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Complementarity and Evolution of Contractual Provisions: An Empirical Study of IT Services Contracts Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

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Page 1: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

Complementarity and Evolution of Contractual Provisions: An Empirical Study of IT Services Contracts

Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer

Presenter: Wen ZHENG

Page 2: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

How learning processes are reflected in systematic relationships between contingency planning and task description contractual provisions?

Research Question

Page 3: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

How learning processes are reflected in systematic relationships between contingency planning and task description contractual provisions?

Research Question

Contingency planning clauses are defined as the parts of a contract that are designed to support within-agreement adjustments by proscribing the ways in which the contractual partners will deal with problematic contingencies that might arise during the execution of the contract.

Page 4: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

How learning processes are reflected in systematic relationships between contingency planning and task description contractual provisions?

Research Question

The contract can include more detailed specification of the task to be completed.

Page 5: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

Systematic theoretical and empirical research of how contract are actually designed and how their structure evolve is limited.

Empirical research fails to investigate the evolutionary patterns in contract structure and mechanisms of learning to contract.

Why this question?

Page 6: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

Transaction cost theories of contract design

Learning and complements

Partner-Specific Learning

Theory

Page 7: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

Transaction Cost Theories of Contract Design

◦ TCE theory of contract design is premised on idea about the functions of contracts that were first emphasized in legal literature.

Theory

Business contracts are designed for the purpose of facilitating a transaction between two parties.

Page 8: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

Transaction Cost Theories of Contract Design

◦ The TCE theory of contracting assumes that parties to a contract have bounded rationality that prevent them from foreseeing all possible future contingencies that may arise.

Theory

Contract terms should reflect certain key characteristics of the transaction. • Degree of bilateral dependency•Degree to which property rights to assets

Safeguard

Page 9: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

Learning and Complements◦ Complements? Substitute?

Theory

Page 10: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

Learning and Complements◦ Complements? Substitute?

Agency Theory Substitute

Theory

Page 11: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

Learning and Complements◦ Complements? Substitute?

Agency Theory Substitute Organization theory Complements

Traditional idea Contemporary idea

Dynamic way of thinking Cross-Provisional learning

Theory

Page 12: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

Learning and Complements◦ Complements? Substitute?

Agency Theory Substitute Organization theory Complements

Traditional idea Contemporary idea

Dynamic way of thinking Cross-Provisional learning

Theory

As they develop one category of contractual provisions for a given contract, the contracting parties may gain understandings about transaction features that are useful in the design of a different category of contractual provisions.

Page 13: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

Partner-Specific Learning

◦ Partners with longer history of working together tend to write more detailed contracts?

Reduce the cost to contain more detailed task description and contingency planning.

Ensure the relationship will not terminated.

Theory

Page 14: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

H1: Contingency planning and task descriptions have reciprocal positive effect on one another.

Hypothesis

Complementary

Page 15: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

H2A: Prior experience with contingency planning is associated more detailed task description in subsequent contracts between the same firms.

H2B: More detailed task description in prior contracts is positively associated with contingency planning in subsequent contracts between the same firms.

Hypothesis

Cross-provisional Learning

Page 16: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

H3A (B): As an exchange relationship between two parties continues, the parties will be more likely to include contingency planning (more detailed task description) in their contracts with each other

Hypothesis

Partner-Specific Learning

Page 17: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

Firm: Compustar Period: 1986-1998 Sample: 405 agreement, 25%

◦ According to the first letters of the customers name

unbiased sample

◦ Remove 8 contracts with missing data, and 11 with abandoned type

Data

Page 18: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

Variables

Dependent and Explanatory

Variable

Contingency planning

Task Description

Relationship History

•Binary Variable•0: contract contains no contingency planning.1: contract contains contingency planning

Page 19: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

Variables

Dependent and Explanatory

Variable

Contingency planning

Task Description

Relationship History

•Binary Variable•0: contract contains no contingency planning.1: contract contains contingency planning

Page 20: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

Variables

Dependent and Explanatory

Variable

Contingency planning

Task Description

Relationship History

•1-7 Liket-type scale• 1: contract include very little details in the description of the task to be completed.7: contract contains very extensive technical description

Page 21: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

Variables

Dependent and Explanatory

Variable

Contingency planning

Task Description

Relationship History

• Capture partner-specific learning over time•Measures the amount of time in weeks that Compustar had worked with a particular business unit of a partner company prior to signing the contract.

Page 22: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

VariablesControl Variable

Name Variable Description Predicted Effect

Mainframe •Measure the degree of uncertainty associated with project•Coded as one if Compustar would be working with the buyer’s mainframe computer and otherwise zero.

Contingency: +/-/=

Task: +/-/=

Measurement Cost

•Identify whether the technology used in the project makes it difficult to verify the quality of the output•Coded as one if quality is difficult to determine and zero if it is readily apparent.

Contingency: -

Task: +/-/=

Programming •Coded as one if project requires programming and zero otherwise

Contingency: noTask: -

Previous Fixed Fee

•Measures aspects of incentive structure of the contracts• Fixed fee takes the value of one if the contract was based on a fixed fee and zero otherwise.Previous fixed fee is the number of fixed-fee contracts that parties to a given contract have used in the past

Contingency: no

Task: +

Page 23: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

VariablesControl Variable (Continued)

Variable Name

Variable Description Predicted Effect

Interdependence •Control for the potential for holdup in each project•Coded as one if customer personnel are listed as being responsible for some portion of the project deliverables and zero otherwise.

Contingency :+

Task : +

Dollar value •Control for the total money value of the project•Enter as log value since it is highly screwed.

Time •Account for time trends and measure the passage of time from 1986 to 1998.•Zero for 1986, one for 1987 etc.

Innovation • Captures the degree to which the project required innovation from Compustar. •A seven point Likert-type scale.

Contingency: -

Task: +

Page 24: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

Models Basic OLS Model

Basic Probit Model

Page 25: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

Task Description

a. Likert-scale measure Continuous variable

b. Endogeneity Problem Durbin-Wu-Hausman

c. 2SLS Regression model Ivendog

Contingency Planning

a. Endogeneity Problem Smith-Blundell test (Probexog)

b. Instrument Predicted value for task description

Methodology

Page 26: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

OLS Estimates, Models of task Description Details

Results

Page 27: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

OLS Estimates, Models of task Description Details

Results

Page 28: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

OLS Estimates, Models of task Description Details

Results

H1

Page 29: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

OLS Estimates, Models of task Description Details

Results

Page 30: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

OLS Estimates, Models of task Description Details

Results

H2A

Page 31: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

OLS Estimates, Models of task Description Details

Results

Page 32: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

OLS Estimates, Models of task Description Details

Results

H3B

Page 33: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

MLE, Probit Model of Contingency Planning

Results

Page 34: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

MLE, Probit Model of Contingency Planning

Results

Page 35: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

MLE, Probit Model of Contingency Planning

Results

H1

Page 36: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

MLE, Probit Model of Contingency Planning

Results

Page 37: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

MLE, Probit Model of Contingency Planning

Results

H2B

Page 38: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

MLE, Probit Model of Contingency Planning

Results

Page 39: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

MLE, Probit Model of Contingency Planning

Results

H3A

Page 40: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

Results

Page 41: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

Results

Page 42: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

Results

Page 43: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

Results

Page 44: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

Results

Page 45: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

Contingency Planning and task description tend to act as complements in contract design, this complementarity likely results from learning spillovers between these two categories of contractual provisions.

Unexpected finding: Task Description tended to become less detailed over time.

The development of a relationship between Compustar and a given customer had a positive, though insignificant, effect on the detail of the task description.

Findings

Page 46: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

Lack measure of trust and a long enough sample period

Unable to test whether trust effects dominate the learning effects over time.

Lack data on performance of the project Unable to evaluate whether increases in one tended

to lead to better performance when the level of the other is higher.

Limitation

Page 47: Nicholas S. Argyres, Janet Bercovitz and Kyle J. Mayer Presenter: Wen ZHENG

Explore relationship of other important contract provisions. (e.g., IPR in Biotechnology contract, payment terms)

Examine when different components of a contract as well as components between those components have a greater effect performance.

Future Research