I Will Survive: Predicting Business Failures from Customer Ratings

References

• Abhishek V, Fader P, Hosanagar K (2012) Media exposure through the funnel: A model of multi-stage attribution. Preprint, submitted October 8, DOI: https://dx.doi.org/10.2139/ssrn.2158421.Google Scholar
• Altman EI (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J. Finance 23(4):589–609.Crossref, Google Scholar
• 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
• Argenti J (1976) Corporate Collapse: The Causes and Symptoms (John Wiley and Sons, New York).Google Scholar
• Ascarza E, Hardie BGS (2013) A joint model of usage and churn in contractual settings. Marketing Sci. 32(4):570–590.Link, Google Scholar
• Ascarza E, Netzer O, Hardie BGS (2018) Some customers would rather leave without saying goodbye. Marketing Sci. 37(1):54–77.Link, Google Scholar
• Asubonteng P, McCleary KJ, Swan JE (1996) SERVQUAL revisited: A critical review of service quality. J. Services Marketing 10(6):62–81.Crossref, Google Scholar
• Babić Rosario A, Sotgiu F, de Valck K, Bijmolt TH (2016) The effect of electronic word of mouth on sales: A meta-analytic review of platform, product, and metric factors. J. Marketing Res. 53(3):297–318.Crossref, Google Scholar
• Barbu VS, Limnios N (2008) Semi-Markov Chains and Hidden Semi-Markov Models Toward Applications: Their Use in Reliability and DNA Analysis (Springer, New York).Google Scholar
• Barron K, Kung E, Proserpio D (2021) The effect of home-sharing on house prices and rents: Evidence from Airbnb. Marketing Sci. 40(1):23–47.Link, Google Scholar
• Bayou M, Bennet LB (1992) Profitability analysis for table-service restaurants. Cornell Hotel Restaurant Admin. Quart. 33(2):49–55.Google Scholar
• Beaver WH (1966) Financial ratios as predictors of failure. J. Accounting Res. 4:71–111.Crossref, Google Scholar
• Berger J, Humphreys A, Ludwig S, Moe WW, Netzer O, Schweidel DA (2020) Uniting the tribes: Using text for marketing insight. J. Marketing 84(1):1–25.Crossref, Google Scholar
• Buttle F (1996) SERVQUAL: Review, critique, research agenda. Eur. J. Marketing 30(1):8–32.Crossref, Google Scholar
• Camillo AA, Connolly DJ, Kim WG (2008) Success and failure in Northern California. Cornell Hospitality Quart. 49(4):364–380.Crossref, Google Scholar
• Chen Z, Lurie NH (2013) Temporal contiguity and negativity bias in the impact of online word of mouth. J. Marketing Res. 50(4):463–476.Crossref, Google Scholar
• Chevalier JA (2004) What do we know about cross-subsidization? evidence from merging firms. Adv. Econom. Anal. Policy 4(1):1–27.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
• Chiappa S (2014) Explicit-duration Markov switching models. Foundations Trends Machine Learn. 7(6):803–886.Crossref, Google Scholar
• Chintagunta PK, Gopinath S, Venkataraman S (2010) The effects of online user reviews on movie box office performance: Accounting for sequential rollout and aggregation across local markets. Marketing Sci. 29(5):944–957.Link, Google Scholar
• Culotta A, Cutler J (2016) Mining brand perceptions from Twitter social networks. Marketing Sci. 35(3):343–362.Link, Google Scholar
• Dellarocas C, Narayan R (2006) A statistical measure of a population’s propensity to engage in post-purchase online word-of-mouth. Statist. Sci. 21(2):277–285.Crossref, Google Scholar
• Dellarocas C, Zhang XM, Awad NF (2007) Exploring the value of online product reviews in forecasting sales: The case of motion pictures. J. Interactive Marketing 21(4):23–45.Crossref, Google Scholar
• Ding AW, Li S, Chatterjee P (2015) Learning user real-time intent for optimal dynamic web page transformation. Inform. Systems Res. 26(2):339–359.Link, Google Scholar
• Fader PS, Winer RS (2012) Introduction to the special issue on the emergence and impact of user-generated content. Marketing Sci. 31(3):369–371.Link, Google Scholar
• Fader PS, Hardie BG, Liu Y, Davin J, Steenburgh T (2018) “How to project customer retention” revisited: The role of duration dependence. J. Interactive Marketing 43:1–16.Crossref, Google Scholar
• Gelman A, Hwang J, Vehtari A (2014) Understanding predictive information criteria for Bayesian models. Statist. Comput. 24(6):997–1016.Crossref, Google Scholar
• Godes D, Mayzlin D (2004) Using online conversations to study word-of-mouth communication. Marketing Sci. 23(4):545–560.Link, Google Scholar
• Gopalakrishnan A, Bradlow E, Fader P (2018) Limited dynamic forecasting of hidden Markov models. Preprint, submitted July 13, DOI: https://dx.doi.org/10.2139/ssrn.3206425.Google Scholar
• Hatt T, Feuerriegel S (2020) Early detection of user exits from clickstream data: A Markov modulated marked point process model. Huang Y, King I, Liu TY, van Steen M, eds. Proc. Web Conference (Association for Computing Machinery, New York), 1671–1681.Google Scholar
• Helsen K, Schmittlein DC (1993) Analyzing duration times in marketing: Evidence for the effectiveness of hazard rate models. Marketing Sci. 12(4):395–414.Link, Google Scholar
• Henderson AD (1999) Firm strategy and age dependence: A contingent view of the liabilities of newness, adolescence, and obsolescence. Admin. Sci. Quart. 44(2):281–314.Crossref, Google Scholar
• Heskett JL, Jones TO, Loveman GW, Sasser WE, Schlesinger LA (1994) Putting the service-profit chain to work. Harvard Bus. Rev. 2:164–170.Google Scholar
• Ho YCC, Wu J, Tan Y (2017) Disconfirmation effect on online rating behavior: A structural model. Inform. Systems Res. 28(3):626–642.Link, Google Scholar
• Ittner CD, Larcker DF (2003) Coming up short on nonfinancial performance measurement. Harvrad Bus. Rev. 81(11):88–95.Google Scholar
• Kamakura WA, Mittal V, de Rosa F, Mazzon JA (2002) Assessing the service-profit chain. Marketing Sci. 21(3):294–317.Link, Google Scholar
• Ladhari R (2009) A review of twenty years of SERVQUAL research. Internat. J. Quality Service Sci. 1(2):172–198.Crossref, Google Scholar
• Laitinen EK (1991) Financial ratios and different failure processes. J. Bus. Finance Accounting 18(5):649–673.Crossref, Google Scholar
• Lee TY, Bradlow ET (2011) Automated marketing research using online customer reviews. J. Marketing Res. 48(5):881–894.Crossref, Google Scholar
• Lee YJ, Hosanagar K, Tan Y (2015) Do I follow my friends or the crowd? Information cascades in online movie ratings. Management Sci. 61(9):2241–2258.Link, Google Scholar
• Lervik-Olsen L, Witell L, Gustafsson A (2014) Turning customer satisfaction measurements into action. J. Service Management 25(4):556–571.Crossref, Google Scholar
• Li S, Sun B, Montgomery AL (2011) Cross-selling the right product to the right customer at the right time. J. Marketing Res. 48(4):683–700.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
• Luca M (2011) Reviews, reputation, and revenue: The case of Yelp.com. Preprint, submitted September 16, DOI: http://dx.doi.org/10.2139/ssrn.1928601.Google Scholar
• Luca M, Zervas G (2016) Fake it till you make it: Reputation, competition, and Yelp review fraud. Management Sci. 62(12):3412–3427.Link, Google Scholar
• Ludwig S, de Ruyter K, Friedman M, Brüggen EC, Wetzels M, Pfann G (2013) More than words: The influence of affective content and linguistic style matches in online reviews on conversion rates. J. Marketing 77(1):87–103.Crossref, Google Scholar
• Luo T, Stark PB (2015) Nine out of 10 restaurants fail? Check, please. Significance 12(2):25–29.Crossref, Google Scholar
• Lussier RN (1995) A nonfinancial business success versus failure prediction model for young firms. J. Small Bus. Management 33(1):8–20.Google Scholar
• Mahajan V, Srinivasan R, Wind J (2002) The dot.com retail failures of 2000: Were there any winners? J. Acad. Marketing Sci. 30(4):474–486.Crossref, Google Scholar
• Mittal V, Anderson EW, Sayrak A, Tadikamalla P (2005) Dual emphasis and the long-term financial impact of customer satisfaction. Marketing Sci. 24(4):544–555.Link, Google Scholar
• Moe WW, Schweidel DA (2012) Online product opinions: Incidence, evaluation, and evolution. Marketing Sci. 31(3):372–386.Link, Google Scholar
• Moe WW, Trusov M (2011) The value of social dynamics in online product ratings forums. J. Marketing Res. 48(3):444–456.Crossref, Google Scholar
• Montgomery AL, Li S, Srinivasan K, Liechty JC (2004) Modeling online browsing and path analysis using clickstream data. Marketing Sci. 23(4):579–595.Link, Google Scholar
• Montoya R, Gonzalez C (2019) A hidden Markov model to detect on-shelf out-of-stocks using point-of-sale data. Manufacturing Serv. Oper. Management 21(4):932–948.Link, 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
• Moon S, Kamakura WA, Ledolter J (2007) Estimating promotion response when competitive promotions are unobservable. J. Marketing Res. 44(3):503–515.Crossref, Google Scholar
• Mun SG, Jang S (2018) Restaurant operating expenses and their effects on profitability enhancement. Internat. J. Hospitality Management 71:68–76.Crossref, Google Scholar
• Murphy KP (2002) Hidden semi-Markov models (HSMMs). Working paper, University of British Columbia, Canada.Google Scholar
• Murphy KP (2012) Machine Learning: A Probabilistic Perspective (MIT Press, Cambridge, MA).Google Scholar
• Netzer O, Lattin JM, Srinivasan V (2008) A hidden Markov model of customer relationship dynamics. Marketing Sci. 27(2):185–204.Link, 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
• Ohlson JA (1980) Financial ratios and the probabilistic prediction of bankruptcy. J. Accounting Res. 18(1):109–131.Crossref, Google Scholar
• Olsen M, Bellas C, Kish LV (1983) Improving the prediction of restaurant failure through ratio analysis. Internat. J. Hospitality Management 2(4):187–193.Crossref, Google Scholar
• Parasuraman A, Zeithaml VA, Berry LL (1988) SERVQUAL: A multi-item scale for measuring consumer perceptions of service quality. J. Retailing 64(1):12–40.Google Scholar
• Parsa HG, Gregory A, Terry M (2011a) Why do restaurants fail? Part III: An analysis of macro and micro factors. Working paper, Dick Pope Sr. Institute for Tourism Studies, University of Central Florida, Orlando.Google Scholar
• Parsa HG, Self J, Sydnor-Busso S, Yoon HJ (2011b) 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
• Pretorius M (2009) Defining business decline, failure and turnaround: A content analysis. Southern African J. Entrepreneurship Small Bus. Management. 2(1):1–16.Crossref, Google Scholar
• Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2):257–286.Crossref, Google Scholar
• Rust RT, Zahorik AJ, Keiningham TL (1995) Return on quality (ROQ): Making service quality financially accountable. J. Marketing 59(2):58–70.Crossref, Google Scholar
• Sarkar S, Sriram RS (2001) Bayesian models for early warning of bank failures. Management Sci. 47(11):1457–1475.Link, Google Scholar
• Schneider C, Weinmann M, Mohr PN, vom Brocke J (2021) When the stars shine too bright: The influence of multidimensional ratings on online consumer ratings. Management Sci. 67(6):3871–3898.Link, Google Scholar
• Schwartz EM, Bradlow ET, Fader PS (2014) Model selection using database characteristics: Developing a classification tree for longitudinal incidence data. Marketing Sci. 33(2):188–205.Link, Google Scholar
• Schweidel DA, Bradlow ET, Fader PS (2011) Portfolio dynamics for customers of a multiservice provider. Management Sci. 57(3):471–486.Link, Google Scholar
• Schweidel DA, Fader PS, Bradlow ET (2008) Understanding service retention within and across cohorts using limited information. J. Marketing 72(1):82–94.Crossref, Google Scholar
• Sharma S, Mahajan V (1980) Early warning indicators of business failure. J. Marketing 44(4):80–89.Crossref, Google Scholar
• Sismeiro C, Bucklin RE (2004) Modeling purchase behavior at an e-commerce web site: A task-completion approach. J. Marketing Res. 41(3):306–323.Crossref, Google Scholar
• Sonnier GP, McAlister L, Rutz OJ (2011) A dynamic model of the effect of online communications on firm sales. Marketing Sci. 30(4):702–716.Link, Google Scholar
• Swaminathan A (1996) Environmental conditions at founding and organizational mortality: A trial-by-fire model. Acad. Management J. 39(5):1350–1377.Crossref, 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
• Toubia O, Stephen AT (2013) Intrinsic vs. image-related utility in social media: Why do people contribute content to Twitter? Marketing Sci. 32(3):368–392.Link, Google Scholar
• Wang L, Gopal R, Shankar R, Pancras J (2015) On the brink: Predicting business failure with mobile location-based checkins. Decision Support Systems 76:3–13.Crossref, Google Scholar
• Yoon E, Jang S (2005) The effect of financial leverage on profitability and risk of restaurant firms. J. Hospitality Financial Management 13(1):35–47.Crossref, Google Scholar
• Yu SZ (2016) Hidden Semi-Markov Models: Theory, Algorithms and Applications (Elsevier, Amsterdam, Netherlands).Crossref, Google Scholar
• Zhang JZ, Netzer O, Ansari A (2014a) Dynamic targeted pricing in B2B relationships. Marketing Sci. 33(3):317–337.Link, Google Scholar
• Zhang KZ, Zhao SJ, Cheung CM, Lee MK (2014b) Examining the influence of online reviews on consumers’ decision-making: A heuristic–systematic model. Decision Support Systems 67(November):78–89.Crossref, Google Scholar
• Zhong N, Schweidel DA (2020) Capturing changes in social media content: A multiple latent changepoint topic model. Marketing Sci. 39(4):827–846.Link, Google Scholar
• Zhu F, Zhang XM (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