Ambidextrous Learning,Resource Acquisition,and Venture Performance: The Moderating Effect of the Entrepreneurial Ecosystem Network Relationships

Abstract

The entrepreneurial ecosystem (EE) has always been a current research hotspot. However, there is currently limited empirical research at the micro-level of EE. Based on organizational learning theory, this study constructs a model integrating the ambidextrous learning, resource acquisition, venture performance, and EE network relationships of new ventures under EE. Based on survey data of 193 new ventures in EE in Hangzhou, China, our results illustrate that (1) the ambidextrous learning of new ventures in EE positively affects venture performance through resource acquisition and (2) the EE network relationships have a negative moderating effect between ambidextrous learning and resource acquisition. We contribute to research on the bidirectional connection between the entrepreneurial activities of new ventures and EE from the perspectives of ambidextrous learning and EE network relationships, organizational learning theory, and micro-foundations of EE.

Keywords

ambidextrous learning resource acquisition venture performance entrepreneurial ecosystem network relationships new ventures

Introduction

Nowadays, digital technologies and factors are flowing and updating quickly. New ventures of various types and industries increasingly need ambidextrous learning, defined as the behavior of ventures engaged in exploratory and exploitative learning (C. Y. Lee et al., 2013; March, 1991; Yi et al., 2022). Ambidextrous learning is conducive to the survival and development of new ventures and improves their competitiveness (Hu et al., 2023). For example, ambidextrous learning can improve the ecological innovation performance of new ventures by acquiring and accumulating knowledge (Huang et al., 2020). To achieve sustainable long-term development, ventures must improve performance through continuous ambidextrous learning (Boso et al., 2019; Su et al., 2022). According to organizational learning theory, the core issues of ambidextrous learning in entrepreneurship include creating and developing new ventures in discovering and exploring entrepreneurial opportunities and the specific process of learning (C. L. Wang & Chugh, 2014). Simply put, ambidextrous learning (exploratory and exploitative learning) is a crucial type of learning for understanding what and how new ventures learn in exploring and developing entrepreneurial opportunities (C. L. Wang & Chugh, 2014), and it is also the foundation for understanding the entrepreneurial process.

Resource acquisition is an appropriate pathway analyzing ambidextrous learning and venture performance (Greve, 2011). Newly established ventures need considerable resources in their survival and development stages (Reypens et al., 2021). We thus assume that ambidextrous learning could affect venture resource acquisition (i.e., human, organizational, market, intellectual, and physical resources; Hertel et al., 2021), thus affecting venture performance.

In addition, the entrepreneurial ecosystem (EE) is a meaningful entrepreneurial context that cannot be ignored in ambidextrous learning. However, existing EE literature mainly focuses on macro-level research, such as the element composition, evolution, development (E. Stam & Van de Ven, 2021; Wurth et al., 2022), and governance of EE, which weakens the connection between new ventures and EEs, making it difficult to provide targeted policy recommendations for developing new ventures. Wurth et al. (2022) and Roundy and Lyons (2022) have actively called for research on the micro-level of EE, especially the interaction between new ventures and EE. Increasingly, new ventures are choosing to land in the EE as the ecosystem activates an influential network: the EE network relationships, which connect new ventures, universities, intermediaries, financial institutions, and so on (Autio et al., 2018; E. Stam & Van de Ven, 2021), forming a set of entrepreneurial opportunities. New ventures embedded in EE network relationships need first to gain legitimacy in EE to truly gain opportunities and resources (Kuratko et al., 2017). Legitimacy in EE network relationships mainly refers to the fact that the behavioral norms of new ventures must comply with the social norms of EE and the public order of EE (Kuratko et al., 2017). Therefore, the promoting effect of ambidextrous learning on resource acquisition could also be influenced by EE network relationships.

This leads to the following research questions: (1) what role does resource acquisition play in the relationship between ambidextrous learning and venture performance and (2) how does the EE network relationship affect this relationship? It is essential to answer these questions for several reasons. First, we need to understand the relationship between ambidextrous learning and venture performance, which is an essential foundation for the application of organizational learning theory in the field of entrepreneurship and can promote our theoretical understanding of ambidextrous learning for new ventures in EE (Clark et al., 2021; Rippa et al., 2019). Second, focusing on resource acquisition will help reveal the impact mechanism of the ambidextrous learning of new ventures on venture performance (see Cai et al. [2014] for a more detailed understanding of the role and path of the ambidextrous learning of new ventures). Finally, focusing on the impact of EE network relationships on the relationship between ambidextrous learning and resource acquisition in new ventures can help promote micro-level EE research and enrich the research context of organizational learning theory (Cunningham et al., 2019; Roundy & Lyons, 2022).

To answer our research questions, we focused on new ventures established in the park for less than 8 years and operated in the venture park (Z. Cao & Shi, 2021; T. Wang et al., 2017). Based on the ambidextrous learning of new ventures under EE, we proposed relevant hypotheses and analyzed the survey results of 193 new ventures in EE in Hangzhou, China. This study makes three main contributions to the literature. First, we explore the impact of the EE network relationships on ambidextrous learning and resource acquisition, which enriches organizational learning theory (Argote et al., 2021). Second, we study the role of ambidextrous learning through resource acquisition (Ge et al., 2016) and elaborate on how ambidextrous learning affects venture performance through resource acquisition based on the potential mechanism of opportunity identification. Finally, by revealing whether EE network relationships can moderate the relationship between ambidextrous learning and resource acquisition in new ventures, we provide relevant insights for subsequent micro research on EE and respond to the call of Roundy and Lyons (2022) for micro-level research on EE.

Theoretical Background and Hypotheses Development

Organization Learning Theory and Ambidextrous Learning

According to organizational learning theory, organizational learning refers to an organization’s ability to process knowledge, characterized by continuity, periodicity, and dynamism (Cangelosi & Dill, 1965). Continuity indicates that organizational learning is a continuous process that interacts and connects with the organization’s past, present, and future (Hernes & Irgens, 2013). Periodicity indicates that organizational learning could result in different learning tasks and goals depending on the developmental stage of the organization. Dynamism indicates that organizational learning needs to continuously adjust learning modes according to changes in the external environment to achieve fast and efficient learning (Santoro et al., 2019). Subsequently, Duncan (1976) introduced the concept of “ambidexterity” into the field of management, using ambidexterity to describe organizational capabilities, as new ventures need to operate effectively on existing resources and adapt to future changes. Only by possessing this ambidexterity ability can they adapt to increasingly dynamic and complex environments (Luger et al., 2018).

March (1991) proposed the concept of “ambidextrous learning” and proposed that ambidextrous learning includes exploratory learning and exploitative learning. In entrepreneurship, the core issue of ambidextrous learning includes the creation and development of new ventures in exploring and exploiting entrepreneurial opportunities but also the specific process of learning (Argote et al., 2021; C. L. Wang & Chugh, 2014). According to organizational learning theory, ambidextrous learning helps new ventures maintain competitiveness in dynamic environments (Camps et al., 2016). Referring to Shao et al. (2022) and Ali (2021), we divide ambidextrous learning into exploratory and exploitative learning. Levinthal and March (1993, p. 105) condensed it into “the pursuit of new knowledge” and “the use and development of things already known.” New ventures can use exploratory learning to discover external new opportunities and drive their future development in the process of realizing these opportunities (C. Wang & Zhang, 2020). Exploitative learning can also be used to fully explore internal opportunities and stabilize the existing development of the new venture while deeply exploiting internal opportunities (C. Wang & Zhang, 2020). Overall, how and when ambidextrous learning occurs is the foundation for understanding the survival and development of new ventures (C. L. Wang & Chugh, 2014).

Ambidextrous Learning and Venture Performance

Lumpkin (2005) introduced a creativity-based model of opportunity recognition and identified three ambidextrous learning approaches: behavioral, cognitive, and action. Therefore, new ventures must learn to discover, identify, and apply these opportunities to improve performance (Lumpkin, 2005).

According to organizational learning theory, exploratory learning is mainly reflected in learning new knowledge, which can expand the learning breadth of new ventures. The impact of exploratory learning on venture performance is mainly reflected in the following two aspects. First, exploratory learning enables new ventures to receive new information (Ali, 2021), understand new industry and market trends, and help them discover new entrepreneurial opportunities. With the rapid changes in the market and technology, most new ventures have gained cognitive methods to improve performance through exploratory learning to recognize and understand new knowledge and discover new opportunities (Cai et al., 2017). Second, effective exploratory learning promotes new ventures to learn new knowledge (Reyt & Wiesenfeld, 2015), improve innovation enthusiasm, and better implement new entrepreneurial opportunities. When exploring the phenomenon of high-tech innovation in emerging market international new ventures, Buccieri et al. (2023) found that exploratory learning helps new ventures create new products and business models, promoting international performance. Overall, new ventures can discover and exploit new entrepreneurial opportunities in exploratory learning and improve their performance through innovative products that meet new customer needs (Huang et al., 2020). Consequently, we formulate our first hypothesis:

Hypothesis 1a (H1a): Exploratory learning is positively related to venture performance.

Exploitative learning is mainly reflected in re-learning existing knowledge, which can expand the learning depth of new ventures. The impact of exploitative learning on venture performance is mainly reflected in two aspects. Exploitative learning deepens the understanding of existing industries and markets by re-mining existing knowledge (Yi et al., 2022), which can help new ventures discover existing entrepreneurial opportunities. Contrastingly, exploitative learning enhances a deep understanding of existing knowledge in ventures (Seo & Park, 2022), guiding them to make minor improvements and adjustments to existing markets and helping them continue to seize existing entrepreneurial opportunities (Westerlund & Rajala, 2010). Ali (2021) believes exploitative learning mainly involves identifying existing knowledge, updating the knowledge stock, seizing opportunities, and developing products without changing the essence of existing knowledge. Shao et al. (2022) believe that the exploitative learning of digital startups is reflected in a deep understanding of the existing functions of digital technology, which can promote the identification and grasp of digital innovation opportunities (Schönherr et al., 2023), promote product development, and improve venture performance. Overall, new ventures can discover and seize existing entrepreneurial opportunities in exploitative learning (J. Yang & Zhang, 2022) and improve their performance through standardized production and effective promotion. Accordingly, we propose the following hypothesis:

Hypothesis 1b (H1b): Exploitative learning is positively related to venture performance

The Mediating Effect of Resource Acquisition

Obtaining outside venture resources has been as a critical entrepreneurial task (Kim & Jin, 2017). As the core of entrepreneurship (Peterson & Wu, 2021), learning is crucial to opportunity recognition and resource acquisition (Chen & Liu, 2020; Ribeiro et al., 2021). Compared with established firms, the ambidextrous learning of new ventures significantly impacts resource acquisition (Huang et al., 2020). Because new ventures have only been established for a short time, existing resources generally find it difficult to meet current development, which is why ambidextrous learning can help new ventures find and identify new opportunities and achieve low-cost and efficient resource acquisition (Dushnitsky & Matusik, 2019). For example, owing to the lack of exploratory learning abilities to interact with the external environment, Intuit Company fails to timely discover and identify new opportunities and needs in the market and rebuild new resources, so they missed the best time point for sustained growth (Ge et al., 2016). Conversely, HASEE Computer accurately identifies and understands opportunities through ambidextrous learning, which helps them reasonably allocate resources internally and obtain scarce external resources timely. Consequently, HASEE Computer balances internal and external resources with low-cost strategies and achieves new venture development (Ge et al., 2016).

The research on the relationship between resource acquisition and venture performance is also well established. Compared with resource deployment in the case of innovation, resource acquisition in the case of entrepreneurship can ensure initial capitalization (Kahn, 2022), thus directly affecting venture performance. Consequently, the newly established ventures obtain sufficient resources and implement the identified opportunities, which brings profit and improves their performance (Nair et al., 2022). For instance, medical technology ventures recognize the importance of resources, actively acquire resources in the development process (Reypens et al., 2021), and flexibly adjust resources according to identified opportunities, thus improving venture performance. From the international marketing perspective, the knowledge resources acquired by international new ventures will affect marketing capabilities, thus affecting the performance of export ventures (Martin & Javalgi, 2019).

Resource acquisition mediates the relationship between ambidextrous learning and venture performance. Exploratory learning can help ventures develop new resources and obtain unique advantages (Cai et al., 2011). This helps new ventures establish a resource base, lay competitive advantages, and improve performance. Because ventures will find and identify market opportunities in new fields in exploratory learning, using opportunities requires acquiring the necessary resources. In this process, a venture will generate new demand for resources until the obtained resources truly realize market opportunities (Y. A. Zhang et al., 2022), ensuring the effectiveness of innovation. We hypothesize the following:

Hypothesis 2a (H2a): Resource acquisition mediates the positive relationship between exploratory learning and venture performance.

Contrastingly, exploitative learning can help ventures update and adjust existing resources, optimize their internal resource structure and allocation (Park et al., 2020), stabilize the existing market, and improve venture performance. Because ventures will find new opportunities in specific knowledge fields through the collision of old and new knowledge in the process of exploitative learning (J. Zhang et al., 2010), they will achieve internal resource acquisition through adjustment and optimization and improve performance through product upgrading (C. H. Lee & Chiravuri, 2019). We therefore form the following hypothesis:

Hypothesis 2b (H2b): Resource acquisition mediates the positive relationship between exploitative learning and venture performance.

The Moderating Effect of EE Network Relationships

According to organizational learning theory, ambidextrous learning is dynamic, which means that new ventures are influenced by the external environment when conducting ambidextrous learning. As new ventures choose to enter EE, scholars also pay attention to the reasons behind the phenomenon. EE has gathered many new ventures, universities, intermediaries, and financial institutions (Autio et al., 2018; E. Stam & Van de Ven, 2021), who interact with each other, forming EE network relationships. Compared to general network relationships, EE network relationships focus more on the interaction of entrepreneurial activities (Knox & Arshed, 2022) and generate a lot of entrepreneurial resources. Therefore, new ventures can more accurately obtain knowledge, information, and policies related to entrepreneurship (Audretsch & Belitski, 2021). EE network relationships are a crucial background factor that cannot be ignored in entrepreneurial activity research (Leyden et al., 2014). However, for new ventures to truly gain knowledge and information related to entrepreneurship, they must first obtain legitimacy in EE network relationships (Kuratko et al., 2017). In addition, the knowledge, information, and other resources present in EE also require new ventures to understand and digest them to apply them, and this also requires ambidextrous learning to achieve (Di Gregorio et al., 2021).

New ventures with high EE network relationships find it difficult to find better opportunities and access resources in ambidextrous learning. High EE network relationships mean new ventures have gained legitimacy in EE and can access corresponding opportunities and resources (Kuratko et al., 2017). However, obtaining legitimacy entails excessive cognitive attention, exacerbating the situation in which the resources, time, and energy of a new venture are already scarce (Tao et al., 2023; Xu et al., 2021). High EE network relationships negatively moderate the relationship between exploratory learning and resource acquisition. First, in the process of exploratory learning, ventures are highly embedded in EE network relationships, which narrows their information source channels and hinders them from obtaining accurate and comprehensive new industry and market information (Kuratko et al., 2017), leading to a decline in their ability to identify and discover entrepreneurial opportunities and a decrease in the quality of resource acquisition (Xu et al., 2021). Second, high EE network relationships could also increase the inertia and dependence of new ventures, which makes it difficult for ventures to improve their innovation enthusiasm even when engaging in exploratory learning (Wu et al., 2021), and the amount of resource acquisition decreases.

Contrastingly, high EE network relationships negatively moderate the relationship between exploitative learning and resource acquisition. First, new ventures spend too much time and effort to obtain legitimacy, and high EE network relationships make it difficult for ventures to focus on exploring the potential value of existing resources in exploitative learning, resulting in a low utilization rate of resource acquisition. Second, high EE network relationships suggest that the venture focuses on building and maintaining social relationships outside production and operation. There is still a certain distance before social relationships can be transformed into resources available to the venture (Yin et al., 2021). The exploitative learning in high EE network relationships leads to a longer cycle of resource acquisition, which could exacerbate the original resource shortage situation of the new venture. Overall, when the new venture is in a high EE network relationship, the promoting effect of ambidextrous learning on resource acquisition will be weakened. We therefore propose the following:

Hypothesis 3a (H3a): The relationship between exploratory learning and resource acquisition is negatively moderated by EE network relationships.

Hypothesis 3b (H3b): The relationship between exploitative learning and resource acquisition is negatively moderated by EE network relationships.

In Figure 1, we show how the ambidextrous learning of new ventures in EE affects resource acquisition and how they affect venture performance. In addition, EE network relationships can moderate the ambidextrous learning and resource acquisition of a venture.

Figure 1.

Theoretical model framework.

Methods

We used the survey data from Hangzhou in Zhejiang Province, China. Hangzhou provides an ideal research context. According to the 2022 Global Entrepreneurial System Report released by start-up genome, China remains a start-up powerhouse despite the strict COVID-19 restrictions. Further, Hangzhou, which ranks 36 in the overall ranking, is fourth in the knowledge category, just behind Silicon Valley, Beijing, and Shanghai. Its learning ability coincides with our research scope. Contrastingly, Hangzhou has become the cradle of high-quality and high-growth entrepreneurial companies in China (China Industry Investment Promotion Network, 2022). According to the 2022 China Emerging Cities White Paper released by the Economist Intelligence Unit, Hangzhou ranks first among the most promising emerging cities. Concurrently, Hangzhou ranked first for two consecutive years because of its strong population inflow, superior natural and cultural environment, solid industrial foundation, and active entrepreneurial atmosphere. In short, Hangzhou is an ideal background for studying ambidextrous learning, resource acquisition, venture performance, and EE network relationships.

Data Collection

We focus on new ventures in the park, which have been established for less than 8 years and operate within the entrepreneurial park (Z. Cao & Shi, 2021; T. Wang et al., 2017). Before designing the questionnaire, we reviewed relevant literature and generated a project pool to mine each constructed domain. Since the scale used for data collection was originally in English, the questionnaire was translated two-way to better adapt to the Chinese context (Zhao et al., 2022). Before the formal investigation, we piloted and pretested the survey. The pretesting consisted of two phrases: (1) Two professors in entrepreneurship, two professors in organizational behavior, and three middle and senior managers of new ventures in the park were invited for face-to-face interviews using the protocol method. Participants thought aloud while completing the questionnaire (Hunt et al., 1982); and (2) Sending the web survey to 10 managers of new ventures in the park to evaluate the survey structure, wording, and overall length. The two pretesting phases led to minor changes in our wording.

Compared to employees, middle and senior managers of new ventures have a better understanding of the overall situation of the venture (Standaert et al., 2022). Therefore, we mainly distribute questionnaires to middle and senior managers of new ventures in the park. The survey was distributed online and offline from May 10, 2021, to August 20, 2021, using a combination of online and offline questionnaires. Based on the evaluation results of the 2019 National Science and Technology Enterprise Incubator (Torch Center of the Ministry of Science and Technology, 2020), the survey questionnaire was mainly collected through the following methods. First, offline on-site visits were conducted to university-oriented and venture-oriented entrepreneurship parks in Hangzhou. Middle and senior managers of new ventures in the parks completed 72 survey questionnaires. Second, to facilitate internships at Upward Venture Capital Management Company, multiple venture park managers were contacted and invited to fill out a survey questionnaire in the form of online distribution for the middle and senior management of new ventures in the park, totaling 138 questionnaires. Owing to the impact of COVID-19, all parks strengthened pandemic control, and it was inconvenient to continue offline visits. Therefore, we adopted online distribution. Out of the 210 questionnaires collected, 193 valid questionnaires were collected, excluding three types of invalid questionnaires that had been established for more than 8 years, participants who were not middle or senior managers, and missing items in the questionnaire. Information on new ventures is shown in Table 1.

Table 1.

Sample Descriptive Statistics.

Characteristic	Range	n	%	Characteristic	Range	n	%
Venture age	<1 year	31	16.063	Venture size	<20	82	42.487
	1–3 years	55	28.497		21–50	39	20.207
	3–5 years	47	24.352		51–100	26	13.472
	5–8 years	60	31.088		>100	46	23.834
Venture industry	High-tech	66	34.197	Venture stage	Start-up	34	17.617
	Traditional manufacturing	42	21.762		Growing	90	46.632
	Trade/service	58	30.052		Scaling	44	22.798
	Others	27	13.989		Maturity	25	12.953

Variables

When developing the questionnaire, we mainly used validated constructs (see Table 2 for an overview of the questionnaire items). To ensure that the original meaning of these items was preserved, we carefully translated them into Chinese and then back-translated them into English (Chisnall, 2007; Kriauciunas et al., 2011). Moreover, we followed Chisnall’s (2007) principles of constructing web surveys, thus ensuring maximum usability for respondents and avoiding potential biases.

Table 2.

Description of the Main Variables.

Variable and properties	Statements (translated from English to Chinese) responses range from 1 (strongly disagree) to 5 (strongly agree)	Factor loading	CR	AVE	Cronbach’s alpha
Venture performance	1. Sales growth is relatively fast in the industry.	0.764	0.902	0.606	.867
	2. Employees are growing rapidly in the industry.	0.722
	3. The venture scale is growing rapidly in the industry.	0.725
	4. The main business has maintained a high market share.	0.837
	5. The profit margin remains high.	0.755
	6. The investment returns maintain their leading position in the industry.	0.858
Ambidextrous learning	Exploratory learning (CR = 0.912; AVE = 0.720; Cronbach’s alpha = .870)		0.945	0.682	.905
	1. The venture explored the knowledge and information of new technologies and products in the industry.	0.856
	2. The venture has explored new knowledge and information outside the industry.	0.837
	3. Ventures try to apply the knowledge and information of new technologies and products in the industry.	0.843
	4. Ventures try to apply new knowledge and information from outside the industry.	0.859
	Exploitative learning (CR = 0.878; AVE = 0.564; Cronbach’s alpha = .814)
	1. The venture considers and optimizes the knowledge and information for existing technology and products.	0.784
	2. The venture considers and optimizes the knowledge and information of the existing industry.	0.847
	3. The venture integrates and applies the knowledge and information optimized for the existing technology and products.	0.805
	4. The venture integrates and applies the knowledge and information optimized for the existing industry.	0.770
Resource acquisition	1. The venture has identified and acquired human resources.	0.698	0.868	0.568	.808
	2. The venture has identified and acquired organizational resources.	0.750
	3. The venture has identified and acquired market resources.	0.772
	4. The venture has identified and acquired intellectual resources.	0.770
	5. The venture has identified and acquired physical resources.	0.774
EE network relationships	1. There is frequent daily contact and communication between ventures and other ventures in the park.	0.873	0.910	0.672	.877
	2. There is frequent business participation and cooperation between ventures and other ventures in the park.	0.879
	3. There is frequent contact between ventures and other entities in the park.	0.794
	4. The venture can obtain information from other ventures in the park and give timely feedback.	0.861
	5. The venture can obtain other information from the park and give timely feedback.	0.672

Dependent Variables

To capture the venture performance, we adapted the subjective measures used in prior research (Adomako et al., 2018). Subjective measures are beneficial for evaluating the non-financial dimensions of venture performance (W. Stam & Elfring, 2008) and have been proven to be highly consistent with the performance reported by objective measures (Poon et al., 2006). Respondents were asked to give scores from 1 to 5 on the six items of venture performance.

Independent Variables

To capture the influence of ambidextrous learning, we divided it into exploratory and exploitative learning (March, 1991). As this study focuses on ambidextrous learning at the venture level, it mainly refers to the scales of Ali (2021) and Huang et al. (2020). The respondents had to rate the observed degree of ambidextrous learning on a five-point Likert-type scale (1 = strongly disagree; 5 = strongly agree).

Mediating Variable

Considering that new ventures need many resources to help improve performance, we constructed resource acquisition variables to measure the degree of venture resources. According to Sirmon et al. (2007) and Li et al. (2017), resource acquisition has five items, and respondents were required to provide a score from 1 to 5 for each item.

Moderating Variables

EE network relationships mainly refer to the interaction and network relationships between the central bodies in the EE (E. Stam & Van de Ven, 2021). Generally, they are measured by the interactivity, timeliness, intimacy, and emotion in the relations between actors (Hoang & Antoncic, 2003). According to Tsai (2001) and Zhu and Li (2011), we revised the design of relevant items and finally included five items.

Control Variables

We include industry- and firm-level control variables to ensure valid results because they influence venture performance (Gueguen et al., 2021; C. Y. Lee & Huang, 2012), and control variables can impact ambidextrous learning, resource acquisition, and venture performance. At the firm level, we measured venture age as the year of the questionnaire minus the year when the venture was registered. Further, we define the venture as a new venture if it has been established for less than 8 years (Z. Cao & Shi, 2021; T. Wang et al., 2017) and divide the venture age into four groups within 1 year, 1–3 years, 3–5 years, and 5–8 years, with scores from 1 to 4. Picken (2017) divided the development stage of new ventures into four stages: start-up, transition, scaling, and exit. Based on the current stage of new venture development, we divided the venture development stage into the start-up, growing, scaling, and maturity stages. The venture scale is divided into four groups according to the number of employees: 1–20, 21–50, 51–100, and above 100, with scores from 1 to 4. The industries are divided into four groups, high-tech, traditional manufacturing, trade/service, and others, with scores from 1 to 4.

Results

Reliability and Validity Test

SPSS 26.0 and MPLUS 8.3 were used to test the reliability and validity. As shown in Table 2, Cronbach’s alpha values are mostly above 0.800, which indicates that the scale has good reliability as a whole (Tavakol & Dennick, 2011). According to Fornell and Larcker (1981), CR was 0.868 to 0.912, showing good internal consistency; and AVE was 0.568 to 0.682, which means convergence validity is acceptable. Concurrently, the confirmatory factor analysis showed that the model had a good fit (χ²/df = 2.879 < 3, SRMR = 0.076 < 0.08, SRMR = 0.070 < 0.08, TLI = 0.914 > 0.90, CFI = 0.926 > 0.90). All indicators reached the standard, and discrimination validity was good.

Descriptive Results

Table 3 shows the descriptive statistics and correlations for the entire sample. The average venture age in our sample is 4 years, and the mean venture scale is 70 employees. Concurrently, ventures are primarily growing to scale stages. The coefficients on exploratory learning, exploitative learning, resource acquisition, EE network relationships, and venture performance are between 0.244 and 0.766, all reaching a significant level and providing preliminary data support for some hypotheses.

Table 3.

Summary Statistics and Correlation Matrix.

Variables	Mean	SD	1	2	3	4	5	6	7	8	9
Exploratory learning	3.728	0.796	1
Exploitative learning	3.937	0.653	0.766***	1
Resource acquisition	3.961	0.640	0.535***	0.654***	1
Venture performance	3.449	0.762	0.577***	0.486***	0.402***	1
EE network relationships	3.405	0.932	0.377***	0.337***	0.244***	0.767***	1
Venture age	2.720	1.101	0.222***	0.187***	0.092	0.191***	0.168**	1
Venture size	2.311	1.453	0.223***	0.063	−0.037	0.412***	0.461***	0.406***	1
Venture industry	2.503	1.362	−0.171**	−0.062	0.056	−0.229***	−0.276***	−0.090	−0.271***	1
Venture stage	2.311	0.911	0.209***	0.222***	0.155**	0.193***	0.116	0.549***	0.273***	−0.068	1
CR			0.912	0.878	0.868	0.902	0.910
AVE			0.720	0.643	0.568	0.606	0.672

p ≤ .1. **p ≤ .05. ***p ≤ .01.

Regression Analysis Results

Table 4 shows the results for ambidextrous learning and venture performance and the mediating effect of resource acquisition. H1a proposes that exploratory learning is positively related to venture performance. In Model 2, the regression coefficient of the exploratory learning on venture performance is positive and significant (β = .485, p < .01). Therefore, H1a is fully supported. H1b posits that exploitative learning is positively related to venture performance. In Model 3, the regression coefficient of exploitative learning and venture performance is positively significant (β = .542, p < .01). Thus, H1b is also supported.

Table 4.

The Mediating Effect Test of Resource Acquisition.

Variables	Venture performance						Resource acquisition
Variables	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8
Venture age	−0.014	−0.049	−0.047	−0.027	−0.047	−0.044	−0.007	−0.014
Venture size	0.189***	0.157***	0.199***	0.208***	0.175***	0.205***	−0.067**	−0.026
Venture industry	−0.071*	−0.036	−0.057*	−0.082**	−0.050	−0.067**	0.055*	0.038
Venture stage	0.081	0.033	0.015	0.026	0.017	0.007	0.065	0.031
Exploratory learning		0.485***			0.365***		0.461***
Exploitative learning			0.542***			0.375***		0.644***
Resource acquisition				0.505***	0.261***	0.260***
Constant	3.039***	1.424***	1.094***	1.188***	0.868***	0.742**	2.130***	1.355***
R ²	.192	.425	.395	.366	.457	.422	.332	.441
ΔR²	.175	.409	.379	.349	.439	.403	.314	.426
F	11.181	27.618	24.464	21.569	26.066	22.637	18.615	29.504

p ≤ .1. **p ≤ .05. ***p ≤ .01.

When considering the mediating effect of resource acquisition, the relationship between ambidextrous learning and resource acquisition must first be considered (Baron & Kenny, 1986; Qu & Perron, 2007). In Models 7 and 8, ambidextrous learning and resource acquisition regression coefficients are all positively significant (β = .461, 0.644, p < .01). As shown in Model 5, the effect of exploratory learning on venture performance is still significant, but the coefficient decreased by 0.120 (β = .365, p < .01). This indicates partial mediation and that H2a is supported. In Model 6, the effect of exploitative learning on venture performance is also significant, but the coefficient decreased by 0.167 (β = .375, p < .01). This also indicates partial mediation, meaning H2b is supported.

Table 5 presents the moderating effect of EE network relationships. H3a proposes that EE network relationships strengthen the relationship between exploratory learning and resource acquisition. In Model 11, we observe that the interaction term between exploratory learning and EE network relationships is weakened significantly (β = −.096, p < .1), thus indicating negative moderation. Given the sign of the moderation effect, H3a is supported. In Model 12, the interaction term between exploitative learning and EE network relationships is also weakened significantly (β = −.108, p < .05). This indicates negative moderation, and H3b is supported.

Table 5.

The Moderating Effect Test of EE Network Relationships.

	Resource acquisition
Variables	Model 9	Model 10	Model 11	Model 12
Venture age	−0.002	−0.010	−0.010	−0.020
Venture size	−0.098***	−0.047	−0.093***	−0.049
Venture industry	0.066**	0.046*	0.066**	0.047*
Venture stage	0.069	0.035	0.067	0.042
Exploratory learning	0.420***		0.697***
Exploitative learning		0.610***		0.915***
Resource acquisition
EE network relationships	0.123**	0.074	0.51**	0.532**
Exploratory learning × EE network relationships			−0.096*
Exploitative learning × EE network relationships				−0.108**
Constant	1.887***	1.248***	0.793	−0.023
R ²	.354	.449	.367	.460
ΔR²	.333	.431	.343	.440
F	17.011	25.230	15.297	22.540

p ≤ .1. **p ≤ .05. ***p ≤ .01.

To further explain the moderating effect of EE network relationships, we conducted a simple slope test and plotted the significant interaction according to the recommendation of Aiken and West (1994). As specified in Figure 2, the nature of interactions proved our expectation that the relationship between exploratory learning and resource acquisition was weakened when EE network relationships were at a higher level than low. Thus, H3a was supported. As plotted in Figure 3, exploitative learning will weaken resource acquisition in the high level of EE network relationships. Thus, H3b was supported.

Figure 2.

The moderating effect of EE network relationships on exploratory learning and resource acquisition.

Figure 3.

The moderating effect of EE network relationships on exploitative learning and resource acquisition.

Robustness Test

To ensure the reliability of the results, two robustness tests were conducted. First, we used an alternative measure of ambidextrous learning. Q. Cao et al. (2009) used two dimensions—BD (balanced dimension) and CD (combined dimension)—to measure ambidextrous learning. However, Xie et al. (2022) and Tao et al. (2023) posit that neither BD nor CD can explain ambidextrous learning thoroughly. The reason is that the BD only considers the competing relationship between exploratory and exploitative learning. Contrastingly, the CD only considers the synergic nature of these two types of learning. These two types of learning may be resources competing in some new ventures and complementary in others. Ambidextrous learning should consider both kinds of situations. To reflect both dimensions of ambidextrous learning, we used the formula in Equation 1 to measure the ambidextrous learning of the new venture (Tao et al., 2023; Xie et al., 2022):

\begin{matrix} Ambidextrous learning = h (X_{explor}, X_{exploi}) = \\ \frac{(n - | x_{explor} - x_{exploi} |) * \sqrt{x_{explor} * x_{exploi}}}{n} \end{matrix}

(1)

where x_explor and x_exploi denote exploratory and exploitative learning, respectively; |x_explor−x_exploi| and |x_explor*x_exploi| represent BD and CD of ambidextrous learning, with either a small BD or a large CD indicating high levels of ambidextrous learning; and n represents the score on the Likert scale—overall, the greater the value of equation (1), the higher the level of ambidextrous learning.

Subsequently, we regressed the ambidextrous learning from the new measurement method with other variables and retested the hypothesis (Table 6 for specific results). At this point, the research hypothesis that H1a and H1b should be adjusted to H1, indicating that ambidextrous learning positively promotes venture performance; H2a and H2b should be adjusted to H2; that is, resource acquisition mediated ambidextrous learning and venture performance; H3a and H3b should be adjusted to H3, which means that the EE network relationships negatively regulates ambidextrous learning and resource acquisition. From Model 14, ambidextrous learning positively promotes venture performance (β = .451, p < .01), assuming H1 holds. Model 15 shows that after resource acquisition is added, the impact of ambidextrous learning on venture performance decreases by 0.133 (β = .318, p < .01), indicating that resource acquisition has a partial mediating effect, assuming H2 holds. Model 18 shows that the EE network relationships negatively moderate the relationship between ambidextrous learning and resource acquisition (β = −.106, p < .05), assuming H3 holds.

Table 6.

Results of Robustness Tests for Mediation and Moderation.

Variables	Venture performance			Resource acquisition
Variables	Model 13	Model 14	Model 15	Model 16	Model 17	Model 18
Venture age	−0.014	−0.044	−0.043	−0.044	0.002	−0.007
Venture size	0.189***	0.174***	0.19***	0.174***	−0.091***	−0.088***
Venture industry	−0.071*	−0.045	−0.06*	−0.045	0.061**	0.061**
Venture stage	0.081	0.022	0.007	0.022	0.058	0.061
Ambidextrous learning		0.451***	0.318***	0.451***	0.402***	0.707***
Resource acquisition			0.300***
EE network relationships					0.147***	0.56***
Ambidextrous learning × EE network relationships						−0.106**
Constant	3.039***	1.634***	0.949***	1.634***	1.955***	0.785
R ²	.192	.393	.437	.393	.343	.359
ΔR²	.175	.377	.419	.377	.322	.335
F	11.181	24.261	24.085	24.261	16.185	14.817

p ≤ .1. **p ≤ .05. ***p ≤ .01.

Second, we re-examined the mediating effect of resource acquisition and the moderating effect of EE network relationships using the process plugin in SPSS. The specific results are shown in Tables 7 and 8. Table 7 shows that the mediating effect of exploratory and exploitative learning on venture performance through resource acquisition is 0.120 and 0.167 (p < .01). The 95% confidence interval of indirect effect is [0.037, 0.202] and [0.190, 0.288], respectively, excluding 0. Therefore, the mediating effect of resource acquisition is established, and H2a and H2b are verified, respectively.

Table 7.

The Mediating Effect Test of Resource Acquisition.

Path		Effect	SE	LLCI	ULCI
Exploratory learning	Total effect	0.485	0.056	0.375	0.595
– Resource acquisition	Direct effect	0.365	0.065	0.236	0.494
– Venture performance	Indirect effect	0.120	0.043	0.037	0.202
Exploitative learning	Total effect	0.542	0.068	0.407	0.677
– Resource acquisition	Direct effect	0.375	0.088	0.201	0.549
– Venture performance	Indirect effect	0.167	0.068	0.190	0.288

Table 8.

The Moderating Effect Test of EE Network Relationships.

Variables	Resource acquisition
	Model 19			Model 20
	Coefficient	95% CI	90% CI	Coefficient	95% CI	90% CI
Venture age	−0.010	[−0.096, 0.075]	[−0.082, 0.062]	−0.020	[−0.099, 0.059]	[−0.086, 0.046]
Venture size	−0.093	[−0.156, −0.030]	[−0.146,−0.040]	−0.049	[−0.108, 0.010]	[−0.099.0.000]
Venture industry	0.066	[0.008, 0.123]	[0.018, 0.114]	0.047	[−0.006, 0.100]	[0.003.0.091]
Venture stage	0.067	[−0.031, 0.165]	[−0.015, 0.149]	0.042	[−0.049, 0.133]	[−0.034, 0.118]
Exploratory learning	0.370	[0.255, 0.485]	[0.274, 0.466]
Exploitative learning				0.546	[0.414, 0.677]	[0.436, 0.656]
Resource acquisition
EE network relationships	0.153	[0.052, 0.253]	[0.068, 0.237]	0.105	[0.010, 0.200]	[0.025, 0.184]
Exploratory learning × EE network relationships	−0.096	[−0.196, 0.004]	[−0.180, −0.012]
Exploitative learning × EE network relationships				−0.108	[−0.216, −0.001]	[−0.198, −0.019]
Constant	3.910	[3.620, 4.200]	[3.667, 4.153]	3.936	[3.669, 4.204]	[3.712, 4.160]

Model 19 in Table 8 shows that the EE network relationships negatively moderate exploratory learning and resource acquisition, with a moderating effect at a 90% confidence interval of [−0.180, −0.012], excluding 0. The regression coefficient and significance effect are consistent with the previous text (β = −.096, p < .1, Table 5), assuming H3a is validated. Model 20 shows that under high EE network relationships, the promoting effect of exploitative learning on resource acquisition weakens, with a moderating effect at a 95% confidence interval of [−0.216, −0.001], excluding 0. The regression coefficient and significance effects are consistent with the previous text (β = −.108, p < .05, Table 5), assuming H3b is validated.

Discussion

Main Research Results

This study is based on organizational learning theory and examines the impact of ambidextrous learning in new ventures on venture performance under EE. It also explores the mediating role of resource acquisition and the moderating role of EE network relationships. Taking 193 new ventures under EE in Hangzhou, China, as an example, the primary situation is summarized as follows.

First, research has shown that ambidextrous learning of new ventures under EE can promote venture performance. New ventures engage in ambidextrous learning, expanding the breadth and depth of learning. Specifically, exploratory learning helps ventures discover and utilize new opportunities, while exploitative learning helps ventures deeply explore entrepreneurial opportunities in existing markets. Both learning methods can help ventures identify entrepreneurial opportunities and promote venture performance.

Second, resource acquisition positively and partially mediates the relationship between ambidextrous learning and venture performance. According to organizational learning theory, we believe that resource acquisition is a suitable variable to uncover the black box of the impact of ambidextrous learning on venture performance, as ambidextrous learning helps ventures identify entrepreneurial opportunities, enabling them to acquire resources and improve venture performance through product upgrades and innovation more accurately and effectively.

Finally, the EE network relationship plays a negative moderating role between ambidextrous learning and resource acquisition. The research results show that high EE network relationships enable new ventures to gain legitimacy after embedding EE network relationships (Kuratko et al., 2017) and to obtain corresponding opportunities and resources. However, obtaining legitimacy requires ventures to invest more time and effort, exacerbating the already scarce resources, time, and energy of a new venture (Tao et al., 2023; Xu et al., 2021), leading to the failure of ambidextrous learning to play a positive role in resource acquisition fully.

Theoretical Contributions

First, our research enriches the research context of organizational learning theory. In the past, research on ambidextrous learning in entrepreneurship mainly focused on the individual level of entrepreneurs (C. L. Wang & Chugh, 2014). We extended ambidextrous learning in entrepreneurship to the enterprise level. Further, we elucidated the importance of EE network relationship background in explaining the relationship between ambidextrous learning and resource acquisition, thus promoting the context research of organizational learning theory in entrepreneurship. C. Y. Lee and Huang (2012) argue that ambidextrous learning by ventures can improve their performance. The promoting effect of ambidextrous learning on venture performance still exists in the EE environment. In addition, our research further explores the relationship between ambidextrous learning and venture performance black box based on C. Y. Lee and Huang (2012) and the moderating role of EE network relationships in the relationship between ambidextrous learning and resource acquisition. This study starts with the relationship between the EE network and applies organizational learning theory to the EE context, enriching the research context of organizational learning theory (Argote et al., 2021; C. L. Wang & Chugh, 2014).

Second, our findings contribute to the study of resource acquisition in entrepreneurship. The research on the impact of different types of resource acquisition on venture performance and its underlying mechanisms has been widely studied by scholars (Ko & McKelvie, 2018; Oo et al., 2019). For new ventures, all types of resources are scarce (Reypens et al., 2021). Therefore, resource acquisition needs to emphasize the acquisition and utilization of comprehensive resources. Based on this research, this study refers to the measurements of Sirmon et al. (2007) and Li et al. (2017), considers the impact of comprehensive resource acquisition on the entrepreneurial process, and introduces comprehensive resource acquisition into entrepreneurial research. By studying the relationship between ambidextrous learning, resource acquisition, and venture performance and further focusing on the moderating role of EE network relationships in the relationship between ambidextrous learning and resource acquisition. To enhance the applicability of resource acquisition in entrepreneurial research so that entrepreneurial resources can more accurately support the development of new ventures.

Third, our research enriches the research attempts of EE in the micro field and has been validated using empirical methods. The existing research on EE mainly focuses on macro-level research and has made substantial progress (E. Stam & Van de Ven, 2021; Wurth et al., 2022). However, ecosystem metaphor has generated an implicit trend in EE theory that focuses on macro- and ecosystem-level dynamics without explaining the micro-foundations of EEs (Roundy & Lyons, 2022). To accompany the macro-dynamics focus in EE theory, Roundy and Lyons (2022) call for a micro-foundations approach that emphasizes the bidirectional connections between entrepreneurs’ strategizing and organizing activities and their ecosystems. We introduce EE to the micro-level, focusing on the moderating effect of EE network relationships on ambidextrous learning and resource acquisition for new ventures and using empirical methods to test it. This promotes the micro research of EE and help new ventures obtain targeted policy recommendations.

Practical Implications

In addition to theoretical contributions, this research has some practical significance and values. The ambidextrous learning of new ventures under EE can not only promote venture performance but also promote venture performance through resource acquisition. Therefore, new ventures need to recognize the importance of ambidextrous learning and avoid missing many entrepreneurial opportunities and resources owing to insufficient learning ability. For example, if a new venture has just partnered with a partner (S. Yang et al., 2021), it can use exploitative learning to acquire resources and improve venture performance. Contrastingly, high EE network relationships can weaken the positive promoting effect of ambidextrous learning and resource acquisition for new ventures. For new ventures, all resources are scarce and precious, and the cost of trial and error is also very high (Gueler & Schneider, 2021; Yin et al., 2021). In the early stages of entrepreneurship, the focus of the new venture should still be on internal production operations, product research, and development innovation. Therefore, new ventures need to embed EE network relationships actively but cannot overly rely on them and still need to focus on improving the core competitiveness of the venture (Bachmann et al., 2021).

Our research findings also have significant implications for park operators. Promoting the development of new ventures in the EE is the primary task for many countries in developing their economies and improving their long-term competitiveness (Guerrero et al., 2021). Our research shows that new ventures under EE still need to engage in ambidextrous learning to acquire resources and improve venture performance. However, the premise for obtaining resources through embedding EE network relationships is that the new venture needs to obtain legitimacy (Kuratko et al., 2017). New ventures in high EE network relationships indicate that they have invested much time, human resources, and material resources in EE network relationships (Xu et al., 2021), which leads to insufficient energy for high-quality and efficient resource acquisition. According to our research results, venture park operators can expand the learning opportunities and forms of new ventures (Cumming et al., 2019), such as providing various free learning platforms, conducting primary technology exchange and sharing activities, allowing ventures to focus on research and innovation while obtaining the legitimacy of EE and achieving a win-win situation (Gueguen et al., 2021).

Limitations and Future Research Directions

Our research has some limitations, which provide valuable opportunities for future research. First, this study focuses on a representative EE in Hangzhou, Zhejiang Province, China, and on collecting and analyzing cross-sectional data. Considering economic and regional differences, in the future, large-scale quantitative studies can be conducted on new ventures within different regions and countries of EE (Audretsch & Belitski, 2021), and panel data can be used for analysis (Wei, 2022).

Second, this study only focuses on the mediating role of resource acquisition between ambidextrous learning and venture performance under EE. Future research can further explore the new changes and manifestations of the “ambidextrous learning → resource acquisition → venture performance” path for new ventures within EE in the context of digital and artificial intelligence. Contrastingly, future research can explore how ambidextrous learning affects venture performance from different disciplinary perspectives to improve other mechanisms of venture performance (Rui & Ma, 2020; Xie et al., 2022), such as business process management (Aljumah et al., 2021; Binci et al., 2020), digital innovation (Shao et al., 2022), digital opportunities, and so on.

Third, this study focuses on the moderating role of EE network relationships and attempts to conduct micro-level research on EE. In the future, we can further research the micro-level of EE from multiple perspectives (Wurth et al., 2022). EE can also be a digital environment ecosystem to serve the survival and development of new ventures (Sahut et al., 2021; Sussan & Acs, 2017). Contrastingly, we can also use affordance theory (Zahra et al., 2023), actor-network theory (Schneider et al., 2020), and other theories to explore the impact of EE network relationships on the relationship between ambidextrous learning, resource acquisition, and venture performance from multiple perspectives, which may further promote the new development of ambidextrous learning.

Conclusions

In recent years, more and more new ventures have settled in EE. Scholars have also emphasized the importance of EE and ambidextrous learning for new ventures (Bertello et al., 2022). However, there is little empirical analysis on how ambidextrous learning in new ventures under EE improves venture performance (Roundy & Lyons, 2022). Our research addresses these gaps in the literature. It explores the internal mechanisms and external environmental impacts of a new venture based on organizational learning theory from resource acquisition and EE network relationships. Empirical research involving data on new ventures in Hangzhou Entrepreneurship Park, China, has supported all proposed hypotheses. Specifically, the ambidextrous learning of new ventures under EE can still promote venture performance and promote venture performance through resource acquisition. When in a high EE network relationship, the positive promoting effect of ambidextrous learning on resource acquisition will be weakened. In summary, our research not only enriches the research context of organizational learning theory and expands the research of EE in the micro field (Argote et al., 2021; Wurth et al., 2022) but also believes that EE provides practical guidance for new ventures and venture park operators.

Footnotes

Acknowledgements

The authors thank the Editor-in-Chief and anonymous reviewers for their constructive insights and suggestions.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the General Program of National Natural Science Foundation of China (Grant No. 72172142).

ORCID iD

Mixiang Peng

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