Population analysis of organizational innovation and learning
| Published date | 01 December 2022 |
| Author | Jean Hartley |
| Date | 01 December 2022 |
| DOI | http://doi.org/10.1111/padm.12771 |
ORIGINAL ARTICLE
Population analysis of organizational innovation
and learning
Jean Hartley
The Open University Business School, The
Open University, Milton Keynes, UK
Correspondence
Jean Hartley, The Open University Business
School, The Open University, Milton Keynes
MK7 6AA, UK.
Email: jean.hartley@open.ac.uk
Abstract
This article argues for a population level of analysis, addressing
a theoretical and empirical gap and enabling the analysis of tran-
sition, tempo, and timing at the macro level. The article exam-
ines four theories of population-level innovation: population
ecology, neo-institutional theory, innovation diffusion, and
population-level learning theory. A population-level empirical
study of innovation and organizational learning addresses three
research questions: the first and second examine patterns of
innovation underpinned by learning over space and over time.
The third concerns the processes and dynamics of those pat-
terns. The data derive from the local government using mixed
methods and multiple respondents over 9 years. The research
shows the uneven spread of learning across the population,
with the emergence of two subpopulations. Over time, innova-
tion and learning strategies shifted. Learning in the population
occurred through both direct interaction and vicarious learning
from others in the population. Implications for population-level
theory, innovation, and learning are explored.
1|INTRODUCTION
Theories using a population level of analysis are deployed in a wide variety of biological and social science disciplines,
including organization studies, demography, sociology, ecology, epidemiology, and microbiology. This level of analysis
enables observation and analysis of patterns and dynamics of transition, tempo, and timing at the macro level not
observable at the micro level. Baum and Shipilov (2006) note that in population-level organizational analysis “diversity
is a property of aggregates of organizations that has no analogue at the level of the individual organization”(p. 55).
Population here is deployed with a clear and specific definition. In organization studies, it is defined as “a set of
organizations engaged in similar activities and with similar patterns of resource utilisation”(Baum & Shipilov, 2006,
p. 55). This builds on the seminal work of Hannan and Freeman (1977) who noted that populations are organizations
Received: 31 January 2020 Revised: 7 July 2021 Accepted: 8 July 2021
DOI: 10.1111/padm.12771
942 © 2021 John Wiley & Sons Ltd. Public Admin. 2022;100:942–959.wileyonlinelibrary.com/journal/padm
which share a “common fate with respect to environmental variations”or put another way “classes of organization
that are relatively homogeneous in terms of environmental vulnerability”(Hannan & Freeman, 1977, p. 934).
Scott (2013) uses this definition in his overview of institutional theory. Hannan et al. (2007) later described a popula-
tion as “bounded sets of entities with a common form, often interacting with each other and struggling over common
resources”(p. 86). To be clear through contrast, population is not here the statistical concept of the entity from
which a sample is drawn, nor is it members of a species in a defined geographical area or time.
Population analysis of organizations is valuable for several reasons. First, in theoretical terms, it can provide insights
about change and transmission processes which are not ob servable at more micro levels, showingpatterns across organi-
zations over space and over time. Second, there may be variation within a population not evident at the organizational
level (Baum & Shipilov, 2006). Third, population studies avoid the biases introduced by a focus on successful organiza-
tions (Denrell, 2003;Overman&Boyd,1994) because the full range of organizational performance is analyzed.
The value of a population level of analysis in understanding innovation is that it examines learning head-on and
importantly enables the development and use of theory to explain the dynamics by which learning occurs across a
population, going beyond the organizational level. There can be additional dynamics across a population due to vicar-
ious learning which occurs through observation of, and reflection on, other organizations (Baum & Berta, 1999;
Miner & Robinson, 1994). Indeed, analysis shows that learning from failure can be beneficial at population level, even
when harmful to the individual organization (Miner et al., 1999).
A population-level approach also enhances the analysis of changes over time. There are surprisingly few studies
of innovation or of organizational learning over time (Kim, 1998) or of institutional processes with timing and tempo-
rality more generally (Thelen, 2000), despite learning being a dynamic process with inevitably chronological aspects.
Instead, research has often relied on retrospective accounts of organizational processes, with inevitable problems of
recall and under-sampling of failures. Finally, the population level helps to enhance awareness of the effects of eco-
logical and historical interactions, and the macro context (Argote, 2012; Levitt & March, 1988).
Studies of population-level innovation and learning have concentrated on the private sector (e.g., Hannan
et al., 2007) but this current article extends analysis to public organizations. It draws on four theories about innova-
tion and learning at the population level and applies these to public services for the first time.
The focus on population-level analysis enables three research questions to be explored here. First, what are the
patterns of innovation and learning which occur over space in a population. Second, what are the patterns of change
over time across the population. Third, what are the dynamics of innovation and learning which explain those pat-
terns over space and time.
The empirical examination is in English local government. Local authorities are a population sharing a legal and
regulatory context, many service functions, and are under similar environmental pressures. The Beacon Scheme,
which was a public service reform initiative over a decade involving all English local authorities (then 388 organiza-
tions), can be conceptualized as providing opportunities for learning and innovation within and across the whole pop-
ulation. Data collection and analysis extended over 9 years.
We contribute to the literature on organizational and interorganizational learning in three significant ways. First,
by reviewing and comparing four key theories relevant to population-level analysis of innovation and organizational
learning, this article brings population analysis to public administration, assessing the value of each framework to the
public sector. The article uses the four theoretical frameworks to extend theory to public service organizations,
drawing particularly on learning capacity, subpopulations and vicarious learning as shaping change. Such theories
tend to be largely absent in the field of public administration, but this article argues that theory at this level of analy-
sis is highly relevant and informative.
Second, the article addresses how learning takes place at the macro or whole population level. This analysis
reveals dynamics not observable at other levels and enables theory development over space and time. Analysis
uncovers uneven development across the population, with both spread and nonspread of innovation, subpopulation
differences and differences in learning capacity. Such population-level analysis takes research beyond the organiza-
tional unit, the network or learning by the most successful organizations.
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