10,266 research outputs found

    On the Mechanism of Building Core Competencies: a Study of Chinese Multinational Port Enterprises

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    This study aims to explore how Chinese multinational port enterprises (MNPEs) build their core competencies. Core competencies are firms’special capabilities and sources to gain sustainable competitive advantage (SCA) in marketplace, and the concept led to extensive research and debates. However, few studies include inquiries about the mechanisms of building core competencies in the context of Chinese MNPEs. Accordingly, answers were sought to three research questions: 1. What are the core competencies of the Chinese MNPEs? 2. What are the mechanisms that the Chinese MNPEs use to build their core competencies? 3. What are the paths that the Chinese MNPEs pursue to build their resources bases? The study adopted a multiple-case study design, focusing on building mechanism of core competencies with RBV. It selected purposively five Chinese leading MNPEs and three industry associations as Case Companies. The study revealed three main findings. First, it identified three generic core competencies possessed by Case Companies, i.e., innovation in business models and operations, utilisation of technologies, and acquisition of strategic resources. Second, it developed the conceptual framework of the Mechanism of Building Core Competencies (MBCC), which is a process of change of collective learning in effective and efficient utilization of resources of a firm in response to critical events. Third, it proposed three paths to build core competencies, i.e., enhancing collective learning, selecting sustainable processes, and building resource base. The study contributes to the knowledge of core competencies and RBV in three ways: (1) presenting three generic core competencies of the Chinese MNPEs, (2) proposing a new conceptual framework to explain how Chinese MNPEs build their core competencies, (3) suggesting a solid anchor point (MBCC) to explain the links among resources, core competencies, and SCA. The findings set benchmarks for Chinese logistics industry and provide guidelines to build core competencies

    Building body identities - exploring the world of female bodybuilders

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    This thesis explores how female bodybuilders seek to develop and maintain a viable sense of self despite being stigmatized by the gendered foundations of what Erving Goffman (1983) refers to as the 'interaction order'; the unavoidable presentational context in which identities are forged during the course of social life. Placed in the context of an overview of the historical treatment of women's bodies, and a concern with the development of bodybuilding as a specific form of body modification, the research draws upon a unique two year ethnographic study based in the South of England, complemented by interviews with twenty-six female bodybuilders, all of whom live in the U.K. By mapping these extraordinary women's lives, the research illuminates the pivotal spaces and essential lived experiences that make up the female bodybuilder. Whilst the women appear to be embarking on an 'empowering' radical body project for themselves, the consequences of their activity remains culturally ambivalent. This research exposes the 'Janus-faced' nature of female bodybuilding, exploring the ways in which the women negotiate, accommodate and resist pressures to engage in more orthodox and feminine activities and appearances

    Coloniality and the Courtroom: Understanding Pre-trial Judicial Decision Making in Brazil

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    This thesis focuses on judicial decision making during custody hearings in Rio de Janeiro, Brazil. The impetus for the study is that while national and international protocols mandate the use of pre-trial detention only as a last resort, judges continue to detain people pre-trial in large numbers. Custody hearings were introduced in 2015, but the initiative has not produced the reduction in pre-trial detention that was hoped. This study aims to understand what informs judicial decision making at this stage. The research is approached through a decolonial lens to foreground legacies of colonialism, overlooked in mainstream criminological scholarship. This is an interview-based study, where key court actors (judges, prosecutors, and public defenders) and subject matter specialists were asked about influences on judicial decision making. Interview data is complemented by non-participatory observation of custody hearings. The research responds directly to Aliverti et al.'s (2021) call to ‘decolonize the criminal question’ by exposing and explaining how colonialism informs criminal justice practices. Answering the call in relation to judicial decision making, findings provide evidence that colonial-era assumptions, dynamics, and hierarchies were evident in the practice of custody hearings and continue to inform judges’ decisions, thus demonstrating the coloniality of justice. This study is significant for the new empirical data presented and theoretical innovation is also offered via the introduction of the ‘anticitizen’. The concept builds on Souza’s (2007) ‘subcitizen’ to account for the active pursuit of dangerous Others by judges casting themselves as crime fighters in a modern moral crusade. The findings point to the limited utility of human rights discourse – the normative approach to influencing judicial decision making around pre-trial detention – as a plurality of conceptualisations compete for dominance. This study has important implications for all actors aiming to reduce pre-trial detention in Brazil because unless underpinning colonial logics are addressed, every innovation risks becoming the next lei para inglĂȘs ver (law [just] for the English to see)

    Data-to-text generation with neural planning

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    In this thesis, we consider the task of data-to-text generation, which takes non-linguistic structures as input and produces textual output. The inputs can take the form of database tables, spreadsheets, charts, and so on. The main application of data-to-text generation is to present information in a textual format which makes it accessible to a layperson who may otherwise find it problematic to understand numerical figures. The task can also automate routine document generation jobs, thus improving human efficiency. We focus on generating long-form text, i.e., documents with multiple paragraphs. Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or its variants. These models generate fluent (but often imprecise) text and perform quite poorly at selecting appropriate content and ordering it coherently. This thesis focuses on overcoming these issues by integrating content planning with neural models. We hypothesize data-to-text generation will benefit from explicit planning, which manifests itself in (a) micro planning, (b) latent entity planning, and (c) macro planning. Throughout this thesis, we assume the input to our generator are tables (with records) in the sports domain. And the output are summaries describing what happened in the game (e.g., who won/lost, ..., scored, etc.). We first describe our work on integrating fine-grained or micro plans with data-to-text generation. As part of this, we generate a micro plan highlighting which records should be mentioned and in which order, and then generate the document while taking the micro plan into account. We then show how data-to-text generation can benefit from higher level latent entity planning. Here, we make use of entity-specific representations which are dynam ically updated. The text is generated conditioned on entity representations and the records corresponding to the entities by using hierarchical attention at each time step. We then combine planning with the high level organization of entities, events, and their interactions. Such coarse-grained macro plans are learnt from data and given as input to the generator. Finally, we present work on making macro plans latent while incrementally generating a document paragraph by paragraph. We infer latent plans sequentially with a structured variational model while interleaving the steps of planning and generation. Text is generated by conditioning on previous variational decisions and previously generated text. Overall our results show that planning makes data-to-text generation more interpretable, improves the factuality and coherence of the generated documents and re duces redundancy in the output document

    TOWARDS AN UNDERSTANDING OF EFFORTFUL FUNDRAISING EXPERIENCES: USING INTERPRETATIVE PHENOMENOLOGICAL ANALYSIS IN FUNDRAISING RESEARCH

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    Physical-activity oriented community fundraising has experienced an exponential growth in popularity over the past 15 years. The aim of this study was to explore the value of effortful fundraising experiences, from the point of view of participants, and explore the impact that these experiences have on people’s lives. This study used an IPA approach to interview 23 individuals, recognising the role of participants as proxy (nonprofessional) fundraisers for charitable organisations, and the unique organisation donor dynamic that this creates. It also bought together relevant psychological theory related to physical activity fundraising experiences (through a narrative literature review) and used primary interview data to substantiate these. Effortful fundraising experiences are examined in detail to understand their significance to participants, and how such experiences influence their connection with a charity or cause. This was done with an idiographic focus at first, before examining convergences and divergences across the sample. This study found that effortful fundraising experiences can have a profound positive impact upon community fundraisers in both the short and the long term. Additionally, it found that these experiences can be opportunities for charitable organisations to create lasting meaningful relationships with participants, and foster mutually beneficial lifetime relationships with them. Further research is needed to test specific psychological theory in this context, including self-esteem theory, self determination theory, and the martyrdom effect (among others)

    From wallet to mobile: exploring how mobile payments create customer value in the service experience

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    This study explores how mobile proximity payments (MPP) (e.g., Apple Pay) create customer value in the service experience compared to traditional payment methods (e.g. cash and card). The main objectives were firstly to understand how customer value manifests as an outcome in the MPP service experience, and secondly to understand how the customer activities in the process of using MPP create customer value. To achieve these objectives a conceptual framework is built upon the Grönroos-Voima Value Model (Grönroos and Voima, 2013), and uses the Theory of Consumption Value (Sheth et al., 1991) to determine the customer value constructs for MPP, which is complimented with Script theory (Abelson, 1981) to determine the value creating activities the consumer does in the process of paying with MPP. The study uses a sequential exploratory mixed methods design, wherein the first qualitative stage uses two methods, self-observations (n=200) and semi-structured interviews (n=18). The subsequent second quantitative stage uses an online survey (n=441) and Structural Equation Modelling analysis to further examine the relationships and effect between the value creating activities and customer value constructs identified in stage one. The academic contributions include the development of a model of mobile payment services value creation in the service experience, introducing the concept of in-use barriers which occur after adoption and constrains the consumers existing use of MPP, and revealing the importance of the mobile in-hand momentary condition as an antecedent state. Additionally, the customer value perspective of this thesis demonstrates an alternative to the dominant Information Technology approaches to researching mobile payments and broadens the view of technology from purely an object a user interacts with to an object that is immersed in consumers’ daily life

    Omics measures of ageing and disease susceptibility

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    While genomics has been a major field of study for decades due to relatively inexpensive genotyping arrays, the recent advancement of technology has also allowed the measure and study of various “omics”. There are now numerous methods and platforms available that allow high throughput and high dimensional quantification of many types of biological molecules. Traditional genomics and transcriptomics are now joined by proteomics, metabolomics, glycomics, lipidomics and epigenomics. I was lucky to have access to a unique resource in the Orkney Complex Disease Study (ORCADES), a cohort of individuals from the Orkney Islands that are extremely deeply annotated. Approximately 1000 individuals in ORCADES have genomics, proteomics, lipidomics, glycomics, metabolomics, epigenomics, clinical risk factors and disease phenotypes, as well as body composition measurements from whole body scans. In addition to these cross-sectional omics and health related measures, these individuals also have linked electronic health records (EHR) available, allowing the assessment of the effect of these omics measures on incident disease over a ~10-year follow up period. In this thesis I use this phenotype rich resource to investigate the relationship between multiple types of omics measures and both ageing and health outcomes. First, I used the ORCADES data to construct measures of biological age (BA). The idea that there is an underlying rate at which the body deteriorates with age that varies between individuals of the same chronological age, this biological age, would be more indicative of health status, functional capacity and risk of age-related diseases than chronological age. Previous models estimating BA (ageing clocks) have predominantly been built using a single type of omics assay and comparison between different omics ageing clocks has been limited. I performed the most exhaustive comparison of different omics ageing clocks yet, with eleven clocks spanning nine different omics assays. I show that different omics clocks overlap in the information they provide about age, that some omics clocks track more generalised ageing while others track specific disease risk factors and that omics ageing clocks are prognostic of incident disease over and above chronological age. Second, I assessed whether individually or in multivariable models, omics measures are associated with health-related risk factors or prognostic of incident disease over 10 years post-assessment. I show that 2,686 single omics biomarkers are associated with 10 risk factors and 44 subsequent incident diseases. I also show that models built using multiple biomarkers from whole body scans, metabolomics, proteomics and clinical risk factors are prognostic of subsequent diabetes mellitus and that clinical risk factors are prognostic of incident hypertensive disorders, obesity, ischaemic heart disease and Framingham risk score. Third, I investigated the genetic architecture of a subset of the proteomics measures available in ORCADES, specifically 184 cardiovascular-related proteins. Combining genome-wide association (GWAS) summary statistics from ORCADES and 17 other cohorts from the SCALLOP Consortium, giving a maximum sample size of 26,494 individuals, I performed 184 genome-wide association meta-analyses (GWAMAs) on the levels of these proteins circulating in plasma. I discovered 592 independent significant loci associated with the levels of at least one protein. I found that between 8-37% of these significant loci colocalise with known expression quantitative trait loci (eQTL). I also find evidence of causal associations between 11 plasma protein levels and disease susceptibility using Mendelian randomisation, highlighting potential candidate drug targets

    GENDERED EMBODIMENT, STABILITY AND CHANGE: WOMEN’S WEIGHTLIFTING AS A TOOL FOR RECOVERY FROM EATING DISORDERS

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    This thesis explores the everyday embodied experiences of women who use amateur weightlifting as a vehicle for recovery from eating disorders. Within online spaces and on social media, women frequently share their experiences of using weightlifting to overcome issues relating to disordered eating, body image, and mental health. In particular, women with a history of eating disorders credit weightlifting to be integral to their recovery journey. However, there is a dearth of research on women’s experiences with exercise during eating disorder recovery and no research that identifies weightlifting as beneficial to this process. To the contrary, discursive links are drawn between the practices of self-surveillance exercised by both eating disorder sufferers and weightlifters alike. In this regard, engagement with weightlifting during eating disorder recovery may signal the transferal of pathology from one set of behaviours to another. That is, from disordered eating to rigid and self-regulatory exercise routines. This thesis examines how women subjectively navigate and make sense of this pathologisation. The data for this research comes from longitudinal semi-structured interviews and photo elicitation with 19 women, living in the United Kingdom, who engaged in weightlifting during their eating disorder recovery. In addition, to build up a holistic picture and to explore how this phenomenon also ‘takes place’ online, I conducted a netnography of the overlapping subcultures of female weightlifting and eating disorder recovery on Instagram. Women’s standpoint theory and interpretative phenomenological analysis are combined to form the underpinning theoretical and analytical tools used to engage with these three rich data sets. Moreover, throughout I draw on an eclectic range of disciplinary perspectives, in order to bring together multiple fields of research and develop novel theoretical frameworks. In the findings, I argue that women’s experiences using weightlifting as a tool for recovery from eating disorders manifests in an embodied sense of multiplicity. In this sense, understandings of the body that are often viewed as ontologically distinct (muscularity/thinness/fatness) hang-together at once in the lived experience of a single individual. I argue that women, particularly those who have previously struggled with an eating disorder, are too readily positioned as vulnerable to media and representation. To theoretically combat these ideas regarding women’s assumed passivity, I develop the concept of ‘digital pruning’ to account for women’s agency in relation to new media. I contend that weightlifting offers women in recovery from eating disorders a new framework for approaching eating and exercise. Specifically, weightlifting’s norms and values legitimate occupying a larger body, which gives women in recovery permission to eat and gain-weight in a way that is both culturally sanctioned and health-promoting. Finally, I explore identity transformation as a specific tenet of recovery from eating disorders. I argue that, on social media, recovery identities are characterised by personal empowerment, resilience, and independence. While offline, quieter and less culturally glorified aspects of recovery (such as relationships of care) are central to women’s accounts of developing a new sense of self as they transition away from an eating disorder identity. In summary, this thesis is an examination of the ways in which women strategically navigate pathology in relation to their bodies, social media, food/exercise practices, and identity. I argue that women develop a set of ‘DIY’ recovery practices that allow them to consciously channel and draw on their negative experiences with eating disorders, to develop new ways of living that serve their overall wellbeing. Weightlifting is integral to this process, as it provides women transitioning out of this difficult phase in their lives with new ways of relating to their bodies and of being in the world. I situate this phenomenon within a neoliberal socio-political climate in which individuals are required to take personal responsibility for their mental health and wellbeing, despite living within conditions which are not conducive to recovery

    Linguistic- and Acoustic-based Automatic Dementia Detection using Deep Learning Methods

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    Dementia can affect a person's speech and language abilities, even in the early stages. Dementia is incurable, but early detection can enable treatment that can slow down and maintain mental function. Therefore, early diagnosis of dementia is of great importance. However, current dementia detection procedures in clinical practice are expensive, invasive, and sometimes inaccurate. In comparison, computational tools based on the automatic analysis of spoken language have the potential to be applied as a cheap, easy-to-use, and objective clinical assistance tool for dementia detection. In recent years, several studies have shown promise in this area. However, most studies focus heavily on the machine learning aspects and, as a consequence, often lack sufficient incorporation of clinical knowledge. Many studies also concentrate on clinically less relevant tasks such as the distinction between HC and people with AD which is relatively easy and therefore less interesting both in terms of the machine learning and the clinical application. The studies in this thesis concentrate on automatically identifying signs of neurodegenerative dementia in the early stages and distinguishing them from other clinical, diagnostic categories related to memory problems: (FMD, MCI, and HC). A key focus, when designing the proposed systems has been to better consider (and incorporate) currently used clinical knowledge and also to bear in mind how these machine-learning based systems could be translated for use in real clinical settings. Firstly, a state-of-the-art end-to-end system is constructed for extracting linguistic information from automatically transcribed spontaneous speech. The system's architecture is based on hierarchical principles thereby mimicking those used in clinical practice where information at both word-, sentence- and paragraph-level is used when extracting information to be used for diagnosis. Secondly, hand-crafted features are designed that are based on clinical knowledge of the importance of pausing and rhythm. These are successfully joined with features extracted from the end-to-end system. Thirdly, different classification tasks are explored, each set up so as to represent the types of diagnostic decision-making that is relevant in clinical practice. Finally, experiments are conducted to explore how to better deal with the known problem of confounding and overlapping symptoms on speech and language from age and cognitive decline. A multi-task system is constructed that takes age into account while predicting cognitive decline. The studies use the publicly available DementiaBank dataset as well as the IVA dataset, which has been collected by our collaborators at the Royal Hallamshire Hospital, UK. In conclusion, this thesis proposes multiple methods of using speech and language information for dementia detection with state-of-the-art deep learning technologies, confirming the automatic system's potential for dementia detection
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