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Developing a workplace resilience instrument

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Developing a Workplace Resilience Instrument

Larry A. Mallak, Ph.D.

Professor, Department of Industrial and Entrepreneurial Engineering & Engineering Management

Mustafa Yildiz

Doctoral student, Department of Educational Leadership, Research, and Technology Western Michigan University Kalamazoo, Michigan, USA 49008

Mallak, L.A. & Yildiz, M. (2016). The Development of a Workplace Resilience Instrument. WORK: A Journal of Prevention, Assessment and Rehabilitation, 54(2), 241-253.

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Developing a Workplace Resilience Instrument

Abstract

BACKGROUND: Resilience benefits from the use of protective factors, as opposed to

risk factors, which are associated with vulnerability. Considerable research and

instrument development has been conducted in clinical settings for patients. The need

existed for an instrument to be developed in a workplace setting to measure resilience of

employees.

OBJECTIVE: This study developed and tested a resilience instrument for employees in

the workplace.

PARTICIPANTS AND METHODS: The research instrument was distributed to

executives and nurses working in the United States in hospital settings. Five-hundred-

forty completed and usable responses were obtained. The instrument contained an

inventory of workplace resilience, a job stress questionnaire, and relevant

demographics. The resilience items were written based on previous work by the lead

author and inspired by Weick’s [1] sense-making theory.

RESULTS: A four-factor model yielded an instrument having psychometric properties

showing good model fit. Twenty items were retained for the resulting Workplace

Resilience Instrument (WRI). Parallel analysis was conducted with successive iterations

of exploratory and confirmatory factor analyses. Respondents were classified based on

their employment with either a rural or an urban hospital. Executives had significantly

higher WRI scores than nurses, controlling for gender. WRI scores were positively and

significantly correlated with years of experience and the Brief Job Stress Questionnaire.

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CONCLUSIONS: An instrument to measure individual resilience in the workplace

(WRI) was developed. The WRI’s four factors identify dimensions of workplace

resilience for use in subsequent investigations: Active Problem-Solving, Team Efficacy,

Confident Sense-Making, and Bricolage.

Keywords:, , nursing, , sense-making, hospital, bricolage, coping

1. Introduction

The primary objective of this study was to build and test a resilience instrument

for use in the workplace. Most studies have focused on patients in clinical settings rather

than employees in the workplace. Our study focused on employees and how we can

characterize the level of resilience in how they approach work. This study used the

resilience scales developed by Mallak [2] as the basis for such a tool, using the relevant

research and application of resilience tools developed to date.

With resilient individuals able to withstand stress better than others, the ability

to reduce stress through the ability to measure and improve resilience has enormous

consequences. Stress costs U.S. businesses an estimated $300 billion annually [3].

With resilient individuals able to withstand stress better than others [4], the

ability to reduce stress through the ability to measure and improve resilience has

enormous consequences. Estimates are that 67-90% of all office visits to a physician can

be traced to stress-related symptoms [5] [6]. Stress creates adverse effects on 43% of all

adults [6]. Stress is a major contributor to heart disease, cancer, stomach problems, lung

problems, accidents, cirrhosis of the liver, and suicide; the common cold and skin rashes

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can often be traced back to stress conditions [5] [6]. There is much to be gained from an

instrument that can effectively measure individual resilience in the workplace and lead

to interventions to increase resilience.

Resilience is a key construct in the performance of targeted behaviors for solving

problems and taking action in the face of adversity. The increasing need for quicker

decision making in complex systems having severe consequences requires individuals

and organizations to have the capacity to make high quality decisions and take effective

actions. In the U.S., the recent increase in the frequency of costly natural disasters and

continued vigilant action to thwart terrorist actions represent high-profile situations

benefiting from resilient behavior.

Resilience research, especially the measurement of resilience dimensions, is

found predominantly in the psychological, medical, and nursing professions and their

associated journals. Resilience has been identified as having “enormous utility for

nursing” [7]. Rew and Horner [8] found that resilient individuals have positive

outcomes in the face of adversity. The classic study in resilience is a 32-year effort led by

Werner [9], following 698 children born on Hawaii’s island of Kauai. Her study

participants were exposed to perinatal stress, poverty, and a “family environment

troubled by chronic discord and parental psychopathology” [9] (p. 503). Her work

surfaced the use of adaptive processes to adverse conditions by children facing these

situations and fueled subsequent work by researchers attempting to build valid and

reliable measurement instruments to characterize an individual’s level of resilience.

The resilience instrument literature shows 1) these instruments were developed

in clinical/counseling settings, not workplace settings; 2) the instruments have some

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common dimensions, but are differentiated; and 3) the need to develop a resilience

instrument for the workplace.

2. Theory & Models

Many models and theories have been advanced in the psychological and clinical

literature. Several of these models and theories are shared here for their relevance to a

study of workplace resilience.

The resilience literature appears to have three developmental phases over the

past 50 years. Although this is not meant to be a comprehensive literature review, the

literature relevant to this study is representative of the material published during each

timeframe. These are the three phases of resilience construct development:

1) Foundation: the 32-year longitudinal study (1955-1987) by Werner [9] set the

stage by discovering protective factors among a group of 698 children born in

Kauai.

2) Conceptualization: the 1990s saw the publication of management books on

resilience (cf. [10] [11], a now-classic analysis of a 1949 disaster [1], and resilience

scales [12] [2]).

3) Measurement: the 2000s produced resilience scales for use primarily in clinical

settings for patients [13] [14] [15] [16].

Although resilience articles have been published since 2004, the most-cited have

focused on the scales shown in Table 1 and discussed later in the literature review for

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this study. This study extends the resilience work by developing resilience scales for use

by employees in a workplace setting.

Insert Table 1 about here

Resilience is often studied in contrast with vulnerability, with the associated

elements of protective factors (those that promote resilient behaviors) and risk factors

(those that promote vulnerable behaviors) [17]. Risk factors often emanate from one’s

personal life. How one proceeds from the point of being confronted with adverse events

and the associated risk factors defines the extent of the individual’s resilience. The types

of protective factors deployed vary whether the individual is studied in a clinical setting,

a work setting, or a disaster setting.

In general, many of the empirical findings in each of these three settings support

a more inclusive theory of resilience where positive, adaptive behaviors that directly

address the needs of the situation are viewed as protective factors and where negative or

neutral behaviors that are relatively “fixed” or do not directly address the needs of the

situation are viewed as risk factors. Several models embody the resilience-vulnerability

phenomena along with protective factors.

Several research streams have led to the current theoretical foundation for the

study of workplace resilience. These research streams are focused on the processes

deployed in response (or even in advance) of situations requiring resilience. In physical

systems, resilience refers to a material’s ability to store and return elastic energy [18].

Similarly, in the workplace, we seek the ability for an employee to absorb energy from a

stressful situation and to return to their original (or improved) condition once the

stressor is removed. Unlike an inanimate material, a person needs to perform one or

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more processes to be able to return to their original state—these processes typically take

the form of protective factors [17]. Protective factors exist in contrast with risk factors

[19] which are associated with vulnerability. In Werner’s classic study [17] [9], the risk

factors facing children born in Kauai (Hawaii) included absent or alcoholic parents,

abuse, and teen motherhood, to name a few. In the workplace, protective factors

emanate from the theories of coping [20] and how job stress is handled [21]. Within the

context of resilience, coping and responses to job stress move the person’s psychological

state to a different “place” than that before the adverse situation was encountered. It is

akin to the quote often attributed to Friedrich Nietzsche, “That which does not kill me

makes me stronger.”

Similarly, the construct of stress originates in engineering—stress is defined as

the force per unit area, but can be conceived as “internal forces that neighboring

particles of a continuous material exert on each other” [22]. Transferring this

engineering definition to the individual, stress is indeed an internal phenomenon and,

like engineering materials, it is manifested physically. Stress is often contrasted with

anxiety; anxiety is a cognitive phenomenon of uncertain origin while stress involves

physical symptoms having a known origin (adapted from definitions in [23] [24]).

Several models from the literature illustrate the relationships between risk

factors and protective factors with the construct of resilience. The Adolescent Resilience

Model [25] contains both individual and family components for risk and protective

factors aimed toward the outcomes of increased resilience and quality of life. The Youth

Resilience Model [8] portrays the interaction between risk factors (vulnerability) and

protective resources (protection), while treating family and community as part of the

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sociocultural context for resilience. Hunter and Chandler’s [26] continuum of resilience

in adolescents has risk factors at one extreme and adaptive behaviors/self-efficacy at its

other extreme. The resilience scales developed by Wagnild and Young [12] were based

on Block and Block’s [27] ego-resilience (a high level of resilience) and ego-brittleness

(vulnerability) and on Rutter’s [28] “buffering effect.”

Resilience models and theories also recognized the interaction among body,

mind, and spirit in producing effective behaviors and outcomes. These appear as

“biopsychosocial factors” [29], “biopsychospiritual homeostasis” [13] and

“psychoneuroimmunology” [30]. Tusaie and Dyer’s [30] model has its roots in the

psychological aspects of coping and in the physiological aspects of stress. Their model

shows the 1990s emergence of the resilience construct as a direct successor to the

concept of psychoneuroimmunology. Their model shows protective factors on the

psychological side and homeostasis on the physiological side. Carver et al.’s [20] study

resulted in an instrument to measure coping; its items range from risk factors (e.g.,

turning to substance abuse) to protective factors (e.g., active coping).

A study of military personnel’s response to adverse conditions found that

resilience is based on complex biopsychosocial factors in short- and long-term reactions

to traumatic stress [29] and is not restricted to events such as disaster response or post-

traumatic stress disorder. Using the Mann Gulch smokejumping disaster of 1949 as a

case study, Weick [1] modeled resilience as a function of sense-making, attitude of

wisdom, virtual role system, and Levi-Strauss’ concept of bricolage [31] [32]—or the

ability to use available materials and methods to solve a problem. Weick’s work

provided a basis for designing the original items in Mallak’s [2] instrument. Similarly,

8

Kobasa [33] found that hardy (or resilient) executives exhibited a stronger commitment

to self, an attitude of vigorousness toward their environment, a sense of meaningfulness,

and an internal locus of control. Whereas Kobasa [33] measured commitment, control,

and challenge as the larger factors from which she drew her resilience conclusions,

Bartone et al. [29] studied commitment, control, and change among military personnel

dealing with trauma from a military plane crash involving fatalities.

The dominant resilience scales found in the literature have been developed

primarily with clinical populations, not workplace populations. As such, the validity of

these instruments for use in the workplace is questionable until psychometric properties

can be established with a workplace population. Resilience scales have been developed

based on work with the military [29], elderly women [12], adolescents [15], general

population/psychiatric patients [13], mental health outpatients in Norway/control

group [14], adult rheumatoid patients [16], and nursing staff [2]. Only the instruments

by Mallak [2] and Bartone et al. [29] were developed solely with data from working

adults. With Bartone’s instrument anchored in the military, the need exists for a

resilience instrument tailored to civilian workplaces.

However, the resilience scales developed by these authors provide input into the

redesign of the Mallak [2] scales, called the Workplace Resilience Instrument (WRI).

See Table 1 for an analysis of the resilience instruments’ content.

3. Resilience in the Workplace

The resilience instrument literature shows 1) these instruments were developed

in clinical/counseling settings, not workplace settings; 2) the instruments have some

9

common dimensions, but are differentiated; and 3) the need to develop a resilience

instrument for the workplace.

The most-used resilience instruments were developed in clinical/counseling

settings, not more general work settings. The resilience instruments in use today and

shown in Table 1 were developed primarily in clinical and counseling settings. While

these are helpful for instrument design and to establish psychometric properties, the

generalization to work settings is not clear. A second major difference is that the

resilience scales in Table 1 were designed and used primarily with patients. This study

builds an instrument to measure the resilience of employees in the workplace.

The main resilience scales in use are CD-RISC [13], Resilience Scales [12], and

Dispositional Resilience Scales [29]. There are many articles and books on the concept

of resilience in work settings (e.g., [10] [4] [11]) and an emerging research stream on

workplace resilience [34] [35]. Resilience instruments share common dimensions, but

are differentiated. A review of the psychology, management, medical, and nursing

literature produced research on a small set of resilience scales. As mentioned earlier,

these scales have been used primarily in clinical and counseling settings (Table 1) as

shown in the column labeled “Primary Population(s).” Of the 294 studies listed in the

CD-RISC user manual [36], only 18 (about 6%) were conducted with employees in the

workplace.

Analysis of the resilience instruments shows some common or related

dimensions. Personal competence appears in three of them as the highest-loading

factor, with a related factor of commitment in the Bartone et al. [29] instrument

showing the highest loading.

10

A workplace resilience instrument needs relevant dimensions that have solid

psychometric properties. The instruments shown in Table 1 may be used in workplace

settings, but the generalizability of the instrument items for other than the Bartone et al.

[29] and Mallak [2] scales is an open question. Beyond perception of one’s own self, the

items in these instruments primarily concern interactions and relationships with family

members and friends [13] [14] [12].

4 Methods

4.1 Instrumentation

The instrument package contained the revised resilience scales (25 items), the 16-

item Brief Job Stress Questionnaire [21], demographic items—gender, location of

hospital (urban or rural), respondent age, respondent years of healthcare experience,

and US state where employed. The resilience scales were based on the Mallak [2] scales

and modified to accommodate work settings in various sectors, not just healthcare.

Resilience items were rewritten to focus on the individual’s response concerning

resilience. The original scales had some items focused on the individual and others on

the team in which the individual worked.

During the design phase of this study, a concern regarding the negatively-worded

(reverse-coded) items was raised. Several authors [37] [38] [39] have discovered that

negatively-worded items, when mixed with positively-worded items, create their own

variance due to their negative nature. In order to avoid possible method effects, those

that were negatively-worded were converted to positive statements so that a positive

endorsement corresponded to a high score on that specific item as in the other items.

11

Response scales were changed from an agree-disagree format to an extent-of-

truth format (e.g., “not true at all” to “true all the time”). The Brief Job Stress

Questionnaire (BJSQ) [21] was modified from statements starting with “you” to starting

with “I.” Some wording was modified to work better with the English-speaking

respondent population, as the BJSQ was translated from Japanese into English for

publication. Urban or rural hospital location was included to discover any differences

between those locations. Years of healthcare experience was included to discover the

degree of correlation between experience and the subscales of WRI.

4.2 Samples

The resilience scales were distributed to 3,291 employees in the healthcare sector

in two campaigns. The first campaign targeted hospital officers in the Great Lakes

region of the United States and produced 177 usable responses out of 2601 sent, for a

6.8% response rate. A second campaign targeted hospital-based nursing staff in the

United States and produced 363 usable responses out of 690 sent, for a 52.6% response

rate. In aggregate, 540 usable responses were received for an overall response rate of

16.4%.

Respondents ranged in age from 16-75, with two-thirds of respondents between

the ages of 45-64. Analyses were conducted on all age ranges. Informed consent was

obtained from each of the study participants, and the study protocol was approved by

the university’s Institutional Review Board. Respondents were 84% female. Three-

fourths of the respondents worked in urban hospitals, 99% had some college education,

with a mean 25 years of healthcare experience. See Table 2 for demographics on the

study samples.

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Insert Table 2 about here

4.3 Data Preparation

Instrument responses were reviewed for their level of completeness and missing

data. Fifty-four observations had some missing responses. Three of those 54

observations had more than 50% or more of responses missing. Those three

observations were deleted entirely. The remaining 51 observations had 3 or fewer

responses missing on the resilience scales which corresponded to less than 1% of the

whole dataset. The demographic information was used to understand the pattern of

missing data. Descriptive statistics and some basic analysis at the individual item level

provided the evidence to decide that the data were missing at random (MAR) [40].

Because the data were missing at random, the missing values were imputed by using

Markov Chain Monte Carlo (MCMC) multiple imputations implemented in Mplus [41].

Five datasets were generated. The one with largest amount of variance was chosen for

the investigation of the psychometric qualities of the WRI, although the differences

among the five datasets were rather small. The other four datasets are available upon

request.

4.4 Analytical Strategy, Estimation, and Fit

The 1998 [2] resilience scale was factor analyzed in the framework of exploratory

factor analysis via a varimax rotation that set the inter-factor correlations to be zero.

With this condition, it is impossible to estimate the model-data fit in the framework of

confirmatory factor analysis due to identification reasons. For example, the factors

Source Reliance (SR), and Role Dependence (RD) were measured by two items for each.

As a result, another condition of the initial model was violated in addition to revising the

13

wording of some items from negative to positive statements, and changing the scale

property of the original scale. These rearrangements actually changed the nature of this

study from a strictly confirmatory approach to an alternative models or model-

generating approach as described in Jöreskog and Sörbom [42].

Maximum likelihood was the first estimation method that was considered

because of the desirable test statistics it provides. However, maximum likelihood

requires that the items are both univariate and multivariate normal. Tests of univariate

and multivariate normality indicated that the data were not normally distributed

(Shapiro-Wilk test for univariate normality ranged from 0.65 to 0.90, p<0.0001;

multivariate normality of Mardia Skewness=5897, p<0.0001; multivariate normality of

Mardia Kurtosis= 29.66, p<0.0001). Therefore, other estimation methods that rely on

normal theory were not considered for further analysis. Considering that the data were

not normal, and the five-point Likert scale as ordinal in nature, weighted least squares

mean and variance (WLSMV) implemented in Mplus [43] was selected for estimating

model data fit.

To evaluate the model-data fit, root mean square error of approximation

(RMSEA) [44] , comparative fit index (CFI) [45], and Tucker-Lewis fit index (TLI) [46]

were used. According to Hu and Bentler [47], model-data fit should be evaluated based

on multiple fit indexes, and acceptable levels of model-data fit are RMSEA≤0.06,

CFI≥0.95, TLI≥0.95.

5 Results

The 1998 [2] resilience model was developed based on the assumption that the

inter-factor correlations were zero. The model-data fit of this simple structure derived

14

from this model was under-identified (three inputs in the variance-covariance matrix, 3

or 4 parameters to be estimated) for some factors such as SR and RD. Therefore, the

goodness-of-fit statistics for this model were not available. Therefore, to get an

approximate estimate of the goodness-of-fit tests in the framework of confirmatory

factor analysis, the assumption of orthogonal solutions was violated and all of the

factors were allowed to correlate, so that the model could be estimated. The test of the

1998 model [2] containing the original 25-item instrument with additional correlated

factors was analyzed in the framework of confirmatory factor analysis and provided the

following fit statistics: RMSEA = 0.092, CFI = 0.918, TLI = 0.905. In addition to poor

model data fit statistics, the two factors, SR and RD, showed Heywood cases [48],

meaning that the standardized item-to-factor correlations were greater than one.

Further investigation of the modification indices and the possible sources of misfit led to

the decision that the factor structure of the resilience scales had to be reinvestigated via

a model building approach by employing both the tools of exploratory and confirmatory

factor analysis.

An exploratory factor analysis conducted on the polychoric correlation matrix

revealed two items with too low communality estimates (square multiple correlation less

than 0.40): I21 and I24 (Table 3). I21 is essentially a reverse-coded item, where a higher

score indicates lower resilience levels. I24 concerns the use of resources, even if

unauthorized to use them, and therefore the responses indicating higher resilience

levels can be confounded with responses indicating conformance to rules. These two

items do not relate well with the other scale items. These two items were flagged, and

further analyses were conducted without them.

15

Insert Table 3 about here

Eigenvalues, an eigenvalue plot, and parallel analysis were used to determine the

number of possible factors. (See Figure 1 for parallel analysis.) These analyses indicated

there could be three or four factors. As a result, an exploratory factor analysis starting

from a single factor model to a six-factor model were investigated. All of these

investigations were conducted in Mplus 6.1 [43]. The estimation method was WLSMV,

with geomin rotation for multi-factor solutions (Table 4). Items I21 and I24 were not

included in these analyses.

Insert Figure 1 about here

Insert Table 4 about here

The purpose of the exploratory factor analysis was to set up a simple structure

where one item is allowed to load on only one factor and error variances of the items are

uncorrelated. A combination of two paths was followed to achieve a simple structure.

One path was to use statistical reasoning which was to allow an item to load only on a

factor on which it had a higher loading. The second path was the theoretical necessity

(content validity) which was always a priority. As methodological decisions were

required, the information considered was statistical and theoretical, with respect to the

design of a valid instrument to measure workplace resilience.

Table 4 clearly shows that the single-factor EFA model did not have desirable fit

statistics. This single-factor EFA was identical to its counterpart CFA solution. The two

factor model’s factor structure was used to achieve a simple structure by using the

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salient loadings (loading≥0.40) from the EFA two-factor model. The test of this model

in the framework of CFA via WLSMV revealed the following fit statistics: RMSEA=0.13,

CFI=0.83, TLI=0.81. Modification indices were examined. It was found out that the

residuals of item 8 and 9, and items 11 and 12 would improve model-data fit if these two

pairs were allowed to correlate. The addition of these correlations one-at-a-time

improved model-data fit to the following fit statistics: RMSEA=0.09, CFI=0.92,

TLI=0.91. The fit statistics were still below the acceptable range. Then, the examination

of a three-factor model that is based on the EFA (loadings≥ 0.40) yielded the following

fit statistics: RMSEA=0.093, CFI=0.92, TLI=0.91. Modification indices were examined.

It was found out that the addition of correlated error variances of items 8 and 9 would

improve model-data fit. The addition of the correlated residual variances of item 8 and 9

improved the fit statistics to RMSEA=0.085, CFI=0.935, TLI=0.927. Then, a four-factor

model was examined that was also developed based on the EFA solution. This model

was a complex model which means that there were some items that were allowed to load

on multiple factors. This model had the following fit statistics: RMSEA = 0.081, CFI =

0.944, TLI = 0.934.

As the steps were taken in order to arrive to a simple structure, a total of four

loadings were deleted by the use of the previously described paths. As a result, when the

simple structure was achieved, the model had the following fit statistics: RMSEA =

0.084, CFI = 0.938, TLI = 0.929. The examination of the modification indices indicated

that the residuals of item 8 and 9, then items 19 and 20 would improve the fit if they

were allowed to correlate. The model with the correlated errors had the following fit

statistics: RMSEA = 0.077, CFI = 0.948, TLI = 0.941. Because items 8 and 9 were quite

similar in terms of content, item 9 was removed from the instrument due to having a

17

smaller loading than item 8. The same procedure was applied with the items 19 and 20,

and then item 20 was deleted. With this condition, the model-data fit became: RMSEA

= 0.078, CFI = 0.948, TLI = 0.940.

Further analysis showed the deletion of item 18 would improve model fit

statistics. A theoretical review of item 18’s fit with its corresponding factor showed that,

although the item measures an underlying concept of value to resilience, it did not have

a close fit with the other items in the factor. Item 18 was therefore deleted and the

resulting model-data fit became: RMSEA = 0.078, 90% CI = 0.071-0.083, CFI=.951,

TLI=.943 (Figure 2). This final model produced a 20-item instrument called the

Workplace Resilience Instrument (WRI).

Insert Figure 2 about here

The five-factor model was examined in the framework of EFA. In that case, the

fifth factor had an insufficient number of items loading on it. The six-factor solution had

the same condition. Therefore, the further examination of these candidate models was

terminated and the four-factor model was adopted: active problem-solving, team

efficacy, confident sense-making, and bricolage. Table 5 shows the four factors and an

example item from each of those factors.

Insert Table 5 about here

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6. Discussion

6.1 The Meaning Behind the Analyses

Through confirmatory factor analysis, four factors related to workplace resilience

emerged. These results show that the four factors of workplace resilience, namely, active

problem-solving, team efficacy, confident sense-making, and bricolage, as assessed by

the WRI were applicable to this target population in the workplace. Each of the four

factors showed evidence of internal consistency (alpha: 0.77-0.83; omega: 0.77-0.83).

Essentially, the four factors of the WRI are protective factors, in terms of the resilience

literature. Protective factors work to increase the resilience capacity of an individual and

exist in contrast to risk factors, which work to increase one’s vulnerability.

The inter-factor correlations of the WRI subscales are mostly moderate and

significant at p<.05 (Table 6). This indicates that the subscales are related, but have

sufficient statistical evidence that they are measuring distinct dimensions of workplace

resilience. The correlations between the WRI subscales and the BJSQ subscales provide

evidence of convergent validity, which is expected because of the theoretical

relationships between job stress and resilience.

A counterintuitive finding in the convergent validity testing with job stress was

the positive correlations between WRI factors and the BJSQ factor of job demand. We

expected measures of job stress to be negatively correlated with resilience on the

assumption that a more resilient individual is likely to score lower on job stress. Put

another way, the resilience person’s use of protective factors should indeed protect

him/her from the forces of job stress. However, the significant positive correlation of the

job demand factor with all four WRI factors may provide some insight into how

19

protective factors are deployed effectively. Perhaps a more resilient individual

experiences stress differently than the person with lower resilience. The job demand

items in the BJSQ may represent behaviors that are important to performance at higher

levels of resilience in the workplace. These job demand items concern the focus of

attention, the difficulty of the job, and the inability to complete all of one’s tasks in the

time given. Based on the underlying dimensions being tested, better performance

against these job demands bears a logical relationship with the expectation of higher

workplace resilience.

Insert Table 6 about here

6.2 The Four Factors of WRI

The resulting model had four factors: Active Problem-Solving, Team Efficacy,

Confident Sense-Making, and Bricolage. These four factors track well with existing

resilience and coping research.

Active Problem-Solving. An active approach to problem-solving demonstrates a

need to do something positive, rather than merely talking about the problem or hoping

it will go away. In the workplace, this requires employees to have a bias for action and

the ability to focus on the problem instead of worrying about why things aren’t going

well. This factor corresponds with Carver et al.’s [20] scale on coping. Their highest

scale assignment in the coping instrument was “active coping,” which consisted of items

concerning the taking of action to solve a problem.

20

Team Efficacy. A resilient individual not only “works well in teams,” but has a

systemic understanding of how the team operates and achieves its objectives. Rather

than assume that a fellow team member knows what he or she is supposed to do, the

resilient individual discusses team member roles with other team members. Goals are

made known and shared with everyone on the team and, in turn, guide each team

member’s actions.

Confident Sense-Making. The ability to extract order out of chaos is a mark of the

resilient individual. Making sense of one’s reality requires accessing the right resources

quickly; to do so confidently is a key factor in workplace resilience. Classically, the types

of behavior exhibiting this factor have saved lives and led to long-lasting innovations [1].

More notably, in today’s workplaces, confident sense-making requires the individual to

quickly filter out unnecessary signal and information and to focus on the relevant

stimuli for decision making and action.

Bricolage. This French term, from Levi-Strauss’ The Savage Mind [32], captures

another unique factor of the resilient individual. The bricoleur practices a highly applied

engineering approach, much like the 1980s U.S. television character MacGyver.

Resilience benefits from fashioning solutions creatively to address the situation. When

confronted with chaotic, extreme, and dangerous situations, the resilient individual

takes intelligent risks and realizes there is time to “STOP”—stop, think, observe, and

plan1.

1 As used by the U.S. Army [53] and outdoor survival agencies to communicate the need for being mindful in the face of danger.

21

Tables 7-9 display some of the demographic variables and their relationship to

the WRI subscales. In terms of gender, there are statistically significant differences

between males (range: 0.15 to 0.20) and females (range: -0.03 to -0.04) with respect to

each of the WRI’s four factors (Table 7). This finding shows males scoring higher as a

group than females on the resilience factors. In comparison, Connor and Davidson’s [13]

use of the CD-RISC resilience instrument showed no significant differences by gender,

race, or age. When looking at factor scores across the two samples, executives scored

higher on all four resilience factors compared with nurses (Table 8). Although the

executive sample had a higher percentage of males than the nursing sample (39% vs.

5%), the executive sample was still predominantly female, suggesting that the executive

position was the dominant factor in this comparison. When males were removed from

the two samples, the same relationships held—the executive sample scored significantly

higher than the nursing sample on all four factors. Finally, we tested for differences

between those in urban versus rural hospital locations and found no statistical

differences (Table 9).

Insert Tables 7, 8, & 9 about here

Analyses conducted by age range showed only minor differences on Factor 2:

Team Efficacy and Factor 3: Confident Sense-Making. For both factors, respondents

aged 65-74 scored significantly higher than those aged 25-34. Years of healthcare

experience was positively correlated with each of the four WRI factors.

6.3 Generalizability and Limitations of the WRI

The 1998 model [2] was a good model for its time, but resilience theory and

psychometric methods have evolved since then. This has allowed for the design for an

22

instrument that has validity for use in the workplace with employees. The improvement

of resilience in the workplace can be aided by the use of a relevant, valid, and reliable

instrument. The WRI has shown promising psychometric qualities and is grounded in

the resilience research.

Improving individual resilience is one area where managers can play a role in

their employees’ development. With knowledge of the primary resilience factors and the

results from the use of the WRI, those efforts can be focused on specific actions.

Expected outcomes of improved workplace resilience include: more effective actions

taken in a crisis, reduced stress, higher quality decision making, decreased use of sick

days, and higher job satisfaction. Future research on resilience and outcomes can verify

the extent and conditions in which these outcomes exist.

With the study sample being solely in the healthcare sector, care must be taken

when attempting to generalize these findings to other sectors. Additionally, the majority

of the study sample was nurses, a distinct job class in a specific sector. Future work will

study the behavior of the WRI in sectors such as manufacturing, service, and education

to assess the validity of using the WRI in sectors other than healthcare. Although no

significant findings emerged from the analysis of resilience by U.S. regions, future work

could focus on resilience differences across countries known to differ on other

workplace variables. Finally, future work could investigate whether the measurement

precision of the WRI is the same across sample groups such as gender, age, and type of

work.

23

7. Conclusion

We have shown the psychometric qualities for an instrument to measure

resilience in the workplace. The WRI was shown to have four factors and convergent

validity with a job stress instrument.

The self-administered instrument was completed by 540 participants across two

samples—healthcare executives and hospital-based nursing staff. The instrument

contained items measuring resilience and job stress, and captured demographics of

gender, age interval, employment location, and years of healthcare experience. The WRI

used a five-point “extent-of-truth” scale.

The WRI’s psychometric properties provide evidence for a four-factor model.

This model has an acceptable RMSEA and has good fit indices as indicated by CFI and

TLI. Analyses by hospital location (urban vs. rural) showed no significant differences in

resilience levels, but males scored significantly higher as a group than females among all

four WRI factors and hospital executives scored significantly higher than nurses on all

four factors, even when analyzed on female-only subsets. Years of healthcare experience

was positively correlated with each of the four WRI factors. These findings suggest

future research may wish to focus on investigating if and why protective factors

(inducing higher resilience) are more likely to be deployed by males than females and

how protective factors are developed through the course of one’s career and how

development of protective factors could be accelerated among those showing lower

levels of resilience.

This instrument development study produced a resilience instrument designed

with employees in the workplace, not patients or clients in a clinical setting.

24

Psychometrics provided validation and support for the quality of the tool for use in

workplace settings. The WRI has the potential to provide organizations and managers a

useful tool for improving workplace resilience and helping employees achieve their

potential.

Acknowledgments

This work was supported by the Faculty Research and Creative Activities Award

program at Western Michigan University, Kalamazoo, Michigan, USA.

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Table captions

Table 1. Summary of the major resilience scales.

Table 2. Demographics of the study respondents.

Table 3. Initial communality estimates

Table 4. EFA solutions with Geomin rotation

Table 5. WRI factors and example items

Table 6. Correlations among WRI subscales, BJSQ subscales, experience, and subscale reliability

Table 7. Factor scores and gender

Table 8. Factor scores and sample

Table 9. Factor scores and hospital location

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Figure captions

Figure 1. Eigenvalues and parallel analysis plot

Figure 2. Factor structure of the Workplace Resilience Instrument.

31

Table 1. Summary of the major resilience scales.

Instrument Source Dimensions Primary Population(s)

CD-RISC 25 items

Connor & Davidson [13]

Sood et al. [49]

Personal competence Trust in one’s instincts, strengthening by stress Positive acceptance of change Control Spiritual influences

Psychiatric/ PTSD population

Medical staff General population

DRS 45 items (32-short form)

Bartone et al. [29]

Commitment Control Change

Military survivor assistance officers

Resilience Scales (RS)

Wagnild & Young [12]

Personal competence Acceptance of self and life

Elderly women

Resilience Scale for Adults (RSA)

Friborg et al. [14]

Personal competence Social competence Family coherence Social support Personal structure

Mental health outpatients & control group

32

Table 2. Demographics of the study respondents.

Sample Frequency % U.S. hospital-based nurses 362 67 Midwest U.S. hospital executives 175 33 Gender Female 447 84 Male 85 16 Hospital Location Urban 337 66 Rural 174 34 Age 16-24 19 4 25-34 38 7 35-44 59 11 45-54 150 28 55-64 225 42 65-74 32 6 75+ 2 0 Prefer not to answer 9 2 Regions of U.S. West 67 13 Midwest 269 50 Northeast 90 17 South 108 20

33

Table 3. Initial Communality Estimates

I1 I2 I3 I4 I5 I6 I7 I8 I9 I10 0.60 0.65 0.70 0.56 0.73 0.57 0.65 0.66 0.64 0.70 I11 I12 I13 I14 I15 I16 I17 I18 I19 I20 0.81 0.74 0.48 0.64 0.68 0.70 0.61 0.70 0.80 0.77 I21 I22 I23 I24 I25 0.17 0.40 0.46 0.19 0.51

34

Table 4. EFA solutions with Geomin rotation*

Solution RMSEA CFI TLI Inter-factor correlation range 1 Factor 0.16 0.74 0.72 2 Factor 0.12 0.88 0.85 0.27 3 Factor 0.10 0.92 0.89 0.03-0.45 4 Factor 0.08 0.95 0.92 0.17-0.59 5 Factor 0.07 0.97 0.94 0.17-0.64 6 Factor 0.06 0.98 0.96 0.14-0.58

*EFA solutions were based on WLSMV

35

Table 5. WRI factors and example items

Factor Example Item Active Problem-Solving I take delight in solving difficult problems. Team Efficacy I understand my team’s overall goals. Confident Sense-Making I make sense of the situation when it becomes chaotic. Bricolage When under extreme pressure, I still take time to try

new methods.

36

Table 6. Correlations among WRI subscales, BJSQ subscales, experience, and subscale reliability

WRI Factors

BJSQ Factors

Active Problem-Solving

Team Efficacy

Confident Sense-Making

Bricolage

Job Control -0.31 -0.30 -0.30 -0.28 Support -0.27 -0.40 -0.36 -0.24 Job Demand 0.15 0.17 0.13 0.10 WRI Factors Active

Problem-Solving

0.68

0.68

0.75

Team Efficacy 0.77 0.69 Confident

Sense-Making 0.73

Job Satisfaction

0.30 0.35 0.37 0.29

Correlation Experience 0.15 0.21 0.22 0.11 Internal Alpha* 0.80 0.79 0.77 0.83 Consistency/ Reliability

Omega** 0.80 0.80 0.77 0.83

*Reliability coefficient alpha, **Reliability coefficient omega [50]

37

Table 7. Factor scores and gender

Gender Male Female t df p

Active Problem Solving

0.20 -0.04 -2.99 1 0.002

Team Efficacy

0.15 -0.03 -2.61 1 0.009

Confident Sense-Making

0.16 -0.04 -2.41 1 0.01

Bricolage 0.17 -0.03 -2.83 1 0.004

38

Table 8. Factor scores and sample

Sample Executives Nurses t df p Active Problem-Solving

0.34 -0.18 -8.49 1 <0.001

Team Efficacy

0.30 -0.16 -8.92 1 <0.001

Confident Sense-Making

0.32 -0.17 -8.21 1 <0.001

Bricolage 0.28 -0.14 -7.57 1 <0.001

39

Table 9. Factor scores and hospital location

Location Rural Urban t df p Active Problem-Solving 0.06 -0.03 -1.44 1 0.15 Team Efficacy 0.009 -0.006 -0.24 1 0.81 Confident Sense-Making 0.02 -0.02 -0.69 1 0.48 Bricolage 0.03 -0.01 -0.88 1 0.37

40

Figure 1. Eigenvalues and parallel analysis plot2

2 The parallel analysis was based on maximum likelihood.

41

Figure 2. Factor structure of the Workplace Resilience Instrument.

RMSEA=0.077, CFI=0.953, TLI=0.945

42

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