Technology Proximity of CVC Parties and Local-distant Dual Technology Exploration of the Venture Enterprise: Differential Effects and Boundary Conditions

Changlin Zhu; Guiyang Zhang

doi:doi:10.11648/j.innov.20260701.12

Research Article |

| Peer-Reviewed

Technology Proximity of CVC Parties and Local-distant Dual Technology Exploration of the Venture Enterprise: Differential Effects and Boundary Conditions

Changlin Zhu, Guiyang Zhang^*

Published in Innovation (Volume 7, Issue 1)

Received: 23 January 2026 Accepted: 25 March 2026 Published: 2 April 2026

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

As an emerging form of strategic investment, Corporate Venture Capital (CVC) and its consequences on venture enterprises’ technology exploration have received more and more attention from scholars, especially in the context of open innovation that highlights interorganizational collaborative and external knowledge acquisition. To solve the inconsistency of existing research conclusions on the relationship between CVC and enterprise innovation, this research tries to explore the differential impacts of technological proximity on dual technological exploration of the venture enterprise from the knowledge-based view and the local-distant knowledge search theory. Based on multi-source matching data, including CVC records, patent, and financial information from 2010 to 2020 of 42 companies that are quoted on the Science and Technology Innovation Board, this paper conducts the Bayes Negative Binomial regression model for empirical analysis. The results show that technology proximity of CVC parties has an inverted U-shaped effect on local technological exploration of the venture enterprise. Both the density and degree centralization of inventor cooperative network of the venture enterprise positively moderate the main effect. Meanwhile, technology proximity of CVC parties has a positive effect on distant technological exploration of the venture enterprise. Both the density and degree centralization of inventor cooperative network of the venture enterprise negatively moderate the main effect. This study enriches the research perspective of CVC from the investor side to the venture enterprise side, clarifies the boundary conditions of the impact of technological proximity, and provides important theoretical guidance and practical reference for venture enterprises to select CVC partners and optimize internal inventor network structure.

Published in	Innovation (Volume 7, Issue 1)
DOI	10.11648/j.innov.20260701.12
Page(s)	11-20
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Corporate Venture Capital (CVC), Technology Proximity, Dual Technology Exploration, Inventor Cooperation Network

1. Introduction

In the context of open innovation, an increasing number of firms are crossing organizational boundaries to engage in collaborative innovation. Effective collaborative innovation forms a symbiotic system aimed at sharing homogeneous resources or complementing heterogeneous ones

[1]

. Corporate Venture Capital (CVC), as a distinctive form of strategic investment

[2]

, refers to equity investments made by established non-financial corporations with core businesses into entrepreneurial firms

[3]

. As an effective channel for knowledge and technology transfer, CVC has become an emerging mode of inter-firm collaborative innovation.

Given that prior research on collaborative innovation emphasizes that the technological foundations and matching characteristics between partners significantly affect the achievement of collaborative goals, this study introduces the matching feature of technological proximity between CVC parties

[4]

to explore how CVC influences the technological exploration of venture enterprises. On the one hand, after receiving CVC, entrepreneurial firms may leverage the investor’s knowledge resources to conduct local search; on the other hand, they may also utilize the investor’s financial, business, and other resources to engage in distant search. Therefore, from the perspective of local–distant search, this study distinguishes between two forms of technological exploration: Local technological exploration, which relies on the CVC investor’s knowledge resources; Distant technological exploration, which is driven by other external knowledge sources. Because the knowledge sources and generative mechanisms of these two types of exploration differ, they may require different levels of technological proximity between CVC parties.

Moreover, the structural characteristics of internal collaboration networks within entrepreneurial firms may influence the diffusion and absorption of external knowledge, thereby moderating the effect of technological proximity on technological exploration. Therefore, this study empirically examines the boundary effects of internal inventor cooperation networks on the relationship between CVC technological proximity and entrepreneurial firms’ local–distant technological exploration.

2. Literature Review

Most existing research examines the impact of CVC from the perspective of investing firms, exploring how CVC affects their innovation strategy or innovation performance. For example, Thomas

[5]

argues that CVC investment differs from independent venture capital and can significantly enhance the technological exploration efficiency of investing firms. Dushnik et al.

[6]

further explain from a proximity perspective how technological, industrial, and geographic proximities shape the innovation outcomes of investors. However, from the perspective of entrepreneurial firms, how CVC technological proximity influences their technological exploration remains unclear.

Beyond the CVC context, scholars have studied the role of technological proximity from multiple theoretical lenses—collaborative innovation, knowledge spillover, technological recombination, and cognitive distance. Mainstream views suggest that moderate technological proximity facilitates the transfer and sharing of heterogeneous knowledge, thereby increasing innovation opportunities. Jakobsen and Steinmo

[7]

find that technological proximity among competitors positively affects their collaborative innovation output. Tian and Xu

[8]

show that industry-based technological proximity strengthens the effects of specialization on regional innovation output.

However, excessive proximity may reduce access to novel technological opportunities. Guan and Yan

[9]

argue that high proximity results in overly similar knowledge pools, reducing recombination potential. Chu and Hu

[10]

; Cao and Song

[11]

; and Xia et al.

[12]

all find inverted U-shaped relationships between technological proximity and collaborative research outputs. Similarly, Nooteboom et al.

[13]

suggest that while increasing cognitive distance promotes complementarity and innovation, beyond a threshold, excessively high cognitive distance impedes mutual understanding and collaborative outcomes.

In the CVC context, the complexity increases further. First, varying theoretical perspectives and measurement choices may yield inconsistent conclusions. Research grounded in technological recombination and cognitive distance emphasizes exploration based on investor-sourced knowledge and may observe inverted U-shaped effects, whereas studies focusing on broader knowledge sources tend to find linear positive relationships. Thus, distinguishing local versus distant search is essential. Second, heterogeneity in entrepreneurial firms’ internal absorptive capabilities may also explain inconsistencies. Cohen and Levinthal

[14]

propose that absorptive capacity—rooted in firms’ prior knowledge—shapes the ability to acquire, assimilate, and apply external knowledge. Internal collaborative networks significantly influence absorptive capacity. Hu

[15]

finds that network heterogeneity enhances cognitive diversity and facilitates effective knowledge integration.

In summary, existing research has three limitations: First, overemphasis on the impact of CVC on investing firms, with limited understanding of how technological proximity affects entrepreneurial firms. Second, lack of consensus on the effect of CVC technological proximity on technological exploration, largely due to insufficient distinction between different knowledge sources. Third, overlooking the role of internal inventor collaboration networks in shaping external knowledge acquisition and utilization. To address these gaps, this study integrates the knowledge-based view, local–distant knowledge search, and absorptive capacity framework, using multi-source matched data from 42 STAR Market firms (2010–2020) to empirically test the heterogeneous effects of CVC technological proximity and the moderating role of internal inventor networks.

3. Theoretical Argument and Hypotheses

3.1. CVC Technological Proximity and Local Technological Exploration

External knowledge acquisition significantly influences firms’ technological exploration

[16]

. CVC provides entrepreneurial firms with a flexible and adaptive environment for knowledge exchange and expands the knowledge pool available for recombination. When technological proximity is low, knowledge bases are highly heterogeneous, limiting mutual understanding and suppressing local search

[12]

. As proximity increases, shared knowledge, common languages, and similar cognitive structures enhance communication and learning

[17]

. Yet, excessive proximity reduces knowledge complementarity and recombination opportunities

[18]

, weakening local search motivation and outcomes.

H1: CVC technological proximity has an inverted U-shaped effect on the local technological exploration of entrepreneurial firms.

3.2. CVC Technological Proximity and Distant Technological Exploration

Technological proximity also reflects alignment in knowledge accumulation and technological strategy between CVC partners. Thomas et al.

[5]

highlight that strategic alignment is embedded in technological proximity. High proximity implies shared strategic goals, reducing conflict over technological directions

[19, 20]

. Therefore, distant technological exploration—which does not rely on investor-sourced complementary knowledge—is unlikely to be hindered by excessive proximity

[13]

. Conversely, low proximity increases strategic conflict and constraints. Hence:

H2: CVC technological proximity positively influences distant technological exploration.

3.3. Moderating Role of Internal Inventor Networks

Internal inventor networks reflect the flow of knowledge and information within the firm. Network density indicates relational closeness, while degree centralization reflects concentration of ties

[21]

. High density enhances trust, knowledge sharing, and efficiency of knowledge integration

[22-24]

. However, when technological proximity is excessively high, high-density networks amplify redundancy and hinder recognition of heterogeneous knowledge

[25]

H3: Inventor network density positively moderates the inverted U-shaped relationship between CVC technological proximity and local technological exploration.

Distant search involves uncertain, heterogeneous knowledge and requires experimentation. High density leads to redundant ties and cohesive subgroups, reducing openness, hindering external knowledge inflow, and lowering willingness to seek new knowledge

[26, 27]

. Thus:

H4: Inventor network density negatively moderates the positive effect of technological proximity on distant technological exploration.

High centralization features star-shaped structures with efficient governance

[28]

. When proximity is low, central actors help absorb heterogeneous knowledge. When proximity is high, excessive dependence on central inventors restricts knowledge flow to peripheral actors

[29, 30]

, reducing exploration effectiveness.

H5: Inventor network centralization positively moderates the inverted U-shaped relationship between technological proximity and local exploration.

Distant search requires decentralized, diverse participation. High centralization limits access to external knowledge among peripheral inventors

[31]

, reduces initiative, and reinforces risk-averse tendencies

[32]

. Thus:

H6: Inventor network centralization negatively moderates the positive relationship between technological proximity and distant exploration.

4. Methodology

4.1. Sample and Data

Given that this study focuses on the technological exploration of entrepreneurial firms, we select companies listed on the Science and Technology Innovation Board (STAR Market) as our sample. The STAR Market of the Shanghai Stock Exchange primarily serves technology-driven firms that align with national strategies, achieve breakthroughs in key core technologies, and possess strong market recognition. Following prior studies, we examine CVC investment records of STAR Market firms from 2017 backward for a seven-year window in the CVSource database. The dataset includes CVC investor names, investment amounts, investment stages, and investment rounds. To identify valid CVC investment events, we adopt the following criteria: First, we exclude investors that are real estate developers, financial institutions, or companies unrelated to the core business of the investee firm, as these investors prioritize financial returns rather than strategic value and thus do not constitute CVC. Second, the investee firm must have filed invention patents within the three years preceding the investment.

Patent data are obtained from the PatSnap (Zhihuiya) patent database. Data collection and cleaning follow these steps: (1) Using the firm name as the assignee keyword, we initially retrieve invention patents of the entrepreneurial firm within three years before and after the CVC event, and those of the CVC investor within three years prior to the event. (2) We analyze variations in assignee names and refine them into secondary precise searches. (3) We download records containing assignee name, inventors, application dates, and four-digit IPC codes. (4) Based on inventor co-occurrence, we construct the internal inventor cooperation network of each entrepreneurial firm using Science of Science Tool and Ucinet. If the firm has too few patents or inventor relations within the three years prior to CVC to form a network, we remove the firm and its CVC records. Finally, 42 entrepreneurial firms and their CVC events meet all criteria. Additional firm-level data—region, business scope, firm size, R&D input—are collected from the Wind financial database.

4.2. Variables

(1) Dependent variable: Technological exploration (TE). Measures of technological exploration vary in prior research, including the ratio of R&D personnel, new product development expenditure, number of new products, and invention patents. Following Belderbos et al.

[33]

, we classify firms’ technological exploration activities using the International Patent Classification (IPC) codes embedded in patent documents. IPC codes reflect the technological attributes of patents and group them into sections, classes, subclasses, and groups

[34]

. Consistent with prior studies, we adopt the four-digit IPC classification

[35, 36]

. This study measures technological exploration by counting invention patents containing new IPC subclasses

[37, 38]

. Using IPC data from three years before and after each CVC event, we identify patents with newly appearing IPC classes. To distinguish between local and distant exploration: Local Technological Exploration (LTE): Number of invention patents filed by the entrepreneurial firm within three years after the CVC event that contains IPC subclasses appearing in the CVC investor’s patents. Distant Technological Exploration (DTE): Total number of invention patents with new IPC subclasses filed by the entrepreneurial firm after the CVC event, minus the patents used to measure LTE.

(2) Independent variable: Technological proximity (TP). Technological proximity measures the similarity of technological knowledge structures between the CVC investor and the entrepreneurial firm

[39]

. Using Jaffe’s method

[40]

, we calculate the cosine similarity between the IPC distribution vectors of the two parties.

(3) Moderation variables: Inventor network density (Den) and Inventor network centralization (Cen)

[41]

. We follow the Reinholt et al. ’s (2011) method to calculate these variables.

(4) Control variables. There are three categories. The first is R&D Input (Input), which is calculated by the average R&D expenditure of the entrepreneurial firm within three years after the CVC event. The second is technological foundation, including the entrepreneur’s pre-CVC patent count (Patent_i), investor’s pre-CVC patent count (Patent_j), entrepreneur’s pre-CVC IPC diversity (IPC_i), and investor’s pre-CVC IPC diversity (IPC_j). The third includes geographic proximity (Region), business similarity (Business), employee size difference (Staff), and asset size difference (Asset).

4.3. Models and Methods

Both LTE and DTE are non-negative count variables, which typically require Poisson or Negative Binomial regression. However, both exhibit over-dispersion (variance > mean), violating the Poisson assumption

[42]

. Therefore, we use the Negative Binomial model. Moreover, CVC research often encounters small sample sizes, potentially reducing estimation efficiency. Bayesian estimation improves small-sample stability, prevents overfitting, and allows iterative updating. Therefore, we adopt the Bayesian Negative Binomial regression model.

5. Results

5.1. Descriptive Statistics and Correlations

Table 1 presents the descriptive statistics of the variables. The mean of local technological exploration (LTE) is 5.452 with a standard deviation of 10.650; the mean of distant technological exploration (DTE) is 16.235 with a standard deviation of 20.364. In both cases, the standard deviation exceeds the mean substantially, indicating pronounced over-dispersion. Therefore, a Bayesian Negative Binomial model is appropriate.

Table 1. Descriptive Statistics of Main Variables.

Variable	Obs	Min	Max	Mean	Std. Dev.
TP_ij	42	0	0.877	0.178	0.238
LTE	42	0	62	5.452	10.650
DTE	42	0	115	16.238	20.364
Den	42	0.015	0.734	0.188	0.226
Cen	42	0	0.972	0.354	0.245
Input	42	0.083	7.586	1.254	1.490
Patent_i	42	2	195	34.024	36.175
Patent_j	42	0	40 346	3 286.357	7 078.262
IPC_i	42	1	58	12.833	10.587
IPC_j	42	0	208	55.310	62.554
Region	42	0	1	0.381	0.492
Business	42	0.087	0.568	0.204	0.096
Staff	42	25	9 925	1 243.214	2 656.605
Asset	42	2.965	32 599.780	1 282.179	4 979.897

We also calculate correlations. CVC technological proximity shows significant positive correlations with both local and distant technological exploration (r = 0.309, p < 0.05; r = 0.398, p < 0.05). LTE and DTE are also positively correlated (r = 0.541, p < 0.05). Correlations between inventor network density, centralization, and other variables are generally non-significant. Variance Inflation Factors (VIFs) are all below 5, with the maximum at 1.2, indicating no multicollinearity concerns.

5.2. Regression Results

Table 2. Heterogeneous effects of CVC technological proximity on LTE and DTE.

Variable	LTE			DTE
Variable	Model 1	Model 2	Model 3	Model 4	Model 5
TP_ij		7.711***	10.55***		2.075***
TP_ij²			-3.193***
Controls	Y	Y	Y	Y	Y
Obs	42	42	42	42	42
Log likelihood	-196.7***	-201.4***	-210.0***	-275.2***	-289.3***

Note: *, **, ***, indicate significance at 5%, 1%, and 0.1% respectively.

Table 3. Moderating effects of inventor networks.

Variable	LTE		DTE
Variable	Model 6	Model 7	Model 8	Model 9
TP_ij	2.055***	5.141***	2.863***	2.912***
TP_ij²	11.96***	3.339***
Den	-6.310***		-1.353***
Den×TP_ij	94.90***		-5.973***
Den×TP_ij²	-180.1***
Cen		0.437**		0.5715***
Cen×TP_ij		24.02***		-2.326***
Cen×P_ij²		-29.76***
Controls	Y	Y	Y	Y
Obs	42	42	42	42
Log likelihood	-252.685***	-246.262***	-311.823***	-315.226***

Note: *, **, ***, indicate significance at 5%, 1%, and 0.1% respectively.

Tables 2 and 3 present the Bayesian Negative Binomial regression results. Models 1–3 display the results of regressions on LTE, and Models 4–5 display the results of regressions on DTE. Model 2 and Model 3 include technological proximity and its squared term. For LTE, technological proximity has a significant positive linear term (β = 10.55, p < 0.001) and a significant negative quadratic term (β = −3.193, p < 0.001), confirming an inverted U-shaped effect. Thus, H1 is supported. For DTE, TP has a significant positive effect (β = 2.075, p < 0.001). Thus, H2 is supported.

Regarding the moderating role of network density (Table 3), the product term Den × TP is positive and significant (β = 94.90, p < 0.001), and Den × TP² is negative and significant on LTE (β = −180.1, p < 0.001). This indicates that high network density strengthens the inverted U-shaped effect of technological proximity on LTE. Thus, H3 is supported. Model 8 suggests that network density significantly weakens the positive relationship between TP and DTE (β = −5.973, p < 0.001). Thus, H4 is supported. Plots (Figure 1a, 1b) illustrate these moderation patterns.

Regarding the moderating role of network centralization, the product term Den × TP is positive and significant (β = 24.02, p < 0.001), and Den × TP² is negative and significant on DTE (β = −29.76, p < 0.001). This indicates that high network centralization strengthens the inverted U-shaped effect of technological proximity on LTE. Thus, H5 is supported. Model 8 suggests that network centralization significantly weakens the positive relationship between TP and DTE (β = −2.326, p < 0.001). Thus, H6 is supported. Plots (Figure 1c, 1d) illustrate these moderation patterns.

Download: Download full-size image

Figure 1. Moderation effects.

5.3. Robustness Tests

To verify robustness, we replace the dependent variables with the number of new IPC subclasses, instead of the number of patents containing new IPC subclasses. Table 4 shows that all main effects and moderating effects remain significant and consistent with baseline results.

Table 4. Regression with Alternative Dependent Variables.

Variable	LTE			DTE
Variable	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
TP_ij	3.8***	0.3***	-3.3***	1.2***	1.5***	1.9***
TP_ij²	-0.8***	3.0***	11.5***
Den		-4.7***			-2.1***
Den×TP_ij		66.3***			-3.1***
Den×TP_ij²		-128.9***				1.3***
Cen			0.2***
Cen×TP_ij			27.2***			-2.4***
Cen×TP_ij²			-33.9***
Controls
Log likelihood	-199.0***	-234.5***	-241.6***	-251.7***	-273.7***	-278.0***

Note: *, **, ***, indicate significance at 5%, 1%, and 0.1% respectively.

6. Conclusions and Implications

Using multi-source matched data (CVC, patent, financial) from 42 STAR Market firms (2010–2020), this study employs Bayesian Negative Binomial regression to test the heterogeneous effects of CVC technological proximity on entrepreneurial firms’ local–distant exploration and examines boundary conditions shaped by internal inventor networks.

6.1. Main Findings

Technological proximity has an inverted U-shaped effect on local technological exploration. Moderate proximity maximizes local exploration, while excessive similarity reduces recombination opportunities. Technological proximity positively influences distant technological exploration. Unlike local search, distant exploration does not suffer from diminishing returns at high technological proximity. Internal inventor network density and centralization magnify the inverted U-shaped relationship between proximity and local exploration. Both network density and centralization weaken the positive effect of proximity on distant exploration.

6.2. Theoretical Contributions

There are three points of theoretical contributions. First, this study extends CVC research from the investor perspective to the entrepreneurial firm perspective. This enriches understanding of how technological proximity affects the innovation behavior of investee firms. Second, this study differentiates between local and distant technological exploration. Prior studies often treat technological exploration as homogeneous, leading to inconsistent conclusions. Third, this study introduces internal inventor networks as boundary conditions in CVC research. These Findings highlight the importance of internal absorptive structures in shaping external knowledge benefits.

6.3. Managerial Implications

Regarding CVC partner selection, moderate proximity enhances mutual understanding and communication, while excessive proximity reduces innovation novelty. Entrepreneurial firms should consider technological proximity when selecting CVC investors. Managers must choose partners based on whether the strategic goal is local or distant exploration.

Regarding internal inventor network configuration. High-density and high-centralization networks benefit local exploration under low proximity. However, low-density, decentralized structures better promote distant exploration and external knowledge absorption. Managers should encourage cross-team inventor collaboration, avoid siloed or clique-like R&D groups and create rotational and cross-departmental exchange mechanisms to enhance knowledge integration.

Abbreviations

CVC	Corporate Venture Capital

Author Contributions

Changlin Zhu: Data curation, Resources, Writing – original draft, Writing – review & editing

Guiyang Zhang: Conceptualization, Methodology, Writing – original draft, Writing – review & editing

Funding

This work is supported by 2025 Jiangsu Provincial Education Science Planning Project (Grant No.B/2025/01/21).

Data Availability Statement

The data is available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Wang W, Ma H. Technology Proximity, R&D Investment and Innovation Performance in Technology Mergers and Acquisitions: From a Perspective of Corporate Life Cycle [J]. Business Review, 2020, 32(06): 104-113. https://doi.org/10.14120/j.cnki.cn11-5057/f.20191127.008
[2]	Yang Y, Narayanan V K, Zahra S. Developing the selection and valuation capabilities through learning: The case of corporate venture capital [J]. Journal of Business Venturing, 2009, 24(3): 261-273. https://doi.org/10.1016/j.jbusvent.2008.05.001
[3]	Wan K, Lu W. Corporate Venture Capital and Firm Technology Innovation: The Moderating Effect of Absorptive Capacity, Involvement Intensity and CVC Program Government Structure [J]. Science of Science and Management of S. &. T., 2014, 35(11): 117-128. https://doi.org/10.20201/j.cnki.ssstm.2014.11.012
[4]	Orlando M J. Measuring Spillovers from Industrial R&D: On the Importance of Geographic and Technological Proximity [J]. Rand Journal of Economics, 2004, 35(4): 777-786. https://doi.org/10.2307/1593773
[5]	Thormas J C, Elena L, Xuan T. Corporate Venture Capital, Value Creation, and Innovation [J]. Review of Financial Studies, 2014, 27(8): 2434-2473. https://doi.org/10.2139/ssrn.1364213
[6]	Dushnik G, Yu L, Lyu J. Corporate Venture Capital: A Literature Review and Research Agenda [J]. Management World, 2021, 37(07): 198-216+14+18-25. https://doi.org/10.19744/j.cnki.11-1235/f.2021.0101
[7]	Jakobsen S, Steinmo M. The Role of Proximity Dimensions in the Development of Innovations in Coopetition: A Longitudinal Case Study [J]. International Journal of Technology Management, 2016, 71(2): 100-122. https://doi.org/10.1504/IJTM.2016.077976
[8]	Tian X, Xu X. The Spatial Effects and Structural Effects of Innovating Production [J]. Business Review, 2018, 30(3): 95-101. https://doi.org/10.14120/j.cnki.cn11-5057/f.2018.03.009
[9]	Guan J, Yan Y. Technological proximity and recombinative innovation in the alternative energy field [J]. Research Policy, 2016, 45(7): 1460-1473. https://doi.org/10.1016/j.respol.2016.04.008
[10]	Chu D, Hu Z. The Impact of Proximities on Scientific Research Collaboration: Evidence from Biotechnological Field in Guangdong Province [J]. Journal of Beijing Institute of Technology (Social Science Edition), 2016, 18(5): 67-74.
[11]	Cao X, Song C. Impact of Geographical Proximity and Cognitive Proximity on Ambidextrous Innovation of Technological Innovation Network [J]. China Soft Science, 2017(04): 120-131.
[12]	Xia L, Xie F, Wang H. The impact of institutional proximity and technological proximity on industry-university collaborative innovation performance: An analysis of joint-patent data [J]. Studies in Science of Science, 2017, 35(5): 782-791. https://doi.org/10.16192/j.cnki.1003-2053.2017.05.017
[13]	Bart N, Wim V H, Geert D, Victor G, Advanden O. Optimal Cognitive Distance and Absorptive Capacity [J]. Research Policy, 2007, 36(7) 1016-1034. https://doi.org/10.1016/j.respol.2007.04.003
[14]	Cohen W M, Levinthal D A. Absorptive capacity: A new perspective on learning and innovation [J]. Administrative Science Quarterly, 1990, 35(1), 128-152. https://doi.org/10.2307/2393553
[15]	Hu L, Randel A E. Knowledge Sharing in Teams: Social Capital, Extrinsic Incentives, and Team Innovation [J]. Group & Organization Management, 2014, 39(2): 213-243. https://doi.org/10.1177/1059601114520969
[16]	Sun Y, Liu J, Ding Y. Analysis of the relationship between open innovation, knowledge management capability and dual innovation [J]. Technology Analysis and Strategic Management, 2022, 32(1): 15-28. https://doi.org/10.1080/09537325.2019.1624704
[17]	Wan K, Lu W. Entrepreneurial enterprises' knowledge heterogeneity and corporate investors' knowledge creation [J]. Science Research Management, 2016, 37(02): 9-19. https://doi.org/10.19571/j.cnki.1000-2995.2016.02.002
[18]	Xia L, Xie F. A Literature Review of Collaborative Innovation from a Perspective of Multi-dimensional Proximity [J]. Foreign Economies and Management, 2014, 36(11): 45-54. https://doi.org/10.16538/j.cnki.fem.2014.11.006
[19]	Shum P, Lin G. A world class new product development best practices model [J]. International Journal of Production Research, 2007, 45(7): 1609-1629. https://doi.org/10.1080/00207540600943925
[20]	Margaret L S, Nathaniel N H. Impact of subsidiaries' cross-border knowledge tacitness shared and social capital on MNCs' explorative and exploitative innovation capability [J]. Journal of International Management, 2019, 25(4): 100-105. https://doi.org/10.1016/j.intman.2019.04.002
[21]	Zhang X, Lu X, Huang X. Regional Collaborative Innovation Network Structure Based on the Triple Helix Theory [J]. Forum on Science and Technology in China, 2016(11): 82-88. https://doi.org/10.13580/j.cnki.fstc.2016.11.013
[22]	Phelps C. A Longitudinal Study of the Influence of Alliance Network Structure and Composition on Firm Exploratory Innovation [J]. Academy of Management Journal, 2010, 53(4): 890-913. https://doi.org/10.5465/amj.2010.52814627
[23]	Schilling M, Phelps C. Interfirm Collaboration Networks: The Impact of Large-Scale Network Structure on Firm Innovation [J]. Management Science, 2007, 53(7): 1113-1126. https://doi.org/10.1287/mnsc.1060.0624
[24]	Leenders R, Gabbay S M. Corporate Social Capital and Liability [J]. Springer Science & Business Media, 2013, 22(3): 1-14. https://doi.org/10.1007/978-1-4615-5027-3
[25]	Gilsing V, Nooteboom B, Vanhaverbeke W, et al. Network embeddedness and the exploration of novel technologies: Technological distance, betweenness centrality and density [J]. Research Policy, 2008, 37(10): 1717-1731. https://doi.org/10.1016/j.respol.2008.08.010
[26]	Choi E, Lee K C. Relationship between social network structure dynamics and innovation: Micro-level analyses of virtual cross-functional teams in a multinational B2B firm [J]. Computers in Human Behavior, 2016, 65(DEC.): 151-162. https://doi.org/10.1016/j.chb.2016.07.045
[27]	Zheng Y, Yang H. Does Familiarity Foster Innovation? The Impact of Alliance Partner Repeatedness on Breakthrough Innovations [J]. Journal of Management Studies, 2015, 52(2): 213-230. https://doi.org/10.1111/joms.12112
[28]	Ahuja G. The duality of collaboration: inducements and opportunities in the formation of inter-firm linkages [J]. Strategic Management Journal, 2000, 21(3): 317-343. https://doi.org/10.1002/(SICI)1097-0266(200003)21:3<317::AID-SMJ90>3.0.CO;2-B
[29]	Macher J T, Boerner C. Technological development at the boundaries of the firm: a knowledge-based examination in drug development [J]. Strategic Management Journal, 2012, 33(9): 1016-1036. https://doi.org/10.1002/smj.1965
[30]	Zeng D, Zhang D, Wen J. A Study of the Impact of Network Technological Diversity on Exploratory Innovation Based on Patent Cooperation: The Moderating Role of Network Structure [J]. Journal of Intelligence, 2015, 34(02): 104-110. https://doi.org/10.3969/j.issn.1002-1965.2015.02.019
[31]	Zheng X. Can the Centralization of Intra-Group Knowledge Network Improve Group's Capability in Product Innovation: Based on Knowledge Network Among Business Group Members [J]. R&D MANAGEMENT, 2019, 31(06): 104-114. https://doi.org/10.13581/j.cnki.rdm.20190820.002
[32]	Fleming L. Science as a Map in Technological Search [J]. Strategic Management Journal, 2004, 25(8): 909-928. https://doi.org/10.1002/smj.384
[33]	Belderbos R, Faems D, Leten B, et al. Technological activities and their impact on the financial performance of the firm: exploitation and exploration within and between firms [J]. Journal of Product Innovation Management, 2010, 27(6): 869-882. https://doi.org/10.1111/j.1540-5885.2010.00757.x
[34]	Guan J, Liu N. Invention profiles and uneven growth in the field of emerging nano-energy [J]. Energy Policy, 2015, 76(1): 146-157.
[35]	Zhang G, Tang C. How could firm's internal R & D collaboration bring more innovation? Technological Forecasting and Social Change, 2017, 12(125): 299-308.
[36]	Zhang G, Tang C. How R&D partner diversity influences innovation performance: an empirical study in the nano-biopharmaceutical field? Scientometrics, 2018, 116(3): 1487-1512. https://doi.org/10.1007/s11192-018-2810-7
[37]	Zhang G, Tang C. How the egocentric alliance network impacts firm ambidextrous innovation: a three-way interaction model [J]. European Journal of Innovation Management, 2022, 25(1), pp. 19-38. https://doi.org/10.1108/EJIM-07-2020-0295
[38]	Zhang G, Tang C. The influences of characteristics of three intrafirm networks on firm exploitative and exploratory innovation. International Journal of Technology Management, 2020, 83(4): 205-227. https://doi.org/10.1504/IJTM.2020.110338
[39]	Wang C, et al. Research on the Change and Influencing Factors of Key Inventors Cooperation Network of Merger and Acquisition Enterprises [J]. Science & Technology Progress and Policy, 2019, 36(10): 66-75. https://doi.org/10.6049/kjjbydc.2018050264
[40]	Adam B J. Technological Opportunity and Spillovers of R & D: Evidence from Firms' Patents, Profits, and Market Value [J]. The American Economic Review, 1986, 76(5): 984-1001. https://doi.org/10.2307/1816464
[41]	Reinholt M, Pedersen T, Foss N J. Why a central network position isn’t enough: the role of motivation and ability for knowledge-sharing in employee networks [J]. Strategic Management Journal, 2011, 54(6): 1277-1297. https://doi.org/10.5465/amj.2009.0007
[42]	Hausman J A, Hall B H, Griliches Z. Econometric Models for Count Data with an Application to the Patents-R&D Relationship [J]. Social Science Electronic Publishing. 1984, 52(4): 909-938. https://doi.org/10.2307/1913191

Cite This Article

Plain Text BibTeX RIS

APA Style

Zhu, C., Zhang, G. (2026). Technology Proximity of CVC Parties and Local-distant Dual Technology Exploration of the Venture Enterprise: Differential Effects and Boundary Conditions. Innovation, 7(1), 11-20. https://doi.org/10.11648/j.innov.20260701.12

Copy | Download

ACS Style

Zhu, C.; Zhang, G. Technology Proximity of CVC Parties and Local-distant Dual Technology Exploration of the Venture Enterprise: Differential Effects and Boundary Conditions. Innovation. 2026, 7(1), 11-20. doi: 10.11648/j.innov.20260701.12

Copy | Download

AMA Style

Zhu C, Zhang G. Technology Proximity of CVC Parties and Local-distant Dual Technology Exploration of the Venture Enterprise: Differential Effects and Boundary Conditions. Innovation. 2026;7(1):11-20. doi: 10.11648/j.innov.20260701.12

Copy | Download

@article{10.11648/j.innov.20260701.12,
  author = {Changlin Zhu and Guiyang Zhang},
  title = {Technology Proximity of CVC Parties and Local-distant Dual Technology Exploration of the Venture Enterprise: Differential Effects and Boundary Conditions},
  journal = {Innovation},
  volume = {7},
  number = {1},
  pages = {11-20},
  doi = {10.11648/j.innov.20260701.12},
  url = {https://doi.org/10.11648/j.innov.20260701.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.innov.20260701.12},
  abstract = {As an emerging form of strategic investment, Corporate Venture Capital (CVC) and its consequences on venture enterprises’ technology exploration have received more and more attention from scholars, especially in the context of open innovation that highlights interorganizational collaborative and external knowledge acquisition. To solve the inconsistency of existing research conclusions on the relationship between CVC and enterprise innovation, this research tries to explore the differential impacts of technological proximity on dual technological exploration of the venture enterprise from the knowledge-based view and the local-distant knowledge search theory. Based on multi-source matching data, including CVC records, patent, and financial information from 2010 to 2020 of 42 companies that are quoted on the Science and Technology Innovation Board, this paper conducts the Bayes Negative Binomial regression model for empirical analysis. The results show that technology proximity of CVC parties has an inverted U-shaped effect on local technological exploration of the venture enterprise. Both the density and degree centralization of inventor cooperative network of the venture enterprise positively moderate the main effect. Meanwhile, technology proximity of CVC parties has a positive effect on distant technological exploration of the venture enterprise. Both the density and degree centralization of inventor cooperative network of the venture enterprise negatively moderate the main effect. This study enriches the research perspective of CVC from the investor side to the venture enterprise side, clarifies the boundary conditions of the impact of technological proximity, and provides important theoretical guidance and practical reference for venture enterprises to select CVC partners and optimize internal inventor network structure.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Technology Proximity of CVC Parties and Local-distant Dual Technology Exploration of the Venture Enterprise: Differential Effects and Boundary Conditions
AU  - Changlin Zhu
AU  - Guiyang Zhang
Y1  - 2026/04/02
PY  - 2026
N1  - https://doi.org/10.11648/j.innov.20260701.12
DO  - 10.11648/j.innov.20260701.12
T2  - Innovation
JF  - Innovation
JO  - Innovation
SP  - 11
EP  - 20
PB  - Science Publishing Group
SN  - 2994-7138
UR  - https://doi.org/10.11648/j.innov.20260701.12
AB  - As an emerging form of strategic investment, Corporate Venture Capital (CVC) and its consequences on venture enterprises’ technology exploration have received more and more attention from scholars, especially in the context of open innovation that highlights interorganizational collaborative and external knowledge acquisition. To solve the inconsistency of existing research conclusions on the relationship between CVC and enterprise innovation, this research tries to explore the differential impacts of technological proximity on dual technological exploration of the venture enterprise from the knowledge-based view and the local-distant knowledge search theory. Based on multi-source matching data, including CVC records, patent, and financial information from 2010 to 2020 of 42 companies that are quoted on the Science and Technology Innovation Board, this paper conducts the Bayes Negative Binomial regression model for empirical analysis. The results show that technology proximity of CVC parties has an inverted U-shaped effect on local technological exploration of the venture enterprise. Both the density and degree centralization of inventor cooperative network of the venture enterprise positively moderate the main effect. Meanwhile, technology proximity of CVC parties has a positive effect on distant technological exploration of the venture enterprise. Both the density and degree centralization of inventor cooperative network of the venture enterprise negatively moderate the main effect. This study enriches the research perspective of CVC from the investor side to the venture enterprise side, clarifies the boundary conditions of the impact of technological proximity, and provides important theoretical guidance and practical reference for venture enterprises to select CVC partners and optimize internal inventor network structure.
VL  - 7
IS  - 1
ER  -

Copy | Download

Author Information

Changlin Zhu

Science and Technology Research Institute, Nanjing University of Science and Technology, Nanjing, China

Contact Email
Guiyang Zhang

School of Economics and Management, Nanjing University of Science and Technology, Nanjing, China

Contact Email

http://orcid.org/0000-0002-4781-9158

Download PDF

Submit an Article

Figure 1

Figure 1. Moderation effects.

Plain Text BibTeX RIS

APA Style

Zhu, C., Zhang, G. (2026). Technology Proximity of CVC Parties and Local-distant Dual Technology Exploration of the Venture Enterprise: Differential Effects and Boundary Conditions. Innovation, 7(1), 11-20. https://doi.org/10.11648/j.innov.20260701.12

Copy | Download

ACS Style

Zhu, C.; Zhang, G. Technology Proximity of CVC Parties and Local-distant Dual Technology Exploration of the Venture Enterprise: Differential Effects and Boundary Conditions. Innovation. 2026, 7(1), 11-20. doi: 10.11648/j.innov.20260701.12

Copy | Download

AMA Style

Zhu C, Zhang G. Technology Proximity of CVC Parties and Local-distant Dual Technology Exploration of the Venture Enterprise: Differential Effects and Boundary Conditions. Innovation. 2026;7(1):11-20. doi: 10.11648/j.innov.20260701.12

Copy | Download

@article{10.11648/j.innov.20260701.12,
  author = {Changlin Zhu and Guiyang Zhang},
  title = {Technology Proximity of CVC Parties and Local-distant Dual Technology Exploration of the Venture Enterprise: Differential Effects and Boundary Conditions},
  journal = {Innovation},
  volume = {7},
  number = {1},
  pages = {11-20},
  doi = {10.11648/j.innov.20260701.12},
  url = {https://doi.org/10.11648/j.innov.20260701.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.innov.20260701.12},
  abstract = {As an emerging form of strategic investment, Corporate Venture Capital (CVC) and its consequences on venture enterprises’ technology exploration have received more and more attention from scholars, especially in the context of open innovation that highlights interorganizational collaborative and external knowledge acquisition. To solve the inconsistency of existing research conclusions on the relationship between CVC and enterprise innovation, this research tries to explore the differential impacts of technological proximity on dual technological exploration of the venture enterprise from the knowledge-based view and the local-distant knowledge search theory. Based on multi-source matching data, including CVC records, patent, and financial information from 2010 to 2020 of 42 companies that are quoted on the Science and Technology Innovation Board, this paper conducts the Bayes Negative Binomial regression model for empirical analysis. The results show that technology proximity of CVC parties has an inverted U-shaped effect on local technological exploration of the venture enterprise. Both the density and degree centralization of inventor cooperative network of the venture enterprise positively moderate the main effect. Meanwhile, technology proximity of CVC parties has a positive effect on distant technological exploration of the venture enterprise. Both the density and degree centralization of inventor cooperative network of the venture enterprise negatively moderate the main effect. This study enriches the research perspective of CVC from the investor side to the venture enterprise side, clarifies the boundary conditions of the impact of technological proximity, and provides important theoretical guidance and practical reference for venture enterprises to select CVC partners and optimize internal inventor network structure.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Technology Proximity of CVC Parties and Local-distant Dual Technology Exploration of the Venture Enterprise: Differential Effects and Boundary Conditions
AU  - Changlin Zhu
AU  - Guiyang Zhang
Y1  - 2026/04/02
PY  - 2026
N1  - https://doi.org/10.11648/j.innov.20260701.12
DO  - 10.11648/j.innov.20260701.12
T2  - Innovation
JF  - Innovation
JO  - Innovation
SP  - 11
EP  - 20
PB  - Science Publishing Group
SN  - 2994-7138
UR  - https://doi.org/10.11648/j.innov.20260701.12
AB  - As an emerging form of strategic investment, Corporate Venture Capital (CVC) and its consequences on venture enterprises’ technology exploration have received more and more attention from scholars, especially in the context of open innovation that highlights interorganizational collaborative and external knowledge acquisition. To solve the inconsistency of existing research conclusions on the relationship between CVC and enterprise innovation, this research tries to explore the differential impacts of technological proximity on dual technological exploration of the venture enterprise from the knowledge-based view and the local-distant knowledge search theory. Based on multi-source matching data, including CVC records, patent, and financial information from 2010 to 2020 of 42 companies that are quoted on the Science and Technology Innovation Board, this paper conducts the Bayes Negative Binomial regression model for empirical analysis. The results show that technology proximity of CVC parties has an inverted U-shaped effect on local technological exploration of the venture enterprise. Both the density and degree centralization of inventor cooperative network of the venture enterprise positively moderate the main effect. Meanwhile, technology proximity of CVC parties has a positive effect on distant technological exploration of the venture enterprise. Both the density and degree centralization of inventor cooperative network of the venture enterprise negatively moderate the main effect. This study enriches the research perspective of CVC from the investor side to the venture enterprise side, clarifies the boundary conditions of the impact of technological proximity, and provides important theoretical guidance and practical reference for venture enterprises to select CVC partners and optimize internal inventor network structure.
VL  - 7
IS  - 1
ER  -

Copy | Download