Tiedon jakaminen tiimityössä

Knowledge-sharing in a teamwork The study examines the link between knowledge-sharing that takes place in a team and the dimensions and objectives of the team s activities. The question the study poses is: How does knowledge-sharing in a team relate to the team s activities? The exchange of knowledge is examined using knowledge-sharing networks and the conversion model, which describes the process of knowledge formation. The answer to the question is sought through four empirical articles describing the activities of a team from the viewpoint of quality, fairness, power related to knowledge management, and performance. One of the articles used in the study describes the role of networks in work life more generally. It attempts to shed light on the manner in which team-related networks operate as part of a more extensive structure of organizational networks. Finland is one of the most eager users of teamwork, if numbers are used as a yardstick. About half of all Finnish wage earners worked in teams in 2009, and comparisons show that the use of teams in Finland is above the EU average. This study focuses on so-called semi-autonomous teams, which carry out permanent work tasks. In such teams, tasks are interdependent, and teams are jointly responsible for ensuring that the work is done. Team members may also, at least to some extent, agree between themselves on how the tasks are carried out and are able to take part in the decision-making process. Such teamwork makes knowledge-sharing an important element for the team s activities. Knowledge and knowledge-sharing have become a major resource, allowing organizations to operate and even compete in today s increasingly competitive markets. A single team or a single organization cannot, however, possess all the knowledge required for carrying out the tasks assigned to it. Although it is difficult to copy the knowledge generated in an organization, it is important to share the knowledge within and between organizations. External links supply teams and organizations with important knowledge that allows them to keep their operations up-to-date and their structures well-functioning. In fact, knowledge provides teams and organizations with an intangible resource that improves their capacity to interact with their environment and to adjust to it. For this reason, it is important to examine both the internal and external knowledge-sharing taking place in a team. The findings of the study show that in terms of quality, fairness, performance and the knowledge management issues concerning a team, its social network structure is both internally and externally connected with its activities. A team structure that is internally coherent and at the same time open to external contacts, is, with certain restrictions, connected with the quality, fairness, and performance of the team. The restrictions concern differences between procedural and interactional justice, public and private sectors, and the team leaders and ordinary team members. The role of the team leader is closely connected with the management of networks that are considered valuable. The results of the study indicate that teamwork is supervisor-dominated. Thus, teamwork does not substantially strengthen the influence of individual employees as players in knowledge-transfer networks. However, ordinary team members possess important peer contacts inside the organization. Teamwork clearly allows employees to interact in a democratic manner, and here the transfer of tacit knowledge plays an important role. Keywords: teamwork, knowledge-sharing, social networks, organization

Stability of multi-cluster synchronization

A system of many coupled oscillators on a network can show multicluster synchronization. We obtain existence conditions and stability bounds for such a multicluster synchronization. When the oscillators are identical, we obtain the interesting result that network structure alone can cause multicluster synchronization to emerge even when all the other parameters are the same. We also study occurrence of multicluster synchronization when two different types of oscillators are coupled.

Tutorial on NK model

NK model, proposed by Kauffman (1993), is a strong simulation framework to study competing dynamics. It has been applied in some social science fields, for instance, organization science. However, like many other simulation methods, NK model has not received much attention from Management Information Systems (MIS) discipline. This tutorial, thus, is trying to introduce NK model in a simple way and encourage related studies. To demonstrate how NK model works, this tutorial reproduces several Levinthal’s (1997) experiments. Besides, this tutorial attempts to make clear the relevance between NK model and agent-based modeling (ABM). The relevance can be a theoretical basis to further develop NK model framework for other research scenarios. For example, this tutorial provides an NK model solution to study IT value cocreation process by extending network structure and agent interactions.

Governance theory as a framework for empirical research : a case study on local environmental policy-making in Helsinki, Finland

Governance has been one of the most popular buzzwords in recent political science. As with any term shared by numerous fields of research, as well as everyday language, governance is encumbered by a jungle of definitions and applications. This work elaborates on the concept of network governance. Network governance refers to complex policy-making situations, where a variety of public and private actors collaborate in order to produce and define policy. Governance is processes of autonomous, self-organizing networks of organizations exchanging information and deliberating. Network governance is a theoretical concept that corresponds to an empirical phenomenon. Often, this phenomenon is used to descirbe a historical development: governance is often used to describe changes in political processes of Western societies since the 1980s. In this work, empirical governance networks are used as an organizing framework, and the concepts of autonomy, self-organization and network structure are developed as tools for empirical analysis of any complex decision-making process. This work develops this framework and explores the governance networks in the case of environmental policy-making in the City of Helsinki, Finland. The crafting of a local ecological sustainability programme required support and knowledge from all sectors of administration, a number of entrepreneurs and companies and the inhabitants of Helsinki. The policy process relied explicitly on networking, with public and private actors collaborating to design policy instruments. Communication between individual organizations led to the development of network structures and patterns. This research analyses these patterns and their effects on policy choice, by applying the methods of social network analysis. A variety of social network analysis methods are used to uncover different features of the networked process. Links between individual network positions, network subgroup structures and macro-level network patterns are compared to the types of organizations involved and final policy instruments chosen. By using governance concepts to depict a policy process, the work aims to assess whether they contribute to models of policy-making. The conclusion is that the governance literature sheds light on events that would otherwise go unnoticed, or whose conceptualization would remain atheoretical. The framework of network governance should be in the toolkit of the policy analyst.

Clustering in Concept Association Networks

We view association of concepts as a complex network and present a heuristic for clustering concepts by taking into account the underlying network structure of their associations. Clusters generated from our approach are qualitatively better than clusters generated from the conventional spectral clustering mechanism used for graph partitioning.

Multiple Perspectives on Networks: Conceptual Development, Application and Integration in an Entrepreneurial Context

Multiple Perspectives on Networks: Conceptual Development, Application and Integration in an Entrepreneurial Context. The purpose of this thesis is to enhance cross-fertilization between three different approaches to network research. The business network approach may contribute in terms of how relationships are created, developed and how tie content changes within ties, not only between them. The social network approach adds to the discussion by offering concepts of structural change on a network level. The network approach in entrepreneurship contributes by emphasizing network content, governance and structure as a way of understanding and capturing networks. This is discussed in the conceptual articles, Articles 2 and 3. The ultimate purpose of this thesis is to develop a theoretical and empirical understanding of network development processes. This is fulfilled by presenting a theoretical framework, which offers multiple views on process as a developmental outcome. The framework implies that change ought to be captured both within and among relationships over time in the firm as well as in the network. Consequently, changes in structure and interaction taking place simultaneously need to be included when doing research on network development. The connection between micro and macro levels is also stressed. Therefore, the entrepreneur or firm level needs to be implemented together with the network level. The surrounding environment impacts firm and network development and vice versa and hence needs to be integrated. Further, it is necessary to view network development not only as a way forward but to include both progression and regression as inevitable parts of the process. Finally, both stability and change should be taken into account as part of network development. Empirical results in Article 1 show support for a positive impact of networks on SME internationalization. Article 4 compares networks of novice, serial and portfolio entrepreneurs but the empirical results show little support for differences in the networks by type of entrepreneur. The results demonstrate that network interaction and structure is not directly impacted by type of entrepreneur involved. It indicates instead that network structure and interaction is more impacted by the development phase of the firm. This in turn is in line with the theoretical implications, stating that the development of the network and the firm impacts each other, as they co-evolve.

The first D-pi-A octupolar cryptand molecule to exhibit bulk non-linearity

The cryptand derivative has H-bond mediated trigonal network structure that leads to octupolar bulk nonlinearity.

Discriminative learning of Bayesian networks via factorized conditional log-likelihood

We propose an efficient and parameter-free scoring criterion, the factorized conditional log-likelihood (ˆfCLL), for learning Bayesian network classifiers. The proposed score is an approximation of the conditional log-likelihood criterion. The approximation is devised in order to guarantee decomposability over the network structure, as well as efficient estimation of the optimal parameters, achieving the same time and space complexity as the traditional log-likelihood scoring criterion. The resulting criterion has an information-theoretic interpretation based on interaction information, which exhibits its discriminative nature. To evaluate the performance of the proposed criterion, we present an empirical comparison with state-of-the-art classifiers. Results on a large suite of benchmark data sets from the UCI repository show that ˆfCLL-trained classifiers achieve at least as good accuracy as the best compared classifiers, using significantly less computational resources.

Synthesis and mesogenic behavior of metal-containing liquid crystalline networks

A new hydroxy functionalized liquid crystalline (LC) polyazomethine has been synthesized by the solution polycondensation of a dialdehyde with a diamine. The polymer was characterized by IR, H-1-, and C-13-NMR spectroscopy. Studies on the liquid crystalline properties reveal the nematic mesomorphic behavior. This polymer functions as a polymeric chelate and forms a three-dimensional network structure through the metal complexation. Influence of various metals and their concentration on the liquid crystalline behavior of the network has been studied. Networks up to 30 mol % of the metal show LC phase transitions; above this the transitions are suppressed and the network behaves like an LC thermoset. (C) 1996 John Wiley & Sons, Inc.

Predictive uncertainty of chaotic daily streamflow using ensemble wavelet networks approach

Perfect or even mediocre weather predictions over a long period are almost impossible because of the ultimate growth of a small initial error into a significant one. Even though the sensitivity of initial conditions limits the predictability in chaotic systems, an ensemble of prediction from different possible initial conditions and also a prediction algorithm capable of resolving the fine structure of the chaotic attractor can reduce the prediction uncertainty to some extent. All of the traditional chaotic prediction methods in hydrology are based on single optimum initial condition local models which can model the sudden divergence of the trajectories with different local functions. Conceptually, global models are ineffective in modeling the highly unstable structure of the chaotic attractor. This paper focuses on an ensemble prediction approach by reconstructing the phase space using different combinations of chaotic parameters, i.e., embedding dimension and delay time to quantify the uncertainty in initial conditions. The ensemble approach is implemented through a local learning wavelet network model with a global feed-forward neural network structure for the phase space prediction of chaotic streamflow series. Quantification of uncertainties in future predictions are done by creating an ensemble of predictions with wavelet network using a range of plausible embedding dimensions and delay times. The ensemble approach is proved to be 50% more efficient than the single prediction for both local approximation and wavelet network approaches. The wavelet network approach has proved to be 30%-50% more superior to the local approximation approach. Compared to the traditional local approximation approach with single initial condition, the total predictive uncertainty in the streamflow is reduced when modeled with ensemble wavelet networks for different lead times. Localization property of wavelets, utilizing different dilation and translation parameters, helps in capturing most of the statistical properties of the observed data. The need for taking into account all plausible initial conditions and also bringing together the characteristics of both local and global approaches to model the unstable yet ordered chaotic attractor of a hydrologic series is clearly demonstrated.

Electrochemical copolymerization of thiophene derivatives; a precursor to photovoltaic devices

This work presents an electrochemical technique for the polymerization and copolymerization of thiophene derivatives like 7,9-dithiophene-2yl-8H-cyclopenta[a]acenaphthalene-8-one and 3-hexylthiophene. The structural characterization of chemically synthesized monomers and electro-chemically synthesized polymers was carried out by nuclear magnetic resonance and Fourier transform infrared spectroscopy. Thermal characterizations indicate that copolymer has increased thermal stability than that of homopolymer. Morphological studies of the polymerized films carried out by scanning electron microscopy shows network structure of copolymer. Optical properties of the homopolymers and copolymer were studied by UV-visible spectrometer and it was observed that band gap of copolymer is less than the homopolymers. HOMO and LUMO levels, band gap values of the respective polymers were also calculated from the cyclic voltammetry technique with various scan rates. By the peak current obtained from various scan rates shows that all polymerization reactions are diffusion controlled process. Charge transfer resistances of polymers were determined using Nyquist plots. Conductivity of synthesized polymers shows higher conductivity for copolymer than homopolymers. (C) 2011 Elsevier Ltd. All rights reserved.

Prediction of Ground Water Levels in the Uplands of a Tropical Coastal Riparian Wetland using Artificial Neural Networks

Artificial Neural Networks (ANNs) have been found to be a robust tool to model many non-linear hydrological processes. The present study aims at evaluating the performance of ANN in simulating and predicting ground water levels in the uplands of a tropical coastal riparian wetland. The study involves comparison of two network architectures, Feed Forward Neural Network (FFNN) and Recurrent Neural Network (RNN) trained under five algorithms namely Levenberg Marquardt algorithm, Resilient Back propagation algorithm, BFGS Quasi Newton algorithm, Scaled Conjugate Gradient algorithm, and Fletcher Reeves Conjugate Gradient algorithm by simulating the water levels in a well in the study area. The study is analyzed in two cases-one with four inputs to the networks and two with eight inputs to the networks. The two networks-five algorithms in both the cases are compared to determine the best performing combination that could simulate and predict the process satisfactorily. Ad Hoc (Trial and Error) method is followed in optimizing network structure in all cases. On the whole, it is noticed from the results that the Artificial Neural Networks have simulated and predicted the water levels in the well with fair accuracy. This is evident from low values of Normalized Root Mean Square Error and Relative Root Mean Square Error and high values of Nash-Sutcliffe Efficiency Index and Correlation Coefficient (which are taken as the performance measures to calibrate the networks) calculated after the analysis. On comparison of ground water levels predicted with those at the observation well, FFNN trained with Fletcher Reeves Conjugate Gradient algorithm taken four inputs has outperformed all other combinations.

Stochastic analysis of epidemics on adaptive time varying networks

Many studies investigating the effect of human social connectivity structures (networks) and human behavioral adaptations on the spread of infectious diseases have assumed either a static connectivity structure or a network which adapts itself in response to the epidemic (adaptive networks). However, human social connections are inherently dynamic or time varying. Furthermore, the spread of many infectious diseases occur on a time scale comparable to the time scale of the evolving network structure. Here we aim to quantify the effect of human behavioral adaptations on the spread of asymptomatic infectious diseases on time varying networks. We perform a full stochastic analysis using a continuous time Markov chain approach for calculating the outbreak probability, mean epidemic duration, epidemic reemergence probability, etc. Additionally, we use mean-field theory for calculating epidemic thresholds. Theoretical predictions are verified using extensive simulations. Our studies have uncovered the existence of an ``adaptive threshold,'' i.e., when the ratio of susceptibility (or infectivity) rate to recovery rate is below the threshold value, adaptive behavior can prevent the epidemic. However, if it is above the threshold, no amount of behavioral adaptations can prevent the epidemic. Our analyses suggest that the interaction patterns of the infected population play a major role in sustaining the epidemic. Our results have implications on epidemic containment policies, as awareness campaigns and human behavioral responses can be effective only if the interaction levels of the infected populace are kept in check.

Solvation dynamics of tryptophan in water-dimethyl sulfoxide binary mixture: In search of molecular origin of composition dependent multiple anomalies

Experimental and simulation studies have uncovered at least two anomalous concentration regimes in water-dimethyl sulfoxide (DMSO) binary mixture whose precise origin has remained a subject of debate. In order to facilitate time domain experimental investigation of the dynamics of such binary mixtures, we explore strength or extent of influence of these anomalies in dipolar solvation dynamics by carrying out long molecular dynamics simulations over a wide range of DMSO concentration. The solvation time correlation function so calculated indeed displays strong composition dependent anomalies, reflected in pronounced non-exponential kinetics and non-monotonous composition dependence of the average solvation time constant. In particular, we find remarkable slow-down in the solvation dynamics around 10%-20% and 35%-50% mole percentage. We investigate microscopic origin of these two anomalies. The population distribution analyses of different structural morphology elucidate that these two slowing down are reflections of intriguing structural transformations in water-DMSO mixture. The structural transformations themselves can be explained in terms of a change in the relative coordination number of DMSO and water molecules, from 1DMSO:2H(2)O to 1H(2)O:1DMSO and 1H(2)O:2DMSO complex formation. Thus, while the emergence of first slow down (at 15% DMSO mole percentage) is due to the percolation among DMSO molecules supported by the water molecules (whose percolating network remains largely unaffected), the 2nd anomaly (centered on 40%-50%) is due to the formation of the network structure where the unit of 1DMSO:1H(2)O and 2DMSO:1H(2)O dominates to give rise to rich dynamical features. Through an analysis of partial solvation dynamics an interesting negative cross-correlation between water and DMSO is observed that makes an important contribution to relaxation at intermediate to longer times.

Scalable Preference Aggregation in Social Networks

In social choice theory, preference aggregation refers to computing an aggregate preference over a set of alternatives given individual preferences of all the agents. In real-world scenarios, it may not be feasible to gather preferences from all the agents. Moreover, determining the aggregate preference is computationally intensive. In this paper, we show that the aggregate preference of the agents in a social network can be computed efficiently and with sufficient accuracy using preferences elicited from a small subset of critical nodes in the network. Our methodology uses a model developed based on real-world data obtained using a survey on human subjects, and exploits network structure and homophily of relationships. Our approach guarantees good performance for aggregation rules that satisfy a property which we call expected weak insensitivity. We demonstrate empirically that many practically relevant aggregation rules satisfy this property. We also show that two natural objective functions in this context satisfy certain properties, which makes our methodology attractive for scalable preference aggregation over large scale social networks. We conclude that our approach is superior to random polling while aggregating preferences related to individualistic metrics, whereas random polling is acceptable in the case of social metrics.