Knowledge Transfer Activities among Information Technology Related Academic Researchers

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1 Knowledge Transfer Activities among Information Technology Related Academic Researchers Yaacob. A., Ibrahim J. A., Mohamed Udin Z. and Abdullah, C. S. Universiti Utara Malaysia (UUM), Malaysia, {noorulsadiqin, jafni, jafni, zulkifli, ABSTRACT This paper presents the results from a survey conducted on academic researchers working on information technology related research from Universiti Utara Malaysia. Factor analysis and reliability tests were conducted to identify dimensions of commercialisation activities and to determine statistical reliability of the dimensions. Subsequently, a series of one-way ANOVA tests were conducted to examine whether perceptions on commercialisation activities results differ based on the demographic background of the researchers. The findings of this study reveal significant differences in perceptions on the commercialisation dimensions based on the demographic groups. The findings from the analysis will provide more insights into the extent to which demographic background influences commercialisation activities of information technology related research in Universiti Utara Malaysia. Keywords: academic researchers, information technology, commercialisation activities I I TRODUCTIO Universities as institutions of higher learning have long performed their traditional role as centers of academic scholarship, inquiry and learning, production and transmission of knowledge and some services. How to get the universities to better contribute to innovation process has become an important issue in the international agenda and also in Malaysia. Transferring the results of university research to industry may take several forms and thus can be achieved in different ways. These include publications, conferences, consulting, conversations, recruitment of graduates, cosupervising, collaborative research, patents and licenses (Agrawal & Henderson, 2002). Some of these methods involve the transfer of knowledge about new technologies to the economy as a public good (Gu & Whewell, 1999). These methods include publications, conferences, conversations, recruitment of graduates and co-supervising. Estimates of relative importance of different knowledge channels suggest that these noncommercial methods represent the majority of knowledge transferred from universities to industry (Agrawal & Henderson, 2002). At the heart of knowledge and technology transfer is the individual academic researcher who is making decisions about how to disseminate the results of their research in the university. They are deciding whether or not to collaborate with industry, whether or not to disclose their inventions to their university and whether or not to start a company based on their knowledge. Although there had been other researches on knowledge and technology transfer which are focused on faculty members, the research had been done mainly in developed countries (refer works by Zucker, Darby & Armstrong, 1998; Zucker, Darby & Brewer, 1998; Zucker & Darby, 2001; Louis, Blumenthal, Gluck & Stoto, 1989; Bercovitz & Feldman, 2004). Furthermore, most of the technology transfer studies use a Technology Transfer Office (TTO) report as a performance guideline. Jensen, Thursby & Thursby (2003) noted that many technology transfer office directors believe that substantially less than half of the inventions with commercial potential are disclosed to their office. Due to that, a potential issue is that there may be gaps between the declared commercialisation activities of TTO and how the academicians view their research commercialisation activities. Noticeably absent from the institution and technology transfer literature is a systematic and broad based analysis of the commercialisation activity of research result. The integration of the demographic variables would be useful to explain differences in perceptions based on the background of the respondents. Based on literature, there are certain demographic variables which known to have significant an insignificant impact on commercialisation activities. Previous research (Allen, Link & Rosenbaum, 2007; Morgan, Kruytbosh & Kannankutty, 2001) tends to indicate that some demographic variables may influence individual perceptions thus affecting their commercialisation activities. Thus, this research will investigate the extent to which commercialisation activities differs by demographic background. This paper focuses on commercialisation activities at Universiti Utara Malaysia. The primary unit of analysis is academic researcher who involve in information technology related research. Although the scope of the research limited to Universiti Utara Malaysia, there is still possibility to derive some general trends, indicators and facts which would contribute to the theory and guide further research. The rest of the paper is followed 261

2 by a description of the research methods and procedures used in the study. This is followed by findings of the study. Finally, the article concludes with a summary of the study s research contributions and directions for future research. II METHODS This section presents methods utilized in this research including sampling, data collection, measurements and analysis. A. Sampling Listing of academic researchers was obtained from the UUM Portal. The list include active academic researcher at School of Computing (SOC) and School of Multimedia Technology and Communication (SMMTC) comprising of computer networking, artificial intelligence, software engineering, information management, multimedia technology and other information technology related field of research. Simple random sampling technique was used in data gathering. According to Black (2004), simple random sampling can be viewed as the basis for other sampling techniques. The sampling procedure assures that each academic researcher has equal chance of being chosen as the sample. B. Data Collection This study utilized a questionnaire in identifying the perceptions of academic researchers who are involve in information technology related research rather than relying on commercialisation activities reported by their Technology Transfer Office. A modified and shortened version of a questionnaire measuring research result using items developed in earlier studies (Jusoh, 2008) and Yaacob (2011) that were originally adapted from Landry, Amara & Ouimet (2006) has been used for this research. C. Measurements Eleven questions that targeted pressing commercialisation activities of research result, which are also believed to be relevant to academic researcher productivity, were selected as dependent measures. The questions specifically referred to commercialisation activities of their research result. Commercialisation activities of research result measures include (1) publishing academic writing, (2) communicated to other users outside the academic environment/priority parties such as private firms or government agencies through seminar, conference, exhibition, report in printed or electronic media, (3) invited to present research results to group and organization who could make direct use of them, (4) been involved in comittee which is interested in using and exploiting new knowledge based on the research result, (5) given consultation service/technical (based on technology field/research result) to private firm, government agency or others, (6) disclosed the invention based on my research result, (7) applied patent based on my research result, (8) got patent based on research result, (9) gave the licence to other party or organization to produce or market the product from my research (10) the license that have been given to other party, have been resulted in monetary return and (11) research result has created spin off company that specifically produce and commercialize the research product. Academic researchers responded to these eleven questions on a 5-point frequency scale (1= Never, 2= Rarely, 3=Sometimes, 4=Often and 5=Very Often).The independent variable of interest for this study was academic researchers demographic background. The demographic information solicited is as follows: research experience, highest level of education, experience as administrator/top level management and academic post status. D. Analysis The analyses used in this study are essentially exploratory and broadly address the research question of interest. The purpose here is mainly to document any differences in opinion between demographic backgrounds group not to explain why such differences might exist. The latter is certainly important and will be addressed briefly in this paper, but a more involved discussion is beyond the scope of this study. III RESULTS At first data was analysed using descriptive statistics using measures of central tendency to provide basic understanding of the questionnaire items. Subsequently, different of means for each item assessing commercialisation activities of research result were compared for each demographic variable using one-way analysis of variance (ANOVA). Comparison of means based on demographic background of the academic researchers was conducted using one-way ANOVA for demographic groupings that are more than two. A. Descriptive Analysis of Demographic Background of Academicians Working on Information Technology Related Researches in University Forty academicians working on information technology related researches in Universiti Utara Malaysia participated in the survey. The descriptive analysis over the collected data illustrated the diverse background of respondents even though they originated from the same university. With reference to Table 1, half of the respondents (50%) posses Master. Whereas, PhD holder 262

3 and Post Doctorate holder hold second and third place respectively. Regarding the academic post, the highest percentage of the respondents was Lecturer (50%) followed by Senior Lecturer (42.5%), Professor (5%), Associate Professor (2.5%). Table 1. Frequency Analysis on Demographic Background Education Level Frequency Valid Master PhD Post doctorate Total Academic Post Frequency Valid Cumulative Cumulative Lecturer Senior Lecturer Associate Professor Professor Total B. Factor Analysis and Reliability Test In general, construct validity is the extent to which a particular item relates to other items consistent with theoretically derived hypotheses concerning the variables that are being measured. The factor analysis used a principal component analysis as the extraction method and Varimax with Kaiser Normalisation rotation method to explain the item variance. Two statistical tests should be done in order to allow for the application of factor analysis, namely, Kaiser- Meyer-Olkin (KMO) sampling adequacy test and the Barlett s test of sphericity. The KMO sampling adequacy test statistic for this study is which is higher than the threshold value of 0.5 (Hair, Anderson, Tatham & Black, 1998). This is supported by the Barlett s test of sphericity value of 0.00 that is less than These two tests seem to support the usage of the factor analysis method using Varimax rotation with Kaiser Normalisation and Principal Component Analysis. Varimax with Kaiser Normalisation was applied prior to factor rotation, thus keeping factors with an eigen value of one and greater. This procedure was chosen to eliminate error variance (Tinsley & Tinsley, 1987). Whereas, a principal component analysis was the chosen extraction method to describe the data set with a smaller set of new variable. The factor analysis extracted two factors based on eigen value criteria more than one. Table 2 presents the factor loading, eigen value and percentage of variance explained and reliability coefficient for every group. These two factors together accounted for 72.5% of the total variance. Based on Table 2, since the scale reliability coefficients using Cronbach Coefficient Alpha for both groups are greater than 0.7 all the dimensions are deemed as statistically reliable. Nunnally (1978) suggested that a set of items with a coefficient alpha greater than 0.7 can also be considered as internally consistent. Within the context of this study, typology development has been used as analytical strategy where a quantitative survey was conducted, developed factors through a factor analysis and using this factors as a typology (Caracelli & Greene, 1993). For the eleven questionnaire items, there are forty cases in the sample, which are sufficiently enough for conducting a single factor analysis using Varimax rotation method with Kaiser Normalisation and Principal Component Analysis. The factor analysis generated two descriptive components for commercialisation activities which are presented in Table 2. Table 2. Result of the Factor Analysis and Reliability Analysis Commercialisation Activities Components of Research Results 1 2 get patent ( 8).945 spin off (11).933 monetary return (10).919 License ( 9).878 patent application ( 7).857 communicated research result (2).853 Consultation (5).756 published research result (1).748 invited to present (3).725 disclosed invention (6).710 involved in committee (4).676 Eigen value age of Variance Explained Reliability coefficient The first group can be classified as technology transfer (TT) and comprises of five items from the commercialisation activities of research results. The following are the items of TT: (8) got patent based on research result, (11) research result has created spin off company that specifically produce and commercialize the research product, (10) the license that have been given to other party, have been resulted in monetary return, (9) gave the licence to other party or organization to produce or market the product from my research and (7) applied patent based on my research result. The second group can be classified as committee and network building (CNB) and comprises of six items from the commercialisation activities of research 263

4 results. The following are the items of CNB: (2) communicated to other users outside the academic environment/priority parties such as private firms or government agencies through seminar, conference, exhibition, report in printed or electronic media, (5) given consultation service/technical (based on technology field/research result) to private firm, government agency or others, (1) publishing academic writing, (3) invited to present research results to group and organization who could make direct use of them, (5) given consultation service/technical (based on technology field/research result) to private firm, government agency or others, (6) disclosed the invention based on my research result and (4) been involved in comittee which is interested in using and exploiting new knowledge based on the research result. C. Comparing Means of Commercialisation Activities of Research Results by Demographic Background Using one-way A OVA A series of one-way ANOVA was conducted to assess the difference of mean for the 11 commercialisation activities based on two demographic variables: highest level of education and academic post status. With reference to Table 3 (see APPENDIX A), five commercialisation activity of research results demonstrated differences in means based on highest level of education since the p-value is more than The five commercialisation activities are (1) publishing academic writing, (2) communicated to other users outside the academic environment/priority parties such as private firms or government agencies through seminar, conference, exhibition, report in printed or electronic media, (4) been involved in comittee which is interested in using and exploiting new knowledge based on the research result, (5) given consultation service/technical (based on technology field/research result) to private firm, government agency or others and (6) disclosed the invention based on my research result. With reference to Table 4 (see APPENDIX B), only commercialisation activity (3) invited to present research results to group and organization who could make direct use of them, demonstrated differences in means based on highest level of education of academic researchers since the p-value is less than The rest of commercialisation activities of research results demonstrated differences in means based on academic post status since the p-value is more than Overall, the study shows that TT are not affected by highest level of education as in CNB. This finding is supported by Morgan et al., (2001) study that found level of education give influence on the patenting and inventive activity of academic scientists. However, academic post status has some effect on the TT and CNB. This finding also supported by previous work done by Allen et al., (2007) that indicate faculty research productivity according to appointment type (tenure-track faculty were more research productive than were faculty on other appointments) and research productivity by rank (e.g., full professor, associate professor, and assistant professor) were significant predictors of faculty research productivity. IV CO CLUSIO Whether shaped by the actual or perceived significance of demographic background, the finding of this study shows that demographic background have some effect on academic researcher who were involved in commercialisation activities of their research result in information technology related research. By classifying the commercialisation activities of research result into different categories or groups, it is possible to identify and develop a more focused commercialisation activities of research result categories CNB and TT for each different group of academic researchers in commercialisation activities of research results in information technology related research in Universiti Utara Malaysia. The conducted one-way ANOVA test further showed that there are differences between demographic backgrounds in commercialisation activities of research result. Therefore, it is recommended that university should practice an approach or policy to take into account demographic background such as level of education and academic post status in motivating the commercialisation activities of research results among academic researchers. Although this study shows broad demographic background differences in perception at academic researcher level, it is not completely clear how those differences play out at the institutional level. Here, qualitative studies might have an advantage over quantitative ones in providing a richer and deeper understanding of how academic researcher benefit from commercialisation activities of their research results. V REFERE CES Agrawal, A. & Henderson, R. (2002). Putting Patents in Context: Exploring Knowledge Transfer from MIT. Management Science. 48(1), Allen, Link & Rosenbaum. (2007). Entrepreneurship and Human Capital: Evidence of Patenting Activity from the Academic Sector, Entrepreneurship: Theory and Practice

5 Bercovitz, J. & Feldman, M. (2004) Academic Entrepreneurs: Social Learning and Participation in University Technology Transfer. Mimeograph, University of Toronto. Black, K. (2004). Business Statistics for Contemporary Decision Making. United States of America: Wiley. Caracelli, V. J. & Greene, J. C. (1993). Data analysis Strategies for Mixedmethod Evaluation Designs. Educational Evaluation and Policy Analysis. 15(2), Gu, W. & Whewell, L. (1999). University Research and the Commercialization of Intellectual Property in Canada. Occasional Paper No. 21. Hair, J.F., Jr., Anderson, R.E., Tatham, R.L. & Black, W.C. (1998). Multivariate Data Analysis (5th ed.). Upper Saddle River, New Jersey: Prentice Hall. Jensen, R.A., Thursby, J.C. & Thursby, M.C. (2003). Disclosure and Licencing of University Inventions: the Best We Can Do with the S We Get to Work With. International Journal of Industrial Organization. 21(9), Jusoh, A. (2008). Hubungan Amalan Pengurusan Kualiti Menyeluruh Dengan Tahap Pemindahan Teknologi: Suatu Kajian Empirikal Mengikut Perspektif Penyelidik Universiti. PhD Thesis. Universiti Utara Malaysia. Landry, R., Amara, N. & Ouimet, M. (2006). Determinants of Knowledge Transfer: Evidence from Canadian University Researchers in Natural Sciences and Engineering. Louis, K.S., Blumenthal, D., Gluck, M.E. & Stoto, M.A. (1989). Entrepreneurs in academe: An exploration of behaviors among life sciences. Administrative Science Quarterly. 34, Morgan R. P., Kruytbosh C. & Kannankutty N. (2001). Patenting and Invention Activity of U.S. Scientists and Engineers in the Academic Sector: Comparisons with Industry. The Journal of Technology Transfer. 26(1-2), Nunnally, J. C. (1978). Psychometric theory (2 nd ed.). New York: McGraw-Hill. Sekaran, U. (2003). Research Methods for Business: A Skill Building Approach (4th ed.). New York: John Wiley & Sons Inc. Tinsley, H. E. A., & Tinsley, D. J. (1987).Uses of Factor Analysis in Counseling Psychology Research. Journal of Counseling Psychology, 34, Yaacob, N.A., (2011). Issues of Commercialization of Biotechnology Related Researches in Malaysian Research Universities. PhD Thesis. Universiti Teknologi Malaysia. Zucker, L.G., Darby, M.R. & Brewer, M.B. (1998). Intellectual Human Capital and the Birth of U.S. Biotechnology Enterprises. American Economic Review. 88(1), Zucker, L.G., Darby, M.R. & Armstrong, J. (1998). Geographically Localized Knowledge: Spillovers or Markets? Economic Inquiry. 36(1), Zucker, L.G & Darby, M.R (2001). Capturing Technological Opportunity via Japan s Star Scientists: Evidence from Japanese Firms Biotech Patents and Products. Journal of Technology Transfer. 26,

6 APPE DIX A Table 3. Analysis of Highest Level of Education using A OVA ANOVA Sum of Squares df Mean Square F Sig. published research result Between Groups Within Groups Total communicated research result Between Groups invited to present Within Groups Total Between Groups Within Groups Total involved in committee Between Groups Within Groups Total consultation Between Groups Within Groups Total disclosed invention Between Groups patent application get patent license monetary return spin off Within Groups Total Between Groups Within Groups Total Between Groups Within Groups Total Between Groups Within Groups Total Between Groups Within Groups Total Between Groups Within Groups Total

7 APPE DIX B Table 4. Analysis of Academic Post Status using A OVA A OVA Sum of Squares df Mean Square F Sig. published research result Between Groups Within Groups Total communicated research result Between Groups invited to present (professor) Within Groups Total Between Groups Within Groups Total involved in committee Between Groups Within Groups Total consultation Between Groups Within Groups Total disclosed invention Between Groups Within Groups Total patent application Between Groups Within Groups Total get patent Between Groups Within Groups Total license Between Groups Within Groups Total monetary return Between Groups Within Groups Total spin off Between Groups Within Groups Total

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