Korreck, 2020; Bollingtoft & Ulhoi, 2005). Incubator firms vigorously have pursued opportunities for profitability and growth through business incuba‐ tors (Chen, 2009). However, research evidence of the quantifiable benefits of business incubation ser‐ vices has been contradictory (Al‐Mubaraki & Busler, 2013). This paper examined a critical performance criterion for the incubator startups, namely their in‐ novation speed. Innovation speed, also called speed to market, represents the time elapsed between in‐ novative ideation and the final market launch of the innovation (Kessler & Chakrabarti, 1996). Although innovation speed is a significant performance crite‐ rion for incubator firms, little empirical research has 1. INTRODUCTION Technology startups have been identified by academicians and policymakers as an essential source of innovation and economic growth for local, regional, and national levels (Dee et al., 2019). Be‐ cause startups are more susceptible to failure due to their liability of newness, policymakers widely promote business incubation as a protective mech‐ anism for early‐stage firms (Eveleens et al., 2017). Entrepreneurship scholars thus have shown a keen interest in understanding the types and effects of business incubation services over the last decade (Bruneel et al., 2012; Mian et al., 2016; Hausberg & Startups suffer from very high mortality rates—much higher than those of larger, well‐established companies. Many researchers believe that business incubation (BI) acts as a helping hand and encourages entrepreneurial development by creating a network of knowledge and resources. Still, few have studied the effect of the incubation on knowledge gain, which leads to quicker innovation compared to their competitors. This study used a resource‐based view and or‐ ganizational learning theory to explore the effect and mechanism of incubation on the absorptive capacity (ACAP) and innovation speed (IS) of startups in the Indian context. Using survey data from a sample of 344 technology intensive incubator firms in India, we demonstrated that the incubation mechanisms, namely business incubator selection per‐ formance, monitoring, and business assistance intensity and resource munificence, help startups to explore, transform, and exploit knowledge and information to bring technology and products to market quickly. Data were analyzed using hierarchical regression, and mediation was tested using Baron and Kenny’s (1986) method and a Process macro de‐ veloped by Hayes (2013). The results of this study prove that absorptive capacity dimensions such as exploratory, transformative, and exploitative learning among startups are augmented by business incubation services, which results in the rapid commercialization of technological products. Keywords: Business Incubation, Startups, Innovation Speed, Absorptive Capacity BUSINESS INCUBATION AND INNOVATION SPEED: MEDIATING ROLE OF ABSORPTIVE CAPACITY Vinney Zephaniah Vincent Cochin University of Science and Technology, Kerala, India vinneyzephaniah@gmail.com Zakkariya K.A. Cochin University of Science and Technology, Kerala, India zakkariya@gmail.com Dynamic Relationships Management Journal, Vol. 10, No. 1, May 2021 57 Abstract Vol. 10, No. 1, 57‐72 doi:10.17708/DRMJ.2021.v10n01a04 DRMJ vol10 no01 2021 (print)4.qxp_Prelom 26/05/2021 10:39 Page 57 Dynamic Relationships Management Journal, Vol. 10, No. 1, May 2021 58 Vinney Zephaniah Vincent, Zakkariya K.A.: Business Incubation and Innovation Speed: Mediating Role of Absorptive Capacity been conducted on this topic (Clausen & Kor‐ neliussen, 2012). Therefore, a better understanding of how incubator startups can achieve the required innovation speed is imperative. Business incubators act as hubs through which emerging young firms can network (Hughes et al., 2007), and thus exposes developing young startups to synergetic opportunities to collaborate, learn, and ac‐ quire knowledge (Dee et al., 2019). Learning refers to the ability of the organization to gain new skills, which it can use in its processes (Lichtenthaler, 2009; Lane & Lubatkin, 1998). Grounded on the resource‐based view and organizational learning perspective, incuba‐ tion opens the door to new networking contexts of various organizational systems and structures, causing businesses to learn the best practices in global mar‐ kets (Wu, 2007). Business incubators also improve the capacity of an organization to leverage its established capacities and resources when exploring innovative options (Pettersen et al., 2015). Exploitative learning among startups focuses on using the existing informa‐ tion, abilities, and resources of the organization in ex‐ isting and first‐hand markets, as well (Zahra, 2005). However, excessive emphasis on transforming new and existing capacities can lead to organizational clink‐ ers and roll‐back (Lichtenthaler, 2009). Business incu‐ bation lessens this risk by stimulating exploration activities among young firms (Hughes et al., 2007). This can promote innovation and expand the variety of strategic options open to the firm (Hackett & Dilts, 2004; Bruneel et al., 2012). The preceding argument indicates that merely registering with a business incubator or engaging in business incubation activities does not ensure inno‐ vation speed (Clausen & Kornelieussen, 2012) and performance (Voisey et al., 2006). Much depends on the ability of the startup to find effective ways of ex‐ ploiting its skills and capabilities (Hughes et al., 2007) and acquiring new knowledge (Theodorakopoulos et al., 2014) from the networked business incubator (Pena, 2004) and the startup’s competing markets (Mian, 2016). This ability depends to a certain extent on the absorptive capacity of the startup firm (Jansen et al., 2005; Zahra, 2005; Lane et al., 2006). Consequently, innovation speed, which is a sig‐ nificant criterion for firm performance, depends on the firm’s absorptive capacity and its applications, and it has major implications for the early‐stage develop‐ ment of firms, mainly firms which have limited busi‐ ness resources and network experience (Wu, 2007). Such constraints limit the accumulation of resources which could extend the firm’s knowledge base, which, in effect, limits the entrepreneurial capacity of the startup firm (Zahra et al., 2009). Compared to other stable factors such as the founder’s education qualifi‐ cations and experience, the absorptive capacity of a firm can be changed and developed (Zahra et al., 2005; Zahra et al., 2009). Business incubation is con‐ sidered to be one solution to these constraints, be‐ cause it provides access to a variety of on‐site live resources, services, mentoring, and training (Hackett & Dilts, 2004a; Hackett & Dilts, 2004b). Although the ability of incubation to help early‐stage firms is implicit and convincing, the extent to which it supports the startups in knowledge exploration and transformation of opportunities by exploiting new and existing infor‐ mation is unknown (Eveleens & Rijnsoever, 2017). This study examined how the incubation pro‐ cess can augment the knowledge attribute of ab‐ sorptive capacity to help improve the innovation speed of new technology startups. The study exam‐ ined the mechanism by which a technology business incubator increases the startup’s absorptive capacity by allowing the exploration of knowledge and criti‐ cally promoting the transformation of information into resources that supports business performance through innovation, creating sustainable develop‐ ment. Empirically, this study is built on survey data collected from a sample of 344 technology‐intensive incubator firms registered with government‐sup‐ ported technology business incubators in India. We used the lens of organizational learning to suggest that startups participate in business incubation activities to gain new skills and capabilities that allow them to leverage new opportunities and innovation in competitive markets effectively. Consequently, busi‐ nesses should build a stock of relevant information within their own innovative operations. This paper dis‐ cusses the topic in greater depth. The paper is ar‐ ranged in four sections. First, we clarify the concepts of business incubation, absorptive capacity, and inno‐ vation speed. Secondly, we propose a relationship be‐ tween them. The third section discusses the research methodology and the empirical results. Lastly, man‐ agerial and theoretical implications are discussed. DRMJ vol10 no01 2021 (print)4.qxp_Prelom 26/05/2021 10:39 Page 58 Dynamic Relationships Management Journal, Vol. 10, No. 1, May 2021 59 2. THEORETICAL BACKGROUND 2.1 Business Incubation and Innovation Speed The key objective for both business incubators and incubated startups is the speedy commercializa‐ tion of new technology and innovation (Grimaldi & Grandi, 2005). The success of the incubator depends on the ability of its tenant ventures to develop and market new products and processes quickly (Patton et al., 2009). Rapid commercialization is critical for in‐ cubator firms to achieve the required market share, early sales, growth, and survival (Chen, 2009; Clausen & Korneliussen, 2012). Most of India’s technology in‐ cubators are supported in whole or in part by the government, which provides a learning ground for startups, and they are focused on the commercializa‐ tion of science‐ and technology‐oriented processes and applications rather than on the generation and growth of the general business. The creation of tech‐ nology‐based incubators in India stems from the gov‐ ernment’s belief that fostering such mentoring practices promotes the development of a knowledge‐ based economy that is competitive enough to com‐ pete on the global marketplace (NASSCOM, 2018). Innovation speed is a capability that can yield a significant competitive advantage for a firm when combined with core practices (Kessler & Chakrabarti, 1996). Consequently, by increasing the speed of effec‐ tive innovation, companies can fully exploit research‐ related assets, amortize research project costs through the more‐efficient introduction of new prod‐ ucts, and thus maximize profit (National Research Council, 2003). Due to unpredictable markets and sys‐ temic let‐downs that restrict the ability of small tech‐ nology‐based startups to survive and overcome the complexity and challenges associated with the early stages of ideation and development, policymakers widely promote a business incubation environment (Eveleens et al., 2017; Dee et al., 2019). Although pol‐ icymakers have acknowledged that the incubation process can bring a competitive advantage by expand‐ ing the knowledge base of incubator firms (Hillermane et al., 2019), the majority of the literature to date has concentrated on recording the effects of incubator outcomes, and not on the aftermath of incubation (Hackett & Dilts, 2004; Hackett & Dilts, 2008; Bergek & Norman, 2008; Stephens & Onofrei, 2012). There‐ fore, several studies have called for more incubatee– incubator research that explores incubation through the lens of incubator firms (Voisey et al., 2006, Stephens & Onofrei 2012; Hackett & Dilts, 2008; Pat‐ ton et al., 2009), incubator best practices (Bergek & Norrman, 2008), the process within startups (Ayatse et al., 2017), networking among startups (Pettersen et al., 2015), and interactions with incubation net‐ works (Bollingtoft & Ulhoi, 2005; Pettersen et al., 2015). There is no consensus about what constitutes the quality of successful business incubation services and how these services can generate the requisite value‐added outcomes (Mian, 1996; Patton et al., 2009; Dee et al., 2019; Hillemane et al., 2019). A major gap in the literature on innovation is that most innovation speed studies are conducted at the process level, and the firm level should be considered. Therefore, we focused on the firm‐level innovation speed and, in particular, the position of external and strategic constructs that can promote faster innovation speed. From the literature on speed to market, which is synonymous to innova‐ tion speed (Claussen & Kornelieussen, 2012), we take the view that it is especially important to ex‐ amine constructs that directly affect the organiza‐ tion and are subject to firm‐level influence. The role of strategic support’ is among the factors high‐ lighted as areas for future research. Established on the resource‐based view, business incubation is a construct that illustrates the role of strategic ap‐ proaches and the role of management support as a driving force behind the rapid commercialization of new products (Hackett & Dilts, 2008; Voisey et al., 2006, Patton et al., 2009; Pettersen et al., 2015; Eveleens & Rijnsoever; 2017) in new and emerging firms. This study argues that business incubation can be a significant antecedent of innovation speed. Hypothesis 1: Business incubation is positively re‐ lated to the innovation speed of startups. 2.2 Business Incubation and Absorptive Capacity Business incubation provides an essential sys‐ tematic environment for startups to acquire knowl‐ edge for innovation and growth, and it provides access to critical resources, which relates to the un‐ derlying concept of the resource‐based view. Based on organizational learning theory, a sustainable en‐ DRMJ vol10 no01 2021 (print)4.qxp_Prelom 26/05/2021 10:39 Page 59 Dynamic Relationships Management Journal, Vol. 10, No. 1, May 2021 60 vironment is necessary for continuous learning. Business incubation leads to continuous learning of new skills and capabilities that significantly improve the ability of a firm to innovate, take risks, and de‐ velop new revenue streams (Bruneel et al., 2012; Etzkowitz, 2002; Arshad et al., 2020). It also acts as a guiding mechanism that enhances a firm’s ability to explore new knowledge, transform useful and adequate information, and exploit its new and ex‐ isting capabilities and resources for achieving growth and innovation (Al‐Mubaraki & Busler, 2013; Freund et al., 2020). Emerging startups obtain their knowledge and perform their learning mainly through network re‐ lations (Hughes et al., 2007), and such learning man‐ ifests the logic underpinning incubators (Pena, 2004). Incubators should help develop and coordi‐ nate such dynamic linkages to ensure that firms learn to explore, transform, and exploit the knowl‐ edge acquired (Patton, 2014; Borges & Beuno, 2020). Absorptive capacity is a key dynamic capabil‐ ity which refers to the ability of the startup to gain new knowledge that it can utilize in its innovation and venturing operations (Zahra & Hayton, 2008). Every piece of new knowledge that a firm has gained will contribute to performance and innova‐ tion, and thus represents a potential source of com‐ petitive advantage (Zahra & Hayton, 2008; Xin et al., 2020) among incubator firms. Absorptive capacity consists of three dimensions, encompassing ex‐ ploratory learning via new knowledge, the exploita‐ tion of the existing knowledge, and the combination of the two through transformative learning (Lane et al., 2006). This study employed the framework of Lane et al. (2006) to describe absorptive capacity, because it is useful in the specific context of incu‐ bator startups that promote technology advance‐ ments through innovative new products and services (Gebauer, Worch & Truffer, 2012). Exploratory learning is a way of discovering and comprehending new external knowledge (Jansen et al., 2005). When the approach of a firm guides its efforts, the firm’s previous knowledge, which is a function of prevailing models, affects the vital as‐ sessment of new external information (Tzokas et al., 2015). Transformative learning necessitates many mechanisms that influence how the newly acquired knowledge and the existing are integrated (Jansen et al., 2005; Lane et al., 2006). Knowledge manage‐ ment methods influence how such knowledge is dis‐ seminated and passed to various areas of the firm (Argote, McEvily & Reagans, 2003). The inclusive outcome of these processes is a transformation of the combined nature of various organizational divi‐ sions arising from the assimilation of new knowl‐ edge (Tzokas et al., 2015; Jansen et al., 2005; Lane et al., 2006). The last dimension reveals how exploitative learning is used to apply the assimilated external knowledge (Lane et al., 2006). Companies do not simply achieve the absorptive capacity they need to thrive, primarily because of their liability of new‐ ness. In reality, it takes an adequate and critical cli‐ mate to help improve the requisite absorptive capacity required to achieve a competitive edge. Business incubation exposes the companies to varying networked environments, operating in a world marked by rapid and persistent transition, which provides the opportunity for persistent learning from the incubator network (Hutabarat & Pandin, 2014). This type of transition makes room for startups to learn continuously to survive, let alone succeed (Patton, 2014). Inevitably, this rou‐ tine allows firms to learn continuously from highly experienced experts, mentors, and peer networks to advance their knowledge, skills, and capabilities (Grandinetti, 2016). Thus, the incubator environment allows the firms to improve the techniques of exploration, trans‐ formation, and exploitation of knowledge to achieve their commercial ends (Jansen et al., 2005). Such ex‐ posure makes it possible for a firm to forecast more accurately the nature and commercial prospect of technological advances (Cohen & Levinthal, 1990) and to develop specialized innovative products (Tzokas et al., 2015). Therefore, it is best to assume that business incubation will have a positive impact on absorptive capacity, which can be understood as a combination of exploratory, transformative, and ex‐ ploitative learning. Based on these arguments, we propose the following hypothesis: Hypothesis 2: Business incubation positively relates to a startup’s absorptive capacity in terms of three dimensions, exploratory learning, transformative learning, and exploitative learning. Vinney Zephaniah Vincent, Zakkariya K.A.: Business Incubation and Innovation Speed: Mediating Role of Absorptive Capacity DRMJ vol10 no01 2021 (print)4.qxp_Prelom 26/05/2021 10:39 Page 60 Dynamic Relationships Management Journal, Vol. 10, No. 1, May 2021 61 2.3 Absorptive Capacity and Innovation Speed Current success and future sustainability in a rapidly changing world rely on continuously learning to do things differently and better (Talwar et al., 2015). Knowledge is both the raw material that un‐ derpins learning and the outcome of it, offering new opportunities and new revenue streams (Gibb, 2002). Economic growth always has been driven by the desire to invent and evolve, which is to create new knowledge and ideas realized through pro‐ cesses, products, and ventures (Al‐Mubaraki & Busler, 2013). Current thinking goes beyond merely recognizing it as a source of competitive advantage for a new venture; instead, it is argued that the con‐ version of knowledge into innovation is the main reason for startups to exist and establish. Startups operating in global markets need to absorb tremendous knowledge rapidly to leverage new business opportunities in those markets and gain an economical advantage (Zahra & Hayton, 2008; Paliokaite, 2019). This challenge is compli‐ cated by the fact that the knowledge gained often is complex and contextual, and represents the cul‐ tures and locations in which it was created (Talwar et al., 2015). A startup’s ability to absorb, transform, and exploit this knowledge can influence the extent to which it rapidly can innovate and generate profits or growth in revenues in the presence of supporting external mechanisms (Zahra & Hayton, 2008). Ab‐ sorptive capacity can accelerate innovation pro‐ cesses in small firms, which can confer strategic advantage (Huang & Rice, 2009). Accelerating inno‐ vation is important, because any window to exploit technological advances is shrinking continually due to the spillover of information, the replication of processes and operations by rivals, and technologi‐ cal obsolescence that makes most of the benefits temporary (Huang & Rice, 2009). Despite differ‐ ences in terms of organizational knowledge, there is increasing consensus in the new venture literature that absorbing capacity explains new venture inno‐ vation at the firm level. Cohen and Levinthal (1990) described absorp‐ tive capacity as the ability of a startup to explore, understand, transform, and assimilate the knowl‐ edge acquired from external sources. Consistent with this definition, we focused on the antecedent effect of absorptive capacity on innovation speed at the level of the startup firm in a business incu‐ bator by focusing on the sources of knowledge fos‐ tered through incubation activities and networks. Although much has been written about absorptive capacity and its value, very little attention has fo‐ cused on how absorptive capacity is created and used in a new venture to accelerate innovation. Whereas the majority of the literature focused on the absorptive capacity of established firms (Zahra & George, 2002; Lichtenthaler, 2009; Zahra et al., 2009), the present study focused on its influence on incubated early‐stage firms’ growth and devel‐ opment, because absorptive capacity is the knowl‐ edge which is acquired externally (Zahra & George, 2002). Based on these arguments, the following hy‐ pothesis is proposed: Hypothesis 3: Absorptive capacity dimensions such as exploratory learning, transformative learning, and exploitative learning are positively related to in‐ novation speed. 2.4 Mediating Role of Absorptive Capacity Organizational learning, described here as ab‐ sorptive capacity, is a firm’s collaborative and itera‐ tive processes to acquire new technologies and know‐how from business incubation (Lane & Lu‐ batkin, 1998). Based on organizational learning the‐ ory, business incubation facilitates the development of new skills and capacities that strongly enhance the capacity of a company to innovate, take risks, and build new revenue streams (Patton et al., 2009). Ab‐ sorptive capacity is the ability of a firm to utilize knowledge held externally through the three pro‐ cesses of exploratory learning, transformative learn‐ ing, and exploitative learning (Tzokas et al., 2015). It involves a path from identifying and acquiring exter‐ nal knowledge (exploratory learning) through assim‐ ilation, understanding, and retention (transformative learning), to its transmutation and application (ex‐ ploitative learning) (Tzokas et al., 2015; Jansen, 2005). Business incubation introduces a startup to new opportunistic market environments with un‐ tapped potential, which encourages companies to explore, transform, and exploit in order to survive the competitive market (Patton et al., 2009). DRMJ vol10 no01 2021 (print)4.qxp_Prelom 26/05/2021 10:39 Page 61 Dynamic Relationships Management Journal, Vol. 10, No. 1, May 2021 62 The diversity of mentors, investors, university networks, client groups, peer startups, and political systems associated with business incubation also can expand a startup’s search for new knowledge (Scillitoe & Chakraborti, 2010). Business incubation often links startups in their fields of business or other sectors with allied companies and networks (Bruneel et al., 2012). According to the theory of or‐ ganizational learning, the introduction of such spe‐ cific and diverse expertise into the operations of small firms enhances and accelerates the absorptive capacity, augmenting their innovative output (Zahra & Hayton, 2008). Business incubation services paves way to ACAP’s exploratory, transformative, and exploitative learning (Hillemane et al., 2019). To achieve rapid innovation, incubator startups require strong tech‐ nological support, mentoring and assistance, and al‐ lied services from incubators for the exploration of new knowledge and technologies (Lalkaka, 2001). When an incubator firm is in a new product devel‐ opment process, it is more likely to be open to new external knowledge available from its incubator net‐ work (Scillitoe & Chakraborti, 2010). This receptivity further increases the speed of innovation by allow‐ ing the firm to access new technological develop‐ ments, workshops, and live interactive sessions as a reinforcing cycle of exploratory innovation (Lane et al., 2006; Lichtenthaler, 2009). Thus it offers a lean process which increases the speed with which the firm is able to discover new opportunities and de‐ creases the cycle time of build, measure, and learn (Carayannis & Zedtwitz, 2005; Ghezzi, 2019). There‐ fore, the higher the degree of assistance and re‐ sources offered by the incubator, the greater is the potential for start‐ups to participate in exploratory innovation (McAdam & McAdam, 2008). At the same time, business incubators should ensure that the knowledge acquired by the tenant firms is accumulated and stored for the sustainable growth and development before and after the cycle time of new product development (Schwartz & Hornych, 2008; Lalkaka, 2002). The accumulation of knowledge not only increases product innovation skills, but also a firm’s ability to engage in the trans‐ formation learning process through assessment, use, and application of new technologies (Gebauer et al., 2012; Tzokas et al., 2015). Similarly, an entrepreneurial venture with strong incubation support tends to engage in more exploitation alliances to gain access to complemen‐ tary assets (Hughes & Morgan, 2007). As firms ac‐ cumulate more knowledge and skills from the incubator, they become more effective in deploying their existing knowledge, and thus develop more ex‐ ploitative activities due to the obvious self‐reinforc‐ ing nature of learning (Kowalski, 2018). Thus, the higher the level of incubation support, the greater is the probability of catalyzing more exploitation in that field (Klofsten et al., 2019). At the same time, innovation speed also can be associated with a firm’s performance (Claussen & Kornelieussen, 2012). In the context of rapid technological innova‐ tions, a strong resourceful environment, along with ACAP, enhances the benefits of novel innovations and contribute positively to a firm’s innovative and financial performance (Zahra & George, 2002; Lane et al., 2006; Hughes & Morgan, 2007; Patton, 2014). Despite the prospective value addition of busi‐ ness incubation services, they may not guarantee boosting a startup’s knowledge (Dee et al., 2019). Unwillingness to share knowledge by team mem‐ bers and the reluctance of peer startup teams to share information might be the reason for this problem (Cardon et al., 2017). Even if team mem‐ bers and peer startup groups share their skills, the recipient startup may not have the absorptive ca‐ pacity necessary to explore, transform, and exploit the knowledge (Cardon et al., 2017) it acquires from business incubation. We argue that the greater the absorptive capacity, the stronger is the relationship between a firm’s use of business incu‐ bation services and innovative performance. Therefore: Hypothesis 4a: Exploratory learning mediates the relationship between business incubation and inno‐ vation speed. Hypothesis 4b: Transformative learning mediates the relationship between business incubation and innovation speed. Hypothesis 4c: Exploitative learning mediates the relationship between business incubation and inno‐ vation speed. Vinney Zephaniah Vincent, Zakkariya K.A.: Business Incubation and Innovation Speed: Mediating Role of Absorptive Capacity DRMJ vol10 no01 2021 (print)4.qxp_Prelom 26/05/2021 10:39 Page 62 Dynamic Relationships Management Journal, Vol. 10, No. 1, May 2021 63 The relationships expressed through the four formulated hypotheses are represented in the re‐ search model with continued associations (Fig. 1). 3. METHODOLOGY 3.1 Sample and collection of data To test the hypothesized relationships between BI, ACAP , and IS empirically, the researchers adopted a descriptive research design using survey data ob‐ tained from incubator firms. Survey data were gath‐ ered in the context of government‐supported technology business incubators in India. These in‐ cubators are open to enterprises with a new prod‐ uct idea and are established for less than five years. An important goal of the technology business incu‐ bator system is a technology development and the commercialization of new innovative products (Claussen & Kornelieussen, 2012). This background is appropriate in testing the influence of business in‐ cubation on startup innovation speed in the context of absorptive capacity. Hence the population of the study was CEOs of technology‐based startups incu‐ bated in government‐supported technology busi‐ ness incubators in the state of Kerala, India. There were 540 such startups incubated in 32 technology business incubators established within the major cities of the state of Kerala. A list of firms with the names and email addresses of the CEOs was obtained from respective incubator managers, and also from the government agency that supports the technology business incubators. Follow‐ ing a census survey method, survey questionnaires were administered directly to all 540 CEOs (and emailed to those who were not physically present in the incubator during the time of data collection) to col‐ lect the required data. All incubator managers were asked to motivate the founder CEOs in their incubation facilities to respond to the survey questionnaire. The researchers collected 344 valid responses out of 540 incubated businesses, following numerous follow‐ups and visits. This is a response rate of 64%, which is a much higher rate than in most other survey‐based re‐ search aimed at new ventures and startup firms. The survey data were gathered from September to Decem‐ ber, 2019. Some of these respondents started ventures immediately after college; a few are still in college; the majority of them worked for corporations before start‐ ing their own businesses. The demographic profile of the respondents is given in Table 1. Figure 1: Research model with hypotheses DRMJ vol10 no01 2021 (print)4.qxp_Prelom 26/05/2021 10:39 Page 63 Dynamic Relationships Management Journal, Vol. 10, No. 1, May 2021 64 3.2 Measurement Independent Variable The items to measure business incubation were adopted from Khalid et al. (2012), which was based on the seminal work by Hackett and Dilts (2008). The items were measured from the incubator firm perspective, taking into account the Indian startup and business incubation environment, which re‐ sulted in a set of measures that encompassed busi‐ ness incubator selection performance, monitoring, business assistance intensity, and resource munifi‐ cence involving 31 items. The items were measured using a five‐point Likert‐type scale ranging from 1 = strongly disagree to 5 = strongly agree, with greater values representing higher amounts. Mediating Variable The new venture literature identifies three im‐ portant indicators for absorptive capacity: ex‐ ploratory, transformative, and exploitative learning. The measurement of absorptive capacity followed the method used by Tzokas et al. (2015), which is a summated scale including all three dimensions which includes 11 items, was adopted for this study. All items were measured using a five‐point Likert‐ type scale ranging from 1 = strongly disagree to 5 = strongly agree. Dependent Variable We began by synthesizing scales from the liter‐ ature in developing measures that reflect product speed, speed to market, and innovation speed. We considered speed to market to be synonymous with innovation speed (Claussen & Kornelieussen, 2012) Thus, to measure innovation speed, we adopted the scale of Fang (2008), which consisted of four items. The items were measured using a five‐point Likert‐ type scale ranging from 1 = strongly disagree to 5 = strongly agree. Control Variables Data were collected for several control vari‐ ables: firm age, CEO gender, education, prior startup experience, prior work experience, and family en‐ trepreneurial experience. Firm age and gender are two significant basic features that may be associ‐ ated with innovation speed. We controlled for edu‐ cation, because it may lead to better business performance because the founders can use their knowledge from formal education to better exploit resources needed to develop and market the prod‐ uct faster. We also controlled for prior startup experience by measuring whether the founding CEO had started a previous new venture. According to the lit‐ Vinney Zephaniah Vincent, Zakkariya K.A.: Business Incubation and Innovation Speed: Mediating Role of Absorptive Capacity Table 1: Demographic profile of respondents Variable Category N Percentage Firm age (years) ≤2 229 66.6 >2 115 33.4 Gender Male 315 91.6 Female 29 8.4 Education Completed 284 82.6 Not completed yet 60 17.4 Previous startup experience Had previous startups 272 79.1 No previous startups 72 20.9 Previous work experience No work experience 66 19.2 Work experience 278 80.8 Family‐owned business Family has business 97 28.2 No family business 247 71.8 DRMJ vol10 no01 2021 (print)4.qxp_Prelom 26/05/2021 10:39 Page 64 Dynamic Relationships Management Journal, Vol. 10, No. 1, May 2021 65 erature, prior work experience of the founding team also has a significant impact on small‐firm innova‐ tion. Family business and entrepreneurial family has a significant influence on startup founders. We used binary variables 0 and 1 to measure all the control variables. We sought to reduce common method bias because each questionnaire came from one re‐ spondent. We also implemented corresponding control measures in research design and statistical analysis, as suggested by Podsakoff et al., (2003). In line with our research design, we assured par‐ ticipants that their responses would be anony‐ mous, that there would be no correct or incorrect answers, and that they should answer questions as fairly as possible. We applied Harman’s single‐fac‐ tor method for statistical remedies. A variance in‐ flation factor (VIF) test was conducted using SmartPLS 3, because it is one of the most reliable tests and can spot the presence of this bias if sin‐ gle‐factor analysis fails (Kock, 2015). The variance inflation factor test it is an automatic procedure in SmartPLS 3 which allows the full collinearity test for all the latent variables in the model. A VIF higher than 3.3 indicates the presence of an unac‐ ceptable level of common method bias. In this study, the VIF values of all latent variables were under the prescribed limit. We subjected all the main variables to factor analysis and then assigned the number of factors that accounted for the variance in the measures. In addition, the reliability of the multi‐item scales was assessed with Cronbach’s alpha. All variables had re‐ liability scores above 0.7 (Table 2), and hence the subsequent step in the analysis could be performed. Table 2: Results of reliability test Table 3: Descriptive statistics and correlations Note: SD = standard deviation. *p < .05, **p < .01, ***p<.001 Variable No. of items Total variance accounted for (%) Cronbach’s alpha Business incubation 31 14.43 .923 Exploratory learning 4 27.73 .850 Transformative learning 3 39.82 .857 Exploitative learning 4 50.11 .876 Innovation speed 4 55.68 .777 No. Variable Mean SD 1 2 3 4 5 6 7 8 9 10 11 1 Startup age 1.33 0.472 1 2 Gender 1.08 0.278 0.015 1 3 Education 1.18 0.382 0.008 −0.012 1 4 Prior startup experience 1.21 0.425 −0.182 −0.393 −0.030 1 5 Prior work experience 1.81 0.393 −0.052 0.123 −0.054 −0.019 1 6 Family‐owned business 1.71 0.460 −0.024 −0.049 0.031 0.043 −0.054 1 7 Business incubation 3.63 0.915 −0.005 0.089 −0.031 −0.044 0.124* −0.044 1 8 Exploratory learning 3.95 0.830 −0.043 −0.079 −0.128* 0.029 0.071 −0.032 0.293** 1 9 Transformative learning 3.89 0.790 −0.027 −0.028 −0.118* −0.025 0.103 −0.103 0.312** 0.726** 1 10 Exploitative learning 3.85 0.815 0.003 −0.013 −0.080 −0.004 0.102 −0.062 0.383** 0.619** 0.692** 1 11 Innovation speed 3.11 0.900 −0.063 0.039 −0.044 −0.060 0.067 −0.068 0.505** 0.378** 0.314** 0.340** 1 DRMJ vol10 no01 2021 (print)4.qxp_Prelom 26/05/2021 10:39 Page 65 Dynamic Relationships Management Journal, Vol. 10, No. 1, May 2021 66 4. RESULTS Table 3 reports the means, standard deviations, and coefficients of Pearson correlation for the major variables. The results showed that the correlations between the key research variables were signifi‐ cantly interrelated. These were in line with the aforementioned research hypotheses. The potential presence of multicollinearity was investigated using SPSS software using the variance inflation factor es‐ timated for each variable in the ordinary least squares (OLS) regression. VIF statistics greater than 10 indicate multicollinearity issues. VIF statistics ranged from 1.005 to 1.714, indicating multi‐ collinearity is not an issue in our analysis. We followed the procedure recommended by Baron and Kenny (1986) to test the mediation hy‐ pothesis. First, the dependent variable was re‐ gressed on the independent variable. Subsequently, the mediating variable (separate dimensions) was regressed against the independent variable. Finally, the dependent variable was regressed against both the independent variable and the mediating vari‐ Vinney Zephaniah Vincent, Zakkariya K.A.: Business Incubation and Innovation Speed: Mediating Role of Absorptive Capacity Table 5: Business incubation regressed on absorptive capacity Variable Unstandardized coefficients Startup age −0.140 −0.140 Gender −0.133 −0.097 Education −0.165 −0.145 Prior startup experience 0.118 −0.005 Prior work experience 0.006 −0.068 Family‐owned business −0.121 −0.097 Business incubation — 0.494*** R 2 0.019 0.265 Adjusted R 2 0.001 0.250 ΔR 2 — 0.246 F 1.067 17.338*** Exploratory learning Transformative learning Exploitative learning Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Star‐up age −0.071 −0.071 −0.054 −0.054 0.009 0.009 Gender −0.371 −0.351 −0.319 −0.300 −0.217 −0.193 Education −0.040 −0.029 −0.114 −0.103 −0.032 −0.018 Prior startup experience 0.140 0.073 0.180 0.115 0.190 0.106 Prior work experience −0.206 −0.245 −0.135 −0.174 −0.072 −0.122 Family‐owned business −0.053 −0.040 −0.164 −0.151 −0.096 −0.080 Business incubation — 0.267*** — 0.262*** — 0.336*** R 2 0.031 0.115 0.037 0.127 0.020 0.159 Adjusted R 2 0.014 0.097 0.020 0.109 0.002 0.142 F 1.784 6.251*** 2.171 6.983*** 1.140 9.093*** Table 4: Business incubation regressed on innovation speed DRMJ vol10 no01 2021 (print)4.qxp_Prelom 26/05/2021 10:39 Page 66 Dynamic Relationships Management Journal, Vol. 10, No. 1, May 2021 67 able dimensions. If there was a mediation effect, the effect of the independent variable would decrease or even become insignificant. Tables 4–6 present the results of hierarchical multiple regressions. Table 4 reports the regression results of business incubation and innovation speed. The results showed that the incubation mechanism had a significant impact on innovation speed. This empirical result firmly supports H1. Table 5 shows the regression analysis results of business incubation and absorptive capacity dimen‐ sions. Models 1, 3, and 5 involved only the control variable, whereas Models 2, 4, and 6 indicated that business incubation had a significant impact on ab‐ sorptive capacity dimensions such as exploratory learning, transformative learning, and exploitative learning. Thus H2, H2a, H2b, and H2c are supported by the data. Overall, there was a significant positive effect of business incubation on absorptive capacity dimensions. Table 6 presents the mediating effect of absorp‐ tive capacity dimensions on the relationship be‐ tween business incubation and innovation speed. Model 1 involved only control variables, and Model 2 tested the effect of business incubation on innova‐ tion speed. On the basis of the two models, ex‐ ploratory, transformative, and exploitative learning were added to the analysis. Compared with Model 2, the results of Models 3–5 indicated that ex‐ ploratory learning (β = 0.272, p < 0.001), transforma‐ tive learning (β = 0.189, p < 0.001), and exploitative learning (β = 0.187, p < 0.001) had a direct effect on innovation speed and had a significant positive me‐ diating effect. Therefore H3, H4a, H4b, and H4c are supported. In Model 6, when the three types of learning were included, each had a significant effect on innovation speed, whereas transformative learn‐ ing had a negative effect (β = −0.047, p < 0.001). To further test Hypothesis 4 to determine the mediation effect of absorptive capacity, we followed the bootstrapping method recommended by Hayes (2013), using a Process macro. We bootstrapped 5,000 samples to obtain a 95% bias‐corrected confi‐ dence interval for the mediation effect. The path (di‐ rect effect) from business incubation to absorptive capacity was positive and significant (b = 0.2929, standard error = 0.0600, p = <0.001), indicating that if improved business incubation facilities are pro‐ vided, the absorptive capacity of startups increases. The direct effect of absorptive capacity on innovation speed also was positive and significant (b = 0.2858, Table 6: Mediation analysis Innovation speed Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Startup age −0.140 −0.140 −0.121 −0.130 −0.142 −0.124 Gender −0.133 −0.097 −0.001 −0.040 −0.061 −0.004 Education −0.165 −0.145 −0.137 −0.126 −0.142 −0.141 Prior startup experience 0.118 −0.005 −0.025 −0.027 −0.025 −0.026 Prior work experience 0.006 −0.068 −0.001 −0.035 −0.045 −0.002 Family‐owned business −0.121 −0.097 −0.086 −0.069 −0.082 −0.089 Business incubation 0.494*** 0.422*** 0.445*** 0.432*** 0.416*** Exploratory learning 0.272*** 0.273*** Transformative learning 0.189*** −0.047*** Exploitative learning 0.187*** 0.054*** R 2 0.019 0.265 0.321 0.289 0.289 0.322 Adjusted R 2 0.001 0.250 0.305 0.250 0.272 0.302 F 1.067 17.338*** 19.797*** 17.053*** 17.053*** 15.835*** DRMJ vol10 no01 2021 (print)4.qxp_Prelom 26/05/2021 10:39 Page 67 Dynamic Relationships Management Journal, Vol. 10, No. 1, May 2021 68 Vinney Zephaniah Vincent, Zakkariya K.A.: Business Incubation and Innovation Speed: Mediating Role of Absorptive Capacity standard error = 0.0566, p < 0.001), indicating that greater absorptive capacity and knowledge building ability of startups increases rapid technology com‐ mercialization and innovation speed. The results ob‐ tained prove that the indirect effect of business incubation on innovation speed through absorptive capacity dimensions (indirect effect = 0.2041; 95% confidence interval = 0.1014, 0.3003) is significantly positive. The results again prove H4a, H4b, and H4c. 5. DISCUSSION AND CONCLUSION We found that business incubation and absorp‐ tive capacity dimensions (mediator) are positively and directly associated with innovation speed. From a theoretical perspective, our study provides addi‐ tional empirical evidence that giving young startups effective business incubation may lead to higher lev‐ els of innovation and product success. The results are consistent with those of previous studies by Pat‐ ton (2014) and of Scillitoe and Chakraborti (2009) regarding the incubator knowledge ecosystem and its impact on innovative outcomes of startups. It also addresses a call made by Eveleens and Rijnso‐ ever (2016) related to the need to examine the qual‐ ity of relationships within the incubator ecosystem in order to better understand the dynamics of knowledge sharing for generating innovation among incubator firms. Additionally, the findings provide an understanding of how entrepreneurial learning influences small‐firm innovation. In their pursuit of knowledge, intellectual entrepreneurs push their firms to achieve rapid innovation through the incu‐ bation network. However, the pursuit of rapid inno‐ vation by intellectual entrepreneurs requires an entrepreneurial process that leads to fulfilling the goal. ACAP realizes this role (Zahra & George, 2002). Through its emphasis on exploratory, transforma‐ tive, and exploitative learning in the pursuit of new opportunities, ACAP enables the identification and development and of creative ideas for small‐firm performance (Jansen et al., 2005). One of the major objectives of this study was to analyze the mediating effect of absorptive capacity, and the study found a significant mediating effect of absorptive capacity, which is consistent with previ‐ ous studies (Aljanabi, 2018; Hernández‐Perlines & Xu, 2018). Unlike previous research that focused on the nature of business incubators (Hackett & Dilts, 2008; Voisey et al., 2006; Grimaldi & Grandi, 2005), this study highlighted the content that flows through the relationship between an incubator and incubator firm, specifically the internal and external knowledge flow. Although the type, model, and use of services may differ among incubator firms (Voisey et al., 2006), as previous research suggests, the resources that every startup seek within incubators and the use they make of these resources may be the same. This finding is also consistent with those of Voisey et al. (2006) and Claussen and Kornelieussen (2012), which indicates that all startups receive a similar type of support from their business incubators, but they excel differently due to their varying capabilities in learning to explore, transform, and exploit. Thus the study used the concept of learning to develop the theoretical arguments concerning how a learning strategy such as ACAP (Engelens et al., 2014) can en‐ hance faster innovation in a resourceful environ‐ ment, thereby addressing Hackett and Dilts’ (2008) call for research on how to improve business incu‐ bation mechanisms for fruitful outcomes. 5.1 Theoretical and Practical Implications This study could be the starting point for proposing new or improved public policies to invest in the world of innovation and entrepreneurship through the role of incubators. From a theoretical point of view, this manuscript has advanced the or‐ ganizational learning literature by highlighting the role of incubation as an antecedent for learning and innovation. One of the study’s key propositions is that business incubation induces and enhances or‐ ganizational learning. The results are equally impor‐ tant for incubators and incubator firms because it documents the various types of knowledge a firm might gain from incubation. This finding indicates that potential market speed depends on having the requisite absorptive capacity. Therefore, startups need to identify gaps within their knowledge, re‐ ceive adequate incubation support to resolve these gaps, and eventually, through learning, apply their enhanced competencies to attain rapid innovation. Incubation acts as a tool for startups, mentors, and managers to collaborate effectively to build the re‐ quired absorptive capacity. DRMJ vol10 no01 2021 (print)4.qxp_Prelom 26/05/2021 10:39 Page 68 Dynamic Relationships Management Journal, Vol. 10, No. 1, May 2021 69 Therefore, absorptive capacity is connected not only to new entry but also to how easily new entry is achieved. This paper highlighted and confirmed that absorptive capacity is one such source of startup innovation performance. Another important aspect is that we concentrated on incubator com‐ panies rather than on incubators to explain why in‐ cubator companies vary in terms of performance (i.e., innovation speed). It has been argued (Ayatse et al., 2017; Dee et al., 2019) that much research has been done at the incubator level on the eco‐ nomic performance of incubators, whereas few studies have examined the driving forces behind in‐ cubator firm performance. Our findings suggest that such a firm‐level outlook is indeed useful and that more work should be conducted at the firm level to better understand incubator outcomes. We also seek to open a new research area fo‐ cusing solely on the innovation speed of incubator firms. Most previous research focused on incuba‐ tor and incubator firm performance by contrasting and comparing incubator firm performance with that of non‐incubator firms as a controlled group. As an alternative to focusing on economic perfor‐ mance, which is similar to the measure of large companies, this study explained the driving force for performance. We assume that this is a signifi‐ cant strand of literature that provides interesting insights into whether incubator programs have their intended effects on startups to achieve the required momentum for innovation. Furthermore, we contend that it is important to focus solely on incubator firms and to explain the innovation per‐ formance, which provides parallel insights and in‐ terpretations in contrast with research focusing solely on incubator economic performance. In‐ sight into how incubator managers can better as‐ sist and enhance their tenant firms’ growth, speed, and performance can be obtained when re‐ searchers analyze performance sources within in‐ cubated firms. 5.2 Limitations and Future Research This paper adopted the Hackett and Dilts (2008) model of business incubation, which includes three factors explaining the business incubation process. Although it is the most‐accepted framework of in‐ cubation, there may be additional mechanisms af‐ fecting incubated firms depending on the nature of business incubation services offered by a specific country. Therefore future research should consider other attributing mechanisms, mainly due to vary‐ ing country contexts that require investigation and develop contributing relationships. In addition, because the results are based on business incubation survey measures, other sources of data should be considered. Although we presented results of reliability and validity, sec‐ ondary data can contribute to the accuracy of our empirical findings. The study used the exploratory, transformative, and exploitative measures of the firm to evaluate the function of the absorptive ca‐ pacity, which is a common measure in the litera‐ ture. The robustness of our findings can be established by alternative measures of absorptive capacity. These measures might include a firm’s ability to absorb, assimilate, transform, and exploit available knowledge. One of the main propositions of the study is that business incubation facilitates organizational learning. Learning is not an automatic outcome of business incubation, and incubators have to dedi‐ cate the resources necessary to form the processes that initiate and encourage learning among star‐ tups. Therefore, researchers need to explore a gen‐ eralized incubator model that can enhance learning through business incubation. Because in this study business incubation consisted of selection criteria, monitoring and assistance, and resource munifi‐ cence, it would be helpful to determine whether these areas can be improved, augmented, and al‐ tered by the incubator managers to cater to the spe‐ cific requirements of incubated firms. Another important question is whether busi‐ ness incubation, absorptive capacity, and innovation speed are connected with an incubator firm’s eco‐ nomic performance. It is important to examine this relationship. In the end, what is important is the ac‐ tual economic results produced by incubator firms. Another limitation of our study is that all the main variables were measured based on a single survey, which might lead to common method bias. How‐ ever, the data were collected in a cross‐sectional de‐ sign, measured as a paper‐and‐pencil exercise, and DRMJ vol10 no01 2021 (print)4.qxp_Prelom 26/05/2021 10:39 Page 69 Dynamic Relationships Management Journal, Vol. 10, No. 1, May 2021 70 EXTENDED SUMMARY/IZVLEČEK Za zagonska podjetja (angl. startups) je značilna visoka stopnja »umrljivosti« – le‐ta je veliko višja kot pri večjih, že dobro uveljavljenih podjetjih. Številni raziskovalci verjamejo, da podjetniški inkubatorji pomagajo kot podpora in spodbujajo podjetniški razvoj z ustvarjanjem mreže znanja in virov. Kljub temu jih je vpliv podjetniških inkubatorjev na pridobivanje znanja, ki vodi do hitrejših inovacij v primerjavi s konkurenco, proučevalo le nekaj. Ta študija je s pomočjo različnih virov in teorije organizacijskega učenja raziskovala učinek ter mehanizem podjetnniških inkubatorjev na absorpcijsko sposobnost in hitrost in‐ ovacij zagonskih podjetij na področju Indije. Z uporabo anketnih podatkov iz vzorca 344 tehnološko in‐ tenzivnih inkubatorskih podjetij v Indiji smo pokazali, da inkubacijski mehanizmi, kot na primer uspešnost izbire podjetniškega inkubatorja, spremljanje in intenzivnost poslovne pomoči ter pomembnost virov, pomagajo zagonskim podjetjem pri raziskovanju, preoblikovanju in izkoriščanju znanja ter informacij za hitro uvedbo tehnologije in izdelkov na trg. Podatki so bili analizirani s pomočjo hierarhične analize, me‐ diacija pa je bila preizkušena z uporabo metode Barona in Kennya (1986) in PROCESS makrom, ki ga je razvil Hayes (2013). Rezultati študije dokazujejo, da so razsežnosti absorpcijske zmogljivosti, kot so ek‐ splorativno, transformacijsko in eksploatacijsko učenje med zagonskimi podjetji, povečane s storitvami podjetniških inkubatorjev, kar rezultira v hitri komercializacija tehnoloških izdelkov. 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