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Self-organizing type, Innovation Path and Enterprise
Performance: Mechanism and Empirical Analysis
Jinchao Guoa, Gelegjamts*
Graduate School of Business The Graduate University of Mongolia Ulaanbaatar, Mongolia
ae-mail: mdguojinchao@xhsysu.edu.cn
* Corresponding author: gelegjamts tech @yahoo.com
Abstract
To measure the relationship between Self-organizing type, innovation path and firm performance, the study uses a
structural equation model and a confirmatory factor analysis method to conduct empirical tests on the basis of in-
depth interviews with electronic information enterprises in the Pearl River Delta. The research shows that the higher
the degree of synergistic self-organization, the better the enterprise performance. The higher the degree of
collaborative self-organization, the more enterprises are inclined to choose the independent innovation path, and the
influence of collaborative self-organization on the independent innovation path is obviously higher than the positive
influence on the imitation innovation path. Additionally, eventually, the more inclined the enterprise is to choose the
independent innovation path, the better the enterprise performance will be, while the imitative innovation path has no
significant impact on the enterprise performance. Simultaneously, by building a model and survey data, it is found that
the imitative innovation path can promote the independent innovation path, it shows that the incremental imitative
innovation path also plays a certain role in promoting the improvement of enterprise performance.
Keywords-Collaborative Self-organization; Innovation Path; Enterprise Performance
1. INTRODUCTION
J.A Schumpeter (1912) put forward the economics of
innovation, pointing out that “innovation is an engine of
organizational economic development”[1]. With the
increasingly prominent status of the knowledge economy,
especially under the situation of global economic
development slowing since the COVID-19 epidemic, the
circular economy theory of self-organization system and
its sustainable development has become the focus of
academic circles at home and abroad. At present, the
world is experiencing a new round of technological
change and industrial update critical period, such as
cloud computing, artificial intelligence and the Internet
of things technology innovation emerging endlessly, the
enterprise profit model and industrial agglomeration
process have had a profound effect, new industries, new
energy have become the core factors that affect economic
development. Enterprises constantly seek self-organizing
forms suitable for their own development to improve
corporate performance and expect to win the first
opportunity in the fierce market competition. Enterprise
organization is a complex system, and it is difficult to
describe the operational laws of the enterprise system
through several variables. Only through field
investigation and research can the agglomeration law
among enterprises be revealed. This research idea is to
establish the production activities of enterprises on the
basis of self-organization theory, using competition or
collaborative cooperation strategy. In order to adapt to
the external environment, enterprises must constantly
conduct innovation and organizational reform.
2. LITERATURE REVIEW AND THEORETICAL
ASSUMPTIONS
2.1. Self-organization type and firm
performance
First, according to H. Haken, a German theoretical
physicist, from the perspective of the evolution of
organizations, they can be divided into two types: other
organizations and self-organizations [2]. If a system
relies on external instructions to form an organization, it
is an other organization. If there is no external instruction,
the internal elements of the system in accordance with a
tacit understanding of some rules, each to do their job
and coordinated automatic formation of an orderly
© The Author(s) 2023
G. Vilas Bhau et al. (Eds.): MSEA 2022, ACSR 101, pp. 131–, 2023.
https://doi.org/10.2991/978-94-6463-042-8_21
structure, is self-organization. Self-organization includes
two types, competitive self-organization and cooperative
self-organization. Competitive self-organization refers to
an uncertain and dynamic unbalanced process in which
two or more elements or individuals hinder or restrict
each other and oppose, exclude, or compete with each
other for their own “ economic interests ”. The second
type of self-organization is collaborative self-
organization, which means that elements or subsystems
cooperate, learn from and support each other to achieve
the overall goal of the organizational system and form a
virtuous cycle of growth [3].
The new idea of co-opetition was first proposed in
1995 by Barry J. Nalebuff, which is a management
professor at Yale, and Adam M. Brandenburger, which is
a professor of business administration at Harvard [4].
They believe that enterprise production is a complex
linear game system, and a non-zero-sum game that can
reach win-win. The business activities of enterprise
organizations should not only perform competition but
also cooperate. In fact, individual enterprises in an
enterprise organization have the behavior of competition
and cooperation, referred to as the phenomenon of
competition and cooperation. They are often interwoven
together, which is the inherent root of the nonlinearity
and spontaneity of the complex system organization.
Competitive self-organization adheres to the market
demand as the guidance and stimulates the internal
potential of enterprises through competition. To survive,
individual enterprises constantly conduct independent
innovation, improve production technology and develop
new products. Under the circumstance of limited
resource factors, enterprises often choose the innovation
path of independent r&d(research and development) to
win in the cruel market, which intensifies the degree of
competition among enterprises. In other words, the result
of the zero-sum game is that the winning side tends to
increase corporate performance. When the competition
between enterprises is orderly, the overall benefit of the
enterprise is better, and the continuous profit. However,
once excessive competition occurs, it will cause
disastrous losses to the entire industry chain. The internal
coordination mechanism will be broken, which may lead
to the decline in innovation ability, profit, enterprise
scale and product market share, thus affecting the
cultural system and performance of the entire enterprise
organization. Therefore, the hypothesis is proposed:
H1: Competitive self-organization is positively
correlated with enterprise performance;
H2: Collaborative self-organization is positively
correlated with enterprise performance.
2.2. Self-organization type and innovation path
According to the early marketing theory, innovation
generally refers to the transformation of original products,
innovative design methods and improvement of
technological processes, as well as the upgrading and
updating of sales channels, and the adoption of new
business strategies and competitive means, to occupy the
product market. Joseph Schumpeter (1921), an American
economist, proposed the theory of creative destruction,
which explained the real root of economic development-
innovation [5], emphasized the importance of production
technology and method reform, greatly enriched the
connotation of innovation. Additionally, Joseph
Schumpeter explained the concept of innovation from the
perspective of a macroeconomy, and connected
innovation and innovation management closely with the
current external economic development, extending the
connotation of innovation from product innovation to the
production process and enterprise management
organization. Simultaneously, some scholars have put
forward a broader concept from the perspective of
enterprise innovation network system, believing that
innovation is multi-dimensional, including innovation
subject, innovation objects and external support systems
[6]. The innovation path refers to the enterprises to
improve the enterprise performance, the use of their
accumulated experience and knowledge base, and strive
to achieve the expected goals of technological innovation
and management innovation. Innovation path is a
complex system with the characteristics of multi-
dimension, multi-layer, and externality. According to the
degree of originality of knowledge or the degree of
technology introduction and absorption, innovation path
can be divided into independent innovation path and an
imitative innovation path. The independent innovation
path belongs to an innovative path of original product
r&d, continuous innovation of production processes and
sublimation of management. The system organization
relies on existing resources to construct new intellectual
property rights. The imitation innovation refers to a path
to improve enterprise performance or increase
knowledge and skills in a short time through imitation
after actively introducing the technologies of other
organizations based on existing external knowledge and
digesting and absorbing them [7]. With to the
relationship between self-organization and the innovation
path, different schools have drawn different conclusions.
Have put forward by some scholars, the path of
innovation type can directly affect the enterprise
decision-making effect, and the imitation innovation path
will drive enterprise organization coordinated self-
organizing behavior; through the specialized division of
labor have a complementary effect between the
organization, prompting behavior, synergy, the
production behavior of r&d and sales together, which
establishes a relationship of competition and causes
products to have a more competitive advantage. Thus,
the industrial cluster has been expanded [8]. Both the
independent innovation path and the imitative innovation
path are a form of self-organization for the continuous
development and self-expansion of enterprises. It can be
J. Guo and Gelegjamts
132
said that the choice of self-organization type and
innovation path is cross-influenced and complement each
other. Therefore, the following hypothesis is proposed:
H3: Competitive self-organization is positively
correlated with the independent innovation path;
H4: Competitive self-organization is negatively
correlated with the imitative innovation path.
Collaborative self-organization is a development
mode with less dissipation and greater benefit than
competitive self-organization, which is characterized by
the linear orderly and stable state of individuals in an
organization through association, cooperation and mutual
learning, aiming at maximizing resources. Collaborative
self-organization covers two aspects: from the
perspective of the process, collaboration is a way of life
opposite to the competition. Enterprises are mainly
established on the basis of mutual cooperation and
require competition and cooperation among various
elements of the system to maintain the overall
optimization of the system organization. From the
perspective of this effect, synergy means less friction
among members of the system organization, clearer
service objectives and stronger profitability. The premise
of successful collaboration is that organizational
elements are interrelated and interact with each other in
the process of product creation, which is a long-term
positive feedback mechanism. Therefore, the following
hypothesis is proposed:
H5: Collaborative self-organization is positively
correlated with the independent innovation path;
H6: Collaborative self-organization is positively
correlated with the imitative innovation path.
2.3. Innovation path and enterprise
performance
By selecting appropriate innovation paths, enterprise
organizations constantly improve production technology
and process flow, improve product performance, reduce
unit product r&d cost, constantly meet customer
personalized needs, create more customer transfer value,
gain stronger competitiveness in the market, to promote
the development of the entire industrial cluster.
Simultaneously, the independent innovation of
enterprises covers a series of economic activities,
including not only the technological patent invention, but
also production method improvement, the technological
process innovation, marketing strategies and other
activities, which ultimately achieve corporate profits,
promote the continuous optimization of financial
indicators, and enhance the sustainable development
ability of enterprises. Therefore, the products of
independent innovation are not only endowed with value,
but also endowed unique patents, as well as the value
carriers for the exchange of market subjects. Through
independent r&d, enterprise organizations constantly
improve the value of products and service quality, gain
market competitive advantages, to achieve profits.
Therefore, the following hypothesis is proposed:
H7: Independent innovation path is positively
correlated with firm performance.
Imitative innovation, also known as imported
innovation, refers to the enterprise organization
according to its own scale and strength, constantly learn
knowledge and management experience from
neighboring enterprises, in order to realize the
improvement of production technology and product and
service quality. The specific implementation path of
enterprises is to introduce advanced equipment or
purchase technology developed by other enterprises, and
actively accept knowledge spillover, digestion and
absorption, and even realize technology catch-up. In
early start-up of Huawei company, for example, lack of
financial support, not only less qualified r&d team, in
this case can only learn to imitate the foreign technology
of homogeneous product enterprise, fully incremental
innovation again after digestion and absorption, can be
said to be a very practical and efficient mode of imitation
innovation, reduce the early high investment risk. Later,
when Huawei’s r&d strength developed to a certain
extent, it actively worked with universities and other
scientific research institutions at home and abroad to
jointly develop product technologies, jointly set up
laboratories, and formed strategic alliances with world-
class enterprises with strong technologies to promote
technological development. At the same time, accelerate
the learning of management methods and organizational
structure of world-class technology giants, implement
standardized management, so as to achieve leapfrog
development of enterprise performance. Finally, the
innovation ecosystem with close combination of industry,
university and research has been actively constructed to
realize the sublimation of self-research value. Therefore,
the following hypothesis is proposed:
H8: Imitative innovation path is positively correlated
with enterprise performance.
Based on the above assumptions, the theoretical
model of this paper is shown in Figure I.
Figure 1. Model of relationships between variables
Self-organizing type, Innovation Path and Enterprise Performance: Mechanism and Empirical Analysis 133
3. RESEARCH METHODS
3.1. Scale design
Since the organization theory was put forward, it has
exerted an important influence in the fields of natural
science and sociology, and is an important methodology
to explore the law of industrial cluster development.
However, few scholars study self-organization theory
from the perspective of scale. Therefore, this study
explains the evolution process of self-organization from
the perspective of marketing. The variables related to this
paper include collaborative self-organization,
independent innovation path, and imitative innovation
paths, and firm performance, which are also illustrated
by the likert scale. To ensure the effectiveness of the
measurement tool, a maturity scale widely used today
will be used in this paper. The two variables of
synergistic self-organization and enterprise performance
are both adopted in the scale developed by Ramani and
Ku-Mar [9]. The collaborative self-organization scale
mainly contains 6 items, while the enterprise
performance scale includes 12 items, which are mainly
based on three dimensions of financial performance, r&d
performance and management performance, and each
dimension has 3-5 items. It should be noted in particular
that the scale of corporate performance in this study
mainly refers to the steps and practices commonly used
in marketing, that is, the interviewees are more inclined
to corporate executives, to improve the accuracy of the
answers. According to existing studies, this method can
effectively improve the interview effect, and the
conclusions obtained are highly consistent with the
expected goals [10]. The scale of independent innovation
paths and imitative innovation paths are derived from the
scale developed by He and Wong [11]. And moderately
modified according to the actual situation. The scale of
independent innovation path and imitative innovation
path have 5 items, respectively. For the improved scale,
the consistency test coefficient of the independent
innovation path is 0.827, and that of the imitative
innovation path is 0.759, indicating good overall validity.
3.2. Data collection
At present, there are not many applications of
synergetic self-organization scale in the industrial
economy. Therefore, to ensure the scientific nature and
effectiveness of this study, questionnaires were
distributed to on-the-job MBA students in colleges and
universities in advance, and a small sample of pre-survey
was carried out, and good results were achieved. Then,
the core factors in the synergy self-organization scale are
discovered, that is, the variance of the collected data is
explained by the least Factor to the maximum extent. For
this purpose, this paper adopts the Exploratory Factor
Analysis (EFA method). In the case of multiple influence
factors, to intercept effective data, it is necessary to set
the characteristic root value greater than 1.
Simultaneously, the orthogonal rotation method is used
to conduct factor analysis. The results showed that the
KMO (Kaiser-Meyer-Olkin) test statistic value was 0.81,
and the SPSS test result also indicated that Bartlett’s
sphere test was passed (P < 0.001). This study has four
characteristic roots with values greater than 1, in other
words, these factors explain 63.5% of the total variance.
The formal survey was conducted by entrusts third-party
research company to obtain data and randomly sampling.
Senior executives of the investigated enterprises were
interviewed. The designed questions were completed in
the form of face-to-face interview. To ensure the
authenticity and universality of the data sources, online
questionnaires and paper questions and answers are also
used to collect data. In accordance with the scientific
principle, the managers of relevant enterprises must fill
carefully according to the research objectives, to improve
the accuracy of data and strive to reduce the overall
sample error. A total of 187 questionnaires were sent out
and 150 were collected in this survey. Simultaneously,
the collected questionnaires were optimized, that is, after
15 invalid questionnaires with incorrect filling,
incomplete filling and inobjective filling were removed,
135 valid questionnaires remained. The questionnaire
recovery rate was 80% and the effective rate was 72%.
3.3. Sample characteristics
The main research object of this study is the
managers of electronic information enterprises in the
pearl River Delta. The industries surveyed included ERP
software development companies, chip manufacturing
enterprises, upstream and downstream suppliers and
other high-tech enterprises, and the specific geographical
scope was mainly from Guangzhou, Shenzhen and
Dongguan. Located in the core area of Guangzhou-
Shenzhen Science and Technology Innovation Corridor,
these three cities are the concentration places of the
electronic information industry in the Pearl River Delta,
with good industrial clusters and complete industrial
supply chains, which are relatively representative. The
interviewees work in various departments and participate
in various management departments of the enterprise.
They have a good understanding of corporate policies,
production processes, external policies and
organizational structure; and have a clear understanding
of the purpose of this study. From the length of service of
the respondents, under the background of rapid
development in the Internet era of big data, high and new
technology enterprise management younger, more
dynamic and innovative spirit, the length of service of
the selected respondents mostly between 3 and 10 years,
the life of the staff of experienced, dynamic understand
more deeply on the development of the industry, Besides,
he was loyal to the enterprise and can give feasible
suggestions and implementation plans. In terms of age,
most of the interviewees are over 30 years old, and this
J. Guo and Gelegjamts
134
age group is the middle and senior managers of
enterprises, who can develop or implement the
development strategic plans of enterprises. Finally, from
the perspective of educational background, 99% of the
interviewees have a bachelor’s degree or above; and have
rich practical experience in the industry, high
professional quality level and strong sensitivity to data,
so they can provide objective evaluation opinions. The
number of employees of enterprises is between 50 and
2000, and both the size and nature of enterprises are
typical. These samples met the requirements of the study.
Additionally, the enterprise has been established for a
relatively long time, has rich experience, and is mainly a
joint-stock company, flexible mechanism, in line with
the pearl River Delta industrial development strategy. In
this study, the efficiency of the questionnaire was
relatively high after the questionnaire was collected and
evaluated by the factor test.
4. DATA ANALYSIS AND MODEL TESTING
4.1. Scale reliability and validity test
After sorting out the survey data and processing the
data with SPSS 24 software, it can be seen from the
operation results that the correlation coefficient between
each variable and the common factor, namely, a factor
load, is greater than 0.5. The coefficient values( df/
2
)
of collaborative self-organization, independent
innovation path, imitator innovation path, and internal
consistency of enterprise performance are 0.815, 0.829,
0.798, 0.813, values are all greater than 0.7, indicating
that the scale has high reliability.
To ensure the accuracy and validity of sample data,
the evaluation method proposed by Anderson and
Gerbing (1988) was adopted. After optimized fitting with
the structural equation model, the validity of the scale
was tested. In this study, AMOS 24 software was used
for factor analysis of scale data (See Table I). AVE
(Average Variance Extracted) and Construct Reliability
were adopted as the concrete criteria. As can be seen
from the data in the table, the correlation coefficients or
factor loadings of common factors in the scale are higher
than 0.50, indicating good aggregation validity.
According to theoretical deduction and actual
measurement, if the correlation coefficient between two
indicators with the same potential characteristics is less
than 0.70, it indicates that the scale has good
discriminative validity. As shown in Table 1, the
maximum correlation coefficient between each construct
is only 0.615, which is lower than the requirement of
0.70. Additionally, according to the research results of
fornell et al., when the square root of the AVE value of
these latent variables that cannot be directly measured is
greater than the correlation coefficient of each construct,
it indicates that the scale has good discriminative validity.
TABLE 1. Descriptive statistics, Pearson correlation
coefficient, AVE square root
4.2. Structural equation model testing
Meier (1976) proposed that fitting degree analysis
should be conducted using the structural equations before
scale validity measurement. In this study, AMOS24.0
software was used to describe the overall framework of
the report, and the path map of the structural equation
model was obtained. Then, relevant indicators were used
to evaluate the model. Generally speaking, the coefficient
matrix or covariance matrix formed between variables
can be measured using the structural fit index. If the
value ( df/
2
) is less than 3, the fit index NFI is
greater than 0.9, indicating that the fitting degree of the
variable relationship matrix in the overall model
constructed in this study is high with the actual data
relationship matrix. If we want to reflect the suitability of
the model and actual data in a more concise way, we can
use the index of reduced fit degree to test. In other words,
on the premise of ensuring a reasonable number of model
variables and samples, the more concise the hypothesis
model is, the more predictive validity it has. In other
words, it passed the AMOS test. If the PNFI of the model
is assumed to be higher than 0.5, the PGFI is also greater
than 0.5, suggesting that the simplified fit of the model is
reasonable. Value-added fitting degree measures the
degree of fitting between the hypothetical model and the
actual data; and can describe the degree of difference in
the covariance matrix between variables in reality. If CFI
is greater than 0.9, it indicates that the value-added fitting
degree of this model is good.As there are many
measurement items designed in this study, only the path
analysis of the following indicators is selected, as shown
in Table 2. Table 2 shows that the overall model fitting
index of AMOS output is consistent with the actual data,
which meets the requirements of this study.
TABLE 2. Analysis of the fitting degree of structural
equation model
Order to further optimize the model, AMOS24.0 was
used to calculate the data again. The running results of
the model show that the fitting effect of the model is
more ideal after the degree of freedom of the model is
optimized. It is worth mentioning that there may be a
correlation between the independent innovation path and
the imitative innovation path, but the positive and
Self-organizing type, Innovation Path and Enterprise Performance: Mechanism and Empirical Analysis 135
negative directions of the relationship cannot be
determined. Therefore, this study adopts the exploratory
validation function of models in AMOS to construct
four competitive models, and finally captures some
important characteristics, namely, imitative innovation
paths also have a significant positive impact on
autonomous innovation paths, which is called H-
Unidentified hypothesis (HU). After model exploration
and modification, the fitting degree of the model is
significantly improved, and all the indexes agree with
the research objectives.
To save space, some steps are omitted in this study,
and the optimal model is presented directly after
modification and model exploration. 2
= 225.8, DF
=165, absolute fitting index ( df/
2
) = 1.256, value
less than 2. The square root of the approximation error
RMSEA = 0.061, less than 0.08, indicating that the
model has a high degree of fitting and is consistent with
the actual data.IFI =0.942, TLI = 0.917, CFI = 0.952, all
greater than 0.9, indicating better fitting degree of the
model. The optimized model architecture is shown in
Figure 2. The solid line indicates that the above
assumptions H1, H2, H3, H4, H5, H6; and H7, have been
verified, while the dashed line indicates that H8 has not
been verified, while HU has passed the exploratory test,
and the running results also show that there is a positive
influence relationship.
Figure 2. Model diagram of verification results
In order to accurately measure and deal with the
relationship between variables, structural equation model
is used to test the collected data, and the results are
shown in Table 3.
TABLE 3. Path analysis results of the structural
equation model
In this study, the P values of H1, H2, H3, H4, H5, H6,
H7 and HU are all less than 0.05, which has been proved.
It is noteworthy that in the linear correlation analysis of
H8, the significance level between variables is
significantly higher than 0.05, and the fitting degree of
path analysis indicates that H8The hypothesis is not true.
Additionally, the positive relationship between
collaborative self-organization and autonomous
innovation path (𝛽= 0.682, 𝑡= 3.471, P < 0.001) is
greater than that between collaborative self-organization
and imitative innovation path (𝛽= 0.469, t=2.563, P=
0.009).
5. CONCLUSION AND MANAGEMENT
ENLIGHTENMENT
Firstly, competitive self-organization, collaborative
self-organization, and firm performance have a
significant positive relationship. The competition is one
of the most basic behavior characteristics of enterprise
organization, is also an important driving force of
innovation, simultaneously, the coordinated ability
stronger enterprises generally have the adaptive, self-
development and self-adjusted the corrective function,
through the exchange with the outside material,
information and energy, promote the orderly
development of the entire system organization, to
improve business performance and economic scale.
Therefore, it also indicates that although emerging
economies like China are experiencing great
development in economy, network data and institutional
environment, competitiveness and collaborative self-
organization have a significant impact on enterprise
performance, which is consistent with the actual
situation.
Secondly, collaborative self-organization can
significantly promote the independent innovation path
and imitative innovation pathway. Simultaneously, it
was also found that the degree of positive impact of
collaborative self-organization on independent
J. Guo and Gelegjamts
136
innovation paths is significantly higher than that of the
imitative innovation path, and the degree of impact of
collaborative self-organization on different innovation
paths is also different. Therefore, it is a meaningful
supplement to the existing research results. According to
the principle of cooperative self-organization theory, the
competition between individual organizations is not a
zero-sum game behavior, but a multi-win behavior
established in a certain competitive state. Despite a
complex and changeable external environment,
individual organizations, especially high-tech electronic
enterprises, cannot gain a place in the fierce market
system by themselves. Therefore, organizations must
cooperate with each other, choose the innovation path of
competition and cooperation, let the innovation elements
flow freely; and promote the value maximization of
network node organization members.
Thirdly, from the perspective of the impact of the
innovation path on firm performance, autonomous
innovation path has a significant positive impact on firm
performance, while the reverse imitative innovation path
has no significant impact on firm performance. In the
scenario independent innovation model, individual
enterprises invest many human and material resources
and obtain high-quality resources to the maximum
extent through the competition mechanism; and obtain
the dominant position in the market by the survival of
the fittest. However, imitative innovation path is
difficult to obtain breakthrough technology in a short
time and may be annexed at any time. When enterprises
implement the imitative innovation path, they tend to be
complacent, and the technology will stagnate, with high
uncertainty, and they always face huge business risks.
Therefore, from the perspective of sustainable
development strategies, imitative innovation path is
negatively correlated with enterprise performance.
Fourth, after data inspection and calculation, it is
found that the imitative innovation path has a positive
impact on the independent innovation path, which
confirms that the independent innovation path and the
imitative innovation path can coexist. Independent
innovation path and imitative innovation paths can be
integrated, complement each other, and promote the
development of enterprise performance together.
Therefore, an enterprise organization should adopt two
innovation path modes. Simultaneously, advocate
balanced-development, change from the previous
competition and unbalanced development state, not only
to joint r&d; but also to encourage independent r&d, to
ensure the advancement of products, to shape the core
capabilities of the enterprise organization.
ACKNOWLEDGMENT
Sponsors: The 2016 General Project of Guangdong
Province Philosophy and Social Science “Thirteenth
Five-Year Plan ” (Project ID: GD16CGL07).
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