Action research as a school-based strategy in intercultural professional development.doc

This paper presents a novel structured approach to evaluate and select the best agile e-CRM framework in a rapidly changing manufacturing environment. The e-CRM frameworks are evaluated with respect to their customer and financial oriented features to achieve manufacturing agility. Initially, the e-CRM frameworks are prioritized according to their financial oriented characteristics using a fuzzy group real options analysis (ROA) model. Next, the e-CRM frameworks are ranked according to their customer oriented characteristics using a hybrid fuzzy group permutation and a four-phase fuzzy quality function deployment (QFD) model with respect to three main perspectives of agile manufacturing (i.e., strategic, operational and functional agilities). Finally, the best agile e-CRM framework is selected using a technique for order preference by similarity to the ideal solution (TOPSIS) model. We also present a case study to demonstrate the applicability of the proposed approach and exhibit the efficacy of the procedures and algorithms.Article Outline1. Introduction2. Literature review3. The mathematical notations and definitions4. The proposed method 4.1. Phase 1: prioritization of the e-CRM frameworks (financial oriented evaluation)Process 1.1: establishment of the financial teamProcess 1.2: identification of the e-CRM frameworksProcess 1.3: construction of the individual fuzzy real option matricesProcess 1.4: calculation of the fuzzy weighted collective real option matrixProcess 1.5: determination of the best order of the e-CRM frameworks (financial oriented evaluation)4.2. Phase 2: prioritization of the e-CRM frameworks (customer oriented evaluation)Process 2.1: prioritization of the e-CRM frameworks (strategic perspective of agile manufacturing)Step 2.1.1: establishment of the strategic agility teamStep 2.1.2: identification of the agile manufacturing strategic criteriaStep 2.1.3: building the fuzzy individual houses of quality in the first phase of the QFD modelStep 2.1.4: building the fuzzy weighted collective house of quality in the first phase of the QFD modelStep 2.1.5: determination of the best order of the e-CRM frameworks (strategic perspective of agile manufacturing)Procedure 2.1.5.1: determination of the permutations of the e-CRM frameworksProcedure 2.1.5.2: determination of the permutation matricesProcedure 2.1.5.3: determination of the best order of e-CRM frameworksProcess 2.2: prioritization of the e-CRM frameworks (operational perspective of agile manufacturing)Step 2.2.1: establishment of the operational agility teamStep 2.2.2: identification of the agile manufacturing operational criteriaStep 2.2.3: building the fuzzy individual houses of quality in the second phase of the QFD modelStep 2.2.4: building the fuzzy weighted collective house of quality in the second phase of the QFD modelStep 2.2.5: determination of the best order of the e-CRM frameworks (operational perspective of agile manufacturing)Procedure 2.2.5.1: determination of the permutations of the e-CRM frameworksProcedure 2.2.5.2: determination of the permutation matricesProcedure 2.2.5.3: determination of the best order of e-CRM frameworksProcess 2.3: prioritization of the e-CRM frameworks (functional perspective of agile manufacturing)Step 2.3.1: establishment of the functional agility teamStep 2.3.2: identification of the agile manufacturing functional criteriaStep 2.3.3: building the fuzzy individual houses of quality in the third phase of the QFD modelStep 2.3.4: building the fuzzy weighted collective house of quality in the third Phase of the QFD modelStep 2.3.5: determination of the best order of the e-CRM frameworks (functional perspective of agile manufacturing)Procedure 2.3.5.1: determination of the permutations of the e-CRM frameworksProcedure 2.3.5.2: determination of the permutation matricesProcedure 2.3.5.3: determination of the best order of the e-CRM frameworks4.3. Phase 3: selection of the agile e-CRM framework (financial and agility oriented evaluations)Process 3.1: establishment of the leadership teamProcess 3.2: building the aggregated house of quality in the fourth phase of the QFD modelProcess 3.3: determination of the best order of the e-CRM frameworks (financial and customer oriented evaluations)Step 3.3.1: identification of the ideal and nadir agile e-CRM frameworksStep 3.3.2: calculation of the relative closeness5. The case study 5.1. Phase 1: prioritizing the e-CRM frameworks (financial oriented) evaluation5.2. Phase 2: prioritizing the e-CRM frameworks (customer oriented evaluation)5.3. Phase 3: selecting the agile e-CRM framework (financial and agility oriented evaluations)6. Conclusions and future research directionsAcknowledgementsReferencesPurchase$ 37.95Research highlights A structured approach is proposed to select the best agile e-CRM framework. It is grounded in the quality function deployment (QFD) model. It uses real options analysis (ROA) to assess risks. It employs fuzzy sets and fuzzy logic to consider imprecise or vague judgments. It uses TOPSIS to aggregate qualitative and quantitative data.23Acute hemodynamic effects of adaptive servo ventilation in patients with pulmonary hypertensionInternational Journal of Cardiology, Volume 148, Issue 1, 1 April 2011, Pages 125-127Shusuke Yagi, Masashi Akaike, Takashi Iwase, Kenya Kusunose, Toshiyuki Niki, Koji Yamaguchi, Kunihiko Koshiba, Yoshio Taketani, Noriko Tomita, Hirotsugu Yamada, Takeshi Soeki, Tetsuzo Wakatsuki, Masataka SataClose preview| Related articles|Related reference work articles AbstractAbstract | Figures/TablesFigures/Tables | ReferencesReferences No abstract is available for this article.Article OutlineReferencesPurchase$ 31.5024What drives financial performanceresource efficiency or resource slack?: Evidence from U.S. Based Manufacturing Firms from 1991 to 2006Original Research ArticleJournal of Operations Management, Volume 29, Issue 3, March 2011, Pages 254-273Sachin B. Modi, Saurabh MishraClose preview| Related articles|Related reference work articles AbstractAbstract | Figures/TablesFigures/Tables | ReferencesReferences AbstractExtant research in operations management has revealed divergent insights into the value potential of resource efficiency. While one view relates efficiency with good operations management and asserts that slack resources are a form of waste that should be minimized, the other view suggests that limited resource slack can impose heavy costs on firms by making them brittle. In this research, the authors build on these views to investigate the relationship of inventory, production, and marketing resource efficiency of firms with three metrics of financial performance (i.e., Stock-Returns, Tobins Q, and Returns-on-Assets). The authors evaluate the theoretical framework using secondary information on all U.S. based publicly-owned manufacturing firms across the 16-year time period of 19912006. Analysis utilizing a mixed-model approach reveals that a focus on resource efficiency is positively associated with firm financial performance. However, findings also support the arguments favoring slack, indicating that the financial gains from resource efficiency exhibit diminishing returns.Article Outline1. Introduction2. Conceptual framework 2.1. Inventory resource efficiency and financial performance2.2. Production resource efficiency and financial performance2.3. Marketing resource efficiency and financial performance3. Research methodology 3.1. Variable operationalization 3.1.1. Dependent measures: firm financial performance3.1.2. Independent measures: resource efficiency3.1.3. Control variables3.2. Empirical model4. Analysis and results 4.1. Additional analysis5. Implications and directions for future researchAcknowledgementsAppendix A. Stock-response modelingAppendix B. Log-linear and quadratic model specificationsAppendix C. AppendixAppendix D. Example calculations and tables for a benchmarking toolReferencesPurchase$ 41.9525Enterprise architecture patterns for business process support analysisOriginal Research ArticleJournal of Systems and Software, In Press, Corrected Proof, Available online 17 March 2011Ana aa, Marjan KrisperClose preview| Related articles|Related reference work articles AbstractAbstract | Figures/TablesFigures/Tables | ReferencesReferences AbstractThe field of enterprise architectures lacks architecture patterns that would support analysis of a given enterprise architecture, comparison of different enterprise architecture solutions and provide guidelines for development of a target enterprise architecture based on the analysis of existing enterprise architecture. In this paper, we focus on business process support analysis using information derived from enterprise architecture description. We give a systematic overview of important aspects. We establish and formally define foundational enterprise architecture patterns for business process support analysis. They are implementation independent and enable more efficient qualitative architecture analysis of business process support, which is the basis for achieving more optimal business operation. We have defined the patterns using the standard enterprise architecture language ArchiMate. They are formalized in a way that enables their implementation in enterprise architecture tools. This is an important characteristic that allows for efficient work by automatic detection of different, more or less suitable, architecture structures. We have derived the patterns based on real-world enterprise architecture descriptions and have used and verified them in enterprise architecture analysis and planning projects for four large organizations. The enterprise architecture analysis patterns address an important research issue in the field of enterprise architectures that has so far not been systematically researched.Article Outline1. Introduction2. Preliminaries3. Related work and motivation4. Approach5. Business process support elements of the ArchiMate metamodel6. Enterprise architecture patterns for business process support analysis 6.1. Enterprise architecture business process support analysis viewpoints 6.1.1. Business process execution support viewpoint6.1.2. Business object realization viewpoint6.2. Business process execution support analysis patterns 6.2.1. Fully automated business process pattern6.2.2. Partially automated business process pattern6.2.3. Unautomated business process pattern6.2.4. Fully manually supported business process pattern6.2.5. Partially manually supported business process pattern6.2.6. Manually unsupported business process pattern6.2.7. Strictly unsupported business process pattern6.2.8. Partially supported business process pattern6.2.9. Fully supported business process pattern6.2.10. Heterogeneous application support of a business process pattern6.2.11. Homogenous application support of a business process pattern6.2.12. Full redundant business process automation pattern6.2.13. Full non-redundant business process automation pattern6.2.14. Fully manually supported business process with multiple role-assignments pattern6.2.15. Fully manually supported business process with single role-assignment pattern6.2.16. Fully redundantly supported business process pattern6.2.17. Fully non-redundantly supported business process pattern6.3. Business object realization analysis patterns 6.3.1. Business object fully realized by application layer pattern6.3.2. Business object partially realized by application layer pattern6.3.3. Business object without application layer realization pattern6.3.4. Full concrete business object realization pattern6.3.5. Partial concrete business object realization pattern6.3.6. Business object without concrete realization pattern6.3.7. Fully realized business object pattern6.3.8. (Strictly) unrealized business object pattern6.3.9. Partially realized business object pattern6.3.10. Multiple business object realization pattern6.3.11. Single business object realization pattern6.3.12. Multiple application layer business object realization pattern6.3.13. Single application layer business object realization pattern6.3.14. Multiple concrete business object realization pattern6.3.15. Single concrete business object realization pattern6.4. Discussion and comparison of business process support analysis patterns7. Proof of concept8. Implications and further work9. ConclusionReferencesVitaePurchase$ 31.5026Aspects of schooluniversity research networks that play a role in developing, sharing and using knowledge based on teacher researchOriginal Research ArticleTeaching and Teacher Education, Volume 27, Issue 1, January 2011, Pages 147-156Frank Cornelissen, Jacqueline van Swet, Douwe Beijaard, Theo BergenClose preview| Related articles|Related reference work articles AbstractAbstract | Figures/TablesFigures/Tables | ReferencesReferences AbstractSchooluniversity research networks aim at closer integration of research and practice by means of teacher research. Such practice-oriented research can enhance teachers professional knowledge development, and can benefit both schools and university. This paper reports on 21 participants of a schooluniversity research network embedded in a Masters program. The main question was: which aspects of a schooluniversity research network play a role in processes of developing, sharing and using knowledge based on research by Masters students? Fifteen network aspects were distinguished, which together provide a useful framework for better understanding and further study of knowledge processes in schooluniversity research networks.Article Outline1. Introduction2. Theoretical framework 2.1. Network infrastructures for developing, sharing and using professional knowledge2.2. Schooluniversity research networks embedded in Masters programs2.3. Knowledge development, sharing and use2.4. Network elements 2.4.1. Members2.4.2. Relationships2.4.3. Context of events2.5. Research questions3. Method 3.1. Selection of participants 3.1.1. Context of the Masters program3.2. Data collection3.3. Data analysis4. Results 4.1. Network element one: network members 4.1.1. Nature of knowledge4.1.2. Activities4.1.3. Cognitions4.1.4. Meta-cognitions4.1.5. Emotions4.2. Network element two: relationships between network members 4.2.1. Trust4.2.2. Power4.2.3. Engagement4.2.4. Expertise4.3. Network element three: context of events in the network 4.3.1. Purpose4.3.2. Collaboration4.3.3. Inquiry4.3.4. Leadership4.3.5. Accountability4.3.6. Capacity5. Conclusion and discussionAppendix. Final coding systemReferencesPurchase$ 19.95Research highlights The integration of three perspectives on a network (i.e. members, relationships, context of events) contributes to better understanding of knowledge processes that take place within schooluniversity research networks; Fifteen network aspects were distinguished that play a role in processes of developing, sharing and using knowledge based on research by Masters students. Together they provide a useful framework for better understanding and further study of knowledge processes in schooluniversity research networks; A coding system was developed to analyze aspects that play a role in knowledge processes within a schooluniversity research network. This coding system can support further exploration of the relationships between the three knowledge processes (developing, sharing, using) and the aspects of the three network elements (members, relationships, context of events).27A review of data mining applications for quality improvement in manufacturing industryReview ArticleExpert Systems with Applications, In Press, Uncorrected Proof, Available online 21 April 2011Glser Kksal, İnci Batmaz, Murat Caner TestikClose preview| Related articles|Related reference work articles AbstractAbstract | Figures/TablesFigures/Tables | ReferencesReferences AbstractMany quality improvement (QI) programs including six sigma, design for six sigma, and kaizen require collection and analysis of data to solve quality problems. Due to advances in data collection systems and analysis tools, data mining (DM) has widely been applied for QI in manufacturing. Although a few review papers have recently been published to discuss DM applications in manufacturing, these only cover a small portion of the applications for specific QI problems (quality tasks). In this study, an extensive review covering the literature from 1997 to 2007 and several analyses on selected quality tasks are provided on DM applications in the manufacturing industry. The quality tasks considered are; product/process quality description, predicting quality, classification of quality, and parameter optimisation. The review provides a comprehensive analysis of the literature from various points of view: data handling practices, DM applications for each quality task and for each manufacturing industry, patterns in the use of DM methods, application results, and software used in the applications are analysed. Several summary tables and figures are also provided along with the discussion of the analyses and results. Finally, conclusions and future research directions are presented.Article Outline1. Introduction2. Quality improvement tasks3. The knowledge discovery in databases process and data mining 3.1. Data preparation3.2. Data preprocessing3.3. Data mi