1. University of Southern California
Contracting for innovation: Defining an exchange that fosters creativity while mitigating opportunism
Kyle J. Mayer
Adele Xing
Pablo Mondal
University of Southern California
OSWC Feb

2. Inter-firm Influences on Innovation
Extant research has examined the roles of alliances and networks on innovation
Ties with other firms can enhance the innovation output of the focal firm
Effect of learning in alliances on firm-level innovation
Current gap: Innovation that occurs in the context of multiple firms working together
i.e., when the need to innovate arises from within the inter-firm transaction the firms are working on

3. Research Question
How can firms design and govern inter-firm transactions that require innovation to complete?

4. Governance and Framing
Tension: TCE  Governance  Innovation
TCE: Limit downside risk (i.e., avoiding a negative outcome)
Innovation: Create a positive environment that fosters creativity (i.e., creating a positive outcome)
(Could also view this as a tension between value creation—innovation and value capture—TCE)
Governance needs to provide safeguards to mitigate downside risk may be suboptimal for promoting more innovative solutions
These safeguards may diminish innovation if they:
Lower intrinsic motivation,
Lead to a perception of control by the buyer,
Create a prevention frame, and/or
Are too focused on how to achieve the outcome.

5. Contracts and Governance
Task Description
Specification of what must be done; includes:
How—processes, specific tasks, etc.
What—outcome standards and/or specifications
Contingency Planning
How to deal with situations that might arise during execution of the exchange
Payment Mechanism (Contract Type)
Fixed Fee: Needs to be concrete
Time & Materials: Can be more open ended
Hybrid—T&M with a cap: A combination of FF and T&M

6. Innovation and Exchange Hazards
What types of exchange hazards may be most disruptive to creating an environment to encourage innovation?
Measurement cost likely the biggest issue as it can cloud the true performance metrics of the exchange outcome
Need for innovation + Measurement cost ?
Other hazards seem less likely to play a role in how a contract might influence innovative outcomes
Asset specificity—just assign ownership and price it
Appropriability—again, assign ownership ex ante

7. Innovation and Task Description
Innovation may lead to more contractual attention to specifying what exactly is required to complete the task
Not telling the supplier how to do the task (i.e. focus on the outcome)
Outcome based contract is problematic when output of the task is difficult to measure
Buyers will focus on controlling or monitoring the input process rather than the output
H1: Need for innovation  more task description
H2: Negatively moderated by measurement cost
Not telling the supplier how to do the task, but putting clear parameters on the performance objectives, architectural issues, etc. (i.e., a focus on the outcome)
Since something new is required, the supplier needs to know what results are expected

8. Innovation and Contingency Planning
CP addresses how you deal with issues that may arise
Can be open-ended and provide flexibility
Can specify how to deal with challenges that may occur during the innovation effort
CP may be more valuable when measurement cost is high with need for innovation
Can address contingencies related to challenges in the innovation effort while also addressing hazards
H3: Need for innovation  more contingency planning
H4: Positively moderated by measurement cost

9. Contract Type/Payment Mechanism
Hybrid contract (i.e., T&M with cap) may be better for innovation
Problems with Fixed Fee and T&M
Caps costs  addresses incentive to pad costs
If below cap, controls temptation to shirk on quality
Key: Allows flexibility to deal with uncertainty of need for innovation, within boundaries
Avoids need to exercise too much control
Hybrid payment may be less effective when measurement cost is high
Uncertainty increases significantly from hazards + need for innovation  Harder to agree on cap
H5: Need for innovation  Hybrid contracts
H6: Negatively moderated by measurement cost

10. Overall Framework Contract Design Task Description
H1 (+)
Contingency Planning
H3 (+)
Need for Innovation
H2 (-)
H5 (+)
H4 (+)
Hybrid Payment
H6 (-)
Measurement Cost

11. Data
Sample
405 contracts from a single IT supplier (Compustar) with 141 buyer firms—buyers are mainly large firms or government entities
Compustar was traditionally a hardware firm (primarily mainframes) and entered the IT services industry in 1986
Each contract governs a separate project
They range in size from a few days (and about $1,000) to over a year (and over $1M)
These contracts are not outsourcing of the entire IT function, but instead involve Compustar performing some type of IT service for the buyer
Coding
Two experienced Compustar engineers familiar with the contracts library coded several sample variables

12. Measurement Dependent Variables
TASK DESCRIPTION: coded by our engineers on a 1-7 Likert-type scale;
1: very generic--minimal “what” and no “how” specified
7: detailed specs for what to do and how it will be done
CONTINGENCY PLANNING:
0 = None
1 = Some contingency planning was included in the contract
PAYMENT:
1 = Hybrid: Time and materials contract with a cap
0 = other payment mechanism (fixed fee or pure time & materials contract)

13. Measurement Key Independent Variables
INNOVATION: an ordinal variable that ranges from 1 for projects that “require no innovation to complete” to 7 for projects that “cannot be completed without a technological breakthrough”
Nothing was coded as 7, so effective range is 1 to 6
MEASUREMENT COST: a dummy variable that is coded as 1 if the quality of the final product is difficult to ascertain immediately upon completion of the project

14. Measurement Control Variables Exchange Hazards Firm-level capabilities
Appropriability
Interdependence
Ability to Disrupt
Firm-level capabilities
Programming (0/1)
Other Firm’s Hardware (0/1)
Own Hardware (0/1)
Mainframe (0/1)
Criticality of the project to the customer (i.e., the cost of a mistake on a project): Dummy variable
Relationship between Compustar and the customer
Prior IT projects Compustar has completed for the customer
Other Compustar products purchased by the customer
Dollar Value of the Project
Year Fixed-Effects

15. Preliminary Analysis
Hypotheses testing using raw data

16. Results (H1, H2)
1) Robust standard errors in parentheses; 2) Year Fixed-Effects included; 3)*** p<0.01, ** p<0.05, * p<0.1 for two-tailed tests
1) Robust standard errors in parentheses; 2) Year Fixed-Effects included;
3)*** p<0.01, ** p<0.05, * p<0.1 for two-tailed tests

17. Results (H3, H4)
1) Robust standard errors in parentheses; 2) Year Fixed-Effects included;
3)*** p<0.01, ** p<0.05, * p<0.1 for two-tailed tests

18. Results (H5, H6)
1) Robust standard errors in parentheses; 2) Year Fixed-Effects included;
3)*** p<0.01, ** p<0.05, * p<0.1 for two-tailed tests

19. Overall Results Contract Design Task Description Contingency Planning
H1 (+)
Contingency Planning
H3 (+)
Need for Innovation
H2 (-)
H5 (+)
Dashed Line is not supported
H4 (+)
Hybrid Payment
H6 (-)
Measurement Cost

20. Additional Tests
Simulation-based approach to test the interaction effect (Zelner, 2009)
Aggregate all types of hazards
Results still hold
Measurement cost drives the findings
Where to go if not Hybrid? FF or T&M?
Split Sample into Hybrid vs. FF and Hybrid vs. T&M
T&M! firms use T&M contracts to avoid the difficulty of identifying the cap in a hybrid payment mechanism when the exchange hazard is high and innovation is required

21. Implications
Contract design is influenced by considerations of safeguards and innovation
Task description gives way to contingency planning when hazards and innovation are both required
Hybrid contracts can’t accommodate both innovation and significant exchange hazards shift to T&M
More outsourcing of innovative activities may lead firms to modify how they write their contracts
Failure to adapt the contract may lead to suboptimal outcome  Need more research in this area

22. Thank you!!

23. Simulation based approach/Graphic Interpretation
Why
because the coefficient of interaction term in nonlinear models (e.g., probit model) may not represent the true interaction effect as we see in linear models (Norton et al., 2004).
How (Zelner, 2009)
We simulated the distribution of the estimated coefficients directly by repeatedly drawing new values of them from the normal distribution (instead of constructing confidence intervals based on standard errors)
Then we calculated the predicted probabilities (and the differences of predicted probabilities) based on the simulated distribution of coefficients
We plotted the mean of predicted probabilities and calculate the significance interval.
Norton et al., (2004) shows that in nonlinear models, 1) the sign of the true interaction effect may be different for different observations, and thus cannot be inferred only from the coefficient of the interaction term; 2) the true statistical significance of the interaction effect cannot be determined from the z-statistic reported in the regression output of the interaction term; and 3) the marginal effect of a change in both interacted variables does not equal the marginal effect of changing just the interaction term.
the results are different in a nonlinear model represented as
(3) Pr(Y=1|X1X2)=Φ(β0+β1X1+β2X2+β12X1X2)
A probit model assumes that ε conforms the standard normal distribution, so Φ is the cumulative distribution function of the standard normal distribution. Therefore, the interaction effect – the cross partial derivative of the expected value of y – is
(4) β12Φ’(.)+(β1+β12X2) (β2+β12X1)Φ”(.)
Equation (4) clearly shows that the interaction effect does not equal β12,