Published and Working Papers

Zhong, H., Wang, G., & Dai, T. (2024). Wheels on the Bus: Impact of Vac­cine Roll­outs on Demand for Pub­lic Trans­porta­tion [Johns Hop­kins Uni­ver­si­ty Work­ing Paper]. https://doi.org/10.2139/ssrn.3874150

Dai, T., Glea­son, K., Hwang, C.-W., & David­son, P. (2019). Heart ana­lyt­ics: Ana­lyt­i­cal mod­el­ing of car­dio­vas­cu­lar care. Naval Research Logis­tics (NRL), 68(1), 30–43. https://doi.org/10.1002/nav.21880

Wang, G., Zhang, M., & Dai, T. (2024). The Spillover Effect of Sus­pend­ing Non-essen­tial Surgery: Evi­dence from Kid­ney Trans­plan­ta­tion [Johns Hop­kins Uni­ver­si­ty Work­ing Paper]. https://doi.org/10.2139/ssrn.3719662

Xu, Y., Dai, T., Sycara, K., & Lewis, M. (2010). Ser­vice Lev­el Dif­fer­en­ti­a­tion in Mul­ti-robots Con­trol. 2010 IEEE/RSJ Inter­na­tion­al Con­fer­ence on Intel­li­gent Robots and Sys­tems, 2224–2230. https://doi.org/10.1109/IROS.2010.5649366

Sycara, K., & Dai, T. (2010). Agent Rea­son­ing in Nego­ti­a­tion. In D. M. Kil­go­ur & C. Eden (Eds.), Hand­book of Group Deci­sion and Nego­ti­a­tion (pp. 437–451). Springer Nether­lands. https://doi.org/10.1007/978–90-481‑9097-3_26

Dai, T., & Qi, X. (2007). An Acqui­si­tion Pol­i­cy for a Mul­ti-sup­pli­er Sys­tem with a Finite-time Hori­zon. Com­put­ers & Oper­a­tions Research, 34(9), 2758–2773. https://doi.org/10.1016/j.cor.2005.10.011

Xu, Y., Dai, T., Sycara, K., & Lewis, M. (2012). A Mech­a­nism Design Mod­el in Robot-ser­vice-queue Con­trol with Strate­gic Oper­a­tors and Asym­met­ric Infor­ma­tion. 2012 IEEE 51st IEEE Con­fer­ence on Deci­sion and Con­trol (CDC), 6113–6119. https://doi.org/10.1109/CDC.2012.6426391

Sanchez-Anguix, V., Dai, T., Sem­nani-Azad, Z., Sycara, K., & Bot­ti, V. (2012). Mod­el­ing Pow­er Dis­tance and Individualism/Collectivism in Nego­ti­a­tion Team Dynam­ics. 2012 45th Hawaii Inter­na­tion­al Con­fer­ence on Sys­tem Sci­ences, 628–637. https://doi.org/10.1109/hicss.2012.436

Turan, N., Dai, T., Sycara, K., & Wein­gart, L. (2013). Toward a Uni­fied Nego­ti­a­tion Frame­work: Lever­ag­ing Strengths in Behav­ioral and Com­pu­ta­tion­al Com­mu­ni­ties. In K. Sycara, M. Gelfand, & A. Abbe (Eds.), Mod­els for Inter­cul­tur­al Col­lab­o­ra­tion and Nego­ti­a­tion (pp. 53–65). Springer Nether­lands. https://doi.org/10.1007/978–94-007‑5574-1_3

Dai, T. (2015). Incen­tives in U.S. Health­care Oper­a­tions. Deci­sion Sci­ences, 46(2), 455–463. https://doi.org/10.1111/deci.12136

Zheng, R., Dai, T., Sycara, K., & Chakraborty, N. (2016). Auto­mat­ed Mul­ti­lat­er­al Nego­ti­a­tion on Mul­ti­ple Issues with Pri­vate Infor­ma­tion. INFORMS Jour­nal on Com­put­ing, 28(4), 612–628. https://doi.org/10.1287/ijoc.2016.0701

Dai, T., & Jerath, K. (2016). Impact of Inven­to­ry on Quo­ta-Bonus Con­tracts with Rent Shar­ing. Oper­a­tions Research, 64(1), 94–98. https://doi.org/10.1287/opre.2015.1461

Dai, T., Cho, S.-H., & Zhang, F. (2016). Con­tract­ing for On-Time Deliv­ery in the U.S. Influen­za Vac­cine Sup­ply Chain. Man­u­fac­tur­ing & Ser­vice Oper­a­tions Man­age­ment, 18(3), 332–346. https://doi.org/10.1287/msom.2015.0574

Dai, T., Sycara, K., & Lewis, M. (2011). A Game The­o­ret­ic Queue­ing Approach to Self-Assess­ment in Human-Robot Inter­ac­tion Sys­tems. 2011 IEEE Inter­na­tion­al Con­fer­ence on Robot­ics and Automa­tion, 58–63. https://doi.org/10.1109/ICRA.2011.5980353

Zheng, R., Chakraborty, N., Dai, T., Sycara, K., & Lewis, M. (2013). Auto­mat­ed Bilat­er­al Mul­ti­ple-issue Nego­ti­a­tion with No Infor­ma­tion About Oppo­nent. 2013 46th Hawaii Inter­na­tion­al Con­fer­ence on Sys­tem Sci­ences, 520–527. https://doi.org/10.1109/hicss.2013.626

Dai, T., & Jerath, K. (2013). Sales­force Com­pen­sa­tion with Inven­to­ry Con­sid­er­a­tions. Man­age­ment Sci­ence, 59(11), 2490–2501. https://doi.org/10.1287/mnsc.2013.1809

Dai, T., Akan, M., & Tayur, S. (2017). Imag­ing Room and Beyond: The Under­ly­ing Eco­nom­ics Behind Physi­cians’ Test-Order­ing Behav­ior in Out­pa­tient Ser­vices. Man­u­fac­tur­ing & Ser­vice Oper­a­tions Man­age­ment, 19(1), 99–113. https://doi.org/10.1287/msom.2016.0594

Phan, P., Lee, S.-H., Dai, T., Moran, N., Math­u­ranayagam, V. J., & Stonemetz, J. (2018). Explorato­ry Study of Fac­tors Influ­enc­ing Surgery Sched­uled Length, Devi­a­tion from Sched­uled Length, and Impact on Length of Stay. Jour­nal of the Amer­i­can Col­lege of Sur­geons, 227(4), e160. https://doi.org/10.1016/j.jamcollsurg.2018.08.436

Dai, T., & Tayur, S. (2018). Hand­book of Health­care Ana­lyt­ics: The­o­ret­i­cal Min­i­mum for Con­duct­ing 21st Cen­tu­ry Research on Health­care Oper­a­tions. John Wiley & Sons.

Dai, T., & Tayur, S. (2017). The Evo­lu­tion­ary Trends of POM Research in Man­u­fac­tur­ing. In M. Starr & S. Gup­ta (Eds.), Rout­ledge Com­pan­ion to Pro­duc­tion and Oper­a­tions Man­age­ment (pp. 647–662). Rout­ledge. https://doi.org/10.4324/9781315687803–35

Dai, T. (2018). Game The­o­ry and Infor­ma­tion Eco­nom­ics. In T. Dai & S. Tayur (Eds.), Hand­book of Health­care Ana­lyt­ics (pp. 337–354). John Wiley & Sons, Ltd. https://doi.org/10.1002/9781119300977.ch15

Dai, T., & Jerath, K. (2019). Sales­force Con­tract­ing Under Uncer­tain Demand and Sup­ply: Dou­ble Moral Haz­ard and Opti­mal­i­ty of Smooth Con­tracts. Mar­ket­ing Sci­ence, 38(5), 852–870. https://doi.org/10.1287/mksc.2019.1171

Wuest, T., Kusi­ak, A., Dai, T., & Tayur, S. R. (2020). Impact of COVID-19 on Man­u­fac­tur­ing and Sup­ply Net­works — The Case for AI-Inspired Dig­i­tal Trans­for­ma­tion (Johns Hop­kins Uni­ver­si­ty Work­ing Paper No. ID 3593540). https://doi.org/10.2139/ssrn.3593540

Dai, T., Bai, G., & Ander­son, G. F. (2020). PPE Sup­ply Chain Needs Data Trans­paren­cy and Stress Test­ing. Jour­nal of Gen­er­al Inter­nal Med­i­cine. https://doi.org/10.1007/s11606-020–05987‑9

Dai, T., & Tayur, S. (2020). OM Forum—Healthcare Oper­a­tions Man­age­ment: A Snap­shot of Emerg­ing Research. Man­u­fac­tur­ing & Ser­vice Oper­a­tions Man­age­ment, 22(5), 869–887. https://doi.org/10.1287/msom.2019.0778

Lee, S.-H., Dai, T., & Phan, P. H. (2020). Health Orga­ni­za­tion­al Design: Infor­ma­tion Exchange and Account­abil­i­ty. In N. Bal­akr­ish­nan, T. Colton, B. Everitt, W. Piegorsch, F. Rug­geri, & J. L. Teugels (Eds.), Wiley Stat­sRef: Sta­tis­tics Ref­er­ence Online (pp. 1–9). John Wiley & Sons, Ltd. https://doi.org/10.1002/9781118445112.stat08229

Court­ney, A., How­ell, A.-M., Daulatzai, N., Sav­va, N., War­ren, O., Mills, S., Rasheed, S., Milind, G., Tekkis, N., Gar­diner, M., Dai, T., Safar, B., Efron, J. E., Darzi, A., Kon­tovouni­sios, C., & Tekkis, P. (2020). Col­orec­tal can­cer ser­vices dur­ing the COVID-19 pan­dem­ic. British Jour­nal of Surgery, 107(8), e255–e256. https://doi.org/10.1002/bjs.11706

Chen, Y.-J., Dai, T., Kor­peoglu, C. G., Kör­peoğlu, E., Sahin, O., Tang, C. S., & Xiao, S. (2020). OM Forum—Innovative Online Plat­forms: Research Oppor­tu­ni­ties. Man­u­fac­tur­ing & Ser­vice Oper­a­tions Man­age­ment, 22(3), 430–445. https://doi.org/10.1287/msom.2018.0757

Dai, T., Zaman, M. H., Padu­la, W. V., & David­son, P. M. (2021). Sup­ply Chain Fail­ures Amid Covid-19 Sig­nal a New Pil­lar for Glob­al Health Pre­pared­ness. Jour­nal of Clin­i­cal Nurs­ing, 30(1–2), e1–e3. https://doi.org/10.1111/jocn.15400

Fat­tahi, A., Dada, M., & Dai, T. (2020). Sub­scrip­tions for Pre­scrip­tions: Impli­ca­tions and Exe­cu­tion of the “Net­flix Mod­el” (Johns Hop­kins Uni­ver­si­ty Work­ing Paper No. ID 3634063). https://doi.org/10.2139/ssrn.3634063

Dai, T., Zheng, R., & Sycara, K. (2020). Jump­ing the Line, Char­i­ta­bly: Analy­sis and Rem­e­dy of Donor-Pri­or­i­ty Rule. Man­age­ment Sci­ence, 66(2), 622–641. https://doi.org/10.1287/mnsc.2018.3266

Dai, T., & Singh, S. (2020). Con­spic­u­ous by Its Absence: Diag­nos­tic Expert Test­ing Under Uncer­tain­ty. Mar­ket­ing Sci­ence, 39(3), 540–563. https://doi.org/10.1287/mksc.2019.1201

Yuan, X., Dai, T., Chen, L. G., & Gavir­neni, S. (2021). Co-Ope­ti­tion in Ser­vice Clus­ters with Wait­ing-Area Enter­tain­ment. Man­u­fac­tur­ing & Ser­vice Oper­a­tions Man­age­ment, 23(1), 106–122. https://doi.org/10.1287/msom.2019.0815

Dai, T., Ke, R., & Ryan, C. T. (2021). Incen­tive Design for Oper­a­tions-Mar­ket­ing Mul­ti­task­ing. Man­age­ment Sci­ence, 67(4), 2211–2230. https://doi.org/10.1287/mnsc.2020.3651

Jain, A., Dai, T., Myers, C. G., Jain, P., & Aggar­w­al, S. (2021). Pri­ori­tis­ing sur­gi­cal cas­es deferred by the COVID-19 pan­dem­ic: An ethics-inspired algo­rith­mic frame­work for health lead­ers. BMJ Leader, 5(2), 124–126. https://doi.org/10.1136/leader-2020–000343

Dai, T., & Tayur, S. (2022). Design­ing AI-aug­ment­ed health­care deliv­ery sys­tems for physi­cian buy-in and patient accep­tance. Pro­duc­tion and Oper­a­tions Man­age­ment, 31(12), 4443–4451. https://doi.org/10.1111/poms.13850

Li, Y., Dai, T., & Qi, X. (2022). A The­o­ry of Inte­ri­or Peaks: Activ­i­ty Sequenc­ing and Selec­tion for Ser­vice Design. Man­u­fac­tur­ing & Ser­vice Oper­a­tions Man­age­ment, 24(2), 993‑1001. https://doi.org/10.1287/msom.2021.0970

Wang, G., Zheng, R., & Dai, T. (2022). Does Trans­porta­tion Mean Trans­plan­ta­tion? Impact of New Air­line Routes on Shar­ing of Cadav­er­ic Kid­neys. Man­age­ment Sci­ence, 68(5), 3660–3679. https://doi.org/10.1287/mnsc.2021.4103

Dai, T., & Tang, C. S. (2022). Inte­grat­ing ESG mea­sures and sup­ply chain man­age­ment: Research oppor­tu­ni­ties in the post­pan­dem­ic era. Ser­vice Sci­ence, 14(1), 1–12. https://doi.org/10.1287/serv.2021.0295

Dai, T., & Abrà­moff, M. D. (2023). Incor­po­rat­ing Arti­fi­cial Intel­li­gence into Health­care Work­flows: Mod­els and Insights. In Tuto­ri­als in Oper­a­tions Research: Advanc­ing the Fron­tiers of OR/MS: From Method­olo­gies to Appli­ca­tions (pp. 133–155). INFORMS. https://doi.org/10.1287/educ.2023.0257

Dai, T., & Song, J.-S. (2021). Trans­form­ing COVID-19 vac­cines into vac­ci­na­tion. Health Care Man­age­ment Sci­ence, 24(3), 455–459. https://doi.org/10.1007/s10729-021–09563‑3

Mak, H.-Y., Dai, T., & Tang, C. S. (2022). Man­ag­ing two-dose COVID-19 vac­cine roll­outs with lim­it­ed sup­ply: Oper­a­tions strate­gies for dis­trib­ut­ing time-sen­si­tive resources. Pro­duc­tion and Oper­a­tions Man­age­ment, 31(12), 4424–4442. https://doi.org/https://doi.org/10.1111/poms.13862

Lee, S.-H., Dai, T., Phan, P. H., Moran, N., & Stonemetz, J. (2022). The asso­ci­a­tion between tim­ing of elec­tive surgery sched­ul­ing and oper­at­ing the­ater uti­liza­tion: A cross-sec­tion­al ret­ro­spec­tive study. Anes­the­sia & Anal­ge­sia, 134(3), 455–462. https://doi.org/10.1213/ane.0000000000005871

Dai, T., Wang, X., & Hwang, C.-W. (2022). Clin­i­cal Ambi­gu­i­ty and Con­flicts of Inter­est in Inter­ven­tion­al Car­di­ol­o­gy Deci­sion Mak­ing. Man­u­fac­tur­ing & Ser­vice Oper­a­tions Man­age­ment, 24(2), 864–882. https://doi.org/10.1287/msom.2021.0969

Dai, T., & Tang, C. S. (2024). De-risk­ing Glob­al Sup­ply Chains: Look­ing Beyond Mate­r­i­al Flows. Asia Pol­i­cy, 19(4), 153–176. https://doi.org/10.1353/asp.2024.a942841

Abrà­moff, M. D., Dai, T., & Zou, J. (2024). Scal­ing Adop­tion of Med­ical AI — Reim­burse­ment from Val­ue-Based Care and Fee-for-Ser­vice Per­spec­tives. NEJM AI, 1(5), AIpc2400083. https://doi.org/10.1056/AIpc2400083

Ho, C. N., Tian, T., Ayers, A. T., Aaron, R. E., Phillips, V., Wolf, R. M., Math­ioudakis, N., Dai, T., & Klonoff, D. C. (2024). Qual­i­ta­tive met­rics from the bio­med­ical lit­er­a­ture for eval­u­at­ing large lan­guage mod­els in clin­i­cal deci­sion-mak­ing: a nar­ra­tive review. BMC Med­ical Infor­mat­ics and Deci­sion Mak­ing, 24(1). https://doi.org/10.1186/s12911-024–02757‑z

Dai, T., & Tang, C. S. (2024). Nat­ur­al Haz­ards and Sup­ply Chain. In Oxford Research Ency­clo­pe­dia of Nat­ur­al Haz­ard Sci­ence. Oxford Uni­ver­si­ty Press. https://doi.org/10.1093/acrefore/9780199389407.013.512

Dai, T., Lee, H. L., & Tang, C. S. (2024). Toward Sup­ply-Chain-Aware ESG Mea­sures. In C. S. Tang (Ed.), Respon­si­ble and Sus­tain­able Oper­a­tions (Vol. 24, pp. 235–252). Springer. https://doi.org/10.1007/978–3‑031–60867-4_15

Dai, T., & Singh, S. (2025). Arti­fi­cial Intel­li­gence on Call: The Physician’s Deci­sion of Whether to Use AI in Clin­i­cal Prac­tice. Jour­nal of Mar­ket­ing Research, (con­di­tion­al­ly accept­ed). https://doi.org/10.2139/ssrn.3987454