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Daria Dzyabura

Personal Details

First Name:Daria
Middle Name:
Last Name:Dzyabura
Suffix:
RePEc Short-ID:pdz32
[This author has chosen not to make the email address public]
https://sites.google.com/a/nes.ru/daria-dzyabura/

Affiliation

New Economic School (NES)

Moscow, Russia
http://www.nes.ru/
RePEc:edi:nerasru (more details at EDIRC)

Research output

as
Jump to: Working papers Articles Chapters

Working papers

  1. Daria Dzyabura & Siham El Kihal & John R. Hauser & Marat Ibragimov, 2019. "Leveraging the Power of Images in Managing Product Return Rates," Working Papers w0259, New Economic School (NES).
  2. Daria Dzyabura & Renana Peres, 2019. "Visual Elicitation of Brand Perception," Working Papers w0260, New Economic School (NES).
  3. Liu Liu & Daria Dzyabura & Natalie Mizik, 2017. "Visual Listening In: Extracting Brand Image Portrayed on Social Media," Working Papers w0258, New Economic School (NES).
  4. Liu Liu & Daria Dzyabura, 2017. "Capturing Heterogeneity Among Consumers with Multi-taste Preferences," Working Papers w0257, New Economic School (NES).

Articles

  1. Daria Dzyabura & Siham El Kihal & John R. Hauser & Marat Ibragimov, 2023. "Leveraging the Power of Images in Managing Product Return Rates," Marketing Science, INFORMS, vol. 42(6), pages 1125-1142, November.
  2. Linda Hagen & Kosuke Uetake & Nathan Yang & Bryan Bollinger & Allison J. B. Chaney & Daria Dzyabura & Jordan Etkin & Avi Goldfarb & Liu Liu & K. Sudhir & Yanwen Wang & James R. Wright & Ying Zhu, 2020. "How can machine learning aid behavioral marketing research?," Marketing Letters, Springer, vol. 31(4), pages 361-370, December.
  3. Raluca M. Ursu & Daria Dzyabura, 2020. "Retailers’ product location problem with consumer search," Quantitative Marketing and Economics (QME), Springer, vol. 18(2), pages 125-154, June.
  4. Daria Dzyabura & Srikanth Jagabathula & Eitan Muller, 2019. "Accounting for Discrepancies Between Online and Offline Product Evaluations," Marketing Science, INFORMS, vol. 38(1), pages 88-106, January.
  5. Daria Dzyabura & John R. Hauser, 2019. "Recommending Products When Consumers Learn Their Preference Weights," Marketing Science, INFORMS, vol. 38(3), pages 417-441, May.
  6. Daria Dzyabura & Srikanth Jagabathula, 2018. "Offline Assortment Optimization in the Presence of an Online Channel," Management Science, INFORMS, vol. 64(6), pages 2767-2786, June.
  7. Daria Dzyabura & John R. Hauser, 2011. "Active Machine Learning for Consideration Heuristics," Marketing Science, INFORMS, vol. 30(5), pages 801-819, September.
    RePEc:inm:ormksc:v:39:y:2020:i:4:p:669-686 is not listed on IDEAS

Chapters

  1. Daria Dzyabura & Siham El Kihal & Renana Peres, 2022. "Image Analytics in Marketing," Springer Books, in: Christian Homburg & Martin Klarmann & Arnd Vomberg (ed.), Handbook of Market Research, pages 665-692, Springer.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Daria Dzyabura & Siham El Kihal & John R. Hauser & Marat Ibragimov, 2019. "Leveraging the Power of Images in Managing Product Return Rates," Working Papers w0259, New Economic School (NES).

    Cited by:

    1. Alex Burnap & John R. Hauser & Artem Timoshenko, 2019. "Product Aesthetic Design: A Machine Learning Augmentation," Papers 1907.07786, arXiv.org, revised Nov 2022.

  2. Daria Dzyabura & Renana Peres, 2019. "Visual Elicitation of Brand Perception," Working Papers w0260, New Economic School (NES).

    Cited by:

    1. Zecong Ma & Sergio Palacios, 2021. "Image-mining: exploring the impact of video content on the success of crowdfunding," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(4), pages 265-285, December.
    2. Dariusz Dudek & Marcin Lipowski & Ilona Bondos, 2021. "Changing Energy Supplier on the Market with a Strong Position of Incumbent Suppliers—Polish Example," Energies, MDPI, vol. 14(13), pages 1-16, June.

  3. Liu Liu & Daria Dzyabura & Natalie Mizik, 2017. "Visual Listening In: Extracting Brand Image Portrayed on Social Media," Working Papers w0258, New Economic School (NES).

    Cited by:

    1. Brett R Gordon & Kinshuk Jerath & Zsolt Katona & Sridhar Narayanan & Jiwoong Shin & Kenneth C Wilbur, 2019. "Inefficiencies in Digital Advertising Markets," Papers 1912.09012, arXiv.org, revised Feb 2020.
    2. Alex Burnap & John R. Hauser & Artem Timoshenko, 2019. "Product Aesthetic Design: A Machine Learning Augmentation," Papers 1907.07786, arXiv.org, revised Nov 2022.
    3. Ishita Chakraborty & Minkyung Kim & K. Sudhir, 2019. "Attribute Sentiment Scoring With Online Text Reviews : Accounting for Language Structure and Attribute Self-Selection," Cowles Foundation Discussion Papers 2176, Cowles Foundation for Research in Economics, Yale University.

Articles

  1. Daria Dzyabura & Siham El Kihal & John R. Hauser & Marat Ibragimov, 2023. "Leveraging the Power of Images in Managing Product Return Rates," Marketing Science, INFORMS, vol. 42(6), pages 1125-1142, November.

    Cited by:

    1. de Haan, Evert & Padigar, Manjunath & El Kihal, Siham & Kübler, Raoul & Wieringa, Jaap E., 2024. "Unstructured data research in business: Toward a structured approach," Journal of Business Research, Elsevier, vol. 177(C).

  2. Linda Hagen & Kosuke Uetake & Nathan Yang & Bryan Bollinger & Allison J. B. Chaney & Daria Dzyabura & Jordan Etkin & Avi Goldfarb & Liu Liu & K. Sudhir & Yanwen Wang & James R. Wright & Ying Zhu, 2020. "How can machine learning aid behavioral marketing research?," Marketing Letters, Springer, vol. 31(4), pages 361-370, December.

    Cited by:

    1. Akter, Shahriar & Dwivedi, Yogesh K. & Sajib, Shahriar & Biswas, Kumar & Bandara, Ruwan J. & Michael, Katina, 2022. "Algorithmic bias in machine learning-based marketing models," Journal of Business Research, Elsevier, vol. 144(C), pages 201-216.
    2. Chatterjee, Sheshadri & Chaudhuri, Ranjan & Vrontis, Demetris, 2022. "AI and digitalization in relationship management: Impact of adopting AI-embedded CRM system," Journal of Business Research, Elsevier, vol. 150(C), pages 437-450.
    3. Lyu, Wenjing & Liu, Jin, 2021. "Soft skills, hard skills: What matters most? Evidence from job postings," Applied Energy, Elsevier, vol. 300(C).
    4. Herhausen, Dennis & Bernritter, Stefan F. & Ngai, Eric W.T. & Kumar, Ajay & Delen, Dursun, 2024. "Machine learning in marketing: Recent progress and future research directions," Journal of Business Research, Elsevier, vol. 170(C).
    5. Erik Hermann, 2022. "Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective," Journal of Business Ethics, Springer, vol. 179(1), pages 43-61, August.
    6. Lyu, Wenjing & Liu, Jin, 2021. "Artificial Intelligence and emerging digital technologies in the energy sector," Applied Energy, Elsevier, vol. 303(C).
    7. Ngai, Eric W.T. & Wu, Yuanyuan, 2022. "Machine learning in marketing: A literature review, conceptual framework, and research agenda," Journal of Business Research, Elsevier, vol. 145(C), pages 35-48.
    8. Lutz, Bernhard & Pröllochs, Nicolas & Neumann, Dirk, 2022. "Are longer reviews always more helpful? Disentangling the interplay between review length and line of argumentation," Journal of Business Research, Elsevier, vol. 144(C), pages 888-901.
    9. Andreas Falke & Harald Hruschka, 2022. "Analyzing browsing across websites by machine learning methods," Journal of Business Economics, Springer, vol. 92(5), pages 829-852, July.

  3. Raluca M. Ursu & Daria Dzyabura, 2020. "Retailers’ product location problem with consumer search," Quantitative Marketing and Economics (QME), Springer, vol. 18(2), pages 125-154, June.

    Cited by:

    1. Greminger, Rafael, 2022. "Essays on consumer search," Other publications TiSEM 404020a9-8337-4950-b57f-0, Tilburg University, School of Economics and Management.
    2. Honka, Elisabeth & Seiler, Stephan & Ursu, Raluca, 2024. "Consumer search: What can we learn from pre-purchase data?," Journal of Retailing, Elsevier, vol. 100(1), pages 114-129.
    3. Fan, Ying & Fu, Yuqi & Yang, Zan & Chen, Ming, 2024. "Search frictions in rental markets: Evidence from urban China," China Economic Review, Elsevier, vol. 83(C).
    4. Fan, Ying & Fu, Yuqi & Yang, Zan, 2024. "Door-in-the-face heuristics: Intermediaries’ diversion in rental markets," Working Paper Series 24/2, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance.
    5. Harris, Mark N. & Novarese, Marco & Wilson, Chris M., 2022. "Being in the right place: A natural field experiment on the causes of position effects in individual choice," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 24-40.
    6. Giovanni Compiani & Gregory Lewis & Sida Peng & Peichun Wang, 2024. "Online Search and Optimal Product Rankings: An Empirical Framework," Marketing Science, INFORMS, vol. 43(3), pages 615-636, May.
    7. Raluca M. Ursu & Qianyun Zhang & Elisabeth Honka, 2023. "Search Gaps and Consumer Fatigue," Marketing Science, INFORMS, vol. 42(1), pages 110-136, January.

  4. Daria Dzyabura & Srikanth Jagabathula & Eitan Muller, 2019. "Accounting for Discrepancies Between Online and Offline Product Evaluations," Marketing Science, INFORMS, vol. 38(1), pages 88-106, January.

    Cited by:

    1. Roelen-Blasberg, Tobias & Habel, Johannes & Klarmann, Martin, 2023. "Automated inference of product attributes and their importance from user-generated content: Can we replace traditional market research?," International Journal of Research in Marketing, Elsevier, vol. 40(1), pages 164-188.
    2. Neslin, Scott A., 2022. "The omnichannel continuum: Integrating online and offline channels along the customer journey," Journal of Retailing, Elsevier, vol. 98(1), pages 111-132.
    3. Berg, Hanna & Lindström, Annika, 2021. "Online product size perceptions: Examining liquid volume size perceptions based on online product pictures," Journal of Business Research, Elsevier, vol. 122(C), pages 192-203.
    4. Lin, Yun Hui & Wang, Yuan & Lee, Loo Hay & Chew, Ek Peng, 2022. "Omnichannel facility location and fulfillment optimization," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 187-209.
    5. Zhang, Ting & Feng, Xiaohui & Wang, Ningning, 2021. "Manufacturer encroachment and product assortment under vertical differentiation," European Journal of Operational Research, Elsevier, vol. 293(1), pages 120-132.
    6. Cai, Ya-Jun & Lo, Chris K.Y., 2020. "Omni-channel management in the new retailing era: A systematic review and future research agenda," International Journal of Production Economics, Elsevier, vol. 229(C).
    7. Torsten J. Gerpott & Jan Berends, 2022. "Competitive pricing on online markets: a literature review," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(6), pages 596-622, December.
    8. Daria Dzyabura & John R. Hauser, 2019. "Recommending Products When Consumers Learn Their Preference Weights," Marketing Science, INFORMS, vol. 38(3), pages 417-441, May.
    9. Wai Kit Tsang & Dries F. Benoit, 2020. "Gaussian processes for daily demand prediction in tourism planning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 551-568, April.
    10. Hamsa Bastani, 2021. "Predicting with Proxies: Transfer Learning in High Dimension," Management Science, INFORMS, vol. 67(5), pages 2964-2984, May.
    11. Brooks Oppenheimer, 2024. "Including “touch-and-feel” in online consumer research: optimizing information gain given costs of data online versus in-person," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(2), pages 411-416, June.

  5. Daria Dzyabura & John R. Hauser, 2019. "Recommending Products When Consumers Learn Their Preference Weights," Marketing Science, INFORMS, vol. 38(3), pages 417-441, May.

    Cited by:

    1. Xitong Li & Jörn Grahl & Oliver Hinz, 2021. "How Do Recommender Systems Lead to Consumer Purchases? A Causal Mediation Analysis of a Field Experiment," Working Papers hal-03869071, HAL.
    2. Grenet, Julien & He, YingHua & Kübler, Dorothea, 2022. "Preference Discovery in University Admissions: The Case for Dynamic Multioffer Mechanisms," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 130(6), pages 1-1.
    3. Liu, Fan & Liao, Huchang & Al-Barakati, Abdullah, 2023. "Physician selection based on user-generated content considering interactive criteria and risk preferences of patients," Omega, Elsevier, vol. 115(C).
    4. Julien Grenet & YingHua He & Dorothea Kubler, 2021. "Decentralizing Centralized Matching Markets: Implications from Early Offers in University Admissions," Papers 2107.01532, arXiv.org, revised Jun 2022.
    5. Huang, Ming-Hui & Rust, Roland T., 2022. "A Framework for Collaborative Artificial Intelligence in Marketing," Journal of Retailing, Elsevier, vol. 98(2), pages 209-223.
    6. Bitty Balducci & Detelina Marinova, 2018. "Unstructured data in marketing," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 557-590, July.
    7. Kris J. Ferreira & Sunanda Parthasarathy & Shreyas Sekar, 2022. "Learning to Rank an Assortment of Products," Management Science, INFORMS, vol. 68(3), pages 1828-1848, March.
    8. Song, Yongming & Li, Yanhong & Zhu, Hongli & Li, Guangxu, 2023. "A decision support model for buying battery electric vehicles considering consumer learning and psychological behavior," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    9. Jose A. Carrasco & Rodrigo Yañez, 2022. "Sequential search and firm prominence," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 74(1), pages 209-233, July.
    10. Ming-Hui Huang & Roland T. Rust, 2021. "A strategic framework for artificial intelligence in marketing," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 30-50, January.
    11. Zhao, Meina & Wang, Xuqi, 2021. "Perception value of product-service systems: Neural effects of service experience and customer knowledge," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    12. Erdem Dogukan Yilmaz & Ivana Naumovska & Milan Miric, 2023. "Does imitation increase or decrease demand for an original product? Understanding the opposing effects of discovery and substitution," Strategic Management Journal, Wiley Blackwell, vol. 44(3), pages 639-671, March.

  6. Daria Dzyabura & Srikanth Jagabathula, 2018. "Offline Assortment Optimization in the Presence of an Online Channel," Management Science, INFORMS, vol. 64(6), pages 2767-2786, June.

    Cited by:

    1. Timoumi, Ahmed & Gangwar, Manish & Mantrala, Murali K., 2022. "Cross-channel effects of omnichannel retail marketing strategies: A review of extant data-driven research," Journal of Retailing, Elsevier, vol. 98(1), pages 133-151.
    2. Zhisong Chen & Shong-Iee Ivan Su, 2021. "Consignment supply chain cooperation for complementary products under online to offline business mode," Flexible Services and Manufacturing Journal, Springer, vol. 33(1), pages 136-182, March.
    3. Hübner, Alexander & Hense, Jonas & Dethlefs, Christian, 2022. "The revival of retail stores via omnichannel operations: A literature review and research framework," European Journal of Operational Research, Elsevier, vol. 302(3), pages 799-818.
    4. Cai, Ya-Jun & Lo, Chris K.Y., 2020. "Omni-channel management in the new retailing era: A systematic review and future research agenda," International Journal of Production Economics, Elsevier, vol. 229(C).
    5. Hense, Jonas & Hübner, Alexander, 2022. "Assortment optimization in omni-channel retailing," European Journal of Operational Research, Elsevier, vol. 301(1), pages 124-140.
    6. Jin, Delong & Caliskan-Demirag, Ozgun & Chen, Frank (Youhua) & Huang, Min, 2020. "Omnichannel retailers’ return policy strategies in the presence of competition," International Journal of Production Economics, Elsevier, vol. 225(C).
    7. Schäfer, Fabian & Hense, Jonas & Hübner, Alexander, 2023. "An analytical assessment of demand effects in omni-channel assortment planning," Omega, Elsevier, vol. 115(C).
    8. Daria Dzyabura & John R. Hauser, 2019. "Recommending Products When Consumers Learn Their Preference Weights," Marketing Science, INFORMS, vol. 38(3), pages 417-441, May.
    9. Daria Dzyabura & Srikanth Jagabathula & Eitan Muller, 2019. "Accounting for Discrepancies Between Online and Offline Product Evaluations," Marketing Science, INFORMS, vol. 38(1), pages 88-106, January.
    10. Gupta, Vishal Kumar & Ting, Q.U. & Tiwari, Manoj Kumar, 2019. "Multi-period price optimization problem for omnichannel retailers accounting for customer heterogeneity," International Journal of Production Economics, Elsevier, vol. 212(C), pages 155-167.
    11. Vasilyev, Andrey & Maier, Sebastian & Seifert, Ralf W., 2023. "Assortment optimization using an attraction model in an omnichannel environment," European Journal of Operational Research, Elsevier, vol. 306(1), pages 207-226.

  7. Daria Dzyabura & John R. Hauser, 2011. "Active Machine Learning for Consideration Heuristics," Marketing Science, INFORMS, vol. 30(5), pages 801-819, September.

    Cited by:

    1. Rosales-Tristancho, Abel & Brey, Raúl & Carazo, Ana F. & Brey, J. Javier, 2022. "Analysis of the barriers to the adoption of zero-emission vehicles in Spain," Transportation Research Part A: Policy and Practice, Elsevier, vol. 158(C), pages 19-43.
    2. Shervin Shahrokhi Tehrani & Andrew T. Ching, 2024. "A Heuristic Approach to Explore: The Value of Perfect Information," Management Science, INFORMS, vol. 70(5), pages 3200-3224, May.
    3. Bradlow, Eric T. & Gangwar, Manish & Kopalle, Praveen & Voleti, Sudhir, 2017. "The Role of Big Data and Predictive Analytics in Retailing," Journal of Retailing, Elsevier, vol. 93(1), pages 79-95.
    4. Hema Yoganarasimhan, 2020. "Search Personalization Using Machine Learning," Management Science, INFORMS, vol. 66(3), pages 1045-1070, March.
    5. Dongling Huang & Lan Luo, 2016. "Consumer Preference Elicitation of Complex Products Using Fuzzy Support Vector Machine Active Learning," Marketing Science, INFORMS, vol. 35(3), pages 445-464, May.
    6. James Agarwal & Wayne DeSarbo & Naresh K. Malhotra & Vithala Rao, 2015. "An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 2(1), pages 19-40, March.
    7. Maldonado, Sebastián & Montoya, Ricardo & Weber, Richard, 2015. "Advanced conjoint analysis using feature selection via support vector machines," European Journal of Operational Research, Elsevier, vol. 241(2), pages 564-574.
    8. Wang, Xin (Shane) & Ryoo, Jun Hyun (Joseph) & Bendle, Neil & Kopalle, Praveen K., 2021. "The role of machine learning analytics and metrics in retailing research," Journal of Retailing, Elsevier, vol. 97(4), pages 658-675.
    9. Olivier Toubia & Eric Johnson & Theodoros Evgeniou & Philippe Delquié, 2013. "Dynamic Experiments for Estimating Preferences: An Adaptive Method of Eliciting Time and Risk Parameters," Management Science, INFORMS, vol. 59(3), pages 613-640, June.
    10. Colin F. Camerer & Gideon Nave & Alec Smith, 2019. "Dynamic Unstructured Bargaining with Private Information: Theory, Experiment, and Outcome Prediction via Machine Learning," Management Science, INFORMS, vol. 65(4), pages 1867-1890, April.
    11. Bruno Jacobs & Dennis Fok & Bas Donkers, 2021. "Understanding Large-Scale Dynamic Purchase Behavior," Marketing Science, INFORMS, vol. 40(5), pages 844-870, September.
    12. Matthew J Salganik & Karen E C Levy, 2015. "Wiki Surveys: Open and Quantifiable Social Data Collection," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-17, May.
    13. Brighton, Henry & Gigerenzer, Gerd, 2015. "The bias bias," Journal of Business Research, Elsevier, vol. 68(8), pages 1772-1784.
    14. Hauser, John R., 2014. "Consideration-set heuristics," Journal of Business Research, Elsevier, vol. 67(8), pages 1688-1699.
    15. Ma, Liye & Sun, Baohong, 2020. "Machine learning and AI in marketing – Connecting computing power to human insights," International Journal of Research in Marketing, Elsevier, vol. 37(3), pages 481-504.
    16. Shasha Lu & Li Xiao & Min Ding, 2016. "A Video-Based Automated Recommender (VAR) System for Garments," Marketing Science, INFORMS, vol. 35(3), pages 484-510, May.
    17. Asim Ansari & Yang Li & Jonathan Z. Zhang, 2018. "Probabilistic Topic Model for Hybrid Recommender Systems: A Stochastic Variational Bayesian Approach," Marketing Science, INFORMS, vol. 37(6), pages 987-1008, November.
    18. Yufeng Huang & Bart J. Bronnenberg, 2018. "Pennies for Your Thoughts: Costly Product Consideration and Purchase Quantity Thresholds," Marketing Science, INFORMS, vol. 37(6), pages 1009-1028, November.
    19. Daria Dzyabura & Srikanth Jagabathula & Eitan Muller, 2019. "Accounting for Discrepancies Between Online and Offline Product Evaluations," Marketing Science, INFORMS, vol. 38(1), pages 88-106, January.
    20. Falke Andreas & Hruschka Harald, 2016. "A Monte Carlo Study of Design Procedures for the Semi-parametric Mixed Logit Model," Review of Marketing Science, De Gruyter, vol. 14(1), pages 21-67, June.
    21. Andrea MAKINGS & Brian BARNARD, 2019. "The Heuristics of Entrepreneurs," Expert Journal of Business and Management, Sprint Investify, vol. 7(2), pages 179-203.
    22. Daniel R. Cavagnaro & Richard Gonzalez & Jay I. Myung & Mark A. Pitt, 2013. "Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach," Management Science, INFORMS, vol. 59(2), pages 358-375, February.
    23. Mengxia Zhang & Lan Luo, 2023. "Can Consumer-Posted Photos Serve as a Leading Indicator of Restaurant Survival? Evidence from Yelp," Management Science, INFORMS, vol. 69(1), pages 25-50, January.
    24. Denis Sauré & Juan Pablo Vielma, 2019. "Ellipsoidal Methods for Adaptive Choice-Based Conjoint Analysis," Operations Research, INFORMS, vol. 67(2), pages 315-338, March.
    25. Mingyu Joo & Michael L. Thompson & Greg M. Allenby6, 2019. "Optimal Product Design by Sequential Experiments in High Dimensions," Management Science, INFORMS, vol. 65(7), pages 3235-3254, July.
    26. Mele, Cristina & Russo Spena, Tiziana & Kaartemo, Valtteri & Marzullo, Maria Luisa, 2021. "Smart nudging: How cognitive technologies enable choice architectures for value co-creation," Journal of Business Research, Elsevier, vol. 129(C), pages 949-960.
    27. Ming-Hui Huang & Roland T. Rust, 2021. "A strategic framework for artificial intelligence in marketing," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 30-50, January.
    28. Rust, Roland T., 2020. "The future of marketing," International Journal of Research in Marketing, Elsevier, vol. 37(1), pages 15-26.
    29. Hema Yoganarasimhan & Ebrahim Barzegary & Abhishek Pani, 2020. "Design and Evaluation of Personalized Free Trials," Papers 2006.13420, arXiv.org.
    30. Roland T. Rust & Ming-Hui Huang, 2014. "The Service Revolution and the Transformation of Marketing Science," Marketing Science, INFORMS, vol. 33(2), pages 206-221, March.
    31. Bremer, Lucas & Heitmann, Mark & Schreiner, Thomas F., 2017. "When and how to infer heuristic consideration set rules of consumers," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 516-535.

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Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 2 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-BIG: Big Data (2) 2020-05-04 2020-05-04
  2. NEP-CMP: Computational Economics (2) 2020-05-04 2020-05-04
  3. NEP-IPR: Intellectual Property Rights (1) 2020-05-04
  4. NEP-MKT: Marketing (1) 2020-05-04
  5. NEP-ORE: Operations Research (1) 2020-05-04
  6. NEP-PAY: Payment Systems and Financial Technology (1) 2020-05-04

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