Strength in Numbers: How do data-driven decision-making practices affect firm productivity?

Transcription

1 Strength in Numbers: How do data-driven decision-making practices affect firm productivity? May 19, 2011 Erik Brynjolfsson and Heekyung Kim MIT Sloan School of Management Lorin Hitt University of Pennsylvania, Wharton School 1

2 The Nanodata Revolution Clickstream/Page views/web transactions Mobile phone/gps/location data messages RFID (Radio Frequency Identification), Bar Code Scanner Data Web links/blog references/facebook ERP/CRM/SCM transactions Real-time machinery diagnostics/engines/equipment Google/Bing/Yahoo Searches Stock market transactions Twitter feeds Wikipedia updates Etc. 2 2

3 Examples of Data-Driven Decision-making (DDD) Truck Routing Wine Chemistry Housing Sales 3 3

4 Research Questions Do more data-driven decision-making practices improve firm performance (productivity, profitability, and market value)? What makes a firm more data-driven? 4 4 4

5 Literature Data-driven decision-making (Davenport, 2009; Loveman, 2003; Lavalle et al., 2010) Information technology and firm performance (Weill 1992; Dewan et al, 1997; Brynjolfsson et al., 1995, 1996, 2002; Bharadwaj et al, 1999, 2000; Bloom et al., 2008; many others) Codified knowledge and organizational learning (Nelson and Winter, 1982; Zander and Kogut, 1995) 5 5 5

6 Key Findings Data-driven decision-making (DDD) may explain a 4-6% of output and productivity, controlling for traditional inputs and IT use. DDD is also correlated with other performance measures return on assets, return on equity, asset utilization, market value), controlling for other firm-specific characteristics. Firms with more consistent business practices are more datadriven Younger firms also tend to be more data-driven

7 2009 Digital Advantage survey overview Companies (330 total respondents; 179 matched to Compustat and IT use data) Respondents by category Percent Manu- facturing Health Care and Social Assistance Wholesale Trade Finance and Insurance Transportation and Warehousing Utilities & Natural Resources Respondents by revenue group Count SOURCE: Digital Advantage Survey 7 7

8 Data-Driven Decision-Making (DDD) How are decisions made for the creation of a new product or service? (1 to 5 scale: Experience and expertise=1, Data=5) To what extent do the following statement describe the work practices and environment of your entire company. We depend on data to support our decision making (1: Describes not at all, 5: Completely describes) We have the data we need to make decisions (1: Describes not at all, 5: Completely describes) 8 8 8

9 Data-driven decision making I: Typical basis for a new product/service Percent of respondents Minerals, Oil & Gas, Utilities, and Construction Manufacturing Wholesale/Retail Trade, Transport, Accommod./Food Information Finance and Insurance Professional and Other Services SOURCE: 2009 Digital Advantage survey 9 9

10 Data-driven decision making II: Use data to make decisions in the entire company Percent of respondents Minerals, Oil & Gas, Utilities, and Construction Manufacturing Wholesale/Retail Trade, Transport, Accommod./Food Not at all Completely Not at all Completely Not at all Completely Information Finance and Insurance Professional and Other Services Not at all Completely Not at all Completely Not at all Completely SOURCE: 2009 Digital Advantage survey 10 10

11 Data-driven decision making III: Have data we need Percent of respondents Minerals, Oil & Gas, Utilities, and Construction Manufacturing Wholesale/Retail Trade, Transport, Accommod./Food Not at all Completely Not at all Completely Not at all Completely Information Finance and Insurance Professional and Other Services Not at all Completely Not at all Completely Not at all Completely SOURCE: 2009 Digital Advantage survey 11 11

12 Estimation of the impact of DDD on productivity Ln(Sales) it = β 0 + β 1 Ln(Materials) it + β 2 Ln(Physical Capital) it + β 3 Ln(IT Labor) it + β 4 Ln(Non-IT Labor) it + β 5 (DDD) i + Other controls > 0? 12 i: firm t: year ( ) Sales, Physical Capital, Employee from Compustat IT Labor from a job-posting site (Tambe and Hitt, 2008) Non-IT Labor = Employee IT Labor Other controls = 1.5 digit NAICS industry, year, employees human capital (importance of typical employee s education, % of employees using PC/ s, and/or Avg. workers wage) 12

13 Productivity and Data-Driven Decision-Making (DDD) Dependent variable = Ln(Sales) DDD (0.019) Ln(Material) (0.042) Ln(Capital) (0.023) Ln(IT-Employee) (0.022) Ln(Non-IT Employee) 0.224(0.032) Constant 1.133(0.182) Industry and Year Control Yes Number of Firms 189 Observations 682 R-squared 0.92 Robust standard errors were clustered around firms. p<0.01,. p<0.05, p<0.1. Industry classification was based on NAICS 2 digit for manufacturing and 1 digit for other industries

14 Why do some firms adopt DDD more than others? What are the drivers of DDD? 1. Adjustment Cost: Firms with a higher adjustment cost have high organizational inertia and do not find it optimal to make an organizational change (Nelson and Winter, 1982) - Constructed from 7 survey questions: Please rate whether the following factors at your company facilitate or inhibit the ability to make organizational changes: 1) financial resources; 2) skill mix of existing staff; 3) employment contracts; 4) work rules; 5) organizational cultures; 6) customer relationships; 7) senior management involvement 14 14

15 Why do some firms adopt DDD more than others? What are the drivers of DDD? 2. Firm Age: -: Older firms have high inertia and cannot make organizational change (Hannan and Freeman, 1977, 1984, 1989; Bresnahan, Greenstein and Henderson, 2010; others) => Cov (firm-age, DDD) <

16 Why do some firms adopt DDD more than others? What are the drivers of DDD? 2. Firm Age: -: Older firms have high inertia and cannot make organizational change (Hannan and Freeman, 1977, 1984, 1989; Bresnahan, Greenstein and Henderson, 2010; others) => Cov (firm-age, DDD) < 0 +: Selection on productivity survived firms are more productive than exit firms due to more resources, better adjusting ability to environment, learning-bydoing (Haltiwanger et al. 1999). 16 => Cov (firm-age, ε) > 0 -> underestimation not overestimation 16

17 Why do some firms adopt DDD more than others? What are the drivers of DDD? 3. Consistency of Business Practices Cases and Literature CVS Enterprise IT system over 4,000 retail stores. (McAfee, 2008; Brynjolfsson and McAfee, 2009; Brynjolfsson 2009) Wal-Mart inventory management Harrah s - customer management Consistency of business practices across their branches let their firms gain a higher performance through data-driven decision-making. Thus, firms with consistent business practices have more incentive to adopt DDD in the first place

18 18 Construction of Consistency Measure Survey Question Looking across your entire company, please rate the level of consistency in behaviors and business processes across operating units (HR survey q1) Regarding the first core activity of your company, the consistency within business unit (HR survey q9a) Regarding the first core activity of your company, the consistency across functions (e.g., sales, finance, etc) (HR survey 9b) Regarding the first core activity of your company, the consistency across geographies (HR survey q9c) Effectiveness of IT in building consistent systems and processes for each operating unit (IT survey q13b) 18 Scale

19 DDD drivers can be potential instrumental variables (IV). DDD Productivity Instrument Variable (IV) 1.Adjustment Cost 2.Firm Age 3.Consistency 19 19

20 Productivity and DDD: OLS and IV OLS DDD (0.019) (0.035) Ln (Material) (0.042) 0.504(0.034) Ln(Physical Capital) Ln (Non-IT Employee) IV (0.023) (0.023) (0.032) (0.032) Ln (IT-Employee) (0.022) (0.022) Industry and Year Control Yes Yes R-squared Overid Test: Hansen s J 0.68 Hausman Test 0.58 Robust standard errors were clustered around firms. p<0.01,. p<0.05, p<0.1. Industry classification was based on NAICS 2 digit for manufacturing and 1 digit for other industries

21 Does DDD improve the other performance measures? 1. Return on Assets: Pretax Income per total assets 2. Return on Equity: Pretax Income per equity 3. Asset Utilization: Output per total assets 21 21

22 Interpretation Return on Asset Return on Equity Asset Utilization Dependent Variable= Log(Pretax Income) Log(Pretax Income) Log(Sales) OLS 2SLS OLS 2SLS OLS 2SLS DDD (0.049) 0.19 (0.11) (0.029) (0.063) Log(IT Employee) (0.054) (0.053) (0.037) (0.036) Log(Total Asset) (0.07) (0.08) Log(Equity) 0.90 (0.04) 0.89 (0.04) (0.034) (0.035) 0.42 (0.05) (0.062) (0.035) 0.43 (0.06) Number Firms Number Observations of of R-square Controls: Industry, Year, Log(R&D expense), Log(Advertising expense), Log(Capital), Log(Total number of employees), Log(Market share), Importance of employees education 22 22

23 Does DDD increase market value? Market Value = β i A i (Market Value of firm = Sum of Value of Each Asset, A i ) (e.g. Hall, 2001; Hall et al., 2000; Baily et al., 1981; Brynjolfsson et al., 2002) Market Value = β i A i + α x DDD i x A i Coefficient (α and β i ) is an indicator of how much investors value a firm with each type of asset Can DDD be thought of as an asset? 23 23

24 Market Value and DDD Market Value = β 0 + β 1 (Physical Capital) + β 2 (Computer Capital) + β 3 (Other Asset) (Brynjolfsson, Hitt, and Yang, 2002) Computer Capital = f(it) (Tambe and Hitt, 2008) Dependent variable = Market Value Property, Plant and Equipment - Total (Net) (PPE) (0.495) (0.454) (0.429) (0.431) (0.458) IT-Employee (2.003) (1.649) (1.635) (1.864) (1.714) Other assets (0.034) (0.031) (0.029) (0.033) (0.026) DDD x IT-Employee (1.267) DDD x Employee (0.073) DDD x PPE (0.379) DDD x Other assets (0.127) Constant -5,494-4,487-5,060-5,953-5,332 ( ) ( ) ( ) ( ) ( ) 24 Observations R-squared Robust standard errors in parentheses p<0.01, p<0.05, p<0.1 24

25 Conclusion 1. Data-driven decision-making (DDD) may explain a 4-6% of output and productivity, controlling for traditional inputs and IT use. 2. DDD is also correlated with other performance measures (return on assets, return on equity, asset utilization, market value), controlling for other firmspecific characteristics. 3. Firms with more consistent business practices are more data-driven; Younger firms tend to be more datadriven