Kang Li & D. Pimchangthong /Future Technology August 2025| Volume 04 | Issue 03 | Pages 251-258 251 Article The impact of job substitution and job intensity on job performance in the process of enterprise digital transformation Kang Li, Daranee Pimchangthong* Institute of Science, Innovation and Culture, Rajamangala University of Technology Krungthep (RMUTK), Bangkok, Thailand A R T I C L E I N F O Article history: Received 30 April 2025 Received in revised form 07 June 2025 Accepted 20 June 2025 Keywords: Digital Transformation, Job Substitution, Job Intensity, Unemployment Insecurity, Job Mobility Insecurity, Job Performance *Corresponding author Email address: daranee.p@mail.rmutk.ac.th DOI: 10.55670/fpll.futech.4.3.23 A B S T R A C T This study explores the effects of job substitution and job intensity on employee performance in the context of digital transformation, focusing on the mediating role of job insecurity (unemployment insecurity and job mobility insecurity). Using confirmatory research methods, we analyzed 1,002 valid samples from seven Chinese furniture manufacturers. A structural equation model (SEM) developed via AMOS 27.0 revealed: (1) Job substitution (standardized coefficient = -0.254, p < 0.001) and job intensity (standardized coefficient = - 0.264, p < 0.001) significantly negatively impact job performance; (2) Unemployment insecurity (mediating effect = -0.087 for job substitution; -0.10 for job intensity) and job mobility insecurity (mediating effect = -0.083 for job substitution; -0.113 for job intensity) fully mediate these relationships. This research validates relevant theories, clarifies variable relationships, and enriches digital transformation and human resource management theories. Practically, it provides HR management advice for enterprises, facilitating performance improvement and sustainable development. Methodologically, it constructs a comprehensive framework considering multiple variables, offering a new perspective to analyze the impact of transformation on employees. 1. Introduction Enterprise DT utilizes digital and information communication technologies to redesign and optimize business processes, organizational structures, working methods, and interactions with customers, suppliers, and partners. It can improve efficiency, reduce costs, enhance innovation capabilities, and optimize the customer experience, and has thus become a strategic focus for numerous enterprises [1]. The world has fully entered the digital age. According to data from Globe Newswire [2], the DT market is expected to grow significantly at a compound annual growth rate (CAGR) of 17.42% from 2023 to 2028. In CHN, when elaborating on the Five-Year Plan for National Economic and Social Development and the Long-Range Goals for 2035 in the 2021 government report, it emphasized "accelerating digital development and building a Digital CHN", positioning digitalization as a top-level strategic priority [3]. Chinese furniture manufacturing enterprises are crucial to CHN's manufacturing industry. In the context of DT, some large-scale furniture manufacturers have launched DT and upgrading plans. These plans aim to achieve data sharing and system integration among sales, design, the Manufacturing Execution System (MES), and the Enterprise Resource Planning (ERP) system. By leveraging digital and information technologies, they comprehensively transform their business models, operational processes, organizational structures, and corporate cultures to pursue sustainable development [4]. The success of their digital integration depends not only on technology but also on the performance of employees [5]. However, DT can lead to job displacements, triggering employee anxiety, depression, and fatigue, which reduces overall performance. The widespread use of digital tools has changed the work process. Employees need to adapt to new processes and learn new skills, and the learning curve affects work efficiency [6]. With the development of intelligence and digitalization, some jobs are being replaced, leading to unemployment. Employees have to re-evaluate their careers and often need to undergo retraining or learn new skills. Learning new skills is both time-consuming and laborious. The introduction of these technologies also reallocates job substitution and increases job intensity, which can harm their short-term job performance. Therefore, job substitution and increased job intensity during the digital transformation process pose challenges to employees' job performance. Enterprises need to address the insecurities of employees caused by job substitution and increased job intensity. May 2025| Volume 01 | Issue 01 | Pages 01- 03 ISSN 2832-0379 Open Access Journal Journal homepage: https://fupubco.com/futech https://doi.org/10.55670/fpll.futech.0.0.0 Future Technology August 2025| Volume 04 | Issue 03 | Pages 251-258 Journal homepage: https://fupubco.com/futech https://doi.org/10.55670/fpll.futech.4.3.23 Future Technology Open Access Journal ISSN 2832-0379 mailto:daranee.p@mail.rmutk.ac.th https://fupubco.com/futech https://doi.org/10.55670/fpll.futech.0.0.0 https://fupubco.com/futech https://doi.org/10.55670/fpll.futech.4.3.23 Kang Li & D. Pimchangthong /Future Technology August 2025| Volume 04 | Issue 03 | Pages 251-258 252 These factors have long-term negative impacts on employees' mental health and career development. During DT, unemployment and job mobility insecurities are the primary psychological hurdles employees face. These not only impact daily JP but also erode loyalty and long-term commitment to the organization. When career prospects seem uncertain, employees may prioritize short-term goals, reducing their dedication to the company's long-term aims. Additionally, such insecurities create a tense workplace atmosphere, hampering team collaboration and communication, and ultimately affecting the team's overall performance and synergy [7,8]. Against this backdrop, this study constructs a comprehensive theoretical framework to explore the occupational insecurity experienced by employees in large-sized furniture manufacturing enterprises during the DT process and its related impacts. The framework encompasses variables such as JS, JI, UI, JMI, and JP. Numerous studies explore the relationships among JS, JI, and JP. In DT, JS means technology replacing traditional jobs, changing employees' roles [9]. JP, used to evaluate employees, includes productivity, quality, innovation, and customer satisfaction aspects [10]. When enterprises use automated equipment, employees may need to learn new skills, which impacts JP. Research shows a complex relationship between JS and performance. Some studies find JS may lower performance in the short term as employees adapt, but long-term, if they adapt and master new skills, performance may rise [11]. Regarding work intensity, it includes the time and energy employees invest in their work, as well as the pressure they face when completing tasks [12]. High-intensity work may cause negative emotions such as fatigue and anxiety among employees, thus affecting JP [13]. When employees are in a state of high-intensity work for a long time, work efficiency may decline, and the error rate may increase [14]. Based on these literature studies, the following hypotheses are proposed: H1. The JS has an effect on JP. H2. The JI has an effect on JP. UI is the worry and anxiety about job loss or unemployment, caused by factors such as economic changes, layoffs, or technological advancements. It can cause employees to become more anxious, stressed, and dissatisfied with their work [15]. JMI is employees' concerns about job transfers or position changes. It shows in responsibility and task changes, difficulty adapting to new job aspects, and learning - related unease. Also, it makes employees worry about promotions and career development, adding to career advancement uncertainty [16]. Tu et al. [17] explain the link between technological innovation and workers' job insecurity, showing how tech advancements can cause job displacement. Constant innovation may automate or replace traditional jobs, making employees more worried about losing their jobs. Varshney [18] claims that DT can change job content and requirements, affecting employees' job-mobility insecurity. DT may require employees to learn new skills, which can make them anxious about job mobility. Dengler and Gundert [19] found a connection between computerization levels and job insecurity. Their study showed that more computerization makes workers more worried about losing their jobs. JI refers to the psychological and physical load that employees bear in their jobs, and it is a crucial factor influencing employee JP, occupational health, and overall quality of life. Shao et al. [20] established a negative relationship between employees' JI and their intention to stay in a job. An increase in JI leads to a decrease in the intention to stay, suggesting that high JI may be linked to employees' perceptions of occupational stability and satisfaction. Chen [21] suggested that as JI rises, employees may face greater pressure and discomfort, thereby strengthening their intention to leave the job. Karamessini et al. [22] identified several critical risk factors contributing to insecurity, including low educational levels, gender and racial discrimination, remote geographical locations, impoverished family backgrounds, economic mobility, and unfavorable policy environments. The escalation in JI can amplify the pressure and discomfort experienced by employees, subsequently diminishing their sense of occupational stability and satisfaction. Jardak and Ben Hamad [23] suggest that employees' sense of job insecurity reduces their motivation and efficiency, thereby affecting the company's performance. Therefore, during DT, it is essential for companies to address the issue of employee job insecurity and implement measures to alleviate their anxiety and stress, thereby enhancing the effectiveness of DT. Sverke et al. [24] suggest that job insecurity is a common outcome of DT and significantly increases employees' psychological pressure. If this pressure is not controlled, it may lead to a decline in employee performance. From a negative perspective, when employees perceive an inability to cope with job insecurity, it can result in diminished performance. UI and JMI are two crucial factors that influence organizational performance. Abolade [25] establishes a certain impact of job insecurity and employee turnover rate on performance. Job insecurity can affect organizational performance in multiple ways, including reducing employee productivity, increasing turnover, and diminishing employee satisfaction. The turnover of employees can further impair organizational performance due to the loss of experienced personnel and the necessity to recruit and train new employees. Employee job insecurity emerges as a pivotal organizational concern closely tied to employee performance [26]. Both employee insecurity and turnover can significantly impact organizational performance. Employees harboring job security concerns are troubled about their economic, occupational, and personal security. Job insecurity can Abbreviations CHN China DT Digital Transformation JS Job Substitution JI Job Intensity JMI Job Mobility Insecurity JP Job Performance JSA Task Substitution JSB Role Substitution JSC Tools Substitution JIA Workload JIB Job Difficulty JIC Job Urgency JPA Job Time JPB Job Quality JPC Job Quantity JMIA Changes in Responsibilities and Tasks JMIB Change in Skill Requirements JMIC Uncertainty in Career Development Opportunities UIA Employment Uncertainty UI Unemployment Insecurity UIB Psychological Impact UIC Financial Situation Kang Li & D. Pimchangthong /Future Technology August 2025| Volume 04 | Issue 03 | Pages 251-258 253 adversely affect employee mental health, job satisfaction, and JP [27]. Therefore, the following hypotheses are proposed: H3. UI is a mediator between JS and JP. H4. JMI is a mediator between JS and JP. H5. UI is a mediator between JI and JP. H6. JMI is a mediator between JI and JP. All the hypotheses were formulated according to the research framework illustrated in Figure 1. Figure 1. Research framework diagram This study explores the impact of JS and JI on employees' JP during enterprise DT, with a focus on the mediating roles of UI and JMI. Using confirmatory research methods, it analyzes 1,002 valid sample data from seven well-known Chinese furniture manufacturers, and establishes a Structural Equation Model (SEM) to verify the direct effects of JS and JI on JP, as well as the mediating mechanisms of UI and JMI. This aims to provide references for the theory and practice of human resource management in the context of DT. 2. Methodology 2.1 Population and sample The population in this study consisted of employees, middle-level management, and executives from seven medium-sized furniture manufacturing companies. The number of populations was 16,704, and the sample size was determined using Kline [28] as the optimal sample size for performing SEM or path analysis. The effective sample size of this study needs to be greater than 980. 2.2 Measurement The research dimensions of all variables in the research model are taken from existing literature and slightly modified to fit the research context. Specifically, the observed indicators in the study are JS and JI. JS proposed by Barley et al. [29], which is manifested in JSA, JSB, and tool substitution. Iranmanesh et al. [30] proposed that the components of JI include JIA, task difficulty, and task urgency. The latent variable studied in this study is JP proposed by Na-Nan et al. [31], which is mainly categorized into job time, JPB, and JPC. The study's mediator variables are UI and JMI. UI proposed by De Witte [32], which mainly comprises three dimensions: UIA, UIB, and UIC impact. JMI proposed by Adekiya [16] mainly consists of three dimensions: JMIA, JMIB, and JMIC. 2.3 Questionnaire design The design of the questionnaire mainly consists of three parts as follows: (1) Introduction of the survey purpose, including brief instructions on respondent confidentiality and the non- biased nature of responses. (2) Personal profile, such as gender, age, marital status, level of education, years of tenure in the current company, and current position. (3) Survey questions designed to measure the main variables of the research model. In this study, three dimensions were allocated to each variable, resulting in a total of 49 questions using a five-point Likert scale. The collected data were statistically analyzed for sample characteristics using SPSS 26.0 and AMOS 27.0 software. Data collection was conducted online. The survey took place from May to July 2024, and 1002 valid samples were confirmed. The effective sample rate was approximately 86.77%. This study was approved by the ethical review board of Mahachulalongkorn-rajavidyalaya University, certification number R.355/2024. 2.4 Reliability and validity Reliability analysis results, as shown in Table 1, revealed that for JS, Cronbach's Alpha was 0.832; JI was 0.813; JP was 0.859; UI was 0.823; and JMI was 0.826. The overall scale (ALL), had a Cronbach's Alpha of 0.708. A Cronbach's Alpha above 0.7 implies good scale internal consistency, meaning the measurement items consistently measure the intended constructs [33]. Regarding content validity, three experts - one academic scholar and two enterprise CEOs - rated the 49- item survey questionnaire using the IOC (Index of Content Validity). Their diverse perspectives and expertise ensured a comprehensive evaluation. Based on the scoring, four items received an average score of 0.67, indicating discrepancies or uncertainties among experts regarding the influence, importance, or assessment confidence of these items. However, 45 items had an average score of 1, indicating experts unanimously agreed on their significant influence, importance, and high assessment confidence [34]. Table 1. The reliability of the pre-survey comes from the author's analysis NO. Variables Cronbach's Alpha IOC Index N of Items 1 JS 0.832 1 9 2 JI 0.813 1 9 3 JP 0.859 0.92 13 4 UI 0.823 1 9 5 JMI 0.826 0.96 9 6 ALL 0.708 0.97 49 3. Results and discussion 3.1 Frequency analysis of personal information According to the statistical analysis of personal information in Table 2, the gender ratio of employees is relatively balanced, with slightly more men than women. Employees aged 31 - 40 account for the highest proportion, reaching 46.5%. 62.6% of the employees are married. 54.3% of the employees have a three-year college education background. Work experience is mainly concentrated in the range of 1 - 5 years, accounting for 70.2%. Regarding job hierarchy, operators or basic staff constitute 81.9% (the majority) of the sample. Based on the data analysis, enterprises need to pay attention to the education and skills training of employees to meet the needs during the DT process. At the same time, it is necessary to focus on the cultivation and development of management personnel to improve competitiveness. 3.2 Descriptive analysis of research variables The mean values of the variables range from 2.864 to 3.149, as shown in Table 3, indicating that respondents' ratings for these variables tend to be neutral overall. Standard deviations range from 0.690 (JP) to 0.768 (JS), showing consistent levels of dispersion in responses. These statistical JS JI UI JP JMI Kang Li & D. Pimchangthong /Future Technology August 2025| Volume 04 | Issue 03 | Pages 251-258 254 results provide a reliable data foundation for further research and analysis [35]. Table 2. Analysis of Personal Information Distribution comes from the author's analysis Table 3. Descriptive analysis of variables from the statistical software Category Subcategory Frequency Percent Total Gender Male 538 53.70% 1002 Female 464 46.30% Age 21-30 Years old 383 38.20% 1002 31-40 Years old 466 46.50% 41-50 Years old 135 13.50% > 50 Years old 18 1.80% Marital Single 335 33.40% 1002 Married 627 62.60% Divorced 34 3.40% Widowed 6 0.60% Educatio nal High School or Lower 206 20.60% 1002 3 Years College Education 544 54.30% Bachelor's Degree 224 22.40% Master's Degree or Higher 28 2.80% Length of Service in the Current Company 1-5 Years 703 70.20% 1002 6-10 Years 254 25.30% > 10 Years 45 4.50% Employe es' Current Position Operators/ Basic Staff 821 81.90% 1002 Basic Managers 97 9.70% Middle Management 70 7% Executive 14 1.40% 3.3 Convergent validity analysis According to the convergent validity analysis in Table 4. The Average Variance Extracted (AVE) for all dimensions exceeds 0.5, and the Composite Reliability (CR) exceeds 0.7, demonstrating high explanatory power and internal consistency among the measurement indicators for each dimension. Specifically, the path coefficients for each dimension are also high, further confirming the strong explanatory ability of the measurement indicators for the latent variables [36]. 3.4 Discriminant validity analysis According to the data presented in the Table 5, following the Fornell and Larcker [36] criterion, which requires the square root of Average Variance Extracted (AVE) for each construct to be greater than its correlations with other constructs, the analysis reveals that each construct's square root of AVE is indeed greater than its correlations with other constructs, as shown in both columns and rows. This indicates that constructs such as JSA, JSB, JSC, etc., are distinct from each other, reflecting the uniqueness of their measurements. Furthermore, it confirms the effectiveness of measuring latent variables, highlighting significant differences between constructs. This ensures the reliability and validity of the model, thereby enhancing the credibility and scientific rigor of the research results. Such a discriminant validity analysis provides confidence in using these latent variables for subsequent structural equation modeling or other statistical analyses. It helps ensure that research conclusions are based on a reliable and effective measurement foundation, providing solid support for theoretical validation and empirical research. Table 4. Data of convergent validity analysis from the AMOS software Path Estimate AVE CR Q1 <--- JSA 0.856 0.608 3 0.8226 Q2 <--- JSA 0.734 Q3 <--- JSA 0.744 Q4 <--- JSB 0.852 0.607 0.8218 Q5 <--- JSB 0.739 Q6 <--- JSB 0.741 Q7 <--- JSC 0.862 0.629 2 0.8353 Q8 <--- JSC 0.753 Q9 <--- JSC 0.76 Q10 <--- JIA 0.851 0.610 1 0.8237 Q11 <--- JIA 0.757 Q12 <--- JIA 0.73 Q13 <--- JIB 0.885 0.618 4 0.8282 Q14 <--- JIB 0.72 Q15 <--- JIB 0.744 Q16 <--- JIC 0.848 0.605 9 0.8212 Q17 <--- JIC 0.756 Q18 <--- JIC 0.726 Q19 <--- JPA 0.876 0.570 7 0.8405 Q20 <--- JPA 0.703 Q21 <--- JPA 0.709 Q22 <--- JPA 0.72 Q23 <--- JPB 0.883 0.563 1 0.8647 Q24 <--- JPB 0.686 Q25 <--- JPB 0.719 Q26 <--- JPB 0.726 Q27 <--- JPB 0.722 Q28 <--- JPC 0.901 0.603 4 0.8578 Q29 <--- JPC 0.734 Q30 <--- JPC 0.719 Q31 <--- JPC 0.739 Q32 <--- UIA 0.839 0.615 2 0.8271 Q33 <--- UIA 0.747 Q34 <--- UIA 0.764 Q35 <--- UIB 0.826 0.576 9 0.8029 Q36 <--- UIB 0.722 Q37 <--- UIB 0.726 Q38 <--- UIC 0.861 0.618 3 0.8286 Q38 <--- UIC 0.729 Q40 <--- UIC 0.763 Q41 <--- JMIA 0.843 0.614 4 0.8266 Q42 <--- JMIA 0.752 Q43 <--- JMIA 0.753 Q44 <--- JMIB 0.864 0.614 9 0.8265 Q45 <--- JMIB 0.738 Q46 <--- JMIB 0.744 Q47 <--- JMIC 0.849 0.625 5 0.8332 Q48 <--- JMIC 0.775 Q49 <--- JMIC 0.745 Variables Mean SD JS 3.112 0.768 JI 3.149 0.738 JP 2.864 0.690 UI 3.132 0.761 JMI 3.134 0.755 Kang Li & D. Pimchangthong /Future Technology August 2025| Volume 04 | Issue 03 | Pages 251-258 255 3.5 Structural validity analysis A structural equation model (SEM) was developed, and the model fit was assessed. The results indicated that the chi- square p-value was less than 0.05. Anderson and Gerbing [37] demonstrated that researchers could systematically refine the residual correlations among measurement variables to enhance the model fit. Thus, a covariance path was established between the residuals of JS and JI. The p-value was 0.1 (> 0.05), which confirmed the adequate fit of the model to the data. Based on the model fit analysis of the structural equation model in this study, as shown in Table 6, all indicators proposed by Fornell and Larcker [36] suggest a good model fit. 3.6 Path analysis Standardized regression weight analyses help understand model path impacts [38]. As shown in Table 7, the negative impacts of JS and JI on JP are significant, with standardized coefficients of -0.254 and -0.264, respectively, and p-values both less than 0.05. The negative impacts of UI and JMI on JP are -0.308 and -0.316, respectively, with p- values also significantly less than 0.05. Table 6. Model fit analysis from the AMOS software X²/df RMSEA GFI TLI CFI Standard value <3 <0.1 >0.9 >0.9 >0.9 Actual value 1.206 0.014 0.987 0.991 0.993 Table 7. Analysis of standardized regression weights from the AMOS software Parameter Estimate Lower Upper P JP <--- JS -.254 -.352 -.142 .000 JP <--- JI -.264 -.373 -.145 .001 UI <--- JS .283 .166 .392 .000 UI <--- JI .325 .211 .438 .000 JMI <--- JS .263 .148 .378 .000 JMI <--- JI .359 .247 .477 .000 JP <--- UI -.308 -.406 -.198 .001 JP <--- JMI -.316 -.420 -.206 .000 Table 5. Data of the discriminant validity analysis from the AMOS software JSA JSB JSC JIA JIB JIC UIA UIB UIC JMIA JMIB JMIC JPA JPB JPC JSA 0.60 8 JSB 0.24 4*** 0.60 7 JSC 0.29 6*** 0.26 3*** 0.62 9 JIA 0.07 4*** 0.06 6*** 0.10 5*** 0.61 0 JIB 0.08 5*** 0.07 6*** 0.10 4*** 0.21 7*** 0.61 8 JIC 0.1** * 0.04 5*** 0.09 1*** 0.22 5*** 0.21 4*** 0.60 6 UIA 0.18 1*** 0.16* ** 0.17 4*** 0.17 1*** 0.12 3*** 0.14* ** 0.61 5 UIB 0.13 7*** 0.09 7*** 0.11 8*** 0.13 1*** 0.15 5*** 0.11 6*** 0.52 2*** 0.57 7 UIC 0.12 2*** 0.15 8*** 0.11 5*** 0.16 8*** 0.18 6*** 0.11 9*** 0.51 7*** 0.56 9*** 0.61 8 JMIA 0.08 2*** 0.09 2*** 0.10 6*** 0.06 8*** 0.08 2*** 0.08 5*** 0.17 9*** 0.16 5*** 0.14 4*** 0.61 4 JMIB 0.08* ** 0.08 8*** 0.10 4*** 0.10 7*** 0.06 1*** 0.08 7*** 0.14 1*** 0.12 7*** 0.18 1*** 0.24 4*** 0.61 5 JMIC 0.07 3*** 0.09 3*** 0.14 5*** 0.11 8*** 0.08 3*** 0.09 6*** 0.15 1*** 0.09 1*** 0.17 3*** 0.27 1*** 0.23 5*** 0.62 6 JPA - 0.17 3*** - 0.20 3*** - 0.16 4*** - 0.16 5*** - 0.24* ** - 0.18* ** - 0.31 4*** - 0.21 8*** - 0.29 2*** - 0.18 7*** - 0.15* ** - 0.2** * 0.57 1 JPB - 0.2** * - 0.22 7*** - 0.28 1*** - 0.19 8*** - 0.18 3*** - 0.18 7*** - 0.31 4*** - 0.32 6*** - 0.39 6*** - 0.22 6*** - 0.22 2*** - 0.22 3*** 0.41 *** 0.5 63 JPC - 0.13 3*** - 0.17 7*** - 0.24 9*** - 0.22 4*** - 0.20 7*** - 0.18 5*** - 0.29 6*** - 0.26 6*** - 0.41 3*** - 0.24 8*** - 0.20 6*** - 0.24 6*** 0.43 7*** 0.5 35* ** 0.60 3 AVE square root 0.78 0 0.77 9 0.79 3 0.78 1 0.78 6 0.77 8 0.78 4 0.76 0 0.78 6 0.78 4 0.78 4 0.79 1 0.75 5 0.7 50 0.77 7 Note: *** indicates p-value less than 0.05; diagonal values represent AVE (Average Variance Extracted) Kang Li & D. Pimchangthong /Future Technology August 2025| Volume 04 | Issue 03 | Pages 251-258 256 3.7 Hypothesis testing Based on the data in Figure 2, the analysis results with all p-values < 0.05 can be described as follows: JS affects JP with an estimate of -0.254, a negative correlation confirming hypothesis H1. The confidence interval (-0.142, - 0.352) in Table 8 supports this. JI affects JP with an estimate of -0.264, a negative correlation confirming hypothesis H2. The confidence interval (-0.145, -0.373) in Table 8 supports this [39]. JS impacts UI (estimate: 0.283), which in turn affects JP (estimate: -0.308), All p-values significant, confirming hypothesis H3, meaning UI mediates between JS and JP, and the value of the mediating effect is -0.087 (M3). JS impacts JMI (estimate: 0.263), which affects JP (estimate: -0.316), All p- values are significant, confirming hypothesis H4, meaning JMI mediates between JS and JP, and the value of the mediating effect is -0.083 (M4). JI impacts UI (estimate: 0.325), which affects JP (estimate: -0.308), All P-values are significant, confirming hypothesis H5, meaning UI mediates between JI and JP, and the value of the mediating effect is -0.10 (M5). JI impacts JMI (estimate: 0.359), which affects JP (estimate: - 0.316), All P-values are significant, confirming hypothesis H6, meaning JMI mediates between JI and JP, and the value of the mediating effect is -0.113 (M6) [39]. Figure 2. SEM impact effects analysis 4. Conclusion This study uses Structural Equation Modeling (SEM) for data analysis to explore the relationships among JS, JI, UI, JMI, and JP through six hypotheses. The results show that JS and JI have significant negative impacts on JP, with UI and JMI acting as mediating factors, which verifies relevant theoretical perspectives. The findings provide theoretical support and practical guidance for enterprises to formulate and implement DT strategies to improve employee performance. However, the study has limitations: the sample is limited to seven large furniture manufacturing enterprises, resulting in insufficient industry representativeness; cross-sectional data fail to enable dynamic tracking; important variables are ignored; and insufficient attention is paid to dynamic adaptation strategies at the enterprise level. Future research can conduct cross-industry comparisons, adopt longitudinal designs, introduce more variables for multivariate analysis, and explore the dynamic adaptation mechanism between enterprises and employees during DT, so as to provide targeted management suggestions for various industries and help enterprises maintain their competitiveness and sustainability. Acknowledgements First, extend sincere gratitude to the employees of the seven Chinese furniture manufacturing enterprises who participated in this survey. Their active cooperation and valuable feedback provided the essential data foundation for this research. Special thanks are due to the Research Ethics Committee of Mahachulalongkorn-rajavidyalaya University for granting ethical approval (certification number R.355/2024), which ensured the scientific and ethical rigor of the study. Finally, to acknowledge the guidance and support from academic experts and industry professionals who contributed to the questionnaire design and content validation. Their insights helped refine the research framework and measurement tools. Ethical issue The authors are aware of and comply with best practices in publication ethics, specifically with regard to authorship (avoidance of guest authorship), dual submission, manipulation of figures, competing interests, and compliance with policies on research ethics. The authors adhere to publication requirements that the submitted work is original and has not been published elsewhere. Data availability statement The manuscript contains all the data. 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