Impact of knowledge brokering on performance heterogeneity among business models

Purpose – The strategic management literature lacks a comprehensive explanation as to why seemingly similar business models in the same industry perform differently. This paper strives to explain this phenomenon. Design/methodology/approach – The model is conceptualized and accompanied by a case study on the airline industry to explain knowledge brokerage that creates value from the effective utilization of knowledge resources acquired from intra- and inter-firm environments. Findings – The model explains a cyclical view of business model flexibility in which the knowledge-based resource accumulation of the business model is spread across the intra- and inter-firm environments. Knowledge brokerage strategies from the inter- and intra-firm environments result in improved performance of the business model. The flexibility that the business model acquires is determined by how efficiently resource accumulation is aligned with its external environment. Originality/value – The paper effectively integrates the concepts of knowledge brokerage and business models from a resource accumulation-based view and simultaneously arrives at the performance heterogeneity of seemingly similar business models within the same industry. It has performance implications for firms that start out without any distinct resources of their own, or that use an imitated business model, to attain better performance through business model evolution aligned with successful knowledge brokerage strategies. It adds to the resource accumulation literature by explaining how resources can be effectively acquired to create value.

Proof of concept of an imaging system demonstrator for PET applications with SiPM

A PET imaging system demonstrator based on LYSO crystal arrays coupled to SiPM matrices is under construction at the University and INFN of Pisa. Two SiPM matrices, composed of 8×8 SiPM pixels, and 1,5 mm pitch, have been coupled one to one to a LYSO crystals array and read out by a custom electronics system. front-end ASICs were used to read 8 channels of each matrix. Data from each front-end were multiplexed and sent to a DAQ board for the digital conversion; a motherboard collects the data and communicates with a host computer through a USB port for the storage and off-line data processing. In this paper we show the first preliminary tomographic image of a point-like radioactive source acquired with part of the two detection heads in time coincidence.

The Role of Knowledge Modeling Techniques in Software Development: A General Approach Based on a Knowledge Management Tool

The aim of the paper is to discuss the use of knowledge models to formulate general applications. First, the paper presents the recent evolution of the software field where increasing attention is paid to conceptual modeling. Then, the current state of knowledge modeling techniques is described where increased reliability is available through the modern knowledge acquisition techniques and supporting tools. The KSM (Knowledge Structure Manager) tool is described next. First, the concept of knowledge area is introduced as a building block where methods to perform a collection of tasks are included together with the bodies of knowledge providing the basic methods to perform the basic tasks. Then, the CONCEL language to define vocabularies of domains and the LINK language for methods formulation are introduced. Finally, the object oriented implementation of a knowledge area is described and a general methodology for application design and maintenance supported by KSM is proposed. To illustrate the concepts and methods, an example of system for intelligent traffic management in a road network is described. This example is followed by a proposal of generalization for reuse of the resulting architecture. Finally, some concluding comments are proposed about the feasibility of using the knowledge modeling tools and methods for general application design.

Application of knowledge engineering acquisition techniques to requirements capture: a case study

In this paper we want to point out, by means of a case study, the importance of incorporating some knowledge engineering techniques to the processes of software engineering. Precisely, we are referring to the knowledge eduction techniques. We know the difficulty of requirements acquisition and its importance to minimise the risks of a software project, both in the development phase and in the maintenance phase. To capture the functional requirements use cases are generally used. However, as we will show in this paper, this technique is insufficient when the problem domain knowledge is only in the "experts? mind". In this situation, the combination of the use case with eduction techniques, in every development phase, will let us to discover the correct requirements.

Dynamic Studies of Shell and Spatial Structures

For the past 20 years, dynamic analysis of shells has been one of the most fascinating fields for research. Using the new light materials the building engineer soon discovered that the subsequent reduction of gravity forces produced not only the desired shape freedom but the appearance of ecologic loads as the first factor of design; loads which present strong random properties and marked dynamic influence. On the other hand, the technological advance in the aeronautical and astronautical field placed the engineers in front of shell structures of nonconventional shape and able to sustain substantialy dynamic loads. The response to the increasingly challenger problems of the last two decades has been very bright; new forms, new materials and new methods of analysis have arosen in the design of off-shore platforms, nuclear vessels, space crafts, etc. Thanks to the intensity of the lived years we have at our disposition a coherent and homogeneous amount of knowledge which enable us to face problems of inconceivable complexity when IASS was founded. The open minded approach to classical problems and the impact of the computer are, probably, important factors in the Renaissance we have enjoyed these years, and a good proof of this are the papers presented to the previous IASS meetings as well as that we are going to consider in this one. Particularly striking is the great number of papers based on a mathematical modeling in front of the meagerness of those treating laboratory experiments on physical models. The universal entering of the computer into almost every phase of our lifes, and the cost of physical models, are –may be- reasons for this lack of experimental methods. Nevertheless they continue offering useful results as are those obtained with the shaking-table in which the computer plays an essential role in the application of loads as well as in the instantaneous treatment of control data. Plates 1 and 2 record the papers presented under dynamic heading, 40% of them are from Japan in good correlation with the relevance that Japanese research has traditionally showed in this area. Also interesting is to find old friends as profesors Tanaka, Nishimura and Kostem who presented valuable papers in previous IASS conferences. As we see there are papers representative of all tendencies, even purely analytical! Better than discuss them in detail, which can be done after the authors presentation, I think we can comment in the general pattern of the dynamical approach are summarized in plate 3.

A visual framework to accelerate knowledge discovery based on dimensionality reduction minimizing degradation of quality

Tradicionalmente, el uso de técnicas de análisis de datos ha sido una de las principales vías para el descubrimiento de conocimiento oculto en grandes cantidades de datos, recopilados por expertos en diferentes dominios. Por otra parte, las técnicas de visualización también se han usado para mejorar y facilitar este proceso. Sin embargo, existen limitaciones serias en la obtención de conocimiento, ya que suele ser un proceso lento, tedioso y en muchas ocasiones infructífero, debido a la dificultad de las personas para comprender conjuntos de datos de grandes dimensiones. Otro gran inconveniente, pocas veces tenido en cuenta por los expertos que analizan grandes conjuntos de datos, es la degradación involuntaria a la que someten a los datos durante las tareas de análisis, previas a la obtención final de conclusiones. Por degradación quiere decirse que los datos pueden perder sus propiedades originales, y suele producirse por una reducción inapropiada de los datos, alterando así su naturaleza original y llevando en muchos casos a interpretaciones y conclusiones erróneas que podrían tener serias implicaciones. Además, este hecho adquiere una importancia trascendental cuando los datos pertenecen al dominio médico o biológico, y la vida de diferentes personas depende de esta toma final de decisiones, en algunas ocasiones llevada a cabo de forma inapropiada. Ésta es la motivación de la presente tesis, la cual propone un nuevo framework visual, llamado MedVir, que combina la potencia de técnicas avanzadas de visualización y minería de datos para tratar de dar solución a estos grandes inconvenientes existentes en el proceso de descubrimiento de información válida. El objetivo principal es hacer más fácil, comprensible, intuitivo y rápido el proceso de adquisición de conocimiento al que se enfrentan los expertos cuando trabajan con grandes conjuntos de datos en diferentes dominios. Para ello, en primer lugar, se lleva a cabo una fuerte disminución en el tamaño de los datos con el objetivo de facilitar al experto su manejo, y a la vez preservando intactas, en la medida de lo posible, sus propiedades originales. Después, se hace uso de efectivas técnicas de visualización para representar los datos obtenidos, permitiendo al experto interactuar de forma sencilla e intuitiva con los datos, llevar a cabo diferentes tareas de análisis de datos y así estimular visualmente su capacidad de comprensión. De este modo, el objetivo subyacente se basa en abstraer al experto, en la medida de lo posible, de la complejidad de sus datos originales para presentarle una versión más comprensible, que facilite y acelere la tarea final de descubrimiento de conocimiento. MedVir se ha aplicado satisfactoriamente, entre otros, al campo de la magnetoencefalografía (MEG), que consiste en la predicción en la rehabilitación de lesiones cerebrales traumáticas (Traumatic Brain Injury (TBI) rehabilitation prediction). Los resultados obtenidos demuestran la efectividad del framework a la hora de acelerar y facilitar el proceso de descubrimiento de conocimiento sobre conjuntos de datos reales. ABSTRACT Traditionally, the use of data analysis techniques has been one of the main ways of discovering knowledge hidden in large amounts of data, collected by experts in different domains. Moreover, visualization techniques have also been used to enhance and facilitate this process. However, there are serious limitations in the process of knowledge acquisition, as it is often a slow, tedious and many times fruitless process, due to the difficulty for human beings to understand large datasets. Another major drawback, rarely considered by experts that analyze large datasets, is the involuntary degradation to which they subject the data during analysis tasks, prior to obtaining the final conclusions. Degradation means that data can lose part of their original properties, and it is usually caused by improper data reduction, thereby altering their original nature and often leading to erroneous interpretations and conclusions that could have serious implications. Furthermore, this fact gains a trascendental importance when the data belong to medical or biological domain, and the lives of people depends on the final decision-making, which is sometimes conducted improperly. This is the motivation of this thesis, which proposes a new visual framework, called MedVir, which combines the power of advanced visualization techniques and data mining to try to solve these major problems existing in the process of discovery of valid information. Thus, the main objective is to facilitate and to make more understandable, intuitive and fast the process of knowledge acquisition that experts face when working with large datasets in different domains. To achieve this, first, a strong reduction in the size of the data is carried out in order to make the management of the data easier to the expert, while preserving intact, as far as possible, the original properties of the data. Then, effective visualization techniques are used to represent the obtained data, allowing the expert to interact easily and intuitively with the data, to carry out different data analysis tasks, and so visually stimulating their comprehension capacity. Therefore, the underlying objective is based on abstracting the expert, as far as possible, from the complexity of the original data to present him a more understandable version, thus facilitating and accelerating the task of knowledge discovery. MedVir has been succesfully applied to, among others, the field of magnetoencephalography (MEG), which consists in predicting the rehabilitation of Traumatic Brain Injury (TBI). The results obtained successfully demonstrate the effectiveness of the framework to accelerate and facilitate the process of knowledge discovery on real world datasets.

A Knowledge-based Approach for Automatic Generation of Summaries of Behavior

Effective automatic summarization usually requires simulating human reasoning such as abstraction or relevance reasoning. In this paper we describe a solution for this type of reasoning in the particular case of surveillance of the behavior of a dynamic system using sensor data. The paper first presents the approach describing the required type of knowledge with a possible representation. This includes knowledge about the system structure, behavior, interpretation and saliency. Then, the paper shows the inference algorithm to produce a summarization tree based on the exploitation of the physical characteristics of the system. The paper illustrates how the method is used in the context of automatic generation of summaries of behavior in an application for basin surveillance in the presence of river floods.

An Analysis Of The Recommended Knowledge For The Software Project Management Discipline

According to the PMBOK (Project Management Body of Knowledge), project management is “the application of knowledge, skills, tools, and techniques to project activities to meet the project requirements” [1]. Project Management has proven to be one of the most important disciplines at the moment of determining the success of any project [2][3][4]. Given that many of the activities covered by this discipline can be said that are “horizontal” for any kind of domain, the importance of acknowledge the concepts and practices becomes even more obvious. The specific case of the projects that fall in the domain of Software Engineering are not the exception about the great influence of Project Management for their success. The critical role that this discipline plays in the industry has come to numbers. A report by McKinsey & Co [4] shows that the establishment of programs for the teaching of critical skills of project management can improve the performance of the project in time and costs. As an example of the above, the reports exposes: “One defense organization used these programs to train several waves of project managers and leaders who together administered a portfolio of more than 1,000 capital projects ranging in Project management size from $100,000 to $500 million. Managers who successfully completed the training were able to cut costs on most projects by between 20 and 35 percent. Over time, the organization expects savings of about 15 percent of its entire baseline spending”. In a white paper by the PMI (Project Management Institute) about the value of project management [5], it is stated that: “Leading organizations across sectors and geographic borders have been steadily embracing project management as a way to control spending and improve project results”. According to the research made by the PMI for the paper, after the economical crisis “Executives discovered that adhering to project management methods and strategies reduced risks, cut costs and improved success rates—all vital to surviving the economic crisis”. In every elite company, a proper execution of the project management discipline has become a must. Several members of the software industry have putted effort into achieving ways of assuring high quality results from projects; many standards, best practices, methodologies and other resources have been produced by experts from different fields of expertise. In the industry and the academic community, there is a continuous research on how to teach better software engineering together with project management [4][6]. For the general practices of Project Management the PMI produced a guide of the required knowledge that any project manager should have in their toolbox to lead any kind of project, this guide is called the PMBOK. On the side of best practices 10 and required knowledge for the Software Engineering discipline, the IEEE (Institute of Electrical and Electronics Engineers) developed the SWEBOK (Software Engineering Body of Knowledge) in collaboration with software industry experts and academic researchers, introducing into the guide many of the needed knowledge for a 5-year expertise software engineer [7]. The SWEBOK also covers management from the perspective of a software project. This thesis is developed to provide guidance to practitioners and members of the academic community about project management applied to software engineering. The way used in this thesis to get useful information for practitioners is to take an industry-approved guide for software engineering professionals such as the SWEBOK, and compare the content to what is found in the PMBOK. After comparing the contents of the SWEBOK and the PMBOK, what is found missing in the SWEBOK is used to give recommendations on how to enrich project management skills for a software engineering professional. Recommendations for members of the academic community on the other hand, are given taking into account the GSwE2009 (Graduated Software Engineering 2009) standard [8]. GSwE2009 is often used as a main reference for software engineering master programs [9]. The standard is mostly based on the content of the SWEBOK, plus some contents that are considered to reinforce the education of software engineering. Given the similarities between the SWEBOK and the GSwE2009, the results of comparing SWEBOK and PMBOK are also considered valid to enrich what the GSwE2009 proposes. So in the end the recommendations for practitioners end up being also useful for the academic community and their strategies to teach project management in the context of software engineering.

A multidisciplinary reference framework to support innovative design, implementation and assessment of chronic care models

Hoy en día, por primera vez en la historia, la mayor parte de la población podrá vivir hasta los sesenta años y más (United Nations, 2015). Sin embargo, todavía existe poca evidencia que demuestre que las personas mayores, estén viviendo con mejor salud que sus padres, a la misma edad, ya que la mayoría de los problemas de salud en edades avanzadas están asociados a las enfermedades crónicas (WHO, 2015). Los sistemas sanitarios de los países desarrollados funcionan adecuadamente cuando se trata del cuidado de enfermedades agudas, pero no son lo suficientemente eficaces en la gestión de las enfermedades crónicas. Durante la última década, se han realizado esfuerzos para mejorar esta gestión, por medio de la utilización de estrategias de prevención y de reenfoque de la provisión de los servicios de atención para la salud (Kane et al. 2005). Según una revisión sistemática de modelos de cuidado de salud, comisionada por el sistema nacional de salud Británico, pocos modelos han conceptualizado cuáles son los componentes que hay que utilizar para proporcionar un cuidado crónico efectivo, y estos componentes no han sido suficientemente estructurados y articulados. Por lo tanto, no hay suficiente evidencia sobre el impacto real de cualquier modelo existente en la actualidad (Ham, 2006). Las innovaciones podrían ayudar a conseguir mejores diagnósticos, tratamientos y gestión de pacientes crónicos, así como a dar soporte a los profesionales y a los pacientes en el cuidado. Sin embargo, la forma en las que estas innovaciones se proporcionan no es lo suficientemente eficiente, efectiva y amigable para el usuario. Para mejorar esto, hace falta crear equipos de trabajo y estrategias multidisciplinares. En conclusión, hacen falta actividades que permitan conseguir que las innovaciones sean utilizadas en los sistemas de salud que quieren mejorar la gestión del cuidado crónico, para que sea posible: 1) traducir la “atención sanitaria basada en la evidencia” en “conocimiento factible”; 2) hacer frente a la complejidad de la atención sanitaria a través de una investigación multidisciplinaria; 3) identificar una aproximación sistemática para que se establezcan intervenciones innovadoras en el cuidado de salud. El marco de referencia desarrollado en este trabajo de investigación es un intento de aportar estas mejoras. Las siguientes hipótesis han sido propuestas: Hipótesis 1: es posible definir un proceso de traducción que convierta un modelo de cuidado crónico en una descripción estructurada de objetivos, requisitos e indicadores clave de rendimiento. Hipótesis 2: el proceso de traducción, si se ejecuta a través de elementos basados en la evidencia, multidisciplinares y de orientación económica, puede convertir un modelo de cuidado crónico en un marco descriptivo, que define el ciclo de vida de soluciones innovadoras para el cuidado de enfermedades crónicas. Hipótesis 3: es posible definir un método para evaluar procesos, resultados y capacidad de desarrollar habilidades, y asistir equipos multidisciplinares en la creación de soluciones innovadoras para el cuidado crónico. Hipótesis 4: es posible dar soporte al desarrollo de soluciones innovadoras para el cuidado crónico a través de un marco de referencia y conseguir efectos positivos, medidos en indicadores clave de rendimiento. Para verificar las hipótesis, se ha definido una aproximación metodológica compuesta de cuatro Fases, cada una asociada a una hipótesis. Antes de esto, se ha llevado a cabo una “Fase 0”, donde se han analizado los antecedentes sobre el problema (i.e. adopción sistemática de la innovación en el cuidado crónico) desde una perspectiva multi-dominio y multi-disciplinar. Durante la fase 1, se ha desarrollado un Proceso de Traducción del Conocimiento, elaborado a partir del JBI Joanna Briggs Institute (JBI) model of evidence-based healthcare (Pearson, 2005), y sobre el cual se han definido cuatro Bloques de Innovación. Estos bloques consisten en una descripción de elementos innovadores, definidos en la fase 0, que han sido añadidos a los cuatros elementos que componen el modelo JBI. El trabajo llevado a cabo en esta fase ha servido también para definir los materiales que el proceso de traducción tiene que ejecutar. La traducción que se ha llevado a cabo en la fase 2, y que traduce la mejor evidencia disponible de cuidado crónico en acción: resultado de este proceso de traducción es la parte descriptiva del marco de referencia, que consiste en una descripción de un modelo de cuidado crónico (se ha elegido el Chronic Care Model, Wagner, 1996) en términos de objetivos, especificaciones e indicadores clave de rendimiento y organizada en tres ciclos de innovación (diseño, implementación y evaluación). Este resultado ha permitido verificar la segunda hipótesis. Durante la fase 3, para demostrar la tercera hipótesis, se ha desarrollado un método-mixto de evaluación de equipos multidisciplinares que trabajan en innovaciones para el cuidado crónico. Este método se ha creado a partir del método mixto usado para la evaluación de equipo multidisciplinares translacionales (Wooden, 2013). El método creado añade una dimensión procedural al marco. El resultado de esta fase consiste, por lo tanto, en una primera versión del marco de referencia, lista para ser experimentada. En la fase 4, se ha validado el marco a través de un caso de estudio multinivel y con técnicas de observación-participante como método de recolección de datos. Como caso de estudio se han elegido las actividades de investigación que el grupo de investigación LifeStech ha desarrollado desde el 2008 para mejorar la gestión de la diabetes, actividades realizadas en un contexto internacional. Los resultados demuestran que el marco ha permitido mejorar las actividades de trabajo en distintos niveles: 1) la calidad y cantidad de las publicaciones; 2) se han conseguido dos contratos de investigación sobre diabetes: el primero es un proyecto de investigación aplicada, el segundo es un proyecto financiado para acelerar las innovaciones en el mercado; 3) a través de los indicadores claves de rendimiento propuestos en el marco, una prueba de concepto de un prototipo desarrollado en un proyecto de investigación ha sido transformada en una evaluación temprana de una intervención eHealth para el manejo de la diabetes, que ha sido recientemente incluida en Repositorio de prácticas innovadoras del Partenariado de Innovación Europeo en Envejecimiento saludable y activo. La verificación de las 4 hipótesis ha permitido demonstrar la hipótesis principal de este trabajo de investigación: es posible contribuir a crear un puente entre la atención sanitaria y la innovación y, por lo tanto, mejorar la manera en que el cuidado crónico sea procurado en los sistemas sanitarios. ABSTRACT Nowadays, for the first time in history, most people can expect to live into their sixties and beyond (United Nations, 2015). However, little evidence suggests that older people are experiencing better health than their parents, and most of the health problems of older age are linked to Chronic Diseases (WHO, 2015). The established health care systems in developed countries are well suited to the treatment of acute diseases but are mostly inadequate for dealing with CDs. Healthcare systems are challenging the burden of chronic diseases by putting more emphasis on the prevention of disease and by looking for new ways to reorient the provision of care (Kane et al., 2005). According to an evidence-based review commissioned by the British NHS Institute, few models have conceptualized effective components of care for CDs and these components have been not structured and articulated. “Consequently, there is limited evidence about the real impact of any of the existing models” (Ham, 2006). Innovations could support to achieve better diagnosis, treatment and management for patients across the continuum of care, by supporting health professionals and empowering patients to take responsibility. However, the way they are delivered is not sufficiently efficient, effective and consumer friendly. The improvement of innovation delivery, involves the creation of multidisciplinary research teams and taskforces, rather than just working teams. There are several actions to improve the adoption of innovations from healthcare systems that are tackling the epidemics of CDs: 1) Translate Evidence-Based Healthcare (EBH) into actionable knowledge; 2) Face the complexity of healthcare through multidisciplinary research; 3) Identify a systematic approach to support effective implementation of healthcare interventions through innovation. The framework proposed in this research work is an attempt to provide these improvements. The following hypotheses have been drafted: Hypothesis 1: it is possible to define a translation process to convert a model of chronic care into a structured description of goals, requirements and key performance indicators. Hypothesis 2: a translation process, if executed through evidence-based, multidisciplinary, holistic and business-oriented elements, can convert a model of chronic care in a descriptive framework, which defines the whole development cycle of innovative solutions for chronic disease management. Hypothesis 3: it is possible to design a method to evaluate processes, outcomes and skill acquisition capacities, and assist multidisciplinary research teams in the creation of innovative solutions for chronic disease management. Hypothesis 4: it is possible to assist the development of innovative solutions for chronic disease management through a reference framework and produce positive effects, measured through key performance indicators. In order to verify the hypotheses, a methodological approach, composed of four Phases that correspond to each one of the stated hypothesis, was defined. Prior to this, a “Phase 0”, consisting in a multi-domain and multi-disciplinary background analysis of the problem (i.e.: systematic adoption of innovation to chronic care), was carried out. During phase 1, in order to verify the first hypothesis, a Knowledge Translation Process (KTP) was developed, starting from the JBI Joanna Briggs Institute (JBI) model of evidence-based healthcare was used (Pearson, 2005) and adding Four Innovation Blocks. These blocks represent an enriched description, added to the JBI model, to accelerate the transformation of evidence-healthcare through innovation; the innovation blocks are built on top of the conclusions drawn after Phase 0. The background analysis gave also indication on the materials and methods to be used for the execution of the KTP, carried out during phase 2, that translates the actual best available evidence for chronic care into action: this resulted in a descriptive Framework, which is a description of a model of chronic care (the Chronic Care Model was chosen, Wagner, 1996) in terms of goals, specified requirements and Key Performance Indicators, and articulated in the three development cycles of innovation (i.e. design, implementation and evaluation). Thanks to this result the second hypothesis was verified. During phase 3, in order to verify the third hypothesis, a mixed-method to evaluate multidisciplinary teams working on innovations for chronic care, was created, based on a mixed-method used for the evaluation of Multidisciplinary Translational Teams (Wooden, 2013). This method adds a procedural dimension to the descriptive component of the Framework, The result of this phase consisted in a draft version of the framework, ready to be tested in a real scenario. During phase 4, a single and multilevel case study, with participant-observation data collection, was carried out, in order to have a complete but at the same time multi-sectorial evaluation of the framework. The activities that the LifeStech research group carried out since 2008 to improve the management of diabetes have been selected as case study. The results achieved showed that the framework allowed to improve the research activities in different directions: the quality and quantity of the research publications that LifeStech has issued, have increased substantially; 2 project grants to improve the management of diabetes, have been assigned: the first is a grant funding applied research while the second is about accelerating innovations into the market; by using the assessment KPIs of the framework, the proof of concept validation of a prototype developed in a research project was transformed into an early stage assessment of innovative eHealth intervention for Diabetes Management, which has been recently included in the repository of innovative practice of the European Innovation Partnership on Active and Health Ageing initiative. The verification of the 4 hypotheses lead to verify the main hypothesis of this research work: it is possible to contribute to bridge the gap between healthcare and innovation and, in turn, improve the way chronic care is delivered by healthcare systems.