TY - JOUR
AU - Choung, Jae-Yong
AB - Abstract While understandings on the institutional environment and individual motivations have been at the center of describing the antecedents of academic entrepreneurship, findings from this research trigger skepticism in the currently dominant perspective. By building on the traditional institutional theory with insights from the stakeholders and attention-based perspectives, it is possible to postulate that reaching multiple stakeholders’ needs is essential in maximizing the effectiveness of a collective entrepreneurial process. We argue that aligning selective interests, or attentions, among key stakeholders is a critical factor that promotes academic entrepreneurship. Quantitative and qualitative investigations on Korean research universities confirm that involved stakeholders are subject to severe discrepancy in how they place attentional prioritizations in technology, formal institution, and informal institution capabilities. In spite of favorable national and university incentives, the different degrees of misaligned interests among key players prove to hold heavy repercussions in the collective planning and execution of academic entrepreneurship. 1. Introduction Transition to an entrepreneurial academic context has become the center of the global shift toward developing a third mission in universities(Etzkowitz and Leydesdorff 1999; Hsu et al. 2015; Lam and Lambermont-Ford 2010; Readings 1996). Case in point, European universities are learning to compete by behaving more like firms, whereas governments are increasingly implementing reforms to national research systems to intensify commercialization of academic knowledge (Mascarenhas et al. 2018; McKelvey and Holmén 2010). Asian governments are also seeking new ways to industrialize academic research (Hershberg et al. 2007) and increase faculty-based academic entrepreneurship (AE) through major restructuration of formalized practice models and institutional mechanisms (Kwon 2011; Hu 2009; Mathews and Hu 2007; Wong et al. 2011). National initiatives particularly focus on enhancing entrepreneurship in research-oriented universities since their cutting-edge technologies are considered essential in creating industrial opportunities (Clarysse et al. 2005; Hu 2009; Kwon 2011; Perkmann et al. 2013). However, recent evidence indicates that this transition has proven to be more difficult than expected. For example, research-oriented universities in Brazil are significantly associated with low entrepreneurial propensity and quality of entrepreneurial content (Fischer et al. 2019), while those in the UK also show considerably less entrepreneurial activities compared with teaching-led universities (Abreu et al. 2016). Current understandings on AE are largely dominated by investigations on the environmental antecedents (Vega-Gomez et al. 2018; Yoon and Lee 2013) and dynamic drives of individual agents (Hayter 2013; Hayter et al. 2017; Martinelli et al. 2008; Meoli and Vismara 2016; O’kane et al. 2015) in pursuing faculty-based entrepreneurship (Miranda et al. 2018). However, approaching this issue from the perspective of stakeholder theory, the effectiveness of an organization or network in pursuing AE is highly dependent on the articulation of a shared vision and the alignment of distributed roles among involved stakeholders (Brugha and Varvasovszky 2000; Van Doorn et al. 2013; Walston and Chou 2011) rather than on promoted policies and individual motivations. In this regard, current AE literature has yet to fully account for the conflicts that occur between involved actors such as faculty, technology office, or university administrators from the perspective of stakeholders’ contrasting interests when pursuing entrepreneurial activities. Lack of studies that explore the extent of actors’ diverging ‘attitudinal splits’ (Philpott et al. 2011) has especially constrained recent understandings that strive to recognize how motivations and interests of AE stakeholders gradually evolve (Galati et al. 2020; Hayter 2015; Ocasio and Joseph 2018; Siegel and Wright 2015). To address this gap, this article adopts perspectives from the attention-based view (ABV) and builds on the institutional theory by investigating the 1) degree of misalignments in AE stakeholders’ selective interests and the 2) reason why it is difficult for these actors to reach attentional consensus. We categorize stakeholders into technology experts (e.g. professors, researchers) and nontechnology experts (e.g. technology transfer office (TTO) personnel, university regulators, incubators, administrators) and divide attentions along the dimensions of technology, formal institution, and informal institution capabilities. Case analysis of research-oriented universities in Korea is performed through three stages as follows: 1) investigation of entrepreneurial activities in major research universities based on archival data; 2) analysis of actors’ prioritization of attention when engaging in AE based on analytical hierarchy process (AHP); and 3) exploration of drives behind such prioritizations based on semi-structured interviews with relevant stakeholders. This analytical process reveals surprisingly contrasting attentions between technology and nontechnology experts, and uncovers functional limitations in the current trajectory of promoting AE. This article is structured as follows. The following section will review theories stemming from the institutional, stakeholder, and attention-based perspectives to investigate the shortcomings in current understandings on AE. Section 3 provides the research framework, while Section 4 specifies the methodology. Section 5 analyzes the case of AE in Korea, while Sections 6 and 7 discuss and conclude the article. 2. Theoretical review Traditional understandings on the shift to the ‘Entrepreneurial Scientist’ role in universities (Etzkowitz and Leydesdorff 1997; Etzkowitz et al. 2000; Hershberg et al. 2007) stem heavily from the institutional theory because the economic, social, and political configurations affect the ability of universities to create and deploy new knowledge that can contribute to industrial growth (Bercovitz and Feldman 2006; Li and Kozhikode 2008). Scott’s (2008) conceptualization of the three pillars of institutions—regulatory (policies or legally sanctioned rules), normative (morally governed obligations), and cultural–cognitive (shared logics and commonly agreed understandings)—has played a pivotal role in contextualizing how the changes in social and cultural environment influence academics’ entrepreneurial intention and attitude (Díaz-Casero et al. 2012). Much of past research explores the effect of formal institutions on the quantity and quality of AE. Restructuration of formalized channels of interaction among policymakers, administrators, and entrepreneurs is found to significantly improve dynamic coordination between researchers, venture support units, and agents responsible for seed funding (Wong et al. 2007; Kwon 2011; Yoon and Lee 2013). For example, favorable reconfigurations in government policy directives (Kwon 2011), education systems (Mok 2012; Yoon and Lee 2013), or public venture capital (Lerner and Schoar 2005) have proven to be prerequisite factors in creating an entrepreneurial environment. However, government policies that promote university–industry–government R&D networks are revealed to hold only temporary success in the early stages of implementation, while the sustainable partnerships and prosperities of technology-based venture firms are rarely guaranteed (Lee and Kim 2016). This is because modifications of formal structures made through selective industrial linkages (Giuliani and Arza 2009), direction-setting by technocrats (Liefner et al. 2016; Nakwa and Zawdie 2016; Schmitz and Altenburg 2016), and resource reinforcement (Hu 2011; Hsu and Yuan 2013) may create an environment that fails to integrate changes in academics’ normative or cultural–cognitive motivations, conflicting goals, and work identities (Meek and Wood 2016; Sandström et al. 2018; Civera et al. 2020). Although reforms in policies and regulations may lead to a corresponding change in the informal institution, this process is rarely predictable and depends highly on the consistency between informal institutions that exist at different levels (e.g. national, organizational, individual) (Eesley et al. 2016) and appropriate configurations in regional political climates (Gianiodis et al. 2019). Reasons behind the existence of large differences in entrepreneurial activities across countries despite the convergence of formal institutions are dominantly explained by informal institutional arrangements that are either supportive or unsupportive of the expected changes (Eesley et al. 2018). Although literature’s understanding of AE weighs heavily on the debate between the forces of formal and informal institutional environments, the accrued knowledge commonly contributes to exploring the enabling conditions for academics’ entrepreneurial intention. According to Miranda et al. (2018), 72 per cent of literary works published in the last two decades on the topic of AE has focused on studying the inherent characteristics and antecedents of individual (e.g. motivations, attitudes), firm (e.g. management practices, financing), or institutional contexts (policies, financial support). For instance, academics are likely to pursue entrepreneurial activities because of economic incentives, research benefits, risk-taking characteristics, or personal networks (Angel Ferrero and Bessière 2016; Bourelos et al. 2012; Fernández-Pérez et al. 2015; Muscio et al. 2016; Vega-Gomez 2018) and are highly affected by institutional contexts such as policies at national and university level, structures of TTO, or proximity to science–technology parks (Hu 2009; Perkmann et al. 2015; Rasmussen et al. 2015; Shane et al. 2015). However, despite these extensive investigations, there is surprisingly scant understanding on the collective conflict of interests among multiple stakeholders, such as academics, TTOs, or administrators, involved in the AE process (Sandström et al. 2018). The importance of meeting multiple stakeholder needs to maximize organizational and network effectiveness has been centric in the stakeholder theory (Brugha and Varvasovszky 2000; Rowley 1997). Articulating a shared vision and aligning distributed roles and interests of various stakeholders across organizational functions contribute to an effective changing effort such as creating entrepreneurial orientation (Kellermanns et al. 2005; Van Doorn et al. 2013; Walston and Chou 2011). This perspective suggests that shared understanding in how multiple actors think, perceive, and reach strategic agreement enables a coordinated action toward a unified direction (Evans and Baker 2012; Okhuysen and Bechky 2009; Vlaar et al. 2006). Following the logics of stakeholder theory, reaching cognitive consensus in the multi-actor collaboration toward achieving AE enables an active transition to an entrepreneurial university. The gap in current AE literature is that the conflicts that occur between university stakeholders when pursuing entrepreneurial activities are not fully accounted for (Bischoff et al. 2018; Gianiodis and Meek 2020). For example, recent works indicate that much of currently established institutions are mostly designed to maximize the number of academic spin-offs while essentially neglecting conflicts that relevant stakeholders experience in the context of cultures, attitudes, habits, or interests when engaging in entrepreneurial activities (Allen and O'Shea 2014; Bischoff et al. 2018; Koo et al. 2018; Siegel and Wright 2015). This line of thought implies the need to expand beyond traditional understandings of identifying individual or contextual antecedents of AE and investigate the internal complications that involved stakeholders with diverging attitudes or motivations encounter during the pursuit of entrepreneurial activities. The ABV has been actively utilized in the past to understand how the dynamic motivations of an organization and its coalition of stakeholders can be aligned to achieve effective strategies and value creation (Ocasio and Joseph 2018). The ABV is largely based on the conjecture that the process and channels of decision-making are dependent on an actor’s particular focus of attention (Ocasio 1997). It is through these channels that stakeholders communicate and agree on critical and discrete decisions that involve the use of key resources (Ocasio and Joseph 2005). Refined conceptualizations on the perspective indicate that agents operate under selective attentions, a process by which individuals focus information processing on a specific set of stimuli at a moment in time (Ocasio 2011). Applying the ABV to AE literature, incoherence in selective interests of major agents, such as academics and faculty (Hayter 2013; Huyghe et al. 2016; Martinelli et al. 2008), technology office personnel (Siegel et al. 2004; O’kane et al. 2015), graduate students (Hayter et al. 2017), or university administrators (Meoli and Vismara 2016), can impair the process of reaching consistent decisions on entrepreneurial activities regardless of a formalized changing effort (i.e. government or university-level policies and regulations). This is a bold claim in AE literature because collective interests of key stakeholders are often misaligned in terms of perceptions of the market among TTOs, university spin-offs, and venture capital investors (Wright et al. 2006), motivations among university, investors, licensees and technology license offices (Kenney and Patton 2011), competencies between researchers and TTOs (Fernández-Alles et al. 2015), and goals among public officials, universities, academics, and private investors (Hayter et al. 2018) when engaging in entrepreneurial activities. To address the issue of aligning attentional gaps, developments in the ABV theory have focused on conceptualizing the role of communicative channels (e.g. communication practices, vocabularies, rhetoric tactics) between stakeholders to promote strategic change (Ocasio and Joseph 2018). Emphasis has been placed on intermediary actors in processing novel information into an organization (Holm et al. 2020), utilization of shared vocabulary to form strong network ties between organizational subunits (Tasselli et al. 2020), or digression from hierarchy and bureaucracy to prevent collective short-termism (Kleinknecht et al. 2020). Although these works explore collective solutions to the problem, they have yet to address the extent to which individual stakeholders are expected to make voluntary efforts to reach attentional balance. This is a critical problem because the outcome of communicative channels adopted to minimize attentional gaps becomes questionable in the context of heterogeneity in attentions among individuals pursuing strategic change (Helfat and Peteraf 2015; Ocasio and Joseph 2018). Implementing generalized communicative solutions (e.g. communication practices, vocabularies, intermediary actors) without understanding the points of misaligned interests among stakeholders would be a repetition of applying top-down directives that fails to integrate changes in cultural–cognitive motivations and goals. The fact that motivations and interests of AE stakeholders gradually evolve (Hayter 2015; Galati et al. 2020) makes it that much more challenging to determine the extent of attentional misalignments between relevant actors. Therefore, we construct the following research question: To what degree are major stakeholders’ attentions misaligned and why is it difficult for them to reach attentional consensus in the process of AE. 3. Research framework The process of planning, executing, and maintaining AE involves the collaboration of a wide range of highly functioning agents, such as faculty or researchers developing the technology, TTO establishing institutional support, start-up centers relaying managerial feedback, or incubators providing an entrepreneurial environment (Clarysse et al. 2011; Sandström et al. 2018). In order to differentiate varying attentions between generalized actors, we divide major stakeholders along the dimensions of technology experts and nontechnology experts. Technology experts include faculty, researchers, or scientists who develop emerging technologies and are direct agents that pursue entrepreneurial activities. These academic entrepreneurs play a critical role in launching a start-up company and sustaining business performance based on their levels of motivation and commitment (Hayter 2011). Nontechnology experts hold supporting functionalities and are comprised of TTO personnel, start-up incubators, or university administrators. AE initiatives are highly contingent upon the role of these actors who may allocate resources (e.g. financial, human), arrange infrastructure (e.g. office space, testing center), provide equipment (e.g. material, machine), and cultural settings (e.g. entrepreneurial atmosphere) to entrepreneurs at the level of the university or department (Sandström et al. 2018). Building a shared understanding on entrepreneurship in an academic environment requires complex efforts to integrate multiple functionalities that promote technological collaboration (Hu 2011; Kwon 2011; Perkmann et al. 2013), regulatory environment (Kwon 2011), and entrepreneurial culture (Díaz-Casero et al. 2012; Hayter 2015). This approach is shared in recent research that proposes renewed actions that direct new technologies toward application, organize flexible intellectual property policies, and synchronize technology and business incubation cultures (Cantu-Ortiz et al. 2017). In line with this line of thinking, we categorize the main dimensions of stakeholder interests into the capabilities in technology, formal institution, and informal institution factors (IFs). Technology capability encompasses characteristics that determine the superiority of the product on which the start-up is based on. Subsequent attentions of technology capability include: 1) specification of artifact in terms of technical performance of the product (Kwon and Martin 2011); 2) market appropriateness that describes the existence of the market, degree of demand, or market saturation (Martin and Scott 2000); and 3) complementary infrastructure that signifies the existence of collaborative partners or necessary physical and knowledge infrastructure (Bonaccorsi et al. 2014; O'Shea et al. 2005). Formal institution capability indicates written institutional mechanisms such as standards, laws, rules, regulations, and general legal systems (Smith 2000) required in AE. Attentions of the formal institution capability are as follows: 1) government policy such as state-level policies, regulations, laws, and programs (Yoon and Lee, 2013); 2) university regulation concerning rules on the qualification, exemption, and compensation for engaging in AE (Wong et al. 2007); and 3) incubation activity that includes formalized education, consulting, and incubation programs offered by supporting offices (Rothaermel and Thursby 2005). Informal institution capability are factors that incorporate a wider context of social values that shape the objectives of implicit rules (Smith 2000; Woolthuis et al. 2005) required in forming an entrepreneurial culture. Attentions of IF contain: 1) entrepreneurial motivation of academic entrepreneurs that allow them to drive start-up activities (Hayter 2011); 2) internal conflict between colleagues or students within the university (Hayter et al. 2017); 3) external conflict with private investors or nonacademic communities (Clarysse et al. 2007; Sandström et al. 2018); and 4) departmental culture that encourages (or discourages) entrepreneurial norm in departments (Etzkowitz et al. 2008; O'Shea et al. 2005). The developed constructs categorize two major stakeholders (i.e. technology experts and nontechnology experts) and three primary capabilities (i.e. technology, formal institution, informal institution) involved in AE. As depicted in Fig. 1, the framework aims to investigate how much the two stakeholders’ attentions, under respective capabilities, align in the process of engaging in AE. The scope of the construct ‘engagement in academic entrepreneurship’ generalizes the stakeholders’ commitment in the stages of planning to proceed with entrepreneurship and maintaining start-up activities. Figure 1. Open in new tabDownload slide Research framework. Figure 1. Open in new tabDownload slide Research framework. 4. Methodology This article investigates the varying degrees of attentional misalignment among AE stakeholders and explores possible barriers that may exist in reaching a collective consensus by targeting the case of research-oriented universities in South Korea (hereafter Korea). Since Korea has been introducing diverse policies and regulations to promote AE for decades especially among research-intensive universities with advanced technology capability (Park and Leydesdorff 2010), we believe that the selected case is ideal for this research. Because case studies often ensue the assumption that homogeneity is retained only for particular variables rather than for broad populations, it is imperative to understand the historical and contextual background before performing inductive investigations (Bennett and Elman 2006; Mahoney and Goertz 2006). Therefore, background information on the Korean government’s policies on AE and entrepreneurial activities in major Korean research universities, Korea Advanced Institute of Science and Technology (KAIST), Ulsan National Institute of Science and Technology (UNIST), and Gwangju Institute of Science and Technology (GIST),1 were extracted from an extensive assortment of formal archival data available in the National Assembly Library and the respective universities’ TTOs. These included records of the National Assembly and ministerial meetings, reports published by public research institutes, and official documents provided by university offices. Collected documents were carefully reviewed to provide a descriptive background of the historical development of national policies, implementation of university regulations, and introduction of academics’ entrepreneurial activities. Technology and nontechnology experts’ prioritization of attention when engaging in AE was investigated based on the AHP,2 which is an effective means of examining subjective opinions of a small number of key players as reference points when making important decisions (refer to Cheng and Li (2001) and Sambasivan and Fei (2008)). Because of the selective number of technology and nontechnology experts involved in AE in Korean research universities, the AHP approach is considered to be an ideal method for analyzing stakeholders’ respective attentions. Adopting Saaty’s (1994) approach, we developed a hierarchical structure that categorizes the objective (level 1: engagement in AE), criteria (level 2: capabilities), and sub-criteria (level 3: attentions), as shown in Fig. 2. Based on a nine-point scale (ranging from equally preferred factors = 1, strongly preferred = 5, extremely strongly preferred = 9) (Saaty 1994), technology and nontechnology experts were asked to compare the three criteria (level 2: capabilities) and ten sub-criteria (level 3: attentions) to prioritize the relative importance of the factors when engaging in AE. Figure 2. Open in new tabDownload slide AHP hierarchical structure for evaluating priorities when engaging in AE. Figure 2. Open in new tabDownload slide AHP hierarchical structure for evaluating priorities when engaging in AE. Entrepreneurs (e.g. faculty, researcher, professor) and supporting personnel (e.g. TTO, administrator, incubator) directly related to AE in the three leading research universities (i.e. KAIST, UNIST, GIST) were first contacted through email and face-to-face meetings to explain the purpose of this research and request participation in the AHP study. After a week, nonrespondents were contacted again through email to remind participation in the study. Respective data, as shown in Table 1, were collected over the span of three weeks in July 2019. Table 1. Sources of sample in AHP and semi-structured interviews AHP . . Sample pool (N) . Response . Negligent response . Sample total (n) . Technology experts 58 17 1 16 Non-technology experts 20 9 0 9 Total 78 26 1 25 Semi-structured interview Collection period Description Main content Technology expert 1–2 (March 2019) Faculty entrepreneur Motivations and barriers when engaging in start-up activities 3–23 (March–April 2019) Faculty entrepreneur Conflicts between stakeholders in the process of academic entrepreneurship Non-technology expert 1–5 (January–August 2019) OUIC and start-up center Success and failures of faculty-based entrepreneurship and effectiveness of university regulations 6 (June 2019) Ministry of SMEs and Startups Process of planning and implementing government policies 7–9 (October 2019) Entrepreneurship Innovation Committee Conflicts between stakeholders in the process of academic entrepreneurship AHP . . Sample pool (N) . Response . Negligent response . Sample total (n) . Technology experts 58 17 1 16 Non-technology experts 20 9 0 9 Total 78 26 1 25 Semi-structured interview Collection period Description Main content Technology expert 1–2 (March 2019) Faculty entrepreneur Motivations and barriers when engaging in start-up activities 3–23 (March–April 2019) Faculty entrepreneur Conflicts between stakeholders in the process of academic entrepreneurship Non-technology expert 1–5 (January–August 2019) OUIC and start-up center Success and failures of faculty-based entrepreneurship and effectiveness of university regulations 6 (June 2019) Ministry of SMEs and Startups Process of planning and implementing government policies 7–9 (October 2019) Entrepreneurship Innovation Committee Conflicts between stakeholders in the process of academic entrepreneurship Open in new tab Table 1. Sources of sample in AHP and semi-structured interviews AHP . . Sample pool (N) . Response . Negligent response . Sample total (n) . Technology experts 58 17 1 16 Non-technology experts 20 9 0 9 Total 78 26 1 25 Semi-structured interview Collection period Description Main content Technology expert 1–2 (March 2019) Faculty entrepreneur Motivations and barriers when engaging in start-up activities 3–23 (March–April 2019) Faculty entrepreneur Conflicts between stakeholders in the process of academic entrepreneurship Non-technology expert 1–5 (January–August 2019) OUIC and start-up center Success and failures of faculty-based entrepreneurship and effectiveness of university regulations 6 (June 2019) Ministry of SMEs and Startups Process of planning and implementing government policies 7–9 (October 2019) Entrepreneurship Innovation Committee Conflicts between stakeholders in the process of academic entrepreneurship AHP . . Sample pool (N) . Response . Negligent response . Sample total (n) . Technology experts 58 17 1 16 Non-technology experts 20 9 0 9 Total 78 26 1 25 Semi-structured interview Collection period Description Main content Technology expert 1–2 (March 2019) Faculty entrepreneur Motivations and barriers when engaging in start-up activities 3–23 (March–April 2019) Faculty entrepreneur Conflicts between stakeholders in the process of academic entrepreneurship Non-technology expert 1–5 (January–August 2019) OUIC and start-up center Success and failures of faculty-based entrepreneurship and effectiveness of university regulations 6 (June 2019) Ministry of SMEs and Startups Process of planning and implementing government policies 7–9 (October 2019) Entrepreneurship Innovation Committee Conflicts between stakeholders in the process of academic entrepreneurship Open in new tab The reasoning behind the prioritizations revealed by the AHP was simultaneously explored through series of semi-structured interviews with relevant stakeholders in KAIST. As KAIST holds the longest history of AE among the target research-oriented universities, it was perceived to be an ideal environment to understand advanced formal and informal institutional dynamics. Inductive approaches are critical in the process of contributing to theory-building efforts especially in areas where scarce empirical research exists (Hayter et al. 2017). A total of thirty-two technology (faculty, researchers) and nontechnology (Office of University Industry Cooperation (OUIC) personnel, regulators, administrators, mediators, ministry official)) experts were interviewed from January to October 2019 in length ranging from 30 min (unrecorded interviews) to 90 min (recorded interviews). Primary content covered in the interviews included (1) enabling and constraining factors (for the respective stakeholder) when engaging in AE, (2) influence of relevant capabilities (i.e. technology, formal institution, informal institution) on AE, and (3) primary sources of conflict in attention between technology and nontechnology experts. Texts from the interviews were coded manually by the authors under the criteria of (1) technology, (2) formal institution, and (3) informal institution (environmental and individual) capabilities. 5. Analysis 5.1. Case background Korea’s pursuit of the entrepreneurial role in universities began with the initiation of the ‘Special Act for Nurturing Venture Firms’ (hereafter ‘Special Act’) by the government in 1997, which allowed for the state to support the development of venture firms through means such as financing, technology transfer, and human resource management. In June of the same year, the Special Act was modified to allow professors and researchers to become entrepreneurs, and in August extended the number of incentives to encourage professors’ start-up activities. These policies provided incentives in the form of allowing individuals to take a leave of the absence or operate as a concurrent professor while pursuing start-up activities, granting permits to allow university research to be developed into commercial products, and opening university research facilities for proprietary activities. Later that year, the Ministry of Science and Technology expanded the Special Act to encourage venture firms to be founded specifically in KAIST, a government-funded research university in Daejeon city. This created numerous channels for the government to support venture firms, such as offering infrastructural support, maintaining strong bonds between industry and university cooperation, and providing education for faculty-based entrepreneurship. In the year 2001, the government nominated KAIST as the ‘Technology Transfer Center’ for government research institutes in the Daedeok Innopolis, an R&D district in Daejeon city. The primary role of the center was to identify market-ready technologies, certify the technology, and provide guidelines for managerial strategies. These policies and formalized drives were directly responsible for the venture boom in the Daedeok Innopolis, which was shortly followed by active start-up activities by KAIST faculty. By the year 2003, the government passed a new act called ‘Enforcement Degree of the Promotion of Industrial Education and Industry–Academic Cooperation Act’, which pushed universities to expand their focus beyond basic science and be more directly involved in industrial activities. Universities were expected to generate revenue, encourage entrepreneurial activity among staff and students, found university stock firms, and pursue other means that directly benefited economic growth (Nah et al. 2014). This movement was further emphasized in 2011 when the ‘Science and Technology Oriented University Development Act’ mandated entrepreneurship education programs for research universities such as KAIST, UNIST, GIST, or Pohang University of Science and Technology, and provided regulatory incentives to encourage faculty’s commitment to entrepreneurial activities (Park and Park 2013). Respective universities also designed policies and regulations that would encourage entrepreneurship (see Supplementary Appendix A for specifics). Incubation centers and other supporting agencies dedicated to promoting start-up activities were further founded, independent from the TTO or the OUIC (e.g. ‘Start-up KAIST’ (ISK) in KAIST, Center for Start-up and Entrepreneurship Development in GIST, and Business Incubation Center in UNIST). KAIST took additional steps to establish a committee called ‘Entrepreneurship Innovation Committee’ that aimed to mediate the interests of professors and university policymakers in early 2019. Figure 3 depicts the formalized relation between stakeholders involved in AE. Figure 3. Open in new tabDownload slide Relationship between stakeholders involved in AE. (Source: Reconfigured from ISK, 2018a.) Figure 3. Open in new tabDownload slide Relationship between stakeholders involved in AE. (Source: Reconfigured from ISK, 2018a.) Despite the establishment of numerous government programs and university support agencies, the performance of AE in research universities failed to show consistent growth. For instance, in the case of KAIST, the average accrued revenue of start-ups showed no significant increase from 22.5 billion won in 2012 to 22.6 billion won in 2016, while the average number of employees decreased from 83 in 2012 to 51 in 2017 (ISK 2015, 2016, 2017, 2018b). Unsatisfactory increase in faculty-based entrepreneurship resulted in plans for implementing even more government-enforced incentives such as expanding education platforms, enhancing academic entrepreneurs’ performance evaluation, restructuring university policies to accept start-up experience as ‘industrial performance’, or discarding regulations on reducing compensation for academic entrepreneurs (MSIT, 2017). 5.2. Analytical hierarchy process on stakeholders’ prioritizations 5.2.1. Pair-wise comparison matrix, criteria weights, and consistency ratio The geometric mean of collected responses from our sample was used to obtain average values, which were then depicted in a comparison matrix, as shown in Supplementary Appendix B. The represented values indicate the relative importance of criteria A over B, such that the significance of criteria B would be the reciprocal of A. For instance, when the influence of criteria A is considered more important than that of B by a degree of 8 (ranging from 1 to 9), the perceived importance of criteria B in comparison to criteria A will be portrayed as 1/8. Normalized matrix was then established by dividing each pair-wise comparison values by the sum of the values in each respective column. Average of the row entries of the subsequent normalized matrix determines the criteria weight, which refers to the priority of each criterion over others (Dhochak and Sharma 2016). The next step of analysis involves calculating the consistency ratio (CR), which determines the consistency of experts’ opinions. The CR was calculated using the following formulae, where aij represents wi/wj (i, j = 1,2,3,…n) of corresponding criteria weights, w, while the random index (RI) value was obtained from Saaty (1994): λmax=∑j=1naijWjWi, CI=λmax-nn-1, CR=CIRI As depicted in Supplementary Appendix B, all obtained CR values are below 0.1, indicating acceptable consistency in experts’ judgments (Saaty 1994). 5.2.2. Local weights, global weights, and rankings Local weights indicate the respondents’ preference of the particular attention within the respective capabilities, while global weights refer to their prioritization of the attention across all capabilities. Global weights are calculated by the following formula where ai represents the local weight of criterion (level 2) i, and bij indicates the local weight of sub-criterion (level 3) j in criterion i: Global weight= ∑(ai×bij) Weighted prioritizations, as shown in Table 2, indicate that technology and nontechnology experts hold reverse priorities on capabilities required when engaging in AE. Case in point, level 2 analysis shows that technology experts perceive ‘technological factor’ (TF, 0.598) to be the most critical capability, while ‘informal institutional factor’ (IF, 0.147) is considered to be the least important aspect. Nontechnology experts, however, believe that IF (IF, 0.429) is the most critical capability, while TF (TF, 0.233) is considered to be the least important aspect when engaging in AE. Specifically, technology experts dedicate most attention to technology-centric attributes, ‘complementarity’ (TF3, 0.219), ‘appropriateness’ (TF2, 0.213), and ‘specification’ (TF1, 0.167), while nontechnology experts emphasize institutional elements, ‘university regulation’ (FF2, 0.175), ‘entrepreneurial motivation’ (IF1, 0.165), and ‘departmental culture’ (IF4, 0.138), in the process of AE. Table 2. Weighted prioritizations of technology and nontechnology experts Hierarchy level . Factor categories/criteria . Technology experts . Nontechnology experts . Local weight . Weight ranking . Global weight . Weight ranking . Local weight . Weight ranking . Global weight . Weight ranking . Level 2 TF 0.598 1 0.598 1 0.233 3 0.233 3 FF 0.255 2 0.255 2 0.337 2 0.337 2 IF 0.147 3 0.147 3 0.429 1 0.429 1 Level 3 TF TF1 0.279 3 0.167 3 0.162 3 0.038 10 TF2 0.355 2 0.213 2 0.547 1 0.128 4 TF3 0.366 1 0.219 1 0.292 2 0.068 7 FF FF1 0.311 2 0.079 5 0.162 3 0.055 8 FF2 0.537 1 0.137 4 0.520 1 0.175 1 FF3 0.152 3 0.039 7 0.319 2 0.108 5 IF IF1 0.419 1 0.061 6 0.384 1 0.165 2 IF2 0.223 3 0.033 9 0.183 3 0.078 6 IF3 0.119 4 0.017 10 0.112 4 0.048 9 IF4 0.239 2 0.035 8 0.321 2 0.138 3 Hierarchy level . Factor categories/criteria . Technology experts . Nontechnology experts . Local weight . Weight ranking . Global weight . Weight ranking . Local weight . Weight ranking . Global weight . Weight ranking . Level 2 TF 0.598 1 0.598 1 0.233 3 0.233 3 FF 0.255 2 0.255 2 0.337 2 0.337 2 IF 0.147 3 0.147 3 0.429 1 0.429 1 Level 3 TF TF1 0.279 3 0.167 3 0.162 3 0.038 10 TF2 0.355 2 0.213 2 0.547 1 0.128 4 TF3 0.366 1 0.219 1 0.292 2 0.068 7 FF FF1 0.311 2 0.079 5 0.162 3 0.055 8 FF2 0.537 1 0.137 4 0.520 1 0.175 1 FF3 0.152 3 0.039 7 0.319 2 0.108 5 IF IF1 0.419 1 0.061 6 0.384 1 0.165 2 IF2 0.223 3 0.033 9 0.183 3 0.078 6 IF3 0.119 4 0.017 10 0.112 4 0.048 9 IF4 0.239 2 0.035 8 0.321 2 0.138 3 Open in new tab Table 2. Weighted prioritizations of technology and nontechnology experts Hierarchy level . Factor categories/criteria . Technology experts . Nontechnology experts . Local weight . Weight ranking . Global weight . Weight ranking . Local weight . Weight ranking . Global weight . Weight ranking . Level 2 TF 0.598 1 0.598 1 0.233 3 0.233 3 FF 0.255 2 0.255 2 0.337 2 0.337 2 IF 0.147 3 0.147 3 0.429 1 0.429 1 Level 3 TF TF1 0.279 3 0.167 3 0.162 3 0.038 10 TF2 0.355 2 0.213 2 0.547 1 0.128 4 TF3 0.366 1 0.219 1 0.292 2 0.068 7 FF FF1 0.311 2 0.079 5 0.162 3 0.055 8 FF2 0.537 1 0.137 4 0.520 1 0.175 1 FF3 0.152 3 0.039 7 0.319 2 0.108 5 IF IF1 0.419 1 0.061 6 0.384 1 0.165 2 IF2 0.223 3 0.033 9 0.183 3 0.078 6 IF3 0.119 4 0.017 10 0.112 4 0.048 9 IF4 0.239 2 0.035 8 0.321 2 0.138 3 Hierarchy level . Factor categories/criteria . Technology experts . Nontechnology experts . Local weight . Weight ranking . Global weight . Weight ranking . Local weight . Weight ranking . Global weight . Weight ranking . Level 2 TF 0.598 1 0.598 1 0.233 3 0.233 3 FF 0.255 2 0.255 2 0.337 2 0.337 2 IF 0.147 3 0.147 3 0.429 1 0.429 1 Level 3 TF TF1 0.279 3 0.167 3 0.162 3 0.038 10 TF2 0.355 2 0.213 2 0.547 1 0.128 4 TF3 0.366 1 0.219 1 0.292 2 0.068 7 FF FF1 0.311 2 0.079 5 0.162 3 0.055 8 FF2 0.537 1 0.137 4 0.520 1 0.175 1 FF3 0.152 3 0.039 7 0.319 2 0.108 5 IF IF1 0.419 1 0.061 6 0.384 1 0.165 2 IF2 0.223 3 0.033 9 0.183 3 0.078 6 IF3 0.119 4 0.017 10 0.112 4 0.048 9 IF4 0.239 2 0.035 8 0.321 2 0.138 3 Open in new tab Table 3. Summary of attentional dilemmas between technology and nontechnology experts Capabilities and attentions . Technology experts (weight ranking) . Nontechnology experts (weight ranking) . Degree of misalignment . Technology factor  Specification Technology perfection Technology imperfection High  Appropriateness N/A N/A Low  Complementarity Competence dependency Competence independency High Formal institution factor  Government policy Missed opportunity Inconsequential condition Intermediate  University regulation Practical discouragement Conceptual encouragement Intermediate  Incubation activity N/A N/A Low Informal institution factor  Entrepreneurial motivation Artifact specification Individual entrepreneurship Intermediate  Internal conflict Mission augmentation Mission substitution Intermediate  External conflict N/A N/A Low  Departmental culture Actor-driven motivation Norm-driven promotion High Capabilities and attentions . Technology experts (weight ranking) . Nontechnology experts (weight ranking) . Degree of misalignment . Technology factor  Specification Technology perfection Technology imperfection High  Appropriateness N/A N/A Low  Complementarity Competence dependency Competence independency High Formal institution factor  Government policy Missed opportunity Inconsequential condition Intermediate  University regulation Practical discouragement Conceptual encouragement Intermediate  Incubation activity N/A N/A Low Informal institution factor  Entrepreneurial motivation Artifact specification Individual entrepreneurship Intermediate  Internal conflict Mission augmentation Mission substitution Intermediate  External conflict N/A N/A Low  Departmental culture Actor-driven motivation Norm-driven promotion High Note: Not Available (N/A) indicates that misalignments did not exist in the respective attentions of technology and nontechnology experts. Open in new tab Table 3. Summary of attentional dilemmas between technology and nontechnology experts Capabilities and attentions . Technology experts (weight ranking) . Nontechnology experts (weight ranking) . Degree of misalignment . Technology factor  Specification Technology perfection Technology imperfection High  Appropriateness N/A N/A Low  Complementarity Competence dependency Competence independency High Formal institution factor  Government policy Missed opportunity Inconsequential condition Intermediate  University regulation Practical discouragement Conceptual encouragement Intermediate  Incubation activity N/A N/A Low Informal institution factor  Entrepreneurial motivation Artifact specification Individual entrepreneurship Intermediate  Internal conflict Mission augmentation Mission substitution Intermediate  External conflict N/A N/A Low  Departmental culture Actor-driven motivation Norm-driven promotion High Capabilities and attentions . Technology experts (weight ranking) . Nontechnology experts (weight ranking) . Degree of misalignment . Technology factor  Specification Technology perfection Technology imperfection High  Appropriateness N/A N/A Low  Complementarity Competence dependency Competence independency High Formal institution factor  Government policy Missed opportunity Inconsequential condition Intermediate  University regulation Practical discouragement Conceptual encouragement Intermediate  Incubation activity N/A N/A Low Informal institution factor  Entrepreneurial motivation Artifact specification Individual entrepreneurship Intermediate  Internal conflict Mission augmentation Mission substitution Intermediate  External conflict N/A N/A Low  Departmental culture Actor-driven motivation Norm-driven promotion High Note: Not Available (N/A) indicates that misalignments did not exist in the respective attentions of technology and nontechnology experts. Open in new tab 5.3. Descriptive analysis of attentional discrepancy 5.3.1. Technology factor 5.3.1.1. Specification (TF1): Dilemma between technology perfection and technology imperfection (high) Stakeholders’ high misalignment concerning the specification of the technology revolves around technology experts’ opinion on how reaching technological perfection is a critical objective to be achieved before even considering entrepreneurship. Because KAIST professors are experts at an extremely specialized area, they have a higher chance of succeeding [at academic entrepreneurship] when the technology is that much more advanced… If the maximum potential of the technology’s performance is considered to be 100, professors expect their technology to be at least ninety before they even think about start-up activities (Technology Expert 11). In contrast to technology experts’ resolute opinion, nontechnology experts believe that the heavy concentration on technical performance effectively impairs managerial potential. Higher technological advancement would likely surpass the standard demanded in the market, and the persistence of reaching technological perfection would continue to exacerbate the gap between developing the technology and managing the company. When there is a mismatch between what is applicable in the market and what is developed in the university, it is necessary to pivot the technology’s functionality to decrease this gap. However, most professors strongly believe that the full extent of their technology is critical for the future society and thus tend to stubbornly pursue start-up activities with their original technology (Non-technology expert 1). Although this issue is considered to be universal in all faculty-based start-up, research university’s ‘pride in having led industrial growth in the past’ (Non-technology expert 3) has been noted to be one of the major barriers in shifting academics’ perception on developing high performing technology. 5.3.1.2 Appropriateness (TF2): N/A (low) There exists a general consensus between technology and nontechnology experts concerning the importance of demand articulation in the form of developing technologies that the market requires. In fact, academic entrepreneurs are even aware of their weakness in incorporating market conditions to start-up activities, while nontechnology experts agree on the need to complement this gap. I agree that it is important to think about how much the market requires the technology… This becomes an especially critical barrier that many professors face when developing their business plan (Technology expert 12). They [professors] need to develop technologies that are more universal and in line with the market in order to succeed in academic entrepreneurship (Non-technology expert 3). Both stakeholders placed significant attention on market appropriateness, thus making attentional discrepancy negligible. A caveat in this issue, however, is that while technology experts believe that a higher performing technology would complement existing markets and drive new opportunities, nontechnology experts seek ways to ‘pivot’ technological functions to align with the existing demands. Therefore, although the two types of stakeholders placed similar prioritizations on market appropriateness, they hold contrasting perspectives on how to approach the issue. 5.3.1.3 Complementarity (TF3): Dilemma between competence dependency and competence independency (high) Technology experts showed a high tendency for requesting a third party to provide or connect them to supporting infrastructure, such as industrial networks or expert managers. Because their core competence is in technological development, their appeals for connecting with professional consultants are justified in the process of creating market-ready products. The University needs to connect us with existing industrial networks that include players such as investors or market analysts, who can help us align technologies to be more market-ready (Technology expert 6). Nontechnology experts, however, believed that entrepreneurs’ attention on expecting others to connect them to preexisting infrastructure should be replaced by efforts to inherently develop such capabilities themselves. They criticize many academic entrepreneurs’ tendencies to detach managerial functions from technological functions in the process of AE. Many professors expect the University or supporting offices to perform functions that they are responsible for… They tend to focus on the role of a professor in “developing the technology”, while expecting others to perform the role of an entrepreneur (Non-technology expert 8). The resulting dilemma is therefore caused by the stakeholders’ contrasting definition of an academic entrepreneur. Although professors perceive the entrepreneur to be a technology specialist, administrators or incubators view them as managers. 5.3.2. Formal institution factor 5.3.2.1 Government policy (FF1): Dilemma between missed opportunity and inconsequential condition (intermediate) While technology and nontechnology experts both hold government policies with relatively low regard in directly affecting AE, the former stakeholders believe that it is an area of possible opportunity. For instance, public incentives are always deemed to be a potential source of increasing capital, while genuine concerns exist for missed opportunities. A side effect of extensive government funding is that they [the government] may also endorse companies that are not self-sufficient to begin with, while those that are potentially viable may miss out on the opportunity (Technology expert 1). On the other hand, attention on government policies is often spared by nontechnology experts, who perceive that entrepreneurial motivations cannot be pressured by exogenous factors, but are created by endogenous characteristics. Rather, national-level policies are more directly related to forming entrepreneurial ecosystems, which is a scope that is beyond academic entrepreneurs or university personnel. The government’s top-down policy drives are largely unrelated with academic entrepreneurship. While they may be partly helpful in terms of supporting a venture ecosystem, most start-ups occur due to lower-level activities transpiring within universities, departments, or individuals (Non-technology expert 6). The stakeholders’ misalignment in attention for government policies appears to arise from the contrasting approach in pursuing AE, in that technology experts tend to seek funding from exogenous sources, while nontechnology experts appoint more emphasis on endogenously generating capital and revenue. 5.3.2.2 University regulation (FF2): Dilemma between practical discouragement and conceptual encouragement (intermediate) The majority of direct recipients of university regulations on AE generally hold negative opinions on the formalized rules entrepreneurs are expected to follow. Technology experts argue that much of designed regulations hold more constraining than enabling effect as they are perceived to benefit the university at the cost of the entrepreneurs. The university’s demand for so many things are strikingly out of context with the global standard. For example, the university is reluctant to allow patent transfer, expects coercive donation, or mandates three percent of revenue as technology royalty [from academic entrepreneurs]… (Technology expert 21). Nontechnology experts, however, hold completely contrasting views on the effect of university regulation on AE as they perceive faculty in KAIST to be especially advantageous in terms of pursuing start-up activities compared to those in any other institutes. Policies that allow exemption from teaching responsibilities, leave of absence, or other government-funded support programs are expected to significantly boost entrepreneurial activities. Our university provides much more incentives to academic entrepreneurs compared to any other universities… because government funding tends to be heavily centric on our university, academic entrepreneurship would be that much more viable [for professors] (Non-technology expert 2). Dilemma in university regulation is an ironic representation of how conditions that allow incentives to take place effectively constrain the outcome that it was intended to promote. 5.3.2.3 Incubation activity (FF3): N/A (low) Although both technology and nontechnology experts agree on the relative importance of incubation activities, thus the lack of attentional dilemma, the extent of how incubation is expected to be carried out remains misaligned. While academic entrepreneurs perceive that the incubation office merely provides educational or conceptual assistance, nontechnology experts believe that their function is closer to active consulting services. It appears as if the Office of University and Industry Cooperation is only interested in meeting the demands of the Ministry of SMEs and Start-ups… The incubation office is unable to provide feasible assistance to academic entrepreneurs other than education… (Technology expert 17). Because academic entrepreneurs often bring technologies that are developed based on academic standards, it is difficult to find patents that are directly applicable for start-up. Only by modifying the technology through multiple incubation programs can these technologies be remotely acceptable for commercialization’ (Non-technology expert 1). The difference in the stakeholders’ perceived effectiveness of incubation activities stems from the contrasting opinions on the extent of one another’s role in the process of AE. For instance, technology experts expect the implementation of useful services such as assistance in developing business plans or evaluating investment reports, while nontechnology experts perceive that conjectural guidelines are sufficient in directing the company. 5.3.3. Informal institution factor 5.3.3.1 Entrepreneurial motivation (IF1): Dilemma between artifact specification and individual entrepreneurship (Intermediate) The fundamental gap in entrepreneurial motivation for engaging in AE is that technology experts’ decisions are based on the technology’s performance, while nontechnology experts pursue ways to enhance individuals’ entrepreneurial character. Academic entrepreneurs believe that entrepreneurial drives are insufficient, if not irrelevant, when the subject technology is not advanced beyond global standards. While I personally did not believe that the technology was sufficiently advanced, investors showed high hopes, and the technology was evaluated to hold higher performance compared to the global standard. Acceptance of technological advancement was the deciding factor [that determined my commitment to engage in start-up activities] (Technology expert 1). Nontechnology experts, however, argue that technological superiority has minimal influence on AE, and it is the lack of entrepreneurial spirit that prevents faculty from engaging in start-up activities. Therefore, fostering inherent motivation to overcome the fear of start-up failure is considered to be one of the most critical objectives in promoting AE. One of the major barriers [to academic entrepreneurship] is the professors’ indifference toward start-ups. Start-ups would indicate encompassing high financial risks, while forgoing stable rewards they would receive through more traditional ways of technology transfer (Non-technology expert 2). Mismatch in stakeholders’ perception of what drives academic entrepreneurs arises from the contrast in perceived identity. To elaborate, technology experts place particular emphasis on the artifact because they are more engineers and scientists than they are managers, while nontechnology experts seek enhanced entrepreneurial intentions because they are more administrators than technology developers. 5.3.3.2 Internal conflict (IF2): Dilemma between mission augmentation and mission substitution (intermediate) Although it is commonly believed that conflicts of interest among colleagues or students occur because entrepreneurs would pursue start-up activities at the expense of faculty activities, this was revealed to be an outdated concern. Technology experts’ decision to engage in AE rarely indicates a substitution of their functionality in the university with an industrial one, but rather signifies an expansion to encompass both teaching and managing roles. Because I embrace all the required roles designated by the department [during start-up], there have never been conflicts with other faculty members… In fact, some interested students even volunteered to be affiliated with the company (Technology expert 15). On the other hand, nontechnology experts believe that conflicts raised in the past continue to negatively affect AE. Therefore, strong concerns for nonentrepreneurial faculty’s disapproving opinions and pressures on entrepreneurial academics to forgo start-up lead to the development of regulations or policies that are misaligned with technology experts’ experience. Since the venture-boom in the early 2000s, academic entrepreneurs were pardoned of many departmental functions such as providing lectures. As other faculty members were expected to compensate for this gap, they became highly discontent and began to foster negative perceptions on academic entrepreneurs. This norm in itself became the major reason for [current] professors’ reluctance in engaging in start-up activities (Non-technology expert 3). Although possible conflicts of interest with colleagues and students is a genuine concern for both stakeholders, technology experts’ caution to minimize such problems has rendered non-technology experts’ attention in such aspect obsolete. 5.3.3.3 External conflict (IF3): N/A (low) Both technology and nontechnology experts showed low concerns for conflicts with external parties such as private investors. This was particularly so because of growing global and national initiatives that expected universities to be a novel source of industrial activities. Investors actually saw my [young] age to be a merit. Because tenured professors rarely fully commit to start-up, they viewed young untenured professors to be more willing to actively participate [in the process] (Technology expert 2). Although some investors may be concerned with an academic’s commitment to start-up, their willingness to invest depends more on the technology’s market receptivity than on any other factor (Non-technology expert 4). There certainly have been cases where investors were skeptical toward the faculty’s commitment to start-up, or communities were displeased at the prospect of faculty seeking individual profit using research funded by the government. However, these are considered to be diminishing concerns as academics become growingly invested in entrepreneurial activities and the industry increasingly benefits from technologies available in such start-up firms. 5.3.3.4 Departmental culture (IF4): Dilemma between actor-driven motivation and norm-driven promotion (high) Technology experts’ individualized inspiration neglects the rationale of nontechnology experts’ pursuit of institutionalized stimulation for AE. Academic entrepreneurs think little of the effect of an entrepreneurial culture especially because their technology-centric motivations are rarely encouraged or discouraged by the collective affinity to start-up activities. Also, the entry barrier for start-up is considered to be lower for faculty because they individually possess the option of fully revert to the original teaching and research occupation. While entrepreneurial culture indicates an institutionalized safety system that allows for failed entrepreneurs to get back up, professors already have many personally embedded safety systems. They have the option to go back to research whenever they want (Technology expert, 6). In contrast, nontechnology experts place significant emphasis on demolishing the constraining pressure created by the university or respective departments that hold traditional views on defining a professor’s role. Therefore, attention is placed on transforming how individual and collective bodies of faculty and researchers perceive entrepreneurial activities in academia. An organization’s negative perception on entrepreneurship, dominant views on professor’s role to be in education and research, and disapproving opinions on individuals using the university as means of earning money are critical barriers to academic entrepreneurship that exist at the department’s cultural level (Non-technology expert 5). The misalignment in perceived importance of culture between technology and nontechnology experts arise from actors’ inherent identities, in which the former endeavors to enhance technological specialization above all other competence, while the latter seeks to nurture an entrepreneurial ecosystem. 6. Discussion This research supports previous claims that argue the ineffectiveness of implementing various acts, policies, and regulations to promote AE in research-oriented universities without incorporating conflicts between individual stakeholders (Civera et al. 2020; Sandström et al. 2018). Although Fischer et al. (2019) reached similar conclusions on the low entrepreneurial propensity in Brazilian research-intensive universities, they mainly criticized the lack of R&D orientation and entrepreneurial ecosystem. In contrast, our archival study reveals that even in the context of high R&D orientation (e.g. Daedeok Innopolis) and entrepreneurial environment (e.g. decades of entrepreneurial movements), the same results apply. Thus, while creating a favorable environment—including both legal and cultural aspects—may be a prerequisite condition (Bischoff et al. 2018; Yoon and Lee 2013), it is not a deciding factor that promotes AE. Although Scott’s (2008) institutional pillar has been a powerful tool in understanding the entrepreneurial context at national, regional, and individual level, this indicates the necessity to diverge from the traditional explorations of environmental antecedents and individual motivations. Integration of the stakeholder theory and the ABV perspective shows that understanding the attentional arrangements of major actors involved in AE is critical in maximizing a collective transition to an entrepreneurial university. This may prove to be particularly meaningful in emerging literature that seeks to conceptualize the heterogeneity and evolution of AE stakeholders’ motivations (Galati et al. 2020; Hayter 2015; Helfat and Peteraf 2015; Ocasio and Joseph 2018). Utilization of the AHP analysis supplements previous works that have strived to identify the various motivating factors of AE stakeholders by measuring the degree of individuals’ prioritizations. Although recent conceptualizations of communicative channels (e.g. vocabulary, rhetoric tactics) are indeed a prominent solution to diverging attentions (Ocasio and Joseph 2018; Tasselli et al. 2020), we argue that it is necessary to develop a contextual understanding of the degree of misalignments in stakeholders’ selective interests before endorsing actions to reach attentional consensus. Although traditional qualitative approaches are effective in accounting descriptive proceedings of strategic changes, they are limited in providing empirical understandings on cognitive foundations (Ocasio and Joseph 2018). Recent works have strived to address this issue. Case in point, Vega-Gomez et al. (2018) utilized conjoint analysis to explore the relative importance among various motivating factors in promoting AE. Although the statistical approach utilized in this methodology was effective in presenting attitudinal preference of a generalized sample of entrepreneurial academics, its dependency on a large number of participants tends to be ineffective in a highly specialized context where the number of involved actors tends to be selective (e.g. research-oriented university’s TTO personnel). We suggest that the AHP may prove to be an effective alternative under circumstances that mandate measuring prioritizations of a small number of dynamic actors involved in academics’ decision to pursue AE. In this research, findings revealed severe discrepancy between the selective stakeholders, such that technology experts place prioritized attentions in the order of technology, formal institution, and informal institution capabilities, while the reverse applies for nontechnology experts when collectively pursuing AE. Therefore, we were able to understand that in the context of a research-oriented university, academic entrepreneurs, and TTO personnel or university administrators often face conflicts of interests concerning how technology, formal institution, and informal institution are expected to influence AE output. Prioritizations revealed in the sub-criteria (level 3: attentions) may provide more specific areas of attentional discrepancy that can be compromised through emerging solutions offered by recent stream of research (Ocasio and Joseph 2018; Tasselli et al. 2020). Through a descriptive investigation, we were able to provide three points that explain why it is difficult for stakeholders to reach attentional consensus. First, especially in an R&D intensive environment, academics’ inherent attribute for seeking technological perfection acts to exacerbate the discrepancy with nontechnology experts. Case in point, while entrepreneurs of research-intensive institutes are driven toward augmenting technology specification, incubators and TTO personnel consider excessive advancement to be harmful, especially when the pursuit of technological excellence negates the release of follow-up products or bypasses the level of sophistication demanded in the market. This explains how the polarized attentions between technology experts (e.g. technical performance) and nontechnology experts (e.g. risk-taking culture) contribute to undermining the legitimacy of either actors’ interests. Second, producer-driven regulations and programs (e.g. exemptions from lecturing duties) lead to ineffective or even constraining results when academic entrepreneurs’ selective attentions are placed in differing areas (e.g. forming partnerships with industrial experts). For example, the university’s efforts to support entrepreneurs through means such as improved resource allocation or exemption from departmental activities unintentionally created subsidiary regulations (e.g. 3% royalty, coercive donation, complicated patent transfer) that acted as a major barrier in faculties’ early start-up careers. Similarly, while nontechnology experts continue to place significant emphasis on the need to create an entrepreneurial culture by minimizing expected workload (e.g. lectures, participation in department activities), academics rarely considered additional work to be a deterring factor when pursuing start-up activities. Third, discord among stakeholders’ individually perceived roles in AE led to the formation of functional gaps. Academic entrepreneurs’ requests for connections with industrial players (e.g. investors, accelerators, market analysts) are often rejected by start-up centers on the belief that an entrepreneur should simultaneously equip technological and managerial roles. Likewise, while nontechnology experts continue to place extensive efforts to guide academic entrepreneurs to be the flagship agent in expanding an entrepreneurial culture, technology experts perceive such movement to be irrelevant because they can easily return to a full-time faculty in the case of start-up failure. From these findings, it is possible to surmise that the cognitive conflict between technology-push (technology experts) and demand-pull (nontechnology experts) schema is evident at the individual-level and contributes to the challenge in reaching attentional consensus. Therefore, procedures to simply increase interaction between technology and non-technology experts, such as through formalized programs (Fischer et al. 2019), shared communication channels (Ocasio and Joseph 2018), or intermediary actors (Holm et al. 2020), need to be replaced by efforts to alter the cognitive logics that determine actors’ decision-making process. For instance, the point of largest attentional divergence in Korean research universities lies in how the stakeholders perceive technological perfection and entrepreneurial culture. University start-up centers are expected to employ intermediary actors with technological backgrounds in order to minimize the attentional gap with academic entrepreneurs. Similarly, academic entrepreneurs are encouraged to utilize business jargons during daily routine in order to be synchronized with other business-oriented stakeholders when pursuing start-up activities. Strategic approaches designed by understanding the micro-level organizational routines, habits, and individual preferences of involved stakeholders would prove to be an effective alternative to the traditional enforcement of spin-off creation strategies (Cunningham and Menter 2020). By recognizing where and why attentional discrepancy exists among key stakeholders, solutions recently provided by the ABV literature may prove to be that much more effective. While the purpose of this research was not to suggest strategic solutions to stakeholders’ attentional discrepancy, we were able to provide specified dimensions in which technology and non-technology experts fail to align when pursuing AE. 7. Conclusion This research began with skepticism on the effectiveness of regulation-centric approaches that strive to promote AE in research-oriented universities. Although extant literature indicates that the legitimization of universities’ adoption of the third mission requires establishment of new formalized rules, cognitive logics, and collaborative activities (Axler et al. 2017; Eesley et al. 2018; Sandström et al. 2018), they have dominantly focused on exploring the environmental antecedents and individual motivations (Miranda et al. 2018). Applying perspectives from the stakeholder theory and ABV, we postulate that since reaching multiple stakeholders’ needs is essential in maximizing organizational and network effectiveness, it is also critical to find consensus in the selective interests of various actors involved in AE to augment entrepreneurial output. This article strives to add to the literature by suggesting that the varying degrees of misalignment in attentions among key stakeholders may prove to be a critical barrier in the AE process. Findings reveal that the categorized stakeholders place priorities in reverse to one another, indicating a severe attentional discrepancy. These misalignments exist because the (1) dependency on technology-intensive agency for AE indicates a greater attentional gap the stakeholders are expected to overcome, (2) implementation of producer-driven regulations lead to unintended negative repercussions, and (3) ambiguity in stakeholders’ expected functionality results in the creation of performance gaps. Findings of this article support recent efforts to evolve from the traditional dependency on the institutional theory (Galati et al. 2020; Gianiodis et al. 2019; Ocasio and Joseph 2018; Vega-Gomez et al. 2018) by integrating multiple perspectives that consider the dynamics in collective cognitions among AE stakeholders. This research also adds to the ABV literature’s recent conceptualizations of shared communicative channels (Holm et al. 2020; Tasselli et al. 2020) to lower attentional discrepancies among stakeholders by providing an alternative method to measure the extent to which particular actors are expected to commit to the change. Adoption of AHP to evaluate the degree of intra-organizational or inter-organizational misalignment may prove useful in exploring attentional configurations that are particularly in need of applying communication tactics such as utilizing shared vocabulary or intermediary agents. Practical implications of the study indicate the need for a serious reconsideration of top-down implementation of policies and regulations in terms of who to target (demerits of research university’s technology-intensive characteristics), how to execute (unintended constraints on policy recipients), and when to expect AE outcomes (undecided functionalities among stakeholders). Managers are expected to understand that while research-intensive universities definitely hold the potential to create disruptive industrial opportunities, respective entrepreneurs’ innate affinity to academia may contribute to the attentional discrepancy among involved stakeholders. Although policymakers’ restructuration of the formalized entrepreneurial environment has been effective in the early stages of universities’ transition, it is important to place growing attention to user-driven policies (concerning involved stakeholders) to minimize academics’ discord with administrative personnel. Universities are encouraged to implement new movements or strategic events that aim at creating a collective consensus on the functionalities among academics, TTO personnel, and various start-up incubation centers when pursuing AE. This article is susceptible to limitations of an exploratory analysis, in that the investigation of a unique case impairs the validity of the result and provides only descriptive interpretations of the findings. Therefore, future research needs to expand into economic contexts with varying institutional environments, R&D orientation, and entrepreneurial history to confirm the correlation between academic entrepreneurs’ misaligned attentions and start-up tendencies. The employed methodology was also open to self-selection bias, in that respondents to the AHP and interviews may be more intensively involved in entrepreneurial activities compared to the general population. In order to control this factor, random sampling methods should be employed with significant participant incentives to increase the response rate. In terms of the scope of research, this article investigated discrepancies between stakeholders (i.e. technology and nontechnology experts) and neglected those that exist within a single agent. For instance, some academic entrepreneurs may also be affiliated with AE mediating offices, thereby essentially possessing attentions on both technology and nontechnology perspectives. Interesting follow-up research would be examinations into how stakeholders’ attention scope differs based on individual actors’ diversified institutional associations. Supplementary data Supplementary data is available at Science and Public Policy Journal online. Funding This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government [NRF-2020R1A2B5B01002243] and the Ministry of Science and ICT, Korea, under an ITRC Program [IITP-2020-2018-0-01402] supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation). Conflict of interest statement. None declared. Footnotes 1 Korea Advanced Institute of Science and Technology (KAIST, 1971), Ulsan National Institute of Science and Technology (UNIST, 2009), and Gwangju Institute of Science and Technology (GIST, 1993) are public research-oriented institutes established by the government to increase the economy’s production of science and engineering knowledge. 2 Because the AHP approach was traditionally developed to understand the process of decision-making among a small number of experts, a sample size of one is theoretically sufficient to implement the methodology (Herath 2004). Many past works have made useful contributions based on small sample numbers such as five (Peterson et al. 1994), twelve (Mavi 2014), eighteen (Somsuk and Laosirihongthong 2014), or twenty-two (Sambasivan and Fei 2008) respondents. Refer to Dhochak and Sharma (2016) for a detailed review on the methodology. References Abreu M. , Demirel P., Grinevich V., Karataş-Özkan M. ( 2016 ) ‘ Entrepreneurial Practices in Research-Intensive and Teaching-led Universities ’, Small Business Economics , 47 / 3 : 695 – 717 . Google Scholar OpenURL Placeholder Text WorldCat Allen T. J. O'Shea R. P. ( 2014 ) Building Technology Transfer within Research Universities: An Entrepreneurial Approach . England : Cambridge University Press . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Angel Ferrero M. C. , Bessière V. ( 2016 ) ‘ From Lab to Venture: Cognitive Factors Influencing Researchers' Decision to Start a Venture ’, Journal of Enterprising Culture , 24 / 02 : 101 – 31 . Google Scholar OpenURL Placeholder Text WorldCat Axler R. E. , Miller F. A., Lehoux P., Lemmens T. ( 2017 ) ‘ The Institutional Workers of Biomedical Science: Legitimizing Academic Entrepreneurship and Obscuring Conflicts of Interest ’, Science and Public Policy , 45 / 3 : 404 – 15 . Google Scholar OpenURL Placeholder Text WorldCat Bennett A. , Elman C. ( 2006 ) ‘ Complex Causal Relations and Case Study Methods: The Example of Path Dependence ’, Political Analysis , 14 / 3 : 250 – 67 . Google Scholar OpenURL Placeholder Text WorldCat Bercovitz J. , Feldman M. ( 2006 ) ‘ Entrepreneurial Universities and Technology Transfer: A Conceptual Framework for Understanding Knowledge-based Economic Development ’, The Journal of Technology Transfer , 31 / 1 : 175 – 88 . Google Scholar OpenURL Placeholder Text WorldCat Bischoff K. , Volkmann C. K., Audretsch D. B. ( 2018 ) ‘ Stakeholder Collaboration in Entrepreneurship Education: An Analysis of the Entrepreneurial Ecosystems of European Higher Educational Institutions ’, The Journal of Technology Transfer , 43 / 1 : 20 – 46 . Google Scholar OpenURL Placeholder Text WorldCat Bonaccorsi A. , Colombo M. G., Guerini M., Rossi-Lamastra C. ( 2014 ) ‘ The Impact of Local and External University Knowledge on the Creation of Knowledge-Intensive Firms: Evidence from the Italian Case ’, Small Business Economics , 43 / 2 : 261 – 87 . Google Scholar OpenURL Placeholder Text WorldCat Bourelos E. , Magnusson M., McKelvey M. ( 2012 ) ‘ Investigating the Complexity Facing Academic Entrepreneurs in Science and Engineering: The Complementarities of Research Performance, Networks and Support Structures in Commercialisation ’, Cambridge Journal of Economics , 36 / 3 : 751 – 80 . Google Scholar OpenURL Placeholder Text WorldCat Brugha R. , Varvasovszky Z. ( 2000 ) ‘ Stakeholder Analysis: A Review ’, Health Policy and Planning , 15 / 3 : 239 – 46 . Google Scholar OpenURL Placeholder Text WorldCat Cantu-Ortiz F. J. , Galeano N., Mora-Castro P., Fangmeyer J. Jr ( 2017 ) ‘ Spreading Academic Entrepreneurship: Made in Mexico ’, Business Horizons , 60 / 4 : 541 – 50 . Google Scholar OpenURL Placeholder Text WorldCat Cheng E. W. , Li H. ( 2001 ) ‘ Information Priority-Setting for Better Resource Allocation Using Analytic Hierarchy Process (AHP) ’, Information Management & Computer Security , 9 / 2 : 61 – 70 . Google Scholar OpenURL Placeholder Text WorldCat Civera A. , Meoli M., Vismara S. ( 2020 ) ‘ Engagement of Academics in University Technology Transfer: Opportunity and Necessity Academic Entrepreneurship ’, European Economic Review , 123 : 103376 . Google Scholar OpenURL Placeholder Text WorldCat Clarysse B. , Wright M., Lockett A. et al. ( 2005 ) ‘ Spinning Out New Ventures: A Typology of Incubation Strategies from European Research Institutions ’, Journal of Business Venturing , 20 / 2 : 183 – 216 . Google Scholar OpenURL Placeholder Text WorldCat Clarysse B. , Wright M., Lockett A. et al. ( 2007 ) ‘ Academic Spin-offs, Formal Technology Transfer and Capital Raising ’, Industrial and Corporate Change , 16 / 4 : 609 – 40 . Google Scholar OpenURL Placeholder Text WorldCat Clarysse B. , Tartari V., Salter A. ( 2011 ) ‘ The Impact of Entrepreneurial Capacity, Experience and Organizational Support on Academic Entrepreneurship ’, Research Policy , 40 / 8 : 1084 – 93 . Google Scholar OpenURL Placeholder Text WorldCat Cunningham J. A. , Menter M. ( 2020 ) ‘ Micro-level Academic Entrepreneurship: A Research Agenda ’, Journal of Management Development Google Scholar OpenURL Placeholder Text WorldCat Dhochak M. , Sharma A. K. ( 2016 ) ‘ Identification and Prioritization of Factors Affecting Venture Capitalists’ Investment Decision-Making Process: An Analytical Hierarchal Process (AHP) Approach ’, Journal of Small Business and Enterprise Development , 23 / 4 : 964 – 83 . Google Scholar OpenURL Placeholder Text WorldCat Díaz-Casero J. C. , Ferreira J. J. M., Mogollón R. H., Raposo M. L. B. ( 2012 ) ‘ Influence of Institutional Environment on Entrepreneurial Intention: A Comparative Study of Two Countries University Students ’, International Entrepreneurship and Management Journal , 8 / 1 : 55 – 74 . Google Scholar OpenURL Placeholder Text WorldCat Eesley C. , Li J. B., Yang D. ( 2016 ) ‘ Does Institutional Change in Universities Influence High-tech Entrepreneurship? Evidence from China’s Project 985 ’, Organization Science , 27 / 2 : 446 – 61 . Google Scholar OpenURL Placeholder Text WorldCat Eesley C. E. , Eberhart R. N., Skousen B. R. et al. . ( 2018 ) ‘ Institutions and Entrepreneurial Activity: The Interactive Influence of Misaligned Formal and Informal Institutions ’, Strategy Science , 3 / 2 : 393 – 407 . Google Scholar OpenURL Placeholder Text WorldCat Etzkowitz H. , Leydesdorff L. ( 1997 ) ‘ Introduction to Special Issue on Science Policy Dimensions of the Triple Helix of University-Industry-Government Relations ’, Science and Public Policy , 24 / 1 : 2 – 5 . Google Scholar OpenURL Placeholder Text WorldCat Etzkowitz H. , Leydesdorff L. ( 1999 ) ‘ The Future Location of Research and Technology Transfer ’, The Journal of Technology Transfer , 24 / 2–3 : 111 – 23 . Google Scholar OpenURL Placeholder Text WorldCat Etzkowitz H. , Webster A., Gebhardt C. et al. . ( 2000 ) ‘ The Future of the University and the University of the Future: Evolution of Ivory Tower to Entrepreneurial Paradigm ’, Research Policy , 29 / 2 : 313 – 30 . Google Scholar OpenURL Placeholder Text WorldCat Etzkowitz H. , Ranga M., Benner M. et al. ( 2008 ) ‘ Pathways to the Entrepreneurial University: Towards a Global Convergence ’, Science and Public Policy , 35 / 9 : 681 – 95 . Google Scholar OpenURL Placeholder Text WorldCat Evans J. M. , Baker G. R. ( 2012 ) ‘ Shared Mental Models of Integrated Care: Aligning Multiple Stakeholder Perspectives ’, Journal of Health Organization and Management , 26 / 6 : 713 – 36 . Google Scholar OpenURL Placeholder Text WorldCat Fernández-Alles M. , Camelo-Ordaz C., Franco-Leal N. ( 2015 ) ‘ Key Resources and Actors for the Evolution of Academic Spin-offs ’, The Journal of Technology Transfer , 40 / 6 : 976 – 1002 . Google Scholar OpenURL Placeholder Text WorldCat Fernández-Pérez V. , Alonso-Galicia P. E., Rodríquez-Ariza L. et al. . ( 2015 ) ‘ Professional and Personal Social Networks: A Bridge to Entrepreneurship for Academics? ’, European Management Journal , 33 / 1 : 37 – 47 . Google Scholar OpenURL Placeholder Text WorldCat Fischer B. B. , de Moraes G. H. S. M., Schaeffer P. R. ( 2019 ) ‘ Universities’ Institutional Settings and Academic Entrepreneurship ’, Technological Forecasting and Social Change , 147 : 243 – 52 . Google Scholar OpenURL Placeholder Text WorldCat Galati F. , Bigliardi B., Passaro R., and Quinto, I. ( 2020 ) ‘ Why do Academics Become Entrepreneurs? How do their Motivations Evolve? Results from an Empirical Study ’, International Journal of Entrepreneurial Behavior & Research, 26/7: 1477–503. Google Scholar OpenURL Placeholder Text WorldCat Gianiodis P. T. , Meek W. R., Chen W. ( 2019 ) ‘ Political Climate and Academic Entrepreneurship: The Case of Strange Bedfellows? ’, Journal of Business Venturing Insights , 12 : e00135 . Google Scholar OpenURL Placeholder Text WorldCat Gianiodis P. T. , Meek W. R. ( 2020 ) ‘ Entrepreneurial Education for the Entrepreneurial University: A Stakeholder Perspective ’, The Journal of Technology Transfer , 45 / 4 : 1167 – 95 . Google Scholar OpenURL Placeholder Text WorldCat Giuliani E. , Arza V. ( 2009 ) ‘ What Drives the Formation of ‘Valuable’ University–Industry Linkages?: Insights from the Wine Industry ’, Research Policy , 38 / 6 : 906 – 21 . Google Scholar OpenURL Placeholder Text WorldCat Hayter C. S. ( 2011 ) ‘ In Search of the Profit-Maximizing Actor: Motivations and Definitions of Success from Nascent Academic Entrepreneurs ’, The Journal of Technology Transfer , 36 / 3 : 340 – 52 . Google Scholar OpenURL Placeholder Text WorldCat Hayter C. S. ( 2013 ) ‘ Harnessing University Entrepreneurship for Economic Growth: Factors of Success Among University Spin-offs ’, Economic Development Quarterly , 27 / 1 : 18 – 28 . Google Scholar OpenURL Placeholder Text WorldCat Hayter C. S. ( 2015 ) ‘ Social Networks and the Success of University Spin-offs: Toward an Agenda for Regional Growth ’, Economic Development Quarterly , 29 / 1 : 3 – 13 . Google Scholar OpenURL Placeholder Text WorldCat Hayter C. S. , Lubynsky R., Maroulis S. ( 2017 ) ‘ Who is the Academic Entrepreneur? The Role of Graduate Students in the Development of University Spinoffs ’, The Journal of Technology Transfer , 42 / 6 : 1237 – 54 . Google Scholar OpenURL Placeholder Text WorldCat Hayter C. S. , Nelson A. J., Zayed S., O’Connor A. C. ( 2018 ) ‘ Conceptualizing Academic Entrepreneurship Ecosystems: A Review, Analysis and Extension of the Literature ’, The Journal of Technology Transfer , 43 / 4 : 1039 – 82 . Google Scholar OpenURL Placeholder Text WorldCat Helfat C. E. , Peteraf M. A. ( 2015 ) ‘ Managerial Cognitive Capabilities and the Microfoundations of Dynamic Capabilities ’, Strategic Management Journal , 36 / 6 : 831 – 50 . Google Scholar OpenURL Placeholder Text WorldCat Herath G. ( 2004 ) ‘ Incorporating Community Objectives in Improved Wetland Management: The Use of the Analytic Hierarchy Process ’, Journal of Environmental Management , 70 / 3 : 263 – 73 . Google Scholar OpenURL Placeholder Text WorldCat Hershberg E. , Nabeshima K., Yusuf S. ( 2007 ) ‘ Opening the Ivory Tower to Business: University–Industry Linkages and the Development of Knowledge-Intensive Clusters in Asian Cities ’, World Development , 35 / 6 : 931 – 40 . Google Scholar OpenURL Placeholder Text WorldCat Holm D. B. , Drogendijk R., ul Haq H. ( 2020 ) ‘ An Attention-Based View on Managing Information Processing Channels in Organizations ’, Scandinavian Journal of Management , 36 / 2 : 101106 . Google Scholar OpenURL Placeholder Text WorldCat Hsu D. W. , Yuan B. J. ( 2013 ) ‘ Knowledge Creation and Diffusion of Taiwan's Universities: Knowledge Trajectory from Patent Data ’, Technology in Society , 35 / 3 : 172 – 81 . Google Scholar OpenURL Placeholder Text WorldCat Hsu D. W. , Shen Y. C., Yuan B. J. et al. . ( 2015 ) ‘ Toward Successful Commercialization of University Technology: Performance Drivers of University Technology Transfer in Taiwan ’, Technological Forecasting and Social Change , 92 : 25 – 39 . Google Scholar OpenURL Placeholder Text WorldCat Hu M. C. ( 2009 ) ‘ Developing Entrepreneurial Universities in Taiwan: The Effects of Research Funding Sources ’, Science, Technology and Society , 14 / 1 : 35 – 57 . Google Scholar OpenURL Placeholder Text WorldCat Hu M. C. ( 2011 ) ‘ Evolution of Knowledge Creation and Diffusion: The Revisit of Taiwan's Hsinchu Science Park ’, Scientometrics , 88 / 3 : 949 – 77 . Google Scholar OpenURL Placeholder Text WorldCat Huyghe A. , Knockaert M., Piva E. et al. . ( 2016 ) ‘ Are Researchers Deliberately Bypassing the Technology Transfer Office? An Analysis of TTO Awareness ’, Small Business Economics , 47 / 3 : 589 – 607 . Google Scholar OpenURL Placeholder Text WorldCat ISK ( 2015 ) KAIST start-up performance survey. Startup KAIST (Text in Korean). ISK ( 2016 ) KAIST start-up performance survey. Startup KAIST (Text in Korean). ISK ( 2017 ) KAIST start-up performance survey. Startup KAIST (Text in Korean). ISK ( 2018a ) Faculty startup guide book. Startup KAIST (Text in Korean). ISK ( 2018b ) KAIST start-up performance survey. Startup KAIST (Text in Korean). Kellermanns F. W. , Walter J., Lechner C., Floyd S. W. ( 2005 ) ‘ The Lack of Consensus About Strategic Consensus: Advancing Theory and Research ’, Journal of Management , 31 / 5 : 719 – 37 . Google Scholar OpenURL Placeholder Text WorldCat Kenney M. , Patton D. ( 2011 ) ‘ Does Inventor Ownership Encourage University Research-Derived Entrepreneurship? A Six University Comparison ’, Research Policy , 40 / 8 : 1100 – 12 . Google Scholar OpenURL Placeholder Text WorldCat Kleinknecht R. , Haq H. U., Muller A. R. et al. . ( 2020 ) ‘ An Attention-Based View of Short-Termism: The Effects of Organizational Structure ’, European Management Journal , 38 / 2 : 244 – 54 . Google Scholar OpenURL Placeholder Text WorldCat Kwon K. S. ( 2011 ) ‘ The Co-evolution of Universities' Academic Research and Knowledge-Transfer Activities: The Case of South Korea ’, Science and Public Policy , 38 / 6 : 493 – 503 . Google Scholar OpenURL Placeholder Text WorldCat Kwon K. S. , Martin B. R. ( 2011 ) ‘ Synergy or Separation Mode: The Relationship Between the Academic Research and the Knowledge-Transfer Activities of Korean Academics ’, Scientometrics , 90 / 1 : 177 – 200 . Google Scholar OpenURL Placeholder Text WorldCat Koo B. J. , Kim S. W., Kim H. B. ( 2018 ) ‘ A Study on Vitalization of Faculty-Associated Startups: Focusing on Conflicts of Interests Regulations ’, Journal of Technology Innovation , 26 / 1 : 59 – 83 . (Text in Korean) Google Scholar OpenURL Placeholder Text WorldCat Lam A. , Lambermont-Ford J. P. ( 2010 ) ‘ Knowledge Sharing in Organisational Contexts: A Motivation-Based Perspective ’, Journal of Knowledge Management , 14 / 1 : 51 – 66 . Google Scholar OpenURL Placeholder Text WorldCat Lee Y. H. , Kim Y. ( 2016 ) ‘ Analyzing Interaction in R&D Networks Using the Triple Helix Method: Evidence from Industrial R&D Programs in Korean Government ’, Technological Forecasting and Social Change , 110 : 93 – 105 . Google Scholar OpenURL Placeholder Text WorldCat Lerner J. , Schoar A. ( 2005 ) ‘ Does Legal Enforcement Affect Financial Transactions? The Contractual Channel in Private Equity ’, The Quarterly Journal of Economics , 120 / 1 : 223 – 46 . Google Scholar OpenURL Placeholder Text WorldCat Li J. , Kozhikode R. K. ( 2008 ) ‘ Knowledge Management and Innovation Strategy: The Challenge for Latecomers in Emerging Economies ’, Asia Pacific Journal of Management , 25 / 3 : 429 – 50 . Google Scholar OpenURL Placeholder Text WorldCat Liefner I. , Kroll H., Peighambari A. ( 2016 ) ‘ Research-Driven or Party-Promoted? Factors Affecting Patent Applications of Private Small and Medium-Sized Enterprises in China’s Pearl River Delta ’, Science and Public Policy , 43 / 6 : 849 – 58 . Google Scholar OpenURL Placeholder Text WorldCat Mahoney J. , Goertz G. ( 2006 ) ‘ A Tale of Two Cultures: Contrasting Quantitative and Qualitative Research ’, Political Analysis , 14 / 3 : 227 – 49 . Google Scholar OpenURL Placeholder Text WorldCat Martin S. , Scott J. T. ( 2000 ) ‘ The Nature of Innovation Market Failure and the Design of Public Support for Private Innovation ’, Research Policy , 29 / 4–5 : 437 – 47 . Google Scholar OpenURL Placeholder Text WorldCat Martinelli A. , Meyer M., Von Tunzelmann N. ( 2008 ) ‘ Becoming an Entrepreneurial University? A Case Study of Knowledge Exchange Relationships and Faculty Attitudes in a Medium-Sized, Research-Oriented University ’, The Journal of Technology Transfer , 33 / 3 : 259 – 83 . Google Scholar OpenURL Placeholder Text WorldCat Mascarenhas C. , Ferreira J. J., Marques C. ( 2018 ) ‘ University–Industry Cooperation: A Systematic Literature Review and Research Agenda ’, Science and Public Policy , 45 / 5 : 708 – 18 . Google Scholar OpenURL Placeholder Text WorldCat Mathews J. A. , Hu M. C. ( 2007 ) Universities and Public Research Institutions as Drivers of Economic Development in Asia. In: Yusuf S., Nabeshima K. (eds), How Universities Promote Economic Growth , pp. 91 – 109 . The World Bank . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Mavi R. K. ( 2014 ) ‘ Indicators of Entrepreneurial University: Fuzzy AHP and Fuzzy TOPSIS Approach ’, Journal of the Knowledge Economy , 5 / 2 : 370 – 87 . Google Scholar OpenURL Placeholder Text WorldCat McKelvey M. , Holmén M. (eds) ( 2010 ) Learning to Compete in European Universities: From Social Institution to Knowledge Business . Edward Elgar Publishing . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Meek W. R. , Wood M. S. ( 2016 ) ‘ Navigating a Sea of Change: Identity Misalignment and Adaptation in Academic Entrepreneurship ’, Entrepreneurship Theory and Practice , 40 / 5 : 1093 – 120 . Google Scholar OpenURL Placeholder Text WorldCat Meoli M. , Vismara S. ( 2016 ) ‘ University Support and the Creation of Technology and Non-technology Academic Spin-offs ’, Small Business Economics , 47 / 2 : 345 – 62 . Google Scholar OpenURL Placeholder Text WorldCat Miranda F. J. , Chamorro A., Rubio S. ( 2018 ) ‘ Re-thinking University Spin-off: A Critical Literature Review and a Research Agenda ’, The Journal of Technology Transfer , 43 / 4 : 1007 – 38 . Google Scholar OpenURL Placeholder Text WorldCat Mok K. H. ( 2012 ) ‘ Bringing the State Back In: Restoring the Role of the State in Chinese Higher Education ’, European Journal of Education , 47 / 2 : 228 – 41 . Google Scholar OpenURL Placeholder Text WorldCat MSIT ( 2017 ) Measures to Promote University Entrepreneurship . Ministry of Science and Information Technology (Text in Korean). Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Muscio A. , Quaglione D., Ramaciotti L. ( 2016 ) ‘ The Effects of University Rules on Spinoff Creation: The Case of Academia in Italy ’, Research Policy , 45 / 7 : 1386 – 96 . Google Scholar OpenURL Placeholder Text WorldCat Nah S. M. , Kim C. O., Lee H. ( 2014 ) ‘ A Comparative Study of the Effect of University Competence on Technology Transfer and Commercialization and Start-ups ’, Journal of Korean Institute of Industrial Engineers , 40 / 5 : 462 – 76 . Google Scholar OpenURL Placeholder Text WorldCat Nakwa K. , Zawdie G. ( 2016 ) ‘ The ‘Third Mission’ and ‘Triple Helix Mission’ of Universities as Evolutionary Processes in the Development of the Network of Knowledge Production: Reflections on SME Experiences in Thailand ’, Science and Public Policy , 43 / 5 : 622 – 9 . Google Scholar OpenURL Placeholder Text WorldCat Ocasio W. ( 1997 ) ‘ Towards an Attention‐Based View of the Firm ’, Strategic Management Journal , 18 / S1 : 187 – 206 . Google Scholar OpenURL Placeholder Text WorldCat Ocasio W. ( 2011 ) ‘ Attention to Attention ’, Organization Science , 22 / 5 : 1286 – 96 . Google Scholar OpenURL Placeholder Text WorldCat Ocasio W. , Joseph J. ( 2005 ) ‘ An Attention-Based Theory of Strategy Formulation: Linking Micro- and Macroperspectives in Strategy Processes ’, Strategy Process , 22 : 39 – 61 . Google Scholar OpenURL Placeholder Text WorldCat Ocasio W. , Joseph J. ( 2018 ) ‘ The Attention-Based View of Great Strategies ’, Strategy Science , 3 / 1 : 289 – 94 . Google Scholar OpenURL Placeholder Text WorldCat O’kane C. , Mangematin V., Geoghegan W. et al. . ( 2015 ) ‘ University Technology Transfer Offices: The Search for Identity to Build Legitimacy ’, Research Policy , 44 / 2 : 421 – 37 . Google Scholar OpenURL Placeholder Text WorldCat Okhuysen G. A. , Bechky B. A. ( 2009 ) ‘ Coordination in Organizations: An Integrative Perspective ’, The Academy of Management Annals , 3 / 1 : 463 – 502 . Google Scholar OpenURL Placeholder Text WorldCat O'Shea R. P. , Allen T. J., Chevalier A. et al. . ( 2005 ) ‘ Entrepreneurial Orientation, Technology Transfer and Spinoff Performance of US Universities ’, Research Policy , 34 / 7 : 994 – 1009 . Google Scholar OpenURL Placeholder Text WorldCat Park H. W. , Leydesdorff L. ( 2010 ) ‘ Longitudinal Trends in Networks of University–Industry–Government Relations in South Korea: The Role of Programmatic Incentives ’, Research Policy , 39 / 5 : 640 – 9 . Google Scholar OpenURL Placeholder Text WorldCat Park K. B. , Park M. S. ( 2013 ) ‘ The Role of Research-Oriented Universities in Promoting Technology Transfer and Start-up ’, Science and Technology Policy , 191 : 166 – 72 . Google Scholar OpenURL Placeholder Text WorldCat Perkmann M. , Tartari V., McKelvey M. et al. ( 2013 ) ‘ Academic Engagement and Commercialization: A Review of the Literature on University-Industry Relations ’, Research Policy , 42 / 2 : 423 – 42 . Google Scholar OpenURL Placeholder Text WorldCat Perkmann M. , Fini R., Ross J. M.. et al. ( 2015 ) ‘ Accounting for Universities’ Impact: Using Augmented Data to Measure Academic Engagement and Commercialization by Academic Scientists ’, Research Evaluation , 24 / 4 : 380 – 91 . Google Scholar OpenURL Placeholder Text WorldCat Peterson D. L. , Silsbee D. G., Schmoldt D. L. ( 1994 ) ‘ A Case Study of Resources Management Planning with Multiple Objectives and Projects ’, Environmental Management , 18 / 5 : 729 – 42 . Google Scholar OpenURL Placeholder Text WorldCat Philpott K. , Dooley L., O’Reilly C. et al. ( 2011 ) ‘ The Entrepreneurial University: Examining the Underlying Academic Tensions ’, Technovation , 31 : 161 – 70 . Google Scholar OpenURL Placeholder Text WorldCat Rasmussen E. , Mosey S., Wright M. ( 2015 ) ‘ The Transformation of Network Ties to Develop Entrepreneurial Competencies for University Spin-offs ’, Entrepreneurship & Regional Development , 27 / 7-8 : 430 – 57 . Google Scholar OpenURL Placeholder Text WorldCat Readings B. ( 1996 ) The University in Ruins . Harvard University Press . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Rothaermel F. T. , Thursby M. ( 2005 ) ‘ Incubator Firm Failure or Graduation? The Role of University Linkages ’, Research Policy , 34 / 7 : 1076 – 90 . Google Scholar OpenURL Placeholder Text WorldCat Rowley T. J. ( 1997 ) ‘ Moving Beyond Dyadic Ties: A Network Theory of Stakeholder Influences ’, Academy of Management Review , 22 / 4 : 887 – 910 . Google Scholar OpenURL Placeholder Text WorldCat Saaty T. L. ( 1994 ) ‘ How to Make a Decision: The Analytic Hierarchy Process ’, Interfaces , 24 / 6 : 19 – 43 . Google Scholar OpenURL Placeholder Text WorldCat Sambasivan M. , Fei N. Y. ( 2008 ) ‘ Evaluation of Critical Success Factors of Implementation of ISO 14001 Using Analytic Hierarchy Process (AHP): A Case Study from Malaysia ’, Journal of Cleaner Production , 16 / 13 : 1424 – 33 . Google Scholar OpenURL Placeholder Text WorldCat Sandström C. , Wennberg K., Wallin M. W. et al. . ( 2018 ) ‘ Public Policy for Academic Entrepreneurship Initiatives: A Review and Critical Discussion ’, The Journal of Technology Transfer , 43 / 5 : 1232 – 56 . Google Scholar OpenURL Placeholder Text WorldCat Schmitz H. , Altenburg T. ( 2016 ) ‘ Innovation Paths in Europe and Asia: Divergence or Convergence? ’, Science and Public Policy , 43 / 4 : 454 – 63 . Google Scholar OpenURL Placeholder Text WorldCat Scott W. R. ( 2008 ) Institutions and Organizations: Ideas and Interests . Sage Publications . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Shane S. , Dolmans S. A., Jankowski J. et al. ( 2015 ) ‘ Academic Entrepreneurship: Which Inventors do Technology Licensing Officers Prefer for Spinoffs? ’, The Journal of Technology Transfer , 40 / 2 : 273 – 92 . Google Scholar OpenURL Placeholder Text WorldCat Siegel D. S. , Waldman D. A., Atwater L. E. et al. . ( 2004 ) ‘ Toward a Model of the Effective Transfer of Scientific Knowledge from Academicians to Practitioners: Qualitative Evidence from the Commercialization of University Technologies ’, Journal of Engineering and Technology Management , 21 / 1 – 2 : 115 – 42 . Google Scholar OpenURL Placeholder Text WorldCat Siegel D. S. , Wright M. ( 2015 ) ‘ Academic Entrepreneurship: Time for a Rethink? ’, British Journal of Management , 26 / 4 : 582 – 95 . Google Scholar OpenURL Placeholder Text WorldCat Somsuk N. , Laosirihongthong T. ( 2014 ) ‘ A fuzzy AHP to Prioritize Enabling Factors for Strategic Management of University Business Incubators: Resource-Based View ’, Technological Forecasting and Social Change , 85 : 198 – 210 . Google Scholar OpenURL Placeholder Text WorldCat Smith K. ( 2000 ) ‘ Innovation as a Systemic Phenomenon: Rethinking the Role of Policy ’, Enterprise and Innovation Management Studies , 1 / 1 : 73 – 102 . Google Scholar OpenURL Placeholder Text WorldCat Tasselli S. , Zappa P., Lomi A. ( 2020 ) Bridging Cultural Holes in Organizations: The Dynamic Structure of Social Networks and Organizational Vocabularies Within and Across Subunits . Organization Science, 31/5: 1292–312. Google Scholar OpenURL Placeholder Text WorldCat Van Doorn S. , Jansen J. J., Van den Bosch F. A. et al. . ( 2013 ) ‘ Entrepreneurial Orientation and Firm Performance: Drawing Attention to the Senior Team ’, Journal of Product Innovation Management , 30 / 5 : 821 – 36 . Google Scholar OpenURL Placeholder Text WorldCat Vega-Gomez F. I. , Miranda F. J., Chamorro Mera A. et al. . ( 2018 ) ‘ The Spin-off as an Instrument of Sustainable Development: Incentives for Creating an Academic USO ’, Sustainability , 10 / 11 : 4266 . Google Scholar OpenURL Placeholder Text WorldCat Vlaar P. W. , Van den Bosch F. A., Volberda H. W. ( 2006 ) ‘ Coping with Problems of Understanding in Interorganizational Relationships: Using Formalization as a Means to Make Sense ’, Organization Studies , 27 / 11 : 1617 – 38 . Google Scholar OpenURL Placeholder Text WorldCat Walston S. , Chou A. F. ( 2011 ) ‘ CEO Perceptions of Organizational Consensus and its Impact on Hospital Restructuring Outcomes ’, Journal of Health Organization and Management , 25 / 2 : 176 – 94 . Google Scholar OpenURL Placeholder Text WorldCat Wong P. K. , Ho Y. P., Singh A. ( 2007 ) ‘ Towards an “Entrepreneurial University” Model to Support Knowledge-Based Economic Development: The Case of the National University of Singapore ’, World Development , 35 / 6 : 941 – 58 . Google Scholar OpenURL Placeholder Text WorldCat Wong P. K. , Ho Y. P., Singh A. ( 2011 ) Contribution of Universities to National Innovation Systems in Asia: Technology Commercialization and Academic Entrepreneurship. In: Wong P. K. (ed), Academic Entrepreneurship in Asia: The Role and Impact of Universities in National Innovation Systems , pp. 1 – 28 . Edward Elgar Publishing . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Woolthuis R. K. , Lankhuizen M., Gilsing V. ( 2005 ) ‘ A System Failure Framework for Innovation Policy Design ’, Technovation , 25 / 6 : 609 – 19 . Google Scholar OpenURL Placeholder Text WorldCat Wright M. , Lockett A., Clarysse B., Binks M. ( 2006 ) ‘ University Spin-out Companies and Venture Capital ’, Research Policy , 35 / 4 : 481 – 501 . Google Scholar OpenURL Placeholder Text WorldCat Yoon H. , Lee J. J. ( 2013 ) ‘ Entrepreneurship Education and Research Commercialization of Engineering-Oriented Universities: An Assessment and Monitoring of Recent Development in Korea ’, International Journal of Engineering Education , 29 / 5 : 1068 – 79 . Google Scholar OpenURL Placeholder Text WorldCat © The Author(s) 2021. Published by Oxford University Press. 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TI - Academic entrepreneurship and attentional discrepancy among key stakeholders: Evidence from research universities in Korea
JO - Science and Public Policy
DO - 10.1093/scipol/scaa064
DA - 2021-03-03
UR - https://www.deepdyve.com/lp/oxford-university-press/academic-entrepreneurship-and-attentional-discrepancy-among-key-Ws0UoeDdLb
SP - 1
EP - 1
VL - Advance Article
IS - 
DP - DeepDyve
ER -