dr. G. (Gabriele) Paolacci

Academic (20)

• Paolacci, G., & Andre, Q. (Accepted/In press). Probabilistic Outcomes Are Valued Less in Expectation, Even Conditional on Their Realization. Management Science. https://doi.org/10.17605/OSF.IO/4MUE3

• Leung, E., Cito, MC., Paolacci, G., & Puntoni, S. (2022). Preference for Material Products in Identity-Based Consumption. Journal of Consumer Psychology, 32(4), 672-679. https://doi.org/10.1002/jcpy.1272

• Paolacci, G., & Yalcin, G. (2020). Fewer but poorer: Benevolent partiality in prosocial preferences. Judgment and Decision Making, 15(2), 173-181.

• Paolacci, G., & Chandler, J. (2018). Direct replications in the era of open sampling (commentary on “Making replication mainstream” by Zwaan, Etz, Lucas, and Donnellan). Behavioral and Brain Sciences, 41(2018), E144. https://doi.org/10.1017/S0140525X18000808

• Leung, E., Paolacci, G., & Puntoni, S. (2018). Man Versus Machine: Resisting Automation in Identity-Based Consumer Behavior. Journal of Marketing Research, 55(6), 818-831. https://doi.org/10.1509/jmr.16.0443

• Rick, S., Paolacci, G., & Burson, K. (2018). Income Tax and the Motivation to Work. Journal of Behavioral Decision Making, 31(5), 619-631. https://doi.org/10.1002/bdm.2078

• Stewart, N., Chandler, J., & Paolacci, G. (2017). Crowdsourcing Samples in Cognitive Science. Trends in Cognitive Sciences, 21(10), 736-748. https://doi.org/10.1016/j.tics.2017.06.007

• Zwaan, R., Pecher, D., Paolacci, G., Bouwmeester, S., Verkoeijen, P., Dijkstra, K., & Zeelenberg, R. (2017). Participant Nonnaiveté and the reproducibility of cognitive psychology. Psychonomic Bulletin and Review, 25(5), 1968-1972. https://doi.org/10.3758/s13423-017-1348-y

• Chandler, J., & Paolacci, G. (2017). Lie for a Dime: When most prescreening responses are honest but most study participants are imposters. Social Psychological and Personality Science, 8(5), 500-508. https://doi.org/10.1177/1948550617698203

• Goodman, JK., & Paolacci, G. (2017). Crowdsourcing Consumer Research. Journal of Consumer Research, 44(1), 196-210. https://doi.org/10.1093/jcr/ucx047

• Stewart, N., Ungemach, C., Harris, A., Bartels, D., Newell, B., Paolacci, G., & Chandler, J. (2015). The Average Laboratory Samples a Population of 7,300 Amazon Mechanical Turk Workers. Judgment and Decision Making, 10(5), 479-491.

• Chandler, J., Paolacci, G., Peer, E., Mueller, P., & Ratliff, KA. (2015). Using nonnaive participants can reduce effect sizes. Psychological Science, 26(7), 1131-1139. https://doi.org/10.1177/0956797615585115

• Paolacci, G., Straeter, L., & Hooge, IE. (2015). Give Me Your Self: Gifts are Liked More When They Match the Giver's Characteristics. Journal of Consumer Psychology, 25(3), 487-494. https://doi.org/10.1016/j.jcps.2015.01.006

• Dannenberg, A., Löschel, A., Paolacci, G., Reif, C., & Tavoni, A. (2015). On the Provision of Public Goods with Probabilistic and Ambiguous Thresholds. Environmental and Resource Economics, 61(3), 365-383. https://doi.org/10.1007/s10640-014-9796-6

• Sweldens, S., Puntoni, S., Paolacci, G., & Vissers, M. (2014). The Bias in the Bias: Comparative Optimism as a Function of Event Social Undesirability. Organizational Behavior and Human Decision Processes, 124(2), 229-244. https://doi.org/10.1016/j.obhdp.2014.03.007

• Paolacci, G., & Chandler, J. (2014). Inside the Turk: Understanding Mechanical Turk as a Participant Pool. Current Directions in Psychological Science, 23(3), 184-188. https://doi.org/10.1177/0963721414531598

• Chandler, J., Mueller, P., & Paolacci, G. (2013). Nonnaïveté among Amazon Mechanical Turk workers: Consequences and solutions for behavioral researchers. Behavior Research Methods, 46(1), 112-130. https://doi.org/10.3758/s13428-013-0365-7

• Paolacci, G., & Legrenzi, E. P. (2012). Financial literacy e abilità cognitive: uno studio empirico. Giornale Italiano di Psicologia, 39(3), 707-716. https://doi.org/10.1421/38779

• Paolacci, G., Burson, K., & Rick, S. (2011). The intermediate alternative effect: Considering a small tradeoff increases subsequent willingness to make large tradeoffs. Journal of Consumer Psychology, 21(4), 384-392. https://doi.org/10.1016/j.jcps.2011.04.005

• Paolacci, G., Chandler, J., & Ipeirotis, P. (2010). Running experiments on Amazon Mechanical Turk. Judgment and Decision Making, 5(5), 411-419. http://hdl.handle.net/1765/97680

Professional (2)

• Paolacci, G. (2018). Crowdsourced consumer data: how do we make sure it's good? RSM Discovery - Management Knowledge, 34(2), 10-11. http://hdl.handle.net/1765/108776

• Paolacci, G. (2015). Consumer insights: think of yourself when buying for others. RSM Discovery - Management Knowledge, 23(3), 12-13. http://hdl.handle.net/1765/78691

Academic (4)

• Zallot, C., Paolacci, G., Chandler, J., & Sisso, I. (2022). Crowdsourcing in Observational and Experimental Research. In Handbook of Computational Social Science (Vol. 2, pp. 140-157). Routeledge (expected). https://doi.org/10.4324/9781003025245-12

• Leung, E., Paolacci, G., & Puntoni, S. (2019). How technology shapes identity-based consumer behavior. In Handbook of Research on Identity Theory in Marketing (pp. 240-254). Edward Elgar Publishing. https://doi.org/10.4337/9781788117739.00027

• Hauser, D., Paolacci, G., & Chandler, J. (2019). Common concerns with mturk as a participant pool: Evidence and solutions. In F. R. Kardes, P. M. Herr, & N. Schwarz (Eds.), Handbook of Research Methods in Consumer Psychology (pp. 319-337). Taylor and Francis Inc.. https://doi.org/10.4324/9781351137713

• Chandler, J., Paolacci, G., & Mueller, P. (2013). Risks and Rewards of Crowdsourcing Marketplaces. In P. Michelucci (Ed.), Handbook of Human Computation (pp. 377-392). Springer-Verlag. http://hdl.handle.net/1765/51293