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Helping Small Businesses become more Data-Driven: A Field Experiment on eBay

Bar-Gill, Sagit, Brynjolfsson, Erik, Hak, Nir·2023

Abstract

As more and more activities in the economy become digitized, analytics and data-driven decision-making (DDD) are becoming increasingly important. The adoption of analytics and DDD has been slower in small-to-medium enterprises (SMEs) compared to large firms, and reliable causal estimates of the impacts of analytics tools for small businesses have been lacking. We derive experiment-based estimates of the effect of an analytics tool on SME outcomes, analyzing the randomized introduction of eBay’s Seller Hub (SH), a data-rich seller dashboard. We find that SH adoption is associated with increased DDD, and that access to SH increases e-retailers’ revenues by 3.6% on average, as more items are transacted and service quality increases, without increases in average prices. Our results suggest that analytics and DDD help SMEs establish a competitive advantage. Managerial practices play an important role in reaping the benefits from the analytics dashboard, as over a third of the SH impact is driven by active performance monitoring. This suggests that digital platforms could increase revenues by supporting the adoption of analytics tools by SMEs. Furthermore, policies supporting small businesses' transition to the data era could address gaps in analytics and data-driven decision-making (DDD) by providing access to tools such as SH, as well as offering appropriate managerial training.

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Research works printed but not linked: 58

  1. J C Aker, 2010, Information from Markets Near and Far: Mobile Phones and Agricultural Markets in Niger, Am. Econ. J. Appl. Econ, 2, 46, 10.1257/app.2.3.46 · DOI
  2. P Angle, 2018, Does IT level the playing field for small establishments? Evidence from manufacturing. SSRN Electron
  3. J D Angrist, 1996, Identification of Causal Effects Using Instrumental Variables, J. Am. Stat. Assoc, 91, 444, 10.1080/01621459.1996.10476902 · DOI · Paper page
  4. J D Angrist, 2009, Mostly harmless econometrics : an empiricist's companion, 10.1515/9781400829828 · DOI · Paper page
  5. J D Angrist, 2010, The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of, Econometrics. J. Econ. Perspect, 24, 3, 10.1257/jep.24.2.3 · DOI · Paper page
  6. Y Bakos, 1998, Firm Size Distribution Goes Online: The Evolution of eBay Firms' Sales Distribution, Commun. ACM, 41, 35
  7. A Barua, 1995, Information Technologies and Business Value: An Analytic and Empirical Investigation, Inf. Syst. Res, 6, 3, 10.1287/isre.6.1.3 · DOI
  8. R Berman, 2022, The Value of Descriptive Analytics: Evidence from Online Retailers. Mark. Sci
  9. S Bharadwaj, 2007, The Performance Effects of Complementarities Between Information Systems, Marketing, Manufacturing, and Supply Chain Processes, Inf. Syst. Res, 18, 437, 10.1287/isre.1070.0148 · DOI
  10. 43 For instance, the US Small Business Administration (SBA) spent $201.15 million on grants in 687 transactions with SMEs in 2019 (pre-Covid19), i.e., approximately $292,795 for an average transaction
  11. N Bloom, 2014, IT and Management in America, Cent. Econ
  12. J R Brown, 2002, Does the internet make markets more competitive? Evidence from the life insurance industry, J. Polit. Econ, 110, 481, 10.1086/339714 · DOI
  13. E Brynjolfsson, 1993, The productivity paradox of information technology, Commun. ACM, 36, 66, 10.1145/163298.163309 · DOI
  14. E Brynjolfsson, 1996, Paradox lost? Firm-level evidence of high returns to information systems spending, Manage. Sci, 42, 541, 10.1287/mnsc.42.4.541 · DOI
  15. E Brynjolfsson, 2011, Strength in numbers: How does data-driven decision-making affect firm performance? ICIS 2011 Proc
  16. E Brynjolfsson, 2021, The power of prediction: predictive analytics, workplace complements, and business performance, Bus. Econ, 56, 217, 10.1057/s11369-021-00224-5 · DOI
  17. E Brynjolfsson, 2016, The rapid adoption of data-driven decision-making, Am. Econ. Rev, 106, 133, 10.1257/aer.p20161016 · DOI · Paper page
  18. E Brynjolfsson, 2019, Data in action: Data-driven decision making and predictive analytics in US manufacturing
  19. E Brynjolfsson, 2000, Frictionless commerce? A comparison of Internet and conventional retailers, Manage. Sci, 46, 563, 10.1287/mnsc.46.4.563.12061 · DOI · Paper page
  20. J Bughin, 2016, Ten big lessons learned from Big Data analytics, Appl. Mark. Anal
  21. M Cao, 2015, Big Data Analytics in Financial Statement Audits, Account. Horizons, 29, 423, 10.2308/acch-51068 · DOI
  22. M Cardona, 2013, ICT and productivity: Conclusions from the empirical literature, Inf. Econ. Policy, 25, 109, 10.1016/j.infoecopol.2012.12.002 · DOI
  23. J Chan, 2014, Internet's Dirty Secret, MIS Q, 38, 955, 10.25300/MISQ/2014/38.4.01 · DOI · Paper page
  24. E K Clemons, 2002, Price Dispersion and Differentiation in Online Travel: An Empirical Investigation, Manage. Sci, 48, 534, 10.1287/mnsc.48.4.534 · DOI · Paper page
  25. T Davenport, 2003, Information technology and economic performance: A critical review of the empirical evidence, ACM Comput. Surv, 35, 1, 10.1145/641865.641866 · DOI
  26. S Devaraj, 2003, Performance impacts of information technology: Is actual usage the missing link?, Manage. Sci, 49, 273, 10.1287/mnsc.49.3.273.12736 · DOI
  27. S Gallino, 2014, Integration of Online and Offline Channels in Retail: The Impact of Sharing Reliable Inventory Availability Information, Manage. Sci, 60, 1434, 10.1287/mnsc.2014.1951 · DOI
  28. F Germann, 2014, Do Retailers Benefit from Deploying Customer Analytics?, J. Retail, 90, 587, 10.1016/j.jretai.2014.08.002 · DOI
  29. F Germann, 2013, Performance implications of deploying marketing analytics, Int. J. Res. Mark, 30, 114, 10.1016/j.ijresmar.2012.10.001 · DOI
  30. B N Greenwood, 2017, Show Me the Way to Go Home: An Empirical Investigation of Ride-Sharing and Alcohol Related Motor Vehicle Fatalities, MIS Q, 41, 163, 10.25300/MISQ/2017/41.1.08 · DOI
  31. K B Hendricks, 2007, The impact of enterprise systems on corporate performance: A study of ERP, SCM, and CRM system implementations, J. Oper. Manag, 25, 65, 10.1016/j.jom.2006.02.002 · DOI
  32. Hui, 2019, Facilitating Inclusive Global Trade: Evidence from a Field Experiment, Manage. Sci, 66, 1737, 10.1287/mnsc.2018.3263 · DOI
  33. J Hunton, 2003, Enterprise Resource Planning Systems: Comparing Firm Performance of Adopters and Nonadopters, Int. J. Account. Inf. Syst, 4, 165, 10.1016/S1467-0895(03)00008-3 · DOI
  34. H Ibrahim, 2020, Rapid Development and Utilization of a Clinical Intelligence Dashboard for Frontline Clinicians to Optimize Critical Resources During Covid-19, Acta Inform. Med, 28, 209, 10.5455/aim.2020.28.209-213 · DOI
  35. R Jensen, 2007, Market Performance, and Welfare in the South Indian Fisheries Sector, The Digital Provide: Information (Technology), 122, 879
  36. Jin W Mcelheran, 2018, Economies Before Scale: Survival and Performance of Young Plants in the Age of Cloud Computing, SSRN Electron. J
  37. S S Khairat, 2018, The Impact of Visualization Dashboards on Quality of Care and Clinician Satisfaction: Integrative Literature Review, JMIR Hum. factors, 5, 10.2196/humanfactors.9328 · DOI
  38. M R Llave, 2017, Business Intelligence and Analytics in Small and Medium-sized Enterprises: A Systematic Literature Review, Procedia Comput. Sci, 121, 194, 10.1016/j.procs.2017.11.027 · DOI
  39. Y Lu, 2019, Information Transparency in Business-to-Business Auction Markets: The Role of Winner Identity Disclosure, 65, 4261
  40. A Mcafee, 2012, Big data: The management revolution, Harv. Bus. Rev, 2
  41. S Mithas, 2011, How Information Management Capability Influences Firm Performance, Manag. Inf. Syst. Q, 35, 10.2307/23043496 · DOI
  42. O M�ller, 2018, The Effect of Big Data and Analytics on Firm Performance: An Econometric Analysis Considering Industry Characteristics, J. Manag. Inf. Syst, 35, 488, 10.1080/07421222.2018.1451955 · DOI
  43. E Overby, 2015, The effect of electronic commerce on geographic purchasing patterns and price dispersion, Manage. Sci, 61, 431, 10.1287/mnsc.2014.1977 · DOI
  44. X Pan, 2002, Can price dispersion in online markets be explained by differences in e-tailer service quality?, J. Acad. Mark. Sci, 304, 433, 10.1177/009207002236915 · DOI
  45. C Parker, 2016, Is IT Enough? Evidence from a Natural Experiment in India's Agriculture Markets, Manage. Sci, 62, 2481, 10.1287/mnsc.2015.2270 · DOI
  46. K Pauwels, 2014, It's Not the Size of the Data --It's How You Use It : Smarter Marketing with Analytics and Dashboards
  47. M E Porter, 1985, How information gives you competitive advantage, Harv. Bus. Rev, 63, 149
  48. V Ravi, 2017, Big Data Analytics Enabled Smart Financial Services: Opportunities and Challenges, Lect. Notes Comput. Sci. 10721 LNCS, 15, 10.1007/978-3-319-72413-3_2 · DOI
  49. E M Rogers, 2003, Diffusion of Innovations 5
  50. D Simchi-Levi, 2014, A review of analytics and clinical informatics in health care, J. Med. Syst, 38, 1
  51. H Subramanian, 2017, Electronic commerce, spatial arbitrage, and market efficiency, Inf. Syst. Res, 28, 97, 10.1287/isre.2016.0653 · DOI
  52. P Tambe, 2012, The Productivity of Information Technology Investments: New Evidence from IT Labor Data, 23, 599
  53. E Waarts, 2002, The dynamics of factors affecting the adoption of innovations, J. Prod. Innov. Manag, 19, 412, 10.1111/1540-5885.1960412 · DOI
  54. W Yu, 2021, Integrating big data analytics into supply chain finance: The roles of information processing and data-driven culture, England: Evidence from a Theory-Based Evaluation. Int. Small Bus. J. Res. Entrep, 236, 557
  55. S Roper, 2001, Grant Assistance and Small Firm Development in Northern Ireland and the Republic of Ireland, Scott. J. Polit. Econ, 48, 99, 10.1111/1467-9485.00187 · DOI
  56. A S�derblom, 2015, Inside the black box of outcome additionality: Effects of early-stage government subsidies on resource accumulation and new venture performance, Res. Policy, 44, 1501, 10.1016/j.respol.2015.05.009 · DOI
  57. S Srhoj, 2019, Closing the Finance Gap by Nudging: Impact Assessment of Public Grants for Women Entrepreneurs
  58. S J Wallsten, 2000, The Effects of Government-Industry R&D Programs on Private R&D: The Case of the Small Business Innovation Research Program, RAND J. Econ, 31, 10.2307/2601030 · DOI · Paper page

Metadata

Authors
Bar-Gill, S., Brynjolfsson, E., & Hak, N.
Publisher
Elsevier BV
Published
2023
Cited by
0
References
3
Type
Preprint

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