15,067 research outputs found

    Testing the nomological network for the Personal Engagement Model

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    The study of employee engagement has been a key focus of management for over three decades. The academic literature on engagement has generated multiple definitions but there are two primary models of engagement: the Personal Engagement Model of Kahn (1990), and the Work Engagement Model (WEM) of Schaufeli et al., (2002). While the former is cited by most authors as the seminal work on engagement, research has tended to focus on elements of the model and most theoretical work on engagement has predominantly used the WEM to consider the topic. The purpose of this study was to test all the elements of the nomological network of the PEM to determine whether the complete model of personal engagement is viable. This was done using data from a large, complex public sector workforce. Survey questions were designed to test each element of the PEM and administered to a sample of the workforce (n = 3,103). The scales were tested and refined using confirmatory factor analysis and then the model was tested determine the structure of the nomological network. This was validated and the generalisability of the final model was tested across different work and organisational types. The results showed that the PEM is viable but there were differences from what was originally proposed by Kahn (1990). Specifically, of the three psychological conditions deemed necessary for engagement to occur, meaningfulness, safety, and availability, only meaningfulness was found to contribute to employee engagement. The model demonstrated that employees experience meaningfulness through both the nature of the work that they do and the organisation within which they do their work. Finally, the findings were replicated across employees in different work types and different organisational types. This thesis makes five contributions to the engagement paradigm. It advances engagement theory by testing the PEM and showing that it is an adequate representation of engagement. A model for testing the causal mechanism for engagement has been articulated, demonstrating that meaningfulness in work is a primary mechanism for engagement. The research has shown the key aspects of the workplace in which employees experience meaningfulness, the nature of the work that they do and the organisation within which they do it. It has demonstrated that this is consistent across organisations and the type of work. Finally, it has developed a reliable measure of the different elements of the PEM which will support future research in this area

    Investigating the Drivers & Challenges of Implementing Immersive Sensory Technology within Construction Site Safety

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    The use of immersive sensory technology for safety management is generally shown positively in academic literature. Many researchers have demonstrated applications of this technology for improving safety training in a risk-free environment. Despite the reported benefits and a global pandemic forcing the digital agenda, the uptake of this technology for this purpose remains slow. This study aims to investigate current drivers and challenges of implementing this technology for safety from an industry-based perspective. To achieve this, qualitative data was collected through 4 online focus groups involving 21 industry professionals working within the field. The findings identified that even amongst these experts, the technology was rarely implemented on projects specifically for safety. Despite this lack of adoption, participants agreed that if implemented correctly this technology has the potential to enhance site safety processes such as inductions, toolbox talks and general safety training. The commitment to safety and legislative requirements were identified as key drivers, whilst deep rooted challenges surrounding client demand, costs and leadership dominated the discussion. The onsite practicalities, personal comfort and lack of digital skills were also identified as concerns if this technology was to be adopted more mainstream in safety training. Further recommendations are made to develop understanding of these specific challenges, including investigating the industry need and availability of specific skills in immersive safety applications. In addition, it is recommended that further empirical evidence including the impact of this technology when implemented for safety on projects is provided in literature

    The developing maternal-infant relationship: a qualitative longitudinal study

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    Aim The study aimed to explore maternal perceptions and the use of knowledge relating to their infant’s mental health over time using qualitative longitudinal research. Background There has been a growing interest in infant mental health over recent years. Much of this interest is directed through the lens of infant determinism, through knowledge regarding neurological development resulting in biological determinism. Research and policy in this field are directed toward individual parenting behaviours, usually focused on the mother. Despite this, there is little attention given to maternal perspectives of infant mental health, indicating that a more innovative approach to methodology is required. Methods This study took a qualitative longitudinal approach, and interviews were undertaken with seven mothers from the third trimester of pregnancy and then throughout the first year of the infant’s life. Interviews were conducted at 34 weeks of pregnancy, and then when the infant was 6 and 12 weeks, 6, 9, and 12 months, alongside the collection of researcher field notes—a total of 41 interviews. Data were analysed by creating case profiles, memos, and summaries, and then cross-comparison of the emerging narratives. A psycho-socially informed approach was taken to the analysis of data. Findings Three interrelated themes emerged from the data: evolving maternal identity, growing a person, and creating a safe space. The theme of evolving maternal identity dominated the other themes of growing a person and creating a safe space in a way that met perceived socio-cultural requirements for mothering and childcare practices. Participants’ personal stories give voice to their perceptions of the developing maternal-infant relationship in the context of their socio-cultural setting, relationships with others, and experiences over time. Conclusions This study adds new knowledge by giving mothers a voice to express how the maternal-infant relationship develops over time. The findings demonstrate how the developing maternal-infant relationship grows in response to their mutual needs as the mother works to create and sustain identities for herself and the infant that will fit within their socio-cultural context and individual situations. Additionally, the findings illustrate the importance of temporal considerations, social networks, and intergenerational relationships to this evolving process. Recommendations for practice, policy, and education are made that reflect the unique relationship between mother and infant and the need to conceptualise this using an ecological approach

    A productive response to legacy system petrification

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    Requirements change. The requirements of a legacy information system change, often in unanticipated ways, and at a more rapid pace than the rate at which the information system itself can be evolved to support them. The capabilities of a legacy system progressively fall further and further behind their evolving requirements, in a degrading process termed petrification. As systems petrify, they deliver diminishing business value, hamper business effectiveness, and drain organisational resources. To address legacy systems, the first challenge is to understand how to shed their resistance to tracking requirements change. The second challenge is to ensure that a newly adaptable system never again petrifies into a change resistant legacy system. This thesis addresses both challenges. The approach outlined herein is underpinned by an agile migration process - termed Productive Migration - that homes in upon the specific causes of petrification within each particular legacy system and provides guidance upon how to address them. That guidance comes in part from a personalised catalogue of petrifying patterns, which capture recurring themes underlying petrification. These steer us to the problems actually present in a given legacy system, and lead us to suitable antidote productive patterns via which we can deal with those problems one by one. To prevent newly adaptable systems from again degrading into legacy systems, we appeal to a follow-on process, termed Productive Evolution, which embraces and keeps pace with change rather than resisting and falling behind it. Productive Evolution teaches us to be vigilant against signs of system petrification and helps us to nip them in the bud. The aim is to nurture systems that remain supportive of the business, that are adaptable in step with ongoing requirements change, and that continue to retain their value as significant business assets

    Studies of strategic performance management for classical organizations theory & practice

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    Nowadays, the activities of "Performance Management" have spread very broadly in actually every part of business and management. There are numerous practitioners and researchers from very different disciplines, who are involved in exploring the different contents of performance management. In this thesis, some relevant historic developments in performance management are first reviewed. This includes various theories and frameworks of performance management. Then several management science techniques are developed for assessing performance management, including new methods in Data Envelopment Analysis (DEA) and Soft System Methodology (SSM). A theoretical framework for performance management and its practical procedures (five phases) are developed for "classic" organizations using soft system thinking, and the relationship with the existing theories are explored. Eventually these results are applied in three case studies to verify our theoretical development. One of the main contributions of this work is to point out, and to systematically explore the basic idea that the effective forms and structures of performance management for an organization are likely to depend greatly on the organizational configuration, in order to coordinate well with other management activities in the organization, which has seemingly been neglected in the existing literature of performance management research in the sense that there exists little known research that associated particular forms of performance management with the explicit assumptions of organizational configuration. By applying SSM, this thesis logically derives some main functional blocks of performance management in 'classic' organizations and clarifies the relationships between performance management and other management activities. Furthermore, it develops some new tools and procedures, which can hierarchically decompose organizational strategies and produce a practical model of specific implementation steps for "classic" organizations. Our approach integrates popular types of performance management models. Last but not least, this thesis presents findings from three major cases, which are quite different organizations in terms of management styles, ownership, and operating environment, to illustrate the fliexbility of the developed theoretical framework

    How can digital technology be used to maximise the social value delivered through major infrastructure projects?

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    The primary aim of this working paper is to inform readers as to what opportunities exist to use digital technology to maximise social value through major infrastructure projects. This research looks through the lenses of social value, infrastructure, and digital business, and addresses the gap around the intersection between social value, digital technology, and the infrastructure sector. This research methodology is a literature research, followed by the collection of qualitative data from 12 interviews, with participants in 12 organisations, during August 2020. The research findings reported in this working paper are structured around 2 questions: 1. What are the opportunities for the use of digital technology in maximising social value? 2. What are the risks and barriers to the use of digital technology to maximise social value? This research makes 12 actionable and practical recommendations as a contribution to the discussion and implementation of policy in the UK

    Machine learning and large scale cancer omic data: decoding the biological mechanisms underpinning cancer

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    Many of the mechanisms underpinning cancer risk and tumorigenesis are still not fully understood. However, the next-generation sequencing revolution and the rapid advances in big data analytics allow us to study cells and complex phenotypes at unprecedented depth and breadth. While experimental and clinical data are still fundamental to validate findings and confirm hypotheses, computational biology is key for the analysis of system- and population-level data for detection of hidden patterns and the generation of testable hypotheses. In this work, I tackle two main questions regarding cancer risk and tumorigenesis that require novel computational methods for the analysis of system-level omic data. First, I focused on how frequent, low-penetrance inherited variants modulate cancer risk in the broader population. Genome-Wide Association Studies (GWAS) have shown that Single Nucleotide Polymorphisms (SNP) contribute to cancer risk with multiple subtle effects, but they are still failing to give further insight into their synergistic effects. I developed a novel hierarchical Bayesian regression model, BAGHERA, to estimate heritability at the gene-level from GWAS summary statistics. I then used BAGHERA to analyse data from 38 malignancies in the UK Biobank. I showed that genes with high heritable risk are involved in key processes associated with cancer and are often localised in genes that are somatically mutated drivers. Heritability, like many other omics analysis methods, study the effects of DNA variants on single genes in isolation. However, we know that most biological processes require the interplay of multiple genes and we often lack a broad perspective on them. For the second part of this thesis, I then worked on the integration of Protein-Protein Interaction (PPI) graphs and omics data, which bridges this gap and recapitulates these interactions at a system level. First, I developed a modular and scalable Python package, PyGNA, that enables robust statistical testing of genesets' topological properties. PyGNA complements the literature with a tool that can be routinely introduced in bioinformatics automated pipelines. With PyGNA I processed multiple genesets obtained from genomics and transcriptomics data. However, topological properties alone have proven to be insufficient to fully characterise complex phenotypes. Therefore, I focused on a model that allows to combine topological and functional data to detect multiple communities associated with a phenotype. Detecting cancer-specific submodules is still an open problem, but it has the potential to elucidate mechanisms detectable only by integrating multi-omics data. Building on the recent advances in Graph Neural Networks (GNN), I present a supervised geometric deep learning model that combines GNNs and Stochastic Block Models (SBM). The model is able to learn multiple graph-aware representations, as multiple joint SBMs, of the attributed network, accounting for nodes participating in multiple processes. The simultaneous estimation of structure and function provides an interpretable picture of how genes interact in specific conditions and it allows to detect novel putative pathways associated with cancer
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