TOPIC What is Strategic PlanningFollowing the co.docx

TOPIC:
What is Strategic Planning?
Following the completion of this week’s reading/content
assignments, complete each of the following:
· Write a one-two sentence personal definition of strategic
planning. Base your definition on what you have encountered in
the reading/materials this week, as well as on what you have
already learned about the topic.
· Discuss this statement by Roger L. Martin: ". . . good strategy
is not the product of hours of careful research and modeling that
lead to an inevitable and almost perfect conclusion. Instead, it’s
the result of a simple and quite rough-and-ready process of
thinking through what it would take to achieve what you want
and then assessing whether it’s realistic to try. If executives
adopt this definition, then maybe, just maybe, they can keep
strategy where it should be: outside the comfort zone.”
· Finally, describe the differences between strategic planning
and business planning.
JOURNAL OF MANAGERIAL. ISSUES
Vol. XXXI Number 2 Summer 2019
Is Customer Satisfaction Really a Catch-All?
The Discrepancy between Financial Performance
and Survey Results
Kevin W. James
Assistant Professor o f Marketing
The University of Texas at Tyler

[email protected]
Hui James
Assistant Professor o f Finance
The University of Texas at Tyler
[email protected]
Barry J. Babin
Chair, Department o f Marketing and Analysis
Louisiana Tech University
[email protected]
Janna M. Parker
Assistant Professor o f Marketing
James Madison University
[email protected]
Marketing as a discipline traditionally places customer
satisfaction as a focal theme,
thereby encouraging considerable amounts of marketing
research (Churchill and
Surprenant, 1982). Satisfaction is indeed a core marketing
concept and, in many cases,
retail marketing managers and academicians alike treat the
concept as a catch-all term
that captures the entirety of consumer results from consumption
(Dixon et at., 2010).
The expectancy-disconfirmation model provides marketers with
a deep understanding
of how expectations align with current performance outcomes to
arrive at a level of
JOURNAL OF MANAGERIAL ISSUES VOL. XXXI NUMBER
2 Summer 2019
( 137)

mailto:[email protected]
138 Is Customer Satisfaction a Catch-All?
satisfaction (Oliver, 1980; Ganesh et al., 2000). Satisfaction
research covers topics
including the “gaps” model (Zeithaml et al., 1993), satisfied
switchers (Maxham and
Netemeyer, 2002), and an index termed the American Customer
Satisfaction Index
(theacsi.org), which remains a measuring stick for performance
for many companies
worldwide, including many retailers (Fornell, 1992).
Despite the richness of the satisfaction concept, researchers find
evidence that
merely satisfying the retail customer might not be enough to
secure strong performance
(Blankson et al., 2017; Balabanis et al, 2006; Dahlsten, 2003).
Evidence suggests that all
too often companies try to retrofit current practice to fit an
outdated customer demand
model (Dahlsten, 2003), while other evidence suggests the
relationship between
satisfaction and loyalty is nonlinear (Balabanis et al., 2006). In
fact, Volvo Motor
Company surprisingly discovered a negative reladonship
between their customers’
reported satisfaction levels and loyalty to Volvo, suggesting
satisfaction may not be as
vital to success as once thought (Dahlsten, 2003), or at the
least, the evidence suggests

that satisfaction is insufficient to drive overall performance.
So, what more can there be? Value is emerging as paramount in
importance to the
marketing community. Marketing authors such as Holbrook
(1994), Woodruff (1997),
Zeithaml (1988), Woodall (2003), and Babin et al. (1994)
emphasize value as the ultimate
outcome from any consumption experience. The emerging
value-dominance theory
defines value as an outcome placed at the same level of
satisfaction rather than a
predictor of satisfaction. Vargo and Lusch (2004) present
service-dominant logic
paradigm (SDL) that positions value-in-use as a focal marketing
concept that requires
more attention as the key outcome variable resulting from
sendee. SDL prescribes
moving beyond the transaction point as the climax of marketing
efforts towards
understanding how customers receive value from firm resources.
SDL positions value as
the result of a company doing something for the benefit of
another, in this case, the
customer (Vargo and Lusch, 2004). This research compares
different value perspectives
with satisfaction in a retail context. The overarching research
question this work
attempts to answer is what drives consumer loyalty and firm
performance in a retail
setting, and are both driven by the same antecedents? Does
customer value or customer
satisfaction matter more in retailing?
RESEARCH QUESTION

The overarching research question investigates the roles that
value and satisfaction
play in creating customer loyalty and positive financial
performance. The implications
may assist retail managers in closely aligning resources with
desired outcomes. In other
words, what return comes from an investment in satisfaction
vis-a-vis value? Loyalty and
both earnings per share (EPS) and return on assets (ROA) serve
as major organizational
success metrics; however the antecedents to a survey outcome
like loyalty and market
outcomes like EPS and ROA may differ. Considering excessive
outside debt can lead to
lower profitability (Titman and Wessels, 1988; Rajan and
Zingales, 1995), retail
managers investing in resources, be it satisfaction or value, will
benefit from
understanding if the investment will provide a return in the
form of either increased
loyalty, better financial performance, both, or neither.
JOURNAL O F MANAGERIAL ISSUES VOL. XXXI
NUMBER 2 SUMMER 2019
J am es, J am es , Ba bin , and Parker 139
OVERALL VALUE
Zeithaml (1988) suggests four interpretations of value from her
research identifying
consumer means-end chains from a qualitative laddering
approach. (1) Value as low-
price, where consumers use value and price interchangeably. (2)

What I want, where
consumers place high value on things with proportionately more
benefits. (3) Quality
for price, where consumers place high weight on the price-
quality heuristic. (4) A get-
for-give perspective, where consumers weigh off all that is
received versus all that must
be sacrificed in consuming a product. The fourth view presents
the most encompassing
interpretation of value. In it, value received is derived through a
deeply personal, and
thus subjective, process involving tradeoffs between resources
received and resources
relinquished.
Babin and Harris (2018) and Babin and James (2010) present
the view of value as
all the customer gets minus all the customer gives. In this get
versus give tradeoff, the
greater the customer involvement with a project, the greater the
potential that the
customer will derive more from the get component, all things
being equal. If a customer
enjoys a task and thus feels prestige or nostalgia from the
purchase or use of a product,
the customer then is deriving added value from the task. Thus,
the customer receiving
emotional benefits can enhance the get components beyond
simple ease-of-use features.
This research explores the theoretical notion of overall
customer value as a get
versus give trade-off from the customer’s perspective. The logic
follows from Thaler
(1985) and from the theoretical consumer value framework
(CVF) in Babin and Harris

(2018). Overall value includes a tradeoff where the customer
weighs the net benefits
received to everything perceived to have been given up (or
invested) to receive the
benefits. The theme is to compare the above view of value with
other marketing
conceptualizations of value and with satisfaction to determine
how each affects outcome
measures. In so doing, the relationship between overall value
and both financial results
(EPS and ROA) and loyalty is captured to determine if overall
value affects survey results
and financial performance equally. Earnings per share (EPS)
and Return on assets
(ROA) were chosen as the two dependent variables along with
loyalty. EPS is the portion
of a company’s profit allocated to each outstanding share of
common stock and is often
considered an indicator of firm profitability and ability to
generate sustainable internal
funding. EPS is derived from net income after considering
shares outstanding. ROA is
seen as a profit indictor regarding management effectiveness in
use of assets to generate
earnings, or similarly stated, the profit per dollar of assets
(Ross et al, 1999).
Firms that deliver the most value should create incentives for
customer loyalty that
ultimately realize themselves in relatively high returns, based
on the relative cost of
customer retention versus continued customer acquisition. The
overriding set of
relationships is below in HI and is subsequently derived into
individual testable
relationships.

HI: Overall value relates positively to loyalty, EPS, and ROA.
Therefore, Hla predicts
overall value relates positively to loyalty. Hlh predicts overall
value relates
positively to EPS. Lastly, Hlc predicts overall value relates
positively to ROA.
JOURNAL OF MANAGERIAL ISSUES Vol. XXXI Number 2
Summer 2019
VALUE AS HEDONIC AND UTILITARIAN
Value is not merely a financial price (Carr and Ring, 2017).
Babin et al. (1994) take
value derived from a shopping experience and break it into two
components: hedonic
and utilitarian value. The personal shopping value scale (PSV)
consists of utilitarian and
hedonic value and was developed specifically for retail
research. Utilitarian value
represents the ability to complete efficiently the shopping task
while hedonic value is
the extent to which the customer enjoys and extracts value from
the experience itself
even aside from any purchase the consumer may make. A
hedonically rewarding
experience generates positive emotions or feelings independent
of any gratification
from product acquisition or task completion.
The (PSV) has seen wide interest and study within retailing.

Examples in retailing
include explaining customer share via positive and negative
emotions and image
transfer (Babin and Attaway, 2000; El Hedhli et al., 2017),
explaining online shopping
and hedonism online (Mazaheri el al, 2010), explaining patient
and provider
expectation congruency (Camp et al, 2017), and explaining
tourists’ behavior (Duman
and Mattila, 2005). Internadonal retailing applications have
recently been undertaken
and support hedonic value relating to survey items such as
satisfaction and behavioral
intentions (Atulkar and Kesari, 2017). Still other authors have
related value to
behavioral loyalty including both number of retail store visits
and sales (Mencarelli and
Lombart, 2017). In a review of the uses and applicability of
hedonic and utilitarian value,
researchers state the concept has been studied in thousands of
applications (Babin and
James, 2010). However, few studies have successfully studied
value's effect on loyalty and
financial results simultaneously to determine if loyalty and
financial results have the
same predictors. Accordingly, the relationships between hedonic
and utilitarian value
and loyalty and two performance metrics (EPS and ROA)
deserve attention. The
overriding set of relationships is below in H2 and H3 and is
subsequently derived into
individual testable relationships.
H2: Hedonic value relates positively to loyally, EPS, and ROA.
Therefore, H2a
predicts hedonic value relates positively to loyalty. H2b

predicts hedonic value
relates positively to EPS. Lastly, IT2c predicts hedonic value
relates positively to
ROA.
H3: Utilitarian value relates positively to loyalty, EPS, and
ROA. Therefore, IT3a
predicts utilitarian value relates positively to loyalty. H3h
predicts utilitarian
value relates positively to EPS. lastly, H3c predicts utilitarian
value relates
positively to ROA.
CUSTOMER SATISFACTION
Fornell (1992) developed the America Customer Satisfaction
Index (ACSI), which
is a “weighted composite index based on annual survey data
from customers of about
100 leading companies in some 30 industries.” This index’s
intent is to provide a
snapshot of the health of the (1) country’, (2) industry, and (3)
individual firm. The idea
is that higher scoring firms should see higher levels of customer
loyalty. Satisfaction is
defined as an overall evaluation based on the purchase and
consumption experience
2 SUMMER 2019
J am es, J am es, Ba bin , and Parker 141
with a product or product offering (Anderson et al., 1994).

Satisfaction has been tied to
financial performance in the B2B industry considering a single
focal firm and includes
outcomes studied here including EPS (Williams and Naumann,
2011). Other studies
address the satisfaction and sales link across a single retailer
and find the relationship
to be asymmetric (Gomez et al., 2004). Service research
suggests direct relationships
from value and satisfaction to intentions, which may
complement the magnitude of
indirect effects on firm performance (Cronin et al., 2000).
Finally, studies have shown
evidence of satisfaction in the retail sector to positively aff ect
outcomes such as consumer
share but don’t include financial outcomes (Carpenter, 2008).
Researchers have raised the question about the diagnosticity of
satisfaction in
relation to performance. The authors of the satisfaction index
themselves realize that
skewness is problematic in that 80 percent of customers report
satisfaction. Additionally,
research reports satisfaction having only a one percent
explanation of variance when
considering market returns due to the high cost to create
satisfaction and little
corresponding increases to loyalty' or share of wallet
(Keiningham et al, 2014).
Returning to skewness, are practically all customers satisfied?
If everyone is satisfied but
companies still show a disparity in performance, then what is
driving this apparent
disparity? This research will relate satisfaction to both loyalty
and two performance
metrics (EPS and ROA) to determine if satisfaction is the catch-

all concept within
retailing while simultaneously considering the role of value as a
complimentary or
alternative marketing outcome. The overriding set of
relationships is below in H4 and
is subsequently derived into individual testable relationships.
114: Satisfaction relates positively to loyalty, EPS, and ROA.
Therefore, H4a predicts
satisfaction relates positively to loyalty. H4b predicts
satisfaction relates positively
to EPS. lastly, II4c predicts satisfaction relates positively to
ROA.
METHODS SECTION
A professional consumer panel company provided access to U.S.
consumer
respondents in an effort to create some ability to generalize
beyond what crowd-sourced
data or data from students would allow. The effective sample
size is 436. Each
respondent rated each retailer they were familiar with from all
retailers appearing in
the ACS1 listings under either the discount retailer or specialty
retailer category within
the previous five years and have financial results to relate. The
five concepts assessed for
this research include hedonic value, satisfaction, utilitarian
value, overall value, and
loyalty. If a respondent wras unfamiliar with a given retailer,
then he/she did not rate that
particular retailer, thus accounting for differing sample sizes in
Table 1. The instructions
and wording used for each single-item scale is provided below.
Given the nature of access

and the need to limit respondent fatigue, single-items were the
only practical solution
in this instance. The list of retailers and subsequent number of
responses for each is
presented in Table 1. Respondents replied to the following
items, which were presented
in a random order:
• Hedonic value: Think about a typical shopping trip at each
retailer shown below.
Rate each retailer on the extent to which the shopping trip itself
is enjoyable or
exciting. In other words, the visit is worthwhile even if you
don’t buy anything.
JOURNAL OF MANAGERIAL ISSUES Von. XXXI Number 2
Summer 2019
142 Is Custom er Satisfaction a Catch-All?
• Utilitarian value: Please rate each retailer below based on the
extent to which you
are able to accomplish the specific task of shopping (find
products you need to
buy, buy them at a reasonable price). 0 means that zero percent
of the task would
get completed and 100 means that 100 percent of the task would
get completed.
• Overall value: Think about everything gained from your
shopping experience
with these particular retailers weighed against all the costs of
shopping in these
stores, and rate the overall value you received from your most

recent interactions
with that retailer. A score below 50 means the costs outweighed
the benefits. A
score above 50 means the benefits outweighed the costs.
• Satisfaction: Rate each retailer based on your opinion of how
satisfied you are
with shopping at each retailer.
• Loyalty: What is the likelihood of continuing to shop at the
retailer based on your
recent experiences? Zero means zero percent chance and 100
means 100 percent
chance.
Company size is included as a control variable through
gathering number of
employees and dichotomizing the outcome to a one or zero
based on a median split
(median size = 138,000). Thus, large firms are defined as
having above 138,000
employees and medium-size firms are defined here as below'
138,000 employees. Of the
thirteen firms rated, seven firms were above the median value
and six firms were below
the median value. Two variables were operationalized to
represent firm financial
performance using financial data collected from SEC.gov and
standardized for analysis.
Table 1
Retailer Effective Sample Size
Retailer
Effective

Sample Size
Dillard’s 213
Dollar General 321
]C Penney 376
Kohl’s 342
Kroger 233
Macy’s 306
Nordstrom 203
Publix 172
Safeway 200
Sears 389
Target 406
Wal-Mart 436
Whole Foods 207
JOURNAL OF MANAGERIAL ISSUES Vol.XXXI Number 2
Summer 2039
J am es, J am es , Babin , and Parker 143
Each respondent rated each retailer using the scales described
above. The
presentation of the retailers was randomized to minimize any
order effect. Sliding scales
were used to capture respondent feedback. A 100-point scale
was used in each case. A
“don’t know” option was provided to respondents who either
could not recall a recent
experience or simply had not patronized the particular retailer.
DATA ANALYSIS
Demographic analysis finds the gender breakdown is 50 percent
female, the median

age falls between 41-50 years of age, with the two largest age
groups being the 61-70
year olds and the 51-60 year olds (24.8 percent and 20.9
percent, respectively). Fifty
percent of the sample lives in a household earning less than
$50,000. Forty-five percent
of the respondents report a high school degree as the highest
educational attainment
and 38 percent of the sample report earning an undergraduate
degree. No
demographic variables discussed above were significant when
inserted as control
variables; thus demographic variables will not be included in
the analysis.
In order to assess possible multicollinearity issues, variance
inflation factors (VIF)
and the related tolerance scores were obtained for the relevant
variables and are
presented in Table 2.
Table 2
Retail Multicollinearity Table Including VIF and Tolerance
MEASURE Tolerance VIF
HV 0.31 3.24
uv 0.21 4.66
OVALUE 0.22 4.53
SAT 0.21 4.81
A VIF value of one means that no mulitcollinearity exists and
higher values mean
more multicollinearity. Research indicates that VIFs of five are
a concern, and
sometimes multiple VIFs greater than two can be problematic
(Babin and Zikmund,

2016). For this analysis three of the variables display a value
above four. In this case,
satisfaction has a variance inflation factor of 4.81, and both
utilitarian value and overall
value show a variance inflation factor approaching 5. In fact,
satisfaction has a
correlation near 0.90 with other variables in the model, thus
inflating the standard
errors problematically. Thus, the VIFs provide evidence of
potentially problematic
multicollinearity.
To rectify the multicollinearity, principal component analysis
was employed to
transform the independent variables. The four variables were
entered into the principal
components analysis with four components extracted. The four
components were
rotated using the varimax procedure, yielding orthogonal
components. In the end, each
variable loads highly on only one component providing an
obvious interpretation of
each component as matching the single corresponding variable.
Component scores were
computed for each, and because of the varimax rotation, each
component represents a
Summer 2019
144 Is Cu sto m er Sa t isfa c tio n a Ca t c h -Al l?
mathematically transformed but independent representation of
each original variable.

Table 3 represents the correlation matrix (PCA loadings)
between the components and
the original variables. As can be seen, the matching loadings are
0.84, 0.77, 0.80, and
0.75, for hedonic value, utilitarian value, overall value, and
satisfaction, respectively.
Table 3
Retail Evaluation Principal Component Score Variable
Correlations (Loadings)
Measured Variables
Component HV ov SAT uv
Hedonic Value Component 0.84 0.34 0.38 0.39
Overall Value Component 0.31 0.80 0.40 0.38
Satisfaction Component 0.30 0.36 0.75 0.35
Utilitarian Value Component 0.32 0.37 0.37 0.77
Thus, the component scores will be used as independent
variables to assess the
research questions. A general linear model approach will
operationalize multivariate
regression analysis with firm size, the hedonic factor, overall
value factor, satisfaction
factor, and utilitarian value factor as independent variables
predicting EPS, ROA, and
loyalty. This analysis will allow further insight into the research
questions with the
advantage of uncorrelated predictor variables.
Wilks’ Lambda and the corresponding multivariate F provide
tests of each model
for the overall effect on all dependent variables. All three
models yield significant F-
statistics (p value < 0.001). The significant multivariate F-
statistics provide support for

proceeding to the univariate regression analyses and to analyze
the relationship between
the dependent and independent variables.
Table 4
Univariate Results for EPS
Lro df F Sig. t P B Lower Upper
Corrected Model 5 67.43 0.00
Intercept 0.00 17.87 1.17 1.04 1.30
Size 1 0.00 15.36 0.25 1.38 1.20 1.55
Hedonic Value 1 0.00 7.04 0.11 0.31 0.23 0.40
Overall Value 1 0.00 4.76 0.08 0.21 0.12 0.30
Satisfaction 1 0.01 2.65 0.04 0.12 0.03 0.20
Utilitarian Value 1 0.39 0.87 0.01 0.04 -0.05 0.13
EPS R Squared = 0.088
Summer 2019
J a m e s , J a m e s , Ba b in , a n d P a r k e r 145
Table 5
Univariate Results for ROA
ROA C.I.
df F Sig. t P B Lower Upper
Intercept 0.00 73.25 5.13 5.00 5.27
Size 1 0.00 21.98 0.35 2.11 1.92 2.30
Hedonic Value 1 0.00 4.20 0.07 0.20 0.11 0.29
Overall Value 1 0.00 4.01 0.06 0.19 0.10 0.28
Satisfaction 1 0.07 1.80 0.03 0.09 -0.01 0.18

Utilitarian Value 1 0.02 -2.28 -0.04 -0.11 -0.20 -0.02
ROAR Squared = 0.13
Table 6
Univariate Results for Loyalty
Loyalty C.I.
df F Sig. t P B Lower Upper
Intercept 0.00 150.60 64.41 63.57 65.24
Size 1 0.03 2.22 0.02 1.28 0.15 2.40
Hedonic Value 1 0.04 2.02 0.35 12.01 11.46 12.56
Overall Value 1 0.00 10.89 0.44 14.88 14.33 15.44
Satisfaction 1 0.00 29.43 0.54 18.18 17.63 18.73
Utilitarian Value 1 0.00 8.49 0.39 13.23 12.67 13.79
Loyalty R Squared = 0.76
Tables 4, 5, and 6 provide univariate regression results. As is
clear, each model
predicts a significant portion of variance in the corresponding
dependent variable. The
resulting R2 values are 0.088, 0.13, and 0.76, for EPS, ROA,
and loyalty, respectively.
The standardized (3 coefficients, allowing for a relative
comparison of effect sizes, are
presented along with unstandardized B, which allow for a direct
assessment of the slope
in real units. Also, the tables reflect the 95% confidence
intervals (Cl) as a better
indicator of effect size than a reliance on p-values. Hedonic
satisfaction all relate significantly to EPS. The findings indicate
that hedonic value (H2b:
(3=0.11, B=0.31, p<0.001) and overall value (Hlb: (3=0.08,
B=0.21, p<0.001) relate

more strongly to EPS than does satisfaction (H4b: (3=0.04,
B=0.12, p<0.01). Utilitarian
value’s Cl contains zero and thus, it is not a significant
predictor of EPS thus not
supporting H3b. All of the effects control for a significant and
positive influence of firm
size (|3=0.25, B=1.38, p<0.001).
Hedonic value, overall value, and satisfaction components all
significantly predict
ROA, as evidence by the lack of 0 in the corresponding Cl
(Table 5). Hedonic value
(H2c: (3 = 0.07, B=0.20, p<0.001) and overall value (Hlc:
(3=0.06, B=0.19, p<0.001)
display the largest effects on ROA, compared to utilitarian
value (H3c: (3=-0.04,
B=-0.11, p<0.05), while the effect of the satisfaction component
on ROA is not
Summer 2019
significant thus not supporting H4c. Once again, the effects
control for firm size, which
relates to ROA significantly indicating relatively large
companies experience more
positive returns on assets (P =0.35, B=2.11, p<0.001).
In contrast to the two financial performance metrics, all loyalty
predictors show
significant, positive effects. Satisfaction relates positively, with
the highest standardized

effect on loyalty (H4a: [1=0.54, B=18.18, p<0.001), with
overall value second (Hla:
(3=0.44, B=14.88; p<0.001), followed by utilitarian value (H3a:
p=0.39, B=0.13.23,
p<0.001) and hedonic value (H2a: P=0.35, B = 12.01, p <
0.001). Once again, these
effects take into account size as a control variable. For
perceived loyalty, size displays a
relatively small but significant effect suggesting more loyalty
for larger firms (p=0.02,
B= 1.28, p=0.03).
DISCUSSION
The research question presented in this paper asks, “What are
the dominant drivers
of loyalty and firm financial performance?” A survey modeled
after the ACSI provides
data on the diagnostic effects of value, its key dimensions,
satisfaction, and loyalty, in
terms of predicting firm performance. Specifically, how do
consumers’ value
perceptions compare to asking customers about their
satisfaction in explaining firm
performance? This research relates multiple value perspectives
and the traditional
satisfaction construct with EPS, ROA, and loyalty for retailers.
Results, perhaps
unsurprisingly, show that satisfaction, value, and loyalty are
positively related in all
contexts. Thus, a correction for multicollinearity allows a more
valid comparison of the
effects. The relationship between perceived overall value
provided by a retailer and both
EPS and ROA is stronger than the relationship between
satisfaction and EPS or ROA.

The question seems to be answered best by separating loyalty,
captured here as an
attitudinal concept, from financial performance, because the
drivers of the two appear
to diverge. If loyalty is the goal, satisfaction seems to be most
diagnostic but, perhaps,
the reason for this finding is the measurement of the two and
the inability of the
customer to separate the two concepts. When financial
performance is measured,
satisfaction appears to take a backseat to the value components’
predictability, including
particularly the role of hedonic value and EPS and hedonic
utilitarian value and ROA.
Marketing managers competing in the SDL era strive to
understand what operant,
resources the retailer provides and how these resources relate to
marketing
management outcomes. Loyalty, here being an attitudinal survey
measure similar to
satisfaction, is often a stated goal of marketing managers.
Similarly, financial outcomes
such as EPS and ROA are important outcomes for marketers if
marketing variables affect
these outcomes.
Satisfaction is often seen as a catch-all measure thought to drive
all outcomes. In
this light, the relationship between value and outcome variables
(survey and market-
based) often is examined with value operating through
satisfaction (Zhong and Mitchell,
2010; Caruana, 2000; and Orth et al, 2010). Results here
suggest that theories

supposing satisfaction as mediating retail variables’ effects on
retail market success may
be inaccurate. The results presented here suggest positive and
significant effects for
hedonic shopping value and EPS, an effect that does not depend
on loyalty to work and
when placed with satisfaction in fact overshadows the concept.
EPS is thought to be a
JOURNAL OF MANAGERIAL ISSUES VOL.XXXI NUMBER 2
SUMMER 2019
J am es, J am es, Ba bin , and Parker 147
good measure of a firm’s profitability and stock price and thus
the findings that hedonic
value relate to this measure more so than do the other variables
(UV, satisfaction, and
overall value) is important to retail decision-makers. The
findings which correlate
hedonic value with EPS and ROA is theoretically appealing and
more diagnostic given
the traditional model which places the hedonic and utilitarian
value components
relating to overall value and satisfaction. The hedonic value
relationship with both EPS
and ROA suggest that retailers can influence these market
variables with a pleasing
experience as an operant resource. Additionally, the relationship
between hedonic
value, utilitarian value, and overall value and ROA is stronger
than the relationship
between satisfaction and ROA which fails to reach significance.
Thus, for retailers,

customers expect to be satisfied, however, a valuable
experience where the task is simple
to complete combined with a pleasing retail experience results
in higher total profits
with respect to ROA.
LIMITATIONS, CONTRIBUTIONS, AND FUTURE
RESEARCH
The limitations and contributions will be discussed next. This
work attempts to
assess the relative relationship between value-in-use, as
captured by utilitarian, hedonic,
and overall value, and performance metrics such as EPS and
ROA. In this case, given
that thirteen retailers are included, a multi-level or mixed-
models analysis is not
reported to allow for a more straight-forward presentation.
Future research should
increase the number of focal firms and respondents to allow for
such an analysis.
A second limitation in this work deals with using single-item
independent variables.
However, every attempt was made to ensure that the definition
for each variable
coincided as closely as possible with the measure used above to
ensure valid measures.
Future research should attempt to replicate the work with a
greater variety of attitudinal
and market constructs.
Finally, future research should investigate and include other
predictor variables
including tax rates, interest rates, and dividend rates, which can
affect ROA and EPS if

the goal is to fully explain ROA or EPS. These predictors
towards financial outcome
variables could capture variance not explained in this research.
These predictors have
been used in previous research (Rosset al., 1999) and are
beyond the scope of the current
research.
The practical contributions assess the extent to which providing
and measuring
success based on satisfaction is enough. The disconnect between
customer satisfaction
and outcome variables is well documented (see Woodruff, 1997;
Reichheld, 2003). This
research suggests that retail success in terms of EPS and ROA
occurs by providing
customers an overall experience they value (overall value), and
by directing resources
towards providing customers with a gratifying experience.
Future research should
expand the number of and variety of financial outcomes
including profit and stock price.
Additionally, experimental research manipulating value
elements (hedonic and
utilitarian value) holding other factors constant across several
comparable branded
stores would allow causation to be analyzed with store sales as
an attractive outcome.
From a managerial point of view, an examination of the change
in share value given
a change in hedonic value is appropriate. The hedonic value
effect on EPS of 0.311.
Taking it and transforming it back to its original metric would
equate to an equivalent
beta of 0.012. That means that a single point change in hedonic

value increases EPS by
SUMMER 2019
0.012 after controlling for firm size. While this may not seem
like a huge difference, for
every 1,000 shares the one point difference in HV returns $12.
Many of the firms here
have millions of shares outstanding.
Finally, firms finance their projects either internally or
externally. The growth
generated from obtaining financing from new creditors or
shareholders may not be
sustainable because the higher costs associated with using them.
Corporate debt has the
possibility of default. Excessive borrowing may significantly
increase firms’ financial
distress costs, which increases cost of using capital and
consequently reduces return from
investment. Issuing new equity is generally viewed by investors
as a signal for overvalued
stocks. In addition, additional equity financing dilutes profits
attributable to existing
shareholders. As such, equity financing in general leads to a
drop in current stock price.
Pecking order theory proposed by Myers (1984) suggest firms
prefer internal funds to
external funds to reduce costs of using capital and creates
higher return for
shareholders. Thus, higher EPS and ROA, holding all else

constant, would result in
more internal fund generation leading to lower cost of
investment and therefore more
positive long-term growth potential.
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2 SUMMER 2019
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betically arranged, signed, and with indi-
vidual bibliographies—provide scholarly
yet clearly written overviews of the many
aspects of identity from the many dis-

ciplines that treat it: religion, the visual
arts, cultural studies, psychology, soci-
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stereotypes to embeddedness/embedded
identity, ethnolinguistic identity theory,
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and self-enhancement theory. While a
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many academic libraries, as well as for
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ing individual terms within every article.
Highly recommended.—Marcia Welsh, Dart-
mouth Coil. Libs., Hanover, NH
Encyclopedia of Political Theory. 3 vols.
SAGE. (SAGE Reference). 2010. c.3011p. ed.
by Mark Bevir. index. ISBN 9781412958653.
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In this three-volume set, Bevir (Univ.
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discuss an assortment of political subjects
ranging from abortion to Wycliffism,
including religious and secular political
theories, major political ideologies, leaders
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job of exploring their political beliefs and
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entries here strive for political neutrality.
Article length varies from half a page to
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public might find it useful for research-
ing a political ideology or theory, college
students majoring in political science will
probably tlnd it most useful. Libraries that
own the Encyclopedia of Political Thought

edited by Garrett Ward Sheldon (Facts
On File 2001) might consider a newer
book. Purchase if your budget permits.—
James Langan, Univ. of Pittsburgh Lib.
^Encyclopedia of Research Design. 3
vols. SAGE. (SAGE Reference). Sept. 2010.
c.3427p. ed. by Neil J. Salkind. index. ISBN
9781412961271. $425; Online: SAGE
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Salkind and associate editors Bruce Frey
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REFERENCE
"sufficiently technical to enlighten the
naive but educated reader." Volume 1 be-
gins with a very useful Reader's Guide,
where entries are organized into 28 broad
categories and which includes a list of the
entries that fall into each category. The
two things that make this work distinc-
tive are the inclusion of reviews of 15
seminal articles in the field (identified
in the Reader's Guide under "Important
Publications") and reviews of 11 popu-
lar research tools (in the Reader's Guide
under "Software Applications"). Entries
are organized alphabetically and range in
length from 1000 to 3000 words (roughly
four paragraphs to four or five pages). En-
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editors' judgments of each topic's impor-
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explicit instructions for their calculation
as well as the underlying assumptions
and, often, a brief example (e.g., the en-
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are pointers to further readings. Entries
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most high-school libraries.—Sarah Sutton,
Texas A&M Univ. Lih., Corpus Christi
SPORTS & RECREATION
Martial Arts of the World: An Encyclopedia
of History and Innovation. 2d ed. 2 vols.
ABC-CLIO. 2010. 833p. ed. by Thomas A.
Green & Joseph R. Svinth. photogs. maps,
index. ISBN 9781598842432. $180; Online:
ABC-CUD eBook Collection REF
In this second edition of a 2001 set.
Green, a longtime martial artist and an-
thropology professor at Texas A&M Uni-
versity, returns as editor and joins forces
with Svinth, editor of Electronic Journals of
Martial Arts and Science, to offer something
of a mixed bag: more articles (111 now,
96 then) but fewer pages (705 now, 894
then). The original alphabetical arrange-
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"Regions and Individual Arts," which

entails broad geographical headings, such
as "Africa," "Americas," and so on. Under
each appear entries for specific styles as-
sociated with that part of the world, as in
"Japan; Aikido," "Japan: Judo," etc. Vol-
ume 2 takes on "Themes," which make
up the social and philosophical underpin-
nings of these fighting forms. The editors
explain their decision to change the or-
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while still allowing for coordination and
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Its strength lies in its historical, social, and
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RESEARCH ARTICLE Open Access
Excluded versus included patients in a
randomized controlled trial of infections
caused by carbapenem-resistant Gram-
negative bacteria: relevance to external
validity
Vered Daitch1,2* , Mical Paul3,4, George L. Daikos5,6,
Emanuele Durante-Mangoni7, Dafna Yahav1,8,
Yehuda Carmeli1,9,10, Yael Dishon Benattar7,11, Anna
Skiada5,6, Roberto Andini7, Noa Eliakim-Raz1,2, Amir
Nutman1,10,
Oren Zusman1,2ˆ, Anastasia Antoniadou6,12, Giusi Cavezza7,

Amos Adler13, Yaakov Dickstein3, Ioannis Pavleas14,
Rosa Zampino7, Roni Bitterman3,4, Hiba Zayyad3, Fidi
Koppel3, Yael Zak-Doron3, Inbar Levi1,10, Tanya Babich1,2,
Adi Turjeman1,2, Haim Ben-Zvi15, Lena E. Friberg16, Johan
W. Mouton17ˆ, Ursula Theuretzbacher18 and
Leonard Leibovici1,2
Abstract
Background: Population external validity is the extent to which
an experimental study results can be generalized
from a specific sample to a defined population. In order to
apply the results of a study, we should be able to assess
its population external validity. We performed an investigator-
initiated randomized controlled trial (RCT) (AIDA
study), which compared colistin-meropenem combination
therapy to colistin monotherapy in the treatment of
patients infected with carbapenem-resistant Gram-negative
bacteria. In order to examine the study’s population
external validity and to substantiate the use of AIDA study
results in clinical practice, we performed a concomitant
observational trial.
Methods: The study was conducted between October 1st, 2013
and January 31st, 2017 (during the RCTs
recruitment period) in Greece, Israel and Italy. Patients
included in the observational arm of the study have fulfilled
clinical and microbiological inclusion criteria but were
excluded from the RCT due to receipt of colistin for > 96 h,
refusal to participate, or prior inclusion in the RCT. Non-
randomized cases were compared to randomized patients.
The primary outcome was clinical failure at 14 days of infection
onset.
(Continued on next page)

© The Author(s). 2021 Open Access This article is licensed
under a Creative Commons Attribution 4.0 International
License,
which permits use, sharing, adaptation, distribution and
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(http://creativecommons.org/publicdomain/zero/1.0/) applies to
the
data made available in this article, unless otherwise stated in a
credit line to the data.
* Correspondence: [email protected]
ˆOren Zusman and Johan W. Mouton are deceased.
1Sackler Faculty of Medicine, Tel Aviv University, Ramat-
Aviv, Tel Aviv, Israel
2Department of Medicine E, Rabin Medical Center, Beilinson
Hospital,
Jebotinski 39, Petah Tikva, Israel
Full list of author information is available at the end of the
article
Daitch et al. BMC Infectious Diseases (2021) 21:309
https://doi.org/10.1186/s12879-021-05995-y

http://crossmark.crossref.org/dialog/?doi=10.1186/s12879-021-
05995-y&domain=pdf
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(Continued from previous page)
Results: Analysis included 701 patients. Patients were infected
mainly with Acinetobacter baumannii [78.2% (548/
701)]. The most common reason for exclusion was refusal to
participate [62% (183/295)]. Non-randomized and
randomized patients were similar in most of the demographic
and background parameters, though randomized
patients showed minor differences towards a more severe
infection. Combination therapy was less common in
non-randomized patients [31.9% (53/166) vs. 51.2% (208/406),
p = 0.000]. Randomized patients received longer
treatment of colistin [13 days (IQR 10–16) vs. 8.5 days (IQR 0–
15), p = 0.000]. Univariate analysis showed that non-
randomized patients were more inclined to clinical failure on
day 14 from infection onset [82% (242/295) vs. 75.5%
(307/406), p = 0.042]. After adjusting for other variables, non-
inclusion was not an independent risk factor for clinical
failure at day 14.
Conclusion: The similarity between the observational arm and
RCT patients has strengthened our confidence in
the population external validity of the AIDA trial. Adding an
observational arm to intervention studies can help
increase the population external validity and improve
implementation of study results in clinical practice.
Trial registration: The trial was registered with

ClinicalTrials.gov, number NCT01732250 on November 22,
2012.
Keywords: Population external validity, Antimicrobial
resistance, Antibiotic treatment
Background
Randomized controlled trials (RCTs) are the gold stand-
ard for guidelines and evidence-based medicine. Internal
validity of an RCT reflects the strengths to support a
clinical decision based on study results and the extent to
which the results are influenced by bias [1]. Adequate
randomization, allocation concealment, blinding, non-
selective reporting of outcomes and intention-to-treat
analysis, have been identified as important factors in
study design to minimize bias in RCTs and increase in-
ternal validity [1, 2]. External validity is defined as the
extent and manner in which the results of an experi-
mental study can be generalized to different subjects and
settings. It has two components: population validity, the
extent to which the results can be generalized from the
specific sample to a defined population, and ecological
validity, the extent to which the results can be general-
ized from the set of environmental conditions created by
the researcher to other environmental conditions/set-
tings [3].
The population external validity of RCTs relies firstly
on the inclusion and exclusion criteria. Secondly, it relies
on the population of patients actually recruited. Inclu-
sion and exclusion criteria should be defined precisely,
clearly and unambiguously [2]. Studies have shown that
patients recruited into RCTs were sometimes different
from those who were eligible but not recruited in terms
of age, gender, educational status, socioeconomic status,
place of residence, ability to provide informed consent

and severity of disease. Patients that could not provide
informed consent, and thus were not included, had more
severe disease and their outcome was often worse com-
pared to patients included in trials [4–6]. The problem
of external validity is particularly relevant to registration
trials, which typically specify numerous exclusion cri-
teria. In order to apply a study’s results, one should be
able to assess its population external validity; however,
few studies to date have done so [7–12].
We performed an investigator-initiated, multicenter,
open-label, parallel group, randomized controlled trial
(AIDA study), which compared colistin-meropenem
combination therapy to colistin monotherapy in the
treatment of patients infected with carbapenem-resistant
Gram-negative bacteria (CR GNB). The RCT differed
from typical registration trials in its design, particularly
in its broad eligibility criteria and in its limited exclusion
criteria that were meant to reflect “real life patients”.
The design, methods, and results have been previously
published [13, 14]. In order to examine the study’s popu-
lation external validity and to substantiate the use of
AIDA study results in clinical practice, we performed a
concomitant observational trial that compared the char-
acteristics and outcomes of randomized (included) and
non-randomized (excluded) AIDA study patients.
Methods
Study design and participants
We compared patients randomized in the trial (interven-
tional arm) to those fulfilling clinical and microbiological
inclusion criteria who were not randomized due to ex-
clusion from the trial (observational arm).
The study was conducted between October 1st, 2013

and January 31st, 2017 (during the RCT recruitment
period) in Laikon and Attikon Hospitals in Athens,
Greece; Tel Aviv Sourasky Medical Center (Tel Aviv),
Rabin Medical Center, Beilinson Hospital (Petah-Tikva)
and Rambam Health Care Center (Haifa), Israel; and
Monaldi Hospital, Naples, Italy.
Study population included adults (18+) with severe in-
fections (requiring hospitalization or hospital acquired),
caused by CR GNB that are susceptible to colistin,
aminoglycosides, sulbactam, tetracyclines, tigecycline,
Daitch et al. BMC Infectious Diseases (2021) 21:309 Page
2 of 9
https://clinicaltrials.gov/ct2/show/NCT01732250
and co-trimoxazole. Infections included bacteraemia,
definite ventilator associated or hospital-acquired pneu-
monia, probable ventilator-associated pneumonia, and
urosepsis. Polymicrobial infections comprising
carbapenem-susceptible GNB were excluded from the
RCT and from the observational arm.
Treatment in the interventional arm included intra-
venous colistin or colistin combined with meropenem.
Colistin was administered as a 9-million unit (MIU)
loading dose, followed by 4.5-MIU maintenance doses
every 12 h, adjusted for renal function in patients with
creatinine clearance of less than 50mL/min. Meropenem
was given as a 2 g extended-infusion (3 h) every 8 h, ad-
justed for renal function.
Patients excluded from the RCT for one or more rea-
sons, but otherwise fulfilling clinical and microbiological

inclusion criteria were included in the observational
arm: refusal to participate; previous colistin treatment
for more than 96 h at eligibility assessment; and prior in-
clusion in the RCT. Treatment in the observational arm
was based on the attending physicians’ decisions.
Outcomes
The primary outcome was clinical failure at 14 days after
the first positive culture was obtained. The outcome was
a composite of: patient deceased, systolic blood pres-
sure < 90 mmHg or the need for vasopressor support, no
stability or improvement in Sequential Organ Failure As-
sessment (SOFA) score, and for patients with bacteremia
due to growth of the initial isolate in blood cultures
taken on day 14. Secondary outcomes collected for this
study were mortality at 14 and 28 days.
We also compared demographic data, background
conditions, source of infection, devices present at in-
fection onset, infection characteristics, and antibiotic
treatment.
Ethics
Both RCT and observational study were approved by
local ethics committee in each site. Data on excluded pa-
tients (observational arm) were collected through elec-
tronic records. Informed consent was obtained for all
RCT participants (interventional arm). In Israel, the
RCT was approved as ‘emergency research’; patients
who were not able to provide informed consent and did
not have a legal guardian were included by the consent
of an approved independent physician (providing direct
patient care but not participating in the trial) and a fam-
ily member. In Italy and Greece, a relative was an ac-
ceptable surrogate for patients that were unable to
provide informed consent. In both cases, if the patient

has improved, he was asked to provide an informed con-
sent for participation. In the case of refusal, the patient
was removed from the trial.
Statistical analysis
Analyses were performed using the Statistical Package
for the Social Sciences 25 (SPSS Inc.). Categorical
data were compared using the chi-square test. A
Kolmogorov-Smirnov test was carried out in order to
determine whether the distributions of continuous
variables were normal. Continuous variables were ana-
lyzed using t-test or Mann-Whitney-U test as appro-
priate. To examine risk factors for clinical failure on
day 14 focusing on exclusion from the RCT, we
performed a multivariable logistic regression. For the
selection of our final model, we used Akaike’s Infor-
mation Criterion. Nine models were tested to find the
best fit. Different sets of significant variables (p < 0.1)
were entered in consideration of clinical relevance. In-
teractions between exclusion from the RCT and other
variables were not tested due to lack of clinical
reasoning.”
Results
Analysis was performed on 701 patients, including 295
non-randomized patients in the observational arm and
406 RCT patients. Patients were infected mainly with
Acinetobacter baumannii [78.2% (548/701)].
The most common reason for not including suitable
patients in the RCT was refusal to participate [62% (183/
295)]. 20.7% (62/295) of patients were excluded due to
treatment with colistin for more than 96 h, and 16.9%
(50/295) were excluded for prior inclusion in the RCT.
Patients’ characteristics

Non-randomized and RCT patients were similar in most
of the demographic and background parameters. There
were more patients with dementia in the RCT [10.7%
(49/406) vs. 5.8% (17/295), p = 0.050]. Hematological
malignancies were more common in non- randomized
patients [8.5% (25/295) vs. 3.4% (14/406), p = 0.004]. At
infection onset, RCT patients had more arterial lines
[37.2% (151/406) vs. 25.8% (76/295), p = 0.001] central
venous catheters [55.4% (225/406) vs. 40.3% (119/295),
p = 0.000] and urinary catheters [87.2% (354/406) vs.
77.3% (228/295), p = 0.001] than non-randomized pa-
tients (Table 1).
Infection characteristics and antibiotic treatment
Severity of infection was similar in the two groups, as ev-
idenced by similar SOFA scores, need for hemodynamic
support, blood pressure and body temperature. Patients
not randomized were less likely to acquire their infection
in the intensive care unit [22.7% (67/295) vs. 30.5% (124/
406). p = 0.022], to be infected with Enterobacteriacaeae
[35/295 (11.9%) vs. 73/406 (18%), p = 0.027]; and more
likely to have urinary tract infection [32/295 (10.8%) vs.
26/406 (6.4%), p = 0.035]. The minimum inhibitory
3 of 9
concentration (MIC) of > 0.5 mg/L for colistin was more
prevalent in randomized patients [24.3% (85/350) vs.
7.7% (18/233), p = 0.000] [15].
RCT patients received higher rates of colistin-
meropenem combination therapy than non-randomized
patients [51.2% (208/406) vs. 31.9% (53/166), p = 0.000].

Colistin loading dose was administered more often to
randomized patients [92.6% (376/406) vs. 73.5% (122/
166), p = 0.000]. No difference was observed in mean co-
listin maintenance dose per day between the two groups.
Among 14-day survivors, treatment with colistin was
longer in randomized patients than in non-randomized
patients [13 days (IQR 10–16) vs. 8.5 days (IQR 0–15),
p = 0.000] (Table 2).
Outcomes
More non-randomized patients met the criteria for the
primary outcome, clinical failure at day 14, than ran-
domized patients [82% (242/295) vs. 75.5% (307/406),
p = 0.042]. Mortality rates were higher in non- random-
ized patients [40.2% (117/295) vs. 33% (134/406 in the
RCT patients, p = 0.051]. The difference between the
Table 1 Patients’ characteristics
Excluded from randomized
controlled trial (N = 295)
Included in randomized
P value
Demographics and background
Age (Mean ± SD), year 65 ± 18 66 ± 17 0.411
Gender (female) 101 (34.2%) 151 (37.2%) 0.421
Country 0.000

Israel 274 (92.9%) 270 (66.5%)
Greece 16 (5.4%) 76 (18.7%)
Italy 5 (1.7%) 60 (14.8%)
Admitted from home 204 (69.2%) 276 (68%) 0.742
BMI, kg/m2 27.1 (6.7) 27.4 (5.8) 0.610
Charlson Score (Mean ± SD) 2 ± 2 2 ± 2 0.497
Dementia 17 (5.8%) 49 (10.7%) 0.050
Diabetes 61 (20.7%) 90 (22.2%) 0.636
Chronic kidney disease 71 (24.1%) 79 (19.5%) 0.129
Hematological Malignancy 25 (8.5%) 14 (3.4%) 0.004
Congestive heart failure 66 (22.4%) 92 (22.7%) 0.928
Chronic pulmonary disease 57 (19.3%) 91 (22.4%) 0.322
Immune suppressive therapy 54 (18.3%) 61 (15%) 0.247
Known colonization by pathogen before infection 69 (23.4%) 96
(23.6%) 0.937
Recent surgery 83 (28.1%) 114 (28.1%) 0.987
Status at infection onset (culture taken time)
Temperature, °C (SD) 37.9 (1.7) 38.0 (1.7) 0.655
Systolic blood pressure, mm Hg (SD) 106 (24) 109 (21) 0.054

Haemodynamic support 68 (24.2%) 75 (18.5%) 0.069
Mechanical ventilation (invasive) 198 (69.5%) 264 (65%) 0.221
Haemodialysis 11 (3.9%) 27 (6.7%) 0.118
SOFA score (Mean ± SD) 6 ± 3 6 ± 3 0.755
Creatinine Clearance (Cockcroft-Gault Equation),
mL/min (Percentiles 25–75)
59.79 (32.54–108.58) 69.95 (41.21–126.27) 0.012
Albumin, g/dL (SD) 2.3 (0.6) 2.4 (0.7) 0.285
White blood cells, thousands/mL (SD) 13.22 (9.85) 14.12 (8.89)
0.212
Arterial line 76 (25.8%) 151 (37.2%) 0.001
Central venous catheter 119 (40.3%) 225 (55.4%) 0.000
Urinary catheter 228 (77.3%) 354 (87.2%) 0.001
Nasogastric tube 201 (68.1%) 285 (70.2%) 0.559
4 of 9
two groups waned at the end of study: 28-day mortality
was 47.8% (138/295) in the non- randomized patients vs.
44.3% (180/406) in RCT patients.
Univariate analysis for clinical failure at day 14 is dis-

played in Table 3.
At multivariable logistic regression, male gender, age,
hemodynamic support, and acquisition of the infection
in the intensive care unit were associated with higher
rates of 14-day clinical failure. Pseudomonas/other bac-
teria as initial isolate were associated with lower rates of
14-day clinical failure. Non-inclusion in the RCT was
not an independent risk factor for clinical failure at day
14 (Table 4).
Discussion
In our study, patients not randomized in the trial were
similar to randomized patients in their baseline charac-
teristics, though RCT patients showed minor differences
towards a more severe infection. They had more lines
and catheters and acquired their infection more often in
the intensive care unit. Non- randomized patients were
less infected by Enterobacteriaceae, showed lower MIC
distributions for colistin, and were presented with higher
rates of urinary tract infection.
Univariate analysis showed that non- randomized pa-
tients were more inclined to clinical failure on day 14
from infection onset. However, on multivariate analysis
exclusion from the RCT was not an independent risk
factor for clinical failure.
The major reason for exclusion from the RCT was re-
fusal of the patient, the legal guardian, or the treating
physician to participate in the trial. In this study, we
were authorized by the local ethics committees to recruit
patients who were not able to provide informed consent
and did not have a legal guardian, with the consent of an
approved independent physician or a family member (as

described in the Ethics section). This allowed the inclu-
sion of severely ill patients that characterize the AIDA
trial. On the other hand, patient refusal implied that pa-
tients who were able to consent refused randomization,
and this translated into the inclusion of less severely ill
patients in the observational arm. Non-randomized pa-
tients suffered more often from hematological malignan-
cies. This could be a result of the patients’ or treating
physicians’ concern regarding the inclusion of a patient
with a compromised immune system. Creatinine clear-
ance levels were lower in non- randomized patients, per-
haps reflecting the reluctance to include patients with
Table 2 Infection characteristics and antibiotic treatmenta
Excluded from randomized
Included in randomized
P value
Infection characteristics
Acquisition of infection in the intensive care unit 67 (22.7%)
124 (30.5%) 0.022
Pathogen
Acinetobacter baumannii 236 (80%) 312 (76.8%) 0.318
Enterobacterales 35 (11.9%) 73 (18%) 0.027
Pseudomonas/other 24 (8.1%) 21 (5.2%) 0.114

Type of infection
Bacteraemia 109 (36.9%) 173 (42.6%) 0.131
Ventilator-associated or hospital-acquired pneumonia 140
(47.5%) 182 (44.8%) 0.490
Probable ventilator-associated pneumonia 14 (4.7%) 25 (6.2%)
0.421
Urinary tract infection 32 (10.8%) 26 (6.4%) 0.035
Colistin MIC distribution > 0.5 mg/L 18 (7.7%), n = 233 85
(24.3%), n = 350 0.000
Antibiotic treatment 0.000
Colistin 113 (68.1%), n = 166 198 (48.8%)
Colistin and meropenem 53 (31.9%), n = 166 208 (51.2%)
Colistin loading dose 122 (73.5%), n = 166 376 (92.6%) 0.000
Treatment days in patients alive ≥14 days, median
(Percentiles 25–75)
8.5 (0–15), n = 200 13 (10–16), n = 273 0.000
Mean colistin maintenance dose per day, million units
(percentiles 25–75)
Creatinine clearance< 50ml/min 4.2 (2.1–6.0) 4.0 (3.0–6.0)
0.629
Creatinine clearance≥50ml/min 8.6 (5.8–9.0) 8.5 (7.0–9.0)
0.239

aNumbers apply to all patients in the group unless stated
otherwise
5 of 9
impaired kidney function into a trial involving a nephro-
toxic drug such as colistin.
No significant difference between colistin monotherapy
and combination therapy was observed for clinical failure at
day 14 in included and excluded patients. Per AIDA RCT
protocol, ~ 50% of patients received colistin-meropenem
combination therapy. Colistin was administered as a 9-
million-unit (MIU) loading dose followed by mainten-
ance doses, with a minimum treatment period of 7
days. Non-randomized patients received mainly colis-
tin monotherapy, reflecting the standard of care, with
a lower rate of colistin loading dose administration
and a shorter treatment period. The difference in
management and the significantly related variates
Table 3 Univariate analysis for clinical failure at day 14a
Clinical success at
day 14 (N = 152)
Clinical failure at
day 14 (N = 549)
P value
Age (Mean ± SD), year 62.79 (18.514) 66.08 (16.975) 0.038

Gender (female) 68 (44.7%) 184 (33.5%) 0.011
Country 0.001
Israel 113 (74.3%) 431 (78.5%)
Greece 32 (21.1%) 60 (10.9%)
Italy 7 (4.6%) 58 (10.6%)
Hematological malignancy 2 (1.3%) 37 (6.7%) 0.010
Congenative heart failure 25 (16.4%) 133 (24.2%) 0.042
Arterial line 35 (23%) 192 (35%) 0.005
Chronic pulmonary disease 23 (15.1%) 125 (22.8%) 0.041
Systolic blood pressure, mm Hg (SD) 111.97 (20.539) 106.66
(22.499) 0.009
Haemodynamic support 16 (10.6%) 127 (23.7%) 0.000
Mechanical ventilation (invasive) 81 (53.6%) 381 (70.6%) 0.000
Haemodialysis 1 (0.7%) 37 (6.9%) 0.003
Creatinine clearance (Cockcroft-Gault Equation),
mL/min (Percentiles 25–75)
72.60 (41.16–132.14) 64.01 (36.08–118.69) 0.199
Albumin, g/dL (SD) 2.46 (0.678) 2.327 (0.6383) 0.035
Nasogastric tube 92 (60.5%) 394 (71.8%) 0.008

Pathogen
Acinetobacter baumannii 100 (65.8%) 448 (81.6%) 0.000
Enterobacteriacaeae 35 (23%) 73 (13.3%) 0.003
Pseudomonas/other 17 (11.1%) 28 (5.1%) 0.007
Type of infection
Bacteraemia 68 (44.7%) 214 (39%) 0.200
Ventilator-associated or hospital-acquired pneumonia 55
(36.2%) 267 (48.6%) 0.006
Probable ventilator-associated pneumonia 10 (6.6%) 29 (5.3%)
0.537
Urinary tract infection 19 (12.5%) 39 (7.1%) 0.033
Acquisition of infection in the intensive care unit 24 (15.6%)
167 (30.4%) 0.000
Exclusion from the RCT 53 (34.9%) 242 (44.1%) 0.042
Colistin MIC distribution > 0.5 mg/L 27 (21.3%), n = 127 76
(16.7%), n = 456 0.230
Antibiotic treatment
Combination arm: colistin and meropenem 68 (50.7%), n = 134
193 (44.1%), n = 438 0.174
No loading dose 12 (9.0%), n = 134 62 (14.2%), n = 438 0.117

Treatment days in patients alive ≥14 days, median
13 (8–16) 8 (4–14) 0.000
Mean colistin maintenance dose per day, million units
7.9 (5.0–9.0) 7.2 (4.0–9.0) 0.330
aNumbers apply to all patients in the group unless stated
otherwise
6 of 9
described in the logistic regression can explain the
higher rates of clinical failure in non- randomized
patients.
In a trial published in 2015, Paul et al. examined the
external validity of a RCT comparing trimethoprim-
sulfamethoxazole versus vancomycin for the treatment
of invasive methicillin-resistant Staphylococcus aureus
(MRSA) infections. The major point of difference from
AIDA study was that patients that were not able to sign
an informed consent and did not have a legal guardian
could not enter the MRSA RCT- thus excluded patients
were more ill than included patients, and the differences
between the two populations were more substantial, in-
cluding primary outcomes, with excluded patients show-
ing significantly higher clinical failure and 30-day all-
cause mortality rates [5].
In order to minimize differences between the study

sample and “real-world” patients, the AIDA RCT did not
exclude patients for underlying conditions or sepsis se-
verity while taking into account the potential comprom-
ise of internal validity caused by increasing heterogeneity
of the recruited patients. This is of major importance,
especially in comparison with registration or pharma-
ceutical company-sponsored trials. Ha et al. examined
the proportion of patients encountered during routine
clinical practice who would qualify for enrollment into a
pivotal RCT of biological agents for inflammatory bowel
disease (IBD). In this retrospective cohort study, the eli-
gible patients were examined for inclusion in at least
one of seven selected published RCTs. Only ~ 30% of
patients would have qualified for enrollment due to nu-
merous exclusion criteria [16]. A literature review pub-
lished in 2015 identified the use of restrictive inclusion/
exclusion criteria as one of the key factors that limited
external validity of trial findings [17]. This issue raises
the importance of designing an RCT to include a diverse
population with limited exclusion criteria so that the re-
sults can be generalized to the population in hand.
Our study has few limitations. First, the observa-
tional cohort included patients excluded due to three
out of seven exclusion criteria which account for
most of the observational sample [81.7% (295/361)],
thus not all RCT excluded patients entered the obser-
vational arm. We chose to focus on these exclusion
criteria since they truly reflect patients compatible for
recruitment. Second, this study focuses on one aspect
of external validity- comparison of characteristics and
outcomes of excluded and included patients. This as-
pect refers to the population validity component and
addresses the question of whether the findings of a
study can be generalized to patients with characteris-

tics that are different from those in the study, or pa-
tients who are treated or followed up differently. For
a broader evaluation of external validity, it will be in-
teresting to test ecological validity which specifically
examines whether the findings of a study can be gen-
eralized to different clinical settings in everyday life.
Conclusions
The similarity between patients in the observational and
RCT arms has strengthened our confidence in the popu-
lation external validity of the AIDA trial. Limited exclu-
sion criteria and access to recruiting the most severely ill
patients into the trial population are key elements con-
ferring the high population external validity in the AIDA
trial, and overall for this type of infectious disease trials.
Extending the RCT to include an observational study
arm strengthens and optimizes the evidence emerging
from the study. The other major benefit of a hybrid
study is that it alleviates concerns in real life clinical
implementation.
Abbreviations
RCT: Randomized controlled trial; CR GNB: Carbapenem-
resistant gram-
negative bacteria; SOFA: Sequential organ failure assessment;
MIC: Minimum
inhibitory concentration; IBD: Inflammatory bowel disease;
MIU: Million units
Table 4 Logistic regression analysis of independent risk factors
for clinical failure at day 14 of infection onset
OR (95% CI) P value
Exclusion from the RCT 1.341 (0.818–2.200) 0.245

Agea 1.018 (1.005–1.031) 0.006
Gender (female) 0.543 (0.345–0.854) 0.008
Enterobacterales 0.658 (0.361–1.202) 0.173
Pseudomonas/other 0.416 (0.183–0.416) 0.037
Systolic blood pressure, mm Hgb 0.992 (0.981–1.002) 0.119
Haemodynamic support 2.561 (1.188–5.520) 0.016
Mechanical ventilation (invasive) 1.481 (0.920–2.384) 0.106
Acquisition of infection in the intensive care unit 2.061 (1.170–
3.632) 0.012
N = 701Akaike’s information criterion goodness of fit =
516.012; constant: β = 2.311; risk for clinical failure at day 14:
OR > 1
aAge- per 1 year increment
bSystolic blood pressure - per 1 mm Hg increment
7 of 9
Acknowledgments
This work was performed in partial fulfillment of the
requirements for a Ph.D.
degree of Vered Daitch, Sackler Faculty of medicine, Tel Aviv
University,
Israel.
Authors’ contributions

MP, GLD, YC, AS, AA2, LEF, JWM, UT, YD, VD and LL:
Substantial contributions
to the conception or design of the work; YDB, AS, AA1, GC,
IP, RA, NE-R, RZ,
RB, HZ, AT, HB-Z, YZ-D, VD, FK, IL, and TB acquisition of
data for the work; VD,
MP, GLD, ED-M, YC, YD, DY, AS, LEF, OZ, AA2, AN, JWM,
UT, and LL acquisi-
tion, analysis, or interpretation of data for the work. All authors
contributed
to the critical revision of the final manuscript. The author(s)
read and ap-
proved the final manuscript.
Funding
This work was funded by the EU AIDA grant (Health-F3-2011-
278348(and by
the Israel National Institute for Health Policy and Health
Services Research
(NIHP) (grant number 2016/80). The funders had no role in
study design,
data collection and analysis, decision to publish, or preparation
of the
manuscript.
Availability of data and materials
The datasets used and/or analysed during the current study are
available
from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
The study was approved by the ethics (Helsinki) committee at
the Rabin
Medical Center and by the local institutional ethics committee

at each site.
Written informed consent was obtained for all RCT participants.
Institutional
ethics committee approval numbers: Rambam Health Care
Center (Haifa),
Israel: 0292–12-RMB. Rabin Medical Center, Beilinson
Hospital (Petah-Tikva),
Israel: 0236–12-RMC. Tel Aviv Sourasky Medical Center (Tel
Aviv), Israel: 0540–
12-TLV. Laikon and Attikon Hospitals in Athens, Greece:
ΔΥΓ3/89292/2003.
Monaldi Hospital, Naples, Italy: Parere CE 1417684.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1Sackler Faculty of Medicine, Tel Aviv University, Ramat-
Aviv, Tel Aviv, Israel.
2Department of Medicine E, Rabin Medical Center, Beilinson
Hospital,
Jebotinski 39, Petah Tikva, Israel. 3Institute of Infectious
Diseases, Rambam
Health Care Campus, Haifa, Israel. 4The Ruth and Bruce
Rappaport Faculty of
Medicine, Technion – Israel Institute of Technology, Haifa,
Israel. 5First
Department of Medicine, Laikon General Hospital, Athens,
Greece. 6National
and Kapodistrian University of Athens, Athens, Greece.
7Internal Medicine,
University of Campania ‘L Vanvitelli’, and AORN dei Colli-
Monaldi Hospital,

Naples, Italy. 8Unit of Infectious Diseases, Rabin Medical
Center, Beilinson
Hospital, Petah Tikva, Israel. 9Division of Epidemiology and
Preventive
Medicine, Tel Aviv Sourasky Medical Centre, Tel Aviv, Israel.
10National
Institute for Antibiotic Resistance and Infection Control,
Ministry of Health,
Tel Aviv, Israel. 11Cheryl Spencer Department of Nursing,
University of Haifa,
Haifa, Israel. 12Fourth Department of Medicine, Attikon
University General
Hospital, Athens, Greece. 13Microbiology Laboratory, Tel-Aviv
Sourasky
Medical Center, Tel-Aviv, Israel. 14Intensive Care Unit, Laikon
General Hospital,
Athens, Greece. 15Clinical Microbiology Laboratory, Rabin
Medical Center,
Beilinson Hospital, Petah Tikva, Israel. 16Department of
Pharmaceutical
Biosciences, Uppsala University, Uppsala, Sweden.
17Department of Medical
Microbiology and Infectious Diseases, Erasmus MC, Rotterdam,
The
Netherlands. 18Center for Anti-Infective Agents, Vienna,
Austria.
Received: 25 June 2020 Accepted: 17 March 2021
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AbstractBackgroundMethodsResultsConclusionTrial
registrationBackgroundMethodsStudy design and
participantsOutcomesEthicsStatistical analysisResultsPatients’
characteristicsInfection characteristics and antibiotic
treatmentOutcomesDiscussionConclusionsAbbreviationsAcknow
ledgmentsAuthors’ contributionsFundingAvailability of data
and materialsDeclarationsEthics approval and consent to
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