1. Preliminary version, please do not cite or circulate
Start, Stay or Stop? The Adoption and Diffusion of Organizational Innovation by Lisa M. Lynch Tufts University, NBER, IZA
November 2006
Preliminary version, please do not cite or circulate
This research was supported in part by the National Science Foundation Program on Innovation and Organizational Change. The research in this paper was conducted while the author was a Census Bureau Associate at the Boston Research Data Center. Research results and conclusions are those of the author and do not necessarily indicate concurrence by the Bureau of the Census. This paper has been screened to ensure that no confidential data are revealed. I would like to thank Sandra Black for her wonderful collaboration on previous papers using these data.

2. Role of Total Factor Productivity in Productivity Story:
Capital deepening (much of this being IT investment) – explains roughly 60% of increase in labor productivity growth
TFP contributes 37% (Jorgenson, Ho and Stiroh (2004))
Organizational innovation – non IT component of TFP growth?

3. A Working Definition of Organizational Innovation Changes in firm structure or management methods that are intended to improve a firm’s use of knowledge, the quality of goods and services, or the efficiency of work flows.
Training
% of workers trained
Number of hours of training
% of payroll spent on training
Types of training offered
Worker Voice
Works councils
TQM
Employees meeting in teams
Self-managed teams
Unions
Work Design
Reenginnering
# of employees per supervisor
Levels of organization
Job rotation & multi-tasking
Outsourcing
Shared Rewards

4. Impact of Organizational Innovation on Productivity
Intra-industry studies – HR practices such as flexible job definitions, cross-training, team work, and extensive reliance on incentive pay are associated with substantially higher levels of productivity than more traditional human resource management practices.
Nationally representative surveys -- There is a large impact of workplace practices on productivity but mixed evidence of bundling of practices. These effects are large at the macro level as well.
Complementarity between investments in IT and organizational practices
Adoption process less well understood and limited by lack of data over time

5. If this is so good why isn’t everyone doing it? 5 Main Research Issues
1.) Those who are doing badly need to adopt and it is easier to get people to change in a crisis – incidence is lower in “good” companies
but: These are expensive systems to put in place so only those who are doing well can afford it – extent is higher (operating profits, industry)
2.) Firms with more external focus/networks (exports, R&D, benchmarking, diversity, multi establishments) are more likely to innovate
3.) IT and other investments result in more product and process innovation along with promoting the adoption of high performance workplace practices
4.) Firms with greater internal capacity (education, soft skills) more likely to adopt and have higher diffusion
5.) Forces that will resist organizational change – entrenched management, unions, different worker occupations who “win” or “lose”

6. Enablers: Human capital Technology External focus Profitability
Education
Soft Skills – Communication
Managerial Expertise
Technology
External focus
Trade
Benchmarking
Multi-establishment
Diversity of workforce
Profitability
Employment Security
Unions

7. Entrenched management Lack of knowledge of best practice Unions
Sources of Resistance
Entrenched management
Lack of knowledge of best practice
Unions
Specific occupations
Lack of crisis

8. Characteristics of Data Used
Nationally representative telephone survey of establishments
1621 manufacturing plants and 1546 in non-manufacturing in 1994 and 1827 manufacturing plants and 1254 in non-manufacturing in 1997
panel almost 700 establishments with 70% in manufacturing.
Can match the manufacturing businesses with the Census LRD
High response rate (72% in 1994 and 78% in 1997)
Depth of questions on
business characteristics – book value of the capital stock, age of equipment, materials costs, employment by 5 occupational categories;
worker characteristics – average education by occupation, % female, % minority
workplace practices – teams, employee involvement in decision making, profit sharing, training, union status, job rotation, job sharing, # of organizational levels, TQM, re-engineering;
technology usage - percent of managers using computers and percent of non-managers using computers
outcome measures including labor productivity, wages and labor demand
1.) with match with LRD can add in export share and lagged average values of operating profits and investment per worker for manufacturing establishments only

9. Incidence and Extent of Workplace Practices (weighted Means):
Practice Manufacturing Non-Manufacturing
Incidence (% of establishments that have this):
Production Worker Training
Production Workers meeting regularly
Workers in self-managed teams
Job Rotation
Reengineering since
Extent (% of workers):
Production Worker Training
Production Workers meeting regularly
Workers in Self-managed teams
Job Rotation

10. Percentage of businesses that changed investments in Organizational Innovation (Unweighted)
Practice Manufacturing Non-Manufacturing
(% of Workers) up down unchanged up down unchanged
Production Worker Training % % % % % %
Workers Meeting Regularly
Workers in Self-Managed Teams
Job Rotation

11. Incidence and Extent of Workplace Practices (weighted Means):
Non-Manufacturing
Manufacturing

12. Probability of Being Organizationally Innovative: Manufacturing*
Dependent Variable: Count of Practices Diffusion of Practices Index of Practices Means
Independent Variables ____
Multi-establishment ** ** * .17*
Share of product exported * * *
Benchmark ** ** ** ** .29** **
% minority **
% female * ** * *
% Non-managers use computers ** ** **
average investment per worker * **
average operating profit * * * .44* *
Average years of education ** ** **
Communication skills ** * ** .20* **
Unionized * ** ** -.32** **
Total workers (00’s) ** *
Number of observations:
Pseudo R-squared
Adjusted R-squared
standard errors in (), * significant at 10%, ** significant at 1%
Other variables included: share of workers who were production, managers, supervisors, technical and industry controls and for 1994 equations R&D and age.

13. Probability of Having a Specific Innovation: Manufacturing*
Dependent Variable: Reengineer Shared Rewards rotate training meeting self-managed
Independent Variables ____
Multi-establishment .27* * ** * *
Share of product exported * * * *
Benchmark .40** * * ** **
Do R&D ** * * *
% minority * * *
% female * .01** **
% Non-managers use computers .004* * ** **
average investment per worker * *
average operating profit .53* * *
Average years of education * * ** * **
Communication skills * * * * *
Unionized * * ** * **
Age of plant
Total workers (00’s) * * * **-9.9e *.001** * **
Number of Establishments
Pseudo R-squared
standard errors in (), * significant at 10%, ** significant at 1%
Other variables included: share of workers who were production, managers, supervisors, technical and industry controls and for 1994 equations R&D and age.

14. Diffusion of Specific Innovations: Manufacturing*
Dependent Variable: %rotate % trained % meeting %self-managed
Independent Variables
Multi-establishment
Share of product exported
Benchmark * ** ** 7.03** 5.66* ** *
Do R&D ** ** *
% minority * *
% female .19** **
% Non-managers use computers ** .11* * **
Average investment per worker .16** * .23** .14* ** *
Average operating profit * **
Average years of education * ** *
Communication skills ** * 6.98* ** * *
Unionized **-6.09** * -7.75** ** -9.32**
Age of plant * *
Total workers (00’s) *
Adjusted R-squared
standard errors in (), * significant at 10%, ** significant at 1%
Other variables included: share of workers who were production, managers, supervisors, technical and industry controls and for
1994 equations R&D and age.

15. Dependent Variable: ΔCount of Practices Re-engineering
Changes in Organizational Innovation: Manufacturing (ordered probit N=191)
Dependent Variable: ΔCount of Practices Re-engineering
Independent Variables Eq. (1)____Eq. (2)_____ Eq. (1)____ Eq. (2)_____
ΔMulti-establishment .21* *
ΔShare of product exported *
ΔBenchmark *
Δ% Non-managers use computers *
Δaverage investment per worker *
Δaverage operating profit
ΔAverage years of education
ΔCommunication skills .21* .43* .21* .60**
ΔUnionized
ΔTotal workers (00’s) ** .0008** .0005** -1.05e-06
1994 Values
Multi-establishment * - .92*
Share of product exported *
Benchmark
% Non-managers use computers *
average investment per worker
Average operating profit *
Average years of education *
Communication skills * - .46
Unionized
Total workers (00’s)
Pseudo R-squared

16. In Sum
1.) These are expensive systems to put in place so only those who are doing well can afford it – extent is higher (operating profits, industry). Early adopters are those who are more profitable and later adopters are those in more trouble.
2.) Firms with more external focus/networks (exports, R&D, benchmarking, diversity, multi establishments) are more likely to innovate
3.) IT and firms that invest in R&D more likely to innovate their organization
4.) Firms with more Internal capacity (education, soft skills) more likely to adopt and have higher diffusion
5.) Forces will resist organizational change – entrenched management (proxied by age of plant) and unions. Larger plants more likely to do more practices. Plants with a higher proportion of production and technical workers more likely to adopt but those with a proportion of supervisors NOT less likely.
6.) Industries more likely to do this intensively – primary and fabricated metals, food and tobacco; industry less likely to do this – textile and apparel, everything else constant.

17. Probability of Being Organizationally Innovative: Non-Manufacturing*
Dependent Variable: Mean Count of Practices Diffusion of Practices Index of Practices
Independent Variables
Multi-establishment ** .28** .32** ** .007
Benchmark ** .28** .21* *** .07
% minority * * *
% female ** ** **
Non-managers use computers ** ** ** .16
Average years of education ** *
Communication skills *
Unionized
Total workers *
Number of Observations
Pseudo R-squared
Adjusted R-square
standard errors in (), * significant at 10%, ** significant at 1%
Also included in estimation: share of workers who were production, managers, supervisors, technical and industry controls

18. Probability of Having a Specific Innovation: Non-Manufacturing*
Dependent Variable: Reengineer Shared Rewards Rotate Train Workers Meet Self-managed
Independent Variables __
Multi-establishment * ** .57**
Benchmark ** .62* * * ** *
Do R&D ** **
% minority * * * * ** *
% female ~ * * **
% Non-managers ** ~
using computers
Average yrs of education ** ** * **
Communication skills *
Unionized *
Age *
Total workers (00’s) * e * 2e e e-06 5e-05
Number of obs
Pseudo R-squared
Also included in estimation: share of workers who were production, managers, supervisors, technical and industry controls

19. Diffusion of Specific Innovations: Non-Manufacturing*
Dependent Variable: % rotate % training % meeting % self-managed
Independent Variables
Multi-establishment **
Benchmark *
Do R&D * * *
% minority .10** *
% female * * ** *
%Non-managers use computers *
Average years of education * **
Communication skills *
Unionized * * *
Age * * *
Total workers (00’s) * -.003* *
Number of observations
Adjusted R-squared
Also included in estimation: share of workers who were production, managers, supervisors, technical and industry controls

20. Differences with Manufacturing Sector Results
Can not include controls for past profitability or investment
firm size – bigger firms do less
union status not significant except % employees meeting on a regular basis
Significant industries include – (+) construction (-) finance for self-managed teams; (+) communications & utilities and wholesale trade for rotate; (+) wholesale trade for meet; (+) wholesale trade and business services and (-) construction for employer training

21. Implications for Trend Productivity Growth in the Medium Term
Good News – profits are high, managers increasingly outward focused
IT investment has decreased from 1990s but not gone away
Worry – skills of the workforce
Missing data – we don’t have systematic collection of information on young business or organizational innovation