Testing a theoretical model of clinical nurses' intent to stay.

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Testing a theoretical model of clinical nurses’ intent to stay Tracy L. Cowden Greta G. Cummings Background: Published theoretical models of nurses’ intent to stay (ITS) report inconsistent outcomes, and not all hypothesized models have been adequately tested. Research has focused on cognitive rather than emotional determinants of nurses’ ITS. Purpose: The aim of this study was to empirically verify a complex theoretical model of nurses’ ITS that includes both affective and cognitive determinants and to explore the influence of relational leadership on staff nurses’ ITS. Methodology: The study was a correlational, mixed-method, nonexperimental design. A subsample of the Quality Work Environment Study survey data 2009 (n = 415 nurses) was used to test our theoretical model of clinical nurses’ ITS as a structural equation model. Results: The model explained 63% of variance in ITS. Organizational commitment, empowerment, and desire to stay were the model concepts with the strongest effects on nurses’ ITS. Leadership practices indirectly influenced ITS. Practice Implications: How nurses evaluate and respond to their work environment is both an emotional and rational process. Health care organizations need to be cognizant of the influence that nurses’ feelings and views of their work setting have on their intention decisions and integrate that knowledge into the development of retention strategies. Leadership practices play an important role in staff nurses’ perceptions of the workplace. Identifying the mechanisms by which leadership influences staff nurses’ intentions to stay presents additional focus areas for developing retention strategies.

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lobally, most countries and professional nursing organizations report a shortage of qualified nurses willing to work in the health care sector (Fox & Abrahamson, 2009). Retaining nurses in their current positions is one way to minimize effects of the nursing shortage. Clinical nurses’ stated intentions to stay have consistently Key words: intent to stay, leadership, nurses, structural equation modeling, theoretical model Tracy L. Cowden, BSN, MHA, PhD, RN, is Senior Practice Consultant, Interprofessional Education, Health Professions Strategy and Practice, Alberta Health Services, Cold Lake, Canada. Greta G. Cummings, PhD, RN, is Professor, Faculty of Nursing, University of Alberta, Canada. E-mail: [email protected]. This research received funding/grant from the Social Sciences and Humanities Research Council of Canada. The authors have disclosed that they have no significant relationship with, or financial interest in, any commercial companies pertaining to this article. DOI: 10.1097/HMR.0000000000000008

Health Care Manage Rev, 2015, 40(2), 169Y181 Copyright B 2015 Wolters Kluwer Health, Inc. All rights reserved.

been reported as predictors of retention. Empirical testing of theoretical models of intent to stay (ITS) has identified up to 52% of the variance in ITS (Boyle, Bott, Hansen, Woods, & Taunton, 1999; Mrayyan, 2008); however, inconsistent outcomes have been reported across studies, which contribute to uncertainty about the formation of clinical nurses’ intentions to remain in their current positions and hinder the development of effective retention strategies. In addition, not all hypothesized models have been adequately tested nor the causal sequence of effects identified, which limits the legitimacy of study conclusions. The aim of this study was to increase understanding of clinical nurses’ ITS in their current positions by empirically testing a complex theoretical model of frontline, hospital-based nurses’ ITS.

Theoretical Framework Nursing retention and turnover has been widely studied, which provides a solid foundation from which to refine theory and seek causal mechanisms influencing the development of behavioral intentions. Turnover research commonly reflects a general perspective that distal antecedents

Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.

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(work, nurse, organization, and work characteristics) influence attitudinal antecedents (cognitive and affective responses to work), which in turn affect direct antecedents (stated intentions) and actual turnover (Hom, Mitchell, Lee, & Griffeth, 2012), Price’s (2001) conceptual framework on turnover has been used in many studies investigating nursing retention. Price’s framework portrays three categories of predictors of turnover. These are the categories of work attitudes and perception, job opportunities, and personal characteristics. Nurse researchers have enhanced this model by adding specific variables and additional categories such as shocks, family conflict, and economic measures (Brewer, Kovner, Greene, Tukov-Shuser, & Djukic, 2012); managerial support (Boyle et al., 1999); and burnout (Tourangeau & Cranley, 2006) with limited and often conflicting findings. The model tested is our theoretical model of clinical nurses’ ITS (Cowden & Cummings, 2012), which was developed based on the literature (Cowden, Cummings, & Profetto-McGrath, 2011), previous models of ITS, personal experience, and hypothesized relationships among factors influencing clinical nurses’ intentions to remain in their current positions. Our model differs from previously tested models in the specificity of variables, the introduction of the concept of desire to stay, and the emphasis on both cognitive and affective influences on the development of ITS. In Figure 1, we present our theoretical framework, where concepts thought to influence nurses’ ITS are grouped into six categories: manager, organization, work and nurse characteristics, and the cognitive and affective responses to one’s work. Empirical support for the hypothesized relationships among study variables is described in detail elsewhere (Cowden & Cummings, 2012) including recognition of a need for distinctions between affective and cognitive de-

terminants of behavioral intentions (Meyer, Allen, & Smith, 1993; Mrayyan, 2008; Gregory, Way, LeFort, Barrett, & Parfrey, 2007). Manager characteristics identified in the model and commonly studied in nursing retention and turnover research are nurses’ perception of leadership (Cowden et al., 2011; Taunton, Boyle, Woods, Hansen, & Bott, 1997), leadership practices of shared decision making (Mrayyan, 2008), praise and recognition (Tourangeau & Cranley, 2006), and supervisor support (Tourangeau & Cranley, 2006). The literature is unclear on the mechanisms by which leadership practices influence the development of nurses’ ITS. Several studies have reported that leadership practices directly influence the development of ITS (Boyle et al., 1999; Cowden et al., 2011; Taunton et al., 1997), whereas others did not find a direct link (Tourangeau & Cranley, 2006). Nurses on units with positive perceptions of leadership are associated with higher levels of job satisfaction and lower turnover rates (O’Brien-Pallas et al., 2012). We hypothesize that leadership practices influence nurses’ ITS, but as most of the models have not measured indirect effects, the mechanism of influence has not been well documented. Organizational characteristics in the model are career development opportunities (Boyle et al., 1999; Taunton et al., 1997), perception of staffing adequacy (Estryn-Behar, van den Heijden, Fry, & Hasselhorn, 2010), and time available to provide patient care (Larrabee et al., 2010). Career development opportunities within the model refer to promotion and educational activities available in the organization, which are linked to job satisfaction and nurse retention (Hayes et al., 2006; Kovner, Brewer, Greene, & Fairchild, 2009). Work place characteristics of importance to the model include incidence or threat of abuse (Roche, Diers, Duffield,

Figure 1

Theoretical framework of clinical nurses’ intent to stay


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Nurses’ Intention To Stay

& Catling-Paul, 2010), presence of autonomy (Boyle et al., 1999; Estryn-Behar et al., 2010; Taunton et al., 1997), and work group cohesion (Boyle et al., 1999; Tourangeau & Cranley, 2006). These elements of the work environment are reported to influence the development of ITS. Nurse-specific characteristics in the model and common to retention research are age (Larrabee et al., 2010), education (Larrabee et al., 2010; Tourangeau & Cranley, 2006), position (Tourangeau & Cranley, 2006), and tenure (Larrabee et al., 2010; Taunton et al., 1997; Tourangeau & Cranley, 2006). Increased age, tenure, and full-time equivalent are all positively associated with ITS. Higher educational preparation is associated with higher intentions to leave. Cognitive responses to nursing work reported in the literature and reflected in the model include perceptions of empowerment (Laschinger, Leiter, Day, & Gilin, 2009; Taunton et al., 1997), organizational commitment (Taunton et al., 1997; Tourangeau & Cranley, 2006), quality of care (Estryn-Behar et al., 2010), and career opportunities elsewhere (Boyle et al., 1999; Taunton et al., 1997). Empowerment, within the model, is defined as the workplace perception arising from psychological and structural attributes that promote optimal performance (Cowden & Cummings, 2012; Laschinger et al., 2009). Affective responses to the environment found to influence ITS are job satisfaction (Boyle et al., 1999; Taunton et al., 1997), desire to stay, joy experienced at work (Manion, 2003), and job stress (Larrabee et al., 2010; Taunton et al., 1997). Desire to stay refers to the positive feelings nurses have toward staying in their positions. The inclusion of this variable provides an opportunity to isolate the influence of the emotional response to work on the development of ITS. Desire to stay refers to feelings related to staying, whereas ITS is a stated likelihood of remaining. Job stress in our study is defined as workplace factors that interfere with nurses’ ability to provide care (Boswell, 1992). Job stress in our model is measured by the presence of abuse and level of moral distress. These variables are both identified as job stressors (Pauly, Varscoe, Storch, & Newton, 2009; Sofield & Salmond, 2003). Although we used a broad cross-section of literature to inform our model development (Cowden et al., 2011), two models (Boyle et al., 1999; Tourangeau & Cranley, 2006) had the greatest influence on our thinking about influences on the development of ITS and, subsequently, were used as a basis for theoretical model development. Boyle et al. employed four sets of predictor variables to understand nurses’ ITS: manager, organizational, nurse, and work characteristics. Tourangeau and Cranley, building on Boyle et al.’s work, used the predictor variables of job satisfaction, manager ability and support, organizational commitment, burnout, work group cohesion and collaboration, and nurse characteristics in their study. Both of these models postulated a relationship between leadership and nurses’ ITS, which is integral to our model.

Methods Quality work environment study design and sample. The Quality Work Environment Study (QWEST; Cummings et al., 2010) was a correlational study based on a mixed-method, nonexperimental design, which investigated relationships between nurse managers’ leadership practices, features of the nursing work environment, and outcomes for the organization and nurses across three contextual settings (long-term care facilities, teaching hospitals, and community care hospitals). The study consisted of three phases. In phase 1, individual interviews with managers (n = 31) and focus group interviews with registered nurses (RNs) and licensed practical nurses (LPNs) (n = 65) were used to identify and align study constructs with the reality of the work environment, to refine study tools. In phase 2, a stratified sample of 180 frontline managers, 60 per each contextual setting, were invited to respond to an electronic Web-based survey. In phase 3, we identified study units (those where a manager had completed a survey in phase 2) and invited full-time and part-time RNs and LPNs to also participate in the study by completing a written survey. This study design was purposive to allow for hierarchical modeling analyses using nested data structures. The study reported here used a subsample of the QWEST nurse sample, which included all full-time and part-time responding RNs and LPNs (n = 415), who worked on acute care study units in nine hospitals (two teaching hospitals and seven community hospitals). Both RNs and LPNs were included in the sample as the scope of practice and job performance expectations for both professional designations are very similar in acute care hospital setting in this province (see Table 1 for sample demographics). QWEST received ethics approval from the University Health Research Ethics Board. All study participants consented to participate and were assured confidentiality. Nurse survey data collection procedures. Data were collected between April and June 2009 in the former Capital Health Region of Alberta Health Services, located in Edmonton, Alberta, Canada. The nurse survey data were collected via paper-based surveys distributed to each care unit included in the study, because of lack of computer access for all staff. A sufficient number of surveys were sent to each unit up to a maximum of 50 surveys. Study participants mailed completed surveys to the QWEST project manager.

Measures The research team developed the QWEST survey for RNs and LPNs, which included a number of valid and reliable instruments used in prior research. The Cronbach’s " reliability coefficients for study items varied between .71 and .94. Instruments used in the survey were the Resonant


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Table 1

Demographic characteristics of sample (n = 415) Demographic characteristic Age (n = 403), years Less than or equal to 29 30Y44 Greater than 44 Tenure (n = 406), years Less than or equal to 5 6Y15 16Y25 Greater than 25 Education (n = 404) Diploma Bachelor Master Other Work status (n = 401) Permanent Temporary

Frequency (%)

93 (23) 126 (31) 184 (46) 176 90 80 60

(43) (22) (20) (15)

223 171 4 6

(55) (42) (1) (2)

360 (90) 41 (10)

Leadership Scale (10 items; Estabrooks, Squires, Cummings, Birdsell, & Norton, 2009), Global Empowerment (two items; Laschinger & Finegan, 2005), Areas of Work Life Questionnaire (29 items; Leiter & Maslach, 1999), Maslach Burnout Inventory (nine items; Maslach, Jackson, & Leiter, 1996), Global Job Satisfaction (three items; Quinn & Shephard, 1974), Stanford Safety Culture Instrument (16 items; Ginsberg, Norton, Casebeer, & Lewis, 2005), and the Revised Nursing Work Index Questionnaire (29 of 52 items; Aiken & Patrician, 2000). Sample demographic information on age, gender, educational level attained, job title, tenure, and employment status was also collected.

Data Analysis Structural equation modeling (SEM) was used to test our model. SEM is a confirmatory statistical technique used to investigate causal consequences postulated in theories and the plausibility of that theory as reflecting the real world (Hayduk, Cummings, Boadu, Pazderka-Robinson, & Boulianne, 2007). Care must be exercised that modifications to the model do not result in a factor analysis exploratory approach to identifying the best model (Tabachnick & Fidell, 2007). Relationships in the model are postulated to be both causal and linear. A sample size of 200 participants or more (Tabachnick & Fidell, 2007) and a ratio of a minimum of 10 cases to each indicator (Violato & Hecker, 2007) are recommended. Our model meets the sample and caseY indicator ratio with 415 cases and 24 indicators. SEM includes a theoretical latent concepts and a measured structural component. Latent concepts in SEM are

identified as exogenous or endogenous. Exogenous concepts are background variables that influence endogenous concepts but do not receive effects from other concepts. Endogenous concepts are internal to the model, influenced by and receiving effects from other concepts. Latent concepts are measured through observed indicator variables. Each indicator is assigned an error variance reflective of the model theory (Hayduk, 1987; Tabachnick & Fidell, 2007). The researcher-assigned error variance is an integral component of the postulated theory. Error variance is the cumulative effect of all nonlatent concept causal impacts associated with the indicator and adjusts for measurement unreliability (Hayduk, 1987). For our model, we used the techniques of Hayduk (1987) and Hayduk and Littvay (2012) and assigned a single indicator to each latent concept. The use of one quality indicator demands a clear articulation of the theory to be tested. When using one indicator, the researcher must not only choose the ‘‘best’’ indicator to measure the concept of interest but must also identify the theoretical difference or error variance between the indicator and the concept (Hayduk et al., 2007; Hayduk & PazderkaRobinson, 2007). The error variances assigned to indicators function as an adjustment for measurement unreliability (Hayduk, 1987). The validity of the theory is apparent when the proposed model (theoretically implied matrix), tested as an SEM, matches the worldly structures (data matrix) (Hayduk & Littvay, 2012).

Transformation of Model into an SEM We transformed our theory into an SEM by specifying the concepts and their interrelationships (both direct and indirect). Postulated relationships within the SEM are presented in Table 2. We reviewed the QWEST survey questions in detail and chose the best single indicator for each concept within the model. We then assigned error variances to indicators of each latent concept ranging from 2% to 20%. The percentage of assessed measurement error of concepts was determined based on how closely each latent variable was tied to the model theory, the theoretical understanding of the underlying causal world, and our assessment of how well the survey question measured the latent concept. Survey questions were assessed for clarity, potential for confusion or misinterpretation, context, and response options. For example, the survey question on time-to-nurse was assigned a 10% measurement error as the answer to the question was thought to potentially vary across nurses, based on their experience, education, and perspectives of the quality of the working environment. See Table 3 for covariances and correlations among the variables. Model estimation. The theoretical model was estimated using LISREL 8.8 software and maximum likelihood


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Table 2

Transforming the theoretical model into an SEM: Hypothesized relationships within the model From Exogenous Variables Perceived leadership

Work status Position preference Opportunity elsewhere Career development Abuse Age Tenure Education Endogenous Variables Shared decision making Supervisor support Autonomy Empowerment Time to nurse Quality of care Staffing Work group cohesion Joy Praise and recognition Moral distress Job satisfaction Organizational commitment Desire to stay

To

Shared decision making, supervisor support, autonomy, empowerment, staffing, work group cohesion, joy, praise and recognition, moral distress, job satisfaction, desire to stay, and intent to stay Job satisfaction and organizational commitment Job satisfaction, organizational commitment, desire to stay, and intent to stay Desire to stay and intent to stay Job satisfaction, organizational commitment, and intent to stay Time to nurse, job satisfaction, and intent to stay Staffing, moral distress, job satisfaction, organizational commitment, desire to stay, and intent to stay Job satisfaction, organizational commitment, and intent to stay Job satisfaction and intent to stay Quality of care Job satisfaction and organizational commitment Quality of care, joy, moral distress, job satisfaction, and intent to stay Quality of care, work group cohesion, joy, job satisfaction, organizational commitment, desire to stay, intent to stay Quality of care, joy, and moral distress Joy, job satisfaction, organizational commitment, desire to stay, and intent to stay Time to nurse, quality of care, and job satisfaction Time to nurse, quality of care, joy, moral distress, job satisfaction, organizational commitment, and desire to stay Job satisfaction and desire to stay Joy, job satisfaction, desire to stay, and intent to stay Quality of care, joy, job satisfaction, and desire to stay Organizational commitment, desire to stay, and intent to stay Desire to stay and intent to stay Intent to stay

estimation (Joreskog & Sorbom, 1996). LISREL does not calculate the significance of specific single indicators; it calculates the chain of indirect effects and measures its combined value. Total effects are the combination of values of direct and indirect effects (Hayduk, 1987). We evaluated model fit using the chi-square (22) statistic. If the difference between the model-implied and data matrices 2 2 is nonsignificant (p 9 .05), the model is deemed to be a potential representation of the causal world and random sampling fluctuations alone could account for any inconsistencies between the model and observed covariances (Hayduk et al., 2007). The use of other fit criteria in the absence of a nonsignificant 2 2 can be construed as accepting of ill fit (Hayduk & Pazderka-Robinson, 2007). The initial model estimated results were 22 = 482.2, df = 183, p G .001, AGFI = 0.836, GFI = 0.908y, CFI = 0.962, and RMSEA = 0.068, indicating lack of fit between theory and data. Modification indices identified that the influence of age, autonomy, leadership, moral distress, and time-to-

nurse required greater specification. New relationships for estimation were added only if they were congruent with the original theory, theoretically reasonable, and in the appropriate direction of effect (see Figure 2 for additional pathways estimated). No pathways were deleted. The model is complex; the amendments to the model reflect undertheorizing in the initial transformation of the model.

Results The final model estimation results, 22 = 169.9, df = 148, p = .105, AGFI = 0.933, GFI = 0.967, CFI = 0.997, and RMSEA = 0.016, indicated a fitting model. Figure 2 illustrates the significant direct and indirect effects within the final model. Our analysis of the final model focused on variables that had direct effects on ITS and most strongly explained variance in ITS. This was followed by an assessment


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Table 3

Covariance and correlation matrix (n = 415) Casual variable

Shared decision Supervisor making support Autonomy Empowerment

Shared decision making Supervisor support Autonomy Empowerment Time to nurse Quality of care Staffing Work group cohesion (1) Work group cohesion (2) Joy Praise and recognition Moral distress Job satisfaction Organizational commitment Desire to stay Leadership Work status Position Preference Opportunity elsewhere Career development Abuse Age Tenure Education

.56 .30 .19 .28 j.47 .20 .18 .17 .23 .27 .26 j.13 .23 .19 .13 .27 .31 .03 .03 .10 .21 j.10 j.01 j.28 j.04

a

.49* .63 .23 .41 j.39 .20 .20 .20 .27 .27 .35 j.18 .34 .24 .26 .39 .49 .02 .03 .04 .25 j.13 j.08 j.52 j.05

.34** .41** .52 .32 j.55 .30 .25 .18 .27 .23 .29 j.20 .26 .22 .25 .29 .25 .02 .03 .14 .20 j.11 j.10 .03 .02

.37** .52** .44** 1.00 j.59 .35 .25 .31 .45 .45 .37 j.24 .49 .42 .50 .70 .42 .02 .03 .10 .31 j.12 j.10 .03 .02

Time to nurse

Quality of care

Staffing

.29** .22** .35** .27** 4.77 j1.32 j.82 j.22 j.40 j.49 j.53 .35 j.59 j.29 j.54 j.60 j.42 j.01 j.02 .05 j.32 1.06 j.90 j3.22 .14

.24** .22** .35** .31** .52** 1.33 .44 .14 .22 .31 .23 j.21 .37 .11 .29 .37 .23 .01 j.01 .015 .16 j.42 .20 .19 .02

.31** .33** .45** .32** .49** .50** .58 .16 .19 .20 .23 j.17 .28 .14 .24 .25 .23 .02 .01 .06 .16 j.13 .22 .43 j.04

Covariances in lower left half of matrix; variances on diagonal; correlation is in upper right half of matrix.

*Correlation is significant at .05 level (two-tailed). **Correlation is significant at .01 level (two-tailed) Four decimals were used for analysis, table contents rounded to two decimal places.

of the concept, desire to stay. Finally, we examined the influence of perceived leadership in the model.

Key Concepts in the Model The final model estimates explaining 63% of the variance in ITS, effects and standard errors, are shown in Table 4. Three conceptsVorganizational commitment, empowerment and desire to stayVpositively and directly influenced ITS. These concepts were intervening variables for a number of workplace factors. The significant direct and indirect effects on ITS that mediated these concepts are portrayed in Figure 2. The final model also explained 31% of the variance in organizational commitment, 48% of the variance in empowerment, and 54% of the variance in desire to stay. Approximately one half of the hypothesized effects on desire to stay were supported through model estimation. Empowerment, job satisfaction, organizational commitment, opportunity elsewhere, and age all had direct and significant effects on desire to stay. Quality of care, joy, work group cohesion, and leadership all had significant indirect effects on desire to stay. Leadership and autonomy had indirect

effects on desire to stay through the intervening variable of empowerment. Quality of care, work group cohesion, and joy all had an indirect effect on desire to stay, through job satisfaction. Work group cohesion and age had an indirect effect on desire to stay, through organizational commitment as an intervening variable. Perceived leadership had several strong, direct, positive, and significant effects within the model. A good manager and leader resulted in higher clinical nurses’ perceptions of shared decision making, supervisor support, nurses’ ability to practice autonomously, personal empowerment, adequate staffing levels, work group cohesion, and praise and recognition received. Having a good leader resulted in a lower incidence of moral distress. Leadership crossed the model via several different significant indirect pathways, presented in Figure 2, which enhances understanding of the sequence of the development of intentions. The most direct path led from leadership, through empowerment, to ITS. A second pathway led from leadership through the concept of shared decision making, to time to nurse, to autonomy, to empowerment, and then to ITS. A third led from leadership through autonomy, to empowerment, to work group cohesion, to job


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Table 4

Covariance and correlation matrix (n = 415) Work group cohesion (1) .27** .30** .29** .36** .12** .15** .25** .73 .46 .26 .22 j.13 .31 .31 .24 .33 .20 .00 .02 .07 .15 j.01 j.04 .15 .00

Work group cohesion (2) Joy .32** .36** .39** .48** .19** .20** .26** .57** .90 .30 .27 j.24 .34 .46 .28 .49 .27 .02 .02 .18 .17 .04 .02 .00 .01

Praise and Moral Job Organizational recognition distress satisfaction commitment

.24** .42** .23** .52** .21** .48** .30** .44** ** .15 .29** .18** .24** .17** .36** .21** .31** ** .21 .34** 2.20 .28** .35 .69 j.19 j.18 .47 .27 .45 .26 .38 .24 .44 .30 .31 .38 .05 .02 .05 .02 .18 .05 .23 .27 j.20 j.10 j.09 .08 j.64 .59 j.00 j.06

j.26** j.34** j.42** j.36** j.24** j.28** j.34** j.24** j.38** j.20** j.33** .43 j.21 j.21 j.25 j.26 j.22 j.01 j.01 j.13 j.16 .07 j.18 j.56 j.01

satisfaction, to organizational commitment, to desire to stay, and finally, to ITS. The final pathway led from leadership through work group cohesion, to organizational commitment, and to ITS.

Discussion Statistical testing supports the plausibility of our theoretical model as an explanation for relationships in the real world. ITS is directly influenced by both affective and cognitive responses to factors in the workplace. Our model estimation provides support for reported outcomes, challenges findings of less complex models, and provides new nursing knowledge.

Support for Previous Research Outcomes Model estimation confirmed Tourangeau and Cranley’s (2006) findings that organizational commitment directly influences ITS. Some of our findings were also in agreement with conclusions of other research. That is, organizational commitment was a better predictor of ITS than job satis-

.32** .45** .37** .51** .28** .34** .39** .38** .38** .33** .34** j.33** .92 .45 .62 .65 .34 j.01 .00 .14 .23 j.11 .14 .37 j.02

.23** .27** .27** .38** .12** .01 .17** .33** .44** .28** .28** j.29** .42** 1.22 .80 .82 .24 j.01 j.01 .01 .20 j.01 .24 1.75 j.04

Desire to stay

Leadership

.14** .27** .28** .40** .20** .20** .25** .23** .24** .20** .23** j.30** .52** .58** 1.56 1.01 .29 j.04 j.01 j.08 .20 j.08 .52 2.64 j.06

.28** .37** .31** .54** .21** .25** .26** .30** .40** .23** .28** j.31** .52** .57** .62** 1.70 .39 j.00 .01 .07 .27 j.11 .20 1.18 j.05

faction (Wagner, 2007), and organizational commitment has a direct effect on ITS (Gregory et al., 2007). Empowerment is an important variable in understanding and predicting job satisfaction, organizational commitment, and ITS (Laschinger et al., 2009). In our study, it was one of the three strongest influences on clinical nurses’ ITS and has significant positive effects on the other two conceptsVorganizational commitment and desire to stay. Empowerment was also the key mediating variable between leadership and ITS. Consistent with Ellenbecker, Samia, Cushman, and Porell (2007) and Mrayyan (2008), our model results supported empowering environments as essential to clinical nurses’ ITS. Our findings were the same as Hayes et al. (2006), that empowerment has a direct effect on job satisfaction, and Storey, Cheater, Ford, and Leese (2009), that empowerment directly influences job satisfaction. Nursing researchers have theorized the importance of the influence of emotion on ITS and explored the concepts of job satisfaction, burnout, and emotional exhaustion (Laschinger et al., 2009; Tourangeau & Cranley, 2006). Our study also shows that emotions play a role in cognitive decision making and confirms that affective variables influence cognitive processes (Blanchette & Richards, 2010).


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Table 3

Covariance and Correlation Matrix n = 415, Continued Work status

Position

Preference

Opportunity elsewhere

Career development

Abuse

Age

Tenure

Education

.46** .69** .39** .48** .22** .23** .34** .27** .32** .23** .52** j.038** .41** .24** .26** .34** .79 .03 .02 .08 .28 j.08 j.15 j.68 j.03

.13** .010* .07 .08 .01 .01 .08 .01 .05 .12* .10 j.03 j.02 j.04 j.09 j.00 .10 .09 .06 j.01 .01 j.01 j.17 j.65 .03

.13* .12* .13* .10* .03 j.03 .04 .08 .09 .13* .09 j.03 .10 j.04 j.04 .04 .10 .78** .07 j.02 .02 .00 j.15 j.52 .02

.10 .03 .14** .07 j.02 .09 .06 .06 .14** .09 .05 j.14** .11* .01 j.05 .04 .07 j.01 j.04 1.91 .13 .05 j.10 j1.67 .02

.40** .44** .38** .43** .21** .20** .30** .24** .25** .22** .45** j.34** .34** .25** .22** .29** .43** .05 .08 .13* .51 j.11 j.07 j.34 j.04

j.08 j.11* j.10 j.08 j.31 j.24** j.12 j.01 .03 j.10 j.08 .06 j.07 j.02 j.04 j.06 j.06 j.00 .03 .03 j.11* 2.34 j.76 j2.65 .11

j.01 j.04 j.08 j.0 .17** .07 .12* j.02 .08 j.02 .04 j.11* .06 .09 .17** .06 j.07 j.23** j.22** j.03 j.04 j.20** 6.10 18.20 j.56

j.04 j.06 j.04 .00 j.14** .02 .05 .02 .00 j.04 .07 j.08 .04 j.15** .20** .09 j.07 j.20** j.18** j.12* j.05 j.17** .70** 109.60 j1.89

j.08 j.11 .06 .04 j.11* .03 j.09 .01 .01 j.00 j.08 j.02 j.03 j.06 j.08 j.07 j.05 .14** .12** .03 j.10 .12 j.38** j.30** .36

Note. Four decimals were used for analysis; table contents were rounded to two decimal places. a

Covariances are in the lower left half of matrix, variances are on diagonal, and correlation is in the upper right half of matrix.

*Correlation is significant at the .05 level (two-tailed). **

Correlation is significant at the .01 level (two-tailed).

Inconsistencies Among Studies The introduction of a greater number of concepts in our model as compared with other published models allowed us to identify the causal sequence of the development of behavioral intentions. The introduction of the concept desire to stay resulted in the detection of indirect effects on ITS that, in previous studies, were reported to be nonsignificant, as the analytic approach used did not allow for intervening variables. Two of the variables in Boyle et al.’s (1999) modelVopportunity elsewhere and job satisfactionVwere found to have a direct effect on desire to stay and not ITS in our model and only indirectly influenced ITS because desire to stay, in our model, had a moderate effect on ITS. Similarly, Tourangeau and Cranley (2006) reported that age and job satisfaction were direct predictors of ITS, whereas we concluded that they had a direct effect on desire to stay and an indirect influence on ITS. The lack of a direct and significant relationship between age and ITS is supportive of recent research findings (Brewer et al., 2012; LavoieTremblay, Paquet, Marchionni, & Drevniok, 2011), which are contrary to many ITS study findings (Shader, Broome, Broome, West, & Nash, 2001; Tai, Bame, & Robinson, 1998).

This may be reflective of a changing workforce and indicative of a need to revisit previous assumptions about the formation of ITS in different generations. The significant relationships between age and perception of staffing and presence of moral distress may reflect the demographics of the sample. Job satisfaction was identified as a direct predictor of ITS in other studies (Taunton et al., 1997), but in our model, this relationship was mediated by organizational commitment and desire to stay. As the relationship of desire to stay and ITS has not been tested in other models, this may have resulted in variables in those models appearing to be direct predictors of ITS. In our model, empowerment also identified some indirect effects, which in other studies, were reported as direct. Autonomy has been reported to be a direct predictor of ITS (Hayhurst, Saylor, & Stuenkel, 2005; Storey et al., 2009) and a consistent predictor of job satisfaction (Kovner et al., 2009). In our model, the relationship of autonomy to ITS was indirect, mediated by empowerment and job satisfaction. Leadership practices were found to indirectly influence ITS through empowerment, rather than through job satisfaction as per Tourangeau and Cranley (2006). Other study outcomes reported to have a direct


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Figure 2

Significant direct/indirect effects within the final model

influence on ITS - supervisor support (Hayhurst et al., 2005) and recognition (Storey et al., 2009) had no significant effects on ITS in our model.

New Knowledge The greatest contribution of this study is the identification of the causal chain of influence on the development of staff nurses’ ITS through simultaneous estimation of multiple relationships in a complex model. Each link along the chain identifies a potential area for development of retention strategies. The introduction of the affective concept of desire to stay identified a key concept important to retention research. The discovery of desire to stay as a mediating concept acknowledges that the intention decision includes both emotional and rational components. The identification of the mechanisms by which perceptions of leadership practices influence the development of staff nurses’ ITS is another important new contribution to the understanding of nurse retention. Leadership had significant, direct effects on autonomy and empowerment, in which the latter also directly influenced ITS. Identifying the role that empowerment plays in the formation of organizational commitment and ITS, as well as the influence that perceived leadership has on empowerment, assists in understanding the complexity of staff nurses’ ITS.

Limitations This study is not without limitations. Although the model resulted in a fitting model, caution should be exercised in stating the correctness of the final model. Adjustments to the model may have merely followed the data modification indices. The final model is not the same as the initial model, and although a reasonable fit was attained, it does not prove the model to be true (Hayduk, 1987). The empirical findings from the literature used to build the overall model were primarily arrived at via regression techniques, which do not adjust for measurement error nor identify indirect effects. The study took place in nine different hospitals. All facilities were located within the same organization and on acute care patient units. Depending on the lens that is used, it can be argued that each facility and unit would have its own culture and leadership approach or viewed that overarching organizational values and expectations could limit the generalizability of findings. Although all participants were full time or part time in employment status, 10% of the study sample were currently assigned to temporary positions. This may affect study findings as, although their employment is guaranteed, their next assignment may yet have been unknown, which may influence their behavioral intentions.


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AprilYJune & 2015

Table 4

Estimated effects and standard errors in the final model (n = 415) Causal variablesa Outcome variables

Shared decision Supervisor Quality making support Autonomy Empowerment Time to nurse of care

Shared decision making Supervisor support Autonomy Empowerment Time to nurse 0.52b (.17) Quality of care j0.07 (.10) Staffing Work group cohesion Joy Praise and recognition Moral distress Job satisfaction Organizational commitment Desire to stay Intent to stay a

0.08 (.09)

0.15 (.10) 0.01 (.09)

j0.06 (.14) 0.16b (.06) j0.22b (.05) 0.02 (.08) 0.06 (.09)

0.18b (.05)

0.04b (.02)

0.18b (.08) 0.13 (.07)

0.20b (.03)

0.17b (.07) 0.21 (.12)

0.00 (.05)

0.21b 0.21b 0.18b 0.34b

j0.01 (.02) (.07) (.07) (.08) (.08)

Staffing

1.2b (.13) 0.4b (.08) 0.08 (.09) 0.10b (.04)

0.12 (.07)

0.05 (.05)

Effects run from the variable heading in the column to the variable in the row. Significant coefficient as it exceeds more than two standard errors.

b

Practice Implications

Conclusion

A shortage of nurses will continue to challenge health care organizations for years to come. Effective retention strategies are essential to ensure that organizations have an adequate number of nurses to provide quality patient care. An awareness of factors in the workplace that negatively and positively influence nurses’ cognitive and affective responses to the workplace is necessary to inform approaches to enhance the retention of nurses in both their position and the nursing profession. Health care administrations that ensure quality, supportive work environments are possible, and nurse managers that make empowering, quality workplaces a reality will retain more of their staff. The establishment of positive work relationships can facilitate nurse managers’ understanding of staffs’ feelings and concerns and fostering of work group cohesion (Cummings et al., 2010). Attention needs to be focused on the emotional response to one’s work. This is of particular importance for new graduates as they work through the emotional context of the transition from the role of student to independent practitioner (Duchscher, 2008). Hospital administrative structures that support and educate managers as relational leaders will facilitate the creation and maintenance of empowering work environments that positively influence ITS. Incorporating quality-of-life measures into accountability and reporting frameworks will not only emphasize the importance of the work environment to the organization but also function as a recruitment tool and positive message to accrediting and governing bodies that the organization cares for its employees.

The theoretical model of clinical nurses’ ITS is a useful model applicable to future nursing retention research. The model explained 63% of the variance in ITS and is a plausible reflection of how nurses make decisions to remain in their current positions. Our results indicate that emotions and cognition both influence how nurses evaluate and respond to their work environments. Leadership practices play an important role in staff nurses’ perception of the workplace and the establishment of empowering work environments. In the development and implementation of retention strategies, health care organizations need to appreciate the influence that nurses’ feelings and perceptions of their work setting have on their intention decisions. Acknowledgments

This project was made possible by the Social Sciences and Humanities Research Council of Canada. We would like to thank the following people for their contributions to this project: Dr. Greta Cummings (principal investigator), University of Alberta; Dr. Judith Spiers (co-investigator), University of Alberta; Dr. Heather Laschinger (collaborator), University of Western Ontario; Dr. Michael Leiter (collaborator), Acadia University; Dr. Carol Wong (collaborator), University of Western Ontario; Dr. Peter Norton (collaborator), University of Calgary; Dr. William K. Midodzi (collaborator), University of Alberta; Tara MacGregor (project coordinator); Susan Lynch (CLEAR Outcomes Research Program Manager); Ozden Yurtseven (statistical analyst);


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Table 3

Work group cohesion

Joy

Praise and recognition Moral distress

Job satisfaction

Organizational commitment Desire to stay

Leadership 0.72b 0.93b 0.48b 0.89b

j0.13 (.14) j0.05 (.08)

j0.04 0.21b 0.26b j0.13

(.05) (.06) (.08) (.08)

0.57b (.06) 0.37b (.10) 0.24 (.30) 0.79b (.08) j0.33b (.07) 0.30 (.30)

j0.12 (.11) 0.21 (.16)

j0.07 (.17)

j0.16 (.09)

j0.03 (.10)

j0.2 (.04) j0.03 (.11) j0.17 (.11)

j0.18 (.11)

0.16 (.12) 0.09b (.03)

0.27b (.07) 0.41b (.07) 0.12 (.07)

Eliza Lo (graduate research assistant); Heather McArthur (graduate research assistant); Riva Benditt (research assistant); and Joanna Czupryn (research assistant). This project is also supported by a Population Health Investigator award, Alberta Heritage Foundation for Medical Research to Dr. Greta Cummings.

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Table 4

Estimated effects and standard errors in the final model (n = 415), Continued Causal variablesa Work status

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Tenure

Education

0.16b (.05) 0.04b (.01)

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a

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j0.05 (.11)

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Significant coefficient as it exceeds more than two standard errors.

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