The Moderating Effect of Value Cognition and Market Competition:

A Study of the Relationship between R&D Intensity and Performance

of AI Enterprise

Litian Chen and Yufeng Wang

Zhejiang Gongshang University, School of Business Administration, Hangzhou, Zhejiang, China

Keywords: R&D Intensity of AI Enterprises, Enterprise Performance, Complexity of Value Cognition, Market

Competition.

Abstract: From the perspective of value cognition and innovation theory, using panel data of 75 listed companies in

China’s AI concept stocks from 2011 to 2019 as samples, this paper analyzes and examines the influence of

R&D intensity of AI enterprises on enterprise performance and the regulatory effect of value cognition and

market competition in this process. Studies have shown that the R&D intensity of AI enterprises is positively

correlated with their performance. In addition, the complexity of value cognition and the pressure of market

competition have a negative regulatory effect on the relationship between the R&D intensity and performance

of AI enterprises.

1 INTRODUCTION

In the field of AI, technology is updated rapidly and

R&D investment is high. Except for a few leading

enterprises with sufficient funds, it is difficult for

business managers and science and technology

decision-makers to balance the survival and

development of start-ups between market efficiency

and R&D investment (Xu 2021

). AI enterprises are

high-tech companies; R&D and innovation are the

keys to maintaining their market competitiveness.

However, this issue is rarely mentioned in existing

studies.

First, there are controversies surrounding the

relationship between R&D intensity and enterprise

performance. In some researches, it is believed that

the R&D investment intensity of an enterprise is

positively correlated with enterprise performance

(Mudambi 2014). In other researches, it is believed

that the two are negatively correlated or have a

nonlinear relationship (Racela 2016, KANG 2013).

However, in the existing researches, there are few

discussions about the relationship between the R&D

intensity and performance of AI enterprises.

Furthermore, value cognition and market

competition affect the relationship between R&D

intensity and performance of AI enterprises. This is

because the R&D and innovation process of

enterprises belongs to the value creation process, and

the decision-making of R&D is affected by the

cognitive level of business managers. It is generally

believed that the more complex the manager’s value

cognition, the more exploration opportunities the

enterprise has, the higher the enterprise’s exploration

capabilities (Daniella 2018, Stabell 1978), and the

greater the possibility that the enterprise will increase

R&D investment. However, when the value cognition

is very complex, enterprises should place resources in

multiple links of the innovation value chain, resulting

in resource fragmentation and resource waste.

Enterprise performance cannot be improved in a short

time. Finally, in most existing studies, it is believed

that under the high pressure of market competition,

leading enterprises will maintain their competitive

advantage by increasing R&D investment. However,

for AI enterprises in the early stages of development,

there is currently no research on whether R&D and

innovation can ensure the growth of enterprise

performance.

In order to make up the research gap, from the

perspective of value cognition and innovation theory,

with 75 listed companies in AI concept stocks from

2011 to 2019 as the objects of study, this paper

studies the relationship between the R&D intensity

and performance of AI enterprises and the

moderating effect of the complexity of value

762

Chen, L. and Wang, Y.

The Moderating Effect of Value Cognition and Market Competition: A Study of the Relationship between RD Intensity and Performance of AI Enterprise.

DOI: 10.5220/0011290300003440

In Proceedings of the International Conference on Big Data Economy and Digital Management (BDEDM 2022), pages 762-768

ISBN: 978-989-758-593-7

cognition and market competition on this relationship

to reveal the boundary conditions of the influence of

the R&D intensity of AI enterprises on enterprise

performance.

2 THEORETICAL BASIS AND

HYPOTHESES

2.1 R&D Intensity and Performance of

AI Enterprises

According to the innovation theory of Joseph Alois

Schumpete, science and technology are the driving

force for the development of enterprises, and the core

competitiveness of enterprises is formed through

R&D activities. The investment in innovation of an

enterprise is mainly reflected in the intensity of R&D

investment (SUN 2019). In the field of AI,

technology is updated rapidly and R&D investment is

high. Through R&D and innovation, AI enterprises

can obtain heterogeneous resources, promote the

launch of high-quality products, help enterprises to

seize a larger market share, and develop lasting

competitive advantages. In addition, according to

some scholars, by increasing innovation investment,

high-tech enterprises can improve their future

business performance (Pand 2011, Ciftci 2011). As

AI enterprises are high-tech enterprises, their R&D

expenditures are in line with the needs of the

enterprise’s long-term development strategies. The

successful transformation of R&D achievements will

ultimately lead to the economic growth of enterprises.

On this basis, Hypothesis 1 was proposed.

H1: The R&D intensity of AI enterprises has a

positive influence on enterprise performance.

2.2 The Influence of Value Cognition

on the Relationship between R&D

Intensity and Performance of AI

Enterprises

The complexity of value cognition refers to the

breadth of the knowledge covered by the managers’

knowledge structure (Walsh 1995, Nadkarni 2008).

From the perspective of value creation, it is the

number of links in the value chain that managers paid

attention to. If the complexity of value cognition is

high, it means that managers recognize various core

concepts (SHANG 2014) and ideas about the

technology field comprehensively in the process of

R&D investment, thereby increasing the possibility

of enterprises obtaining long-term value through

R&D investment. However, the ideas are so

comprehensive that they will use up more resources.

The more complex the value cognition in the R&D

investment process of AI enterprises, the more value

chains they pay attention to. This will lead to the

reallocation of resources distributed in multiple value

chains, which does not help with the concentration of

enterprise resources and may cause higher sunk costs

(CHEN 2021). Enterprise performance cannot be

improved. Therefore, the negative influence of the

increased R&D investment on performance in the

short term will not be reduced due to the higher

complexity of value cognition. On this basis,

Hypothesis 2 was proposed.

H2: The complexity of value cognition has a

negative moderating effect on the relationship

between R&D intensity and performance of AI

enterprises.

2.3 The Influence of the Pressure of

Market Competition on the

Relationship between R&D

Intensity and Performance of AI

Enterprises

The R&D intensity and performance of AI enterprises

are affected by both internal and external factors. The

R&D strategies of the enterprises are affected by the

intensity of market competition (JIAN 2017), and the

competition in China’s AI industry is intense. Once

the competitor has launched new products or offered

the same products at lower prices, management is

forced to make targeted R&D decisions (ZHENG

2018). With the increasingly intense market

competition, AI enterprises continue to increase R&D

investment in order to maintain their competitive

advantages. However, in the early stage of enterprise

R&D activities, the operating pressure of funds was

relatively high, and it was difficult to obtain returns

in a short time. At the same time, the risks of R&D

increase, and the possibility that the innovative

achievements of an enterprise are imitated or being

surpassed by follow-suitors increased (JIANG 2021).

On top of that, the AI industry in China is still at an

early stage of development. If enterprises in the

growth stage invest too much, their future

performance will be poor (Fedyk 2017).

On this basis,

Hypothesis 3 was proposed.

H3: The pressure of the market competition

pressure has a negative moderating effect on the

relationship between R&D intensity and performance

of AI enterprises.

The Moderating Effect of Value Cognition and Market Competition: A Study of the Relationship between RD Intensity and Performance of

AI Enterprise

763

3 METHODS

3.1 Samples and Data Sources

Sample selection: The research samples of this paper

were selected from Tonghuashun AI concept stocks

listed companies from 2011 to 2019. At the same

time, considering the lag effect of R&D intensity of

enterprises on enterprise performance, in this paper,

1 year was considered as the lag phase. The data from

2011 to 2018 were used as independent variables,

regulated variables, and control variables, whereas

the data from 2012 to 2019 were used as the

dependent variables. After excluding ST and *ST

companies, there were 75 listed companies in the

final samples. STATA15 software is used to process

the above-mentioned data.

Data source: Annual reports and social

responsibility reports of listed companies originated

from cninfo.com.cn, combined with the enterprises’

official websites for supplementary verification.

Most of the data on R&D intensity, enterprise

performance, and control variables are sourced from

CSMAR, where some R&D intensity research data

have been supplemented through the enterprises’

annual reports. By identifying and manually

collecting key sentences in the enterprise’s annual

report and social responsibility report, the complexity

of value cognition was obtained.

3.2 Variable Definition and Metric

1) Dependent variable: enterprise performance

(TBQ).

Based on the reference (HE 2021), in this paper,

we decided to measure the enterprise performance

with the profit index. In other words, we measure the

enterprise performance with the ratio of market value

to total assets.

2) Independent variable: R&D intensity (RD).

R&D intensity refers to the intensity of

enterprises investing limited resources in R&D. For

the measurement of R&D intensity, refer to the

practices of Barker & Mueller (Barker, Mueller 2002)

and Lv Diwei et al. (Diwei 2018). This paper

measures the R&D intensity of an enterprise with the

ratio of its R&D expenditure to total sales in year. The

greater the value of this variable, the higher the R&D

investment intensity of the enterprise.

3) Moderator variable: complexity of value

cognition (NC).

The coding research design of Nadkarni and

Narayanan (Nadkarni, Narayanan 2007) and Wu

Dong (Wu 2011) were used for reference and

integration. In this research, the text analysis method

was used to describe the complexity of cognition of

AI enterprises in the process of R&D and innovation.

There are mainly the following two steps.

Step 1: The statements were identified. The

annual reports and social responsibility reports of the

companies were studied, and according to the coding

vocabulary in the link of value creation summarized

by Wu Dong (Wu 2011) in his research, the sentences

showing that the enterprises have considered the

factors in each link of value creation in the innovation

strategy planning in each annual report were

scientifically identified and recorded.

Step 2: In the selected sentences, the number of

links (such as R&D, production, market, manpower,

and operation) in the chain of value creation

considered was determined and recorded as the value

of complexity of value cognition. The greater the

value of complexity, the more links of value creation

are considered in the cognition process of decision-

making of the enterprise.

4) Moderator variable: Market competition

pressure (HHI).

By using previous researches as a reference, this

paper measures the pressure of market competition of

the industry in which the enterprise is based with the

Herfindahl-Hirschman Index (HHI). The HHI is a

comprehensive index used to measure industrial

concentration. The smaller the index, the lower the

market concentration of the industry, and the more

intense the competition. It can be calculated as

follows.

HHI=













In which, x



represents the operating revenue of

the i enterprise, “x” represents the sum of the

operating revenues of all the enterprises in the

industry,







represents the market share of the “i”

enterprise, and “n” represents the total number of

enterprises in the industry.

5) Control variable

This paper selects the size of the enterprise (Size),

whether the chairman serves as the general manager

(CEO), industry (IND), asset-liability ratio (LEV),

the nature of enterprise ownership (SOE), and slack

resources (SLK) as control variables.

BDEDM 2022 - The International Conference on Big Data Economy and Digital Management

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4 RESULTS

4.1 Narrative Statistics and Analysis

The descriptive statistics and correlation analysis of

the variables are shown in Table 1. The average

performance of AI enterprises is 2.593, which

indicates an average performance from the AI

enterprises in China, whereas the average R&D

intensity is 0.110. In other words, the R&D

investment accounts for more than 10% of operating

revenue. This shows that AI enterprises in China

attach importance to R&D investment. The average

complexity of value cognition is 4.410, which

indicates that many links of value creation are

evaluated by AI enterprises in China in the process of

R&D and innovation, and there are big differences.

The average value of HHI is 0.187, which indicates

that the pressure of the market competition of

enterprises in different industries is relatively high. It

can be seen from the table that R&D intensity is

significantly positively correlated with enterprise

performance, preliminarily supporting H1.

Table 1 Pearson correlation analysis and Descriptive statistics of all variables

Variabl

1 2 3 4 5 6 7 8 9 10 11

TBQ

0.206**

0.180**

-0.0290 1

HHI

0.0390 -

0.108**

-0.075* 1

Size

0.142**

0.127**

0.361**

0.0330 1

LEV

0.235**

0.219**

0.395**

-0.0410 0.609**

SOE

-0.0170 -0.0520 0.149**

0.115**

0.293**

0.083** 1

CEO

-0.0360 -0.0110 0.0470 0.00100 0.311**

0.229**

0.120**

SLK

-0.0110 0.281**

-0.00600 -0.0340 -

0.272**

0.416**

-0.0420 -

0.236**

IND

0.245**

0.224**

0.254**

-0.072* -

0.207**

0.325**

0.119**

0.077** -0.0530 1

Year

0.0180 0.00200 0.078** 0.0250 0.382**

0.205**

-0.00800 0.146**

0.164**

0.035

0 1

Mean

2.593 0.113 4.410 0.187 22.09 0.310 0.269 1.615 5.063 2.942 2,01

S.D

1.766 0.125 1.823 0.212 1.275 0.175 0.444 0.487 11.02 1.963 2.58

Note:*p<0.1，**p<0.05，***p<0.01

4.2 Hypothesis Testing

In order to verify the hypothesis proposed above, this

paper analyzed data with stata15.0. Since the original

hypothesis was rejected by the Hausman test, a fixed-

effect model was used.

1) Test on the main effect of R&D intensity of

AI enterprises and the enterprise performance

As shown in Table 2, Model (1) is a regression

model that only contains control variables, and in

Model (2) R&D intensity of the enterprise, an

independent variable is added. It can be seen from

Model (2) that the R&D intensity of an enterprise is

significantly positively correlated with enterprise

performance, supporting H1. In other words, the

higher the R&D intensity of an enterprise, the better

the enterprise performance.

2) Test of the moderating effect of the

complexity of value perception on the relationship

The Moderating Effect of Value Cognition and Market Competition: A Study of the Relationship between RD Intensity and Performance of

AI Enterprise

765

between the R&D intensity of AI companies and

the enterprise performance

As shown in Table 2，in Models (3), it can be

seen that the interaction terms of R&D of an

enterprise and complexity of value cognition are

significantly negatively correlated, which indicates

that the complexity of value cognition has a negative

moderating effect on the relationship between the

R&D intensity of an enterprise and enterprise

performance, supporting H2.

3) Test of the moderating effect of the pressure

of the market competition on the relationship

between the R&D intensity of AI companies and

the enterprise performance

In Models (4), it can be seen that the interaction

terms of R&D intensity of an enterprise and the

pressure of the market competition are significantly

negatively correlated, which indicates that the

pressure of market competition has a negative

moderating effect on the relationship between R&D

intensity of an enterprise and enterprise performance,

supporting H3.

Table 2: Stratified regression analysis results.

M(1) M(2) M(3) M(4)

Size -0.131 -0.202 -0.154 -0.233

(-0.83) (-1.15) (-0.86) (-1.38)

LEV -1.914*** 0.296** 0.289* 0.277*

(-5.83) (2.38) (2.14) (2.13)

SOE 0.182* -1.190*** -0.747 -1.032**

(2.02) (-3.54) (-1.73) (-2.46)

CEO 0.211 0.208** 0.240*** 0.193**

(1.82) (3.04) (3.73) (2.69)

SLK -0.009*** -0.012*** -0.013*** -0.018***

(-6.89) (-4.96) (-8.70) (-4.33)

Constant -382.989 5.003 4.326 5.555

(-1.56) (1.39) (1.15) (1.63)

RD 2.537* 3.535** 2.622*

(2.01) (3.35) (2.05)

-0.130***

(-3.77)

RD_NC

-0.578***

(-4.29)

HHI

0.529**

(3.17)

RD_HHI

-8.921***

(-5.89)

Year Controlled Controlled Controlled Controlled

IND Controlled Controlled Controlled Controlled



0.136 0.361 0.379 0.380

15.51 16.74 15.92 15.62

Notes:* p＜0.1, **p＜0.05, ***p＜0.01.

4.3 Robustness Test

In order to improve the robustness of the conclusion,

this paper tested the robustness with the following

method. In previous related literature, the enterprise

growth rate was used as the control variable instead

of the asset-liability ratio (XIAO 2016). Therefore,

this paper replaced the asset-liability ratio (LEV) with

the enterprise growth rate (Growth) (the difference

between the main operating revenue of the current

period and the previous period/the main operating

revenue of the previous period). All test results are

consistent with the original results.

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5 RESEARCH CONCLUSION

AND INSPIRATION

5.1 Research Conclusion

From the perspective of innovation theory and value

cognition, this paper empirically analyzes and tests

the influence of R&D intensity of an enterprise on

enterprise performance and the regulatory effect of

the complexity of value cognition and market

competition, and comes to three conclusions. The

R&D intensity of AI enterprises has a positive impact

on enterprise performance; the complexity of value

cognition has a negative regulatory effect on the

relationship between the R&D intensity and

performance of AI enterprises; the pressure of market

competition has a negative regulatory effect on the

relationship between R&D intensity and performance

of AI enterprises.

5.2 Theoretical Contribution and

Practical Significance

Firstly, in the field of AI, there are few studies on the

relationship between R&D intensity of an enterprise

and enterprise performance. This paper explored the

relationship between them through the innovation

theory and expanded the application of the innovation

theory. It provided some theoretical basis for the

improvement of enterprise performance by AI

enterprises through R&D and innovation.

Secondly, there is no research in which the

complexity of value cognition is considered as a

situational variable to study its influence on the R&D

intensity and performance of AI enterprises. To make

up this gap, in-depth research from the perspective of

value cognition was done. In the context of the high

complexity of value cognition, it is difficult for AI

enterprises to gather resources for R&D and

innovation, so the enterprise performance cannot be

improved in the short term. Therefore, when making

R&D decisions, enterprise managers should prevent

resources from being too fragmented and solve the

problem of resource waste caused by excessive

attention to the value chain.

Thirdly, AI enterprises are emerging technology

enterprises. Under the high pressure of market

competition, managers should not blindly increase

R&D costs because of the decisions of competitors,

which is a short-sighted behavior. If it is separated

from the actual situation of the enterprise, the

substantial increase in R&D costs will not only

increase sunk costs, but also will not help with the

improvement of enterprise performance.

5.3 Limitations and Future Research

The following limitations of this study can be used as

a reference for future research. First of all, since there

were few listed companies of AI concept stocks from

2011 to 2019, in the end, only 75 enterprises were

considered as samples, which is a relatively small

sample size. In the future, we can conduct research

based on more enterprise samples. Next, we only

measure the R&D intensity of AI enterprises with

financial resources, but other resources such as the

R&D personnel and technology may also affect

enterprise performance. The influence of different

types of resources on enterprise performance should

also be discussed in future researches. Last but not

least, in this study, the text analysis method was used

to measure the complexity of value cognition of

managers of AI enterprises. There will be subjective

factors of researchers.

ACKNOWLEDGMENT

This research was substantially supported by research

grants from the Zhejiang philosophy and social

science planning project (20NDJC099YB), National

Natural Science Foundation of China (71972170),

Zhejiang Provincial Natural Science Foundation of

China (LY18G020003).

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