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Asian Business Research Journal 
Vol. 10, No. 8, 49-58, 2025 
ISSN: 2576-6759 
DOI: 10.55220/2576-6759.536 
© 2025 by the authors; licensee Eastern Centre of Science and Education, USA 

 
 

 

 
The Impact of Customer Demand Orientation on Digital Transformation of 
Enterprises 

 
Wenshuai Chen1  
Haochen Wang2

  
Saixing Zeng3 
 

 
 

1,2,3Antai college of Economics & Management, Shanghai Jiao Tong University, China. 
Email: chenws2021@163.com   
Email: francis666@sjtu.edu.cn   
Email: zengsaixing@sjtu.edu.cn  
(Corresponding Author) 

 

 
Abstract 

With the continuous development of science and technology, the digital transformation of 
enterprises has become one of the core forces to maintain competitiveness and create lasting value. 
However, this transformation is not only a technical change but also a strategic process that 
requires deep insight and flexibility. Building on theories of customer value theory and data-
driven decision-making, this paper explores the impact of customer demand orientation on the 
level of enterprise digital transformation based on the data collected from manufacturing 
enterprises in China's A-share and Shanghai stock markets from 2010 to 2021. We find that the 
degree of customer demand orientation has a positive impact on the level of digital 
transformation; further, this main effect is moderated by industry concentration externally and 
investor interaction response internally. More specifically, the more intense the competition in the 
industry and the shorter the response time of enterprises to investors’ questions on the interactive 
e-platform, the stronger the impact of customer demand orientation on the level of digital 
transformation. These insights introduce novel effects from several perspectives on enterprise 
digital transformation, offering significant theoretical and practical implications for policy-makers 
and enterprise decision-makers regarding business digital transformation. 

 
Keywords: Customer demand orientation, Industry concentration, Investor interaction response, Level of digital transformation. 

 
1. Introduction 

With the rapid development of the economy and the universal popularization of the Internet, the speed of the 
development of the digital economy, the breadth of its radiation scope, and the extent of its far-reaching influence 
have reached an unprecedented point. Due to economic growth and social progress, consumer demand is constantly 
upgraded, and customer demand has become an important issue that enterprises must address. Customer demand 
orientation requires enterprises not only to meet the diversified needs of customers but also to reflect the 
personalized elements in products and services to enhance the sense of customer participation and experience. This 
requires enterprises to conduct in-depth market research and analysis to master and understand customer needs 
and consumption trends in order to implement precision marketing and customized services. 

Digital transformation is an important means for enterprises to adapt to the development of the times and can 
help them improve production efficiency, reduce costs, optimize resource allocation, and enhance market 
competitiveness (Schweidel et al., 2022). Under the influence of customer demand orientation, enterprises need to 
increase investment in R&D and innovation so as to obtain technology and production capacity that can meet 
customer demand, and realize the common development of enterprises and customers in the process of focusing on 
customer demand and digital transformation.  

Building on theories of customer value theory and data-driven decision-making, this paper explores the impact 
of customer demand orientation on the level of enterprise digital transformation based on the data collected from 
manufacturing enterprises in China's A-share and Shanghai stock markets from 2010 to 2021. Through text 
analysis to explore the key information in enterprise annual reports, we count the word frequency of keywords 
such as personalized, customized, and customer-oriented in these reports and take the logarithmic processing as 
the enterprise's score of customer demand orientation. At the same time, we define the level of digital 
transformation of enterprises by digital and intelligent readiness and contribution to digital intelligence. Digital 
and intelligent readiness is measured by CIO (Chief Information Officer), innovation-oriented foresight, 
innovation-oriented continuity, and innovation-oriented intensity. Contribution to digital intelligence is measured 
by normalizing the number of Numerical Intelligence-related patents obtained by a company each year.  

Our research provides several interesting results that can inform business strategy regarding enterprise's 
digital transformation. Firstly, the degree of customer demand orientation positively affects digital transformation. 
In order to meet customers' personalized needs, enterprises need to continuously upgrade technology and digital 

mailto:chenws2021@163.com
mailto:francis666@sjtu.edu.cn
mailto:zengsaixing@sjtu.edu.cn
https://doi.org/10.55220/2576-6759.536


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transformation, optimize business processes and production processes, and improve their digital transformation 
level. Secondly, industry concentration negatively moderates the relationship between customer demand 
orientation and the level of digital transformation. In industries with lower industry concentration, enterprises face 
more intense competition, and in order to excel in the competition, they need to respond to customers' personalized 
needs faster and more efficiently. Finally, investor interaction response negatively moderates the relationship 
between customer demand orientation and the level of digital transformation. By responding quickly to investor 
needs and feedback, listed companies can better understand market dynamics and customer needs, and then adjust 
their own strategies and business models to improve their level of digital transformation. 

Our research also makes the following theoretical contributions: firstly, it provides theoretical support and 
guidance for enterprise to facilitate digital transformation. Studying the influencing factors and evaluation methods 
of their digital transformation level can help enterprises clarify their advantages and disadvantages in the process 
of digital transformation, so as to formulate a more scientific and reasonable transformation strategy. Secondly, we 
bridge the gap between the research on enterprise digital transformation and customer demand which is under 
explored in literature. This study combines these two fields, and provides novel insights through using updated 
text analysis methods. Thirdly, our research provides practical guidance for policymakers and enterprise decision 
makers regarding digital transformation strategy. For example, policymakers can use this study to understand the 
difficulties and challenges faced by enterprises in the process of digital transformation, so as to formulate more 
targeted policy measures. With regard to enterprise decision makers, this study can help them understand the 
impact of customers' personalized needs on the digital transformation of enterprises, so that they can formulate 
more accurate marketing strategies. 
 

2. Theoretical Background and Hypothesis Development 
2.1. Customer Demand Orientation 

Customer orientation refers to the degree to which an organization and its employees are committed to 
meeting customer needs and improving the customer experience (Kiffin-Petersen & Soutar, 2020). It guides 
companies to continually improve their offerings to customers (Ghouri et al., 2021), facilitate the development of 
new products and/or services, ensure that customer value management is more customer driven than IT driven, 
develop customer service quality to improve customer satisfaction, loyalty, retention.  

Since the 1990s, with rapid advancements in productivity, more firms have acknowledged the impact of 
customer orientation on product development and performance. The higher the degree of customer orientation, the 
more emphasis firms place on understanding and satisfying customer needs. Correspondingly a good 
understanding of customer orientation is a key requirement for companies to be competitive in the marketplace 
(Alt et al., 2019), which in turn promotes the level of digital transformation. As a catalyst, digital marketing 
promotes consumer interaction, recognition of the brand, and broadening company growth (Zannat et al., 2024). 

Personalization refers to tailoring products, services, or interactions based on customer data and preferences 
inferred from their behavior, aiming to meet their unique needs and expectations. There are three types of 
personalization: user-, transaction- and context-driven personalization. User-driven personalization refers to users 
specifying in advance the desired web layout and content that matches the tools and options provided (Tam & Ho, 
2006). This kind of personalization enhances customer satisfaction and leads to a better overall experience (Sun & 
Zhang, 2021). Research also indicates that personalized demand significantly boosts customer retention and 
loyalty, as consumers feel more understood and valued.  

Customization is the process of meeting the different preferences and personalized needs of different individual 
consumers; therefore, customization can also be interpreted as a set of solutions tailored to customers' specific 
needs and interests, and providing products or services for the personalized preferences of different customers 
(Shao, 2020). Due to technological advances, customizing one's own preferences has become more autonomous with 
the help from information technology and other means; therefore, customization can also be defined as a system 
that uses information technology to provide customers with a wide range of products and services for specific needs 
by constructing flexible design processes and organizational structures (Da Silveira et al., 2001). In addition to this, 
although the preferences of different consumers for customized products and services are highly heterogeneous and 
stylistically diverse, they have also been shown to be very attractive to consumers who could pay a significant 
premium and tolerate longer waiting times, which are the main reasons why companies are striving to upgrade 
their technological innovations to achieve mass customization (Jost & Süsser, 2020). 

In the past, customization of products was oriented to individual consumers or groups with small-volume 
demand, and the production cost was high and difficult to be popularized. While in the digital age, production 
capacity and people's living standards have made rapid progress, and traditional mass production has been unable 
to meet the growing demand for personalization; in order to keep abreast of the times, the concept of mass 
customization came into being. Many scholars believe that mass customization is a mode by which manufacturing 
firms must accept and embrace globally in order to survive in a continuously dynamic and unstable environment 
(Ullah & Narain, 2018). Mass customization to some extent satisfies consumers' personalized needs, increases 
consumer loyalty to the brand, and enables firms to earn more profits and enhance their competitiveness.  

 

2.2. Customer Demand Orientation and the Level of Digital Transformation 
From the data-driven decision-making viewpoint, data is the basis for enterprise decision-making. Through 

digital intelligence technology, enterprises can better grasp the needs and expectations of customers as the basis 
for digital transformation and improve their level of digital intelligence. The digitalization of processes is beneficial 
since it transmits information in real time, allowing managers to correct problems more effectively and with less 
waste (Broday, 2022). In the era of digitalization, customer demand orientation has become an important factor 
affecting enterprises' level of digital intelligence. A certain level of process maturity can be put in relation to a 
certain innovation level and a certain level of digital change and an adaptable process could respond to changes in 
customer demands better (Sehlin et al., 2019). In the process of the market constantly calling on enterprises to 
improve their production quality and pay attention to consumer demand, enterprises have become more innovative 



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in the process of being pushed forward (Fichman et al., 2014). Strengthening digital infrastructure can scale up the 
energy transition (Olugbenga, 2025). The challenge for a manufacturing company is not only how customer- or 
innovation-oriented it is, but also whether it is able to respond positively to market demands (Berthon et al., 2004) 
and adapt to changes in the market, which at this stage is becoming more and more personalized as people's 
standard of living improves and technological advances are made (Davenport et al., 2020). With regard to 
enterprises, once the target strategy oriented to customers' personalized needs is determined, they will attach great 
importance to finding ways that can provide customers with quality products and services, and in this process will 
also continuously promote technological innovation, which is key to the success of digital transformation (Dung & 
Duc, 2025). 

The main mission of the enterprise is to make profits, and the main strategy to make profits is to be able to 
occupy enough market share. Most of the products produced by manufacturing enterprises have strong 
substitutability, forcing marginal returns lower and lower. As a result, how to stand out in the market has become 
the most important task of all enterprises. But large-scale customized production still has a large room to grow in 
the manufacturing industry; along with the improvement in the standard of living and consumption ability, 
consumers are increasingly focusing on their own personalized customization needs, and the ability of enterprises 
to achieve early mass customization of production will enable them in future to better capture the market. The 
higher the enterprise's degree of customer demand orientation,  the more it will invest in upgrading its technology 
to better meet the personalised and customised needs of its customers, as digital transformation can improve the 
accuracy of demand forecasting (TRAN, 2025), correspondingly, the higher the level of its digital transformation 
will also be. Based on this, we formulate the following hypothesis: 

Hypothesis 1: The degree of customer demand orientation will have a positive impact on the level of digital transformation 
of enterprises. 

 

2.3. Industry Concentration  
Industry concentration measures the degree of competition in an industry, which directly affects the 

enterprises’ formulation of strategies and the implementation of plans. Environment significantly influences firms' 
investment decisions (Lueg & Borisov, 2014). Firms with market concentration and monopoly power have the 
financial support and risk prevention needed for R&D activities, and effective technological innovation activities 
will further enhance the monopoly power of the firms and secure monopoly rents. By analyzing data on firms from 
several countries, Cette et al. (2017) found that restricted competition significantly stimulates technological 
innovation and increases firms' productivity. 

However, many scholars have questioned whether competition between firms has a positive effect on 
innovation, compared to the relatively inefficient management and low incentive to innovate of firms without 
competition. For example, Jiang et al. (2024) found that industry concentration enhances the positive impact of 
customer concentration on firm innovation performance.  

Many studies on market competition and firm innovation provide an interesting insight that market 
competition has an inverted U-shaped effect on firm innovation (Aghion et al., 2005). In highly competitive 
industries, firms invest more in innovation in order to gain a relative competitive advantage. On the other hand, 
when firms are in a weak position to catch up with strong firms, too large a technology gap may cause them to 
engage in pendulum behavior.  

A growing body of research suggests that a firm's strategic choices depend heavily on the nature of its external 
environment, the most important of which is the degree of competition among its industry peers (Rahmayati, 
2021). When the market environment is very competitive, the variety of products available is more diversified, 
alternative products and services continue to emerge, the fierce competitive environment brings fewer market 
opportunities, and for firms, the profitability of homogeneous products is continually suppressed due to competition 
from peers (Dou et al., 2021). In order to maintain their competitive advantage, meeting customers' individual 
needs can help enterprises stand out better, and in this process, enterprises will continuously increase R&D 
investment in technological innovation, thus improving their degree of digital transformation. 

Different industries have different degrees of competition, which also affects the willingness of enterprises to 
carry out digital transformation. For industries where enterprises are evenly matched, the more intense the 
competition is, the more attention these enterprises will pay to the customization and personalization needs of 
consumers, which can stimulate enterprises to carry out digital transformation. The higher the industry 
concentration, the less intense the competition within the industry. Therefore, we make the following hypothesis: 

Hypothesis 2: Industry concentration negatively moderates the relationship between customer demand orientation and the 
level of digital transformation. 

 
2.4. Investor Interaction Response 

Based on H1, we tentatively believe that enterprises that pay attention to the personalized needs of customers 
have a higher degree of digital transformation, and rapid identification of external information can enable them to 
respond to changes in the market in a timelier manner; also, more efficient identification of the needs of customers 
and the ability of enterprises to acquire external knowledge can effectively promote corporate innovation. The 
interaction between investors and listed firms can effectively encourage managers to increase R&D investment and 
promote technological progress. Since technological change is a long-term uncertain and risky process, and has the 
characteristics of information asymmetry, it easily causes resistance from managers (Abdoh & Liu, 2021); and the 
openness of interactive e-platforms enables investors and corporate managers to better interact and communicate, 
and in the process of interaction, managers are able to better understand investor needs. If investors cannot 
provide timely solutions to issues related to production processes, technological innovation, etc. raised by 
investors, investors on the interactive platform may follow up with more questions; thereby generating the 
“spotlight effect”. The “spotlight effect” will attract management's attention, both to stabilize investor confidence 
and to satisfy customer demand for products among investors, which will encourage managers to increase 
investment in innovation and R&D, and thus promote the level of digital transformation of the enterprise. If the 



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firm does nothing, it may cause a large number of investors to pressure management by divesting their shares, thus 
strengthening their supervision (Reiter, 2021). We can therefore make the following hypothesis: 

Hypothesis 3: Investor interaction response negatively moderates the relationship between customer demand orientation 
and the level of digital transformation. 

 

3. Data and Methodology 

3.1. Data 
In this paper, we take the data of A-share listed manufacturing enterprises from 2010-2021 as a sample, and 

refer to the practice of existing studies, excluding the samples with missing main variables, with 16,783 effective 
observations to study the impact of enterprise customer demand orientation on the level of digital transformation. 
Financial data and corporate governance data are obtained from the CSMAR database, enterprise customer demand 
orientation is obtained from enterprise annual reports, and digital transformation data are obtained by crawling 
through enterprise annual reports and the patent network. Due to the high number of 0 values in CDO and LDT, 
and also to avoid the influence of outliers, we shrank the variables by 5% and 95%.  

 

3.2. Measures 
Porter et al. (2002) suggests enhancing traditional literature reviews by utilizing text mining's extensive 

research analysis methodology because "this extensive scanning of the contextual literature can extend the span of 
science by better connecting work in the research field". 

Research overviews are made possible through sophisticated text-mining tools combined with modern search 
engines and scientific databases (Porter et al., 2002). Text mining allows for automated or partially automated 
processing of text; technically, it is the process of digitizing text documents and extracting patterns from them 
using common data mining techniques (Delen & Crossland, 2008). 
 
3.2.1. Independent Variable 

The independent variable of our research is CDO (customer demand orientation): CDO = num_CDO, where 
num_CDO is the frequency of keywords related to personalization, customization, and customer orientation. 
Through text analysis exploring key information in annual reports, we count the word frequency of keywords such 
as personalized, customized, and customer-oriented and take the logarithmic processing as the enterprise's score of 
customer demand orientation. (Data source: Annual reports of listed companies obtained after text analysis and 
processing). 
Keyword phrases are shown in Table 1 below. 
 

Table 1. Customer Demand Orientation Keywords. 

Customer Demand Orientation 

Personalization, User Needs, Customer Needs, Customer Demand, User Preferences, Customer Preferences, User 
Satisfaction, Customer Satisfaction, High-end Demand, Customization, DIY, Diy, Intelligent Services, Recommender 
Systems, User Models, E-commerce, Collaborative Filtering, Data Mining, Web Usage Mining, User Profiles, Ontology, 
Adapted, Information Retrieval, Privacy, Clustering, Situational Awareness, Internet, Machine Learning, Mass 
Customization, Product Families, Supply Chain Management, Platform Products, Genetic Algorithms, Modularity, Product 
Configurator, Product Development, Flexibility, Extension, Services, User Profiles, Case Studies, Design, Context-
awareness 

 
3.2.2. Dependent Variable 

The dependent variable of this research is LDT (Level of digital transformation of Enterprises). The core of 
digital transformation is to solve the uncertainty of complex systems in an environment defined by data and 
algorithms by means of intelligent data services and other related technologies, so as to improve the efficiency of 
resource allocation and build the relative competitive advantage of enterprises. digital transformation is customer-
centric, through the deep integration of digital technology with business, operations, management, and other 
aspects, to achieve the rapid delivery of data flow, the depth of value mining and creation, the cycle of iteration, and 
to re-construct a new value chain and digital ecology. For enterprises to carry out digital transformation is 
inevitable if they are to comply with the trend of the times; most enterprises are making an effort, to various 
degrees, to carry out digital transformation. How to measure the level of digital transformation of enterprises is a 
highlight of this paper. 
 

Table 2.  Digital Intelligence Readiness Metrics. 

Variable Measurement 

CIO Whether or not there is a Chief Information/Technology/Digital Officer, 0-1 dummy 
variable, standardized treatment 

Forward_looking 
(Innovation-Oriented 
Forward-Looking) 

2022 - year_first_report_digital 
Year of first occurrence of digital keywords in the MD&A section of the annual report, 
normalized 

Sustainable 
(Innovation-Oriented 
Sustainability) 

num_report_digital 
Total number of years with numerical keywords in the MD&A section of the annual report, 
normalized 

Intensity 
(Intensity of Innovation 
Orientation) 

digital_word/report_word 
Ratio of digitized keywords to the total number of words in the MD&A section of the 
annual report for each year, normalized to the total number of words in the section. 

 
In this paper we measure the level of digital transformation from the following two perspectives: digital and 

intelligent readiness (DIR: Digital_intelligence_readiness) and contribution to digital intelligence (CDI: 
Contribution_to_digital_intelligence). Whether an enterprise has set up positions related to digital intelligence and 



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whether it has taken digital transformation as a strategic goal are the most important reflections of an enterprise's 
digital transformation level. We calculate metrics to determine an enterprise's digital intelligence readiness, as seen 
in Table 2. 
DIR = (CIO+Forward_looking+Sustainable+Intensity)/4 CDI = NUM_digital_patent, NUM_digital_patent is 
the normalized data of the number of digital intelligence-related patents obtained by enterprises every year. 

We downloaded the patent data in the Guotai Junan csmar database and then searched for patents according to 
their patent codes on the PatentGuru (www.patentguru.com) website to obtain the abstract content of the patent 
introduction, analyze the text of the abstract to determine whether it is a patent related to digital and intelligence, 
and then sum up the number of digital and intelligence patents of the enterprise in the current year. The keywords 
of the digital and intelligence patents are shown in Table 3. 
 
Table 3. 
Digital and Intelligence Patent Keywords. 

Digital and Intelligence Patents 
Artificial Intelligence, AI, Business Intelligence, Image Understanding, Robotics, Machine Learning, Deep Learning, 
Semantic Search, Biometrics, Face Recognition, Speech Recognition, Identity Verification, Autonomous Driving, Natural 
Language Processing, NLP, Supervised Learning, machine translation, OCR, computer vision, machine vision, intelligent 
Q&A, expert systems, neural networks, automated reasoning, unmanned vehicles, drones, automated reasoning, driverless, 
unmanned vehicles, brain-computer interfaces, knowledge graphs, neuroscience, federated learning, automation, Gesture 
Recognition, Voice Interaction, Drones, Smart Driving, Digital Identity, RPA, Vehicle-Road Collaboration, Blockchain, 
Digital Currency, Smart Contracts, Distributed Digital Currency, Smart Contracts, Distributed Computing, 
Decentralization, Bitcoin, Coalition Chain, Differential Privacy, Consensus Mechanisms, Distributed Ledger, Public Chain, 
Cross-Chain, DLT, Cloud Computing, In-Memory Computing, Streaming Computing, Graph Computing, Multi-Party 
Secure Computing, Brain-Like Computing, Green Computing, Brain-like Computing, Cognitive Computing, Converged 
Architecture, Cloud Native, Privacy Computing, Edge Computing, BaaS, SaaS, IaaS, PaaS, Fog Computing, Cloud Services, 
Cloud-Network Convergence, Hybrid Cloud, Public Cloud, Cloud Technology, Cloud Storage, Cloud-Edge Collaboration, 
Distributed Architecture, Big Data, Data Mining, Text Mining, Data Visualization, Heterogeneous Data, Augmented 
Reality, Mixed Reality, Virtual Reality, Text Capture, AR, VR, MR, Data Analytics, Data Management, Cloud Platform, 
Anonymization, Internet of Things, Internet of Vehicles, IoT, Industrial Internet, Mobile Internet, Wireless Medical 
Networking, Wireless Access, Inter-vehicle Network, Smart Terminals, Smart Homes, Remote Monitoring, Mobile 
Terminals, Intelligent Control, Smart Factories, Digital Twins, Live Twins, Intelligent Transportation, BIM, CIM, 3D, 2D, 
5G, 6G, LTE, Internet+, Quantum Secure Communication, Mobile Communication, Smart Grid, E-commerce, AGV, CNC 
Machine Tools, Digitization, Smart Hardware, Core Shield, Smart Wearable, Smart Manufacturing, Digital Model, Smart 
Construction, Smart Public Transportation, Intelligent, Digital Intelligence, Smart Ports, Smart Factories, Quantum 
Computing, FinTech, Supply Chain Finance, Smart Customer Service, Internet Finance, Digital Finance, Fintech, Cross-
border Payment, Mobile Payment, NFC Payment, CAD, CAM, Investment Decision Aid System, Intelligent Data Analysis, 
Intelligent Robot, Biometrics, OCR Technology, CPO, Expert System, Learning Algorithms, Differential Privacy 
Technology, Billion Level Concurrency, EB Level Storage, Information Physical System, Artificial Intelligence Laboratory, 
Artificial Intelligence Platform, Artificial Intelligence Facility, Artificial Intelligence Equipment, Artificial Intelligence 
System, Intelligent Information System, Cloud Laboratory, Cloud System, Cloud Equipment, Cloud Facilities, Cloud 
Terminal, Cloud Community, Cloud Technology System, Big Data Lab, Big Data Platform, Big Data Facilities, Big Data 
Equipment, Big Data Information System, Big Data Technology System, Digital Patent, Digital Network, Digital Terminal, 
Digital Technology System, 3D Printing Equipment, Meta-Universe, Virtual Human, 3D Printing, 5G Technology, Mobile 
Internet, Digital Technology, Nano-computing, Intelligent Planning, Intelligent Optimization, Intelligent Marketing, 
Digital Marketing, Unmanned Retail, Unmanned Factory, Third Party Payment, NFC Payment, Human-Computer 
Interaction, Social Networking, Intelligent Agriculture, Intelligent Transportation, Intelligent Healthcare, Intelligent 
Investment, Intelligent Culture and Tourism, Intelligent Environmental Protection, Intelligent Energy, Internet Healthcare, 
Quantitative Finance, Open Banking, Netflix 

 
Level of Digital Transformation (LDT): We sum the above readiness and contribution to the level of digital 

transformation (LDT). LDT = DIR+CDI 
 
3.2.3. Moderating Variables 

The moderating variables of our research are Reply_interval (Investor Interaction Response) and Industry 
Concentration (LernerIndex). The first moderating variable in this study is Reply_interval. Investor interaction 
response: ln(mean(answer_time - question_time)+1), answer_time for the enterprise to answer investor questions 
for the time, unit for the day, question_time for the investor question time, unit for the day. 

The best data to measure the responsiveness of a company to its customers is based on the interaction between 
the company and its downstream customers, but the difficulty of obtaining this type of data, as well as the 
personalized differences between different companies can also make a big difference in the results (some companies 
have more mature system platforms, but some do not; however, there may be offline or even other ways to pay 
close attention to the personalized needs of customers); therefore, we decided to use the interaction time to measure 
the responsiveness of the company to its customers.), we therefore decided to approximate the response rate of 
firms to consumer comments through the interactions of investors and listed firms captured by the Interactive e 
platform, thus approximating the level of importance that firms attach to customer needs. We take the logarithm of 
the difference between investor comments and listed firms' response time by counting the difference between 
investor comments and listed firms' response time in each year, and then take the logarithm of the mean value as 
the response to investor interactions (Source: Guotai Junan csmar database). 

The second moderating variable in this study is Industry Concentration. Measures of the degree of competition 
in a market are mainly divided into two types: objective measures and subjective evaluations. Objective 
measurement usually uses indicators such as industry concentration and Lerner index to quantify the actual level of 
market competition. On the other hand, subjective evaluation uses market questionnaires and other methods to 
obtain the subjective feelings of market players about the degree of market competition. These two methods have 
their own advantages and disadvantages. Subjective evaluation can reflect the intuitive views of market 
participants, while objective measurement focuses more on objective analysis through data and indicators. The 



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combined use of these two methods can help us understand and assess the state of competition in a more 
comprehensive manner. Objective measures often use industry concentration, Lerner index and other related 
indicators to measure the intensity of competition in the market. Meanwhile, some scholars also use instrumental 
variables such as trade policy changes and tariff changes to replace the level of market competition. However, all 
these indicators are somewhat one-sided in measuring the level of market competition. 

In this paper, we adopt the industry Lerner index. The industry Lerner index (also known as the industry 
concentration index) is a measure of the degree of monopoly of manufacturers in an industry, which assesses the 
market dominance of different manufacturers in an industry by comparing their output or sales. The advantage of 
the Industry Lerner Index is that it provides a visual measure of the degree of market monopoly and can be used to 
compare the market structure between different industries. In addition, the industry Lerner index can also reflect 
the potential monopoly power of manufacturers in the industry. 

LernerIndex [Industry Lerner Index]: si/∑ si𝑛
0 *Lerner_stock, obtained by weighting the Lerner Index of 

individual stocks using the ratio of operating income of individual companies to the operating income of individual 
industries. 

si: Operating income of individual company 
∑ si𝑛

0 : Total operating income in the industry 
Lerner_stock: Individual stock Lerner index cumulative 

 

3.2.4. Control Variables 
Some company-level factors that may affect the dependent variable are included. The definition of control 

variables (Shou et al., 2021) is shown in Table 4. 
 

Table 4. Summary of  Variable Measures. 

Variable Name Variable Symbol Variable-Specific Measures 

Customer Demand Orientation CDO LN(1+num_CDO), where num_CDO is the number of personalization-
related word frequencies in the annual report. 

Investor Interactive Response Reply_interval LN (1+qa_time), where qa_time is the mean value of the annual investor 
Q&A intervals. 

Industry Concentration LernerIndex Individual Company Operating Income / Total Industry Operating 
Income * Individual Lerner Index Cumulative, obtained by weighting the 
Individual Lerner Index using the ratio of Individual Company Operating 
Income to Individual Industry Operating Income. 

Level of  digital transformation 
of  Enterprises 

LDT Digital Intelligence Patents and Digital Intelligence Readiness Weighting 

Firm Size Size LN (firm's total assets at year-end) 

Firm Age Age LN (the number of years since the firm's incorporation) 

Revenue Growth Rate Growth 
(current period amount of operating income for the current year - amount 
of operating income for the same period of the previous year) / (amount of 
operating income for the same period of the previous year) 

State Ownership of Enterprise SOE 1: state-owned; 0: non-state-owned 
Leverage Leverage total liabilities at year-end/total assets at year-end 

Return on Assets ROA net profit at the end of the year/total assets at the end of the year 
Total Asset Turn Over TATO year-end sales revenue/year-end total assets 

Book-To-Market Ratio MTB shareholders' equity/market capitalization 
Tobin's Q Tobin's Q market capitalization A/ total assets  

Share of Tangible Assets Tangibility (total assets - net intangible assets - net goodwill) / total assets 

 

3.3. Regression Specification 
We used spspro statistical analysis software to analyze the model selection for the hypotheses we wanted to 

test, and the Table 5 are illustrated as follows: based on the results of the model selection in the table above, the 
three tests were combined to select the most appropriate model.  
 

Table 5. Measurement model selection. 

Type of Test Statistic P Conclusion 

F-test 23.825 0.000*** FE model 

Breusch-Pagan test 173453.271 0.000*** RE model 

Hausman test 957.336 0.000*** FE model 
Note: ***、**、*represent 1%, 5%, and 10% significance levels respectively. 

 
The sample of this paper is selected as the data of manufacturing industry of A-share listed companies from 

2010 to 2021, which is a panel data type. Therefore, for the empirical model of this paper, we choose the time 
individual fixed effect model, which can effectively remove the influence of year and enterprise. Meanwhile, because 
the research contains two moderating variables, in order to prevent the existence of multicollinearity between the 
independent variables and the moderating variables as well as the interaction terms, the independent variables and 
the two moderating variables are centered, and the interaction terms are constructed afterwards. 

Firstly, in order to test the relationship between the core explanatory variables of this paper, customer demand 
orientation and the level of digital transformation, the following econometric regression model 1 is constructed.  

 

LDTi,t  = α0 + β1CDOi,t + β2Agei,t + β3Sizei,t + β4SOEi,t +  β5Growthi,t + β6Leveragei,t + β7ROEi,t + β8TATOi,t

+ β9MTBi,t + β10Tobin′s Qi,t + β11Tangibilityi,t + μi + τt + εi,t 
(Model 1)              

where α0 denotes the constant term; βi denotes the coefficients to be estimated for the explanatory variables; i 

and t denote enterprise i and year t, respectively; εi,t denotes the residual term; τt denotes the time fixed effect, and 

μi is the individual fixed effect. 



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In order to verify the possible moderating effect of industry concentration and response speed, we introduce 
the two variables of industry concentration (industry Lerner index) and investor interaction response speed as the 
moderating variables to construct Model 2 and Model 3 on the basis of the existing Model 1, and conduct a 
regression test by adding the moderating variables and interaction terms between the moderating variables and the 
customer demand orientation on the basis of Model 1. The regression test is conducted by adding the moderating 
variable and the interaction term between the moderating variable and customer demand orientation to Model 1, 
respectively, and the significance of the correlation coefficients is used to verify the moderating role of the 
moderating variable in the relationship between the customer demand orientation and the level of digital 
transformation. Model 2 and Model 3 are shown below: 

LDTi,t  = α0 + γ1CDOi,t + γ2Lernerindexi,t + γ3CDOi,t × Lernerindexi,t + γ4Agei,t + γ5Sizei,t + γ6SOEi,t +
γ7Growthi,t + γ8Leveragei,t + γ9ROEi,t + γ10TATOi,t + γ11MTBi,t + γ12Tobin′s Qi,t + γ13Tangibilityi,t + μi +
τt + εi,t                                   (Model 2) 
 

LDTi,t  = α0 + η1CDOi,t + η2Reply_speedi,t + η3CDOi,t × Reply_speedi,t + η4Agei,t + η5Sizei,t + η6SOEi,t +
η7Growthi,t + η8Leveragei,t + η9ROEi,t + η10TATOi,t + η11MTBi,t + η12Tobin′s Qi,t + η13Tangibilityi,t + μi +
τt + εi,t                                                       (Model 3) 
 

4. Results 
4.1. Main Effect Regression Results 

Summary statistics (means, standard deviations, and pairwise correlations) for all variables are provided in 
Table 6. The variance inflation factor (VIF) values of the regression models are all less than 10 and the correlation 
coefficients between the variables are all well below 0.7, suggesting that multicollinearity is not a concern. 
 

Table 6.  Descriptive Statistics and Correlations. 

Variable Mean S.D. 1 2 3 4 5 6 7 8 9 10 11 12 13 

1. CDO 0 0.67              
2. LernerIndex 0 0.06 .06             
3. Reply_interval 0 1.23 .04 .08            
4. Age 2.87 0.34 .05 .20 .14           
5. Size 21.98 1.17 .11 .01 .24 .23          
6. SOE 0.24 0.42 -.05 -.07 .13 .21 .34         
7. Growth 0.12 0.21 .06 .05 -.02 -.06 .02 -.09        
8. Leverage 1.34 1.02 -.07 -.10 .04 .05 .18 .16 -.11       
9. ROA 0.09 0.08 .00 .15 .04 .03 .10 -.05 .18 -.24      
10. TATO 0.63 0.38 -.04 -.16 .03 .08 .15 .11 .06 .01 .19     
11. MTB 0.36 0.16 -.08 .01 -.08 -.11 -.07 -.11 -.11 -.17 -.19 -.08    
12. Tobin's Q 2.11 1.26 .03 .09 .01 .02 -.24 -.07 .06 -.14 .22 -.06 -.63   
13. Tangibility 0.93 0.08 -.09 -.04 .04 .00 -.03 .09 -.11 .00 .08 .13 .01 -.01  
14. LDT -0.08 1.40 .64 .03 .08 .13 .21 -.16 .03 -.05 -.01 -.05 -.13 .05 -.08 

 
Through the above analysis, we took the time individual fixed effect model and used the OLS regression model 

for regression analysis so as to verify our hypothesis. In the study of customer demand orientation on the level of 
digital transformation, we used three models: Model 1, Model 2, and Model 3. Model 1 verifies the effect of 
customer demand orientation on the level of digital transformation of enterprises. In Model 2, the interaction of 
industry concentration with customer demand orientation is added to verify the moderating effect of industry 
concentration on the relationship between customer demand orientation and the level of digital transformation. In 
Model 3, the interaction of investor interaction response speed with customer demand orientation is added to verify 
the moderating effect of investor interaction response speed on the level of digital transformation. The specific 
regression model is shown in Table 7. 
 

Table 7. Main Effect Regression Results. 

Variable Model 1 Model 2 Model 3 

Age -0.041 (0.038) -0.036 (0.038) -0.041 (0.038) 
Size 0.032*** (0.006) 0.033*** (0.006) 0.032*** (0.006) 
SOE 0.007 (0.012) 0.009 (0.012) 0.007 (0.012) 
Growth -0.012 (0.009) -0.013 (0.009) -0.012 (0.009) 

Leverage -0.004* (0.002) -0.004** (0.002) -0.004* (0.002) 
ROA 0.003 (0.026) 0.007 (0.026) 0.003 (0.026) 
TATO 0.017* (0.01) 0.019* (0.01) 0.017 (0.01) 
MTB -0.018 (0.022) -0.019 (0.022) -0.017 (0.022) 
Tobin's Q 0 (0.002) 0 (0.002) 0 (0.002) 
Tangibility 0.091*** (0.033) 0.093*** (0.033) 0.093*** (0.033) 
CDO 0.201*** (0.004) 0.202*** (0.004) 0.201*** (0.004) 
LernerIndex   -0.321*** (0.093)   

CDO*LernerIndex   -0.248*** (0.067)   
Reply_interval     0 (0.002) 
CDO*Reply_interval     -0.004* (0.003) 
const -0.752*** (0.174) -0.78*** (0.174) -0.749*** (0.174) 
F 358.813*** 309.987*** 304.397*** 

 



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We use two-tailed tests for all of our regressions, among which * represents p <0.10, ** represents p <0.05, 
and *** represents p < 0.01 for Table 7 through to Table 10.  

Model 1 is mainly to verify the influence of customer demand orientation on the level of digital transformation 
of enterprises. The regression analysis results in the above figure show that the coefficient of customer demand 
orientation (CDO; 0.201) is significantly positive at the 1% level, which indicates that the more attention is paid to 
customer demand, the higher the level of digital transformation of the enterprise, and hypothesis 1 is supported. 
Enterprises with strong customer demand orientation tend to introduce innovative digital technologies to meet the 
personalized needs of different customers more flexibly. With the introduction of AI technologies, companies are 
able to intelligently design and customize products according to customers' individual needs, improving the level of 
product personalization.  

Model 2 mainly verifies the moderating effect of industry concentration on the relationship between customer 
demand orientation and the level of digital transformation of enterprises. We can find that the coefficient of the 
LernerIndex is -0.321 and the corresponding P-value is 0.001***, which indicates that when the competition in the 
industry is more intense, enterprises will try to improve their production technology in order to obtain competitive 
advantages, and thus improve their level of digital transformation. In order to further verify the moderating effect 
of industry concentration, we introduce the variable CDO *LernerIndex, and we find that its coefficient is -0.248 
and the corresponding P-value is 0.000***, so we can draw the following conclusions that H2 is supported, and 
industry concentration can negatively modulate the effect of the orientation of customer demand orientation on the 
level of digital transformation. 

When the competitive ability between enterprises in the industry is similar, in the face of customer demand, 
only the enterprises that take the lead in meeting customer demand more efficiently through technological 
transformation can achieve competitive advantage; therefore, in a fiercely competitive environment, customer 
demand orientation stimulates enterprises more to carry out digital transformation. Intense competition under low 
industry concentration forces enterprises to seek differentiation and innovation to stand out in the market. This 
competitive pressure prompts enterprises to pay more attention to the personalized needs of customers, thus 
promoting digital transformation. In a competitive market, customers have more choices, so enterprises are more 
willing to listen to and satisfy customer demand, and provide customized services through digital technology to 
gain market share.  

Model 3 verifies the moderating role of investor interactive response in the relationship between customer 
demand orientation and the level of digital transformation of enterprises. Through the above analysis results, we 
can find that the coefficient corresponding to CDO * Reply_interval is -0.004 and the corresponding P-value is 
0.000*, which suggests that the shorter the response time of the enterprise to investor questions, the more effective 
the promotion of customer demand orientation on the level of digital transformation. We can conclude that H3 
holds that the faster the response time to investor interactions of listed companies, the more effective they can be in 
promoting the impact of customer demand orientation on the level of digital transformation. 

A faster response rate of investor interaction helps to reduce the information asymmetry between enterprises 
and investors, which makes enterprises more aware of market demand and investor concerns. Reducing 
information asymmetry helps enterprises more accurately grasp market dynamics, better meet customer demand, 
and promote the in-depth implementation of digital transformation. Rapidly responding to investor opinions and 
issues enables companies to adjust their strategies more quickly and better adapt to market demand. Faster 
strategic adjustments enable companies to meet customer demand more flexibly, thus promoting the smooth 
implementation of digital transformation. 

 

4.2. Causality Test 
In order to conduct a reverse causality test for the effect of CDO on LDT, we select the last three years of 

data in 2020 to conduct a Granger causality test with a lag order of 2. The results of the ADF test can be seen in 
Table 8. 

 
Table 8. ADF Test Results. 

Variable t p 
Threshold Value 

1% 5% 10% 

CDO -5.083 0.000*** -3.435 -2.864 -2.568 
LDT -20.15 0.000*** -3.435 -2.864 -2.568 

 
Based on the variable CDO shrinkage treatment, the significance P-value is 0.000***, presenting significance 

and rejecting the original hypothesis that the series is a smooth time series. Based on the variable LDT, the 
significance p-value is 0.000***, presenting significance and rejecting the original hypothesis that the series is a 
smooth time series. Granger causality tests were conducted on CDO and LDT and the results of the tests are 
shown in Table 9. 
 

Table 9. Granger Causality Test Results. 

Matched Sample F p 
CDO LDT 6.774 0.001*** 

LDT CDO 0.203 0.816 

 
Based on the variable CDO with LDT, the significance p-value is 0.001***, presenting significance and 

rejecting the original hypothesis, that CDO can cause changes in LDT. Based on the variable LDT with CDO, the 
significance p-value is 0.816, which does not present significance, and the original hypothesis cannot be rejected, 
i.e., LDT cannot cause changes in CDO. Therefore, there is no reverse causality between CDO and LDT. 
 
 
 



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4.3. Robustness Checks 
In order to avoid the problem of endogeneity to affect the results, we changed the measure of the outcome 

variables to avoid the problem of endogeneity. 
Considering that there are various ways to measure the level of digital transformation, different measures may 

affect this paper's results. In order to further calibrate the robustness of the model, we chose the digital 
transformation index in the CSMAR database to replace our outcome variables. The regression results are shown 
in Table 10. 
  

Table 10. Robustness Check Results. 
Variable Model 4 Model 5 Model 6 

Age -0.011 (0.019) -0.008 (0.019) -0.011 (0.019) 
Size 0.032*** (0.003) 0.033*** (0.003) 0.032*** (0.003) 
SOE -0.004 (0.006) -0.003 (0.006) -0.004 (0.006) 
Growth -0.012*** (0.004) -0.013*** (0.004) -0.012*** (0.004) 
Leverage -0.003*** (0.001) -0.003*** (0.001) -0.003*** (0.001) 
ROA -0.014 (0.012) -0.009 (0.012) -0.014 (0.012) 
TATO 0.011** (0.005) 0.012** (0.005) 0.011** (0.005) 
MTB -0.028*** (0.011) -0.03*** (0.011) -0.028** (0.011) 
Tobin's Q 0 (0.001) 0 (0.001) 0 (0.001) 
Tangibility -0.02 (0.016) -0.019 (0.016) -0.018 (0.016) 
CDO 0.101*** (0.002) 0.102*** (0.002) 0.101*** (0.002) 
LernerIndex   -0.308*** (0.046)   
CDO*LernerIndex   -0.108*** (0.034)   
Reply_interval     0.001 (0.001) 
CDO*Reply_interval     -0.003** (0.001) 
const 2.934*** (0.086) 2.912*** (0.086) 2.94*** (0.086) 
F 358.813*** 309.987*** 304.397*** 

 
Model 4. The coefficient (0.101) of customer demand orientation (CDO) is significantly positive at the 1% level, 

further verifying that H1 holds; 
Model 5. The coefficient of CDO*LernerIndex (-0.108) is significantly negative at the 1% level, further 

verifying that H2 holds; 
Model 6. The coefficient of CDO*Reply_interval (-0.003) is significantly negative at the 5% level, further 

verifying that H3 holds. 
 

5. Discussion and Limitations 
In today's market environment, customer demand orientation has become an important driving force for the 

digital transformation of enterprises. This paper focuses on the impact of firms' customer demand orientation on 
the level of digital transformation with two moderating variables incorporated: industry concentration and investor 
interaction response respectively. We summarized our findings as follows:  

Firstly, the degree of customer demand orientation positively affects the level of digital transformation. The 
higher the degree of customer demand orientation, the higher the level of digital transformation of enterprises. 
Against the backdrop of the increasing speed of product renewal and iteration, enterprises pay more and more 
attention to customers' personalized needs. To meet these needs, enterprises need to continuously upgrade 
technology, optimize business and production processes, and improve their digital transformation level.  

Secondly, industry concentration negatively moderates the relationship between customer demand orientation 
and the level of digital transformation. The lower the industry concentration, the more intense the competition 
within the industry, and the more it can strengthen the impact of customer demand orientation on the level of 
enterprises' digital transformation. In industries with lower industry concentration, enterprises face more intense 
competition, and in order to stand out from the competition, they need to respond to customers' personalized needs 
faster and more efficiently, so as to win market share.  

Finally, investor interactive response negatively moderates the relationship between customer demand 
orientation and the level of digital transformation. Shorter and faster response time of investor interaction of listed 
firms can effectively promote the impact of customer demand orientation on their digital transformation level. 
Investor interaction is one of the important ways for enterprises to obtain market feedback, and the interval time of 
investor interaction Q&A is an important indicator of enterprises' market feedback, reflecting the timeliness of 
their access to external information.  

While summarizing the above research results, we also need to acknowledge the limitations of this research. 
First of all, the influence mechanism of factors such as customer demand orientation, industry concentration, and 
the response speed of listed companies' investor interaction on the level of digital  transformation needs to be 
further explored; our measurement of the market response speed of enterprises is relatively rough; and the use of 
investor Q&A response time to measure the speed of enterprises' acquisition of external information is not precise 
enough. Secondly, the focus of this study is mainly on the analysis of the current situation, and future development 
trends and possible solutions have not been explored in depth. In addition, the generalizability to different 
industries, different types of enterprises, and different regions needs to be further explored. 
 

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