253 research outputs found

    Business Functions Capabilities and Small and Medium Enterprises’ Internationalization

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    Ineffective global expansion can adversely affect small and medium enterprises (SMEs) business outcomes. Business leaders are concerned with developing effective global expansion strategies to penetrate potential international markets, thus enhancing sustainability. Grounded in the business management systems theory, the purpose of this qualitative multi-case study was to explore strategies that leaders of Sub-Saharan Africa manufacturing SMEs use for global expansion. The participants were five manufacturing value-adding SME leaders participating in export markets. Using Yin’s five steps data analysis process, six themes emerged: (a) enterprise characterization, (b) understanding the enterprise’s product, (c) intra-enterprise factor-based strategies for export participation, (d) the enterprise’s external factor-based strategies for successful export venture, (e) global expansion strategies, and (f) serendipitous findings. A key recommendation for SME leaders is to analyze the critical components of their products and prepare to adjust them to the demand dimensions of the target market. The implications for positive social change include the potential to increase the enterprise’s wealth, increase employment, reduce poverty for all value chain participants, and growth in gross domestic product

    Erosion Of Credibility: A Mixed Methods Evaluation of Twitter News Headlines from The New York Times, Washington Post, Wall Street Journal, Los Angeles Times, And USA Today

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    To entice and commodify social media news consumers, contemporary news organizations have increasingly relied on data analytics to boost audience engagement. Clicks, likes, and shares are the metrics that now guide the editorial process and shape decisions about content and coverage. As such, news headlines are regularly manipulated to attract the attention of those who quickly scroll through social media networks on computers and smartphones. However, few studies have examined the typologies of news content most likely to be manipulated in social media news headlines or the impact of news headline manipulation on news source credibility. For this research, source credibility theory has been updated for a practical application of today’s social media news landscape and used as a lens to examine the phenomenon, its impact on audience engagement, and association with traditional standards of journalism and credibility. A mixed methods content analysis was conducted of news headlines published on Twitter compared to headlines and content published on the websites of five traditional newspapers: the New York Times, Washington Post, Wall Street Journal, Los Angeles Times, and USA Today. The results indicated that the typologies of news most likely to be manipulated for Twitter publication (opinion, politics, health/medical), were also the least credible. Conversely, typologies of news that were least likely to be manipulated for Twitter publication (international, consumer, disaster), were rated the most credible

    A Qualitative Analysis of Corporate Responsibility for the Education of U.S. Citizens

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    Educated and trained workers represent the primary critical success factor needed for all organizations to achieve their mission. Organizations depend on a constant flow of educated applicants competing for their jobs at any given time. Traditionally, public, private, and charter schools prepared U.S. citizens for college, trade schools, military, or university, enabling them to then compete successfully for jobs of the era. Today, a myriad of problems face these schools, including disruptive change, uninvolved parents, lack for funding, teacher unions, politics, school overcrowding, COVID-19, outdated training methods, security, race issues, and more. The result is that this education model is in decline and the flow of skilled workers into companies is affecting the United States, which risks losing its ability to compete locally and globally. Consensus that transcends party politics, religious infighting, and greedy decision-making must be reached in time to analyze this big-picture problem. The United States has reached a strategic inflection point and must respond to this disruptive change by developing creative, innovative, and state-of-the-art solutions to this problem, or she may not fulfill God’s will for this country. Companies strive to reach critical mass where they are self-sustaining, but this cannot be done without a change in how people are educated in the United States, which may require business and education to collaborate to reach the same goals, combining education and opportunity. This qualitative case study examined the problem that organizational leaders in the United States face, and specifically the challenges they encounter when strategically planning initiatives that will ensure a pool of educated, skilled, and talented workers available to their organizations now and in the future. Semi-structured interviews with the working population in Eastern Tennessee provided insights to this problem facing organizations across the spectrum

    Metaverse. Old urban issues in new virtual cities

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    Recent years have seen the arise of some early attempts to build virtual cities, utopias or affective dystopias in an embodied Internet, which in some respects appear to be the ultimate expression of the neoliberal city paradigma (even if virtual). Although there is an extensive disciplinary literature on the relationship between planning and virtual or augmented reality linked mainly to the gaming industry, this often avoids design and value issues. The observation of some of these early experiences - Decentraland, Minecraft, Liberland Metaverse, to name a few - poses important questions and problems that are gradually becoming inescapable for designers and urban planners, and allows us to make some partial considerations on the risks and potentialities of these early virtual cities

    Variability-aware Neo4j for Analyzing a Graphical Model of a Software Product Line

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    A Software product line (SPLs) eases the development of families of related products by managing and integrating a collection of mandatory and optional features (units of functionality). Individual products can be derived from the product line by selecting among the optional features. Companies that successfully employ SPLs report dramatic improvements in rapid product development, software quality, labour needs, support for mass customization, and time to market. In a product line of reasonable size, it is impractical to verify every product because the number of possible feature combinations is exponential in the number of features. As a result, developers might verify a small fraction of products and limit the choices offered to consumers, thereby foregoing one of the greatest promises of product lines — mass customization. To improve the efficiency of analyzing SPLs, (1) we analyze a model of an SPL rather than its code and (2) we analyze the SPL model itself rather than models of its products. We extract a model comprising facts (e.g., functions, variables, assignments) from an SPL’s source-code artifacts. The facts from different software components are linked together into a lightweight model of the code, called a factbase. The resulting factbase is a typed graphical model that can be analyzed using the Neo4j graph database. In this thesis, we lift the Neo4j query engine to reason over a factbase of an entire SPL. By lifting the Neo4j query engine, we enable any analysis that can be expressed in the query language to be applicable to an SPL model. The lifted analyses return variability-aware results, in which each result is annotated with a feature expression denoting the products to which the result applies. We evaluated lifted Neo4j on five real-world open-source SPLs, with respect to ten commonly used analyses of interest. The first evaluation aims at comparing the performance of a post-processing approach versus an on-the-fly approach computing the feature expressions that annotate to variability-aware results of lifted Neo4j. In general, the on-the-fly approach has a smaller runtime compared to the post-processing approach. The second evaluation aims at assessing the overhead of analyzing a model of an SPL versus a model of a single product, which ranges from 1.88% to 456%. In the third evaluation, we compare the outputs and performance of lifted Neo4j to a related work that employs the variability-aware V-Soufflé Datalog engine. We found that lifted Neo4j is usually more efficient than V-Soufflé when returning the same results (i.e., the end points of path results). When lifted Neo4j returns complete path results, it is generally slower than V-Soufflé, although lifted Neo4j can outperform V-Soufflé on analyses that return short fixed-length paths

    Assessment of Founders in Venture Capital Investment Decisions

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    This manuscript documents a research project that employs grounded theory to determine what criteria contemporary investors in early-stage startups use to assess founders. One of the first questions posed by entrepreneurship researchers - even before entrepreneurship had formalized as a field - was, what criteria do investors consider when making investments in startups? Initially, the central concern was whether it was the founder(s) or the business model, often characterized as the “jockey” or the “horse.” From the start, it was generally accepted that the founder was the primary consideration, especially in early-stage ventures. Nonetheless, while business model considerations were parsed into separate factors (e.g., market, financial, product), understanding of founder characteristics evolved rather slowly and centered on the macroeconomic construct of human capital - an aggregate measure of investment in human factors that is ill-suited to measuring micro-level characteristics of individuals. Of course, years of education and experience are reasonable criteria and in many cases are a sound foundation for assessing entrepreneurs. Recently, researchers have been increasingly examining the role of individual-level traits and behaviors across a range of entrepreneurship questions. For example, characteristics such as passion and persistence have been examined for their roles in issues like entrepreneurial intention, performance, and decision-making. Some researchers have examined how such characteristics affect investment decisions, especially among angel investors who essentially have no reliable business model information to include in their decision-making process. Also relatively recently, other organizational researchers, especially those outside of entrepreneurship, have begun serious inquiries into the constituent elements of leadership and the role of purpose, character and emotion in organizations. To assess whether these factors have infiltrated the investment selection criteria of professional early-stage venture investors, the project described herein assesses whether these topics are now openly considered during the investment selection process. Over a dozen investors were interviewed to solicit the criteria they use when evaluating the founders of early-stage ventures prior to investment. All study participants stated that they assessed the startup founder, and most underscored the importance of assessing the startup team. Participants suggested several criteria are important when assessing a startup founder, which included hard skills, soft skills, personality, character, and mindset or mental attitude. Hard skills essentially correspond to human capital and other legacy criteria that have long been understood to be part of founder assessment. The other criteria are clusters of traits and abilities that are related to the once-frowned-upon factors. This research contributes to the discussion of founder assessment by linking it explicitly to established theories from the broader management, leadership, and social science arenas

    24th Nordic Conference on Computational Linguistics (NoDaLiDa)

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    Full Issue: Volume 56, No. 1

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    Understanding virus and microbial evolution in wildlife through meta-transcriptomics

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    Wildlife harbors a substantial and largely undocumented diversity of RNA viruses and microbial life forms. RNA viruses and microbes are also arguably the most diverse and dynamic entities on Earth. Despite their evident importance, there are major limitations in our knowledge of the diversity, ecology, and evolution of RNA viruses and microbial communities. These gaps stem from a variety of factors, including biased sampling and the difficulty in accurately identifying highly divergent sequences through sequence similarity-based analyses alone. The implementation of meta-transcriptomic sequencing has greatly contributed to narrowing this gap. In particular, the rapid increase in the number of newly described RNA viruses over the last decade provides a glimpse of the remarkable diversity within the RNA virosphere. The central goal in this thesis was to determine the diversity of RNA viruses associated with wildlife, particularly in an Australian context. To this end I exploited cutting-edge meta-transcriptomic and bioinformatic approaches to reveal the RNA virus diversity within diverse animal taxa, tissues, and environments, with a special focus on the highly divergent "dark matter" of the virome that has largely been refractory to sequence analysis. Similarly, I used these approaches to detect targeted common microbes circulating in vertebrate and invertebrate fauna. Another important goal was to assess the diversity of RNA viruses and microbes as a cornerstone within a new eco-evolutionary framework. By doing so, this thesis encompasses multiple disciplines including virus discovery, viral host-range distributions, microbial-virus and host–parasite interactions, phylogenetic analysis, and pathogen surveillance. In sum, the research presented in this thesis expands the known RNA virosphere as well as the detection and surveillance of targeted microbes in wildlife, providing new insights into the diversity, evolution, and ecology of these agents in nature

    Differential evolution of non-coding DNA across eukaryotes and its close relationship with complex multicellularity on Earth

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    Here, I elaborate on the hypothesis that complex multicellularity (CM, sensu Knoll) is a major evolutionary transition (sensu Szathmary), which has convergently evolved a few times in Eukarya only: within red and brown algae, plants, animals, and fungi. Paradoxically, CM seems to correlate with the expansion of non-coding DNA (ncDNA) in the genome rather than with genome size or the total number of genes. Thus, I investigated the correlation between genome and organismal complexities across 461 eukaryotes under a phylogenetically controlled framework. To that end, I introduce the first formal definitions and criteria to distinguish ‘unicellularity’, ‘simple’ (SM) and ‘complex’ multicellularity. Rather than using the limited available estimations of unique cell types, the 461 species were classified according to our criteria by reviewing their life cycle and body plan development from literature. Then, I investigated the evolutionary association between genome size and 35 genome-wide features (introns and exons from protein-coding genes, repeats and intergenic regions) describing the coding and ncDNA complexities of the 461 genomes. To that end, I developed ‘GenomeContent’, a program that systematically retrieves massive multidimensional datasets from gene annotations and calculates over 100 genome-wide statistics. R-scripts coupled to parallel computing were created to calculate >260,000 phylogenetic controlled pairwise correlations. As previously reported, both repetitive and non-repetitive DNA are found to be scaling strongly and positively with genome size across most eukaryotic lineages. Contrasting previous studies, I demonstrate that changes in the length and repeat composition of introns are only weakly or moderately associated with changes in genome size at the global phylogenetic scale, while changes in intron abundance (within and across genes) are either not or only very weakly associated with changes in genome size. Our evolutionary correlations are robust to: different phylogenetic regression methods, uncertainties in the tree of eukaryotes, variations in genome size estimates, and randomly reduced datasets. Then, I investigated the correlation between the 35 genome-wide features and the cellular complexity of the 461 eukaryotes with phylogenetic Principal Component Analyses. Our results endorse a genetic distinction between SM and CM in Archaeplastida and Metazoa, but not so clearly in Fungi. Remarkably, complex multicellular organisms and their closest ancestral relatives are characterized by high intron-richness, regardless of genome size. Finally, I argue why and how a vast expansion of non-coding RNA (ncRNA) regulators rather than of novel protein regulators can promote the emergence of CM in Eukarya. As a proof of concept, I co-developed a novel ‘ceRNA-motif pipeline’ for the prediction of “competing endogenous” ncRNAs (ceRNAs) that regulate microRNAs in plants. We identified three candidate ceRNAs motifs: MIM166, MIM171 and MIM159/319, which were found to be conserved across land plants and be potentially involved in diverse developmental processes and stress responses. Collectively, the findings of this dissertation support our hypothesis that CM on Earth is a major evolutionary transition promoted by the expansion of two major ncDNA classes, introns and regulatory ncRNAs, which might have boosted the irreversible commitment of cell types in certain lineages by canalizing the timing and kinetics of the eukaryotic transcriptome.:Cover page Abstract Acknowledgements Index 1. The structure of this thesis 1.1. Structure of this PhD dissertation 1.2. Publications of this PhD dissertation 1.3. Computational infrastructure and resources 1.4. Disclosure of financial support and information use 1.5. Acknowledgements 1.6. Author contributions and use of impersonal and personal pronouns 2. Biological background 2.1. The complexity of the eukaryotic genome 2.2. The problem of counting and defining “genes” in eukaryotes 2.3. The “function” concept for genes and “dark matter” 2.4. Increases of organismal complexity on Earth through multicellularity 2.5. Multicellularity is a “fitness transition” in individuality 2.6. The complexity of cell differentiation in multicellularity 3. Technical background 3.1. The Phylogenetic Comparative Method (PCM) 3.2. RNA secondary structure prediction 3.3. Some standards for genome and gene annotation 4. What is in a eukaryotic genome? GenomeContent provides a good answer 4.1. Background 4.2. Motivation: an interoperable tool for data retrieval of gene annotations 4.3. Methods 4.4. Results 4.5. Discussion 5. The evolutionary correlation between genome size and ncDNA 5.1. Background 5.2. Motivation: estimating the relationship between genome size and ncDNA 5.3. Methods 5.4. Results 5.5. Discussion 6. The relationship between non-coding DNA and Complex Multicellularity 6.1. Background 6.2. Motivation: How to define and measure complex multicellularity across eukaryotes? 6.3. Methods 6.4. Results 6.5. Discussion 7. The ceRNA motif pipeline: regulation of microRNAs by target mimics 7.1. Background 7.2. A revisited protocol for the computational analysis of Target Mimics 7.3. Motivation: a novel pipeline for ceRNA motif discovery 7.4. Methods 7.5. Results 7.6. Discussion 8. Conclusions and outlook 8.1. Contributions and lessons for the bioinformatics of large-scale comparative analyses 8.2. Intron features are evolutionarily decoupled among themselves and from genome size throughout Eukarya 8.3. “Complex multicellularity” is a major evolutionary transition 8.4. Role of RNA throughout the evolution of life and complex multicellularity on Earth 9. Supplementary Data Bibliography Curriculum Scientiae Selbständigkeitserklärung (declaration of authorship
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