An Investigation of the Interaction between Organizational Culture and Knowledge Sharing through Socialization: A Multi-Level Perspective

Knowledge management (KM) has been determined by many researchers as one of the most important domains within the information systems (IS) field, and knowledge sharing (KS) has been identified as the most vital component of KM. Lack of KS within organizations has been approached from many perspectives. One perspective that has been outlined in recent studies is the organizational culture (OC) perspective, which examines the interaction between OC and KS behaviors. Although research has been conducted on OC and KS, the findings of recent studies have been contradictory. These conflicts were due to the different operationalization of KS. The purpose of this research was to conduct a multi method study to investigate the interaction between KS and OC in detail. A case study within a Fortune 50 organization was undertaken to address the problem. By focusing on socialization adopted from the socialization, externalization, combination, internalization (SECI) model, the iceberg theory, and the Competing Values Framework (CVF), two questions were explored to address an unexamined area within the body of knowledge. Per the recent calls for research, the questions addressed KS itemized into knowledge seeking and knowledge contributing, and investigated the phenomenon at multiple levels of the organization. The first question examined the interaction between OC and KS via socialization amongst peers for: (a) overall organization, (b) non-managers, (c) first level managers, and (d) second-level managers. The second question examined the interaction between OC and KS via socialization amongst various levels for: (a) subordinates and managers in overall organization, (b) non-managers and first level managers, and (c) first level managers and second level managers. Data were collected through 82 surveys, 23 interviews, 23 observations, and company records for the calendar year of 2017 to provide multiple types of data for triangulation. The quantitative data were analyzed through descriptive statistics, correlation tables, multivariate analysis of covariance (MANCOVA), and visualization. The qualitative data were analyzed through open coding, axial coding, and selective coding. The combined results were triangulated to reach the conclusions. The MANCOVA displayed a significant interaction between OC and KS via socialization. Furthermore, the triangulated results showcased that perceived bureaucratic culture and perceived competitive- bureaucratic culture had a negative relationship with KS via socialization amongst peers, knowledge seeking for manager to subordinate, and subordinate to manager, but not for between level knowledge contributing. While perceived clan culture had a positive relationship with KS via socialization amongst peers, and for knowledge seeking from managers, but not for between level knowledge contributing. Perceived competitive culture was only discovered to have a negative relationship with knowledge seeking for level two managers, while having a positive relationship with knowledge contributing to employees, and knowledge contributing amongst peers with knowledge seeking as moderating variable. The various organizational levels also showcased distinct results which requires further investigation. Future research suggestions were made to extend the body of knowledge through various directions, alongside an IS solution recommendation for organizations to improve KS

Coastal risk adaptation: the potential role of accessible geospatial Big Data

Increasing numbers of people are living in and using coastal areas. Combined with the presence of pervasive coastal threats, such as flooding and erosion, this is having widespread impacts on coastal populations, infrastructure and ecosystems. For the right adaptive strategies to be adopted, and planning decisions to be made, rigorous evaluation of the available options is required. This evaluation hinges on the availability and use of suitable datasets. For knowledge to be derived from coastal datasets, such data needs to be combined and analysed in an effective manner. This paper reviews a wide range of literature relating to data-driven approaches to coastal risk evaluation, revealing how limitations have been imposed on many of these methods, due to restrictions in computing power and access to data. The rapidly emerging field of ‘Big Data’ can help overcome many of these hurdles. ‘Big Data’ involves powerful computer infrastructures, enabling storage, processing and real-time analysis of large volumes and varieties of data, in a fast and reliable manner. Through consideration of examples of how ‘Big Data’ technologies are being applied to fields related to coastal risk, it becomes apparent that geospatial Big Data solutions hold clear potential to improve the process of risk based decision making on the coast. ‘Big Data’ does not provide a stand-alone solution to the issues and gaps outlined in this paper, yet these technological methods hold the potential to optimise data-driven approaches, enabling robust risk profiles to be generated for coastal regions

Uncertainty in Neural Networks; Bayesian Ensembles, Priors & Prediction Intervals

The breakout success of deep neural networks (NNs) in the 2010's marked a new era in the quest to build artificial intelligence (AI). With NNs as the building block of these systems, excellent performance has been achieved on narrow, well-defined tasks where large amounts of data are available. However, these systems lack certain capabilities that are important for broad use in real-world applications. One such capability is the communication of uncertainty in a NN's predictions and decisions. In applications such as healthcare recommendation or heavy machinery prognostics, it is vital that AI systems be aware of and express their uncertainty – this creates safer, more cautious, and ultimately more useful systems. This thesis explores how to engineer NNs to communicate robust uncertainty estimates on their predictions, whilst minimising the impact on usability. One way to encourage uncertainty estimates to be robust is to adopt the Bayesian framework, which offers a principled approach to handling uncertainty. Two of the major contributions in this thesis relate to Bayesian NNs (BNNs). Specifying appropriate priors is an important step in any Bayesian model, yet it is not clear how to do this in BNNs. The first contribution shows that the connection between BNNs and Gaussian Processes (GPs) provides an effective lens to study BNN priors. NN architectures are derived which mirror the combining of GP kernels to create priors tailored to a task. The second major contribution is a novel way to perform approximate Bayesian inference in BNNs using a modified version of ensembling. Novel analysis improves an understanding of a technique known as randomised MAP sampling. It's shown this is particularly effective when strong correlations exist between parameters, making it well suited to NNs. The third major contribution of the thesis is a non-Bayesian technique that trains a NN to directly output prediction intervals for regression tasks through a tailored objective function. This advances over related works that were incompatible with gradient descent, and ignored one source of uncertainty.EPSRC, Alan Turing Institut

Burden of rare variants in ALS genes influences survival in familial and sporadic ALS

published_or_final_versio

Utilizing static and dynamic software analysis to aid cost estimation, software visualization, and test quality management

The main results presented in the thesis are related to the semi- or fully-automated analysis of the software and its development processes. My overall research goal is to provide meaningful insights, methods, and practical tools to help the work of stakeholders during various phases of software development. The thesis statements have been grouped into three major thesis points, namely "Measuring, predicting, and comparing the productivity of developer teams"; "Providing immersive methods for software and unit test visualization"; and "Spotting the structures in the package hierarchy that required attention using test coverage data"

Exploration of Chemical Space: Formal, chemical and historical aspects

Starting from the observation that substances and reactions are the central entities of chemistry, I have structured chemical knowledge into a formal space called a directed hypergraph, which arises when substances are connected by their reactions. I call this hypernet chemical space. In this thesis, I explore different levels of description of this space: its evolution over time, its curvature, and categorical models of its compositionality. The vast majority of the chemical literature focuses on investigations of particular aspects of some substances or reactions, which have been systematically recorded in comprehensive databases such as Reaxys for the last 200 years. While complexity science has made important advances in physics, biology, economics, and many other fields, it has somewhat neglected chemistry. In this work, I propose to take a global view of chemistry and to combine complexity science tools, modern data analysis techniques, and geometric and compositional theories to explore chemical space. This provides a novel view of chemistry, its history, and its current status. We argue that a large directed hypergraph, that is, a model of directed relations between sets, underlies chemical space and that a systematic study of this structure is a major challenge for chemistry. Using the Reaxys database as a proxy for chemical space, we search for large-scale changes in a directed hypergraph model of chemical knowledge and present a data-driven approach to navigate through its history and evolution. These investigations focus on the mechanistic features by which this space has been expanding: the role of synthesis and extraction in the production of new substances, patterns in the selection of starting materials, and the frequency with which reactions reach new regions of chemical space. Large-scale patterns that emerged in the last two centuries of chemical history are detected, in particular, in the growth of chemical knowledge, the use of reagents, and the synthesis of products, which reveal both conservatism and sharp transitions in the exploration of the space. Furthermore, since chemical similarity of substances arises from affinity patterns in chemical reactions, we quantify the impact of changes in the diversity of the space on the formulation of the system of chemical elements. In addition, we develop formal tools to probe the local geometry of the resulting directed hypergraph and introduce the Forman-Ricci curvature for directed and undirected hypergraphs. This notion of curvature is characterized by applying it to social and chemical networks with higher order interactions, and then used for the investigation of the structure and dynamics of chemical space. The network model of chemistry is strongly motivated by the observation that the compositional nature of chemical reactions must be captured in order to build a model of chemical reasoning. A step forward towards categorical chemistry, that is, a formalization of all the flavors of compositionality in chemistry, is taken by the construction of a categorical model of directed hypergraphs. We lifted the structure from a lineale (a poset version of a symmetric monoidal closed category) to a category of Petri nets, whose wiring is a bipartite directed graph equivalent to a directed hypergraph. The resulting construction, based on the Dialectica categories introduced by Valeria De Paiva, is a symmetric monoidal closed category with finite products and coproducts, which provides a formal way of composing smaller networks into larger in such a way that the algebraic properties of the components are preserved in the resulting network. Several sets of labels, often used in empirical data modeling, can be given the structure of a lineale, including: stoichiometric coefficients in chemical reaction networks, reaction rates, inhibitor arcs, Boolean interactions, unknown or incomplete data, and probabilities. Therefore, a wide range of empirical data types for chemical substances and reactions can be included in our model

Measuring the impact of knowledge sharing on quality improvement initiatives

The contributions of knowledge-based processes in quality initiatives, though unfamiliar, may have been realized by the improvement in the efficiency and effectiveness of knowledge workers (Malhotra 2000). Effective knowledge management (KM) can provide an organization with a competitive edge through effective management of product and process quality. It is possible that quality initiatives can be directed using knowledge sharing practices by focusing on common themes throughout the organization. Hence, it stands to reason that knowledge sharing can be facilitated by understanding those factors that are critical to the success of quality improvement initiatives and, conversely, that the impact of quality initiatives can be enhanced through effective knowledge sharing. The purpose of this study is to identify the critical organizational factors that contribute to the impact of knowledge sharing on quality improvement initiatives. It will focus on quality and on production managers\u27 perception of the way knowledge is being shared to improve results. It will examine the key independent variables that influence public and private organizations\u27 and overall manufacturing industries\u27 performances. These variables include customer focus, involvement of leaders and employees, and horizontal and vertical communication. A 30-item survey instrument provided the data on which the statistical analysis was based, validating the conclusions presented in this thesis

Land use change rather than surrounding vegetation affects fungal endophyte assemblages in the African wild olive

DATA AVAILABILITY : Data collected for this manuscript is not publicly available but may be made available upon reasonable request.CONTEXT : Land use change can significantly affect plant-fungal interactions. OBJECTIVES : We assessed how fungal endophytes within African wild olive (Olea europaea subsp. cuspidata) twigs are influenced by different levels of land use change and differences in surrounding vegetation types. METHODS : Twigs were sampled in the Western Cape Province (South Africa) and their fungal endophyte assemblages were characterised using culture-independent DNA metabarcoding. We assessed the effects of land use change (natural, semi-natural and planted (completely transformed)) and differences in surrounding vegetation types (grasses/low-growing plants versus shrubs/trees versus other olives) using fungal endophyte alpha and beta diversity measures. Co-occurrence networks were constructed to assess assemblage connectivity under different scenarios and to identify OTUs of potential ecological significance. RESULTS : OTU richness, but not abundance, was significantly influenced by both land use change and differences in the surrounding vegetation types. Planted African olives and those surrounded by heterospecific trees harboured the highest OTU richness. Only levels of land use change significantly influenced fungal endophyte assemblage composition. Specifically, fungal assemblages from natural habitats were distinct from those in planted and semi-natural habitats, which were similar to each other. Co-occurrence network analyses revealed that cohesive and species rich networks could only be maintained within the natural habitats. CONCLUSION : These findings suggest that although the African olive is widespread, the identity and composition of their associated fungal assemblages are particularly sensitive to land use change. This study highlights the importance of conserving natural habitats, not just for the plants, but also for the maintenance of their associated fungal endophytes.Open access funding provided by Stellenbosch University. This work was supported by the Department of Science and Technology/National Research Foundation Centre of Excellence in Tree Health Biotechnology.http://link.springer.com/journal/10980hj2024BiochemistryGeneticsMicrobiology and Plant PathologySDG-15:Life on lan

An integration framework for managing rich organisational process knowledge

The problem we have addressed in this dissertation is that of designing a pragmatic framework for integrating the synthesis and management of organisational process knowledge which is based on domain-independent AI planning and plan representations. Our solution has focused on a set of framework components which provide methods, tools and representations to accomplish this task.In the framework we address a lifecycle of this knowledge which begins with a methodological approach to acquiring information about the process domain. We show that this initial domain specification can be translated into a common constraint-based model of activity (based on the work of Tate, 1996c and 1996d) which can then be operationalised for use in an AI planner. This model of activity is ontologically underpinned and may be expressed with a flexible and extensible language based on a sorted first-order logic. The model combines perspectives covering both the space of behaviour as well as the space of decisions. Synthesised or modified processes/plans can be translated to and from the common representation in order to support knowledge sharing, visualisation and mixed-initiative interaction.This work united past and present Edinburgh research on planning and infused it with perspectives from design rationale, requirements engineering, and process knowledge sharing. The implementation has been applied to a portfolio of scenarios which include process examples from business, manufacturing, construction and military operations. An archive of this work is available at: http://www.aiai.ed.ac.uk/~oplan/cpf