Organisational Learning and Innovation

Organisational Learning and Innovation

Edward LorenzUniversity 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 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 ‘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.

.

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.

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.

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

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

Knowledge agent(autonomy and control)

Individual Organisation

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)

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.

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:

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)

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

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,

Discretionary learning Lean organisation Taylorism Simple organisation Total

Austria 47.4 26.6 12.4 13.6100

Belgium 41.3 25.5 15.9 17.2100

Bulgaria 19.3 23.9 27.1 29.7100

Cyprus 30.7 20.6 21.7 27100

Czech Republic 32.4 23.1 24.1 20.5100

Denmark 61.9 16.9 8.3 16.9100

Estonia 37.6 40.2 9.4 12.8100

Finland 42.2 36.5 9.8 11.6100

France 30.6 27.7 19.7 22.1100

Germany 44.4 22.6 16 17.1100

Greece 19.4 24.7 28.8 27.1100

Hungary 30 27.8 29 13.2100

Ireland 25.1 41.4 21.8 11.8100

Italy 31.4 24.4 21.2 22.9100

Latvia 48.3 26 11.5 14.2100

Lithuania 29.9 24.3 26.1 19.7100

Luxembourg 36 35.3 15.3 13.4100

Malta 50.6 30 10.3 9.5100

Netherlands 59.8 12.6 13 14.6100

Norway 54.7 27.8 11.7 5.8100

Poland 38.7 21.6 16.9 22.8100

Portugal 31.5 32 23.8 12.7100

Romania 22.9 36.5 18 22.6100

Slovakia 28.6 27.9 22.1 21.4100

Slovenia 47.1 24 14.4 14.4100

Spain 28.7 29.7 22.3 19.3100

Sweden 61.9 20.1 8.6 9.5100

United Kingdom 28.4 36.6 19.6 15.5100

EU-2836.3 27.0 18.4 18.3

100

National differences in forms of work organisation EU-28(Source: 5th European Working Conditions Survey)

Aggregate correlations between forms of work organisation and innovation performance: 5th EWCS

and CIS-2010

BE

DK

DE

ESFR IT

LU

NL

AT

PT

FI

SE

UK

NO

BG

CZ

EE

CY

LV

LTHU

MT

PL

RO

SI

SK

2030

4050

60%

Disc

retio

nary

lear

ning

.05 .1 .15 .2 .25% New-to-market innovators

% Discretionary learning by % New-to-market innovators

BE

DK

DE

ES

FR

IT

LU

NL

AT

PT

FI

SE

UK

NO

BGCZ

EE

CY

LV

LT

HU

MT

PL

RO

SI

SK

1020

3040

% L

ean

orga

nisa

tion


% Lean organisation by % New-to-market innovators

Aggregate correlations between forms of work organisation and innovation performance: 5th EWCS

and CIS-2010

BE

DK

DE

ES

FR

IT

LU

NLAT

PT

FI

SE

UK

NO

BG

CZ

EE

CY

LV

LT

HU

MT

PLRO

SI

SK

1015

2025

30%

Tay

loris

t org

anisa

tion


% Taylorism by % New-to-market innovators

BEDK DE

ES

FRIT

LUNL

ATPT

FI

SE

UK

NO

BG

CZ

EE

CY

LV

LT

HU

MT

PLRO

SI

SK

510

1520

2530

% S

impl

e or

gani

satio

n


% Simple organisation by % New-to-market innovators

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

BE

DK

DE

IE

ES

FR

IT

LU

NL

AT

PT

FI

SE

UK

NO

GRBG

CZ

EE

CY

LV

LT

HU

MT

PL

RO

SI

SK

1020

3040

% L

ean

orga

nisa

tion

20 30 40 50 60% Process innovation: EWCS

% Lean organisation by % process innovation

BE

DK

DE

IE

ESFRIT

LU

NL

AT

PT

FI

SE

UK

NO

GRBG

CZ

EE

CY

LV

LTHU

MT

PL

RO

SI

SK

2030

4050

60%

Disc

retio

nary

lear

ning


% Discretionary learning by % process innovation

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

BE

DK

DE

IEES

FR

IT

LU

NLAT

PT

FI

SE

UK

NO

GR

BG

CZ

EE

CY

LV

LT

HU

MT

PLRO

SI

SK

1015

2025

30%

Tay

loris

t org

anisa

tiont


% Taylorist organisation by % process innovation

BE DKDE

IE

ES

FRIT

LUNL

ATPT

FI

SE

UK

NO

GR

BG

CZ

EE

CY

LV

LT

HU

MT

PL RO

SI

SK

510

1520

2530

% S

impl

e or

gani

satio

n20 30 40 50 60

% Process innovation: EWCS

% Simple organisation by % Process innovation

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) (odds ratios)

Discretionary learning 3.00*** 2.73***

Lean organisation 4.04*** 3.66***

Taylorism 1.96*** 1.82***

Simple organisation reference

n 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

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 1.02*** 1.01*

Share Lean 1.01 1.00 1.00 1.00

Share Taylorism .98** .99

Log GDP/capita 1.24*** 1.28***

Random

Intercept .08 (.02) .06 (.02) .09 (.03) .07 (.02)

n 13172 13172 13172 13172

LR test vslogistic 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-281

* 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 problem-solving 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.

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Organisational Learning and Innovation