Cited By
View all- Geng JWang SGan WYuan HChen ZYuan ZDai TDeng K(2019)Promoting Geospatial Service from Information to Knowledge with Spatiotemporal SemanticsComplexity10.1155/2019/93014202019Online publication date: 21-Jan-2019
Data mining-based variable assessment methodology for evaluating the contribution of knowledge services of a public research institute to business performance of firms
Authors: 
Jeongsub Choi, Byunghoon Kim, Hyuk Hahn, Hun Park, Yongil Jeong, Jaeyoung Yoo, Myong Kee JeongAuthors Info & Claims
Pages 37 - 48
Abstract
A methodology for assessing the attributes of knowledge services is proposed.A data mining-based variable importance method is used for service-level scoring.Knowledge services from a public research institute in Korea are investigated.Product types of knowledge services boost firms business performance are assessed. This study proposes a methodology for assessing the contribution of knowledge services (KSs) provided by a Korean public research institute to the business performance of firms. A new methodology based on a data mining-based variable assessment method in a regression model is proposed for the service-level assessment. The contribution of the KSs to firms business performance is analyzed using their attributes and specific business performance indicators through the conditional variable permutation method in the random forest regression. This reduces the ambiguity in variable importance caused by the correlations among input variables. The proposed methodology is applied to the survey dataset collected from firms. The survey dataset is examined 1) for the whole data and 2) for a subset of the data, namely, small- and medium-sized enterprises (SMEs). The empirical results show behavioral properties of firms with regard to the given KSs in general and SMEs in particular. Practical and user-friendly service product types increase the firms expectation on business performance. Also, flexibility in the service products helps firms acquire much-needed knowledge and boosts their expectation on business performance. In particular, SMEs expect better business performance from the KSs that help them create business plans and strategies.
References
[1]
G. Abramo, C.A. D'Angelo, M. Solazzi, Assessing publicprivate research collaboration: Is it possible to compare university performance, Scientometrics, 84 (2010) 173-197.
[2]
J.-H. Ahn, S.-G. Chang, Assessing the contribution of knowledge to business performance: The KP 3 methodology, Decision Support Systems, 36 (2004) 403-416.
[3]
M. Alavi, D.E. Leidner, Review: Knowledge management and knowledge management systems: Conceptual foundations and research issues, MIS Quarterly, 25 (2001) 107-136.
[4]
S. Arvanitis, N. Sydow, M. Woerter, Do specific forms of university-industry knowledge transfer have different impacts on the performance of private enterprises? An empirical analysis based on Swiss firm data, The Journal of Technology Transfer, 33 (2008) 504-533.
[5]
R. Azen, D.V. Budescu, The dominance analysis approach for comparing predictors in multiple regression, Psychological Methods, 8 (2003) 129.
[6]
M. Beise, H. Stahl, Public research and industrial innovations in Germany, Research Policy, 28 (1999) 397-422.
[7]
L. Breiman, Random forests, Machine Learning, 45 (2001) 5-32.
[8]
Y.B. Chang, V. Gurbaxani, Information technology outsourcing, knowledge transfer, and firm productivity: An empirical analysis, MIS Quarterly, 36 (2012) 1043-1053.
[9]
M. Coccia, A scientometric model for the assessment of scientific research performance within public institutes, Scientometrics, 65 (2005) 307-321.
[10]
M. Coccia, Research performance and bureaucracy within public research labs, Scientometrics, 79 (2009) 93-107.
[11]
B.P. Cozzarin, Performance measures for the socio-economic impact of government spending on R&D, Scientometrics, 68 (2006) 41-71.
[12]
S. Da Veiga, Global sensitivity analysis with dependence measures, Journal of Statistical Computation and Simulation, 85 (2015) 1283-1305.
[13]
R. Daz-Uriarte, S.A. De Andres, Gene selection and classification of microarray data using random forest, BMC Bioinformatics, 7 (2006) 3.
[14]
A.C. Edmondson, A.B. Winslow, R.M.J. Bohmer, G.P. Pisano, Learning how and learning what: Effects of tacit and codified knowledge on performance improvement following technology adoption, Decision Sciences, 34 (2003) 197-224.
[15]
L. Eriksson, E. Johansson, N. Kettaneh-Wold, S. Wold, Multi-and megavariate data analysis: Principles and applications, Umetrics, 2001.
[16]
W. Fan, Contribution of the institutional repositories of the Chinese Academy of Sciences to the webometric indicators of their home institutions, Scientometrics, 105 (2015) 1889-1909.
[17]
C. Grimpe, U. Kaiser, Balancing internal and external knowledge acquisition: The gains and pains from R&D outsourcing, Journal of Management Studies, 47 (2010) 1483-1509.
[18]
U. Grmping, Variable importance assessment in regression: Linear regression versus random forest, The American Statistician, 63 (2009) 308-319.
[19]
J.W. Johnson, A heuristic method for estimating the relative weight of predictor variables in multiple regression, Multivariate Behavioral Research, 35 (2000) 1-19.
[20]
B. Kogut, U. Zander, Knowledge of the firm, combinative capabilities, and the replication of technology, Organization Science, 3 (1992) 383-397.
[21]
K. Laursen, A. Salter, Open for innovation: The role of openness in explaining innovation performance among UK manufacturing firms, Strategic Management Journal, 27 (2006) 131-150.
[22]
J.W. Lee, J.B. Lee, M. Park, S.H. Song, An extensive comparison of recent classification tools applied to microarray data, Computational Statistics & Data Analysis, 48 (2005) 869-885.
[23]
A. Leiponen, C.E. Helfat, Innovation objectives, knowledge sources, and the benefits of breadth, Strategic Management Journal, 31 (2010) 224-236.
[24]
J.M. Levitt, M. Thelwall, Long term productivity and collaboration in information science, Scientometrics, 108 (2016) 1103-1117.
[25]
A. Liaw, M. Wiener, Classification and regression by Random Forest, R News, 2 (2002) 18-22.
[26]
M.J. Mol, J. Birkinshaw, The sources of management innovation: When firms introduce new management practices, Journal of Business Research, 62 (2009) 1269-1280.
[27]
F. Morillo, Publicprivate interactions reflected through the funding acknowledgements, Scientometrics, 108 (2016) 1193-1204.
[28]
S.M. Mudambi, S. Tallman, Make, buy or ally? Theoretical perspectives on knowledge process outsourcing through alliances, Journal of Management Studies, 47 (2010) 1434-1456.
[29]
F. Narin, K.S. Hamilton, D. Olivastro, The increasing linkage between US technology and public science, Research Policy, 26 (1997) 317-330.
[30]
R.R. Nelson, P.M. Romer, Science, economic growth, and public policy, Challenge, 39 (1996) 9-21.
[31]
I. Nonaka, A dynamic theory of organizational knowledge creation, Organization Science, 5 (1994) 14-37.
[32]
I. Nonaka, H. Takeuchi, The knowledge-creating company: How Japanese companies create the dynamics of innovation, Oxford University Press, 1995.
[33]
R. Nez-Snchez, A. Barge-Gil, A. Modrego-Rico, Performance of knowledge interactions between public research centres and industrial firms in Spain: a project-level analysis, The Journal of Technology Transfer, 37 (2012) 330-354.
[34]
P. OECD, Performance-based funding for public research in tertiary education institutions: Workshop proceedings, OECD, 2010.
[35]
OECD, Public research institutions: Mapping sector trends, OECD Publishing, 2011.
[36]
J.B. Quinn, Intelligent enterprise: A knowledge and service based paradigm for industry, Simon and Schuster, 1992.
[37]
E.J. Rinia, T.N. Van Leeuwen, E.E. Bruins, H.G. Van Vuren, A.F. Van Raan, Measuring knowledge transfer between fields of science, Scientometrics, 54 (2002) 347-362.
[38]
S. Robin, T. Schubert, Cooperation with public research institutions and success in innovation: Evidence from France and Germany, Research Policy, 42 (2013) 149-166.
[39]
J.-W. Shiu, C.-Y. Wong, M.-C. Hu, The dynamic effect of knowledge capitals in the public research institute: insights from patenting analysis of ITRI (Taiwan) and ETRI (Korea), Scientometrics, 98 (2014) 2051-2068.
[40]
R. Sternberg, Innovative linkages and proximity: Empirical results from recent surveys of small and medium sized firms in German regions, Regional Studies, 33 (1999) 529-540.
[41]
C. Strobl, A.-L. Boulesteix, T. Kneib, T. Augustin, A. Zeileis, Conditional variable importance for random forests, BMC Bioinformatics, 9 (2008) 307.
[42]
S.B. Tallman, O. Shenkar, A managerial decision model of international cooperative venture formation, Journal of International Business Studies, 25 (1994) 91-113.
[43]
A. Verikas, A. Gelzinis, M. Bacauskiene, Mining data with random forests: A survey and results of new tests, Pattern Recognition, 44 (2011) 330-349.
[44]
P. Wei, Z. Lu, J. Song, Variable importance analysis: a comprehensive review, Reliability Engineering & System Safety, 142 (2015) 399-432.
[45]
C.-C. Yeh, F. Lin, C.-Y. Hsu, A hybrid KMV model, random forests and rough set theory approach for credit rating, Knowledge-Based Systems, 33 (2012) 166-172.
[46]
Q. Zhou, H. Zhou, T. Li, Cost-sensitive feature selection using random forest: Selecting low-cost subsets of informative features, Knowledge-Based Systems, 95 (2016) 1-11.
- Data mining-based variable assessment methodology for evaluating the contribution of knowledge services of a public research institute to business performance of firms
Recommendations
Methodology for assessing the contribution of knowledge services during the new product development process to business performance
Highlights- Contributions of knowledge-intensive services in new product development are assessed.
AbstractKnowledge intensive service (KIS) is a key resource for new product development (NPD) of a firm. As a KIS provider, public research organization plays a critical role in supporting the success of firms’ NPD process, thereby promoting ...
The Effect of Interactivity between Knowledge Intensive Business service (KIBS) Firms and Customers on Innovations in KIBS Firms
HICSS '10: Proceedings of the 2010 43rd Hawaii International Conference on System SciencesIt is now well established that innovation and knowledge management can improve firm's performance. However, questions like what kind of factors affect the innovations and how knowledge is related to innovation remain largely uninvestigated. In this ...
Multifaceted Applications of Data Mining, Business Intelligence, and Knowledge Management
This article reviews the literature in the search for the multifaceted applications of data mining DM, business intelligence BI, and knowledge management KM. The literature review highlights the overviews of DM, BI, and KM; the practical applications of ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Copyright © Elsevier Ltd.
Publisher
Pergamon Press, Inc.
United States
Publication History
Published: 30 October 2017
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 04 Jan 2025
Other Metrics
Citations
Cited By
View all- Geng JWang SGan WYuan HChen ZYuan ZDai TDeng K(2019)Promoting Geospatial Service from Information to Knowledge with Spatiotemporal SemanticsComplexity10.1155/2019/93014202019Online publication date: 21-Jan-2019