Taxing the Taxpayers: An Empirical Investigation of the Drivers of Baseline Changes in U.S. Federal Government Technology Programs

Published Online:https://doi.org/10.1287/msom.2020.0942

References

  • Ai C, Norton EC (2003) Interaction terms in logit and probit models. Econom. Lett. 80(1):123–129.CrossrefGoogle Scholar
  • Allison PD, Waterman RP (2002) Fixed-effects negative binomial regression models. Sociol. Methodology 32(1):247–265.CrossrefGoogle Scholar
  • Altonji JG, Elder TE, Taber CR (2005) Selection on observed and unobserved variables: Assessing the effectiveness of Catholic schools. J. Political Econom. 113(1):151–184.CrossrefGoogle Scholar
  • Atefi Y, Ahearne M, Maxham JG III, Donavan DT, Carlson BD (2018) Does selective sales force training work? J. Marketing Res. 55(5):722–737.CrossrefGoogle Scholar
  • Austin RD, Devin L (2009) Weighing the benefits and costs of flexibility in making software: Toward a contingency theory of the determinants of development process design. Inform. Systems Res. 20(3):462–477.LinkGoogle Scholar
  • Baldwin C, Clark K (2000) Design Rules: The Power of Modularity (MIT Press, Cambridge, MA).CrossrefGoogle Scholar
  • Ball A (2016) Feds freeze funding for troubled state technology project. Accessed November 17, 2020, https://www.statesman.com/NEWS/20160904/Feds-freeze-funding-for-troubled-state-technology-project.Google Scholar
  • Calvo E, Cui R, Serpa JC (2019) Oversight and efficiency in public projects: A regression discontinuity analysis. Management Sci. 65(12):5651–5675.LinkGoogle Scholar
  • Carrillo J, Gaimon C (2004) Managing knowledge-based resource capabilities under uncertainty. Management Sci. 50(11):1504–1518.LinkGoogle Scholar
  • Chao RO, Kavadias S (2008) A theoretical framework for managing the new product development portfolio: When and how to use strategic buckets. Management Sci. 54(5):907–921.LinkGoogle Scholar
  • Chao RO, Kavadias S, Gaimon C (2009) Revenue driven resource allocation: Funding authority, incentives, and new product development portfolio management. Management Sci. 55(9):1556–1569.LinkGoogle Scholar
  • Clark KB, Fujimoto T (1991) Product Development Performance: Strategy, Organization, and Management in the World Auto Industry (Harvard Business School Press, Boston).Google Scholar
  • Clark JJ, Littrell PD (2002) Breaking down the work breakdown structure. Program-Project Management 31(2):104–107.Google Scholar
  • Coviello D, Guglielmo A, Spagnolo G (2017) The effect of discretion on procurement performance. Management Sci. 64(2):715–738.LinkGoogle Scholar
  • Crama P, Sting FJ, Wu Y (2018) Encouraging help across projects. Management Sci. 65(3):1408–1429.LinkGoogle Scholar
  • Department of Justice (2010) Information technology (IT) investment baseline management guide. Accessed November 17, 2020, https://www.justice.gov/jmd/file/705791/downloadGoogle Scholar
  • Dey A, Nikolaev V, Wang X (2015) Disproportional control rights and the governance role of debt. Management Sci. 62(9):2581–2614.LinkGoogle Scholar
  • Dibbern J, Winkler J, Heinzl A (2008) Explaining variations in client extra costs between software projects offshored to India. MIS Quart. 32(2):333–366.CrossrefGoogle Scholar
  • Edwards C, Kaeding N (2015) Federal government cost overruns. Accessed November 17, 2020, https://www.downsizinggovernment.org/government-cost-overruns.Google Scholar
  • Eisenhardt KM, Tabrizi BN (1995) Accelerating adaptive processes: Product innovation in the global computer industry. Admin. Sci. Quart. 40(1):84–110.Google Scholar
  • Ferdows K (2006) Transfer of changing production know-how. Production Oper. Management 15(1):1–9.CrossrefGoogle Scholar
  • Ford DN, Sterman JD (2003) The liar’s club: Concealing rework in concurrent development. Concurrent Engrg. 11(3):211–219.CrossrefGoogle Scholar
  • Gallivan MJ, Spitler VK, Koufaris M (2005) Does information technology training really matter? A social information processing analysis of coworkers’ influence on IT usage in the workplace. J. Management Inform. Systems 22(1):153–192.CrossrefGoogle Scholar
  • Gokpinar B, Hopp WJ, Iravani SMR (2010) The impact of misalignment of organizational structure and product architecture on quality in complex product development. Management Sci. 56(3):468–484.LinkGoogle Scholar
  • Gopal A, Sivaramakrishnan K, Krishnan MS, Mukhopadhyay T (2003) Contracts in offshore software development: An empirical analysis. Management Sci. 49(12):1671–1683.LinkGoogle Scholar
  • Government Accountability Office (2008) Agencies need to establish comprehensive policies to address changes top projects’ cost, schedule, and performance goals. Accessed November 17, 2020, https://www.gao.gov/assets/280/279189.pdf.Google Scholar
  • Government Accountability Office (2009) GAO cost estimating and assessment guide. Accessed November 17, 2020, https://www.gao.gov/new.items/d093sp.pdfGoogle Scholar
  • Government Accountability Office (2012) Effective practices and federal challenges in applying agile methods. Accessed November 17, 2020, https://www.gao.gov/assets/600/593091.pdf.Google Scholar
  • Gregory RW, Keil M, Muntermann J, Mähring M (2015) Paradoxes and the nature of ambidexterity in IT transformation programs. Inform. Systems Res. 26(1):57–80.LinkGoogle Scholar
  • Haas MR, Hansen MT (2005) When using knowledge can hurt performance: The value of organizational capabilities in a management consulting company. Strategic Management J. 26(1):1–24.CrossrefGoogle Scholar
  • Harris ML, Collins RW, Hevner AR (2009) Control of flexible software development under uncertainty. Inform. Systems Res. 20(3):400–419.LinkGoogle Scholar
  • Huckman RS, Pisano GP (2006) The firm specificity of individual performance: Evidence from cardiac surgery. Management Sci. 52(4):473–488.LinkGoogle Scholar
  • Jiang JJ, Chang JY, Chen HG, Wang ET, Klein G (2014) Achieving IT program goals with integrative conflict management. J. Management Inform. Systems 31(1):79–106.CrossrefGoogle Scholar
  • Joglekar NR, Davies J, Anderson EG (2016) The role of industry studies and public policies in production and operations management. Production Oper. Management 25(12):1977–2001.CrossrefGoogle Scholar
  • Kennedy P (2008) A Guide to Econometrics (Blackwell Publishing, Malden, MA).Google Scholar
  • Kerzner HR (2013) Project Management: A Systems Approach to Planning, Scheduling and Controlling (John Wiley & Sons, Hoboken, NJ).Google Scholar
  • Kwak YH, Anbari FT (2012) History, practices, and future of earned value management in government: Perspectives from NASA. Project Management J. 43(1):77–90.CrossrefGoogle Scholar
  • Kwon HD, Lippman SA, McCardle KF, Tang CS (2010) Project management contracts with delayed payments. Manufacturing Service Oper. Management 12(4):692–707.LinkGoogle Scholar
  • Langer N, Slaughter SA, Mukhopadhyay T (2014) Project managers’ practical intelligence and project performance in software offshore outsourcing: A field study. Inform. Systems Res. 25(2):364–384.LinkGoogle Scholar
  • Lee HL, Tang CS (2018) Socially and environmentally responsible value chain innovations: New operations management research opportunities. Management Sci. 64(3):983–996.LinkGoogle Scholar
  • Loch C, DeMeyer A, Pich M (2006) Managing the Unknown: A New Approach to Managing High Uncertainty and Risks in Projects (John Wiley & Sons, Hoboken, NJ).CrossrefGoogle Scholar
  • MacCormack A, Verganti R, Iansiti M (2001) Developing products on “Internet time”: The anatomy of a flexible development process. Management Sci. 47(1):133–150.LinkGoogle Scholar
  • Maruping L, Venkatesh V, Agarwal R (2009) A control theory perspective on agile methodology use and changing user requirements. Inform. Systems Res. 20(3):377–399.LinkGoogle Scholar
  • Mihm J (2010) Incentives in new product development projects and the role of target costing. Management Sci. 56(8):1324–1344.LinkGoogle Scholar
  • Mihm J, Loch C, Huchzermeier A (2003) Problem-solving oscillations in complex engineering projects. Management Sci. 49(6):733–750.LinkGoogle Scholar
  • McKinsey (2017) The opportunity in government productivity. Report, McKinsey Center for Government (San Francisco, California).Google Scholar
  • Miller S, Ward D (2016) Update 2016: Considerations for using agile in DoD acquisition. Technical Note CMU/SEI-2016-TN-001, Software Engineering Institute, Carnegie Mellon University, Pittsburgh, PA.Google Scholar
  • Mishra A, Sinha KK (2016) Work design and integration glitches in globally distributed technology projects. Production Oper. Management 25(2):347–369.CrossrefGoogle Scholar
  • Mishra A, Das SR, Murray JJ (2016) Risk, process maturity and project performance: An empirical analysis of US federal government technology projects. Production Oper. Management 25(2):210–232.CrossrefGoogle Scholar
  • Mithas S, Krishnan MS (2008) Human capital and institutional effects in the compensation of information technology professionals in the United States. Management Sci. 54(3):415–428.LinkGoogle Scholar
  • Moczar L (2013) Why agile isn’t working: bringing common sense to agile principles. CIO (June 4), https://www.cio.com/article/2385322/agile-development-why-agile-isn-t-working-bringing-common-sense-to-agile-principles.html.Google Scholar
  • Moy M (2016) Evaluating federal information technology program success based on earned value management. Walden Dissertations and Doctoral Studies 2075. Walden University, Minneapolis, MN.Google Scholar
  • Mussweiler T, Strack F (2001) The semantics of anchoring. Organ. Behav. Human Decision Processes 86(2):234–255.CrossrefGoogle Scholar
  • Narayanan S, Balasubramanian S, Swaminathan JM (2011) Managing outsourced software projects: An analysis of project performance and customer satisfaction. Production Oper. Management 20(4):508–521.CrossrefGoogle Scholar
  • Office of the Chief Information Officer (2010) Information technology (IT) investment baseline management guide. Department of Justice Office of the Chief Information Officer, Washington, DC. Google Scholar
  • Office of Personnel Management (2012) Information technology (IT) baseline management policy. United States Office of Personnel Management, Washington, DC.Google Scholar
  • Orlikowski WJ (2002) Knowing in practice: Enacting a collective capability in distributed organizing. Organ. Sci. 13(3):249–273.LinkGoogle Scholar
  • Pang M, Tafti A, Krishnan MS (2016) Do CIO IT budgets explain bigger or smaller governments? Theory and evidence from U.S. state governments. Management. Sci. 62(4):1020–1041.LinkGoogle Scholar
  • Park S, Gupta S (2012) Handling endogenous regressors by joint estimation using copulas. Marketing Sci. 31(4):567–586.LinkGoogle Scholar
  • Privett N, Erhun F (2011) Efficient funding: Auditing in the nonprofit sector. Manufacturing Service Oper. Management 13(4):471–488.LinkGoogle Scholar
  • Rahmani M, Roels G, Karmarkar US (2017) Collaborative work dynamics in projects with co-production. Production Oper. Management 26(4):686–703.CrossrefGoogle Scholar
  • Rai A, Maruping LM, Venkatesh V (2009) Offshore information systems project success: The role of social embeddedness and cultural characteristics. Management Inform. Systems Quart. 33(3):617–641.CrossrefGoogle Scholar
  • Ramasubbu N, Bharadwaj A, Tayi GK (2015) Software process diversity: Conceptualization, measurement, and analysis of impact on project performance. Management Inform. Systems Quart. 39(4):787–807.CrossrefGoogle Scholar
  • Ramesh B, Mohan K, Cao L (2012) Ambidexterity in agile distributed development: An empirical investigation. Inform. Systems Res. 23(2):323–339.LinkGoogle Scholar
  • Simon H (1979) Rational decision making in business organizations. Amer. Econom. Rev. 69(4):493–513.Google Scholar
  • Small Business Administration (2011) IT Investment Performance Baseline Management (PBM) Policy. Accessed November 17, 2020, https://www.sba.gov/sites/default/files/SOP_90-52.pdfGoogle Scholar
  • Staats BR, Milkman KL, Fox CR (2012) The team scaling fallacy: Underestimating the declining efficiency of larger teams. Organ. Behav. Human Decision Processes 118(2):132–142.CrossrefGoogle Scholar
  • Stiglitz JE, Rosengard JK (2015) Economics of the Public Sector (WW Norton & Company, Manhattan, New York).Google Scholar
  • Subramanyam R, Ramasubbu N, Krishnan MS (2012) In search of efficient flexibility: Effects of software component granularity on development effort, defects, and customization effort. Inform. Systems Res. 23(3):787–803.LinkGoogle Scholar
  • Tang CS (2016) OM Forum—Making OM research more relevant: “Why?” and “how?” Manufacturing Service Oper. Management 18(2):178–183.LinkGoogle Scholar
  • Terwiesch C, Loch CH (1999) Measuring the effectiveness of overlapping development activities. Management Sci. 45(4):455–465.LinkGoogle Scholar
  • Terza JV, Basu A, Rathouz PJ (2008) Two-stage residual inclusion estimation: Addressing endogeneity in health econometric modeling. J. Health Econom. 27(3):531–543.CrossrefGoogle Scholar
  • U.S. Government Printing Office (2007) Senate Hearing 110-409: High-Risk Information Technology Projects: Is Poor Management Leading Billions in Waste? (BiblioGov, Washington, DC).Google Scholar
  • Wheelwright SC, Clark KB (1992) Revolutionizing Product Development: Quantum Leaps in Speed, Efficiency, and Quality (Simon and Schuster, New York, NY).Google Scholar
  • Wooldridge JM (2010) Econometric Analysis of Cross Section and Panel Data (MIT Press, Cambridge, MA).Google Scholar
  • Yaraghi N (2015) Doomed: Challenges and solutions to government IT projects. Brookings TechTank (August 25), https://www.brookings.edu/blog/techtank/2015/08/25/doomed-challenges-and-solutions-to-government-it-projects/.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.