1. Organisational Learning and Innovation Edward Lorenz University of Nice and CNRS Sophia Antipolis, France Lecture prepared for the doctoral course on: ‘The Innovative Firm’ Norwegian School of Management BI May 6-8, 2013

2. Organisational Learning and Innovation I will start this lecture with a quote from C. Freeman’s in his1995 CJE article, ‘The 'National System of Innovation' in Historical Perspective’ (p. 18): “… it is essential to emphasise the interdependencies between innovations and between technical innovations and organisational innovations. A theory of technical change which ignores these interdependencies is no more helpful than a theory of economics which ignores the interdependencies of prices and quantities in the world economy.

3. Objectives/arguments The neglect of work organisation in the ‘core’ innovation studies research Conceptual progress on micro processes mainly coming from the field of management The limitations of CIS measures of organisational innovation The need for a EU wide harmonised survey including measures of organisational design, work organisation and innovation performance Possibilities and limits of using employee-level surveys of working conditions Empirical research of the relation between forms of work organisation and innovation performance for the EU-27 and Norway The use of multi-level models to explore national innovation dynamics

4. The analysis of work organisation in the field of innovation studies While the role of work organisation has always been recognised in innovation studies research, I think it is fair to say that it hasn’t been a central preoccupation of researchers in this field, at least not in the ‘core’ literature. Freeman’s (1987) classic study of the Japanese innovation system was exceptional in focusing in on the interdependencies between technical innovation and organisational change.

5. Work organisation: a neglected dimension in innovation studies? Subsequent to Freeman’s classic analysis, innovation studies scholars have given relatively little attention to the role of workers and work organisation in innovation processes and the emphasis has rather been on the role of formal R&D and on the skills and expertise of engineers, scientists and managers. Fagerberg and Verspagen (2009) in their use of citations in Research Policy to identify the core literature in innovations studies recognised only two publications that focus on the organisation of the firm, the classic studies by Cohen and Levinthal (1989, 1990) on absorptive capacity.

6. Conceptual progress mainly from outside the ‘core’ of innovation studies Most of the recent contributions to conceptualising the interrelations between work organisation, employee learning and innovation processes have come from outside the field of innovation studies. For example: Research on organisational design and innovation including work on learning organisations (e.g. Lam; Senge) Research on ‘communities of practice’ (e.g. Lave and Wenger) Research on creativity at the workplace (e.g. Amabile) Research on dynamic capabilities (e.g. Teece) Burgeoning innovation management literature (much too vast to cite, now with specialised journals)

7. Progress in measuring organisational change and innovation During the 2000s there has been a growing interest in measuring organisation innovation, largely inspired by recognition that the classic Oslo Manual based measures of TPP innovation poorly capture innovation processes in service sectors. From 2005 the CIS incorporates the revised Oslo Manual definitions of innovation including organisational and marketing innovations. Researchers now have access to data measuring for the EU-27 the frequency and the amount of expenditure not only on product and process innovations but also on organisational and marketing innovations.

8. A misleading distinction between ‘technical’ and ‘non-technical’ innovation CIS measures are of questionable value for getting a better empirical understanding of the interdependencies between organisational design, work organisation and product and process innovation. The Oslo Manual framework lends itself to the idea that workplace organisation is a separate ‘social’ or ‘non- technical’ dimension that can be analysed independently of the ‘technical’ dimension which is equated with product and process innovation Measures of marketing and organisational innovation are essentially add-ons to a survey framework designed to capture product and process innovations..

9. Policy ramifications This approach impacts on the policy relevance of CIS survey results since it’s not clear how policy- makers are supposed to make use of a general measures of how much organisational change has taken place over a 3-year period. Policy-relevant measures of work organisation would focus not only on how much change has occurred but also on the direction of change. The key question is what kinds of organisational designs and forms of work organisation promote learning and innovation, and the policy challenge is how to promote the adoption of these good designs and forms.

10. Surveys of working conditions: a window into the hidden dimension of employee learning? Surveys of working conditions carried out at the employee-level provide a valuable window into the hidden dimension of employee learning and problem-solving at the work place. What successive waves of the European Working Conditions Survey show is that there are large and persistent inequalities across EU member states in the percentage of employees having access to learning environments at work. Further, there are differences in the degree of inequality within nations in terms of workers’ and managers’ access to learning in work.

11. An update of Lorenz and Valeyre (2005) and Arundel et al. (2007) Research based on the fifth European Working Conditions Survey (EWCS) carried out by the European Foundation for the Improvement of Living and Working Conditions in 2010  EU-27, Norway, Turkey, Croatia, the former Yugoslav Republic of Macedonia, Turkey, Albania, Montenegro and Kosovo Survey methodology based on a multi-stage random sampling (method called ‘random walk’)  with face-to-face interviews at employees’ home  (about 1000 persons in each country). Field of our study : salaried employees working :  in establishments with at least 10 persons  in both industry and services, but excluding agriculture and fishing; public administration and social security; education; health and social work; and private domestic employees. Total population studied : 13172 persons in EU-27 and Norway

12. Statistical methodology Factor and cluster analysis in order to group individual employees into distinct organisational clusters or forms on the basis of measures of work organisation Use of logisitic regression to explore the determinants of the likelihood of the different forms of work organisation including HRM practices Aggregate correlation analysis: systemic relations between innovation performance and the frequency of forms of work organisation at the national level Micro analysis of the impact of work organisation on the likelihood of process innovation

13. Knowledge agent (autonomy and control) IndividualOrganisation High standardisation of knowledge and work Professional bureaucracy (embrained knowledge) Machine bureaucracy (encoded knowledge) Low standardisation of knowledge and work Operating Adhocracy (embodied knowledge) J-form Organisation (embedded knowledge) Organisational coordination and dominant forms of knowledge (Lam, 1998, Mintzberg, 1979 Blackler, 1995)

14. Professional bureaucracy Embrained knowledge Narrow learning inhibits innovation Machine bureaucracy Encoded knowledge Shallow learning, limited innovation Operating adhocracy, Embodied knowledge Dynamical learning, radical innovation J-form organisation Embedded knowledge Cumulative learning, incremental innovation Contrasting organisational models with different learning/innovation capabilities; Lam 1998.

15. Work Organisation Variables Learning new things in work Generally, does your main paid job involve, or not, learning new things? Problem solving activities Generally, does your main paid job involve, or not, solving unforeseen problems on your own? Complexity of tasks Generally, does your main paid job involve, or not, complex tasks? Autonomy in work methods Are you able, or not, to choose or change your methods of work? Autonomy in work pace Are you able, or not, to choose or change your speed or rate of work? Team work Does your job involve, or not, doing all or part of your work in a team? Job rotation Does your job involve, or not, rotating tasks between yourself and colleagues? Responsibility for quality control Generally, does your main paid job involve, or not, assessing yourself the quality of your own work?

16. Work Organisation Variables Quality norms Generally, does your main paid job involve, or not, meeting precise quality standards? Repetitiveness of tasks Please tell me, does your job involve short repetitive tasks of less than a minute? Monotony of tasks Generally, does your main paid job involve, or not, monotonous tasks? Automatic constraints on work rate On the whole, is your pace of work dependent, or not, on automatic speed of a machine or movement of a product? Norm-based constraints on work rate On the whole, is your pace of work dependent, or not, on numerical production targets? Hierarchical constraints on work rate On the whole, is your pace of work dependent, or not, on the direct control of your boss? Horizontal constraints on work rate On the whole, is your pace of work dependent, or not, on the work done by colleagues?

17. Summary of results for the 4-cluster solution (percent of employees in each cluster) Discretionary Learning Lean production TaylorismTraditional organisation All Autonomy fixing work methods83.761.321.237.557.7 Autonomy setting work rate80.862.437.049.362.0 Learning new things in work88.290.530.223.666.3 Problem solving activities97.595.753.545.079.3 Complexity of tasks78.685.922.114.958.5 Responsibility for quality control85.392.759.823.571.3 Quality norms79.097.690.232.477.6 Team work63.176.963.946.964.0 Job rotation45.660.350.034.448.3 Monotony of tasks27.459.683.244.149.4 Repetitiveness of tasks15.136.060.617.129.5 Horizontal constraints on work rate30.683.266.323.450.0 Hierarchical constraints on work rate23.273.664.624.044.6 Norm-based constraints on work rate35.483.066.517.350.7 Automatic constraints on work rate5.544.459.78.326.5 Source : Fifth European Working Condition survey. European Foundation for the Improvement of Living and Working Conditions

18. The forms of work organisation in the EU Discretionary Learning forms of work organisation:  autonomy in work  learning dynamics (learning new things, problem solving)  complexity of tasks  responsibility for quality control  low work rate constraints, repetitiveness and monotony  team working and job rotation not characteristic “Swedish socio-technical” model “Operating adhocracy” model (Mintzberg) Lean forms of work organisation:  team working  job rotation  quality management (quality norms and quality control)  learning dynamics  work rate constraints, repetitiveness and monotony  relatively low autonomy in work “Lean production” (Womack et alii; MacDuffie et alii) “Controlled autonomy” model (Appay; Coutrot)

19. The forms of work organisation in the EU Taylorist forms of work organisation:  work rate constraints, repetitiveness and monotony  low autonomy, low learning dynamics, low complexity, low responsibility in quality control  team working and job rotation at average levels traditional taylorism and “flexible taylorism” Traditional or simple structure or forms of work organisation:  under-representation of all organisational variables, except tasks monotony simple organisational structure informal and non codified work methods

20. Forms of work organisation across European nations ‘Learning’ forms of work organisation:  + : Netherlands, Denmark, Sweden, Norway, Malta  - : Greece, Bulgaria, Romania ‘Lean’ forms of work organisation:  + : UK, Ireland, Finland, Luxembourg, Estonia  - : Netherlands, Denmark, Sweden, Cyprus, Poland ‘Taylorist’ forms of work organisation:  + : Southern countries, Ireland, Bulgaria, Lithuania, Hungary  - : Netherlands, Denmark, Norway, France, Sweden, Estonia, Latvia, Malta ‘Simple’ forms of work organisation:  + : Southern countries, France, Bulgaria, Czech Republic, Poland, Slovakia  - : Netherlands, Denmark, Sweden, Norway, Ireland, Finland, Malta,

21. Discretionary learningLean organisationTaylorismSimple organisationTotal Austria47.426.612.413.6 100 Belgium41.325.515.917.2 100 Bulgaria19.323.927.129.7 100 Cyprus30.720.621.727 100 Czech Republic32.423.124.120.5 100 Denmark61.916.98.316.9 100 Estonia37.640.29.412.8 100 Finland42.236.59.811.6 100 France30.627.719.722.1 100 Germany44.422.61617.1 100 Greece19.424.728.827.1 100 Hungary3027.82913.2 100 Ireland25.141.421.811.8 100 Italy31.424.421.222.9 100 Latvia48.32611.514.2 100 Lithuania29.924.326.119.7 100 Luxembourg3635.315.313.4 100 Malta50.63010.39.5 100 Netherlands59.812.61314.6 100 Norway54.727.811.75.8 100 Poland38.721.616.922.8 100 Portugal31.53223.812.7 100 Romania22.936.51822.6 100 Slovakia28.627.922.121.4 100 Slovenia47.12414.4 100 Spain28.729.722.319.3 100 Sweden61.920.18.69.5 100 United Kingdom28.436.619.615.5 100 EU-28 36.327.018.418.3 100 National differences in forms of work organisation EU-28 (Source: 5 th European Working Conditions Survey)

22. Aggregate correlations between forms of work organisation and innovation performance: 5 th EWCS and CIS-2010

24. Limitations of the aggregate correlation analysis A deeper understanding of the organisational basis for these national differences and interrelations would require micro survey data linking organisational structure to both forms of work organisation and enterprise innovation performance.  DISKO survey framework; collaboration between researchers in innovation studies, human resource management and industrial relations  The MEADOW survey framework of linked employer/employee surveys provides a possible way forward The direction of macro-level relations does not necessarily mirror that of micro-level relations. In order to investigate this there is a need for internationally harmonised survey data that can be used to investigate simultaneously the micro and macro levels in a multi-level approach

25. Measuring process innovation with the 5 th EWCS The 5 th EWCS carried out in 2010 includes a question asking whether the introduction of new processes or technologies over the last 3 years has affected the employee’s immediate work environment. The data can be used to explore the relation between forms of work organisation and the frequency of process innovations at both the micro and aggregate levels for the EU. However the results of the two levels of analysis lead to somewhat different conclusions regarding the impact of different forms of work organisation on process innovation outcomes

26. Aggregate correlation relations between forms of work organisation and process innovation: 5 th EWCS

28. Logistic Regressions: Predicting the Odds of Process Innovation for the EU-28 Model 1 (with country controls) Model 2 (with country and sector controls) (odds ratios) Discretionary learning3.00***2.73*** Lean organisation4.04***3.66*** Taylorism1.96***1.82*** Simple organisationreference n 13172 * significant at.10 level; ** significant at.05 level; *** significant at.01 level

29. The value of a multi-level approach While the aggregate correlation analysis points to a clear superiority of the discretionary learning (DL) forms in terms of the national frequency of process innovations, the micro-level analysis shows that the Lean forms are more likely to be associated with process innovations. A possible explanation is that the DL forms are superior in terms of generating knowledge externalities. If the DL forms generate new knowledge for process innovations that can be used by firms and employees in general, while the Lean forms are good at exploiting available knowledge, this could account for the stronger positive correlation between the frequency of DL and process innovations at the aggregate level

30. Model 2Model 3Model 4Model 5 Fixed: Employee level Odds ratios Discretionary learning 2.72*** 2.74***2.73*** Lean 3.65***3.47***3.66***3.65*** Taylorism 1.82*** Simple reference Fixed: Country level Share DL1.02***1.01* Share Lean1.011.00 Share Taylorism.98**.99 Log GDP/capita1.24***1.28*** Random Intercept.08 (.02).06 (.02).09 (.03).07 (.02) n13172 LR test vs logistic regression chi2(7) = 124.60 chi2(7) = 94.95 chi2(7) = 151.78 chi2(7) = 106.19 Multi-level logistic models predicting the odds of process innovations with country-level effects for the EU-28 1 * significant at.10 level; ** significant at.05 level; *** significant at.01 level 1. The models include controls for sector

31. Results of multi-level analysis The multi-level analysis provides support for the hypothesis that there are positive knowledge externalities associated with increases in the national share of the DL forms of work organisation. However the results also indicate that the positive and negative correlations between process innovations and the national shares of DL and Taylorism respectively can be accounted for in part by the level of economic development as measured by GDP per capita

32. Main conclusions The results of both the correlation and multi-level analysis show that in countries where a large share of employees are engaged in forms of work organisation that support high levels of discretion in complex problem-solving the innovation performance of enterprises tends to be higher, whether the focus is on process innovations or on new-for-the market product innovations. In countries where learning and problem- solving on the job are more constrained, and little discretion is left to the employee, innovation performance tends to drag.

33. Main conclusions The results imply that European and national policy efforts to improve innovation performance need to take a close look at the effects of organisational practice on innovation. The bottleneck to improving the innovative capabilities of European firms might not be low levels of R&D expenditures, which are strongly determined by industrial sector, but the widespread presence of working environments that are unable to provide a fertile environment for innovation. If this is the case, European policy makers should make a major effort to develop policy instruments that could stimulate the adoption of ‘pro-innovation’ organisational practice, particularly in countries with poor innovative performance.