TY - JOUR
AU - Jang, Deok-Ho
AB - Abstract This article analyzes and compares expert groups’ (science policy experts and field researchers in engineering) perceptions of the national scientific agenda in South Korea. The national agenda seeks to identify the conditions necessary for creativity and innovation. In general, policy experts and field academics share a common notion that investment in human resources and increased interdisciplinary cooperation are prerequisites for global technological competence. However, comparison of semantic network analysis results reveals that policy officials and field scientists differ in their views of how the field of innovation, the academy and laboratory, should be governed and reformed. The analysis implies that more fundamental conditions need to be discussed in scientific governance, especially recognizing the importance of educational reform, encouraging collaborative culture in the academy and empowering a coordinative body in the government. However, these are yet to be included in the public deliberation. 1. Introduction1 Organizational influences on scientific innovation and creativity have long been of interest to the academy. Contrary to the myth of scientific achievement coming from a solitary genius, contemporary laboratories are complex environments, with large workforces and multiple resources. Effective generation of knowledge requires ‘problem definition, information gathering, information organization, conceptual combination, idea generation, idea evaluation, implementation planning, and monitoring’ (Hemlin et al. 2013: 11). In a similar vein, Valente and Rogers (1995) depict innovation diffusion as a dynamic form of network that is mediated by active interaction of information exchange, problem solving, and mutual learning. The strategy and dynamics of scientific innovation is also the subject of literatures on systems of innovation from national (Freeman 1987, 1995) and regional (Cooke and Morgan 1998) levels, to the more microscopic gaze on the ‘Triple Helix’ of government–academy–industry relations (Etzkowitz and Leydesdorff 2000) and networks of salient knowledge (Callon et al. 1986). Universities, governmental research institutes, and firms that cooperate with them have emerged as key components of national innovation systems (Etzkowitz and Leydesdorff 2000; Freeman 1987). These organizations interact and share both explicit and tacit knowledge that eventually leads to product and/or process innovation. Throughout this process, effective communication of engaged actors is a key concern for governing bodies. Keun Lee (2013) reports that South Korea has recently moved from catch-up and short cycle industry to the post catch-up, long cycle, and science-based industry stage. Lee and Eom’s (2010: 3) analysis of Korean industrial data in the early 2000s, however, revealed that the government research institute–university–industry collaboration had significantly increased neither the probability of innovation success, nor sales, but only increased the number of patents. This is in contrast to many European countries where more explicit linkage between inter-organizational collaboration and innovation success is being reported. Lee and Eom also report that government financial support for R&D turns out to be the main factor in firms’ propensity to cooperate with other institutions, which reflects the early stage of knowledge industrialization where government plays a leading role. In this paper, we explore the views of policy makers and scientists on the success of government tactics to facilitate creativity and innovation. In terms of the amount of expenditure spent on national research and development, South Korea ranks highest in the world: Korea spent 4.15 per cent of GDP in R&D and invested 68,936 million PPP dollar in 2013 (Table 1), reaching an annual average increase rate of 10.3 per cent (OECD 2014a: OECD.StatExtracts). This amount of expenditure calculated as a percentage of GDP is the top among all OECD countries and the second in the world after Israel, which invested 4.21 as a percentage of GDP. Table 1. Gross domestic expenditure on R&D (2013). Country  Korea  USA  Japan  Germany  France  UK  Gross Domestic Expenditure on R&D (million PPP $)  68,936  453,544  160,246  103,909  55,218  39,858  R&D Expenditure as a percentage of GDP  4.15  2.81  3.49  2.94  2.23  1.63  Country  Korea  USA  Japan  Germany  France  UK  Gross Domestic Expenditure on R&D (million PPP $)  68,936  453,544  160,246  103,909  55,218  39,858  R&D Expenditure as a percentage of GDP  4.15  2.81  3.49  2.94  2.23  1.63  Source: OECD. OECD.StatExtracts (http://stats.oecd.org/Index.aspx). This aggressive investment in R&D has represented the state’s desire to promote scientific research and economic growth. As shown in Fig. 1, investment in R&D correlates with impressive economic growth in Korea. However, the slowed economic growth rate casts doubt on the sustainable growth of the economy and compels policy-makers to seek solutions in research innovation. Despite the contribution science and technology (hereafter S&T) has made to South Korea’s economic development, indicators of excellence in scientific research are lacking: Korea ranks 32nd in the world for citations per science journal in the last 5 years (Korea Institute of Science and Technology Evaluation and Planning (KISTEP) 2012), and has not produced a Nobel science laureate—something that is regularly bemoaned by Korean mass media.2 As Ko (2015) points out, ‘changes in investment priority and the emergence of new concepts about “basic research” are co-products of a policy paradigm of the linear model and a policy norm of industrial competitiveness’ in South Korea, which are growingly emphasizing the somewhat oxymoronic ‘application-oriented basic research’. Figure 1. View largeDownload slide Relation between R&D and economic growth in industrial nations 1994–2008. Source: German Federal Ministry of Education and Research (2010). Figure 1. View largeDownload slide Relation between R&D and economic growth in industrial nations 1994–2008. Source: German Federal Ministry of Education and Research (2010). Concerns over competence combined with the rapid change of the economic and social environment (Hong and Lee 2012), such as the advent of a global knowledge-based economy, energy shortage, and climate change (Korean Ministry of Science and Technology and Education (KMEST) 2008) has led to serious questions about whether South Korea can sustain its position as a major world economy. Industrial experts also point out that Korea can no longer simply copy seed technologies abroad and produce better applied-goods in the global market, because a number of Korean firms have taken the lead in several fields, missing a role model. Therefore, a shift into a first mover through S&T innovations has been regarded as both necessary and imminent (National Science and Technology Commission (NSTC) 2012). For this to happen, endogenous creativity is seen as vital, where in the past, patterns of economic catch-up were mainly driven by collaboration with foreign companies (Lee and Lim 2001). To cope with these challenges, the Korean government has placed emphasis on S&T in order to transform into a knowledge-based nation. Several attempts to reform have been made by administrations. Most visible changes were made by the creation of new roles in, or reshuffling of the cabinet, such as the creation of the Office of Science and Technology Innovation in the Roh Mu-hyun administration (2003–8), merging the Ministry of Science and Technology with the Ministry of Education, and the creation of a National Science and Technology Council (NSTC) in the Lee Myung-bak administration (2008–13), and recently, the establishment of the Ministry of Science, ICT, and Future Planning (MSIP) in the current Park Geun-hye administration (2013–17 March). Alongside the reorganization of the Ministries, concerns have been raised from academic and political communities. The increase of R&D expenditure and the number of researchers is insufficient (Kim et al. 2012; MSIP 2013). The quantity-driven strategy to increase SCI publications and patents brings about considerable governmental intervention, monitoring, and inefficient evaluation of R&D projects (OECD 2014b). Jang and Kim (2013) compared international and domestic (South Korean) scholars’ opinions on the institutional sponsorship of the World Class University (WCU) project, concluding that the evaluative system concentrating on numbers of publications hampered spontaneous collaboration among researchers. Higher-education academics suggest that this kind of practice ignores the nature of research processes, which might in turn lead to a decrease in long-term research capacity (Marginson 2011). Likewise, Kim and Park (2015) argued that the research evaluation system adopted by the Korean government has undermined the collaborative spirit among biomedical scientists. The existing paradigm has been heavily criticized for failing to respond to the demands of a society where diverse factors contribute to the capacity to innovate. Leadership in science and technology field calls not only for the innovation of technology but also for the overall reconstruction of markets, institution, and new lifestyles (Geels 2004; Song et al. 2011). Recognizing the need for a holistic change, the Park Geun-hye administration has given urgent attention to a ‘creative economy’ that reflects both the terminology and intention of Howkins (2001). But the implementation of a ‘creative economy’ has never been clear enough for policy makers and researchers to act. Recent academic reviews (Im 2015; Jeong 2016; Kim 2013b) criticize the Park’s regime’s failure to deliver the much desired scientific innovation, especially in that it has weakened private actors’ innovative capabilities and coordinative function within the government, which are essential to its creative economy (Im 2015; Kim 2013b). Besides the institutional interactions, this article explores how heterogeneous scientific actors (science policy experts and field researchers in engineering) perceive the conditions for creativity and innovation, and reflect how the aforementioned institutional orientations might have affected their views and attitudes. This article asks, what encourages or hampers scientific creativity and innovation in the South Korean context? By exploring the question, the authors attempt to visualize and compare the characteristics of the two expert groups’ opinion, and discuss how the difference reflects socio-cultural challenges for science policy in South Korea. Methodologically, semantic network analysis (SNA) is applied to focus group conversations between members of these groups by visualizing and comparing core characteristics of the two groups’ discussions. Through the analysis, the authors attempt to highlight the overlooked area of existing scientific activities and discourses that could be vital to creativity and innovation in research. 2. Methodology 2.1 Focus group discussion We recruited focus groups from academic researchers and science policy experts; and analyzed the perceptions of the two expert groups and verified if there is a notable difference between them. The authors systematically compared their frames by operating an SNA. In comparison to existing qualitative analysis, the SNA of focus group interview transcripts enables the researcher to structure text with derived key concepts, finding hierarchical relations and informing the researcher which concept and thematic cluster to focus on (Jang and Kim 2013). In this study, the authors conducted two group interviews using semi-structured questionnaires to stimulate discussions on fundamental topics including global scientific trends and governance. Questions were designed to reflect the current status of a South Korea that is moving to global science and technology leadership, but still held back by fast-followership mindset. Taking into account the historically embedded limitations, the interviewer(s) also asked about perceived obstacles to innovation. Thus, the baseline questions are: What kind of major technological, organizational, and socio-cultural transformations in the globe are being observed in science and technology? What do you think is their deep-lying cause? How do you evaluate the way that the government, academy, and industry in Korea respond to such global transformation? Do you think there are some differences from that of other first world countries? If so, what are they? How do you evaluate the overall national support system to encourage science and technological innovation over the last 5 years? And what are the reasons? What do you think is the main objective of the nation in order to actively respond to changes in the global science and technology environment? What changes do you think are necessary from government, the academy, and industry to effectively meet the science and technology policy agenda?To recruit for group (a) we approached engineering faculty members in universities and senior researchers in governmental research institutes. Science policy experts (group b) were recruited from various government-affiliated institutions3 (Table 2). Table 2. Participants of FGD. Science policy experts   Academic participants   Participant  Affiliation  Participant  Affiliation  GP1  KITECH  AP1  Sangmyung University  GP2  KISTEP  AP2  Electronics and Telecommunications Research Institute (ETRI)  GP3  STEPI  AP3  Korea University  GP4  STEPI  AP4  University of Seoul  GP5  Hankuk University of Foreign Studies  AP5  Yonsei University  Science policy experts   Academic participants   Participant  Affiliation  Participant  Affiliation  GP1  KITECH  AP1  Sangmyung University  GP2  KISTEP  AP2  Electronics and Telecommunications Research Institute (ETRI)  GP3  STEPI  AP3  Korea University  GP4  STEPI  AP4  University of Seoul  GP5  Hankuk University of Foreign Studies  AP5  Yonsei University  2.2 Representation of semantic network for discourse analysis In order to represent the qualitative outcomes of the focus group discussions in a systematic form, we utilized SNA to interpret the linkage pattern of keywords that was considered to reflect the typical frame of a social group (Tewksbury and Scheufele 2009; Jang and Kim 2013). SNA is a form of content analysis which extracts the network of relations between objects as expressed in a text, in order to represent a discursive model as a visible map (Carley 1993). Coding texts as maps focuses the user on investigating meaning among texts by finding relationships among words and themes, and by identifying central words in specified relations. In the traditional co-word analysis (Callon et al. 1986; Danowski 1993) that has been utilized for content analysis, it is assumed that search terms identifying actors or issues that appear close to each other (two words are ‘close’ if they co-occur often in the same document or paragraph) in a text indicate an association between these actors or issues. The drawback of this method, however, is that it ignores the semantics of concepts, context, and expressed relations (van Atteveldt and Takens 2010), and the links become too complex to concisely denote the relation of reference. Also, co-occurring words in a sentence or a paragraph presuppose the relation of reference, but those words do not necessarily form truly referential, that is, preceding and anteceding, relationships. In contrast, our approach of defining the relation of a directed link between two concepts has to do with whether the first concept is seen to have some type of ‘prior’ relationship to the second concept (Carley 1993; Kronberger and Wagner 2007). Various types of prior relationship can be thought of. For example, ‘a implies b,’ ‘a comes before b,’ ‘if a is true, then b is true,’ ‘a qualifies b,’ or ‘a (subject) <verb> b (descriptive).’ This coding directionality, automatically performed with natural language processing (NLP) technology, can provide information about the way in which the impact of new information propagates through the network and affects decisions, and the structure of meaning (Carley 1993: 96). The selective links of concepts in the semantic network represent a symptom of the social representation (Moscovici 2000) of utterers, and they are incorporated into discourse analyses that delve into microscopic relations of power among actors—mediated by language. Social representation theory (Deaux and Philogene 2001; Moscovici 2000) presents a formal way of considering multiple levels of signification in science communication, by actively incorporating both experts’ and lay people’s knowledge and perception of science. Acquiring similar knowledge through socialization, and constructing their knowledge in similar ways from public discourse, people integrate similar information in similar ways and form similar, social representation (Baden 2010). In this way, various cultural groups are defined as sharing specific discourses and interpretations, which also implies on-going competition between multiple frames and discourses. These social signification processes highlight the dynamic process of social representation mediated by semantic and social psychological interactions that are not reduced to individual cognition, but remain highly abstract without formal methods of classifying the levels of signification in practice. The analytical term ‘denotation’ in this regard clarifies a relation that serves to connect the expression and the content of sign with means of salient rhetoric, and ‘connotation’ reveals an underlying contextual meaning or ideology that is manifested through a converging cultural object. By focusing on the pragmatic and contextual nature of sense making, Suerdem (2013) emphasizes that the structural aspect of signification can be methodologically captured from the distribution of words internal to a large text corpus produced by the members of a culture. From this perspective, the theory of social representation can particularly highlight the connotative aspect that is ‘implicit, cultural, sensational, and phenomenal side of the sense making process’ reflecting utterers’ embedded social context and shared phenomenological experiences (Peirce 1998; Wittgenstein 2001). To do this, ‘semiotic theory of social representation should be the relational mechanism decoding this order rather than the discrete units such as words; themes; or thought units’ (Suerdem 2013). The dialogue, depicted graphically in the SNA, is a social action that creates a collective narrative, or an ‘occurring’ event (Bourdieu 1991) of conceptual relations. The event represents specific interests and socio-historical contexts; and words and concepts become ideological units of life that both reflect and refract particular social relations. Therefore, when a word is uttered ‘it is not merely an individual’s identity that is invoked … but also a social and historical whole through which the utterance has been indicated and through which it has gained a specific evaluation’ (Crossley and Roberts 2004: 77, 85). Hence, the visualized discourses as a semantic network form can facilitate to explain how the socially controversial issues, like the conditions of creativity and innovation discussed in this article, are being defined, what are the salient causal interpretations, what are the associated value references, and what is the converging solution and desire (Entman 1993) signified by central organizing idea (Gamson and Modigliani 1987). For the technical analysis, interaction between two nonadjacent nodes of concepts is likely to depend on another concept for reference that functions as a ‘catalysis’ to join metalanguages of concepts (Barthes 1967). This function of ‘denotation’ is translated into a node with the highest betweenness centrality (Freeman 1979; Kim 2013a) in the semantic network, when the keyword lies on the paths between the trigger of information and referent, performing a mediating role as a semiological facilitator of communication. On the other hand, the ‘flow’ or sequence of denotative communication has an ultimate end(s), which becomes a converging point of connotation. This can be represented as an individual keyword that has the highest input-closeness centrality (Freeman 1979; Kim 2013a). In this study, keywords with the highest input-closeness centrality are calculated and highlighted as ‘connotation’. To derive a consistent outcome without human intervention in coding a textual data, utilization of an automatic algorithm based on the aforementioned assumption becomes vital. Based on the methodological assumptions and procedure, the keywords in the text were coded and analyzed automatically by the commercial text-mining tool Optimind (ver. 1.0).4Optimind is an automatic semantic network tool, based on the distance and story line model for coding (Carley 1993), which extracts and analyzes links among words to model the author(s)’s ‘mental map’ as a network of links. For the standardized extraction of a core network, the backbone extraction model (Serrano et al. 2009) was adopted. An important aspect of this model is that the ensuing reduction algorithm does not belittle small nodes and links in terms of frequency while offering a stable automatic procedure to reduce the number of connections by taking into account all of the scales. By operating the computerized system, the text analysis goes through the following stages: Preprocessing: Checking words and listing context-specific thesauri of synonyms. Automatic lemmatization of variable words (transformation into basic form) based on the NLP library and system. Automatic deletion of syntactically functional words such as articles and adverbs. Processing: Transformation of the remaining text into an adjacency matrix of keywords, with the window size of every paragraph. Applying a backbone threshold that extracts a core set of nodes (keywords). Verifying keywords with high centralities with the original texts in which those keywords were used. Visualization Visualization of network by Optimind based on the calculation of centrality indices and grouping algorithm. Interpretation of the represented semantic network.In contrast to the relatively simple procedure of inference derived from the correlation between text and research question in traditional content analysis, SNA goes further, exposing the multilayered contexts of texts and research questions, and proceeds to infer the answer through an active feedback loop between network representation and background knowledge. Background knowledge was substantiated by an explicit ethnographical review (Tambayong and Carley 2012) and/or implicitly what German sociologist Max Weber refers to as Nach erleben (reliving) through the ideal-typically reconstructed representation. Therefore, the methodology of SNA is positioned in the academic tradition of hermeneutics and interpretive sociology, and aims to invoke critical questioning instead of presenting a confirmatory answer (Kim and Kim 2015). 3. Results 3.1 Semantic network results The network representation of the extracted core frame elucidates the meaning of the characteristic differences. Figure 2 is the extracted semantic map of participant responses reorganized by Optimind. Different word classes (themes) are presented as different circles, and the directed edges and their width, respectively, represent the sequential flow of statements and the frequency of linkages between the two word classes. Among the keywords in the same circle, the one with the highest betweenness centrality (most salient denotation) is placed at the center. In this arrangement, we can detect the core concepts, related words, and the sequential flow of identified themes that collectively construct the pattern of respondents’ opinion. Figure 2. View largeDownload slide Discursive frame of academic researchers. Figure 2. View largeDownload slide Discursive frame of academic researchers. For brevity, in the following text, the bracketed word is the one placed in the center of each thematic circle, and the quoted word or phrase is the one placed in the periphery of each thematic circle. For the engineering professors and senior researchers in the governmental laboratories, governmental policies are seen as a [problem], and blame of ‘public official[s]’ open up initial debates (Fig. 2): there were discontented voices over the Ministry of Education, Science and Technology’s (MEST) WCU project that concentrated funding to only a few prominent candidates, and similarly expressed frustrations about the way of granting ‘R&D’ ‘project’s ‘budget’ (AP4, AP5). Those faculty members in the academy felt that [change] was needed, and they mainly sought the solution in governmental reorganization, that is, deconstructing the current MEST and reviving the older organization, the Ministry of Science and Technology (MST), that was considered more favorable to professors of engineering (referential linkages indicated by circled <no. 1>) in terms of ‘recognizing scientists’ (AP3, AP4) and ‘facilitating funding’ (AP4, AP5). On the other hand, the academic participants unanimously agreed that ‘convergence’ of technology was a visible trend in the current industrial world, and that ‘policy effort’ and ‘school’ should encourage ‘individual’ ‘effort’ and [study] to cope with the demand (circled <no. 2>). Respondents exhibited dualistic attitude toward the project-based evaluative (‘PBS’ system) that is related to the theme of [convergence]: While one respondent criticized its competitive characteristics (AP2), the interviewees overall recognize the PBS as a predominant rule of a game to which they should adapt. What [professors] felt was that the [university] system was locked in the current [evaluation] system (circled <no. 3>) that was excessively quantity-oriented, especially in terms of the number of publications (AP1, AP2, AP4, AP5). Although most respondents thought that such evaluation was heavily influenced by the academic culture in the ‘USA’, one faculty member (AP4) asserted that the Korean system had become far more quantity-oriented vis-à-vis publication numbers, thus being more superficial than the ‘USA’. The theme of [university] and related keywords ‘governance’ and ‘government’ are placed between the story line from [change] to [evaluation], somewhat narrowing down the role of government (and governance) into an evaluative function. Participants agreed that a new pathway of education and research was required to respond to changes in technology. According to the participants, however, it was ‘government’ that was responsible for such ‘governance’ of the ‘university’ ‘system’, rather than university itself. The typical frame of science policy experts (Fig. 3), on the other hand, shows that initial reflections on [evaluation] in research, the role of [government], and the overall [system] converge on emphasizing the ‘importance’ of paying attention to [human] capability or ‘professionalism’ while acknowledging human resource training practice in the ‘USA’ as a viable model (circled <no. 1>). Figure 3. View largeDownload slide Discursive frame of science policy experts. Figure 3. View largeDownload slide Discursive frame of science policy experts. As for the word class of [evaluation], it summarizes that Korea’s researchers have been excessively ‘objective oriented’, that those objectives are mostly short-term, and driven by ‘competition’; [government] should recognize the value of ‘knowledge’ per se and the research capability of small- and medium-sized enterprises that have been ‘overlooked in the existing economic environment where large sized manufacturing firms like Samsung and Hyundai have dominated’ (GP2, GP4). In relation to the discussions emanating from the concept of [idea] (circled <no. 2>), the adjacent [system] should focus on developing human resources by bringing a ‘superior level’ of ‘culture’ into systemic reflection. This means that the administrative [idea] should go beyond ‘budget’ allocation and coordinative ‘adjustment’ of conflicting interests, actively engaging in the task of ‘human resource cultivation’ in the [science technology] field. According to GP4, ‘the variable of culture has long been neglected in the administration’ and ‘lack of cooperative culture and severe competition have been serious obstacles to research’. From these perspectives, the eventually converging theme of [governance] (circled <no. 3>) requires a new form of social ‘contract’, rather than ‘simple project contracting and government bureaucracy’ (GP1), that allows ‘efficient cooperation through vertical and horizontal relationships’ (GP4). Although these two groups’ discussions represented by semantic networks look similar, they reveal a critical difference. The discursive structure of the academic participants’ group mainly criticizes the government’s resource allocation practice and evaluative policy, but accepts, either consciously or unconsciously, the value of competition and the government’s involvement in the university affairs. The policy experts’ group similarly criticizes the practice of government and the current evaluative system, but also problematizes the present competitive culture itself. The policy experts’ discussion positions the competitive culture as hindering human resource development and the accumulation of knowledge. 3.2 Comparison of frames To compare the most salient denotations and connotations that, respectively, have the highest betweenness and in-closeness centrality, Table 3 captures some notable differences between the groups. For academic participants, ‘university’ and ‘evaluation’ were the main topics, and the ‘fusion’ of technology and ‘government’ ‘problem’ followed. In terms of connotation, disputes in the budget allocation of a government-initiated ‘project’ and pressures imposed on a ‘research-oriented university’ were the most important concerns. The discussion and subsequent criticism converge on the ‘fault’ of governmental ‘official[s]’ and the related ‘program’. Table 3. Top five keywords of denotation and connotation. Science policy experts   Academic researchers   Rank  Denotation  Connotation  Rank  Denotation  Connotation  1  Human  System  1  University  Project  2  Culture  Human resources  2  Evaluation  Research-oriented University  3  Governance  Attention  3  Fusion  Fault  4  Government  Integration  4  Government  Government official  5  Society  Contract  5  Problem  Program  Science policy experts   Academic researchers   Rank  Denotation  Connotation  Rank  Denotation  Connotation  1  Human  System  1  University  Project  2  Culture  Human resources  2  Evaluation  Research-oriented University  3  Governance  Attention  3  Fusion  Fault  4  Government  Integration  4  Government  Government official  5  Society  Contract  5  Problem  Program  For science policy experts, the usual catalysis of communication was about ‘human’ [actors], ‘culture’, ‘governance’, ‘government’, and ‘society’; and these topics eventually converged on discussions of a ‘system’, ‘human resources’, ‘attention’, ‘integration’, and a (social) ‘contract’ to seek a societal solution. The comparison of extracted frames in Table 4 represents each group’s identity as well as expertise. The academic researchers’ frame reflects their frustrations on a more personal level, mainly caused by severe competition over project funding and evaluation. Science policy experts speculate on a broader system to facilitate scientific governance that includes the mitigation of competitive culture and formation of a new social contract. The two groups share the key concern that the current evaluative regime is undermining creative activity. Table 4. Comparison of frames.   Academic researchers  Science policy experts  Key concerns  Evaluative system R&D budget allocation  Evaluative system Competitive culture Lack of human resource development  Main solutions  Promotion of graduate school and student Government intervention in R&D policy Reorganization of governmental bodies  Human resource cultivation Promotion of collaborative, inter-disciplinary culture Mitigation of competition  Critical factors of innovation  Budget and recognition Recognition of scientist Government organization  Effective communication Research culture Social contract  Target of governance  Fair evaluative system in the university  Creative and integrative social interaction in research fields    Academic researchers  Science policy experts  Key concerns  Evaluative system R&D budget allocation  Evaluative system Competitive culture Lack of human resource development  Main solutions  Promotion of graduate school and student Government intervention in R&D policy Reorganization of governmental bodies  Human resource cultivation Promotion of collaborative, inter-disciplinary culture Mitigation of competition  Critical factors of innovation  Budget and recognition Recognition of scientist Government organization  Effective communication Research culture Social contract  Target of governance  Fair evaluative system in the university  Creative and integrative social interaction in research fields  In terms of the solution, however, they are fundamentally different. The policy experts envision a new form of social contract and transformation of the competitive culture to encourage cooperation based on more transparent and spontaneously constructed rules by participating actors. In contrast, most of the academic participants pay little attention to the competitive culture, and look to government to encourage innovation by facilitating budget allocation and modifying the evaluative rules. In the interview, the academic participants agreed that the concept of governance was not simply a matter of ‘top-down’ directing, but were skeptical that coordination and adjustment could be made spontaneously within the university because of the lack of trust among faculty members. Academics were too immersed in competition for funding and pursuing beneficial bureaucratic intervention, to consider creating the means for innovation. 4. Conclusion and discussion Through the focus group discussion and textual analysis, the authors have derived the following main agenda: Global trends: The convergence of technologies is deemed to be an imminent issue. In order to induce successful convergence of technologies to bring about innovation, the interviewees agree that inter-disciplinary and trans-organizational cooperation has become a prerequisite. The concept is not new, but the need for an integrative approach to the ‘socialization of innovation’ has become much more visible. South Korea’s technological model: There is a unanimous agreement among experts that the scientific development model should quickly shift from the fast-followership to more independent and innovative first mover. But a ‘cultural lag’ in the structure of governance, the scientific policy paradigm, and collaborative practice in the academy hampers this move. The subject of innovation: In the past, the state directed the roadmap of science and technology policy. Now, it should be individuals who spontaneously take the lead creatively. Thus, the nurturing of competent actors should be emphasized over formal systemization. The prerequisite to change: Above all, the concept of ‘governance’ should be reframed as a method of cooperation between actors, rather than a simple funding distribution system of research projects or the organization of governmental bodies to support it. Consequently, a coordinative body should be established or empowered to encourage heterogeneous actors to cooperate with an effective communicative system. Various actors should propose ways to promote autonomous and cooperative culture in the research field, including academia.To recapture, President Park’s government has prioritized science and technological development, and empowered a new MSIP incorporating the technology-related functions of the Ministry of Science and Technology, the Communications Commission, and the Ministry of Knowledge Economy. The creation of a bigger ministry, the MSIP, has become a visible demonstration of promoting science and technology on the part of the government. The reformulation of governmental bodies and heavy investments in R&D projects alone, however, has not been an effective solution. After the creation of the super Ministry, its bureaucratic power has become stronger than ever, weakening private actors’ innovative capabilities and coordinative function within the government, which are essential to its creative economy (Im 2015; Kim 2013b). Giving a vast Ministry and its bureaucratic routines the responsibility for the ‘creative economy’ is to make it the dominant player in the national innovation system. This is the reverse of the global zeitgeist, where private actors have risen and bureaucrats declined in terms of S&T policy agenda setting capability (Kim 2013b). Academic reviews usually find the cause of the reversal of policy from the Park administration’s bureaucratic approach to science5: the current funding system that is centralized toward government officials have made academic actors vulnerable to bureaucratic interference in any scientific research (Jang et al. 2015). Yet, in our analysis of experts’ discourses, we found a lack of academic vigilance to check excessive and ineffective bureaucratization. The interviewed academic researchers’ perceptions were overwhelmed by day-to-day competition and evaluation in the field. Even though the academic interviewees expressed frustrations and complaints about the evaluative system, they did not propose to change the rules of the competitive game by forming a solidarity within the academic arena or beyond. In this regard, even the critical voice of academics represented that of the ‘institutional researcher’ (Bourdieu 2004) hoping to thrive in the bureaucratic sphere, rather than pursuing academic liberty and autonomous research.6 Without a reformative vision and serious reflection springing out of the current academic conducts (Kim and Park 2015), institutional change alone is uncertain to change the scientific perspective and action. It was rather the policy experts who emphasized the value of individual researchers’ capacity to collaborate and innovate, supported by a nurturing system and cooperative culture; however, they did not present a concrete pathway to accomplish these ends, perhaps because the central fields that require change, the academy and laboratory, were beyond their direct oversight. The two groups’ differences in the perception of scientific creativity and innovation in actual sense remind the two-communities theory (Caplan 1979) that highlighted the different sets of knowledge use between (social) scientists and policy practitioners. The classic thesis seems to resonate in natural scientific field as Choi et al. (2005) note that scientists and policy makers have different goals, attitudes toward information, languages, perception of time, and career path; and they often lack the ability to take into account the realities or perspectives of one another. Recommended solutions are for some scientists to develop a sense of the ‘big picture’, build organization capacity to intervene in public policy, and define research far more broadly to embrace social issues (Choi et al. 2005: 634–5). In this sense, we think that cultural obstacles to cooperation and mutual reflection in the South Korean context should be considered as an explicit target to change, rather than looking to an administrative Leviathan for the solution. Instead, ‘authoritative and non-partisan bodies and other independent scientific societies need to assume more responsibility for helping scientists place the significance of their research into a policy context’ (Pielke 2002: 416). In this context, the foremost social task to follow might be to encourage junior individual actors to reform the academic environment, revitalize innovative research beyond numerous publications or patents, and speak out for social issues. To do so, the quantity-oriented evaluation system requires reconsideration to encourage a spontaneous research atmosphere, rather than imposing additional policy burdens. Ultimately, actors will need to find a pragmatic way to deliver a clear signal to society that innovation is beyond the economic and industrial achievements imposed by the official evaluative system. The consensual philosophy and motivation of both governmental and academic actors are crucial to this kind of signaling. Our research has made a contribution by proposing a systematic way to study social representational frames of science policy and academic actors, and to diagnose current impasses to cooperative innovation. In sum, an epistemological shift from the existing top-down management to more coordinative and spontaneous governance to promote collaborative culture, autonomy, and creativity among actors will be helpful to entrench newly envisaged scientific and technological leadership in South Korea. The research method of utilizing the SNA proved useful to visualize focus groups’ core frames and present some prioritized concepts; however, this approach has limits particularly in that it only highlights those concepts iteratively expressed by the interviewees. To substantiate our initial findings and argument, further qualitative analyses with widened research objects would be required to study further the causes of distrust in the academy, academic and policy actors’ mutual interaction, and microscopic strategies that are exacerbating the bureaucratization of science. Footnotes 1 This research was funded by the National Science and Technology Commission and completed in November 2012. 2 ‘Science Korea, Nobel Prize Project’ of Korea Broadcasting System (KBS). 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TI - Expert views on innovation and bureaucratization of science: Semantic network analysis of discourses on scientific governance
JF - Science and Public Policy
DO - 10.1093/scipol/scx035
DA - 2018-02-01
UR - https://www.deepdyve.com/lp/oxford-university-press/expert-views-on-innovation-and-bureaucratization-of-science-semantic-8WyLuQUM48
SP - 36
EP - 44
VL - 45
IS - 1
DP - DeepDyve
ER -