D2.4 – Updated use case models based on ID2.11 and Advice on Configuration Management based on ID2.17

Méndez, C., Arjona, M., Lemmers, R., & Kluijfhout, E. (2009). D2.4 – Updated use case models based on ID2.11 and Advice on Configuration Management based on ID2.17. TENCompetence. [Report delivered 02-11-2009]This deliverable contains a) configuration guidelines for the implementation of the PCM for the domains of Personal Competence Management, eLearning, and Knowledge Management, and b) work done on the interaction design of the PCM from the perspective of distinct usage profiles.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

e-LION: Data integration semantic model to enhance predictive analytics in e-Learning.

The surge in online education emphasizes Learning Management Systems' (LMSs) crucial role in organizing learning resources and enabling teacher-learner communication. COVID-19 accelerated this, spiking engagement and substantial learning data. Academic institutions now have extensive data for comprehensive analysis to inform educational planning. However, integrating this diverse, sizable dataset from heterogeneous sources with semantic inconsistencies is challenging. Standardized integration schemes are needed for efficient utilization in machine learning models. Semantic web technologies offer a promising framework for semantic integration of e-learning data, enabling systematic consolidation, linkage, and advanced querying. We propose the e-LION (e-Learning Integration ONtology) semantic model to consolidate diverse e-learning knowledge bases and enhance analytical capabilities. Populated with real-world data from various LMSs, focusing on Software Engineering courses from the University of Malaga (Spain) and the Open University Learning, we validate it through four in-depth case studies. Advanced semantic querying techniques feed predictive models, perform time-series forecasting of student interactions based on final grades, and develop SWRL reasoning rules for student behavior classification. Validation study results are highly promising, suggesting e-LION as an ontological mediator scheme for integrating future semantic models within the e-learning domain. This opens exciting possibilities for leveraging the e-LION model to enhance educational planning, predictive modeling, and behavioral analysis, ultimately advancing e-learning through effective semantic integration and diverse learning-related data utilization.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Transdisciplinarity as a means for capacity development in water resources management

Water resources management has to deal with complex real life problems under uncertain framework conditions. One possibility for encountering such challenges is integrated water resources management (IWRM). However, IWRM is often understood as prescriptive manual, not acknowledging the need for adaptive solutions and capacity development (CD). These challenges demonstrate that sustainable water resources management requires transdisciplinarity, i.e. the integration of several scientific disciplines, as well as the collaboration between science and local actors. Transdisciplinarity is inherently related to CD since it facilitates collaboration and provides mutual learning and knowledge on complex interrelationships. This correlates with the evidence that CD can be seen as a key factor for water resources management (Alaerts et al. 1991, Alaerts 2009). Consequently, the objective of this thesis is to strengthen water resources management by connecting processes of IWRM and CD in a transdisciplinary sense, i.e. (i) interrelating disciplinary research within an interdisciplinary research team that collaborates with local actors, and (ii) conducting a political process for knowledge and capacity development. Based on general insights, an embedded case study in the Western Bug River Basin, Ukraine, was conducted to evaluate the concept. It is shown that CD is essential for shifting from IWRM theories towards implementation and accordingly advantages of harmonizing CD into the IWRM process are presented (Leidel et al. 2012). Next to capacity issues, also other coordination gaps were assessed. River Basin Organisations are frequently proposed as a response to the administrative gap; however, coordination efforts cannot be simply reduced by transferring tasks from jurisdictional institutions to a river basin authority, because they will always need to coordinate with organizations from within or outside the water sector (von Keitz and Kessler 2008). Thus, coordination mechanisms across the boundaries of relevant policy fields are essential. Therefore, a management framework is established linking technical development and capacity development that describes interrelations between environmental pressures and capacity and information gaps for different levels of water management (Leidel et al. 2014). The developed model-based and capacity-based IWRM framework combines model-based systems analysis and capacity analysis for developing management options that support water management actors. This is aligned with a political process for capacity development. It constitutes a boundary object for approaching cross-scale challenges that converges analyses, assessments and participation into one strategy. As concluded by Mollinga (2008), this can improve the performance of sustainable resources management by approaching transdisciplinarity. Within the model and capacity-based IWRM framework, the results of the integrated analysis are made explicit and transparent by introducing a matrix approach. Technical issues, institutional challenges, organizational and human resources development, and information needs are jointly assessed and interrelated by confronting pressures and coordination gaps on a subsystem basis. Accordingly, the concept supports a transparent decision making process by identifying knowledge and capacities required for the implementation of technical intervention options and vice versa. The method is applied in the International Water Research Alliance Saxony (IWAS) model region ‘Ukraine’. It could be shown that the approach delivers management options that are scientifically credible and also accepted by and relevant for the actors. The case study revealed that technical intervention measures for the urban and rural water management have to be jointly implemented with appropriate CD measures and an accompanying political process on (i) strengthening the institutional framework and interministerial collaboration, (ii) fitting RBM into the existing institutional framework, (iii) setting up prerequisites for realistic RBM (Monitoring, information management, legal enforcement), (iv) a revision of effluent standards and a differentiated levy system, (v) cost covering tariffs, (vi) association work. For the Western Bug River Basin (WBRB), the strengthening of the collaboration between actors on all levels has to be continued. For increasing the usability, the approach needs to be institutionalized and become more practice relevant, e.g. by extending it to a water knowledge management system. Developing a roadmap for establishing transboundary water management is a subsequent step. For strengthening future water management actors, IWRM curricula development at uni-versities in Ukraine was supported. And we developed the e-learning module IWRM-education that links interactively different aspects of water management to comprehend the complexity of IWRM (Leidel et al. 2013). The evaluation showed that participants under-stand the content, appreciate this way of learning, and will use this module for further activities. The case study showed that technical cooperation can be a facilitator for political processes and that it can support decision making in a transparent way. Yet, it also showed that IWRM is highly political process and that the developed approach cannot cover all obstacles. In summary, exploring and reducing simultaneously environmental pressures and capacity and information gaps is essential for water sector evolution worldwide. Accordingly, transdisciplinarity as a means for capacity development can support the implementation of real integrated water resources management

Teaching Construction in the Virtual University: the WINDS project

This paper introduces some of the Information Technology solutions adopted in Web based INtelligent Design Support (WINDS) to support education in A/E/C design. The WINDS project WINDS is an EC-funded project in the 5th Framework, Information Society Technologies programme, Flexible University key action. WINDS is divided into two actions: ·The research technology action is going to implement a learning environment integrating an intelligent tutoring system, a computer instruction management system and a set of co-operative supporting tools. ·The development action is going to build a large knowledge base supporting Architecture and Civil Engineering Design Courses and to experiment a comprehensive Virtual School of Architecture and Engineering Design. During the third year of the project, more than 400 students all over Europe will attend the Virtual School. During the next three years the WINDS project will span a total effort of about 150 man-years from 28 partners of 10 European countries. The missions of the WINDS project are: Advanced Methodologies in Design Education. WINDS drives a breakdown with conventional models in design education, i.e. classroom or distance education. WINDS implements a problem oriented knowledge transfer methodology following Roger Schank's Goal Based Scenario (GBS) pedagogical methodology. GBS encourages the learning of both skills and cases, and fosters creative problem solving. Multidisciplinary Design Education. Design requires creative synthesis and open-end problem definition at the intersection of several disciplines. WINDS experiments a valuable integration of multidisciplinary design knowledge and expertise to produce a high level standard of education. Innovative Representation, Delivery and Access to Construction Education. WINDS delivers individual education customisation by allowing the learner access through the Internet to a wide range of on-line courses and structured learning objects by means of personally tailored learning strategies. WINDS promotes the 3W paradigm: learn What you need, Where you want, When you require. Construction Practice. Construction industry is a repository of ""best practices"" and knowledge that the WINDS will profit. WINDS system benefits the ISO10303 and IFC standards to acquire knowledge of the construction process directly in digital format. On the other hand, WINDS reengineers the knowledge in up-to-date courses, educational services, which the industries can use to provide just-in-time rather than in-advance learning. WINDS IT Solutions The missions of the WINDS project state many challenging requirements both in knowledge and system architecture. Many of the solutions adopted in these fields are innovative; others are evolution of existing technologies. This paper focuses on the integration of this set of state-of-the-art technologies in an advanced and functionally sound Computer Aided Instruction system for A/E/C Design. In particular the paper deals with the following aspects: Standard Learning Technology Architecture The WINDS system relies on the in progress IEEE 1484.1 Learning Technology Standard Architecture. According to this standard the system consists of two data stores, the Knowledge Library and the Record Database, and four process: System Coach, Delivery, Evaluation and the Learner. WINDS implements the Knowledge Library into a three-tier architecture: 1.Learning Objects: ·Learning Units are collections of text and multimedia data. ·Models are represented in either IFC or STEP formats. ·Cases are sets of Learning Units and Models. Cases are noteworthy stories, which describes solutions, integrate technical detail, contain relevant design failures etc. 2.Indexes refer to the process in which the identification of relevant topics in design cases and learning units takes place. Indexing process creates structures of Learning Objects for course management, profile planning procedures and reasoning processes. 3.Courses are taxonomies of either Learning Units or a design task and Course Units. Knowledge Representation WINDS demonstrates that it is possible and valuable to integrate a widespread design expertise so that it can be effectively used to produce a high level standard of education. To this aim WINDS gathers area knowledge, design skills and expertise under the umbrellas of common knowledge representation structures and unambiguous semantics. Cases are one of the most valuable means for the representation of design expertise. A Case is a set of Learning Units and Product Models. Cases are noteworthy stories, which describe solutions, integrate technical details, contain relevant design failures, etc. Knowledge Integration Indexes are a medium among different kind of knowledge: they implement networks for navigation and access to disparate documents: HTML, video, images, CAD and product models (STEP or IFC). Concept indexes link learning topics to learning objects and group them into competencies. Index relationships are the base of the WINDS reasoning processes, and provide the foundation for system coaching functions, which proactively suggest strategies, solutions, examples and avoids students' design deadlock. Knowledge Distribution To support the data stores and the process among the partners in 10 countries efficiently, WINDS implements an object oriented client/server as COM objects. Behind the DCOM components there is the Dynamic Kernel, which dynamically embodies and maintains data stores and process. Components of the Knowledge Library can reside on several servers across the Internet. This provides for distributed transactions, e.g. a change in one Learning Object affects the Knowledge Library spread across several servers in different countries. Learning objects implemented as COM objects can wrap ownership data. Clear and univocal definition of ownerships rights enables Universities, in collaboration with telecommunication and publisher companies, to act as "education brokers". Brokerage in education and training is an innovative paradigm to provide just-in-time and personally customised value added learning knowledg

Exploring social collaborative e-learning in higher education: a study of two universities in Uganda

The emerging social collaborative technologies such as Facebook and Twitter are greatly influencing the evolution of e-learning in higher education. As these technologies become more easily available to students and lecturers, the approach to e-learning continues to evolve leading to a social collaborative e-learning (SoCeL) model. SoCeL involves social interactions and collaborations among students and lecturers in order to make it easy for them to construct and share knowledge. They exchange ideas and share their own digital products using these technologies to facilitate learning. Studies have however, shown that using social collaborative technologies in the learning process has not always had definite success. This may be attributed to lack of a framework to guide effective integration. The perceived absence of suitable frameworks is addressed in this work by developing frameworks to guide effective integration of SoCeL. This empirical study follows the requirements engineering process and uses a mixed methods approach involving case study and human-computer interaction ethnography to explore the environment in which social collaborative technologies are adopted in two universities in Uganda. Data were analysed using qualitative and quantitative approaches to establish requirements for SoCeL effective integration. The findings of this study are grouped in two broad areas: learning environment and adoption of social collaborative technologies. Based on these findings, the SoCeL environment framework and SoCeL adoption frameworks were developed. These provide the basis on which important recommendations are made. In conclusion, the thesis argues that SoCeL can be effectively integrated in higher education if the learning environment focuses on an integrated design. The design should bring together: informal learning, social networking and learning management

CBR model for the intelligent management of customer support centers

[EN] In this paper, a new CBR system for Technology Management Centers is presented. The system helps the staff of the centers to solve customer problems by finding solutions successfully applied to similar problems experienced in the past. This improves the satisfaction of customers and ensures a good reputation for the company who manages the center and thus, it may increase its profits. The CBR system is portable, flexible and multi-domain. It is implemented as a module of a help-desk application to make the CBR system as independent as possible of any change in the help-desk. Each phase of the reasoning cycle is implemented as a series of configurable plugins, making the CBR module easy to update and maintain. This system has been introduced and tested in a real Technology Management center ran by the Spanish company TISSAT S.A.Financial support from Spanish government under grant PROFIT FIT-340001-2004-11 is gratefully acknowledgeHeras Barberá, SM.; Garcia Pardo Gimenez De Los Galanes, JA.; Ramos-Garijo Font De Mora, R.; Palomares Chust, A.; Julian Inglada, VJ.; Rebollo Pedruelo, M.; Botti, V. (2006). CBR model for the intelligent management of customer support centers. En Lecture Notes in Computer Science. Springer Verlag (Germany). 663-670. https://doi.org/10.1007/11875581_80S663670Acorn, T., Walden, S.: SMART: SupportManagement Automated Reasoning Technology for Compaq Customer Service. In: Scott, A., Klahr, P. (eds.) Proceedings of the 2 International Conference on Intelligent Tutoring Systems, ITS-92 Berlin, vol. 4, pp. 3–18. AAAI Press, Menlo Park (1992)Simoudis, E.: Using Case-Based Retrieval for Customer Technical Support. IEEE Intelligent Systems 7, 10–12 (1992)Kriegsman, M., Barletta, R.: Building a Case-Based Help Desk Application. IEEE Expert: Intelligent Systems and Their Applications 8, 18–26 (1993)Shimazu, H., Shibata, A., Nihei, K.: Case-Based Retrieval Interface Adapted to Customer-Initiated Dialogues in Help Desk Operations. In: Mylopoulos, J., Reiter, R. (eds.) Proceedings of the 12th National Conference on Artificial Intelligence, vol. 1, pp. 513–518. AAAI Press, Menlo Park (1994)Raman, R., Chang, K.H., Carlisle, W.H., Cross, J.H.: A self-improving helpdesk service system using case-based reasoning techniques. Computers in Industry 2, 113–125 (1996)Kang, B.H., Yoshida, K., Motoda, H., Compton, P.: Help Desk System with Intelligent Interface. Applied Artificial Intelligence 11, 611–631 (1997)Roth-Berghofer, T., Iglezakis, I.: Developing an Integrated Multilevel Help-Desk Support System. In: Proceedings of the 8th German Workshop on Case-Based Reasoning, pp. 145–155 (2000)Goker, M., Roth-Berghofer, T.: The development and utilization of the case-based help-desk support system HOMER. Engineering Applications of Artificial Intelligence 12, 665–680 (1999)Roth-Berghofer, T.R.: Learning from HOMER, a case-based help-desk support system. In: Melnik, G., Holz, H. (eds.) Advances in Learning Software Organizations, pp. 88–97. Springer, Heidelberg (2004)Bergmann, R., Althoff, K.D., Breen, S., Göker, M., Manago, M., Traphöner, R., Wess, S.: Developing Industrial Case-Based Reasoning Applications. In: The INRECA Methodology, 2nd edn. LNCS (LNAI), vol. 1612. Springer, Heidelberg (2003)eGain (2006), http://www.egain.comKaidara Software Corporation (2006), http://www.kaidara.com/Empolis Knowledge Management GmbH - Arvato AG (2006), http://www.empolis.com/Althoff, K.D., Auriol, E., Barletta, R., Manago, M.: A Review of Industrial Case-Based Reasoning Tools. AI Perspectives Report. Goodall, A., Oxford (1995)Watson, I.: Applying Case-Based Reasoning. Techniques for Enterprise Systems. Morgan Kaufmann Publishers, Inc. California (1997)empolis: empolis Orenge Technology Whitepaper. Technical report, empolis GmbH (2002)Tissat, S.A. (2006), http://www.tissat.esGiraud-Carrier, C., Martinez, T.R.: An integrated framework for learning and reasoning. Journal of Artificial Intelligence Research 3, 147–185 (1995)Corchado, J.M., Borrajo, M.L., Pellicer, M.A., Yanez, J.C.: Neuro-symbolic system for Business Internal Control. In: Perner, P. (ed.) ICDM 2004. LNCS (LNAI), vol. 3275, pp. 1–10. Springer, Heidelberg (2004)Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations and system approaches. AI Communications 7(1), 39–59 (1994)Tversky, A.: Features of similarity. Psychological Review 84(4), 327–352 (1997

A social network-based organizational model for improving knowledge management in supply chains

Purpose: This paper aims to provide a social network-based model for improving knowledge management in multi-level supply chains formed by small and medium-sized enterprises (SMEs). Design/methodology/approach: This approach uses social network analysis techniques to propose and represent a knowledge network for supply chains. Also, an empirical experience from an exploratory case study in the construction sector is presented. Findings: This proposal improves the establishment of inter-organizational relationships into networks to exchange the knowledge among the companies along the supply chain and create specific knowledge by promoting confidence and motivation. Originality/value: This proposed model is useful for academics and practitioners in supply chain management to gain a better understanding of knowledge management processes, particularly for the supply chains formed by SMEs. © Emerald Group Publishing Limited.Capó-Vicedo, J.; Mula, J.; Capó I Vicedo, J. (2011). A social network-based organizational model for improving knowledge management in supply chains. Supply Chain Management: An International Journal. 16(5):379-388. doi:10.1108/13598541111155884S379388165Archer, N., Wang, S., & Kang, C. (2008). Barriers to the adoption of online supply chain solutions in small and medium enterprises. Supply Chain Management: An International Journal, 13(1), 73-82. doi:10.1108/13598540810850337Arend, R. J., & Wisner, J. D. (2005). Small business and supply chain management: is there a fit? Journal of Business Venturing, 20(3), 403-436. doi:10.1016/j.jbusvent.2003.11.003BERNARDES, E. S. (2010). THE EFFECT OF SUPPLY MANAGEMENT ON ASPECTS OF SOCIAL CAPITAL AND THE IMPACT ON PERFORMANCE: A SOCIAL NETWORK PERSPECTIVE. Journal of Supply Chain Management, 46(1), 45-55. doi:10.1111/j.1745-493x.2009.03185.xBORGATTI, S. P., & LI, X. (2009). ON SOCIAL NETWORK ANALYSIS IN A SUPPLY CHAIN CONTEXT. Journal of Supply Chain Management, 45(2), 5-22. doi:10.1111/j.1745-493x.2009.03166.xBorgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network Analysis in the Social Sciences. Science, 323(5916), 892-895. doi:10.1126/science.1165821Boschma, R. A., & ter Wal, A. L. J. (2007). Knowledge Networks and Innovative Performance in an Industrial District: The Case of a Footwear District in the South of Italy. Industry & Innovation, 14(2), 177-199. doi:10.1080/13662710701253441Cadilhon, J.J. and Fearne, A.P. (2005), “Lessons in collaboration: a case study from Vietnam”,Supply Chain Management Review, Vol. 9 No. 4, pp. 11‐12.Carter, C. R., Ellram, L. M., & Tate, W. (2007). THE USE OF SOCIAL NETWORK ANALYSIS IN LOGISTICS RESEARCH. Journal of Business Logistics, 28(1), 137-168. doi:10.1002/j.2158-1592.2007.tb00235.xChen, I. J., & Paulraj, A. (2004). Understanding supply chain management: critical research and a theoretical framework. International Journal of Production Research, 42(1), 131-163. doi:10.1080/00207540310001602865Cheng, J., Yeh, C., & Tu, C. (2008). Trust and knowledge sharing in green supply chains. Supply Chain Management: An International Journal, 13(4), 283-295. doi:10.1108/13598540810882170CHOI, T. Y., & WU, Z. (2009). TRIADS IN SUPPLY NETWORKS: THEORIZING BUYER-SUPPLIER-SUPPLIER RELATIONSHIPS. Journal of Supply Chain Management, 45(1), 8-25. doi:10.1111/j.1745-493x.2009.03151.xCrone, M., & Roper, S. (2001). Local Learning from Multinational Plants: Knowledge Transfers in the Supply Chain. Regional Studies, 35(6), 535-548. doi:10.1080/00343400120065705Egbu, C. O., Hari, S., & Renukappa, S. H. (2005). Knowledge management for sustainable competitiveness in small and medium surveying practices. Structural Survey, 23(1), 7-21. doi:10.1108/02630800510586871Fong, P. S. W., & Kwok, C. W. C. (2009). Organizational Culture and Knowledge Management Success at Project and Organizational Levels in Contracting Firms. Journal of Construction Engineering and Management, 135(12), 1348-1356. doi:10.1061/(asce)co.1943-7862.0000106Giannakis, M. (2008). Facilitating learning and knowledge transfer through supplier development. Supply Chain Management: An International Journal, 13(1), 62-72. doi:10.1108/13598540810850328Giuliani, E. (2007). The selective nature of knowledge networks in clusters: evidence from the wine industry. Journal of Economic Geography, 7(2), 139-168. doi:10.1093/jeg/lbl014Giuliani, E., & Bell, M. (2005). The micro-determinants of meso-level learning and innovation: evidence from a Chilean wine cluster. Research Policy, 34(1), 47-68. doi:10.1016/j.respol.2004.10.008Gunasekaran, A., Patel, C., & Tirtiroglu, E. (2001). Performance measures and metrics in a supply chain environment. International Journal of Operations & Production Management, 21(1/2), 71-87. doi:10.1108/01443570110358468Hogarth‐Scott, S. (1999). Retailer‐supplier partnerships: hostages to fortune or the way forward for the millennium? British Food Journal, 101(9), 668-682. doi:10.1108/00070709910288865Javernick-Will, A. N., & Scott, W. R. (2010). Who Needs to Know What? Institutional Knowledge and Global Projects. Journal of Construction Engineering and Management, 136(5), 546-557. doi:10.1061/(asce)co.1943-7862.0000035Johnsen, T. E., Johnsen, R. E., & Lamming, R. C. (2008). Supply relationship evaluation: European Management Journal, 26(4), 274-287. doi:10.1016/j.emj.2007.10.001Kinder, T. (2003). Go with the flow—a conceptual framework for supply relations in the era of the extended enterprise. Research Policy, 32(3), 503-523. doi:10.1016/s0048-7333(02)00021-5Lambert, D. M., Cooper, M. C., & Pagh, J. D. (1998). Supply Chain Management: Implementation Issues and Research Opportunities. The International Journal of Logistics Management, 9(2), 1-20. doi:10.1108/09574099810805807Lamming, R., Caldwell, N., & Phillips, W. (2006). A Conceptual Model of Value-Transparency in Supply. European Management Journal, 24(2-3), 206-213. doi:10.1016/j.emj.2006.03.010Lamming, R., Caldwell, N., Phillips, W., & Harrison, D. (2005). Sharing Sensitive Information in Supply Relationships: European Management Journal, 23(5), 554-563. doi:10.1016/j.emj.2005.09.010Levy, M., Loebbecke, C., & Powell, P. (2003). SMEs, co-opetition and knowledge sharing: the role of information systems. European Journal of Information Systems, 12(1), 3-17. doi:10.1057/palgrave.ejis.3000439McCarthy, T. M., & Golicic, S. L. (2002). Implementing collaborative forecasting to improve supply chain performance. International Journal of Physical Distribution & Logistics Management, 32(6), 431-454. doi:10.1108/09600030210437960Malhotra, A., Gosain, S. and El Sawy, O.A. (2001), “Absorptive capacity configurations in supply chains: gearing for partner‐enabled market knowledge creation”,MIS Quarterly, Vol. 29 No. 1, pp. 145‐87.Matopoulos, A., Vlachopoulou, M., Manthou, V., & Manos, B. (2007). A conceptual framework for supply chain collaboration: empirical evidence from the agri‐food industry. Supply Chain Management: An International Journal, 12(3), 177-186. doi:10.1108/13598540710742491Mentzas, G., Apostolou, D., Kafentzis, K., & Georgolios, P. (2006). Inter-organizational networks for knowledge sharing and trading. Information Technology and Management, 7(4), 259-276. doi:10.1007/s10799-006-0276-8Morrison, A. (2008). Gatekeepers of Knowledgewithin Industrial Districts: Who They Are, How They Interact. Regional Studies, 42(6), 817-835. doi:10.1080/00343400701654178Morrison, A., & Rabellotti, R. (2009). Knowledge and Information Networks in an Italian Wine Cluster. European Planning Studies, 17(7), 983-1006. doi:10.1080/09654310902949265Newell, S., Bresnen, M., Edelman, L., Scarbrough, H., & Swan, J. (2006). Sharing Knowledge Across Projects. Management Learning, 37(2), 167-185. doi:10.1177/1350507606063441Nonaka, I. (1994). A Dynamic Theory of Organizational Knowledge Creation. Organization Science, 5(1), 14-37. doi:10.1287/orsc.5.1.14Ozkul, A., & Barut, M. (2009). Measuring supply chain relationships: a social network approach. International Journal of Integrated Supply Management, 5(1), 38. doi:10.1504/ijism.2009.026204Ramírez-Pasillas, M. (2010). International trade fairs as amplifiers of permanent and temporary proximities in clusters. Entrepreneurship & Regional Development, 22(2), 155-187. doi:10.1080/08985620902815106Sanderson, J., & Cox, A. (2008). The challenges of supply strategy selection in a project environment: evidence from UK naval shipbuilding. Supply Chain Management: An International Journal, 13(1), 16-25. doi:10.1108/13598540810850283Seggie, S. H., Kim, D., & Cavusgil, S. T. (2006). Do supply chain IT alignment and supply chain interfirm system integration impact upon brand equity and firm performance? Journal of Business Research, 59(8), 887-895. doi:10.1016/j.jbusres.2006.03.005Soosay, C. A., Hyland, P. W., & Ferrer, M. (2008). Supply chain collaboration: capabilities for continuous innovation. Supply Chain Management: An International Journal, 13(2), 160-169. doi:10.1108/13598540810860994Vaaland, T. I., & Heide, M. (2007). Can the SME survive the supply chain challenges? Supply Chain Management: An International Journal, 12(1), 20-31. doi:10.1108/13598540710724374Venters, W., Cornford, T., & Cushman, M. (2005). Knowledge about Sustainability: SSM as a Method for Conceptualising the UK Construction Industry�s Knowledge Environment. Journal of Computing and Information Technology, 13(2), 137. doi:10.2498/cit.2005.02.05Wagner, B. A., Fillis, I., & Johansson, U. (2003). E‐business and e‐supply strategy in small and medium sized businesses (SMEs). Supply Chain Management: An International Journal, 8(4), 343-354. doi:10.1108/13598540310490107Walter, J., Lechner, C., & Kellermanns, F. W. (2007). Knowledge transfer between and within alliance partners: Private versus collective benefits of social capital. Journal of Business Research, 60(7), 698-710. doi:10.1016/j.jbusres.2007.01.026Wu, C. (2008). Knowledge creation in a supply chain. Supply Chain Management: An International Journal, 13(3), 241-250. doi:10.1108/13598540810871280Zheng, W., Yang, B., & McLean, G. N. (2010). Linking organizational culture, structure, strategy, and organizational effectiveness: Mediating role of knowledge management. Journal of Business Research, 63(7), 763-771. doi:10.1016/j.jbusres.2009.06.00

The effectiveness of crowdsourcing in knowledge-based industries: the moderating role of transformational leadership and organisational learning

[EN] Crowdsourcing provides an opportunity for SMEs to exploit collective knowledge that is located outside the organisation. Crowdsourcing allows organisations to keep pace with a fast-changing environment by solving business problems, supporting R&D activities, and fostering innovation cheaply, flexibly, and dynamically. Nevertheless, managing crowdsourcing is difficult, and positive outcomes are not guaranteed. Drawing on the Resource-based View, we study transformational leadership and organisational learning capability as complementary assets to help SMEs deploy crowdsourcing. An empirical study of Spanish telecommunications and biotechnology companies confirmed the moderating effect of organisational learning on the relationship between crowdsourcing and organisational performance.Devece Carañana, CA.; Palacios Marqués, D.; Ribeiro-Navarrete, B. (2019). The effectiveness of crowdsourcing in knowledge-based industries: the moderating role of transformational leadership and organisational learning. Economic Research-Ekonomska Istra ivanja. 32(1):335-351. https://doi.org/10.1080/1331677X.2018.1547204S335351321Amitay, M., Popper, M., & Lipshitz, R. (2005). Leadership styles and organizational learning in community clinics. The Learning Organization, 12(1), 57-70. doi:10.1108/09696470510574269Atapattu, M., & Ranawake, G. (2017). Transformational and Transactional Leadership Behaviours and their Effect on Knowledge Workers’ Propensity for Knowledge Management Processes. Journal of Information & Knowledge Management, 16(03), 1750026. doi:10.1142/s0219649217500265Aragón-Correa, J. A., García-Morales, V. J., & Cordón-Pozo, E. (2007). Leadership and organizational learning’s role on innovation and performance: Lessons from Spain. Industrial Marketing Management, 36(3), 349-359. doi:10.1016/j.indmarman.2005.09.006Bal, A. 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Effect of exploitation and exploration on the innovative as outcomes in entrepreneurial firms

[EN] The main aim of this study is to establish the effect of the Exploitation and Exploration; and the influence of these learning flows on the Innovative Outcome (IO). The Innovative Outcome refers to new products, services, processes (or improvements) that the organization has obtained as a result of an innovative process. For this purpose, a relationship model is defined, which is empirically contrasted, and can explains and predicts the cyclical dynamization of learning flows on innovative outcome in knowledge intensive firms. The quantitative test for this model use the data from entrepreneurial firms biotechnology sector. The statistical analysis applies a method based on variance using Partial Least Squares (PLS). Research results confirm the hypotheses, that is, they show a positive dynamic effect between the Exploration and the Innovative as outcomes. 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