Frontiers in Business, Economics and Management 
ISSN: 2766-824X | Vol. 8, No. 1, 2023 

 

82 

How	Does	Data	Input	Drive	Enterprise’s	Technological	
Innovation:	Concepts,	Mechanisms	and	Paths?	
Xinyue Ruan1, *, Xiaohua Feng2 

1 Business School, Hubei University, Wuhan 430062, China 
2 Business School, Hubei University, Hubei Open Economic Research Center, Wuhan 430062, China 
* Corresponding at 2529645804 @qq.com 

 

Abstract: In the era of digital economy, data has become not only a production factor, but also an innovation factor, which has 
already played a profound impact on technological innovation of enterprises. What is the data factor? How does data input drive 
technological innovation of enterprises? How to promote the positive effects of data input on enterprise’s technological 
innovation. Such questions have attracted attentionof academic world. This paper first explores the connotation and economic 
characteristics of data, and further analyzes the mechanism of data input driving enterprise’s technological innovation from four 
aspects:cost reduction, knowledge creation, optimal allocation and enterprise governance. Finally, this paper proposes that 
reducing the cost of data transactions, reconstructing the cooperation network of industry-university-research, accelerating the 
integration of elements andconstructing high-level data rules are effective paths for data-input enterprises to developping their 
technological innovation. 

Keywords: Data input, Enterprise, Technological innovation. 
 

1. Introduction 
Now, China's economy has been shifted from the stage of 

high-speed growth to high-quality development, which also 
puts forward new and higher requirements for enterprise's 
technology innovation. However, in this new stage, the factor 
constraints faced by enterprises' technological innovation are 
increasingly tightened. In addition to the significant rise in 
factor costs[1], under the dual impact of the outbreak of the 
epidemic and the rise of protectionism, the global flows of 
innovation elements such as talents and capital have been 
obviously blocked. It is evident that the innovation dividend 
of traditional factors has gradually peaked, and enterprises 
have fallen into the exhaustion of innovation momentum and 
urgently need to switch to new momentum. It is worth noting 
that, along with the accelerated evolution of a new round of 
scientific and technological revolution and industrial 
transformation, the world has entered the era of "data is king", 
and the development of data elements has become an 
important breakthrough in solving the problem of "where is 
the new power". After 2010, major economies such as the US, 
UK and EU have successively elevated data and related issues 
to the national strategic level, competing to formulate and 
implement big data strategies in an attempt to seize an 
advantageous position in the field of data and stimulate the 
role of data in economic growth and national innovation. As 
one of the pioneers of big data strategy, China's mobile 
Internet users rank first in the world, with natural advantages 
in data scale and application market. According to the 
International Data Corporation (IDC), China will become the 
world's largest data circle by 2025. In this context, China is 
ahead of many other countries in the field of data, and has 
earlier committed to maximizing the innovation value of data 
to provide strong support for high-level technological 
innovation and high-quality development. On August 31, 
2015, the State Council released the Action Plan for 
Promoting the Development of Big Data, which clearly 
pointed out that "data has become an important basic strategic 

resource for the country and is leading a new round of 
scientific and technological innovation." On December 8, 
2017, the Political Bureau of the CPC Central Committee 
conducted the second collective study on the implementation 
of the national big data strategy, and Xi Jinping General 
clearly pointed out the need to "play the role of data as a basic 
resource and innovation engine, and form a data-driven 
innovation system and development model". In October, 2019, 
the Fourth Plenary Session of the Nineteenth Central 
Committee of the Communist Party of China adopted the " 
Decision of the Central Committee of the Communist Party 
of China on Several Major Issues Concerning Adhering to and 
Perfecting the Socialist System with Chinese Characteristics 
to Promote the Modernization of the National Governance 
System and Governance Capacity. " It is proposed to " 
improve the mechanism for the market to evaluate the 
contribution of production factors such as labor, capital, land, 
knowledge, technology, management and data, and to 
determine the remuneration according to the contribution." 
since then data has officially been a production element. The 
introduction of this document has directly accelerated the 
formation and development of data-driven innovation system. 
The above-mentioned series of government documents have 
fully demonstrated that data-driven enterprise’s technology 
innovation has become a fundamental and key issue for 
Chinese enterprises in the period of high-quality development. 
Globally, Google, Amazon, Alibaba and other large 
multinational companies are all relying on massive data 
elements for technological innovation [2].    

At present, the theoretical research on data factors 
promoting enterprise technological innovation can be 
summarized into two categories. The first type of research 
focuses on the driving effect of data attributes on enterprise 
technological innovation. For example, Ghasemaghaei et al. 
(2020) pointed out that the large-capacity attributes of data 
elements cannot directly improve enterprise innovation 
performance, but data speed and data diversity have a 
significant role in promoting enterprise innovation 



 

83 

performance. The second type of research believes that data 
elements can indirectly promote enterprise technological 
innovation by reducing costs, optimizing resource allocation, 
knowledge innovation, market matching and other ways. 
Some scholars emphasize that individual data elements do not 
have a significant innovation incentive effect. The realization 
of the innovation value of data elements depends on the new 
combination of data elements, human capital, labor and other 
elements. For example, the combination of data elements and 
capital elements can reduce the cost of enterprises to obtain 
complementary assets, and then promote enterprise 
technological innovation, but such research is either based on 
questionnaire data or focuses on a single perspective, and the 
mechanism is not completely clear and unified, which still 
needs to be discussed. 

In this context, we have to think about the following 
questions: What is the data as a factor of production? How do 
data inputs drive enterprise technology innovation? What is 
the corresponding realization path? It has not only become a 
frontier issue of concern to scholars and policy makers at 
present, but also a question about the dynamics of enterprises 
to achieve a high level of independent innovation. Based on 
this, this paper aims to reveal the mechanism and realization 
path of data input-driven enterprise technology innovation, 
which is conductive to deeply understand the driving effect of 
data input on enterprise technology innovation, and to provide 
a clear idea for constructing a theoretical framework of data 
input-driven enterprise technology innovation and a data-
driven innovation system in the context of high-quality 
development. 

2. Concepts and Economic 
Characteristics of Data  

As for data, there is not yet a mature and unified 
authoritative definition in academic circles, and its economic 
characteristics are still under discussion. However, the 
concept of data and its economic characteristics will be the 
first and foremost issue that must be addressed in the study of 
data input-driven enterprise technology innovation and 
related issues at present and for a longer period of time in the 
future, otherwise it will hinder the accumulation of relevant 
theories and the construction of theoretical research 
paradigms. 

2.1. The Concept of Data  
Up to now, the mainstream research on data has chosen to 

interpret and define data as a new factor of production from 
the term information.  

According to the International Organization for 
Standardization (ISO), information is knowledge about an 
object (e.g., facts, things, events, processes, or ideas) that has 
a specific meaning in a particular context; data is a formal 
representation of information that facilitates better 
communication, interpretation, or processing. Or, facts or 
information that can be used for analysis and decision making 
can be broadly defined as data[3]. 

In the digital economy, data as a new factor of production 
is defined as "information that can be encoded as a binary 
sequence of zeros and ones"[4], and data under this definition 
include both digital products such as digital music, e-books, 
and online videos, and data products such as statistical data 
and transaction records[5][6].Similar definitions include data 
as parts of information (economic goods represented by 

binary bit strings) that is not knowledge and creativity[7], and 
data is labor-processed data products with use value[8]. It is 
worth noting that the same data product can be used as both 
final consumer goods and intermediate inputs. Synthesizing 
the above views, it can be found that in the context of digital 
economy, data as a factor of production is basically equivalent 
to information, although there are subtle differences between 
data and information. The data under examination in this 
paper is the information content contained in data products. 

2.2. Economic characteristics of Data  
As a new production factor in the era of digital economy, 

data shows many economic characteristics that are different 
from traditional production factors such as labor, capital and 
land. 

First, the data is virtual. Clearly different from traditional 
tangible factors of production such as labor, capital, and land, 
data is a production factor that has a virtual form and exists in 
the database and Internet space. The virtual nature of the data 
means that it must be stored in modern network carriers such 
as computers and cloud disks, and also determines that it 
cannot exist in the enterprise production activities in the form 
of an independent element[9].Data can only release its value 
in combination with other factors of production such as labor 
and capital to achieve the effect of improving the efficiency 
of enterprise operation[10]. 

Secondly, data is immediacy. The large-scale application of 
modern information technology such as mobile Internet, 
3G/4G/5G communication, artificial intelligence, and basic 
algorithms, ensures the instantaneous generation (collection), 
transmission, and processing and analysis of data [11]. 
Especially in scenarios with strong requirements for 
timeliness, such as unmanned driving, unmanned retail, and 
telemedicine, the immediacy of data plays a key role .In 
recent years, the present-time prediction of the trend of the 
new crown pneumonia epidemic utilizes the immediacy 
feature of data as well. 

Third, data is non-competitive and the marginal cost tends 
to be zero. The same data set can be simultaneously accessed 
and used by multiple economic entities in different scenarios, 
and the addition of new users does not cause a decrease in the 
utility of existing users, so the data are non-competitive. In 
this process, the value of the data itself is also not diminished 
and even realizes value addition. For example, 100 million 
browsing records of mom-and-pop websites can be used by 
any number of e-retailers at the same time, and in the process 
of use, the direction and magnitude of changes in the market 
demand of mother and baby products can be predicted with 
high probability by matching and comparing with data from 
other sources, thus enhancing the value of the original dataset. 
In addition, although the first creation of data requires a large 
one-time investment, its marginal cost has been close to zero, 
which is the premise that the non-competitive characteristics 
of data can exist. 

Fourth, data is non-exclusive or partial exclusive. 
Exclusivity refers to the possibility of excluding others from 
using it. For data, its production process often involves 
multiple economic agents, while data holders can proliferate 
data on a large scale with very low or even zero cost of 
reproduction and dissemination, making it difficult to fully 
realize the exclusive occupation of data. Therefore, data is 
non-exclusive in the process of use[12]. In a few cases, some 
users may also be excluded from data usage due to encryption 
technology, patent protection, cross-border restrictions, and 



 

84 

other factors. Thus, data can also be partially exclusive. 
Fifth, data have the nature of increasing returns to scale in 

specific situations.Based on non-rivalry, as the size of data 
grows, the growth of total output will exceed the growth of 
factor inputs, i.e., data are incrementally rewarded by 
scale[13]. Some scholars suggest that the payoff nature of 
data depends on specific contexts, and data are not always 
able to maintain the incremental payoff of scale, and the 
incremental payoff of data often appears in complex contexts 
such as autonomous driving, intelligent healthcare, and face 
recognition[14]. 

Finally, data is characterized by externalities. For users, 
they do not have the ability and access to collect and process 
personal data [15]. In contrast, various data platforms can 
collect or even sell user data on a large scale, but such data 
transactions come at the cost of user privacy and thus have 
negative privacy externalities. On the other hand, the spillover 
effect of data is significant, and big data analysis and 
processing technology usually exhibit the characteristics of 
"general-purpose technologies", which indirectly promote the 
productivity of enterprises and thus generating positive 
externalities [16]. 

3. Theoretical Mechanism of Data 
input-driven Enterprise Technology 
Innovation 

Nowadays, companies such as Amazon, Uber, and Twitter 
have completed the digital transformation of their 
development models through data-driven innovation[17]. The 
deep integration of data and technological innovation 
activities is both the basic fact of current enterprise 
development and represents the development trend of future 
innovation models. However, compared with the pioneering 
practices of enterprises, there are few in-depth theoretical 
discussions about the mechanism of the role of data elements 
on enterprise technology innovation, but this driving process 
is still in the "black box". Therefore, based on the existing 
literature, this paper proposes that there are cost reduction 
effect, knowledge creation effect, optimal allocation effect 
and corporate governance effect between the two, and the 
positive impact of data input on enterprise technology 
innovation is realized through these four channels. 

3.1. Cost Saving Effect 
One of the important motivations for enterprises to 

introduce data in the process of technological innovation is to 
reduce costs. The virtual, non-competitive, and instantaneous 
nature of data enable it to significantly reduce costs and 
increase profits when it’s invested in the production process, 
which in turn enhances the motivation of enterprises to 
engage in technological innovation. Compared with 
traditional methods, data shows significantly in decreasing 
marginal cost and reducing transaction cost[18]. 

First, the diminishing marginal cost effect. For the data 
itself, thanks to the large-scale commercial application of 
various new-generation information technologies such as 
3G/4G/5G communication, cloud computing, and artificial 
intelligence, its marginal cost shows a decreasing trend, even 
close to zero, except for the first production (collection) of 
data, which requires a large amount of input on time. From 
the perspective of other production factors, when the prices of 
other input factors rise rapidly or enter the stage of 
diminishing marginal returns, enterprises can replace other 

production factors with data. For example, enterprises can use 
big data technology to realize online office, thus offsetting the 
rising land rent. It can be said that, within a certain range, with 
the gradual increase in the elasticity of substitution between 
data and other production elements, enterprises' technological 
innovation activities show the trend of de-landing, de-
laboring and de-management, driving the continuous 
diminishing marginal cost.  

Second, the transaction cost reduction effect. It is mainly 
manifested in the following three dimensions of transaction 
costs: first, the search cost reduction effect. Based on the 
virtual, non-competitive and low-cost replication 
characteristics of data elements, the massive amount of data 
containing rich information enters into the technological 
innovation activities of enterprises, which substantially 
reduces various explicit and invisible costs caused by 
information asymmetry and greatly reduces the search cost 
before the transaction[19]. Second, the communication cost 
reduction effect. With the increasing degree of data on the 
behavior of each subject within the innovation system, 
interactive communication becomes convenient and efficient, 
thus directly reducing the communication costs of negotiation, 
contracting, and supervision among enterprises. Third, the 
verification cost reduction effect. For micro enterprises, 
technological innovation is a high-input, high-risk economic 
behavior that requires product verification and market 
validation[20]. With the continuous input of data, the 
production and operation behavior of enterprises can be 
tracked and recorded in the form of data, which makes the 
performance records and market evaluation of enterprises can 
be obtained by multiple parties at low or even zero cost at the 
same time, thus effectively reducing the verification cost of 
the reputation of innovation subjects and the value of 
innovation results. 

3.2. Knowledge Creation Effect 
The second motivation for enterprises to introduce data in 

the process of technological innovation is to create knowledge. 
From the perspective of knowledge creation, the process of 
enterprise technological innovation is essentially a process by 
which firm creates new knowledge by combining its own 
knowledge with external knowledge, which in turn leads to 
innovative output.In this sense, a continuous process of 
knowledge creation is indispensable for the lasting innovation 
of enterprises[21]. After entering into the digital economy, 
with the continuous development of machine learning and 
artificial intelligence technologies, data plays an increasingly 
important role in knowledge creation.Knowledge creation can 
be divided into two parts: knowledge exchange and 
knowledge combination [22], so data can drive enterprise 
technological innovation by influencing knowledge exchange 
and knowledge combination respectively. 

First, the degree of knowledge exchange is enhanced. 
Knowledge creation relies on the exchange and sharing of 
knowledge among knowledge subjects, so knowledge 
exchange is a necessary precondition for knowledge creation. 
From the perspective of the breadth of knowledge exchange, 
the instantaneous, virtual and non-competitive characteristics 
of data, coupled with the emergence of various data platforms, 
greatly enhance the scope and frequency of knowledge 
exchange and accelerate the spatial overflow of knowledge. 
At the same time, the continuous investment of data will also 
reduce the cost of knowledge exchange gradually.For 
example, compared with offline meetings, online meetings 



 

85 

not only spread widely, but also save the expenses of venue 
and labor. Therefore, data can help enterprises acquire a huge 
amount of diversified new knowledge. From the perspective 
of the depth of knowledge exchange, the instantaneous 
feature of data can realize the time lag of interaction among 
various knowledge subjects up to the second level, and this 
high-frequency interaction can not only the strong timeliness 
of knowledge, but also activate a large amount of inert 
knowledge, and part of the implicit knowledge is also 
structured and manifested through data coding, which makes 
the digestion and understanding of new knowledge by 
interactors more profound and thorough. Second, the 
usefulness of knowledge combination is enhanced. The 
knowledge combination process has the characteristics of 
high uncertainty, high risk and high mismatch rate, so the 
process of enterprise technology innovation is often filled 
with a large number of inefficient and useless knowledge 
combinations, which seriously hinders the efficiency and 
level of knowledge creation. From the perspective of data, 
features such as networking of data effectively break the 
convention and dependent path of knowledge combinations, 
increase the chance of interactive integration among high-
quality knowledge, and promote the emergence of a large 
number of knowledge combinations with economic and 
innovative value. In addition, along with the progress of big 
data analysis technology, it effectively improves the accuracy 
of algorithms in predicting the next round of useful 
knowledge combinations, and at the same time clarifies the 
optimizing direction of knowledge combination paths, while 
in the past, the cycle drives knowledge combinations to 
evolve in the direction of high value in the process of 
technological innovation. 

3.3. Optimal Allocation Effect 
The third motivation for enterprises to introduce data in the 

process of technological innovation is to optimize the 
allocation of innovation elements. Spatial mismatch, 
proportional mismatch and differences in input structure of 
innovation elements may lead to the loss of efficiency of 
enterprises' technological innovation [23]. The problem of 
innovation element mismatch has been difficult to eradicate 
due to the limited rationality of corporate managers, the 
limited marginal technological substitution rate of factors and 
the objective existence of information asymmetry [24]. It is 
worth noting that data, as a new innovation element, has a 
significant role in optimizing the allocation of other 
innovation elements, which can mitigate or even offset the 
negative effects of misallocation [25]. Specifically, the data 
can optimize the allocation of innovation elements through 
three channels: enhancing the mobility of innovation 
elements, improving the matching of supply and demand of 
innovation elements, and improving the precision of 
enterprise allocation decisions, so that it can continuously 
converge to the Pareto optimal state and thus drivng the 
improvement of enterprise technological innovation 
performance. 

First, enhance the mobility of innovation elements. Good 
mobility is the main method to improve the mismatch of 
innovation factors[26]. Under the traditional innovation 
paradigm, the mobility of innovation elements, such as talent 
and capital, is weak due to physical time and space constraints. 
Entering into the digital economy, the characteristics of non-
competitive, non-exclusive, and instantaneous data can 
greatly reduce the time and cost of innovation elements 

mobility by creating a good circulation channel and 
environment[27]. For example, the emergence of new mobile 
payment means, such as e-CNY, Alipay, and WeChat Pay, has 
made the time lag of R&D capital flow across zones in 
seconds [28]. Second, the supply and demand matching of 
innovation elements is improved. How to match the supply 
and demand of innovation elements is a key issue for the 
sustainable development of enterprise technological 
innovation. Compared with traditional elements such as labor, 
capital, and land, the greatest value of data is to provide real 
and reliable economic information and reduce the uncertainty 
of economic activities[29]. Therefore, data can lead to precise 
matching between supply and demand by reducing market 
failures generated by information asymmetry and releasing 
immediate and accurate market signals. Third, it improves the 
precision of enterprise allocation decisions. In the process of 
technological innovation, compared with the manager-driven 
empirical decision-making model, the data-driven allocation 
model, as an "unconscious" allocation model that highly 
relies on the results of data analysis [30], takes the advantage 
of making allocation decisions based on factual evidence 
rather than personal intuition by enterprises or relevant 
stakeholders, which makes up for the problem of limited 
rationality of enterprise managers. The advantage of this 
model is that companies or stakeholders make allocation 
decisions based on factual evidence rather than personal 
intuition, which compensates for the limited rationality of 
managers and other problems that lead to resource mismatch, 
and achieves scientific allocation of innovation factors. 

3.4. Corporate governance effect 
The fourth motivation of introducing data in the process of 

technological innovation is to improve the effectiveness of 
corporate governance. Good internal governance is the 
institutional basis for technological innovation for 
enterprises[31].Good corporate governance is conducive to 
the establishment of a long-term investment mechanism for 
technological innovation. There are two paths of corporate 
governance, namely internal governance and external 
governance, which complement each other and together 
constitute a complete corporate governance structure. In the 
era of big data, data has the dual attributes of production 
element and governance element[32].The data directly drives 
enterprise technological innovation through two governance 
paths:enhancing information disclosure and improving 
innovation opportunity identification.  

First, it enhances the degree of corporate information 
disclosure. From the perspective of an external governance, 
based on signaling theory, the characteristics of data such as 
non-competition, non-exclusion, and immediacy compensate 
for the information disadvantage of external investors and the 
public sector, helping them to monitor the dynamic changes 
of firms in real time, which will directly increases the risk and 
economic cost of firms' disclosure of false information [33]. 
For example, once the government discovers the illegitimacy 
of a firm's activities, it will immediately stop providing 
innovative support such as financing incentives, tax breaks 
and financial subsidies to the firm and pursue its related legal 
responsibilities. From the perspective of an internal 
governance, advantages in big data analytics reduce the 
likelihood of management concealing private information or 
disclosing false information, and the quality of voluntary 
information disclosure is improved, not only alleviating 
conflicts between principal and agent, but also reducing the 



 

86 

damage to technological innovation activities caused by 
manager's short-sighted behavior [34]. In summary, the 
information sharing environment created by data will force 
firms to improve the degree and quality of information 
disclosure, which in turn will draw an effect of enhancing 
reputation and attracting R&D investment. Second, it 
enhances the ability of enterprises to identify technological 
innovation opportunities. In reality, most companies often 
miss innovation opportunities and incur significant sunk costs 
due to risk aversion, lack of knowledge, and time lag of 
information of managers. Nowadays, benefiting from the rise 
of various data platforms and the continuous progress of big 
data analysis, enterprises can collect massive data resources 
from market feedback and extract valuable information from 
them, so as to accurately analyze the timing and cost of 
implementing innovation investment[35], which reduces the 
number of trial and error of technological innovation, avoids 
the duplication and waste of R&D investment, and can even 
predict industry trends and market demand changes, thus 
seizing new round of innovation opportunities. 

4. The Realization Path of Data input-
driven Enterprise Technology 
Innovation 

The emergence and rise of data-input-driven enterprise 
technology innovation model has undoubtedly created a new 
opportunity for China to "overtake" in the new round of 
global innovation competition.However, behind the vigorous 
development of this new model, there are still certain 
blindness, confusion and inefficiency, and it is urgent to plan 
a realization path that can promote its benign development. 
This paper proposes the corresponding realization paths from 
the four levels of cost reduction, knowledge creation, optimal 
allocation and enterprise governance in turn. 

4.1. Cost Reduction: Promoting low- cost Data 
Transaction  

Data trading is a market-oriented tool that plays a decisive 
role in the allocation of data resources. However, the value of 
data is difficult to be clarified, and the relevant pricing 
mechanism and trading rules are not yet clear, especially 
under the continuous impact of the global spread of the new 
crown epidemic, most enterprises are facing the pressure of 
financing difficulties and capital shortage, and can hardly 
afford the large amount of capital investment for the first 
production (collection) of data , which directly leads to the 
weakened willingness of enterprises to innovate and the lack 
of innovation backbone. Therefore, it is not only necessary 
but also urgent to promote the diversified development of data 
trading mode with low cost as the main feature under the 
realistic constraint of insufficient innovation funds. The so-
called low-cost data trading mainly refers to the value of 
materialized data. For example,in a "data-for-data" trading 
model, enterprises can transfer their own data to other data 
pools as the consideration or "open condition" for obtaining 
other data, saving the input cost of data elements. For startups 
with insufficient technical resources, the transaction mode of 
"data-for-technology" and "data-for-talent" can directly or 
indirectly reduce the technical cost and R&D time of 
enterprises and improve the efficiency of technological 
innovation. 

4.2. Knowledge Creation: Reconstructing 
Industry-University-Research Cooperation 
Network 

Industry-university-research cooperation can provide 
knowledge sources for enterprise technology innovation 
system, promote knowledge exchange and sharing, and 
facilitate the application and transformation of advanced 
innovation knowledge to solve practical problems. The report 
of the 19th National Congress of the Communist Party of 
China pointed out that we should "establish a technological 
innovation system with enterprises as the main body, market 
as the guide, and deep integration of industry, academia and 
research". On April 27, 2020, General Secretary Xi Jinping 
presided over the 13th meeting of the Central Committee for 
Comprehensively Deepening Reform and pointed out that 
"the innovation chain, industry chain and value chain of 
industry-university-research should be opened up". Therefore, 
in the era of digital economy, it is necessary to reconstruct the 
chain of industry-university-research from the perspective of 
data elements to eliminate the adverse effects of spatial 
distribution differences and communication barriers. For 
example, big data technology can be applied to the 
construction process of virtual reality industry-university-
research platform.Through mining and processing data 
resources, the application value of data can be better brought 
into play, and a scientific and efficient virtual reality industry-
university research platform can be established, so as to 
improve the effect of industry-university-research. For 
instance, enterprises take the initiative to open real data and 
business scenarios to the cooperative research units in the 
industry-university-research chain to achieve instant 
communication and supervision among three parties and 
break the docking barriers. 

4.3. Optimization of allocation: accelerating 
the integration of elements 

In the era of digital economy, the emergence of data 
elements implies an inevitable reform of factor allocation 
methods. Based on the perspective of efficiency standard, the 
efficiency standard should be fully reflected on the decision 
of data flow and full factor combination, rather than the 
standard of ownership difference, scale difference, industry 
difference, etc. For example, among innovation activities, 
when land rent is too high, the degree of substitution between 
land and data factors should be determined by the efficiency 
of innovation output. This will not only guide the factor 
resources to be concentrated to high-efficiency enterprises, 
but also stimulate the innovation energy of the whole industry. 
Based on the perspective of circular supply chain 
development, enterprises should efficiently dock capital flow, 
information flow and logistics, and promote the efficient 
concentration of various data resources at the end of the chain. 
Based on this, enterprises should build a unified application 
platform for the application of various data resources and 
unblock the data resource gathering point. On the other hand, 
in order to promote the full influx of all-factor resources, 
enterprises should establish an efficient all-factor resource 
flow system based on the unified all-factor resource 
application platform and around technological innovation, so 
as to complete the unblocking of all kinds of factors and 
strengthen technological innovation capability. 



 

87 

4.4. Enterprise governance: accelerate the 
high-level construction of data rules 

Although the data input-driven enterprise technology 
innovation model is in a rapid development stage, the 
matching data rules have not yet been formed. Influenced by 
industry characteristics, institutional environment, 
development stage and other factors, data rules show strong 
enterprise heterogeneity in terms of formation mode, 
formation time and content. There are significant differences 
in the effects of data rules of different breadth, depth and 
quality on the technological innovation capability of 
enterprises. Specifically, compared with large data platform 
enterprises such as Huawei, Alibaba and Tencent, the data 
rules construction of many startups or small and medium-
sized enterprises has lagging, low-quality and non-
standardized problems, which will seriously hinder the ability 
of enterprises to release data innovation value to improve, and 
will likely lead to enterprises being locked in a 
disadvantageous position of global data competition for a 
long time. Therefore, it can be expected that the construction 
of unified standardization and high quality of data rules is 
undoubtedly an important governance direction for carrying 
out data input to realize enterprise technology innovation in 
the future. Therefore, a data standard sharing and co-
construction platform can be built among enterprises to 
promote the coordinated and unified development of data 
standards and realize the high-level construction of 
standardization and internationalization, thus reducing the 
data trial and error costs and learning costs of enterprisesas 
well as driving the development of enterprise technology 
innovation. 

5. Conclusion and Prospect 
This paper aims to reveal the mechanism and realization 

path of data input-driven enterprise technology innovation, 
firstly, we analyze the connotation of data factor, and point 
out that it has the economic characteristics of virtual, non-
competitive, instantaneous, non-exclusive/partially exclusive, 
which are obviously different from traditional factors such as 
land, labor and capital. After that, this paper proposes that 
with the increase of data investment, it can make a positive 
driving influence on enterprise technological innovation 
through cost-saving effect, knowledge creation effect, 
optimal allocation effect and enterprise governance effect, 
and accordingly distills four realization paths of data 
transaction cost reduction, reconstructing the cooperation 
network of industry-academia-research, accelerating factor 
integration construction, and accelerating the high-level 
construction of data rules. However, we must realize that the 
development and application of data elements are still in the 
initial stage, and the related academic research and theoretical 
construction are obviously lagging behind, so there are still 
many crucial issues to be studied in the future. 

1.How to take into account the stability of enterprise 
technology innovation and effective risk prevention when 
data elements drive the efficiency and performance of 
technology innovation. In the future, how to play the role of 
data elements as the engine of innovation, and at the same 
time, how to prospectively prevent new types of risks arising 
from improper data application, and how to use new 
technologies and policy systems to prevent data risks and 
maintain the stability of the innovation system is also 
undoubtedly an important research direction.     

2. How regulators from all sectors can formulate policies 
that not only encourage data elements to play an innovative 
role but also at the same time effectively curb the negative 
effects they may generate. Some studies have identified the 
negative economic effects of data elements, such as data 
monopoly derived through algorithmic collusion which 
seriously undermines fair competition and cuts public welfare. 
At the same time, some data trading institutions take 
advantage of the lack of regulation to illegally arbitrage by 
innovation in a brutal way. Therefore, the future development 
of data elements must be placed under effective government 
regulation, while at the same time it must be fully stimulated 
to develop continuously in a positive direction. 

3. The dynamic issue of how to quantify the data elements 
for technological innovation development in a multi-
dimensional manner. This, in turn, involves the definition of 
the basic concept of data elements and the balance between 
data sharing and privacy protection. At present, scholars' 
disagreement on the concept of data elements has blurred the 
understanding of its essence, thus also hindering the 
quantitative research on the relationship between data 
elements and technological innovation. 

Acknowledgment 
Financial support from the Major Project of Philosophy and 

Social Science Research in Universities in Hubei Province of 
China(Grant 20ZD012) is gratefully acknowledged. 

References 
[1] Li W.,The New Economic Normal and Supply-Side Structural 

Reform [J]. Management World,2016,(07):1-9. 

[2] Wang X.L. and Yang Z.H., Mechanisms of Digital 
Transformation of Enterprises in The Perspective of High-
quality Development [J]. Seeking, 2022,(04):126-134. 

[3] Cai Y.Z. and Ma W.J., How Data Influence High-quality 
Development as a Factor and the Restriction of Data Flow[J]. 
Quantitative Economic and Technical Economics Research, 
2021, 38(03):64-83. 

[4] Farboodi M.,Veldkamp L.,2020,A Growth Model of the Data 
Economy[R],Columbia Business School Working Paper. 

[5] Xu X.,Li K.,Tian X.X., Research Progress on Data as a Factor 
of Production [J]. Economic Dynamics,2021(04):142-158. 

[6] Xiong Q.Q. and Tang K., Research Progress on the Right 
Delimitation, Exchange and Pricing of Data [J]. Dynamics of 
Economics,2021,(02):143-158.  

[7] Jones C.I. and C.Tonetti(2020), "Nonrivalry and the economics 
of data" [R],NBER Working Paper,No.26260. 

[8] Cai J.M.,Liu Y.,Gao H.,Chen C., The Approach of Data Factor 
Participating in Value Creation: A General Equilibrium 
Analysis Based on the General Theory of Value [J]. 
Management World, 2022, 38(07):108-121. 

[9] Mueller M. and Grindal K.,2019, "Data Flows and the Digital 
Economy:information as a Mobile Factor of Production", 
Digital Policy,Regulation and Governance,21,pp.71~87. 

[10] Lin Z.J.and Meng Z.X., The Integration Mechanism of Data 
and Core Production Factors from the Perspective of 
Complementary Assets [J]. Journal of Beijing Jiaotong 
University (Social Science Edition),2021,20(02):28-38. 

[11] Li H.J. and Zhao L., Data Becomes a Factor of Production: 
Characteristics, Mechanisms, and the Evolution of Value Form 
[J]. Shanghai Economic Research,2021(08):48-59. 



 

88 

[12] Ding W.L., "The legal system basis of data competition" [J], 
Research on Financial Issues, 2018, No. 2. 

[13] Prufer J.and C.Schottmmller.,Competing with Big Data [J]. 
Journal of Industrial Economics, 2021, 69(4) :967-1008. 

[14] Wang C.X., Zhang W.D.,Yan M., Is More Data always Better-
-An Interdisciplinary Analysis of the Nature of Returns to Data 
[J/OL]. China Industrial Economics,2022(07):46-66. 

[15] Zuboff, S.,The age of surveillance capitalism. New 
York:Public Affairs, 2019. 

[16] Li Y.J., The economic meaning of data elements and related 
policy suggestions [J]. Jiangxi Social Science,2022,42(03):50-
63. 

[17] Huang P. and Chen L., World Economic Operating Mechanism 
and Rules Building-up under the Digital Economic 
Globalization: Based on the Perspective of Factor Mobility 
Theory [J]. World Economic Research, 2021, (03): 3-13+134. 

[18] Zhu H.L. and Wang C.J.,Digital Economy Leads High-quality 
Development of Industry: Theory,Mechanism and Path [J]. 
Theory and Practice of Finance and Economics,2020,41(05):2-
10. 

[19] Zhao S.G. and Li Z.,Information Asymmetry, Transaction Cost 
and Modernization of National Governance:On the Application 
Logic of Big Data in the Modernization of National 
Governance [J]. Research on Finance and Economics, 2021, 
(04):28-36. 

[20] Xie K., Wu J., Xiao J.H., User portrait and user behavior 
analysis based on big data platform[J]. China Informatization, 
2018(03):100-104. 

[21] Popadiuk S. and Choo C.W.,Innovation and Knowledge 
Creation:How Are These Concepts Related? International 
Journal of Information Management,2006,26(4):302-312. 

[22] Nieves J .and Osorio J., 2013,“The role of social netw orks in 
know ledge creation", Know ledge Management 
Research&Practice, 11(1) :62-77. 

[23] Dong Z.Q. and Hu S.M., Spatial Misallocation of Innovation 
Factors and Efficiency Loss: Model Analysis and China's 
Evidence [J]. Journal of East China Normal University 
(Philosophy and Social Science Edition),2020,52(1):162-
178,200. 

[24] Dai J.,Yang Z.,Liu G.C.,Xu C.H., Bank Competition, 
Innovation Resource Allocation, and Firm Innovation Output: 
Empirical Evidence from the China Industry Census Database 
[J]. Financial Research,2020,(02):51-70. 

[25] Tao C.Q. and Xu M., Measuring the Allocation Level of China 
Innovation Elements from the Perspective of Economic High-
Quality Development [J]. Quantitative Economic and 
Technical Economics Research, 2021,38(3):3-22. 

[26] Klenow H. P. J., Misallocation and manufacturing TFP in 
China and India[J].Quarterly Journal of Economics, 2009, 4(4): 
1403-1448. 

[27] Yang Y., Wang L., Liao Z.J., Data Elements: Multiplier Effect 
and Per Capital Output-- -From the Perspective of Data 
Elements Flow Environment [J]. Exploration of Economic 
Issues, 2021,42(12):118-135. 

[28] Bai J.H., Wang Y., Jiang F.X., Li J., R&D Element Flow, 
Spatial Knowledge Spillovers and Economic Growth [J]. 
Economic Research,2017,52(07):109-123. 

[29] Peng Y. and Li S.M., Flow of Innovation Elements and Urban 
Green Innovation Development: The Spatial Moderating Effect 
of Data Elements' Flow Environment [J/OL]. Science and 
Technology Progress and Countermeasures:1-10[2022-09-03].  

[30] Liu Y.,Xie K., Deng H.L.,Data-Driven R&D Transformation 
of New Product:A Case Study from the Perspective of 
Adaptive Change in Organizational Practices [J]. Management 
World,2020,36(03):164-183. 

[31] Belloc F., 2012, "Corporate Governance and Innovation: a 
Survey", Journal of Economic Surveys,26(5),835-864 . 

[32] Chadefaux T. Early warning signals for war in the 
news[J].Journal of Peace Research, 2014, 51(01):51-58.  

[33] Lu Y.,Zhu Y.J.,Hu X.Y., Institutional Shareholding and 
Corporate Fraud:Evidence from China [J]. Nankai 
Management Review, 2012, 15(01):13-23.  

[34] Zhang J.W.,Hu D.D.,Zhou Le., Can the Digital Economy 
Alleviate Management Myopia? Empirical Evidence from Real 
Earnings Management [J]. Economic Management, 2022, 
44(01): 122-139. 

[35] C. C. Lee, C. W. Wang, W. C. Chiu, "Managerial Ability and 
Corporate InvestmenOpportunity," International Review of 
Financial Analysis, Vol. 57 ,No. 5, 2018, p. 65-76.