Abstract
Employee engagement remains a growing interest in the academic research literature and especially in the practitioner-industry mainstream literature and practice. Despite the increase in literature on constructs of and associations between enablers and indicators of employee engagement, the empirical examination of employee engagement measures offered by the mainstream practitioner-industry market has received little attention. The study redresses this shortcoming by examining the internal construct validity and reliability of the Flow@Work Engagement Survey (FWES) as applied to a global aggregated cross-sectional data set of (N = 39 310) clients. We further employed structural equation modelling and relative weight analysis to assess the extent to which the proposed FWES engagement enabling working conditions functioned as predictors of the measure's employee engagement indicators on a randomly selected data subset of (n = 3000) clients. The study established the internal convergent and discriminant validity and reliability of the FWES. The findings contributed new insights to the engagement research literature by revealing the nature and relative importance of different engagement enablers in predicting employee willingness and commitment as indicators of organisational employee engagement. Practically, the study uncovers opportunities for future engagement research and well-informed employee engagement measurement and intervention planning in industry.
Keywords: Flow@Work engagement survey, Employee engagement, Engagement indicators, Engagement enablers, Job engagement, Organisational engagement, Self-determination, Work engagement
1. Introduction
Employee engagement is a multidimensional psychological construct denoting employees' emotional ties with and deep passion for the job and organisation [[1], [2], [3], [4], [5], [6],61,62]. Positive working conditions foster experiences of self-determination and flow characterised by an engaged, energetic connection to the job, work enjoyment, a sense of autonomy and competence, and an intrinsic motivation (willingness) and commitment to work at one's highest level of ability [2,5,[7], [8], [9], [10]].
Employee engagement remains a growing interest in the academic research literature and especially in the practitioner-industry mainstream literature and practice [5,6,8,[11], [12], [13], [14], [15]]. Several empirical-based, individual-level and organisational-level outcomes of engaged employees have been reported by Ref. [6]. On an individual level, employee engagement is known to inter alia increase proactive work behaviour, affective commitment, improved job and task performance, innovative behaviour, job satisfaction, and reduced intention to leave. On an organisational level, employee engagement enhances important performance outcomes in organisations, such as for example, person-job or talent fit, company competitiveness and performance, extra-role customer service, innovation, organisational citizenship behaviours, client satisfaction, employee retention, employee wellbeing, and organisational commitment [6,16].
Despite empirical evidence of the benefits of employee engagement [13,14], a United States-based global analysis and advisory firm on employee engagement, reports concerning trends on the decline of employee engagement because of workplaces still adapting to the new normal of hybrid and fully remote ways of working which seems to erode the emotional connection of workers to their organisation's mission and goals [8,17]. This emotional disconnect between employees and their jobs and organisations are, for example, evident in the “quiet quitting” phenomena widely reported in the mainstream media [4].
The call for more people-centric organisations to re-establish employees' organisational affinity shifted management's attention to organisational strategies that promote employees' states of engagement and wellbeing through the creation of positive working conditions [2,8,11,12,18]. One such strategy is the use of employee engagement surveys to measure employees' state of engagement in relation to important working conditions for intervention planning purposes [8,19]. Measuring employees' levels of engagement is seen to signal caring towards employees' wellbeing and helps employers gauge their employees' job enthusiasm, motivation, and organisational connectedness while establishing a baseline understanding of the company's workforce and their needs [8,11,12,19]. In this regard, scholars and practitioners deem the accurate and reliable measurement of employee engagement as being more important than ever [4].
2. Study objective
Despite lack of consensus on the conceptualisation and measurement of employee engagement in the practitioner-industry mainstream and academic research literature [5,6,8,11,12,16,20,21], there seems to be consensus on the measurement of key working conditions that enable or promote employees' state of engagement. Such working conditions allude to employees’ degree of satisfaction with inter alia their jobs, the organisation, supervisor support and performance feedback, career growth and skills development opportunities, performance goals, manager effectiveness, trust in team members and leadership, communication and job resources, diversity and inclusion, company culture, psychological and physical safety, teamwork, reward and recognition, and organisational policies and procedures (see for example [3,5,6,[9], [10], [11], [12], [13], [14], [15],18,[20], [21], [22], [23], [24], [25]],
The practitioner-industry market offers a plethora of employee engagement surveys claimed to be reliable measures of employee engagement [8,[11], [12], [14], [15],19]. The approach to measuring employee engagement differs. Some of the practitioner-industry employee engagement surveys consist of single items measuring employees' satisfaction with key working conditions as drivers of engagement (see for example, Gallup's [13], 12-item, Q12 Employee Engagement Survey). Other employee engagement surveys offer more items per construct to measure various dimensions of employee engagement (see for example, Quantumworkplace's [25], 21-item Employee Engagement Survey, Achievers' [22] 22 -item Employee Engagement Survey, and Leapsome's [23] 70-item Employee Engagement Survey). However, research on the construct validity and reliability of such mainstream practitioner-industry employee engagement surveys is scant and more research is needed to help organisations make informed decisions on the survey products they utilise to gauge their employees' state of engagement [[4], [5], [6],16,20,21,26]. Our purpose was to redress this shortcoming regarding practitioner-industry's measurement of employee engagement.
The objective of the present study was to evaluate the Flow@Work Engagement Survey (FWES: [19]) as a valid and reliable practitioner-industry measure of employee engagement drivers and indicators. The study also aimed to assess the extent to which the FWES engagement enabling working conditions (enablers) function as predictors of the measure's employee engagement indicators. Empirical evidence of the psychometric soundness of the FWES may enrich the practitioner-industry mainstream and research literature on both the construct of, and valid and reliable measurement of employee engagement.
2.1. Conceptual framework of employee engagement
In this study, the construct of employee engagement captures intrinsically motivating experiences associated with working conditions that promote individuals' work engagement (the positive investment of cognitive energy in one's work, i.e. vigour, dedication, and absorption), flow at work (absorption, work enjoyment and intrinsic work motivation), job engagement (individuals' degree of psychological immersion into a job role), and organisational engagement (employees' roles as members of a particular organisation and the willingness and commitment to perform for the benefit of the organisation: [[4], [5], [6],20,21,27,61].
The Flow@Work Engagement Survey (FWES: [19]) purports a holistic approach to employee engagement measurement and intervention [12,15]. Mindsetmanage (2024), the developers of the FWES and its theoretical premises, uses the term Flow@Work engagement to capture the likeliness for the prevalence of high flow at work when the survey indicates high overall levels of employee engagement for an organisation. This reasoning aligns with the argument of Yan and Donaldson (2023) that high levels of engagement generally allude to high overall positive motivational states of mind that activate personal flow at work resources such as positive emotions, absorption, and intrinsic work motivation. However, in critique, the term Flow@Work may be misleading as a label for the FWES because the scale does not fully capture the actual measurement of momentarily states of flow at work.
As shown in Fig. 1, the FWES measures nine working conditions that function as enablers of employee engagement (strategic alignment, manager intent, employee voice, performance feedback, personal development, recognition, and praise, enabling organisational environment, team relations and talent fit) that presuppose to predict two indicators of engagement states (willingness and commitment: [11]. While the engagement indicators (willingness and commitment) signify an overall state of employee engagement, the engagement enablers explain the probable underlying working conditions precipitating or predicting the observed state(s) of engagement [11].
Fig. 1.
Conceptual framework of the Flow@Work Engagement Survey constructs (Authors' own work).
The engagement indicator of willingness encapsulates the intrinsic motivation to engage in discretionary efforts beyond the call for duty to help the organisation meet its goals and objectives [19]. The engagement indicator of commitment reflects employees' positive emotional attachment to the organisation [19]. Willingness and commitment represent a compound simultaneous state of employee engagement which characterizes a strong cognitive-emotional attachment to one's job. Individuals feel intrinsically motivated and are behaviourally, mentally, and emotionally committed to invest their energy and effort into their work, which generally results in higher levels of performance and loyalty. Employees who are mentally involved and emotionally connected to their jobs are willing to work harder for their organisation's interest [5,6,20,21]. The compound state of willingness and commitment can be plotted in terms of four states of engagement [15].
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Fully engaged or star performers who exhibit high levels of commitment and willingness to contribute beyond the expectations placed on them by the organisation.
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Engaged or key contributors who are seen as reliable employees delivering what is expected of them with a potential tendency to be complacent and risk averse.
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Not engaged or passengers who seem indifferent and frustrated with organisational conditions and who may exhibit low energy levels while doing the minimum.
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Disengaged or saboteurs or derailers who are dissatisfied employees with a potential destructive influence and who are most likely looking for another job.
Generally, employees’ state of engagement reflects the intensity (willingness and commitment) of self-determined invested behavioural, cognitive, and emotional energy in active work experiences for positive personal, team, and organisational outcomes [3,11,15,18,20,21,28]. Being a state-oriented psychological construct, employee engagement is modifiable because of changes in working conditions impacting flow at work experiences [10,26]. The simultaneous assessment of both engagement enablers and engagement indicators allows for targeted intervention strategies on individual, team, management, and organisational level to maximise the impact of organisational engagement initiatives [11,15].
Systematic literature reviews and empirical studies on the antecedents and outcomes of employee engagement provide empirical support for the conceptualisation of employee engagement, and the nine core engagement enabling working conditions (enablers) and two indicators of employee engagement purported by the FWES [5,6,16,17,20,21].
Strategic alignment (employees' understanding of the contribution of their work to the organisation's vision, mission, strategy, and performance goals) is associated with high levels of organisational commitment which results from the desire to advance the organisation's goals, exert the effort for greater job performance, and to accept the prevailing organisational values [19,[29], [30], [31]]. Manager intent (employees' perceptions of their managers' behaviour espousing organisational values, leading by example, being treated fairly, and trusting their managers) are associated with organisational trust and perceptions of fairness, including outcomes such as job satisfaction, commitment, job and organisational performance, and employee engagement [10,26,32,33].
Employee voice (employees' satisfaction that their ideas and opinions count at work while feeling safe and encouraged to speak up and being invited to participate in decision-making) function as a valuable source of fresh perspectives and ideas, and creative problem-solving which increase psychological safety, employee engagement, team wellbeing, business innovation and individual, team and business performance [19,34,35]. Performance feedback (employees' satisfaction with the frequency and quality of management feedback on achievement of organisational, team and individual goals and performance, including improvement planning) is an important outlet for employees' job efforts to be recognised and to promote employee engagement [19,36]. Personal development (employees’ positivity toward the career, personal and professional skills development and growth opportunities offered by the organisation) creates competent and productive employees who are committed to achieve performance goals [37]. Recognition and praise (the extent to which employees feel appreciated for the work they do, being compensated fairly and receiving recognition or praise for doing a good job) drive individual motivation and innovative problem-solving and enhance job and organisational performance. Formal and informal recognition practices are characteristic of strong human-centric supportive organisational cultures [19,36].
Enabling organisational environment (the extent to which employees feel physically and personally safe and secure in the work environment and having all the tools and equipment or resources they need to do their job well) have a direct impact on team relations and employees' sense of wellbeing, security, and safety in carrying out their job tasks [19,30]. Team relations (the extent to which employees trust their team members, feel comfortable in reaching out to team members for help and receive team social support for achieving goals) foster the willingness and commitment to fully engage and perform in one's work [19. 30]. Talent fit (employees' perceptions of having opportunities for utilising and developing their strengths or things they are naturally good at) is associated with job performance, actual turnover, organisational commitment, and individual motivational strivings for high performance [19,38,39].
Generally, the academic research literature on employee engagement provides ample empirical support for the construct of employee engagement, including the employee engagement drivers (enablers) and indicators purported by the Flow@Work Engagement Survey's [19,40] conceptual framework. However, scholars agree that much more work is needed regarding the measurement of employee engagement and especially testing the psychometric soundness and validity of the measures offered by practitioners in industry [6,4,20,21]. We formulated two hypotheses.
Hypothesis 1
The FWES has internal construct (convergent and discriminant) validity and reliability.
Hypothesis 2
The FWES engagement drivers (enablers) are significant predictors of willingness and commitment as uniquely separate and as compound indicators of employee engagement.
3. Method
3.1. Participants
The initial availability-based sample was an aggregated data set of N = 219 001 records of individual respondents who completed the Flow@Work Engagement Survey on the open real-time survey platform of Mindset Manage [15]. Consent was obtained from all participants before participating in the research. The respondents were from global organisations in South Africa, Rest of Africa, United States of America, United Kingdom, the Middle East, and Australia. Once missing values were removed from the aggregated data set, the final sample included response data from n = 39 310 respondents. The sample was represented by 46 % women and 34 % men with 20 % unspecified. In terms of age, 29 % of the respondents were between 20 and 30 years of age, 28 % were between 31 and 40 years of age, 15 % were older than 41 years and 28 % did not report their age.
3.2. Measure
The Flow@Work Engagement Survey (FWES: [19]) is administered in English and has 38 items measuring employee engagement across two employee engagement indicators (willingness and commitment) and nine engagement enabling working conditions (enablers) that are seen to impact individuals’ state of engagement in the organisation [19].
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Willingness (3 items), e.g. “Does your job motivate you to do more than what is required of you?).
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Commitment (3 items), e.g. ”Do you see yourself still working for your organisation a year from now?”
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Strategic alignment (5 items), e.g.” To what extent does your work help your organisation to achieve its goals?”
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Manager intent (4 items), e.g. “Does your direct manager act in a way that is consistent with the organisational values?”
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Performance feedback (3 items), e.g. “How often does your direct manager give you feedback on your performance?”
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Employee voice (3 items), e.g. “Do your ideas and opinions count at work?”
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Recognition and praise (4 items), e.g. “Does your organisation have a formal reward and recognition program for doing good work?”
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Personal development (3 items), e.g.” Does your organisation provide you with sufficient opportunities to develop and grow professionally?”
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Enabling organisational environment (3 items), e.g. “Do the policies and procedures in your organisation enable you to do your job well?”
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Team relations (3 items), e.g. “Do you feel that your co-workers treat you with dignity and respect?”
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Talent fit (4 items), e.g. “At work, how often do you get the opportunity to use your strengths (things that you are naturally good at)?”
Responses are measured on a 10-point Likert-type scale (1–2: Never/Not at all; 9–10: always/completely). The response scores are categorised as follows: Disengaged (scores of 0–5.0), not engaged (scores of 5.1–6.4), engaged (scores of 6.5–7.99) and fully engaged (scores of 8.0–10.0) [41]. reports good internal consistency reliability coefficients (≥.75) for the eleven construct dimensions of the FWES [19]: willingness α = .75; commitment α = .79; strategic alignment α = .75; manager intent α = .92; performance feedback α = .79; employee voice α = .81; recognition & praise α = .76; personal development α = .80; enabling organisational environment α = .82; team relations α = .83; and talent fit α = .77.
3.3. Procedure and ethical considerations
[15] runs a real-time online employee survey platform (Engage EX) for clients who request a contextualised personal engagement profile report from completing the FWES [19]. Response data are automatically captured on an EXCELL spreadsheet generated by the Engage EX software program hosted on Amazon Web Services [40]. The data were transformed into a SPSS data file for statistical purposes.
The authors received permission from Mindset Manage to utilise the data set for research purposes. The College of Economic and Management Sciences_ERC Human Resource Department granted ethical clearance for conducting the research (NHREC Ref#:4057). The data set was anonymous group-based data and posed no risk to respondents’ privacy and confidentiality.
3.4. Data analysis
The IBM SPSS version 29.0.0 [[42], [43]] and IBM SPSS Amos Version 28.0 statistical software programs with the robust maximum likelihood estimator (MLE) were utilised for the data analysis. A confirmatory factor analysis (CFA) was conducted with the 38-item FWES to confirm the eleven-factor solution hypothesized by the theoretical model. We scrutinized the following fit indices to evaluate acceptable model fit [44,45]: CMIN/DF (≤5); comparative fit index (CFI ≥.90), Tucker-Lewis index (TLI ≥.90); root mean square error of approximation (RMSEA) and standardised root mean square residual (SRMR) ≤ .08.
We then assessed the reliability (internal consistency and composite reliability) and validity (convergent and discriminant validity) of the best fitting CFA model for the FWES. We applied the Fornell-Larcker criterion [46] to test convergent validity by calculating the Average Variance Extracted (AVE) and Composite Reliability (CR) with AVE values of ≥ .50 and CR coefficients ≥.70 indicating acceptable convergent validity. Heterotrait-Monotrait (HTMT) ratio values smaller than 1.0 indicated acceptable discriminant validity among the scale factors [47]. Cronbach alpha coefficients ≥.70 indicated acceptable internal consistency reliability. Descriptive statistics (means, standard deviations) and bi-variate correlations were also calculated to evaluate the magnitude and direction of associations among the FWES factors.
Next, we conducted structural equation modelling (SEM) to assess the empirical fit of the hypothesized model for the structural predictor (enabler) – engagement indicator (outcome) relationships between the FWES variables. We considered the following indices of acceptable model fit [48]: CMIN\DF (≤5), RMSEA and SRMR (≤.08), CFI (≥.90), GFI (goodness of fit index ≥.90) and TLI (≥.90).
We further analysed the relative weighted importance of each of the significant predictor engagement enablers indicated by the SEM models using a post hoc relative weights analysis (RWA: [49) with metric of percentage of predicted variance.
4. Results
4.1. Internal validity and reliability of the FWES measurement model
We first conducted three CFA models on the full sample of N = 39 310 and then on a random sample of n = 3000 drawn from the full sample. Table 1 summarises the results of the CFA analysis.
Table 1.
Confirmatory factor analysis results for the FWES.
| CMIN/DF | CFI | TLI | RMSEA | SRMR | AIC | |
|---|---|---|---|---|---|---|
| Sample: N = 39 310 | ||||||
| Model 1 | 257.55∗∗∗ | .78 | .77 | .08 | .06 | 171 420.60 |
| Model 2 | 92.16∗∗∗ | .93 | .92 | .05 | .04 | 56 481.50 |
| Model 3 | 122.59∗∗∗ | .90 | .89 | .05 | .04 | 80, 347.10 |
| Sample: n = 3000 | ||||||
| Model 1 | 20.98∗∗∗ | .78 | .77 | .08 | .06 | 14 104.30 |
| Model 2 | 8.33∗∗∗ | .93 | .91 | .05 | .04 | 5340.00 |
| Model 3 | 10.53∗∗∗ | .90 | .90 | .05 | .05 | 7582.60 |
Note: ∗∗∗p ≤ .001. Harman's one factor: 39.71 % (N = 39 310). Harman's one factor = 39.69 % (n = 3000).
CFA Model 1 was a single latent factor model with all FWES items loading onto an overall employee engagement construct. Overall, for both the N = 39 310 sample and the n = 3000 sample the single latent factor CFA models did not have a good fit with the data (CFI: .78; TLI: .77).
CFA Model 2 was a first order multi-dimensional measurement model with the FWES items loading onto their respective constructs in the model. CFA Model 3 was a second-order multi-dimensional model, with the FWES items loading onto their respective constructs, and the eleven FWES constructs loading onto an overall construct employee engagement. Both samples’ first-order and second-order multidimensional CFA models had an acceptable fit with the data. As such, further statistical analyses involved only the n = 3000 sample.
As shown in Table 2, the FWES constructs further converged significantly and positively onto an overall employee engagement construct with path loadings ≥.71 (e.g., n = 3000): CMIN/DF = 10.53 (p ≤ .001); CFI = .90; TLI = .90; RMSEA = .05, SRMR = .05.
Table 2.
FWES Second-order multifactor CFA: Standardised regression weigths
| Parameter | Estimate | Lower level CI | Upper level CI | p | ||
|---|---|---|---|---|---|---|
| WIL | <--- | Engagement | .87 | .84 | .89 | .000 |
| COM | <--- | Engagement | .79 | .77 | .82 | .000 |
| SA | <--- | Engagement | .91 | .89 | .93 | .000 |
| MI | <--- | Engagement | .84 | .82 | .85 | .000 |
| PF | <--- | Engagement | .79 | .77 | .81 | .000 |
| EV | <--- | Engagement | .91 | .90 | .93 | .000 |
| RP | <--- | Engagement | .91 | .89 | .93 | .000 |
| PD | <--- | Engagement | .87 | .85 | .89 | .001 |
| EE | <--- | Engagement | .85 | .83 | .88 | .000 |
| TR | <--- | Engagement | .71 | .68 | .74 | .000 |
| TF | <--- | Engagement | .89 | .86 | .90 | .000 |
Note: n = 3000. WIL: willingness. COM: commitment. SA: strategic alignment. MI: manager intent. PF: performance feedback. EV: employee voice. RP: recognition & praise. PD: personal development. EE: enabling environment. TR: team relations. TF: talent fit. CI: Confidence intervals (95 %).
We assessed for potential common method variance by conducting a Harman's single factor test which revealed that for both the N = 39 310 sample and n = 3000 sample, the single factor explained less than 50 % of the variance (≤39.71 %). Considering the single latent factor CFA fit indices (poor fit) and Harman's single factor tests' results, the presence of common method bias was considered as being negligible.
As shown in Table 3, the HTMT analysis on the n = 3000 sample's first-order multi-dimensional CFA results revealed that the heterotrait-monotrait ratios were smaller than 1.0 which provided evidence for the discriminant validity of the FWES (i.e., the true correlation between the scale factors differed: Henseler et al., 2015).
Table 3.
HTMT analysis results – testing discriminant validity among the FWES factors.
| WIL | COM | EV | SA | MI | PF | RP | PD | EE | TR | TF | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| WIL | – | ||||||||||
| COM | .55 | – | |||||||||
| EV | .52 | .51 | – | ||||||||
| SA | .59 | .62 | .60 | – | |||||||
| MI | .46 | .53 | .77 | .62 | – | ||||||
| PF | .42 | .46 | .63 | .60 | .62 | – | |||||
| RP | .51 | .57 | .66 | .56 | .60 | .61 | – | ||||
| PD | .55 | .65 | .58 | .59 | .54 | .50 | .64 | – | |||
| EE | .44 | .56 | .55 | .62 | .56 | .48 | .57 | .56 | – | ||
| TR | .41 | .44 | .52 | .53 | .53 | .41 | .47 | .46 | .51 | – | |
| TF | .58 | .52 | .69 | .59 | .62 | .58 | .65 | .65 | .54 | .53 | – |
Note: n = 3000. WIL: willingness. COM: commitment. SA: strategic alignment. MI: manager intent. PF: performance feedback. EV: employee voice. RP: recognition & praise. PD: personal development. EE: enabling environment. TR: team relations. TF: talent fit.
Table 4 summarises the descriptive statistics and bi-variate correlations of the multi-dimensional second-order CFA model (n = 3000). Regarding evidence of convergent validity, the AVE values were predominantly higher than, or equal to .50 and the CR values (as equivalents of omega coefficients) (with the exception of manager intent CR = .61) were higher than .70. Even with some AVE values lower than .50, the convergent validity of the FWES was acceptable because the CR values were higher or equal to .60 [50,51,59,60]. The Cronbach's alpha coeffcients provided evidence of acceptable (α = .60) to high (α ≥ .71) internal consistency reliability of the FWES.
Table 4.
Descriptive statistics and bi-variate correlations.
| AVE | CR | α | Mean (SD) | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | WIL | .35 | .61 | .60 | 7.50 (1.70) |
– | |||||||||||
| 2 | COM | .57 | .80 | .79 | 7.82 (1.99) |
.55 | – | ||||||||||
| 3 | SA | .38 | .75 | .75 | 7.93 (1.57) |
.59 | .63 | – | |||||||||
| 4 | MI | .73 | .91 | .91 | 7.36 (2.31) |
.46 | .53 | .62 | – | ||||||||
| 5 | PF | .60 | .81 | .81 | 7.42 (2.29) |
.43 | .46 | .61 | .62 | – | |||||||
| 6 | EV | .61 | .82 | .82 | 6.91 (2.23) |
.53 | .51 | .61 | .78 | .64 | – | ||||||
| 7 | RP | .44 | .75 | .75 | 6.16 (2.09) |
.52 | .57 | .58 | .60 | .61 | .65 | – | |||||
| 8 | PD | .53 | .78 | .78 | 6.83 (2.20) |
.56 | .65 | .60 | .54 | .49 | .58 | .64 | – | ||||
| 9 | EE | .45 | .71 | .71 | 7.47 (1.91) |
.44 | .56 | .63 | .56 | .48 | .55 | .56 | .56 | – | |||
| 10 | TR | .55 | .78 | .78 | 7.64 (1.87) |
.41 | .44 | .54 | .53 | .41 | .52 | .47 | .46 | .51 | – | ||
| 11 | TF | .50 | .79 | .78 | 6.78 (2.10) |
.59 | .52 | .60 | .63 | .59 | .70 | .65 | .65 | .54 | .53 | – | |
| 12 | ENG | .73 | .93 | .97 | 7.26 (1.57) |
.70 | .75 | .81 | .82 | .75 | .84 | .81 | .79 | .75 | .68 | .82 | – |
Note: n = 3000. All correlations are signifcant at p ≤ .001. Composite reliability (CR) and average variance extracted (AVE) estimates are all significant at the 95 % confidence level interval. WIL: willingness. COM: commitment. SA: strategic alignment. MI: manager intent. PF: performance feedback. EV: employee voice. RP: recognition & praise. PD: personal development. EE: enabling environment. TR: team relations. TF: talent fit. ENG: overall employee engagement.
Table 4 shows that the bi-variate correlations among the eleven FWES variables were positive and significant (p = .001), and ranged between r ≥ .41 and r ≤ .78. All the FWES variables correlated significantly and positively with the overall employee engagement construct (r ≥ .68 and r ≤ .84; p = .001). The range of the bi-variate correlations suggested that the potential presence of multi-collinearity was negligible in interpreting the findings. In terms of the sample's engagement levels, the mean scores ranged predominantly between 6.91 (SD = 2.23) and 7.93 (SD = 1.57: engaged), except for the mean score on recognition and praise (mean = 6.16; SD = 2.09: not engaged).
In summary, in support of research hypothesis 1, the results provided empirical evidence of the FWES having sound internal convergent and discriminant validity and internal consistency reliability.
4.1.1. Engagement enablers as predictors of employee engagement indicators
Having established a measurement model with acceptable fit, we conducted five SEM models to test the proposed relationships between the FWES engagement enablers (predictor variables) and the engagement indicators (outcomes). Table 5 summarises the SEM goodness of fit results.
Table 5.
Structural equation modelling results.
| SEM models | CMIN/DF | CFI | GFI | TLI | RMSEA | SRMR | AIC |
|---|---|---|---|---|---|---|---|
| Model 1 | 8.80∗∗∗ | .93 | .91 | .92 | .05 | .04 | 5453.00 |
| Model 2 | 8.23∗∗∗ | .93 | .91 | .91 | .05 | .04 | 4775.30 |
| Model 3 | 8.33∗∗∗ | .93 | .91 | .91 | .04 | .04 | 6126.90 |
| Model 4 | 8.52∗∗∗ | .92 | .91 | .91 | .05 | .04 | 5,5100.00 |
| Model 5 | 9.48∗∗∗ | .92 | .94 | .92 | .05 | .04 | 2358.07 |
Note: n = 3000. ∗∗∗p ≤ .001.
In SEM Model 1, the items of the nine engagement enablers loaded onto their respective construct, and each of the nine engagement enablers was treated as predictors of commitment as outcome (with the items of commitment loading onto its construct). The model had acceptable fit with the data: CMIN/df = 8.80; p ≤ .001; CFI = .93; GFI = .91; TLI = .92; RMSEA = .05; SRMR = .04.
In SEM Model 2, the items of the nine engagement enablers loaded onto their respective construct, and each of the nine engagement enablers was treated as predictors of willingness as outcome (with the items of willingness loading onto its construct). The model had acceptable fit with the data: CMIN/df = 8.23; p ≤ .001; CFI = .93; GFI = .91; TLI = .92; RMSEA = .05; SRMR = .04.
In SEM Model 3, the items of the nine engagement enablers loaded onto their respective construct, and each of the nine engagement enablers was treated as predictors of willingness and commitment as two separate outcomes (with the items loading onto its respective construct). The model had acceptable fit with the data: CMIN/df = 8.33; p ≤ .001; CFI = .93; GFI = .91; TLI = .91; RMSEA = .04; SRMR = .04.
In SEM Model 4, the items of the nine engagement enablers loaded onto their respective construct, and each of the nine engagement enablers was treated as predictors of overall employee engagement as outcome (with willingness and commitment as indicators of overall employee engagement). The model had acceptable fit with the data: CMIN/df = 8.52; p ≤ .001; CFI = .92; GFI = .91; TLI = .91; RMSEA = .05; SRMR = .04.
In SEM Model 5, only strategic alignment (SA), perfomance feedback (PF), recognition and praise (RP), personal development (PD), and enabling environment (EE) were treated as significant predictors of overall employee engagement as outcome (with willingness and commitment as indicators of overall employee engagement). The model fit indices showed a lower AIC (2, 358.07) than SEM model 4's AIC, and acceptable fit estimates: CFI = .93, GFI = .94, and TLI = .92, RMSEA = .05 and SRMR = .04.
In summary, Table 5 shows that the five SEM models all had good model fit indices which provided empirical evidence of the FWES engagement enablers acting as significant predictors of the employee engagement construct with its indicators of willingness and commitment. However, closer scrutiny of the standardised regression weights to assess the prediction effect of the engagement drivers revealed the following:
In SEM Model 1, the standardised regression weights indicated that enabling environment (β = −.07; p = .54; CI = −.27.12) and team relations (β = −.03; p = .48; CI = −.10.05) did not significantly predict commitment. Performance feedback (β = −.22; p = .001; CI = −.36;-.08), employee voice (β = −.25; p = .04; CI = −.52; −.01) and talent fit (β = −.21; p = .01; CI = −.36;-.05) significantly and negatively predicted commitment. Manager intent (β = .20; p = .01; CI = .05.37), strategic alignment (β = .61; p = .001; CI = .35.88), recognition and praise (β = .33; p = .004; CI = .11.55) and personal development (β = .50; p = .000; CI = .31.67) significantly and positively predicted commitment.
In SEM Model 2, the standardised regression weights indicated that employee voice (β = .16; p = .30; CI = −.18.49), personal development (β = .07; p = .49; CI = −.17.29) and team relations (β = −.05; p = .33; CI = −.16.05) did not significantly predict willingness. Manager intent (β = −.23; p = .03; CI = −.43;-.03) and enabling environment (β = −.43; p = .001; CI = −.74;.-.19) significantly and negatively predicted willingness. Strategic alignment (β = 1.06; p = .001; CI = .75; 1.49), recognition and praise (β = .35; p = .01; CI = .09.69), and talent fit (β = .34; p = .002; CI = .13.55) significantly and positively predicted willingness.
In SEM Model 4, the standardised regression weights indicated that the following engagement working conditions did not significantly predict employee engagement (with willingness and commitment as indicators): manager intent (β = .03; p = .73; CI = −.13.20), employee voice (β = −.09; p = .50; CI = −.38.16), team relations (β = −.05; p = .29; CI = −.13.04) and talent fit (β = .02; p = .83; CI = −.14.19). Based on these findings, SEM Model 5 excluded these engagement enablers.
In SEM Model 5 and in Fig. 2, the standardised regression weights indicated that the engagement enablers of strategic alignment (β = .82; p = .001; CI = .60.1.10), recognition and praise (β = .31; p = .001; CI = .15.51), and personal development (β = .39; p = .000; CI = .22.52), acted as significant and positive predictors of the employee engagement construct (with willingness and commitment as indicators). Performance feedback (β = −.33; p = .001; CI = −.49; −.20) and enabling environment (β = −.21; p = .02; CI = −.44;-.04), acted as significant and negative predictors. The engagement indicators of willingness (β = .90; p = .000; .87; .93) and commitment (β = .88; p = .000; CI = .86.90) also had significant and positive path loadings on the overall employee engagement construct, implying that they were valid compound indicators of the overall employee engagement construct.
Fig. 2.
SEM Model 5 path loadings. Note: n = 3000.
4.1.2. The relative importance of the engagement enablers in predicting engagement indicators
Fig. 3 summarises the results of the post hoc relative weights analysis (RWA: [49]) to determine which engagement enablers were more important than others in predicting willingness and commitment as indicators of employee engagement. Overall, three RWA regression models were computed by including the significant predictors (engagement enablers) identified in SEM models 1, 2 and 5: RWA model 1 (commitment as engagement indicator, R2 = .46; large significant practical effect); RWA model 2 (willingness as engagement indicator, R2 = .54; large significant practical effect); and RWA model 3 (overall employee engagement as a compound state of the indicators of willingness and commitment, R2 = .62; large significant practical effect). In Fig. 3, the rescaled relative weight percentages shown in brackets highlighted the engagement enablers that explain the largest portion of variance in the employee engagement indicators. The weights of the predictors (engagement enablers) were all significantly greater than zero.
Fig. 3.
FWES engagement enablers as predictors of employee engagement indicators.
Note: Rescaled relative weights % shown in brackets.
In summary, in RWA model 1 (commitment as outcome), strategic alignment (20.66 %), talent fit (17.49 %) and personal development (16.22 %) were the most important engagement enablers (predictors) in explaining the total variance in commitment, followed by employee voice (10.41 %) and recognition and praise (10.39 %). Manager intent (6.21 %) and performance feedback (5.67 %) were the least important engagement drivers.
In RWA model 2 (willingness as outcome), strategic alignment (18.32 %) was the most important engagement enabler in explaining the total variance in willingness, followed by enabling environment (12.20 %) and recognition and praise (11.98 %). Manager intent (8.96 %), talent fit (7.49 %) and performance feedback (5.58 %) were the least important engagement drivers.
In RWA model 3 (willingness and commitment as compound indicators of overall employee engagement), personal development (28.96 %) and strategic alignment (28.53 %) were the most important engagement enablers in explaining the total variance in employee engagement, followed by recognition and praise (18.11 %) and enabling environment (14.60 %). Performance feedback (9.80 %) was the least important engagement driver.
Team relations did not act as a significant predictor in the various models. Generally, as positive predictors, strategic alignment and personal development had the largest % relative weights across the three RWA models. Performance feedback as negative predictor had the smallest % relative weight across the three RWA models.
In summary, in support of research hypothesis 2, the results indicated that the FWES engagement enablers acted as significant predictors of willingness and commitment, both as uniquely separate and as compound indicators of employee engagement.
5. Discussion
The present study established the psychometric soundness of the FWES [19] in measuring employee engagement as a set of enabling, intrinsic motivating working conditions that promote willingness and commitment as indicators of employee engagement in a valid and reliable manner. Additionally, the study yielded empirical evidence in support of the basic premise that the nine engagement enabling working conditions of the FWES measurement model significantly predict employees’ willingness and commitment as indicators of their overall employee engagement. The findings contributed new insights to the engagement research literature by revealing the nature and relative importance of the engagement enablers in predicting employee engagement.
The significant and positive prediction effect of strategic alignment, personal development, and recognition and praise on employee engagement (with willingness and commitment as compound indicators) as well as on commitment as an outcome, are in agreement with research highlighting the importance of these three engagement enablers in fostering employee engagement [5,29,30,36,37]. These three enabling working conditions made the strongest contribution to raising employees' states of willingness and commitment as indicators of their engagement levels. Empirical evidence shows that the strategic alignment of individual performance goals with business goals, strategy, mission and vision, opportunities for skills and growth development opportunities, and receiving fair compensation, recognition and praise for the work one delivers, are important psychological mechanisms for experiencing one's work as meaningful, worthwhile and engaging [5,52].
The findings further support the view that strategic alignment and recognition and praise are key engagement enabling conditions for fostering individuals' emotional commitment to the organisation and their willingness to translate this commitment into discretionary efforts aimed at helping the organisation achieve its business goals [19]. Personal development opportunities seem especially important to raise individuals' emotional connection with the organisation (commitment). Regulatory focus theory [53] explains that personal development opportunities promote employees’ engagement states by activating their future aspirations and understanding of the broader meaningfulness of their work in the organisation [52].
The findings also revealed seemingly paradoxical results. For example, talent fit acted respectively as a significant positive and negative enabler of the two engagement indicators of willingness and commitment. As significant and positive enabler of willingness, talent fit made a relative weak contribution in comparison to its role as a significant and negative enabler of commitment. According to the meta-theoretical principle of “too-much-of-a-good-thing” (TMGT) effect [54], such inconsistencies in results may occur because of an apparent beneficial antecedent (e.g. talent fit as engagement enabler) in a predictor-outcome relation having a context-specific inflection point after which the predictor ceases to be positive or beneficial and thus resulting in a less desired outcome.
Talent fit (engaging employees in roles where they can utilise and grow their personal strengths: [19]) seems important to promote or enable individuals' willingness to actively engage in work improvement activities and go above and beyond what is expected of them. Talent fit is a psychological mechanism for harnessing individuals’ selves to their work roles. Individuals develop a sense of autonomy and competence when the demands of job tasks are optimally matched to their skills and strengths [52]. Self-determination theory [7] explains that individuals feel willing and able to interact effectively within their environment when they feel competent in having the intellect, skills and strengths for successful goal achievement.
Self-determination theory [7] also explains the finding that talent fit and employee voice may lower individuals’ commitment to the organisation. The sense of autonomy and competence of self-determined behaviour is intrinsically driven. Individuals may be willing to go beyond the call of duty not necessarily for the reward or recognition, but rather for self-satisfaction, interest and enjoyment of the behaviour itself. Talent fit as an internal source of motivation, helps individuals gain independence and a sense of mastery over their environment which may lower their emotional connection (commitment) to the organisation and what it stands for as an external source of motivation [7,52].
Employee voice is an upward-directed and improvement-oriented communicative enabler of engagement [55]. Contrary to research attesting to the positive direct association between employee voice and employee engagement [5,55], in this study employee voice made only a relatively small and negative contribution to the employee engagement indicator of commitment. The FWS [19] measures employee voice as a psychological sense of work empowerment that encourages individuals to voice their ideas and opinions and participate in decisions that impact them. Based on the TMGT effect principle [54], the current findings suggest that the increased sense of empowerment resulting from encouraged voice behaviour may at times start to drive lower levels of psychological attachment (commitment) to the organisation. Drawing from self-determination theory [7], employee voice as an external source of motivation seems to feed individuals' intrinsic motivational need for independence, autonomy and self-realised competence, which may at some point start to reduce individuals’ psychological attachment to the organisation as an external focal resource of self-determination.
The TMGT effect [54] also seems apparent in the manager intent predictor (enabler)-engagement indicator relation. Manager intent made a relatively weak contribution to explaining employee engagement and its indicators of willingness and commitment. However, the findings corroborate research highlighting the positive association between employees' organisational commitment and engagement, and their perceptions of managers espousing organisational values, leading by example and treating employees fairly [32,33]. Paradoxically, our findings additionally suggest that escalating the credibility of manager intent as a focal engagement enabler may lower employees' willingness in the employee engagement enabler-indicator equation because, as argued by Ref. [54], at some point employees may perceive them gaining no additional beneficial motivational outcomes from manager behaviour and intentions. Alternatively, employees may also come to expect managers to behave in a certain manner with a mismatch between employees’ expectations and perceived manager behaviour resulting in a lower willingness to engage in discretionary efforts that facilitate organisational goal achievement [21,33].
The engagement enabler of enabling environment (psychological and physical safety, job resources, and employee-supportive policies and procedures) made a relative moderate and negative contribution to driving employee engagement (with willingness and commitment as compound indicators), and willingness as separate outcome. The finding contradicts research indicating organisational conditions and resources as important positive drivers of engagement [1,6,55]. Enabling environment is an important external motivational resource that provides a supportive context through which employees' psychological need for self-determination and organisational connectedness are fulfilled [1]. Drawing from the TMGT effect principle [54], enabling environment as a desirable engagement working condition may also lead to unanticipated negative consequences for employee engagement. Increases in supportive organisational conditions and resources may reach an inflection point which diminishes the positive effects on employee engagement, and especially the intrinsic motivation to initiate work improvement activities and doing more than what is required of one. At times organisational resources, policies and practices might also prompt employees to feel obligated to do more than what is expected of them, which potentially diminishes the positive effect on employee engagement [52]. Additionally, the shift towards flexible work arrangements and hybrid and remote models of working could potentially have contributed to the lowering of employees’ engagement levels. Research suggests that promoting employee engagement in remote and hybrid working environments have become more challenging because of feelings of isolation and lower quality of relatedness and two-way interaction and communication [17].
Performance feedback made the weakest contribution to employees' engagement levels with a lowering effect on employees' willingness and commitment as separate and as compound indicators of employee engagement. The finding corroborates previous contradicting research indicating that performance appraisal and feedback can either increase or lower or have no significant direct effect on employee engagement [56]. The FWES [19] measured the frequency and quality of performance feedback. Generally, individuals tend to have an emotional orientation toward performance feedback that adversely influences their engagement levels [56]. Research highlights employees’ perceptions of the fairness of performance appraisals and experiences of the quality of performance and job feedback as important factors influencing their engagement levels [56,57].
6. Implications for theory and practice
The findings contributed new insights to the employee engagement literature by highlighting the relative unique importance of a set of engagement enabling working conditions in predicting willingness and commitment as indicators of employee engagement. The study's findings may be useful for practice and especially for assessing employees' levels of engagement as compound states of willingness and commitment and for planning interventions targeted at working conditions that promote engagement driver levels. However, the TMGT effect principle [54] evident in the paradoxical complexities of the associations between the FWES engagement enablers and indicators should be considered when planning engagement interventions. The findings revealed that the measuring of presumed positive linear or monotonic relations (e.g. engagement enablers as positive predictors of employee engagement indicators) may lead to asymptotic or paradoxical consequences (no significant effect, neutral, or even negative) when those antecedents (predictor enablers) reach high levels. The presence of some curvilinear engagement enabler predictor – employee engagement indicator relationships, underscores the importance to consider potential unexpected negative effects or consequences of interventions aimed at enhancing the engagement driver levels of the negative predictors of employee engagement. A balanced and informed approach toward enhancing engagement enabling levels may be more appropriate to limit or avoid detrimental TMGT effect consequences for individuals and their organisations. In this regard, the findings of our study serve as useful guidelines for the planning of engagement intervention recommendations for clients.
7. Limitations and future research
The large, aggregated data set promotes the potential generalisability of our findings to the sample population involved in the study. However, future studies should consider the socio-demographic characteristics of the data set to be able to assess the validity of the FWES for different nationalities, industries, occupational professions and racial, gender and age groups. The cross-sectional nature of the data-set limits establishing causality between the predictors (engagement enablers) and the employee engagement indicators. Longitudinal studies based on follow-up data sets are recommended with a minimum of three measurement points of a targeted sample group. The study focused only on the internal validity of the FWES. Future studies should test its external validity by including constructs of other measures of engagement and Flow@Work. The FWES measures eleven constructs associated with enabling working conditions and indicators of employee engagement rather than momentarily states of flow at work. It is recommended that the developers of the FWES revise the appropriateness of the use of the term Flow@Work engagement or include items that specifically measure the construct of flow at work in the engagement context. Future research might focus on the development of a short form of the FWES and its stand-alone administration to assess whether its internal psychometric properties and external validity uphold. Further refinement of the FWES could also consider revising the construct domains, pool of items for constructs, and adopting principles of the ideal point model of response measurement in the scoring of responses to increase the accuracy of the estimates of items which potentially contributed to the paradoxical or curvilinear associations between the FWES engagement drivers and indicators. Future research could also test the construct-relevant psychometric dimensionality of the Flow@Work Engagement Survey by employing for example, bifactor exploratory structural equation modeling (SEM) as recommended by Marin et al. (2016) [58]. The findings' interpretation aligned with the Flow@Work engagement construct that originated from the practitioner-industry market. Future research could interrogate the theoretical foundation of the construct as a distinct concept of employee engagement. Tests of external validity in relation to the constructs of other employee engagement scales are also recommended.
8. Conclusion
The findings of the study suggest that there is utility in the FWES as holistic measure of engagement enabling working conditions and employee engagement indicators. Despite our supportive results, further refinement of the FWES is recommended to strengthen its value as a valid and reliable practitioner-industry-based measure of employee engagement. The present study extends engagement theory by contributing new insights into the relative importance of working conditions that act as significant promoters of individuals’ employee engagement. The present research further uncovers opportunities for future engagement research and well-informed employee engagement measurement and intervention planning for practitioners and their clients.
CRediT authorship contribution statement
Melinde Coetzee: Writing – original draft, Methodology, Formal analysis, Conceptualization. Dieter Veldsman: Writing – review & editing, Data curation, Conceptualization. Ingrid L. Potgieter: Writing – review & editing, Writing – original draft, Resources, Conceptualization. Nadia Ferreira: Writing – review & editing, Writing – original draft, Conceptualization.
Data availability
The data is available upon reasonable request from the corresponding author.
Disclaimer
The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of any affiliated agency of the authors.
Funding information
This research received no specific grant from any funding agency.
Declaration of competing interest
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
We declare that the paper is original and our own work and has not been submitted elsewhere.
We declare that we have no conflict of interest.
Acknowledgments
The authors would like to acknowledge Mindset Manage for their consent to use their survey data for research purposes.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2025.e42386.
Contributor Information
Melinde Coetzee, Email: drmelindecoetzee@gmail.com.
Dieter Veldsman, Email: dieter.veldsman@aihr.com.
Ingrid L. Potgieter, Email: visseil@unisa.ac.za.
Nadia Ferreira, Email: Ferren@unisa.ac.za.
Appendix A. Supplementary data
The following is the Supplementary data to this article:
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Data Availability Statement
The data is available upon reasonable request from the corresponding author.