TOPIC EVOLUTION OF INNOVATION ACADEMIC RESEARCHES Yu-Shan Lin National Taitung University, Taiwan ysl.nttu@gmail.com ABSTRACT Innovation is an important issue of academic research for several decades. However, no comprehensive study in the trend of innovation topics is available. Therefore, this paper proceeds to review the innovation research. In general, the common methods for research review are content analysis, bibliometrics, and literature mining techniques, but these methods don’t provide a comprehensive viewpoint. Based on big data thinking, this study introduces Corpus-based Approach which makes the structural text database possible to extract the hidden knowledge through analyzing the text. This work uses 1,460 abstracts published from 1973 to 2015 in Journal of Business Research as the text. Moreover, the text is divided into two groups (1973-1995 and 1996-2015) to analyze the evolution of innovation research topics using WordSmith Tools with three main functions — Concord, WordList, and KeyWords. Finally, this study provides a basis for future academic research on innovation, and offers important references for industrial practices. Keywords: innovation, topic evolution, comparative analysis, corpus-based approach, big data 25 Journal of Small Business Strategy Vol. 26 ● No. 1 ● 2016 INTRODUCTION The publication of Big Data in 2013 is a big shock for business. All businesses start to create more business opportunities through the power of data. Data revolution will come after the digital revolution. Big data promote the innovative economic value. Arriving of the big data era generates a new discipline, namely Culturomics, which belongs to the field of computational lexicology. Comprehending humanity behavior and cultural trend through the quantitative analysis for text is expected (Mayer-Schönberger & Cukier, 2013). Innovation issue is valued accompanying technological development and internet popularization all the time in the business research. Not only nations endeavor to promote innovation policy, but also businesses regard innovation as superior competitive strategy. Therefore, the objective of this study is to draw a conclusion towards innovation research trends hiding in the literature through textual analysis. The conclusion can be the base for future academic research. LITERATURE REVIEW Innovation related researches are plentiful and diversified for a long time. However, no overall and complete review research has been done for innovation topic evolution trends. In the past, review researches of innovation topics are not much. Review topics can roughly divide into three types. The first type focuses on innovation issues, such as success and failure of innovation (Van der Panne, Van Beers, & Kleinknecht, 2003), innovation management measurement (Adams, Bessant, & Phelps, 2006), and organizational innovation (Crossan & Apaydin, 2010). The second type discusses innovation in different subjects, sectors, or industries, such as innovation in marketing science (Hauser, Tellis, & Griffin, 2006), innovation in the manufacturing sector (Becheikh, Landry, & Amara, 2006), innovation in services (Gallouj & Savona, 2009), and innovation in tourism (Hjalager, 2010). The third type explores the relationship between innovation and other variables, such as social capital and innovation (Li, 2009), and innovation and entrepreneurship (Niu & Hsin, 2011). Concise interpretations of these researches are as follows. Van der Panne, Van Beers, and Kleinknecht, (2003) examine 43 studies of factors in the success and failure of innovative projects. Of these, nine studies reported and ranked success factors. Comparisons show that the 10 highest-ranking success factors in these studies are very similar; however, the studies are far from consistent when lower ranking factors are considered. The literature agree on the positive impact of factors such as firm culture, experience with innovation, multidisciplinary R&D team and explicit recognition of the collective character of the innovation process or the advantages of the matrix organization. However, many studies are either inconsistent or inconclusive regarding the effects of factors such as strength of competition, R&D intensity, the degree to which a project is innovative or technologically advanced and top management support. Adams, Bessant, and Phelps (2006) review the literature relevant to the measurement of innovation management at the level of the firm. They develop a synthesized framework of seven categories of factors in the innovation management process: inputs management, knowledge management, innovation strategy, organizational culture and structure, portfolio management, project management and commercialization. They then identified factors empirically shown to have significant 26 Journal of Small Business Strategy Vol. 26 ● No. 1 ● 2016 effects in the innovation process, and illustrative measures to map the territory of innovation management measurement. This review makes two important contributions. First, it takes the difficult step of incorporating a vastly diverse literature into a single framework. Second, it provides a framework that practitioners can use to evaluate their own innovation management activity, to explore the extent to which their organization is nominally innovative, to explore whether innovation is embedded throughout their organization, and to identify areas where attention and resources might be focused. Crossan and Apaydin (2010) comprehensively review the state of academic research on innovation. Based on their systematic review of studies published during 1981-2008, they integrate diverse research perspectives into an exhaustive multi-dimensional framework of organizational innovation. In their framework, leadership, managerial levers, business processes, innovation as a process, and innovation as an outcome are linked. They also suggest measures for determinants of organizational innovation and propose implications for both academic and managerial practice. Hauser, Tellis, and Griffin (2006) identify 16 topics relevant to marketing science, which they classify into five research fields. The consumer response to innovation field includes attempts to measure consumer innovativeness, models of new product growth, and recent ideas on network externalities. The organizations and innovation field includes contextual and structural drivers of innovation, organizing for innovation, and adoption of new tools and methods. The market entry strategies field includes recent research on technology revolution, extensive marketing science research on strategies for entry, and issues of portfolio management. The prescriptive techniques for product development processes field includes techniques which have been transformed through global pressures, increasingly accurate customer input, Web- based communication for dispersed and global product design, and new tools for dealing with complexity over time and across product lines. The innovation outcomes field includes defending against market entry and capturing the rewards of innovating. For each topic, authors summarize key concepts and highlight research challenges. After a systematic review of empirical studies published during 1993 to 2003, Becheikh, Landry, and Amara (2006) propose a research framework which brings together a set of variables related to innovation and the internal and contextual factors driving it. The dependent variable is innovation, and three major issues considered include type of innovation, investigation method, and measurement. The internal factors include firm’s general characteristics, global strategies, structure, culture, control activities, management team, and functional assets and strategies. The contextual factors include firm’s industry, region, networking, knowledge/technology acquisition, government and public policies, and surrounding culture. The following results emphasize several means which could help managers and policy makers to better promote innovation and researchers to better direct their efforts in exploring the phenomenon. Gallouj and Savona (2009) review the arguments for service innovation and suggest a research agenda for the evolutionary theory to integrate the conceptualization of 27 Journal of Small Business Strategy Vol. 26 ● No. 1 ● 2016 innovation in services. They discuss whether, and the extent to which, the ill-definition and mis-measurement of service output have influenced the conceptualization and analysis of innovation in services. They then reclassified the diverse contributions according to their assimilation, demarcation or integrative nature with respect to the more consolidated literature focused on technological innovation in the manufacturing sector. They also review the synthesizing contributions of other studies, and suggest a taxonomy for the forms of innovation in services, based on the Lancasterian characteristics-based approach to product definition. Hjalager (2010) reviews the literature on tourism innovation in the past two decades. Several categories of innovation are addressed, such as product or service, process, managerial, marketing and institutional innovations. Important determinants and driving forces of innovation are identified, including the role of entrepreneurship, technology push and the existence of industrial districts. Representation of knowledge is also considered essential for innovation. Their review shows that there is still little systematic and comparable empirical study for the innovative activity levels and their influences and implications for destinations and national economies. The authors also recommend further quantitative and qualitative studies of the foundations, processes, implications and policies of innovation in tourism. Li (2009) integrates current knowledge of the relationship between social capital and innovation after an extensive review of empirical studies of the relationship between social capital and innovation. The study analyzes and makes comparison based on relations between innovation and the three dimensions — structural, relational, and cognitive components of social capital structure of Nahapiet and Ghoshal (1998). Consensus, discordances, and gaps in the social capital-innovation connection are identified and directions for future research are generated. Niu and Hsin (2011) apply the content analysis method in a review of the literature on innovation and entrepreneurship published from 2000 to 2010 accessed through the ISI database. They analyze each paper through author’s nationality, research type, research field, journal types based on publication year. Then, they process relation analysis on journal rank and keywords to analyze the variation of research contents over the years and understand the research trend of innovation and entrepreneurship. Their analytical results show that studies of innovation and entrepreneurship have significantly increased in recent years. The USA, UK and Germany are found to be the most productive countries. Finally, this research finds that the trend of innovation and entrepreneurship research issues are in technology related industries, policy and economic themes, and academic subjects. As a whole, the importance of innovation in management field is mentioned continuously, and innovation is an important research tendency in the future. However, the evolution of innovation issue only focuses on specific business function; it seems that a study of comprehensive research issue trend isn’t appeared so far. This study hopes to make effort to the part. 28 Journal of Small Business Strategy Vol. 26 ● No. 1 ● 2016 METHODOLOGY In the past, the most commonly used methods to conduct review researches are content analysis. Content analysis is a method that transforms materials of qualitative research into quantitative data. Content analysis is a research method that makes an objective and systematic quantification towards concrete mass contents. However, content analysis describes the obvious contents of information, and doesn’t deal with potential and concealing contents. This is a big defect of content analysis. Textual analysis and discourse analysis can supplement this deficiency of quantitative content analysis. Fairclough (2003) considers that textual analysis for social research has at least four values-- theoretical, methodological, historical, and political reasons. The methodological reason is that text is an important source of offering evidences, and textual analysis can get the research declaration based on the detailed characteristics of the text. Based on the analytical thinking of big data, text is also statistic having giant potential values. But traditional textual analysis is restricted to the limited samples and the stereotype of qualitative researches, it can’t satisfy the breadth and depth of contents mining in the big data era. In the situation, using the Corpus-based Approach coming from applied linguistics can make the construction of structured text statistics database possible. Furthermore, combining the content analysis of linguistics and rhetoric field can supply some new thinking for methodology and research tools (Yu & Li, 2014). This study will use abstracts containing the word ‘innovation’ of papers published in Journal of Business Research (JBR) as the text. The publication period is from 1973- 2015, and text will be classified into two groups for evolution trend analysis of innovation issues. The research framework appears in Figure 1. Figure 1. Research framework The study adopts ‘Corpus-based approach.’ Officially speaking, Corpus is a set of natural language usage passing specified theoretical principles. Usually the usage contains oral or written language, and is stored in a mode of computer files. Corpus resources in the written style can be news media, literary works, or personal writing pieces. Oral style resources can come from narratives, interviews, conversations, or other oral data of tapes and videos. Then, the oral resources can be transformed into written styles. The size of Corpus can be several tens of thousands to tens of millions, even hundred millions words. Abstracts of Innovation - Related Publications in JBR Clusters of Words in Period 1973-1995 Clusters of Words in Period 1996-2015 Trend Evolution of Innovation Topics 29 Journal of Small Business Strategy Vol. 26 ● No. 1 ● 2016 Bigger Corpus usually offers for large scale research projects, for example, compiling a dictionary or composing a grammar book. Even though small oral Corpus of several tens of thousands words also can influence language teaching. After the construction of Corpus, we can use software to analyze and generate wordlist, word by word index, and other data (Corpus Portal, 2014). The text chosen is abstracts containing the word ‘innovation’ of papers published in JBR. There are 1,460 papers between 1973 and 2015 up to January, 2015. Choosing JBR is because the journal applies theory developed from business research to actual business situations and recognizes the intricate relationships between the many areas of business activity. Therefore, JBR can comprehend every dimension research of business functions. The filter function of JBR website classifies papers according to year and topic. The most innovation related papers appear in 2014. In the next place, there are 185 papers in 2013. Then 174 papers appear in 1995 and before, and 124 papers in 2012 (see table 1). Paper amount in other years are all below 100. This shows scholars pay much attention to innovation issue in the past three years. From the view of topic, market orientation is the most popular topic and there are 34 papers. Secondly, 22 papers about China and firm respectively. Moreover, 20 papers about firm performance and Internet individually. Other topics are innovation, Latin American, business research, competitive advantage, marketing strategy, new product, service quality, organizational learning, company, jog satisfaction, organizational performance, Costa Rica, social capital, marketing. But topic classification can only offer the rough bracket, not pure classification of innovation. Therefore, the study provides a further issue of analysis. Table 1 Numbers of innovation related papers published in JBR by year Years 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 Number of papers 77 206 185 124 81 78 61 40 41 58 58 Years 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 and before Number of papers 48 38 47 30 38 20 20 14 22 174 The appearance and widespread application of Internet have already exerted influence over global economics. From the era of year 1990, the global internet market shows an extremely 30 Journal of Small Business Strategy Vol. 26 ● No. 1 ● 2016 fast growth trend. For example, comparison with 1995, the market capacity of global internet hardware industry experiences a high growth rate in 1996. The development of internet on transnational commerce application makes progress at a tremendous pace. Global internet population is over fifty million in 1996. Over 80% businesses in Fortune 500 construct websites (Institute for information industry, 1997). Furthermore, the year is concordant with the year borderline of the journal database. Therefore, the study analyzes texts over the past forty years, and splits them into two periods, 1973-1995 and 1996-2015. The software used in this study is WordSmith Tools. Illustrations of three main functions— Concord, WordList, KeyWords are as follows (WordSmith Tools, 2014). (1) Concord Concord is a program which makes a concordance using plain text or web text files. To use it we can specify a search word or phrase, which Concord will seek in all the text files we have chosen. It will then present a concordance display, and give us access to information about collocates of the search word, dispersion plots showing where the search word came in each file, cluster analyses showing repeated clusters of words (phrases) etc. Briefly, Concord is for finding all instances of a word or phrase. When conducting the software analysis, we can use the function of cluster and set up word numbers accompanying both sides of target vocabulary. (2) WordList This program generates word lists based on one or more plain text files. The word lists are automatically generated in both alphabetical and frequency order, and optionally we can generate a word index list too. In short, the function lists the words in our text(s) in alphabetical and frequency order. We can also apply the Lemma function to find out vocabularies that have the same meanings but show in the text with a different manner. Generally speaking, high frequency of one word means that it is more important in the text than other low frequency words. (3) KeyWords This is a program for identifying the ‘key’ words in one or more texts. Key words are those whose frequency is unusually high in comparison with some norm. The function helps find salient words in a text or set of texts. The meaning of keywords here is different from the representation of important meaning traditionally. It indicates vocabularies having distinctive frequency when compare with Reference Corpus. Proceeding the KeyWords function, the study adopts British National Corpus（BNC）as reference corpus. BNC is a speaking and writing language sample including a hundred million vocabularies set. The KeyWords function not only verifies the result of WordList, but also confirms keyword’s exact place in the texts. DATA ANALYSIS AND RESULTS First, Wordlist function generates wordlist according to the frequency. The top ten words (frequency in parentheses) in turn are study (1,241), research (959), performance (818), firms (807), marketing (716), results (635), market (602), firm (561), business (556) and innovation (547). Second, we use the Keyword function to identify the ‘key’ words in texts. Wordsmith tools can calculate the keyness of each word and build a sorted list based on keyness value. Innovation (4,437.95) is the number four, and top three words are study (6,110.20), firms (4,943.50) and marketing (4,710.63). The number in parentheses is the ‘keyness’ of that word. The 31 Journal of Small Business Strategy Vol. 26 ● No. 1 ● 2016 study focuses on the target word, innovation. Finally, we use Concordance function to extract the repeated clusters of words and phrases through cluster analyses. We set up 2 words in cluster, minimum frequency is 5, and horizons 1L, 1R. When we process to 4 words in cluster, minimum frequency is only 3 (lower than 5). Therefore, we stop at 3 words per cluster. Then, we use innovative as the target words and repeat the same process. At last, we get the top 20 repeated clusters of words as table 2. We merge two pairs of synonyms among the list. Innovation performance (frequency: 35) is combination of ‘innovation performance’ (25) and ‘innovative performance’ (10). Entrepreneurship and innovation (frequency: 13) is combination of ‘entrepreneurship and innovation’ (8) and ‘innovation and entrepreneurship’ (5). The sources of ‘community innovation’ (frequency: 5) and ‘community innovation survey’ (frequency: 5) are the same, so two clusters are merged into one. The following discussions base on these clusters of words and their appearance on the title of publications in JBR. Service innovation and innovation performance are simultaneously in the first place. The concept of service innovation is first discussed in Miles (1993) and has been developed in the past 2 decades. The first paper focusing on service innovation in JBR appears in 1998. Frambach, Barkeman, Nooteboom, and Wedel make an empirical test about adoption of a service innovation in the business market. The greater part appears in the last three years. As to innovation performance, Manu and Sriram’s study exploring innovation, marketing strategy, environment, and performance in 1996 is the earliest paper focuses on innovation performance published in JBR. About three- fourths papers appear in the last five years. Product innovation is the third. Numerous examples of product innovation include introducing new products, enhanced quality and improving its overall performance. Product innovation, alongside cost-cutting innovation and process innovation, are three different classifications of innovation which aim to develop a company's production methods (Hoang, 2010). Thus product innovation can be divided into two categories of innovation: radical innovation which aims at developing a new product, and incremental innovation which aims at improving existing products (Wong, 2014). Radical innovation and incremental innovation also rank 6th and 19th respectively. Although the earliest appearance of product innovation in JBR is Iwinska-Knop’s paper in 1986, the widespread research emerges in last five years. Organizational innovation is the fourth, but the topic appears in JBR earlier than the top three. Cummings and O'Connell’s study in 1978 is the earliest one. About two-thirds papers appear in the last five years. Firm innovation (firm’s innovation) is the fifth. Nearly three-fourths studies publish in the last three years. The earliest study in JBR is Forrester’s paper in 2000. He investigates the use of innovation teams in Japanese and American automotive firms. Except for the top five and foregoing clusters, others still include entrepreneurship and innovation, technological innovation, innovation activities, innovation outcomes, R&D innovation, learning and innovation, innovation strategy, innovation capability, innovation success, process innovation, innovation adoption, innovation survey, virtual innovation, community innovation, management innovation, innovation orientation, radical product innovation, performance and innovation, and innovative behavior. 32 Journal of Small Business Strategy Vol. 26 ● No. 1 ● 2016 Table 2 Repeated clusters of words in period 1996-2015 Rank Cluster Frequency 1 Service innovation 35 1 Innovation performance 35 3 Product innovation 27 4 Organizational innovation 19 5 Firm(’s) innovation 18 6 Radical innovation 13 6 Entrepreneurship and innovation 13 8 Technological innovation 11 8 Innovation activities 11 10 Innovation outcomes 10 11 R&D innovation 9 12 Learning and innovation 8 13 Innovation strategy 7 13 Innovation capability 7 13 Innovation success 7 16 Process innovation 6 16 Innovation adoption 6 16 Innovation survey 6 19 Incremental innovation 5 19 Virtual innovation 5 19 Community innovation 5 19 Management innovation 5 19 Innovation orientation 5 19 Radical product innovation 5 19 Performance and innovation 5 19 Innovative behavior 5 Some topics emerge earlier than 1995. Therefore, we use Concordance function to extract the repeated clusters of words and phrases through cluster analyses for the period of 1973-1995. We set up 2 words in cluster, minimum frequency is 5, and horizons 1L, 1R. When we process to 3 words in cluster, minimum frequency is only 2, so we stop at 2 words in cluster. Then, we use innovative as the target words and repeat the same process. We set up 2 words in cluster, minimum frequency is 3, and horizons 1L, 1R. At last, we get the top 3 repeated clusters of words as table 3. The result has a wide gap with the above one. That’s because the paper quantity difference is remarkable, 1,286 papers in 1996-2015 and only 174 papers in 1973-1995. The result also reveals that innovation becomes a flourishing research topic over the past two decades. Table 3 Repeated clusters of words in period 1973-1995 Rank Cluster Frequency 1 Organizational innovation 5 2 Product innovation 5 3 Innovative products 3 Only organizational innovation and product innovation appear simultaneously in the two periods. No doubt the two topics emerge earlier than other ones, and their importance remains high in period 1996-2015 (4th and 3rd respectively). In the later period, diverse topics appear in droves. Service innovation deserves respect following the rise of service industry. The conduct and result of innovation are deemed important issues, such as innovation performance (1st), firm (’s) innovation (5th), innovation activities (8th), innovation outcomes (10th), and etc. CONCLUSION The study based on big data thinking adopts the cross-field methodology, corpus-based approach, to analyze academic research papers published in JBR. Corpus-based approach is originated and commonly used in linguistics research. The study attempts to apply the approach in business research and 33 Journal of Small Business Strategy Vol. 26 ● No. 1 ● 2016 analyze the trend evolution of innovation researches over past 40 years. Because the text quantity is up to 1,460, it is like a big database so that we can fully master the trend evolution of innovation topics. Organizational innovation and product innovation are highly valued for a long time, whereas service innovation, innovation performance, firm (’s) innovation and other innovation related topics rise and flourish over the past twenty years. The result can offer an important reference value for future academic research about innovation field. As to the future research, two words--firms and marketing--sieved out from the keyword function seem very important and can be further studied and probed into their relations with innovation. Furthermore, the study anticipates supplying a direction towards innovation practice for industry. The practitioners should synchronize internal innovation management and actual actions with academic research results. This step is an extremely urgent task. Academic research needs to take the lead, and industry need to apply research results in practice. REFERENCES Adams, R., Bessant, J., & Phelps, R. (2006), Innovation Management Measurement: A Review. International Journal of Management Reviews, 8(1), 21-47. Becheikh, N., Landry, R., & Amara, N. (2006), Lessons From Innovation Empirical Studies in the Manufacturing Sector: A Systematic Review of the Literature from 1993–2003. Technovation, 26(5), 644-664. Corpus Portal, Retrieved from http://calper.la.psu.edu/corpus_portal/ch inese_corp_guide_trad.php. Crossan, M. M., & Apaydin, M. (2010), A Multi‐dimensional Framework of Organizational Innovation: A Systematic Review of the Literature. Journal of Management Studies, 47(6), 1154-1191. Cummings, L. L., & O'Connell, M. J. 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(2010), A Review of Innovation Research in Tourism. Tourism Management, 31(1), 1-12. Hoang, P. (2010), Business & Managemen, [Melton, Vic.]: IBID Press. p. 686. ISBN 978-1876659639. Institute for Information Industry (1997), Development Trends of Electronic Commerce and Opportunity Analysis of Our Industry. Institute for Information Industry, Taipei, Taiwan. Iwinska-Knop, K. (1986), Product-innovation Propensity at the Company Level in Centrally Planned Economies. Journal of Business Research, 14 (4), 321-327. 34 Journal of Small Business Strategy Vol. 26 ● No. 1 ● 2016 Koch, P., & Hauknes, J. (2005), On Innovation in the Public Sector – Today and Beyond. Retrieved from http://survey.nifu.no/step/publin/reports/ d20-innovation.pdf. Li, Y.-P. (2009), A Review of the Relationship between Social Capital and Innovation. Journal of Dahan Institute of Technology, 24, 1-14. Manu, F. A., & Sriram, V. (1996), Innovation, Marketing Strategy, Environment, and Performance. Journal of Business Research, 35 (1), 79-91. Mayer-Schönberger, V., & Cukier, K. (2013), Big Data: A Revolution That Will Transform How We Live, Work, and Think, Houghton Mifflin Harcourt. Miles, I. (1993), Services in the New Industrial Economy. Futures, 25(6), 653- 672. doi:10.1016/0016-3287(93)90106-4 Nahapiet, J., & Ghoshal, S. (1998), Social Capital, Intellectual Capital, and the Organizational Advantage. Academy of management review, 23(2), 242-266. Niu, H.-J., & Hsin M.-C. (2011), The Development of Innovation and Entrepreneurship: A Literature Review. Journal of Innovation and Management, 8(4), 33-62. Van der Panne, G., Van Beers, C., & Kleinknecht, A. (2003), Success and Failure of Innovation: A Literature Review. International Journal of Innovation Management, 7(3), 309-338. Wong, S.K.S. (2014), Impacts of Environmental Turbulence on Entrepreneurial Orientation and New Product Success. European Journal of Innovation Management, 17 (2), 229- 249. WordSmith Tools website, http://www.lexically.net/wordsmith/inde x.html. Yu, G.-M., & Li, H.-J. (2014), The Value of Corpus-based Approach in Communication Researches in the Era of Big Data. Retrieved from http://media.people.com.cn/BIG5/n/20 14/1126/c390949-26099195.html. Yu-Shan Lin is an assistant professor in the department of Information Science and Management Systems, National Taitung University, Taitung, Taiwan. Dr. Lin received her Ph.D. degree of Business Management from National Sun Yat-sen University, Kaohsiung, Taiwan, and majored in Marketing. Her research interesting areas include marketing, marketing strategy, tourism marketing, consumer behavior, internet marketing, social marketing, and research methods. She serves as reviewer for many important academic journals, Tourism Management, Journal of Business Research, etc. 35 http://media.people.com.cn/BIG5/n/2014/1126/c390949-26099195.html http://media.people.com.cn/BIG5/n/2014/1126/c390949-26099195.html