LEADER 05145nam 2200613 450 001 9910822191203321 005 20230808205256.0 010 $a1-119-30747-3 010 $a1-119-30746-5 010 $a1-119-30745-7 035 $a(CKB)4330000000010119 035 $a(EBL)4558122 035 $a(MiAaPQ)EBC4558122 035 $a(Au-PeEL)EBL4558122 035 $a(CaPaEBR)ebr11223888 035 $a(CaONFJC)MIL933685 035 $a(OCoLC)952247590 035 $a(EXLCZ)994330000000010119 100 $a20160711h20162016 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aCircular economy, industrial ecology and short supply chain /$fDelphine Gallaud, Blandine Laperche 210 1$aLondon, England ;$aHoboken, New Jersey :$cISTE :$cWiley,$d2016. 210 4$d©2016 215 $a1 online resource (143 p.) 225 0 $aInnovation, Entrepreneurship, Management Series - Smart Innovation Set ;$vVolume 4 300 $aDescription based upon print version of record. 311 $a1-84821-879-6 320 $aIncludes bibliographical references and index. 327 $aCover; Title Page; Copyright; Contents; Preface; Introduction; 1: Building Region-based Sustainable Development: Vocabulary and Tools; 2: Difficulties, Barriers and Stakes in Transitioning Towards Sustainable Regions; Conclusion; Bibliography; Index; Other titles from ISTE in Innovation, Entrepreneurship and Management; EULA; 1.1. Circular economy; 1.2. Industrial ecology; 1.3. Short supply chains; 1.4 Industrial ecology, short supply chains and sustainable regional development; 2.1. Barriers to the implementation of industrial ecology and short supply chains 327 $a2.2. How to overcome or reduce these obstacles: the role of service activities2.3. Challenges for public policy; 1.1.1. The circular economy according to the MacArthur Foundation; 1.1.2. Experiments in circular economy; 1.1.3. Factual and scientific origins of circular economy; 1.2.1. Industrial ecology and sustainable development; 1.2.2. Industrial metabolism and symbiosis; 1.2.3. Experiments in industrial ecology; 1.3.1. Origins of short food supply chains: criticism of industrial "long" supply chains; 1.3.2. Forms and functioning of short food supply chains 327 $a1.3.3. Short supply chains: generators of social innovation1.4.1. Links among these different concepts: the creation of sustainable territories; 1.4.2. Proximity and innovative "milieu": key ingredients for sustainable regional development; 1.4.3. An assessment of the regional impacts of industrial ecology and short supply chains; 2.1.1. The case of industrial ecology; 2.1.2. The case of short food supply chains; 2.2.1. Definition of service activities; 2.2.2. What role do service activities have in the implementation of industrial ecology and short food supply chains? 327 $a2.3.1. The issue of governance2.3.2. The issue of coordination; 2.3.3. What is the relevant territorial scale?; 2.1.1.1. Technical barriers to synergy; 2.1.1.2. Economic barriers; 2.1.1.3. Informational barriers; 2.1.1.4. Organizational barriers; 2.1.1.5. Regulatory barriers; 2.1.1.6. Infrastructural barriers; 2.1.1.7. The human dimension; 2.1.2.1. Obstacles to the implementation of short food supply chains; 2.1.2.2. Obstacles to the complete attainment of the positive effects of short food supply chain; 2.2.2.1. The organization of market relations 327 $a2.2.2.2. Acquisition or maintenance of agents' capacities2.2.2.3. The development of new practices 330 $aIn contrast to the linear "take-make-dispose" model of resource consumption, a new industrial model is proposed in the form of a circular economy. This model aims to optimize the use of resources and to reduce or eliminate waste, and is based on re-use, repair, ecodesign, industrial ecology, sustainable supply and responsible consumption. Industrial ecology and short supply chains can contribute - particularly on a territorial scale - to the emergence of a real sustainable development. This book develops these concepts and presents experiments that are taking place in France and other countries, in addition to an integrated model which details the mechanisms through which industrial ecology and short supply chains can generate economic, social and environmental profits. The possible issues and obstacles facing these new practices are also analyzed, in order to develop the outline of an adapted management and governance which will enable them to be fully realized. 606 $aIndustrial management 606 $aIndustrial ecology 606 $aBusiness logistics 615 0$aIndustrial management. 615 0$aIndustrial ecology. 615 0$aBusiness logistics. 676 $a658 700 $aGallaud$b Delphine$01725062 702 $aLaperche$b Blandine 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910822191203321 996 $aCircular economy, industrial ecology and short supply chain$94127667 997 $aUNINA LEADER 05330nam 22005775 450 001 9910254088303321 005 20250327022422.0 010 $a9783319433769 010 $a3319433768 024 7 $a10.1007/978-3-319-43376-9 035 $a(CKB)3710000000872816 035 $a(DE-He213)978-3-319-43376-9 035 $a(MiAaPQ)EBC4701218 035 $a(PPN)19551257X 035 $a(EXLCZ)993710000000872816 100 $a20160929d2016 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aExcel 2016 for Marketing Statistics $eA Guide to Solving Practical Problems /$fby Thomas J. Quirk, Eric Rhiney 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource 225 1 $aExcel for Statistics,$x2570-4613 300 $aIncludes index. 311 08$a9783319433752 311 08$a331943375X 327 $aIntroduction -- Sample size, mean, standard deviation, standard error of the mean -- Random number generator -- Confidence interval about the mean using the TINV function and hypothesis testing -- One-group t-test for the mean -- Two-group t-test of the difference of the means for independent groups -- Correlation and simple linear regression -- Multiple correlation and multiple regression -- One-way analysis of variance (ANOVA) -- Appendix A -- Appendix B -- Appendix C -- Appendix D -- Appendix E -- Index. 330 $aThis is the first book to show the capabilities of Microsoft Excel in teaching marketing statistics effectively. It is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical marketing problems. If understanding statistics isn?t your strongest suit, you are not especially mathematically-inclined, or if you are wary of computers, this is the right book for you. Excel, a widely available computer program for students and managers, is also an effective teaching and learning tool for quantitative analyses in marketing courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. However, Excel 2016 for Marketing Statistics: A Guide to Solving Practical Problems is the first book to capitalize on these improvements by teaching students and managers how to apply Excel to statistical techniques necessary in their courses and work. Each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand marketing problems. Practice problems are provided at the end of each chapter with their solutions in an appendix. Separately, there is a full Practice Test (with answers in an Appendix) that allows readers to test what they have learned. Includes 167 illustrations in color Suitable for undergraduates or graduate students Prof. Tom Quirk spent six years in educational research at The American Institutes for Research and Educational Testing Service.  He is Professor of Marketing in the Walker School of Business & Technology at Webster University in St. Louis, Missouri (USA).  He holds a B.S. in Mathematics from John Carroll University, both an M.A. in Education and a Ph.D. in Educational Psychology from Stanford University, and an MBA from The University of Missouri-St. Louis. Prof. Eric Rhiney is currently an Assistant Professor of Marketing in The Walker School of Business at Webster University in St. Louis, Missouri (US) where he teaches Research Design, Marketing Research and Marketing Strategies.  He holds a B.S.B.A. with an Emphasis in Marketing from University of Central Missouri, an M.B.A. with an Emphasis in Marketing from Webster University, and a Ph.D. in Marketing and International Business from St. Louis University.  He did marketing research professionally for over ten years engaging in research for companies such as Pizza Hut, Monsanto, Chrysler and Hardee?s.  He is involved in a number of quantitative research studies focused on in-group out-group orientation on consumer attitudes, digital marketing behavior, and cross-cultural marketing and has presented is work at a number of conferences including The American Marketing Association, the International Business Association, and the Marketing Management Association and the University of Missouri-St. Louis (UMSL) Digital Marketing Conference. 410 0$aExcel for Statistics,$x2570-4613 606 $aStatistics 606 $aMarketing 606 $aSocial sciences$xData processing 606 $aStatistical Theory and Methods 606 $aMarketing 606 $aComputer Application in Social and Behavioral Sciences 615 0$aStatistics. 615 0$aMarketing. 615 0$aSocial sciences$xData processing. 615 14$aStatistical Theory and Methods. 615 24$aMarketing. 615 24$aComputer Application in Social and Behavioral Sciences. 676 $a519.5 700 $aQuirk$b Thomas J$4aut$4http://id.loc.gov/vocabulary/relators/aut$0721655 702 $aRhiney$b Eric$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910254088303321 996 $aExcel 2016 for Marketing Statistics$92044175 997 $aUNINA