Our purpose is to answer the following questions: Are Peruvian universities delivering lecture notes on time and in accordance with the outlined syllabus of the course? Is the information up-to-date and does it meet international standards? In order to find out the answers, we used a LP such as Moodle jointly with a MAS. We propose a model of KT called KTIT which use Moodle and a MAS. Our approach uses ontology designed with Protegé to verify lectures notes. The themes and keywords to construct the course’s ontology were extracted from its syllabus. We are validating the fundamental assumption that Peruvian universities deliver lecture notes to students under best conditions. This is an assumption that may hold true in others countries, but it has not been proven in Peru. To the best of our knowledge, there is not previous research on this topic. Therefore, we believe that our research is original as it is the first of its kind. The outcomes of the application could be applied to 140 Peruvian universities with at least 8 undergraduate programs of approximately 40 courses each. This gives a total of 44,800 potential applications of our KTIT model. We believe that our research will change Peruvian undergraduate education to higher levels of quality. Peruvian higher education will never be the same.

This study discusses the main findings from a systematic literature review about Big Data and analytics in the healthcare field. Although the use of Big Data in healthcare is emerging as fashionable research topic, policy-makers and healthcare professionals still struggle on how to unfold the potential value locked in Big Data. In fact, despite the promises that Big Data will change the way knowledge will be generated and managed in healthcare, little is known about how knowledge generated by big data can be used for managerial and decision making purposes. We conducted a systematic literature review in PubMed and Scopus databases, by combining “big-data” and “health care” with the following keywords: decision making, management, knowledge, knowledge generation, and knowledge creation. From 229 articles identified, 77 studies were selected coherently to our inclusion/exclusion criteria. Emerging topics on the use of big data for decision making in healthcare are: Knowledge development from Big-Data, Big-Data for clinical decision making, Big-Data ethical and legal issues, Big-Data definition and opportunities, and Big-Data for policy making. The large majority of selected studies were theoretical or conceptual, without empirical data. Empirical studies are mainly based on experiments, single/multiple case studies. These results call for empirical research in the next years about Big Data in healthcare to extent current theories and crystallize good practices. Four previous literature reviews have been carried out on concepts related to Big Data and analytics in healthcare. Although their value, one focused to Big Data definition, one of them was limited to Electronic Medical Records (EMRs) as source of Big Data, the other two articles present Big Data challenges in scientific research and healthcare. Moreover, the 4 reviews have been published on healthcare journals, with limited attention to management insights. Our literature review offers a more comprehensive understanding of the state of art of Big Data and analytics in healthcare, narrowing the current gap about managerial implications. In this view, our literature reviews adds value to our current understanding and provide scholars of knowledge management with directions for future research. Big Data are expected to radically change the way evidence is generated in healthcare. Results from this systematic literature review show topics/applications that have been investigated so far, thus supporting policy-makers, healthcare professionals as well as practitioners to identify best practices and set priorities for the next years.

Health Administrative Databases are among the most promising data in healthcare because of the capability to generate population-based knowledge. This study shows that Health Administrative Databases produce significant knowledge for evaluating hospitals’ performances and supporting policy-makers to set improvement strategies. The empirical setting is the Lombardy Region (Italy) and hospitals treating patients affected by Heart Failure(HF). Thirty-day mortality and readmission can be used to identify high-performing hospitals in the case of HF patients. High- and low-performing hospitals were identified as outliers through a funnel plot comparing expected and observed cases of death and re-admission. Expected cases were estimated using a 2-level logistic model. Funnel plot results on mortality showed that 48 hospitals were located outside the 95th percentile. Twenty-nine of these showed an observed mortality higher than the expected. As for re-admission, the number of hospitals “out of control” was equal to 8, equally distributed between the upper and the lower part of the plot. Health Administrative Databases can provide policy-makers and hospital managers with population-based knowledge that complement the traditional sources such as clinical trials and medical registers. While administrative data have been used mainly for epidemiological analyses, this study leverage on them to evaluate hospital performances, in doing so it paves the way for further research. The application of mixed models and funnel plot to detect hospital outliers based on administrative data is unusual and promising. Although our model has been developed for HF patients, we argue that it can be extended easily to other pathologies. Health Administrative Databases can generate robust knowledge about hospital performances. Our results about hospital treating HF patients show that private hospitals are more in-control than public ones. Public hospitals offer both the best and the worst performances. Moreover, hospitals that over-perform in term of re-admission are likely to over-perform also in term of mortality: this paves the way to strategies that could improve both performances at the same time. Finally, being based on a very large basis of evidence, policy-makers will be able to win resistance to change.

The development of technologies contributed to the exponential increase in the volume of the collected spatio-temporal data. It was determined that parallel processing of large series of spatio-temporal data can contribute to their analysis takes only a few seconds instead of hours. Big data applications require a combination of different process techniques, data sources and formats of storage. Spark SQL allows programmers great advantages in relational processing such as calling complex analytic libraries in Spark (eg. Machine learning). Also provides a general framework for the transformation of the structure, which we use to perform the analysis, planning and code generation in real time, expanding with new data sources, including data such as JSON and “smart” data warehouse over which is possible filtering (such as HBase) with user defined functions and user-defined types and domains such as machine learning. Model for managing large volumes of spatio-temporal data is implemented in Apache Spark platform for storing and processing large sets of data. The algorithms for processing spatio-temporal data are defined according to the rules of Spark SQL programming model and relational operations on dataframes (specialized system of data frames) using domain specific language (domain – specific – language → DSL). Data are stored on external storage systems that support new data types. Implementation of algorithm is performed in the programming language Scala. There is relatively small number of research in the field spatio-temporal Big Data and there are only several publications related to our research. Our model is based on the Spark which currently represents de-facto standard for Big Data processing. We utilise all advantages provided by Spark, such as user-defined types, user-defined functions, and DSL in order to support new spatio-temporal data types. The functionality of our model is accessible through SQL and DSL constructs and therefore available to wide spectrum of users and not only programmers. Amount of spatio-temporal data grows continuously at very fast pace. In order to use and maximally utilise the potential of such amounts of data, new solutions for storage, distribution, indexing, processing, and presentation, are necessary. The model we proposed is based on well established platforms for Big Data, such as Spark, Hadoop, and HBase, which inherently provides the solutions for the most of problems mentioned. We introduced new data types in those platforms in order to provide support for spatio-temporal data which were defined in accordance with existing OGC and ISO standards.

Design in general and the innovation method ‘Design Thinking’ specifically are currently receiving a high level of attention from the sectors of economics and management. Numerous large enterprises are using Design Thinking, on the one hand, for development of customer-orientated products and, on the other, with the goal in mind of scrutinising traditional organisational structures. Design is, accordingly, not limited to aesthetic creation of products; rather, it is placed in a larger context. Design and Design Thinking are seen as possibilities – with the aid of a mental model – for working on fundamental problems in economic and societal systems. Crucial aspects here are the empathetic and human-centred approach of Design Thinking as well as the typical actions, for designers, involving problem-solving and planning. In order to develop a unified understanding of Design Thinking, it appears to be necessary to link the theoretical and historical roots of design with the modern management approach. Therefore, the goal of this article is to form a theoretical and practice-orientated categorisation system for Design Thinking, based on examples from non-material approaches to our current comprehension of the concept. The draft and design-ideals of Design Thinking can be tracked backwards from today’s digital and mobile age to German modernism and the Bauhaus art school in Weimar at the beginning of the 20th century. At that time ’designing’ was awarded a level of significance that went far beyond mere notions of aestheticisation and visualisation. Walter Gropius (1923), founder of the Staatliches Bauhaus Weimar, declared the credo ’art and technology – a unity’, which can be found once again in today’s approach to Design Thinking. Later on, a methodological-analytical perception of design asserted itself (Rittel 1987; Simon 1994). On this basis, Design Thinking was defined as a mindset and a problem-solving process, in which the issue revolved primarily around detection of the actual structure of a problem, as opposed to concrete results (Brown 2009). The contemporary business approach views Design Thinking overridingly as a methodology for development of creative solutions and innovative products (Brown 2009; Plattner et al. 2012; Martin 2009). This paper classifies Design Thinking in a historical and economic context. The significance of creative thinking as a premise for general actions involving problem-solving and planning is carved out hereby – regardless of temporary trends.

The purpose of this paper is to examine how management consulting is linked to the performance of the firm. We claim that the acquiring consulting services in order to gain expertise or Best Practices improves the customer’s decision and finally leads to better organizational performance. The theoretical model consisted of six theoretical constructs about the motivations to acquire consulting, the impact of consulting on decision making, and the impact of consulting on performance. Four hypotheses about the linkages between these constructs were formed. For the empirical analysis a data (N=1,127) was collected among managers who acquire services of management consultants. The analysis followed the two-step procedure where two types of assessment were conducted, measurement model assessment and structural model assessment. The measurement model assessment was conducted using Confirmatory Factor Analysis (CFA), and the structural model assessment using Structural Equation Modeling (SEM). This study shows how purchasing expertise and best practices are connected to organizational performance via improved decision-making. The results widen the view on client-consultant relationship in existing theory on knowledge intensive client work (cf. Sturdy et al., 2009; Todorova, 2004; Bitner et al., 1997) and especially in the reciprocal processes of consulting service delivery (cf. Nikolova and Devinney 2012). The proposed results of the analysis provides a framework, which allows the clients of management consultants better design, brief and guide the consultancy processes. From the consultant’s perspective, the results of this study contribute to designing facilitation methods, which strengthen the need for emphasizing the cooperation with the client.

Joint action and knowledge sharing are fundamental aspects of business model innovation (BMI) for businesses to meet the complex BMI agenda of today; 12 independent businesses initially joint innovating on the establishment of a new concept of a balanced energy plant project, where the businesses had to work in symbioses from the very first moment and have now reached the second phase of the BMI project – the final and closing of the conceptualization phase. In the previous paper presented at the IFKAD conference 2015 the GreenLab Skive project (GLS) was still in the very initial and “happy days” of the BMI project. The second phase is now filled with the final contract negotiations, other network partners who wants to join the project from different business model ecosystems (BME). This now plays an important aspect that influences the BMI project in both positive and negative ways. Business model (BM) science and best practice tools have had little to say on the network aspect of BMI projects – particularly the BM relation axiom topic – which we call the quadrant 3 (Lindgren and Rasmussen, 2013). For many years’ perception, action, and cognition of network partners in a BMI project have been studied without investigating network partner’s BM´s real roles and interaction related to network-based business model innovation (NBBMI) intensively. However, the establishment of a “room” for social BMI interaction is essential both at the very first moment of the meeting between the involved participants – who often are different and unknown to each other. But as we show in this paper it is also needed in the later phases of the BMI process – the second phase in this paper. It gives the foundation for knowledge sharing and learning throughout the BMI project’s lifetime – and the base for whether the NBBMI project will succeed and even be implemented. However, as we show in the paper external BMI ecosystems and the project’s own BME in itself can – although the NBBMI project is functioning well and progressing – lay some serious barriers and challenges to further BMI progress. The paper aims to understand the cognitive mechanisms “at the later moment of a NBBMI process”, supporting participants’ ability and motivation to act together with each other in this project that have already taken off. The study is a new NBBMI study related to the 3. Quadrant of the Relations Axiom (Lindgren and Rasmussen, 2013), where BMI takes place outside the business and inside the BM of focus. The paper addresses knowledge sharing and learning in high risk and sensitive areas of NBBMI, where businesses open up their core business, core BMs and core competences and expects trust, progression and fulfilment of success criteria – in other words results and momentum. This research paper addresses one longitudinal case study of NBBMI project commencing in 2013.

The purpose of the paper is to investigate the process of selecting regional smart specializations in designing and implementing Regional Innovation Strategy (RIS). It presents conditions concerning selection of three smart specializations, i.e., power sector, medicine and information and communication technologies (ICT) on the background of selected smart specializations in the remaining regions of Poland and other EU states. They are characterized in the aspect of using the knowledge for the purposes of implementation of goal adopted in RIS. The basic research problem (in the methodology aspect) was to create a model allowing for planning and implementing content-related works concerning innovation strategy, which should ensure development of a region and obtain necessary financing. Methodology consists of two parts. The first one applies to developing (designing) RIS, the other to its implementation. The basic content-related assumption of the design stage is to support the concept and strategy implementation on smart specializations. This means effective and synergistic utilization of public support to strengthen innovative capacities by focusing on the most promising areas in which the region could reveal a competitive advantage. The second part of the methodology refers to implementation model of RIS which is based on the assumption of smart specialization development in the region, but much more refers to the practical aspects, identified projects in the region and possibilities of financing it. The value of the paper consists in presenting a designing method and RIS implementation model in one of regions in Poland, namely Śląskie Voivideship. In particular, smart specialisations selected in the region were characterized in a strategic aspect and their role in creating and implementing RIS was emphasized. RIS implementation model must be perceived as original, because it identifies areas of activity where strengthening and development of smart specializations will be noticed during the implementation of the strategy (until 2020). The result of application of the developed RIS designing method and the model of its implementation is manifested in two important strategic documents for the Upper Silesia Region, the first one is Regional Innovation Strategy of the Śląskie Voivodeship for the years 2013-2020, the other one is the Implementation model of the Regional Innovation Strategy of the Śląskie Voivodeship for the years 2013-2020.

The typical feature of Chinese property rights is incomplete property rights and informal property rights, which seriously affect the effectiveness of property rights. Transformation of Chinese property rights must point to effective property rights. Abolition of informal property rights which damage the interests of the main body of property rights and achievement of the integrity of property rights is the key to transformation of Chinese property rights. Tax plays an extremely important role in the process of constructing of effective property right system, so it is necessary to construct a set of tax system to confirm property rights and protect property rights.

This paper continues a research program designed to better understand the connections between different types of intangible assets, from data/information to knowledge to intelligence. As a consequence, theory and data are presented from different disciplines, allowing us to identify, by industry, where big data and explicit knowledge are applied as well as those where more tacit knowledge and analytical insights can be found. By better understanding the range of intangibles that can add value and their conditions of use, we can better advise decision-makers on when and how to invest in knowledge management systems, big data systems, intelligence systems, and related installations. The paper will have a substantial theoretical component, focusing on Ackoff’s (1989) DIKW (data/information/knowledge/wisdom) hierarchy and a more contemporary version initiated by Kurtz & Snowden (2003). Given the objective of bringing together different disciplines, scholarship from knowledge management (KM), intellectual capital, big data, and competitive intelligence will be reviewed and conceptualized. Data from financial statements (five years, almost 2,000 firms), a competitive intelligence survey (five years, almost 1,000 individuals), and a McKinsey big data research report will provide support for the analysis. By means of the databases and conceptualization, we can identify industries within which the different intangibles are more or less exploited (Erickson & Rothberg 2012). Industries with extensive use of big data can be uncovered. Industries emphasizing knowledge management installations (especially explicit-focused IT systems) can also be found, as can those more focused on individual tacit knowledge or insight managed through analytics and intelligence. These findings can be analysed through what we know about theory and practice in each of the identified industries. By outlining conditions in an industry–what intangibles lead to competitive advantage, where and how they are applied (operations? R&D? marketing?), what current best practices are—we can offer better advice to decision-makers looking at potential investments in big data and business analytics systems, KM systems (explicit- or tacit-oriented), competitive intelligence operations or other related activities. If monitoring and reacting to operational or transactional information is the key to success in an industry, that is one thing. If it is discerning deep strategic or tactical insights from analysing knowledge and information, that is quite another and calls for a different approach. Helping decision-makers assess their intangibles environment, take sensible intangibles management actions and make appropriate investments would help move the entire knowledge field forward.

The provision of food information to consumers is well recognized to be problematic and complex, since it includes health, social, legal, as well economic aspects. Information provision through mass media and labelling is usually product/producer centred and it could not meet specific consumers’ needs. Novel approaches tend to adopt mobile app-based services as a way for consumers to get better food information. Potentially, these services could provide relevant information to consumers as they could address particular needs or expectations by leveraging on context-awareness, ubiquity, pervasiveness, and mobility features. Do current app-based services actualize these potentialities? This paper has a twofold to answer question delimiting the research scope to olive oil information domain and to propose a methodological approach that can be easily adapted to conduct reviews of app-based services providing consumers with information related to other food domains. The review methodological approach we propose consists in a sequence of steps (sample selection, data coding and extraction, and data analysis) aimed to investigate dimensional aspects (and their inter-relationships) of food information flow that occur in the interaction between a consumer/user and a food app-based service. In particular, we have applied it to discover possible relations between information categorization, provider type, social network capability, and information search capability in app-based olive oil information services. Although there are countless mobile apps aimed to assist consumers with their everyday food practices, little research has been devoted to investigate services providing consumers with food information delivered through a mobile app. The available research lacks in defining a methodological framework to be employed in analyzing the relevance of food information provided to targeted audiences by app-based services. Our research gives a contribution to fill this knowledge gap by presenting some methods that can be employed to conduct reviews of app-based food information services. Applicability of these methods has been shown for the olive oil information domain. Results from our review of mobile olive oil apps suggest some direction to conceive and develop innovative mobile olive oil apps that better exploit mobile/ubiquitous technology features. Moreover, methods we presented could be used to conduct other reviews in order to obtain fruitful insights for the design of a new generation of food-apps or to identify potential service delivery improvements within the so-called “internet of food” domain.

What makes reverse logistics difficult on the operative level is that it is an exception-driven process and requires higher knowledge intensity. Whereas the current body of research documented the link between knowledge management and reverse logistics effectiveness, it remains unknown, which aspects of reverse logistics in companies are known well and which not. The paper, therefore, explores the knowledge of motives, managerial attention, barriers to- and ICT support for reverse logistic to enlighten the ways the knowledge management is able to support effectiveness of reverse logistics. The propositions formulated in the paper are based on an exploratory survey, which employed multiple informants approach, 18 respondents described 8 companies. Variance in respondents’ answers is analysed to show the extent of subjectivity and the lack of precise knowledge. A supplementary analysis of 93 companies tested the impact of respondents’ job position on the perception of reverse logistic to show different views between experts and non-experts The paper extends the literature on knowledge management in reverse logistics by the empirical identification of knowledge gaps and it proposes areas, on which the knowledge management should focus to improve the reverse logistics effectiveness. Insufficient knowledge is one of the barriers of effective reverse logistics, as documented in the literature. The paper elaborates on knowledge management in reverse logistics as the way to diminish the barriers of “insufficient knowledge”. The paper suggests that knowledge management can alter the perception of drivers and triggers of reverse logistic by improving the contextual understanding of them. Specifically, a precise understanding of drivers enables to design a system of reverse logistics, which is will focus at direct and indirect benefits (and not to the cost aspects only), resulting in its higher effectiveness.

The article presents the results of the study focused on the elaboration of a method to identify knowledge deficits, particularly from the point of view of the implementation of make-to-order contracts in a machine-building enterprise. The study allowed to identify existing links between key elements of logistic customer service and knowledge processes implemented in selected polish machine-building industry enterprises. The proposed method enables to identify knowledge deficits and makes it possible to determine active actions to improve the level of logistic customer service. The research process consisted of two stages and was aimed primarily at acquiring qualitative information in the field of knowledge processes implemented in logistic processes concerning the execution of make-to-order contracts in a selected group of machine-building industry enterprises. The first stage was focused on the general determination of knowledge deficits in all logistic processes in analysed companies. The second stage was to characterize and structure identified deficits to get their sources and consequences basing on direct interviews with internal and external experts. The result of the research was the elaboration of a knowledge deficits matrix as an authorial method enabling the identification and evaluation of knowledge deficits in a machine-building enterprise. Hitherto, there have been the lack of elaborated methods for the identification of knowledge deficits in the processes of logistic costumer service, taking into consideration internal relations within a company, as well as external conditions and recipients’ requirements. The undertaken study allowed to identify main knowledge deficits in each logistic process, particularly in an order fulfilment process, in analysed machine-building industry enterprises. As a result, the study has resulted in elaborating the original tool, enabling the identification and evaluation of knowledge deficits in the form of a knowledge deficits matrix. The matrix allows to determine and structure knowledge deficits and to indicate key areas of actions improving the execution of customer orders in a machine-building enterprise. The specificity of machine-building enterprises requires an individual approach to knowledge management concerning logistic processes. The proposed method, even though it was elaborated for a determined group of companies, i.e. machine-building enterprises executing individual make-to-order contracts, includes versatile aspects and can be used in manufacturing companies, as well as in other companies, which are the participants of a supply chain. The application of an above-mentioned method allows to identify knowledge deficits in individual logistic processes, and as a result it makes possible to take active actions for improving logistic customer service.

Company innovation strategies have recently been characterized by a trend towards more collaboration, through the sharing of ideas, knowledge, and expertise across firms’ or industries’ boundaries. Innovation clusters are considered as privileged places for collaborative innovation to develop. A recent stream of literature highlights the key role of cluster governance in facilitating and promoting these collaborative dynamics. However, little is known about the practices of cluster governance for managing knowledge at the cluster level although it is considered as a key determinant of cluster’s competitiveness. In this research, we study the knowledge brokering practices of cluster governance in three French innovation clusters, Advancity, Axelera and Imaginove. The three clusters are a top-down national initiative to foster regional competitiveness. We propose a comparative qualitative approach based on the analysis of 34 semi-structured, face-to-face interviews with strategic and operational members of the cluster governance. Very few researches have so far investigated the concrete practices of knowledge management at the cluster level, and fewer have attempted to highlight the role of cluster governance as a knowledge broker and the impact of these brokering practices on collaborative innovation at the cluster level. We hope to contribute to the recent stream of the Knowledge-based-View of Cluster as well as the cluster governance. The outcomes of this comparative research on knowledge brokering activities within French clusters induce the elaboration of practical knowledge management tools to create specific value at the territorial and cluster level. In particular we point out how these brokering activities focused on knowledge management help to reinforce the integration of SMEs in collaborative innovation dynamics within the cluster.

The purpose of this systematic literature review is to survey and evaluate the key works in the field of organizational unlearning and forgetting. Through analyzing and synthesizing common themes, we aim to highlight research gaps and new avenues for research in the field of organizational unlearning and forgetting. We intend to contribute to the debate and enhance the field by providing a better understanding of the topic. We conducted a systematic literature review to identify significant studies in the field of organizational unlearning and forgetting. After applying certain inclusion/exclusion criteria, we selected a final number of 63 relevant works which we thoroughly analyzed. Structuring the analysis and synthesis around various constructs, theories, typologies, and related themes allowed us to highlight several research gaps and propose avenues for further research. Although it is an under-researched field characterized by a blurry and fragmented understanding, the field of organizational unlearning and forgetting has received increased scholarly attention. We reveal fresh insights into this field of research and provide new avenues for future research. This paper contributes to a better understanding of the field and raises several additional questions to spark further debate. We highlight various processes and mechanisms of unlearning and forgetting and expose common themes in the analyzed literature. By reviewing relevant works in detail, we theoretically expand the overarching fields of knowledge management and organizational learning. Researchers from other disciplines might consider various aspects of this work for their own fields and, in turn, enhance interdisciplinary research efforts. This paper also aims at raising awareness of organizational unlearning and forgetting in companies. The outcomes of this work could benefit managers, consultants, employees, or other practitioners in terms of its strategic significance in order to enhance the competitive advantage and performance of their organizations. Particularly in knowledge-intensive environments, the effective and efficient management of knowledge is crucial. Successfully managing processes of knowledge loss can positively influence an organization’s performance. We outline some important factors that practitioners can consider when dealing with purposeful or involuntary knowledge loss.

The study aims to investigate the role of transformational leadership style in knowledge management related practices particularly knowledge acquisition, creation, sharing and storage that strive for achieving goals of organizational resilience together with organizational innovation. Based on the tentative suppositions derived from knowledge based view (KBV) of an organization, analyses have been performed including partial least square (PLS) for structural equation modeling (SEM) in order to examine these hypotheses. The sample in the intended cross-sectional study contains empirical or primary data collected from managerial employees in IT companies of Pakistan. There is empirical indication of mediating role of knowledge management practices i.e. acquisition, creation, sharing and storage between transformational leadership and organizational innovation in order to sustain resilience of the organization. Conforming to literature based studies and theory, it has been witnessed that leaders with their inspirational guidance help encourage their managers to enhance organizational innovation through emancipating KM practices. Consequently organizational innovation of IT based organizations leads exceptional resilience. Findings hold the substantiation that km practices exercise an ambidextrous capability of exploration and exploitation using knowledge acquisition, creation, sharing and storage respectively by the virtue of transformational leadership, thereby resulting in an increased resilience and vigor of IT based organizations through the entrenchment of organization innovation. The paper takes valuable and evidence based insights based on objectivist approach from previous empirical and exploratory research studies and reveals an empirically proven relationship between transformational leadership and organizational innovation through mediating role of knowledge management practices, which consequently leads to high organizational resilience. Top management and leadership teams at IT companies can gain maximum input from this research in order to maximize the potential for organizational innovation using knowledge management practices and finally make their organizations highly resilient to effectively bounce back any likely disruptions. Knowledge management practices encompassing acquisition, creation, sharing and storage of new knowledge evolve as a result of the efforts made by transformational leaders that play a noteworthy role in managing even the most technical and critical aspects of workplace disposed to external vulnerabilities and turbulence and help support innovation based performance of the organization. Additionally, the outcomes of organizational innovation as a result of executing knowledge management practices owing to the influential motivation of transformational leaders help an organization become highly adaptable, flexible, bouncy or resilient under uncertain environmental conditions.

Innovation is associated with concepts such as change and value creation, because it is a transformative process that expands the possibilities of a country, productive sector or company; besides being the driving force behind growth, innovations allow farmers in a country to move up the value chain of international agricultural export markets. Innovation and its transfer are fundamental for the development and source of competitive advantage that goes beyond the endowment of natural resources, capital and labor engine; making innovation networks indispensable, which are a form of flexible collaboration between members, organizations or institutions working together in pursuit of a common goal. Mexico has a tremendous potential in the agricultural sector to venture into international markets for its excellent environmental conditions, in addition to a high and diverse agricultural production, especially in lemon’s production and exportation; unfortunately, despite the competitive advantages of the country, agricultural marketing has diminished by problems that have blocked the development of the lemon industry. The research was conducted in the Valley of Apatzingan, integrated with ten municipalities that concentrate most of the lemon production of Michoacan. The object of the study were 4200 producers, 50 packers, 10 industries, 13 suppliers and 15 nurseries. This region is at the top in agricultural production in Michoacan, and the problems the lemon production has faced, despite its benefits, it hasn’t been adequately promoted in international marketing; thereby preventing further integration of innovation networks. Based on the above, the purpose of this paper, is to describe which are the variables that drive the increase in export competitiveness. Lemon-System-Product’s of Michoacan state, based on innovation networks and actors. For the design of methodology, we propose an approach, the following hypothesis, There is a positive relationship between innovation network and the actors who compose, reflecting in the export competitiveness of Lemon-System-Product’s in Michoacan’s state. This methodology puts in evidence twofold, the dynamics of technological innovation and analyzing innovation networks, exchanging information between both approaches to enrich the analysis. The outcomes of the application, proposes the Lemon Product System’s Innovation Network based on what actors involved in it.

The subject of the research work is characteristic of coopetition in the low-tech SMEs in Poland on the example of the brewing industry. The author intends to combine the two streams of research dealing with issues of cross-organizational cooperation, coopetition theory and network theory. We propose an approach of interpretative symbolic paradigm, qualitative research method, use of grounded theory and data collection will be made by interviews. Majority of research was conducted in large and/or hi tech companies, also the author is interested in two main factors, how small low-tech companies can establish and profit from coopetition and are there any differences between low-tech and hi-tech coopetition networks in regard to knowledge sharing and success factors the author wants to examine how the phenomenon of coopetition, which has been very well described in the hi-tech sectors and in large organizations, can be used by small organizations belonging to the low-tech industries. An additional aim is to show how knowledge is distributed in conditions of very little trust.

Purpose of this paper is to show the work in progress of the author’s Ph.D. which is about the development of a method to support the communication between SME and their respective bank regarding intellectual capital (IC) within the lending process. Based on a theoretical and practical requirement analysis selected methods on intellectual capital reporting (ICR) will be analysed in order to identify suitable modules to support the communication interface between small and medium-sized enterprises (SME) and banks. This paper presents the approach and the development process of the project “IC Future Check” (Wuscher et al., 2014) where the requirements of financial experts and SMEs were collected by case study research activities and structured interviews. Based on the collected requirements the paper will outline the main challenges of ICR in the use of the lending process of banks. Approach – The definition of IC and the methodology of proceeding ICR in small and medium-sized enterprises as well as in larger companies and organizational networks have been adequately researched in the last years. But how can the concept of ICR help SME to get a better rating and better conditions of credits? Companies and especially SME are mainly assessed on the basis of their financial data and financial guarantees. However these data do not cover intangible assets, which represent a significant share of the value and the future development potential of an enterprise. This missing information might lead to an uncertain assessment of the company and in consequence to unfavourable credit conditions for SME. To reduce the risk assessment on the banking side and to enhance the credit conditions on the company side, structured and additional information on non-financial information can reduce the existing information asymmetry. In this context detailed field reports and surveys have shown that complementing financial data with information on intangibles can sharpen the view on creditworthiness of an organisation. Especially SME are facing the challenge that the intangibles which thus far have not been taken into account are to make transparent for their respective banks. Value and The paper is about the theoretical and practical requirements of banks and SME as the basis for the development a suitable methodology for assessing and communicating intellectual capital of SME in the context of the lending process of banks.

The development of a knowledge-based economy implies a change in the paradigms of behavior of our society that affects, among other things, two areas of life, i) the need of improving professional skills, which becomes a must for individuals who wish to be valid on the labor market, and ii) the use of explicit knowledge existing in learning contexts, which becomes a key factor to socioeconomic development. In this way, our research is aimed to analyse some of the network effects that occur in online education platforms for lifelong learning, which are based on increasing the tacit knowlege (internalization) through the use of explicit knowledge (externalization). We propose an approach based on study network effects related to e-learning by analysing the HarvardX and MITx courses of the edX platform public dataset in six European countries. We estimate and compare the value of the network of this dataset by using three laws related to netwok effects (Metcalfe, Odlyzko, and Sarnoff) and several metrics related to this network (nodes, edges, degree, diameter, density, path lenght, and topology). Thus, we analyse two areas of knowledge-based content activities in e-learning courses, i) processes of transforming explicit knowledge (content interactions, number of events, number of days, number of play videos, and number of chapters explored) into tacit knowledge (certificate); and ii) measurement of value of network externalities based on explicit knowledge existing in e-learning courses. This article shows how observable knowledge related to content interactions in online education is related to the effects of direct and indirect network externalities. Also, it puts in evidende its dependency by the intrinsic value of different types of explicit knowledge (events, days, videos, and chapters), the marginal value (the contribution of a new content-student interaction), and the relative size of the network (power-laws). The outcomes of the application are useful for estimating a power-law valid in student-content networks in online education. Moreover, the analysis of relationships between explicit and tacit knowledge in online education can help to design learning strategies that take into account how processes related to explicit knowledge management work in online education, which are very useful for detecting potential dropout.