Selecting Cover Images for Restaurant Reviews: AI vs. Wisdom of the Crowd

Published Online:https://doi.org/10.1287/msom.2021.0531

References

  • Adamopoulos P, Ghose A, Todri V (2018) The impact of user personality traits on word of mouth: Text-mining social media platforms. Inform. Systems Res. 29(3):612–640.LinkGoogle Scholar
  • Adulyasak Y, Benomar O, Chaouachi A, Cohen MC, Khern-am-nuai W (2023) Using AI to detect panic buying and improve products distribution amid pandemic. AI Soc., ePub ahead of print April 15, https://doi.org/10.1007/s00146-023-01654-9.CrossrefGoogle Scholar
  • Aguiar L, Claussen J, Peukert C (2018) Catch me if you can: Effectiveness and consequences of online copyright enforcement. Inform. Systems Res. 29(3):656–678.LinkGoogle Scholar
  • Aouad A, Saban D (2023) Online assortment optimization for two-sided matching platforms. Management Sci. 69(4):2069–2087.LinkGoogle Scholar
  • Bai B, Dai H, Zhang DJ, Zhang F, Hu H (2022) The impacts of algorithmic work assignment on fairness perceptions and productivity: Evidence from field experiments. Manufacturing Service Oper. Management 24(6):3060–3078.LinkGoogle Scholar
  • Cao X, Zhang D, Huang L (2022) The impact of the Covid-19 pandemic on the behavior of online gig workers. Manufacturing Service Oper. Management 24(5):2611–2628.LinkGoogle Scholar
  • Cavusoglu H, Phan TQ, Cavusoglu H, Airoldi EM (2016) Assessing the impact of granular privacy controls on content sharing and disclosure on Facebook. Inform. Systems Res. 27(4):848–879.LinkGoogle Scholar
  • Cheng YH, Ho HY (2015) Social influence’s impact on reader perceptions of online reviews. J. Bus. Res. 68(4):883–887.CrossrefGoogle Scholar
  • Cohen MC (2018) Big data and service operations. Production Oper. Management 27(9):1709–1723.CrossrefGoogle Scholar
  • Cohen MC, Fiszer MD, Kim BJ (2022) Frustration-based promotions: Field experiments in ride-sharing. Management Sci. 68(4):2432–2464.LinkGoogle Scholar
  • Cohen MC, Fiszer MD, Ratzon A, Sasson R (2023) Incentivizing commuters to carpool: A large field experiment with Waze. Manufacturing Service Oper. Management 25(4):1263–1284.LinkGoogle Scholar
  • Cui R, Li M, Zhang S (2022) AI and procurement. Manufacturing Service Oper. Management 24(2):691–706.LinkGoogle Scholar
  • Cui Y (2020) Artificial Intelligence and Judicial Modernization (Springer, Berlin).CrossrefGoogle Scholar
  • Datta R, Joshi D, Li J, Wang JZ (2006) Studying aesthetics in photographic images using a computational approach. Leonardis A, Bischof H, Pinz A, eds. Eur. Conf. Comput. Vision (Springer, Berlin), 288–301.Google Scholar
  • Feldman P, Frazelle AE, Swinney R (2023) Managing relationships between restaurants and food delivery platforms: Conflict, contracts, and coordination. Management Sci. 69(2):812–823.LinkGoogle Scholar
  • Fresneda JE, Gefen D (2019) A semantic measure of online review helpfulness and the importance of message entropy. Decision Support Systems 125:113117.CrossrefGoogle Scholar
  • Fügener A, Grahl J, Gupta A, Ketter W (2022) Cognitive challenges in human–artificial intelligence collaboration: Investigating the path toward productive delegation. Inform. Systems Res. 33(2):678–696.LinkGoogle Scholar
  • Gallino S, Moreno A (2018) The value of fit information in online retail: Evidence from a randomized field experiment. Manufacturing Service Oper. Management 20(4):767–787.LinkGoogle Scholar
  • Ghadiyaram D, Bovik AC (2015) Massive online crowdsourced study of subjective and objective picture quality. IEEE Trans. Image Processing 25(1):372–387.CrossrefGoogle Scholar
  • Grill T, Scanlon M (1990) Photographic Composition (Amphoto Books, New York).Google Scholar
  • Guo J, Zhang W, Fan W, Li W (2018) Combining geographical and social influences with deep learning for personalized point-of-interest recommendation. J. Management Inform. Systems 35(4):1121–1153.CrossrefGoogle Scholar
  • Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. Preprint, submitted April 17, https://arxiv.org/abs/1704.04861.Google Scholar
  • Huang N, Sun T, Chen P, Golden JM (2019) Word-of-mouth system implementation and customer conversion: A randomized field experiment. Inform. Systems Res. 30(3):805–818.LinkGoogle Scholar
  • Iandola FN, Moskewicz MW, Ashraf K, Keutzer K (2016) Firecaffe: Near-linear acceleration of deep neural network training on compute clusters. Bajcsy R, Li F-F, Tuytelaars T, eds. Proc. IEEE Conf. Comput. Vision Pattern Recognition (IEEE, Piscataway, NJ), 2592–2600.Google Scholar
  • Järvelin K, Kekäläinen J (2000) IR evaluation methods for retrieving highly relevant documents. Harman D, Kelly D, eds. ACM SIGIR Conf. Res. Development Inform. Retrieval (Association for Computing Machinery, New York), 41–48.Google Scholar
  • Keding C (2021) Understanding the interplay of artificial intelligence and strategic management: Four decades of research in review. Management Rev. Quart. 71:91–134.Google Scholar
  • Khern-am-nuai W, Ghasemkhani H, Qiao D, Kannan K (2023a) The impact of online Q&As on product sales: The case of Amazon answer. Inform. Systems Res., ePub ahead of print June 6, https://doi.org/10.1287/isre.2023.1233.LinkGoogle Scholar
  • Khern-am-nuai W, Hashim MJ, Pinsonneault A, Yang W, Li N (2023b) Augmenting password strength meter design using the elaboration likelihood model: Evidence from randomized experiments. Inform. Systems Res. 34(1):157–177.LinkGoogle Scholar
  • Kittur A, Chi E, Pendleton BA, Suh B, Mytkowicz T (2007) Power of the few vs. wisdom of the crowd: Wikipedia and the rise of the bourgeoisie. Holmquist LE, Brown B, eds. Alt.CHI (Association for Computing Machinery, New York), 1–9.Google Scholar
  • Koh TK (2019) Adopting seekers’ solution exemplars in crowdsourcing ideation contests: Antecedents and consequences. Inform. Systems Res. 30(2):486–506.LinkGoogle Scholar
  • Kohavi R, Thomke S (2017) The surprising power of online experiments. Harvard Bus. Rev. 95(5):74–82.Google Scholar
  • Kokkodis M, Lappas T (2020) Your hometown matters: Popularity-difference bias in online reputation platforms. Inform. Systems Res. 31(2):412–430.LinkGoogle Scholar
  • Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Pereira F, Burges CJ, Bottou L, Weinberger KQ, eds. Adv. Neural Inform. Processing Systems (Association for Computing Machinery, New York), 1097–1105.Google Scholar
  • Kumar S, Mookerjee V, Shubham A (2018) Research in operations management and information systems interface. Production Oper. Management 27(11):1893–1905.CrossrefGoogle Scholar
  • Kyung N, Kwon HE (2022) Rationally trust, but emotionally? The roles of cognitive and affective trust in laypeople’s acceptance of ai for preventive care operations. Production Oper. Management, ePub ahead of print June 30, https://doi.org/10.1111/poms.13785.CrossrefGoogle Scholar
  • Lee D, Hosanagar K (2019) How do recommender systems affect sales diversity? A cross-category investigation via randomized field experiment. Inform. Systems Res. 30(1):239–259.LinkGoogle Scholar
  • Liu QB, Karahanna E (2017) The dark side of reviews: The swaying effects of online product reviews on attribute preference construction. MIS Quart. 41(2):427–448.CrossrefGoogle Scholar
  • Lou J, Yang H (2018) Food image aesthetic quality measurement by distribution prediction. Working paper, Stanford University, Stanford, CA.Google Scholar
  • Manshadi V, Rodilitz S, Saban D, Suresh A (2022) Online algorithms for matching platforms with multi-channel traffic. Preprint, submitted April 22, http://dx.doi.org/10.2139/ssrn.4036904.Google Scholar
  • Miller A (2016) Finding beautiful yelp photos using deep learning. Accessed November 3, 2021, https://engineeringblog.yelp.com/2016/11/finding-beautiful-yelp-photos-using-deep-learning.html.Google Scholar
  • Mithas S, Chen ZL, Saldanha TJ, De Oliveira Silveira A (2022) How will artificial intelligence and industry 4.0 emerging technologies transform operations management? Production Oper. Management 31(12):4475–4487.CrossrefGoogle Scholar
  • Montabone S, Soto A (2010) Human detection using a mobile platform and novel features derived from a visual saliency mechanism. Image Vision Comput. 28(3):391–402.CrossrefGoogle Scholar
  • Nishiyama M, Okabe T, Sato I, Sato Y (2011) Aesthetic quality classification of photographs based on color harmony. Boult T, Kanade T, Peleg S, eds. IEEE Conf. Comput. Vision Pattern Recognition (IEEE Computer Society, Piscataway, NJ), 33–40.Google Scholar
  • Otterbacher J (2009) ‘Helpfulness’ in online communities: A measure of message quality. Olsen DR, Arthur RB, eds. Proc. SIGCHI Conf. Human Factors Comput. Systems (Association for Computing Machinery, New York), 955–964.Google Scholar
  • Overgoor G, Rand W, Dolen WV (2020) The champion of images: Understanding the role of images in the decision-making process of online hotel bookings. Bui T, ed. Proc. 53rd Hawaii Internat. Conf. System Sci. (University of Hawai’i at Mānoa, Honolulu, HI), 4069–4078.Google Scholar
  • Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans. Knowledge Data Engrg. 22(10):1345–1359.CrossrefGoogle Scholar
  • Park K, Hong S, Baek M, Han B (2017) Personalized image aesthetic quality assessment by joint regression and ranking. Medioni G, Michael D, Sarkar S, eds. 2017 IEEE Winter Conf. Appl. Comput. Vision (WACV) (IEEE Computer Society, Piscataway, NJ), 1206–1214.Google Scholar
  • Pomerol JC (1997) Artificial intelligence and human decision making. Eur. J. Oper. Res. 99(1):3–25.CrossrefGoogle Scholar
  • Ponomarenko N, Ieremeiev O, Lukin V, Egiazarian K, Jin L, Astola J, Vozel B, et al. (2013) Color image database TID2013: Peculiarities and preliminary results. Beghdadi A, ed. Eur. Workshop Visual Inform. Processing (EUVIP) (IEEE, Piscataway, NJ), 106–111.Google Scholar
  • Ren X, Malik J (2003) Learning a classification model for segmentation. Triggs B, Zisserman A, ed. IEEE Internat. Conf. Comput. Vision (IEEE, Piscataway, NJ), 10–17.Google Scholar
  • Rubner Y, Tomasi C, Guibas LJ (1998) A metric for distributions with applications to image databases. Chandran S, Desai U, eds. Sixth Internat. Conf. Comput. Vision (IEEE Catalog No. 98CH36271) (IEEE, Piscataway, NJ), 59–66.Google Scholar
  • Shin D, He S, Lee GM, Whinston AB, Cetintas S, Lee KC (2020) Enhancing social media analysis with visual data analytics: A deep learning approach. MIS Quart. 44(4):1459–1492.CrossrefGoogle Scholar
  • Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. Preprint, September 4, https://arxiv.org/abs/1409.1556.Google Scholar
  • Spring M, Faulconbridge J, Sarwar A (2022) How information technology automates and augments processes: Insights from artificial-intelligence-based systems in professional service operations. J. Oper. Management 68(6–7):592–618.CrossrefGoogle Scholar
  • Sun T, Viswanathan S, Zheleva E (2021) Creating social contagion through firm-mediated message design: Evidence from a randomized field experiment. Management Sci. 67(2):808–827.LinkGoogle Scholar
  • Surowiecki J (2004) The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business (Doubleday, New York).Google Scholar
  • Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. Bajcsy R, Li F-F, Tuytelaars T, eds. Proc. IEEE Conf. Comput. Vision Pattern Recognition (IEEE Computer Society, Piscataway, NJ), 2818–2826.Google Scholar
  • Talebi H, Milanfar P (2018) Nima: Neural image assessment. IEEE Trans. Image Processing 27(8):3998–4011.CrossrefGoogle Scholar
  • Tan C, Sun F, Kong T, Zhang W, Yang C, Liu C (2018) A survey on deep transfer learning. Kůrková V, Manolopoulos Y, Hammer B, Iliadis L, Maglogiannis I, eds. Internat. Conf. Artificial Neural Networks (Springer, Berlin), 270–279.Google Scholar
  • Tan TF, Netessine S (2020) At your service on the table: Impact of tabletop technology on restaurant performance. Management Sci. 66(10):4496–4515.LinkGoogle Scholar
  • Vernon R, Bartel D (1985) Effect of hue, saturation, and intensity on color selection by the onion fly, Delia antiqua (meigen)(diptera: Anthomyiidae) in the field. Environ. Entomology 14(3):210–216.CrossrefGoogle Scholar
  • Wang Y, Goes P, Wei Z, Zeng D (2019) Production of online word-of-mouth: Peer effects and the moderation of user characteristics. Production Oper. Management 28(7):1621–1640.CrossrefGoogle Scholar
  • Wang Y, Wang L, Li Y, He D, Liu TY (2013) A theoretical analysis of NDCG ranking measures. Shalev-Shwartz S, Steinwart I, eds. Proc. 26th Annual Conf. Learn. Theory (COLT) (Proceedings of Machine Learning Research (PMLR), New York), 25–54.Google Scholar
  • Xu Y, Armony M, Ghose A (2021) The interplay between online reviews and physician demand: An empirical investigation. Management Sci. 67(12):7344–7361.LinkGoogle Scholar
  • Xu Y, Lu B, Ghose A, Dai H, Zhou W (2023) The interplay of earnings, ratings, and penalties on sharing platforms: An empirical investigation. Management Sci., ePub ahead of print April 19, https://doi.org/10.1287/mnsc.2023.4761.Google Scholar
  • Yu Y, Khern-am-nuai W, Pinsonneault A, Wei Z (2023) The impacts of social interactions and peer evaluations on online review platforms. J. Management Inform. Systems Forthcoming.CrossrefGoogle Scholar
  • Zajonc RB (2001) Mere exposure: A gateway to the subliminal. Current Directions Psych. Sci. 10(6):224–228.CrossrefGoogle Scholar
  • Zhang K, Sarvary M (2015) Differentiation with user-generated content. Management Sci. 61(4):898–914.LinkGoogle Scholar
  • Zhang Q, Yang LT, Chen Z, Li P (2018) A survey on deep learning for big data. Inform. Fusion 42:146–157.CrossrefGoogle Scholar
  • Zhang S, Lee D, Singh PV, Srinivasan K (2022) What makes a good image? Airbnb demand analytics leveraging interpretable image features. Management Sci. 68(8):5644–5666.LinkGoogle Scholar
  • Zheng J, Qi Z, Dou Y, Tan Y (2019) How mega is the mega? Exploring the spillover effects of WeChat using graphical model. Inform. Systems Res. 30(4):1343–1362.LinkGoogle Scholar
  • Zheng X, Hong Y, Ren X, Cao J, Yang S (2018) Information inconsistencies in multi-dimensional rating systems. Baskerville R, Nickerson R, eds. 39th Internat. Conf. Inform. Systems (Association for Information Systems, Atlanta), 1–17.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.