Can Consumer-Posted Photos Serve as a Leading Indicator of Restaurant Survival? Evidence from Yelp

References

• Archak N, Ghose A, Ipeirotis PG (2011) Deriving the pricing power of product features by mining consumer reviews. Management Sci. 57(8):1485–1509.Link, Google Scholar
• Arnheim R (1965) Art and Visual Perception: A Psychology of the Creative Eye (University of California Press, Berkeley, CA).Google Scholar
• Ataman MB, Van Heerde HJ, Mela CF (2010) The long-term effect of marketing strategy on brand sales. J. Marketing Res. 47(5):866–882.Crossref, Google Scholar
• Athey S, Imbens G (2016) Recursive partitioning for heterogeneous causal effects. Shiffrin RM, ed. Proc. Natl. Acad. Sci. USA 113(27):7353–7360.Crossref, Google Scholar
• Athey S, Wager S (2019) Estimating Treatment Effects with Causal Forests: An Application (Observational Studies), 5(2):37–51.Google Scholar
• Athey S, Tibshirani J, Wager S (2019) Generalized random forests. Ann. Statist. 47(2):1148–1178.Crossref, Google Scholar
• Audretsch DB, Mahmood T (1995) New firm survival: New results using a hazard function. Rev. Econom. Statist. 77(1):97–103.Crossref, Google Scholar
• Audretsch DB, Houweling P, Roy Thurik A (2000) Firm survival in the Netherlands. Rev. Indust. Organ. 16(1):1–11.Crossref, Google Scholar
• Barasch A, Zauberman G, Diehl K (2017a) How the intention to share can undermine enjoyment: Photo-taking goals and evaluation of experiences. J. Consumer Res. 44(6):1220–1237.Crossref, Google Scholar
• Barasch A, Diehl K, Silverman J, Zauberman G (2017b) Photographic memory: The effects of volitional photo taking on memory for visual and auditory aspects of an experience. Psych. Sci. 28(8):1056–1066.Crossref, Google Scholar
• Bates T (1990) Entrepreneur human capital inputs and small business longevity. Rev. Econom. Statist. 72(4):551–559.Google Scholar
• Bates T (1995) A comparison of franchise and independent small business survival rates. Small Bus. Econom. 7(5):377–388.Crossref, Google Scholar
• Bates T (1998) The use of the area under the ROC curve in the evaluation of machine learning algorithms. J. Bus. Venturing 13(2):113–130.Crossref, Google Scholar
• Bekkar M, Djemaa HK, Alitouche TA (2013) Evaluation measures for models assessment over imbalanced data sets. J. Inform. Engrg. Appl. 3(10):27–38.Google Scholar
• Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J. Machine Learn. Res. 3:993–1022.Google Scholar
• Boden RJ Jr, Nucci AR (2000) On the survival prospects of men’s and women’s new business ventures. J. Bus. Venturing 15(4):347–362.Crossref, Google Scholar
• Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30(7):1145–1159.Crossref, Google Scholar
• Brodersen KH, Ong CS, Stephan KE, Buhmann JM (2010) The balanced accuracy and its posterior distribution. IEEE 20th Internat. Conf. Pattern Recognition, 3121–3124.Google Scholar
• Brüggen EC, Foubert B, Gremler DD (2011) Extreme makeover: Short-and long-term effects of a remodeled servicescape. J. Marketing 75(5):71–87.Crossref, Google Scholar
• Bujisic M, Hutchinson J, Parsa HG (2014) The effects of restaurant quality attributes on customer behavioral intentions. Internat. J. Contemporary Hospitality Management 26(8):1270–1291.Crossref, Google Scholar
• Carroll GR (1983) A stochastic model of organizational mortality: Review and reanalysis. Soc. Sci. Res. 12(4):303–329.Crossref, Google Scholar
• Chang J, Gerrish S, Wang C, Boyd-Graber J, Blei D (2009) Reading tea leaves: How humans interpret topic models. Adv. Neural Inform. Processing Systems 22:288–296.Google Scholar
• Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. Proc. 22nd ACM Sigkdd Internat. Conf. Knowledge Discovery Data Mining, 785–794.Google Scholar
• Chen T, Benesty M, He T (2018) Understand Your Dataset with Xgboost (R Document).Google Scholar
• Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, Newey W, Robins J (2018) Double/debiased machine learning for treatment and structural parameters. Econom. J. 21:C1–C68.Crossref, Google Scholar
• Chevalier JA, Mayzlin D (2006) The effect of word of mouth on sales: Online book reviews. J. Marketing Res. 43(3):345–354.Crossref, Google Scholar
• Cooper AC, Gimeno-Gascon FJ, Woo CY (1994) Initial human and financial capital as predictors of new venture performance. J. Bus. Venturing 9(5):371–395.Crossref, Google Scholar
• Diehl K, Zauberman G, Barasch A (2016) How taking photos increases enjoyment of experiences. J. Personality Soc. Psych. 111(2):119–140.Crossref, Google Scholar
• Duarte Alonso A, O’Neill M, Liu Y, O’Shea M (2013) Factors driving consumer restaurant choice: An exploratory study from the Southeastern United States. J. Hospitality Marketing Management 22(5):547–567.Crossref, Google Scholar
• Dzyabura D, Hauser JR (2011) Active machine learning for consideration heuristics. Marketing Sci. 30(5):801–819.Link, Google Scholar
• Dzyabura D, Peres R (2021) Visual elicitation of brand perception. J. Marketing 85(4):44–66.Google Scholar
• Dzyabura D, El Kihal S, Hauser JR, Ibragimov M (2019) Leveraging the power of images in managing product return rates. Preprint, submitted September 4, http://dx.doi.org/10.2139/ssrn.3209307.Google Scholar
• Fawcett T (2004) ROC graphs: Notes and practical considerations for researchers. Machine Learn. 31(1):1–38.Google Scholar
• Fawcett T (2006) An introduction to ROC analysis. Pattern Recognition Lett. 27(8):861–874.Crossref, Google Scholar
• Fellbaum C (1998) WordNet: An Electronic Lexical Database (MIT Press, Cambridge, MA).Crossref, Google Scholar
• Fong C, Hazlett C, Imai K (2018) Covariate balancing propensity score for a continuous treatment: Application to the efficacy of political advertisements. Ann. Appl. Statist. 12(1):156–177.Crossref, Google Scholar
• Freeman M (2007) The Photographer’s Eye: Composition and Design for Better Digital Photos (Routledge, New York).Crossref, Google Scholar
• Friedman J (2001) Greedy function approximation: A gradient boosting machine. Ann. Statist. 29(5):1189–1232.Crossref, Google Scholar
• Fritsch M, Brixy U, Falck O (2006) The effect of industry, region, and time on new business survival—A multi-dimensional analysis. Rev. Indust. Organ. 28(3):285–306.Crossref, Google Scholar
• Getz D, Brown G (2006) Critical success factors for wine tourism regions: A demand analysis. Tourism Management 27(1):146–158.Crossref, Google Scholar
• Ghose A, Ipeirotis PG, Li B (2012) Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content. Marketing Sci. 31(3):493–520.Link, Google Scholar
• Greene W (2004) The behaviour of the maximum likelihood estimator of limited dependent variable models in the presence of fixed effects. Econom. J. 7(1):98–119.Crossref, Google Scholar
• Guo T, Sriram S, Manchanda P (2021) The effect of information disclosure on industry payments to physicians. J. Marketing Res. 58(1):115–140.Google Scholar
• Haapanen M, Tervo H (2009) Self-employment duration in urban and rural locations. Appl. Econom. 41(19):2449–2461.Crossref, Google Scholar
• Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1):29–36.Crossref, Google Scholar
• Hartmann J, Heitmann M, Schamp C, Netzer O (2021) The power of brand selfies in consumer-generated brand images. J. Marketing Res. 58(6):1159–1177.Google Scholar
• Hauser JR, Dong S, Ding M (2014) Self-reflection and articulated consumer preferences. J. Product Innovation Management 31(1):17–32.Crossref, Google Scholar
• Hirano K, Imbens GW (2004) The propensity score with continuous treatments. Gelman A, Meng X-L, eds. Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives (John Wiley & Sons, Ltd., West Sussex, England), 73–84.Google Scholar
• Hollenbeck B (2018) Online reputation mechanisms and the decreasing value of chain affiliation. J. Marketing Res. 55(5):636–654.Crossref, Google Scholar
• Huang D, Luo L (2016) Consumer preference elicitation of complex products using fuzzy support vector machine active learning. Marketing Sci. 35(3):445–464.Link, Google Scholar
• Hutto CJ, Gilbert E (2014) VADER: A parsimonious rule-based model for sentiment analysis of social media text. Proc. Eighth Internat. AAAI Conf. Weblogs Social Media, 8(1):216–225.Google Scholar
• Hyun SS (2010) Predictors of relationship quality and loyalty in the chain restaurant industry. Cornell Hospitality Quart. 51(2):251–267.Crossref, Google Scholar
• Isola P, Parikh D, Torralba A, Oliva A (2011) Understanding the intrinsic memorability of images. Adv. Neural Inform. Processing Systems 24:2429–2437.Google Scholar
• Kalleberg AL, Leicht KT (1991) Gender and organizational performance: Determinants of small business survival and success. Acad. Management J. 34(1):136–161.Crossref, Google Scholar
• Kalnins A, Lafontaine F (2013) Too far away? The effect of distance to headquarters on business establishment performance. Amer. Econom. J. Microeconomics 5(3):157–179.Crossref, Google Scholar
• Kalnins A, Mayer KJ (2004) Franchising, ownership, and experience: A study of pizza restaurant survival. Management Sci. 50(12):1716–1728.Link, Google Scholar
• Kamshad KM (1994) Firm growth and survival: Does ownership structure matter? J. Econom. Management Strategy 3(3):521–543.Crossref, Google Scholar
• Ko EE, Bowman D (2020) The effect of sentiment and complexity on consumer engagement with brand-themed user-generated content. Proc. 49th Eur. Marketing Acad. (63560).Google Scholar
• Kotsiantis S, Kanellopoulos D, Pintelas P (2006) Handling imbalanced datasets: A review. GESTS Internat. Trans. Comput. Sci. Engrg. 30(1):25–36.Google Scholar
• Krages B (2005) Photography: The Art of Composition (Allworth Press, New York).Google Scholar
• Kreitler H, Kreitler S (1972) Psychology of the Arts (Duke University Press, Durham, NC).Google Scholar
• Kullback S, Leibler RA (1951) On information and sufficiency. Ann. Math. Statist. 22(1):79–86.Crossref, Google Scholar
• Lafontaine F, Zapletal M, Zhang X (2018) Brighter prospects? Assessing the franchise advantage using census data. J. Econom. Management Strategy 28(2):175–197.Crossref, Google Scholar
• Larsen V, Luna D, Peracchio LA (2004) Points of view and pieces of time: A taxonomy of image attributes. J. Consumer Res. 31(1):102–111.Crossref, Google Scholar
• Lee D, Manzoor EA, Cheng Z (2019) Focused concept miner (FCM): An interpretable deep learning for text exploration. Working paper, Boston University, Questrom School of Business, Boston, MA.Google Scholar
• Li Y, Xie Y (2020) Is a picture worth a thousand words? An empirical study of imagery content and social media engagement. J. Marketing Res. 57(1):1–19.Google Scholar
• Li X, Shi M, Wang XS (2019) Video mining: Measuring visual information using automatic methods. Internat. J. Res. Marketing 36(2):216–231.Crossref, Google Scholar
• Liu Y (2006) Word-of-mouth for movies: Its dynamics and impact on box office revenue. J. Marketing 70(3):74–89.Crossref, Google Scholar
• Liu L, Dzyabura D, Mizik N (2020) Visual listening in: Extracting brand image portrayed on social media. Marketing Sci. 39(4):669–848.Link, Google Scholar
• Liu X, Lee D, Srinivasan K (2019) Large scale cross category analysis of consumer review content on sales conversion leveraging deep learning. J. Marketing Res. 56(6):918–943.Crossref, Google Scholar
• Liu Y, Shankar V, Yun W (2017) Crisis management strategies and the long-term effects of product recalls on firm value. J. Marketing 81(5):30–48.Crossref, Google Scholar
• Lundberg S, Lee SI (2017) A unified approach to interpreting model predictions. 31st Conf. Neural Inform. Processing Systems, Long Beach, CA.Google Scholar
• Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, Katz R, Himmelfarb J, Bansal N, Lee S-I (2020) From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence 2(1):56–67.Crossref, Google Scholar
• Malik N, Singh PV, Srinivasan K (2020) A dynamic analysis of beauty premium. Working paper, University of Southern California, Marshall School of Business, Los Angeles.Google Scholar
• McGrath T (2017) Social Listening Meets Image Sharing: A Picture Is Worth 1,000 Words (Marketing Science Institute).Google Scholar
• Mela CF, Gupta S, Lehmann DR (1997) The long-term impact of promotion and advertising on consumer brand choice. J. Marketing Res. 34(2):248–261.Crossref, Google Scholar
• Miller GA (1995) Wordnet: A lexical database for English. Comm. ACM 38(11):39–41.Crossref, Google Scholar
• Molnar C (2021) Interpretable machine learning: A guide for making black box models explainable. https://christophm.github.io/interpretable-ml-book/.Google 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.Crossref, Google Scholar
• Montoya R, Netzer O, Jedidi K (2010) Dynamic allocation of pharmaceutical detailing and sampling for long-term profitability. Marketing Sci. 29(5):909–924.Link, Google Scholar
• Morgan SL, Winship C (2015) Counterfactuals and Causal Inference (Cambridge University Press, New York).Google Scholar
• Mudambi SM, Schuff D (2010) What makes a helpful online review? A study of customer reviews on Amazon.com. Management Inform. Systems Quart. 34(1):185–200.Crossref, Google Scholar
• Narang U, Shankar V, Narayanan S (2019) The impact of mobile app failures on purchases in online and offline channels. Working paper, University of Illinois at Urbana–Champaign, Champaign, IL.Google Scholar
• Netzer O, Lemaire A, Herzenstein M (2019) When words sweat: Identifying signals for loan default in the text of loan applications. J. Marketing Res. 56(6):960–980.Crossref, Google Scholar
• Netzer O, Feldman R, Goldenberg J, Fresko M (2012) Mine your own business: Market-structure surveillance through text mining. Marketing Sci. 31(3):521–543.Link, Google Scholar
• Newman D, Lau JH, Grieser K, Baldwin T (2010) Automatic evaluation of topic coherence. Human Language Tech Annual Conf. North Amer. Chapter Assoc. Comput. Linguistics (Los Angeles), 100–108.Google Scholar
• Nishiyama M, Okabe T, Sato I, Sato Y (2011) Aesthetic quality classification of photographs based on color harmony. Comput. Vision Pattern Recognition, 33–40.Google Scholar
• Parsa HG, Gregory A, Terry MD (2010) Why do restaurants fail? Part III: An analysis of macro and micro factors. Emerging Aspects Redefining Tourism Hospitality 1(1):16–25.Google Scholar
• Parsa HG, Self JT, Njite D, King T (2005) Why restaurants fail. Cornell Hotel Restaurant. Admin. Quart. 46(3):304–322.Crossref, Google Scholar
• Parsa HG, Self J, Sydnor-Busso S, Yoon HJ (2011) Why restaurants fail? Part II—The impact of affiliation, location, and size on restaurant failures: Results from a survival analysis. J. Foodservice. Bus. Res. 14(4):360–379.Crossref, Google Scholar
• Parsa HG, Van Der Rest JPI, Smith SR, Parsa RA, Bujisic M (2015) Why restaurants fail? Part IV: The relationship between restaurant failures and demographic factors. Cornell Hospitality Quart. 56(1):80–90.Crossref, Google Scholar
• Pauwels K, Hanssens DM, Siddarth S (2002) The long-term effects of price promotions on category incidence, brand choice, and purchase quantity. J. Marketing Res. 39(4):421–439.Crossref, Google Scholar
• Pearl J (2014) The deductive approach to causal inference. J. Causal Inference 2(2):115–129.Crossref, Google Scholar
• Puranam D, Narayan V, Kadiyali V (2017) The effect of calorie posting regulation on consumer opinion: A flexible latent Dirichlet allocation model with informative priors. Marketing Sci. 36(5):726–746.Link, Google Scholar
• Rafieian O, Yoganarasimhan H (2021) Targeting and privacy in mobile advertising. Marketing Sci. 40(2):193–218.Google Scholar
• Rajaram P, Puneet M, Schwartz E (2021) Finding the sweet spot: Ad targeting on streaming media. Preprint, submitted August 6, http://dx.doi.org/10.2139/ssrn.3496039.Google Scholar
• Röder M, Both A, Hinneburg A (2015) Exploring the space of topic coherence measures. Proc. Eighth ACM Internat. Conf. Web Search Data Mining, 399–408.Google Scholar
• Rosenbaum PR, Rubin DB (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70(1):41–55.Crossref, Google Scholar
• Rubin DB (1973) Matching to remove bias in observational studies. Biometrics 29(1):159–183.Crossref, Google Scholar
• Ryu K, Lee H-R, Kim WG (2012) The influence of the quality of the physical environment, food, and service on restaurant image, customer perceived value, customer satisfaction, and behavioral intentions. Internat. J. Contemporary Hospitality Management 24(2):200–223.Crossref, Google Scholar
• Seiffert C, Khoshgoftaar TM, Van Hulse J, Napolitano A (2008) Resampling or reweighting: A comparison of boosting implementations. 20th IEEE Internat. Conf. Tools Artificial Intelligence, vol. 1, 445–51.Google Scholar
• Sulek JM, Hensley RL (2004) The relative importance of food, atmosphere, and fairness of wait: The case of a full-service restaurant. Cornell Hotel Restaurant Admin. Quart. 45(3):235–247.Crossref, Google Scholar
• Takahashi K, Doman K, Kawanishi Y, Hirayama T, Ide I, Deguchi D, Murase H (2016) A study on estimating the attractiveness of food photography. 2016 IEEE Second Internat. Conf. Multimedia Big Data, 444–449.Google Scholar
• Timoshenko A, Hauser JR (2019) Identifying customer needs from user-generated content. Marketing Sci. 38(1):1–20.Link, Google Scholar
• Tirunillai S, Tellis GJ (2012) Does chatter really matter? Dynamics of user-generated content and stock performance. Marketing Sci. 31(2):198–215.Link, Google Scholar
• Tirunillai S, Tellis G (2014) Extracting dimensions of consumer satisfaction with quality from online chatter: Strategic brand analysis of big data using latent Dirichlet allocation. J. Marketing Res. 51(4):463–479.Crossref, Google Scholar
• Toubia O, Netzer O (2016) Idea generation, creativity, and prototypicality. Marketing Sci. 36(1):1–20.Link, Google Scholar
• Troncoso I, Luo L (2020) Look the part? The role of profile pictures in online labor markets. Working paper, University of Southern California, Marshall School of Business, Los Angeles.Google Scholar
• Tvetera^s R, Eide GE (2000) Survival of new plants in different industry environments in Norwegian manufacturing: A semi-proportional Cox model approach. Small Bus. Econom. 14(1):65–82.Crossref, Google Scholar
• Vogel DR, Dickson GW, Lehman JA (1986) Persuasion and the Role of Visual Presentation Support: The UM/3M Study (Management Information Systems Research Center, School of Management, University of Minnesota, Minneapolis).Google Scholar
• Wadhera D, Capaldi-Phillips ED (2014) A review of visual cues associated with food on food acceptance and consumption. Eating Behav. 15(1):132–143.Crossref, Google Scholar
• Wager S, Athey S (2018) Estimation and inference of heterogeneous treatment effects using random forests. J. Amer. Statist. Assoc. 113(523):1228–1242.Crossref, Google Scholar
• Wagner J (1994) The post-entry performance of new small firms in German manufacturing industries. J. Indust. Econom. 42(2):141–154.Crossref, Google Scholar
• Wooldridge J (2009) Introductory Econometrics (South-Western Cengage Learning, Mason, OH).Google Scholar
• Xiao L, Ding M (2014) Just the faces: Exploring the effects of facial features in print advertising. Marketing Sci. 33(3):338–352.Link, Google Scholar
• Yoganarasimhan H (2019) Search personalization using machine learning. Management Sci. 66(3):1045–1070.Link, Google Scholar
• Zhang S, Lee D, Singh P, Srinivasan K (2018) How much is an image worth? Airbnb property demand analytics leveraging a scalable image classification algorithm. Working paper, Harvard University Business School, Boston, MA.Google Scholar
• Zhu F, Zhang X (2010) Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics. J. Marketing 74(2):133–148.Crossref, Google Scholar