Human performance, psychological well-being, and behavioral adaptation in isolated, confined, and extreme (ICE) environments—such as intensive care units, remote healthcare settings, disaster zones, and space-analog missions—are increasingly mediated through digital and interactive technologies. Understanding how users perceive, experience, and sustain engagement with such systems requires methodological approaches capable of modeling complex psychosocial constructs under constrained conditions. This paper positions Partial Least Squares Structural Equation Modelling (PLS-SEM) as a robust and practical analytical framework for Human–Computer Interaction (HCI) research in ICE contexts. We review the methodological limitations of traditional statistical approaches and demonstrate how PLS-SEM enables the simultaneous modeling of latent experiential, cognitive, and behavioral variables central to technology use in extreme environments. Using an illustrative case involving a mobile health (mHealth) system designed for remote and resource-constrained settings, we examine how user experience, user satisfaction, perceived usefulness, perceived ease of use, and system quality influence behavioral intention. The findings indicate that experiential and affective factors—particularly user experience and satisfaction—play a dominant role in shaping continued usage intentions, while functional attributes exert their influence primarily through indirect pathways. By reframing PLS-SEM as an enabling methodology for ICE research, this paper contributes a theoretically grounded and practically applicable roadmap for researchers investigating human–technology interaction in extreme environments.
behavioral intentionHCIICE environmentsisolated and confined environmentsPLS-SEMpsychosocial factorstelemedicineuser experienceThe author(s) declared that financial support was not received for this work and/or its publication.section-at-acceptanceDigital ImpactsIntroduction
Human–Computer Interaction (HCI) plays a critical role in supporting human performance, psychological well-being, and decision-making in isolated, confined, and extreme (ICE) environments (Melo Vargas and Barriga Medina, 2025). Such environments include intensive care units (ICUs), remote and rural healthcare settings, offshore platforms, polar research stations, disaster response zones, and space-related missions (Palinkas and Suedfeld, 2021; Thomas, 2023). In these contexts, technology is not merely a productivity tool; it becomes a psychological mediator that shapes stress regulation, trust, resilience, and behavioral adaptation (Anderson et al., 2023; Nezami, 2025).
As humanity confronts global challenges driven by climate change, pandemics, geopolitical instability, and rapid technological transformation, individuals are increasingly required to live and work in conditions characterized by isolation, confinement, uncertainty, and high cognitive load. Digital and interactive systems—ranging from telemedicine platforms and decision-support tools to immersive training (Blandford, 2019; Candra et al., 2025; Sharp et al., 2025)—are central to sustaining performance and mental health in such settings (Melo Vargas and Barriga Medina, 2025). Understanding how users perceive, experience, and continue to engage with these systems is therefore a fundamental research challenge.
HCI research seeks to examine not only usability and functionality but also the emotional, cognitive, and behavioral dimensions of human interaction with technology (Tang et al., 2024; Upreti et al., 2026). Constructs such as perceived ease of use, trust, satisfaction, and user experience are especially salient in ICE environments, where system failure or disengagement can have serious psychological or operational consequences. However, these constructs are inherently latent: they cannot be measured directly and must be inferred through observable indicators (Gudergan et al., 2025; Hair et al., 2021; Rosseel and Loh, 2024).
Traditional statistical techniques, such as multiple regression or analysis of variance, struggle to capture the complex, interdependent relationships among such latent constructs (Cepeda et al., 2024; Gudergan et al., 2025; Hair et al., 2021), particularly when indirect effects, mediation, or formative indicators are involved. Structural Equation Modeling (SEM) offers a powerful alternative by enabling the simultaneous estimation of measurement and structural relationships (Gefen et al., 2000; van Kesteren and Oberski, 2019; Schamberger et al., 2025). Among SEM approaches, Partial Least Squares Structural Equation Modeling (PLS-SEM) is particularly well suited to ICE-related HCI research due to its flexibility, tolerance for moderate samples, robustness to non-normal data, and strong predictive orientation (Ferraro et al., 2026; Hair et al., 2019; Shah et al., 2026).
This paper argues that PLS-SEM provides a methodological lens uniquely aligned with the demands of HCI research in ICE environments. By demonstrating how PLS-SEM can model psychosocial and experiential factors central to sustained technology use, this study contributes both methodological guidance and practical insight for researchers working at the intersection of human behavior, technology, and extreme contexts. Rather than advancing a new empirical theory, this paper offers a methodological perspective on the use of PLS-SEM for studying human–technology interaction in isolated, confined, and extreme environments, supported by an illustrative application.
Human–Computer Interaction and ICE environmentsHuman–Computer Interaction as a psychosocial discipline
Human–Computer Interaction is an interdisciplinary field concerned with the design, evaluation, and implementation of interactive systems for human use (Clemmensen et al., 2016; Kaewkitipong et al., 2022). It integrates perspectives from computer science, psychology, cognitive science, and the social sciences to ensure that technologies align with human capabilities, limitations, and needs (Tang et al., 2024; Upreti et al., 2026). Beyond functionality, HCI emphasizes usability, emotional engagement, trust, and satisfaction—dimensions that become critical when users operate under stress or isolation.
In ICE environments, the stakes of HCI design are amplified. Poorly designed systems can increase cognitive load, exacerbate stress, and undermine user confidence, while well-designed systems can enhance resilience, support mental health, and promote sustained engagement (Melo Vargas and Barriga Medina, 2025). As a result, HCI research in these contexts increasingly focuses on latent psychological and experiential constructs that influence how users adapt to and rely on technology.
Methodological challenges in ICE-oriented HCI research
Research in ICE contexts often involves small or specialized populations, such as healthcare professionals, emergency responders, or individuals in remote locations. Data collection is frequently constrained, and survey responses may violate assumptions of normality (Anderson et al., 2023; Melo Vargas and Barriga Medina, 2025). Moreover, theoretical models in these domains are often exploratory or evolving, reflecting the novelty of the technologies involved (Mehta et al., 2025; Ramírez-Correa et al., 2020; Sharp et al., 2025).
Conventional statistical methods are ill-equipped to address these challenges. They typically assume direct, independent relationships between observed variables and lack the capacity to model latent constructs, mediation effects, or complex causal pathways (Ghazali and Yaacob, 2025; Hair et al., 2021). These limitations can lead researchers to oversimplify theoretical models, reducing their explanatory power.
Structural Equation Modeling for ICE-focused HCI researchWhy PLS-SEM?
Structural Equation Modeling provides a comprehensive framework for testing theoretical models involving latent variables (Almeida, 2024; Lebakula et al., 2025). While covariance-based SEM (CB-SEM) is effective for theory confirmation under strict assumptions, it requires large samples, multivariate normality, and well-established theoretical foundations (Hair et al., 2021; Vuković, 2024)—conditions rarely met in ICE-related HCI studies. PLS-SEM, by contrast, is a variance-based approach designed to maximize predictive accuracy. It performs well with small to large samples, handles non-normal data effectively, and accommodates both reflective and formative measurement models (Goktas and Dirsehan, 2025; Greifenstein et al., 2026; Hair et al., 2019). These characteristics make PLS-SEM particularly suitable for modeling psychosocial constructs in constrained environments.
Applications of PLS-SEM in related domains, such as smart health services adoption (Sun and Zhao, 2025), demonstrate its effectiveness in identifying nuanced drivers of behavioral intention. These studies show that both functional factors and emotional constructs jointly influence user outcomes. Beyond healthcare, PLS-SEM has been widely applied across multiple disciplines. More broadly, PLS-SEM has been widely investigated and applied across diverse disciplines, including marketing (Goktas and Dirsehan, 2025), information systems (Sharma et al., 2024), extended reality (Oncioiu and Priescu, 2022), psychology (de Rooij et al., 2023), behavioral sciences (Naseer et al., 2022), environmental studies (Mobeen et al., 2026), healthcare research (Senapati et al., 2026), and related fields (Sarstedt et al., 2022).
Latent constructs relevant to ICE environments
In ICE contexts, constructs such as user satisfaction, trust, system quality, and user experience are closely linked to psychological well-being and behavioral persistence. For example, a telemedicine platform deployed in a remote setting must not only function correctly but also instill confidence, reduce anxiety, and provide a positive experiential flow. PLS-SEM allows researchers to model these constructs simultaneously and examine how they interact to influence behavioral intention. Such modeling can be conducted at either the pre-usage or post-usage stage of the system.
Models of technology acceptance such as the Technology Acceptance Model (TAM) (Davis, 1989) and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003) emphasize latent constructs like perceived usefulness, perceived ease of use, satisfaction, and behavioral intention. These constructs have been implicated in user engagement across contexts including telemedicine and wearable technologies.
In ICE environments, such latent psychosocial and experiential constructs can be critical determinants of sustained technology engagement, necessitating analytical frameworks that permit simultaneous estimation of direct, indirect, and mediated effects.
Illustrative case: evaluating an mHealth system for constrained environmentsCase context
To illustrate the application of PLS-SEM in ICE-oriented HCI research, we present a case involving a mobile health (mHealth) application designed to support users with chronic conditions in remote and resource-constrained environments. Such systems are increasingly deployed in rural healthcare, emergency response settings, and other contexts where direct access to medical professionals is limited—conditions that closely mirror key characteristics of ICE environments.
Remote healthcare settings and ICE analogs share several common constraints, including restricted access to clinicians, bandwidth and connectivity limitations, and elevated psychological and emotional demands on users. These similarities make mHealth systems an appropriate proxy for examining human–technology interaction in ICE-related research contexts.
The study investigates how user experience, user satisfaction, system quality, and perceived ease of use influence behavioral intention to continue using the system. Data from users with prior experience using an mHealth application, originally collected for a different study, were utilized as a secondary dataset. The dataset was subsequently pre-processed to align with the objectives of the present study. The final dataset comprises 500 observations and includes six latent constructs: user experience (UX), user satisfaction (US), system quality (SQ), perceived usefulness (PU), perceived ease of use (PEOU), and behavioral intention (BI). Each construct is modeled reflectively and measured using three observed indicators.
Conceptual model and hypotheses
Drawing on the Technology Acceptance Model (TAM) and user experience theory (Hassenzahl and Tractinsky, 2006), the conceptual model integrates both instrumental and experiential constructs. Rather than treating user experience as a single outcome, it is modeled as a multidimensional latent construct encompassing aesthetic appeal, emotional response, and trust—dimensions especially relevant under constrained conditions. Figure 1 depicts the conceptual model that guides this illustrative case.
Conceptual framework of the proposed model (authors’ own creation).
Conceptual diagram showing relationships among constructs: PEOU, PU, UX, and SQ lead to US and BI through hypotheses H1 to H8. All entities are connected by labeled arrows indicating hypothesized effects.
Hypotheses were formulated to examine the relationships among perceived ease of use, perceived usefulness, system quality, user experience, user satisfaction, and behavioral intention. The proposed hypotheses are as follows:
H1: Perceived usefulness (PU) has a significant positive effect on user satisfaction (US).
H2: Perceived usefulness (PU) has a significant positive effect on behavioral intention (BI).
H3: Perceived ease of use (PEOU) has a significant positive effect on user satisfaction (US).
H4: Perceived ease of use (PEOU) has a significant positive effect on behavioral intention (BI).
H5: User experience (UX) has a significant positive effect on user satisfaction (US).
H6: User experience (UX) has a significant positive effect on behavioral intention (BI).
H7: User satisfaction (US) has a significant positive effect on behavioral intention (BI).
H8: System quality (SQ) has a significant positive effect on behavioral intention (BI).
Illustrative application of PLS-SEM
The PLS-SEM analysis was conducted in accordance with the standard application procedure recommended by Hair et al. (2021). The sequential steps involved in this process are illustrated in Supplementary Figure S1. The procedure begins with the specification of the measurement and structural models, followed by data collection and model estimation. Subsequently, the results are evaluated through assessment of both the measurement and structural models, with particular attention to reliability, validity, and predictive performance. Where appropriate, additional analyses—such as multi-group analysis (MGA), mediation, moderation, and higher-order construct (HOC) modeling—may be undertaken prior to final interpretation and conclusion.
Following this procedure, the PLS-SEM analysis was implemented in R (R Core Team, 2025), an open-source statistical environment using the seminr package (Ray et al., 2025) (version 2.3.7). Figure 2 presents the resulting measurement and structural model estimates generated in R. The evaluation of the measurement and structural models is summarized in the subsequent section.
Measurement and structural model results (authors’ output from RStudio).
Path diagram visualizing relationships between constructs SQ, PEOU, UX, PU, US, and BI with arrows indicating regression coefficients (β values) and measurement loadings (λ values). Hexagons represent latent variables, rectangles are observed variables, and arrow thickness reflects effect size. BI and US display R squared values of 0.505 and 0.467, respectively.
Detailed procedural steps, estimation settings, and additional outputs are provided in the Supplementary material.
Summary of measurement and structural results
Figure 2 presents the estimated measurement and structural models, including standardized path coefficients and the explanatory power (R2) of the endogenous variables.
A closer examination of the structural paths provides further insight into the hypothesized relationships among the constructs. Perceived usefulness exerted a strong and statistically significant effect on user satisfaction (H1: β = 0.521, p < 0.001), indicating that users’ evaluations of the system’s functional benefits substantially shaped their overall satisfaction. However, its direct effect on behavioral intention was not significant (H2: β = 0.065, p = 0.247), suggesting that perceived usefulness influences continued use primarily through indirect mechanisms rather than direct motivational pathways. Perceived ease of use demonstrated significant positive effects on both user satisfaction (H3: β = 0.143, p = 0.003) and behavioral intention (H4: β = 0.132, p = 0.002), highlighting its role as an enabling factor that lowers interaction barriers and facilitates positive experiential outcomes. User experience emerged as a key driver within the model, significantly influencing user satisfaction (H5: β = 0.146, p < 0.001) and behavioral intention (H6: β = 0.316, p < 0.001), underscoring the importance of affective and experiential dimensions in constrained environments. Consistent with expectation, user satisfaction had a strong positive effect on behavioral intention (H7: β = 0.343, p < 0.001), reinforcing its central mediating role. In contrast, system quality did not exhibit a statistically significant direct effect on behavioral intention (H8: β = 0.071, p = 0.069), suggesting that technical performance alone may be insufficient to sustain continued use in ICE-related contexts. Detailed structural estimates, effect sizes, collinearity diagnostics, and confidence intervals for all hypotheses are reported in the Supplementary Table S4.
The illustrative PLS-SEM analysis demonstrated satisfactory measurement quality and structural consistency, supporting the adequacy of the proposed model specification for examining psychosocial and experiential constructs in constrained environments. The results indicate that user experience and user satisfaction play a central role in shaping behavioral intention, while perceived ease of use primarily acts as an enabling factor. In contrast, perceived usefulness and system quality exerted limited direct influence on continued use, highlighting the importance of experiential and affective dimensions in ICE contexts. Comprehensive measurement diagnostics, structural estimates, and model evaluation results are reported in the Supplementary material.
Implications for ICE environment research
From an ICE and HCI perspective, the findings suggest that sustained technology use in constrained and high-stress environments is driven more by users’ lived experiences than by purely functional considerations. The strong effects of user experience and user satisfaction on behavioral intention indicate that how a system feels to use—whether it inspires confidence, reduces frustration, and supports users emotionally—matters greatly in environments characterized by isolation, limited support, and heightened cognitive load. While perceived usefulness and system quality are necessary foundations, their non-significant direct effects on behavioral intention imply that functional adequacy alone is insufficient to sustain engagement in such contexts. Instead, perceived ease of use appears to act as an enabler, lowering barriers to interaction and indirectly supporting positive experiential outcomes. These results align with HCI research in ICE environments, where technologies must go beyond technical performance to actively support psychological comfort, trust, and resilience, especially when users operate under uncertainty, resource constraints, and prolonged exposure to stress. For researchers, the study illustrates how PLS-SEM can serve as a methodological backbone for analyzing complex psychosocial dynamics in extreme environments.
Challenges and limitations
Despite the analytical strengths of PLS-SEM, several limitations should be acknowledged. First, the validity of PLS-SEM results depends on the correct specification of measurement models, particularly the distinction between reflective and formative constructs. In HCI and ICE-oriented research, constructs such as user experience, satisfaction, and system quality are conceptually rich and multifaceted, and misspecification may lead to biased parameter estimates or misleading theoretical inferences. Careful theoretical grounding and indicator selection therefore remain essential.
Second, while PLS-SEM is well suited for modeling complex relationships among latent variables and for prediction-oriented analysis, it remains a quantitative technique that cannot fully capture the nuanced, context-dependent experiences of individuals operating in isolated, confined, and extreme environments. Psychological states such as stress, fatigue, uncertainty, and trust dynamics often evolve situationally and may not be adequately represented through structured survey indicators alone.
Third, the illustrative application relies on secondary cross-sectional data derived from an mHealth context used as an ICE analog. Although such settings share key characteristics with ICE environments, including resource constraints and elevated cognitive demands, caution is warranted when generalizing the findings to other extreme contexts without further empirical validation.
For these reasons, future research would benefit from integrating PLS-SEM with qualitative approaches such as interviews, ethnographic observations, or diary studies to provide richer contextual insight and enhance ecological validity. Mixed-method designs can strengthen interpretation by linking statistical relationships to lived user experiences, thereby supporting more human-centered and context-sensitive technology design in ICE environments. Such integrative approaches are particularly important in ICE environments, where technology use is closely intertwined with psychological resilience, trust formation, and sustained human performance.
Conclusion
This paper positions Partial Least Squares Structural Equation Modelling (PLS-SEM) as a critical methodological framework for advancing Human–Computer Interaction research in isolated, confined, and extreme (ICE) environments. By enabling the systematic modeling of latent psychosocial, cognitive, and experiential constructs, PLS-SEM provides researchers with a robust means of examining how users perceive, experience, and sustain engagement with technology under constrained and high-pressure conditions. In such environments, where human performance, psychological well-being, and operational effectiveness are closely intertwined, the ability to capture complex relationships among experiential factors is particularly valuable.
Through an illustrative application, this study demonstrates how PLS-SEM can bridge the gap between theoretical models of technology use and the practical realities faced by users operating in remote, resource-limited, or psychologically demanding contexts. The findings reinforce the idea that effective technology design in ICE settings must extend beyond functional performance to address emotional comfort, usability, trust, and overall user experience. As digital systems increasingly mediate critical tasks in healthcare, emergency response, and space-related operations, methodological approaches that account for these human-centered dimensions become indispensable.
At the same time, the findings should be interpreted in light of the methodological limitations of quantitative modeling approaches, underscoring the value of complementary qualitative and mixed-method research for capturing the lived experiences of users in isolated, confined, and extreme environments.
Looking ahead, future research can extend this methodological perspective to the growing adoption of immersive technologies, such as virtual reality (VR), augmented reality (AR), and extended reality (XR), in ICE environments. These technologies are increasingly being explored for training, psychological support, remote collaboration, and stress mitigation in settings characterized by isolation and confinement. Applying PLS-SEM to study the adoption, sustained use, and experiential impact of VR and AR systems can offer valuable insights into how immersive experiences influence cognitive load, emotional regulation, presence, and behavioral intention in extreme contexts. Integrating experiential constructs with emerging XR applications holds significant potential for informing the design of technologies that not only enhance performance but also actively support human resilience and well-being in ICE environments.
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: 10.5281/zenodo.18195037.
Author contributions
IO: Conceptualization, Data curation, Methodology, Software, Writing – original draft, Writing – review & editing. AC: Conceptualization, Data curation, Methodology, Software, Writing – original draft, Writing – review & editing.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that Generative AI was used in the creation of this manuscript. The authors acknowledge the use of ChatGPT-5.2 for copy-editing and language refinement in specific parts of the manuscript. All conceptual, analytical, and interpretive components were solely developed by the authors.
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Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fhumd.2026.1784355/full#supplementary-material
ReferencesAlmeidaF. (2024). Editorial: performing a structural equation modeling (SEM) in innovation science studies. Int. J. Innov. Sci.16, 1005–1011. doi: 10.1108/IJIS-12-2024-289AndersonA.StankovicA.CowanD.FellowsA.BuckeyJ. (2023). Natural scene virtual reality as a behavioral health countermeasure in isolated, confined, and extreme environments: three isolated, confined, extreme analog case studies. Hum. Factors65, 1266–1278. doi: 10.1177/00187208221100693, 35604867BlandfordA. (2019). HCI for health and wellbeing: challenges and opportunities. Int. J. Hum.-Comput. Stud.131, 41–51. doi: 10.1016/j.ijhcs.2019.06.007CandraS.FredericaE.PutriH. A.LoangO. K. (2025). The UTAUT approach to Indonesia’s behavioral intention to use mobile health apps. J. Sci. Technol. Policy Manag.16, 892–911. doi: 10.1108/JSTPM-10-2022-0175CepedaG.RoldánJ. L.SabolM.HairJ.ChongA. Y. L. (2024). Emerging opportunities for information systems researchers to expand their PLS-SEM analytical toolbox. Ind. Manag. Data Syst.124, 2230–2250. doi: 10.1108/IMDS-08-2023-0580ClemmensenT.KaptelininV.NardiB. (2016). Making HCI theory work: an analysis of the use of activity theory in HCI research. Behav. Inf. Technol.35, 608–627. doi: 10.1080/0144929X.2016.1175507DavisF. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q.13:319. doi: 10.2307/249008de RooijM.KarchJ. D.FokkemaM.BakkZ.PratiwiB. C.KeldermanH. (2023). SEM-based out-of-sample predictions. Struct. Equ. Modeling30, 132–148. doi: 10.1080/10705511.2022.2061494FerraroG.ScandurraG.ThomasA. (2026). The emergence of innovation capabilities supporting technological shifts among SMEs: a mediated moderated model. J. Innov. Knowl.11:100868. doi: 10.1016/j.jik.2025.100868FornellC.LarckerD. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res.18, 39–50. doi: 10.2307/3151312GefenD.StraubD.BoudreauM.-C. (2000). Structural equation modeling and regression: guidelines for research practice. Commun. Assoc. Inf. Syst.4:7. doi: 10.17705/1CAIS.00407GhazaliZ. M.YaacobW. (2025). Integrated PLS-SEM-latent growth curve model: a new conditional time invariant method for analysing panel survey data. MethodsX15:103635. doi: 10.1016/j.mex.2025.103635, 41048324GoktasP.DirsehanT. (2025). Using PLS-SEM and XAI for causal-predictive services marketing research. J. Serv. Mark.39, 53–68. doi: 10.1108/JSM-10-2023-0377GreifensteinM.NordhoffS.WangX.AtluriB. (2026). Riding into the future: what drives the use of robotaxis in San Francisco?Transp. Res. Part A Policy Pract.203:104763. doi: 10.1016/j.tra.2025.104763GuderganS. P.MoisescuO. I.RadomirL.RingleC. M.SarstedtM. (2025). Special issue editorial: advanced partial least squares structural equation modeling (PLS-SEM) applications in business research. J. Bus. Res.188:115087. doi: 10.1016/j.jbusres.2024.115087HairJ. F.HultG. T. M.RingleC. M.SarstedtM.DanksN. P.RayS. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: a workbook. Cham: Springer International Publishing.HairJ. F.RisherJ. J.SarstedtM.RingleC. M. (2019). When to use and how to report the results of PLS-SEM. Eur. Bus. Rev.31, 2–24. doi: 10.1108/EBR-11-2018-0203HassenzahlM.TractinskyN. (2006). User experience—a research agenda. Behav. Inf. Technol.25, 91–97. doi: 10.1080/01449290500330331HenselerJ.RingleC. M.SarstedtM. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci.43, 115–135. doi: 10.1007/s11747-014-0403-8KaewkitipongL.ChenC.HanJ.RacthamP. (2022). Human–Computer Interaction (HCI) and trust factors for the continuance intention of Mobile payment services. Sustainability14:14546. doi: 10.3390/su142114546KockN. (2015). Common method bias in PLS-SEM. Int. J. E-Collab.11, 1–10. doi: 10.4018/ijec.2015100101LebakulaV.PorterB.Stubbs-RichardsonM.RayT.CosbyA.BethelC. (2025). A structural equation modeling approach to leveraging the power of extant sentiment analysis tools. J. Comput. Soc. Sci.8, 1–21. doi: 10.1007/s42001-024-00334-yMehtaR. K.KangJ.ShiY.DuJ. (2025). Effectiveness of training under stress in immersive VR: an investigation of firefighter performance, gaze entropy, and pupillometry. Front. Virtual Real.6, 1–15. doi: 10.3389/frvir.2025.1542507Melo VargasE.Barriga MedinaH. R. (2025). Voices from the ice: exploring wellbeing and job performance in a Latin American Antarctic expedition. Front. Sociol.10:1687669. doi: 10.3389/fsoc.2025.1687669, 41458322MobeenM.KabirK. H.SchneiderU. A.AhmedT.AhmadN.ScheffranJ. (2026). Essential livelihood capitals for effective climate adaptation in Pakistan’s irrigated agriculture: insights from PLS-SEM and NCA. Heliyon12:e44281. doi: 10.1016/j.heliyon.2025.e44281NaseerK.QaziJ.QaziA.AvuglahB. K.TahirR.RasheedR. A.. (2022). Travel behaviour prediction amid COVID-19 underlaying situational awareness theory and health belief model. Behav. Inf. Technol.41, 3318–3328. doi: 10.1080/0144929X.2021.1984579NezamiA. (2025). Space psychology: a comprehensive approach to the future of astronaut wellbeing. Front. Virtual Real.5, 1–6. doi: 10.3389/frvir.2024.1446796OncioiuI.PriescuI. (2022). The use of virtual reality in tourism destinations as a tool to develop tourist behavior perspective. Sustainability14:4191. doi: 10.3390/su14074191PalinkasL. A.SuedfeldP. (2021). Psychosocial issues in isolated and confined extreme environments. Neurosci. Biobehav. Rev.126, 413–429. doi: 10.1016/j.neubiorev.2021.03.032, 33836214R Core Team. (2025). R: a language and environment for statistical computing. Available online at: https://www.r-project.org/ (Accessed September 20, 2025).Ramírez-CorreaP.Ramírez-RivasC.Alfaro-PérezJ.Melo-MarianoA. (2020). Telemedicine acceptance during the COVID-19 pandemic: an empirical example of robust consistent partial least squares path modeling. Symmetry12:1593. doi: 10.3390/sym12101593RayS.DanksN. P.AndréC. V. (2025). seminr: building and estimating structural equation models (R package version 2.3.4). Available online at: https://cran.r-project.org/package=seminr (Accessed September 27, 2025).RosseelY.LohW. W. (2024). A structural after measurement approach to structural equation modeling. Psychol. Methods29, 561–588. doi: 10.1037/met0000503, 36355708SarstedtM.HairJ. F.PickM.LiengaardB. D.RadomirL.RingleC. M. (2022). Progress in partial least squares structural equation modeling use in marketing research in the last decade. Psychol. Mark.39, 1035–1064. doi: 10.1002/mar.21640SchambergerT.SchuberthF.HenselerJ.RosseelY. (2025). Structural equation modeling with latent variables and composites. Avaialable online at: http://arxiv.org/abs/2508.06112 (Accessed September 26, 2025).SenapatiS.MishraD. N.BeheraS. K. (2026). Reconciling the dimensions of healthcare service quality and experience quality using PLS-SEM. Int. J. Qual. Reliab. Manag.43, 295–319. doi: 10.1108/IJQRM-03-2025-0100ShahS. S.van DamJ.FanL.KumarS.SinghA.BundelaD. S.. (2026). Understanding farmers’ protective behavior towards pesticide use in northern India: a health belief model approach using PLS-SEM and fsQCA. Environ. Impact Assess. Rev.117:108193. doi: 10.1016/j.eiar.2025.108193SharmaP. N.SarstedtM.RingleC. M.CheahJ.-H.HerfurthA.HairJ. F. (2024). A framework for enhancing the replicability of behavioral MIS research using prediction oriented techniques. Int. J. Inf. Manag.78:102805. doi: 10.1016/j.ijinfomgt.2024.102805SharpJ.KelsonJ.SouthD.SalibaA.KabirM. A. (2025). Virtual reality and artificial intelligence as psychological countermeasures in space and other isolated and confined environments: a scoping review. Acta Astronaut.232, 666–677. doi: 10.1016/j.actaastro.2025.04.002SunM.-X.ZhaoZ.-F. (2025). Exploring behavioral factors and emotional mechanisms underlying older adult users’ adoption of smart health services: evidence from PLS-SEM and fsQCA. Front. Public Health13:1673340. doi: 10.3389/fpubh.2025.1673340, 41080852TangL.YuanP.ZhangD. (2024). Emotional experience during human-computer interaction: a survey. Int. J. Hum.-Comput. Interact.40, 1845–1855. doi: 10.1080/10447318.2023.2259710ThomasL. J. (2023). The future potential of virtual reality countermeasures for maintaining behavioural health during long duration space exploration. Front. Virtual Real.4, 1–6. doi: 10.3389/frvir.2023.1180165UpretiK.KumariP.ShankarU.RadhakrishnanG.UdhayaS. K.MalikK. (2026). “Human–computer interaction for cognitive, emotional and learning well-being” in Intelligent systems for neurocognition and human–robot–computer interaction. Eds. S. Mahajan, D. S. Kapoor, K. J. Singh (Elsevier), 43–65.van KesterenE.-J.OberskiD. (2019). Flexible Extensions to Structural Equation Models Using Computation Graphs. Struc Equa Model J. Multidiscip. 29, 233–247. doi: 10.1080/10705511.2021.1971527VenkateshV.MorrisM. G.DavisG. B.DavisF. D. (2003). User acceptance of information technology: toward a unified view. MIS Q.27, 425–478. doi: 10.2307/30036540VukovićM. (2024). CB-SEM vs PLS-SEM comparison in estimating the predictors of investment intention. Croat. Oper. Res. Rev.15, 131–144. doi: 10.17535/crorr.2024.0011
Edited by: Laura Thomas, PARSEC Space, United Kingdom
Reviewed by: Benjamin Stephanus Botha, University of the Free State, South Africa