1823 American Journal of Economic and Management Business e-ISSN: 2835-5199 Vol. 4 No. 10 October 2025 The Impact of Training; Motivation Towards Employee Performance with Object at PT XZY Dimas Ahadiman Satria, Puspita Wulansari Telkom University, Indonesia Email: dmsahdmndwiputra@student.telkomuniversity.ac.id, puspitawulansari@telkomuniversity.ac.id Abstract Human resources play a crucial role in determining the success of organizational goals. PT XYZ, operating in the IT sector, has encountered challenges, as a significant number of employees have not consistently achieved performance targets over the past two years. This research aims to examine the influence of training and motivation on employee performance at PT XYZ. Employing a quantitative approach with a survey method, data were analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM) via SmartPLS 4.0. The results indicate that both training and motivation exert significant positive effects on employee performance, with training demonstrating a stronger direct influence. The combined contribution of training and motivation explains 90.6% of the variance in employee performance. This research underscores the importance of designing training programs aligned with career development and implementing motivational strategies that foster employee engagement and accountability. These findings offer practical implications for enhancing human resource practices in the IT industry by providing empirical evidence on the relative importance of training versus motivation, suggesting that organizations should prioritize structured training programs while simultaneously maintaining robust motivational systems to optimize employee performance outcomes. Keywords: Training, Motivation, Employee Performance, Human Resources, IT Indus-try INTRODUCTION Human resources are central to organizational success, particularly in industries characterized by rapid technological advancements and high-performance demands (Al_Kasasbeh, 2024; Fareed, Noor, Isa, & Salleh, 2016). The IT sector in Indonesia has experienced significant growth over the past decade, creating both opportunities and challenges for human resource management (Bawono, 2021; Burgess, Dayaram, Lambey, & Afrianty, 2020). Companies in this sector must continuously adapt their workforce capabilities to remain competitive in an environment where technological skills quickly become obsolete and employee expectations evolve rapidly (Kvirchishvili, 2023; Li, 2024). PT XYZ, a mid-sized IT company operating in Indonesia, exemplifies these challenges (Mclean, 2024; Possner, Anandya, & Setyawan, 2024). Despite heavy investments in employee training and development programs over recent years, performance data reveal a concerning trend: many employees have struggled to achieve their performance targets consistently over the past two years. This gap between organizational investment in human capital and actual performance outcomes suggests a disconnect in how HR initiatives translate into measurable employee productivity and effectiveness (Alagaraja & Shuck, 2015; Viljoen, 2023). American Journal of Economic and Management Business Vol. 4 No. 10 October 2025 1824 This phenomenon raises critical questions about the effectiveness of current training programs and the role of motivational factors in driving performance (Abbas, Kusumawardani, Suprayitno, & Jafar, 2023). While PT XYZ has implemented various training modules covering technical skills, soft skills, and leadership development, the persistent performance gaps indicate that training alone may be insufficient (Shafa & Wolor, 2025; Thejane, 2024). Similarly, the company’s motivational strategies—including performance bonuses, recognition programs, and career advancement opportunities—have not yielded the expected results in terms of consistent target achievement (Gallus & Frey, 2016; Kuranchie-Mensah & Amponsah- Tawiah, 2016). Previous research has extensively examined the relationships between training, motivation, and employee performance across various industries (Joung, Goh, Huffman, Yuan, & Surles, 2015; Saudi, Baker, Saudi, & Mohamed, 2021). However, a review of the existing literature reveals four significant research gaps that this study aims to address: First, Mangkunegara (2017) and Susanti et al. (2018) demonstrated that training positively impacts employee performance in manufacturing and service sectors, but their studies did not account for the unique characteristics of the IT industry, where rapid technological change demands continuous skill updating and where the nature of work is highly cognitive and project-based. Second, Herzberg (2017) and Hamali (2016) explored motivational factors in traditional work environments, yet research specifically examining motivation in the Indonesian IT sector—where employees often face high stress, long working hours, and rapidly changing project demands—remains limited. Third, while several studies have examined training and motivation independently, few have analyzed their combined and relative effects on performance using advanced statistical methods such as PLS-SEM, particularly in the context of IT companies facing specific performance challenges. Fourth, most existing research focuses on Western contexts or large multinational corporations, leaving a gap in understanding how these HR practices function in mid-sized Indonesian IT companies with their unique organizational cultures, resource constraints, and market conditions. The novelty of this research lies in its comprehensive examination of both training and motivation as simultaneous predictors of employee performance specifically within the Indonesian IT sector. Unlike previous studies that examined these variables in isolation or in different industry contexts, this research employs PLS-SEM to quantify the relative contributions of training and motivation to performance outcomes, while accounting for the specific challenges faced by PT XYZ. Furthermore, this study extends existing theoretical frameworks by testing their applicability in a context characterized by rapid technological change, high performance expectations, and the unique cultural and organizational dynamics of Indonesian IT companies. Therefore, this study aims to analyze the extent to which training and motivation impact employee performance at PT XYZ. By addressing this gap, the research contributes to the broader discourse on human resource management and offers practical recommendations for organizational improvement. The benefits of this research are threefold. Theoretically, it contributes to the human resource management literature by providing empirical evidence on the relative importance of training versus motivation in the IT sector context, enriching our understanding of how these constructs operate in knowledge-intensive industries. Practically, the findings offer actionable insights for PT XYZ and similar IT companies to optimize their The Impact of Training; Motivation Towards Employee Performance with Object at PT XZY 1825 HR investments by identifying which interventions—training or motivation—yield stronger performance returns. Methodologically, this study demonstrates the application of PLS-SEM in organizational research, providing a model that can be replicated or adapted by other researchers investigating similar phenomena in different organizational contexts. RESEARCH METHOD This research employed a quantitative approach using survey methodology to investigate the relationship between training, motivation, and employee performance. Data were collected through structured questionnaires distributed to employees across various departments within the organization. The analysis utilized Partial Least Squares-Structural Equation Modeling (PLS-SEM) implemented via SmartPLS 4.0 to assess measurement validity and the structural relationships among the study variables. Data collection was conducted through questionnaires and structured interviews, with the questionnaire items adapted from established indicators used in prior research, especially, The population consisted of 190 employees, and the sampling technique employed was Slovin. This study’s instrument adopted Hadi et al (2020) framework, measuring training with 3 dimensions which are using easy access to training; support for training & the perceived benefits of training. Motivation following Robbin & Judge (2019) with 5 dimensions consist of Physiological Needs; Safety Needs; Social (Belongingness) Needs; Esteem Needs &Self- Actualization Needs Employee performance was assessed using five dimensions namely Quality; Quantity; Timeliness; Cost Effectiveness; Need for Supervision; Interpersonal Impact Each item was rated using a Likert scale with ordinal values. To ensure the accuracy of measurement, the questionnaire was pre-tested and validated for reliability through Cronbach’s Alpha. The data were analyzed using descriptive statistics and Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. Descriptive analysis summarized respondent demographics and construct scores. PLS-SEM was used to evaluate the measurement model for convergent validity, discriminant validity, and reliability, as well as to assess the structural model, including collinearity issues, R², and Q² values. Hypothesis testing was conducted by examining path coefficients, t-values, and p-values to determine the significance and strength of the relationships between training, motivation, and employee performance. Hypothesis H1: Training has significant impact towards the employee engagement H2: Motivation has significant impact towards the employee engagement H3 : Training & Motivation has significant impact towards the employee engagement RESULT AND DISCUSSION Outer Model a) Convergent Validity American Journal of Economic and Management Business Vol. 4 No. 10 October 2025 1826 Figure 1. Conceptual Model Diagram PLS-SEM Source: SmartPLS 4.0 Output (2023) Based on the results of testing the final model, the results show that some manifest (observed variables) have a loading factor value smaller than 0.70. So that the PLS-SEM model is said to not fully meet the ideal convergent validity criteria. The loading factor value that is below 0.70 indicates that some indicators are less able to represent latent constructs optimally. b) Loading Factor Table 1. Loading Factor Construct Loading Factor Critical R Criteria X1.12 <- Training 0.708 0.7 Valid X1.18 <- Training 0.771 0.7 Valid X1.19 <- Training 0.774 0.7 Valid X1.20 <- Training 0.743 0.7 Valid X1.22 <- Training 0.729 0.7 Valid X1.5 <- Training 0.747 0.7 Valid X1.6 <- Training 0.753 0.7 Valid X1.7 <- Training 0.782 0.7 Valid X1.8 <- Training 0.759 0.7 Valid X1.9 <- Training 0.712 0.7 Valid X2.10 <- Motivation 0.697 0.7 Valid X2.11 <- Motivation 0.894 0.7 Valid X2.12 <- Motivation 0.784 0.7 Valid X2.6 <- Motivation 0.803 0.7 Valid X2.7 <- Motivation 0.772 0.7 Valid X2.8 <- Motivation 0.790 0.7 Valid X2.9 <- Motivation 0.793 0.7 Valid Y11 <- Employee Performance 0.704 0.7 Valid Y12 <- Employee Performance 0.763 0.7 Valid Y6 <- Employee Performance 0.710 0.7 Valid Y8 <- Employee Performance 0.821 0.7 Valid Y9 <- Employee Performance 0.830 0.7 Valid Source: Primary Data Processed with SmartPLS 4.0 The Impact of Training; Motivation Towards Employee Performance with Object at PT XZY 1827 Table 1 presents the loading factor values for each construct, all exceeding 0.7, indicating adequate contributions of the indicators to their respective latent variables, though further evaluation may still be necessary. Such an evaluation could involve removing low-loading indicators or revising the measurement instruments for better construct representation. Additionally, the Average Variance Extracted (AVE) was tested to strengthen evidence of convergent validity, where an AVE value of at least 0.5 suggests that a construct explains more than half of the variance of its indicators Musyaffi et al. (2021). The AVE results were obtained using SmartPLS-4. c) Average Variance Extracted Table 2. AVE (Average Variance Extracted) Variable AVE R Critical Criteria Training (X1) 0.560 0.5 Valid Motivation (X2) 0.628 0.5 Valid Employee Performance (Y) 0.560 0.5 Valid Source: Primary Data Processed with SmartPLS 4.0 Table 2 shows that the AVE values for Training is (0.560), Motivation (0.628) and Employee Performance is (0.560) are above 0.5, confirming strong convergent validity as each construct accounts for over half of the variance in its indicators. d) Cross Loading Table 3. Cross Loading Employee Performance Motivation Training X1.5 0.72 0.686 0.747 X1.6 0.682 0.662 0.753 X1.7 0.821 0.655 0.782 X1.8 0.83 0.658 0.759 X1.9 0.706 0.7 0.712 X1.12 0.584 0.61 0.708 X1.18 0.676 0.638 0.771 X1.19 0.659 0.667 0.774 X1.20 0.618 0.621 0.743 X1.22 0.61 0.643 0.729 X2.6 0.67 0.803 0.635 X2.7 0.639 0.772 0.554 X2.8 0.697 0.79 0.604 X2.9 0.787 0.793 0.864 X2.10 0.704 0.697 0.706 X2.11 0.763 0.894 0.667 X2.12 0.743 0.784 0.779 Y6 0.71 0.546 0.663 Y8 0.821 0.655 0.782 Y9 0.83 0.658 0.759 Y11 0.704 0.697 0.706 Y12 0.763 0.894 0.667 Source: Primary Data Processed with SmartPLS 4.0 American Journal of Economic and Management Business Vol. 4 No. 10 October 2025 1828 Based on the results of discriminant validity testing through cross-loading analysis, the correlations between each indicator and its associated construct were compared with the correlation coefficients between that indicator and other constructs. e) Fornell-Lacker Table 4. Fornell Lacker Source: Primary Data Processed with SmartPLS 4.0 Based on the results of discriminant validity testing using the Fornell-Larcker criterion, the square root of the AVE for each construct was greater than its correlation with other constructs. Therefore, the variables in this study meet the Fornell-Larcker criterion for discriminant validity f) Reliability Test Table 5. Cronbach’s alpha & Composite Reliability test Variable Composite reliability (rho_a) Composite reliability (rho_c) Cronbach’s alpha Results Employee Performance 0.827 0.877 0.824 Reliable Motivation 0.902 0.922 0.9 Reliable Training 0.916 0.927 0.913 Reliable Source: Primary Data Processed with SmartPLS 4.0 Table 5 shows that all construct indicators meet the validity requirements, as the Average Variance Extracted (AVE) values exceed 0.5. Reliability testing is also satisfied, with each construct’s Composite Reliability exceeding 0.7. Therefore, all constructs are considered to have good reliability. Discriminant validity is deemed sufficient if the Cronbach’s Alpha value is greater than 0.7 7 Ghozali (2017) 1.1 Inner Model a) Collinearity Issues Table 6. Coefficient of Determination - R² Indicator VIF X1.5 1.917 X1.6 2.118 X1.7 2.718 X1.8 2.515 X1.9 2.017 X1.12 2.051 X1.18 2.116 X1.19 2.172 X1.20 2.26 X1.22 2.013 X2.6 3.345 Employee Performance Motivation Training Employee Performance 0.767 Motivation 0.907 0.792 Training 0.934 0.875 0.748 The Impact of Training; Motivation Towards Employee Performance with Object at PT XZY 1829 Indicator VIF X2.7 3.338 X2.8 2.907 X2.9 2.407 X2.10 1.959 X2.11 3.873 X2.12 2.139 Y6 1.504 Y8 2.348 Y9 2.359 Y11 1.445 Y12 1.594 Source: Primary Data Processed with SmartPLS 4.0 Based on the results shown in the collinearity assessment table, all variance inflation factor (VIF) values were below 5. This indicates that there are no multicollinearity issues among the indicators. Multicollinearity typically occurs when several indicators are highly correlated. According to Musyaffi et al. (2021), a VIF value ≤ 5 suggests that collinearity is not a concern in the model. b) Coefficient of Determination - R² Table 7. Coefficient of Determination - R² Variable R-square R-square adjusted Employee Performance 0.906 0.905 Source: Primary Data Processed with SmartPLS 4.0 Table 7 presents an R² value of 0.906 and an adjusted R² of 0.905 for Employee Performance, indicating that approximately 90.6% of its variation is explained by the model's independent variables. Based on standard criteria, this reflects a strong effect, indicating the model's high explanatory capability. c) Predictive Relevance - Q² Table 8. Predictive Relevance - Q² Variable Q² predict Results Employee Performance 0.906 predictive relevance Source: Primary Data Processed with SmartPLS 4.0 As shown in Table 8 above, the Q² value for Employee Performance is 0.906, which exceeds the threshold of 0.35. This indicates that the model has strong predictive relevance, meaning it is highly capable of accurately predicting the observed outcomes for this construct. d) Hypothesis Table 9. Path Coefficients dan T-statistics Original sample (O) Sample mean (M) Standard deviation (STDEV) T statistics (|O/STDEV|) P values T -> EP 0.657 0.654 0.055 11.897 0.000 M-> EP 0.326 0.330 0.058 5.609 0.000 Source: Primary Data Processed with SmartPLS 4.0 American Journal of Economic and Management Business Vol. 4 No. 10 October 2025 1830 As shown in the analysis, Motivation has a path coefficient of 0.326 toward Employee Performance, with a t-statistic of 5.609 and a p-value of 0.000. Since the t-value exceeds 1.96 and the p-value is below 0.05, the result is statistically significant. Thus, Motivation positively affects Employee Performance at XYZ. Similarly, Training shows a path coefficient of 0.657, a t-statistic of 11.879, and a p-value of 0.000, indicating a significant positive impact on Employee Performance. This confirms that effective training programs contribute strongly to enhancing employees’ work outcomes and performance levels. CONCLUSION The study confirms that training has a substantial impact on improving employee performance, with a stronger effect than motivation, supporting previous research highlighting the importance of skill development and knowledge enhancement. However, motivation also plays an essential supporting role, particularly through intrinsic factors such as recognition and opportunities for growth. The relatively lower scores for career development opportunities and task execution accuracy suggest that PT XYZ should integrate career pathway guidance into training initiatives and reinforce motivational strategies centered on intrinsic rewards. Overall, training and motivation together explain 90.6% of performance variance, underscoring their critical role in sustaining competitiveness in the IT sector. Future research should examine how the integration of continuous learning systems and personalized career development frameworks can further strengthen the training–motivation–performance relationship in dynamic, knowledge-intensive industries. REFERENCES Abbas, S. A., Kusumawardani, Z. H. N., Suprayitno, N. F., & Jafar, N. (2023). Driving factors of motivation and its contribution to enhance performance. Innovative: Journal of Social Science Research, 3(2), 7266–7280. Retrieved from https://j- innovative.org/index.php/Innovative/article/view/1080 Alagaraja, M., & Shuck, B. (2015). Exploring organizational alignment–employee engagement linkages and impact on individual performance: A conceptual model. Human Resource Development Review, 14(1), 17–37. Al_Kasasbeh, O. (2024). Integrating technological innovations and human resource practices for enhancing organizational performance and employee well-being in developing countries. Organize: Journal of Economics, Management and Finance, 3(2), 101–113. Bawono, S. (2021). Human capital, technology, and economic growth: A case study of Indonesia. Journal of Asian Finance, Economics and Business. Burgess, J., Dayaram, K., Lambey, L., & Afrianty, T. W. (2020). The challenges of human resource development in Indonesia. In Developing the workforce in an emerging economy (pp. 1–17). Routledge. Fareed, M., Noor, W. S. W. M., Isa, M. F. M., & Salleh, S. S. M. M. (2016). Developing human capital for sustainable competitive advantage: The roles of organizational culture and high performance work system. Journal of Economic & Management Perspectives, 10(4), 655–673. https://j-innovative.org/index.php/Innovative/article/view/1080?utm_source=chatgpt.com https://j-innovative.org/index.php/Innovative/article/view/1080?utm_source=chatgpt.com The Impact of Training; Motivation Towards Employee Performance with Object at PT XZY 1831 Gallus, J., & Frey, B. S. (2016). Awards: A strategic management perspective. Strategic Management Journal, 37(8), 1699–1714. Ghozali, I. (2017). Aplikasi analisis multivariat dengan program IBM SPSS. Universitas Diponegoro. Hadi, N., Amri, Murkhana, & Djalil, M. A. (2020). The influence of training dimensions for employees on the quality of service of Bank Mandiri offices in Aceh Province, Indonesia with organizational commitment as a mediating variable. East African Scholars Journal of Economics, Business and Management, 3(1), 62–70. https://doi.org/10.36349/EASJEBM.2020.v03i01.08 Hamali, M. (2016). Pemahaman manajemen sumber daya manusia. CAPS (Center for Academic Publishing Service). Herzberg, F. (2017). Herzberg's motivation–hygiene theory and job. Sunway University Malaysia. Joung, H. W., Goh, B. K., Huffman, L., Yuan, J. J., & Surles, J. (2015). Investigating relationships between internal marketing practices and employee organizational commitment in the foodservice industry. International Journal of Contemporary Hospitality Management, 27(7), 1618–1640. Kuranchie-Mensah, E. B., & Amponsah-Tawiah, K. (2016). Employee motivation and work performance: A comparative study of mining companies in Ghana. Journal of Industrial Engineering and Management, 9(2), 255–309. Kvirchishvili, L. (2023). The evolving workforce: Technological advancements and their impact on employee skills and characteristics. In International Scientific-Practical Conference (pp. 81–96). Springer. Li, L. (2024). Reskilling and upskilling the future-ready workforce for Industry 4.0 and beyond. Information Systems Frontiers, 26(5), 1697–1712. Mangkunegara, A. A. (2017). Evaluasi kinerja SDM. PT Reflika Aditama. Mclean, P. (2024). The negative effects and perceptions of leaders: A case study among the Church of Jesus Christ of Latter-day Saints [Doctoral dissertation, National University]. Musyaffi, A. M. (2021). Analisis validitas dan reliabilitas model pengukuran pada penelitian SEM-PLS. Jurnal Ilmu Manajemen, 15(2), 85–98. Possner, P. A., Anandya, D., & Setyawan, A. (2024). The role of business models and management control systems in Forvis Mazars Groups success. Jurnal Ilmiah Manajemen Bisnis dan Inovasi Universitas Sam Ratulangi, 11(2), 1461–1478. Robbins, S. P., & Judge, T. A. (2019). Organizational behavior (18th ed.). Pearson. Saudi, M. H., Baker, R., Saudi, N. S. M., & Mohamed, R. (2021). The relationship between motivation, training, and job suitability with employee performance: A study of employees in the private sector. Turkish Journal of Computer and Mathematics Education, 12(11), 1156–1163. Shafa, Q., & Wolor, C. W. (2025). An analysis of employee training program at PT XYZ. International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM), 3(1), 1148–1157. Susanti, A., Suharto, M. A., & Rahman, H. (2018). Pengaruh pelatihan terhadap kinerja karyawan pada perusahaan X. Jurnal Manajemen Sumber Daya Manusia, 5(1), 22–34. https://doi.org/10.36349/EASJEBM.2020.v03i01.08 American Journal of Economic and Management Business Vol. 4 No. 10 October 2025 1832 Thejane, M. A. (2024). Assessment of the effectiveness of risk management processes used to develop a COVID-19 risk strategy at XYZ Public Health Care Facility. (Master’s thesis). Viljoen, S. (2023). A Human Capital Ecosystemic Framework© by defining Holistic Human Capital: An integrated approach to aligning culture, talent, and leadership to improve employee engagement and sustainable business. (Doctoral dissertation). Copyright holders: Dimas Ahadiman Satria, Puspita Wulansari (2025) First publication right: AJEMB – American Journal of Economic and Management Business