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Contract Selection and Software Quality: Outsourcing Insights

Explore how contract choices impact software quality outcomes in outsourcing, focusing on vendor uncertainty and product complexity. Learn strategies for aligning goals and ensuring higher quality.

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Contract Selection and Software Quality: Outsourcing Insights

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  1. Contract Choice and Product Quality Outcomes in Outsourcing:Empirical Evidence from Software Development Sandra Slaughter Donald E. Harter Soon Ang Jonathan Whitaker American University

  2. Software Quality • Problem • In April, a software glitch resulted in the loss of thousands of dollars for US Airways Group Inc. when some tickets were mistakenly priced at $1.86. (ComputerWorld 7/25/05) • A software bug apparently caused the largest power outage in North America, the Northeast blackout of August 2003, which threw millions of people into darkness (ComputerWorld 7/25/05) • Flawed software cost the U.S. economy $60 billion in 2002 (NIST 2002) American University

  3. Research Motivation • Solutions • Process maturity is key to higher quality, lower costs, shorter development time • Harter, Krishnan, Slaughter, Management Science 2000 • Questions remain: • How to encourage higher quality? • Research question • Can contract selection be a vehicle to encourage software quality? • If so, what factors drive contract selection? American University

  4. Contract Selection • Contract theory & agency theory • Choice of contract structure is crucial to ensure that agent’s goals are aligned with principal (Crocker & Reynolds, 1993; Grossman & Hart, 1983; Milgrom & Roberts, 1992) • Issues: • Hidden information leads to adverse selection • Information asymmetry leads to moral hazard • Type of contract can serve as an effective governance mechanism American University

  5. Information Asymmetry • Sources of information asymmetry • uncertainty in product specifications and uncertainty about the vendor’s ability to develop quality products • Artz and Norman 2002 • Stump and Heide 1996 • Kalnins and Mayer 2004 • High uncertainty increases costs of writing specific contract terms American University

  6. Specification Uncertainty Design Complexity Prior Contracting Experience Software Process Maturity Research Model Uncertainty of Product Specifications: H1, H2 Contract Choice Time & Material, Hybrid, Fixed Price Verification & Validation Quality H5a, H5b Uncertainty of Vendor Quality: H3, H4 American University

  7. Product Uncertainty • Issues • Client requirements can be ambiguous (Nidumolu 1995) • Software products are innovations and innovations embody uncertainties (Ang & Beath 1993) • Software development is frequently exploratory (MacCormack 2001) • Client understanding is evolutionary (Richmond 1992) • Hypothesis • H1: Time & Materials contracts more likely when specification uncertainty is high American University

  8. Product Complexity • Issues • Complex designs are more difficult to develop (Brooks 1995) • Effort required for testing complex designs is highly variable (Banker 2002) • Higher software complexity increases technical risk (Barki 1993) • Development cost estimation is more uncertain • Hypothesis • H2: Time & Materials contracts more likely when design complexity is high American University

  9. Vendor Uncertainty • Issues • Inability to determine vendor quality can create problems of adverse selection and moral hazard (Artz & Norman 2002) • Repeated interaction and long-term relationships mitigate adverse selection and moral hazard (Baker 1994) • Repeated transactions provide incentives that decrease likelihood of opportunism (MacNeil 1978; Granovetter 1985) • Corts & Singh (2004) • Repeated interactions reduce contracting costs, leading to fixed price • Interaction reduces opportunism, leading to time & material • Variance of these costs affects contract choice (Kalnins & Mayer 2004) • Hypothesis • H3: Hybrid contracts more likely when contracting experience between vendor and client is low American University

  10. Vendor Uncertainty • Issues • Adverse selection can be addressed using signals designed to reveal private information (Milgrom & Roberts 1992; Mishra 1998) • Qualification process can identify vendors with necessary skills (Stump & Heide 1996) • Process maturity can be used to signal quality (Arora & Asundi 1999) • Hypothesis • H4: Fixed price contracts more likely when software process maturity is high American University

  11. Effect of Contract Choiceon Quality Outcomes • Issues • Opportunity for ex post opportunism by both parties (Williamson 1979) • Vendor has financial incentive to freeze the specification in fixed price contract • Incentives are to develop the software right the first time, according to the specification • Clients may change are articulate new requirements as they discover what they truly need • Vendor profits from new requirements under Time & Materials, and may accommodate client’s requirements • Hypotheses • H5a: Fixed price contracts have higher development and production verification quality • H5b: Time & Materials contracts have higher acceptance validation quality American University

  12. Research Site &Data Collection • Data collected on software projects developed from 1987 to 2004 • 78 contracts were negotiated • 26 time and material • 38 fixed price • 14 hybrid American University

  13. Contract Types • Time & Material • Vendor reimbursed through hourly rate • Technical and financial risks on client • Fixed Price • Vendor agrees to fixed contract value • Technical and financial risks on vendor • Costly to negotiate – requires detailed specifications ex ante • Hybrid • Agreement on cost estimate, but client pays all costs; profit based on initial estimate and performance • Financial risk primarily on client American University

  14. Measures • Contract choice • Categorical variables • 1-T&M, 2-hybrid, 3-Fixed price • Quality • Verification (development & production) – technical issues of whether the software has been developed correctly and performs correctly • Validation (acceptance) – whether the right software has been developed that satisfies the users • Antecedents of contract choice • Specification uncertainty • Design complexity • Prior contracting experience • Software process maturity • Controls • Product size American University

  15. Regression Models: Choice • Stage 1: Contract Choice • multinomial regression using Newton-Raphson maximum likelihood estimation • Prob(yi=j) = e jXi / Σe kXi • Corrections • Non-independence of disturbances across different contract segments • Huber (1967)/White (1982) sandwich estimator • Results • Antecedents significant in predicting choice (p<.001) • Explain significant variance (pseudo R2 = 0.751) • Correlation between predicted and actual contract is 88.5% American University

  16. Uncertainty & Complexity Likelihood of Contract Choice Given Levels of Specification Uncertainty Likelihood of Contract Choice Given Levels of Design Complexity American University

  17. Hypotheses Summary • H1: Time & Materials preferred over fixed price for higher levels of specification uncertainty • 66% likelihood for high specification uncertainty • 10% likelihood for low specification uncertainty • H2: Time & Materials preferred over fixed price and hybrid when there is higher design complexity • 71% likelihood for high design complexity • 8% likelihood for low design complexity American University

  18. Experience & Process Likelihood of Contract Choice Given Levels of Prior Contracting Experience Likelihood of Contract Choice Given Levels of Software Process Maturity American University

  19. Hypotheses Summary • H3: hybrid contracts preferred over fixed price when prior contracting experience is lower • 84% likelihood of hybrid for low experience • 88% likelihood of fixed price for high experience • H4: fixed price preferred for higher levels of process maturity American University

  20. Regression Models: Quality • Stage 2: Quality Outcomes of Contract Choice • Multivariate general linear modeling (GLM) • Two-step multinomial selection bias correction method of Lee (1983) • Models • Development Verification Quality = f(Contract-Choice, Specification-Uncertainty, Design-Complexity, Prior-Contracting-Experience, Software-Process-Maturity, Product-Size) • Production Verification Quality = f(Contract-Choice, Specification-Uncertainty, Design-Complexity, Prior-Contracting-Experience, Software-Process-Maturity, Product-Size) • Acceptance Validation Quality = f(Contract-Choice, Specification-Uncertainty, Design-Complexity, Prior-Contracting-Experience, Software-Process-Maturity, Product-Size) • Results • Hotelling’s T2 test of contract choice significant (p<.001) • Post hoc calculation of power is 0.97 American University

  21. Quality Outcomes American University

  22. Discussion • Information asymmetry arising from product uncertainties (specification uncertainty and design complexity) shifts contract choice to Time & Material • Uncertainty of vendor quality is a strong motivator of contract choice • Vendor quality (30.2%) explains eight times the variance of product uncertainty (3.7%) American University

  23. Discussion • Prior contracting experience is a critical mitigator of information asymmetry • Hybrid contracts more likely when experience between client and vendor is low • Reducing contracting and shirking costs • Vendor quality certification explains highest variance in contract choice (20%) • Quality certification engenders greater confidence in the vendor’s abilities to estimate and deliver software products to specifications American University

  24. Thank You! Questions? American University

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