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000940680 019__ $$a1182852828$$a1191078411
000940680 020__ $$a9789811563003$$q(electronic book)
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000940680 0247_ $$a10.1007/978-981-15-6
000940680 035__ $$aSP(OCoLC)on1184056835
000940680 035__ $$aSP(OCoLC)1184056835$$z(OCoLC)1182852828$$z(OCoLC)1191078411
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000940680 049__ $$aISEA
000940680 050_4 $$aTS155
000940680 08204 $$a658.5/14$$223
000940680 1001_ $$aHosseini, Seyed Mehrshad Parvin.
000940680 24510 $$aBig data approach to firm level innovation in manufacturing :$$bindustrial economics /$$cSeyed Mehrshad Parvin Hosseini, Aydin Azizi.
000940680 260__ $$aSingapore :$$bSpringer,$$c2020.
000940680 300__ $$a1 online resource
000940680 336__ $$atext$$btxt$$2rdacontent
000940680 337__ $$acomputer$$bc$$2rdamedia
000940680 338__ $$aonline resource$$2rdacarrier
000940680 4901_ $$aSpringerBriefs in applied sciences and technology
000940680 504__ $$aIncldues bibliographical references.
000940680 5050_ $$aChapter 1: Introduction to innovation activities -- Chapter 2: The role of SMEs in innovation activities -- Chapter 3: Overview of innovation activities in Southeast Asia -- Chapter 4: From Linear model to Chain Linked model of innovation in reaching firm characteristics that facilitate and lowering the cost of innovation -- Chapter 5: Predicting level of innovation -- Chapter 6: Factors affecting the decision to innovate and related policies.
000940680 506__ $$aAccess limited to authorized users.
000940680 520__ $$aThis book discusses utilizing Big Data and Machine Learning approaches in investigating five aspects of firm level innovation in manufacturing; (1) factors that determine the decision to innovate (2) the extent of innovation (3) characteristics of an innovating firm (4) types of innovation undertaken and (5) the factors that drive and enable different types of innovation. A conceptual model and a cost-benefit framework were developed to explain a firms decision to innovate. To empirically demonstrate these aspects, Big data and machine learning approaches were introduced in the form of a case study. The result of Big data analysis as an inferior method to analyse innovation data was also compared with the results of conventional statistical methods. The implications of the findings of the study for increasing the pace of innovation are also discussed.
000940680 650_0 $$aProduction management.
000940680 650_0 $$aManufacturing processes$$xTechnological innovations.
000940680 650_0 $$aBig data.
000940680 7001_ $$aAzizi, Aydin.
000940680 77608 $$iPrint version:$$z9811562997$$z9789811562990$$w(OCoLC)1154861350
000940680 830_0 $$aSpringerBriefs in applied sciences and technology.
000940680 852__ $$bebk
000940680 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-981-15-6300-3$$zOnline Access$$91397441.1
000940680 909CO $$ooai:library.usi.edu:940680$$pGLOBAL_SET
000940680 980__ $$aEBOOK
000940680 980__ $$aBIB
000940680 982__ $$aEbook
000940680 983__ $$aOnline
000940680 994__ $$a92$$bISE