Vitality and Challenging Commitment in Times of Digital Intensification: Evidence for Healthy Educational Organizations Based on Teacher Engagement in Chile

Abstract

The rapid digital transformation of education systems has profoundly changed teachers’ working conditions, intensified administrative demands, and highlighted territorial and organizational inequalities. In this context, understanding how these dynamics influence teacher engagement is essential for promoting healthy educational organizations. This study examined the factor structure of the UWES-17 and analyzed the relationship between engagement levels and sociodemographic variables in a sample of 314 elementary school teachers from four regions of Chile. Descriptive analyses, exploratory factor analysis with polychoric correlations and unweighted least squares, and confirmatory factor analysis using robust ULS and the Hull method were performed. The results showed a robust two-factor structure—Inspired Vitality and Challenging Commitment—with excellent fit indices. Freeman–Halton exact tests showed that Inspired Vitality was significantly associated with age, gender, region, location, administrative dependency, and professional experience, while Challenging Commitment was associated with gender, region, context, and professional experience. These findings indicate that teacher engagement is influenced by both structural inequalities and individual trajectories. The results underscore the need to strengthen organizational resources, regulate digital intensification, and reduce territorial gaps to promote teacher well-being.

1. Introduction

The accelerated digital transformation of education systems (Lynch et al., 2024; Samala et al., 2024), intensified by the COVID-19 pandemic, has profoundly reshaped the social and working conditions of teachers (Akour & Alenezi, 2022; Røe et al., 2022; Deroncele-Acosta et al., 2023; Bitar & Davidovich, 2024; C. Wang et al., 2024; Zhai, 2025). The expansion of hybrid modalities, the increase in technology-mediated administrative tasks, and the blurring of boundaries between work and personal life have generated new psychosocial stresses that affect teachers’ occupational health (Bacova & Turner, 2023; Siddiqui et al., 2023; Hussain et al., 2024; Lillelien & Jensen, 2025), and the functioning of healthy educational organizations (Akour & Alenezi, 2022; Gulliksen et al., 2023). At the same time, digital tools have also enabled new forms of pedagogical communication, reduced repetitive tasks, and expanded opportunities for instructional innovation, illustrating that technology can operate not only as a source of demand but also as a meaningful resource for teachers.

In this sense, the dual role of technology, as both a potential stressor and a potential support, aligns with the Job Demands–Resources (JD-R) model, which posits that well-being and performance depend on the balance between job demands and the personal and organizational resources available to workers (W. Schaufeli & Bakker, 2003; W. Schaufeli, 2021). When digital systems are implemented without adequate training, infrastructure, or institutional support, they may intensify workload and contribute to technostress. Conversely, when supported by appropriate conditions, digital tools can enhance autonomy, streamline administrative processes, and even strengthen work engagement.

In Latin America, these transformations are expressed unevenly across administrative dependence, territorial contexts, and institutional conditions, deepening digital divides and differences in teachers’ workloads (Bruggeman et al., 2022; Van De Werfhorst et al., 2022; Weber et al., 2025). In schools serving socially vulnerable communities, the rapid incorporation of educational platforms has expanded the repertoire of teaching tasks, often without sufficient technical or pedagogical support, increasing pressure for constant availability and exposure to fragmented information flows (Ahiaku et al., 2025; Cone et al., 2022; Luo et al., 2022; Pavez et al., 2024). These dynamics have contributed to rising digital overload and technostress (Zhong & Rosli, 2025), with documented effects on teachers’ well-being and mental health (Fiore & Decataldo, 2022; Rahmi et al., 2025; Sobral et al., 2025; Yang et al., 2023; K. Wang et al., 2025; Willermark et al., 2023). Such inequalities underscore the need to understand how territorial and organizational conditions shape teachers’ experiences of engagement in digitally intensified environments.

Within this context, work engagement emerges as a key motivational resource. In education, engagement is associated with energy, resilience, pedagogical meaning, and job satisfaction (Greenier et al., 2021; E. E. Sandoval-Obando et al., 2023; E. Sandoval-Obando et al., 2025). However, the relationship between digital workload, technostress, and teacher engagement remains an emerging field (Yang et al., 2023; Xing, 2022; K. Wang et al., 2025; A. Zhang & Yang, 2021), particularly in settings where technological and organizational inequalities may amplify tensions and produce divergent patterns of commitment.

In Chile, where territorial disparities and structural differences between municipal and subsidized private schools persist, there is a pressing need for empirical evidence that characterizes socio-labor conditions, digital workload, and levels of teacher engagement across diverse educational contexts. The purpose of this study was to describe these conditions and evaluate the factorial structure of the UWES-17 in elementary school teachers from four regions of the country, providing contextualized evidence for the design of healthy educational organizations in times of digital intensification.

2. Materials and Methods

The study adopted a quantitative, non-experimental, cross-sectional design aimed at evaluating the factorial structure of the UWES-17 (Occupational Health Psychology Unit Utrecht University, 2011) and describing the levels of teacher engagement among elementary school teachers in four regions of Chile. The stratified sample (Song & Kawai, 2023) consisted of 314 teachers from municipal and subsidized private schools in the Metropolitan, Maule, La Araucanía, and Los Ríos regions of Chile, including both urban and rural schools, with diverse professional experience and academic backgrounds. Although stratification ensured representation across regions and school types, the sampling procedure was non-probabilistic and based on voluntary participation, which aligns with common practices in occupational health research involving school systems. Specifically, the following inclusion criteria were considered: (i) holding a professional degree in primary education; (ii) working in primary education; (iii) carrying out their educational work in school settings (urban or rural) and in schools (municipal or subsidized private); and (iv) having three or more years of classroom teaching experience. Challenges associated with non-probabilistic sampling in educational research were considered, following the methodological recommendations of Kanaki and Kalogiannakis (2023). The response rate could not be calculated precisely because schools disseminated the survey internally; however, no systematic differences were detected between participating and non-participating schools based on available administrative information. Data collection was conducted using a self-administered, anonymous, voluntary digital questionnaire after obtaining informed consent.

The study complied with the ethical principles of the Declaration of Helsinki and was also approved by the scientific research ethics committee of the Autonomous University of Chile, according to resolution No. 11–25 of 22 April 2025.

The instrument used was the Utrecht Work Engagement Scale (UWES-17), which has been widely validated in various work contexts (W. Schaufeli & Bakker, 2003; W. B. Schaufeli et al., 2009; W. B. Schaufeli & Taris, 2014). The UWES-17 assesses engagement through 17 ordinal items which, in their original formulation, are organized into three dimensions: Vigor, Dedication, and Absorption. Given that previous research has shown factorial variations across cultural and occupational contexts, it was considered relevant to examine its structure among Chilean teachers.

Data analysis was carried out in several stages. First, a preliminary examination of the items was conducted using univariate descriptive statistics (mean, variance, skewness, and kurtosis), in accordance with psychometric criteria for ordinal distributions (Ferrando et al., 2022; Ho & Yu, 2015). Sample adequacy was assessed using the sample adequacy measure (SAM) based on the anti-image matrix (Kaiser & Cerny, 1979), the KMO index, and Bartlett’s sphericity test, confirming the relevance of applying factor analysis.

Subsequently, exploratory factor analysis (EFA) was performed using polychoric correlations, the Unweighted Least Squares (ULS) extraction method, and Direct Oblimin oblique rotation with Kaiser normalization. ULS was selected because it performs well with ordinal data, does not assume multivariate normality, and is robust with moderate sample sizes—conditions that characterize the present dataset, as recommended by Krijnen (1996), Ximénez and García (2005), Li (2016), and Morata-Ramírez et al. (2015). EFA allowed us to identify the data’s underlying structure as a preliminary step toward confirmatory analysis.

Next, a confirmatory factor analysis (CFA) was performed using FACTOR software version 12.01.02 of December 2021 (Rovira i Virgili University, Tarragona, Spain) (Ferrando & Lorenzo-Seva, 2017), employing polyarchic correlations, the Robust Unweighted Least Squares (RULS) method, and factor selection using the Hull method (Lorenzo-Seva et al., 2011). This combination of procedures is recommended for ordinal items and avoids the inflation of fit indices that can occur with maximum likelihood estimation in non-normal data. Model fit was evaluated using the indices recommended by Schermelleh-Engel et al. (2003), including: Chi-square divided by degrees of freedom (χ²/df), Root Mean Square Error of Approximation (RMSEA), Adjusted Goodness of Fit Index (AGFI), Goodness of Fit Index (GFI), Comparative Fit Index (CFI), Non-Normed Fit Index (NNFI), Root Mean Square Residual (RMSR), following the interpretation criteria proposed by Kalkan and Kelecioğlu (2016). The internal reliability of the resulting factors was estimated using Cronbach’s alpha coefficient, following the recommendations of Bonett and Wright (2015).

Finally, to analyze the relationship between engagement levels and the sociodemographic characteristics of teachers (age, gender, region, location, administrative dependence, professional experience, and educational level), the exact Freeman–Halton test was applied, an extension of Fisher’s exact test for RxC tables (Freeman & Halton, 1951). This procedure is particularly suitable when expected frequencies are low and maintains the probabilistic accuracy of the exact test, as recommended by Contreras-Cristán and González-Barrios (2009) and Ruxton and Neuhäuser (2010). The calculation was performed using the exact tests option in SPSS version 23 (IBM, New York, NY, USA), which automatically applies this extension to larger contingency tables.

3. Results

The sample characterization showed a heterogeneous distribution across age, gender, professional experience, educational level, administrative dependence, and geographical location. Participants included teachers from municipal (public) and subsidized private (mixed funding) schools located in both urban and rural areas of the Metropolitan, Maule, La Araucanía, and Los Ríos regions. This diversity allowed the analysis of engagement patterns across contrasting territorial and organizational conditions, providing a broad view of the Chilean elementary school context (

Table 1. Teachers sample characterization.

Sociodemographic Variables	Level (Number Code)	n	n%
Gender (GND)	Female (1) *	242	77.1%
	Male (2)	70	22.3%
	No binary (3)	2	0.6%
Age level (AGE) (in years)	AGL < 30 (1)	47	15.0%
	30 ≤ AGL ≤ 39 (2)	104	33.1%
	40 ≤ AGL ≤ 49 (3)	100	31.8%
	50 ≤ AGL ≤ 59 (4)	41	13.1%
	60 ≤ AGL (5)	22	7.0%
Region (REG)	La Araucanía (LAR)	102	32.5%
	Los Ríos (LRI)	70	22.3%
	Maule (MAU)	80	25.5%
	Metropolitan (MET)	62	19.7%
Location (LOC)	Rural (1)	94	29.9%
	Urban (2)	220	70.1%
Dependence (DEP)	Public (1)	121	38.5%
	Mixed funding (2)	193	61.5%
Experience level (EXP) (in years)	EXP < 5 (1)	41	13.1%
	5 ≤ EXP ≤ 10 (2)	100	31.8%
	11 ≤ EXP ≤ 15 (3)	85	27.1%
	16 ≤ EXP ≤ 20 (4)	39	12.4%
	21 ≤ EXP (5)	49	15.6%
Education level (EDU)	Ungraduated (1) **	227	72.3%
	Qualification (2) ***	33	10.5%
	Advanced qualification (3)	5	1.6%
	Master (4)	49	15.6%

* The numbers and acronyms in parentheses (°) correspond to the values assigned in the database. ** Ungraduated includes teachers who have not completed postgraduate studies. *** Qualification, including teachers with varying levels of pedagogical specialization at the diploma and postgraduate levels in educational fields.

).

3.1. Univariate Item Analysis

The 17 items of the UWES-17 showed high means (3.90–4.74), indicating elevated engagement levels in the sample. Variances were adequate, and no items presented insufficient dispersion. Distributions showed moderate negative skewness and acceptable kurtosis values for ordinal data. These patterns supported the use of polychoric correlations and robust estimation methods, as they reduce bias when items deviate from normality (

Table 2. Univariate descriptive statistical analysis.

Variables	N	Mean	Variance	Skewness		Kurtosis
	Statistic	Statistic	Statistic	Statistic	Std. Error	Statistic	Std. Error
WBW1 *	314	4.10	2.188	−0.630	0.138	−0.454	0.274
WBW2 *	314	4.58	2.027	−0.901	0.138	0.141	0.274
WBW3 *	314	4.49	2.136	−0.899	0.138	0.044	0.274
WBW4 *	314	4.31	2.171	−0.762	0.138	−0.262	0.274
WBW5 *	314	4.43	2.323	−1.013	0.138	0.428	0.274
WBW6 *	314	3.91	2.972	−0.673	0.138	−0.411	0.274
WBW7 *	314	4.54	2.147	−0.992	0.138	0.322	0.274
WBW8 *	314	3.90	2.884	−0.588	0.138	−0.594	0.274
WBW9 *	314	4.06	2.645	−0.645	0.138	−0.498	0.274
WBW10 *	314	4.74	1.975	−1.188	0.138	0.883	0.274
WBW11 *	314	4.49	1.803	−0.748	0.138	−0.156	0.274
WBW12 *	314	3.99	2.377	−0.600	0.138	−0.369	0.274
WBW13 *	314	4.68	1.855	−1.350	0.138	1.835	0.274
WBW14 *	314	4.08	2.316	−0.797	0.138	0.325	0.274
WBW15 *	314	4.69	1.890	−1.236	0.138	1.359	0.274
WBW16 *	314	4.03	2.763	−0.752	0.138	−0.305	0.274
WBW17 *	314	4.40	2.491	−1.050	0.138	0.485	0.274
Valid N (listwise)	314

* Variables that satisfy the pre-established parameters of standard deviation, skewness, and kurtosis.

).

3.2. Exploratory Factor Analysis

Exploratory factor analysis (EFA) revealed a two-factor structure, in contrast to the classic three-factor structure of the UWES-17. The first factor grouped items related to energy, enthusiasm, meaning, and positive absorption, while the second factor grouped items associated with persistence, intense absorption, and difficulty disconnecting. This configuration suggests two distinct but related engagement profiles among Chilean teachers.

The first factor explained more than half of the total variance, while the second factor contributed an additional meaningful percentage. Communalities were high for most items, and the sample adequacy indices (KMO and MSA) confirmed the suitability of the factor model (

Table 3. Exploratory factor analysis for two factors.

KMO and Bartlett’s Test
Kaiser–Meyer–Olkin Measure of Sampling Adequacy				0.945
Bartlett’s Test of Sphericity		Approx. Chi-Square		3590.706
		Degree of freedom		136
		Significance		0.000
Pattern Matrix a
ID	Factor 1 (F1)		Factor 2 (F2)
WBW1	0.792
WBW2	0.780
WBW3	0.699
WBW4	0.703
WBW5	0.847
WBW6	0.471
WBW7	0.942
WBW8	0.870
WBW9	0.827
WBW10	0.696
WBW11	0.508
WBW12	0.510
WBW13			0.450
WBW14			0.490
WBW15			0.519
WBW16			0.634
WBW17			0.691
Eigenvalue	8.746		0.878
% of Variance	51.450		5.162
Cumulative %	51.450		56.611
Factor Correlation Matrix b
Factor	1	2
1	1.000	0.618
2	0.618	1.000

^a Extraction Method: Unweighted Least Squares. Rotation Method: Oblimin with Kaiser Normalization. Rotation converged in 6 iterations. ^b Extraction Method: Unweighted Least Squares. Rotation Method: Oblimin with Kaiser Normalization.

).

Figure 1 shows the scree plot, where the elbow point (eigenvalue = 1.368) coincides with the clipping line (eigenvalue = 1), supporting the retention of two factors.

3.3. Confirmatory Factor Analysis

Confirmatory factor analysis (CFA) corroborated the two-factor structure identified in the EFA. The two-factor model showed excellent fit indices, with CFI and NNFI values close to 1.000, RMSEA equal to 0.000, and GFI and AGFI greater than 0.99. In contrast, the unifactorial model showed weaker fit across all indices. The difference between the EFA eigenvalue for Factors and the CFA model-implied eigenvalue is expected because the two procedures rely on different estimation frameworks. EFA eigenvalues are extracted from the unrotated correlation matrix using ULS, whereas CFA eigenvalues are derived from the fitted RULS model and reflect the variance explained under confirmatory constraints. Therefore, eigenvalues from EFA and CFA are not directly comparable.

Factor loadings were high and consistent with the proposed structure, and internal reliability reached satisfactory levels for both factors (

Table 4. Confirmatory factor analysis for two factors.

KMO and Bartlett’s Test
Kaiser–Meyer–Olkin Measure of Sampling Adequacy (confidence interval 90%)			0.944 (0.855; 0.933)
Bartlett’s Test of Sphericity	Approx. Chi-Square		3528.7
	Degree of freedom		136
	Significance		0.000010
Rotated Loading Matrix
Variable (Item)	Factor 1 (F1)	Factor 2 (F2)
WBW1	0.850
WBW2	0.831
WBW3	0.734
WBW4	0.762
WBW5	0.936
WBW6	0.554
WBW7	0.969
WBW8	0.879
WBW9	0.810
WBW10	0.663
WBW11	0.470
WBW12	0.506
WBW13		0.548
WBW14		0.598
WBW15		0.576
WBW16		0.721
WBW17		0.723
Explained Variance	0.602	0.081
Cumulative Variance	0.602	0.683
Eigenvalue	10.234	1.368
Inter Factor Correlation Matrix
Factor	F1	F2
F1	1.000
F2	0.665	1.000

and

Table 5. Reliability statistics.

Scale	Variance	Skewness	Kurtosis	Valid Cases	Number of Items	Cronbach’s Alpha
Factor 1	1.567	−0.431	−0.704	314	12	0.942 ci (0.936 0.948) **
Factor 2	1.380	−0.332	−0.683	314	5	0.795 ci (0.772 0.818) *
Factor Total	1.341	−0.377	−0.626	314	17	0.942 ci (0.936 0.948) **

* Cronbach’s Alpha > 0.7, ** Cronbach’s Alpha > 0.8.

). The correlation between factors was substantial but not redundant, indicating related but distinct dimensions of engagement.

In Table 5, the internal consistency was evaluated using Cronbach’s alpha with 95% confidence intervals. Inspired Vitality showed excellent reliability (α = 0.942, 95% CI [0.936, 0.948]), while Challenging Commitment showed acceptable reliability (α = 0.795, 95% CI [0.772, 0.818]).

To facilitate interpretation for readers unfamiliar with psychometric modeling, a concise model comparison table was added, including the most relevant indices and the thresholds adopted (

Table 6. Validation and reliability versus parameters (Schermelleh-Engel et al., 2003).

Model	Sample	Level	Cronbach’s Alpha	MIF	χ2/df	RMSEA	AGFI	GFI	CFI	NNFI	RMSR
Proposed (Two-factor model)	314	-	0.934 **	5	0.54 **,+	0.000 ** ci (could not be computed)	0.996 ** ci (0.996 0.997)	0.997 ** ci (0.997 0.998)	0.999 ** ci (0.998 1.001)	1.000 ** ci (1.001 1.002)	0.036 ** ci (0.033 0.035)
Contrast (One-factor model)	314	-	0.934 **	17	1.52 ** +	0.072 * ci (0.057 0.078)	0.988 ** ci (0.986 0.992)	0.989 ** ci (0.987 0.993)	0.987 ** ci (0.983 0.993)	0.985 ** ci (0.980 0.992)	0.065 ** ci (0.057 0.069)
Adopted thresholds	≥200	**	[0.80, 0.95)	NR	[0, 2]	[0.00, 0.05]	[0.90, 1.00]	[0.95, 1.00]	[0.97, 1.00]	[0.97, 1.00]	[0.00, 0.05) ++
		*	[0.70, 0.80)	≥3	(2, 3]	(0.05, 0.08]	[0.85, 0.90)	[0.90, 0.95)	[0.95, 0.97)	[0.95, 0.97)	[0.05, 0.08] ++

NR: not reported. ** Good fit. * Acceptable fit. ⁺ Minimum Fit Function Chi Square. ⁺⁺ indicated in Kalkan and Kelecioğlu (2016).

).

The two-factor model clearly outperformed the unifactorial alternative, supporting the conceptual distinction between Inspired Vitality and Challenging Commitment.

3.4. Associations with Sociodemographic Variables

The Freeman–Halton exact test revealed differentiated association patterns between engagement factors and sociodemographic variables.

• Inspired Vitality showed significant associations with age, gender, region, location, administrative dependence, and professional experience, but not with educational level.
• Challenging Commitment was significantly associated with gender, region, location, and experience, but not with age, administrative dependence, or educational level.
• The total UWES-17 score showed significant associations with all variables except educational level.

These results indicate that teacher engagement is shaped by both individual trajectories (e.g., experience, gender) and structural conditions (e.g., region, school location, administrative dependence), reinforcing the relevance of territorial and organizational inequalities in shaping teachers’ motivational resources. To complement statistical significance, effect sizes were calculated using Cramer’s V. Across all associations, effect sizes ranged from small to small-to-moderate (V = 0.115–0.288), indicating limited to modest practical relevance despite statistically significant results (

Table 7. Freeman–Halton (F-H) extension of Fisher’s Exact Tests.

Variable 1	Variable 2	N of Valid Cases	Value F-H +	Significance (2-Sided) +	Correlation Evidence	Cramer’s V	Effect Size
F1	AGE	314	53.203	0.000 ci (0.000 0.000) **	Yes	0.213	small–moderate
	GND	314	26.024	0.001 ci (0.000 0.002) **	Yes	0.210	small–moderate
	REG	314	49.511	0.000 ci (0.000 0.000) **	Yes	0.242	small–moderate
	LOC	314	24.231	0.000 ci (0.000 0.000) **	Yes	0.280	small–moderate (close to medium)
	DEP	314	12.145	0.025 ci (0.021 0.029) *	Yes	0.197	small
	EXP	314	37.204	0.005 ci (0.003 0.006) **	Yes	0.172	small
	EDU	314	19.136	0.172 ci (0.162 0.182)	No	0.139	small
F2	AGE	314	26.833	0.109 ci (0.101 0.117)	No	0.145	small
	GND	314	26.762	0.001 ci (0.000 0.001) **	Yes	0.192	small
	REG	314	46.122	0.000 ci (0.000 0.000) **	Yes	0.227	small–moderate
	LOC	314	12.869	0.018 ci (0.014 0.021) *	Yes	0.207	small–moderate
	DEP	314	9.931	0.062 ci (0.055 0.068)	No	0.180	small
	EXP	314	36.296	0.006 ci (0.004 0.008) **	Yes	0.170	small
	EDU	314	15.148	0.526 ci (0.513 0.539)	No	0.115	small
FT	AGE	314	49.420	0.000 ci (0.000 0.000) **	Yes	0.201	small–moderate
	GND	314	27.007	0.001 ci (0.000 0.002) **	Yes	0.208	small–moderate
	REG	314	57.286	0.000 ci (0.000 0.000) **	Yes	0.255	small–moderate
	LOC	314	24.679	0.000 ci (0.000 0.000) **	Yes	0.288	small–moderate (close to medium)
	DEP	314	24.722	0.000 ci (0.000 0.000) **	Yes	0.283	small–moderate (close to medium)
	EXP	314	39.784	0.002 ci (0.001 0.003) **	Yes	0.179	small
	EDU	314	22.624	0.081 ci (0.074 0.088)	No	0.147	small

* p-value < 0.05, ** p-value < 0.01, ⁺ calculated with Monte Carlo significance based on 10,000 sampled tables with starting seed 2,000,000.

).

4. Discussion

The results of this study provide robust evidence on the configuration of teacher engagement in the Chilean school context, identifying a two-factor structure of the UWES-17: Inspired Vitality and Challenging Commitment. This alternative structure differs from the classic three-factor model (W. Schaufeli & Bakker, 2003) and coincides with research indicating that, in vocational and emotionally demanding professions, the dimensions of vigor, dedication, and absorption tend to be integrated into broader or more intense patterns of work engagement (Rathan et al., 2025; Yuan et al., 2025). Among Chilean teachers, this bifurcation appears to reflect two distinct modalities of commitment shaped by digital intensification, administrative overload, and persistent territorial and historical-cultural inequalities.

Inspired Vitality brings together energy, enthusiasm, meaning, and positive absorption, suggesting a deep pedagogical involvement consistent with evidence on the role of intrinsic motivation, self-efficacy, and professional identity in teacher well-being (Greenier et al., 2021; Yuan et al., 2025). In contrast, Challenging Commitment captures a more demanding form of engagement characterized by persistence, intense absorption, and difficulty disconnecting, a pattern observed in high-demand professions, where intense involvement can coexist with risks of burnout when recovery is insufficient (Fujikawa et al., 2024; H. Zhang & Cao, 2025). Rather than interpreting this factor as inherently maladaptive, the present findings suggest that it may represent a culturally embedded form of professional dedication, particularly salient in contexts where teachers compensate for structural deficiencies through personal effort.

Based on the reported reliability coefficients and association patterns (see Table 6 and Table 7), it is possible to propose a socio-pedagogical analysis focused on the differential stability of teacher engagement patterns. The high internal consistency of Inspired Vitality and its strong association with structural variables indicate that this factor is sensitive to organizational resources, perceived fairness, and accumulated professional capital. In contrast, the moderate reliability of the weaker structural dependence of Challenging Commitment suggests a more transversal configuration, potentially rooted in vocational dispositions, coping strategies, or cultural norms that legitimize persistence under pressure. From this perspective, teachers’ engagement reflects not only objective working conditions but also professional socialization processes that shape how teachers interpret and respond to demands. Structural inequalities do not determine whether commitment exists, but they influence their pedagogical quality, sustainability, and long-term consequences (Delgado-Galindo et al., 2025; Holst et al., 2025; Martin & Benedetti, 2025; Zorde & Lapidot-Lefler, 2025).

The identification of the Challenging Commitment factor suggests the need to differentiate between adaptive and potentially dysfunctional forms of teacher engagement. Although its empirical indicators—high persistence, intense concentration on the task, and difficulty disconnecting from work—show partial convergence with constructs such as overcommitment (Siegrist & Li, 2016; Jin et al., 2025), heavy work investment (W. B. Schaufeli, 2016; Tabak et al., 2021; Wettstein et al., 2022), and strain-based involvement (Muasya, 2024; Provido et al., 2025; Sharif, 2025), the results of this study reveal a specific and differentiating configuration within the field of teacher engagement. Unlike excessive commitment associated with occupational stress—characterized by heightened physiological reactivity, progressive exhaustion, and declines in well-being—Challenging Commitment appears as a form of professional involvement oriented toward sustaining pedagogical continuity in highly demanding institutional contexts. From the perspective of the Job Demands–Resources model, this pattern may represent an adaptive response to workload intensification and organizational constraints. However, the difficulty in disconnecting suggests a blurred boundary between professional vocation and over-investment in work (Bredehorst et al., 2024). Consequently, Challenging Commitment reflects an ambivalent form of engagement that combines professional energy with the risk of cognitive overload, particularly in educational systems experiencing increasing institutional pressures and the digitalization of teaching work.

Sociodemographic analyses using the Freeman–Halton exact test show that Inspired Vitality is highly sensitive to structural inequalities, as it is associated with age, gender, region, location, administrative dependence, and professional experience. This pattern reinforces the idea that vitality and meaning in work depend strongly on organizational resources, consistent with the Job Demands–Resources (JD-R) model and with evidence linking engagement to institutional conditions, leadership, and professional development opportunities (Collie, 2023; Jamal et al., 2023; Timotheou et al., 2023; Cai et al., 2022; Polatbekova et al., 2025; Rathan et al., 2025). In contrast, Challenging Engagement showed greater stability and weaker dependence on structural variables, suggesting that it may reflect personal styles of involvement, emotion-focused coping, or traits such as tolerance for ambiguity, positively associated with engagement in clinical and educational contexts (Fujikawa et al., 2024).

However, the findings also point to the need for further investigation into the coexistence of functional and dysfunctional engagement in digital intensification. High engagement in contexts of technological overload and unequal institutional support (connectivity, and infrastructure) may constitute short-term adaptive responses but may be difficult to sustain. Challenging Commitment may reflect a normative internalization of self-imposed demands, where persistence and difficulty disconnecting are framed as part of the teaching vocation (Saks et al., 2022). Without adequate organizational resources and explicit regulation of digital workload, this configuration may increase vulnerability to progressive burnout.

For this reason, a dialogical perspective on technology is essential. Technology is not inherently a risk factor; rather, its impact depends on how it is integrated into organizational and pedagogical practices (Digón-Regueiro et al., 2023; Perrotta, 2013; Torres-Rivera et al., 2025; Ubal Camacho et al., 2023; Yan et al., 2025). According to the Job Demands-Resources (JD-R) model, digital tools can serve as work resources by optimizing planning, automating evaluation processes, and facilitating asynchronous communication, thereby reducing administrative burden and freeing up pedagogical time.

In schools with strong leadership and clear availability policies, technology can even support teacher disconnection, for example, through staggered scheduling platforms or notification-limiting systems. The problem arises when digitalization increases demand without strengthening resources. Thus, the key issue is not technology itself but the structural conditions that determine whether it becomes a protective resource or a chronic demand.

The results are consistent with evidence showing that emotional intelligence, self-efficacy, and emotional regulation predict teacher engagement (Deng et al., 2022; Hameli et al., 2025; Uzuntiryaki-Kondakci et al., 2022; Yuan et al., 2025). Engagement is strengthened when personal and organizational resources are available to manage emotional and cognitive demands in contexts of uncertainty or digital transformation (Harper-Hill et al., 2022; Polatbekova et al., 2025; Rathan et al., 2025). In Chile, the unequal distribution of these resources across regions, administrative types, and urban/rural contexts limits the capacity to sustain high levels of vitality and meaning in work (Ahiaku et al., 2025; Iturra & Gallardo, 2022).

To illustrate this, consider two contrasting scenarios. In an urban school with stable technological support and distributed leadership, a teacher with ten years of experience may experience high Inspired Vitality, using digital platforms for efficient feedback and professional collaboration, which strengthens their sense of purpose and work energy. In a rural school with unreliable connectivity and limited institutional support, a teacher may exhibit high Challenging Commitment, characterized by persistence and prolonged hyper-connectivity to compensate for structural deficiencies. Both profiles demonstrate commitment, but their functional configurations differ, showing how similar digital demands can lead to healthy engagement or sustained overexertion depending on the balance between demands and resources.

4.1. Limitations

This study has some limitations that should be considered when interpreting the results. First, cross-sectional design prevents establishing causal relationships between the variables analyzed, so future research should incorporate longitudinal designs to examine the evolution of engagement over time. Second, the data are based on self-reports, which may introduce social desirability or subjective perception biases, a common limitation in occupational well-being and health studies (W. Schaufeli, 2021). Third, although the sample is geographically diverse, it does not include private paid establishments or extreme regions, which limits the generalizability of the findings. Additionally, the voluntary nature of participation may have introduced self-selection bias, and the absence of information on non-respondents restricts the assessment of potential systematic differences. Finally, the study focused on the UWES-17; recent research suggests exploring alternative or complementary scales, such as the Engaged Teacher Scale (ETS) or culturally adapted versions (Rathan et al., 2025).

4.2. Organizational and Socio-Pedagogical Implications

Strengthening labor resources is essential for sustaining the Inspired Vitality of teachers, particularly in public and rural schools where structural constraints may weaken teachers’ sense of effectiveness and recognition. Pedagogical leadership emphasizes professional support, emotional accompaniment, and systematic recognition can enhance engagement, alongside administrative simplification strategies that protect pedagogical time. Regulating digital intensification is also key to preventing Challenging Commitment configurations associated with persistence under pressure and difficulty disconnecting. Institutional policies on availability, communication protocols, and training in time management and the functional use of technologies can mitigate these risks (Mexhuani, 2025). Such actions should be adapted to urban or rural contexts and administrative dependency to reduce regional gaps and ensure minimum standards of teacher well-being.

From a pedagogical perspective, integrating engagement into professional development allows continuous training to focus on skills that expand personal and collective resources in the face of growing demands (Moravec & Martínez-Bravo, 2023). Programs that strengthen self-efficacy, professional autonomy, and emotional intelligence promote emotional regulation and teacher dedication in contexts of uncertainty, while initiatives that foster creativity, flexibility, and tolerance for ambiguity facilitate adaptation to technological and organizational changes (Rennstich, 2023; Tomczyk & Majkut, 2025). These guidelines must align with educational policies aimed at psychological well-being, resilience, and continuous teacher development (Shu, 2022; Holzer & Spiel, 2025; Ma & Pongpisanu, 2025). Finally, future lines of research should incorporate longitudinal, comparative, and mixed-methods approaches, integrating variables such as generativity (E. E. Sandoval-Obando et al., 2023; E. Sandoval-Obando et al., 2025), self-efficacy and emotional intelligence (Xiao et al., 2022), and the evaluation of successful organizational interventions (Polatbekova et al., 2025).

5. Conclusions

The study provides contextualized and psychometrically robust evidence on teacher engagement in the Chilean elementary schools, identifying a two-factor structure of the UWES-17 composed of Inspired Vitality and Challenging Commitment. This bifactorial configuration offers a more nuanced understanding of engagement in digitally intensified educational environments, where teachers navigate increasing administrative demands, uneven technological conditions, and persistent territorial inequalities.

Inspired Vitality emerged as a resource-sensitive dimension, strongly associated with structural and organizational variables such as region, school location, administrative dependence, and professional experience. This suggests that teachers’ energy, meaning, and positive absorption are closely linked to the availability of institutional support, technological infrastructure, and equitable working conditions. In contrast, Challenging Commitment reflected a more stable and transversal pattern, less dependent on structural factors and potentially rooted in vocational dispositions, coping strategies, and cultural norms that legitimize persistence under pressure.

Together, these findings highlight that engagement is not a uniform construct, but rather a dynamic interplay between personal resources and contextual demands. In settings where digital intensification is accompanied by adequate organizational support, engagement may foster well-being and pedagogical effectiveness. However, in contexts marked by technological overload, limited institutional resources, or territorial disparities, engagement, particularly in its challenging form, may become difficult to sustain, increasing vulnerability to burnout.

The study underscores the importance of strengthening organizational resources, regulating digital workload, and reducing territorial gaps to promote healthy and sustainable forms of engagement. It also points to the need for professional development initiatives that enhance emotional regulation, self-efficacy, and adaptive coping in the face of technological and organizational change.

Finally, the results open avenues for future research, including longitudinal analyses of engagement trajectories, comparative studies across school types and regions, and the integration of complementary constructs such as generativity, emotional intelligence, and organizational justice. Such approaches would deepen understanding of how teachers maintain vitality and commitment in increasingly complex educational environments.