IDEAS home Printed from https://ideas.repec.org/r/inm/ormksc/v36y2017i5p726-746.html
   My bibliography  Save this item

The Effect of Calorie Posting Regulation on Consumer Opinion: A Flexible Latent Dirichlet Allocation Model with Informative Priors

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Yi Yang & Kunpeng Zhang & Yangyang Fan, 2023. "sDTM: A Supervised Bayesian Deep Topic Model for Text Analytics," Information Systems Research, INFORMS, vol. 34(1), pages 137-156, March.
  2. Soumya Mukhopadhyay & V Kumar & Amalesh Sharma & Tuck Siong Chung, 2022. "Impact of review narrativity on sales in a competitive environment," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2538-2556, June.
  3. Hyowon Kim & Greg M. Allenby, 2022. "Integrating Textual Information into Models of Choice and Scaled Response Data," Marketing Science, INFORMS, vol. 41(4), pages 815-830, July.
  4. Yash Raj Shrestha & Vivianna Fang He & Phanish Puranam & Georg von Krogh, 2021. "Algorithm Supported Induction for Building Theory: How Can We Use Prediction Models to Theorize?," Organization Science, INFORMS, vol. 32(3), pages 856-880, May.
  5. Arianna Marchetti & Phanish Puranam, 2022. "Organizational cultural strength as the negative cross-entropy of mindshare: a measure based on descriptive text," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-14, December.
  6. Jiawei Chen & Yinghui (Catherine) Yang & Hongyan Liu, 2021. "Mining Bilateral Reviews for Online Transaction Prediction: A Relational Topic Modeling Approach," Information Systems Research, INFORMS, vol. 32(2), pages 541-560, June.
  7. Oetzel, Sebastian & Graf, Denise, 2023. "Fragen oder Zuhören? Ein Vergleich von Kundenbefragungen und User Generated Content," PraxisWISSEN Marketing: German Journal of Marketing, AfM – Arbeitsgemeinschaft für Marketing, vol. 8(01/2023), pages 91-107.
  8. Hartmann, Jochen & Huppertz, Juliana & Schamp, Christina & Heitmann, Mark, 2019. "Comparing automated text classification methods," International Journal of Research in Marketing, Elsevier, vol. 36(1), pages 20-38.
  9. Saridakis, Charalampos & Katsikeas, Constantine S. & Angelidou, Sofia & Oikonomidou, Maria & Pratikakis, Polyvios, 2023. "Mining Twitter lists to extract brand-related associative information for celebrity endorsement," European Journal of Operational Research, Elsevier, vol. 311(1), pages 316-332.
  10. Maximilian Matthe & Daniel M. Ringel & Bernd Skiera, 2023. "Mapping Market Structure Evolution," Marketing Science, INFORMS, vol. 42(3), pages 589-613, May.
  11. Feifei Wang & Yang Yang & Geoffrey K. F. Tso & Yang Li, 2019. "Analysis of launch strategy in cross-border e-Commerce market via topic modeling of consumer reviews," Electronic Commerce Research, Springer, vol. 19(4), pages 863-884, December.
  12. Ning Zhong & David A. Schweidel, 2020. "Capturing Changes in Social Media Content: A Multiple Latent Changepoint Topic Model," Marketing Science, INFORMS, vol. 39(4), pages 827-846, July.
  13. Marit Hinnosaar, 2023. "The Persistence of Healthy Behaviors in Food Purchasing," Marketing Science, INFORMS, vol. 42(3), pages 521-537, May.
  14. Schauerte, Nico & Becker, Maren & Imschloss, Monika & Wichmann, Julian R.K. & Reinartz, Werner J., 2023. "The managerial relevance of marketing science: Properties and genesis," International Journal of Research in Marketing, Elsevier, vol. 40(4), pages 801-822.
  15. 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 2176R, Cowles Foundation for Research in Economics, Yale University, revised Sep 2020.
  16. Wei Chen & Karen Xie & Jianwei Liu & Yong Liu, 2019. "How Incumbents Beat Disruptors? Evidence from Hotels’ Responses to Home-sharing Rivals," Working Papers 19-11, NET Institute.
  17. Piyush Anand & Clarence Lee, 2023. "Using Deep Learning to Overcome Privacy and Scalability Issues in Customer Data Transfer," Marketing Science, INFORMS, vol. 42(1), pages 189-207, January.
  18. Liu, Yezheng & Qian, Yang & Jiang, Yuanchun & Shang, Jennifer, 2020. "Using favorite data to analyze asymmetric competition: Machine learning models," European Journal of Operational Research, Elsevier, vol. 287(2), pages 600-615.
  19. Kathleen T. Li, 2024. "Frontiers: A Simple Forward Difference-in-Differences Method," Marketing Science, INFORMS, vol. 43(2), pages 267-279, March.
  20. 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.
  21. 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 2176R2, Cowles Foundation for Research in Economics, Yale University, revised Jun 2021.
  22. Sotaro Katsumata & Seungjin Kim, 2020. "The Text-Score Allocation Model: Finding Latent Topics of Online Review Documents and Multi-Item Ratings," Discussion Papers in Economics and Business 20-01, Osaka University, Graduate School of Economics.
  23. Alantari, Huwail J. & Currim, Imran S. & Deng, Yiting & Singh, Sameer, 2022. "An empirical comparison of machine learning methods for text-based sentiment analysis of online consumer reviews," International Journal of Research in Marketing, Elsevier, vol. 39(1), pages 1-19.
  24. Robert W. Palmatier & Andrew T. Crecelius, 2019. "The “first principles” of marketing strategy," AMS Review, Springer;Academy of Marketing Science, vol. 9(1), pages 5-26, June.
  25. Dinesh Puranam & Vrinda Kadiyali & Vishal Narayan, 2021. "The Impact of Increase in Minimum Wages on Consumer Perceptions of Service: A Transformer Model of Online Restaurant Reviews," Marketing Science, INFORMS, vol. 40(5), pages 985-1004, September.
  26. Bruno Jacobs & Dennis Fok & Bas Donkers, 2021. "Understanding Large-Scale Dynamic Purchase Behavior," Marketing Science, INFORMS, vol. 40(5), pages 844-870, September.
  27. Zhang, Min & Sun, Lin & Wang, G. Alan & Li, Yuzhuo & He, Shuguang, 2022. "Using neutral sentiment reviews to improve customer requirement identification and product design strategies," International Journal of Production Economics, Elsevier, vol. 254(C).
  28. 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.
  29. Alzate, Miriam & Arce-Urriza, Marta & Cebollada, Javier, 2022. "Mining the text of online consumer reviews to analyze brand image and brand positioning," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).
  30. 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.
  31. S. Sajeesh & Ozgur M. Araz & Terry T.‐K. Huang, 2022. "Market positioning in food industry in response to public health policies," Production and Operations Management, Production and Operations Management Society, vol. 31(7), pages 2962-2981, July.
  32. Kim, Jong Min & Park, Keeyeon Ki-cheon & Mariani, Marcello M., 2023. "Do online review readers react differently when exposed to credible versus fake online reviews?," Journal of Business Research, Elsevier, vol. 154(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.