Factors Influencing the Academic Use of Digital Technologies Among Chilean Pre-Service Teachers.

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Miriam León, Cristian Cerda, Camila Salazar-Fernández, Mireia Usart, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7941772/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This research examines the impact of psychological and cultural factors on the academic use of digital technologies by Chilean pre-service teachers. Considering the global digital transformation and the influence of digital technologies, such as artificial intelligence (AI), in education, it is essential to prepare future educators to integrate technology pedagogically. Although the use of technology among pre-service teachers has been studied, the specific factors influencing their academic use require further investigation considering an integral approach. A study was conducted with 1188 pre-service teachers from various Chilean universities. Data were collected through questionnaires assessing academic use of digital technologies, motivation towards Information and Communication Technologies (ICT), technology attitudes, self-directed learning readiness, disposition towards learning and teaching, technological infrastructure availability, and portrait values. Structural Equation Modeling (SEM) was utilized to analyze the data. The results indicated that self-management, disposition towards pedagogical learning, and motivation were the most significant direct predictors of the academic use of digital technologies. Among the cultural factors, self-transcendence, openness to change, and the use of portable PCs had a significant but minor direct influence. The model explained 32.1% of the variance in academic technology use. These findings underscore the significance of both psychological characteristics and cultural values in influencing how future teachers utilize digital technologies for academic purposes. Understanding these influences is crucial for enhancing teacher education programs and equipping educators to integrate technology into teaching and learning effectively. Digital competence pre-service teacher educational technology teacher education self-management Chile Figures Figure 1 Figure 2 1. Introduction The accelerated digital transformation is redefining communication, production, and everyday life, profoundly impacting the educational field. This profound shift stems from the ubiquitous adoption of technologies and the advent of disruptive innovations such as artificial intelligence (Ravi et al, 2025 ). In this context, the role of pre-service teacher education becomes critical, as it must not only foster digital competences but also prepare future educators to integrate technology in pedagogically sound and ethically responsible ways (Falloon, 2020 ). Various national and international initiatives, including teachers' digital competence frameworks such as DigCompEdu, emphasize the importance of developing these competencies from the early stages of teacher education to ensure that new professionals are equipped to respond to the demands of digitalized teaching and learning environments (Redecker, 2017 ). Understanding the intersection between these global demands and the specific contextual conditions of teacher training institutions is essential for rethinking pedagogical practices and informing responsive educational policies. Multiple factors influence how pre-service teachers engage with digital technologies for academic purposes, especially in teaching and learning contexts. These factors include individual psychological dimensions (such as intrinsic motivation, perceived self-efficacy, and attitudes toward technology) that shape the specific purposes for which digital tools are employed, determining whether their use leans toward academic or recreational activities (Gómez-Fernández & Mediavilla, 2022 ). Additionally, cultural elements such as access to functional infrastructure, availability of technological resources, and institutional support mechanisms significantly influence the effective integration of these tools in educational settings (Zhao et al., 2002 ). Prior experiences with digital technology, both in formal education and informal contexts, also play a decisive role in shaping future teachers' confidence, perceptions of usefulness, and strategies for digital engagement (Janeš et al., 2023 ). Together, these findings highlight the multifaceted nature of digital technology adoption in teacher education and underscore the importance of analyzing these factors to understand how digital competence is acquired and enacted by prospective educators. Although numerous studies have examined how pre-service teachers use digital technologies for teaching and learning (e.g., Wang et al., 2025 ), limited research has explored the underlying factors that shape their use for academic purposes. While existing literature identifies general facilitators and barriers, a deeper understanding of the determinants driving technology adoption, particularly in academic contexts, remains underdeveloped (e.g., Chu et al., 2023 ). This lack of insight into the 'why' hinders the development of targeted interventions and informed policies that could promote meaningful and effective technology integration within teacher education programs. Beyond usage patterns, it is essential to examine psychological and cultural influences (such as beliefs, values, and self-regulation strategies) that have proven impactful in other domains (Betancourt, 2015 ; Choden et al., 2019 ) but have yet to be sufficiently addressed in pre-service teacher education research. These gaps highlight the need to investigate the multifaceted factors that influence the academic use of digital technologies among future educators. Building on the context, this study specifically aimed to examine the influence of psychological and cultural factors on the academic use of digital technologies among pre-service teachers in Chile. To thoroughly address this objective, the research analyzed several key psychological factors hypothesized to play a role, including ICT motivation, attitude towards technology use, self-directed learning readiness, and disposition toward learning and teaching. Additionally, recognizing the complex nature of cultural influence, cultural factors were explored through both objective lenses, such as the technological infrastructure available in the training environment, and subjective lenses, explicitly focusing on portrait values, which represent deep individual and social orientations. This approach sought to provide a nuanced understanding of how these distinct sets of factors collectively impact the academic use of technology by future teachers in a specific Latin American context. A deeper understanding of the factors that foster the academic use of digital technologies among pre-service teachers can significantly contribute to various aspects of initial teacher training. Understanding the factors that influence the academic use of digital technologies among Chilean pre-service teachers, can significantly strengthen initial teacher education. At the macro level, these insights offer valuable input for policymakers and curriculum designers, enabling the refinement of teacher training programs for meaningful, pedagogically sound technology integration aligned with national ICT goals. At the micro level, findings help teacher educators design targeted strategies that support the development of academic digital practices in their classrooms. Beyond practical implications, this study contributes theoretically by examining how psychological constructs (like motivation and self-regulation) and cultural factors (like shared norms and values) interact to shape technology use. This addresses the need to expand existing models of technology adoption by incorporating deeper, specific dimensions often overlooked in this population (López-Pérez et al., 2019 ). Addressing these gaps is crucial for enhancing digital readiness in teacher education. 2. Literature review Academic use of digital technologies The Digital Competence Framework for Citizens (DigComp) is a foundational model designed to promote digital literacy across European societies. Initially introduced in 2013 by Ferrari, the framework has evolved through successive versions, with DigComp 2.2 offering refined concepts and vocabulary to better align with the evolving demands of digital environments (Collado-Sánchez et al., 2023 ; Ferrari, 2013 ). DigComp is structured into five core areas: information and data literacy, communication and collaboration, digital content creation, safety, and problem-solving, each comprising specific competencies and proficiency levels. Mastery of these competencies enables individuals to engage meaningfully and responsibly with digital technologies, contributing to personal development, civic participation, and lifelong learning (Martínez-Domingo et al., 2025 ). As such, DigComp not only supports individual digital empowerment but also serves as a strategic tool for fostering inclusive participation in an increasingly digital society. Developing digital competence in pre-service teacher education is crucial for enhancing both pedagogical and disciplinary knowledge. This competence is multidimensional, covering areas like information literacy, communication and collaboration, digital content creation, safety, and problem-solving, which is fundamental for transforming teaching practices (Damianus & Widi, 2023 ). Rather the extensive curriculum overhauls, teacher digital competence should be integrated organically, especially during practicum experiences, to ensure its relevance and applicability (Marais, 2023 ). Pre-service teachers strongly desire to develop this competence, emphasizing the importance of collaborative environments and guided instruction (Katrin et al., 2024 ). Cultivating digital competence equips future educators with the tools to critically engage with technology, significantly enhancing their professional practice and contributing to broader educational goals (Tomczyk, 2024 ). Therefore, promoting this professional competence necessitates robust mechanisms for assessment and support throughout teacher education programs. Various assessment tools for pre-service teacher digital competence draw on the internationally recognized DIGCOMP and DIGCOMPEDU frameworks. For example, Quast et al. ( 2023 ) developed a seven-dimension instrument that found pre-service teachers report higher competence beliefs than in-service teachers. Similarly, Siiman et al. ( 2016 ) created a DIGCOMP-based tool to assess perceived digital competence in science and mathematics learning, focusing on smart device use. In Chile, Cerda et al. ( 2022 ) used DIGCOMP indicators to explore diverse purposes of digital technology use (academic, recreational, social, and economic) among pre-service teachers. These instruments underscore the frameworks’ critical role in defining and assessing digital skills development, informing targeted interventions, and supporting digital inclusion in education. The examples show diverse methods for measuring pre-service teachers’ digital competence, often using frameworks like DIGCOMP and DIGCOMPEDU. However, simply assessing competence is insufficient. To truly foster these skills, it is essential to examine the underlying process guiding their adoption. This requires investigating the psychological and cultural factors that influence how future teachers utilise digital technologies for academic purposes in their practice. Given the complex nature of these influences, an integrated analytical model that considers diverse factors is essential. Such a model facilitates the simultaneous examination of direct and indirect relationships among multiple variables, providing a robust theoretical framework for understanding the complex relationship between psychological and cultural factors and the academic use of digital technologies. Psychological factors influencing the adoption of digital competence among pre-service teachers The adoption of digital competencies among pre-service teachers is a complex process influenced by various psychological factors. These factors include motivation, attitude toward technology use, self-directed learning readiness, and disposition to learning and teaching. This section explores each of these factors in detail, drawing on insights from relevant research papers. Motivation Motivational factors strongly influence the development of digital competencies in pre-service teachers. Both intrinsic motivation (personal interest, enjoyment of learning) and extrinsic drivers (social influence, perceived usefulness) are key to technology adoption among future educators (Şahin & Şahin, 2022 ). Grounded in Self-Determination Theory, Mendoza et al. ( 2023 ) demonstrated that providing need-supportive instruction in digital settings significantly enhances students' intrinsic motivation. The COVID-19 pandemic also emphasized motivation’s critical role: pre-service teachers driven by basic psychological needs and emotional engagement were more likely to adopt digital tools (Şahin & Şahin, 2022 ). Collectively, these findings suggest that cultivating internal drive and recognizing external enablers is essential to support the meaningful development of digital competencies. Attitude toward technology use Pre-service teachers’ attitudes significantly shape their engagement with digital competencies. Positive attitudes, especially perceptions of ease of use and usefulness (core to the Technology Acceptance Model -TAM), strongly predict technology acceptance. For example, Jatmikowati et al. ( 2020 ) found that perceived usefulness influences the intention to use e-learning platforms, often mediated by self-efficacy. Conversely, negative attitudes, such as technology-related anxiety or low confidence, hinder adoption. Bozdoğan and Özen ( 2014 ) observed that pre-service teachers experiencing such barriers were less likely to engage with digital tools. Similarly, Lemon and Garvis ( 2015 ) reported that limited self-efficacy often translates into reluctance to integrate technology into pedagogical practice. These findings highlight that teacher education programs must actively promote positive dispositions by offering sustained training and emotional support to enhance pre-service teachers' readiness to adopt digital teaching tools. Self-directed learning readiness Self-directed learning readiness plays a pivotal role in the development of digital competencies among pre-service teachers, as it empowers them to take control of their learning processes. This form of learning is defined by autonomy, self-regulation, and the strategic use of technology for independent educational pursuits (Liwanag & Leomar, 2023 ). Empirical evidence indicates that technological self-efficacy, the belief in one's ability to use digital tools effectively, is a key predictor of readiness for self-directed learning (Pan, 2020 ). Moreover, technology-mediated collaborative learning has been shown to enhance self-direction by promoting reflective engagement and peer-supported learning practices (Lee & Bonk, 2024 ). Intrinsic goal orientation and the perceived value of learning tasks also serve as significant motivators, fostering a more profound commitment to autonomous learning for professional growth (Liwanag & Leomar, 2023 ). Thus, cultivating self-directed learning readiness among pre-service teachers not only encourages proactive engagement with digital resources but also supports their long-term development as reflective, digitally competent educators. Disposition to learning and teaching The disposition toward learning and teaching refers to the attitudes and beliefs that pre-service teachers hold about their professional development and future roles as educators. A positive disposition toward learning is closely associated with the adoption of digital competencies, as it fosters openness to innovation and willingness to integrate technology into pedagogical practice (Camacho & Salinas, 2020 ). Research has shown that pre-service teachers who perceive technology as a valuable tool for enhancing student learning are more likely to adopt digital teaching practices (Gómez-Trigueros et al., 2024 ). Furthermore, teacher self-efficacy, defined as the belief in one's ability to implement instructional strategies effectively, has been identified as a robust predictor of technology integration (Joo et al., 2018 ; Lemon & Garvis, 2015 ). For example, a study in Ghana revealed that both teacher self-efficacy and perceived usefulness significantly influenced pre-service teachers' intention to adopt ICT in their future classrooms (Adu, 2017 ). Therefore, fostering positive dispositions toward teaching and learning, particularly in relation to digital technologies, is crucial for preparing future educators to develop and apply digital competencies effectively. Cultural factors influencing the adoption of digital competence among pre-service teachers Within the scope of cultural factors influencing pre-service teachers’ digital competence adoption, personal access to digital technology serves as a foundational enabler, providing the necessary tools for engagement. Complementing this, their Portrait values are critical predictors of their intention and effective use of technology. Together, access and these individual-level "cultural" aspects profoundly shape how and why pre-service teachers integrate digital technologies into their practice. Personal access to digital technology Access to digital technologies has become widespread among pre-service teachers, significantly shaping their academic routines using laptops, tablets, and smartphones for daily learning and knowledge management (Khamkaew et al., 2025 ). However, access alone is insufficient. Research indicates that many pre-service teachers lack the digital competencies necessary to effectively utilize these tools for academic and pedagogical purposes, which may hinder their progress (Marais, 2023 ). Furthermore, while access to digital technology can enhance learning, it can also introduce challenges, such as digital distractions (Essafi et al., 2025 ), underscoring the need for structured training and support to develop critical digital literacies (De La Cruz et al., 2023 ). Evidence also suggests that effective pedagogical integration of technology hinges not only on access but also on cultivating strategic competencies and instructional frameworks during initial teacher education (Zoupa & Karlis, 2025 ). Therefore, ensuring meaningful educational impact requires a holistic approach that combines access with purposeful training in digital and pedagogical skills (Gisbert-Cervera et al, 2022 ). Portrait values Portrait values are potential predictors of pre-service teachers’ use of digital technologies. According to Schwartz ( 1992 ), values are enduring beliefs that guide action. Lechner et al. ( 2024 ) classify human values into four higher-order dimensions: Openness to Change (focusing on intellectual and emotional exploration), Conservation (emphasizing tradition and order), Self-Transcendence (promoting concern for others), and Self-Enhancement (seeking personal growth and advancement). Research indicates that Openness to Change positively influences digital behaviours across cultures (Choden et al., 2019 ). In Chile, empirical evidence links teachers' preferences for Openness to Change and Self-Transcendence with both the frequency and perceived appropriation of digital technologies (Labbé, 2006 ). Similarly, among Chilean adolescents, these same values played a central role in explaining patterns of digital immersion (León et al., 2021 ). These findings suggest that pre-service teachers' value orientations, especially those focused on openness and social concern, may significantly shape their willingness to adopt and integrate digital tools into their academic and professional practices (Chaw & Tang, 2022 ). Interplay between factors The DigComp digital competence framework provides a foundational structure for understanding how pedagogy enables students to develop and apply digital competencies in academic and training contexts. Organized into five key areas (information and data literacy, communication and collaboration, digital content creation, safety, and problem-solving) this model transcends mere technical skills by emphasizing the critical and reflective integration of technology in teaching practices (Ferrari, 2013 ; Collado-Sánchez et al., 2023 ). In this sense, initial teacher education acquires relevance by advocating for the integration of digital technologies not as isolated topics but as mediators of pedagogical and disciplinary development (Damianus & Widi, 2023 ; Marais, 2023 ). Digital competence, therefore, is not simply a functional skill set but a transversal dimension that contributes to the formation of professional teaching knowledge, enabling innovative, collaborative, and learner-centered educational practices. From a psychosocial perspective, pre-service teachers’ academic use of digital technologies is shaped by a dynamic interplay of motivational, attitudinal, and contextual factors. Intrinsic motivation (interest and enjoyment in using technologies), technological self-efficacy, and a positive disposition toward learning are key predictor of meaningful technology adoption (Mendoza et al., 2023 ; Pan, 2020 ; Şahin & Şahin, 2022 ). However, while daily access is widespread, it does not ensure effective pedagogical use. It must be accompanied by targeted training in digital competencies and strategies for self-regulation and critical reflection (Khamkaew et al., 2025 ). Additionally, individual value orientations, especially openness to change and self-transcendence, influence digital technology use (Choden et al., 2019 ; León et al., 2021 ). These insights highlight that fostering digital competence requires more than infrastructure; it demands addressing the internal and contextual drivers that shape technology integration into pedagogical practice. The hypothesized relationships between the academic use of digital technologies and the variables assessed in this study are presented in the theoretical model depicted in Fig. 1 . 3. Methods 3.1 Sample This study involved a large, voluntarily sample of 1188 pre-service teachers recruited from multiple Chilean universities, across nine diverse teaching programs. The participants represented fields such as Physical Education (18%), History (17.7%), Early Childhood Education (14.7%), Spanish Language (14.6%), English Language (14.5%), Mathematics (10.3%), Science (5.6%), Special Education (2.6%), and Primary Education (1.9%). The sample comprised 38.5% men and 61.5% women, with an average age of 22.4 years ( SD = 0.39). Additional detailed demographic characteristics of the participants are presented in Table 1 . Table 1 Sociodemographic characteristics of participants Characteristic Male Female Full sample n % n % n % Nationality Chilean 452 98.5 712 97.3 1164 97.7 Foreigner 7 1.5 20 2.7 27 2.3 Ethnicity Mapuche 120 26.1 238 32.5 358 30.1 Non-mapuche 339 73.9 494 67.5 833 69.9 University location North 2 0.4 196 26.8 198 16.6 Center 85 18.6 110 15.0 195 16.4 South 355 77.3 371 50.7 726 61.0 Southern 17 3.7 55 7.5 72 6.0 University year First-year 119 25.9 227 31.0 346 29.1 Second year 69 15.0 120 16.4 189 15.9 Third year 67 14.6 139 19.0 206 17.3 Fourth year 91 19.8 121 16.5 212 17.8 Fifth year 113 24.7 125 17.1 238 19.9 3.2 Instruments For model assessment, the Spanish versions of eight instruments were used. Among these, the Academic Use of Digital Technologies Subscale served as the primary measure of the dependent variable. This instrument included a 17-item subscale focusing on academic digital technology use, rated on a five-point frequency scale ranging from 1 (“Never or almost never”) to 5 (“Always or almost always”). The scale demonstrated acceptable composite reliability (CR = .84) and satisfactory goodness-of-fit indices (TLI = .97, CFI = .99, SRMR = .06, RMSEA = .08, 90% CI [.045 – .14]), as reported by Cerda et al. ( 2022 ). Additionally, a general sociodemographic questionnaire gathered participant characteristics including gender, ethnicity, and university affiliation. Four instruments measure psychological factors. First, the Instrumental Motive Factor, ICT Motivation Scale, 5 items, five-level agreement scale (1 = "Strongly disagree," 5 = "Strongly agree"), showed good reliability (Cronbach's α = .77) and acceptable fit (χ 2 (81, n = 2075) = 372.349, p < .001, CFI = .99, RMSEA = .04) in prior work (Senkbeil, 2018 ). Second, Attitudes towards technology use (three items with a five-level agreement scale), demonstrated high internal consistency (Cronbach's alpha, α = .91) and acceptable fit within its original model (TLI = .97, CFI = .98, SRMR = .03, RMSEA = .05) (Teo, 2011 ). Third, The Self-Directed Learning Readiness Scale (21 items, five-level scale from 1 = "Nothing" to 5 = "Totally") exhibited robust reliability for its factors (self-management α = .87, self-control α = .74, desire for learning α = .83) and satisfactory fit indices (self-management TLI = .94, CFI = .95, SRMR = .04, RMSEA = .07; self-control TLI = .94, CFI = .96, SRMR = .04, RMSEA = .06; desire for learning TLI = .96, CFI = .98, SRMR = .03, RMSEA = .08) (Cerda et al., 2021 ). Four, the Disposition to Learning and Teaching Scale (10 items, five-level scale from 1 = "Nothing" to 5 = "Totally") showed strong reliability across its factors (disposition to disciplinary learning α = .87, disposition to teaching α = .80, disposition to pedagogical learning α = .89) and adequate overall model fit (TLI = .97, CFI = .97, SRMR = .04, RMSEA = .05) (Cerda et al., 2024 ). To comprehensively assess the cultural constructs, this study employed two instruments. First, a custom instrument measured technological infrastructure, including participants' ownership of computers/tablets and their average daily usage time. Second, the Revised Portrait Values Questionnaire (PVQ-R) by Beramendi and Zubieta ( 2017 ) was used to operationalize participants' value orientations. The analysis focused on the self-transcendence and openness to change subscales, each comprising 11 items rated on a 6-point Likert scale (1 = "Not like me at all like me"; 6 = "Very much like me"). Both PVQ-R subscales demonstrated satisfactory psychometric properties in previous validation studies: self-transcendence (CFI = .96, SRMR = .04, RMSEA = .05) and openness to change (CFI = .97, SRMR = .04, RMSEA = .06) (McQuilkin et al., 2016 ). 3.3 Data Collection Method Data collection followed a structured, ethical protocol. The process began by securing collaboration with academics at multiple universities across Chile, granting access to their classrooms. Pre-service teachers were comprehensively informed about the study’s purpose, confidentiality, privacy, and its voluntary nature. Participants provided written informed consent, a protocol approved by the University's Science Ethics Committee (Number 055_23). Participants subsequently completed the instruments during regular class hours in 2023, with the application requiring approximately 30 minutes. Following collection, the raw data underwent rigorous quality control measures before being prepared for statistical analysis. 3.4 Data analysis The data collected were analyzed through a series of statistical procedures aimed at exploring the relationships between cultural and psychological variables and the academic use of digital technologies. Initial exploratory analyses were conducted to detect missing values and assess the distributional characteristics of each variable, verifying assumptions for subsequent multivariate modeling. Based on acceptable levels of internal consistency, mean scores for each construct were computed and used in a path analysis conducted with the lavaan package in R (Rosseel, 2012 ). Given that Mardia’s test indicated a violation of the multivariate normality assumption ( p < .05), model estimation was performed using the MLR estimator (maximum likelihood with robust standard errors), as recommended for such conditions. The initial structural model was specified according to the hypothesized relationships illustrated in Fig. 1 . Model refinement proceeded through the elimination of non-significant paths to achieve greater parsimony. Model fit was evaluated using standard criteria, including the Chi-square statistic (χ²), Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Standardized Root Mean Square Residual (SRMR), and Root Mean Square Error of Approximation (RMSEA), along with its 90% confidence interval. Following established thresholds, model acceptance was defined by values of CFI and TLI above .90, and SRMR and RMSEA equal to or below .08 (Hu & Bentler, 1999 ). Variability due to participants’ university affiliation was controlled for in the model. Additionally, the analysis explored the indirect effects of cultural factors on academic digital technology use, mediated by psychological variables. This analytical strategy enabled a nuanced examination of the complex interrelations among the studied constructs. 4. Results Descriptive statistics and correlations among the variables included in the path model are presented in Table 2 . Analysis of the correlation matrix revealed that Academic Use of Digital Technologies demonstrated the highest positive correlation coefficient with the variable self-management. The second strongest positive correlation was observed with disposition to pedagogical learning, followed by disposition to disciplinary learning, disposition to teach, and self-transcendence, in descending order of magnitude. Table 2 Correlation and descriptive statistics of the main variables. 1 2 3 4 5 6 7 8 9 10 11 1. Tablet – 2. Portable Pc − .040 – 3. Openness to change .007 − .008 – 4. Self-transcendence .003 .006 .472 * – 5. Motivation .012 .089 * .124 * .204 * – 6. Attitude .035 .079 * .082 * .070 * .319 * – 7. Self-management .042 .043 .207 * .200 * .103 * .047 – 8. Disposition to disciplinary learning − .023 .036 .229 * .288 * .125 * .010 .290 * – 9. Disposition to teach .029 − .046 .210 * .386 * .141 * .058 * .282 * .343 * – 10. Disposition to pedagogical learning − .005 .083 * .320 * .436 * .174 * .079 * .291 * .488 * .451 * – 11. Academic use of digital technologies .084 * .100 * .272 * .318 * .263 * .184 * .449 * .333 * .323 * .397 * – Mean 4.290 12.319 4.871 5.348 4.360 3.528 3.530 4.195 3.941 4.281 3.537 Standard Deviation 3.415 7.175 0.726 0.576 0.554 0.765 0.695 0.672 0.810 0.776 0.694 * p < .05. The proposed structural model examining the relationships between cultural and psychological factors and their influence on the academic use of digital technologies was evaluated through path analysis. The initial hypothesized model did not achieve an acceptable fit according to standard indices, yielding fit statistics of χ² (32) = 221.242, p < .01, CFI = .920, TLI = .804, SRMR = .078, and RMSEA = .074 [90% CI: .065, .084]. Specifically, the TLI value fell below the typically accepted threshold of .90 or .95. Inspection of this model revealed that the 'use of tablet' indicator did not exhibit statistically significant associations with other variables; consequently, this indicator was excluded from subsequent analyses. A revised model was then tested after removing the 'use of tablet.' This model showed improved fit, but the TLI remained below acceptable levels: χ 2 (24) = 122.710, p < .01, CFI = .957, TLI = .885, SRMR = .054, RMSEA = .062 [90% CI: .051, .073]. In this revised specification, the 'self-control' variable also demonstrated non-significant associations with other constructs. Therefore, 'self-control' was removed in the subsequent model modification. The final re-specified model, excluding both 'tablet use' and 'self-control' (Fig. 2 ), achieved adequate fit across all evaluated indices, confirming it as the definitive structural model: χ 2 (19) = 85.695, p < .01, CFI = .966, TLI = .904, SRMR = .044, RMSEA = .057 [90% CI: .045, .069]. This final model accounted for 32.1% of the variance in the academic use of digital technologies among pre-service teachers. Within this model, self-management emerged as the most significant direct predictor of academic technology use, followed by disposition to pedagogical learning and motivation. Final model Consistent with the overall model results, the most influential psychological factors demonstrating significant direct pathways to academic use were self-management, disposition to pedagogical learning, and motivation. Regarding the direct influence of cultural factors on academic use, their standardized estimate value was relatively low. Furthermore, analysis of the pathways between cultural and psychological factors indicated significant influences. Specifically, self-transcendence and openness to change were the cultural factors demonstrating the most substantial impact on various psychological aspects. The influence of self-transcendence on the disposition to teach and disposition to pedagogical learning exhibited the highest path coefficients among these inter-factor relationships. The specific path coefficients and their statistical significance for the final model are detailed in Table 3 . Table 3 Paths effects of the final model Paths Standardized estimate p -value Cultural to psychological factors Portable PC \(\:\to\:\) Motivation .091 .002 Openness to change \(\:\to\:\) Motivation .036 .264 Self-transcendence \(\:\to\:\) Motivation .190 < .001 Portable PC \(\:\to\:\) Attitude .083 .005 Openness to change \(\:\to\:\) Attitude .065 .047 Self-transcendence \(\:\to\:\) Attitude .044 .203 Portable PC \(\:\to\:\) Self-management .044 .125 Openness to change \(\:\to\:\) Self-management .145 .001 Self-transcendence \(\:\to\:\) Self-management .131 < .001 Portable PC \(\:\to\:\) Disposition to disciplinary learning .033 .225 Openness to change \(\:\to\:\) Disposition to disciplinary learning .121 .001 Self-transcendence \(\:\to\:\) Disposition to disciplinary learning .230 < .001 Portable PC \(\:\to\:\) Disposition to teach − .049 .065 Openness to change \(\:\to\:\) Disposition to teach .035 .282 Self-transcendence \(\:\to\:\) Disposition to teach .371 < .001 Portable PC \(\:\to\:\) Disposition to pedagogical learning .081 .002 Openness to change \(\:\to\:\) Disposition to pedagogical learning .148 < .001 Self-transcendence \(\:\to\:\) Disposition to pedagogical learning .365 < .001 Psychological to academic use of digital technologies Motivation \(\:\to\:\) Academic use of digital technologies .124 < .001 Attitude \(\:\to\:\) Academic use of digital technologies .095 < .001 Self-management \(\:\to\:\) Academic use of digital technologies .319 < .001 Disposition to disciplinary learning \(\:\to\:\) Academic use of digital technologies .080 .005 Disposition to teach \(\:\to\:\) Academic use of digital technologies .079 .015 Disposition to pedagogical learning \(\:\to\:\) Academic use of digital technologies .144 < .001 Cultural Factors to Academic Use of Digital Technologies of digital technologies Portable PC \(\:\to\:\) Academic use of digital technologies .055 .023 Openness to change \(\:\to\:\) Academic use of digital technologies .071 .009 Self-transcendence \(\:\to\:\) Academic use of digital technologies .075 .056 University (control variable) \(\:\to\:\) Academic use of digital technologies .056 .016 Table 4 presents the decomposition of effects, showing direct, indirect (mediated), and total effects of cultural factors on academic use mediated by psychological factors. Significant indirect effects were observed for the relationship between "portable PCs" and academic use, mediated through Disposition to Pedagogical Learning, Motivation, and Attitude. These mediated pathways accounted for 13% to 18% of the total effect of "portable PCs" on academic use. Additional significant indirect effects were found for the relationship between openness to change and academic use, mediated via self-management (accounting for 39% of the total effect) and disposition to pedagogical learning (accounting for 23% of the total effect). Finally, indirect effects were also found for the association between self-transcendence and academic use, mediated by all psychological factors except attitudes. These mediation effects ranged from 19% to 41% of the total effect of self-transcendence on academic use, with the highest percentage attributed to the pathway mediated by disposition to pedagogical learning. Table 4 Direct, indirect, and total effects of the associations were tested in the final model. Path Direct effects Indirect effects Total effects Mediation percentage Portable PC \(\:\to\:\) Motivation \(\:\to\:\) Academic use of digital technologies .055 * .011 * .066 * 16.66% Portable PC \(\:\to\:\) Attitude \(\:\to\:\) Academic use of digital technologies .055 * .008 * .063 * 12.69% Portable PC \(\:\to\:\) Self-management \(\:\to\:\) Academic use of digital technologies .055 * .014 .069 * - Portable PC \(\:\to\:\) Disposition to disciplinary learning \(\:\to\:\) Academic use of digital technologies .055 * .003 .058 * - Portable PC \(\:\to\:\) Disposition to teach \(\:\to\:\) Academic use of digital technologies .055 * − .004 .051 * - Portable PC \(\:\to\:\) Disposition to pedagogical learning \(\:\to\:\) Academic use of digital technologies .055 * .012 * .067 * 17.91% Openness to change \(\:\to\:\) Motivation \(\:\to\:\) Academic use of digital technologies .071 * .005 .076 * - Openness to change \(\:\to\:\) Attitude \(\:\to\:\) Academic use of digital technologies .071 * .006 .077 * - Openness to change \(\:\to\:\) Self-management \(\:\to\:\) Academic use of digital technologies .071 * .046 * .117 * 39.31% Openness to change \(\:\to\:\) Disposition to disciplinary learning \(\:\to\:\) Academic use of digital technologies .071 * .010 * .081 * - Openness to change \(\:\to\:\) Disposition to teach \(\:\to\:\) Academic use of digital technologies .071 * .003 .074 * - Openness to change \(\:\to\:\) Disposition to pedagogical learning \(\:\to\:\) Academic use of digital technologies .071 * .021 * .092 * 22.82% Self-transcendence \(\:\to\:\) Motivation \(\:\to\:\) Academic use of digital technologies .075 * .024 * .098 * 24.89% Self-transcendence \(\:\to\:\) Attitude \(\:\to\:\) Academic use of digital technologies .075 * .004 .079 * - Self-transcendence \(\:\to\:\) Self-management \(\:\to\:\) Academic use of digital technologies .075 * .042 * .116 * 36.20% Self-transcendence \(\:\to\:\) Disposition to disciplinary learning \(\:\to\:\) Academic use of digital technologies .075 * .018 * .093 * 19.35% Self-transcendence \(\:\to\:\) Disposition to teach \(\:\to\:\) Academic use of digital technologies .075 * .029 * .104 * 27.88% Self-transcendence \(\:\to\:\) Disposition to pedagogical learning \(\:\to\:\) Academic use of digital technologies .075 * .052 * .127 * 40.94% * p < .05. Discussion This study investigated the psychological and cultural factors shaping the academic use of digital technologies among Chilean pre-service teachers, employing a large and diverse national sample. The study provides novel insights into how individual dispositions interact with contextual factors to influence technology integration. By operationalizing complex psychological constructs such as self-management, motivation, and pedagogical disposition, it extends research emphasizing the psychosocial readiness of pre-service teachers, not just access (Mendoza et al., 2023 ; Şahin & Şahin, 2022 ). Furthermore, by integrating both subjective (e.g., value systems) and objective (e.g., device access) cultural indicators, the study responds to calls for multidimensional frameworks that more accurately capture academic technology use (Chu et al., 2023 ). These contributions enrich the international dialogue on digital competence, particularly in under-researched Latin American contexts. The model states that psychological variables, particularly self-management, pedagogical learning disposition, and motivation, were the strongest direct predictors of academic digital technology use. Self-management emerged as the most influential factor, indicating that pre-service teachers capable of organizing and directing their learning are more inclined toward the intentional use of digital tools. This aligns with research highlighting the effectiveness of self-regulated learners in using digital technologies for knowledge construction, collaboration, and performance monitoring (Lee & Bonk, 2024 ; Pan, 2020 ). The relevance of pedagogical learning disposition underscores the role of beliefs about teaching and technology in shaping behavior, implying that pre-service teachers who identify with their future roles as educators are more receptive to technologies aligned with constructivist goals (Tomczyk, 2024 ). Moreover, recent studies stress the importance of incorporating digital identity formation into teacher education curricula to strengthen such dispositions (Fazlıoğlu & Akkuş, 2025 ). These findings support the strategic integration of reflective, technology-mediated pedagogical experiences in initial teacher training. Although cultural factors exerted limited direct effects, their indirect influence was substantial, mediated by key psychological constructs. Significant pathways were identified between openness to change and variables such as self-management and pedagogical disposition, highlighting how adaptability and innovative value orientations foster the internal dispositions essential for academic technology use. Similarly, self-transcendence showed indirect effects through self-management, motivation, and pedagogical disposition, suggesting that values centered on social responsibility and personal growth can frame technology as a vehicle for deeper educational engagement (Watson & Rockinson-Szapkiw, 2021 ). Access-related indicators, especially the use of portable computers, also exhibited indirect effects via motivation and pedagogical variables (Cerda et al., 2022 ). These findings underscore that meaningful academic technology use is not solely determined by access but rather emerges from the interaction of infrastructure with individual identity and psychosocial readiness. This area remains underexplored in Global South contexts. Although specific predictors included in the initial model, such as tablet usage and self-control, did not exhibit statistically significant associations, their exclusion contributed to improved model fit and interpretability. This finding calls for a more nuanced understanding of how specific access types and self-regulatory processes shape technology use. For instance, tablets are often associated with entertainment or casual browsing rather than structured academic engagement. Similarly, self-control, while relevant, may conceptually overlap with self-management or lack a distinct behavioral impact within educational settings. These observations resonate with prior research that highlights functional distinctions among digital devices in educational contexts (Essafi et al., 2025 ). Consequently, the assumption that all digital access is equally beneficial should be reconsidered. Instead, a differentiated approach is needed: one that considers device type, usage context, and user intention. Recent literature supports the adoption of adaptive technology using frameworks, which align instructional strategies with the specific affordances and limitations of each device. Such approaches hold promise for enhancing teacher training programs by making technology integration more purposeful and context-sensitive (Simon & Zeng, 2024 ). Future research should explore several important directions to build upon the insights gained from this study. First, longitudinal designs allow researchers to assess how psychological and cultural factors evolve and how they predict sustained academic use of digital technologies during pre-service teachers' transition into professional practice. Second, qualitative or mixed-methods approaches could further unpack the nuances behind value systems like self-transcendence and openness to change, offering richer interpretations of their influence. Third, comparative studies across Latin American countries could highlight contextual specificities or regional patterns in technology adoption, helping develop culturally grounded models. Additionally, future inquiries might explore the role of emerging technologies, such as generative artificial intelligence or immersive platforms, in reshaping digital competencies and the pedagogical mindsets of pre-service teachers. Integrating these technologies meaningfully into training programs may require redefining digital competence frameworks themselves. Lastly, participatory action research involving teacher educators and students could co-design interventions that strengthen self-regulation and identity formation through digital practices. Together, these future lines of research would advance both theoretical understanding and practical improvements in teacher training systems worldwide. Conclusion The findings of this study highlight the importance of psychological attributes and cultural values in shaping the academic use of digital technologies among Chilean pre-service teachers. Psychological factors such as self-management, willingness to engage in pedagogical learning, and motivation emerged as the most significant direct predictors of academic use of digital tools. Among cultural factors, self-transcendence, openness to change, and access to laptops also showed a direct, though more negligible, influence. These results underscore the need to address both psychological and cultural dimensions within initial teacher education programs to foster meaningful and effective integration of digital technologies. Strengthening self-management, pedagogical learning readiness, and motivation while promoting values such as openness to change and self-transcendence may serve as key strategies for developing digital competence in pre-service teachers. Moreover, while ensuring access to technological devices, such as laptops, is important, it is not sufficient on its own. The development of digital skills, self-regulation, and reflective practices must accompany such access. In summary, this study offers valuable insights to inform future academic research and practical initiatives aimed at enhancing digital readiness in teacher education programs in Chile and similar contexts. Declarations Author Contribution ML: conceptualization, funding acquisition, data curation, formal analysis, methodology, writing –original draft. CC: conceptualization, data curation, formal analysis, methodology, writing –original draft. CS-F: conceptualization, formal analysis, methodology, writing –original draft. 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1","display":"","copyAsset":false,"role":"figure","size":58265,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eA conceptual model to test\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7941772/v1/0808528c458591fabd43ad66.png"},{"id":100368926,"identity":"468f5155-3767-4948-9039-ba4211c8c6b8","added_by":"auto","created_at":"2026-01-16 07:58:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47106,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFinal model.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7941772/v1/c89d83a8fe4f781681df6c46.png"},{"id":100382752,"identity":"d24ed53a-40f5-4bd5-828d-1bb17f69b751","added_by":"auto","created_at":"2026-01-16 10:43:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1241540,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7941772/v1/20d4df18-f60c-4bbc-9d29-7a5e1b3a5a04.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Factors Influencing the Academic Use of Digital Technologies Among Chilean Pre-Service Teachers.","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe accelerated digital transformation is redefining communication, production, and everyday life, profoundly impacting the educational field. This profound shift stems from the ubiquitous adoption of technologies and the advent of disruptive innovations such as artificial intelligence (Ravi et al, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In this context, the role of pre-service teacher education becomes critical, as it must not only foster digital competences but also prepare future educators to integrate technology in pedagogically sound and ethically responsible ways (Falloon, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Various national and international initiatives, including teachers' digital competence frameworks such as DigCompEdu, emphasize the importance of developing these competencies from the early stages of teacher education to ensure that new professionals are equipped to respond to the demands of digitalized teaching and learning environments (Redecker, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Understanding the intersection between these global demands and the specific contextual conditions of teacher training institutions is essential for rethinking pedagogical practices and informing responsive educational policies.\u003c/p\u003e \u003cp\u003eMultiple factors influence how pre-service teachers engage with digital technologies for academic purposes, especially in teaching and learning contexts. These factors include individual psychological dimensions (such as intrinsic motivation, perceived self-efficacy, and attitudes toward technology) that shape the specific purposes for which digital tools are employed, determining whether their use leans toward academic or recreational activities (G\u0026oacute;mez-Fern\u0026aacute;ndez \u0026amp; Mediavilla, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, cultural elements such as access to functional infrastructure, availability of technological resources, and institutional support mechanisms significantly influence the effective integration of these tools in educational settings (Zhao et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Prior experiences with digital technology, both in formal education and informal contexts, also play a decisive role in shaping future teachers' confidence, perceptions of usefulness, and strategies for digital engagement (Janeš et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Together, these findings highlight the multifaceted nature of digital technology adoption in teacher education and underscore the importance of analyzing these factors to understand how digital competence is acquired and enacted by prospective educators.\u003c/p\u003e \u003cp\u003eAlthough numerous studies have examined how pre-service teachers use digital technologies for teaching and learning (e.g., Wang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), limited research has explored the underlying factors that shape their use for academic purposes. While existing literature identifies general facilitators and barriers, a deeper understanding of the determinants driving technology adoption, particularly in academic contexts, remains underdeveloped (e.g., Chu et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This lack of insight into the 'why' hinders the development of targeted interventions and informed policies that could promote meaningful and effective technology integration within teacher education programs. Beyond usage patterns, it is essential to examine psychological and cultural influences (such as beliefs, values, and self-regulation strategies) that have proven impactful in other domains (Betancourt, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Choden et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) but have yet to be sufficiently addressed in pre-service teacher education research. These gaps highlight the need to investigate the multifaceted factors that influence the academic use of digital technologies among future educators.\u003c/p\u003e \u003cp\u003eBuilding on the context, this study specifically aimed to examine the influence of psychological and cultural factors on the academic use of digital technologies among pre-service teachers in Chile. To thoroughly address this objective, the research analyzed several key psychological factors hypothesized to play a role, including ICT motivation, attitude towards technology use, self-directed learning readiness, and disposition toward learning and teaching. Additionally, recognizing the complex nature of cultural influence, cultural factors were explored through both objective lenses, such as the technological infrastructure available in the training environment, and subjective lenses, explicitly focusing on portrait values, which represent deep individual and social orientations. This approach sought to provide a nuanced understanding of how these distinct sets of factors collectively impact the academic use of technology by future teachers in a specific Latin American context. A deeper understanding of the factors that foster the academic use of digital technologies among pre-service teachers can significantly contribute to various aspects of initial teacher training.\u003c/p\u003e \u003cp\u003eUnderstanding the factors that influence the academic use of digital technologies among Chilean pre-service teachers, can significantly strengthen initial teacher education. At the macro level, these insights offer valuable input for policymakers and curriculum designers, enabling the refinement of teacher training programs for meaningful, pedagogically sound technology integration aligned with national ICT goals. At the micro level, findings help teacher educators design targeted strategies that support the development of academic digital practices in their classrooms. Beyond practical implications, this study contributes theoretically by examining how psychological constructs (like motivation and self-regulation) and cultural factors (like shared norms and values) interact to shape technology use. This addresses the need to expand existing models of technology adoption by incorporating deeper, specific dimensions often overlooked in this population (L\u0026oacute;pez-P\u0026eacute;rez et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Addressing these gaps is crucial for enhancing digital readiness in teacher education.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cp\u003e \u003cb\u003eAcademic use of digital technologies\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe Digital Competence Framework for Citizens (DigComp) is a foundational model designed to promote digital literacy across European societies. Initially introduced in 2013 by Ferrari, the framework has evolved through successive versions, with DigComp 2.2 offering refined concepts and vocabulary to better align with the evolving demands of digital environments (Collado-S\u0026aacute;nchez et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ferrari, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). DigComp is structured into five core areas: information and data literacy, communication and collaboration, digital content creation, safety, and problem-solving, each comprising specific competencies and proficiency levels. Mastery of these competencies enables individuals to engage meaningfully and responsibly with digital technologies, contributing to personal development, civic participation, and lifelong learning (Mart\u0026iacute;nez-Domingo et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). As such, DigComp not only supports individual digital empowerment but also serves as a strategic tool for fostering inclusive participation in an increasingly digital society.\u003c/p\u003e \u003cp\u003eDeveloping digital competence in pre-service teacher education is crucial for enhancing both pedagogical and disciplinary knowledge. This competence is multidimensional, covering areas like information literacy, communication and collaboration, digital content creation, safety, and problem-solving, which is fundamental for transforming teaching practices (Damianus \u0026amp; Widi, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Rather the extensive curriculum overhauls, teacher digital competence should be integrated organically, especially during practicum experiences, to ensure its relevance and applicability (Marais, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Pre-service teachers strongly desire to develop this competence, emphasizing the importance of collaborative environments and guided instruction (Katrin et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Cultivating digital competence equips future educators with the tools to critically engage with technology, significantly enhancing their professional practice and contributing to broader educational goals (Tomczyk, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, promoting this professional competence necessitates robust mechanisms for assessment and support throughout teacher education programs.\u003c/p\u003e \u003cp\u003eVarious assessment tools for pre-service teacher digital competence draw on the internationally recognized DIGCOMP and DIGCOMPEDU frameworks. For example, Quast et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) developed a seven-dimension instrument that found pre-service teachers report higher competence beliefs than in-service teachers. Similarly, Siiman et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) created a DIGCOMP-based tool to assess perceived digital competence in science and mathematics learning, focusing on smart device use. In Chile, Cerda et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) used DIGCOMP indicators to explore diverse purposes of digital technology use (academic, recreational, social, and economic) among pre-service teachers. These instruments underscore the frameworks\u0026rsquo; critical role in defining and assessing digital skills development, informing targeted interventions, and supporting digital inclusion in education.\u003c/p\u003e \u003cp\u003eThe examples show diverse methods for measuring pre-service teachers\u0026rsquo; digital competence, often using frameworks like DIGCOMP and DIGCOMPEDU. However, simply assessing competence is insufficient. To truly foster these skills, it is essential to examine the underlying process guiding their adoption. This requires investigating the psychological and cultural factors that influence how future teachers utilise digital technologies for academic purposes in their practice. Given the complex nature of these influences, an integrated analytical model that considers diverse factors is essential. Such a model facilitates the simultaneous examination of direct and indirect relationships among multiple variables, providing a robust theoretical framework for understanding the complex relationship between psychological and cultural factors and the academic use of digital technologies.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePsychological factors influencing the adoption of digital competence among pre-service teachers\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe adoption of digital competencies among pre-service teachers is a complex process influenced by various psychological factors. These factors include motivation, attitude toward technology use, self-directed learning readiness, and disposition to learning and teaching. This section explores each of these factors in detail, drawing on insights from relevant research papers.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMotivation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMotivational factors strongly influence the development of digital competencies in pre-service teachers. Both intrinsic motivation (personal interest, enjoyment of learning) and extrinsic drivers (social influence, perceived usefulness) are key to technology adoption among future educators (Şahin \u0026amp; Şahin, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Grounded in Self-Determination Theory, Mendoza et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) demonstrated that providing need-supportive instruction in digital settings significantly enhances students' intrinsic motivation. The COVID-19 pandemic also emphasized motivation\u0026rsquo;s critical role: pre-service teachers driven by basic psychological needs and emotional engagement were more likely to adopt digital tools (Şahin \u0026amp; Şahin, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Collectively, these findings suggest that cultivating internal drive and recognizing external enablers is essential to support the meaningful development of digital competencies.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAttitude toward technology use\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePre-service teachers\u0026rsquo; attitudes significantly shape their engagement with digital competencies. Positive attitudes, especially perceptions of ease of use and usefulness (core to the Technology Acceptance Model -TAM), strongly predict technology acceptance. For example, Jatmikowati et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that perceived usefulness influences the intention to use e-learning platforms, often mediated by self-efficacy. Conversely, negative attitudes, such as technology-related anxiety or low confidence, hinder adoption. Bozdoğan and \u0026Ouml;zen (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) observed that pre-service teachers experiencing such barriers were less likely to engage with digital tools. Similarly, Lemon and Garvis (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) reported that limited self-efficacy often translates into reluctance to integrate technology into pedagogical practice. These findings highlight that teacher education programs must actively promote positive dispositions by offering sustained training and emotional support to enhance pre-service teachers' readiness to adopt digital teaching tools.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSelf-directed learning readiness\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSelf-directed learning readiness plays a pivotal role in the development of digital competencies among pre-service teachers, as it empowers them to take control of their learning processes. This form of learning is defined by autonomy, self-regulation, and the strategic use of technology for independent educational pursuits (Liwanag \u0026amp; Leomar, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Empirical evidence indicates that technological self-efficacy, the belief in one's ability to use digital tools effectively, is a key predictor of readiness for self-directed learning (Pan, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, technology-mediated collaborative learning has been shown to enhance self-direction by promoting reflective engagement and peer-supported learning practices (Lee \u0026amp; Bonk, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Intrinsic goal orientation and the perceived value of learning tasks also serve as significant motivators, fostering a more profound commitment to autonomous learning for professional growth (Liwanag \u0026amp; Leomar, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, cultivating self-directed learning readiness among pre-service teachers not only encourages proactive engagement with digital resources but also supports their long-term development as reflective, digitally competent educators.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDisposition to learning and teaching\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe disposition toward learning and teaching refers to the attitudes and beliefs that pre-service teachers hold about their professional development and future roles as educators. A positive disposition toward learning is closely associated with the adoption of digital competencies, as it fosters openness to innovation and willingness to integrate technology into pedagogical practice (Camacho \u0026amp; Salinas, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Research has shown that pre-service teachers who perceive technology as a valuable tool for enhancing student learning are more likely to adopt digital teaching practices (G\u0026oacute;mez-Trigueros et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, teacher self-efficacy, defined as the belief in one's ability to implement instructional strategies effectively, has been identified as a robust predictor of technology integration (Joo et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lemon \u0026amp; Garvis, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). For example, a study in Ghana revealed that both teacher self-efficacy and perceived usefulness significantly influenced pre-service teachers' intention to adopt ICT in their future classrooms (Adu, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, fostering positive dispositions toward teaching and learning, particularly in relation to digital technologies, is crucial for preparing future educators to develop and apply digital competencies effectively.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCultural factors influencing the adoption of digital competence among pre-service teachers\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWithin the scope of cultural factors influencing pre-service teachers\u0026rsquo; digital competence adoption, personal access to digital technology serves as a foundational enabler, providing the necessary tools for engagement. Complementing this, their Portrait values are critical predictors of their intention and effective use of technology. Together, access and these individual-level \"cultural\" aspects profoundly shape how and why pre-service teachers integrate digital technologies into their practice.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePersonal access to digital technology\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAccess to digital technologies has become widespread among pre-service teachers, significantly shaping their academic routines using laptops, tablets, and smartphones for daily learning and knowledge management (Khamkaew et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, access alone is insufficient. Research indicates that many pre-service teachers lack the digital competencies necessary to effectively utilize these tools for academic and pedagogical purposes, which may hinder their progress (Marais, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, while access to digital technology can enhance learning, it can also introduce challenges, such as digital distractions (Essafi et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), underscoring the need for structured training and support to develop critical digital literacies (De La Cruz et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Evidence also suggests that effective pedagogical integration of technology hinges not only on access but also on cultivating strategic competencies and instructional frameworks during initial teacher education (Zoupa \u0026amp; Karlis, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Therefore, ensuring meaningful educational impact requires a holistic approach that combines access with purposeful training in digital and pedagogical skills (Gisbert-Cervera et al, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePortrait values\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePortrait values are potential predictors of pre-service teachers\u0026rsquo; use of digital technologies. According to Schwartz (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), values are enduring beliefs that guide action. Lechner et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) classify human values into four higher-order dimensions: Openness to Change (focusing on intellectual and emotional exploration), Conservation (emphasizing tradition and order), Self-Transcendence (promoting concern for others), and Self-Enhancement (seeking personal growth and advancement). Research indicates that Openness to Change positively influences digital behaviours across cultures (Choden et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In Chile, empirical evidence links teachers' preferences for Openness to Change and Self-Transcendence with both the frequency and perceived appropriation of digital technologies (Labb\u0026eacute;, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Similarly, among Chilean adolescents, these same values played a central role in explaining patterns of digital immersion (Le\u0026oacute;n et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These findings suggest that pre-service teachers' value orientations, especially those focused on openness and social concern, may significantly shape their willingness to adopt and integrate digital tools into their academic and professional practices (Chaw \u0026amp; Tang, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eInterplay between factors\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe DigComp digital competence framework provides a foundational structure for understanding how pedagogy enables students to develop and apply digital competencies in academic and training contexts. Organized into five key areas (information and data literacy, communication and collaboration, digital content creation, safety, and problem-solving) this model transcends mere technical skills by emphasizing the critical and reflective integration of technology in teaching practices (Ferrari, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Collado-S\u0026aacute;nchez et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this sense, initial teacher education acquires relevance by advocating for the integration of digital technologies not as isolated topics but as mediators of pedagogical and disciplinary development (Damianus \u0026amp; Widi, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Marais, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Digital competence, therefore, is not simply a functional skill set but a transversal dimension that contributes to the formation of professional teaching knowledge, enabling innovative, collaborative, and learner-centered educational practices.\u003c/p\u003e \u003cp\u003eFrom a psychosocial perspective, pre-service teachers\u0026rsquo; academic use of digital technologies is shaped by a dynamic interplay of motivational, attitudinal, and contextual factors. Intrinsic motivation (interest and enjoyment in using technologies), technological self-efficacy, and a positive disposition toward learning are key predictor of meaningful technology adoption (Mendoza et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pan, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Şahin \u0026amp; Şahin, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, while daily access is widespread, it does not ensure effective pedagogical use. It must be accompanied by targeted training in digital competencies and strategies for self-regulation and critical reflection (Khamkaew et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Additionally, individual value orientations, especially openness to change and self-transcendence, influence digital technology use (Choden et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Le\u0026oacute;n et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These insights highlight that fostering digital competence requires more than infrastructure; it demands addressing the internal and contextual drivers that shape technology integration into pedagogical practice.\u003c/p\u003e \u003cp\u003eThe hypothesized relationships between the academic use of digital technologies and the variables assessed in this study are presented in the theoretical model depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"3. Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sample\u003c/h2\u003e \u003cp\u003eThis study involved a large, voluntarily sample of 1188 pre-service teachers recruited from multiple Chilean universities, across nine diverse teaching programs. The participants represented fields such as Physical Education (18%), History (17.7%), Early Childhood Education (14.7%), Spanish Language (14.6%), English Language (14.5%), Mathematics (10.3%), Science (5.6%), Special Education (2.6%), and Primary Education (1.9%). The sample comprised 38.5% men and 61.5% women, with an average age of 22.4 years (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.39). Additional detailed demographic characteristics of the participants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eSociodemographic characteristics of participants\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eFull sample\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNationality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChilean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e97.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForeigner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMapuche\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-mapuche\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e69.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCenter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e61.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst-year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThird year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFourth year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFifth year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Instruments\u003c/h2\u003e \u003cp\u003eFor model assessment, the Spanish versions of eight instruments were used. Among these, the Academic Use of Digital Technologies Subscale served as the primary measure of the dependent variable. This instrument included a 17-item subscale focusing on academic digital technology use, rated on a five-point frequency scale ranging from 1 (\u0026ldquo;Never or almost never\u0026rdquo;) to 5 (\u0026ldquo;Always or almost always\u0026rdquo;). The scale demonstrated acceptable composite reliability (CR\u0026thinsp;=\u0026thinsp;.84) and satisfactory goodness-of-fit indices (TLI\u0026thinsp;=\u0026thinsp;.97, CFI\u0026thinsp;=\u0026thinsp;.99, SRMR\u0026thinsp;=\u0026thinsp;.06, RMSEA\u0026thinsp;=\u0026thinsp;.08, 90% CI [.045 \u0026ndash; .14]), as reported by Cerda et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, a general sociodemographic questionnaire gathered participant characteristics including gender, ethnicity, and university affiliation.\u003c/p\u003e \u003cp\u003eFour instruments measure psychological factors. First, the Instrumental Motive Factor, ICT Motivation Scale, 5 items, five-level agreement scale (1 = \"Strongly disagree,\" 5 = \"Strongly agree\"), showed good reliability (Cronbach's \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.77) and acceptable fit (χ\u003csup\u003e2\u003c/sup\u003e (81, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2075)\u0026thinsp;=\u0026thinsp;372.349, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, CFI\u0026thinsp;=\u0026thinsp;.99, RMSEA\u0026thinsp;=\u0026thinsp;.04) in prior work (Senkbeil, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Second, Attitudes towards technology use (three items with a five-level agreement scale), demonstrated high internal consistency (Cronbach's alpha, \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.91) and acceptable fit within its original model (TLI\u0026thinsp;=\u0026thinsp;.97, CFI\u0026thinsp;=\u0026thinsp;.98, SRMR\u0026thinsp;=\u0026thinsp;.03, RMSEA\u0026thinsp;=\u0026thinsp;.05) (Teo, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Third, The Self-Directed Learning Readiness Scale (21 items, five-level scale from 1 = \"Nothing\" to 5 = \"Totally\") exhibited robust reliability for its factors (self-management \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.87, self-control \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.74, desire for learning \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.83) and satisfactory fit indices (self-management TLI\u0026thinsp;=\u0026thinsp;.94, CFI\u0026thinsp;=\u0026thinsp;.95, SRMR\u0026thinsp;=\u0026thinsp;.04, RMSEA\u0026thinsp;=\u0026thinsp;.07; self-control TLI\u0026thinsp;=\u0026thinsp;.94, CFI\u0026thinsp;=\u0026thinsp;.96, SRMR\u0026thinsp;=\u0026thinsp;.04, RMSEA\u0026thinsp;=\u0026thinsp;.06; desire for learning TLI\u0026thinsp;=\u0026thinsp;.96, CFI\u0026thinsp;=\u0026thinsp;.98, SRMR\u0026thinsp;=\u0026thinsp;.03, RMSEA\u0026thinsp;=\u0026thinsp;.08) (Cerda et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Four, the Disposition to Learning and Teaching Scale (10 items, five-level scale from 1 = \"Nothing\" to 5 = \"Totally\") showed strong reliability across its factors (disposition to disciplinary learning \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.87, disposition to teaching \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.80, disposition to pedagogical learning \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.89) and adequate overall model fit (TLI\u0026thinsp;=\u0026thinsp;.97, CFI\u0026thinsp;=\u0026thinsp;.97, SRMR\u0026thinsp;=\u0026thinsp;.04, RMSEA\u0026thinsp;=\u0026thinsp;.05) (Cerda et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo comprehensively assess the cultural constructs, this study employed two instruments. First, a custom instrument measured technological infrastructure, including participants' ownership of computers/tablets and their average daily usage time. Second, the Revised Portrait Values Questionnaire (PVQ-R) by Beramendi and Zubieta (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) was used to operationalize participants' value orientations. The analysis focused on the self-transcendence and openness to change subscales, each comprising 11 items rated on a 6-point Likert scale (1 = \"Not like me at all like me\"; 6 = \"Very much like me\"). Both PVQ-R subscales demonstrated satisfactory psychometric properties in previous validation studies: self-transcendence (CFI\u0026thinsp;=\u0026thinsp;.96, SRMR\u0026thinsp;=\u0026thinsp;.04, RMSEA\u0026thinsp;=\u0026thinsp;.05) and openness to change (CFI\u0026thinsp;=\u0026thinsp;.97, SRMR\u0026thinsp;=\u0026thinsp;.04, RMSEA\u0026thinsp;=\u0026thinsp;.06) (McQuilkin et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Data Collection Method\u003c/h2\u003e \u003cp\u003eData collection followed a structured, ethical protocol. The process began by securing collaboration with academics at multiple universities across Chile, granting access to their classrooms. Pre-service teachers were comprehensively informed about the study\u0026rsquo;s purpose, confidentiality, privacy, and its voluntary nature. Participants provided written informed consent, a protocol approved by the University's Science Ethics Committee (Number 055_23). Participants subsequently completed the instruments during regular class hours in 2023, with the application requiring approximately 30 minutes. Following collection, the raw data underwent rigorous quality control measures before being prepared for statistical analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Data analysis\u003c/h2\u003e \u003cp\u003eThe data collected were analyzed through a series of statistical procedures aimed at exploring the relationships between cultural and psychological variables and the academic use of digital technologies. Initial exploratory analyses were conducted to detect missing values and assess the distributional characteristics of each variable, verifying assumptions for subsequent multivariate modeling. Based on acceptable levels of internal consistency, mean scores for each construct were computed and used in a path analysis conducted with the lavaan package in \u003cem\u003eR\u003c/em\u003e (Rosseel, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Given that Mardia\u0026rsquo;s test indicated a violation of the multivariate normality assumption (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05), model estimation was performed using the MLR estimator (maximum likelihood with robust standard errors), as recommended for such conditions. The initial structural model was specified according to the hypothesized relationships illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eModel refinement proceeded through the elimination of non-significant paths to achieve greater parsimony. Model fit was evaluated using standard criteria, including the Chi-square statistic (χ\u0026sup2;), Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Standardized Root Mean Square Residual (SRMR), and Root Mean Square Error of Approximation (RMSEA), along with its 90% confidence interval. Following established thresholds, model acceptance was defined by values of CFI and TLI above .90, and SRMR and RMSEA equal to or below .08 (Hu \u0026amp; Bentler, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Variability due to participants\u0026rsquo; university affiliation was controlled for in the model. Additionally, the analysis explored the indirect effects of cultural factors on academic digital technology use, mediated by psychological variables. This analytical strategy enabled a nuanced examination of the complex interrelations among the studied constructs.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eDescriptive statistics and correlations among the variables included in the path model are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Analysis of the correlation matrix revealed that Academic Use of Digital Technologies demonstrated the highest positive correlation coefficient with the variable self-management. The second strongest positive correlation was observed with disposition to pedagogical learning, followed by disposition to disciplinary learning, disposition to teach, and self-transcendence, in descending order of magnitude.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eCorrelation and descriptive statistics of the main variables.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Tablet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Portable Pc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Openness to change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. Self-transcendence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.472\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. Motivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.089\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.124\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.204\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. Attitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.079\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.082\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.070\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.319\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7. Self-management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.207\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.200\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.103\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8. Disposition to disciplinary learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.229\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.288\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.125\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.290\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9. Disposition to teach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.210\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.386\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.141\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.058\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.282\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.343\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10. Disposition to pedagogical learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.083\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.320\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.436\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.174\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.079\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.291\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.488\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.451\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11. Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.084\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.100\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.272\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.318\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.263\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.184\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.449\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.333\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.323\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.397\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.537\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/p\u003e \u003cp\u003eThe proposed structural model examining the relationships between cultural and psychological factors and their influence on the academic use of digital technologies was evaluated through path analysis. The initial hypothesized model did not achieve an acceptable fit according to standard indices, yielding fit statistics of χ\u0026sup2; (32)\u0026thinsp;=\u0026thinsp;221.242, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01, CFI\u0026thinsp;=\u0026thinsp;.920, TLI\u0026thinsp;=\u0026thinsp;.804, SRMR\u0026thinsp;=\u0026thinsp;.078, and RMSEA\u0026thinsp;=\u0026thinsp;.074 [90% CI: .065, .084]. Specifically, the TLI value fell below the typically accepted threshold of .90 or .95. Inspection of this model revealed that the 'use of tablet' indicator did not exhibit statistically significant associations with other variables; consequently, this indicator was excluded from subsequent analyses. A revised model was then tested after removing the 'use of tablet.' This model showed improved fit, but the TLI remained below acceptable levels: χ\u003csup\u003e2\u003c/sup\u003e(24)\u0026thinsp;=\u0026thinsp;122.710, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01, CFI\u0026thinsp;=\u0026thinsp;.957, TLI\u0026thinsp;=\u0026thinsp;.885, SRMR\u0026thinsp;=\u0026thinsp;.054, RMSEA\u0026thinsp;=\u0026thinsp;.062 [90% CI: .051, .073]. In this revised specification, the 'self-control' variable also demonstrated non-significant associations with other constructs. Therefore, 'self-control' was removed in the subsequent model modification. The final re-specified model, excluding both 'tablet use' and 'self-control' (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), achieved adequate fit across all evaluated indices, confirming it as the definitive structural model: χ\u003csup\u003e2\u003c/sup\u003e(19)\u0026thinsp;=\u0026thinsp;85.695, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01, CFI\u0026thinsp;=\u0026thinsp;.966, TLI\u0026thinsp;=\u0026thinsp;.904, SRMR\u0026thinsp;=\u0026thinsp;.044, RMSEA\u0026thinsp;=\u0026thinsp;.057 [90% CI: .045, .069]. This final model accounted for 32.1% of the variance in the academic use of digital technologies among pre-service teachers. Within this model, self-management emerged as the most significant direct predictor of academic technology use, followed by disposition to pedagogical learning and motivation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFinal model\u003c/b\u003e \u003c/p\u003e \u003cp\u003eConsistent with the overall model results, the most influential psychological factors demonstrating significant direct pathways to academic use were self-management, disposition to pedagogical learning, and motivation. Regarding the direct influence of cultural factors on academic use, their standardized estimate value was relatively low. Furthermore, analysis of the pathways between cultural and psychological factors indicated significant influences. Specifically, self-transcendence and openness to change were the cultural factors demonstrating the most substantial impact on various psychological aspects. The influence of self-transcendence on the disposition to teach and disposition to pedagogical learning exhibited the highest path coefficients among these inter-factor relationships. The specific path coefficients and their statistical significance for the final model are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003ePaths effects of the final model\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaths\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandardized estimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCultural to psychological factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortable PC \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Motivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness to change \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Motivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-transcendence \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Motivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortable PC \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Attitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness to change \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Attitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-transcendence \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Attitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortable PC \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Self-management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness to change \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Self-management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-transcendence \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Self-management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortable PC \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Disposition to disciplinary learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness to change \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Disposition to disciplinary learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-transcendence \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Disposition to disciplinary learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortable PC \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Disposition to teach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness to change \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Disposition to teach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-transcendence \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Disposition to teach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortable PC \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Disposition to pedagogical learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness to change \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Disposition to pedagogical learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-transcendence \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Disposition to pedagogical learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychological to academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotivation \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttitude \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-management \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisposition to disciplinary learning \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisposition to teach \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisposition to pedagogical learning \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCultural Factors to Academic Use of Digital Technologies of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortable PC \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness to change \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-transcendence \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity (control variable) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the decomposition of effects, showing direct, indirect (mediated), and total effects of cultural factors on academic use mediated by psychological factors. Significant indirect effects were observed for the relationship between \"portable PCs\" and academic use, mediated through Disposition to Pedagogical Learning, Motivation, and Attitude. These mediated pathways accounted for 13% to 18% of the total effect of \"portable PCs\" on academic use. Additional significant indirect effects were found for the relationship between openness to change and academic use, mediated via self-management (accounting for 39% of the total effect) and disposition to pedagogical learning (accounting for 23% of the total effect). Finally, indirect effects were also found for the association between self-transcendence and academic use, mediated by all psychological factors except attitudes. These mediation effects ranged from 19% to 41% of the total effect of self-transcendence on academic use, with the highest percentage attributed to the pathway mediated by disposition to pedagogical learning.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eDirect, indirect, and total effects of the associations were tested in the final model.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirect effects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndirect effects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal effects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMediation percentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortable PC \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Motivation \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.055\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.011\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.066\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.66%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortable PC \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Attitude \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.055\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.008\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.063\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.69%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortable PC \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Self-management\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.055\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.069\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortable PC \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Disposition to disciplinary learning \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.055\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.058\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortable PC \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Disposition to teach \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.055\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.051\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortable PC \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Disposition to pedagogical learning \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.055\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.012\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.067\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.91%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness to change \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Motivation \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.071\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.076\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness to change \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Attitude \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.071\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.077\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness to change \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Self-management\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.071\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.046\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.117\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.31%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness to change \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Disposition to disciplinary learning \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.071\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.010\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.081\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness to change \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Disposition to teach \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.071\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.074\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness to change \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Disposition to pedagogical learning \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.071\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.021\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.092\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.82%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-transcendence \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Motivation \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.075\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.024\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.098\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.89%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-transcendence \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Attitude \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.075\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.079\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-transcendence \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Self-management\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.075\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.042\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.116\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-transcendence \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Disposition to disciplinary learning \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.075\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.018\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.093\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.35%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-transcendence \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Disposition to teach \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.075\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.029\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.104\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.88%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-transcendence \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Disposition to pedagogical learning \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Academic use of digital technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.075\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.052\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.127\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.94%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the psychological and cultural factors shaping the academic use of digital technologies among Chilean pre-service teachers, employing a large and diverse national sample. The study provides novel insights into how individual dispositions interact with contextual factors to influence technology integration. By operationalizing complex psychological constructs such as self-management, motivation, and pedagogical disposition, it extends research emphasizing the psychosocial readiness of pre-service teachers, not just access (Mendoza et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Şahin \u0026amp; Şahin, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, by integrating both subjective (e.g., value systems) and objective (e.g., device access) cultural indicators, the study responds to calls for multidimensional frameworks that more accurately capture academic technology use (Chu et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These contributions enrich the international dialogue on digital competence, particularly in under-researched Latin American contexts.\u003c/p\u003e \u003cp\u003eThe model states that psychological variables, particularly self-management, pedagogical learning disposition, and motivation, were the strongest direct predictors of academic digital technology use. Self-management emerged as the most influential factor, indicating that pre-service teachers capable of organizing and directing their learning are more inclined toward the intentional use of digital tools. This aligns with research highlighting the effectiveness of self-regulated learners in using digital technologies for knowledge construction, collaboration, and performance monitoring (Lee \u0026amp; Bonk, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Pan, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The relevance of pedagogical learning disposition underscores the role of beliefs about teaching and technology in shaping behavior, implying that pre-service teachers who identify with their future roles as educators are more receptive to technologies aligned with constructivist goals (Tomczyk, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, recent studies stress the importance of incorporating digital identity formation into teacher education curricula to strengthen such dispositions (Fazlıoğlu \u0026amp; Akkuş, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These findings support the strategic integration of reflective, technology-mediated pedagogical experiences in initial teacher training.\u003c/p\u003e \u003cp\u003eAlthough cultural factors exerted limited direct effects, their indirect influence was substantial, mediated by key psychological constructs. Significant pathways were identified between openness to change and variables such as self-management and pedagogical disposition, highlighting how adaptability and innovative value orientations foster the internal dispositions essential for academic technology use. Similarly, self-transcendence showed indirect effects through self-management, motivation, and pedagogical disposition, suggesting that values centered on social responsibility and personal growth can frame technology as a vehicle for deeper educational engagement (Watson \u0026amp; Rockinson-Szapkiw, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Access-related indicators, especially the use of portable computers, also exhibited indirect effects via motivation and pedagogical variables (Cerda et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These findings underscore that meaningful academic technology use is not solely determined by access but rather emerges from the interaction of infrastructure with individual identity and psychosocial readiness. This area remains underexplored in Global South contexts.\u003c/p\u003e \u003cp\u003eAlthough specific predictors included in the initial model, such as tablet usage and self-control, did not exhibit statistically significant associations, their exclusion contributed to improved model fit and interpretability. This finding calls for a more nuanced understanding of how specific access types and self-regulatory processes shape technology use. For instance, tablets are often associated with entertainment or casual browsing rather than structured academic engagement. Similarly, self-control, while relevant, may conceptually overlap with self-management or lack a distinct behavioral impact within educational settings. These observations resonate with prior research that highlights functional distinctions among digital devices in educational contexts (Essafi et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Consequently, the assumption that all digital access is equally beneficial should be reconsidered. Instead, a differentiated approach is needed: one that considers device type, usage context, and user intention. Recent literature supports the adoption of adaptive technology using frameworks, which align instructional strategies with the specific affordances and limitations of each device. Such approaches hold promise for enhancing teacher training programs by making technology integration more purposeful and context-sensitive (Simon \u0026amp; Zeng, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFuture research should explore several important directions to build upon the insights gained from this study. First, longitudinal designs allow researchers to assess how psychological and cultural factors evolve and how they predict sustained academic use of digital technologies during pre-service teachers' transition into professional practice. Second, qualitative or mixed-methods approaches could further unpack the nuances behind value systems like self-transcendence and openness to change, offering richer interpretations of their influence. Third, comparative studies across Latin American countries could highlight contextual specificities or regional patterns in technology adoption, helping develop culturally grounded models. Additionally, future inquiries might explore the role of emerging technologies, such as generative artificial intelligence or immersive platforms, in reshaping digital competencies and the pedagogical mindsets of pre-service teachers. Integrating these technologies meaningfully into training programs may require redefining digital competence frameworks themselves. Lastly, participatory action research involving teacher educators and students could co-design interventions that strengthen self-regulation and identity formation through digital practices. Together, these future lines of research would advance both theoretical understanding and practical improvements in teacher training systems worldwide.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe findings of this study highlight the importance of psychological attributes and cultural values in shaping the academic use of digital technologies among Chilean pre-service teachers. Psychological factors such as self-management, willingness to engage in pedagogical learning, and motivation emerged as the most significant direct predictors of academic use of digital tools. Among cultural factors, self-transcendence, openness to change, and access to laptops also showed a direct, though more negligible, influence. These results underscore the need to address both psychological and cultural dimensions within initial teacher education programs to foster meaningful and effective integration of digital technologies. Strengthening self-management, pedagogical learning readiness, and motivation while promoting values such as openness to change and self-transcendence may serve as key strategies for developing digital competence in pre-service teachers. Moreover, while ensuring access to technological devices, such as laptops, is important, it is not sufficient on its own. The development of digital skills, self-regulation, and reflective practices must accompany such access. In summary, this study offers valuable insights to inform future academic research and practical initiatives aimed at enhancing digital readiness in teacher education programs in Chile and similar contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eML: conceptualization, funding acquisition, data curation, formal analysis, methodology, writing \u0026ndash;original draft. CC: conceptualization, data curation, formal analysis, methodology, writing \u0026ndash;original draft. CS-F: conceptualization, formal analysis, methodology, writing \u0026ndash;original draft. MU: formal analysis, validation, writing \u0026ndash; review \u0026amp; editing. MG: formal analysis, validation, writing \u0026ndash; review \u0026amp; editing. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe authors will provide the data that supports the findings of this study upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdu, S. (2017). 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The administration of Heritage Language Schools in multicultural societies: The case of the Hellenic School of Ottawa, Canada. \u003cem\u003eAthens Journal of Education\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(1), 9\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.30958/aje.12-1-1\u003c/span\u003e\u003cspan address=\"10.30958/aje.12-1-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Digital competence, pre-service teacher, educational technology, teacher education, self-management, Chile","lastPublishedDoi":"10.21203/rs.3.rs-7941772/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7941772/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis research examines the impact of psychological and cultural factors on the academic use of digital technologies by Chilean pre-service teachers. Considering the global digital transformation and the influence of digital technologies, such as artificial intelligence (AI), in education, it is essential to prepare future educators to integrate technology pedagogically. Although the use of technology among pre-service teachers has been studied, the specific factors influencing their academic use require further investigation considering an integral approach. A study was conducted with 1188 pre-service teachers from various Chilean universities. Data were collected through questionnaires assessing academic use of digital technologies, motivation towards Information and Communication Technologies (ICT), technology attitudes, self-directed learning readiness, disposition towards learning and teaching, technological infrastructure availability, and portrait values. Structural Equation Modeling (SEM) was utilized to analyze the data. The results indicated that self-management, disposition towards pedagogical learning, and motivation were the most significant direct predictors of the academic use of digital technologies. Among the cultural factors, self-transcendence, openness to change, and the use of portable PCs had a significant but minor direct influence. The model explained 32.1% of the variance in academic technology use. These findings underscore the significance of both psychological characteristics and cultural values in influencing how future teachers utilize digital technologies for academic purposes. Understanding these influences is crucial for enhancing teacher education programs and equipping educators to integrate technology into teaching and learning effectively.\u003c/p\u003e","manuscriptTitle":"Factors Influencing the Academic Use of Digital Technologies Among Chilean Pre-Service Teachers.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-13 17:51:56","doi":"10.21203/rs.3.rs-7941772/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b5ae9da4-9b16-4e75-b314-6d107c307e04","owner":[],"postedDate":"January 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T09:38:54+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-13 17:51:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7941772","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7941772","identity":"rs-7941772","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}