Neural Networks: Is it hermeneutic?

This paper proposes a synoptic methodology to evaluate the determinants of audit fees by utilising Neural Networks. First, a brief discussion is presented to highlight the significant application of Neural Network in the areas of financial management; second the framework of proposed methodology has been outlined to examine the implication of audit fees on target sample. The underlying rational of this paper is to establish NNs as a diagnostic tool to assess the effect of audit fees on firms, which indeed warrants further empirical investigation. The importance of NNs emerges from the fact that if external and internal audit fees can be disseminated by employing this methodology which is perceived more significantly robust than other econometric models, then accounting standards can be improved.Neural Networks and Audit Fee

Security Analysts and Market Reaction:Caveat for Monitoring

Security analysts, analyst forecast and market reaction are anecdotal in restructuring transactions, sometime conflicting and some other time imperative to the process of transaction. This article attempts to highlight a consistent association between analyst, market reaction and corporate restructuring. A close intermediation between those themes is analysed in this article, implying the relationship is contiguous. However issues of delayed price adjustment, conglomerate stock break-ups and negative earnings surprises are not discussed in this paper, though such factors are ingeniously important and crucial to the process of corporate restructuring.Security Analysts, Forecasting and Agency Cost

Genetic Algorithms: Genesis of Stock Evaluation

The uncertainty of predicting stock prices emanates pre-eminent concerns around the functionality of the stock market. The possibility of utilising Genetic Algorithms to forecast the momentum of stock price has been previously explored by many optimisation models that have subsequently addressed much of the scepticism. In this paper the author proposes a methodology based on Genetic Algorithms and individual data maximum likelihood estimation using logit model arguing that forecasting discrepancy can be rationalised by combined approximation of both the approaches. Thus this paper offers a methodological overture to further investigate the anomalies surrounding stock market. In the main, this paper attempts to provide a temporal dimension of the methods transposed on recurrent series of data over a fixed window conjecturereGenetic Algorithms, Individual Maximum Likelihood Estimation, Stock Price

Cognitive analytics enabled responsible artificial intelligence for business model innovation: A multilayer perceptron neural networks estimation

Cognitive analytics employs and analyses complex and heterogeneous data sources generating deeper insights that mimic the natural intelligence of the human brain. Cognitive analytics-enabled Artificial Intelligence (AI) that promotes Business Model Innovation (BMI) for the efficiency of the healthcare system is a nascent and undertheorized domain. Within the healthcare management systems, stakeholders’ engagement with AI, particularly with responsible AI, to optimize BMI and improve business performance is bounded by several caveats. Using the Technology Acceptance Model (TAM) and Social Network Theory (SNT) as our conceptual foci, we empirically examine through the Multilayer Perceptron Neural Network the extent to which responsible AI leads to Business Model Innovation (BMI) through the stakeholders’ engagement. Our contributions are novel which demonstrate that cognitive analytics-enabled responsible AI is central to innovation, and healthcare stakeholders exhibit a robust propensity to reorientate and innovate their existing BMI to achieve improved business performance. It has significant implications for innovation, AI and cognitive analytics literature

Are managers myopic? Evidence from take overs

The controversy over take overs has been ongoing since the 1960s. Often, take over attempts are referred to as corporate raiding or predatory behaviour not only for less privileged firms having weaker internal control mechanisms and unresolved debt to equity ratios but also for healthy businesses

Focal firms and interorganisational relationships in small economies: Towards a multi-level theoretical framework for enhancing value co-creation and performance

Underpinning Resource-Based View (RBV) and Relational View (RV) as theoretical premises, we examine the influence of the macro and micro-level factors on inter-organisational relationship/alliances dynamics and competitive advantage of Focal Firms (FF) operating in an emerging Small Island Economy (SIE), Mauritius. Data gathered from in-depth interviews of boundary spanners of FF from diverse industries and sectors, were analysed using the open, axial, and selective coding procedure to establish the main factors influencing inter-organisational relationship dynamics and competitive advantage. Our findings explain and provide insights into the embedded nature of the firms, inter-organisational relationships, value co-creation (VCC), internationalisation capabilities and performance. The novel themes of cultural intelligence and tight-knit society were considered pivotal to firms' relational strategy and advantage, locally; and in their VCC and internationalisation opportunities and capabilities. Our propositions on the emerging themes and the core category web of ties stemming from the situational features define Mauritius, and the multi-level theoretical framework can be extended to understand inter-organisational relationship/alliances in other emerging economies with similar architecture