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NURS-6050 Assignment Week 8

Please see complete instructions and resources – Attached documents

Instructions

The Assignment: (2–4 pages)

In a 2- to 4-page paper, create an interview transcript of your responses to the following interview questions:

Tell us about a healthcare program, within your practice – Psychiatry. What are the costs and projected outcomes of this program?
Who is your target population?
What is the role of the nurse in providing input for the design of this healthcare program? Can you provide examples?
What is your role as an advocate for your target population for this healthcare program? Do you have input into design decisions? How else do you impact design?
What is the role of the nurse in healthcare program implementation? How does this role vary between design and implementation of healthcare programs? Can you provide examples?
Who are the members of a healthcare team that you believe are most needed to implement a program? Can you explain why?

**at least 2 outside resources and 2-3 course specific resources that fully supports the summary provided. **

Running

head:

THE ROLE OF THE RN

M AND

APRN IN POLICY

–

MAKING

DISCUSSION 2: THE ROLE OF THE RN AND APRN IN POLICY-MAKING 2

The Role of the RN and

APRN in Policy

–

Making

Student’s Name

Institution Affiliation

Opportunities in Policy Making

RNs and APRNs have various opportunities that that can enable them to participate actively in policy making. Nurses are registered members of different professional organizations that enable to them to participate actively in policy making. Through enrollment to these organizations in different state jurisdictions enable to take part in policy making. Membership to organizations such as the American Nurses Association makes it possible for nurses to actively participate in policy making (McGuire ET AL., 2017). This is promoted by analyzing and reviewing the existing the health policies. These organizations offer the needed opportunity to assess the different aspects of the health policies and contribute in policy making that safeguards the interests of the RNs and APRNs, and ensure that these interests are in best interest of the patients by ensuring the quality of care is safeguarded.

Besides, the workplace environment provides the RNs and APRNs with another opportunity for policy making. Workplace presents an important opportunity for nurses that can utilize to deliver healthcare to their patients. Since healthcare workers have an influence in their workplace, they can use it to influence healthcare systems by promoting policy review related to healthcare delivery by focusing on policies that contribute to efficiency and improved performance in the workplace by ensuring healthcare standards expected by their patients are met (Nantsupawat et al., 2017). The workplace enables the nurses to advocate for policies that affect efficiency, thus an important opportunity which they use to influence policy change.

Challenges Presented

Despite these opportunities availing policy making role for nurses, there are also challenges that hinder full participation in policy making. One of the challenges is the difficulties that registering in professional organizations present to nurses, thus limit their participation in policy making (Lee, Hwang & Moon, 2020). Focusing on making the process efficient and brief would help reduce the complexity of the registration process. This would make the process for registering in nursing professional organizations simple. This should embark on reviewing the leadership to reduce inefficiencies associated with poor leadership to ensure responsibility and accountability.

Another challenge is presented by the workplace environment. To begin with workplace is faced with lack of cooperation and conflicts that make it hard for the RNs and APRNs to remain united when advocating for a policy change. Such problems can be overcome if there is goodwill among the nurses to work as a team and cooperate when advocating for change. Teamwork should be promoted by ensuring that nurses speak in one voice when reviewing policy change aimed at promotion of quality within the work place. Another challenge can emanate from poor leadership, which might present a hurdle for nurses’ participation in policy making. This can come in form of poor work environment that support nurses dynamism and participation in policy process. Leaders can turn to a more inclusive policy review process so that nurses can participate comfortably.

Strategy Recommendation

To promote policy making among the nurses include proper training and promoting good leadership. Training enables the nurses to comprehend their roles and responsibilities. On the other hand, good leadership would advocate on making a better workplace environment that motivate the workers to participate in policy making process. Leadership also enables the nurses to openly share their views on how policies can be improved to promote efficiency within the workplace. Besides, good leadership presents an opportunity for nurses to register within professional organizations that which are critical in creating an opportunity to participate I policy review.

References

Lee, S., Hwang, C., & Moon, M. J. (2020). Policy learning and crisis policy-making: quadruple-loop learning and COVID-19 responses in South Korea. Policy and Society, 39(3), 363-381.

McGuire, M., Goldstein, C., Claywell, L., & Patton, R. (2017). Analysis of student reflections of experiential learning in nursing health policy courses. Nurse educator, 42(2), 95-99.

Nantsupawat, A., Kunaviktikul, W., Nantsupawat, R., Wichaikhum, O. A., Thienthong, H., & Poghosyan, L. (2017). Effects of nurse work environment on job dissatisfaction, burnout, intention to leave. International nursing review, 64(1), 91-98.

Running head:
THE ROLE OF THE RN
M AND
APRN IN POLICY
–
MAKING

The Role of the RN and
APRN in Policy
–
Making

Student’s Name

Institution Affiliation

Running head: THE ROLE OF THE RNM AND APRN IN POLICY-MAKING 1

The Role of the RN and APRN in Policy-Making

Student’s Name

Institution Affiliation

Deadline : 1/22/2021

Assignment: Advocating for the Nursing Role in Program Design and Implementation

Much like honeyguide birds and honey badgers, nurses and health professionals from other specialty areas can—and should—collaborate to design effective programs. Nurses bring specialties to the table that make them natural partners to professionals with different specialties. When nurses take the requisite leadership in becoming involved throughout the healthcare system, these partnerships can better design and deliver highly effective programs that meet objectives.

In this Assignment, you will practice this type of leadership by advocating for a healthcare program. Equally as important, you will advocate for a collaborative role of the nurse in the design and implementation of this program. To do this, assume you are preparing to be interviewed by a professional organization/publication regarding your thoughts on the role of the nurse in the design and implementation of new healthcare programs.

To Prepare:

· Review the Resources and reflect on your thinking regarding the role of the nurse in the design and implementation of new healthcare programs.

Course Specific Resources

https://www.nursingworld.org/practice-policy/advocacy/

https://www.cdc.gov/injury/pdfs/policy/Brief%204-a

https://www.congress.gov/

APA

(American Psychological Assoc.)

References

Klein, K. J., & Sorra, J. S. (1996). The Challenge of Innovation Implementation. Academy of Management Review, 21(4), 1055–1080.

https://doi-org.ezp.waldenulibrary.org/10.5465/AMR.1996.9704071863

APA

(American Psychological Assoc.)
References

José A., S., & Tatiana, D. (2015). No big data without small data: learning health care systems begin and end with the individual patient. Journal of Evaluation in Clinical Practice, 21(6), 1014–1017. https://doi-org.ezp.waldenulibrary.org/10.1111/jep.12350

APA

(American Psychological Assoc.)
References

Tummers, L., & Bekkers, V. (2014). Policy Implementation, Street-level Bureaucracy, and the Importance of Discretion. Public Management Review, 16(4), 527–547. https://doi-org.ezp.waldenulibrary.org/10.1080/14719037.2013.841978

· Select a healthcare program within your practice and consider the design and implementation of this program.

· Reflect on advocacy efforts and the role of the nurse in relation to healthcare program design and implementation.

Instructions

The Assignment: (2–4 pages)

In a 2- to 4-page paper, create an interview transcript of your responses to the following interview questions:

· Tell us about a healthcare program, within your practice –

Psychiatry

. What are the costs and projected outcomes of this program?

· Who is your target population?

· What is the role of the nurse in providing input for the design of this healthcare program? Can you provide examples?

· What is your role as an advocate for your target population for this healthcare program? Do you have input into design decisions? How else do you impact design?

· What is the role of the nurse in healthcare program implementation? How does this role vary between design and implementation of healthcare programs? Can you provide examples?

· Who are the members of a healthcare team that you believe are most needed to implement a program? Can you explain why?

**at least 2 outside resources and 2-3 course specific resources that fully supports the summary provided. **

Deadline : 1/
22
/2021

Assignment: Advocating for the Nursing Role in Program
Design and Implementation

Much like honeyguide birds and honey badgers, nurses and health professionals from other s
pecialty
areas can
—
and should
—
collaborate to design effective programs. Nurses bring specialties to the
table that make them natural partners to professionals with different specialties. When nurses take
the requisite leadership in becoming involved throug
hout the healthcare system, these partnerships
can better design and deliver highly effective programs that meet objectives.

In this Assignment, you will practice this type of leadership by advocating for a healthcare program.
Equally as important, you wi
ll advocate for a collaborative role of the nurse in the design and
implementation of this program. To do this, assume you are preparing to be interviewed by a
professional organization/publication regarding your thoughts on the role of the nurse in the de
sign
and implementation of new healthcare programs.

To Prepare:

Review the Resources and reflect on your thinking regarding the role of the nurse in the design
and implementation of new healthcare programs.

Course Specific Resources

https://www.nursingworld.org/practice
–
policy/advocacy/

https://www.cdc.gov/injury/pdfs/policy/Brief%204
–
a

https://www.congress.gov/

APA

(American Psychological Assoc.)

References

Klein
, K. J., & Sorra, J. S. (1996). The Challenge of Innovation
Implementation.

Academy

Management

Review
,

21
(4), 1055
–
1080.
https://doi
–
org.ezp.waldenulibrary.org/10.5465/AM
R.1996.9704071863

APA

(American
Psychological Assoc.)

References

José A., S., & Tatiana, D. (2015). No big data without small data: learning health care
systems begin and end with the individual patient.

Journal

Evaluation

Clinical

Deadline : 1/22/2021

Assignment: Advocating for the Nursing Role in Program
Design and Implementation

As their names imply, the honeyguide bird and the honey badger both share an affinity for honey.
Honeyguide birds specialize in finding beehives but struggle to access the honey within. Honey
badgers are well-equipped to raid beehives but cannot always find them. However, these two honey-
loving species have learned to collaborate on an effective means to meet their objectives. The
honeyguide bird guides honey badgers to newly discovered hives. Once the honey badger has
ransacked the hive, the honey guide bird safely enters to enjoy the leftover honey.
Much like honeyguide birds and honey badgers, nurses and health professionals from other specialty
areas can—and should—collaborate to design effective programs. Nurses bring specialties to the
table that make them natural partners to professionals with different specialties. When nurses take
the requisite leadership in becoming involved throughout the healthcare system, these partnerships
can better design and deliver highly effective programs that meet objectives.

In this Assignment, you will practice this type of leadership by advocating for a healthcare program.
Equally as important, you will advocate for a collaborative role of the nurse in the design and
implementation of this program. To do this, assume you are preparing to be interviewed by a
professional organization/publication regarding your thoughts on the role of the nurse in the design
and implementation of new healthcare programs.

To Prepare:
 Review the Resources and reflect on your thinking regarding the role of the nurse in the design
and implementation of new healthcare programs.

Course Specific Resources

https://www.nursingworld.org/practice-policy/advocacy/

https://www.cdc.gov/injury/pdfs/policy/Brief%204-a

https://www.congress.gov/

APA
(American Psychological Assoc.)
References
Klein, K. J., & Sorra, J. S. (1996). The Challenge of Innovation
Implementation. Academy of Management Review, 21(4), 1055–1080. https://doi-
org.ezp.waldenulibrary.org/10.5465/AMR.1996.9704071863

APA
(American Psychological Assoc.)
References
José A., S., & Tatiana, D. (2015). No big data without small data: learning health care
systems begin and end with the individual patient. Journal of Evaluation in Clinical

1/8/2021 Rubric Detail – Blackboard Learn

https://class.waldenu.edu/webapps/bbgs-deep-links-BBLEARN/app/course/rubric?course_id=_16767090_1&rubric_id=_2031043_1 1/5

Rubric Detail
Select Grid View or List View to change the rubric’s layout.

Excellent Good Fair Poor

Program Design

In a 2- to 4-page
paper, create an
interview
transcript of
your responses
to the following
interview
questions.
· Tell us about a
healthcare
program within
your practice.
What are the
costs and
projected
outcomes of this
program?

· Who is your
target
population?

· What is the role
of the nurse in
providing input

for the design of
this healthcare
program? Can you
provide
examples?

· What is the role
of the nurse in
providing input

41 (41%) – 45
(45%)

Response
provides a clear
and complete
summary of the
healthcare
program,
including an
accurate and
detailed
description of
the costs and
projected
outcomes of the
program.

Response
provides a clear
and accurate
description that
fully describes
the target
population.

Response
provides a clear
and accurate
explanation of
the role of the
nurse in
providing input
for the design of
the program,

including
speci�c

examples.

36 (36%) – 40
(40%)

Response
provides a
summary of the
healthcare
program,
including a
description of
the costs and
project
outcomes of the
program.

Response
provides an
accurate
description of
the target
population.

Response
provides an
accurate
explanation of
the role of the
nurse in
providing input
for the design of
the program,
including some
examples.

Response
provides an
accurate
description of
the role of the

31 (31%) – 35
(35%)

Response
provides a
summary of the
healthcare
program that is
vague or
incomplete or
does not
include costs or
projected
outcomes of
the program.

Description of
the target
population is
vague or
inaccurate.

Explanation of
the role of the
nurse in
providing input
for the design
of the program
is vague,
inaccurate, or
does not
include

speci�c
examples.

Description of
the role of the
nurse advocate
for the target

population for

0 (0%) – 30 (30%)
Response
provides a
summary of the
healthcare
program that is
vague and
inaccurate,
does not
include costs or
projected
outcomes of
the program, or
is missing.

Description of
the target
population is
vague and
inaccurate, or is
missing.

Explanation of
the role of the
nurse in
providing input
for the design
of the program,
and speci�c
examples is
vague and
inaccurate, or is
missing.

Description of
the role of the
nurse advocate
for the target

Name: NURS_6050_Module04_Week08_Assignment_Rubric

EXIT

Grid View List View

1/8/2021 Rubric Detail – Blackboard Learn

https://class.waldenu.edu/webapps/bbgs-deep-links-BBLEARN/app/course/rubric?course_id=_16767090_1&rubric_id=_2031043_1 2/5

Excellent Good Fair Poor
for the design of
this healthcare
program? Can you
provide
examples?

· What is your
role as an
advocate for your
target population
for this
healthcare
program? Do you
have input into
design decisions?
How else do you
impact design?

Response
provides an
accurate and
detailed
description of
the role of the

nurse advocate
for the target
population for
the healthcare
program
selected.

Response
provides an
accurate and
detailed
explanation of
how the
advocate’s role
in�uences
design decisions
as well as fully
explaining
impacts to
program design.

nurse advocate
for the target
population for
the healthcare
program
selected.

Response
provides an
accurate
explanation of
how the
advocate’s role
in�uences
design decisions
and somewhat
explains
impacts to
program design.

the healthcare
program
selected is
vague or
inaccurate.

Explanation of
how the
advocate’s role
in�uences
design
decisions and
impacts to
program design
is vague or
inaccurate.

population for
the healthcare
program
selected is
vague and
inaccurate, or is
missing.

Explanation of
how the
advocate’s role
in�uences
design
decisions and
impacts to
program design
is vague and
inaccurate, or is
missing.

Program
Implementation
· What is the role
of the nurse in
healthcare
program
implementation?
How does this
role vary between
design and
implementation
of healthcare
programs? Can
you provide
examples?

· Who are the
members of a
healthcare team
that you believe
are most needed
to implement a
program? Can you
explain why you
think this?

36 (36%) – 40
(40%)

Response
provides a clear,
accurate, and
complete
explanation of
the role of the
nurse in
healthcare
program
implementation.

Response
provides an
accurate and
detailed
explanation of
how the role of
the nurse is
di�erent
between design
and
implementation
of healthcare
programs,

32 (32%) – 35
(35%)

Response
provides an
accurate
explanation of
the role of the
nurse in
healthcare
program
implementation.

Response
provides an
accurate
explanation of
how the role of
the nurse is
di�erent
between design
and
implementation
of healthcare
programs, and
may include
some speci�c

28 (28%) – 31
(31%)

Explanation of
the role of the
nurse in
healthcare
program
implementation
is vague,
inaccurate,
and/or
incomplete.

Explanation of
how the role of
the nurse is
di�erent
between design
and
implementation
of healthcare
programs is
vague or
inaccurate
and/or does
not include

0 (0%) – 27 (27%)
Explanation of
the role of the
nurse in
healthcare
program
implementation
is vague and
inaccurate, or is
missing.

Explanation of
how the role of
the nurse is
di�erent
between design
and
implementation
of healthcare
programs is
vague and
inaccurate, or is
missing.

Description of
the members of

1/8/2021 Rubric Detail – Blackboard Learn

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Excellent Good Fair Poor
including
speci�c
examples.

Response
provides an
accurate and
detailed
description of
the members of
a healthcare
team needed to
implement the
program
selected.

The response
fully integrates
at least 2
outside
resources and
2-3 course
speci�c
resources that
fully supports
the summary
provided.

examples.

Response
provides and
accurate
description of
the members of
a healthcare
team needed to
implement the
program
selected.

The response
integrates at
least 1 outside
resource and 2-
3 course speci�c
resources that
may support the
summary
provided.

speci�c
examples.

Description of
the members of
a healthcare
team needed to
implement the
program
selected is
inaccurate or
incomplete.

The response
minimally
integrates
resources that
may support
the summary
provided.

a healthcare
team needed to
implement the
program
selected is
vague and
inaccurate,
incomplete, or
is missing.

The response
fails to
integrate any
resources to
support the
summary
provided.

1/8/2021 Rubric Detail – Blackboard Learn

https://class.waldenu.edu/webapps/bbgs-deep-links-BBLEARN/app/course/rubric?course_id=_16767090_1&rubric_id=_2031043_1 4/5

Excellent Good Fair Poor

Written
Expression and
Formatting –
Paragraph
Development
and
Organization:

Paragraphs
make clear
points that
support well
developed ideas,
�ow logically,
and demonstrate
continuity of
ideas. Sentences
are carefully
focused–neither
long and
rambling nor
short and lacking
substance. A
clear and
comprehensive
purpose
statement and
introduction is
provided which
delineates all
required criteria.

5 (5%) – 5 (5%)
Paragraphs and
sentences
follow writing
standards for
�ow, continuity,
and clarity.

A clear and
comprehensive
purpose
statement,
introduction,
and conclusion
is provided
which
delineates all
required
criteria.

4 (4%) – 4 (4%)
Paragraphs and
sentences
follow writing
standards for
�ow, continuity,
and clarity 80%
of the time.

Purpose,
introduction,
and conclusion
of the
assignment is
stated, yet is
brief and not
descriptive.

3.5 (3.5%) – 3.5
(3.5%)

Paragraphs and
sentences
follow writing
standards for
�ow, continuity,
and clarity 60%-
79% of the
time.

Purpose,
introduction,
and conclusion
of the
assignment is
vague or o�
topic.

0 (0%) – 3 (3%)
Paragraphs and
sentences
follow writing
standards for
�ow, continuity,
and clarity < 60% of the time.

No purpose
statement,
introduction, or
conclusion was
provided.

Written
Expression and
Formatting –
English writing
standards:

Correct
grammar,
mechanics, and
proper
punctuation

5 (5%) – 5 (5%)
Uses correct
grammar,
spelling, and
punctuation
with no errors.

4 (4%) – 4 (4%)
Contains a few
(1-2) grammar,
spelling, and
punctuation
errors.

3.5 (3.5%) – 3.5
(3.5%)

Contains
several (3-4)
grammar,
spelling, and
punctuation
errors.

0 (0%) – 3 (3%)
Contains many
(≥ 5) grammar,
spelling, and
punctuation
errors that
interfere with
the reader’s
understanding.

1/8/2021 Rubric Detail – Blackboard Learn

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Excellent Good Fair Poor

Written
Expression and
Formatting – The
paper follows
correct APA
format for title
page, headings,
font,
spacing, margins,
indentations,
page numbers,
parenthetical/in-
text citations,
and reference
list.

5 (5%) – 5 (5%)
Uses correct
APA format with
no errors.

4 (4%) – 4 (4%)
Contains a few
(1-2) APA format
errors.

3.5 (3.5%) – 3.5
(3.5%)

Contains
several (3-4)
APA format
errors.

0 (0%) – 3 (3%)
Contains many
(≥ 5) APA
format errors.

Total Points: 100

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PERSONAL VIEW

No big data without small data: learning health care
systems begin and end with the individual patient
José A. Sacristán MD PhD1 and Tatiana Dilla PharmD2

1Medical Director, 2Head of Health Outcomes Research, Medical Department, Lilly, Madrid, Spain

Introduction
We live in the era of big data. Data volume doubles every 2 years
and it has been estimated that every 2 days, more data are gener-
ated than were produced in human history up to 2003. The devel-
opment of technology and new analytical capabilities, which allow
the handling of large data volumes from different sources, have
generated high expectations regarding the potential of big data for
understanding the world and aiding in decision making [1]. Tech-
nological development is so rapid that it is difficult to imagine all
the applications that may result from the analyses of the data that
are generated globally every day.

A broad consensus exists concerning the vast possibilities of big
data in research and in the optimization of medical care, improving
their quality and reducing their cost [2,3]. However, big data
applications in the health sector lag behind those of other areas of
knowledge, such as the physical sciences, economics, businesses
or social networks, where data mining techniques are giving rise to
unprecedented qualitative changes [4].

Variability is the essence of biomedical sciences. In medicine,
the heterogeneity of individuals calls for personalized decisions to
benefit individual patients. Theoretically, the potential of big data
in the field of health may be limited by increasingly personalized
medicine. This paper analyses the potential barriers that may
impede the development of big data in medicine and research and
proposes ways of moving forward to generate a ‘learning health
care system’ that aims to improve health outcomes for current and
future patients in an efficient manner.

Barriers that may slow the
development of big data in research
and medicine
The main limitations of big data in clinical research and in medical
care are well known and are related to methodological, technologic
and legal factors. Among the methodological barriers, the low
quality of data (incomplete data, lack of standardization) and the
existence of an analytical methodology that remains insufficiently
developed are most prominent [5,6]. The biases inherent to the
analyses conducted on databases (often used for administrative and
billing purposes) have been widely described [7]. Obvious technical
and analytical difficulties exist in managing a very large volume of
data that is constantly changing and that resides in different reposi-
tories, along with frequent linkage and interoperability issues. A
significant part of the data is ‘noise’, which presents challenges
when the noise grows faster than the signal. Different databases
with different degrees of quality and completeness generate hetero-

geneous results [8,9], which may increase the risk of ‘biased
fact-finding excursions’ and false discoveries [5]. Finally, restric-
tions in access to databases and privacy problems are generating
growing concern among experts [10].

However, although the barriers mentioned earlier are important,
a problem of even greater significance is hindering the application
of big data in medicine. In the era of personalized medicine, the
real challenge of big data is how to use large-scale population-
based analyses to benefit individual patients. This situation reflects
the classical conflict between the objectives of clinical research
and those of medical care. The purpose of clinical research is to
generate generalizable knowledge that is useful for future patients,
whereas medical care aims to promote the well-being of individual
patients. Whereas clinical research seeks generalization, medical
care seeks personalization.

The previous conflict is exhibited in the two most important
movements that have emerged in health systems in the last
decades: Evidence-based medicine and patient-centred medicine.
Evidence-based medicine has its conceptual anchor in research,
valuing evidence that results from experimental methods such as
the randomized clinical trial (RCT), particularly when such trials
involve large sample sizes. The primary objective of evidence-
based medicine is generalization and development of clinical
guidelines and the standardization of medical care. In contrast,
patient-centred medicine has its conceptual anchor in medical
care; therefore, its reference is the individual patient, a patient
whose personal beliefs, objectives and preferences are, essentially,
unique and non-transferable [11].

The doctor–patient encounter as the
link between clinical research and
medical care
The worlds of populations and individuals must necessarily con-
verge in the path that separates evidence-based medicine and
patient-centred medicine. Every doctor–patient encounter repre-
sents the connecting link between the population and the individ-
ual, between clinical research and medical care [2]. The
progressive implementation of electronic medical records (EMRs)
may help to gradually blur the borders between research and care,
contributing to the creation of a true ‘rapid-learning health system’
[12] in which each medical act has the double objective of gener-
ating and applying clinically relevant medical knowledge. This
approach should produce benefits for present and future patients as
follows: (1) the data generated in each medical act should not only
be used to the benefit of that individual patient but also to generate
knowledge potentially useful for future patients and (2) all of the

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Journal of Evaluation in Clinical Practice ISSN 1365-2753

Journal of Evaluation in Clinical Practice 21 (2015) 1014–1017 © 2015 The Authors. Journal of Evaluation in Clinical Practice Published by John Wiley & Sons, Ltd.1014
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knowledge generated through big data analyses should be applied
to improve clinical decision making and to produce benefits for
individual patients. Neither of these two goals is achieved at
present: most of the information generated in each medical act is
lost and it requires an estimated average of 17 years for only 14%
of new discoveries to enter into daily clinical practice [13]. In
summary, the individual and the population and the small data and
the big data are the two sides of the same coin and EMRs are the
intersection between small data (individual data, relatively easy to
handle, used by the doctor in his/her consultation to personalize
medical care) and big data (Fig. 1).

Figure 2 presents the ‘circle of knowledge’ and describes how
clinical research and medical care begin and end with the individ-
ual patient. Each doctor–patient encounter generates data (small
data), which are collected in the EMR. The sum of millions of
small data gives rise to big data, which should be analysed and
translated into information. The information is only useful if it is

translated into knowledge and knowledge is only useful if it is used
to improve the health of individual patients. One of the main
reasons why big data have not fulfilled its full potential in health
care is that small data are not adequately systematized to generate
useful knowledge for future patients (research) and that big data
are not used to improve health outcomes for individual patients
(care). In the following sections, proposals are offered to attempt
to solve these challenges as follows: (1) how to use small data to
generate knowledge and (2) how to use big data to benefit indi-
vidual patients.

How to use ‘small data’ to generate
knowledge
Although low data quality and biases because of the absence of
randomization are two of the main limitations of observational
research, most efforts to increase the value of big data are oriented
towards analysing all of the existing data through the linkage of
different databases. Interestingly, the obsession of modern science
for measurement (and biomedicine is no exception) has contrib-
uted to the transformation of tools into goals, promoting the idea
that everything that can be measured must be measured and iden-
tifying quantity with quality and big data with ‘reliable data’.
Because massive amounts of data and very powerful technology
exist, we have fallen into ‘data-ism’ [14].

Initiatives to analyse non-structured data (e.g. the analysis of
free text through natural language processing and pattern recogni-
tion) are more than welcome, although they are insufficient. The
value of big data will be greatly limited if the data are not based on
high-quality individual clinical data and structured formats. For
that reason, it appears reasonable to redefine the present priorities,
devoting greater efforts towards generating standardized and com-
plete data that include both quantitative clinical variables as well
as qualitative impressions of the doctors and the preferences and
important variables of the patients [15].

The lack of a control group and the absence of randomization are
other important limitations of observational data, particularly when
the objective is to assess the effectiveness of health interventions
and to predict outcomes [6]. One way of overcoming these limita-
tions is to integrate investigational efforts into clinical practice to
conduct point of care research. For example, patients treated under
conditions of typical clinical practice who met the specific pre-
determined selection criteria might be automatically identified to
participate in randomized registry trials. The idea of conducting
‘randomized database studies’ combining the advantages of initial
randomization (minimization of biases) and the advantages of the
follow-up of patients treated under routine clinical practice was
described for the first time in 1998 [16]. This proposal, which has
been recently been considered as the next revolution in clinical
research [17], has been successfully implemented in recent years
[18,19] because of the technical developments of EMRs that allow
the embedding of trials into regular medical practice. The United
States National Institutes of Health is designing and conducting
several pragmatic trials that exploit routinely collected data, includ-
ing patient-reported outcomes, to quickly demonstrate effective-
ness in real-world care delivery systems [2]. Another way to
integrate experiments into clinical practice would be to conduct
N-of-1 trials. This form of research has only been rarely applied

Figure 1 Learning health care system. Each medical act is the intersec-
tion between the small and big data.

Figure 2 The circle of medical knowledge begins and ends with the
individual patient.

J.A. Sacristán and T. Dilla No big data without small data

despite its clear advantages in benefitting the individual patients
who participate in these studies [20].

The integration of experiments in daily practice requires impor-
tant regulatory and cultural changes oriented towards decreasing
the level of regulatory oversight and adapting the ethical require-
ments to the risk for the patients. Recently, prominent bioethicists
have suggested that informed consent documents could be sub-
stantially simplified (or even suppressed) in the case of pragmatic
trials that assess established interventions for which there are
minimal incremental risks and burdens compared with usual clini-
cal care (e.g. a comparative effectiveness study that compares two
standard-of-care interventions) [21,22]. In these ‘low risk’ condi-
tions, the informed consent document could be similar to the
simple consent document used in clinical practice, as the main
distinguishing feature of these trials is that they replace clinical
uncertainty with randomization [23].

To eliminate the cultural barriers that exist between clinical
research and medical care, it may be useful to realize that all
research begins at the patient’s bedside and that every medical act
is structured similar to an experiment. The increasing use of EMRs
might contribute to the elimination of walls between doctors who
conduct research and those who do not, between patients who
participate in RCTs and the ‘real’ patients who doctors see every
day and between the clinical research form used in RCTs and the
electronic medical history. In all likelihood, the real challenge to
embedding research into daily clinical practice is not the technical
infrastructure to implement randomized database studies but the
understanding that, in the context of learning health care systems,
a clear distinction between research and care should not exist [24].

How to use big data to benefit
individual patients
Among all of the activities that have successfully applied the
analyses of big data, ‘the key has been to go beyond aggregate data
and link information to individual people’ [25]. In health care, big
data analyses have not been systematically transformed into ben-
efits for individual patients. This is likely the main reason why the
use of big data in medicine is relatively delayed compared with
other fields of knowledge. Once new knowledge has been gener-
ated, it is essential to apply it to aid doctors in their daily practice
in an individualized and rapid manner. EMRs will become much
more valuable tools for doctors if they are designed for use in
personalized medical care.

There are several potential ways of applying the knowledge
resulting from big data at the individual level. Perhaps, the most
obvious method is the use of decision-aid systems that help
doctors in the diagnosis and treatment of their patients. Many of
the present tools are linked to evidence-based guidelines and rec-
ommendations. However, very often, these guidelines are based on
the results of large RCTs and meta-analyses containing informa-
tion that, in theory, is applicable to ‘average patients’. However,
doctors do not treat average patients; thus, this ‘generalizable
knowledge’ that may be useful to standardize medical practice is
not the most appropriate to treat individual patients. Currently, the
real challenge is to develop more ‘personalized guidelines’ that
take into account the heterogeneity of patients and aid doctors in
individualizing their clinical decisions [26]. Fortunately, new
guidelines increasingly include tailored recommendations for sub-

groups of patients and examples are evident wherein the prefer-
ences and values of individual patients are the key drivers for the
recommendation [27].

EMRs could also help doctors identify ‘anomalies’ or unexpec-
ted results, test hypotheses and identify possible areas of interven-
tion [25]. For example, predictive analytics may identify situations
in which a given patient exhibits a high risk of complications or
may detect the existence of characteristics that could predict a
certain behaviour (e.g. risk of low compliance to treatment, high
risk of adverse events or readmission) [28]. Patient support pro-
grammes could be linked to EMRs to help doctors handle particu-
larly complex situations and optimize medical care.

EMRs may also be used to engage patients, facilitating shared
decision-making processes [29] and more active participation of
patients in clinical research. For example, EMRs could be linked
to ‘patient decision aids’ designed to help patients better under-
stand the benefits and risks of different alternatives and aid them in
reflecting on the pros and cons of the different options [30]. With
respect to clinical research, EMRs could include information about
the selection criteria of ongoing clinical trials and information on
the participant centres so that doctors might offer patients the
possibility of being candidates for such trials. Finally, EMRs could
contribute to adapting the level of information and regulatory
oversight to the individual characteristics and cultural level of each
patient. For example, the informed consent document could be
adapted to both the level of risk of the study and the literacy of
each patient. In the same way, the system could provide tailored
information on the results of the study at a level of complexity
adapted to the needs of the patient.

Summary
The apparent contradiction between the population focus of big
data and the practice of personalized medicine contributes to the
relatively scarce and slow applications of big data in medicine
compared with other areas of knowledge. The technologic devel-
opment and the implementation of EMRs may give rise to a
learning health care system in which every doctor–patient encoun-
ter becomes the connecting link between the population and the
individual.

To generate valuable knowledge, big data must come from
high-quality individual clinical data. There are no big data without
small data. EMRs may be used to integrate research into medical
care, thereby conducting point of care research (e.g. randomized
database studies). However, big data will not achieve its full
potential if it is not used to improve health outcomes for the
individual patients from whom the data were generated.

EMRs should aid doctors in personalizing medical care and
contribute towards the engagement of patients in research and
care. The continuous interaction between the individual patient
and the population, between clinical research and medical care,
between the world of big data and that of small data is an essential
step towards achieving a true learning health care system.

Conflict of interest
José A. Sacristán and Tatiana Dilla are employees of Lilly. Any
views or opinions presented in this manuscript are solely those of
the authors and do not necessarily represent those of Lilly.

No big data without small data J.A. Sacristán and T. Dilla

Author contributions
José A. Sacristán developed the concept and design of this manu-
script and drafted article. Tatiana Dilla collaborated in the acqui-
sition of information for the manuscript. José A. Sacristán and
Tatiana Dilla both participated in the critical revision of the article
and its final approval.

References
1. Murdoch, T. B. & Detsky, A. S. (2013) The inevitable application of

big data to health care. Journal of the American Medical Association,
13, 1351–1352.

2. Larson, E. B. (2013) Building trust in the power of ‘big data’ research
to serve the public good. Journal of the American Medical Associa-
tion, 309, 2443–2444.

3. Roski, J., Bo-Linn, G. W. & Andrews, T. A. (2014) Creating value in
health care through big data: opportunities and policy implications.
Health Affairs, 33, 1115–1122.

4. Davenport, T. H. & Harris, J. G. (2007) Competing on Analytics: The
New Science of Winning. Boston, MA: Harvard Business School
Press.

5. Wang, W. & Krishnan, E. (2014) Big data and clinicians: a review on
the state of the science. Journal of Medical Internet Research Medical
Informatics, 2, 1–11.

6. Schneeweiss, S. (2014) Learning from big health care data. The New
England Journal of Medicine, 370, 2161–2163.

7. Schneeweiss, S. & Avorn, J. (2005) A review of uses of health care
utilization databases for epidemiologic research on therapeutics.
Journal of Clinical Epidemiology, 58, 323–337.

8. Madigan, D., Ryan, P. B., Schuemie, M., Stang, P. E., Overhage, J. M.,
Hartzema, A. G., Suchard, M. A., DuMouchel, W. & Berlin, J. A.
(2013) Evaluating the impact of database heterogeneity on observa-
tional study results. American Journal of Epidemiology, 178, 645–
651.

9. Psaty, B. M. & Breckenridge, A. M. (2014) Mini-Sentinel and regu-
latory science. Big data rendered fit and functional. The New England
Journal of Medicine, 370 (23), 2165–2167.

10. Ross, J. S. & Krumholz, H. M. (2013) Ushering in a new era of open
science through data sharing. The wall must come down. Journal of
the American Medical Association, 309, 1355–1356.

11. Sacristan, J. A. (2013) Evidence based medicine and patient centered
medicine: some thoughts on their integration. Revista Clínica
Española, 213, 460–464.

12. Etheredge, M. L. (2007) A rapid-learning health system. Health
Affairs, 26, w107–w118.

13. Balas, E. A. & Boren, S. A. (2000) Yearbook of Medical Informatics:
Managing Clinical Knowledge for Health Care Improvement. Stutt-
gart, Germany: Schattauer Verlagsgesellscaft mbH.

14. Brooks, D. (2013) The philosophy of data. New York Times, 4 Feb.
15. Black, N. (2013) Patient reported outcome measures could help trans-

form healthcare. British Medical Journal, 346, f167.

16. Sacristan, J. A., Soto, J., Galende, I. & Hylan, T. R. (1998)
Randomized database studies: a new method to assess drugs’ effec-
tiveness? Journal of Clinical Epidemiology, 51, 713–715.

17. Lauer, M. S. & D’Agostino, R. B. (2013) The randomized registry
trial. The next disruptive technology in clinical research? The New
England Journal of Medicine, 369 (17), 1579–1581.

18. Staa, T. P., Goldacre, B., Gulliford, M., Cassell, J., Pirmohamed, M.,
Taweel, A., Delaney, B. & Smeeth, L. (2013) Pragmatic randomised
trials using routine electronic health records: putting them to the test.
British Medical Journal, 344, e55.

19. van Staa, T. P., Dyson, L., McCann, G., et al. (2014) The opportunities
and challenges of pragmatic point-of-care randomised trials using
routinely collected electronic records: evaluations of two exemplar
trials. Health Technology Assessment, 18, 1–146.

20. Duan, N., Kravitz, R. L. & Schmid, C. H. (2013) Single-patient
(n-of-1) trials: a pragmatic clinical decision methodology for patient-
centered comparative effectiveness research. Journal of Clinical Epi-
demiology, 66 (8 Suppl.), S21–S28.

21. Kass, N., Faden, R. & Tunis, S. (2012) Addressing low-risk compara-
tive effectiveness research in proposed changes to US federal regula-
tions governing research. Journal of the American Medical
Association, 307, 1589–1590.

22. Faden, R. R., Kass, N. E., Goodman, S. N., Pronovost, P., Tunis, S. &
Beauchamp, T. L. (2013) An ethics framework for a learning health
care system: a departure from traditional research ethics and clinical
ethics. Hastings Center Report, 43, S16–S27.

23. Faden, R. R., Beauchamp, T. L. & Kass, N. E. (2014) Informed
consent, comparative effectiveness, and learning health care. The New
England Journal of Medicine, 370, 766–768.

24. Sacristan, J. A. (2013) Patient-centered medicine and patient-oriented
research: improving health outcomes for individual patients. BMC
Medical Informatics and Decision Making, 13, 6.

25. Weber, G. M., Mandl, K. D. & Kohane, I. S. (2014) Finding the
missing link for big biomedical data. Journal of the American Medical
Association, 24, 2479–2480.

26. Montori, V. M., Brito, J. P. & Murad, M. H. (2013) The optimal
practice of evidence based medicine. Incorporating patient preferences
in practice guidelines. Journal of the American Medical Association,
310, 2503–2504.

27. Hayes, J. H. & Barry, M. J. (2014) Screening for prostate cancer
with the prostate-specific antigen test. A review of current evi-
dence. Journal of the American Medical Association, 311, 1143–
1149.

28. Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A. & Escobar, G.
(2014) Big data in health care: using analytics to identify and manage
high-risk and high-cost patients. Health Affairs, 33, 1123–1131.

29. Lipkin, M. (2013) Shared decision making. Journal of the American
Medical Association Internal Medicine, 173, 1204–1205.

30. Hoffmann, T. C., Legare, F., Simmons, M. B., McNamara, K.,
McCaffery, K., Trevena, L. J., Hudson, B., Glasziou, P. P. & Del Mar,
C. B. (2014) Shared decision making: what do clinicians need to know
and why should they bother? Medical Journal of Australia, 201,
35–39.

J.A. Sacristán and T. Dilla No big data without small data

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POLICY
IMPLEMENTATION,
STREET-LEVEL
BUREAUCRACY, AND
THE IMPORTANCE
OF DISCRETION

Lars Tummers and Victor Bekkers

Lars Tummers
Department of Public Administration
Erasmus University Rotterdam
P.O. Box 1738, NL-3000 DR Rotterdam
The Netherlands
E-mail: Tummers@fsw.eur.nl

Victor Bekkers
Department of Public Administration
Erasmus University Rotterdam
P.O. Box 1738, NL-3000 DR Rotterdam
The Netherlands
E-mail: Bekkers@fsw.eur.nl

Abstract

Street-level bureaucrats implementing public
policies have a certain degree of autonomy –
or discretion – in their work. Following
Lipsky, discretion has received wide atten-
tion in the policy implementation literature.
However, scholars have not developed theo-
retical frameworks regarding the effects of
discretion, which were then tested using
large samples. This study therefore develops
a theoretical framework regarding two main
effects of discretion: client meaningfulness
and willingness to implement. The relation-
ships are tested using a survey among 1,300
health care professionals implementing a
new policy. The results underscore the
importance of discretion. Implications of the
findings and a future research agenda is
shown.

Key words
Discretion, public policy, policy implementa-
tion, street-level bureaucracy, quantitative
analysis

Public Management Review, 2014

Vol. 16, No. 4, 527–547, http://dx.doi.org/10.1080/14719037.2013.841978

INTRODUCTION

In his book Street-level bureaucracy: Dilemmas of the individual in public services, Michael
Lipsky (1980) analysed the behaviour of front-line staff in policy delivery agencies.
Lipsky refers to these front-line workers as ‘street-level bureaucrats’. These are public
employees who interact directly with citizens and have substantial discretion in the
execution of their work (1980, p. 3). Examples are teachers, police officers, general
practitioners, and social workers.
These street-level bureaucrats implement public policies. However, street-level

bureaucrats have to respond to citizens with only a limited amount of information or
time to make a decision. Moreover, very often the rules the street-level bureaucrats
have to follow do not correspond to the specific situation of the involved citizen. In
response, street-level bureaucrats develop coping mechanisms. They can do that
because they have a certain degree of discretion – or autonomy – in their work
(Lipsky 1980, p. 14). Following the work of Lipsky, the concept of discretion has
received wide attention in the policy implementation literature (Brodkin 1997; Buffat
2011; Hill and Hupe 2009; Sandfort 2000; Tummers et al. 2009; Vinzant et al. 1998).
However, scholars have not yet developed theoretical frameworks regarding the

effects of discretion, which were subsequently tested using large-scale quantitative
approaches (Hill and Hupe 2009; O’Toole 2000). This study aims to fill this gap by
developing a theoretical framework regarding two effects of discretion.
The first effect, which is often noted, is that a certain amount of discretion can

increase the meaningfulness of a policy for clients (Palumbo et al. 1984). An example
can clarify this. A teacher could adapt the teaching method to the particular circum-
stances of the pupil, such as his/her problems with long-term reading, but ease when
discussing the material in groups. The teacher could devote more attention to the
pupil’s reading difficulties, thereby providing a more balanced development. More
generally, it is argued that when street-level bureaucrats have a certain degree of
discretion, this will make the policy more meaningful for the clients. Client mean-
ingfulness can thus be considered a potential effect of discretion. Here, we note that
client meaningfulness is highly related to concepts such as client utility or usefulness.
Furthermore, it can be argued that providing street-level bureaucrats discretion

increases their willingness to implement the policy (Meyers and Vorsanger 2003;
Sandfort 2000). Tummers (2011) showed this effect while studying ‘policy alienation’,
a new concept for understanding the problems of street-level bureaucrats with new
policies. One mechanism underlying this relationship between discretion and willingness
to implement seems to be that a certain amount of discretion increases the (perceived)
meaningfulness for clients, which in turn enhances their willingness to implement this
policy (Hill and Hupe 2009; Lipsky 1980). This is expected as street-level bureaucrats
want to make a difference to their clients’ lives when implementing a policy (Maynard-
Moody and Musheno 2000). Hence, when street-level bureaucrats perceive that they

528 Public Management Review

have discretion, they feel that they are better able to help clients (more perceived client
meaningfulness), which in turn increases their willingness to implement the policy. This
is known as a mediation effect. This effect is often implicitly argued, and has yet to be
studied empirically.
Based on this rationale the central research question is: To what extent does discretion

influence client meaningfulness and willingness to implement public policies, and does client
meaningfulness mediate the discretion-willingness relationship?
This brings us to the outline of this article. We will first develop a theoretical

framework, outlining the relationships between discretion, client meaningfulness, and
willingness to implement. The ‘Methods’ section describes the operationalization of the
concepts and research design, which is based on a Dutch nationwide survey among
1,300 psychologists, psychiatrists, and psychotherapists implementing a new reimbur-
sement policy. The ‘Results’ section shows descriptive statistics and discusses the
hypotheses. We conclude by discussing the contribution of this article to policy
implementation literature with a particular emphasis on the importance of discretion
of street-level bureaucrats.

THEORETICAL FRAMEWORK

Background on discretion

This article focuses on the discretion of street-level bureaucrats during policy imple-
mentation. Due to the abundance of literature and the intrinsic difficulties with the
discretion concept (such as the different interpretations attached to as well as criticisms
of these interpretations), we will provide only a short overview of the term discretion
(for elaborate overviews, see Evans (2010), Hill and Hupe (2009), Lipsky (1980),
Maynard-Moody and Portillo (2010), Meyers and Vorsanger (2003), Saetren (2005),
and Winter (2007)). For a recent critique on discretion, see Maynard-Moody and
Musheno (2012).
Evans (2010) has noted that for employees, discretion can be seen as the extent of

freedom he or she can exercise in a specific context. Related to this, Davis (1969, p. 4)
states ‘a public officer has discretion whenever the effective limits on his power leave
him free to make a choice among possible courses of action or inaction’ (see also
Vinzant et al. 1998). Lipsky (1980) focuses more specifically on the discretion of street-
level bureaucrats. He views discretion as the freedom that street-level bureaucrats have
in determining the sort, quantity and quality of sanctions, and rewards during policy
implementation (see also Hill and Hupe 2009; Tummers 2012). We then define
discretion as the perceived freedom of street-level bureaucrats in making choices
concerning the sort, quantity, and quality of sanctions, and rewards on offer when
implementing a policy; for instance, to what extent do policemen experience that they
themselves decide whether to give an on-the-spot fine? To what extent do teachers feel

Tummers & Bekkers: Policy implementation and discretion 529

they can decide what and how to teach students about the development of mankind, i.e.
evolution or creationism (Berkman and Plutzer 2010)?
As can be seen from the previous paragraph, we focus on experienced discretion.

This is based on Lewin’s (1936) notion that people behave on the basis of their
perceptions of reality, not on the basis of reality itself (Thomas Theorem). Street-
level bureaucrats may experience different levels of discretion within the same policy
because, for example, (a) they possess more knowledge on loopholes in the rules, (b)
their organization operationalized the policy somewhat differently, (c) they have a
better relationship with their manager which enables them to adjust the policy to
circumstances, or (d) the personality of the street-level bureaucrat is more rule-
following or rebellious (Brehm and Hamilton 1996; Prottas 1979).
In both top-down and bottom-up approaches of policy implementation, the notion of

discretion is important (DeLeon and DeLeon 2002; Hill and Hupe 2009). From a top-
down perspective, discretion is often not welcomed (Davis 1969; Polsky 1993).
Discretion is primarily seen as a possibility that street-level bureaucrats use to pursue
their own, private goals. This can influence the policy programme to be implemented
in a negative way, which undermines the effectiveness and democratic legitimacy of a
programme (Brehm and Gates 1999). In order to deal with this issue, control
mechanisms are often put in place in order to achieve compliance.
In the bottom-up perspective, discretion is assessed differently. Discretion is seen as

inevitable in order to deploy general rules, regulations, and norms in specific situations,
which helps to improve the effectiveness of policy programmes and the democratic
support for the programme. Moreover, given the limited time, money, and other
resources available and the large number of rules, regulations, and norms that have to
be implemented, it is important that street-level bureaucrats are able to prioritize what
rules to apply, given the specific circumstances in which they operate in (Brodkin 1997;
Maynard-Moody and Musheno 2000; Maynard-Moody and Portillo 2010).
From a top-down and bottom-up perspective it can be argued that discretion has a

different meaning for citizens as a client. In the top-down perspective, discretion could
possibly harm the position of a citizen because private considerations and interpretations
of the goals of the policy programme by the street-level bureaucrat prevent citizens
being treated equally. In the bottom-up perspective, discretion will help to strengthen
the value/meaningfulness of a policy for clients, as policy programmes can be targeted
to their specific situation. Hence, from a bottom-up perspective discretion might
increase the client meaningfulness, that is, the value of the policy for clients (Barrick
et al. 2012; Brodkin 1997; May et al. 2004; Maynard-Moody and Musheno 2003;
Tummers 2011). Client meaningfulness can be defined as the perception of street-level
bureaucrats that their implementing a policy has value for their own clients. Client
meaningfulness is therefore about the perception of the street-level bureaucrat that a policy is
valuable for a client (the client may not feel the same way). For instance, a social
worker might feel that when he/she implements a policy focused on getting clients
back to work, this indeed helps the client to get employed and improves the quality of

530 Public Management Review

life for this client. Granting street-level bureaucrats discretion during policy implemen-
tation can increase client meaningfulness as several situations street-level bureaucrats
face are too complicated to be reduced to programmatic formats. Discretion makes it
possible to adapt the policy to meet the local needs of the citizens/clients, increasing
the meaningfulness of the policy to clients.
It seems that discretion could also positively affect the street-level bureaucrats’

willingness to implement the policy. Willingness to implement is defined as a positive
behavioural intention of the street-level bureaucrat towards the implementation of the
policy (Ajzen 1991; Metselaar 1997). Hence, the street-level bureaucrat aims to put
effort in implementing this policy: he/she tries to make it work. Policy implementation
literature, especially the studies rooted in the bottom-up perspective, suggests that an
important factor in this willingness of street-level bureaucrats is the extent to which
organizations are willing and able to delegate decision-making authority to the front line
(Meier and O’Toole 2002). This influence may be particularly pronounced in profes-
sionals whose expectations of discretion and autonomy contradict notions of bureau-
cratic control (Freidson 2001).
To conclude, it seems that discretion can have various effects. In this article, we

specifically examine two possible positive effects of discretion: enhanced client mean-
ingfulness for clients and more willingness to implement the policy. These effects are chosen
given their dominant role in the policy implementation debate (Ewalt and Jennings
2004; Riccucci 2005; Simon 1987; Tummers et al. 2012).

The effects of discretion on client meaningfulness and willingness to
implement

Given the arguments stated previously, we first expect that when street-level bureau-
crats experience high discretion, this positively influences their perception of client
meaningfulness. Sandfort (2000) illustrates this by describing a case in United States
public welfare system (Work First contractors). Regardless of the specifics of the local
office, street-level bureaucrats are given the same resources to carry out their tasks:
standardized forms, policy manuals, complex computer programmes, etc. Such struc-
tures cause the street-level bureaucrats to be isolated from other professionals and
unable to adapt existing practices to altering demands. Hence, it reduces their discre-
tion and this could result in less client meaningfulness. We will study this same process
using a quantitative approach, bringing us to the first hypothesis.

H1: When street-level bureaucrats experience more discretion, this positively influences their
experienced client meaningfulness of the policy

Next, we expect that when street-level bureaucrats feel that they have enough discre-
tion, this positively influences their willingness to implement a policy. Maynard-Moody

Tummers & Bekkers: Policy implementation and discretion 531

and Portillo (2010, p. 259) note, ‘Street-level workers rely on their discretion to manage
the physical and emotional demands of their jobs. They also rely on their discretion to
claim some small successes and redeem some satisfaction’. Examining this more generally,
the mechanism linking discretion to willingness to implement can be traced back to the
human relations movement (McGregor 1960). One of the central tenets of this movement
is that employees have a right to give input into decisions that affect their lives. Employees
enjoy carrying out decisions they have helped create. As such, the human relations
movement argues that when employees experience discretion during their work, this
will positively influence several job indicators by fulfilling intrinsic employee needs. Next
to this, self-determination theory (Deci and Ryan 2004) argues that three psychological
needs must be fulfilled to foster motivation: competence, relatedness, and autonomy. In
short, they argue that when people perceive to have autonomy, they aremoremotivated to
perform.

H2: When street-level bureaucrats experience more discretion, this positively and directly
influences their willingness to implement the policy

Furthermore, we expect that when street-level bureaucrats experience more discre-
tion, this positively influences their client meaningfulness, which in turn positively
influences their willingness to implement a policy. Hence, client meaningfulness could
influence the willingness to implement a policy. This is expected as street-level bureau-
crats want to make a difference to their clients’ lives when implementing a policy. May
and Winter (2009) found that if the front-line workers perceive the instruments at their
disposal for implementing a policy as ineffective, in terms of delivering to clients, this is
likely to add to their frustrations. They do not see how their implementation of the policy
helps their clients, so wonder why they should implement it.
Technically speaking, we expect a mediation effect to occur (Zhao et al. 2010).

Mediation is the effect of an independent variable (here, discretion) on a dependent
variable (willingness to implement) via a mediator variable (client meaningfulness).
Hence, besides hypothesizing the direct effect of discretion on willingness to imple-
ment, we expect that part of this effect is caused by increasing client meaningfulness.
This can be considered a partially mediated effect: part of the effect of discretion on
willingness to implement is mediated by client meaningfulness. Full mediation is not
expected. Some of the influence of discretion on willingness to implement is explained
by factors other than increasing client meaningfulness, i.e. peoples’ intrinsic need for
autonomy in their work (Wagner 1994).

H3: The positive influence of discretion on willingness to implement is partially mediated by
the level of client meaningfulness

This mediation effect can be related to established job design theories like the job
characteristics model of Hackman and Oldham (1980). Hackman and Oldham noted

532 Public Management Review

that autonomy (related to discretion) is one of the core job characteristics, enhancing
experienced responsibility for outcomes. This influences the critical psychological
states, such as experienced meaningfulness of work (related to client meaningfulness).
In turn, experienced meaningfulness of work fosters individual and organizational
outcomes, such as high internal motivation (related to willingness to implement).
Hence, important similarities between their line of reasoning and ours can be found.
An important difference is that we focus on the level of policy implementation instead
of the general job level.
Based on these three hypotheses, a theoretical framework is constructed as shown in

Figure 1.

METHODS

Case

To test the theoretical framework, we undertook a survey of Dutch mental health care
professionals implementing a new reimbursement policy (Diagnosis Related Groups).
First, a short overview of this policy is provided.
In January 2008, the Dutch government introduced Diagnosis Related Groups

(DRGs, DiagnoseBehandelingCombinaties (in Dutch), or DBC’s) in mental health
care. The DRGs are part of the new Law Health Market Organization. The DRGs can
be seen as the introduction of regulated competition into the Dutch health care market, a
move in line with new public management (NPM) ideas. More specifically, it can be seen
as a shift to greater competition and more efficient use of resource (Hood 1991, p. 5).
The system of DRGs was developed as a means of determining the level of financial

exchange for mental health care provision. The DRG-policy differs significantly from
the former method in which each medical action resulted in a financial claim. This
meant that the more sessions a professional caregiver (a psychologist, psychiatrist or
psychotherapist) had with a patient, the more recompense could be claimed. This
former system was considered inefficient by some (Kimberly et al. 2009). The DRG-
policy changed the situation by stipulating a standard rate for each disorder. For

Client
meaningfulness

Discretion

Willingness to

implement
+

Figure 1: Proposed theoretical framework regarding two main effects of discretion

Tummers & Bekkers: Policy implementation and discretion 533

instance, for a mild depression, the mental health care professional gets a standard rate
and can treat the patient (direct and indirect time) between 250 and 800 min.
The DRG-policy these professionals have to implement is related more to service

management than to service delivery. However, this policy does have effects on service
delivery. Professionals have to work in a more ‘evidence-based’ way and are required
to account for their cost declarations in terms of the mental health DSM (Diagnostic
Statistical Manual) classification system. As a result, it becomes harder to use practices
that are difficult to standardize and evaluate, such as psychodynamic treatments.
Discretion regarding the length of treatment is arguably also increasingly limited.
Whereas, in the former system, each medical action resulted in a payment (this was
not the case under the DRG-policy). Under the DRG-policy, a standard rate is
determined for each disorder, meaning it has become more difficult to adjust the
treatment to the specific patient needs. Hence, the number of treatments for a patient
is often limited due to the DRG-policy, thereby changing service delivery. It is
interesting to study how much discretion street-level bureaucrats really experienced
during implementing this policy, and what effects this has.
We noted that we focus on experienced discretion. Even within the same policy, some

street-level bureaucrats will perceive more discretion than others. Indeed, in the open
answers of the survey we witnessed that some respondents felt that they had substantial
discretion when implementing this policy, while others felt very limited. Illustrative quotes
from different respondents are (all from open answers in the survey, which is reported next):

The DRG-policy does not force me into a certain choices. I examine the funding scheme of the treatment

only ‘in second instance’.

I do my work first and foremost according to professional standards and hereafter just attach a DRG-label

which I think fits but best.

With the DRG-policy, I am being forced into a straitjacket.

You are bound by the rules. So that’s a harness.

Sampling and response

Our sampling frame comprised of 5,199 professionals who were members of two nationwide
mental health care associations (the Dutch Association of Psychologists (Nederlands Instituut
van Psychologen (NIP)) and the Netherlands Association for Psychiatry (Nederlandse
Vereniging voor Psychiatrie (NVvP)). They were all members of those associations which
could, in principle, be working with the DRG-policy. Using an email and two reminders, we
received 1,317 answers of our questionnaire, i.e. a 25 per cent response.
Our sampling frame comprised of high-status professionals: psychiatrists, psycholo-

gists, and psychotherapists. Most research analysing discretion focuses on traditional
street-level bureaucrats, such as welfare workers and police officers (Maynard-Moody

534 Public Management Review

and Portillo 2010). However, these mental health care professionals are a specific group
of highly trained professionals who traditionally, due to their professional training, have
substantial autonomy. Furthermore, they also have to implement governmental policies
(in this case, DRGs). Hence, it seems worthwhile to analyse such professional groups
using the theoretical lens of street-level bureaucracy (see also Hupe and Hill 2007).
Of the valid respondents, 36 per cent were men and 64 per cent were women which

is consistent with Dutch averages for mental health care professionals, where 69 per
cent of the workforce are female (Palm et al. 2008). The respondents’ ages ranged
from 23 to 91 years (M = 48), which is slightly older then the Dutch national average
for mental health care professionals (M = 44). Hence, respondents’ mean age and
gender distribution are quite similar to those of the overall mental health care sector.
To rule out a possible non-response bias, we conducted non-response research where
we contacted the non-responders for their reasons for non-participation. Common
reasons for not participating were: lack of time, retirement, change of occupation, or
not working with the DRG-policy. Some organizations, including some hospitals, were
not yet working with this policy. The large number of respondents, their characteristics
in terms of gender and age, and the results of the non-response research indicate that
our respondents are quite a good representation of the population.

Measures

This section reports the measurement of the variables. Unless stated otherwise, the
measures were formatted using five-point Likert scales, ranging from strongly agree to
strongly disagree. For the items tapping discretion, client meaningfulness and will-
ingness to implement, we used templates. Templates allow the researcher to specify an
item by replacing general phrases with more specific ones that better fit the research
context (DeVellis 2003). For example, instead of stating ‘the policy’ or ‘professionals’,
the researcher can rephrase these items using the specific policy and group of profes-
sionals being examined. Here, ‘the DRG-policy’ and ‘health care professionals’
replaced the template terms. Items are therefore easier for professionals to understand,
since items are better tailored to their context and this, in turn, increases reliability and
content validity (DeVellis 2003, p. 62). All items are shown in Appendix 1.

Discretion
Discretion concerns the perceived freedom of the implementer in terms of the type,
quantity and quality of sanctions, and rewards delivered (Lipsky 1980). The scale is
based on the validated measurement instrument of policy alienation, specifically the
dimension ‘operational powerlessness’ (Tummers 2012). Three items were used based
on confirmatory factor analysis (CFA; see section ‘Results’). Cronbach’s alpha = 0.78.

Tummers & Bekkers: Policy implementation and discretion 535

Client meaningfulness

Client meaningfulness (or meaninglessness) was also conceptualized as a dimension of
policy alienation (Tummers 2012). It refers to the perception of professionals about the
benefits of implementing the DRG-policy for their own clients. For instance, do they
perceive that they are really helping their patients by implementing this policy? Three
items were used based on CFA. Cronbach’s alpha = 0.77.

Willingness to implement

Willingness to implement was measured using Metselaar’s (1997) four-item scale. All
items were used based on CFA. Cronbach’s alpha = 0.83.

Control variables
Commonly used individual characteristics were included: gender, age, and management
position (yes/no). We also distinguish between psychiatrists and others, because the
former belong to a medical profession. Psychologists and psychotherapists are non-
medical professionals, which possibly influenced their perceptions.

Statistical method

We used CFA followed by structural equation modelling (SEM). The CFA and SEM
techniques are often used in psychology research, but quite new to most public
administration scholars (see for instance Wright et al. 2012). We therefore discuss a
number of the analyses’ characteristics in detail.
CFA is a technique used to test the factor structure of latent constructs based on

theory and prior research experience. This is appropriate in our case given that prior
analyses have already explored the variables discretion, client meaningfulness, and
willingness to implement. It has several advantages over exploratory factor analysis,
such as more stringent psychometric criteria for accepting models, thereby improving
validity and reliability (Brown 2006).
Using CFA, a measurement model is specified. The measurement model specifies the

number of factors and shows how the indicators (items) relate to the various factors
(Brown 2006, p. 51). Hence, it shows for instance how the items asked to measure
discretion relate to the latent construct of discretion. This measurement model is a
precursor for the SEM analysis. In the SEM analysis, a structural model is constructed
showing how the various latent factors relate to each other. For instance, it shows how
discretion is related to willingness to implement. In this analysis, a complete model can
be tested where variables can be both dependent and independent. This is an advantage
over regression analyses. Given that we hypothesize that client meaningfulness is both
dependent (influenced by discretion) and independent (influencing willingness to

536 Public Management Review

implement), this was appropriate for our model. For mediation models, as is our
model, SEM is preferred over regression analysis (Zhao et al. 2010).
The latent variable programme Mplus was used for the analyses (Muthén and Muthén,

1998–2010). Mplus (http://www.statmodel.com/) is suited for handling non-normally
distributed data, which is often the case when employing surveys. As our data were
(mildly) non-normally distributed, this was an advantage. Robust maximum likelihood
was used, which works well in these circumstances (Brown 2006, p. 379).

Measurement model

Before analysing the structural model (see section ‘Results’), the measurement model is
analysed.
Based on the analyses for the measurement model, some modifications were made to

improve the model. The only modifications were to delete a number of items for the
latent factors: three for discretion, one for client meaningfulness, and one for willingness
to implement. This was based on theoretical grounds, fit of item content with definition
of concept/latent factor, and the minimization of the Akaike information criterion (AIC).
This fit index can be used to compare competing models. As suggested we selected the
model with the lowest AIC, thereby taking into account theoretical plausibility (Schreiber
et al. 2006). More specifics about the measurement model are described in Appendix 2.

RESULTS

Descriptive statistics

Table 1 shows the means and variances/covariances for all items used. A number of
interesting results can be seen. First, many street-level bureaucrats are psychiatrists and
these often occupy management positions. Next, the means for discretion are quite
low, showing that the street-level bureaucrats do not feel that they have a lot of
autonomy in this policy. We also found low scores for willingness to implement and
even lower scores for client meaningfulness. Hence, in general, the street-level bureau-
crats were quite negative about this policy. The covariances for the items linked via our
hypotheses are in the anticipated direction. For example, items regarding willingness to
implement are positively related to discretion.

Structural model

The structural equation model is shown in Figure 2. Table 2 shows the results,
including control variables. First, an effect of discretion on client meaningfulness was

Tummers & Bekkers: Policy implementation and discretion 537

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538 Public Management Review

found (standardized coefficient 0.33, p < 0.01). Hence, we do not reject Hypothesis 1. Second, the empirical tests show a cascading effect from discretion to willingness to implement through the mediating variable client meaningfulness. The effect (standar- dized coefficient) of discretion on client meaningfulness was 0.33 (p < 0.01), while the effect from client meaningfulness on willingness to implement was

0.49

(p < 0.01). The total indirect effect was therefore 0.16 (0.33*0.49, p < 0.01). Based on this, we do not reject Hypothesis 3. Furthermore, the direct effect was also significant (β = 0.27, p < 0.01), thus Hypothesis 2 is not rejected. The total effect of discretion on willingness to implement is the sum of its direct and indirect effects:

Table 2: Results from structural equation modelling

Model

Meaningfulness for
clients (standardized

scores)

Meaningfulness
for clients

(unstandardized
scores)

Willingness to
implement

(standardized scores)

Willingness to
implement
(unstandardized
scores)

Control variables
Gender NS NS NS NS
Age −0.092 −0.006 NS NS
Managing position NS NS 0.144 0.212
Psychiatrist NS NS NS NS

Direct influences
Discretion 0.330 0.334 0.278 0.302
Meaningfulness for

clients
– – 0.491 0.527

Indirect influence
Discretion via

meaningfulness
for clients

– – 0.162 0.176

R2 0.135 – 0.446 –

Notes: NS = Not significant. All shown scores are significant at p < 0.01.

Discretion 0.33
Client

meaningfulness
(R2 = 0.14)

Willingness to
implement
(R2 = 0.45)

0.49

0.28

Figure 2: Structural equation model for relationships between discretion, client meaningfulness, and
willingness to implement (control variables not shown)

Tummers & Bekkers: Policy implementation and discretion 539

0.27 + 0.16 = 0.43. This means that – all other things being equal – when the
perceived discretion of the street-level bureaucrat increases by 1, the willingness to
implement increases by 0.43. As there is both a direct and an indirect significant
effect, there is evidence of partial mediation which was also hypothesized. This
(partially mediated) model proved to be a good fit of the data: root mean square
error of approximation (RMSEA) = 0.04 (criterion ≤ 0.08), comparative fit index
(CFI) = 0.97 (criterion ≥ 0.90), Tucker–Lewis index (TLI) = 0.96 (criterion ≥ 0.90).
To shed more light on the mediating mechanisms, we conducted additional SEM

analyses to test the validity of two alternative models: a model without mediation and a
model with full mediation. The model without mediation did not fit as adequately as
the partially mediated model, given that the AIC was higher compared to the partially
mediated model, and the fit indexes showed a worse fit. The fully mediated model also
had a higher AIC, and worse scores on RMSEA, CFI, and TLI than the partially
mediated model, although differences were small.
We used bootstrapping to test the indirect effect of discretion on willingness to

implement via client meaningfulness. It is the preferred method for testing mediated
effects (Preacher and Hayes 2004; Zhao et al. 2010). It presents estimates and
confidence intervals so that we can test the significance of the mediation effect. The
99 per cent confidence interval for the standardized indirect effect (which was 0.16) is
between 0.11 and 0.22, meaning the indirect effect is not equal to 0 (p < 0.01).1

Hence, a mediation effect is clearly present here. In the discussion and conclusion, we
discuss the implications of these results for both theory and practice.

CONCLUSION

The central goal of this article is to understand the mechanisms at work between
discretion, client meaningfulness, and willingness to implement. Based on a literature
review, a theoretical model was constructed linking discretion, client meaningfulness
and willingness to implement. This model was tested in a survey among 1,317 mental
health care professionals implementing a new policy. The model worked adequately in
that discretion, together with conventional control variables, indeed partly explained
client meaningfulness (R2 = 14 per cent). Furthermore, willingness to implement was
indeed explained by discretion, client meaningfulness, and the control variables (R2 =
45 per cent). Fit criteria were very good for the measurement model and the structural
model, thereby strengthening the reliability and validity of the study. As such, we can
conclude that the approach worked satisfactorily and adds to the literature on street-
level bureaucracy. Having reached this conclusion, we can summarize the results,
highlight limitations, and develop a future research agenda on discretion.
We found that the discretion of street-level bureaucrats influences the willingness to

implement in two ways. First, discretion influences client meaningfulness because
street-level bureaucrats are more able to tailor their decisions and the procedures

540 Public Management Review

they have to follow to the specific situations and needs of their clients. Hence,
discretion gives street-level bureaucrats the possibility to apply their own judgements
when dealing with the needs and wishes of citizens. Our results strengthen the claim
made by several authors that discretion could indeed have positive effects for clients
(Handler 1990; May and Winter 2009).
At the same time, the positive effect that discretion has on the bureaucrat’s perception

of client meaningfulness can be seen as a condition for the second effect: more willingness
to implement the policy. When street-level bureaucrats perceive that their work is
meaningful for their clients, this strongly influences their willingness to implement it.
This is in line with the notion that street-level bureaucrats want to make a difference to
their clients’ lives (Maynard-Moody and Musheno 2003). Furthermore, the results also
point to another, more autonomous, effect that discretion directly influences willingness
to implement; hence, discretion is inherently valued by bureaucrats.
The results have interesting implications for the theory and practice of policy

implementation. From a theoretical point of view, it contributes to the long-lasting
discussion about the validity of a more top-down or bottom-up perspective on policy
implementation. Discretion indeed seems to have a positive effect on the effectiveness
of policy programmes, as it reduces resistance. At the same time it adds to the
legitimacy of the policy implementation process, because it enables street-level bureau-
crats to meet the needs and wishes of citizens (in the eyes of the street-level bureau-
crats). These implications of the findings are strengthened by the large-scale
quantitative analysis and sophisticated techniques. The arguments that are put forward
in the bottom-up perspective on the positive role that discretion plays in the effective-
ness and democratic legitimacy of public policy programmes are being confirmed.
For practitioners, it is important to note that when drafting policy programme it can

be beneficial to give the implementing street-level bureaucrats some (perceived) free-
dom to adjust the policy programme in order to be effective and legitimate. This has
also important consequences for the role of performance and risk management in the
implementation of these programmes. The central role that detailed performance
indicators and risk reduction rules play in the implementation process often leads to
a broad variety of detailed norms and guidelines that the street-level bureaucrats
involved must obey (Power 1997).
Next to this, the results show that client meaningfulness, in itself, proved to be very

important, something which is not often mentioned in the street-level bureaucracy
literature or in more general management literature, which focuses often on influence,
autonomy, and discretion (Green 2008; McGregor 1960; Sowa and Selden 2003;
Spence Laschinger et al. 2001). For instance, Judson (1991) argues that providing
employees with influence is the most powerful lever in gaining acceptance for a change.
However, given the results of this study, we urge practitioners and scholars to also
consider the perceived meaningfulness of the policy for clients, rather than to restrict
their focus on discretion and influence aspects.

Tummers & Bekkers: Policy implementation and discretion 541

This brings us to the limitations and future research suggestions. First, the results
found could be dependent on this research context. This study addresses high status
professionals: psychologists, psychiatrists, and psychotherapists. Furthermore, the spe-
cific policy context (DRG-policy, focused on cost-cutting and transparency) could
influence the results. It would be interesting to conduct studies using the same
theoretical model which focus on other groups of street-level bureaucrats who have
other types of professional training or who are a part of government service bureau-
cracy. Related to this, an interesting venue for research would be to analyse cases which
are more directly related to service delivery and less to service management. Here,
stronger effects of discretion on client meaningfulness could be found. Furthermore, it
would be worthwhile to analyse the developed model in a situation where there was in
general high discretion, client meaninglessness and willingness to implement, contrary
to the case analysed. Are the effects of discretion and client meaningfulness also
important in such rather different policy contexts?
Second, further research could use multiple sources to measure the indicators, and

measure new effects of discretion. It would be worthwhile to measure client mean-
ingfulness by asking the clients themselves. Furthermore, other indicators could be
linked to discretion, such as objective indicators such as the percentage of people
getting a job when implementing re-integration policies. Does granting street-level
bureaucrats discretion in such a policy heighten the ‘success’ of such a policy? Linked to
this, we should note that we have looked at only two possible positive effects of
discretion. We have largely ignored its negative side, such as discrimination of clients or
the ways discretion can break public trust (Sandfort 2000).
Third, future research could investigate other factors influencing client meaningful-

ness and willingness to implement, including other control variables. Scholars could,
for instance, examine the influence of organizational factors such as the level of trust
between professionals and management, incentive systems which promote or stymie
implementing a policy or the way the policy has been implemented (top-down,
bottom-up) within an organization. Next to this, personality characteristics could be
taken into account, such as optimism, self-efficacy beliefs, and locus of control.
To conclude, this study provides important insights that help to understand the

effects of granting street-level bureaucrats discretion in their work. It underscores the
importance of studying discretion. Embracing and further researching this should prove
to be a timely and productive endeavour for both researchers and practitioners alike.

ACKNOWLEDGEMENT

The authors would like to thank the anonymous reviewers for their insightful com-
ments on earlier versions of this article.

542 Public Management Review

NOTE
1 Bootstrap 5,000 times, maximum likelihood estimation is used as robust maximum likelihood is not available

for bootstrapping.

REFERENCES
Ajzen, I. (1991) The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes. 50:2

pp179–211.
Barrick, M., Mount, M. and Li, N. (2012) The Theory of Purposeful Work Behavior: The Role of Personality,

Job Characteristics, and Experienced Meaningfulness. Academy of Management Review, 38:1 pp132–53.
Berkman, M. and Plutzer, E. (2010) Evolution, Creationism, and the Battle to Control America’s Classrooms,

Cambridge: Cambridge University Press.
Brehm, J. and Gates, S. (1999) Working, Shirking, and Sabotage: Bureaucratic Response to a Democratic Public, Ann

Arbor, MI: University of Michigan Press.
Brehm, J. and Hamilton, J. T. (1996) Noncompliance in Environmental Reporting: Are Violators Ignorant, or

Evasive, of the Law? American Journal of Political Science, 40:2 pp444–77.
Brodkin, E. Z. (1997) Inside the Welfare Contract: Discretion and Accountability in State Welfare

Administration. The Social Service Review, 71:1 pp1–33.
Brown, T. A. (2006) Confirmatory Factor Analysis for Applied Research, London: The Guilford Press.
Buffat, A. (2011) Pouvoir Discrétionnaire Et Redevabilité De La Bureaucratie De Guichet: Les Taxateurs D’une Caisse De

Chômage Comme Acteurs De Mise En Oeuvre, Lausanne: Université de Lausanne.
Davis, K. C. (1969) Discretionary Justice: A Preliminary Inquiry, Baton Rouge, LA: Louisiana State University Press.
Deci, E. L. and Ryan, R. M. (2004) Handbook of Self-Determination Research, Rochester, NY: University of

Rochester Press.
DeLeon, P. and DeLeon, L. (2002) What Ever Happened to Policy Implementation? An Alternative Approach.

Journal of Public Administration Research and Theory, 12:4 p467.
DeVellis, R. F. (2003) Scale Development: Theory and Applications, Thousand Oaks, CA: Sage.
Evans, T. (2010) Professional Discretion in Welfare Services: Beyond Street-Level Bureaucracy, London: Ashgate.
Ewalt, J. A. G. and Jennings, E. T. (2004) Administration, Governance, and Policy Tools in Welfare Policy

Implementation. Public Administration Review, 64:4 pp449–62.
Freidson, E. (2001) Professionalism: The Third Logic, Cambridge: Cambridge University Press.
Green, F. (2008) ‘Work Effort and Worker Well-Being in the Age of Affluence’ in R. Burke and C. L. Cooper

(eds) Effects of Working Hours and Work Addiction: Strategies for Dealing with Them, pp115–36. London:
Elsevier.

Hackman, J. R. and Oldham, G. R. (1980) Work Redesign, Reading, MA: Addison Wesley.
Handler, J. F. (1990) Law and the Search for Community, Philadelphia, PA: University of Pennsylvania Press.
Hill, M. and Hupe, P. (2009) Implementing Public Policy, 2nd ed. Thousand Oaks, CA: Sage.
Hood, C. (1991) A Public Management for All Seasons. Public Administration, 19:1 pp3–19.
Hooper, D., Coughlan, J. and Mullen, M. (2008) Structural Equation Modelling: Guidelines for Determining

Model Fit. Electronic Journal of Business Research Methods, 6:1 pp53–60.
Hu, L. and Bentler, P. M. (1999) Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional

Criteria Versus New Alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6:1 pp1–55.
Hupe, P. and Hill, M. (2007) Street-Level Bureaucracy and Public Accountability. Public Administration, 85:2

pp279–99.
Judson, A. S. (1991) Changing Behavior in Organization: Minimizing Resistance to Change, Cambridge, MA: Basil

Blackwell.

Tummers & Bekkers: Policy implementation and discretion 543

Kimberly, J. R., De Pouvourville, G. and Thomas, A. D. A. (2009) The Globalization of Managerial Innovation in
Health Care, Cambridge, MA: Cambridge University Press.

Kline, R. B. (2010) Principles and Practice of Structural Equation Modeling, London: The Guilford Press.
Lance, C. E., Dawson, B., Birkelbach, D. and Hoffman, B. J. (2010) Method Effects, Measurement Error, and

Substantive Conclusions. Organizational Research Methods, 13:3 pp435–55.
Lewin, K. (1936) Principles of Topological Psychology, New York: McGraw-Hill.
Lipsky, M. (1980) Street-Level Bureaucracy, New York: Russell Sage Foundation.
May, D. R., Gilson, R. L. and Harter, L. M. (2004) The Psychological Conditions of Meaningfulness, Safety and

Availability and the Engagement of the Human Spirit at Work. Journal of Occupational and Organizational
Psychology, 77:1 pp11–37.

May, P. J. and Winter, S. C. (2009) Politicians, Managers, and Street-Level Bureaucrats: Influences on Policy
Implementation. Journal of Public Administration Research and Theory, 19:3 p453.

Maynard-Moody, S. and Musheno, M. (2000) State Agent or Citizen Agent: Two Narratives of Discretion.
Journal of Public Administration Research and Theory, 10:2 p329.

Maynard‐Moody, S. and Musheno, M. (2012) Social Equities and Inequities in Practice: Street‐Level Workers as
Agents and Pragmatists. Public Administration Review, 72:s1 pp16–23.

Maynard-Moody, S. and Musheno, M. C. (2003) Cops, Teachers, Counselors: Stories From the Front Lines of Public
Service, Ann Arbor, MI: University of Michigan Press.

Maynard-Moody, S. and Portillo, S. (2010) ‘Street-Level Bureaucracy Theory’ in R. Durant (ed) Oxford Handbook
of American Bureaucracy, pp. 252–77. Oxford: Oxford University Press.

McGregor, D. (1960) The Human Side of Enterprise, New York: Wiley.
Meier, K. J. and O’Toole, L. J. (2002) Public Management and Organizational Performance: The Effect of

Managerial Quality. Journal of Policy Analysis and Management, 21:4 pp629–43.
Metselaar, E. E. (1997) Assessing the willingness to change: Construction and validation of the DINAMO.

Doctoral dissertation, Free University of Amsterdam.
Meyers, M. K. and Vorsanger, S. (2003) ‘Street-Level Bureaucrats and the Implementation of Public Policy’ in

B. Guy Peters and J. Pierre (eds) Handbook of Public Administration, pp245–54. London: Sage.
Muthén, L. and Muthén, B. (1998–2010) Mplus User’s Guide, 6th ed. Los Angeles, CA: Muthén & Muthén.
O’Toole, L. J. (2000) Research on Policy Implementation: Assessment and Prospects. Journal of Public

Administration Research and Theory, 10:2 pp263–88.
Palm, I., Leffers, F., Emons, T., Van Egmond, V. and Zeegers, S. (2008) De GGz Ontwricht: Een Praktijkonderzoek

Naar De Gevolgen Van Het Nieuwe Zorgstelsel in De Geestelijke Gezondheidszorg, Den Haag: SP.
Palumbo, D. J., Maynard-Moody, S. and Wright, P. (1984) Measuring Degrees of Successful Implementation.

Evaluation Review, 8:1 pp45–74.
Podsakoff, P. M. and Organ, D. W. (1986) Self-Reports in Organizational Research: Problems and Prospects.

Journal of Management, 12:4 pp531–44.
Polsky, A. J. (1993) The Rise of the Therapeutic State, Princeton, NJ: Princeton University Press.
Power, M. (1997) The Audit Society: Rituals of Verification, Oxford: Oxford University Press.
Preacher, K. J. and Hayes, A. F. (2004) SPSS and SAS Procedures for Estimating Indirect Effects in Simple

Mediation Models. Behavior Research Methods, 36:4 pp717–31.
Prottas, J. M. (1979) People Processing: The Street-Level Bureaucrat in Public Service Bureaucracies, Lexington, MA:

Lexington Books.
Riccucci, N. M. (2005) How Management Matters: Street-Level Bureaucrats and Welfare Reform, Georgetown:

Georgetown University Press.
Saetren, H. (2005) Facts and Myths About Research on Public Policy Implementation: Out‐of‐Fashion, Allegedly

Dead, but Still Very Much Alive and Relevant. Policy Studies Journal, 33:4 pp559–82.

544 Public Management Review

Sandfort, J. R. (2000) Moving Beyond Discretion and Outcomes: Examining Public Management From the Front
Lines of the Welfare System. Journal of Public Administration Research and Theory, 10:4 pp729–56.

Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A. and King, J. (2006) Reporting Structural Equation Modeling
and Confirmatory Factor Analysis Results: A Review. The Journal of Educational Research, 99:6 pp323–38.

Simon, W. H. (1987) Ethical Discretion in Lawyering. Harvard Law Review, 101:6 pp1083.
Sowa, J. E. and Selden, S. C. (2003) Administrative Discretion and Active Representation: An Expansion of the

Theory of Representative Bureaucracy. Public Administration Review, 63:6 pp700–10.
Spence Laschinger, H. K., Finegan, J. and Shamian, J. (2001) The Impact of Workplace Empowerment,

Organizational Trust on Staff Nurses’ Work Satisfaction and Organizational Commitment. Health Care
Management Review, 26:3 p7.

Tummers, L. G. (2011) Explaining the Willingness of Public Professionals to Implement New Policies: A Policy
Alienation Framework. International Review of Administrative Sciences, 77:3 pp555–81.

Tummers, L. G. (2012) Policy Alienation of Public Professionals: The Construct and Its Measurement. Public
Administration Review, 72:4 pp516–25.

Tummers, L. G., Bekkers, V. J. J. M. and Steijn, A. J. (2009) Policy Alienation of Public Professionals:
Application in a New Public Management Context. Public Management Review, 11:5 pp685–706.

Tummers, L. G., Steijn, A. J. and Bekkers, V. J. J. M. (2012) Explaining Willingness of Public Professionals to
Implement Public Policies: Content, Context, and Personality Characteristics. Public Administration, 90:3
pp716–36.

Van de Schoot, R., Lugtig, P. and Hox, J. (2012) A Checklist for Testing Measurement Invariance. European
Journal of Developmental Psychology, 9:4 pp37–41.

Vinzant, J. C., Denhardt, J. V. and Crothers, L. (1998) Street-Level Leadership: Discretion and Legitimacy in Front-
Line Public Service, Washington, DC: Georgetown University Press.

Wagner III, J. A. (1994) Participation’s Effects on Performance and Satisfaction: A Reconsideration of Research
Evidence. Academy of Management Review, 19:2 pp312–30.

Winter, S. C. (2007) ‘Implementation Perspectives, Status and Reconsideration’ in B. Guy Peters and J. Pierre
(eds) The Handbook of Public Administration, Concise Paperback Edition, pp131–141. New York: Sage.

Wright, B. E., Moynihan, D. P. and Pandey, S. K. (2012) Pulling the Levers: Transformational Leadership,
Public Service Motivation, and Mission Valence. Public Administration Review, 72:2 pp206–15.

Zhao, X., Lynch, J. G. and Chen, Q. (2010) Reconsidering Baron and Kenny: Myths and Truths About
Mediation Analysis. Journal of Consumer Research, 37:2 pp197–206.

APPENDIX 1

Items used for the scales

As indicated in the main text, we used templates to specify the policy. Templates allow
the researcher to specify an item by replacing general phrases with more specific ones
that better fit the research context. Template words are underlined. The templates are
in this case:

Policy: DRG-policy
Clients: Patients
Professionals: Health care professionals
Organization: Institution

Tummers & Bekkers: Policy implementation and discretion 545

Note: Item 4–5 (client meaningfulness) and Item 1–3 (discretion) are not used in the
final model as they negatively influenced fit indices in the CFA.

Client meaningfulness

1 The policy is harmful for my clients’ privacy
2 With the policy I can better solve the problems of my clients
3 The policy is contributing to the welfare of my clients
4 Because of the policy, I can help clients more efficiently than before
5 I think that the policy is ultimately favourable for my clients

Discretion

1 I have freedom to decide how to use the policy
2 While working with the policy, I can be in keeping with the client’s needs
3 Working with the policy feels like a harness in which I cannot easily move
4 When I work with the policy, I have to adhere to tight procedures
5 While working with the policy, I cannot sufficiently tailor it to the needs of my

clients
6 While working with the policy, I can make my own judgements

Willingness to implement

1 I intend to try to convince employees of the benefits the policy will bring
2 I intend to put effort into achieving the goals of the policy
3 I intend to reduce resistance among employees regarding the policy
4 I intend to make time to implement the policy

APPENDIX 2

Measurement model
This Appendix describes some additional reliability and validity checks on the measure-
ment model. Several authors suggest reporting RMSEA, TLI and CFI statistics when
describing model fit (Schreiber et al. 2006; Van de Schoot et al. 2012). The RMSEA –
a widely recommended fit index which tests the absolute fit of the model – was 0.048.
This indicates good fit as Hu and Bentler (1999) suggest that values ≤0.06 indicate good
fit (≤0.08 average fit). The Tucker–Lewis index (TLI) is a comparative fit index that
compares the fit of the model with the baseline model. The TLI here was 0.98, which is
considered excellent (≥0.90, better ≥0.95). The comparative fit index was 0.98 in our

546 Public Management Review

final model showing good fit (≥0.90, better ≥0.95). Note that – based on the
recommendations of Hooper et al. (2008) – we have not used correlated error terms.
In the final model, each item loaded significantly onto its appropriate latent variable.

For instance, an item tapping discretion loaded onto the variable discretion. The values
of the standardized factor loadings were all relatively high (minimum 0.51, maximum
0.91, average 0.75). This shows evidence of convergent validity: items that tap the
same latent construct are related to each other (Kline 2010).
We should also discuss the possibility of common method variance. Self-reported

data based on a single application of a questionnaire can result in inflated relationships
between variables due to common method variance, i.e. variance that is due to the
measurement method rather than the constructs themselves (Podsakoff and Organ
1986). Although a recent study showed that ‘in contrast to conventional wisdom,
common method effects do not appear to be so large as to pose a serious threat to
organizational research’ (Lance et al. 2010, p. 450), we conducted a test to analyse
whether common method bias was a major concern. We compared the three-factor
structure (discretion, client meaningfulness, and willingness to implement) with a one-
factor model. The fit indices show that the one-factor model had a much poorer fit than
the three-factor model. The AIC was higher, and the RMSEA (0.16), CFI (0.58) and
TLI (0.54) indicated much poorer fit. Hence, common method variance does not seem
to be a major problem in this study.

Tummers & Bekkers: Policy implementation and discretion 547

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National Center for Injury Prevention and Control

This brief discusses the implementation of Step 3 of the CDC evaluation
Framework as it applies to the second of the three main phases of policy
evaluation: policy implementation evaluation.

Purposes of Policy Implementation Evaluation
Policy implementation evaluation can have multiple aims or purposes,
including:

Understanding how a policy was implemented. ƒ

Identifying critical differences between planned and actual ƒ
implementation.

Identifying barriers to and facilitators of implementation. ƒ

Documenting and comparing different intensities or variations of policy. ƒ

Collecting information to support interpretation of future evaluations of policy impact. ƒ

Documenting the relationships between logic model components and external influences. ƒ

Improving the implementation process. ƒ

Informing future policy development. ƒ

Policy implementation evaluation may focus on a number of different areas, including

Components of the logic model, such as inputs, activities and outputs. ƒ

Stakeholder attitudes, knowledge, and awareness. ƒ

Facilitators of and barriers to implementation. ƒ

Sample Implementation Evaluation Questions
Once the purpose and focus of the evaluation are determined, specific evaluation questions should be
identified. The following are some sample policy implementation evaluation questions. Identifying the core
components of implementation can be challenging, but doing so can be essential to focusing the evaluation.
The evaluation questions selected will guide the selection of an appropriate evaluation design.

Did the policy clearly identify the critical implementation steps? ƒ

Was the policy implemented according to the policy requirements? ƒ

What inputs and resources were required to implement the policy? Were all of these inputs and resources ƒ
available?

What key activities were completed during policy implementation? ƒ

Brief 4: Evaluating Policy Implementation
Step by Step – Evaluating Violence and Injury Prevention Policies

Step by Step – Evaluating Violence and Injury Prevention Policies2

Did the activities result in the anticipated outputs? ƒ

Was the policy implemented consistently across ƒ
communities or environments?

Were there any unintended consequences? ƒ

What external factors influenced the implementation? ƒ

Evaluation Design Considerations

Describing Implementation1

Implementation evaluation often relies on non-experimental
descriptive or exploratory designs such as case studies
and cross-sectional designs.2 The focus of the design is on
accurately describing the implementation process rather
than on “proving” any specific hypothesis or demonstrating
relations between variables. The evaluation design may
also include exploration of differences in implementation
in different contexts or for different variations of the policy.
Identifying the core components of implementation can be challenging, but this step is vital when developing

the evaluation questions and measures. Components
may be identified by describing the policy (Brief 2),
conducting a policy content evaluation (Brief 3), or both.

Policy Implementation Data

Qualitative and process evaluation data are useful in
evaluating policy implementation, because each can
provide detailed information about how a policy was
implemented or provide insight as to why certain
things happened during implementation. Data for
implementation evaluation is usually intentionally
descriptive and uses a variety of measures and
types of data to complete a thorough picture of the
implementation.3

1 Blais E., & Gagne M. P. (2010). The effect on collisions with injuries of a reduction in traffic citations issued by police officers. Injury Prevention,
16(6), 393-397.

2 CDC, Office of the Associate Director for Policy. (2012). Draft working paper: Policy definition and development framework.
3 Her Majesty’s Treasury. (2011). The magenta book: Guidance for evaluation. London, UK: Author. Retrieved from http://www.hm-treasury.gov.

uk/data_magentabook_index.htm

The Effects of Implementation on Impact

To demonstrate the importance of measuring
the influence of implementation on the impact
of a policy, Blais and Gagne examined whether
the number of traffic citations issued by police
officers (enforcement of traffic laws) had
an impact on the number of collisions. The
evaluation used natural changes in enforcement
over time (due to police union negotiations)
and used neighboring communities as a
comparison group. The evaluation found
a 61% reduction in the monthly volume of
traffic citations, which was associated with
an increase in collisions involving injuries.1 If
the implementation of the policy (levels of
enforcement) had not been included in the
evaluation, assessing its overall impact would
have been difficult.

Policy Implementation Evaluation:
Was the Policy Implemented as
Intended?

Policy implementation evaluation
examines the inputs, activities, and
outputs involved in the implementation
of a policy. It can also provide important
information about stakeholder
perceptions and awareness, as well
as barriers to and facilitators of
implementation. The relation of policy
implementation evaluation to policy
development phases is illustrated in
Figure 1.

Step by Step – Evaluating Violence and Injury Prevention Policies3

Policy Implementation Indicators

Policy implementation indicators often measure
activities or accomplishments that are part of the policy
implementation. Examples could include:

Number of organizations with written policies. ƒ

Adjudication. ƒ

Number of citations issued. ƒ

Effectiveness of training materials. ƒ

Awareness of policy. ƒ

Survey of compliance with core components. ƒ

Comparing Implementation in Different Settings

If the key components of the policy or levels of its
implementation varies across settings, evaluators can
make comparisons between implementing jurisdictions.
In these cases, a cross-sectional design or multiple case
studies may be indicated. Measuring key contextual
differences between the jurisdictions is important to
interpreting results accurately. This information can be
valuable in comparing the relative effectiveness of the
various components.

Measuring Degree of Implementation

Depending on previous research and evaluation results,
specific criteria or standards may be established to
assess implementation.1 The standards should be
established by the stakeholders and should cover
required inputs, activities, and outputs. Each of the
standards should have a corresponding indicator
that will allow it to be measured. Comparing actual
implementation to established standards can
clarify discrepancies between planned and actual
implementation, identify which components or
features of implementation are barriers or catalysts for
implementation, linked to policy impacts, or allow for
comparisons between different levels and components
of evaluation.

Identifying Implementation Challenges

The Southern California Injury Prevention
Research Center conducted an assessment
of the implementation of the CDC-funded
Urban Networks to Increase Thriving Youth
(UNITY) through Violence Prevention in
several U.S. cities. Using surveillance data
and 5-year average annual rates of homicide,
suicide, and firearm deaths, evaluators
categorized each city as a low-, moderate-,
or high-violence city. They then selected 12
cities diverse in violence rates and geography
and conducted key informant interviews
with stakeholders in each community.
These interviews identified barriers to and
facilitators of the implementation of the
UNITY strategies. The knowledge gained
subsequently enabled the development of
technical assistance to address barriers and
improve implementation.

Evaluating Implementation

To evaluate the implementation of Return-to-Play
laws, NCIPC used a case study design to examine
implementation efforts in two states. The
evaluation examined stakeholder perceptions,
barriers to implementation, and successes. In-
depth interviews were conducted with a variety
of stakeholders at different levels, including
state health departments and interscholastic
athletic associations, regional athletic directors,
and school coaches. The evaluation provides an
understanding of key implementation factors
within states and a comparison of barriers and
facilitators across the states to inform the efforts
of other states with similar laws.

Step by Step – Evaluating Violence and Injury Prevention Policies4

Potential Policy Implementation Evaluation Challenges and Solutions

Challenges Solutions
Rapid pace of policy Strive to develop the evaluation plan before implementation if at all ƒ

possible; identify potential indicators up front to plan for their collection.
Challenges of finding an
equivalent comparison group

Identify variables within the implementing community (such as level ƒ
or degree of implementation) that may allow for examination of how
individual variables influence implementation and impact.

Lack of clear responsibility for
evaluation

Create a clearly written evaluation plan with specific roles and ƒ
responsibilities.
Identify and partner with the stakeholder who has responsibility for ƒ
monitoring the implementation (if that is not your agency).

Action Steps
Identify the core components and activities of implementation for a specific policy. ƒ

Identify potential methods and indicators to assess whether or not each of the core components has been ƒ
implemented.

Identify key stakeholders involved in the implementation of the evaluation. What is the optimal method for ƒ
obtaining information from each of the stakeholders?

Identify the stakeholder responsible for monitoring implementation of the policy (if any) to find out if a ƒ
process for tracking the implementation already exists or is under development.

Additional Resources
Introduction to Process Evaluation in Tobacco Use Prevention and Control (CDC Office of Smoking and
Health). Available at http://www.cdc.gov/tobacco/tobacco_control_programs/surveillance_evaluation/
process_evaluation/index.htm

A Guidebook to Strategy Evaluation: Evaluating Your City’s Approach to Community Safety and Youth
Violence Prevention. Available at http://www.ph.ucla.edu/sciprc/pdf/Evaluation_Guidebook_July08

C Academy ot Managernent Revie

1996, Vol. 21. No. 4,

1055

-lDBO,

^ THE CHALLENGE OF
INNOVATION IMPLEMENTATION

KATHERINE I. KLEIN
JOANN SPEER SORRA

University of Maryland at College Park

Implementation is the process of gaining targeted organizational
members’ appropriate and committed use of an innovation. Our model
suggests that implementation eiiectiveness—the consistency an

quality of targeted organizational members’ use oi an innovation—is
a function oi (a) the strength oi an organization’s climate ior the imple-
mentation oi that innovation and (b) the fit of that innovation to targeted
users’ values. The model speciiies a range of implementation outcomes
(including resietance, avoidance, compliance, and commitment): high-
lights the equifinality of an organization’s climate ior implementation;
describes within- and between-organizational diiferences in innova-
tion-values fit; and suggests new topics and strategies for implementa-
tion research.

Innovation implementation within an organization is the process of
gaining targeted employees’ appropriate and committed use of an innova-
tion. Innovation implementation presupposes innovation adoption, tha

is, a decision, typically made by senior organizational managers, that
employees within the organization will use the innovation in their work.
Implementation failure occurs when, despite this decision, employees us

the innovation less frequently, less consistently, or less assiduously tha

required for the potential benefits of the innovation to be realized.

An organization’s failure to achieve the intended benefits of an innova-
tion it has adopted may thus reflect either a failure of implementation or
a failure of the innovation itself. Increasingly, organizational analysts
identify implementation failure, not innovation failure, as the cause of
many organizations’ inability to achieve the intended benefits of the inno-
vations they adopt. Quality circles, total quality management, statistical
process control, and computerized technologies often yield little or n

benefit to adopting organizations, not because the innovations are ineffec-
tive, analysts suggest, but because their implementation is unsuccessful

We are very grateful to Lori Berman. Amy Buhl, Dov Eden. Marlene Fiol, John Gomperts,
Susan Jackson. Steve Kozlowski, Judy Olian. Michelle Paul, Ben Schneider, and the anony-
mous reviewers for their extremely helpful comments on earlier versions oi this article. We
also thank Beth Benjamin, Pamela Carter. Elizabeth Clemmer. and Scott Rails for their hel

in collecting and analyzing the interview data ior the Buildco and Wireco case studies.

1055

1056 Academy of Management Review October

(e.g., Bushe, 1988; Hackman & Wageman, 1995; Klein & Rails, 1995; Reger,
Gustafson, DeMarie, & Mullane, 1994).

Innovation scholars have long bemoaned the paucity of research on
innovation implementation (Beyer & Trice, 1978; Hage, 1980; Roberts-
Gray & Gray, 1983; Tornatzky & Klein, 1982). Although cross-organizational
studies of the determinants of innovation adoption are abundant (see
Damanpour, 1991; Tornatzky & Klein, 1982, for reviews), cross-organiza-
tional studies of innovation implementation (e.g., Nord & Tucker, 1987) are
extremely rare. More common are single-site, qualitative case studies of
innovation implementation. Each of these studies describes pieces of the
implementation story. Largely missing, however, are integrative models
that capture and clarify the multidetermined, multilevel phenomenon of
innovation implementation.

In this article, we present an integrative model of the determinants
of the effectiveness of organizational implementation. The primary prem-
ise of the model, depicted in Figure 1, is that implementation effective-
ness—the quality and consistency of targeted organizational members’
use of an adopted innovation—is a function of (a) an organization’s climate
for the implementation of a given innovation and (b) targeted organiza-
tional members’ perceptions of the fit of the innovation to their values.

HGURE

Determinants and Consequences of Implementation Effectiveness

Climate
for

implementation

Skills

Incentives and
disincentives

Absence of
obstacles

Innovation-
values
fit

Commitment

Implementation
effectiveness

Strategic
accuracy of
innovation
adoption

1996 Klein and Sorra 1057

We begin by defining several key terms and outlining our levels of
theory. We then present the model. We focus first on the organization as
a whole, examining instances, determinants, and consequences of homo-
geneous innovation use within an organization. We then explore between-
group differences, examining instances, determinants, and consequences
of varying levels of innovation use by groups within an organization. Next,
we consider the feedback processes suggested by the model: the iniluences
of implementation and innovation outcomes on an organization’s subse-
quent climate for implementation and on employees’ values. We illustrate
the model with examples from our own and others’ implementation re-
search, and we conclude with a discussion of the implications that the
model may have for implementation researchers.

KEY TERMS

Two types of stage models are commonly used to describe the innova-
tion process. The first, source-based stage models, are based on the per-
spective of the innovation developer or source. They trace the creation of
new products or services from the gestation of the idea to the marketing
of the final product (e.g., research, development, testing, manufacturing
or packaging, dissemination) (Amabile, 1988; Kanter, 1988; Tornatzky &
Fleischer, 1990). Within source-based stage models, an innovation is

new product or service that an organization, developer, or inventor has
created for market.

User-based stage models, in contrast, are based on the perspective
of the user. They trace the innovation process from the user’s awareness
of a need or opportunity for change to the incorporation of the innovation
in the user’s behavioral repertoire (e.g., awareness, selection, adoption,
implementation, routinization) (Beyer & Trice, 1978; Nord & Tucker, 1987;
Tornatzky & Fleischer, 1990). Within user-based stage models (and within
our model), an innovation is a technology or a practice “being used for
the first time by members of an organization, whether or not other organiza-
tions have used it previously” (Nord & Tucker, 1987: 6).

We focus on innovations that require the active and coordinated use
of multiple organizational members to benefit the organization. Because
innovations of this type by definition affect numerous organizational mem-
bers, they are typically implemented within an organization only following
a formal decision on the part of senior managers to adopt the innovation.
Examples of innovations of this kind include total quality management
(TQM), statistical process control (SPC), computer-aided design and manu-
facturing (CAD/CAM), and manufacturing resource planning (MRP).

Implementation is the transition period during which targeted organi-
zational members ideally become increasingly skillful, consistent, and
committed in their use of an innovation. Implementation is the critical
gateway between the decision to adopt the innovation and the routine
use oi the innovation within an organization. We conceptualize innovation

1058 Academy of Management Beview October

use as a continuum, ranging from avoidance of the innovation (nonuse)
to meager and unenthusiastic use (compliant use) to skilled, enthusiastic,
and consistent use (committed use). Implementation effectiveness refers
to the consistency and quality of targeted organizational members’ use
of a specific innovation. Targeted organizational members (or targeted
users) are individuals who are expected either to use the innovation di-
rectly (e.g., production workers) or to support the innovation’s use (e.g.,
information technology specialists, production supervisors).

Innovation effectiveness describes the benefits an organization re-
ceives as a result of its implementation of a given innovation (e.g., improve-
ments in profitability, productivity, customer service, and employee mo-
rale). Implementation effectiveness is a necessary but not sufficient
condition for innovation effectiveness: Although an innovation is ex-
tremely unlikely to yield significant benefits to an adopting organization
unless the innovation is used consistently and well, effective implementa-
tion does not guarantee that the innovation will, in fact, prove beneficial
for the organization.

LEVELS OF THEORY

Klein, Dansereau, and Hall (1994: 206) urged organizational scholars
to specify and explicate the level(s) of their theories and their “attendant
assumptions of homogeneity, independence, or heterogeneity.” We begin
to do so here, weaving further discussion of the levels of the model through-
out the article.

The fundamental organizational challenge of innovation implementa-
tion is to gain targeted organizational members’ use of an innovation: to
change individuals’ behavior. However, for the innovations on which we
focus, the benefits of innovation implementation are dependent on the
use of the innovation not by individuals but by all, or a critical group of
organizational members (Tornatzky & Fleischer, 1990). Thus, although we
acknowledge that innovation use may vary between individuals and be-
tween groups within an organization, we conceptualize implementation
effectiveness as an organization-level construct, describing the overall,
pooled or aggregate consistency and quality of targeted organizational
members’ innovation use. An organization in which all targeted employees
use a given innovation consistently and well is more effective in its imple-
mentation effort than is an organization in which only some of the targeted
employees use the innovation consistently and well. Futher, because the
benefits of innovation implementation depend (again, in the case of the
innovations we describe) on the integrated and coordinated use of the
innovation, an organization in which all or most targeted employees’ inno-
vation use is moderate in consistency and quality shows greater imple-
mentation effectiveness than an organization in which some targeted
members use the innovation consistently and well while others use it
inconsistently and poorly. Thus, to use Klein and colleagues’ (1994) termi-

1996 Klein and Sorra 1059

nology, implementation effectiveness is a homogeneous construct, de-
scribing the quality and consistency of the use of a specific innovation
within an organization as a whole.

Implementation effectiveness results, we argue in the following sec-
tion, from the dual influence of an organization’s climate for the implemen-
tation of a given innovation and the perceived fit of that innovation to
targeted users’ values. We posit that implementation climate, too, is a
homogeneous construct, describing a facet of targeted users’ collective,
perceived work environment. Innovation-values fit, in contrast, may vary
between individuals, between groups, or between organizations. We focus
on between-organization and between-group differences in innovation-
values fit, thus conceptualizing innovation-values fit primarily as a homo-
geneous construct that may characterize the shared values of either an
organization’s targeted users as a whole or distinct groups of targeted
users within an organization.

CLIMATE FOR IMPLEMENTATION

The empirical literature on the implementation of workplace innova-
tions is dominated, as we noted previously, by qualitative, single-site
studies (e.g., Markus, 1987; Roitman, Liker, & Roskies, 1988; Sproull &
Hofmeister, 1986). In rich detail, the authors of these studies have described
a variety of innovation, implementation, organizational, and managerial
policies, practices, and characteristics that may influence innovation use.
These include training in innovation use (Fleischer, Liker, & Arnsdorf,
1988), user support services (Rousseau, 1989), time to experiment with the
innovation (Zuboff, 1988), praise from supervisors for innovation use (Klein,
Hall, & Laliberte, 1990), financial incentives for innovation use (Lawler &
Mohrman, 1991), job reassignment or job elimination for those who do not
learn to use the innovation (Klein et al., 1990), budgetary constraints on
implementation expenses (Nord & Tucker, 1987), and the user-friendliness
of the innovation (Rivard, 1987). (We will use the shorthand phrase “imple-
mentation policies and practices” to refer to the array of innovation, imple-
mentation, organizational, and managerial policies, practices, and charac-
teristics that may influence innovation use.)

Because each implementation case study highlights a different subset
of one or more implementation policies and practices, the determinants
of implementation effectiveness may appear to be a blur, a hodge-podge
lacking organization and parsimony. If multiple authors, studying multiple
organizations, identify differing sources of implementation failure and
success, what overarching conclusion is a reader to reach? The implemen-
tation literature offers, unfortunately, little guidance. To highlight the
collective influence of an organization’s multiple implementation policies
and practices, we introduce the construct of an organization’s climate for
the implementation of an innovation.

1060 Academy of Management Beview October

Our discussion of this construct builds on Schneider’s conceptualiza-
tion of climate (e.g., Schneider, 1975, 1990). Schneider (1990: 384) defined
climate as employees’ “perceptions of the events, practices, and proce-
dures and the kinds of behaviors that are rewarded, supported, and ex-
pected in a setting.” Three distinctive features of Schneider’s conceptual-
ization of climate bear note here. First, Schneider’s conceptualization
highlights employees’ perceptions—^not their evaluations—of their work
environment. Second, Schneider’s conceptualization draws attention to
employees’ shared perceptions, not employees’ individual and idiosyn-
cratic views. And, third, Schneider’s conceptualization focuses on employ-
ees’ shared perceptions of the extent to which work unit practices, proce-
dures, and rewards promote behaviors consistent with a specific strategic
outcome of interest. Schneider’s conceptualization does not focus on em-
ployees’ perceptions of generic work unit characteristics—such as socio-
emotional supportiveness (e.g., Kopelman, Brief, & Guzzo, 1990)—that are
generalizable to any work unit.

An organization’s climate for the implementation of a given innovation
refers to targeted employees’ shared summary perceptions of the extent
to which their use of a specific innovation is rewarded, supported, and
expected within their organization. Employees’ perceptions of their organi-
zation’s climate for the implementation of a given innovation are the
result of employees’ shared experiences and observations of, and their
information and discussions about, their organization’s implementation
policies and practices. Climate for implementation, we emphasize, does
not refer to employees’ satisfaction with the innovation, the organization,
or their jobs; it also does not refer to employees’ perceptions of their
organization’s openness to change or general innovativeness.

The Influence of Climate for Implementation

The more comprehensively and consistently implementation policies
and practices are perceived by targeted employees to encourage, cultivate,
and reward their use of a given innovation, the stronger the climate for
implementation of that innovation. A strong implementation climate fos-
ters innovation use by (a) ensuring employee skill in innovation use,
(b) providing incentives for innovation use and disincentives for innova-
tion avoidance, and (c) removing obstacles to innovation use. An organiza-
tion has a strong climate for the implementation of a given innovation if,
for example, training regarding innovation use is readily and broadly
available to targeted employees (ensuring skill); additional assistance in
innovation use is available to employees following training (ensuring
skill); ample time is given to employees so they can both learn about
the innovation and use it on an ongoing basis (ensuring skill, removing
obstacles); employees’ concerns and complaints regarding innovation use
are responded to by those in charge of the innovation implementation
(removing obstacles); the innovation itself can be easily accessed by the
employees (e.g., TQM meetings scheduled at convenient times, user-

1996̂ Q-J-^ Klein and Sorra 1061
y

friendly computerized technology) (removing obstacles); and employees’
use of the innovation is monitored and praised by managers and supervi-
sors (providing incentives for use and disincentives for innovation
avoidance).

Research on climates for specific strategic outcomes reveals the in-
fluence that an organization’s climate for a specific outcome has on em-
ployees’ behaviors regarding that outcome. Researchers have found, for
example, that climate for safety is related to factory safety (Zohar, 1980),
that climate for innovation in R&D subsystems is related to technological
breakthroughs (Abbey & Dickson, 1983), that climate for technical updating
is related to engineers’ performance (Kozlowski & Hults, 1987), and that
climate for service is related to customers’ perceptions of the quality of
service received (Schneider & Bowen, 1985; Schneider, Parkington, & Bux-
ton, 1980). Thus, we posit that the stronger an organization’s climate for
the implementation of a given innovation, the greater will be the employ-
ees’ use of that innovation, provided employees are committed to innova-
tion use.

The Limits of Climate for Implementation
Our caveat—”provided employees are committed to innovation

use”—indicates the limits of climate. Psychological theories and research
on conformity and commitment (Kelman, 1961; O’Reilly & Chatman, 1986;
Sussman & Vecchio, 1991) have been used to distinguish between compli-
ance, “the acceptance of influence in order to gain specific rewards and
to avoid punishments,” and internalization, “the acceptance of influence
because it is congruent with a worker’s values” (Sussman & Vecchio, 1991:
214).’ Applied to innovation implementation, these works suggest that
employees who perceive innovation use to be congruent with their values
are likely to be internalized—committed and enthusiastic—in their inno-
vation use, whereas individuals who perceive innovation use merely as
a means to obtain and avoid punishments are likely to be compliant—pro
forma and uninvested—in their innovation use.

Because a strong implementation climate provides incentives and
disincentives for innovation use, it may, in and of itself, foster compliant
innovation use. Climate for implementation does not, however, ensure
either the congruence of an innovation to targeted users’ values or internal-
ized and committed innovation use. Skillful, internalized, and commited
innovation use takes more: a strong climate for the implementation of an
innovation and a good fit of the innovation to targeted users’ values.

We discuss the combined effects of implementation climate and
innovation-values fit in greater detail in a subsequent section, but an

‘ Also mentioned in these theories is idenfificafion, the acceptance of iniluence “in order
to engage in a satisfying role-relationship with another person or group” (Sussman 8f Vecchio,
1991: 214). Identification seemed to us to have relatively little relevance to innovation imple-
mentation.

1062 Academy of Management Beview October

example—close to many readers’ academic homes—may be helpful here.
Imagine a university that has historically valued, rewarded, and sup-
ported teaching far more than research. If the university adopts a new
emphasis on research, the university can surely create—through its poli-
cies and practices—a strong climate for research. But how will professors,
drawn to the university for its teaching emphasis, respond to such a
change? Will they not simultaneously recognize the new climate for re-
search and resist it because it is incongruent with their values?

An Example of Climate for Implementation: Buildco, Inc.

Buildco, Inc. (a pseudonym) is a large engineering and construc-
tion company that experienced great difficulty in implementing three-
dimensional computer-aided design and drafting (3-D CADD), a sophisti-
cated computer graphics program used to design and test computerized
representations of products (in this case, buildings and plants). Buildco’s
senior managers complained of “employee resistance to change,” yet re-
searchers (Klein, 1986; Klein et al., 1990) found, in their interviews with 26
targeted users and their supervisors, that targeted users were, in fact,
very enthusiastic about 3-D CADD, per se. For example, one employee
raved, “I think CADD is the greatest thing since sliced bread. I like the
whole concept, the speed, the accuracy, [and] the uniformity of the
drawings.”

Targeted users complained vociferously, however, about many as-
pects of the implementation process. Targeted users were satisfied with
the content of the company’s 60-hour 3-D CADD training program, but often
they had little opportunity to use their 3-D CADD training on the job. As
a result, employee skill in 3-D CADD often decayed sharply following
training. Targeted users complained, too, that managers and supervisors
offered few rewards for 3-D CADD use: “Supervisors fall short of letting
people know when they’re doing a good job,” one employee commented.
“From what I hear, CADD’s made a lot of money for the company, but how
many people who use CADD know it?” In addition, users complained
about a variety of obstacles to their use of 3-D CADD: “The system is
designed to handle 6 or 7 terminals at once, but now there are 17 terminals.
. . . It takes a long time for the computer to do a simple placement, and
this disrupts your train of thought and creativity. It kills your efficiency.”

Despite users’ appreciation of 3-D CADD and the appropriateness of
the content of the company’s training program, the overall climate for the
implementation of 3-D CADD at Buildco was weak: Targeted users’ CAD

skills often grew rusty, rewards for using CADD were slim, and obstacles
to using CADD were many.

INNOVATION-VALUES H

Building on psychological theories of conformity, we posit that em-
ployees’ commitment to the use of an innovation is a function of the per-

1996 Klein and Sorra 1063

ceived fit of the innovation to employees’ values. Values are “generalized,
enduring beliefs about the personal and social desirability of modes of
conduct or ‘end-states’ of existence” (Kabanoff, Waldersee, & Cohen, 1995:
1076). Individuals have values, as do groups, organizations, societies, and
national cultures (Kabanoff et al., 1995).

We focus on organizational and group values in our analysis of
innovation-values fit. Organizational values are implicit or explicit
views, shared to a considerable extent by organizational members,
about both the external adaptation of the organization (i.e., how the
organization should relate to external customers, constituencies, and
competitors) and the internal integration of the organization (i.e., how
members of the organization should relate to and work with one another)
(Schein, 1992). Organizational members come to share values as a result
of their common experiences and personal characteristics (Holland, 1985;
Schein, 1992; Schneider, 1987). Organizational values are stable, but not
fixed, and may evolve in response to changing organizational and
environmental events and circumstances. Organizational values vary
in intensity. High-intensity organizational values encapsulate strong,
fervent views and sharp strictures regarding desirable and undersirable
actions on the part of the organization and its members. Low-intensity
organizational values describe matters of relatively little importance
and passion for organizational members.

Group values are implicit or explicit views, shared to a considerable
extent by the members of a group within an organization, about the exter-
nal adaptation and internal integration of the organization and of the
group itself. Group values vary among groups in an organization, and
they often reflect the self-interests of the group (cf. Guth & MacMillan, 1986).
Functional and hierarchical groups (e.g., senior managers, supervisors,
technicians) are likely to differ in their values as a function of (a) their
roles in the organization (Dougherty, 1992), (b) their common interactions
and experiences (Rentsch, 1990), and (c) their distinctive backgrounds and
traits (Holland, 1985). Like organizational values, group values vary in
their intensity and may evolve over time.

We highlight the fit of innovations to organizational and group values,
rather than individual values, because our aim is to explain organizational
implementation effectiveness, not individual differences in innovation
use. A poor fit between an innovation and organizational or group values
affects relatively large numbers of organizational members, and it is thus
more likely to derail innovation implementation than is a poor fit between
an innovation and any one organizational member’s values.

/nnova(ion-va/ues fit describes the extent to which targeted users
perceive that use of the innovation will foster (or, conversely, inhibit) the
fulfillment of their values. Targeted users assess the objective characteris-
tics of an innovation and its socially constructed meaning (e.g.. Barley,
1986; Goodman & Griffith, 1991; Hattrup & Kozlowski, 1993; Zuboff, 1988) to
judge the fit of the innovation to their values. Because senior managers

1064 Academy of Management Beview October

adopt innovations to alter production, service, or management, innova-
tions often represent an imperfect fit with organizational members’ values.
Innovation-values fit is good when targeted innovation users regard
the innovation as highly congruent with their high-intensity values.
Innovation-values fit is poor when targeted users regard the innovation
as highly incongruent with their high-intensity values. Innovation-values
fit is neutral when targeted users regard the innovation as either moder-
ately congruent or moderately incongruent with their low-intensity values.

Innovation-Values Fit: Some Examples of Poor Fit

Innovation-values fit has not, to our knowledge, been the object of
researchers’ explicit attention. However, several scholars have com-
mented implicitly on the topic. In a case study of the implementation of
statistical process control in a manufacturing plant, for example, Bushe
(1988: 25) suggested that because members of manufacturing plants value
performance (i.e., production) more than change and learning, “both the
implementation of SPC and the nature of the technique are countercultural,
in that learning must be as highly valued as performing for SPC to be
used successfully.” In a similar vein, Schein (1992: 140) has commented.

One of the major dilemmas that leaders encounter when they
attempt to change the way organizations function is how to
get something going that is basically countercultural. . . . For
example, the use of quality circles, self-managed teams, auton-
omous work teams, and other kinds of organizational devices
that rely heavily on commitment to groups may be so counter-
cultural in the typical U.S. individualistic competitive organi-
zation as to be virtually impossible to make work unless they
are presented pragmatically as the only way to get some-
thing done.

Further, Schein (1992) and others (e.g., March & Sproull, 1990) docu-
mented the poor fit between top managers’ and information technology
(IT) specialists’ values. For example, top managers’ assumption that “hier-
archy is intrinsic to organizations and necessary for coordination” (Schein,
1992; 291) clashes with the IT specialists’ assumptions that “a flatter organi-
zation will be a better one” and “a more fully connected organization with
open channels in every direction will be a better one” (Schein, 1992: 286).

A last example of poor innovation-values fit comes from a case study
of the implementation of a computerized inventory control system in a
wire manufacturing company with the pseudonym Wireco (Klein, Rails, &
Carter, 1989). (The conclusions we make are based on interviews with 37
employees: managers, supervisors, and targeted users.) When the decision
to adopt the computerized inventory control system was mandated by
corporate headquarters, Wireco’s manufacturing procedures were unstruc-
tured, fluid, and disorganized. If Customer A placed a rush order for one
kind of wire, preliminary work on Customer B’s order for a different kind
of wire was either put aside (and often lost) or transformed and used to

1996 Klein and Sorra 1065

meet Customer A’s order. Employees at Wireco believed that customers
were well served by the flexibility of their production procedures. The new
computerized inventory control system, however, required employees
(a) to track each customer’s order throughout the production process and
(b) to maintain accurate inventory records. Employees could no longer
use preliminary work on one customer’s order to complete a different
customer’s order. The inventory control system represented a poor fit with
the employees’ values supporting flexible, if disorganized, production pro-
cedures.

THE EFFECTS OF IMPLEMENTATION CLIMATE AND INNOVATION-
VALUES FIT ON INNOVATION USE: WHEN FIT IS HOMOGENEOUS

To predict innovation use, we consider the combined influence of
implementation climate and innovation-values fit. We first describe the
implications of a strong or weak climate for implementation and good,
neutral, or poor innovation-values fit, when innovation-values fit is homo-
geneous (i.e., when there are few within-organization, between-group dif-
ferences in innovation-values fit).

The six cells in Table 1 summarize the predicted influence of varying
levels of implementation climate and innovation-values fit on employees’
affective responses and innovation use. When innovation-values fit is
good and the organization’s implementation climate is strong, employees
are skilled in innovation use, incentives for innovation use and disincen-
tives for innovation avoidance are ample, obstacles to innovation use are
few, and employees are likely to be highly committed to their innovation
use. This is the ideal scenario for innovation implementation. Employees
are enthusiastic about the innovation, and they are skilled, consistent,
and committed in their innovation use.

When innovation-values fit is good, yet the organization’s implemen-
tation climate is weak, targeted users are committed to innovation use, but
they lack skills in and experience few incentives for and many obstacles to
innovation use. Thus, employees’ use of the innovation is likely to be
sporadic and inadequate. Committed to the idea of innovation use, users
are likely to be disappointed and frustrated by their organization’s weak
implementation climate and by their own and their fellow employees’
poor use of the innovation. Good innovation-values fit, in the absence of
a strong implementation climate, is not sufficient to produce skillful and
consistent innovation use.

When innovation-values fit is poor, yet the organization’s implementa-
tion climate is strong, employee resistance is likely. A strong implementa-
tion climate creates an imperative for employees to use an innovation
that, given poor innovation-values fit, employees oppose. If innovation-
values fit is very poor, targeted innovation users may opt to leave the
organization if they can find alternative employment. Those who cannot

1066 Academy of Management fleview October

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1996 Klein and Soria 1067

leave the organization are likely to engage in compliant innovation use,
at best.

When innovation-values fit is poor and implementation climate is
weak, targeted innovation users are likely to regard their organization’s
weak implementation climate—its anemic and erratic implementation
policies and practices—with some relief. Targeted users are likely to be
pleased to face little pressure to use the innovation. Unskilled, unmoti-
vated, and opposed to innovation use, targeted users are unlikely to use
the innovation at all.

Between these extremes of enthusiasm and frustration {when innova-
tion-values fit is good) and resistance and relief (when innovation-values
fit is poor) lies a middle group defined by neutral innovation-values fit.
In this middle ground are innovations that are perceived to be neither
highly congruent nor highly incongruent with organizational values that
are of low intensity. When fit is neutral and the implementation climate
is strong, targeted users are indifferent to the prospect of innovation imple-
mentation, and they face a strong imperative in favor of innovation use.
In this case, we predict adequate innovation use—more than compliant
innovation use but less than committed use. When fit is neutral and the
implementation climate is weak, employees are not likely to use the inno-
vation at all.

We note that employee resistance to innovation implementation is
predicted in only one of the six cases that are depicted in Table 1, that is,
when an organization’s implementation climate is strong and innovation-
values fit is poor. The term resistance connotes protest and defiance
against an opposing pressure or force. A strong implementation climate
is such a force. However, when an organization’s implementation climate
is weak, employees need not “resist” innovation use; there is, by definition,
little pressure on employees to use the innovation. In sum, when an organi-
zation’s climate for innovation implementation is weak, the organization’s
failure to create an imperative for innovation use, not employee resistance,
is the likely cause of employees’ lackluster innovation use.

Implementation Climate and Innovation-Values Fit: Two Examples

Buildco represents a case of a weak implementation climate and good
innovation-values fit. Targeted users complained about many aspects of
the implementation process, but they liked 3-D CADD. They valued their
own and their company’s technical expertise and use of cutting-edge tech-
nologies. They strived to create economical, creative, and fail-safe de-
signs, and these users believed that 3-D CADD enhanced their efforts. As
suggested in Table 1, targeted users were frustrated and disappointed by
their company’s weak implementation policies and practices {its weak
implementation climate) and by employees’ resultant inability to use 3-D
CADD as much or as well as they would have liked to use it.

Markus’s {1987) case study of one company’s attempted implementa-
tion of a computerized financial information system {FIS) provides an

1068 Academy ot Management Review October

example of a strong climate for innovation implementation and poor
innovation-values fit.̂ Championed by corporate headquarters, FIS al-
lowed corporate accountants new access to divisional performance data.
Corporate headquarters fostered a strong climate for the implementation
of FIS in the divisions of the corporation by {a) ensuring divisional accoun-
tants knew how to use the system, (b) fixing technical problems regarding
FIS, and {c) instituting policies that virtually necessitated the divisions’
use of FIS. Nevertheless, divisional accountants actively resisted using
FIS. They valued their financial authority and autonomy and perceived
FIS to be an affront and a threat to these values.

THE EFFECTS OF IMPLEMENTATION CLIMATE
AND INNOVATION-VALUES FIT ON INNOVATION USE:

WHEN FIT DIFFERS BETWEEN GROUPS

In an organization characterized by between-group differences in
high-intensity values, the same innovation may be regarded by the mem-
bers of one group as highly congruent with their values {good fit) and by
the members of a second group as highly incongruent with their values
{poor fit). Such a situation is, of course, ripe for conflict if the effective
implementation of the innovation requires innovation use {or at least sup-
port for innovation use) across both groups. Next, we explore the conse-
quences of between-group differences in innovation-values fit: {a) when
neither of the opposing groups has formal power over the other (horizontal
groups) and {b) when one of the opposing groups does have formal power
over the other {vertical groups).

Horizontal Groups

When innovation-values fit is good for one group within an organiza-
tion and poor for another group, and when neither of the groups has power
over the other, the strength of the organization’s implementation climate
determines the “winner” of the conflict over innovation use. If the organiza-
tion’s climate for implementation is strong, the group in favor of innovation
implementation (whose members find the innovation congruent with their
group’s values) is likely to win for two reasons. First, a strong implementa-
tion climate creates an imperative for innovation use for all targeted users.
Second, a strong implementation climate indicates to targeted innovation
users that managers, who are senior to both groups, support implementa-
tion, thus throwing the weight of management behind the group favoring
implementation. Ultimately, all targeted users are likely to use the innova-
tion. Conflict may be drawn out, however, and implementation may be
slow, as those opposed to innovation implementation actively or passively
resist using the innovation.

^ Because we did not conduct this case study, our knowledge of it is more limited than
our knowledge of the Buildco and Wireco case studies.

1996 Klein and Sorra 1069

Conversely, if the climate is weak, those opposed to implementation
are likely to win, for the same reasons. A weak implementation climate
discourages innovation use and indicates managers’ ambivalence or an-
tipathy toward implementation (and thus their tacit support of those who
oppose innovation). Under these circumstances, employees’ use of the
innovation is likely to be limited at best, after a period of perhaps high
but then declining use of the innovation by those who support innovation
implementation.

An Example of Horizontal Groups:
Production Operators and IT Specialists

We have described Wireco as an example of poor innovation-values
fit. Although the fit of the computerized inventory control system to produc-
tion operators’ values was poor, the fit of the system to the company’s IT
specialists was good. Wireco’s IT specialists valued the computerized
system, believing it to be modern, efficient, organized, and beneficial.
{Recall Schein’s, 1992, description of IT values.) Further, the IT specialists
saw in the prospective implementation of the system an opportunity to
increase their own influence and status in the company.

Wireco’s managers and supervisors, however, tacitly supported pro-
duction operators’ views of the system. As a result, the company’s resulting
implementation climate was very weak. For example, operators experi-
enced few rewards for using the system and few punishments for neglect-
ing it. One operator commented, “Are there any rewards or recognition
for effective use of the system? No. I pet my dog at home more than I get
petted here, and I don’t pet my dog very often.”

Given the poor fit of the inventory control system to production opera-
tors’ values and the weak implementation climate, implementation of the
system was not successful. Operators’ and their managers’ and supervi-
sors’ use of and support for the system declined, and Wireco’s IT specialists
lost the battle for implementation.

Vertical Groups

When innovation-values fit is good for one group within an organiza-
tion and poor for another group and when one group does have power
over the other, the strength of the organization’s implementation climate
again determines the “winner” of conflict over innovation use, yet the
dynamic is a little different than the one just described. If innovation-
values fit is good for the higher authority group and poor for the lower
authority group, then the higher authority group (e.g., supervisors) will
strengthen and augment the organization’s climate for the implementation
of the innovation. For example, the higher authority group may establish
additional incentives or training for innovation use. Under these circum-
stances, lower authority group members—experiencing a strong imple-
mentation climate and poor innovation-values fit—will resist innovation
use and/or engage in compliant innovation use.

1070 Academy of Management Beview October

Conversely, if innovation-values fit is poor for the higher authority
group and good for the lower authority group, then the higher authority
group is likely to undermine the organization’s implementation climate.
Higher authority group members may diminish or constrain lower author-
ity group members’ innovation use by, for example, minimizing the time
available to use the innovation. Under such circumstances, lower authority
group members—experiencing good-innovation values fit and a weak
implementation climate—feel frustrated and disappointed, and they en-
gage in only sporadic and inadequate innovation use.

Examples of Vertical Groups: Supervisors and Their Subordinates

In a study of employee-involvement programs in eight manufacturing
plants, Klein (1984) found that employees generally welcomed opportuni-
ties for greater involvement in plant decision making (good fit). Supervi-
sors, however, often resisted the implementation of employee-involvement
programs, believing that these programs limited their authority and threat-
ened their job security (bad fit). For example, in one plant (Klein, 1984: 88),

the foremen saw [team meetings among employees] as a threat
to their control and authority, which they tried to regain by
bad-mouthing the program. This bad-mouthing, in turn, dis-
couraged many of their subordinates from participating. In the
end, the whole effort just faded away tor lack of interest.

In sum, supervisors created impediments to workers’ involvement, weak-
ening the climate for implementation that their subordinates experienced
and thereby undermining innovation implementation.

THE OUTCOMES OF INNOVATION IMPLEMENTATION: EXPLORING
CONSEQUENCES FOR IMPLEMENTATION CLIMATE AND VALUES

Prior to the 1980s, most researchers who studied the determinants of
innovation adoption did not study its aftermath: implementation {Tornat-
zky & Klein, 1982). Although research on implementation is now more
prevalent, research on its aftermath is, to our knowledge, nonexistent. In
this section, we consider briefly the aftermath of implementation: the ef-
fects {depicted by dashed lines in Figure 1) of varying implementation
outcomes on an organization’s subsequent implementation climate and
values.

Innovation implementation may result in one of three outcomes:
{a) implementation is effective, and use of the innovation enhances the
organization’s performance; {b) implementation is effective, but use of the
innovation does not enhance the organization’s performance; and
(c) implementation fails. Each of these three outcomes may influence an
organization’s subsequent implementation climate and organizational
members’ values.

1996 Klein and Sorra 1071

When Implementation Is Effective and Innovation Use
Enhances Performance

When innovation implementation succeeds and enhances an organi-
zation’s performance, the organization’s implementation climate is
strengthened. Managers’ and supervisors’ support for innovation imple-
mentation increases, yielding likely improvements in implementation
policies and practices {e.g., innovation training for additional employees,
more praise for targeted employees’ innovation use). Further, when
innovation implementation enhances an organization’s performance,
organizational values may be affected. If the innovation is largely
congruent with the organizational members’ homogeneous values, these
values are reinforced and organizational members’ confidence in the
fit of the innovation to their values is strengthened. If the innovation
is incongruent with organizational members’ homogeneous values, mem-
bers’ values may shift. Organizational members’ confidence in new
values congruent with use of the innovation increases, as does the
perceived efficacy of innovation adoption and implementation in general.
As a result of such changes in organizational members’ values, the fit
of future innovations to organizational values is improved. If the innova-
tion fits well with the values of one group of targeted users and it fits
poorly with the values of a second group of targeted users’, the “good-
fit” group that encouraged innovation implementation is vindicated.
Support for this group and its values may grow, whereas support for
the “poor-fit” group and its values declines.

When Implementation Is Effective But Innovation Use
Does Not Enhance Performance

When implementation succeeds but does not enhance an organiza-
tion’s performance, the organization’s climate for implementation is weak-
ened. Managers’ and supervisors’ support for implementation declines. If
innovation-values fit is homogeneous within the organization and poor,
preexisting organizational values are reinforced {e.g., “We should have
known computerization would never work for us.”). If innovation-values
fit is homogeneous and good, existing organizational values are chal-
lenged. At the same time, however, the perceived value of innovation
adoption and implementation in general may be questioned, potentially
leading to pessimism regarding the organization’s implementation of fu-
ture innovations. Finally, if innovation-values fit varies between groups,
support for the group that advocated innovation use lessens.

When Implementation Is Not Effective

When implementation fails, an implementation climate, which has
in all likelihood always been weak, weakens further unless—in response
to initial signs of implementation failure—managers demonstrably in-
crease their support for innovation implementation by changing the

1072 Academy of Management fleview October

organization’s implementation policies and practices to better support
implementation. If the innovation was largely congruent with organiza-
tional members’ homogeneous values, organizational members may
question not just the merits of change, but the very possibility of change.
If the innovation was largely incongruent with organizational members’
homogeneous values, organizational members may feel empowered by
their thwarting of the innovation’s implementation. Finally, if innovation-
values fit varies between groups, the influence within the organization
of the group that advocated innovation implementation is reduced.

The Outcomes of Innovation Implementation: Two Examples

Buildco provides an interesting example of implementation and
innovation outcomes over time. The company’s initial climate for the
implementation of 3-D CADD was weak, and innovation use was,
accordingly, sporadic. However, Buildco’s managers stepped in to
strengthen the company’s climate for implementation. The early organi-
zational benefits of 3-D CADD use further strengthened Buildco’s imple-
mentation climate. Given an ultimately strong climate for implementa-
tion and good fit between 3-D CADD and organizational values, use of
3-D CADD is now routine at Buildco, and the values for computerization
appear even stronger than they were prior to the company’s adoption
oi 3-D CADD.

In contrast, Wireco did not succeed in implementing its computerized
inventory control system. Respect within Wireco for the company’s IT spe-
cialists declined. The company has not, in the years since its foiled imple-
mentation of the inventory control system, adopted any other computerized
technology that would diminish the flexibility of, or change in any other
significant way, the company’s production procedures.

RESEARCH IMPLICATIONS OF THE MODEL

The subject of relatively little research, implementation is the ne-
glected member of the innovation family. Even the Academy of Manage-
ment Review’s Call for Papers on the Management of Innovation (1994:
617-618) had a distinct, if implicit, focus on the development and adop-
tion^not the implementation^of innovations. Our model brings new at-
tention to implementation and invites new research on the topic. In this
section, we underscore key constructs of the model, note additional re-
search topics suggested by the model, and highlight research methods
most useful for the study of implementation.

Key Constructs

Climate for implementation. We have proposed that implementation
effectiveness is in part a function of the strength of an organization’s
climate for implementation. The climate construct subsumes and inte-
grates many of the findings of past implementation research. However,

1996 Klein and Sorra 1073

the contributions of the construct go beyond parsimony. The construct
suggests that an organization’s implementation policies and practices
should be conceptualized and evaluated as a comprehensive, interdepen-
dent whole that together determines the strength of the organization’s
climate for implementation. Further, the construct highlights the equifi-
nality of implementation climate. Implementation climates of equal
strength may ensue from quite different sets of policies and practices.
For example, an organization may ensure employee innovation skill by
training employees, by motivating employees through the reward system,
by selecting employees skilled in innovation use for hire or promotion,
or by shaping the innovation to match employees’ existing skills.

The climate for implementation construct thus pushes researchers
away from the search for the critical determinants of implementation
effectiveness—training or rewards or user friendliness—to the documen-
tation of the cumulative influence of all of these on innovation use. Further,
the climate construct facilitates the comparison of implementation effec-
tiveness across organizations. The specific implementation policies and
practices that facilitate innovation use may vary tremendously from orga-
nization to organization. Training may be critical in one organization,
rewards in a second organization, and so on. Thus, specific implementation
policies and practices may show little consistent relationship to innova-
tion use across organizations. Climate, however, is cumulative and thus,
in concert with innovation-values fit, predictive of innovation use across
organizations.

Innovation-values fit. The construct of innovation-values fit indicates
the limits of implementation climate. In the face of poor innovation-values
fit, a strong implementation climate results in only compliant innovation
use and/or resistance. Further, innovation-values fit may vary across the
groups of an organization, engendering intraorganizational conflict and
lessening implementation effectiveness. The construct of innovation-
values fit thus directs researchers to look beyond an organization’s global
{or homogeneous) implementation policies and practices and to consider
the extent to which a given innovation is perceived by targeted users to
clash or coincide with their organizational and group values.

Implementation effectiveness and innovation efiectiveness. The con-
struct of implementation effectiveness helps to focus researchers’ attention
on the aggregate behavioral phenomenon of innovation use. The construct
of innovation effectiveness, in contrast, directs researchers’ attention to
the benefits that may accrue to an organization as a result of successful
innovation implementation. These two distinct constructs, too often blurred
in prior innovation research and theory, are critical for implementation
research and theory. The first underscores the difficulty of innovation
implementation; targeted organizational members’ consistent and appro-
priate innovation use is not guaranteed. The second underscores the vary-
ing effects of innovation implementation; even when the implementation

1074 Academy ot Management Beview October

of an innovation is effective, the innovation may fail to yield intended
organizational benefits.

Additional Topics for Research

The model invites research not only on the effects of implementation
climate and innovation-values fit on implementation and innovation effec-
tiveness, but it also suggests several questions only hinted at in this
article, given space limitations. We consider four.

Managers and the creation of a strong implementation climate. The
organizational change and innovation literatures (e.g., Angle & Van de
Ven, 1989; Beer, 1988; Leonard-Barton & Krauss, 1985; Nadler & Tushman,
1989; Nutt, 1986) suggest that the primary antecedent of an organization’s
climate for implementation is managers’ support for implementation of
the innovation. If this is true, why do managers fail to support the imple-
mentation of many of the innovations adopted in their organizations?
The available literature, although limited, suggests at least two possible
answers. First, innovation adoption decisions are often made by execu-
tives at corporate headquarters without the participation or input of local,
lower level managers {Guth & MacMillan, 1988; Klein, 1984). Left out of this
decision-making process, local managers may not be inspired to create
a strong climate for innovation implementation. Second, managers may
support innovation implementation, but they may lack an in-depth under-
standing of the innovation. Managers who know little about an innovation
are likely to delegate implementation management to subordinates who
are more knowledgeable but who lack the authority and resources to
create a strong climate for implementation. Although plausible, these
explanations for managers’ failure to support innovation implementation
are tentative and preliminary. The topic warrants further empirical and
conceptual analysis.

“Upward implementation” of innovations. The preceding paragraph,
and much of our model, highlights the roles that managers play in creating
a strong implementation climate among targeted users. Are nonmanagers
powerless to affect their organization’s implementation climate? We know
of no research explicitly designed to answer this question. We suspect,
however, that in all but the most participative, flat organizations, nonman-
agers have relatively little influence in creating a strong implementation
climate. Even though nonmanagers can advocate, or champion, their man-
agers’ adoption of a given innovation {Dean, 1987; Howell & Higgins, 1990),
they lack the authority and resources to institute the policies and practices
that yield a strong implementation climate. Yet as organizations strive to
become both more innovative and flatter, the role of nonmanagers in
fostering implementation becomes an increasingly important topic for re-
search.

Implementing multiple innovations. Can an organization successfully
and simultaneously implement multiple innovations? If an organization’s
multiple innovations necessitate diverse, new, time-consuming, and

^]ein and Sorra 1075

difficult-to-learn behaviors of a common group of targeted users, the likeli-
hood of successful simultaneous implementation of the innovations is
slim. An organization’s climate for the implementation of one such innova-
tion may compete with and undermine its climate for the implementation
of another innovation. For example, rewards for the use of one innovation
may impose obstacles to the use of the second innovation. More likely to
be successful are organizational efforts to implement innovations that
require complementary changes in the behavior of distinct groups of users.
In such a case, the climate for the implementation oi one innovation may
indeed enhance the climate for the implementation of a second innovation.
However, additional research is needed because relatively little is known
about the success or failure of organizations’ attempts to implement multi-
ple innovations.

Fostering innovation-values fit. The actions an organization might
take to strengthen its climate for the implementation of an innovation
are relatively clear, but what can an organization do to foster good
innovation-values fit? The available literature suggests three possible
strategies. First, an organization may provide opportunities for employ-
ees to participate in the decision to adopt the innovation {Kotter &
Schlesinger, 1979). Employees’ participation in the adoption decision
increases the likelihood that the chosen innovation fits their preexisting
values. Employees’ participation in the adoption decision also may
change employees’ values, rendering their new values congruent with the
adopted innovation. Second, an organization may foster good innovation-
values fit by educating employees about the need for {value of) the
innovation for organizational performance. Although senior executives
may recognize the need for an innovation that is discrepant with
organizational members’ preexisting values, lower level employees may
not understand this {Floyd & Wooldridge, 1992; Guth & MacMillan, 1986;
Klein, 1984). Third, employees’ values may shift over time, and innovation-
values fit may increase if an organization’s implementation of an
innovation that represents a poor fit with employees’ preexisting values
yields clear and widely recognized benefits for the organization. This,
however, is a risky strategy; employees’ use of an innovation that
represents a poor fit with their values is likely to be compliant at best,
and compliant innovation use is unlikely to yield great benefits to the
adopting organization. Given the predicted importance of innovation-
values fit in fostering innovation use, the determinants of innovation-
values fit warrant focused research attention.

Methods for the Study of Implementation

Multiorganizational research. As we have noted, single-site, qualita-
tive case studies dominate the implementation literature. To verify the
sources of between-organization differences in implementation effec-
tiveness proposed in the model, however, researchers must move be-
yond single-site research to analyze innovation implementation across

1076 Academy of Management Review October

organizations. The topic is sufficiently complex to warrant studying the
implementation of a single innovation (e.g., a specific computer program),
rather than the implementation of diverse innovations, across organiza-
tional sites. Ultimately, such studies may provide the groundwork for
studies that are used to compare the implementation of different types of
innovations across organizations.

Multilevel research. Although designed to capture between-organiza-
tional differences in innovation implementation, our model is expressly
multilevel. Implementation effectiveness summarizes the innovation use
of multiple individuals. Implementation climate describes the shared per-
ceptions of multiple individuals. And innovation-values fit may vary not
only between organizations but also between groups and even between
individuals. Accordingly, we advocate the collection of data from multiple
individuals across multiple groups, if present, within each organization
in a multiorganizational sample.

Longitudinal data. Implementation is a process that occurs over
time. Ideally, implementation research begins prior to implementa-
tion, with analysis and documentation of the decision to adopt an
innovation. Research then continues over time to capture increases and
decreases in the strength of implementation climate, in the fit of the
innovation to employee values, and in innovation use and innovation
effectiveness.

Qualitative and quantitative data. To gather data from multiple indi-
viduals across multiple groups in multiple organizations over multiple
periods, researchers will surely need to use quantitative survey measures.
The use of qualitative methods across such a sample would be far too
labor intensive, far too time consuming. Further, the use of quantitative
measures will allow researchers to conduct needed statistical tests of
within- and between-group and within- and between-organization vari-
ability in implementation climate, innovation-values fit, innovation use,
and innovation effectiveness.

However, qualitative research on implementation is still valuable.
Preliminary qualitative research is likely to be essential for a researcher
to gain an in-depth understanding of a given innovation and its imple-
mentation across organizations. Qualitative research may foster further
development of our constructs and may provide the groundwork for the
creation of survey instruments that are focused on a specific innovation.
Finally, qualitative methods may be used to gather in-depth information
about specific organizations that were revealed in surveys to be particu-
larly interesting and important (e.g., organizations characterized by
strong implementation climates and poor innovation-values fit).

Few researchers are likely, of course, to collect multiorganizational,
multilevel, longitudinal, quantitative and qualitative data within a single
study. Yet, studies that follow even two of the four research design recom-
mendations proposed in this section will represent a step in the right

1996 Klein and Sorra 1077

direction—a step toward a deeper, more thorough understanding of inno-
vation implementation.

CONCLUSION

When organizations adopt innovations, they do so with high expecta-
tions, anticipating improvements in organizational productivity and per-
formance. However, the adoption of an innovation does not ensure its
implementation; adopted policies may never be put into action, and
adopted technologies may sit in unopened crates on the factory floor. The
organizational challenge is to create the conditions for innovation use: a
strong climate for innovation implementation and good innovation-values
fit. Only then is an organization likely—but, unfortunately, by no means
certain—to achieve the intended benefits of the innovation.

REFERENCES

Abbey, A., & Dickson, J. W. 1983. R&D work climate and innovation in semi-conductors.
Academy ot Management Journal, 26: 362-368.

Amabiie, T. 1988. A model of creativity and innovation in organizations. In B. M. Staw & L. L.
Cummings (Eds.), flesearch in organizafionai behavior, vol. 10: 123-167. Greenwich, CT:
JAI Press.

Angle, H., & Van de Ven, A. 1989. Suggestions for managing the innovation journey. In A.
Van de Ven, H. Angle, & M. S. Poole (Eds.), Research on the management ot innovations:
The Minnesota studies: 663-697. New York: Harper & Row.

Barley, S. R. 1986. Technology as an occasion for structuring: Evidence from observations ol
CT scanners and the social order of radiologry departments. Adminisfrative Science
Quarterly, 31: 78-108.

Beer, M. 1988. The critical path for change: Keys to success and iailure in six companies.
In R, H, Kilmann 8t T, J. Covin (Eds.), Corporate transformation: 17-45. San Francisco:
Jossey-Bass.

Beyer, J. M., & Trice, H. M. 1978. Implementing change. New York: Free Press.

Bushe, G. R. 1988. Cultural contradictions of statistical process control in American manufac-
turing organizations. Journal ot Management. 14: 19-31.

Damanpour, F, 1991, Organizational innovation: A meta-analysis of effects of determinants
and moderators. Academy ot Management Journal. 34: 555-590.

Dean, J, W., Jr, 1987, Deciding fo innovate. Cambridge, MA: Ballinger.

Dougherty, D. 1992. Interpretive barriers to successful product innovation in large firms.
Organizafionai Science, 3: 179-203.

Fleischer, M., Liker, J., & Arnsdorf, D. 1988. Ettective use ot computer-aided design and
computer-aided engineering in manufacturing. Ann Arbor, MI: Industrial Technology In-
stitute,

Floyd. S. W., & Wooldridge, B. 1992. Managing strategic consensus: The foundation of effective
implementation. Academy of Management Executive. 6(4): 27-39,

Goodman, P. S., & Griffith, T. L. 1991. A process approach to the implementation of new
technology. Joumal ot Engineering Technology and Management, 8: 261-285.

Guth, W. D., & MacMillan, I. C, 1986. Strategy implementation versus middle management
self-interest. Strategic Mangagement Journal. 7: 313-327.

1078 Academy of Management fleview October

Hackman, J. R., 8t Wageman, R, 1995. Total quality management: Empirical, conceptual and
practical issues. Administrative Science OuarterJy, 40: 309-342.

Hage, J. 1980. rheories ot organizations. New York: Wiley.

Hattrup, K.. & Kozlowski, S. W. J. 1993. An acioss-organization analysis of the implementation
of advanced manufacturing technologies. Joumal ot High Technology Management Re-
search. 4: 175-196,

Holland. J. L, 1985. Mating vocational choices: A theory ot careers. Englewood Cliffs, NJ:
Prentice Hall.

Howell, J., & Higgins, C. 1990. Champions of technological innovation. Administrative Science
OuarterJy. 35: 317-341.

Kabanofl, B,, Waldersee, R., & Cohen, M. 1995. Espoused values and organizational change
themes. Academy o/Management/oumaL 38: 1075-1104,

Kanter. R. M, 1988. When a thousand flowers bloom: Structural, collective, and social condi-
tions for innovation in organization. In B. M. Staw & L L, Cummings (Eds.}, Research in
organizational behavior, vol. 10: 169-211. Greenwich, CT: JAI Press.

Kelman, H, C, 1961, Processes of opinion change, Puhiic Opinion Quarterly. 25: 57-78.

Klein, J, A. 1984. Why supervisors resist employee involvement. Harvard Business Review.
84(5): 87-95,

Klein, K, J. 1986. Using 3D CADD: The human side. Technical report. College Park: University
oi Maryland, Department of Psychology,

Klein, K. J., Dansereau, F., & Hall, R. J, 1994, Levels issues in theory development, data collec-
tion, and analysis. Academy of Management fleview, 19: 195-229.

Klein, K. J., Hall, R. ],, & Laliberte, M. 1990. Training and the organizational consequences of
technological change: A case study of computer-aided design and drafting. In U. E.
Gattiker & L. Larwood (Eds.), Technoiogicai innovation and human resources; End-user
training: 7-36. New York: de Gruyter.

Klein, K. J., & Rails, R. S. 1995. The organizational dynamics of computerized technology
implementation: A review of the empirical literature. In L, R. Gomez-Mejia & M, W. Law-
less (Eds.), Implementation management of high technology: 31-79, Greenwich, CT:
JAI Press.

Klein, K. J., Rails, R. S,, & Carter, P. O. 1989. The implementation of a computerized inventory
control system. Technical report. College Park: University of Maryland, Department of
Psychology.

Kopelman, R. E., Brief, A. P., & Guzzo, R. A, 1990, The role of climate and culture in productivity.
In B. Schneider (Ed.), Organizationai ciimate and culture: 282-318. San Francisco:
Jossey-Bass,

Kotter, J. P., & Schlesinger, L. A. 1979, Choosing strategies for change. Harvard Business
Review, 57(2): 106-114.

Kozlowski, S, W. J,, 8f Hults, B. M. 1987. An exploration of climates for technical updating and
periormance. PersonneJ Psychology. 40: 539-563.

Lawler, E. E,, & Mohrman, S. A. 1991. Quality circles: After the honeymoon. In B. M. Staw (Ed.),
Psyciioiogica/ dimensions ot organizational behavior: 523-533, New York: Macmillan.

Leonard-Barton, D., & Krauss, W. A. 1985. Implementing new technology. Harvard Business
ReWew. 63(6): 102-110.

March, J. G., & Sproull, L, S. 1990. Technology, management, and competitive advantage. In
P. S. Goodman & L. S. Sproull (Eds.), Technoiogy and organixafions: 144-173. San Fran-
cisco: Jossey-Bass.

1996 Kiein and Sorra 1079

Markus, M. L. 1987. Power, politics, and MIS implementation. In R. M. Becker & W. A, S. Buxton
(Eds.), Readings in human-computer interaction: A muitidisciplinary approach: 68-82,
Los Angeles: Morgan Kaufmann,

Nadler, D, A,, & Tushman, M. L. 1989. Leadership for organizational change. In A. M, Mohrman,
Jr., S. A. Mohrman, G. E, Ledford, Jr., T. G. Cummings, & E. E. Lawler (Eds.), Large-scale
organizational change: 100-119. San Francisco: Jossey-Bass.

Nord, W. R., & Tucker, S. 1987, impiementing routine and radical innovafions. Lexington, MA:
Lexington Books.

Nutt, P. C. 1988. Tactics of implementation. Academy of Management Joumal. 29: 230-261.

CReilly, C, 8f Chatman, J. 1986. Organizational commitment and psychological attachment:
The effects of compliance, identification, and internalization on prosocial behavior. Jour-
nal of Applied Psychology. 71: 492-499.

Reger, R. K., Gustafson, L. T., DeMarie, S. M., & Mullane, J. V, 1994. Reframing the organization:
Why implementing total quality is easier said than done. Academy of Management
fleview, 19: 565-584.

Rentsch, J. R. 1990. Climate and culture: Interaction and qualitative difference in organiza-
tional meanings, /ourna/ of Applied Psychology. 75: 668-681.

Rivard, S. 1987, Successful implementation oi end-user computing. /n(er/aces, 17(3): 25-33.

Roberts-Gray, C, & Gray, T, 1983. The evaluation oi text editors: Methodology and empirical
results. Communications ot the ACM. 26: 265-283,

Roitman, D. B., Liker, J. K., & Roskies, E. 1988. Birthing a factory of the future: When is “all at
once” too much? In R. H. Kilmann & T, J, Covin (Eds,), Corporate trans/ormation: 205-246.
San Francisco: Jossey-Bass,

Rousseau, D. M, 1989, Managing the change to an automated office: Lessons from five case
studies. Office: Technology & People, 4: 31-52.

Schein, E. H. 1992. Organizationai culture and ieadership. San Francisco: Jossey-Bass,

Schneider, B. 1975. Organizational climates: An essay, Personnei Psychology, 28: 447-479,

Schneider, B, 1987, The people make the place. Personnei Psychology, 40: 437-453.

Schneider, B. 1990. The climate for service: An application of the climate construct. In B.
Schneider (Ed.), Organizationai climate and culture: 383-412, San Francisco: Jossey-Bass,

Schneider, B., & Bowen, D. E, 1985. Employee and customer perceptions of service in banks:
Replication and extension. Joumal of Applied Psychology. 70: 423-433.

Schneider, B., Parkington, J, J., & Buxton, V. M. 1980. Employee and customer perceptions of
service in bands. Adminisfrative Science Quarferiy, 25: 252-267.

Sproull, L, S., & Hoimeister, K, R. 1986. Thinking about implementation. Joumal of Manage-
ment, 12: 43-60,

Sussman M., & Vecchio, R. P. 1991. A social influence interpretation of worker motivation. In
R. M. Steers & L. W, Porter (Eds,), Motivation and work behavior: 218-220. New York:
McGraw-Hill.

Tornatzky, L. G., & Fleischer, M, 1990. The process of technological innovation: Reviewing
the literature. Washington, DC: National Science Foundation.

Tornatzky, L. G., & Klein, K. J. 1982, Innovation characteristics and innovation adoption-
implementation: A meta-analysis of findings, IEEE Transactions on Engineering Manage-
ment. 29: 28-45.

Zohar, D. 1980. Safety climate in industrial organizations: Theoretical and applied implica-
tions. Joumal of Applied Psychology, 65: 96-102.

1080 Academy ot Management Review October

Zuboif, S. 1988. in tiie age of the smart machine: The tuture ot work and power. New York:
Basic Books.

loann Speer Sorra received her master’s degree from Michigan State University and
is currently a doctoral candidate in industrial and organizational psychology at th©
University of Maryland. Her research interests include training, technical updating,
organizational climate and culture, and organizational change,

Katherine J. Klein received her Ph.D. from the University oi Texas. She is an associate
professor of psychology at the University of Maryland, Her current research interests
include innovation implementation and organizational change, level-oi-analysis is-
sues, and part-time work.

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