Structural equation modeling of the intelligent manufacturing entrepreneurship’s network characteristics

Abstract

The entrepreneurship of intelligent manufacturing enterprises in manufacturing clusters have significant influences on China’s economic growth in the process of industry 4.0. This paper reveals the entrepreneurship of intelligent manufacturing enterprises by the analysis of network theory. By the questionnaire of 470 effective sample enterprises in the 6 manufacturing industrial clusters selected, Structural Equation Modeling and the AMOS software are used to verify the theoretical hypotheses. The findings are as follows: first, entrepreneurial competence has a positive effect on corporate entrepreneurship; second, network relation, betweenness centrality and network scale have positive effect on entrepreneurial competence; third, network relationship strength and network density have different effects on entrepreneurial competence. Based on the results of empirical analysis, this paper puts forward some policy suggestions to promote enterprise entrepreneurship in industrial clusters.

Keywords

Network characteristics corporate entrepreneurship entrepreneurial competence SMEs structural equation modeling

1 Introduction

The experience of Zhejiang, Jiangsu, Fujian, Guangdong and other developed regions in China show that the traditional manufacturing industrial clusters play an important role in the process of regional economic development [40]. However, facing the challenge of Industry 4.0, the industrial clusters of SMEs following the growth pattern of high input with low efficiency need transformation and upgrading urgently. The experiences of Changsha machinery industrial cluster, Yueqing electrical industrial cluster shows that SMEs’ corporate entrepreneurship is one of the important ways for the industrial clusters transformation and upgrading in China [8].

Despite that current theoretical researches of SMEs industrial clusters have already increasingly turned to the industrial cluster upgrading [14, 18], there still existing the following problems: First, the previous phenomenon of corporate entrepreneurship have not been given sufficient attention. Second, rare theoretical researches about corporate entrepreneurship in clusters only have studied the entrepreneurial phenomena in an isolated position in cluster environment, what exactly caused the differences in corporate entrepreneurship of firms in clusters are still wait to be released. Third, the empirical researches on corporate entrepreneurship in clusters are static analysis that cannot answer how the entrepreneurship transmissions in firms thus promote the cluster upgrading. Actually, as members of local network organization, cluster enterprises are prone to have embeddedness [3], so the entrepreneurial behaviors will be influenced by the network characteristics, the entrepreneurship decision of firms in clusters are very different. Take the firms in clusters as research object, this study aims to analyze the differences in corporate entrepreneurship of firms in clusters with the new perspective of corporate entrepreneurial competence, reveals the influences that network characteristics of firms have on corporate entrepreneurship competence, and further construct a theoretical framework “network characteristics—entrepreneurial competence—corporate entrepreneurship” of firms in clusters.

2 Conceptual framework

2.1 Entrepreneurial competence and corporate entrepreneurship in clusters

Entrepreneurship is not only the phenomenon of building a new enterprise [31], but also the phenomenon that corporate keep the innovative, risk-taking, and advanced features of entrepreneurial action [20]. So corporate entrepreneurship in this paper is characterized by the phenomenon of innovation, risk investment activities [41]. Besides entrepreneurs’ pre-entry experience, both superior hires and regional embeddedness have been suggested as factors contributing to corporate entrepreneurship in clusters [2, 33]. Competence theory holds that the competence is embodied in concrete behavior and can be observed through behavior, so behavior is determined by competence [29]. Teece etc. define the dynamic capabilities of the enterprises as the ability to integrate, establish and reconstruct the internal and external competence to meet the rapidly changing environment [9, 11]. The resource concept believes that entrepreneurship is mainly reflected in the ability to integrate resources [23]. The opportunity concept insists that the exploration ability of entrepreneurial opportunity is the most important ability of the enterprise to achieve the goal [25]. Entrepreneurship has the process of perception, the use of opportunities to build a team, to get the necessary resources, create a new organization and conduct new business activities.

In the different stages of the entrepreneurship process, the entrepreneurial competence needed to match is somewhat different. Therefore, from the perspective of the entrepreneurial process, this paper defines the entrepreneurial competence as the entrepreneurial cognitive ability, entrepreneurial opportunities exploration ability and entrepreneurial resources integration ability. Entrepreneurial cognitive ability includes the entrepreneurial motivation, resources endowment, risk judgment and endurance, the expected value for choices. In addition, the ability to predict future, to work better, to insight the opportunities and strong entrepreneurial motivation will influence entrepreneurial performance [12]. The entrepreneurial opportunities exploration ability is the ability to discover and acquire opportunity. The discovery and use of the valuable opportunity are the important process of entrepreneurial behavior [26]. The entrepreneurial resources integration ability is the ability to reorganize the total various tangible and intangible resources inside or outside the enterprise to make better use of opportunities [5, 24]. Resources integration after the behavior of percept entrepreneurial opportunities mainly includes absorbing team members, raising venture capital.

2.2 Network characteristics and entrepreneurial competence

The network embedded theory pulled out that the economic behavior of an actor is affected by its neighborhood relationship and the location of the whole network at the same time. Social network characteristics have a significant positive impact on individual entrepreneurship intention, and entrepreneurial opportunity identification plays a full mediating role in the impact of network location on entrepreneurship intention [19]. This paper adopts network relation and structure to analysis the influence that network characteristics have on entrepreneurial competence.

It is argued that established entrepreneurs at the regional level become referents of new entrepreneurs, influencing not only the decision to become entrepreneurs but also the characteristics of the new venture, such as its initial size [32]. The neighbor effect of complex social network also shows that the neighbors’ selection normally affects the individual decision-making processes [4]. The relationship between firms in a local network can be represented by the index of Network intensity or frequency [34]. Weak links that means a loose contact relationship may increase relationship diversity in the cluster while strong links may promote trust and cooperation between firms. Weak links between firms will not hinder the firms to have more access to different types of new information, people and resources while strong links help firms easy to catch more resources such as refined, high-quality information and tacit knowledge, and can quickly change the market opportunities into reality activities [17].

Based on the discussion above, the authors propose the hypotheses below (Table 1).

Table 1
Hypotheses of entrepreneurial competence to corporate entrepreneurship

Hypotheses Influence

A1 Entrepreneurial cognitive ability to entrepreneurial behavior Positive

A2 Entrepreneurial opportunities exploration ability to entrepreneurial behavior Positive

A3 Entrepreneurial resources integration ability to entrepreneurial behavior Positive

Hypotheses	Influence
A1	Entrepreneurial cognitive ability to entrepreneurial behavior	Positive
A2	Entrepreneurial opportunities exploration ability to entrepreneurial behavior	Positive
A3	Entrepreneurial resources integration ability to entrepreneurial behavior	Positive

In the same network, there are different contents in the relationship between participants, that is, the specific contact characteristics or types between actors [13]. Distributors, suppliers, competitors and customer relationships are important channels for entrepreneurs to access information and integrated technologies [1, 35]. Therefore, this paper focuses on the influence that the amount of network content has on entrepreneurial ability.

Research on social networks shows that the opportunities and constraints of a participant are often determined by its position in the social structure [28, 39]. This status can be expressed by the number of direct links between participants and other participants, i.e. network size. It represents the main channels available to participants. The greater the value, the more capital and human resources, the more potential customers and suppliers, the stronger the influence and the more industry experience and knowledge [17]. Therefore, a large network scale is helpful to improve the access and evaluation ability of the firms.

Network density is expressed by dividing the actual link by the value of all possible links in the network. The higher the value, the easier it is for network members to share tacit knowledge and expand the relationship between knowledge and information resources, so as to ensure strong control over certain resources and improve the possibility of resource integration [22, 27]. On the other hand, the higher the value, the more centralized transactions among network members may reduce the new opportunities for enterprises to obtain useful information and knowledge from outside [21].

Betweenness centrality is especially suitable for evaluating the strength of participants in the network [15 , 37], which means that participants occupy the middle position on the shortest path between the other two participants and connect the two participants. Enterprises occupy this position can obtain multi-dimensional non-repetitive information and becomes an information distribution center [21].

Based on the discussion above, the authors propose the hypotheses (Table 2).

Table 2

Hypotheses of network characteristics to entrepreneurial competence

Hypotheses	Influence
B1	Network strength to entrepreneurial cognitive ability	Positive
B2	Network strength to entrepreneurial access ability	Negative
B3	Network strength to entrepreneurial resource integration ability	Positive
C1	Network content to entrepreneurial cognitive ability	Positive
C2	Network content to entrepreneurial opportunities exploration ability	Positive
C3	Network content to entrepreneurial resources integration ability	Positive
D1	Network scale to entrepreneurial cognitive ability	Positive
D2	Network scale to entrepreneurial opportunities exploration ability	Positive
D3	Network scale to entrepreneurial resources integration ability	Positive
E1	Network density to entrepreneurial cognitive ability	Positive
E2	Network density to entrepreneurial opportunities exploration ability	Negative
E3	Network density to entrepreneurial resources integration ability	Positive
F1	Betweenness centrality to entrepreneurial cognitive ability	Positive
F2	Betweenness centrality to entrepreneurial opportunities exploration ability	Positive
F3	Betweenness centrality to entrepreneurial resources integration ability	Positive

On the basis of the above analysis, this paper constructs an analysis model with entrepreneurial behavior of cluster enterprises as dependent variable, network characteristics of cluster enterprises (network relationship and network structure) as independent variable, and entrepreneurial capability of cluster enterprises as intermediate variable (Fig. 1).

Fig. 1

Research model.

3 Methodology

3.1 Data collection and analysis

This paper selects six manufacturing SMEs clusters that carried out intelligent manufacturing in the previous five years to do the survey. They are Jinhua automobile industrial cluster, Yueqing electrical industrial cluster, Shengzhou tie industrial cluster, Zhuji Datang socks industrial cluster, Dongyang electronic magnetic materials industrial cluster and Lanxi cotton textile industrial cluster. The respondents included managers of enterprises in these clusters. The issuance and recovery of questionnaires lasted for nearly five months. A total of 918 questionnaires were distributed and 470 valid questionnaires were collected. The overall effective questionnaire recovery rate was 51.2%.

The survey items of the variables mainly come from the related literature, and we use the subjective perception method to measure the variables with the Likert 7 level scale. For the measurement of entrepreneurial behavior of cluster enterprises, this paper designs two items to measure the entrepreneurship behavior of cluster enterprises as “over the past five years, your corporate has innovative entrepreneurship” and “over the past five years, your corporate has venture capital Entrepreneurship”. Measurement of network characteristics of cluster enterprises are designed as follows. Firstly, “your corporate has very high frequency of contact with local enterprises”, “your corporate has very smooth contact with local enterprises”, “your corporate has long-standing links with local enterprises” and “the links with local enterprises are very deep” are used to measure relation strength. Secondly, relation content is measured by “your corporate has more consultation relationship with local enterprises”, “your corporate has more capital relationship with local enterprises” and “your corporate has more customer relationship with local enterprises”. Thirdly, the items of “more local enterprises are associated with your corporate than with your local counterparts”, “more local auxiliary organizations are associated with your corporate than with your local counterparts” are used to measure the network scale. Fourthly, to measure network density, this paper use the items “compared with local counterparts, your established links with local enterprises account for a higher proportion of all possible links” and “compared with local counterparts, your established links with local auxiliary institutions account for a higher proportion of all possible links”. Fifthly, “when establishing linkages with the objects of business contacts, many of them depend on your corporate to connect them” and “without your corporate, many local enterprises will lose their important information sources” are used to measure betweenness centrality. The measurement of corporate entrepreneurship ability are designed as follows. Firstly, “emphasizing and supporting innovation”, “emphasizing leading opponents in action”, “emphasizing forward-looking judgment and thinking about the future”, “being inclined to take bold actions to achieve corporate goals” and “generally accepting the possible consequences of risks” are used to measure entrepreneurial cognitive ability. Secondly, “good at discovering potential opportunities in the market”, “focused on attending lectures or training related to entrepreneurship”, “focused on attending technical exchanges or product exhibitions”, “focused on developing and creating opportunities”, and “good at grasping opportunities” are used to measure entrepreneurial opportunities exploration ability. Thirdly, in order to accomplish a certain task, “your corporate can usually set up a team quickly”, “raise funds more quickly”, “be supported by local government departments”, and “be able to complete the relevant procedures smoothly” to measure the entrepreneurial resources integration ability.

We use the KMO and Bartlett’s test to construct the validity of the measurement index. The results show that the data meet the basic conditions for factor analysis. According to the principle of characteristic root greater than 1, principal component factor was extracted by orthogonal rotation method and maximum variance method. The explanatory power of the total variance satisfies the requirement, and the number of factors obtained is consistent with the variable structure of index setting, which indicates that the index setting in this study has construct validity (Table 3).

Table 3
Factor analysis result

Items Component

1

Entrepreneurial behaviors X 0.908

F 0.892

Items Component

1 2 3

Entrepreneurial cognitive ability R1 0.880 0.315 0.119

R2 0.875 0.184 0.233

R3 0.795 0.236 0.186

R4 0.743 0.401 0.155

R5 0.720 0.336 0.239

Entrepreneurial opportunities exploration ability H1 0.122 0.905 0.102

H2 0.151 0.871 0.243

H3 0.038 0.844 0.394

H4 0.335 0.813 0.228

H5 0.166 0.704 0.261

Entrepreneurial resources integration ability Z1 0.013 0.367 0.893

Z2 0.247 0.079 0.865

Z3 0.306 0.300 0.772

Z4 0.252 0.311 0.759

Items Component

1 2 3 4 5

Relation strength Q1 0.805 0.302 0.266 0.244 0.295

Q2 0.712 0.322 0.338 0.262 0.332

Q3 0.709 0.456 0.223 0.259 0.226

Q4 0.705 0.262 0.439 0.354 0.138

Relation content N1 0.336 0.783 0.308 0.296 0.262

N2 0.344 0.775 0.314 0.286 0.263

N3 0.370 0.744 0.352 0.295 0.234

Network scale G1 0.321 0.315 0.824 0.243 0.206

G2 0.327 0.333 0.808 0.260 0.212

Network density M1 0.328 0.364 0.268 0.764 0.293

M2 0.359 0.327 0.328 0.749 0.269

Betweenness centrality J1 0.360 0.320 0.262 0.309 0.746

J2 0.371 0.441 0.262 0.399 0.677

Items	Component
Entrepreneurial behaviors	X	0.908
	F	0.892
Items		Component
		1	2	3
Entrepreneurial cognitive ability	R1	0.880	0.315	0.119
	R2	0.875	0.184	0.233
	R3	0.795	0.236	0.186
	R4	0.743	0.401	0.155
	R5	0.720	0.336	0.239
Entrepreneurial opportunities exploration ability	H1	0.122	0.905	0.102
	H2	0.151	0.871	0.243
	H3	0.038	0.844	0.394
	H4	0.335	0.813	0.228
	H5	0.166	0.704	0.261
Entrepreneurial resources integration ability	Z1	0.013	0.367	0.893
	Z2	0.247	0.079	0.865
	Z3	0.306	0.300	0.772
	Z4	0.252	0.311	0.759
Items		Component
		1	2	3	4	5
Relation strength	Q1	0.805	0.302	0.266	0.244	0.295
	Q2	0.712	0.322	0.338	0.262	0.332
	Q3	0.709	0.456	0.223	0.259	0.226
	Q4	0.705	0.262	0.439	0.354	0.138
Relation content	N1	0.336	0.783	0.308	0.296	0.262
	N2	0.344	0.775	0.314	0.286	0.263
	N3	0.370	0.744	0.352	0.295	0.234
Network scale	G1	0.321	0.315	0.824	0.243	0.206
	G2	0.327	0.333	0.808	0.260	0.212
Network density	M1	0.328	0.364	0.268	0.764	0.293
	M2	0.359	0.327	0.328	0.749	0.269
Betweenness centrality	J1	0.360	0.320	0.262	0.309	0.746
	J2	0.371	0.441	0.262	0.399	0.677

To verify the rationality and validity of the data before the SEM (structural equation modeling) analysis, this paper uses the SPSS16.0 software to carry on the reliability test (Table 4). Reliability analysis results show that the Cronbach’s alpha coefficients are more than 0.7 and the corrected item-total correlation of all variables measured items are greater than 0.35, which shows that the reliability of each scale is higher, and the variables have high internal consistency.

Table 4

The reliability of the measured variables

Variables	Items	Corrected Item-Total Correlation	Cronbach’s Alpha
CY	X	0.701	0.875
	F	0.522
RZ	R1	0.756	0.763
	R2	0.545
	R3	0.498
	R4	0.612
	R5	0.663
HQ	H1	0.738	0.846
	H2	0.842
	H3	0.567
	H4	0.742
	H5	0.486
ZH	Z1	0.4357	0.771
	Z2	0.465
	Z3	0.642
	Z4	0.856
Q	Q1	0.420	0.808
	Q2	0.457
	Q3	0.556
	Q4	0.565
N	N1	0.391	0.795
	N2	0.498
	N3	0.776
G	G1	0.535	0.7296
	G2	0.622
M	M1	0.553	0.784
	M2	0.691
J	J1	0.538	0.808
	J2	0.488

4 Hypothesis test and result

Considering the latent variables used in the research model, SEM with the software Amos 21.0 are used to conduct the analysis. We select χ2 test, χ2/df, CFI, NFI, RMSEA and SRMR as model evaluation fit indices. The empirical analysis results are show in Fig. 2. According to the model analysis, the hypotheses about the influence mechanism that network characteristics to entrepreneurial behavior have been tested. Hypotheses A1, A2, A3 are verified, indicating the significant positive influence that entrepreneurial cognitive ability, entrepreneurial opportunities exploration ability and resource integration ability have on entrepreneurial behavior. Hypotheses B1, B2, B3, C1, C2, C3, D1, D2, D3, E1, E2, E3, F1, F2, F3 that refer to the influence that network characteristics have on entrepreneurial competence pass test. These indicate: first, network relation content, network scale and betweenness centrality have positive influence on entrepreneurial cognitive ability, entrepreneurial opportunities exploration ability and resources integration ability. The more network relation contents, the larger the network scale and the higher the betweenness centrality, the better entrepreneurial cognitive ability and entrepreneurial opportunities exploration ability and resources integration ability. Second, network relationship strength has positive influence on entrepreneurial cognitive ability and resources integration ability, but negative influence on entrepreneurial opportunities exploration ability. The higher the network relation intensity, the better entrepreneurial cognitive ability and resources integration ability but the weaker entrepreneurial opportunities exploration ability the firms in clusters are. This shows that for the acquisition of entrepreneurial opportunities, firms in clusters should pay also more attention to the association with the firms outside the clusters, when they attach more importance to internal firms of the cluster, thus to obtain new opportunities information and avoid excessive “embedded”. Third, there is a positive influence network density has on entrepreneurial cognitive ability and resources integration ability, but a negative influence on entrepreneurial opportunities exploration ability. Cluster firms with high density network have more entrepreneurial cognitive ability and have strong ability to control the entrepreneurial resources, thus improve the possibility of entrepreneurial resources integration.

Fig. 2

The empirical analysis results.

5 Conclusions

With the empirical study of six SMEs clusters, this paper traces the role of network characteristics in corporate entrepreneurship of intelligent manufacturing enterprises and the influence mechanism between network characteristics and entrepreneurial behaviors. By building a research model about the network characteristics and entrepreneurial behaviors with the intermediary variables of entrepreneurial competence, this paper also conducted the evaluation of the theoretical research model. Based on the results of the above empirical analysis, this paper puts forward the following suggestions. Firstly, the motivation of entrepreneurship should be found in enterprises with strong entrepreneurial cognitive ability, entrepreneurial opportunity acquisition ability and entrepreneurial resource integration ability. At present, there are few regional entrepreneurship policies for industrial clusters in Zhejiang province. The entrepreneurship opportunities of some enterprises still come from participating in some public training and public projects outside the cluster. For the acquisition of entrepreneurship opportunities, cluster enterprises should not only attach importance to the relationship with the main body inside the cluster, but also attach importance to the connection with the outside of the cluster, so as to obtain opportunity information and avoid excessive “embedding”. In the most industrial clusters in Zhejiang province, if a firm has high density network, its individual behavior will meet certain constraints from the intensive affiliated firms, so that the information and knowledge boundary constrained. Furthermore, the heterogeneous information scarcity reduces the firm’s ability to obtain a unique knowledge and finally reduce the entrepreneurial opportunities exploration ability. Secondly, the incentives for sustainable innovation of cluster enterprises are very important. The regional government of the industrial clusters should support the industrialization of innovation results of core enterprises in the cluster. It is necessary for the regional government to provide information consulting services to the support enterprises in clusters and to promote innovation “spillover”. For the production system based on division of labor and cooperation, the innovation of an enterprise often needs other suppliers’ innovations associated with it. Many small and medium-sized enterprises in the cluster are often faster than large enterprises in implementing technological innovation. They are very suitable as “spillover” objects to provide innovation matching for innovative enterprises.

6 Limitations and future research

As a further research to our paper “Influencing mechanism of entrepreneurial behavior: an empirical analysis of firms in manufacturing clusters” submitted to IE&EM 2012, more case studies and more data collection have been conducted in this paper. We complete the interaction test of the adjusted and modified model with a bigger group of data, which can reduce the possibility of “false pass of test” arising from simple data and improve the credibility of the research results. In addition, as for future research [38], empirical analysis may be carried out on more industrial clusters in china or even in abroad. It will require continuous research focusing on the entrepreneurial behavior of the firms in industrial clusters. At present, there are few entrepreneurship policies specifically aimed at industrial clusters. The entrepreneurial cognitive ability and entrepreneurial opportunity discovery ability of internal enterprises almost come from public training and public information outside the clusters. In the future, more in-depth research on the entrepreneurship policies of clusters or local governments should be conducted. Furthermore, with the rapid development of global e-commerce, international entrepreneurial virtual networks in cyberspace are growing [30]. Considering the recent research on the entrepreneurial orientation of electronic commerce adoption in small and medium-sized enterprise [16], the characters of web-enabled entrepreneurial networks [7], the knowledge sharing in e-business virtual industry [6], and open source innovation for entrepreneurship transmission [10], the research on entrepreneurship transmission of e-business virtual industry cluster will be a brand-new research direction.

Footnotes

Acknowledgments

The authors acknowledge National Natural Science Foundation of China (No. 71603235) and National Social Science Fund (No. 18BGL097).

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