Assignment WK 8 Advocating for the Nursing Role in Program Design .docx

Assignment WK 8 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.
· 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.
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. 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?
Milstead, J. A., & Short, N. M. (2019).
Health policy and politics: A nurse's guide (6th ed.).
Jones & Bartlett Learning.
· Chapter 5, “Public Policy Design” (pp. 87–95 only)
· Chapter 8, “The Impact of EHRs, Big Data, and Evidence-
Informed Practice” (pp. 137–146)

· Chapter 9, “Interprofessional Practice” (pp. 152–160 only)
· Chapter 10, “Overview: The Economics and Finance of Health
Care” (pp. 183–191 only)
https://www.nursingworld.org/practice-policy/advocacy/
https://www.cdc.gov/injury/pdfs/policy/Brief%204-a.pdf
https://www.congress.gov
C Academy ot Managernent Review
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 and
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, that
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 use
the innovation less frequently, less consistently, or less
assiduously than
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 no
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 help
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 1
Determinants and Consequences of Implementation
Effectiveness
t
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 a
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-

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

INNOVATION-VALUES HT
Building on psychological theories of conformity, we posit that
em-
ployees' commitment to the use of an innovation is a function of
the per-
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
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
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.
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.
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.

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
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,
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
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.
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level-oi-analysis is-
sues, and part-time work.
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: [email protected]
Victor Bekkers
Department of Public Administration
Erasmus University Rotterdam
P.O. Box 1738, NL-3000 DR Rotterdam
The Netherlands

E-mail: [email protected]
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
© 2013 Taylor & Francis
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
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
life for this client. Granting street-level bureaucrats discretion
during policy implemen-
tation can increase client meaningfulness as several situations
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
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
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
meaningfulnessDiscretion
Willingness to
implement+ +
+
Figure 1: Proposed theoretical framework regarding two main
effects of discretion
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

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.
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
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
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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 (! = 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)
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
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.
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.
NOTE
1 Bootstrap 5,000 times, maximum likelihood estimation is used
as robust maximum likelihood is not available
for bootstrapping.
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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
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
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.
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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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Published by John Wiley & Sons, Ltd.1014
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Commons Attribution License, which permits use, distribution
and reproduction in any medium, provided the
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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
Published by John Wiley & Sons, Ltd. 1015
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
Published by John Wiley & Sons, Ltd.1016
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.
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(2014) Big data in health care: using analytics to identify and
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J.A. Sacristán and T. Dilla No big data without small data
Published by John Wiley & Sons, Ltd. 1017
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10/13/22, 9:47 AMRubric Detail – NURS-6050N-18-Policy &
Advocacy for Pop ...
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student work. Your instructor
linked a rubric to this item and made it available to you. 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
41 (41.00%) - 45
(45.00%)
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
36 (36.00%) - 40
(40.00%)
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

32 (32.00%) - 35
(35.00%)
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
0 (0.00%)
(31.00%)
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
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AssoId=_4149265_1
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?
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.
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.
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
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.
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
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.
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
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
36 (36.00%) - 40
(40.00%)
Response
provides a clear,
accurate, and

32 (32.00%) - 35
(35.00%)
Response
provides an
accurate
28 (28.00%) - 31
(31.00%)
Explanation of
the role of the
nurse in
0 (0.00%)
(27.00%)
Explanation of
the role of the
nurse in
Page 3 of
AssoId=_4149265_1
program
implementation?
How does this
role vary

between design
and implantation
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?
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,
including
speci!c
examples.
Response
provides an
accurate and
detailed
description of
the members of
a healthcare
team needed to
implement the
program
selected.
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
examples.
Response
provides and
accurate
description of
the members of
a healthcare
team needed to
implement the
program
selected.
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
speci!c
examples.
Description of
the members
of a healthcare
team needed to
implement the
program
selected is
inaccurate or
incomplete.
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 a healthcare
team needed to
implement the
program
selected is
vague and
inaccurate,
incomplete, or
is missing.
Written
Expression and
Formatting -
Paragraph
Development
and
Organization:
5 (5.00%) - 5
(5.00%)
Paragraphs and
sentences
follow writing
standards for
"ow, continuity,
and clarity.
4 (4.00%) - 4
(4.00%)
Paragraphs and

sentences
follow writing
standards for
"ow, continuity,
and clarity 80%
3 (3.00%) - 3
(3.00%)
Paragraphs and
sentences
follow writing
standards for
"ow, continuity,
and clarity 60%-
0 (0.00%)
(2.00%)
Paragraphs and
sentences
follow writing
standards for
"ow, continuity,
and clarity <
Page 4 of
AssoId=_4149265_1

Paragraphs
make clear
points that
support well
developed ideas,
low 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.
and clarity.
A clear and
comprehensive
purpose
statement,
introduction,
and conclusion

is provided
which
delineates all
required
criteria.
and clarity 80%
of the time.
Purpose,
introduction,
and conclusion
of the
assignment is
stated, yet is
brief and not
descriptive.
and clarity 60%-
79% of the
time.
Purpose,
introduction,
and conclusion
of the
assignment is
vague or o#
topic.
and clarity <
60% of the
time.
Purpose,
introduction,

and conclusion
of the
assignment is
incomplete or
missing.
Written
Expression and
Formatting -
English Writing
Standards:
Correct
grammar,
mechanics, and
proper
punctuation
5 (5.00%) - 5
(5.00%)
Uses correct
grammar,
spelling, and
punctuation
with no errors.
4 (4.00%) - 4
(4.00%)
Contains a few
(1-2) grammar,
spelling, and
punctuation
errors.

3 (3.00%) - 3
(3.00%)
Contains
several (3-4)
grammar,
spelling, and
punctuation
errors.
0 (0.00%)
(2.00%)
Contains many
(≥5) grammar,
spelling, and
punctuation
errors that
interfere with
the reader’s
understanding.
Written
Expression and
Formatting:
5 (5.00%) - 5
(5.00%)
Uses correct
APA format with
4 (4.00%) - 4
(4.00%)
Contains a few

(1-2) APA format
3 (3.00%) - 3
(3.00%)
Contains
several (3-4)
0 (0.00%)
(2.00%)
Contains many
(≥5) APA format
Page 5 of
AssoId=_4149265_1
The paper
follows correct
APA format for
title page, font,
spacing,
margins,
indentations,
parenthetical/in-
text citations,
and reference
list (if sources
are cited).

APA format with
no errors.
(1-2) APA format
errors.
several (3-4)
APA format
errors.
(≥5) APA format
errors.
Name:NURS_6050_Module04_Week08_Assignment_Rubric
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d=_3280053_1
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?
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
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.
32 (32%) - 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.
0 (0%) - 31 (31%)
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
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· 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?
for the design of
the program,
including
speci!c
examples.
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.
Response
provides an
accurate
description of
the role of the
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.

Description of
the role of the
nurse advocate
for the target
population for
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.
missing.
Description of
the role of the
nurse advocate
for the target
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 implantation
of healthcare
programs? Can
you provide
examples?
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
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
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
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
d=_3280053_1

· 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?
detailed
explanation of
how the role of
the nurse is
di#erent
between design
and
implementation
of healthcare
programs,
including
speci!c
examples.
Response
provides an
accurate and
detailed
description of
the members of
a healthcare
team needed to
implement the
program
selected.

how the role of
the nurse is
di#erent
between design
and
implementation
of healthcare
programs and
may include
some speci!c
examples.
Response
provides and
accurate
description of
the members of
a healthcare
team needed to
implement the
program
selected.
di#erent
between design
and
implementation
of healthcare
programs is
vague or
inaccurate
and/or does
not include
speci!c
examples.

Description of
the members
of a healthcare
team needed to
implement the
program
selected is
inaccurate or
incomplete.
and
implementation
of healthcare
programs is
vague and
inaccurate or is
missing.
Description of
the members
of a healthcare
team needed to
implement the
program
selected is
vague and
inaccurate,
incomplete, or
is missing.
Written
Expression and
Formatting -
Paragraph
Development

and
Organization:
Paragraphs
make clear
points that
support well
developed ideas,
low logically,
and
demonstrate
continuity of
ideas.
Sentences are
carefully
focused--
neither long and
rambling nor
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 (3%) - 3 (3%)
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%) - 2 (2%)
Paragraphs and
sentences
follow writing
standards for
"ow, continuity,
and clarity <
60% of the
time.
Purpose,
introduction,
and conclusion
of the
assignment is
incomplete or
missing.
d=_3280053_1
short and
lacking
substance. A

clear and
comprehensive
purpose
statement and
introduction is
provided which
delineates all
required
criteria.
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 (3%) - 3 (3%)
Contains
several (3-4)
grammar,
spelling, and
punctuation
errors.
0 (0%) - 2 (2%)
Contains many
(≥5) grammar,
spelling, and
punctuation
errors that
interfere with
the reader’s
understanding.
Written
Expression and
Formatting:
The paper
follows correct
APA format for
title page, font,
spacing,
margins,
indentations,
parenthetical/in-
text citations,
and reference

list (if sources
are cited).
5 (5%) - 5 (5%)
Uses correct
APA format with
no errors.
4 (4%) - 4 (4%)
Contains a few
(1-2) APA format
errors.
3 (3%) - 3 (3%)
Contains
several (3-4)
APA format
errors.
0 (0%) - 2 (2%)
Contains many
(≥5) APA format
errors.
Total Points: 100

of 5https://class.waldenu.edu/webapps/bbgs-deep-links-
d=_3280053_1
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Assignment WK 8 Advocating for the Nursing Role in Program Design .docx

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Assignment WK 8 Advocating for the Nursing Role in Program Design .docx