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Article

Enhancing Business Incubator Performances from Knowledge-Based Perspectives

by
Prima Fithri
1,2,*,
Alizar Hasan
1,
Syafrizal Syafrizal
2,* and
Donard Games
2
1
Department of Industrial Engineering, Faculty of Engineering, Universitas Andalas, Padang City 25163, Indonesia
2
Department of Management, Faculty of Economics and Business, Universitas Andalas, Padang City 25163, Indonesia
*
Authors to whom correspondence should be addressed.
Sustainability 2024, 16(15), 6303; https://doi.org/10.3390/su16156303
Submission received: 7 June 2024 / Revised: 19 July 2024 / Accepted: 21 July 2024 / Published: 23 July 2024
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Versions Notes

Abstract

:
The rapid evolution of technology has reshaped the global economy, intensified competition, and prompted industry players to embrace innovation to maintain sustained competitiveness. In Indonesia, business incubators, overseen by universities and provincial/city governments, are pivotal. However, understanding the determinants of business incubator performance remains limited. This study investigates 24 statement items aiming to uncover the factors influencing technology business incubators in Indonesia, focusing on knowledge management as a mediating variable from a knowledge-based perspective. Employing a quantitative approach, structural equation modeling (SEM) is utilized to scrutinize these factors’ impact on business incubator performance. The findings reveal that knowledge management serves as a full and partial mediator among funding support, government assistance, incubator governance, and business incubator technology performance. This research offers valuable insights for entrepreneurs and stakeholders by emphasizing the significance of funding, governmental backing, incubator governance, and knowledge management in enhancing incubator business technology performance in Indonesia.

1. Introduction

Technological developments are the main driver of change in the business world, spurring companies to adapt and compete in a rapidly developing environment. Recently, the ability to innovate and utilize technology has become a key factor in business success [1]. According to [2], these technological developments significantly affect the global trading system, intensifying competition within the global market.
Significant changes have occurred in business operations with the emergence of digital technology. Fast and efficient communication, along with transactions without geographic restrictions, are the result of advances in digital technology. In Indonesia, the growth in internet use has created new opportunities for digital startups to develop [3]. For example, Indonesia ranks sixth in internet usage, where it has empowered digital startups to offer technology-based solutions to overcome various societal problems [4].
Digital startups in Indonesia, which cover sectors such as transportation, e-commerce, news media, and communications, play a vital economic role [5]. Despite receiving support from the government and other parties, many startups face significant challenges that can lead to failure, with approximately 99% experiencing difficulties in developing their business [6]. In addition, entrepreneurs’ fear of startup failure is also an important factor for business incubator performance [7] as well as other risks experienced by startups when running their business [8]. Factors contributing to this failure include insufficient business skills, inadequate planning, poor management, investment problems in technology, and inadequate cash flow. Therefore, targeted assistance and support are needed to help startups compete effectively in an increasingly competitive market [9].
Performance in a company is a benchmark of its ability to achieve its goals effectively. Performance measurement is important for companies because it is a measurement process to show the company’s progress towards its objectives [10]. This can be implemented to measure the performance of technology-based business incubators at universities. Incubators have grown in scale and variety all over the world, and they have a long history of fostering economic development and career production [11]. Apart from that, with the incubation program, business actors will be helped in overcoming business problems by utilizing appropriate facilities and technology. Business incubators have been considered as enablers and launchpads for new startups [11]. This shows that business incubators have a crucial role in increasing the quantity and quality of new businesses. Hence, conducting regular evaluations of business incubators’ performance is necessary.
Although there has been much research on the factors influencing the performance of technology business incubators, there is still much to explore, especially within the context of the knowledge-based view. Emphasizing knowledge management in startups can significantly enhance their performance across financial [12], environmental [13], no. of patent granted, support services offered by incubator business, services offered in incubator business [14], human, market, organizational, relational, technical, and technological domains. Ref. [15] stated that proper knowledge management improves organizational performance. The research results of [16] stated that inertial knowledge provides a complete mediating effect on organizational innovation through organizational learning.
According to research by [17] regarding the performance of technology business incubators, there are no clear and definite standards for measuring incubator performance [18]. Several studies have proposed various indicators to determine incubator performance [19]. For example, Ref. [6] examined university-based incubators based on tenant performance, revenue generated, total funds raised, venture capital funding obtained, and post-incubation company success. While this approach focuses on the success of the resulting business, it may only sometimes be ideal. In general, according to Somsuk and Ref. [20], incubated companies tend to have higher success rates than non-incubated ones, despite the persistent risk of failure. Therefore, this research aims to analyze factors influencing the performance of technology business incubators, including support, facilities, incubator leadership, networking, links with universities, and knowledge management, which are known to mediate between support and performance [21].
While research on business incubator performance has developed rapidly, there is still a gap in understanding how a knowledge-based view (KBV) perspective can influence these performance outcomes. Previous research has focused more on the physical aspects and facilities provided by incubators, but little has examined how the knowledge and capabilities possessed by incubator management can be the main resources that determine incubator performance. In particular, aspects of incubator governance that include internal policies, resource management, and learning processes remain underexplored in relation to how this knowledge is integrated and utilized to support incubated startups. Thus, research that explores the role of incubator governance from a KBV perspective has the potential to provide new insights into improving the performance of business incubators.
Another important consideration in examining incubator performance is the role of government and financial support, which has not been fully studied from a KBV perspective in the context of business incubators. Government support is often identified as an external factor that provides access to additional resources and policies that support startup growth, but the mechanisms by which this knowledge and capability are internalized by incubators remain unclear. Likewise, with financial support, although crucial, further research is needed to understand how incubators can optimize their financial knowledge and risk management to ensure the funds they receive can be used effectively. By examining more deeply the interaction between government support, financial support, and incubator governance from a KBV perspective, it is hoped that a more effective and highly competitive incubator model can be found.
Based on the explanation above, it is necessary to carry out deeper research to find out the factors that influence the performance of technology business incubators in Indonesia from a knowledge-based perspective, i.e., how do the mediating, variable knowledge management, and incubator capabilities influence the performance of technology business incubators?

2. Literature Review

This review examines the knowledge-based view by looking at management knowledge inertia.

2.1. Conceptual Orientation: Knowledge-Based View

Knowledge-based view (KBV) is a theory that complements the previous theory (resource-based view). KBV theory is very closely related to RBV theory, namely that knowledge influences resources. RBV focuses on internal organization capabilities, and it is an important aspect of strategic analysis [22]. The resource-based view theory (RBV) states that resources can give a company a competitive advantage and direct it toward good long-term performance [23]. According to [24], this knowledge-based view theory states that the collection of resources in a company is not the only factor that can provide a company with a competitive advantage. Another important factor is the knowledge and information available in the company. According to this view, appropriate knowledge and information availability will provide long-term and sustainable advantages.
The performance factors for technology business incubators included in the KBV include knowledge management to increase organizational knowledge, such as facilitating communication among staff, providing opportunities to learn, and facilitating knowledge sharing [25]. This business aims to create and maintain increased value from core business competencies by utilizing existing information technology. Knowledge management also encourages logical thinking among employees, resulting in boosted productivity, creativity, and innovation. The advantage of this knowledge is that it makes individuals able to follow up on information that is used as a basis for decision-making and to pursue a certain direction or strategy [26].
Knowledge management is an important discipline that facilitates knowledge transfer, skills, and practical work behavior. Having knowledge management capacity can collect tacit knowledge from workers, organize it, and transform it into explicit knowledge to enhance competence and competitiveness, resulting in a sustained competitive advantage within the industry [27]. In [28] research, knowledge management significantly influences and benefits company performance. The results show that knowledge management through work procedures is pivotal in enhancing employee and company performance. The employee acquires valuable skills and attitudes through knowledge that is received and can be implemented in the company system as a whole. Company performance will increase along with knowledge management that is continuously updated with the importance of employee performance as the driving wheel of the company.
Knowledge is essential for achieving sustainable competitive advantage and high performance. Knowledge is not easily measured or audited. Therefore, organizations must effectively manage knowledge to fully leverage their existing skills and experience, systems and structures, and employee knowledge. Knowledge management encompasses managerial activities to develop, transfer, transmit, store, and apply knowledge within an organization. It also ensures organizational members have access to relevant information to make the right decisions and achieve organizational goals.

2.2. Research Model and Hypotheses Formulation

2.2.1. Knowledge Management

Knowledge management is considered very important in organizations because it can build organizations as learning organizations and can be used as a more effective interaction facility through the availability of information [29]. Knowledge management can function as a manager of organizational knowledge to be used in the long term [29]. In assessing managerial knowledge and skills, it can focus on a critical analysis of the legal definition of the rights and obligations of an executive and their responsibility towards a limited liability company [30]. Various studies reveal that the use of knowledge through the knowledge management process can produce innovation and competitive advantage for organizations or companies [29].
Knowledge management in startups has a significant effect on improving performance through positive contributions to sustainable startup growth by improving financial, environmental, human, market, organizational, relational, technical, and technological performance [31]. Within the startup ecosystem, effective knowledge management fosters innovation by creating, storing, transferring, and applying knowledge. Ref. [15] stated that proper knowledge management improves the performance of organizations in the public and private sectors. They also emphasize that proper employee retention improves skills and builds employee confidence. Knowledge creation is essential for organizational survival. Therefore, it can be concluded that the ability to create knowledge and generate competitive advantage is very important for every organization that wants to remain relevant and achieve wider achievements.
Government support provides essential resources, regulatory frameworks, and policy incentives that can significantly enhance knowledge management practices within business incubators. For example, government grants and subsidies can fund training programs and knowledge-sharing platforms, while regulatory support can create a conducive environment for collaboration and innovation. This approach is feasible through close cooperation between the company and society, which minimizes government intervention [32]. Empirical evidence suggests that government backing plays a crucial role in developing robust knowledge management systems by enabling access to critical information and facilitating partnerships among incubators, academic institutions, and industry experts. Thus, the following is hypothesized:
H1: 
Government support influences knowledge management.
Ref. [33] investigated that initial capital support is necessary for success. Venture capital performs a supporting function to provide necessary funds for incubators and other business entities. Ref. [34] stated that business incubators play an important role in providing supporting funds for fund formation. One of the main challenges for new entrepreneurs is limited access to financial capital. Capital support provides a kind of confidence to incubators as well as encourages incubators to do more with less support. They have positive insight into capital support and new business growth. Capital support is seen as the main source of innovation, employment, and economic growth, so policymakers need to pay attention to entrepreneurship and capital support. Ref. [35] defines capital support as a synonym for innovation, venture capital, and financing for startups. Data from the 2016 Annual Survey of Entrepreneurs show that only 5% to 10% of salaried employees do not need to raise capital when starting a business. Hence, 90% to 95% of entrepreneurs need some funds to start a business. These obstacles and the lack of appropriate capital for new ventures result in many unmet needs among entrepreneurs who need capital, and entrepreneurs are often unable to choose the financing services that best suit their businesses. Ref. [33] indicates that entrepreneurs and researchers often state that lack of access to capital is a significant obstacle that many entrepreneurs face.
Funding support is a critical driver of effective knowledge management within business incubators. Adequate funding allows incubators to invest in knowledge management infrastructure, such as advanced IT systems, databases, and training programs. Moreover, financial resources enable incubators to attract and retain skilled personnel who can effectively manage and disseminate knowledge. Studies have shown that incubators with better funding tend to implement sophisticated knowledge management practices, leading to enhanced innovation and performance. Therefore, the writers hypothesized that the following:
H2: 
Funding support influences knowledge management.
Ref. [36] found a positive relationship between incubator governance and business incubator performance. Governance plays an important role in recommending, reviewing, evaluating, and selecting tenants. According to [37], his research found that incubator governance factors, which include transparency in governance, stakeholder participation in decision-making, and the quality of services provided by business incubators, can positively improve the performance of business incubators. Good incubator governance factors and technological support are important aspects of optimizing business incubator performance [38]. Therefore, it can be concluded that incubator governance factors have a significant impact on incubator business performance [39].
The governance structure of an incubator plays a pivotal role in shaping its knowledge management practices. Effective governance provides strategic direction, establishes policies, and fosters a culture that values knowledge-sharing and continuous learning. Good governance ensures that knowledge management is aligned with the incubator’s objectives and integrated into daily operations. Research indicates that incubators with strong governance frameworks are better equipped to manage knowledge effectively, thereby supporting innovation and business success. Thus, the writers propose that the following hypothesis:
H3: 
Incubator governance influences knowledge management.
Knowledge management is widely recognized as a key factor in driving the performance of business incubators. Effective knowledge management enhances innovation, operational efficiency, and strategic decision-making. Studies have shown that incubators with robust knowledge management systems tend to develop innovative solutions, improve service delivery, and achieve higher performance levels. For instance, Ref. [40] found that knowledge management positively influences the innovation and performance of technology business incubators. Similarly, Ref. [41] demonstrated a direct positive impact of knowledge management on organizational performance, while Ref. [42] highlighted its role in enhancing company performance through improved employee skills and attitudes. Knowledge management acts as a mediator between support and performance [17]. Knowledge management is very important in networking and knowledge-sharing activities in business circles. Knowledge management has a significant effect on the performance of business organizations. The application of knowledge management can be a strategy, especially in utilizing new knowledge [29]. Additionally, Ref. [43] concluded that knowledge management is crucial for creating competitive advantage and improving organizational performance. Knowledge management. Ref. [44] explains that venture capital is support for providing the necessary funds for business incubators and other business entities. According to [36], funding support significantly influences business incubators. Additionally, government support and protection, mentoring, and incubator governance have a strong relationship with reward moderation factors and a good infrastructure system. In [17], rewards have a positive influence on knowledge management. Therefore, the following hypothesis is proposed:
H4: 
Knowledge management mediates government support, funding support, and incubator governance for the performance of business incubators.

2.2.2. Knowledge Inertia

Knowledge inertia refers to the tendency in human behavior to rely on past experiences and existing knowledge without revising and updating them [45]. It is caused by a lack of innovative thinking and behavior and can negatively influence beneficial learning processes and knowledge attainment [46]. Knowledge inertia in research by [16] suggested that the term ‘knowledge inertia’ refers to a situation where individuals rely on their past experiences and knowledge in making decisions or acting. This is caused by a lack of innovative thinking and negatively impacts effective learning and knowledge acquisition. Ref. [16] suggested that knowledge inertia has the following two aspects: experience inertia, where individuals rely on old experience and knowledge to solve problems, and learning inertia, when individuals constantly learn from the same sources without seeking new perspectives or sources of information. A study by [47] found that learning inertia positively moderates the relationship between problem-solving and knowledge integration capabilities. They showed that when firms have a high level of learning inertia, problem-searching exerts a more positive effect on knowledge integration capabilities. Thus, knowledge inertia may act as a distinct moderator in the relationship between various search activities and diverse business model innovations [48]. Therefore, the following hypothesis is proposed:
H5: 
Knowledge inertia moderates the impact of knowledge management on business incubator performance.
Knowledge inertia refers to the resistance to change in knowledge-related processes and practices within an organization. It can arise from various factors, such as entrenched routines, a lack of motivation to update existing knowledge, or a fear of the unknown. In the context of business incubators, knowledge inertia can significantly influence the effectiveness of knowledge management practices. While effective knowledge management can enhance incubator performance by fostering innovation, improving decision-making, and facilitating the sharing of best practices, the presence of knowledge inertia may hinder these benefits.
For instance, even with well-designed knowledge management systems, an incubator may struggle to achieve optimal performance if its members resist adopting new knowledge or established practices. Studies suggest that high levels of knowledge inertia can negate the positive effects of knowledge management by slowing down the implementation of new ideas and preventing the effective utilization of acquired knowledge. Conversely, low knowledge inertia can amplify the benefits of knowledge management, enabling incubators to adapt more quickly to new information and innovations.

3. Methods

This research focuses on the factors that influence the performance of technology business incubators in Indonesia, based on a knowledge-based perspective. This study examines incubator management, government support, financial support, and facilities. Knowledge management (KM) mediates between support and performance [17]. Meanwhile, knowledge inertia is known to moderate knowledge management and performance [49]. This research uses quantitative methods. This method aims to determine the magnitude of the relationship or influence of these factors on the performance of technology business incubators and determine which factors have the most influence on the performance of technology business incubators in Indonesia, as shown in Figure 1. The research framework can be developed as presented in Figure 1 as follows:
The population of this research consists of organizations that are members of the Indonesian Business Incubator Association (AIBI) and are within the scope of the Ministry of Research, Technology, and Higher Education (Kemenristekdikti). This population includes 100 technology business incubators operating throughout Indonesia. Data regarding business incubators were obtained through AIBI and the Ministry of Research, Technology, and Higher Education as the main sources of information. The organization level is the unit of analysis in this research. According to Campbell, DT (1955), a good informant for this research should have access to all the issues under investigation. Therefore, business owners were selected as the only key informants. The sampling design used is purposive sampling, a non-probability sampling technique where the selection of sample elements is based on certain criteria relevant to the research objectives. A sample of 100 respondents from a population of 185 was carefully selected based on their relevance to the research objectives and characteristics in accordance with the research criteria, ensuring valuable insight into the research questions. Data collection was carried out between 2020 and 2021 using Google Forms. Researchers contacted respondents individually to ask for their willingness to fill out this research questionnaire. The variables studied in this research were adopted from various previous studies and can be seen in Table A1. All constructs in this study were measured using a five-point Likert scale. This scale asks respondents to express their level of agreement by selecting one of five different levels of agreement. The aim of using this scale is to measure respondents’ perceptions and views of the variables examined in this research. This scale provides the flexibility necessary to measure the diversity of views among respondents.

4. Results

The descriptive statistical analysis of this study provides a more detailed understanding of the characteristics of the respondents and the research object. Regarding the profession of the respondents, it was found that the majority of respondents were incubator heads (48%) and managers (41%). This indicates that the study received significant participation from leaders and decision-makers within the business incubators. Meanwhile, deputy heads made a minor contribution (1%), suggesting a less dominant role in this study. Furthermore, in terms of gender, the findings showed that men dominated the respondents, with 68% of the total 100 respondents. This suggests a potential gender imbalance in the context of the business incubators studied, which may need to be addressed in further analysis regarding its impact on organizational decisions and dynamics. Regarding the type of business incubator studied, university-affiliated incubators had the highest contribution (62%), followed by government-owned (27%) and private incubators (11%). Meanwhile, analysis of the age range of respondents showed significant variation, ranging from 12 to 65 years old. This board age range reflects the inclusiveness of the research and the possibility of diverse perspectives on the issues studied (Table 1).
Table 2 shows the Cronbach’s alpha, composite reliability (CR), and AVE outputs. It can be observed that all CR values are greater than 0.6. The smallest CR is incubator governance, namely 0.847, much higher than the minimum limit of 0.6. All AVE values are greater than 0.5 and smaller than CR. The smallest AVE value is Facilities, with a value of 0.550, slightly greater than the minimum limit of 0.5. All AVEs are in the range 0.5–1, while all CRs are in the range 0.8–1.0. This shows that all AVEs are smaller than CR. It can be concluded that the model has met the requirements for convergent validity. Table 2 also shows the indicator code, which is the code for each statement on the variable. There is one indicator that is eliminated because it has a small factor loading of 0.5, namely the FS1 indicator with a value of 0.491.
To assess the underlying theoretical model and test the hypotheses, this study uses the following two structural modeling approaches: the two-step method proposed by [50] and the partial least squares–structural equation model (PLS-SEM) developed by [51]. The SmartPLS (v.3.2.9) program is used for this purpose. An advancement in PLS-SEM includes extending the comprehensive SEM method to include formative and reflective model simulations, which aid in measuring more multiplex models and highly complex theoretical model structures. PLS-SEM is particularly advantageous when predicting the structural model that determines the target construct. It is suitable for describing how fundamental characteristics predict the relationship between business incubator performance and knowledge-based view (KBV). To test the hypothesis, 10,000 subsamples were bootstrapped, and a two-sided significance check was performed at the 95% confidence level, including bootstrap-corrected confidence intervals [50]. Figure 2 shows the results of partial least squares equation modeling using SmartPLS. In addition, assessing the PLS-SEM model involves discussing several foundational elements in this study.

4.1. Measurement Model

An AVE value above 0.5 indicates the convergent validity of the item. This investigation confirmed the suitability of the writers’ measurement approach for SEM-PLS analysis, ensuring construct reliability and construct internal consistency, as shown in Table 2. Discriminant validity measures the degree of independence between different quality measures. To establish discriminant validity, the correlation between constructs (heterotrait) must be lower than the correlation within constructs (monotrait). The HTMT ratio is below the established threshold of 0.85, or 0.9 [51]. This examination ensures that the metrics taken for this study have unique constructs and do not reflect underlying factors, as shown in Table 3 and Table 4. The Fornell–Larcker criterion was also used to verify discriminant validity. The results shown in Table 2 direct the AVE for each construct that exceeds the squared correlation with other constructs; the Fornell–Larcker criterion [51] is met. This illustration confirms and ensures that the metrics are separate from each other and accurately represent typical attributes in the measurement framework, as depicted in Table 3. Measurement Model PLS and SEM construct analysis confirm the robustness of this study’s measurement model. Cronbach’s alpha values for all constructs exceed 0.7, which means the strength of internal consistency for all constructs, while the composite reliability values (rho_a and rho_c) are above 0.8, demonstrating construct reliability. The average variance extracted (AVE) value exceeds 0.5, indicating the convergent validity of the items. This investigation confirms the suitability of our measurement approach for SEM-PLS analysis.
The SmartPLS results show that all indicators have individual indicator reliability values that are much greater than the minimum acceptable level of 0.5, ranging from 0.8410 to 1.000. To check the convergent validity of the PLS-SEM model above, it is necessary to evaluate the factor loading and average variance extracted (AVE) values of each latent variable. Based on the results of data processing, it is found that with a threshold value set at 0.5 [51], there is one indicator that is eliminated because it has a small factor loading of 0.5, namely the FS1 indicator with a value of 0.491. Meanwhile, it can be seen that all AVE values are greater than the acceptable threshold of 0.5, confirming convergent validity. Discriminant validity in the reflective measurement model illustrates the significance of the structural path in the bootstrapping analysis. Furthermore, a two-sided t-test with a significance level of 5% was conducted to test the significance of the path coefficients in the inner model. The results show that the proposed path relationships are statistically significant at a 1% significance level (with a T-statistic of 6.668, surpassing the threshold of 2.58), consistent across all hypotheses tested. Meanwhile, the reliability test on the model includes an evaluation of two main aspects, namely Cronbach’s alpha and composite reliability with a threshold value of 0.7.

4.2. Structural Model

Indications of path influence can be evaluated by measuring the cross-loading between constructs and their measurement items. Convergent validity is achieved when the correlation between a construct and its measurement item is higher than the correlation with other latent constructs. Apart from that, Table 5 also shows that the knowledge inertia–business incubator performance relationship construct has a significant moderating influence on incubator business performance with a significance level of 5%. However, the interaction construct knowledge management–business incubator performance does not demonstrate a significant moderating effect on company performance. The results of this study indicate that knowledge inertia plays a significant moderating factor within the relationship. In addition, to evaluate the extent of the moderating influence on the model, the change in R2 in Figure 1 and Figure 2 is important, which produces f2. In addition, discriminant validity can also be assessed by comparing the root average squared variance extracted (AVE) value between latent constructs with the correlation value between these latent constructs. For example, Table 2 shows that the AVE of the latent construct “knowledge inertia” is 0.8186; hence, its square root is 0.669. This value exceeds the correlation in the “facilities” column (0.670 and 0.322). Similar observations were made for other latent constructs such as the “Business Incubator Performance, Funding Support, Government Support, Incubator Governance, Knowledge Inertia, Knowledge Management, Moderating Effect 1”, and the results confirmed that discriminant validity had been fulfilled.
In the context of variance analysis of business incubator performance, the results from SmartPLS reveal various important aspects. R2 of 0.273 for the endogenous latent variable (business incubator performance) shows that around 27.3% of the variation in business incubator performance can be explained by the variables that have been studied in the model. This means that the latent variable Business Incubator Performance significantly influences the variations in startup performance. These results indicate that the factors considered in this study, such as knowledge management strategies, have a significant impact on the performance of business incubators. Therefore, it is crucial to prioritize the management and enhancement of these factors to improve the performance of business incubators and, more broadly, the performance of startups in Indonesia. This shows that elements of knowledge management strategy have an important role in influencing the success of business incubators in supporting startup development in Indonesia.
Table 5 displays the significance of the structural path through bootstrapping. Next, the significance of each path coefficient in the inner model can be checked using a two-sided t-test with a significance level of 5%. The results show the hypothesized path relationship between (Funding Support → Knowledge Management); (Government Support → Knowledge Management); (Incubator Governance → Knowledge Management); (Knowledge Inertia → Business Incubator Performance); (Knowledge Management → Business Incubator Performance); (Knowledge Management → Knowledge Inertia); (Moderating Effect 1 → Business Incubator Performance); and (Knowledge Management → Knowledge Inertia → Business Incubator Performance).
Table 4. Discriminant validity—HTMT.
Table 4. Discriminant validity—HTMT.
Business Incubator PerformanceFunding SupportGovernment SupportIncubator GovernanceKnowledge InertiaKnowledge Management
Business Incubator Performance
Funding Support0.499
Government Support0.4320.576
Incubator Governance0.4440.6590.502
Knowledge Inertia0.4960.5530.6610.767
Knowledge Management 0.4850.6240.7460.6410.772
Table 5. The test results of hypotheses based on the path coefficient.
Table 5. The test results of hypotheses based on the path coefficient.
Original Sample (O)Sample Mean (M)Standard Deviation (STDEV)T-Statistics (|O/STDEV|)p-ValuesResult
Funding Support → Knowledge Management 0.1880.1860.0932.0260.043Supported
Government Support → Knowledge Management 0.4040.3810.0964.2070.000Supported
Incubator Governance → Knowledge Management 0.2980.3000.0893.3680.001Supported
Knowledge Management → * Knowledge Inertia (Moderating Effect 1 → Business Incubator Performance)−0.060−0.0640.0431.4110.159Not
Supported
Knowledge Inertia → Business Incubator Performance0.2980.3100.1262.3750.018Supported
Knowledge Management → Business Incubator Performance0.1410.1400.1331.0600.290Not
Supported
Knowledge Management → Knowledge Inertia0.6990.6840.0897.8610.000Supported

5. Discussions

The first hypothesis shows that the relationship between “funding support” and “knowledge management” has a T-statistic of 2.026 and a p-value of 0.043. A p-value less than the 0.05 significance level indicates that this relationship is statistically significant at that level. The results obtained in this research are in contrast to research by Alpenidze and Pauceanu (2019), which states that funding support provides access to improving performance and knowledge management, so the research results state that funding support is one of the important aspects for an incubator business. Another study by Binsawad et al. (2019) [17] stated that rewards have a positive influence on knowledge management.
The second hypothesis states that government support significantly influences knowledge management in business incubators. These funds support this hypothesis, as shown by the T-statistic value of 4.207. These significant results emphasize the importance of government support for promoting effective knowledge management practices in business incubators. The significance of government support for improving knowledge management aligns with the work of [44]. We emphasize that venture capital and government support play an important role in providing the necessary funding for business incubators. These results also support the findings of [36], which show that funding support has a direct impact on business incubators. The impact of these results is significant, as government initiatives and financial support can significantly contribute to strengthening knowledge management and thereby provide a strong foundation for effective business incubator performance.
The third hypothesis tests the influence of incubator governance on knowledge management. The results, with a T-statistic of 3.368, show significant results, confirming that incubator governance plays a crucial role in promoting effective knowledge management. These results are in line with existing literature and emphasize the importance of effective governance structures in business incubators. This supports the claims of various researchers who argue that governance, alongside government support, contributes to improving knowledge management in incubators [36,44]. Effective governance mechanisms provide a structured environment that facilitates the collection, dissemination, and application of knowledge in business incubators. Therefore, it is important for business incubators to establish a strong governance system to optimize their knowledge management practices.
The fourth hypothesis aims to establish the relationship between knowledge management and business incubator performance. With a T-statistic of 1.060, this hypothesis is not accepted; it is different from the real condition, which indicates that knowledge management positively influences business incubator performance. The results are in line with several previous studies demonstrating how effective knowledge management contributes to organizational performance [40,41,42,43]. However, in the context of startup business incubators in Indonesia, it appears that other factors may significantly impact incubator performance. The fifth hypothesis examines the role of knowledge inertia in moderating the relationship between knowledge management and business incubator performance. The results, with a T-statistic of 1.411, strongly do not support this hypothesis. Knowledge inertia refers to resistance to change in an individual’s knowledge and behavior, often stemming from past experiences [46]. The presence of knowledge inertia can hinder the positive effects of knowledge management. Therefore, understanding and overcoming knowledge inertia is important to enhance business incubator performance. These findings confirm the view that overcoming knowledge inertia can be crucial in optimizing knowledge management and, as a result, improving business incubator performance. This research provides a deeper understanding of the complex dynamics within business incubators and the need to effectively manage knowledge and overcome resistance to change. In summary, this research provides valuable insights into the factors influencing knowledge management and business incubator performance. Government support and incubator governance have been identified as significant knowledge management influencers, positively impacting business incubator performance. Additionally, the moderating role of knowledge inertia emphasizes the importance of overcoming resistance to change in business incubators to optimize their performance. These findings emphasize the need for strategic management of knowledge and resources in business incubators to effectively support startup development and growth.
The implications of this research significantly improve business incubator performance. In general, startups can utilize the findings of this research in various aspects of their management to achieve better results. The practical implications for each variable in this research are as follows: The findings of this research highlight the importance of government support, funding support, and effective governance in business incubators. Startups must understand that government support can help them in terms of obtaining funding, supportive regulations, and necessary guidance. Startup managers must forge partnerships with relevant government agencies and utilize existing support effectively. In terms of funding support, startup managers must be proactive in seeking venture capital and external funding. This will facilitate business expansion and investment in knowledge development. In addition, strong incubator governance is also important. Startup managers should work closely with well-governed incubators to ensure efficient knowledge management. It is important for startup managers to understand the importance of knowledge management in improving their business performance.
Implementing effective knowledge management practices is necessary for identifying, storing, and sharing knowledge to enhance innovation and performance. Managers should prioritize developing and coaching human resources who can understand and manage knowledge well. Knowledge inertia, as a moderator in this study, must be addressed carefully by startup managers. Startups need to find ways to overcome resistance to change in their knowledge management. This could involve training and educating employees to be more open to innovation and change in knowledge management practices. Overcoming knowledge inertia is an important step in optimizing knowledge management and improving startup performance. Apart from that, Table 4 also shows the loading of each indicator, indicating their influences on the variables studied. Startup managers should understand which indicators have significant influence and allocate their resources accordingly. Variables that do not have a significant influence must also be considered. While variables with less significant impact may not directly impact performance, exploring creative ways to influence them can result in unexpected benefits.

6. Conclusions

This research shows a significant relationship among the variables studied, namely external support, knowledge inertia, and incubator business performance in startups in Indonesia. Hypotheses 1, 2, and 3, which investigate the effects of government support, funding support, and incubator governance on incubator business performance, are supported by positive results. However, hypothesis 4, which states “knowledge management influences the performance of business incubators”, is rejected in this study. Conversely, hypothesis 5, which states “knowledge inertia moderates knowledge management on business incubator performance”, is accepted. These results provide important insights for startup managers, stakeholders, and governments in improving practices that support incubator business performance. This research also provides a foundation for further research and development in this field.
The limitation of this research is that it is necessary to add respondents to all technology business incubators in Indonesia and not only business incubators owned by universities but for all types, both private and government. Due to these limitations, it is necessary to add respondents with the same proportion between universities, private companies, and the government for future research. Apart from that, further research can be supported by qualitative research as confirmation of the results of quantitative data processing.
The knowledge-based approach (knowledge-based view (KBV)) emphasizes that knowledge is the main strategic resource that can provide sustainable competitive advantages for organizations. In the context of business incubators, KBV highlights the importance of managing and utilizing knowledge to improve incubator performance and support the development of incubated startups. Effective systems within incubators are vital for collecting, storing, sharing, and using knowledge, both explicit and tacit. Creating an environment that encourages continuous learning through workshops, seminars, and regular training sessions is essential, as is developing a network of diverse and experienced mentors. Integrating internal knowledge with external knowledge from academia, industry, and government can enrich available resources. Moreover, incubators also need to develop metrics to measure the extent to which knowledge is used and its impact on startup performance, as well as build an organizational culture that values knowledge and learning. Another crucial factor is encouraging knowledge-based innovation through providing resources for research and development (R&D). By applying KBV principles, business incubators can improve their performance, provide better support to startups, and create a sustainable ecosystem for innovation and growth.
The theoretical implications of the knowledge-based approach (KBV) on business incubator performance include several things. First, KBV emphasizes that knowledge is a key strategic asset that can provide a sustainable competitive advantage. This means that knowledge management theory becomes central to understanding how incubators can optimize their performance through effective knowledge management. Second, KBV implies that an incubator’s ability to integrate and utilize knowledge from various sources, both internal and external, is very important. Theories about collaboration and social networks become relevant here, as successful incubators tend to develop broad networks that include mentors, academia, and industry. Third, KBV underscores the importance of organizational learning and innovation. Incubators should create an environment that supports continuous learning and encourages innovation among incubated startups, aligning with organizational learning and innovation. Fourth, KBV emphasizes that assessing and measuring knowledge is an important aspect of managing performance. Theories about performance metrics and knowledge evaluation become critical in this context. By applying KBV, incubator management theory can better understand how effective knowledge management contributes to the long-term success and competitiveness of business incubators.

Author Contributions

Conceptualization, P.F., S.S. and D.G.; methodology, A.H., S.S. and D.G.; software, P.F.; resources, P.F., A.H. and D.G.; data curation, P.F.; writing—original draft, P.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Questionnaire design.
Table A1. Questionnaire design.
VariableStatementCode
Funding Support1. There is funding support that provides financing arrangements sourced from venture capital, commercial, and non-commercial loans.FS1
2. There are organizational arrangements in the business incubator.FS2
3. There is good management of supporting data related to incubator funding.FS3
4. Obtaining assistance with compliance with governing regulations.FS4
5. There is intellectual property protection for the results of incubator inventions.FS5
Government Support1. Government support is needed to support the performance of the business incubatorGS1
2. Support is needed in the form of grants or funding from the governmentGS2
3. Good government regulations are needed for university business incubatorsGS3
Incubator Governance1. The need for incubator governance by an experienced incubator managerIG1
2. The need for incubator governance by the main board of directorsIG2
3. The need for incubator governance by an advisory boardIG3
4. A short program with clear policies and programs is needed for incubator governanceIG4
Knowledge Management1. I often share with my colleagues the new skills I learn.KM1
2. I share the information I have with colleagues when they ask for it.KM2
3. Sharing knowledge among colleagues is considered normal in my companyKM3
4. Knowledge-sharing practices carried out within the organization lead to improving the performance of the incubator.KM4
5. Sharing knowledge is considered a normal and grateful practice in technology incubators.KM5
6. When I have learned something new, I tell my colleagues about it.KM6
Business Incubator Performance1. Existence of a program profile (growth in budget, space, facilities, tenant services, and staff)BIB1
2. There are complementary research park facilitiesBIB2
3. There is a level of funding received from major donors, including the state, industry, and universitiesBIB3
4. There is continuity for the life of the tenant company (graduate and exit)BIB4

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Sustainability 16 06303 g001
Figure 1. Research framework. Source: authors.
Figure 1. Research framework. Source: authors.
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Figure 2. Results of the structural equation model.
Figure 2. Results of the structural equation model.
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Table 1. Respondent profiles.
Table 1. Respondent profiles.
VariableDescriptionPercentage (%)
PositionHead of business incubator48%
Manager41%
Director6%
Secretary4%
Vice head of business incubator1%
GenderMale68%
Female32%
Incubator HoldingsGovernment27%
Private11%
University62%
Age20–254%
26–4554%
45–6542%
Table 2. Structural model validity and reliability test.
Table 2. Structural model validity and reliability test.
VariableIndicator CodeLoading FactorCronbach’s AlphaComposite ReliabilityAVE
Business Incubator PerformanceBIB10.8520.93250.94510.7111
BIB20.852
BIB30.844
BIB40.805
BIB50.840
BIB60.884
BIB70.823
Funding SupportFS20.8310.79710.86520.5741
FS30.802
FS40.808
FS50.846
Government SupportGS10.8070.71790.84100.6382
GS20.768
GS30.821
Incubator GovernanceIG10.7550.79990.86620.6185
IG20.826
IG30.760
IG40.802
Knowledge InertiaKI10.8670.92600.94750.8186
KI20.911
KI30.939
KI40.901
Knowledge Management KM10.8650.88360.91440.6455
KM20.824
KM30.529
KM40.807
KM50.873
KM60.868
Table 3. Discriminant validity—Fornell–Larcker criterion.
Table 3. Discriminant validity—Fornell–Larcker criterion.
Business Incubator PerformanceFunding SupportGovernment SupportIncubator GovernanceKnowledge InertiaKnowledge Management
Business Incubator Performance0.843
Funding Support0.4310.758
Government Support0.3580.4300.799
Incubator Governance0.3870.5420.4030.786
Knowledge Inertia0.4690.4670.5460.6820.905
Knowledge Management0.4560.5190.5990.5590.6990.803
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Fithri, P.; Hasan, A.; Syafrizal, S.; Games, D. Enhancing Business Incubator Performances from Knowledge-Based Perspectives. Sustainability 2024, 16, 6303. https://doi.org/10.3390/su16156303

AMA Style

Fithri P, Hasan A, Syafrizal S, Games D. Enhancing Business Incubator Performances from Knowledge-Based Perspectives. Sustainability. 2024; 16(15):6303. https://doi.org/10.3390/su16156303

Chicago/Turabian Style

Fithri, Prima, Alizar Hasan, Syafrizal Syafrizal, and Donard Games. 2024. "Enhancing Business Incubator Performances from Knowledge-Based Perspectives" Sustainability 16, no. 15: 6303. https://doi.org/10.3390/su16156303

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