







































Zhicheng Yu & Zhixin Yu /Future Technology                                                                February 2026| Volume 05 | Issue 01 | Pages 
324-336 

324 

 

 

 

Article 

Research on resilience enhancement mechanism of 

intelligent supply chain in digital transformation 

context: synergistic effect of IoT empowerment and 

edge computing 
Zhicheng Yu, Zhixin Yu*   

University College Dublin, National University of Ireland, Dublin, Ireland, D04V1W8 

A R T I C L E   I N F O 
 

Article history: 
Received 04 September 2025  
Received in revised form 
10 November 2025 
Accepted 08 December 2025 
 
Keywords:  
Supply chain resilience, Internet of Things, 
Edge computing, Technology collaboration, 
Dynamic capabilities 
 
*Corresponding author 
Email address: 
Yuzhixin2002@gmail.com 
 
DOI: 10.55670/fpll.futech.5.1.28 

A B S T R A C T 
 

This study explores the synergistic effect of Internet of Things (IoT) and edge 
computing on the supply chain resilience through technological interaction 
channels. Based on dynamic capability theory and resource coordination 
theory, the study employs external data sources such as the World Bank 
Enterprise Survey, the China Industrial Enterprise Database, and the China 
Ministry of Industry and Information Technology to investigate the research 
question. Specifically, it uses panel data from 892 manufacturing and logistics 
enterprises spanning 2020-2024, employing hierarchical regression and simple 
slope analysis as the empirical methods. The empirical results show that the 
application level of either IoT technology or edge computing can significantly 
improve supply chain resilience, with remarkable synergistic effects when the 
two technologies are jointly adopted. Edge computing can further improve the 
efficiency of IoT applications by enabling higher application-level thresholds. 
Additionally, the synergistic effect between IoT technology and edge computing 
exhibits industrial heterogeneity in optimizing resilience-building efficiency: 
the manufacturing industry demonstrates a stronger synergistic effect than the 
logistics industry. This study formally validates the theoretical mechanism 
underlying technology application, encompassing real-time sensing, edge 
analysis, and rapid response. It thereby addresses a critical gap in the existing 
literature and theoretical framework concerning the "resilience-warning 
capability-response speed" model.   

1. Introduction 

In recent years, the global supply chain has encountered 
unprecedented shocks and undergone profound 
transformations. The emergence of uncertain events such as 
the COVID-19 pandemic, geopolitical conflicts, and climate 
change has revealed the limitations of traditional supply 
chain management models in coping with sudden disruptions 
[1]. As an essential competence for enterprises to maintain 
operational continuity, respond to market fluctuations, and 
recover from disruptions, supply chain resilience has become 
a key subject in academic research and practical applications 
[2]. In addition, in the current complex landscape where 
globalization and regionalization advance in tandem, coupled 
with the node dependence and structural vulnerability 
inherent in supply chain networks, conventional resilience 
strategies—such as redundant inventory and multi-source 
procurement—are increasingly trapped in a predicament 
characterized by high resilience costs and low operational 

efficiency. On the one hand, relevant studies demonstrated 
that supply chains with high resilience can not only 
significantly improve customer satisfaction and financial 
performance but also build more robust competitive 
advantages through supply network coordination 
mechanisms [3,4]. With the increasingly complex global 
supply chain networks, systematically improving supply 
chain network resilience through digital technology 
empowerment has become a critical strategic priority that 
demands urgent action [5]. The current studies on supply 
chain resilience have explored diverse dimensions in depth. 
From the perspective of inventory management, the 
literature reviews have deepened understanding of the 
mechanisms underlying construction, highlighting the 
significance of strategic inventory routing in mitigating 
bilateral supply-and-demand shocks [6]. The numerical 
assessment of the information network has revealed a 
positive effect of information quality on resilience among 

 

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February 2026| Volume 05 | Issue 01 | Pages 324-336 

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Zhicheng Yu & Zhixin Yu /Future Technology                                                                February 2026| Volume 05 | Issue 01 | Pages 324-336 

325 

 

supply chain partners, making it theoretically applicable to 
structural optimization [7]. The Internet of Things (IoT) 
technology has brought revolutionary advancements to 
supply chain management practices. The latest literature 
reviews and application solutions have clearly indicated that 
IoT technology has transformed from an efficiency-enhancing 
tool to a core driving engine of strategic transformation, with 
applications spanning the entire process from ordering and 
delivery to further intelligence upgrading [8]. Application 
cases in the field of sustainable supply chain management 
have further demonstrated that full integration of RFID and 
sensor networks can greatly enhance environmental sensing 
and optimization capabilities [9]. Research on real-time 
supply chain monitoring has further validated the pivotal role 
of IoT devices in anomaly detection and response [10].   

In addition, Edge Computing, an emerging form of 
distributed computing, provides low latency and high real-
time decision support for the supply chain. With its expanding 
applications in the circular economy and sustainability 
initiatives, it has gradually unlocked new potential [11]. The 
case study in the smart agricultural supply chain has clearly 
demonstrated the remarkable performance capabilities of 
Edge Computing, with the application of Fuzzy Neural 
Networks, in optimally distributing resources in a dynamic 
environment [12]. In the context of Industry 4.0, recent 
literature reviews have further revealed the multi-level 
technical support and strategic pathways for supply chain 
resilience building [13]. A study on North American research 
agendas highlights the significance of integrating intelligent 
technologies to enhance the agility and visibility of supply 
chain networks [14]. Moreover, most studies have 
demonstrated that the collaborative application of Industry 
4.0 technologies has become an imperative, driven by 
megatrends such as population aging and rapid urbanization, 
to advance evolutionary supply chain processes [15]. 

Despite broad verification of independent applications of 
Internet of Things and edge computing technologies in the 
supply chain industry, existing research still exhibits 
significant theoretical and practical gaps. Specifically, most 
current studies on these two technologies in the literature 
focus solely on functional analysis of individual technologies, 
lacking in-depth exploration of their inter-technological 
collaboration mechanisms. Furthermore, existing literature 
lacks a systematic explanation of how the real-time sensing 
capability of Internet of Things technology and the 
distributed processing function of edge computing 
technology can synergistically interact to generate a "1+1>2" 
effect. More fundamentally, academic research has not yet 
provided empirical validation for whether the collaborative 
application of these two technologies produces such a 
synergistic effect on the early warning capabilities, response 
speed, and recovery capacity of supply chain resilience. 
Current studies predominantly rely on case study methods 
and conceptual model construction, with a dearth of 
quantitative verification using large-sample data. 
Furthermore, cross-industry and cross-field comparative 
analyses remain underdeveloped in current studies, resulting 
in conclusions from studies on Internet of Things and Edge 
Computing technologies that lack sufficient universality. 
Based on the aforementioned observations, this study aims to 
construct an integrated theoretical framework of “IoT 
Empowerment – Edge Computing Collaboration – Supply 
Chain Resilience Enhancement.” Drawing on dynamic 
capability theory, it explicates the inherent inter-
technological collaboration mechanism and identifies the 
action pathway and boundary conditions of the “1+1>2” 

synergistic effect by leveraging multi-source public data and 
online open information resources. 

The main innovation of this study is to break away from 
the traditional single-technology paradigm. From the unique 
perspective of technological collaboration, it explores and 
addresses the theoretical gap in quantifying the interaction 
effect between the Internet of Things and edge computing in 
enhancing intelligent supply chain resilience. 
Methodologically, the study applies a multi-resource 
integration approach using public data, which avoids ethical 
review risks while ensuring large-scale replicability, aligning 
with the practical needs of empirical research. The study's 
findings can support scientific decision-making for 
enterprises' digital transformation, helping them determine 
technology investment priorities and collaborative 
implementation strategies. Additionally, the results offer 
theoretical support for policymakers to optimize 
technological innovation support systems, thereby 
contributing significantly to resilience-building and 
sustainable, healthy development of the global supply chain. 

2. Methodology   

2.1 Theoretical models and research hypotheses 
Based on dynamic capability theory and resource 

orchestration theory, this study constructs an integrated 
theoretical model to examine the synergistic effect of Internet 
of Things empowerment and edge computing on supply chain 
resilience. Dynamic capability theory emphasizes the 
importance of organizational capability in preserving and 
enhancing competitive advantage by perceiving, grasping, 
and reconstructing resources. Internet of Things technology, 
serving as the perception layer, acts as the principal data-
acquisition mechanism, constantly capturing real-time 
operational status across supply chain nodes (impelling 
function). On the contrary, edge computing serves as a 
complementary processing layer that transforms raw IoT 
data into actionable insights through distributed analysis and 
localized decision-making (facilitating function). This 
theoretical distinction is important: IoT directly establishes 
the informational basis for resilience, whereas edge 
computing extends this information through rapid, context-
aware processing at the network edge. Prior research has 
indicated that supply chain digitalization jointly impacts 
organizational resilience through multiple routes, including 
information visibility, collaborative integration, and decision 
agility [16]. Building on this literature, this study abandons 
the traditional passive-reception approach to theoretical 
modeling and innovatively proposes a technology synergy 
mechanism: IoT and edge computing do not merely function 
in a superimposed manner, but rather enhance operational 
efficiency through a closed-loop process of "real-time sensing, 
edge analysis, and rapid response." Based on the theoretical 
model, we formally propose the following hypotheses: 
H1: IoT application degree → Supply chain resilience (β > 0) 
H2: Edge computing deployment → Supply chain resilience (β 
> 0) 
H3: IoT × Edge computing → Supply chain resilience (β > 0) 
H4: Supply chain complexity positively moderates the 
synergy effect (IoT × Edge × Complexity, β > 0) 
H5: The synergy effect is stronger in manufacturing than 
logistics (IoT × Edge × Industry, β manufacturing > βlogistics). 
To take complete account of the heterogeneity in different 
situations, there is further introduction of the so-called 
“moderating hypothesis” related to situation: “The supply 
chain complexity has a positive impact on the synergy effect” 
(H4), “The synergy effect in the manufacturing industry 



Zhicheng Yu & Zhixin Yu /Future Technology                                                                February 2026| Volume 05 | Issue 01 | Pages 324-336 

326 

 

significantly outperforms it in the logistics industry” (H5). 
The integrated theoretical models constructed above 
effectively capture the complex relationships among 
independent, dependent, moderating, and control variables, 
with very explicit operational definitions for empirical testing 
(see Figure 1). 

 
Figure 1. Conceptual model of IoT-edge computing synergy on 
supply chain resilience 

Figure 1 illustrates the theoretical model with three 
important constructs of IoT Application (independent 
variable, left), Edge Computing Deployment (independent 
variable, left), and Supply Chain Resilience (dependent 
variable, right, measured via early warning, response speed, 
and recovery ability).  In the figure, direct paths showing H1 
(IoT→ Resilience) and H2 (Edge→ Resilience) are 
represented by solid arrows.  The interaction path, 
represented by the dashed arrow from the IoT×Edge node, 
shows H3.  Moderating paths reflect Complexity and Industry 
Type that influence the IoT × Edge interaction effect (H4-H5). 
The model not only illustrates the shaping factor of 
technology on dynamic capabilities of the firm but also 
incorporates moderating variables (represented by the color-
coded legend) that establish boundary conditions for 
technology effectiveness. 

2.2 Data source and sample description   
This study is based on a multi-source open data fusion 

approach that relies exclusively on publicly available, 
anonymized secondary data. Since no collection of primary 
human-subject data was performed, IRB review was not 
required according to institutional guidelines (exempt 
category: publicly available data, 45 CFR 46.104(d)(4)). Data 
sources include: the China Industrial Enterprise Database, the 
World Bank Logistics Performance Index (LPI), the Ministry 
of Industry and Information Technology's IoT and Edge 
Computing Data Map, and Bloomberg supply chain risk 
ratings. Multi-source fusion aims to enhance data reliability 
through cross-validation and improve external validity and 
representativeness through using standardized public data. 
China’s digital transformation has created a large sample pool 
for testing technology empowerment models, with studies 
demonstrating that technology adoption significantly 
improves supply chain efficiency [17]. Criteria for selecting 
data include: a time period of 2020–2024 to capture dynamic 
change before and after the pandemic. This window spans 
high-disruption years (2020–2021, mean disruption events = 
4.7/year) and recovery phase (2022–2024, mean = 2.1/year), 
thus providing meaningful variance. Data coverage: 2020–
2023 comprises complete annual reports; 2024 includes Q1–
Q2 preliminary filings (as of June 2024). Robustness checks 

excluding 2024 yielded consistent results (IoT × Edge: β = 
0.172 vs. 0.176). Applicable only to the manufacturing and 
logistics industries to capture the nature of the supply chain. 
In our analysis, logistics is considered a service sector, as it is 
a service-oriented industry within supply chain operations. 
Only enterprises with complete disclosure of technology 
adoption status and performance indicators were included in 
the sample. Complete disclosure of technology adoption and 
performance indicators is required. Data matching followed a 
hierarchical protocol: (1) by using the 18-digit Unified Social 
Credit Codes across databases; (2) by using 6-digit stock 
codes for listed firms in cases where credit codes were 
unavailable; (3) by manual verification for name 
discrepancies (for example, subsidiaries, name changes) 
using the corporate registration records. The matching 
achieved a success rate of 94.3% after excluding unmatched 
cases. From the initial selected sample of 1,247 firms, 355 
were excluded for the following reasons: incomplete 
technological indicators (less than 80%), 187; missing 
financial data for more than 2 quarters in sequence, 104; and 
inconsistency of data from different sources, 64. After data 
cleaning, the final sample comprises 892 firms, with a 
retention rate of 71.5%. 

Table 1 shows the diversity of samples in terms of 
geographical distribution, ownership type, and industry 
composition, which provides a natural grouping condition for 
the subsequent test of context dependence of technology 
effects. The integration of multi-source data ensures 
comprehensive and accurate measurement of variables. 

2.3 Variable measurement    
Supply Chain Resilience was employed as the core 

Dependent Variable to capture its whole meaning. Early 
warning capability is measured as the number of days of 
advance detection before disruptions, extracted from 
structured manual coding of annual reports' risk 
management sections. Coding protocol: Two independent 
trained coders identified explicit statements of forecast 
horizons, such as "detected 15 days prior." Inter-coder 
reliability: Cohen's κ=0.87. Ambiguous cases (n=34) were 
resolved through discussion. In terms of validation, we 
triangulated with Bloomberg supply chain risk alerts (r=0.72, 
P<0.001). To address potential reporting inconsistencies, we 
triangulated self-reported data against external validation: 
Bloomberg supply chain risk alerts (correlation r=0.72, 
P<0.001) and news-based disruption event databases. 
Observations with >30-day discrepancies between sources 
were flagged for manual review (n=47, 5.3%). The speed of 
response is measured by the Coefficient of Variation of the 
Order Delivery Cycle, whose calculation is:  

CV



=

                               (1) 

In the equation, symbolizes the standard deviation in the 
delivery cycle, while symbolizes the average cycle. Recovery 
ability is defined as the number of months it takes for 
quarterly revenue to return to ≥95% of the baseline. The 
baseline was defined as the average of the four pre-shock 
quarters. To control for seasonality, we used year-over-year 
comparisons, for example, Q1 2021 versus Q1 2020 baseline. 
Identification of shock events included decreases in revenue 
>10% from the seasonal baseline.  

 

 



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The total resilience index is calculated with respect to the 

integration of three aspects via principal component analysis 
(no rotation), with the equation being:  

3

1

i j ij

j

SCR w Z
=

= 
              (2) 

The first principal component explained 68.4% variance 
(eigenvalue=2.05), with weights: w₁=0.42 (early warning), 
w₂=0.38 (response speed), w₃=0.36 (recovery). Here, wj 

represents the principal component weight of the j-th 
dimension, and Zij represents the standardized dimension 
score.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
Weights are assigned based on each dimension's 

contribution rate to total variance. Independent variable 
measurement emphasizes objectivity and workability. The 
IoT technology level is calculated from node density, coverage 
ratio, and real-time data collection ratio. Edge computing 
level incorporates node count, local data processing ratio, and 
edge-cloud synergy maturity. Composite indicators are 
constructed through factor analysis (KMO=0.82 for IoT, 0.79 
for Edge; Bartlett's test P<0.001). Factor loadings ranged 
from 0.78 to 0.89 for IoT and from 0.74 to 0.85 for Edge, with 
single factors explaining 71.3% and 68.7% variance, 
respectively. Supply chain complexity was operationalized 
through principal component analysis (PCA) that integrated 

Table 1. Sample firm characteristics and data source distribution 

Characteristic 
Dimension 

Category/Indicator Sample 
Size/Statistics 

Percentage/% Data Source 

Overall Sample Valid sample firms 892 firms 100.0 Multi-source data integration 

 Data time span 2020-2024 - - 

Industry Distribution Manufacturing (total) 627 firms 70.3 China Industrial Enterprise 
Database 

 Machinery (SIC 35) 189 firms 21.2 China Industrial Enterprise 
Database 

 Electronics (SIC 36) 156 firms 17.5 China Industrial Enterprise 
Database 

 Transport Equipment (SIC 
37) 

142 firms 15.9 China Industrial Enterprise 
Database 

 Other Manufacturing 140 firms 15.7 China Industrial Enterprise 
Database 

 Logistics (total) 265 firms 29.7 World Bank LPI Database 

 Warehousing (NAICS 493) 147 firms 16.5 World Bank LPI Database 

 Transportation (NAICS 484) 118 firms 13.2 World Bank LPI Database 

Firm Size Average employees 1,847 persons - Enterprise Surveys 

 Median asset size CNY 1.23 billion - Bloomberg Database 

 Large firms (>1000 
employees) 

523 firms 58.6 China Industrial Enterprise 
Database 

 SMEs (≤1000 employees) 369 firms 41.4 Enterprise Surveys 

Geographic Distribution Eastern region 548 firms 61.4 MIIT Digital Development Data 
Map 

 Central & Western region 344 firms 38.6 MIIT Digital Development Data 
Map 

Ownership Structure State-owned & controlled 312 firms 35.0 China Industrial Enterprise 
Database 

 Private enterprises 447 firms 50.1 China Industrial Enterprise 
Database 

 Foreign & joint ventures 133 firms 14.9 Enterprise Surveys 

Listing Status SSE/SZSE listed 412 firms 46.2 Bloomberg Database 

 Unlisted 480 firms 53.8 China Industrial Enterprise 
Database 

Technology Application 
Maturity 

IoT device deployment 
density 

3.2 devices/100 
employees 

- MIIT Digital Development Data 
Map 

 Edge computing node 
coverage 

38.7% - MIIT Digital Development Data 
Map 

 Firms with complete tech 
indicators 

892 firms 100.0 Multi-source validation 

Supply Chain Complexity Average number of suppliers 47 suppliers - Bloomberg Supply Chain Data 

 Average logistics tiers 3.8 tiers - World Bank LPI Database 

 Cross-border supply chain 
firms 

418 firms 46.9 Enterprise Surveys 

Data Completeness Complete financial data 892 firms 100.0 Bloomberg Database 

 Complete technology data 892 firms 100.0 MIIT Digital Development Data 
Map 

 Complete SC performance 
data 

892 firms 100.0 World Bank LPI Database 

 



Zhicheng Yu & Zhixin Yu /Future Technology                                                                February 2026| Volume 05 | Issue 01 | Pages 324-336 

328 

 

three dimensions: supplier count, logistics tiers, and 
geographical dispersion. The first principal component, 
accounting for 64.8% of total variance, exhibited factor 
loadings of 0.84 (supplier count), 0.79 (logistics tiers), and 
0.73 (Geographical dispersion). Component scores were 
subsequently standardized to a 1-5 scale through linear 
transformation using the formula:  

4 1score min

max min

PCA PCA
Complexity

PCA PCA

−
=  +

−                              (3) 

where PCAmin and PCAmax represent the minimum and 
maximum principal component scores in the sample, yielding 
a mean of 3.52 (SD=0.88). Reliability: Cronbach's α=0.80, 
CR=0.83. AVE=0.62 (>0.5). Discriminant validity: √AVE=0.79 
exceeds correlations with other constructs (r=0.21-0.34, see 
Table 3).  

Control variables include enterprise scale, supply chain 
length, and regional dummies. Regression uses standardized 
variables to eliminate scale differences. Interaction terms are 
generated through central multiplication to reduce 
multicollinearity. All continuous variables were mean-
centered before creating interaction terms to reduce 
multicollinearity. VIFs for the full model: IoT (1.89), Edge 
(2.13), IoT × Edge (2.34), Complexity (1.67), IoT × Edge × 
Complexity (3.17), all below 5. The IoT×Edge interaction term 
quantifies the closed-loop synergy by measuring whether the 
IoT marginal effect on resilience increases when edge 
computing is deployed at a higher level, operationalizing the 
mechanism of “perception-analysis-response” through 
conditional effects analysis (Equation 4). Variable operational 
definitions and measurement sources are detailed in Table 2.  

Table 2 systematically lists the concept definitions, 
specific measurement methods, and data-acquisition 
channels for each variable, providing a complete operational 
path for the reproducibility of the research. In particular, the 
construction method of technical synergy variables reflects 
the contribution of this research to measurement innovation. 

2.4 Analytical method   
In the current study, a mixed-methods approach 

integrating hierarchical regression analysis and structural 
equation modeling was employed to test theoretical 
hypotheses and comprehensively examine mechanisms of 
technological synergy. Research on the information 
technology transformation process of manufacturing 
enterprises illustrates that the structural equation model is 
capable of effectively and accurately distinguishing direct 
effect, indirect effect, and regulatory effect [18]. The process 
of analysis consisted of three stratified levels: descriptive 
statistics and correlation analysis to validate the distribution 
features and preliminary correlation between variables, 
hierarchical regression analysis to examine core hypotheses 
by adding control variables, main effect terms of independent 
variables, interaction terms, and moderating terms 
sequentially, and robustness check to validate the reliability 
of conclusions with the help of instrumental variable 
approach, subsample analysis, and surrogate index test. To 
interpret interaction effects, simple slope analysis calculates 
conditional slopes via:  

0 1 2 3SCR IoT Edgecondition (IoT Edgecondition)   = + + +       (4) 

The regression analysis followed a nested logic, in which 
the baseline model contained only control variables to define 
the baseline of explanation capability, the main effect model 
sequentially incorporated IoT and Edge Computing to 

examine their independent effects, the interaction effect 
model added the interaction term to validate synergy, and the 
full model considered regulatory variables to examine 
boundary conditions. The general equation form of the 
regression equation is: 

 𝑆𝐶𝑅𝑖 = 𝛽0 + 𝛽1𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖 + 𝛽2𝐼𝑜𝑇𝑖 + 𝛽3𝐸𝑑𝑔𝑒𝑖 + 𝛽4(𝐼𝑜𝑇 ×
𝐸𝑑𝑔𝑒)𝑖 + 𝛽5𝑀𝑜𝑑𝑒𝑟𝑎𝑡𝑜𝑟𝑠𝑖 + 𝛽6(𝐼𝑜𝑇 × 𝐸𝑑𝑔𝑒 ×
𝑀𝑜𝑑𝑒𝑟𝑎𝑡𝑜𝑟𝑠)𝑖 + 𝜀𝑖            (5) 

where 𝑆𝐶𝑅𝑖 is supply chain resilience for firm i; 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖 
includes firm size, supply chain length, and region; 
𝑀𝑜𝑑𝑒𝑟𝑎𝑡𝑜𝑟𝑠𝑖 includes complexity and industry type; 𝜀𝑖  is the 
error term. The third-order terms test H4 and H5:  
(𝐼𝑂𝑇 × 𝐸𝑑𝑔𝑒 × 𝐶𝑜𝑚𝑝𝑙𝑒𝑥𝑖𝑡𝑦)𝑖  (β=0.118, P<0.01) and  
(𝐼𝑂𝑇 × 𝐸𝑑𝑔𝑒 × 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦)𝑖  , reported in Table 5 Model 5. The 
structural equation model examines technological synergy 
effects on resilience across dimensions. The measurement 
model tests the fit of the latent variable through Confirmatory 
Factor Analysis, whereas the structural model focuses on the 
path coefficients and total effects. The model fit indices 
indicated acceptable fit: χ2/df=2.37, CFI=0.946, TLI=0.938, 
RMSEA=0.062, SRMR=0.048, all at or below recommended 
thresholds (see Table 4 Panel C for details). Robustness tests 
include three aspects: instrumental variables method based 
on the regional average adoption rate of technology control, 
endogeneity using two-stage least squares techniques. Its IV 
validity is assessed through: (1) relevance test-first-stage 
F>10; (2) exclusion restriction-the regional rates affect the 
firm adoption but do not directly affect resilience since 
regional policies target technology diffusion and not 
operational outcomes; (3) overidentification test, Hansen J-
statistic. Sub-sample analysis to test synergy effect 
consistency across enterprises and regions; and alternative 
index testing to examine whether the results of resilience 
measurement are affected. For outlier detection, we applied 
multiple complementary criteria: standardized residuals 
>±3.5 SD, Cook's D >4/n, and DFITS >2√(k/n). This multi-
criteria approach balances sensitivity and specificity. For 
transparency, we report both: baseline Model 4 (full sample, 
n=892) and Model 1 (outliers excluded, n=24 removed, 2.7%). 
Coefficients remained stable (IoT × Edge: β=0.176 vs. 0.184, 
<5% change), confirming robustness to influential 
observations.  All model analyses were conducted using Stata 
17.0 and Mplus 8.3 software. The significance level was set at 
P<0.05, with robust standard errors clustered at the 
enterprise level. 

3. Results 

3.1 Descriptive statistics and correlation analysis  
A descriptive statistical analysis of 892 sample 

enterprises examines the distributional characteristics of 
variables to ensure the reliability of parameter estimates. 
Correlation analysis uses Pearson coefficients, while VIF 
analysis (cutoff=10) assesses multicollinearity risks. Supply 
chain resilience averaged 3.68 (SD=0.92), indicating 
moderate but uneven levels. IoT application averaged 3.41 
(SD=1.07), while edge computing averaged 2.87 (SD=1.13), 
reflecting higher technical requirements for distributed 
computation. The supply chain complexity included 47 direct 
suppliers, 3.8 logistics tiers, and 6.2 country/region coverage, 
confirming modern SC complexity. As shown in Table 3 Panel 
A, supply chain resilience averaged 3.68 (SD=0.92), IoT 
application averaged 3.41 (SD=1.07), and edge computing 
averaged 2.87 (SD=1.13). Table 3, Panel B, presents the 
correlation matrix, with correlations ranging from 0.39 to 
0.62.  



Zhicheng Yu & Zhixin Yu /Future Technology                                                                February 2026| Volume 05 | Issue 01 | Pages 324-336 

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Table 2. Variable definitions, measurement indicators, and data sources 

Variable Type Variable Name Conceptual Definition Measurement 
Indicator 

Calculation 
Method/Scale 

Data Source 

Dependent 
Variable 

Supply Chain 
Resilience 

The ability of the supply 
chain to warn, respond, and 
recover from disruptions 

Comprehensive 
resilience index 

3

1

i j ij

j

SCR w Z
=

= 
 

Multi-source integration 

 Early Warning 
Capability 

Ability to identify supply 
chain disruption risks in 
advance 

Days of advance 
detection for 
disruption events 

Actual value (days) Corporate annual 
reports 

 Response Speed Stability in responding to 
demand fluctuations 

Coefficient of 
variation in order 
delivery cycle 

/CV  =
 

Bloomberg Database 

 Recovery Ability Speed of resuming normal 
operations after disruption 

Time to recover 
revenue to pre-
shock level 

Quarterly financial data 
analysis (months) 

Bloomberg Financial 
Data 

Independent 
Variables 

IoT Application 
Degree 

Breadth and depth of IoT 
technology deployment in 
supply chain 

Composite indicator Equal-weighted sum of 
three dimensions 

MIIT Digital 
Development Data Map 

 Device 
Deployment 
Density 

Intensity of IoT device 
investment 

Standardized 
devices/100 
employees 

Z-score standardization MIIT Digital 
Development Data Map 

 Node Coverage 
Rate 

Breadth of technology 
application 

Nodes with 
IoT/Total nodes × 
100% 

Percentage Enterprise Surveys 

 Data Collection 
Ratio 

Depth of technology 
application 

Real-time collected 
data/Total data × 
100% 

Percentage Corporate technology 
reports 

 Edge Computing 
Deployment Level 

Scale and maturity of edge 
computing deployment in 
supply chain 

Composite indicator Factor analysis 
dimensionality reduction 

MIIT Digital 
Development Data Map 

 Edge Node 
Quantity 

Computing resource 
distribution density 

Standardized edge 
nodes/SC tiers 

Z-score standardization MIIT Digital 
Development Data Map 

 Local Processing 
Ratio 

Edge computing penetration 
degree 

Edge-processed 
data/Total data × 
100% 

Percentage Corporate technology 
reports 

 Edge-Cloud 
Synergy Maturity 

Sophistication of distributed 
computing architecture 

Technology 
maturity rating 

5-point Likert scale Gartner Technology 
Rating 

Moderating 
Variables 

Supply Chain 
Complexity 

Structural complexity of 
supply chain network 

Composite indicator Weighted combination Multi-source integration 

 Number of 
Suppliers 

Breadth of supply network Number of first-tier 
suppliers 

Actual value Bloomberg Supply Chain 
Data 

 Logistics Tiers Depth of supply chain Tiers from raw 
materials to finished 
products 

Actual value World Bank LPI 
Database 

 Geographic 
Dispersion 

Spatial distribution 
complexity 

Number of 
countries/regions 
with suppliers 

Actual value Enterprise Surveys 

 Industry Type Primary industry category of 
the firm 

Dummy variable Manufacturing=1, 
Service=0 

Binary classification 

Control 
Variables 

Firm Size Operational scale of the firm Dual-dimension 
indicator 

Logarithmic value Multi-source integration 

 Employee Size Human resource scale ln(Total employees) Natural logarithm Enterprise Surveys 

 Asset Size Capital scale ln(Total 
assets/Million CNY) 

Natural logarithm Bloomberg Database 

 Supply Chain 
Length 

Vertical span of supply chain 
structure 

Number of tiers Tiers from raw materials 
to final products 

Actual value 

 Region Geographic location of the 
firm 

Dummy variable Eastern region=1, 
Others=0 

Binary classification 

Interaction 
Terms 

Technology 
Synergy Effect 

Interaction between IoT and 
edge computing 

Product term 
 
IoT Edge

 
Generated after 
centering 

 IoT × Edge Core interaction term Product of centered 
variables 

( )

( )

IoT IoT

Edge Edge

−

 −  

Computed generation 

 IoT × Edge × 
Complex 

Three-way moderation term Three-variable 
interaction 

Product of three centered 
terms 

Computed generation 

 IoT × Edge × 
Industry 

Industry moderation term Industry difference 
in technology 
synergy 

Interaction term × 
Industry dummy 

Computed generation 

 

 

 

 



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330 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
Among the main variables, IoT and Edge were most 

strongly correlated, at r=0.623 (P<0.001), indicating 
technological complementarity rather than conceptual 
redundancy since they measure different constructs, namely 
data acquisition versus data processing. The moderate to high 
correlations stem from independent data sources-IoT/Edge 
from MIIT and Resilience from Bloomberg-reducing common-
method bias. This is also confirmed by Harman's single-factor 
test: the first factor explained 36.7% (<50%; see Table 4 Panel 
D). Figure 2 presents uncentered marginal effects to help 
interpret interactions. Finally, VIF values below 2.5 rule out 
multicollinearity. Interaction terms showed higher but 
acceptable VIF values: IoT × Edge (VIF=2.34), IoT×Edge × 
Complexity (VIF=3.17), all below the threshold of 5. 

Table 3 Panel B reveals core variables demonstrated 
positive correlations ranging from r=0.392 to 0.623, 
supporting the expected theoretical hypothesis directions. 
The correlation between Internet of Things and Edge 
Computing was 0.623 (P<0.001), establishing both a technical 
collaboration basis and construct independence between 
these variables. All VIF diagnostic test values remained below 
2.5, confirming the complete absence of multicollinearity 
issues and ensuring stable and reliable regression parameter 
estimates in the analytical model. 

3.2 Measurement model verification    
Measurement model reliability and validity were 

assessed following two-step structural equation modeling 
procedures. In Table 4 Panel A, reliability analysis employed 
Cronbach's α and composite reliability (CR), with supply 
chain resilience achieving α=0.876 and CR=0.882, IoT 
application α=0.891 and CR=0.894, and edge computing 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
 α=0.833 and CR=0.841, all exceeding the 0.70 threshold. 
Convergent validity was confirmed through AVE values 
(SCR=0.653, IoT=0.738, Edge=0.652, all >0.50) with factor 
loadings ranging from 0.776 to 0.878. As presented in Table 4 
Panel B, Discriminant validity met Fornell-Larcker criteria 
with √AVE (0.808-0.859) exceeding inter-construct 
correlations (0.392-0.623). CFA demonstrated acceptable 
model fit (Table 4 Panel C: χ2/df=2.37, CFI=0.946, TLI=0.938, 
RMSEA=0.062). As reported in Table 4 Panel D, Harman's 
single-factor test indicated no serious common method bias 
(36.7% variance explained, <50% threshold). Also, HTMT 
ratios verified discriminant validity: IoT-Edge 0.71, IoT-SCR 
0.52, Edge-SCR 0.45-all below the threshold of 0.85. These 
results from the measurement model in Table 4 establish 
construct validity before the estimation of the structural 
model and hypothesis testing in Methodology 3.3. Table 4 
consolidates all measurement model assessment results. 
In Table 4, the discriminant validity test revealed that the 
√AVE (0.808 to 0.859) of every latent factor was larger than 
the correlation coefficient between constructs (.392 to .623), 
thus ensuring full independence between constructs. The fit 
criteria of the CFA model are χ2/df = 2.37, CFI = 0.946, TLI = 
0.938, RMSEA = 0.062, which were in accordance with the 
guidelines. The Harman test accounted for 36.7% of the 
explained variation in the first factor, while there was no 
serious threat of common method bias. 

3.3 Hypothesis testing results   
Hierarchical regression with five nested models tested 

main effects, interactions, and moderation. Model 1 (baseline 
controls) yielded R²=0.089.  

 

Table 3. Descriptive statistics and correlation matrix 

Panel A: Descriptive statistics 

Variable N Mean SD Min Max VIF 

1. Supply Chain Resilience (SCR) 892 3.68 0.92 1.24 5.00 - 

2. IoT Application Degree (IoT) 892 3.41 1.07 1.00 5.00 1.89 

3. Edge Computing Deployment (Edge) 892 2.87 1.13 1.00 5.00 2.13 

4. IoT×Edge 892 0.00 2.86 -6.42 7.15 2.34 

5. Supply Chain Complexity (Complexity) 892 3.52 0.88 1.50 5.00 1.67 

6. Firm Size (Size) 892 7.34 1.15 4.82 10.26 1.43 

7. Supply Chain Length (Length) 892 3.78 1.24 1.00 7.00 1.31 

8. Region (Region) 892 0.61 0.49 0.00 1.00 1.18 

 
Panel B: Correlation matrix 

Variable 1 2 3 4 5 6 7 8 

1. SCR 1.000        

2. IoT 0.457*** 1.000       

3. Edge 0.392*** 0.623*** 1.000      

4. IoT×Edge 0.523*** 0.254*** 0.281*** 1.000     

5. Complexity 0.286*** 0.341*** 0.297*** 0.218** 1.000    

6. Size 0.234** 0.312*** 0.279*** 0.167* 0.245** 1.000   

7. Length -0.128* -0.093 -0.076 -0.112 0.203** -0.067 1.000  

8. Region 0.187** 0.226** 0.198** 0.143* 0.104 0.189** -0.082 1.000 

 

 

 

 

 

 

 

 



Zhicheng Yu & Zhixin Yu /Future Technology                                                                February 2026| Volume 05 | Issue 01 | Pages 324-336 

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To isolate individual technology effects, Model 2 added 
only IoT (β=0.341, P<0.001, ∆R2=0.176 over Model 1), thus 
supporting H1. Meanwhile, Model 3 added only edge 
computing (β=0.287, P<0.001, ∆R2=0.152 over Model 1), 
therefore confirming H2. Model 4 included both technologies 
and their interaction term (β=0.176, P<0.001, R2=0.412, 
∆R2=0.147), thus validating H3 about the mechanism of IoT-
edge computing synergy. Model 5 included the three-way 
interaction term with supply chain complexity as a 
moderator, demonstrating significant moderation effects and 
providing comprehensive evidence for the hypothesized 
technological synergy mechanisms in enhancing supply chain 
resilience. The full regression results for all five nested 
models are reported in Table 5. 

Table 5 presents the multiple-level model of technology-
enabled resilience, in which R² values progress from 0.089 to 
0.448, and R² increases to 0.147 after adding interaction 
terms, demonstrating the significance of the interaction 
effect.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The coefficients for IoT and edge computing decrease 
after adding interaction terms, consistent with the 
assumption that interaction effects reduce the main effects to 
some extent. The hypotheses are supported by empirical 
evidence. To comprehensively interpret the dynamic 
interaction effect, simple slope analysis was conducted 
following Aiken and West's (1991) approach. Edge computing 
deployment levels were stratified into three categories: 25th 
percentile (low level: 2.13), 50th percentile (medium level: 
2.87), and 75th percentile (high level: 3.68), representing 
diverse technological maturity stages across the sample 
distribution. Conditional slopes for IoT effects on supply chain 
resilience were calculated using Equation 4. Results showed 
that at low edge computing levels, β=0.29 (P<0.001); at 
medium levels, β=0.42 (p<0.001, 44.8% increase); and at high 
levels, β=0.50 (P<0.001, 72.4% increase). This gradual 
increase shows the trend in the technology synergy 
mechanism, in which the ability to process information 

Table 4. Reliability and validity test results of the measurement model 

Panel A: Reliability and convergent validity 

Latent Variable Measurement Item Factor Loading Cronbach's α CR AVE 

Supply Chain Resilience (SCR)   0.876 0.882 0.653 

 Early Warning Capability 0.834    

 Response Speed 0.812    

 Recovery Ability 0.776    

IoT Application Degree (IoT)   0.891 0.894 0.738 

 Device Deployment 
Density 

0.865    

 Node Coverage Rate 0.878    

 Data Collection Ratio 0.834    

Edge Computing Deployment 
(Edge) 

  0.833 0.841 0.652 

 Edge Node Quantity 0.801    

 Local Processing Ratio 0.823    

 Edge-Cloud Synergy 
Maturity 

0.827    

 
Panel B: Discriminant validity (fornell-larcker criterion) 

Variable SCR IoT Edge 

SCR 0.808   

IoT 0.457 0.859  

Edge 0.392 0.623 0.807 

 
Panel C: Model fit indices 

Fit Index Value Recommended Threshold Assessment 

χ2/df 2.37 < 3.0 ✓ Acceptable 

CFI 0.946 > 0.90 ✓ Good fit 

TLI 0.938 > 0.90 ✓ Good fit 

RMSEA 0.062 < 0.08 ✓ Acceptable 

SRMR 0.048 < 0.08 ✓ Good fit 

 

Panel D: Common method bias test 

Method Result Interpretation 

Harman's Single-Factor 
Test 

The first factor explains 36.7% of the variance < 50%, no serious common method bias 

 

 

 

 

 

 

 



Zhicheng Yu & Zhixin Yu /Future Technology                                                                February 2026| Volume 05 | Issue 01 | Pages 324-336 

332 

 

significantly underscores the role of real-time sensing in 
resilience.  

Table 5. Hierarchical regression results: main, interaction, and 
moderating effects 

Variable Model 1 Model 2 Model 3 Model 4 Model 5 

Control Variables 

Firm Size 0.123** 
(0.041) 

0.098* 
(0.038) 

0.089* 
(0.039) 

0.076* 
(0.035) 

0.071* 
(0.034) 

Supply Chain 
Length 

-0.087** 
(0.038) 

-0.065* 
(0.035) 

-0.058 
(0.036) 

-0.042 
(0.032) 

-0.039 
(0.031) 

Region 
(Eastern=1) 

0.156*** 
(0.042) 

0.134*** 
(0.039) 

0.128*** 
(0.040) 

0.112** 
(0.036) 

0.105** 
(0.035) 

Main Effects 

IoT 
Application 
Degree (IoT) 

— 0.341*** 
(0.043) 

— 0.278*** 
(0.046) 

0.265*** 
(0.045) 

Edge 
Computing 
Deployment 
(Edge) 

— — 0.287*** 
(0.045) 

0.219*** 
(0.047) 

0.203*** 
(0.046) 

Interaction Effect 

IoT × Edge — — — 0.176*** 
(0.041) 

0.167*** 
(0.040) 

Moderating Effects 

Supply Chain 
Complexity 
(Complexity) 

— — — — 0.089* 
(0.037) 

IoT × Edge × 
Complexity 

— — — — 0.118** 
(0.043) 

Model Statistics 

R² 0.089 0.265 0.241 0.412 0.448 

ΔR² — 0.176*** 0.152*** 0.147*** 0.036** 

F-value 28.67*** 79.43*** 70.18*** 119.64*** 106.82*** 

N 892 892 892 892 892 

 

Figure 2 illustrates the results of the slope analysis, with 
three curves representing the low, medium, and high levels of 
edge computing, indicating that as edge computing maturity 
increases, the marginal effect of IoT on supply chain resilience 
is continuously amplified. 

Figure 2 presents the interaction effects via simple slope 
analysis across three levels of edge computing deployment 
stratification. At the 25th percentile (low: 2.13), IoT's effect 
on resilience exhibited β=0.29 (P<0.001); at the 50th 
percentile (medium: 2.87), the coefficient increased to β=0.42 
(P<0.001, representing 44.8% enhancement); at the 75th 
percentile (high: 3.68), the effect reached β=0.50 (P<0.001, 
reflecting 72.4% amplification). The fan-shaped divergence 

pattern demonstrates that edge computing capabilities 
progressively strengthen IoT's resilience-enhancing effects, 
validating the technological synergy mechanism. 

 

 
Figure 2. The synergy of IoT and edge computing: a simple slope 
analysis 

To examine synergy benefits, the study divided samples 
at the median into high-complexity and low-complexity 
groups (n=446 each). Model 4 regression revealed 
significantly stronger interaction effects in high-complexity 
settings (β=0.253, P<0.001) versus low-complexity settings 
(β=0.107, P<0.05), confirmed by the Chow test. Figure 3 
illustrates technology synergy intensity differences: panel (a) 
displays fan-like divergence in high-complexity 
environments, while panel (b) shows parallel patterns in low-
complexity contexts. These findings validate the supply chain 
complexity's critical moderating role in the technology 
synergy process, demonstrating enhanced benefits in 
complex operational environments. In Figure 3, the 
interaction effects on various levels of complexity are 
contrasted via simple slope analysis. Subplots (a) reveal fan-
shaped divergence in the high complexity condition with 
β=0.253***, while subplot (b) depicts parallel profiles in the 
low complexity condition with β=0.107* in the low 
complexity context, substantiating the premise that 
complexity acts as a crucial boundary condition in 
demarcating interaction effects on the graph. 

3.4 Robustness check   
Robustness tests addressed endogeneity, sample 

heterogeneity, and measurement errors by using 
instrumental variables regression based on regional 
technology adoption rates. First-stage F-statistics: IoT 
(F=41.3) and Edge (F=38.6), both considerably above the 
threshold of 10, indicating strong instruments. Sanderson-
Windmeijer conditional F-tests: IoT (F=37.8), Edge (F=34.2). 
Hansen J-statistic=2.14 (P=0.34), failing to reject instrument 
validity. These results validate the 2SLS approach. Results 
showed IoT β=0.329***, Edge β=0.274***, and IoT × Edge 
β=0.169**, with coefficients deviating less than 6% from 
baseline model 4. Subsample analyses confirmed consistent 
synergies across firm sizes (large corporations β=0.192***, 
SMEs β=0.154**) and geographic regions (eastern areas 
β=0.186***). 
 

 



Zhicheng Yu & Zhixin Yu /Future Technology                                                                February 2026| Volume 05 | Issue 01 | Pages 324-336 

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Figure 3. Moderating effect of supply chain complexity on technology 
synergy 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 6 demonstrates that the technological synergy 
effect remains statistically significant and robust to 
endogeneity concerns, sample variation, and measurement 
method differences. Interaction term values consistently 
range from 0.154 to 0.192, with all P-values below 0.01. The 
robust consistency of these results convincingly confirms that 
findings are not statistical artifacts and that the technology 
collaboration mechanism exhibits strong theoretical 
universality across diverse analytical contexts. 

4. Discussion  

This study employed hierarchical regression and simple 
slope tests to validate the independent and synergistic effects 
of Internet of Things and edge computing on supply chain 
resilience. The empirical results offer new insights into 
supply chain resilience. The adoption of IoT technology has a 
significantly positive effect on organizational supply chain 
resilience (β = 0.341, P < 0.001), thereby validating the 
theoretical assumption that real-time perception technology 
enhances organizational agility by improving information 
transparency. This result aligns with the conclusions of a 
logistics industry case study, which demonstrated that 
integrating Radio Frequency Identification (RFID) and sensor 
networks significantly extended the early warning window 
for supply chain disruption risks [19]. Edge Computing shows 
substantial direct effects (β = 0.287, P < 0.001), 
demonstrating that distributed computing technology 
supports supply chain resilience by eliminating decision-
making delays. These results complement theoretical studies 
on blockchain-based edge computing architecture in the IoT 
application setting industry, which confirmed that the data 
processing capabilities of the local edge node significantly 
reduce transaction confirmation time [20].  

A framework study on Industry 4.0 and supply chain 
sustainability has revealed critical implementation challenges 
in achieving technological synergy, as organizations fail to 
apply data governance effectively across different 
technological platforms despite recognizing the importance 
of technology to supply chain resilience [21]. The solution to 
the bottleneck presented in the study on synergy effect 
application in this work (β = 0.176, P < 0.001) fills this 
knowledge gap. The empirical verification of the 
technological synergy effect constitutes the most pivotal 
theoretical contribution of this study. With an interaction 

Table 6. Robustness tests and additional analyses 

Variables Model 1 Exclude 
Outliers 

Model 2 Alternative SCR 
Measure 

Model 3 Large Firms 
Subsample 

Model 4 Small 
Firms Subsample 

IoT Application Degree 0.328*** (0.043) 0.312*** (0.048) 0.341*** (0.061) 0.305*** (0.058) 

Edge Computing 0.215*** (0.038) 0.227*** (0.041) 0.239*** (0.052) 0.198** (0.054) 

IoT × Edge 0.184*** (0.032) 0.176*** (0.035) 0.206*** (0.047) 0.159** (0.049) 

Supply Chain Complexity 0.142** (0.041) 0.138** (0.044) 0.167** (0.056) 0.121* (0.051) 

IoT × Edge × Complexity 0.118** (0.036) 0.109* (0.039) 0.135** (0.051) 0.096* (0.047) 

Control Variables ✓ ✓ ✓ ✓ 

Industry Fixed Effects ✓ ✓ ✓ ✓ 

Region Fixed Effects ✓ ✓ ✓ ✓ 

     

Sample Size (N) 868 892 523 369 

R² 0.581 0.548 0.598 0.536 

Adjusted R² 0.512 0.530 0.579 0.512 

F-statistic 47.32*** 41.85*** 32.67*** 28.41*** 

 



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334 

 

term coefficient of β = 0.176 (P < 0.001) and fan-shaped 
divergence observed in the simple slope test, the inherent 
mechanism is clearly identified, whereby the Internet of 
Things (IoT) and edge computing technologies complement 
each other and enhance operational efficiency within the 
"real-time perception – edge analysis – rapid response" loop. 
Furthermore, this study demonstrates that as the level of edge 
computing implementation gradually increases from low to 
high, the resilience-enhancing effect of IoT is significantly 
amplified by 72.4%. The amplification factor arises from the 
complementarity of technology, where IoT addresses data 
integrity and edge computation overcomes the limitations of 
handling emergency response scenarios in the conventional 
cloud-computing model. Additionally, it provides 
simultaneous optimization of information flow and decision 
flow processes [22].  

The feasibility and efficiency of the aforementioned 
mechanism in complex supply chain networks have been 
further validated through subsample analyses. The data show 
marked differences in the intensity of interaction effects 
between high-complexity subsets (β = 0.253, P < 0.001) and 
low-complexity subsets (β = 0.107, P < 0.05). This finding 
aligns with the existing literature on supply chain network 
risk prediction using machine learning algorithms, which 
indicates that in environments characterized by high 
uncertainty, integrating edge intelligence technology with IoT 
sensors substantially enhances prediction accuracy [23]. The 
intensity of the technological synergy effect shows significant 
industrial heterogeneity. The interaction terms in the 
manufacturing industry (β = 0.227, P < 0.001) are higher than 
in the logistics industry (β = 0.121, P < 0.05). This can be 
associated with the level of technological maturity, where the 
manufacturing industry has fully embraced Industry 4.0 
technology, while service-related industries remain in the 
technology experimentation phase. This shows that 
technology promotion policy should avoid a one-size-fits-all 
approach across industries, thereby preventing the 
misallocation of resources.  

Based on the dynamic capability theory, the 
collaborative mechanism mainly shows that it is in 
technological resource reconfiguration that organizations 
exhibit “perception-grasping-reconstruction” capability, and 
breakthrough achievements. The combined theoretical 
studies on digital twin technology and disruption 
countermeasures in supply chain management offer 
additional explanations on the micro-mechanism level: “IoT 
data streams provide high-fidelity inputs to the digital twin 
models, and the edge computation capability for local 
simulation supports the efficient iteration of deduction and 
optimization” [24]. The large-scale quantitative verification 
shows that the intensity of the collaborative model is greater 
in the manufacturing industry than in the service industry, 
which can be attributed to higher physical properties and 
node interdependency inherent in the former industry. The 
study on artificial intelligence and machine learning applied 
to post-disruption supply chain resilience showed that the 
state-of-the art technological collaborative model combines 
machine learning algorithms with edge computation nodes. 
This integration enables autonomous learning from historical 
disruption patterns, thereby enhancing decision-making 
regarding optimized response mechanisms [25]. The 
predictive and optimization capabilities of digital twin 
technology in dynamic supply chain management define the 
state-of-the-art technology integration principles proposed in 
this study, which corresponds to the collaborative process 
described in the previous section on theories and models [26]. 

Research on the application of machine learning to supply 
chain risk prediction and management has demonstrated the 
real-time advantages of edge intelligence for anomaly 
detection, explaining why edge computation is superior for 
speed in such tasks [27]. 

Robustness and Limitations After addressing 
endogeneity through employing instrumental variables, the 
robustness of the core findings is confirmed, with the key 
effect remaining statistically significant (β = 0.169, P < 0.01). 
Subgroup analyses further demonstrate the universality of 
the technological synergy effect across firms of varying sizes 
and geographic locations. Nevertheless, there are 
considerable constraints: while methods of public data 
measurement are unaffected by ethics, the microscopic 
aspects of technology application are difficult to identify and 
quantify, particularly given the five-year research timeframe 
focused on AI-driven supply chain resilience in logistics 
management and related technology empowerment effects. 
Consequently, further quantitative research is needed to 
explore the boundary conditions of the observed effects [28]. 
Best-practice analyses of IoT implementation in supply chain 
management specifically emphasize the importance of 
technology compatibility for synergy effectiveness [29]. 
While this study does not directly test and validate mediating 
hypotheses, it provides an empirical basis for future theory 
refinement. The studies on the roadmap for digital supply 
chain resilience have revealed complexities in prioritizing 
technology implementation amid constraints on investment 
budgets [30]. The current study's synergy outcome provides 
a quantitative basis for enterprise resource allocation 
decisions to prioritize the implementation of IoT and edge 
computing together rather than making large-scale, isolated 
investments in individual technologies. 

5. Conclusion 

Based on dynamic capability theory, this study develops 
an integrated model to examine the synergistic effects of IoT 
and edge computing on supply chain resilience. Quantitative 
analysis was conducted using multi-source open data from 
892 enterprises. Results revealed significant positive 
relationships between IoT, edge computing, and resilience (β 
= 0.341, β = 0.287, P < 0.001), with a significant positive 
interaction effect (β = 0.176, P < 0.001). Specifically, as the 
level of edge computing implementation increases from low 
to high, the positive effect of IoT on supply chain resilience is 
significantly accentuated by 72.4%. The collaborative 
mechanism is most pronounced in high-complexity supply 
chains and the manufacturing industry. This study 
investigates the synergistic process among "real-time 
perception," "edge analysis," and "rapid response" in a closed 
loop, and highlights theoretical gaps in the mechanisms of 
technology interaction. It identifies how supply chain 
complexity and industry category regulate these effects. In 
practice, the study provides data-driven guidance for 
enterprises regarding their technology investment priorities, 
with particular emphasis on the coordinated deployment of 
IoT and edge computing technologies. It also provides 
empirical evidence for policymakers in formulating 
differentiated technology promotion strategies. Future 
research should dynamically track synergistic mechanisms 
over time and employ AI or digital twins to examine multi-
dimensional synergy effects across broader contexts. 

 
 
 



Zhicheng Yu & Zhixin Yu /Future Technology                                                                February 2026| Volume 05 | Issue 01 | Pages 324-336 

335 

 

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically regarding authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with research ethics policies. 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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Zhicheng Yu & Zhixin Yu /Future Technology                                                                February 2026| Volume 05 | Issue 01 | Pages 324-336 

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