2.1. Overview of Defense Technology Education Programs

The Fourth Industrial Revolution has shifted the military environment toward a complex, data-driven paradigm. Core technologies—AI, IoT, AR/VR, and Big Data—are now recognized as transformative forces redefining national security (Russell & Norvig, 2010). To cultivate necessary competencies, the ROKICS military launched the "Introductory Course on Fourth Industrial Revolution Technologies" in 2022. By 2025, the program expanded into intermediate levels, focusing on applied data analytics and generative AI to enhance mission-critical capabilities (ROKAF, 2025). This study examines how such education facilitates the transition from labor-intensive systems (As-Is) to intelligent defense ecosystems (To-Be), where automation and human adaptability converge (MND, 2022; KIDA, 2023).

Table 1

Transformation of Military Functions under the Fourth Industrial Revolution

2.2. Core Technologies Shaping Military Security

The curriculum focuses on four strategic technologies, each presenting unique operational implications:

2.3. Technological Maturity and the Strategic Role of Education

Gartner’s Hype Cycle (2023) illustrates the trajectory from the "Peak of Inflated Expectations" to the "Plateau of Productivity." Many defense technologies currently face the "Trough of Disillusionment" due to integration barriers and data reliability issues.

Figure 2

Gartner Hype Cycle (2023)

In the military, where technological failure correlates with mission failure, systematic education serves as a critical bridge. High-quality training helps trainees navigate the discrepancy between expectations and practical limitations, fostering the technical resilience required to achieve stable operational readiness (Hong & Kim, 2025). Thus, educational quality is not merely a measure of satisfaction but a lead indicator of successful technological maturation.

2.4. Service Quality, Satisfaction, and Loyalty in Military Contexts

Similar to general service industries, education is a professional service defined by classic service characteristics such as intangibility, heterogeneity, simultaneity, and perishability, as conceptualized by Parasuraman, Zeithaml, and Berry (1985). To operationalize this, the present study adopts the SERVQUAL framework (Parasuraman et al., 1988), utilizing its five dimensions—Reliability, Assurance, Tangibles, Empathy, and Responsiveness (RATER)—as a higher-order construct (HOC). This approach aligns with recent trends in quality management research, which reaffirm the diagnostic value of traditional SERQUAL dimensions (Kim et al., 2024), thereby justifying the extension of SERQUAL into a higher-order framework within the unique infrastructure of military education. In military technical training, these service dimensions transcend mere learner satisfaction; they function as a critical infrastructure for ensuring that trainees can reliably internalize and operationalize high-stakes emerging technologies in high-pressure combat environments.

Aligned with the Expectation–Disconfirmation Paradigm (EDP), satisfaction understood to emerge when perceived quality meets or exceeds prior expectations (Oliver, 1980, 1997). Consistent with Oliver’s (1999) paradigm, this satisfaction serves as a critical antecedent to loyalty. In a defense context, 'Loyalty' extends beyond simple program re-enrollment; it represents a sustained commitment to applying emerging technologies in future missions—a crucial psychological mechanism for bridging the "valley of disillusionment" (Biggs, 2025; Spirnak & Antani, 2023). By empirically testing the Quality–Satisfaction–Loyalty (Q–S–L) model, this research proposes evidence-based directions for sustainable human capital development and operational readiness in the defense sector.

3.1. Research Model

This study conceptualizes service quality (TechEduQUAL) as a Higher-Order Construct (HOC), composed of five first-order dimensions: reliability, assurance, tangibles, empathy and responsiveness (RATER). This modeling approach reflects the multidimensional and integrative nature of service quality as a construct that captures both functional and relational aspects of learners’ experiences. According to Becker, Klein, and Wetzels (2012) and Sarstedt, Ringle, and Hair (2019), when a latent variable is theoretically holistic yet empirically represented by distinct but correlated dimensions, a reflective-reflective HOC model is appropriate. In line with this guidance, the present study models TechEduQUAL as a reflective higher-order construct measured through five reflective first-order factors.

Figure 3

Research Model

3.2. Research Hypotheses

4.1. Research Context and Sampling

This study examines trainee attitudes toward the Basic New Technology Education Program at the AFICS in Gyeongsangnam-do, ROK. The survey was conducted between March 2024 and December 2025. Out of 160 distributed questionnaires, 115 valid responses were analyzed (71.88% response rate). The sample included 83 males and 32 females, with a predominant representation of Generation M (n=64) and Bachelor’s degree holders (n=99). Service length was evenly distributed, while only 15 respondents (13%) reported prior experience with similar technology training.

4.2. Measurement Instruments

Latent variables were operationally defined based on prior literature and measured using a 0–10 Likert scale to ensure high interval precision (Table 2).

Table 2

Operational definitions and measurement questions

4.3. Analytical Procedures

(a) PLS-SEM with Higher-Order Construct (HOC)

To test the structural model, this study employed Partial Least Squares Structural Equation Modeling (PLS-SEM), which is robust for relatively small sample sizes and non-normal distributions (Hair et al., 2019). TechEduQUAL was modeled as a reflective-reflective HOC using the disjoint two-stage approach (Sarstedt et al., 2019):

Stage 1: Estimated first-order latent variable scores and verified reliability (Cronbach’s α, ρC) and convergent validity (AVE).

Stage 2: Used the scores from Stage 1 as manifest variables to estimate the second-order TechEduQUAL construct. Statistical significance was tested via bootstrapping with 10,000 resamples (95% CI).

(b) Moderation and Conditional Process Analysis (CPA)

To examine the moderating effect of prior experience, Conditional Process Analysis (CPA) was conducted using the PROCESS Macro for R (Hayes, 2015). Interaction terms were created through mean-centering to prevent multicollinearity. Significant moderation was confirmed via 10,000 bootstrap resamples and visualized through simple slope analysis to compare effects across groups.

(c) Text Mining: Word Cloud and Network Analysis

To complement the quantitative results, text mining was performed on open-ended responses. Word cloud analysis visualized key thematic frequencies (Heimerl et al., 2014), while word network analysis identified co-occurrence patterns and centrality among concepts (Hansen et al., 2011). This mixed-method approach integrates qualitative needs with structural model findings for a comprehensive understanding of TechEduQUAL.

5.1. Verification of the PLS-SEM Higher-Order Construct (HOC) Model

(a) Stage 1: First-Order Construct Analysis

In the first stage, all five first-order reflective constructs (RATER) were modeled as the subdimensions of TechEduQUAL and met the recommended reliability and validity thresholds. As shown in Table 3, the results―Cronbach’s α (0.802–0.973), composite reliability (ρc = 0.886–0.974), and average variance extracted (AVE = 0.612–0.925)―exceeding the suggested cutoffs (α, ρc ≥ 0.7; AVE ≥ 0.5) (Hair et al., 2021). These results confirm internal consistency, convergent validity and discriminant validity across all constructs.

Table 3

Reliability and validity verification results of the 1st stage construct

All inter-construct correlations were statistically significant (r = 0.35–0.96). The particularly high correlation between Responsiveness and Assurance (r = 0.963) suggests that trainees perceive “responsive and reliable instructor behavior” and “confidence in the learning process” as highly integrated aspects of service quality (Parasuraman et al., 1988; Brady & Cronin, 2001).

Table 4

Correlation Matrix

Given that the square roots of AVE for these two constructs were 0.962 (Ass) and 0.949 (Res), they marginally satisfy the Fornell–Larcker criterion (1981), indicating partial discriminant validity. This high interrelation supports the interpretation of service quality as a multidimensional yet conceptually unified construct, where distinct dimensions work synergistically within the military educational context.

Accordingly, the study proceeded with a second-order (HOC) analysis, treating TechEduQUAL as a higher-order latent construct (Becker et al., 2012; Sarstedt et al., 2019).

(b) Stage 2: Higher-Order Construct (HOC) Analysis

Based on the validated first-order dimensions, the second-order HOC model was estimated with TechEduQUAL as the higher-order construct encompassing the five subdimensions. The overall reliability and convergent validity of the higher-order construct were satisfactory (α = 0.93, ρc = 0.95, AVE = 0.79). All first-order dimensions significantly contributed to the formation of the higher-order construct, with weights and loadings significant at p < .001. While the loadings indicate how strongly each first-order construct reflects the higher-order latent factor, the weights capture the relative contribution of each dimension to forming the higher-order construct. This confirms that each dimension is a stable and meaningful component of TechEduQUAL, validating its integrative nature (Becker et al., 2012; Sarstedt et al., 2019).

Table 5

Contribution of components of the upper factor (service quality)

Discriminant validity was verified using the Heterotrait–Monotrait Ratio (HTMT), with all upper confidence intervals below 0.90 (Henseler, Ringle, & Sarstedt, 2015). These results confirm that TechEduQUAL, trainee satisfaction, and loyalty were measured as conceptually distinct yet structurally coherent constructs.

Table 6

HTMT Ratio of the 2nd stage construct

5.2. Structural Model Assessment

The structural model explained 54.2% of the variance in trainee satisfaction (R² = 0.542) and 62.5% of the variance in trainee loyalty (R² = 0.625, indicating a moderate explanatory power (Hair et al., 2021).

As summarized in Table 7, TechEduQUAL positively affected trainee satisfaction (β = 0.737, p < .001), supporting H1. Satisfaction, in turn, significantly influenced loyalty (β = 0.612, p < .001), supporting H2. However, the direct path from TechEduQUAL to loyalty was not significant (β = 0.223, p > .05). This indicates a full mediation effect through satisfaction, supporting H4 while rejecting H3. Thus, trainee satisfaction fully transmits the effect of TechEduQUAL to trainee loyalty.

Table 7

Bootstrapped PLS-SEM Path Analysis Results

Figure 4

illustrates the final PLS path model diagram

This finding aligns with Expectancy–Disconfirmation Theory (Oliver, 1980), which posits that satisfaction mediates the link between perceived service quality and behavioral loyalty.

5.3. Moderation Analysis

Conditional Process Analysis (PROCESS Model 7) confirmed the moderating role of pre-similar learning experience on the quality–satisfaction linkage (β = -0.266, p < .05, 95% CI = [–0.485, –0.047]), supporting H5 (Note. The negative coefficient resulted from the dummy variable coding scheme where ‘experience’ is coded as 0 and ‘no experience’ as 1). The negative coefficient suggests that prior experience interacts with quality perceptions; specifically, trainees with prior exposure showed a more sensitive satisfaction response to service quality improvements compared to novices. This can be theoretically explained by ‘Assimilation-Contrast Theory’ or ‘Expectancy Disconfirmation’, where experienced trainees, possessing a clearer internal benchmark, respond more sensitively to the actual delivery of technical education. This suggests that experiential familiarity functions as an amplifier that heights the evaluative impact of service quality on satisfaction. However, the index of moderated mediation was not significant (95% CI [-0.43, 0.23]), suggesting that the overall indirect effect on loyalty does not vary meaningfully by experience level.

Figure 5

visualizes moderation effect of pre-experience

5.4. Word Cloud and Word Network Analysis

Text mining of open-ended responses provided qualitative depth to the SEM findings: Word Cloud Analysis

(a) Word Cloud: As shown in Figure 6, Terms like "education," "AI," "practice," and "helpful" were prominent, indicating that trainees valued the practical, instructor-driven nature of the program. The prevalence of keywords such as ‘experience’ and ‘helpful’ in the word cloud reinforces the quantitative finding of H5, suggesting that trainees’ prior familiarity with the subject matter significantly colors their perception of educational utility.

Figure 6

Word Cloud

(b) Word Network Analysis: The word network (Figure 7) reveals structurally coherent thematic clusters that indicate how trainees cognitively organize their learning experience during the I4.0 military technical program. Three major clusters emerged: (1) core educational experience (trainees, knowledge, helpful), (2) technological integration (AI, VR, future), and (3) logistical environment (facilities, restricted). These clusters reinforce the quantitative findings, particularly the importance of Assurance and Responsiveness in shaping the learning experience.

Figure 7

Word Network Analysis

6.1. Summary of Key Findings

This study empirically examined the structural relationships between educational service quality, satisfaction, and loyalty in the context of military new-technology training. The key findings are as follows:

(a) Structural Validity of TechEduQUAL: The PLS-SEM Higher-Order Construct (HOC) analysis confirmed that TechEduQUAL is a valid second-order reflective construct. All sub-dimensions (RATER) significantly contributed to the higher-order construct (p < .001), validating the use of an integrative quality assessment model in military settings (Becker et al., 2012; Sarstedt et al., 2019).

(b) Full Mediation of Satisfaction: TechEduQUAL significantly enhanced trainee satisfaction (β = 0.737, p < .001), which in turn drove loyalty (β = 0.612, p < .001). The lack of a significant direct path from quality to loyalty confirms a full mediation model, aligning with Oliver’s (1999) paradigm where satisfaction is the essential bridge to long-term engagement (Parasuraman et al., 1988; Cronin & Taylor, 1992).

(c) Moderating Effect of Experience: Prior learning experience significantly moderated the quality–satisfaction linkage (β = -0.266, p < .05) using Hayes’ (2015) Conditional Process Analysis (PROCESS Model 7). Trainees with prior exposure demonstrated higher sensitivity to service quality, suggesting that experiential familiarity functions as a critical filter in evaluating educational services.

(d) Qualitative Insights: Text-mining revealed that "practice," "AI," and "instructor expertise" are central to the learner experience. This reinforces the quantitative finding that Assurance and Responsiveness are pivotal in managing the "Trough of Disillusionment" inherent in emerging technology adoption.

6.2. Theoretical Implications

(a) Extension of SERVQUAL: This research successfully adapts the SERVQUAL framework to the military domain, transitioning the defense education paradigm from simple knowledge transfer to a learner-centered service model.

(b) Validation of Oliver’s (1999) Q–S–L Model: The study confirms that the Quality–Satisfaction–Loyalty (Q–S–L) chain remains robust even in highly technical military environments, providing a theoretical foundation for sustainable human capital development.

(c) Integration of Experiential Learning: By verifying the moderating role of prior experience, this study bridges Kolb’s (1984) Experiential Learning Theory with service quality models, offering a more nuanced understanding of how digital competence is formed.

6.3. Practical Implications

(a) Strengthen Instructor Expertise (Assurance & Empathy): Since these dimensions showed high weights, the military should prioritize the cultivation of "Expert Instructors" who possess both technical proficiency and empathetic communication skills.

(b) Expand Immersive & Practice-Based Modules: Qualitative data suggests a strong demand for hands-on experience with AI and XR. Transitioning from theory-oriented to problem-solving-based curricula will enhance both satisfaction and mission readiness.

(c) Customized Learning Paths: Given the moderating effect of prior experience, the military should institutionalize leveled curricula (e.g., Basic, Intermediate, Advanced) to meet the diverse baseline competencies of trainees.

(d) Continuous Quality Improvement (CQI): Periodic evaluation using IPA (Importance-Performance Analysis) should be established to ensure that resource allocation aligns with the rapidly evolving "To-Be" defense environment.

6.4. Limitations and Future Research Directions

Despite its contributions, this study has limitations that offer directions for future research:

(a) Generalizability and Contextual Diversity: As the research was conducted within a single military institution, the findings may not capture the diverse organizational cultures of the Army, Navy, or Marine Corps. Future studies should expand the sample to various service branches and career fields to enhance the model's broader applicability. Additionally, incorporating diverse ranks and roles would enhance the model’s hierarchical validity and its generalizability.

(b) Measurement Objectivity and Temporal Dynamics: The reliance on self-reported, cross-sectional data may involve social desirability bias and fails to track the long-term transition from educational satisfaction to actual mission performance. Future research should incorporate longitudinal designs and objective metrics, such as learning analytics or on-site competency assessments.

(c) Model Complexity and Variable Integration: While TechEduQUAL is robust, it does not account for external constraints like unit-level resource accessibility or individual technical anxiety. Integrating the Technology Acceptance Model (TAM) and organizational support variables would provide a more comprehensive understanding of digital transformation in defense education.