Sinergizar el rendimiento académico con la competencia comunicativa y la adopción de tecnología Tengku Shahraniza 1 , Mokhtarrudin Ahmad 2 , Nurafni Rubiyanti 3 1 Multimedia University Melaka, Malaysia. E-mail: shahraniza.jalal@mmu.edu.my; ORCID: https://orcid.org/0000-0002-6345-3053 2 Multimedia University Cyberjaya, Malaysia. E-mail:mokhtarrudin@mmu.edu.my; ORCID: https://orcid.org/0000-0002-3696-3015 3 Telkom University Bandung, Indonesia. E-mail: nrubiyanti@telkomuniversity.ac.id; ORCID: https://orcid.org/0000-0002-8268-0227 Resumen. En el contexto de la educación del siglo XXI, la integración de la tecnología y el desarrollo de la competencia comunicativa son fundamentales para mejorar el rendimiento académico de los estudiantes universitarios. El artículo plan- tea la relación entre las competencias comunicativas, la adopción de tecnología y el rendimiento académico, centrándose en el papel mediador de la motivación de los estudiantes en la educación superior de Malasia. El objetivo es proporcionar una com- prensión integral de cómo estos factores interactúan para influir en los resultados del aprendizaje en un panorama educativo en rápida evolución. El estudio empleó un diseño de investigación cuantitativo para analizar el impacto de la competencia comu- nicativa, la adopción de tecnología y la mediación de la motivación en el rendimiento académico de 129 estudiantes de una universidad malaya mediante cuestionarios es- tructurados y análisis de factores confirmatorio (CFA) con Smart PLS 4. Este diseño permite un examen exhaustivo de la relación entre las variables. Los hallazgos indican que tanto la adopción de tecnología como la competencia comunicativa impactan significativamente el rendimiento académico, pero sus efectos se magnifican cuando se combinan con altos niveles de motivación de los estudiantes. Los datos revelan que los estudiantes que utilizan eficazmente herramientas y plataformas digitales tienden a ob- tener mejores resultados académicos, siempre que también posean fuertes habilidades comunicativas que faciliten la interacción efectiva y el intercambio de conocimientos. Palabras clave: rendimiento académico, competencia comunicativa, tecnología, motivación, educación superior. Recibido: 07/10/2024 ~ Aceptado: 14/12/2024 INTERACCIÓN Y PERSPECTIVA Revista de Trabajo Social ISSN 2244-808X ~ Dep. Legal pp 201002Z43506 DOI: https://doi.org/10.5281/zenodo.15080460 Vol. 15 (2): 502 - 518 pp, 2025

Sinergizar el rendimiento académico con la competencia comunicativa y la adopción de tecnología 503 Vol. 15(2) abril-junio 2025/ 502 - 518 Synergizing academic achievement with communicative competence and technology adoption Abstract. In the context of twenty-first century education, the integration of technology and the development of communicative competence are essential to im- prove the academic performance of university students. e article discusses the re- lationship between communicative competence, technology adoption, and academic performance, focusing on the mediating role of student motivation in Malaysian higher education. e goal is to provide a comprehensive understanding of how these factors interact to influence learning outcomes in a rapidly evolving educational landscape. e study employed a quantitative research design to analyze the impact of communi- cative competence, technology adoption, and motivation mediation on the academic performance of 129 students at a Malaysian university using structured questionnaires and confirmatory factor analysis (CFA) with Smart PLS 4. is design allows for a thorough examination of the relationship between variables. e findings indicate that both technology adoption and communicative competence significantly impact aca- demic performance, but their effects are magnified when combined with high levels of student motivation. e data reveal that students who effectively use digital tools and platforms tend to obtain better academic results, provided that they also possess strong communication skills that facilitate effective interaction and knowledge sharing. Keywords: academic performance, communicative competence, technology, motivation, higher education. INTRODUCTION Academic achievement of university undergraduates in the past centuries differ from those in the 21st century. Academic achievement in the 21st century depends a lot on mastering the 21st century digital knowledge and competencies (James, Talin & Bikar, 2022) that are complex, cross-disciplinary and are much more demanding than rote memorization-based skills (Saavedra & Opfer, 2012). To compete and to achieve academically, university undergraduates in the 21st-century are expected to be able to communicate well with others, acquire new skills and information indepen- dently, and adapt to rapidly changing conditions (Lavi et al., 2021; Gewertz, 2008) and concept mapping strategies (Manas, 2023). Malaysia being a developing country has always aimed to pro- duce students who are not only well balanced, but also competent communicators (MOE, 2014). Since 21st century education is inseparable from technology, like it or not, university undergradu- ates who want to obtain better results must master the platform used (Wardoyo et al., 2021). Apart from communicative competence and technology adoption, motivation in digital edu- cation has been receiving attention in recent years (Li & Tsai, 2017; Kyewski & Kramer, 2018; Özhan & Kocadere, 2020) since students have the tendency to participate less (Kyewski & Krämer, 2018) and the alarming completion and dropout rates (Xavier & Meneses, 2020; Lee, Choi, & Kim, 2013; Park & Choi, 2009). Murday et al. (2008) study concluded that keeping motivation at a desired level is tough in online courses.

504 Shahraniza, Ahmad, Rubiyanti Interacción y Perspectiva. Revista de Trabajo Social Vol. 15(2): 2025 As there are various factors influencing academic achievement, it is crucial to understand how the communicative competence and technology adoption influence academic achievement while motivation mediates these among undergraduates in Malaysia. LITERATURE REVIEW Academic achievement is a multi-faceted, complex equation. It is the barometer of students’ competence (Idris et al., 2020; Yağci & Çevik, 2019; Kleijn, Ploeg & Topman, 1994). It measures the knowledge, skills and abilities gained by the students (Sanchez et al., 2021). However, in re- cent years, low academic achievement has been observed among various university undergraduates across the globe (Chowdhury, Rahman. & McCray, 2024; Manas, 2023; Tadese, Yeshaneh & Mulu, 2022; Realyvásquez-Vargas et al., 2020; Adnan & Anwar, 2020; Wan Maziah et al., 2019; Yigermal, 2017). is is caused by a variety of determinants. is study aims to contribute to the investiga- tion of low academic achievement by looking into determinants like communicative competence, technology adoption and motivation. Empirically, many of the researchers in the world applied the GPA to assess the academic achievement of the students (Tadese, 2022; Zheng & Mustappha, 2022; Jan et al., 2020; Steinmayr et al., 2014; Al-Rofo, 2010; Hijaz & Naqvi, 2006; Applegate & Daly, 2006; Stephan & Schaban, 2002; Naser & Peel, 1998). GPA is one of the best predictors of college achievement in academic activities (Moore & Shulock, 2009). e supremacy of GPA among other measures may be attrib- uted to the readily and conveniently available data about students’ achievement in HEIs. Communicative Competence Communicative competence refers to the syntactic, morphological, phonological, that is, lin- guistic knowledge of the language user as well as the social, cultural, discourse and strategic knowl- edge of how and when to use the language appropriately (Geçkin, 2022). Many previous studies have investigated communicative competence and have proposed it as an important predictor of academic achievement (Bo et al., 2023; Al Awaji et al., 2022; Martirosyan et al., 2015; Opoola & Fatiloro, 2014; Othman & Nordin, 2013; Yen & Kuzma, 2009; Light et al., 1987). Communicative competence is measured using various standardized test scores. MUET is a test of English language proficiency that is used specifically in Malaysia and is required for admission to many Malaysian universities. MUET assesses the ability of test-takers to use English effectively for academic purposes and includes a variety of tasks, including listening, speaking, reading, and writing (Baharum et. al., 2021). In Malaysia, several studies demonstrated the significant relationship between MUET scores and academic achievement (Malik et al., 2022; Baharum et al., 2021; Hamid, Ismail & Tapsir, 2019; Krishnan, Yaacob & Veloo, 2019; Buniyamin, Kassim & Mat, 2015; Othman & Nor- din, 2013; Nopiah et al., 2011). All these studies engaged MUET as a common indicator of academic achievement. By collecting data from 300 undergraduates from four public universities in Malaysia, Malik et al. (2022) discovered significant effect of MUET on academic achievement (GPA). Technology Adoption Technology adoption describes how users adopt new technologies, influenced by a variety of factors, such as perceived usefulness and ease of use (Kirwa & Zhiyong, 2020). e successful in-

Sinergizar el rendimiento académico con la competencia comunicativa y la adopción de tecnología 505 Vol. 15(2) abril-junio 2025/ 502 - 518 tegration of new technology into an organization is referred to as technology adoption. Adoption entails more than simply using technology. When new technology is adopted, it will be employed to its maximum capacity and to reap the benefits of the new system. e Technology Acceptance Model (TAM) is a theoretical framework that has been used to explain and predict the adoption and use of technology in various settings, including education. TAM, as the first step of technology adoption, is an attitude towards technology, and it is influenced by various factors. TAM proposes that two key factors, perceived usefulness and perceived ease of use, influence a person’s intention to use technology and ultimately, their actual use of technology (Davis, 1989). In addition, perceived usefulness (PU) and perceived ease of use (PEU) are key fac- tors that directly and indirectly boost academic achievement (Marangunić & Granić, 2015). Motivation Motivation can be defined as a need, a drive supported by expectations, goals, and emotions. Intrinsic motivation means that the student takes a new course just for its pleasure, because it is considered rewarding and motivating in itself. Extrinsic motivation means that the learning activ- ity is carried out for external activities, such as receiving recognition, a certificate, a good grade or avoiding negative situations such as a reprimand (Capone & Lepore, 2022). Motivation is a significant predictor of academic achievement (Steinmayr, et. al., 2019). Stu- dents who are highly motivated to learn and achieve tend to perform better academically than those who lack motivation. Increasing students’ motivation is one of the pedagogical objectives in higher education. A past study suggested that students with higher motivation would actively engage in the learning process and were likely to obtain good learning outcomes (Foong et al., 2021). Students who are highly motivated are more likely to have higher academic achievement (Hōigaard et al., 2015; SuárezÁlvarez et al., 2014). Figure 1. Conceptual Framework METHODOLOGY e study employed a quantitative research design to synergize the impact of communicative competence and technology adoption on academic achievement among undergraduates. Also, the role of motivation in mediating the relationship was analyzed. e study involved a total of 129 undergraduate students, aged 18 to 24, from Malaysian university. Participants were selected using random sampling from a population of undergraduate students. Eligibility criteria included being full-time students and actively using online learning for at least one year. Communicave Competence Technology Adopon Movaon Academic Achievement

506 Shahraniza, Ahmad, Rubiyanti Interacción y Perspectiva. Revista de Trabajo Social Vol. 15(2): 2025 e study utilized a structured questionnaire consisting of 69 items divided into three key areas: communicative competence, technology adoption, and motivation. e communicative com- petence section comprised 35 questions designed to assess participants’ ability to effectively use language in various contexts. Technology adoption was measured using 10 questions that focused on perceived usefulness and perceived ease of use. Lastly, the motivation section included 24 ques- tions aimed at evaluating students’ intrinsic and extrinsic motivation toward academic and techno- logical engagement. e questionnaire was carefully designed to ensure clarity and relevance, with responses collected using a 5-point Likert scale ranging from “strongly disagree” to “strongly agree.” e data were analyzed using second-order confirmatory factor analysis (CFA) with Smart PLS 4 to assess the relationships between various factors influencing academic achievement in online education contexts. Smart PLS 4, a partial least squares structural equation modeling (PLS-SEM) tool, was chosen for its ability to handle complex models and small sample sizes. e analysis fo- cused on the three primary constructs of communicative competence, technology adoption, and motivation, all of which were modeled as second-order latent variables. By utilizing this advanced statistical technique, the study aimed to understand both the direct and indirect effects of these vari- ables on academic achievement. e results provided insights into the strength of the relationships between the constructs and their contributions to students’ academic achievement in an online learning environment. ANALYSIS AND RESULTS e data analysis and results present the details of the data analysis. PLS-SEM analysis that includes the assessment of Measurement and Structural Model. e measurement model establishes the reliability and validity of the construct. e structural model ascertains the significance of hy- pothesized relationships. Different hypotheses were proposed to evaluate the relationship of predic- tors on the outcome. H1. Communicative competence positively and significantly influences motivation H2. Technology adoption positively and significantly influences motivation H3. Motivation positively and significantly influences academic achievement/GPA H4. Motivation mediates the relationship between communicative competence and academic achievement/GPA H5. Motivation mediates the relationship between technology adoption and academic achive- ment/GPA Measurement Model e quality of the constructs in the study is assessed based on the evaluation of the measure- ment model. e assessment of the quality criteria starts with evaluation of the factor loading which is followed by establishing the construct reliability and construct validity. Factor loadings Factor loading refers to the “the extent to which each of the items in the correlation matrix correlates with the given principal component, Factor loadings can range from - to +1.0, with higher

Sinergizar el rendimiento académico con la competencia comunicativa y la adopción de tecnología 507 Vol. 15(2) abril-junio 2025/ 502 - 518 absolute values indicating a higher correlation of the item with the underlying factor” (Pett et al., 2003). None of the item in these study had factor loading less than the recommended value of 0.5 (Hair et al., 2016). Hence, no items were further removed. Indicator Multicollinearity Variance Inflation Factor (VIF) statistic in utilized to assess multicollinearity in the indicators (Fornell & Bookstein, 1982). According to Hair et al (2016) multicollinearity is not a serious issue if the value for VIF is below 5. Table 1 presents the VIF values for the indicators in the study and reveals that VIF for each indicator is below the recommended threshold. Low multicollinearity in regression analysis offers several key benefits that enhance the reliabil- ity and accuracy of the model. One of the primary advantages is stable estimates, where regression coefficients remain more consistent and are less sensitive to changes in the model. is stability is crucial for ensuring that small variations in the data or model structure do not lead to large fluctua- tions in the coefficients, making the results more trustworthy. Another benefit is accurate significance tests. When multicollinearity is low, the tests for de- termining the significance of individual predictors are more reliable. is allows researchers to con- fidently assess the unique contribution of each variable, leading to a clearer understanding of their individual effects on the outcome variable. Alongside this, the model benefits from lower standard errors, as the standard errors of the regression coefficients are not inflated by shared variance among predictors. is contributes to more precise estimates, increasing the overall accuracy of the results. Finally, low multicollinearity leads to clearer interpretation of the model. Since the predic- tors share less variance with each other, it becomes easier to understand the distinct impact of each variable on the dependent variable. is clarity is essential for deriving meaningful insights from the model. By maintaining low multicollinearity, the regression analysis produces more reliable, in- terpretable, and insightful results, providing a solid foundation for understanding the relationships between the predictors and the outcome variable. Figure 2. Measurement model for lower order construct

508 Shahraniza, Ahmad, Rubiyanti Interacción y Perspectiva. Revista de Trabajo Social Vol. 15(2): 2025 TABLE 1. Multicollinearity Statistics (VIF) for indicators Indicators VIF Indicators VIF Indicators VIF D1 2.069 EMIN2 3.633 GP7 2.777 D1 2.825 EMIN2 2.601 GP7 2.642 D2 2.168 EMIN3 3.323 GP8 3.532 D2 3.587 EMIN3 2.147 GP8 2.728 D3 4.891 EMIN4 3.305 GV1 1.754 D3 2.625 EMIN4 4.849 GV1 2.34 D4 3.454 G1 2.965 GV2 2.745 D4 2.507 G1 2.154 GV2 2.161 D5 3.382 G2 2.388 GV3 4.622 D5 2.133 G2 3.587 GV3 2.481 EMER1 2.492 G3 2.642 IMTA1 2.81 EMER1 1.455 G3 4.537 IMTA1 1.917 EMER2 4.222 G4 3.766 IMTA2 2.014 EMER2 3.187 G4 2.328 IMTA2 3.666 EMER3 4.14 G5 4.073 IMTA3 3.733 EMER3 2.62 G5 2.992 IMTA3 2.556 EMER4 3.253 G6 3.85 IMTA4 3.365 EMER4 4.227 G6 3.039 IMTA4 1.714 EMIDI1 1.791 GP2 4.122 IMTE1 1.554 EMIDI1 2.414 GP2 3.927 IMTE1 2.735 EMIDI2 2.393 GP3 3.975 IMTE2 3.053 EMIDI2 4.407 GP3 3.892 IMTE2 2.255 EMIDI3 1.892 GP4 4.934 IMTE3 2.986 EMIDI3 2.894 GP4 4.021 IMTE3 2.393 EMIDI4 1.877 GP5 4.136 IMTE4 2.813 EMIDI4 2.631 GP5 3.645 IMTE4 1.567 EMIN1 2.271 GP6 3.259 IMTK1 2.906 EMIN1 3.383 GP6 3.156 IMTK1 2.168 IMTK2 2.842 PU4 2.375 SO9 2.512 IMTK2 1.982 PU4 2.661 SO9 3.672 IMTK3 1.563 PU5 2.5 ST1 2.646 IMTK3 2.828 PU5 3.453 ST1 2.093 IMTK4 1.843 SO1 2.831 ST2 2.746 IMTK4 2.996 SO1 3.872 ST2 4.196

Sinergizar el rendimiento académico con la competencia comunicativa y la adopción de tecnología 509 Vol. 15(2) abril-junio 2025/ 502 - 518 PEOU1 2.461 SO10 3.962 ST3 4.009 PEOU1 2.6 SO10 4.729 ST3 2.772 PEOU2 3.724 SO2 4.026 GP1 3.58 PEOU2 3.474 SO2 2.418 GP1 3.946 PEOU3 2.766 SO3 4.412 PEOU3 2.201 SO3 3.382 PEOU4 1.486 SO4 4.075 PEOU4 1.599 SO4 2.44 PEOU5 2.452 SO5 2.875 PEOU5 3.073 SO5 2.132 PU1 2.675 SO6 4.968 PU1 2.627 SO6 2.49 PU2 3.905 SO7 2.481 PU2 3.528 SO7 2.28 PU3 2.703 SO8 2.998 PU3 2.835 SO8 2.241 According to Mark (1996), “Reliability is defined as the extent to which a measuring instru- ment is stable and consistent. e essence of reliability is repeatability. If it is administered over and over again, it will yield the same results.” In research, reliability is a critical aspect of ensuring that the measurements taken are not only accurate but can be consistently replicated under similar conditions. is repeatability is essential for the validity of any scientific or statistical analysis, as it assures that the data collected through the instrument is dependable over time. Two of the most commonly used methods for assessing and establishing reliability in the field of quantitative research are Cronbach’s Alpha and Composite Reliability (CR). Cronbach’s Alpha is a measure of internal consistency, which indicates how well a set of items measures a single uni- dimensional latent construct. A higher value of Cronbach’s Alpha suggests that the items within a scale are highly correlated and provide a reliable measure of the underlying construct. Composite Reliability, on the other hand, is an alternative reliability measure that considers the overall reliabil- ity of a latent variable in relation to the measured items and is often preferred in structural equation modeling (SEM) contexts. e results of both Cronbach’s Alpha and Composite Reliability for this study are presented in Table 2. e values of Cronbach’s Alpha ranged from 0.808 to 0.974, indicating a high level of internal consistency across the constructs measured. Similarly, the Composite Reliability statistics ranged from 0.874 to 0.976, further affirming the consistency and stability of the measurement model. Both of these reliability indicators surpass the widely accepted threshold of 0.70 (Hair et al., Table 1. CONTINUATION Indicators VIF Indicators VIF Indicators VIF

510 Shahraniza, Ahmad, Rubiyanti Interacción y Perspectiva. Revista de Trabajo Social Vol. 15(2): 2025 2011), which is considered the minimum level required to establish acceptable reliability in social science research. Given that both Cronbach’s Alpha and Composite Reliability values exceed the required threshold, it can be concluded that the constructs used in this study are reliable. e high reliability scores ensure that the measurement instrument is capable of producing consistent results, which strengthens the overall credibility of the data and the findings derived from the analysis. erefore, construct reliability is well-established, providing a solid foundation for the subsequent phases of data interpretation and analysis. TABLE 2. Construct Reliability Analysis (Cronbach Alpha and Composite Reliability) Construct Validity Cronbach‘s alpha Composite reliability Communicative Competence 0.974 0.976 D 0.93 0.947 EMER 0.862 0.908 EMIDI 0.825 0.884 EMIN 0.891 0.925 G 0.916 0.935 GP 0.944 0.953 GPA 0.953 0.959 GV 0.843 0.905 IMTA 0.846 0.897 IMTE 0.808 0.874 IMTK 0.839 0.892 Motivation 0.956 0.96 PEOU 0.878 0.912 PU 0.916 0.937 SO 0.953 0.96 ST 0.919 0.949 Technology Adoption 0.933 0.944 In statistical analysis using PLS-SEM, construct validity is established when there is convergent validity and discriminant validity. Convergent Validity “Convergent validity is the degree to which multiple attempts to measure the same concept are in agreement. e idea is that two or more measure of the same thing should ovary highly if they are valid measures of the concept” (Bagozzi et al, 1991). When the AVE value is greater than or equal to the recommended value of 0.50, items coverage to measure the underlying construct and hence

Sinergizar el rendimiento académico con la competencia comunicativa y la adopción de tecnología 511 Vol. 15(2) abril-junio 2025/ 502 - 518 convergent validity is established (Fornell & Larcker, 1981). Convergent validity results based on the AVE statistics in the current study show that all the constructs have an AVE greater than 0.50. Hence, convergent validity is established. Table 3 shows the AVE Value for each of the constructs. TABLE 3. Construct Convergent Validity (AVE) Discriminant Validity “Discriminant validity is the degree to which measures of different concepts are distinct. e notion is that if two or more concepts are unique, then valid measures of of each should correlate to highly” (Bagozzi et al, 1991). Fornell and Larcker Criterion According to Fornell and larcker (1981) criterion, discriminant validity is established when the square root of AVE for a construct is greater than its correlation with all other constructs. In this study, square root of AVE (in Bold and Italics) for a construct was found greater than its correlation with other constructs. Hence, providing strong support for establishment of discriminant validity. Cross Loadings Cross loadings help assess if an item belonging to particular constructs load strongly onto its own parent construct instead of other constructs in the study. e results show that factor loading of all the items is stronger on the underlying construct to which they belong instead of the other Average variance extracted (AVE) Communication Competence 0.628 D 0.782 EMER 0.714 EMIDI 0.656 EMIN 0.754 G 0.706 GP 0.719 GPA 0.683 GV 0.761 IMTA 0.685 IMTE 0.634 IMTK 0.675 Motivation 0.503 PEOU 0.678 PU 0.748 SO 0.706 ST 0.860 Technology Adoption 0.629

512 Shahraniza, Ahmad, Rubiyanti Interacción y Perspectiva. Revista de Trabajo Social Vol. 15(2): 2025 constructs in the study (Wasko & Faraj, 2005). Hence, based on the evaluation of cross loadings, discriminant validity is attained. Heterotrait-Monotrait Ratio (HTMT) HTMT is and based on the estimation of the correlation between the constructs. Discrimi- nant validity is established based on the HTMT ratio. However, the threshold for HTMT has been debated in existing literature, Kline (2011) suggested a threshold of 0.85 or less, while eo et al (2008) recommend a liberal threshold of 0.90 or less. e HTMT results in this study indicates that HTMT ratio for required threshold of 0.90. ese higher order constructs were also validated as part of the measurement model assess- ment. Each of these constructs was assessed for reliability and convergent validity. Furthermore, the higher order construct was tested for discriminant validity with lower order constructs in the study as recommended by Sarstedt et al. (2019). e results for reliability and validity of the higher order constructs showed that both reliability and validity was established. e reliability and convergent validity for all other constructs were established as the value for reliability is > 0.70 and the AVE is greater than 0.50 respectively (Table 4). Further to assessment of reliability and validity, dis- criminant validity of the higher order construct was also assessed. e results of Fornell and Larcker (1981) criterion shows that square-root of AVE of the constructs is higher than its correlation with all other constructs (Table 5) whereas HTMT is also lower than 0.90 (Table 6). TABLE 4. Higher Order Construct Reliability and Convergent Validity Cronbach's alpha Composite reliability Average variance extracted (AVE) Communicative Competence 0.94 0.957 0.848 GPA 0.911 0.957 0.918 Motivation 0.927 0.943 0.734 Technology Adoption 0.867 0.938 0.883 TABLE 5. Fornell and Larcker (1981) Criterion – Higher Order Discriminant Validity Communicative Competence GPA Motivation Technology Adoption Communicative Competence GPA 0.856 Motivation 0.743 0.665 Technology Adoption 0.515 0.46 0.669

Sinergizar el rendimiento académico con la competencia comunicativa y la adopción de tecnología 513 Vol. 15(2) abril-junio 2025/ 502 - 518 TABLE 6. HTMT – Higher Order Discriminant Validity Communicative Competence GPA Motivation Technology Adoption Communicative Competence 0.921 GPA 0.885 0.958 Motivation 0.701 0.623 0.857 Technology Adoption 0.466 0.41 0.601 0.939 Subsequently, to confirm the proposed hypothesis, assessment of the hypothesized relationship was carried out. e results revealed that Communicative Competence has significantly influenced Motivation on OP (b = 0.538, t = 6.946, p = 0.000). Technology Adoption has significantly influ- enced Motivation on OP (b = 0.350, t = 4.725, p = 0.000). Motivation has significantly influenced Academic Achievement on OP (b = 0.623, t = 11.238, p = 0.000). erefore, H1, H2 and H3 were supported. H1 Communicative Competence positively and significantly influences Motivation. H2 Technology Adoption positively and significantly influences Motivation. H3 Motivation positively and significantly influences GPA. is finding indicates that higher levels of Communicative Competence are strongly associ- ated with increased Motivation. e result underscores the importance of effective communication skills in enhancing students’ motivational levels, which could be attributed to the confidence and engagement that competent communicators often exhibit. e result also highlights the role of Technology Adoption in fostering Motivation, possibly by making learning more interactive and engaging, thus reinforcing the value of integrating technology into educational practices. In addi- tion, this finding aligns with existing literature suggesting that motivated students are more likely to achieve higher academic performance, as they are more likely to engage with learning materials and put in the necessary effort to excel. e significant relationships identified in this study emphasize the importance of fostering Communicative Competence and encouraging Technology Adoption to enhance Motivation and, consequently, Academic Achievement. Educational institutions and instructors might consider incorporating strategies that develop communication skills and integrate technology to boost student motivation and improve academic outcomes. Additionally, these results suggest that interventions aimed at increasing Motivation could be effective in enhancing students’ academic achievement. TABLE 7. Direct Relationship Results Original sample (O) Standard deviation T statistics p values H1. CC -> M 0.538 0.077 6.946 0 H2. T A -> M 0.35 0.074 4.725 0 H3. M -> GPA 0.623 0.055 11.238 0 For the Mediation Analysis, the results (Table 8) revealed significant (p < 0.05) partial mediat- ing roles of motivation (H4: b = 4.526, p = 0.000). e total effect of Communicative Competence on GPA was significant (b = 6.946, p = 0.000), with the inclusion of the mediator, the direct effect was still significant (b =5.073, p = 0.000). Also, the results (see Table 8) revealed significant (p<

514 Shahraniza, Ahmad, Rubiyanti Interacción y Perspectiva. Revista de Trabajo Social Vol. 15(2): 2025 0.05) partial mediating roles of motivation (H4: b = 5.073, p = 0.000). e total effect of Technol- ogy Adoption on GPA was significant (b = 4.725, p = 0.000), with the inclusion of the mediator, the direct effect was still significant (b = 4.526, p = 0.000). erefore, H4 and H5 were validated. H4 Motivation mediates the relationship between Communication Competence and GPA. H5 Motivation mediates the relationship between Technology Adoption and GPA. ese findings confirm that Motivation is a significant mediator in both contexts, enhancing our understanding of how Communicative Competence and Technology Adoption influence Aca- demic Achievement. e significant mediating role of Motivation suggests that interventions aimed at improving students’ Motivational levels could enhance the impact of Communicative Com- petence and Technology Adoption on Academic Achievement. Educational programs and strate- gies that foster both Communication Skills and Technological Engagement should also consider ways to boost Motivation, as it plays a crucial role in achieving better academic outcomes. Overall, these results emphasize the importance of Motivation in educational contexts and provide a deeper understanding of the mechanisms through which Communicative Competence and Technology Adoption affect Academic Achievement. Future research could further explore additional factors that may influence this mediation process and test interventions designed to enhance Motivation as a pathway to improve academic success. TABLE 8. Mediation Relationship Results Total Effect Direct Effect Indirect Effects Coefficient p-value Coefficient p-value Coefficient p-value CC->GPA 6.946 0 5.073 0 H4. CC->M -> GPA 4.526 0 TA->GPA 4.725 0 4.526 0 H5. TA-> M -> GPA 5.073 0 Figure 3. Measurement Model Higher Order Constructs.

Sinergizar el rendimiento académico con la competencia comunicativa y la adopción de tecnología 515 Vol. 15(2) abril-junio 2025/ 502 - 518 The structural model results further confirm the support for the proposed hypotheses. The positive and significant influence of Communicative Competence on Motivation (Hy- pothesis 1) and the positive and significant influence of Technology Adoption on Motivation (Hypothesis 2) are evident. Additionally, the positive and significant influence of Motivation on GPA (Hypothesis 3) underscores the critical role of Motivation in Academic Achievement. Moreover, the mediating role of Motivation in the relationship between Communicative Competence and GPA (Hypothesis 4) and between Technology Adoption and GPA (Hypoth- esis 5) is supported by the data. This indicates that Motivation acts as a mediator, enhancing the impact of Communication Competence and Technology Adoption on Academic Achieve- ment. In summary, the data analysis and results chapter provide robust evidence supporting all five hypotheses, thereby validating the theoretical framework and providing insights into the complex relationships between Communication Competence, Technology Adoption, Mo- tivation, and Academic Achievement. LIMITATIONS Despite the robust support for the hypotheses, this study has several limitations. Firstly, the cross-sectional nature of the research design limits the ability to draw causal inferences. Longitudinal studies are needed to better understand the directionality of the relationships observed. Secondly, the study relies on self-reported measures, which may introduce bias or inaccuracies in the data. Objective measures or multi-source data could provide more reliable insights. Additionally, the sample may not be representative of all educational contexts, limit- ing the generalizability of the findings. Future research should aim to include diverse popula- tions and educational settings to enhance the external validity of the results. Finally, while the study explores key variables, it does not account for other potential factors influencing academic achievement, such as socio-economic status or prior academic performance, which could further illuminate the complexities of these relationships. FURTHER RESEARCH Future research should consider adopting longitudinal designs to examine how the relation- ships between Communicative Competence, Technology Adoption, Motivation, and Academic Achievement evolve over time. Investigating these dynamics in different educational contexts and with diverse populations can provide a more comprehensive understanding of the variables’ effects. Additionally, incorporating objective measures and multi-source data could enhance the accuracy of findings. Researchers might also explore additional factors that could influence academic achieve- ment, such as socio-economic variables, learning environments, and personal characteristics, to pro- vide a more holistic view of the determinants of academic success. Finally, examining interventions aimed at improving Communicative Competence and Technology Adoption, and their subsequent impact on Motivation and Academic Achievement, could offer practical strategies for enhancing educational outcomes.

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