INTERNATIONAL
JOURNAL
BEHAVIOR, 7(1), 1-21
OF
ORGANIZATION
THEORY
AND
SPRING 2004
A TAXONOMY OF THE UNCERTAINTY SOURCES
PERCEIVED BY PUBLIC SECTOR MANAGERS
IN HONG KONG
Kathleen E. Voges, Richard L. Priem,
Christopher Shook and Margaret Shaffer*
ABSTRACT. Perceived environmental uncertainty (PEU) is a foundational
concept in organization studies. The PEU typologies used in organizational
research were developed using private sector managers. But, do public sector
managers perceive the same uncertainty sources? We asked public sector
managers in Hong Kong to identify and group uncertainty sources facing their
organizations. Multidimensional scaling and cluster analysis yielded classes of
uncertainty sources that differ from those developed using private sector
managers.
INTRODUCTION
Many scholars view the organization’s environment as a source of
information perceived by its members (Daft, Somunen & Parks, 1988;
Gioia & Thomas, 1996; Thomas, Clark & Gioia, 1993). Research has
-----------------------* Kathleen E. Voges is a doctoral candidate, Department of Management,
University of Texas at Arlington. Her research interest is in privatization,
particularly the study of differences between public and private sector
managers. Richard L. Priem, Ph.D., is the Robert L. and Sally S. Manegold
Professor of Management and Strategic Planning, School of Business
Administration, University of Wisconsin-Milwaukee. His research interests
include the strategy making process and chief executive decision-making.
Christopher Shook, Ph.D., is an Assistant Professor, Department of
Management, Auburn University. His research interests include the strategy
making process and methodological issues in strategy research. Margaret
Shaffer, Ph.D., is an Associate Professor, Department of Management, Hong
Kong Baptist University. Her research interests are in the areas of crosscultural studies, expatriation and work role adjustment.
VOGES, PRIEM, SHOOK & SHAFFER
Copyright © 2004 by PrAcademics Press
2
VOGES, PRIEM, SHOOK & SHAFFER
found that managers’ perceptions of environmental uncertainty (PEU)
are related to managerial actions such as: scanning, interpretation,
strategy making processes, and political tactics (Galaskiewicz &
Bielffeld, 1998; Lant, Milliken & Batra, 1992; Priem, Love & Shaffer,
2002; Yasai-Ardekani & Nystrom, 1996). Many theories of organization
- such as open systems theory and strategic contingency theory - allot the
environment a crucial role in the study of organizations (Hofer, 1975;
Katz & Kahn, 1978).
Separately, scholars also note important differences between private
sector and public sector organizations (Nutt, 1999; Perry & Rainey,
1988). For example, the primary goal of the private sector is profitability,
whereas the primary goal of the public sector is to serve citizens
(Willcocks & Currie, 1997). Government regulatory oversight differs and
organizational structures differ between the private and public sectors
(Rainey & Bozeman, 2000). Moreover, organizations with different
structures likely perceive the environment differently, as do
organizations with differing goals and regulatory controls (Aldrich &
Pfeffer, 1976).
Despite these differences between the private and public sectors,
classifications of environmental uncertainty sources derived from the
private sector are being used in public sector research (Bryson, 1995).
Public sector management studies that employ the PEU construct
typically present public managers with categories from a private sectorbase, and then ask them to rate the level of uncertainty facing their
organization in each category. If the chosen typology does not reflect the
classifications used by public managers, however, research results could
lead to basic misunderstandings of both the organizational environment
and public managers’ behavior. Even more troublesome, this use of
private sector PEU typologies could induce private sector researchers
toward heedless generalization of public sector results to private sector
research, thereby blurring potentially crucial distinctions between private
and public managers. Given the importance of the PEU construct in
studying managerial actions and subsequent organizational performance,
a direct examination of the uncertainty sources facing public sector
managers is warranted.
Our study takes an initial step toward addressing two fundamental
research questions:
TAXONOMY OF THE UNCERTAINTY SOURCES PERCEIVED BY PUBLIC
SECTOR MANAGERS
3
1) What are the sources of uncertainty perceived by public sector
managers? and
2) Do public sector managers perceive the same uncertainties as do
private sector managers?
Thus, our goal is to begin determining whether uncertainty
classifications that are specific to the public sector are necessary for
productive public sector research. We begin with a brief review of extant
PEU research. We then present the results of our study of public
managers in Hong Kong. Multidimensional scaling (MDS) was used to
identify perceived sources and dimensions of uncertainty. Cluster
analysis then was used to produce a taxonomy of public sector
uncertainty sources. We discuss the implications of the taxonomy for
future research on public sector managers and organizations.
BACKGROUND
The environment’s role has been recognized from at least as early as
Frederick Taylor’s time, when he observed a relationship between the
environment and the production process (Ashmos & Huber, 1987).
Researchers subsequently conceptualized the environment as being
comprised of subsets, or domains. Dill’s seminal work, for example,
characterized the environment as being comprised of the task domain and
the general domain (Dill, 1958). The works of Alchian (1950), Dill
(1958), and March and Simon (1958) established that uncertainties
associated with domains of the organizational environment should be a
major focus of organizational research.
Many researchers have attempted to classify the environment into
domains that are meaningful for private firms. Duncan identified three
internal components and five external components that he theorized
could contribute to uncertainty along static-dynamic and simple-complex
dimensions (Duncan, 1972). Miles and Snow (1978) developed scales
measuring managers’ perceptions of uncertainty in six environmental
sectors (i.e., suppliers, customers, financial markets, competitors, labor
unions and governmental/regulatory agencies). Likewise, Daft et al.
(1988) refined the PEU construct using Dill’s two domains, general and
task - along with the specific environmental sectors: competition,
customers, technological, regulatory, economic and socio-cultural. Most
recently, Priem et al. (2002) had top private sector managers in Hong
4
VOGES, PRIEM, SHOOK & SHAFFER
Kong develop a PEU taxonomy that included uncertainties associated
with international competitive advantage, industry competition,
production costs, human resources, government, and societal change.
Researchers debating public and private sector differences have
rallied around two positions (Rainey & Boxeman, 2000). The first view,
prevalent in the fields of economics and political science, emphasizes the
superiority of private sector organizations regarding effectiveness and
efficiency. This view emphasizes the differences in public and private
sector organizations. The second, contrary view is that the public vs.
private sector distinction is only a “crude stereotype”, with organizations
in each class having many similar characteristics. This position is more
prevalent in the field of organizational theory.
The environments faced by public and private sector organizations
have been differentiated, however, based on “market” characteristics, cooperation and competition, data availability, political influence,
transactions, scrutiny and ownership (Nutt, 1999; Nutt & Backoff, 1993;
Rainey, Backoff & Levine, 1976; Willcocks & Currie, 1997). One
difference is that public sector organizations often do not operate in
competitive markets that provide resources and reimbursements for
services rendered (Rainey, Backoff & Levine, 1976). Legitimacy rather
than efficiency is often the pivotal element of a public organization
(Meyer & Rowan, 1977). A second difference is in the nature of
competition.
Whereas private sector organizations engage in
competition with other companies, organizations in the public sector
quite often collaborate with one another. For example, many U.S. state
governments are increasingly helping local communities with issues such
as education reform. A third difference is that the political influence in
the private sector is indirect, whereas public sector organizations often
experience direct political influence from authority networks and users.
Finally, ownership in the private sector is vested in owners or
stockholders whose interests are interpreted using financial indicators.
“Owners” of public sector organizations, on the other hand, are citizens
who impose a variety of expectations about the organizations’ activities
(Perry & Rainey, 1988). Public sector organizations are often subject to
intense scrutiny using open records requirements (Nutt, 2000).
In spite of these differences, the PEU domains used in studies of
organizations in public sector settings often have been the same as the
domains developed from private sector studies (e.g., see adaptations of
Duncan’s, 1972 instrument that were used in Koberg, 1987, and
TAXONOMY OF THE UNCERTAINTY SOURCES PERCEIVED BY PUBLIC
SECTOR MANAGERS
5
Milliken, 1990). This generalization of private company uncertainty
sources to the public sector is unsatisfactory for several reasons. First, we
have little or no evidence that private sector-developed uncertainty
classifications are appropriate for public sector organizations. Second,
appropriate classification is a prerequisite for effective theory building
(McKelvey, 1982). Empirical results based on inadequate classification
can find support for “partial theories” at best, and incorrect theories at
worst (Pearce, 2001). Third, public sector organizations are assuming
greater importance to developed and developing societies. This suggests
that more public sector research is warranted. One approach toward
improving this situation is to inductively develop a public sector
manager-based numerical taxonomy of environmental uncertainty
sources, so that public sector research involving PEU can be conducted
without introducing the constraints of previously established private
sector typologies. The taxonomy development process would identify
both the characteristics used by public managers to group similar sources
of environmental uncertainty, and the actual classes (i.e., groupings) that
result. The goal is progress toward a more up-to-date and empirically
grounded framework that ultimately may be helpful for both researchers’
and practitioners’ understandings of uncertainty sources confronting
public managers. Our expectation is that public sector managers will
perceive at least some different sources of uncertainty than do private
sector managers.
METHOD AND RESULTS
Our field study was conducted in three phases. First, we identified
the uncertainty sources seen by public managers, and the underlying
dimensions that these managers use to discriminate among sources of
uncertainty. The public managers listed all sources of uncertainty facing
their organizations, and made similarity judgments that were analyzed
using multidimensional scaling techniques (MDS) (Kruskal & Wish,
1978). A benefit of MDS is that the resulting classificatory dimensions,
when identified by managers themselves through a non-prompted
approach, are less likely to be influenced by researcher presuppositions
than are those of approaches such as factor analysis of survey data
(Buchko, 1994; Werner & Brouthers, 1996). These dimensions represent
the “foundation” on which a taxonomy is based. In the second phase, we
validated the MDS results and dimension labels with a second sample of
6
VOGES, PRIEM, SHOOK & SHAFFER
public managers. Effective classification of the uncertainty sources by
the second sample, based on the dimensions identified by the first
sample, increases confidence in the usefulness of the MDS dimensions.
This permits continuation to the next step. In the third phase, we
inductively derived the managers’ classifications of sources of
environmental uncertainty. Although direct visual examination of the
graphic results of MDS output can be used to identify similar objects,
“great care must be taken when using this technique” to avoid
misinterpretation (Hair, Anderson, Tatham & Black, 1998; Jacoby,
1991). We elected to forego the visual approach. Instead, we used the
uncertainty source coordinates from the MDS output as input for cluster
analyses (Aldenderfer & Blashfield, 1984). This allowed us to group the
uncertainty sources based on the managers’ cognitive representations,
while minimizing interpretations by the researchers. Our sample,
procedures and the rationale for each phase of our study are detailed
below.
Samples
Participants were Executive Officers (EOs) for the Hong Kong
government, who were enrolled in a custom-designed graduate course in
management at a large Hong Kong university. All held at least an
undergraduate degree. EOs are in the “mid-level” general management
ranks and are rotated to a new position every 3 years. This helps to
ensure that they develop the general management skills necessary for
higher level, executive responsibilities. The typical EO supervises
between 3 and 50 subordinates; over half are women. Sample 1
consisted of thirty-four EOs, who participated in the first phase of the
research. Their ages ranged from 26 to 35 and they had an average
tenure of 7.82 years with the Hong Kong government. There were 10
males and 23 females. Thirty-one EOs made up Sample 2, the validation
sample. Their ages ranged from 26 to 35 with one over 36 (exclusion of
this outlier did not affect our results). They averaged 10.29 years tenure
with the Hong Kong government. Nine were males and 22 females.
These Hong Kong samples were selected for two important reasons.
First, we gathered data from the Hong Kong public managers
approximately two months before the scheduled transfer of Hong Kong
sovereignty from the United Kingdom to the People’s Republic of China.
This turnover period was expected to be one of intense uncertainty over
issues such as the rule of law, the type and role of government, business
TAXONOMY OF THE UNCERTAINTY SOURCES PERCEIVED BY PUBLIC
SECTOR MANAGERS
7
changes and transparency, and individual freedoms (Kahanna, 1995).
We considered it vital that we obtain data from public sector managers
who were facing a full range of possible sources of uncertainty. This
allowed us to develop a public sector PEU taxonomy that is as
comprehensive as possible. An effective taxonomy must have a class for
every object (McKelvey, 1982).
Second, the Hong Kong government provides a variety of public
services more common to a country than to a city. In Hong Kong, for
example, government organizations supervise a range of activities,
including hospitals, housing, the airport, ferries, the central bank,
immigration, education, law enforcement, and so on. The EOs in our
sample represented each of these areas of government, plus others.
Moreover, Hong Kong government will retain control over currency,
immigration and foreign relations for a period of fifty years after the
transfer of sovereignty. Hence, this sample was particularly likely to
identify national-level as well as local level uncertainties facing public
organizations, again furthering comprehensiveness.
Dimension Identification Phase
The Sample 1 managers were first asked “to think about and then
develop a comprehensive listing of all the sources of uncertainty facing
your department”. They performed this task individually after asking
clarification questions. The individual responses were combined across
respondents collectively. After redundancies were eliminated, the final
list contained 30 PEU sources. Next, a complete set of cards, each of
which was labeled with one of the uncertainty sources, was prepared for
each study participant. Approximately one week after Step 1, the
Sample 1 managers each were given a set of index cards and asked to
“group the cards into as many groups as may be necessary to properly
reflect the similarities and differences among the uncertainty sources
your group identified during the previous session. When you are
finished, similar uncertainty sources should be grouped together, while
dissimilar sources should be in different groups.” This task was
performed individually after asking clarification questions. When
satisfied with their groupings, they handed the grouped cards back to the
researcher.
A 30x30, ½-diagonal matrix for each respondent was then prepared,
with “1” entered if two uncertainty sources were placed in the same
8
VOGES, PRIEM, SHOOK & SHAFFER
group, and “0” if they were not. These matrices’ values were then
aggregated to produce an overall similarities matrix. The matrix was then
converted to reflect dissimilarity data “in order to avoid [an] inverse
relation between the data values and the geometric model (Jacoby,
1991).” These data were analyzed using the ALSCAL metric MDS
program (Young & Lewykcyj, 1979). Solutions were obtained for one,
two, three, four, and five dimensions, with stress indices of 0.54, 0.30,
0.17, 0.10 and 0.07 respectively (Kruskal, 1964). The three-dimensional
solution exhibited a better fit for our data than did the two-dimensional
solution (r2= .83 vs. r2= .62), but the fit improvement leveled off
somewhat for four dimensions and even more for five dimensions. We
chose the three-dimensional solution for interpretation based on the scree
test, on parsimony relative to the four and five dimensional solutions,
and on the more likely ease of interpretation (Cattell, 1965; Kruskal &
Wish, 1978).
Although each manager made up to 400 comparisons of uncertainty
sources as input to the MDS, one might question whether 34 public
managers, plus the 31-manager validation sample, can be representative
of the population of Hong Kong public sector managers. The work of
Zaltman and his colleagues is instructive. They have developed a
qualitative metaphor elicitation technique (MET) designed to “surface
the mental models that drive consumer thinking and behavior” for
various topics (Zaltman & Coutler, 1995; Zaltman, 1997). This technique
is parallel to the goals in this research: that is, to identify, via nonprompted examination of a relatively small number of participants from a
group, the constructs (in our case, classes) used by that group when
considering a particular topic area (in our case, uncertainty). Zaltman
and Coutler (1995) present data showing that, averaged over several
MET projects, it took only six to eight participant files randomlyselected from their 20-person samples to identify 100% of the constructs
identified by the entire sample for a particular topic area. This suggests
that our more quantitative findings from 34 top managers likely
identified the important classes employed by the group “high potential
public managers in Hong Kong” when considering the topic
“uncertainty”. Generalizability to other groups of public managers
remains an empirical question, as we discuss in later sections.
To minimize carryover from earlier tasks, we waited approximately
one month before asking the Sample 1 managers to label each of the
three dimensions they used to distinguish among uncertainty sources.
TAXONOMY OF THE UNCERTAINTY SOURCES PERCEIVED BY PUBLIC
SECTOR MANAGERS
9
The task was completed first individually and then as a group exercise to
derive dimension labels based on consensus. MDS output dimensions
represent the degree to which an object represents one characteristic
rather than another. Dimensions are frequently labeled based on those
objects that appear at the extremes of the dimensions. The managers
labeled Dimension One, “Socio-economic”. They used this dimension to
distinguish among sources of uncertainty that were related to “external”
conditions such as international trade, competition from other economic
powers in Asia, and structural changes in the economy, versus “internal”
organizational conditions such as privatization of governmental
departments, policies and procedures of the government, and the
structure and policies of the civil service.
The managers labeled Dimension Two, “Political”. They used this
dimension to distinguish among sources of uncertainty that were related
to “local conditions” such as the influx of immigrants from China, the
quality and composition of the Hong Kong population, and public
services such as housing, education and welfare, versus “international
conditions” such as political instability in other Asian countries, changes
in world political systems and direct competition from other cities such
as Shanghai and Taipei.
The managers labeled Dimension Three, “China’s Influence”. They
used this dimension to distinguish among sources of uncertainty that
were related to “micro conditions” such as the increasing expectations of
Hong Kong residents regarding housing, job security, income equalities,
and the public service, versus “macro conditions” such as China’s
potential interventions (after the change in sovereignty) in Hong Kong’s
economic, social and political systems. The specific reference to China
in this dimension is not surprising given the importance of the
sovereignty change at the time of our study. This might be thought to
restrict application of this dimension to China research only, but that is
not the case. Macro political change is an on-going source of potential
uncertainty for public managers, as can be seen in Russia, Central and
Eastern Europe, and many other locations around the world (Gelb &
Tenev, 2000). Even in the U.S., for example, this dimension could
distinguish among macro uncertainties generated by a shift from
Democratic to Republican control, versus the more micro, customeroriented uncertainties associated with shifting public expectations. Our
10
VOGES, PRIEM, SHOOK & SHAFFER
research team relabeled this dimension “Ideological” uncertainty, to
enlarge the Hong Kong managers’ more local, “China-specific” label.
Dimension Validation Phase
In this phase, the Sample 2 managers performed a task designed to
validate the three dimensions, and their labels, developed by the Sample
1 managers. First, the Sample 2 managers were given the three
dimension labels and descriptions generated by Sample 1, and were
asked to position each of the 30 uncertainty sources along each
dimension using a five-point scale. Each Sample 2 manager “located”
each uncertainty source along each dimension based on his or her
understanding of the dimensions and their labels. Next, we used the
MDS results for Dimension 1 to identify the ten uncertainty sources with
the most extreme MDS scores (i.e. the five at each extreme of Dimension
1). The Sample 2 ratings for these uncertainty sources were then tested
for mean differences across the “high-five” and “low-five” Dimension 1
sources. This process was repeated for Dimensions 2 and 3. Significant
“high-five” versus “low-five” rating differences were found for each
dimension (all p< 0.001). This indicates the dimensions and labels
resulting from the first sample were recognized by the second group, and
were used successfully to rate uncertainty sources. Still, identifying the
underlying dimensions used by public managers in classification does
not in itself provide a taxonomy. We turn to that next.
Taxonomy Phase
The MDS analysis indicated that the Hong Kong public managers
distinguish among uncertainty sources by positioning them along
dimensions reflecting their socio-economic (internal versus external),
political (local versus international) and ideological (micro versus macro)
natures. Given the three-dimension MDS solution, if each dimension
were split at the mean and combined in a 3-dimensional cube there
would be eight possible categories of uncertainty sources. It is not clear,
however, whether or not each possible cell would actually contain an
uncertainty source. Clustering the uncertainty sources using the MDS
output (i.e., the 3 coordinates for each uncertainty source) allowed us to
identify which categories actually are meaningful to the public managers,
and to achieve a more parsimonious classification system.
TAXONOMY OF THE UNCERTAINTY SOURCES PERCEIVED BY PUBLIC
SECTOR MANAGERS
11
Clustering results are subject to researcher judgment, as well as to
the inherent biases associated with clustering algorithms (Aldenderfer &
Blashfield, 1984). We recognize these limitations of cluster analysis,
and chose to use a dual-stage clustering method to increase the validity
of the solution (Milligan, 1980; Punj & Stewart, 1983). Following the
recommendations of Ketchen and Shook (1996), we first evaluated the
correlations among the three dimensions. These ranged from -.06 to .10,
indicating the absence of multi-collinearity. Second, a hierarchical,
agglomerative clustering algorithm (Ward’s minimum variance
technique) generated a tentative cluster solution and indicated the
number of clusters. The r-squared (.86) and CCC (2.94) of the Ward’s
output suggested a six-cluster solution (Everett & Der, 1996). Third, a
second cluster analysis was generated using the first analysis’ cluster
means as the initial conditions for an iterative, point kernel procedure (kmeans clustering). The two cluster procedures use extremely dissimilar
algorithms and approaches to clustering (Aldenderfer & Blashfield,
1984). There was strong agreement across these very different clustering
techniques; twenty-nine of the thirty PEU sources were classified in the
same clusters for both the Ward’s and k-means analyses. This increases
confidence in the reliability of the cluster solution.
We labeled the clusters based on the PEU sources they contained.
For instance, the first cluster contained uncertainty sources such as
economic structure changes, the property market and technological
changes. We labeled this cluster “Techno-economic Conditions”. The
resulting clusters and their labels are shown in Table 1. To illustrate
where each cluster is positioned along the distinguishing dimensions,
Figure 1 provides a three-dimensional representation of cluster centroids
and their coordinates.
DISCUSSION
The taxonomic results of our study provide an answer for our first
research question, What are the sources of uncertainty perceived by
public sector managers? The six uncertainty source groups shown in
Table 1 result from three distinct dimensions that were used by the Hong
Kong public managers in classifying uncertainties. These dimensions
included a political dimension (rated as local versus international), a
socio-economic dimension (rated as internal organizational versus
12
VOGES, PRIEM, SHOOK & SHAFFER
external), and an ideological dimension (rated as micro versus macro
conditions).
Our second research question, Do public sector managers perceive
the same uncertainties as do private sector managers?, can be examined
by comparing our public sector PEU taxonomy with other PEU
taxonomies and typologies.
TABLE 1
A Taxonomy of Public Managers’ Perceived Uncertainties
Cluster 1: Techno-economic conditions
Economic structure changes
Property market
Technological changes
Cluster 2: Internal organizational conditions
Laws governing work procedures
Continuity/succession of senior government officials
HK government finances - sources /allocation
Policies/procedures of the government
Structure/policies of civil services
Privatization of government departments
Management style of HK officials
Use of Chinese language
Appointment of PRC officials to government posts
Cluster 3: International politics and competition
Political instability in other Asian countries
Changes in world political systems
Competition from other economic powers in Asia
International trade
Competition from other cities such as Shanghai and Taipei
Cluster 4: Local population characteristics
Influx of unskilled cheap labor from China
Corruption and bribery
Brain drain
Quality and composition of the HK population
Influx of immigrants from China
Cluster 5: Governmental Influence
China’s influence after change in sovereignty 30 June 1997
Intervention by China on Hong Kong economic and social system
Political instability in Hong Kong
Modification of the legal system
TAXONOMY OF THE UNCERTAINTY SOURCES PERCEIVED BY PUBLIC
SECTOR MANAGERS
13
Cluster 6: Societal expectations
Increasing expectations of Hong Kong residents regarding housing,
welfare
Public services: housing, education, welfare
Job security
Income Imbalance
FIGURE 1
Public Managers’ Perceived Uncertainty Clusters
3
2.0
1.0
5
1
Pol itical
2
0.0
4
-1.0
2.0
1.0
0.0
Ideo logical
-1.0
Cluster Label
1. Techno-economic conditions
2. Internal organizational conditions
3. International politics and
competition
4. Local populations characteristics
5. Governmental influence
6. Societal expectations
6
-1.0
Socioeconomic
1.31
-1.61
1.21
.70
.48
-.20
0.0
1.0
2.0
Soc io-eco nomic
Dimension
Political Ideological
.01
.19
1.69
-.66
.08
-.17
-1.27
.03
-.99
.27
1.33
-1.14
Table 2 summarizes the similarities and differences among our
public sector PEU taxonomy, three well-known U.S. private sector
14
VOGES, PRIEM, SHOOK & SHAFFER
typologies, and the recent Priem et al. (2002) Hong Kong private sector
PEU taxonomy. Societal uncertainties are common to all five of these
classification schemes.
Organizational conditions, economic, and
technological uncertainties are common to the pubic sector taxonomy, as
well as to two of the four other private sector taxonomies. These
similarities indicate that private sector and public sector uncertainties are
not totally different. This result is not unexpected, and it provides further
validation of our taxonomy.
TABLE 2
External
Governmental Influence
International Politics and
Competition
Political
Industry Competition
Economic
Societal
Technological
Regulatory
Customers
Suppliers
Public Sector
Taxonomy
Daft,
Sormunen and
Parks (1988)
X
Priem, Love
and Shaffer
(2002)
Internal
Organizational
Conditions
Miles and
Snow (1978)
Duncan (1972)
Comparison of PEU Classification Systems
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
There clearly are important differences that need to be recognized,
however, between the private sector typologies and our public sector
taxonomy. Our taxonomy focuses on political and broad ideological
sources of PEU that are ignored by the other classification schemes (Daft
et al., 1988; Duncan, 1972; Miles & Snow, 1978; Priem et al., 2002).
This focus is evident in that there are cluster categories for 1)
TAXONOMY OF THE UNCERTAINTY SOURCES PERCEIVED BY PUBLIC
SECTOR MANAGERS
15
governmental influence and 2) international politics and competition in
the public sector taxonomy that are not evident in any of the three wellknown taxonomies, nor in the recent parallel private sector Hong Kong
study.
Further, the political cluster category in the public sector
taxonomy is only found in the Duncan taxonomy. The public sector
taxonomy also includes uncertainties internal to the organization, and
previously unobserved international uncertainty sources. Of the three
well-known taxonomies, only Duncan identified internal organizational
sources. Interestingly, the recent Priem et al. (2002) private sector study
also identified internal sources. All three taxonomies (e.g. Duncan’s, our
public sector taxonomy and the Priem et al. work) utilized a
methodology in which managers provided the sources of uncertainty.
This contrasts with the Miles and Snow (1978) and Daft et al. (1988)
typologies, which were derived from previous studies’ research
constructs. On the other hand, our public sector taxonomy does not
include the customer and supplier uncertainties that are common in the
well-known private sector typologies. Thus, the answer for our second
research question is that for some classes of uncertainty the answer is
yes, there are similarities between public sector and private sector
uncertainty sources; for others, no.
The divergence between our public sector, manager-generated
taxonomy of uncertainty sources and the existing, private sector
typologies may be the result of two factors. The first is simply that the
concerns and challenges of public and private sector managers are
different. This explanation supports the view that there are important
differences between public and private organizations (Rainey &
Bozeman, 2000). A second possible explanation is that increasing
globalization adds more uncertainties to almost any organizational type
(Porter, 1990). For example, the opening of trade through accords such
as the North American Free Trade Agreement (NAFTA) and the
European Community (EC), likely added potential sources of
uncertainty. This could contribute to divergence from the well-known
private sector uncertainty typologies because they were developed quite
long ago. This explanation is further supported in the observation that
the recent parallel study recognizes the international sources.
Our findings provide a number of opportunities for extending theory
and research relating PEU to managerial action in the public sector.
First, the taxonomy creates meaningful patterns in a plethora of
16
VOGES, PRIEM, SHOOK & SHAFFER
uncertainty sources facing public managers. It provides a comprehensive
yet parsimonious classification that can contribute to theory building.
The introduction of international political uncertainty sources and the
inclusion of previously little-considered internal organizational
uncertainty sources, when combined with the further specification of
political, ideological, and societal sources, provide a first step toward
enhancing our ability to build and test theories of action for public sector
managers. One might argue, for example, that public managers’ attention
to uncertainty sources associated with international politics and
competition will be greater in countries where a higher percentage of
GDP is involved in international trade. Or, one might argue that public
managers whose governments are planning privatizations will pay more
attention to uncertainties from internal organizational conditions.
Second, our study contributes empirical evidence to the publicprivate sector debate. The taxonomy provides general evidence to
caution researchers about the potential danger of using private sector
classifications for public sector work. It also indicates that Meyer’s
concerns about theoretical overgeneralization if the public-private
distinction is blurred may be well founded (Meyer, 1982). Our PEU
taxonomy contains uncertainty sources that are unique to public sector
managers. Thus, it may help researchers to establish previously
unconsidered relationships between the environment and the activities of
public sector managers. Future inclusion of uncertainty due to potential
ideological changes, for example, could improve our understanding of
how public managers define and clarify goals in their organizations.
Third, the taxonomy may facilitate research to expand knowledge of
differences in the relationships between PEU and organizational action in
public and private sector contexts. One might examine, for example,
across public and private contexts: 1) whether relationships among
similar variables are different, and 2) whether the causally important
variables themselves differ.
Our study has a number of limitations based on the inherent research
tradeoffs among simplicity, accuracy and generalizability (Weick, 1969).
First, in spite of its many advantages noted in the Method and Results
section, ours was a small, convenience sample. The possibility exists
that unique Hong Kong factors influenced the uncertainty classifications.
Thus, the representativeness of our taxonomy for public organizations
worldwide remains to be confirmed through replication. Second,
although major political changes are prevalent in large portions of the
TAXONOMY OF THE UNCERTAINTY SOURCES PERCEIVED BY PUBLIC
SECTOR MANAGERS
17
world, it is possible that managers in a more stable public sector setting
could face an uncertainty source that cannot be classified via our
taxonomy (May, Stewart & Sweo, 2000). We contend, however, that
use of a PEU source classification that captures the multiplicity of
political, ideological, societal and organizational uncertainty sources is
more likely to generate meaningful public sector research results than
would continued use of private sector based classification schemes.
Finally, although we made efforts to exclude researcher bias through our
methodology, it is impossible to rule it out conclusively. The managers
developed lists of all sources of uncertainty, but our judgment was
required to aggregate the lists and eliminate redundancies. Moreover, the
managers labeled the dimensions, but we labeled the clusters. The threat
of unintended bias may be less likely, however, due to the validation of
the MDS results using a different data collection method and a different
public manager sample.
CONCLUSION
Venkatraman and Grant (1986) note that well-defined constructs are
a basic criterion for the interpretation of substantive relationships. Perry
and Rainey argue that organizational theory can advance only through
properly distinguishing between public sector and private sector
organizations (1988). These arguments were made 15 years ago, but little
progress has been made since then toward either defining a public sector
PEU construct or distinguishing it from private sector PEU. Yet
uncertainty is a central concept in theories of public and private
organizations. We hope that the practitioner-derived taxonomy of public
sector PEU sources identified by our research responds to each of these
requirements - that it 1) contributes to a better defined uncertainty
sources construct for the public sector, and 2) helps to further distinguish
between public and private sector organizations along the critical
uncertainty variable. Despite a “blurring” of boundaries between public
and private sector organizations due to activities such as privatization,
there is a continuing need to distinguish between these two organization
types (Bryson, 1995). This taxonomy suggests that public sector and
private sector managers perceive differing sources as generating
uncertainty for their organizations.
ACKNOWLEDGEMENTS
18
VOGES, PRIEM, SHOOK & SHAFFER
We thank Howard Balanoff, Vince Barker, Dave Ketchen, Paul
Nystrom, Abdul Rasheed and Masoud Yasai-Ardekani for helpful
comments.
REFERENCES
Alchian A.A. (1950). “Uncertainty, Evolution and Economic Theory,”
The Journal of Public Economics, 58, 211-221
Aldenderfer, M.K. & Blashfield, R.K. (1984). Cluster Analysis. Beverly
Hills, CA: Sage Publications.
Aldrich, H. & Pfeffer, J. (1976). “Environments of Organizations,”
Annual Review of Sociology, 2, 79-105.
Ashmos, D.P., & Huber, G.P. (1987). “The Systems Paradigm in
Organizational Theory: Correcting the Record and Suggesting the
Future,” Academy of Management Review, 12, 607-621.
Bryson, J.M. (1995). Strategic Planning for Public and Nonprofit
Organizations: A Guide to Strengthening and Sustaining
Organizational Achievement. San Francisco: Jossey-Bass.
Buchko, A.A. (1994). “Conceptualization and Measurement of
Environmental Uncertainty: An Assessment of the Miles and Snow
Perceived Environmental Uncertainty Scale,” Academy of
Management Journal, 37, 410-425.
Cattell, R. (1965). Handbook of Multivariate Experimental Psychology.
Chicago, IL: Rand-McNally.
Daft, R.L., Sormunen, J., & Parks, D. (1988). “Chief Executive
Scanning,
Environmental
Characteristics,
and
Company
Performance: An Empirical Study,” Strategic Management Journal,
9, 123-139.
Dill, W.R. (1958). “Environment as an Influence on Managerial
Autonomy,” Administrative Science Quarterly, 2, 409-443.
Duncan, R. (1972). “Characteristics of Organizational Environments and
Perceived Environmental Uncertainty,” Administrative Science
Quarterly, 17, 313-327.
Everett, B.S., & Der, G. (1996). A Handbook of Statistical Analyses
Using SAS. London: Chapman and Hall.
TAXONOMY OF THE UNCERTAINTY SOURCES PERCEIVED BY PUBLIC
SECTOR MANAGERS
19
Galaskiewicz, J., & Bielfeld, W. (1998). Nonprofit Organizations in an
Age of Uncertainty: A Study of Organizational Change. Hawthorne,
NY: Aldine de Gruyter.
Gelb, A. & Tenev. S. (2000). “Circumstances and Choice: The Role of
Initial Conditions and Policies in Transition Economies,” The World
Bank Economic Review, 15, 1-31.
Gioia, D.A. & Thomas, J.B. (1996). “Identity, Image, and Issue
Interpretation: Sensemaking During Strategic Change in Academia,”
Administrative Science Quarterly, 41, 370-403.
Hair, Jr. J.F., Anderson, R.E., Tatham, R.E., & Black, W.C. (1998).
Multivariate Data Analysis. Upper Saddle, NJ: Prentice Hall, Inc.
Hofer, C.W. (1975). “Toward a Contingency Theory of Business
Strategy,” Academy of Management Journal, 18, 784-801.
Jacoby, W.G. (1991). Data Theory and Dimensional Analysis. Newbury
Park, CA: Sage Publications.
Katz, D., & Kahn, R.L. (1978). The Social Psychology of Organizations.
New York: John Wiley and Sons. .
Ketchen, D.J. & Shook, C.L. (1996). “The Application of Cluster
Analysis in Strategic Management Research: An Analysis and
Critique,” Strategic Management Journal, 17 (6), 441-458.
Khanna, J. (1995). Southern China, Hong Kong, and Taiwan: Evolution
of a Subregional Economy. Washington DC: Center for Strategic
and International Studies.
Koberg, C. (1987). “Resource Scarcity, Environmental Uncertainty and
Adaptive Organizational Behavior,” Academy of Management
Journal, 30, 798-807.
Kruskal, J. (1964). “Multidimensional Scaling by Optimizing Goodness
of Fit to a Monothetic Hypotheses,” Psychometrika, 29, 1-27.
Kruskal, J. &. Wish. M. (1978). Multidimensional Scaling. Beverly Hills,
CA: Sage Publications.
Lant, T.K., Milliken, F.J., & Batra. B. (1992). “The Role of Managerial
Learning and Interpretation in Strategic Persistence and
20
VOGES, PRIEM, SHOOK & SHAFFER
Reorientation: An Empirical Exploration,” Strategic Management
Journal, 13, 585-608.
March, J.G. & Simon, H.A. (1958). Organizations. New York: Wiley.
May, R.C. Stewart, W.H., & Sweo, R. (2000). “Environmental Scanning
Behavior in a Transitional Economy: Evidence from Russia,”
Academy of Management Journal, 43(3), 403-426.
McKelvey, W. (1982). Organizational Systematics:
Taxonomy,
Evolution, Classification. Berkeley, CA: University of California
Press.
Meyer, M.W. (1982). “‘Bureaucratic’ vs. ‘Profit’ Organization.” In Staw,
B.M. & Cummings, L.L. (Eds.), Research in Organizational
Behavior (pp. 89-126). Greenwich, CT: JAI Press.
Meyer, J.W., & Rowan, B. (1977). “Institutionalized Organizations:
Formal Structure As Myth and Ceremony,” American Journal of
Sociology, 83, 340-363.
Miles, R.E., & Snow, C.C. in collaboration with Meyer, A.D., and with
contributions by Coleman, H.J. Jr. (1978). Organizational Strategy,
Structure and Process. New York: McGraw-Hill.
Milligan, G.W. (1980). “An Examination of the Effect of Six Types of
Error
Perturbation on Fifteen Clustering Algorithms,”
Psychometrika, 45, 325-342.
Milliken, F.J. (1990). “Perceiving and Interpreting Environmental
Change: An Examination of College Administrators’ Interpretation
of Changing Demographics,” Academy of Management Journal, 33,
42-63.
Nutt, P. (1999). “Public-Private Differences and Assessment of
Alternatives for Decision Making,” Journal of Public Administration
Research and Theory, 9, 305-349.
Nutt, P. (2000). “Decision Making Success in Public, Private and Third
Sector Organizations: Finding Sector Dependent Best Practices,”
Journal of Management Studies, 37, 77-108.
Nutt, P., & Backoff, R.W. (1993). “Organizational Publicness and its
Implications for Strategic Management,” Journal of Public
Administration Research and Theory, 3, 182-201.
TAXONOMY OF THE UNCERTAINTY SOURCES PERCEIVED BY PUBLIC
SECTOR MANAGERS
21
Pearce, J.L. (2001). Organization and Management in the Embrace of
Government. Mahwah, NJ: Lawrence Erlbaum Associates.
Perry, J.L., & Rainey, H.G. (1988). “The Public-Private Distinction in
Organization Theory: A Critique and Research Strategy,” Academy
of Management Review, 13, 182-201.
Porter, M.E. (1990). The Competitive Advantage of Nations. New York:
Free Press.
Priem, R.L., Love, L., & Shaffer, M. (2002). “Executive’s Perceptions of
Uncertainty Sources: A Numerical Taxonomy and Underlying
Dimensions,” Journal of Management, 28, 725-747.
Punj, G., & Stewart, D.W. (1983). “Cluster Analysis in Marketing
Research: Review and Suggestions for Application,” Journal of
Marketing Research, 20, 134-148.
Rainey, H.G., Backoff, R.W., & Levine, C.H. (1976). “Comparing
Public and Private Organizations,” Public Administration Review,
36, 233-244.
Rainey, H.G., & Bozeman, B. (2000). “Comparing Public and Private
Organizations: Empirical Research and The Power of the A Priori,”
Journal of Public Administration Research and Theory, 10, 447-469.
Thomas, J.B., Clark, S.M., & D.A.Gioia. (1993). “Strategic Sensemaking
and Organizational Performance: Linkages among Scanning,
Interpretation, Action and Outcomes,” Academy of Management
Journal, 3, 239-270.
Venkatraman, N., & Grant, V. (1986). “Construct Measurement in
Organizational Strategy Research: A Critique and Proposal,”
Academy of Management Review, 11 (1), 71-87.
Weick, K.E. (1969). The Social Psychology of Organizing. Reading,
MA: Addison-Wesley.
Werner, S., & Brouthers, L.E. (1996). “International Risk and Perceived
Environmental Uncertainty: The Dimensionality and Internal
Consistency of Miller’s Measure,” Journal of International Business
Studies, 27, 571-588.
22
VOGES, PRIEM, SHOOK & SHAFFER
Willcocks, L.P., & Currie, W.L. (1997). “Information Technology in
Public Services: Towards the Contractual Organization?” British
Journal of Management, 8, 107-120.
Yasai-Ardekani, M., & Nystrom, P.C. (1996). “Designs for
Environmental Scanning Systems: Tests of Contingency Theory.
Management Science, 42, 187-204.
Young, F.W., & Lewykcyj, R. (1979). ALSCAL-4: User’s Guide. Chapel
Hill, NC: Psychometric Laboratory, University of North Carolina.
Zaltman, G. (1997). “Rethinking Marketing Research: Putting People
Back in,” Journal of Marketing Research, 34, 424-437.
Zaltman, G., & Coutler, R.H. (1995). “Seeing the Voice of the Customer:
Metaphor-Based Advertising Research,” Journal of Advertising
Research, 35, 35-51.