Developing CSR giving as a dynamic capability for salient stakeholder management

In this paper, we draw upon the emerging view of strategic cognition and issue salience and show that CSR giving has evolved into more than an altruistic response to being asked for support, to one which is embedded in the strategic frames of management and which supports organizational identity. The managerial action as a result of such strategic cognition suggests that modern organizations are seeking to develop CSR giving processes that provide them with a competitive advantage. We draw on the resource-based view of organizations and the VRIO framework to provide the theoretical foundations for our argument that CSR implementation in the form of corporate giving to charities can be developed as a dynamic capability. This can provide a competitive advantage by allowing organizations to manage key stakeholder relationships (external and internal) more effectively with benefits which could lead to increased organizational productivity and the ability to execute strategy more effectively. We interview CSR implementation managers from large organizations in Australia and find that the CSR giving process in many firms is evolving into a more sophisticated and strategically motivated process with expectations of a return. Central to this evolution is the appointment of a CSR implementation manager who acts as a boundary spanner between the organization and its key stakeholders. We posit that this corporate investment in their role and supporting structures can lead to the better management of stakeholders by organizations through the dynamic capability of the CSR giving process. We develop a table of best practise to help guide managers entering this sphere

Transforming Sentiment Analysis in the Financial Domain with ChatGPT

Financial sentiment analysis plays a crucial role in decoding market trends and guiding strategic trading decisions. Despite the deployment of advanced deep learning techniques and language models to refine sentiment analysis in finance, this study breaks new ground by investigating the potential of large language models, particularly ChatGPT 3.5, in financial sentiment analysis, with a strong emphasis on the foreign exchange market (forex). Employing a zero-shot prompting approach, we examine multiple ChatGPT prompts on a meticulously curated dataset of forex-related news headlines, measuring performance using metrics such as precision, recall, f1-score, and Mean Absolute Error (MAE) of the sentiment class. Additionally, we probe the correlation between predicted sentiment and market returns as an additional evaluation approach. ChatGPT, compared to FinBERT, a well-established sentiment analysis model for financial texts, exhibited approximately 35\% enhanced performance in sentiment classification and a 36\% higher correlation with market returns. By underlining the significance of prompt engineering, particularly in zero-shot contexts, this study spotlights ChatGPT's potential to substantially boost sentiment analysis in financial applications. By sharing the utilized dataset, our intention is to stimulate further research and advancements in the field of financial services.Comment: 10 pages, 8 figures, Preprint submitted to Machine Learning with Application

Viewing ambiguous social interactions increases functional connectivity between frontal and temporal nodes of the social brain.

Social behaviour is coordinated by a network of brain regions, including those involved in the perception of social stimuli and those involved in complex functions like inferring perceptual and mental states and controlling social interactions. The properties and function of many of these regions in isolation is relatively well-understood, but less is known about how these regions interact whilst processing dynamic social interactions. To investigate whether the functional connectivity between brain regions is modulated by social context, we collected functional MRI (fMRI) data from male monkeys (Macaca mulatta) viewing videos of social interactions labelled as "affiliative", "aggressive", or "ambiguous". We show activation related to the perception of social interactions along both banks of the superior temporal sulcus, parietal cortex, medial and lateral frontal cortex, and the caudate nucleus. Within this network, we show that fronto-temporal functional connectivity is significantly modulated by social context. Crucially, we link the observation of specific behaviours to changes in functional connectivity within our network. Viewing aggressive behaviour was associated with a limited increase in temporo-temporal and a weak increase in cingulate-temporal connectivity. By contrast, viewing interactions where the outcome was uncertain was associated with a pronounced increase in temporo-temporal, and cingulate-temporal functional connectivity. We hypothesise that this widespread network synchronisation occurs when cingulate and temporal areas coordinate their activity when more difficult social inferences are being made.SIGNIFICANCE STATEMENT:Processing social information from our environment requires the activation of several brain regions, which are concentrated within the frontal and temporal lobes. However, little is known about how these areas interact to facilitate the processing of different social interactions. Here we show that functional connectivity within and between the frontal and temporal lobes is modulated by social context. Specifically, we demonstrate that viewing social interactions where the outcome was unclear is associated with increased synchrony within and between the cingulate cortex and temporal cortices. These findings suggest that the coordination between the cingulate and temporal cortices is enhanced when more difficult social inferences are being made

Evaluation of seismic hazard for the assessment of historical elements at risk : description of input and selection of intensity measures

The assessment of historical elements at risk from earthquake loading presents a number of differences from the seismic evaluation of modern structures, for design or retrofitting purposes, which is covered by existing building codes, and for the development of fragility curves, procedures for which have been extensively developed in the past decade. This article briefly discusses: the hazard framework for historical assets, including a consideration of the appropriate return period to be used for such elements at risk; the intensity measures that could be used to describe earthquake shaking for the analysis of historical assets; and available approaches for their assessment. We then discuss various unique aspects of historical assets that mean the characterisation of earthquake loading must be different from that for modern structures. For example, historical buildings are often composed of heterogeneous materials (e.g., old masonry) and they are sometimes located where strong local site effects occur due to: steep topography (e.g., hilltops), basin effects or foundations built on the remains of previous structures. Standard seismic hazard assessment undertaken for modern structures and the majority of sites is generally not appropriate. Within the PERPETUATE project performance-based assessments, using nonlinear static and dynamic analyses for the evaluation of structural response of historical assets, were undertaken. The steps outlined in this article are important for input to these assessments

Genomic Underpinnings of Population Persistence in Isle Royale Moose

Island ecosystems provide natural laboratories to assess the impacts of isolation on population persistence. However, most studies of persistence have focused on a single species, without comparisons to other organisms they interact with in the ecosystem. The case study of moose and gray wolves on Isle Royale allows for a direct contrast of genetic variation in isolated populations that have experienced dramatically differing population trajectories over the past decade. Whereas the Isle Royale wolf population recently declined nearly to extinction due to severe inbreeding depression, the moose population has thrived and continues to persist, despite having low genetic diversity and being isolated for ∼120 years. Here, we examine the patterns of genomic variation underlying the continued persistence of the Isle Royale moose population. We document high levels of inbreeding in the population, roughly as high as the wolf population at the time of its decline. However, inbreeding in the moose population manifests in the form of intermediate-length runs of homozygosity suggestive of historical inbreeding and purging, contrasting with the long runs of homozygosity observed in the smaller wolf population. Using simulations, we confirm that substantial purging has likely occurred in the moose population. However, we also document notable increases in genetic load, which could eventually threaten population viability over the long term. Overall, our results demonstrate a complex relationship between inbreeding, genetic diversity, and population viability that highlights the use of genomic datasets and computational simulation tools for understanding the factors enabling persistence in isolated populations

Leveraging data-driven infrastructure management to facilitate AIOps for big data applications and operations

As institutions increasingly shift to distributed and containerized application deployments on remote heterogeneous cloud/cluster infrastructures, the cost and difficulty of efficiently managing and maintaining data-intensive applications have risen. A new emerging solution to this issue is Data-Driven Infrastructure Management (DDIM), where the decisions regarding the management of resources are taken based on data aspects and operations (both on the infrastructure and on the application levels). This chapter will introduce readers to the core concepts underpinning DDIM, based on experience gained from development of the Kubernetes-based BigDataStack DDIM platform (https://bigdatastack.eu/). This chapter involves multiple important BDV topics, including development, deployment, and operations for cluster/cloud-based big data applications, as well as data-driven analytics and artificial intelligence for smart automated infrastructure self-management. Readers will gain important insights into how next-generation DDIM platforms function, as well as how they can be used in practical deployments to improve quality of service for Big Data Applications. This chapter relates to the technical priority Data Processing Architectures of the European Big Data Value Strategic Research & Innovation Agenda [33], as well as the Data Processing Architectures horizontal and Engineering and DevOps for building Big Data Value vertical concerns. The chapter relates to the Reasoning and Decision Making cross-sectorial technology enablers of the AI, Data and Robotics Strategic Research, Innovation & Deployment Agenda [34]

A Conceptual Model of the Factors Affecting the Choice of Nonprofit Organisation by Large Corporations in Australia

This paper develops a new conceptualisation of corporate giving which advances our knowledge in the field of nonprofit marketing through the development of a model which assists in identifying the drivers of corporate giving in Australia. Existing conceptualisations are limited in that the commercial realities of corporate life and the pressures that many organizations face in achieving concrete outcomes from their giving behaviour have not been properly reflected in research results. In an environment of increased competition amongst nonprofits for donations in terms of money, resources, and volunteers the better understanding of how and why corporations give will enable nonprofit organisations to better position themselves in communicating with corporations, targeting requests and competing for corporate giving. Using the extant literature and evidence from qualitative interviews conducted with giving managers of eight large organisations operating in Australia (not just Australian owned organisations) we develop a conceptual model of the managerial interpretation and actualisation of corporate policy which incorporates our finding that organisations chose and support their NPOs differently primarily based on how giving managers classify the value of the NPO relationship. As a key decision maker or influencer in the choice and support ofNPOs the individual giving managers role is explicitly included in our model. This paper adds a further dimension to the literature and an increased understanding of the giving by large corporations to nonprofit organisations

Human in the Loop of AI Systems in Manufacturing

Artificial Intelligence (AI) in manufacturing is typically looked upon from the view- point of its contribution to automation. Additionally, the role of AI in augmenting human activities has been the subject of a wide range of studies with impact on practical applications in manufacturing environments. Recently, the empowering effect of human and AI actors working in synergy has attracted increased atten- tion. After outlining relevant work, this chapter considers the potential emergent outcomes of such a synergy in a way that goes beyond automation or augmenta- tion. Aimed at both developers and work designers, the present work proposes a model of human-AI interaction along with an outline of key concepts and success criteria towards making human-AI interaction more effective