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.

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 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 resilientbehavior.

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 ofresilience.

The resilience instrument literature shows 1) these instruments were developed in clinical/counseling settings, not workplace settings; 2) the instruments have some common dimensions, but are differentiated; and 3) the need to develop a resilience instrument for the workplace.

2 Theory and 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:

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.

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 [2, 12]).

Measurement: The 2000s produced resilience scales for use primarily in clinical settings for patients [13 –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 this study. This study extends the resilience work by developing resilience scales for use by employees in a workplace setting.

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 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 [9, 17], 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 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, 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 involvingfatalities.

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 civilianworkplaces.

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 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., [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.

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 [12 –14].

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 –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.

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 rateof 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 studyparticipants, 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.

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.

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 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 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.

Eigenvalues, an eigenvalue plot, and parallel analysis were used to determine the number of possible factors. (See Fig. 1 for parallel analysis.) Theseanalyses 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.

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 workplaceresilience.

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 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 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 = 0.077, 90% CI = 0.071–0.083, CFI = 0.953, TLI = 0.945 (Fig. 2). This final model produced a 20-item instrument called the Workplace Resilience Instrument (WRI).

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.

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 < 0.05 (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 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.

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.

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.

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).

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 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.

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. 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.

8 Conflict of interest

The authors have no conflict of interest to report.

Footnotes

Acknowledgments

This work was supported by the Faculty Research and Creative Activities Award program at Western Michigan University, Kalamazoo, Michigan, USA.

1

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

2

The parallel analysis was based on maximum likelihood.

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