Transcript Plan du cours : l`approche behavioriste à la firme (March et Simon)

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
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.
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
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.
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.
Conceptual progress mainly from outside
the ‘core’ of innovation studies

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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)
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.
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 ‘nontechnical’ 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.
Policy ramifications


This approach impacts on the policy relevance of
CIS survey results since it’s not clear how policymakers 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.
Surveys of working conditions: a window into
the hidden dimension of employee learning?


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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.
An update of Lorenz and Valeyre (2005)
and Arundel et al. (2007)



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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
Statistical methodology


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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
Organisational coordination and dominant forms
of knowledge (Lam, 1998, Mintzberg, 1979
Blackler, 1995)
Knowledge agent
(autonomy and control)
High
standardisation of
knowledge and
work
Low standardisation
of knowledge and
work
Individual
Organisation
Professional bureaucracy
Machine bureaucracy
(embrained knowledge)
(encoded knowledge)
Operating Adhocracy
J-form Organisation
(embodied knowledge)
(embedded knowledge)
Contrasting organisational models with different
learning/innovation capabilities; Lam 1998.
Professional bureaucracy
Machine bureaucracy
Embrained knowledge
Encoded knowledge
Narrow learning inhibits
innovation
Shallow learning, limited
innovation
Operating adhocracy,
J-form organisation
Embodied knowledge
Embedded knowledge
Dynamical learning, radical
innovation
Cumulative learning,
incremental innovation
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?
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?
Summary of results for the 4-cluster solution
(percent of employees in each cluster)
Discretionary
Learning
Lean
production
Taylorism
Traditional
organisation
All
Autonomy fixing work methods
83.7
61.3
21.2
37.5
57.7
Autonomy setting work rate
80.8
62.4
37.0
49.3
62.0
Learning new things in work
88.2
90.5
30.2
23.6
66.3
Problem solving activities
97.5
95.7
53.5
45.0
79.3
Complexity of tasks
78.6
85.9
22.1
14.9
58.5
Responsibility for quality control
85.3
92.7
59.8
23.5
71.3
Quality norms
79.0
97.6
90.2
32.4
77.6
Team work
63.1
76.9
63.9
46.9
64.0
Job rotation
45.6
60.3
50.0
34.4
48.3
Monotony of tasks
27.4
59.6
83.2
44.1
49.4
Repetitiveness of tasks
15.1
36.0
60.6
17.1
29.5
Horizontal constraints on work rate
30.6
83.2
66.3
23.4
50.0
Hierarchical constraints on work rate
23.2
73.6
64.6
24.0
44.6
Norm-based constraints on work rate
35.4
83.0
66.5
17.3
50.7
Automatic constraints on work rate
5.5
44.4
59.7
8.3
26.5
Source : Fifth European Working Condition survey. European Foundation for the Improvement of Living and
Working Conditions
The forms of work organisation in the EU

Discretionary Learning forms of work organisation:
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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
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“Operating adhocracy” model (Mintzberg)
Lean forms of work organisation:
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

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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)
The forms of work organisation in the EU

Taylorist forms of work organisation:
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
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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
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simple organisational structure
informal and non codified work methods
Forms of work organisation across European
nations

‘Learning’ forms of work organisation:
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‘Lean’ forms of work organisation:
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+ : UK, Ireland, Finland, Luxembourg, Estonia
- : Netherlands, Denmark, Sweden, Cyprus, Poland
‘Taylorist’ forms of work organisation:
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+ : Netherlands, Denmark, Sweden, Norway, Malta
- : Greece, Bulgaria, Romania
+ : Southern countries, Ireland, Bulgaria, Lithuania, Hungary
- : Netherlands, Denmark, Norway, France, Sweden, Estonia, Latvia, Malta
‘Simple’ forms of work organisation:
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+ : Southern countries, France, Bulgaria, Czech Republic, Poland, Slovakia
- : Netherlands, Denmark, Sweden, Norway, Ireland, Finland, Malta,
National differences in forms of work organisation EU-28
(Source: 5th European Working Conditions Survey)
Discretionary learning
Lean organisation
Taylorism
Simple organisation
Total
Austria
47.4
26.6
12.4
13.6
100
Belgium
41.3
25.5
15.9
17.2
100
Bulgaria
19.3
23.9
27.1
29.7
100
Cyprus
30.7
20.6
21.7
27
100
Czech Republic
32.4
23.1
24.1
20.5
100
Denmark
61.9
16.9
8.3
16.9
100
Estonia
37.6
40.2
9.4
12.8
100
Finland
42.2
36.5
9.8
11.6
100
France
30.6
27.7
19.7
22.1
100
Germany
44.4
22.6
16
17.1
100
Greece
19.4
24.7
28.8
27.1
100
Hungary
30
27.8
29
13.2
100
Ireland
25.1
41.4
21.8
11.8
100
Italy
31.4
24.4
21.2
22.9
100
Latvia
48.3
26
11.5
14.2
100
Lithuania
29.9
24.3
26.1
19.7
100
Luxembourg
36
35.3
15.3
13.4
100
Malta
50.6
30
10.3
9.5
100
Netherlands
59.8
12.6
13
14.6
100
Norway
54.7
27.8
11.7
5.8
100
Poland
38.7
21.6
16.9
22.8
100
Portugal
31.5
32
23.8
12.7
100
Romania
22.9
36.5
18
22.6
100
Slovakia
28.6
27.9
22.1
21.4
100
Slovenia
47.1
24
14.4
14.4
100
Spain
28.7
29.7
22.3
19.3
100
Sweden
61.9
20.1
8.6
9.5
100
United Kingdom
28.4
36.6
19.6
15.5
100
EU-28
36.3
27.0
18.4
18.3
100
Aggregate correlations between forms of work organisation
and innovation performance: 5th EWCS and CIS-2010
% Discretionary learning by % New-to-market innovators
60
DK
% Lean organisation by % New-to-market innovators
SE
40
NL
NO
EE
UK
RO
FI
LU
DE
40
FI
30
PL
BE
EE
HU
ES
LT
CZ
PT CY
FR
IT
PT
30
AT
SI
ES
MT
SK
HU
FR
NO
AT
LV
BG
LU
BE
IT
SI
DE
LT
CZ
PL
CY
20
LV
% Lean organisation
50
MT
SK UK
SE
DK
20
RO
NL
.05
10
BG
.1
.15
% New-to-market innovators
.2
.25
.05
.1
.15
% New-to-market innovators
.2
.25
Aggregate correlations between forms of work organisation
and innovation performance: 5th EWCS and CIS-2010
30
% Taylorism by % New-to-market innovators
% Simple organisation by % New-to-market innovators
30
HU
BG
BG
LT
25
25
CY
CY
20
UK
IT
FR
RO
PL
15
DE
BE
SI
AT
10
MT
FR
SK
ES
LU
NL
SI
LV
HU
EE
AT
PT
LU
FI
MT
SE
SE
NO
5
DK
.1
BE
DE
FI
EE
.05
IT
CZ
LT
DK
UK
10
NO
LV
NL
RO PL
20
SK
15
ES
% Simple organisation
CZ
PT
.15
% New-to-market innovators
.2
.25
.05
.1
.15
% New-to-market innovators
.2
.25
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
Measuring process innovation with the
5th EWCS


The 5th 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
Aggregate correlation relations between forms of work
organisation and process innovation: 5th EWCS
% Lean organisation by % process innovation
% Discretionary learning by % process innovation
SE
IE
40
60
DK
EE
NL
UK
RO
FI
LU
DE
40
BE
PL
EE
30
CZ
IT
FR
HU
PT
PT
30
AT
MT
GR
BG
IT
CZ
CY
SE
DK
NL
10
20
DE
CY
LT
SK UK
ES
GR
30
LT
SI
PL
LU
IE
20
NO SK
AT
LVBE
FI
RO
BG
MT
ES
HU
FR
20
SI
LV
% Lean organisation
50
NO
40
50
% Process innovation: EWCS
60
20
30
40
50
% Process innovation: EWCS
60
Aggregate correlation relations between forms of work
organisation and process innovation: 5th EWCS
30
% Simple organisation by % Process innovation
30
% Taylorist organisation by % process innovation
BG
HU
GR
GR
BG
ES
IE SK
BE DE
SI
NL
NO
LV
BE DE
10
15
DK
50
AT
PT
LU
IE
MT
40
SI NL
LV
HUEE
AT
% Process innovation: EWCS
DK
UK
LU
EE
30
LT
ES
FI
10
PL
20
SK
CZ
UK
RO
15
CY
60
FI
SE
MT
NO
5
20
IT
FR
IT
FR
PL RO
20
PT
% Simple organisation
25
CZ
CY
25
LT
20
30
40
50
% Process innovation: EWCS
60
SE
Logistic Regressions: Predicting the Odds of Process
Innovation for the EU-28
Model 1
Model 2
(with country
controls)
(with country
and sector
controls)
(odds ratios)
(odds ratios)
Discretionary learning
3.00***
2.73***
Lean organisation
4.04***
3.66***
Taylorism
1.96***
1.82***
Simple organisation
n
reference
13172
13172
* significant at .10 level; ** significant at .05 level; *** significant at .01 level
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
Multi-level logistic models predicting the odds of process innovations
with country-level effects for the EU-281
Model 2
Model 3
Model 4
Model 5
Fixed: Employee level
Odds ratios
Discretionary learning
2.72***
2.72***
2.74***
2.73***
Lean
3.65***
3.47***
3.66***
3.65***
Taylorism
1.82***
1.82***
1.82***
1.82***
Simple
reference
Fixed: Country level
Share DL
Share Lean
1.02***
1.01*
1.01
1.00
Share Taylorism
Log GDP/capita
1.00
1.00
.98**
.99
1.24***
1.28***
Random
Intercept
n
LR test vs
logistic regression
.08 (.02)
.06 (.02)
.09 (.03)
.07 (.02)
13172
13172
13172
13172
chi2(7) =
124.60
chi2(7) =
94.95
chi2(7) =
151.78
chi2(7) =
106.19
* significant at .10 level; ** significant at .05 level; *** significant at .01 level
1. The models include controls for sector
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
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 problemsolving on the job are more constrained, and little
discretion is left to the employee, innovation
performance tends to drag.
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.