1. 1 A Transaction Cost Approach to Make-or-Buy Decisions Gordon Walker and David Weber Administrative Science Quarterly, 29 (1984): 373-391

2. 2 Purpose of the paper Make or buy decisions Transaction cost -Uncertainty -Asset specificity Production cost

3. 3 Asset specificity Costs Governance cost when AS is low Internal organization > market Governance cost when AS is high Internal organization < market Governance cost difference between internal organization and market A Given moderate uncertainty ΔTC Proxies for AS - Supplier competition - Buyer experience

4. 4 Asset specificity Costs Production cost when AS is low Internal organization > market Production cost when AS is high Internal organization > market BUT get close to each other ΔPC Given moderate uncertainty Proxies for PC Supplier production advantage

5. 5 Asset specificity Costs A ΔPC + ΔTC A’ Given moderate uncertainty Proxies for uncertainty - Volume uncertainty - Technological uncertainty Make or Buy decision

7. 7 Buy decision Volume uncertainty Technological uncertainty negative Uncertainty Hypothesis 1, 2

8. 8 Supplier Competition Buy decision Supplier Production advantage Positive production cost positive Hypothesis 3, 4

9. 9 Supplier Competition Buy decision Buyer experience positive Asset specificity Supplier Production advantage Technological uncertainty Negative Hypothesis 5, 6, 7, 8

10. Hypotheses H1/ β1 Volume uncertainty leads to making rather than buying a component. H2/r1Technological uncertainty increases the likelihood of a make rather than a buy decision. H3/ β2 The higher the supplier production cost advantage, the more likely the firm is to buy rather than make a component. H4/r2The competitiveness of the supplier market increases the production cost advantage of suppliers over buyers. H5/r3Greater supplier market competition should lead to buying the component. H6/r4The experience a buyer has in producing a component reduces the production cost advantage of the supplier over the buyer. H7/r5Buyer experience in producing a component increases the likelihood of a buy decision. H8/r6Buyer experience in component production reduces technological uncertainty associated with the component.

11. 11 Supplier Competition Buy decision Supplier Production advantage Positive positive Buy decision Volume uncertainty Technological uncertainty negative Buyer experience positive Negative Full model

12. 12

13. 13 Method and data  The data consisted of 60 decisions made in a component division of a large U.S. automobile manufacturer over a period of three years  The data were analyzed using the unweighted least squars (ULS) procedure

17. 17 Supplier Competition (reverse scale) Buy decision Supplier Production advantage Volume uncertainty Technological uncertainty Buyer experience -.284*.034.155 -.198 -.315* -.316*.205*.862* Results

18. Results (Mixed support for Williamson’s theory) H1/r1Volume uncertainty leads to making rather than buying a component.Significant effect H2/β1Technological uncertainty increases the likelihood of a make rather than a buy decision. direction opposite to hypothesis; not significant H3/β2The higher the supplier production cost advantage, the more likely the firm is to buy rather than make a component. Strongly supported H4/r2The competitiveness of the supplier market increases the production cost advantage of suppliers over buyers. moderate H5/r3Greater supplier market competition should lead to buying the component. Jackknife coefficient acceptable H6/r4The experience a buyer has in producing a component reduces the production cost advantage of the supplier over the buyer. Jackknife coefficient not acceptable; variance explained low H7/r5Buyer experience in producing a component increases the likelihood of a buy decision. Not significant; small effects H8/r6Buyer experience in component production reduces technological uncertainty associated with the component. Moderate; variance explained low

19. 19 Discussion  Production costs were likely to be salient in the decision-making process  Transaction costs have small but significant effects  Limitations: -small sample size -data from a single corporate division -relative simplicity of the components