Artificial Intelligence Adoption and Organizational
Performance of Chinese SMES
Chen
Hongbo, Ferdinand Somido
somido.ferdinand@uphsl.edu.ph
Abstract
Artificial
Intelligence (AI) has become a strategic driver of organizational
transformation and competitiveness in the digital economy. This study examined
the relationship between AI adoption and organizational performance among
Chinese small and medium enterprises (SMEs). Guided by the
Technology–Organization–Environment (TOE) Framework and Resource-Based View
(RBV) Theory, the study employed a quantitative correlational design involving
300 SME owners and managers from major Chinese economic centers. Data were
collected using a structured questionnaire measuring AI adoption in terms of AI
infrastructure, AI applications, employee AI competency, and AI integration, as
well as organizational performance in terms of operational efficiency,
innovation capability, customer satisfaction, and financial performance.
Weighted mean, Pearson correlation, and multiple regression analyses were used
to analyze the data. Results revealed a high level of AI adoption (M = 4.13)
and very high organizational performance (M = 4.22). Correlation analysis
demonstrated a significant positive relationship between AI adoption and
organizational performance (r = .782, p < .001). Multiple regression
analysis indicated that AI adoption explained 64.2% of the variance in
organizational performance, with AI integration emerging as the strongest
predictor. The findings suggest that AI serves as a strategic resource that
enhances organizational efficiency, innovation, customer satisfaction, and
financial outcomes. The study recommends increased investments in AI
infrastructure, workforce capability development, and comprehensive AI
integration strategies to maximize organizational benefits among Chinese SMEs.
Keywords: Artificial Intelligence,
Organizational Performance, SMEs, China, Digital Transformation
INTRODUCTION
Artificial
Intelligence (AI) has emerged as one of the most influential technological
innovations shaping modern business operations. As organizations continue to
face increasing market competition, changing customer expectations, and growing
demands for operational efficiency, AI technologies provide opportunities for
automation, predictive analytics, intelligent decision-making, and business
innovation. The increasing accessibility of AI-powered solutions has
transformed AI from an experimental technology into a strategic business
necessity.
China has become one
of the world's leading nations in AI development and implementation. National
initiatives such as the New Generation Artificial Intelligence Development Plan
have accelerated technological innovation and digital transformation across
industries. Chinese enterprises increasingly utilize AI technologies to improve
productivity, optimize operational processes, and strengthen competitiveness in
both domestic and international markets. The rapid growth of cloud computing,
big data analytics, and intelligent automation has further enabled
organizations to adopt AI-driven business models.
Small and medium
enterprises (SMEs) represent the backbone of China's economy, contributing
substantially to employment generation, innovation, and economic growth.
Despite their importance, SMEs often face challenges related to resource
constraints, operational inefficiencies, and technological limitations. AI
adoption offers opportunities to overcome these challenges by enabling
data-driven decision-making, process automation, and enhanced customer
engagement. Through AI implementation, SMEs can improve operational performance
while reducing costs and increasing responsiveness to market demands.
Previous studies have
reported positive associations between AI adoption and organizational outcomes,
including innovation capability, customer satisfaction, productivity, and
financial performance. However, much of the existing literature focuses on large
organizations or developed economies. Empirical evidence regarding AI adoption
and organizational performance among Chinese SMEs remains limited.
Consequently, there is a need to examine how AI adoption contributes to
organizational success within the Chinese SME context.
This study is
anchored on the Technology–Organization–Environment (TOE) Framework developed
by Tornatzky and Fleischer (1990) and the Resource-Based View (RBV) Theory
proposed by Barney (1991). The TOE framework explains technology adoption
through technological, organizational, and environmental factors influencing
adoption decisions. Meanwhile, RBV Theory suggests that valuable organizational
resources and capabilities serve as sources of sustainable competitive
advantage. AI technologies can be viewed as strategic organizational resources
capable of generating superior performance outcomes.
Specifically, this
study sought to determine the level of AI adoption among Chinese SMEs, assess
their organizational performance, examine the relationship between AI adoption
and organizational performance, and identify the predictive influence of AI adoption
dimensions on organizational performance.
METHODOLOGY
This study employed a
quantitative correlational research design to examine the relationship between
AI adoption and organizational performance among Chinese SMEs. The design was
appropriate because it enabled the measurement of associations between variables
and the determination of predictive relationships using statistical analysis.
The study was
conducted among SMEs operating in Beijing, Shanghai, Shenzhen, Chengdu, and
Guangzhou. These cities were selected because they represent major economic and
technological centers in China where AI adoption initiatives are highly
visible.
The respondents
consisted of SME owners, general managers, operations managers, and information
technology managers. Using Slovin’s formula with a 5% margin of error, a
minimum sample size of 286 respondents was determined. To improve
representativeness and compensate for possible non-responses, the final sample
consisted of 300 respondents selected through stratified random sampling.
Data were gathered
using a structured questionnaire composed of three sections. The first section
obtained demographic information. The second section measured AI adoption
through four dimensions: AI infrastructure, AI applications, employee AI
competency, and AI integration. The third section measured organizational
performance through operational efficiency, innovation capability, customer
satisfaction, and financial performance.
Responses were
measured using a five-point Likert scale ranging from 1 (Very Low) to 5 (Very
High). Prior to data collection, the instrument underwent expert validation and
pilot testing to establish content validity and reliability.
Data were analyzed
using weighted mean to determine levels of AI adoption and organizational
performance. Pearson Product-Moment Correlation was used to determine the
relationship between AI adoption and organizational performance. Multiple
Regression Analysis was employed to determine the predictive influence of AI
adoption dimensions on organizational performance. Statistical significance was
tested at the .05 level.
RESULTS
The results revealed
that Chinese SMEs demonstrated a high level of AI adoption with an overall mean
score of 4.13. Among the dimensions, AI applications obtained the highest
rating (M = 4.25), followed by AI infrastructure (M = 4.18), AI integration (M =
4.12), and employee AI competency (M = 3.98). These findings indicate that AI
technologies have become integrated into many business operations among Chinese
SMEs.
Organizational
performance was rated very high with an overall mean score of 4.22. Customer
satisfaction received the highest rating (M = 4.31), followed by operational
efficiency (M = 4.24), innovation capability (M = 4.20), and financial
performance (M = 4.11). The results suggest that SMEs perceive strong
organizational outcomes associated with their business operations.
Correlation analysis
revealed a strong positive relationship between AI adoption and organizational
performance (r = .782, p < .001). The findings indicate that higher levels
of AI adoption are associated with better organizational outcomes.
Multiple regression
analysis demonstrated that AI adoption significantly predicts organizational
performance. The regression model explained 64.2% of the variance in
organizational performance (R² = .642, F = 112.45, p < .001). AI integration
emerged as the strongest predictor (β = .324), followed by AI infrastructure (β
= .301), AI applications (β = .287), and employee AI competency (β = .198).
DISCUSSION
The findings
demonstrate that AI adoption has become an important strategic capability among
Chinese SMEs. The high level of AI adoption observed in the study reflects the
increasing accessibility of AI technologies and the supportive digital
ecosystem within China. Government initiatives promoting technological
innovation and digital transformation may have contributed significantly to the
widespread adoption of AI solutions among SMEs.
The strong positive
relationship between AI adoption and organizational performance supports
previous studies suggesting that AI technologies improve organizational
efficiency, innovation, customer engagement, and financial outcomes. AI
applications enable organizations to automate repetitive tasks, improve
forecasting accuracy, and generate actionable business insights. These
capabilities contribute directly to enhanced organizational effectiveness and
competitiveness.
The findings support
the Technology–Organization–Environment Framework, which emphasizes the
importance of technological readiness, organizational capabilities, and
environmental support in successful technology adoption. SMEs with stronger AI
infrastructure, greater technological integration, and more capable employees
appear better positioned to achieve superior organizational outcomes.
The results also
support Resource-Based View Theory. AI capabilities function as strategic
organizational resources that enhance competitive advantage. Organizations
capable of effectively integrating AI into business processes develop unique
competencies that competitors may find difficult to replicate. This strategic
advantage contributes to improved operational performance and long-term
sustainability.
Notably, AI
integration emerged as the strongest predictor of organizational performance.
This finding suggests that the mere adoption of AI technologies is insufficient
to maximize organizational benefits. Rather, organizations must ensure that AI
applications are integrated into strategic and operational processes across
functional areas. Effective integration enables organizations to leverage AI
capabilities more comprehensively and generate greater business value.
CONCLUSION AND
RECOMMENDATIONS
The study concludes
that Chinese SMEs exhibit high levels of AI adoption and organizational
performance. AI adoption is significantly associated with organizational
performance and serves as a strong predictor of business success. Among the
dimensions examined, AI integration contributes most significantly to
organizational outcomes.
Based on the
findings, SMEs should increase investments in AI infrastructure and workforce
development initiatives. Managers should prioritize the integration of AI
technologies into organizational processes and strategic decision-making
systems. Government agencies should continue supporting AI adoption through
incentives, training programs, and technological infrastructure development.
Future studies may utilize mixed-methods approaches, larger samples, and
industry-specific analyses to further examine the impact of AI adoption on
organizational performance.
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