create a website

Beyond accuracy measures: the effect of diversity, novelty and serendipity in recommender systems on user engagement. (2025). Ping, Yanni ; Li, Yang ; Zhu, Jiaxin.
In: Electronic Commerce Research.
RePEc:spr:elcore:v:25:y:2025:i:3:d:10.1007_s10660-024-09813-w.

Full description at Econpapers || Download paper

Cited: 0

Citations received by this document

Cites: 60

References cited by this document

Cocites: 26

Documents which have cited the same bibliography

Coauthors: 0

Authors who have wrote about the same topic

Citations

Citations received by this document

    This document has not been cited yet.

References

References cited by this document

  1. Adamopoulos, P., & Tuzhilin, A. (2015). On unexpectedness in recommender systems. ACM Transactions on Intelligent Systems and Technology, 5(4), 1–32. https://doi.org/10.1145/2559952 .
    Paper not yet in RePEc: Add citation now
  2. Aivazoglou, M., Roussos, A. O., Margaris, D., Vassilakis, C., Ioannidis, S., Polakis, J., & Spiliotopoulos, D. (2020). A fine-grained social network recommender system. Social Network Analysis and Mining, 10(1), 8. https://doi.org/10.1007/s13278-019-0621-7 .
    Paper not yet in RePEc: Add citation now

  3. Baier, D., & Stüber, E. (2010). Acceptance of recommendations to buy in online retailing. Journal of Retailing and Consumer Services, 17, 173–180. https://doi.org/10.1016/j.jretconser.2010.03.005 .

  4. Brodie, R. J., Hollebeek, L. D., Jurić, B., & Ilić, A. (2011). Customer engagement: conceptual domain, fundamental propositions, and implications for research. Journal of Service Research, 14(3), 252–271. https://doi.org/10.1177/1094670511411703 .
    Paper not yet in RePEc: Add citation now

  5. Brodie, R. J., Ilic, A., Juric, B., & Hollebeek, L. (2013). Consumer engagement in a virtual brand community: An exploratory analysis. Journal of Business Research, 66(1), 105–114. https://doi.org/10.1016/j.jbusres.2011.07.029 .

  6. Calder, B. J., Isaac, M. S., & Malthouse, E. C. (2016). How to capture consumer experiences: A context-specific approach to measuring engagement. Journal of Advertising Research, 56(1), 39–52. https://doi.org/10.2501/JAR-2015-028 .
    Paper not yet in RePEc: Add citation now
  7. Castells, Pablo, Hurley, Neil J., & Vargas, Saul. (2015). Novelty and diversity in recommender systems. Recommender systems handbook (pp. 881–918). Springer, US.
    Paper not yet in RePEc: Add citation now

  8. Chen, A., Yaobin, Lu., Wang, B., Zhao, L., & Li, M. (2013). What drives content creation behavior on SNSs? A commitment perspective. Journal of Business Research, 66(12), 2529–2535. https://doi.org/10.1016/j.jbusres.2013.05.045 .

  9. Cheng, P., Wang, S., Ma, J., Sun, J., & Xiong, H. (2017). Learning to recommend accurate and diverse items. In Proceedings of the 26th international conference on world wide web (pp. 183–192). ACM. https://doi.org/10.1145/3038912.3052585 .
    Paper not yet in RePEc: Add citation now
  10. Choi, K., & Suh, Y. (2013). A new similarity function for selecting neighbors for each target item in collaborative filtering. Knowledge-Based Systems, 37, 146–153. https://doi.org/10.1016/j.knosys.2012.07.019 .
    Paper not yet in RePEc: Add citation now
  11. Choi, S.-M., Ko, S.-K., & Han, Y.-S. (2012). A movie recommendation algorithm based on genre correlations. Expert Systems with Applications, 39(9), 8079–8085. https://doi.org/10.1016/j.eswa.2012.01.132 .
    Paper not yet in RePEc: Add citation now
  12. de Gemmis, M., Lops, P., Semeraro, G., & Musto, C. (2015). An investigation on the serendipity problem in recommender systems. Information Processing & Management, 51(5), 695–717. https://doi.org/10.1016/j.ipm.2015.06.008 .
    Paper not yet in RePEc: Add citation now

  13. Eady, G., Nagler, J., Guess, A., Zilinsky, J., & Tucker, J. A. (2019). How Many people live in political bubbles on social media? Evidence from linked survey and twitter data. SAGE Open, 9(1), 215824401983270. https://doi.org/10.1177/2158244019832705 .

  14. Fleder, D., & Hosanagar, K. (2009). Blockbuster culture’s next rise or fall: The Impact of recommender systems on sales diversity. Management Science, 55(5), 697–712. https://doi.org/10.1287/mnsc.1080.0974 .

  15. Giurgiu, L., Bârsan, G., Academia Forrelor Terestre, & Bălcescu, N. (2008). The prosumer-core and consequence of the web 2.0 era.
    Paper not yet in RePEc: Add citation now
  16. Gunawardana, A., & Shani, G. (2015). Evaluating recommender systems. In Recommender systems handbook (pp. 265–308). Boston, MA: Springer US.
    Paper not yet in RePEc: Add citation now
  17. Hamilton, M., & Singal, R. (2021). Churning While learning: maximizing user engagement in a recommendation system. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3871915 .
    Paper not yet in RePEc: Add citation now
  18. Helberger, N., Karppinen, K., & D’Acunto, L. (2018). Exposure diversity as a design principle for recommender systems. Information, Communication & Society, 21(2), 191–207. https://doi.org/10.1080/1369118X.2016.1271900 .
    Paper not yet in RePEc: Add citation now
  19. Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1), 5–53. https://doi.org/10.1145/963770.963772 .
    Paper not yet in RePEc: Add citation now

  20. Hollebeek, L. D., Glynn, M. S., & Brodie, R. J. (2014). Consumer brand engagement in social media: Conceptualization, scale development and validation. Journal of Interactive Marketing, 28(2), 149–165. https://doi.org/10.1016/j.intmar.2013.12.002 .

  21. Hollebeek, L. D., Srivastava, R. K., & Chen, T. (2019). S-D logic-informed customer engagement: Integrative framework, revised fundamental propositions, and application to CRM. Journal of the Academy of Marketing Science, 47(1), 161–185. https://doi.org/10.1007/s11747-016-0494-5 .

  22. Hong, L., & Lalmas, M. (2019). Tutorial on online user engagement: Metrics and optimization. In Companion proceedings of the 2019 World Wide Web conference (pp. 1303–1305). ACM.
    Paper not yet in RePEc: Add citation now
  23. Hurley, N., & Zhang, Mi. (2011). Novelty and diversity in top-N recommendation – Analysis and evaluation. ACM Transactions on Internet Technology, 10(4), 1–30. https://doi.org/10.1145/1944339.1944341 .
    Paper not yet in RePEc: Add citation now
  24. Kaminskas, M., & Bridge, D. (2017). Diversity, serendipity, novelty, and coverage. ACM Transactions on Interactive Intelligent Systems, 7(1), 1–42. https://doi.org/10.1145/2926720 .
    Paper not yet in RePEc: Add citation now
  25. Karimi, M., Jannach, D., & Jugovac, M. (2018). News Recommender systems – Survey and roads ahead. Information Processing & Management, 54(6), 1203–1227. https://doi.org/10.1016/j.ipm.2018.04.008 .
    Paper not yet in RePEc: Add citation now
  26. Kim, K.-J., & Ahn, H. (2008). A Recommender system using GA K-means clustering in an online shopping market. Expert Systems with Applications, 34(2), 1200–1209. https://doi.org/10.1016/j.eswa.2006.12.025 .
    Paper not yet in RePEc: Add citation now
  27. Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30–37. https://doi.org/10.1109/MC.2009.263 .
    Paper not yet in RePEc: Add citation now
  28. Kotkov, D., Veijalainen, J., & Wang, S. (2020). How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm. Computing, 102(2), 393–411. https://doi.org/10.1007/s00607-018-0687-5 .
    Paper not yet in RePEc: Add citation now
  29. Kotkov, D., Wang, S., & Veijalainen, J. (2016). A survey of serendipity in recommender systems. Knowledge-Based Systems, 111, 180–192. https://doi.org/10.1016/j.knosys.2016.08.014 .
    Paper not yet in RePEc: Add citation now
  30. Lehmann, J., Lalmas, M., Yom-Tov, E., & Dupret, G. (2012). Models of user engagement. In User modeling, adaptation, and personalization (pp. 164–75). Springer.
    Paper not yet in RePEc: Add citation now
  31. Li, Y., Ping, Y., Zhong, Y., & Misra, R. (2023). Learning-by-doing in non-homogeneous tasks: An empirical study of content creator performance on a music streaming platform. Electronic Commerce Research and Applications, 58, 101241. https://doi.org/10.1016/j.elerap.2023.101241 .
    Paper not yet in RePEc: Add citation now
  32. Lu, J., Dianshuang, Wu., Mao, M., Wang, W., & Zhang, G. (2015). Recommender system application developments: A survey. Decision Support Systems, 74, 12–32. https://doi.org/10.1016/j.dss.2015.03.008 .
    Paper not yet in RePEc: Add citation now
  33. Lunardi, G. M., Machado, G. M., Maran, V., & de Oliveira., J. P. (2020). A metric for filter bubble measurement in recommender algorithms considering the news domain. Applied Soft Computing, 97, 106771. https://doi.org/10.1016/j.asoc.2020.106771 .
    Paper not yet in RePEc: Add citation now
  34. Lundquist, E. (2020, June 28). Factorization machines for item recommendation with implicit feedback data. Towards Data Science.
    Paper not yet in RePEc: Add citation now
  35. Maslowska, E., Malthouse, E. C., & Hollebeek, L. D. (2022). The role of recommender systems in fostering consumers’ long-term platform engagement. Journal of Service Management, 33(4/5), 721–732. https://doi.org/10.1108/JOSM-12-2021-0487 .
    Paper not yet in RePEc: Add citation now
  36. Nakatsuji, M., Fujiwara, Y., Tanaka, A., Uchiyama, T., Fujimura, K., & Ishida, T. (2010). Classical music for rock fans? In Proceedings of the 19th ACM international conference on Information and knowledge management (pp. 949–958). ACM.
    Paper not yet in RePEc: Add citation now
  37. Nguyen, T. T., Hui, P.-M., Maxwell Harper, F., Terveen, L., & Konstan, J. A. (2014). Exploring the filter bubble. In Proceedings of the 23rd international conference on World Wide Web (pp. 677–686). ACM.
    Paper not yet in RePEc: Add citation now
  38. Pariser, E. (2011). The filter bubble: What the internet is hiding from you. KG: Carl Hanser Verlag GmbH & Co.
    Paper not yet in RePEc: Add citation now

  39. Plümper, T., & Troeger, V. E. (2007). Efficient estimation of time-invariant and rarely changing variables in finite sample panel analyses with unit fixed effects. Political Analysis, 15(2), 124–139. https://doi.org/10.1093/pan/mpm002 .

  40. Preece, J., Nonnecke, B., & Andrews, D. (2004). The Top five reasons for lurking: Improving community experiences for everyone. Computers in Human Behavior, 20(2), 201–223. https://doi.org/10.1016/j.chb.2003.10.015 .
    Paper not yet in RePEc: Add citation now
  41. Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction to recommender systems handbook. In Recommender systems handbook (pp. 1–35). Springer US.
    Paper not yet in RePEc: Add citation now
  42. Roodman, D. (2020). Xtabond2: Stata module to extend xtabond dynamic panel data estimator.
    Paper not yet in RePEc: Add citation now
  43. Roy, D., & Dutta, M. (2022). A systematic review and research perspective on recommender systems. Journal of Big Data, 9(1), 59. https://doi.org/10.1186/s40537-022-00592-5 .
    Paper not yet in RePEc: Add citation now
  44. Salvador, F., Alba, C., Madiedo, J. P., Tenhiälä, A., & Bendoly, E. (2021). Project managers’ breadth of experience, project complexity, and project performance. Journal of Operations Management, 67(6), 729–754. https://doi.org/10.1002/joom.1140 .
    Paper not yet in RePEc: Add citation now
  45. Schomakers, E.-M., Lidynia, C., & Ziefle, M. (2020). All of me? Users’ Preferences for privacy-preserving data markets and the importance of anonymity. Electronic Markets, 30(3), 649–665. https://doi.org/10.1007/s12525-020-00404-9 .
    Paper not yet in RePEc: Add citation now
  46. Seo, Y.-D., Kim, Y.-G., Lee, E., & Kim, H. (2021). Group recommender system based on genre preference focusing on reducing the clustering cost. Expert Systems with Applications, 183, 115396. https://doi.org/10.1016/j.eswa.2021.115396 .
    Paper not yet in RePEc: Add citation now
  47. Silveira, T., Zhang, M., Lin, X., Liu, Y., & Ma, S. (2019). How Good your recommender system is? A survey on evaluations in recommendation. International Journal of Machine Learning and Cybernetics, 10(5), 813–831. https://doi.org/10.1007/s13042-017-0762-9 .
    Paper not yet in RePEc: Add citation now

  48. Srivastava, A., Bala, P. K., & Kumar, B. (2020). New perspectives on gray sheep behavior in E-commerce recommendations. Journal of Retailing and Consumer Services 53. https://doi.org/10.1016/j.jretconser.2019.02.018 .

  49. Sun, J., Song, J., Jiang, Y., Liu, Y., & Li, J. (2022). Prick the filter bubble: A novel cross domain recommendation model with adaptive diversity regularization. Electronic Markets, 32(1), 101–121. https://doi.org/10.1007/s12525-021-00492-1 .

  50. Toffler, A. (1980). The Third wave. William Morrow & Company Inc.
    Paper not yet in RePEc: Add citation now
  51. van Doorn, J., Lemon, K. N., Mittal, V., Nass, S., Pick, D., Pirner, P., & Verhoef, P. C. (2010). Customer engagement behavior: Theoretical foundations and research directions. Journal of Service Research, 13(3), 253–266. https://doi.org/10.1177/1094670510375599 .
    Paper not yet in RePEc: Add citation now
  52. Wu, W., Chen, L., & Zhao, Y. (2018). Personalizing recommendation diversity based on user personality. User Modeling & User-Adapted Interaction, 28(3), 237–276. https://doi.org/10.1007/s11257-018-9205-x .
    Paper not yet in RePEc: Add citation now

  53. Yoganarasimhan, H. (2012). Impact of social network structure on content propagation: A study using YouTube data. Quantitative Marketing and Economics, 10(1), 111–150. https://doi.org/10.1007/s11129-011-9105-4 .

  54. Zhang, D. J., Ming, Hu., Liu, X., Yuxiang, Wu., & Li, Y. (2022). NetEase cloud music data. Manufacturing & Service Operations Management, 24(1), 275–284. https://doi.org/10.1287/msom.2020.0923 .

  55. Zhang, Y. C., Séaghdha, D. Ó., Quercia, D., Jambor, T. (2012). Auralist: Introducing serendipity into music recommendation. In Proceedings of the fifth ACM international conference on web search and data mining (pp 13–22). ACM.
    Paper not yet in RePEc: Add citation now
  56. Zheng, Q., Chan, C.-K., & Ip, H. H. S. (2015). An unexpectedness-augmented utility model for making serendipitous recommendation. In Advances in data mining: Applications and theoretical aspects (pp. 216–30). Springer.
    Paper not yet in RePEc: Add citation now
  57. Zhou, T., Kuscsik, Z., Liu, J.-G., Medo, M., Wakeling, J. R., & Zhang, Y.-C. (2010). Solving the apparent diversity-accuracy dilemma of recommender systems. Proceedings of the National Academy of Sciences, 107(10), 4511–4515. https://doi.org/10.1073/pnas.1000488107 .
    Paper not yet in RePEc: Add citation now
  58. Ziegler, C.-N., McNee, S. M., Konstan, J. A., & Lausen, G. (2005). Improving recommendation lists through topic diversification. In Proceedings of the 14th international conference on World Wide Web – WWW ’05 (p. 22). ACM Press.
    Paper not yet in RePEc: Add citation now
  59. Ziemba, W., Ewa, M. E., Mullins, R., & Dettmer, S. (2019). Prosumers’ engagement in business process innovation – The case of Poland and the UK. Interdisciplinary Journal of Information, Knowledge, and Management, 14, 119–143. https://doi.org/10.28945/4320 .
    Paper not yet in RePEc: Add citation now
  60. Zou, L., Xia, L., Ding, Z., Song, J., Liu, W., & Yin, D. (2019). Reinforcement learning to optimize long-term user engagement in recommender systems. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 2810–2818). New York, NY, USA, ACM.
    Paper not yet in RePEc: Add citation now

Cocites

Documents in RePEc which have cited the same bibliography

  1. Beyond accuracy measures: the effect of diversity, novelty and serendipity in recommender systems on user engagement. (2025). Ping, Yanni ; Li, Yang ; Zhu, Jiaxin.
    In: Electronic Commerce Research.
    RePEc:spr:elcore:v:25:y:2025:i:3:d:10.1007_s10660-024-09813-w.

    Full description at Econpapers || Download paper

  2. Is suggestive selling effective in increasing sales? Investigating its role in store promotion strategy using retail chain data from the U.S.. (2023). Bhatt, Siddharth ; Pai, Dinesh Ramdas.
    In: Journal of Marketing Analytics.
    RePEc:pal:jmarka:v:11:y:2023:i:1:d:10.1057_s41270-021-00152-5.

    Full description at Econpapers || Download paper

  3. Development of methodology for classification of user experience (UX) in online customer review. (2023). Son, Youngdoo ; Kim, Wonjoon.
    In: Journal of Retailing and Consumer Services.
    RePEc:eee:joreco:v:71:y:2023:i:c:s0969698922003034.

    Full description at Econpapers || Download paper

  4. DEVELOPMENT OF MEASUREMENT SCALE FOR PERSONALIZED RECOMMENDED PRODUCT ACCEPTANCE (PRPA-SCALE). (2022). Emmanuel, Debrah ; Seth, Ampadu ; Jiang, Yuanchun ; Gyamfi, Samuel Adu ; Amankwa, Eric.
    In: Malaysian E Commerce Journal (MECJ).
    RePEc:zib:zbmecj:v:6:y:2022:i:2:p:76-85.

    Full description at Econpapers || Download paper

  5. Exploring customers’ likeliness to use e-service touchpoints in brick and mortar retail. (2022). Niemann, Marco ; Becker, Jorg ; Betzing, Jan H ; Barann, Benjamin ; Hoffmeister, Benedikt.
    In: Electronic Markets.
    RePEc:spr:elmark:v:32:y:2022:i:2:d:10.1007_s12525-020-00445-0.

    Full description at Econpapers || Download paper

  6. Enhancing playful customer experience with personalization. (2022). Magrofuoco, Nathan ; Lambillotte, Laetitia ; Vanderdonckt, Jean ; Poncin, Ingrid.
    In: Journal of Retailing and Consumer Services.
    RePEc:eee:joreco:v:68:y:2022:i:c:s0969698922001102.

    Full description at Econpapers || Download paper

  7. Economic corollaries of personalized recommendations. (2022). Lee, Wonjae ; Molaie, Mir Majid.
    In: Journal of Retailing and Consumer Services.
    RePEc:eee:joreco:v:68:y:2022:i:c:s0969698922000960.

    Full description at Econpapers || Download paper

  8. How a €œChina-made€ label influences Chinese Youths product evaluation: The priming effect of patriotic and nationalistic news. (2022). Yu, Shubin ; Liu, Huaming.
    In: Journal of Retailing and Consumer Services.
    RePEc:eee:joreco:v:66:y:2022:i:c:s0969698921004653.

    Full description at Econpapers || Download paper

  9. Is the shopper always the king/queen? Study of omnichannel retail technology use and shopping orientations. (2022). ben Mimoun, Mohamed Slim ; Miltgen, Caroline Lancelot ; Slama, Boulbeba.
    In: Journal of Retailing and Consumer Services.
    RePEc:eee:joreco:v:65:y:2022:i:c:s0969698921004100.

    Full description at Econpapers || Download paper

  10. Artificial intelligence and the new forms of interaction: Who has the control when interacting with a chatbot?. (2021). Pizzi, Gabriele ; Pantano, Eleonora ; Scarpi, Daniele.
    In: Journal of Business Research.
    RePEc:eee:jbrese:v:129:y:2021:i:c:p:878-890.

    Full description at Econpapers || Download paper

  11. Can free-shipping hurt online retailers?. (2020). Gil, Ricard ; Sahin, Ozge ; Korkmaz, Evsen.
    In: Quantitative Marketing and Economics (QME).
    RePEc:kap:qmktec:v:18:y:2020:i:3:d:10.1007_s11129-020-09225-8.

    Full description at Econpapers || Download paper

  12. The evolving role of hit and niche products in brick-and-mortar retail category assortment planning: A large-scale empirical investigation of U.S. consumer packaged goods. (2020). Hoskins, Jake D.
    In: Journal of Retailing and Consumer Services.
    RePEc:eee:joreco:v:57:y:2020:i:c:s096969892030566x.

    Full description at Econpapers || Download paper

  13. New perspectives on gray sheep behavior in E-commerce recommendations. (2020). Bala, Pradip Kumar ; Kumar, Bipul ; Srivastava, Abhishek.
    In: Journal of Retailing and Consumer Services.
    RePEc:eee:joreco:v:53:y:2020:i:c:s0969698918307999.

    Full description at Econpapers || Download paper

  14. Multichannel personalization: Identifying consumer preferences for product recommendations in advertisements across different media channels. (2019). Schreiner, Timo ; Baier, Daniel ; Rese, Alexandra.
    In: Journal of Retailing and Consumer Services.
    RePEc:eee:joreco:v:48:y:2019:i:c:p:87-99.

    Full description at Econpapers || Download paper

  15. Do enriched digital catalogues offer compelling experiences, beyond websites? A comparative analysis through the IKEA case. (2019). Garnier, Marion ; Poncin, Ingrid.
    In: Journal of Retailing and Consumer Services.
    RePEc:eee:joreco:v:47:y:2019:i:c:p:361-369.

    Full description at Econpapers || Download paper

  16. Fashion brands on retail websites: Customer performance expectancy and e-word-of-mouth. (2018). , Sandra ; Cavallero, Luisa ; Miranda, Francisco Javier.
    In: Journal of Retailing and Consumer Services.
    RePEc:eee:joreco:v:41:y:2018:i:c:p:131-141.

    Full description at Econpapers || Download paper

  17. Help welcome or not: Understanding consumer shopping motivation in curated fashion retailing. (2018). Jacob, Frank ; Sebald, Anna Kathrin.
    In: Journal of Retailing and Consumer Services.
    RePEc:eee:joreco:v:40:y:2018:i:c:p:188-203.

    Full description at Econpapers || Download paper

  18. An empirical analysis of factors predicting the behavioral intention to adopt Internet shopping technology among non-shoppers in a developing country context: Does gender matter?. (2016). , Khaled.
    In: Journal of Retailing and Consumer Services.
    RePEc:eee:joreco:v:30:y:2016:i:c:p:140-164.

    Full description at Econpapers || Download paper

  19. A valued agent: How ECAs affect website customers satisfaction and behaviors. (2015). ben Mimoun, Mohammed Slim ; Poncin, Ingrid.
    In: Journal of Retailing and Consumer Services.
    RePEc:eee:joreco:v:26:y:2015:i:c:p:70-82.

    Full description at Econpapers || Download paper

  20. Customization in bicycle retailing. (2015). Osnes, Tone-Lise ; Jafari, Hamid ; Schmitz, Annika ; Nyberg, Anna.
    In: Journal of Retailing and Consumer Services.
    RePEc:eee:joreco:v:23:y:2015:i:c:p:77-90.

    Full description at Econpapers || Download paper

  21. Optimal Pricing of Access and Secondary Goods with Repeat Purchases: Evidence from Online Grocery Shopping and Delivery Fees. (2014). Gil, Ricard ; Sahin, Ozge ; Korkmaz, Evsen.
    In: Working Papers.
    RePEc:net:wpaper:14-10.

    Full description at Econpapers || Download paper

  22. Key determinants of sustainable smartcard payment. (2014). Wong, Wing-Keung ; Liao, Ziqi ; Shi, Xinping.
    In: Journal of Retailing and Consumer Services.
    RePEc:eee:joreco:v:21:y:2014:i:3:p:306-313.

    Full description at Econpapers || Download paper

  23. Image data analysis and classification in marketing. (2012). Frost, Sarah ; Naundorf, Robert ; Daniel, Ines ; Baier, Daniel.
    In: Advances in Data Analysis and Classification.
    RePEc:spr:advdac:v:6:y:2012:i:4:p:253-276.

    Full description at Econpapers || Download paper

  24. How Retailers’ Recommendation and Return Policies Alter Product Evaluations. (2012). Kim, Jun Yong ; Wansink, Brian.
    In: Journal of Retailing.
    RePEc:eee:jouret:v:88:y:2012:i:4:p:528-541.

    Full description at Econpapers || Download paper

  25. Case study€”Embodied virtual agents: An analysis on reasons for failure. (2012). ben Mimoun, Mohammed Slim ; Garnier, Marion ; Poncin, Ingrid.
    In: Journal of Retailing and Consumer Services.
    RePEc:eee:joreco:v:19:y:2012:i:6:p:605-612.

    Full description at Econpapers || Download paper

  26. Can free-shipping hurt online retailers?. (). Sahin, Ozge ; Korkmaz, Evsen.
    In: Quantitative Marketing and Economics (QME).
    RePEc:kap:qmktec:v::y::i::d:10.1007_s11129-020-09225-8.

    Full description at Econpapers || Download paper

Coauthors

Authors registered in RePEc who have wrote about the same topic

Report date: 2025-10-05 19:56:04 || Missing content? Let us know

CitEc is a RePEc service, providing citation data for Economics since 2001. Last updated August, 3 2024. Contact: Jose Manuel Barrueco.