







































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.  

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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) 
 



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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. However, more data will 

be available upon request from the authors. 

Conflict of interest 

The authors declare no potential conflict of interest. 

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