Alok Gupta Explained

Alok Gupta
Occupation:Information scientist, economic engineer, and academic
Thesis Title:A Real-Time Priority Pricing Approach for Resource Allocation in Multi-Service Class Data Communication Networks
Thesis Url:https://www.proquest.com/openview/b45da9bd581693ee2fa30f71c4573fa1/1?pq-origsite=gscholar&cbl=18750&diss=y
Thesis Year:1996
Doctoral Advisor:Andrew B. Whinston
Dale Stahl

Alok Gupta is an American information scientist, economic engineer, and academic. He is the Professor of Information and Decision, a Senior Associate Dean of Faculty, Research and Administration, and Curtis L. Carlson School Wide Chair in Information Management in the Carlson School of Management at the University of Minnesota.[1]

Gupta's research interests include the impact of technology on business model, digital transformation, data-driven decision-making, and the design and adoption of emerging technologies. He is the recipient of the Career award by the National Science Foundation (NSF),[2] and the LEO award by the Association for Information Science (AIS).[3]

Gupta is a Fellow of the Association for Information Science[4] and a Distinguished Fellow of INFORMS ISS.[5] He has held several editorial appointments throughout his career, including serving as the Editor-in-Chief and Senior Editor of Information Systems Research.[6] He also serves as an Associate Editor of the Brazilian Electronic Journal of Economics.[7]

Education

Gupta enrolled at Banaras Hindu University where he completed a Bachelor of Technology in Mining Engineering from its Indian Institute of Technology in 1988. He then completed his master's degree in Mine Electrical Systems from the Pennsylvania State University in 1991. Later, he earned a Ph.D. in Management Science and Information Systems from the University of Texas Austin in 1996 under the supervision of Andrew B. Whinston and Dale Stahl. His thesis was titled, "A Real-Time Priority Pricing Approach for Resource Allocation in Multi-Service Class Data Communication Networks".[8]

Career

Following his PhD Gupta began his academic career as a Visiting assistant professor in the Operations and Information Management Department at the University of Connecticut from 1996 to 1997 and became associate professor in 2001. He moved to the Carlson School of Management at the University of Minnesota in 2001 and was promoted to Professor in 2005. Since 2005 he has been a professor in the Information and Decision department at the University of Minnesota.[9]

Gupta is the Publisher of MIS Quarterly and also holds an appointment as the Senior Associate Dean of Faculty in Research and Administration at the Carlson School of Management, the University of Minnesota.[9]

Research

Gupta's research centers on digital innovation, business analytics, and strategic IT management. His particular focus lies in the areas of electronic commerce, online auction, and bidding strategies. He has authored over 80 articles.[10]

Electronic commerce

Gupta has done research in the area of Electronic commerce particularly focusing on consumer behavior,[11] risk prediction, pricing strategies,[12] and sales management.[13] During his early research, he proposed a stochastic equilibrium concept for a general mathematical model and demonstrated how it supports optimal congestion internet prices[14] and also provided a framework to manage resources in intranets using the concepts of electronic commerce.[15] In 2004, he designed a model named GIST to provide assistance in managing and designing the interactivity and content of customer-centric websites[16] and developed an economic model that captured consumer shopping channel choices based on the characteristics of the shopping channel and consumer risk profiles.[17] He highlighted the use of transparency strategy as an efficient way of enhancing internet-based selling and how this could help in increasing a firm's value on the internet.[18] In related research, he explored the impact of information technology on transparency, market information, and its structure and developed a theoretical framework to understand the process through which emerging dominance of transparent electronic markets can be inhibited.[19] He investigated the concept of smart markets as well, which utilizes computational tools to comprehend intricate trading environments and deliver real-time decision support to human decision-makers.[20] He also analyzed investment incentives for network infrastructure owners and explored two different pricing strategies: congestion-based negative externality pricing and the prevalent flat-rate pricing.[21] In his work titled, "Consumption and Performance: Understanding Longitudinal Dynamics of Recommender Systems via an Agent-Based Simulation Framework," he developed an agent-based modeling and computational simulation approach to investigate several factors that affect the temporal dynamics of recommender systems' performance.[22]

Auction and bidding strategies

Another major area of Gupta's research interest is online auctions[23] and bidding strategies. He focused on analyzing[24] and designing auctions,[25] [26] understanding bidders' behavior,[27] [28] and investigating how these auctions serve as an emerging mercantile process.[29] He conducted research on multi-item online auctions, providing a comparative analysis between the Vickery version and the English version. His findings indicated that while the English version may dominate, the Vickery version exhibited higher allocative efficiency.[30] He then presented a simulation approach using the characteristics of the Yankee auction in order to optimize sellers' revenue.[31] Together with Ravi Bapna and Paulo Goes, he also suggested a cost-effective and risk-free simulation approach to investigate the decision behavior of bid makers and takers in web-based dynamic price-setting processes.[32] Additionally, he presented a novel feedback scheme, specifically designed for multiattribute auctions, which helped in providing protection of buyer's preference information from the supplier and the cost schedule of supplier from the buyer.[33] In 2009, he introduced the concept of auction overlap and examined how market-level factors such as price information, degree of overlap, auction format, and market supply influence the auction prices.[34]

Continuous combinatorial auction

Gupta's research group has also worked on the Continuous Combinatorial Auction (CoCoA) project. The project utilized design science principles to design, build, validate, and evaluate a combinatorial bidding environment that aimed to lower computational and cognitive hurdles in order to realize the potential of the mechanism.[35] Additionally, a key objective of the project was to promote acceptance and utilization of this complex mechanism by providing information and tools tailored to meet users' task requirements.[36] The designed artifacts were subsequently evaluated using economic[37] and behavioral measures.[38]

Next-generation high-speed auction markets

Gupta is known for his work in the field of information systems, including the collaborative effort titled "Designing next-generation high-speed auction markets". The focus of this research project was to create IT tools that enhance quick decision-making in time-sensitive and information-rich B2B auction markets. He and his team established a partnership with the Dutch Flower Auctions (DFA). They developed a stable taxonomy of bidding strategies that allow market operators to adapt and optimize the key auction parameters in real-time[39] and designed a flexible decision support framework that focuses on two models, namely prediction and optimization models. The results of the framework showed that it can help auctioneers make better tradeoffs between revenue and throughput (i.e., market clearing speed) under different market conditions[40] and that it can increase the revenue and price stability.[41] In addition, they developed a Hybrid Auction Mechanism that mitigates market congestion which can speed up the market clearing process without affecting expected revenue, and thus effectively mitigate the congestion problem.[42]

Artificial intelligence in floriculture chain (iFlow)

During his time at Erasmus University, Gupta collaborated with the Rotterdam School of Management (RSM) group on a project called "Artificial Intelligence in the Floriculture Chain" (iFlow). The project was designed to develop advanced analytical methods and tools that would advise floriculture auctioneers on achieving a balance between higher commercial revenues, lower logistical distribution costs, faster deliveries, and reduced carbon emissions in transportation. The group executed eight notable projects, including bidder heterogeneity and the development of a bidder typology based on actual bidding data,[43] multi-transaction auctioning, auctioning sequence, and role of winner bidder identification.[44]

Awards and honors

Selected articles

Notes and References

  1. Web site: Alok Gupta. Carlson School of Management.
  2. Web site: Awards and Honors | Journal of the Association for Information Systems | AIS Journals | Association for Information Systems.
  3. Web site: AIS LEO Award – History of AIS.
  4. Web site: AIS Fellow Award – History of AIS.
  5. Web site: Alok Gupta. INFORMS.
  6. Web site: Editorial Board | Information Systems Research.
  7. Web site: Alok Gupta. College of Science and Engineering.
  8. Gupta . Alok . 1996 . A real-time priority pricing approach for resource allocation in multi service class data communication networks .
  9. Web site: Alok Gupta. Carlson School of Management.
  10. Web site: Alok Gupta. scholar.google.com.
  11. An Empirical Study of Consumer Switching from Traditional to Electronic Channels: A Purchase-Decision Process Perspective. Alok. Gupta. Bo-chiuan. Su. Zhiping. Walter. April 20, 2004. International Journal of Electronic Commerce. 8. 3. 131–161. CrossRef. 10.1080/10864415.2004.11044302. 16054242 .
  12. Designing online selling mechanisms: Transparency levels and prices. Nelson. Granados. Alok. Gupta. Robert J.. Kauffman. November 1, 2008. Decision Support Systems. 45. 4. 729–745. ScienceDirect. 10.1016/j.dss.2007.12.005.
  13. Real-Time Tactical and Strategic Sales Management for Intelligent Agents Guided by Economic Regimes. Wolfgang. Ketter. John. Collins. Maria. Gini. Alok. Gupta. Paul. Schrater. December 20, 2012. Information Systems Research. 23. 4. 1263–1283. CrossRef. 10.1287/isre.1110.0415. 2810884 . 1765/23339. free.
  14. A stochastic equilibrium model of internet pricing. Alok. Gupta. Dale O.. Stahl. Andrew B.. Whinston. May 1, 1997. Journal of Economic Dynamics and Control. 21. 4. 697–722. ScienceDirect. 10.1016/S0165-1889(96)00003-6.
  15. Managing computing resources in intranets: an electronic commerce perspective. Alok. Gupta. Dale O.. Stahl. Andrew B.. Whinston. November 1, 1998. Decision Support Systems. 24. 1. 55–69. ScienceDirect. 10.1016/S0167-9236(98)00063-3.
  16. GIST: A Model for Design and Management of Content and Interactivity of Customer-Centric Web Sites. Albert, Terri C.. Goes, Paulo B.. Gupta, Alok. 2004. MIS Quarterly. 28. 2. 161–182. JSTOR. 10.2307/25148632. 25148632 .
  17. Risk profile and consumer shopping behavior in electronic and traditional channels. Alok. Gupta. Bo-chiuan. Su. Zhiping. Walter. December 1, 2004. Decision Support Systems. 38. 3. 347–367. ScienceDirect. 10.1016/j.dss.2003.08.002.
  18. Book: Advances in the Economics of Information Systems. Nelson. Granados. Alok. Gupta. Robert J.. Kauffman. June 20, 2005. IGI Global. 80–112. www.igi-global.com.
  19. Web site: The Impact of IT on Market Information and Transparency: A Unified Theoretical Framework .
  20. Research Commentary —Designing Smart Markets. Martin. Bichler. Alok. Gupta. Wolfgang. Ketter. December 20, 2010. Information Systems Research. 21. 4. 688–699. CrossRef. 10.1287/isre.1100.0316. 1765/32046. free.
  21. An Analysis of Incentives for Network Infrastructure Investment Under Different Pricing Strategies. Alok. Gupta. Boris. Jukic. Dale O.. Stahl. Andrew B.. Whinston. June 20, 2011. Information Systems Research. 22. 2. 215–232. CrossRef. 10.1287/isre.1090.0253.
  22. Consumption and Performance: Understanding Longitudinal Dynamics of Recommender Systems via an Agent-Based Simulation Framework. Jingjing. Zhang. Gediminas. Adomavicius. Alok. Gupta. Wolfgang. Ketter. March 20, 2020. Information Systems Research. 31. 1. 76–101. CrossRef. 10.1287/isre.2019.0876. 202300723 .
  23. Web site: Online Auctions: A Closer Look.
  24. Analysis and Design of Business-to-Consumer Online Auctions. Ravi. Bapna. Paulo. Goes. Alok. Gupta. January 20, 2003. Management Science. 49. 1. 85–101. CrossRef. 10.1287/mnsc.49.1.85.12754.
  25. Optimal Design of the Online Auction Channel: Analytical, Empirical, and Computational Insights. Ravi. Bapna. Paulo. Goes. Alok. Gupta. Gilbert. Karuga. September 20, 2002. Decision Sciences. 33. 4. 557–578. CrossRef. 10.1111/j.1540-5915.2002.tb01656.x.
  26. Designing Intelligent Software Agents for Auctions with Limited Information Feedback. Gediminas. Adomavicius. Alok. Gupta. Dmitry. Zhdanov. December 20, 2009. Information Systems Research. 20. 4. 507–526. CrossRef. 10.1287/isre.1080.0172.
  27. Predicting Bidders' Willingness to Pay in Online Multiunit Ascending Auctions: Analytical and Empirical Insights. Ravi. Bapna. Paulo. Goes. Alok. Gupta. Gilbert. Karuga. August 20, 2008. INFORMS Journal on Computing. 20. 3. 345–355. CrossRef. 10.1287/ijoc.1070.0247.
  28. Web site: A Data-Driven Exploration of Bidder Strategies in Continuous Combinatorial Auctions.
  29. A theoretical and empirical investigation of multi-item on-line auctions. 2000 . 10.1023/A:1019100419867 . Bapna . Ravi . Goes . Paulo . Gupta . Alok . Information Technology and Management . 1 . 1–23 . 18880263 .
  30. Comparative analysis of multi-item online auctions: evidence from the laboratory . 2001 . 10.1016/S0167-9236(01)00107-5 . Bapna . Ravi . Goes . Paulo . Gupta . Alok . Decision Support Systems . 32 . 2 . 135–153 .
  31. Book: https://ieeexplore.ieee.org/document/927066. Simulating online Yankee auctions to optimize sellers revenue. R.. Bapna. P.. Goes. A.. Gupta. Proceedings of the 34th Annual Hawaii International Conference on System Sciences . January 20, 2001. 10 pp.–. IEEE Xplore. 10.1109/HICSS.2001.927066. 0-7695-0981-9 . 2100365 .
  32. Replicating Online Yankee Auctions to Analyze Auctioneers' and Bidders' Strategies. 2003 . 10.1287/isre.14.3.244.16562 . Bapna . Ravi . Goes . Paulo . Gupta . Alok . Information Systems Research . 14 . 3 . 244–268 .
  33. "Design and Evaluation of Feedback Schemes for Multiattribute Procureme" by Gediminas Adomavicius, Alok Gupta et al.. Icis 2008 Proceedings . January 2008 . Adomavicius . Gediminas . Gupta . Alok . Sanyal . Pallab .
  34. Overlapping Online Auctions: Empirical Characterization of Bidder Strategies and Auction Prices. Bapna, Ravi. Chang, Seokjoo Andrew. Goes, Paulo. Gupta, Alok. 2009. MIS Quarterly. 33. 4. 763–783. JSTOR. 10.2307/20650326. 20650326 .
  35. User acceptance of complex electronic market mechanisms: Role of information feedback – ScienceDirect. Journal of Operations Management . IT, Supply Chain, and Services . September 2013 . 31 . 6 . 489–503 . 10.1016/j.jom.2013.07.015 . Adomavicius . Gediminas . Curley . Shawn P. . Gupta . Alok . Sanyal . Pallab .
  36. Toward Comprehensive Real-Time Bidder Support in Iterative Combinatorial Auctions. 2005 . 10.1287/isre.1050.0052 . Adomavicius . Gediminas . Gupta . Alok . Information Systems Research . 16 . 2 . 169–185 .
  37. Impact of Information Feedback in Continuous Combinatorial Auctions: An Experimental Study of Economic Performance . 43825937 . Adomavicius . Gediminas . Curley . Shawn P. . Gupta . Alok . Sanyal . Pallab . MIS Quarterly . 2013 . 37 . 1 . 55–76 . 10.25300/MISQ/2013/37.1.03 .
  38. Effect of Information Feedback on Bidder Behavior in Continuous Combinatorial Auctions. 2012 . 10.1287/mnsc.1110.1443 . Adomavicius . Gediminas . Curley . Shawn P. . Gupta . Alok . Sanyal . Pallab . Management Science . 58 . 4 . 811–830 .
  39. Exploring Bidder Heterogeneity in multichannel sequential B2B Auctions. 26629031 . Lu . Yixin . Gupta . Alok . Ketter . Wolfgang . Van Heck . Eric . MIS Quarterly . 2016 . 40 . 3 . 645–662 . 10.25300/MISQ/2016/40.3.06 .
  40. Dynamic Decision Making in Sequential Business-to-Business Auctions: A Structural Econometric Approach. 2019 . 10.1287/mnsc.2018.3118 . Lu . Yixin . Gupta . Alok . Ketter . Wolfgang . Van Heck . Eric . Management Science . 65 . 8 . 3853–3876 . 159376075 .
  41. Web site: Information Transparency in B2B Auction Markets: The Role of Winner Identity Disclosure. 2949785 .
  42. Web site: Designing Hybrid Mechanisms to oVercome Congestion in Sequential Dutch Auctions.
  43. User Heterogeneity and Its Impact on Electronic Auction Market Design: An Empirical Exploration. 25148623 . Bapna . Ravi . Goes . Paulo . Gupta . Alok . Jin . Yiwei . MIS Quarterly . 2004 . 28 . 1 . 21–43 . 10.2307/25148623 .
  44. Web site: Information Transparency in B2B Auction Markets: The Role of Winner Identity Disclosure . 2949785 .
  45. Web site: The Design Science Award – INFORMS.
  46. Web site: Information Systems Society President's Service Award .
  47. Web site: ISS Practical Impacts Award – INFORMS .

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