L. M. (2007). Using engineering control principles to inform the design of adaptive interventions: A conceptual introduction. Drug & Alcohol Dependence, 88, S31-S40. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2062527/

Bakkour, A., Leuker, C., Hover, A. M., Giles, N., Poldrack, R. A., & Schonberg, T. (2016). Mechanisms of choice behavior shift using cue- approach training. Frontiers in psychology, 7, 421. https://www.frontiersin.org/articles/10.3389/fpsyg.2016.00421/full

Session 9 Retail and Marketing

Sensory-motor, Pansari, A., & Kumar, V. (2017). Customer engagement: the construct, perceptual and antecedents, and consequences. Journal of the Academy of Marketing embodied decision Science, 45(3), 294-311. making and behavior: https://link.springer.com/article/10.1007/s11747-016-0485-6 Salience, Embodied Cognition/Decision and Spence, C., Puccinelli, N. M., Grewal, D., & Roggeveen, A. L. (2014). Store Affordance atmospherics: A multisensory perspective. Psychology & Marketing, 31(7), 472-488. https://pdfs.semanticscholar.org/9627/b41171a597b6c00f8d80ab0917e47f25 a397.pdf

Milosavljevic, M., & Cerf, M. (2008). First attention then intention: Insights from computational neuroscience of vision. International Journal of Advertising, 27(3), 381-398. https://www.tandfonline.com/doi/abs/10.2501/S0265048708080037

Krishna, A. (2012). An integrative review of sensory marketing: Engaging the senses to affect perception, judgment and behavior. Journal of Consumer Psychology, 22(3), 332-351. https://www.sciencedirect.com/science/article/pii/S1057740811000830

Zhang, S., Lee, D., Singh, P. V., & Srinivasan, K. (2017). How Much Is an Image Worth? Airbnb Property Demand Estimation Leveraging Large Scale Image Analytics. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2976021

Zhao, M., Dahl, D. W., & Hoeffler, S. (2014). Optimal visualization aids and temporal framing for new products. Journal of Consumer Research, 41(4), 1137-1151. http://www- 2.rotman.utoronto.ca/facbios/file/Optimal%20Visualization%20Aids%20and% 20Temporal%20Framing%20JCR%20final.pdf

Van den Bergh, B., Schmitt, J., & Warlop, L. (2011). Embodied myopia. Journal of Marketing Research, 48(6), 1033-1044. http://www.ama.org/documents/emobodied_myopia.pdf

Argo, J. J., & Dahl, D.W. (2017). Standards of Beauty: The Impact of Mannequins in the Retail Context. Journal of Consumer Research, 44(5), 974-990. https://academic.oup.com/jcr/article/44/5/974/3861627

Chae, B., & Hoegg, J. (2013). The future looks “right”: Effects of the horizontal location of advertising images on product attitude. Journal of Consumer Research, 40(2), 223-238. https://www.researchgate.net/publication/276290078_The_Future_Looks_Rig ht_Effects_of_the_Horizontal_Location_of_Advertising_Images_on_Product_ Attitude

Jiang, Y., Gorn, G. J., Galli, M., & Chattopadhyay, A. (2015). Does your company have the right logo? How and why circular-and angular-logo shapes influence brand attribute judgments. Journal of Consumer Research, 42(5), 709-726. https://academic.oup.com/jcr/article/42/5/709/1855577

Lu, S., Xiao, L., & Ding, M. (2016). A video-based automated recommender (VAR) system for garments. Marketing Science, 35(3), 484-510. https://pubsonline.informs.org/doi/pdf/10.1287/mksc.2016.0984

Management – Others and Economics

Hanna, R., Mullainathan, S., & Schwartzstein, J. (2014). Learning through noticing: Theory and evidence from a field experiment. The Quarterly Journal of Economics, 129(3), 1311-1353. http://www.nber.org/papers/w18401

Miranda, S. M., Kim, I., & Summers, J. D. (2015). Jamming with Social Media: How Cognitive Structuring of Organizing Vision Facets Affects IT Innovation Diffusion. MIS Quarterly, 39(3). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.865.2675&rep=rep 1&type=pdf

Gylfe, P., Franck, H., Lebaron, C., & Mantere, S. (2016). Video methods in strategy research: Focusing on embodied cognition. Strategic Management Journal, 37(1), 133-148. https://onlinelibrary.wiley.com/doi/epdf/10.1002/smj.2456

Nagengast, A. J., & Wolpert, D. M. (2011). Risk-sensitivity in sensorimotor control. Frontiers in human neuroscience, 5, 1-1 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3028548/

Nevo, S., Nevo, D., & Pinsonneault, A. (2016). A Temporally Situated Self- Agency Theory of Information Technology Reinvention. Mis Quarterly, 40(1), 157-186. http://web.a.ebscohost.com/ehost/pdfviewer/pdfviewer?vid=0&sid=4fcc0ac8- a76a-48c9-b577-6a3326250036%40sessionmgr4009

Tilcsik, A. (2014). Imprint–environment fit and performance: How organizational munificence at the time of hire affects subsequent job performance. Administrative Science Quarterly, 59(4), 639-668. http://journals.sagepub.com/doi/pdf/10.1177/0001839214549042

Animesh, A., Pinsonneault, A., Yang, S. B., & Oh, W. (2011). An odyssey into virtual worlds: exploring the impacts of technological and spatial environments on intention to purchase virtual products. MIS Quarterly, 789- 810. http://www.jstor.org/stable/23042809?seq=1#page_scan_tab_contents

Davidson, E., & Vaast, E. (2010). Digital entrepreneurship and its sociomaterial enactment. In System Sciences (HICSS), 2010 43rd Hawaii International Conference on (pp. 1-10). IEEE. http://ieeexplore.ieee.org/document/5428439/

Faraj, S., & Azad, B. (2012). The materiality of technology: An affordance perspective. Materiality and organizing: Social interaction in a technological world, 237-258 http://www.oxfordscholarship.com/view/10.1093/acprof:oso/9780199664054. 001.0001/acprof-9780199664054-chapter-12

Medicine, Health and Other Social Sectors

Petit, O., Basso, F., Merunka, D., Spence, C., Cheok, A. D., & Oullier, O. (2016). Pleasure and the Control of Food Intake: An Embodied Cognition Approach to Consumer Self‐Regulation. Psychology & Marketing, 33(8), 608- 619.

Franks, B., Lahlou, S., Bottin, J. H., Guelinckx, I., & Boesen-Mariani, S. (2017). Increasing water intake in pre-school children with unhealthy drinking habits: A year-long controlled longitudinal field experiment assessing the impact of information, water affordance, and social regulation. Appetite, 116, 205-214., https://mcgill.worldcat.org/title/increasing-water-intake-in-pre-school-children- with-unhealthy-drinking-habits-a-year-long-controlled-longitudinal-field- experiment-assessing-the-impact-of-information-water-affordance-and-social- regulation/oclc/7015379797&referer=brief_results

Nemeth, C., Blomberg, J., Argenta, C., Pamplin, J. C., Salinas, J., & Serio- Melvin, M. (2015, October). Support for Salience: IT to Assist Burn ICU Clinician Decision Making and Communication. In Systems, Man, and Cybernetics (SMC), 2015 IEEE International Conference on (pp. 1122-1126). IEEE.. https://goo.gl/zfCuHi

Neuroscience and Psychology

Lepora, N. F., & Pezzulo, G. (2015). Embodied choice: how action influences perceptual decision making. PLoS computational biology, 11(4), e1004110. http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004110

Donnarumma, F., Costantini, M., Ambrosini, E., Friston, K., & Pezzulo, G. (2017). Action perception as hypothesis testing. Cortex, 89, 45-60. https://www.sciencedirect.com/science/article/pii/S0010945217300308

Pezzulo, G., Donnarumma, F., & Dindo, H. (2013). Human sensorimotor communication: A theory of signaling in online social interactions. PLoS One, 8(11), e79876. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0079876

Vincent, B. (2011). Covert visual search: Prior beliefs are optimally combined with sensory evidence. Journal of Vision, 11(13), 25-25. http://jov.arvojournals.org/article.aspx?articleid=2121258

Vincent, B. T. (2011). Search asymmetries: Parallel processing of uncertain sensory information. Vision Research, 51(15), 1741-1750. https://www.sciencedirect.com/science/article/pii/S0042698911001982

Vincent, B. T. (2015). Bayesian accounts of covert selective attention: A tutorial review. Attention, Perception, & Psychophysics, 77(4), 1013-1032 https://link.springer.com/article/10.3758%2Fs13414-014-0830-0

Vincent, B. T., Baddeley, R. J., Troscianko, T., & Gilchrist, I. D. (2009). Optimal feature integration in visual search. Journal of Vision, 9(5), 15-15. http://jov.arvojournals.org/article.aspx?articleid=2122194

Castelo, N., Hardy, E., House, J., Mazar, N., Tsai, C., & Zhao, M. (2015). Moving citizens online: Using salience & message framing to motivate behavior change. Behavioral Science & Policy, 1(2), 57-68. https://behavioralpolicy.org/articles/moving-citizens-online-using-salience- message-framing-to-motivate-behavior-change/

Enax, L., Weber, B., Ahlers, M., Kaiser, U., Diethelm, K., Holtkamp, D., ... & Kersting, M. (2015). Food packaging cues influence taste perception and increase effort provision for a recommended snack product in children. Frontiers in psychology, 6. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4488606/

Barsalou, L. W. (2011). Integrating Bayesian analysis and mechanistic theories in grounded cognition. Behavioral and Brain Sciences, 34(04), 191- 192. https://search.proquest.com/docview/899266063/fulltextPDF/8E79B58EE7A2 4B22PQ/1?accountid=12339

Barsalou, L. W., Niedenthal, P. M., Barbey, A. K., & Ruppert, J. A. (2003). Social embodiment. Psychology of learning and motivation, 43, 43-92. https://www.sciencedirect.com/science/article/pii/S0079742103010119?via% 3Dihub

Dong, P., & Lee, S. W. (2017). Embodiment as procedures: Physical cleansing changes goal priming effects. Journal of Experimental Psychology: General, 146(4), 592. http://psycnet.apa.org/record/2017-14922-010

Gigliotta, O., Pezzulo, G., & Nolfi, S. (2011). Evolution of a predictive internal model in an embodied and situated agent. Theory in biosciences, 130(4), 259-276. https://link.springer.com/article/10.1007%2Fs12064-011-0128-x

Maglio, S. J., & Trope, Y. (2012). Disembodiment: abstract construal attenuates the influence of contextual bodily state in judgment. Journal of Experimental Psychology: General, 141(2), 211. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3199306/

Maglio, S. J., Trope, Y., & Liberman, N. (2013). Distance from a distance: Psychological distance reduces sensitivity to any further psychological distance. Journal of Experimental Psychology: General, 142(3), 644. http://psycnet.apa.org/record/2012-26187-001

Rigoli, L., & Spivey, M. J. (2015). Real-time language processing as embodied and embedded in joint action. In Attention and vision in language processing (pp. 3-22). Springer, New Delhi. https://link.springer.com/chapter/10.1007/978-81-322-2443-3_1 van Rheede, J. J., Richards, B. A., & Akerman, C. J. (2015). Sensory-evoked spiking behavior emerges via an experience-dependent plasticity mechanism. Neuron, 87(5), 1050-1062. http://www.cell.com/neuron/fulltext/S0896-6273(15)00715-1

Karmarkar, U. R. (2017). The Impact of “Display‐Set” Options on Decision‐ Making. Journal of Behavioral Decision Making, 30(3), 744-753. https://doi-org.proxy3.library.mcgill.ca/10.1002/bdm.1998

Weinberg, A., Ferri, J., & Hajcak, G. (2013). Interactions between attention and emotion. Handbook of cognition and emotion, 35-54. https://www.dropbox.com/s/sl74j0foyo5bm09/SALIENCE_CR_Interactions_b etween_attention_and_emotion.pdf?dl=0

Murata, S., Yamashita, Y., Arie, H., Ogata, T., Sugano, S., & Tani, J. (2017). Learning to perceive the world as probabilistic or deterministic via interaction with others: a neuro-robotics experiment. IEEE transactions on neural networks and learning systems, 28(4), 830-848. goo.gl/LzZxFr

AI and Advanced Analytics

Choi, H., Cho, K., & Bengio, Y. (2018). Fine-grained attention mechanism for neural machine translation. Neurocomputing, 284, 171-176. https://www.sciencedirect.com/science/article/pii/S0925231218300225

Körding, K. P., & Wolpert, D. M. (2006). Bayesian decision theory in sensorimotor control. Trends in cognitive sciences, 10(7), 319-326. https://www.sciencedirect.com/science/article/pii/S1364661306001276

Brodeur, S., Perez, E., Anand, A., Golemo, F., Celotti, L., Strub, F., ... & Courville, A. (2017). HoME: A household multimodal environment. arXiv preprint. 1711.11017. https://arxiv.org/abs/1711.11017

Guadagno, R. E., Swinth, K. R., & Blascovich, J. (2011). Social evaluations of embodied agents and avatars. Computers in Human Behavior, 27(6), 2380- 2385. https://pdfs.semanticscholar.org/6b5f/f42cf45269630c18b38bd2e91dbf62110 09e.pdf

Saleem, H. M., Al Zamal, F., & Ruths, D. (2015). Tackling the challenges of situational awareness extraction in Twitter with an adaptive approach. Procedia Engineering, 107, 301-311.

http://cs.mcgill.ca/~hsalee/pdfs/humtech2015_SaleemZamalRuths.pdf

Chuang, C. Y., Li, J., Torralba, A., & Fidler, S. (2017). Learning to Act Properly: Predicting and Explaining Affordances from Images. arXiv preprint. arXiv:1712.07576. https://arxiv.org/abs/1712.07576

Faghri, F., Fleet, D. J., Kiros, J. R., & Fidler, S. (2017). VSE++: Improving Visual-Semantic Embeddings with Hard Negatives. Open Review. https://arxiv.org/abs/1707.05612

Session 10 Retail and Marketing

Emotion- and Winterich, K. P. & Haws,K. L. (2011). Helpful hopefulness: The effect of experience-based future positive emotions on consumption. Journal of Consumer Research, 38, decision making and 505-520. behavior: stress, fun, and http://www.jstor.org/stable/10.1086/659873 emotional intelligence Dunn, L., & Hoegg, J. (2014). The impact of fear on emotional brand attachment. Journal of Consumer Research, 41(1), 152-168. https://academic.oup.com/jcr/article/41/1/152/1810279

Grewal, D., Roggeveen, A. L., Sisodia, R., & Nordfält, J. (2017). Enhancing customer engagement through consciousness. Journal of Retailing, 93(1), 55-64. https://www.sciencedirect.com/science/article/pii/S002243591630080X

Kidwell, B., Hasford, J., & Hardesty, D. M. (2015). Emotional ability training and mindful eating. Journal of Marketing Research, 52(1), 105-119. https://www.researchgate.net/publication/272020061_Emotional_Ability_Train ing_and_Mindful_Eating

Kumar, V., & Pansari, A. (2016). Competitive advantage through engagement. Journal of Marketing Research, 53(4), 497-514. http://web.b.ebscohost.com/ehost/pdfviewer/pdfviewer?vid=0&sid=f2718df6- 06e9-49e2-851e-728d250c47bd%40sessionmgr120

Han, D., Duhachek, A., & Agrawal, N. (2014). Emotions shape decisions through construal level: The case of guilt and shame. Journal of Consumer Research, 41(4), 1047-1064. http://www.acrwebsite.org/volumes/v42/acr_v42_17716.pdf

Management – Others and Economics

Baron, R. A. (2008). The role of affect in the entrepreneurial process. Academy of Management Review, 33(2), 328-340. https://www.jstor.org/stable/20159400?seq=1#page_scan_tab_contents

Cote, S., Lopes, P. N, Salovey, P. and Miners, C. T. (2010). Emotional intelligence and leadership emergence in small groups. The Leadership Quarterly, 21, 496 – 508. https://www.sciencedirect.com/science/article/pii/S1048984310000603

Cote, S., & Morgan, L. M. (2002). A longitudinal analysis of the association between emotion regulation, job satisfaction, and intentions to quit. Journal of organizational Behavior, 23(8), 947-962. http://jwalkonline.org/docs/Grad%20Classes/Fall%2007/Org%20Psy/Cases/s atisfaction/articles/Job%20Sat%20and%20Emotional%20Reg%20- %20Cote%20and%20Morgan.pdf

Døskeland, T., & Pedersen, L. J. T. (2015). Investing with brain or heart? A field experiment on responsible investment. Management Science, 62(6), 1632-1644. https://pubsonline.informs.org/doi/pdf/10.1287/mnsc.2015.2208

Labonte-LeMoyne, E., Labonte-LeMoyne, E., Leger, P. M., Leger, P. M., Robert, J., Robert, J., ... & Michon, J. F. (2017). Business intelligence serious game participatory development: lessons from ERPsim for big data. Business Process Management Journal, 23(3), 493-505. https://www.emeraldinsight.com/doi/full/10.1108/BPMJ-12-2015-0177

Cote, S. (2005). A Social Interaction Model of the Effects of Emotion Regulation on Work Strain. The Academy of Management Review, 30(2), 509 – 530. https://www.jstor.org/stable/20159142?seq=1#page_scan_tab_contents

Casciaro, T., & Lobo, M. S. (2014). Affective primacy in intraorganizational task networks. Organization Science, 26(2), 373-389. https://pubsonline.informs.org/doi/pdf/10.1287/orsc.2014.0939

Cornelissen, J. P., Mantere, S., and Vaara, E. (2013). The contraction of meaning: The combined effect of communication, emotions, and materiality on sensemaking in the Stockwell shooting, Journal of Management Studies, 51:5, https://onlinelibrary.wiley.com/doi/epdf/10.1111/joms.12073

Medicine, Health and Other Social Sectors

Fish, E. W., Shahrokh, D., Bagot, R., Caldji, C., Bredy, T., Szyf, M., & Meaney, M. J. (2004). Epigenetic programming of stress responses through variations in maternal care. Annals of the New York Academy of Sciences, 1036(1), 167-180. https://nyaspubs.onlinelibrary.wiley.com/doi/epdf/10.1196/annals.1330.011

Afifi, W. A., Gangi, K., Blascovich, J., Afifi, T. D., Cornick, J. E., Merrill, A. F., & Sterling, K. (2016). Mothers' Impact on Daughters' Cardiovascular Reactivity in a High-Threat Context: An Immersive Virtual Environment Study. Human Communication Research, 42(3), 371-395. https://onlinelibrary.wiley.com/doi/epdf/10.1111/hcre.12085

Naghieh, A., Montgomery, P., Bonell, C., Thompson, M., & Aber, J. (2013). Organisational interventions for improving wellbeing and reducing work- related stress in teachers. Cochrane Database of Systematic Reviews, 4. http://cochranelibrary-wiley.com/doi/10.1002/14651858.CD010306.pub2/epdf

Yardley, L., Spring, B. J., Riper, H., Morrison, L. G., Crane, D. H., Curtis, K., ... & Blandford, A. (2016). Understanding and promoting effective engagement with digital behavior change interventions. American journal of preventive medicine, 51(5), 833-842. https://pureportal.coventry.ac.uk/files/6220484/Understanding_cover.pdf

Lin, K., Xia, F., Wang, W., Tian, D., & Song, J. (2016). System design for big data application in emotion-aware healthcare. IEEE Access, 4, 6901-6909. https://mcgill.worldcat.org/title/system-design-for-big-data-application-in- emotion-aware-healthcare/oclc/6854950066&referer=brief_results

Martin, A., Ziebland, S., Powell, J., Jenkinson, C., Perera, R., Kelly, L., ... & Locock, L. (2016). Examining the role of patients' experiences as a resource

for choice and decision-making in health care: a creative, inter-disciplinary mixed method study. in digital health. Programme Grants Applied Research, 4(17). http://nrl.northumbria.ac.uk/28943/1/Bookshelf_NBK401199.pdf

Neuroscience and Psychology

Otto, A. R., Fleming, S. M., & Glimcher, P. W. (2016). Unexpected but incidental positive outcomes predict real-world gambling. Psychological science, 27(3), 299-311. http://journals.sagepub.com/doi/full/10.1177/0956797615618366

Ma-Kellams, C., & Blascovich, J. (2012). Inferring the emotions of friends versus strangers: The role of culture and self-construal. Personality and Social Psychology Bulletin, 38(7), 933-945. http://journals.sagepub.com/doi/abs/10.1177/0146167212440291?url_ver=Z3 9.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed

Nash, K., Prentice, M., Hirsh, J., McGregor, I., & Inzlicht, M. (2013). Muted neural response to distress among securely attached people. Social cognitive and affective neuroscience, 9(8), 1239-1245. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4127024/

McEwan, K., Gilbert, P., Dandeneau, S., Lipka, S., Maratos, F., Paterson, K. B., & Baldwin, M. (2014). Facial expressions depicting compassionate and critical emotions: The development and validation of a new emotional face stimulus set. PloS one, 9(2), e88783. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0088783

Tritt, S. M., Inzlicht, M., & Harmon-Jones, E. (2012). Toward a biological understanding of mortality salience (and other threat compensation processes). Social Cognition, 30(6), 715-733. https://guilfordjournals.com/doi/abs/10.1521/soco.2012.30.6.715

Zautra, A. J., Hall, J. S., Murray, K. E., & the Resilience Solutions Group 1. (2008). Resilience: a new integrative approach to health and mental health research. Health Psychology Review, 2(1), 41-64. https://www.tandfonline.com/doi/abs/10.1080/17437190802298568

Dunsmoor, J. E., Otto, A. R., & Phelps, E. A. (2017). Stress promotes generalization of older but not recent threat memories. Proceedings of the National Academy of Sciences, 114(34), 9218-9223. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5576797/

AI and Advanced Analytics

Clerico, A., Chamberland, C., Parent, M., Michon, P. E., Tremblay, S., Falk, T. H., ... & Jackson, P. (2016). Biometrics and classifier fusion to predict the fun- factor in video gaming. In Computational Intelligence and Games (CIG), 2016 IEEE Conference on (pp. 1-8). IEEE. http://musaelab.ca/pdfs/C102.pdf

Khooshabeh, P., Gandhe, S., McCall, C., Gratch, J., Blascovich, J., & Traum, D. (2011, September). The effects of virtual agent humor and gaze behavior on human-virtual agent proxemics. In Proceedings of the 10th international conference on Intelligent virtual agents (pp. 458-459). Springer-Verlag. http://ict.usc.edu/pubs/The%20effects%20of%20virtual%20agent%20humor %20and%20gaze%20behavior%20on%20human- virtual%20agent%20proxemics.pdf

Shafiq, S. I., Sanin, C., Toro, C., & Szczerbicki, E. (2015). Virtual engineering object (VEO): Toward experience-based design and manufacturing for industry 4.0. Cybernetics and Systems, 46(1-2), 35-50. https://www.tandfonline.com/doi/abs/10.1080/01969722.2015.1007734

Session 11 Retail and Marketing

Social decision making Berry, L. (2017). How Service Companies Can Earn Customer Trust and and behavior: Keep It. Harvard Business Review. Loneliness, social https://www.dropbox.com/s/i11dgn7c6p1x9v4/How%20Service%20Companie dis/connectedness, trust, s%20Can%20Earn%20Customer%20Trust%20and%20Keep%20It.pdf?dl=0 empathy, and communication Einav, L., Farronato, C., Levin, J., & Sundaresan, N. (2018). Auctions versus posted prices in online markets. Journal of Political Economy, 126(1), 178- 215. https://web.stanford.edu/~jdlevin/Papers/AFP.pdf

Godes, D., & Silva, J. C. (2012). Sequential and temporal dynamics of online opinion. Marketing Science, 31(3), 448-473. https://pubsonline.informs.org/doi/pdf/10.1287/mksc.1110.0653

Sun, Y., Dong, X., & McIntyre, S. (2017). Motivation of user-generated content: Social connectedness moderates the effects of monetary rewards. Marketing Science, 36(3), 329-337. https://pubsonline.informs.org/doi/pdf/10.1287/mksc.2016.1022

Toubia, O., & Stephen, A. T. (2013). Intrinsic vs. image-related utility in social media: Why do people contribute content to twitter?. Marketing Science, 32(3), 368-392. https://pubsonline.informs.org/doi/pdf/10.1287/mksc.2013.0773

Wang, J., Aribarg, A., & Atchadé, Y. F. (2013). Modeling choice interdependence in a social network. Marketing Science, 32(6), 977-997. https://pubsonline.informs.org/doi/pdf/10.1287/mksc.2013.0811

You, Y., Vadakkepatt, G. G., & Joshi, A. M. (2015). A meta-analysis of electronic word-of-mouth elasticity. Journal of Marketing, 79(2), 19-39. https://business.ucf.edu/wp-content/uploads/2015/04/meta-analysis-JM- published.pdf

Gans, J. S., Goldfarb, A., & Lederman, M. (2017). Exit, Tweets and Loyalty (No. w23046). National Bureau of Economic Research. http://www.law.northwestern.edu/research- faculty/searlecenter/events/antitrust/documents/Gans_Goldfarb_Lederman.p df

Gong, S., Zhang, J., Zhao, P., & Jiang, X. (2017). Tweeting as a marketing tool–field experiment in the TV industry. Journal of Marketing Research: December 2017, Vol. 54, No. 6, pp. 833-850. http://jjzhang.scripts.mit.edu/docs/Gong_Zhang_Zhao_Jiang_2017.pdf

Hamilton, R. W., Schlosser, A., & Chen, Y. J. (2017). Who’s driving this conversation? Systematic biases in the content of online consumer discussions. Journal of Marketing Research, 54(4), 540-555. http://commons.ln.edu.hk/cgi/viewcontent.cgi?article=3294&context=sw_mast er

Gallaugher, J., & Ransbotham, S. (2010). Social media and customer dialog management at Starbucks. MIS Quarterly Executive, 9(4). http://www.samransbotham.com/sites/default/files/GallaugherRansbotham_St arbucks_2010_MISQE.pdf

Lee, J. Y., & Bell, D. R. (2013). Neighborhood social capital and social learning for experience attributes of products. Marketing Science, 32(6), 960- 976. https://pubsonline.informs.org/doi/abs/10.1287/mksc.2013.0796

Ma, L., Krishnan, R., & Montgomery, A. L. (2014). Latent homophily or social influence? An empirical analysis of purchase within a social network. Management Science, 61(2), 454-473. https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2014.1928

Manchanda, P., Packard, G., & Pattabhiramaiah, A. (2015). Social dollars: The economic impact of customer participation in a firm-sponsored online customer community. Marketing Science, 34(3), 367-387. https://www.scheller.gatech.edu/directory/faculty/pattabhiramaiah/pubs/Social Dollars_FinalMKSC.pdf

Ajorlou, A., Jadbabaie, A., & Kakhbod, A. (2016). Dynamic pricing in social networks: The word-of-mouth effect. Management Science, 64(2), 971-979. https://pubsonline.informs.org/doi/pdf/10.1287/mnsc.2016.2657

Naylor, R. W., Lamberton, C. P., & West, P. M. (2012). Beyond the “like” button: The impact of mere virtual presence on brand evaluations and purchase intentions in social media settings. Journal of Marketing, 76(6), 105- 120. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2078586

Ransbotham, S., Kane, G. C., & Lurie, N. H. (2012). Network characteristics and the value of collaborative user-generated content. Marketing Science, 31(3), 387-405. https://pdfs.semanticscholar.org/b0b4/18be678ad290affab1efff178235d8231 3bd.pdf

Su, L., Jiang, Y., Chen, Z., & DeWall, C. N. (2016). Social exclusion and consumer switching behavior: A control restoration mechanism. Journal of Consumer Research, 44(1), 99-117. https://academic.oup.com/jcr/article/44/1/99/2736403

Management – Others and Economics

Kumar, V., Lahiri, A., & Dogan, O. B. (2017). A strategic framework for a profitable business model in the sharing economy. Industrial Marketing Management. https://www.sciencedirect.com/science/article/pii/S0019850117303620

Shriver, S. K., Nair, H. S., & Hofstetter, R. (2013). Social ties and user- generated content: Evidence from an online social network. Management Science, 59(6), 1425-1443. https://pubsonline.informs.org/doi/pdf/10.1287/mnsc.1110.1648

Peysakhovich, A., & Rand, D. G. (2015). Habits of virtue: Creating norms of cooperation and defection in the laboratory. Management Science, 62(3), 631-647. https://pubsonline.informs.org/doi/pdf/10.1287/mnsc.2015.2168

Schofield, H., Loewenstein, G., Kopsic, J., & Volpp, K. G. (2015). Comparing the effectiveness of individualistic, altruistic, and competitive incentives in motivating completion of mental exercises. Journal of health economics, 44, 286-299. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4854446/

Battilana, J., & Casciaro, T. (2013). Overcoming resistance to organizational change: Strong ties and affective cooptation. Management Science, 59(4), 819-836. http://www- 2.rotman.utoronto.ca/facbios/file/Affective%20Cooptation_Final.pdf

Ganju, K. K., & Bassellier, G. (2017). The Impact of Online Platforms on Labor Markets. ICIS 2017 Conference. http://aisel.aisnet.org/cgi/viewcontent.cgi?article=1338&context=icis2017

Oppong-Tawiah, D., Bassellier, G., & Ramaprasad, J. (2016). Social Connectedness and Leadership in Online Communities. ICIS 2016 Conference. https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1229&context=icis2016

Faraj, S., & Johnson, S. L. (2011). Network exchange patterns in online communities. Organization Science, 22(6), 1464-1480. https://pubsonline.informs.org/doi/pdf/10.1287/orsc.1100.0600

Faraj, S., Kudaravalli, S., & Wasko, M. (2015). Leading Collaboration in Online Communities. Mis Quarterly, 39(2). https://www.dropbox.com/s/cy08ybxfwjmyreq/Leading%20Collaboration%20i n%20Online%20Communities..pdf?dl=0

Bapna, R., Liangfei, Q., & Rice, S. (2017). Repeated interactions versus social ties: quantifying the economic value of trust, forgiveness, and reputation using a field experiment. MIS Quarterly, 41(3). http://www.fox.temple.edu/conferences/cist/papers/Session%208A/CIST_201 5_8A_2.pdf

Barki, H., Robert, J., & Dulipovici, A. (2015). Reconceptualizing trust: A non- linear Boolean model. Information & Management, 52, 4, 483-495. https://www.sciencedirect.com/science/article/pii/S0378720615000166

Moldoveanu, M. C., & Baum, J. A. (2011). “I Think You Think I Think You're Lying”: The Interactive Epistemology of Trust in Social Networks. Management Science, 57(2), 393-412. https://pubsonline.informs.org/doi/pdf/10.1287/mnsc.1100.1279

Bottazzi, L., Da Rin, M., & Hellmann, T. F. (2011). The importance of trust for investment: Evidence from venture capital (No. w16923). National Bureau of Economic Research. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.319.5263&rep=rep 1&type=pdf

Medicine, Health and Other Social Sectors

VanderWeele, T. J., Hawkley, L. C., & Cacioppo, J. T. (2012). On the reciprocal association between loneliness and subjective wellbeing. American Journal of Epidemiology, 176(9), 777-784. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3571255/

Yusoff, N., Luhman, M., Caccioppo, J. T. (2013). Explaining the link between loneliness and self rated health with hedonic regulation as a mediator, Procedia Social and Behavioral Science, 97, 156-159 https://www.sciencedirect.com/science/article/pii/S1877042813036616

Austin, J., Dodge, H. H., Riley, T., Jacobs, P. G., Thielke, S., & Kaye, J. (2016). A smart-home system to unobtrusively and continuously assess loneliness in older adults. IEEE journal of translational engineering in health and medicine, 4, 1-11. https://mcgill.worldcat.org/title/a-smart-home-system-to-unobtrusively-and- continuously-assess-loneliness-in-older- adults/oclc/6793629748&referer=brief_results

Durand, M. A., Carpenter, L., Dolan, H., Bravo, P., Mann, M., Bunn, F., & Elwyn, G. (2014). Do interventions designed to support shared decision- making reduce health inequalities? A systematic review and meta- analysis. PloS one, 9(4), e94670. https://mcgill.worldcat.org/title/do-interventions-designed-to-support-shared- decision-making-reduce-health-inequalities-a-systematic-review-and-meta- analysis/oclc/6893803550&referer=brief_results

Neuroscience and Psychology

Harmon-Jones, E., & Inzlicht, M. (2016). A brief overview of social neuroscience. Social Neuroscience: Biological Approaches to Social Psychology, 1. https://michael-inzlicht.squarespace.com/s/A-brief-overview-of-social- neuroscience.pdf

Mu, Y., Kitayama, S., Han, S., & Gelfand, M. J. (2015). How culture gets embrained: Cultural differences in event-related potentials of social norm violations. Proceedings of the National Academy of Sciences, 112(50), 15348-15353. http://www.pnas.org/content/112/50/15348

Murray, S. L., Holmes, J. G., Griffin, D. W., & Derrick, J. L. (2015). The equilibrium model of relationship maintenance. Journal of personality and social psychology, 108(1), 93-113. http://psycnet.apa.org/fulltext/2015-00656-003.html

Luhmann, M., Schonbrodt, F. D., Hawkey, L. C., and Cacioppo, J. T. (2015). Loneliness and social behaviors in a virtual social environment. Cognition and Emotion, 29(3), 548-558. https://www.tandfonline.com/doi/full/10.1080/02699931.2014.922053

Hayward, D. A., Pereira, E. J., Otto, A. R., & Ristic, J. (2018). Smile! Social reward drives attention. Journal of Experimental Psychology: Human Perception and Performance, 44(2), 206. http://otto.lab.mcgill.ca/papers/hayward_et_al_in_press.pdf

AI and Advanced Analytics

Blascovich, J., & McCall, C. (2013). Social influence in virtual environments. In K. E. Dill (Ed.), Oxford library of psychology. The Oxford handbook of media psychology (pp. 305-315). New York, NY, US: Oxford University Press. https://link.springer.com/chapter/10.1007/978-1-4471-0277-9_8#enumeration

Jackson, P. L., Michon, P. E., Geslin, E., Carignan, M., & Beaudoin, D. (2015). EEVEE: The empathy-enhancing virtual evolving environment. Frontiers in human neuroscience, 9, 112. https://www.frontiersin.org/articles/10.3389/fnhum.2015.00112/full

Zhou, H., Huang, M., Zhang, T., Zhu, X., & Liu, B. (2017). Emotional chatting machine: emotional conversation generation with internal and external memory. arXiv preprint arXiv:1704.01074. https://arxiv.org/pdf/1704.01074

Oh, O., Agrawal, M., & Rao, H. R. (2013). Community intelligence and social media services: A rumor theoretic analysis of tweets during social crises. Mis Quarterly, 37(2). https://pdfs.semanticscholar.org/a884/f71a3cd7c198fdd2a4c1951f71f2c7c50 ac6.pdf

Urban, I. V., Sankar, C., Germain, M., Zhang, S., Lin, Z., Subramanian, S., ... & Mudumba, S. (2017). A deep reinforcement learning chatbot. arXiv preprint arXiv:1709.02349. https://arxiv.org/abs/1709.02349

Huang, J., & Fox, M. S. (2005, August). Trust judgment in knowledge provenance. In Database and Expert Systems Applications, 2005. Proceedings. Sixteenth International Workshop on (pp. 524-528). IEEE. http://www.eil.utoronto.ca/wp-content/uploads/km/papers/huang-dexa05.pdf

Session 12 Retail and Marketing

Prospective decision Argo, J. J., Dahl, D. W., & Manchanda, R. V. (2005). The influence of a mere making and behavior: social presence in a retail context. Journal of consumer research, 32(2), 207- Identity, self, other, and 212. interaction http://www.jstor.org/stable/10.1086/432230

Zhang, Y., Trusov, M., Stephen, A. T., & Jamal, Z. (2017). Online shopping and social media: friends or foes?. Journal of Marketing, 81(6), 24-41. https://www.dropbox.com/s/xnuqyt11wsudlrr/Online%20shopping%20and%2 0social%20media-friends%20or%20foes.pdf?dl=0

Borkovsky, R. N., Goldfarb, A., Haviv, A. M., & Moorthy, S. (2017). Measuring and understanding brand value in a dynamic model of brand management. Marketing Science, 36(4), 471-499. http://www-2.rotman.utoronto.ca/facbios/file/brandbuilding2.pdf

Dietvorst, R. C., W. J. M. I. Verbeke, et al. (2009). A sales force-specific theory-of-mind scale: Tests of its validity by classical methods and functional magnetic resonance imaging. Journal of Marketing Research, 46(5), 653-668. https://www.jstor.org/stable/20618926?seq=1#page_scan_tab_contents

Dunn, L., & Dahl, D. W. (2012). Self-threat and product failure: how internal attributions of blame affect consumer complaining behavior. Journal of Marketing Research, 49(5), 670-681. https://www.dropbox.com/s/2ymvxb8nd810tmc/Self- threat%20and%20product%20failure_%20how%20internal%20attributions%2 0of%20blame%20affect%20consumer%20complaining%20behavior..pdf?dl= 0

Goldfarb, A., Lu, Q., & Moorthy, S. (2009). Measuring brand value in an equilibrium framework. Marketing Science, 28(1), 69-86. https://pubsonline.informs.org/doi/pdf/10.1287/mksc.1080.0376

White, Chris J., P. Rajan Varadarajan and Peter A. Dacin (2003), “Market Situation Interpretation and Response: The Role of Cognitive Style, Organizational Culture, and Information Use,” Journal of Marketing. https://www.jstor.org/stable/30040537?seq=1#page_scan_tab_contents

Moorthy, S. (2012). Can brand extension signal product quality?. Marketing Science, 31(5), 756-770. https://pubsonline.informs.org/doi/pdf/10.1287/mksc.1120.0723

Nam, H., Joshi, Y. V., & Kannan, P. K. (2017). Harvesting brand information from social tags. Journal of Marketing, 81(4), 88-108. https://www.rhsmith.umd.edu/files/Documents/Departments/Marketing/nam- joshi-kannan-2017.pdf

Irmak, C., Wakslak, C. J., & Trope, Y. (2013). Selling the forest, buying the trees: The effect of construal level on seller-buyer price discrepancy. Journal of Consumer Research, 40(2), 284-297. https://academic.oup.com/jcr/article/40/2/284/2911022

Aribarg, A., Arora, N., Henderson, T., & Kim, Y. (2014). Private label imitation of a national brand: Implications for consumer choice and law. Journal of Marketing Research, 51(6), 657-675. https://www.mccombs.utexas.edu/Research/~/media/Files/MSB/Research/Pu blications/2015Q1/Private%20Label%20Imitation

Bolton, L. E. (2003). Stickier priors: The effects of nonanalytic versus analytic thinking in new product forecasting. Journal of Marketing Research, 40(1), 65-79. https://www.jstor.org/stable/30038836?seq=1#page_scan_tab_contents

Bolton, L. E., & Reed, A. (2004). Sticky priors: The perseverance of identity effects on judgment. Journal of Marketing Research, 41(4), 397-410. http://www.jstor.org/stable/pdf/30164705.pdf

Cleeren, K., Van Heerde, H. J., & Dekimpe, M. G. (2013). Rising from the ashes: How brands and categories can overcome product-harm crises. Journal of Marketing, 77(2), 58-77. https://lirias.kuleuven.be/bitstream/123456789/419715/3/Accepted+manuscri pt+JM+10-0414.pdf

Ter Braak, A., Deleersnyder, B., Geyskens, I., & Dekimpe, M. G. (2013). Does private-label production by national-brand manufacturers create discounter goodwill?. International Journal of Research in Marketing, 30(4), 343-357. https://www.sciencedirect.com/science/article/pii/S0167811613000761

Weijo, H. A., Martin, D. M., Arnould, E. J., & Ger, G. (2018). Consumer Movements and Collective Creativity: The Case of Restaurant Day. Journal of Consumer Research. https://academic.oup.com/jcr/advance-article/doi/10.1093/jcr/ucy003/4815184

Burson, K. A. & Gershoff, A. D. (2015). Marketing Actions That Influence Estimates of Others Also Shape Identity. Journal of Consumer Psychology, 25. 495-503. https://www.sciencedirect.com/science/article/pii/S1057740815000121

Management – Others and Economics

Dawar, N., & Bagga, C. K. (2015). A better way to map brand strategy. Harvard Business Review, 93(6), 90-97. https://www.dropbox.com/s/91lgrhrau0vjft1/A%20better%20way%20to%20m ap%20brand%20strategy.pdf?dl=0

Kranton, R. E. (2016). Identity Economics 2016: Where Do Social Distinctions and Norms Come From?. The American Economic Review, 106(5), 405-409 https://www.aeaweb.org/articles?id=10.1257/aer.p20161038 Mayer-Schönberger, V. (2009). Edgar A. Whitley & Ian Hosein: Global challenges for identity policies. Identity in the Information Society, 2(3) 359- 361. https://www.dropbox.com/s/tejbks8b3zcnmuf/Global%20challenges%20for%2 0identity%20policies..pdf?dl=0

Lin, M., Prabhala, N. R., & Viswanathan, S. (2013). Judging borrowers by the company they keep: Friendship networks and information asymmetry in online peer-to-peer lending. Management Science, 59(1), 17-35.\ https://www8.gsb.columbia.edu/rtfiles/finance/Finance%20Seminar/Spring%2 02010/p2p_prabhala.pdf

Johnson, S. L., Faraj, S. and Kudaravalli, S. (2014). Emergence of power laws in online communities: The role of social mechanisms and preferential attachment. MIS Quarterly, 38(3), 795-808. https://www.researchgate.net/publication/268386888_Emergence_of_Power_ Laws_in_Online_Communities_The_Role_of_Social_Mechanisms_and_Pref erential_Attachment

Ethiraj, S. K., Ramasubbu, N., & Krishnan, M. S. (2012). Does complexity deter customer‐focus?. Strategic Management Journal, 33(2), 137-161. goo.gl/z6YC54

Medicine, Health and Other Social Sectors

Loprinzi, P. D. (2015). Factors influencing the disconnect between self- perceived health status and actual health profile: implications for improving self-awareness of health status. Preventive medicine, 73, 37-39. https://mcgill.worldcat.org/title/factors-influencing-the-disconnect-between- self-perceived-health-status-and-actual-health-profile-implications-for- improving-self-awareness-of-health- status/oclc/5718495235&referer=brief_results

Moore, R. C., Eyler, L. T., Mausbach, B. T., Zlatar, Z. Z., Thompson, W. K., Peavy, G., ... & Jeste, D. V. (2015). Complex interplay between health and successful aging: role of perceived stress, resilience, and social support. The American Journal of Geriatric Psychiatry, 23(6), 622-632. https://mcgill.worldcat.org/title/complex-interplay-between-health-and- successful-aging-role-of-perceived-stress-resilience-and-social- support/oclc/5616246121&referer=brief_results

Neuroscience and Psychology

Kitayama, S. (2016). The Collective Construction of the Self: Culture, Brain, and Genes. Scientists Making a Difference: One Hundred Eminent Behavioral and Brain Scientists Talk about Their Most Important Contributions, 400. https://www.cambridge.org/core/books/scientists-making-a-difference/the- collective-construction-of-the-self-culture-brain-and- genes/71B41FC695402A94A06DBCDE58720C62

Cunningham, W. A., Johnson, M. K., Gatenby, J. C., Gore, J. C., & Banaji, M. R. (2013). Neural Components of Social Evaluation. Journal of Personality and Social Psychology, 85(4), 639 – 649. http://socialneuro.psych.utoronto.ca/neural%20components%20of%20social %20evaluation.pdf

Hogeveen, J., Inzlicht, M., & Obhi, S. S. (2014). Power changes how the brain responds to others. Journal of Experimental Psychology: General, 143(2), 755. http://michaelinzlicht.com/publications/articles-chapters/power-changes-how- the-brain-responds-to-others-pdf

Achim, A. M., Guitton, M. J., Jackson, P. L., & Monetta, L. (2013). Real-life interactions and the eight sources of information framework (8-SIF): a reply to Champagne-Lavau and Moreau (2013). Psychological Assessment, 25(4), 1407-1408. https://pdfs.semanticscholar.org/5b40/9729761df0ef3892a5ef7231e7fa06ce4 af8.pdf

K. B., & Häubl, G. (2011). Freedom of choice, ease of use, and the formation of interface preferences. MIS Quarterly, 955-976. https://pdfs.semanticscholar.org/b94d/e30e9a36df3cf1240f0da9b894eaced7d 242.pdf

Kettle, K. L., & Häubl, G. (2011). The signature effect: Signing influences consumption-related behavior by priming self-identity. Journal of Consumer Research, 38(3), 474-489. http://www.jstor.org/stable/10.1086/659753

Ravary, A., & Baldwin, M. W. (2018). Self-esteem vulnerabilities are associated with cued attentional biases toward rejection. Personality and Individual Differences, 126, 44-51. https://www.sciencedirect.com/science/article/pii/S019188691830014X

Song, X., Ma, L., Ma, Y., Yang, C., & Ji, H. (2016). Selfishness-and selflessness-based models of pedestrian room evacuation. Physica A: Statistical Mechanics and its Applications, 447, 455-466. https://www.sciencedirect.com/science/article/pii/S0378437115010699

AI and Advanced Analytics

Serban, I. V., Lowe, R., Charlin, L., & Pineau, J. (2016). Generative deep neural networks for dialogue: A short review. arXiv preprint arXiv:1611.06216. https://arxiv.org/abs/1611.06216

Truong, H. P., Parthasarathi, P., & Pineau, J. (2017). MACA: A Modular Architecture for Conversational Agents. arXiv preprint arXiv:1705.00673. http://www.sigdial.org/workshops/conference18/proceedings/pdf/SIGDIAL13. pdf

Session 13 Retail and Marketing

Prospective decision Wu, C. (2015). Matching value and market design in online advertising making and networks: An empirical analysis. Marketing Science, 34(6), 906-921. behavior: Future cognition, mind Luo, L., & Toubia, O. (2015). Improving online idea generation platforms and wandering, creativity, customizing the task structure on the basis of consumers' domain-specific foresight, and prediction knowledge. Journal of Marketing, 79(5), 100-114. for science and practice shaping a future of Roggeveen, A. L., Tsiros, M., & Grewal, D. (2012). Understanding the co- individual and collective creation effect: when does collaborating with customers provide a lift to health, wealth and service recovery?. Journal of the Academy of Marketing Science, 40(6), 771- wellbeing 790

Management – Others and Economics

Weiss, T. (2006). Designing a better world. Forbes.

Brown, T. (2015). When Everyone Is Doing Design Thinking, Is It Still a Competitive Advantage?. Harvard Business Review Digital Articles, 27.

Brown, D. (2008). From blueprint to genetic code: the merits of an evolutionary approach to design. Harvard Business Review.

Hirsch, P. M., & Lounsbury, M. D. (1996). Rediscovering volition: the institutional economics of Douglass C. North. Academy of Management Review, 21(3), 872-884.

Hooshangi, S. & Loewenstein, G. (2016). The Impact of Idea Generation and Potential Appropriation on Entrepreneurship: An Experimental Study. Management Science, 64(1), 64-82.

Kagan, E., Leider, S., & Lovejoy, W. S. (2017). Ideation–Execution Transition in Product Development: An Experimental Analysis. Management Science. Article in Advance.

Nicolaou, N., Shane, S., Cherkas, L., & Spector, T. D. (2009). Opportunity recognition and the tendency to be an entrepreneur: A bivariate genetics perspective. Organizational Behavior and Human Decision Processes, 110(2), 108-117.

Gans, J., & Ryall, M. D. (2017). Value capture theory: A strategic management review. Strategic Management Journal, 38(1), 17-41. https://paper-download.com/wp-content/uploads/2017/02/13.pdf

Medicine, Health and Other Social Sectors

Lamothe, M., Rondeau, É., Malboeuf-Hurtubise, C., Duval, M., & Sultan, S. (2016). Outcomes of MBSR or MBSR-based interventions in health care providers: A systematic review with a focus on empathy and emotional competencies. Complementary therapies in medicine, 24, 19-28. https://mcgill.worldcat.org/title/outcomes-of-mbsr-or-mbsr-based- interventions-in-health-care-providers-a-systematic-review-with-a-focus-on- empathy-and-emotional- competencies/oclc/5995528305&referer=brief_results

Auer, C. J., Glombiewski, J. A., Doering, B. K., Winkler, A., Laferton, J. A., Broadbent, E., & Rief, W. (2016). Patients’ expectations predict surgery

outcomes: a meta-analysis. International journal of behavioral medicine, 23(1), 49-62. https://mcgill.worldcat.org/title/patients-expectations-predict-surgery- outcomes-a-meta-analysis/oclc/5987256975&referer=brief_results

Neuroscience and Psychology

Szpunar, K. K., Spreng, R. N., and Schacter, D. L. (2014). A taxonomy of prospection: Introducing an organizational framework for future oriented cognition. Processing of the National Academy of Sciences, 111(52), 18414- 18421.

Saunders, B., Rodrigo, A. H., & Inzlicht, M. (2016). Mindful awareness of feelings increases neural performance monitoring. Cognitive, Affective, & Behavioral Neuroscience, 16(1), 93-105.

Baird, B., Smallwood, J., Mrazek, M. D., Kam, J. W., Franklin, M. S., & Schooler, J. W. (2012). Inspired by distraction: mind wandering facilitates creative incubation. Psychological Science, 23(10), 1117-1122.

Bacon, P. L., Balle, B., & Precup, D. (2015, July). Learning and Planning with Timing Information in Markov Decision Processes. In UAI (pp. 111-120).

Mehta, R., Dahl, D. W., & Zhu, R. J. (2017). Social-Recognition versus Financial Incentives? Exploring the Effects of Creativity-Contingent External Rewards on Creative Performance. Journal of Consumer Research, 44(3), 536-553.

Pezzulo, G. (2017). Tracing the roots of cognition in predictive processing. Johannes Gutenberg-Universität Mainz.

Spivey, M., & Cargill, S. (2007). Toward a continuity of consciousness. Journal of Consciousness Studies, 14(1-2), 216-233.

Stillman, P.E., Lee, H., Deng, X., Unnava, H.R., Cunningham, W.A. & Fujita, K. (2017). Neurological evidence for the role of construal level future-directed thought. Social Cognitive and Affective Neuroscience, 12(6), 937-947.

Teper, R., & Inzlicht, M. (2012). Meditation, mindfulness and executive control: the importance of emotional acceptance and brain-based performance monitoring. Social cognitive and affective neuroscience, 8(1), 85-92.

Pothos, E. M., Busemeyer, J. R., Shiffrin, R. M., & Yearsley, J. M. (2017). The rational status of quantum cognition. Journal of Experimental Psychology: General, 146(7), 968. http://psycnet.apa.org/record/2017-18579-001

AI and Advanced Analytics

Dutil, F., Gulcehre, C., Trischler, A., & Bengio, Y. (2017). Plan, Attend, Generate: Planning for Sequence-to-Sequence Models. arXiv preprint arXiv:1711.10462. Ferreira, K. J., Lee, B. H. A., & Simchi-Levi, D. (2015). Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing & Service Operations Management, 18(1), 69-88.

Session 14 No readings.

Project presentations