Browsing by Author "Wen, Chue Kar"

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Artificial Intellegence and Porter's Five Forces: An Integration
(Department of Marketing Management, Faculty of Commerce and Management Studies, University of Kelaniya, Sri Lanka., 2021) Wen, Chue Kar; Shan, Lom Hui; Adeline, Y. L.; Chin, Thoo Ai
The influx of Artificial Intelligence (AI) into Marketing has been surging in recent years in various disciplines of Marketing, however the research are only limited to macro-level analyses. Academic criticisms have been built up on Porter's Five Forces (P5F) that the model is limited to macro-level perspective in measuring the attractiveness (or profitability) of an industry, and no longer up to date to latest business practices. It is noticeable that only a few research introduced or integrated technological advancement into the P5F model to keep the P5F model relevant to the modern business world. AI is capable of sensing, reasoning, and acting; especially reasoning is being focused in the recent researches that aims to develop human reasoning into machines to activate them thinking and acting like humans, undertake more complex work and making better informed decisions. The Three-stage Framework idealised by Huang and Rust (2021) consists of Mechanical AI, Thinking AI, and Feeling AI that are applied coherently into each element in the Porter's Five Forces. The Mechanical AI is to perform repetitive or routine work such as data collection; Thinking AI is to process and analyse the data to decision making (without being needed to supervise); and Feeling AI is to perform communication with humans (e.g. customers). The process is looping back to the mechanical AI as there would be feedbacks from humans as inputs. The integration of the Three-stage Framework as a concept of AI categorises Porter's Five Forces into more micro-level on the analysis of the industry, that would ultimately enhance the relevancy of Porter's Five Forces in the modern and ever-changing business environment. This paper aims to integrate Artificial Intelligence into Porter's Five Forces to enhance the relevancy of the model into the latest business practices, by integrating a conceptual model in Artificial Intelligence, Three-stage Framework into Porter's Five Forces. Each element in P5F including Threat of New Entrants, Bargaining Power of Supplier, Threat of Substitution, Bargaining Power of Customers and Degree of Rivalry are integrated with the Three-Stage Framework. The integration would enhance the content of P5F hence P5F would provide researchers to analyse the industry more effectively. Some underlying factors that may be hidden during research may be highlighted or observed by AI. Other than being more analytic, the model could be more predictive in analysing the level of competitiveness and competitors' activities. Conceptually, the integration of the ThreeStage Framework into P5F would create a more possibilities for the market entrants, both suppliers and customers, substitutions, subsequently increases the degree of rivalry and ultimately enhances the attractiveness and profitability of an industry. Following the technological trend, the applications of Artificial Intelligence programs will generally enhance the marketing content in Porter's Five Forces that makes it more relevant in the current and future business environment. This study is currently limited to conceptual research, as the practicality of some AI functions are not yet matured. The conceptual integration of the Three-Stage Framework into P5F could be further discovered in specific industries for more empirical results.
Detecting Fraudulent Financial Reporting and Predicting Business Failure Using Probabilistic Neural Network: Malaysia Chapter
(Department of Marketing Management, Faculty of Commerce and Management Studies, University of Kelaniya, Sri Lanka., 2021) Wen, Chue Kar; Sin, Tiong Jia; Shan, Lom Hui; Dyana, Chang Mui Ling
Methodologies with the integration of machine learning (ML) into fraudulent financial reporting (FFR) and business failure detection have been researched popularly globally however these ML methodologies were not popular researched in Malaysia Specifically. Studies showed that Probabilistic Neural Network (PNN) yielded highest fraudulent detection rate about 98%; and Neural Network achieved overall accuracy of 84% of business failure detection rate. It was also found that there was no specific method mentioned in Malaysia Securities Commission Act 1993 in assessing the financial statements of public listed companies (PLCs), and low expenditure of PLCs in audit functions. Due to the huge impacts resulted from FFR and business failure of public listed companies, there is a need to minimise FFR and business failure incidents with high accuracy detection and prediction tools, which are ML techniques. The applications of ML technique (i.e., PNN) into the research would shorten the analysis time compared to other statistical methods; yield higher accuracy rate that becomes effective layer of screening financial statements; is able to optimise or minimise the loss functions if discrepancies occur in data sets. On the other hand, although the relationship between FFR and business failure has been linked, the two topics have been studied separately in the past. Financially distressed companies may have a higher probability to commit fraudulent financial reporting, and less research that link the two topics although the methodologies and models were found effective in research the two topics. This study aims to firstly determine the accuracy of ML technique, i.e. PNN in the detection of FFR and detection of business failures among the public listed companies in Malaysia. The relationship between business failure and FFR among the PLCs in Malaysia would be identified. This study applies two stage PNN procedures: first stage is to detect FFR among the companies; second stage is to predict business failure of the companies prior to the conduct of FFR. The accuracy of PNN in the applications and the relationship between FFR and business failure will be discovered. Secondary data is to be collected through financial reports from the PLCs that have been identified fraudulent by the Securities Commission Malaysia in the past. A set of identical non-fraudulent and non-failed companies (similar size in same industries) would be as pairs to the fraudulent companies in the study. PNN is expected to yield high accuracy rates in detecting fraudulent companies and predicting business failures. The ML methodology would also be expected to detect the relationship between FFR and business failure (as supported by fraud triangle theory that financial distress is one of the elements in committing frauds). The research should enhance the detectability of frauds and business failures among the PLCs, improves overall corporate governance of the companies and increase public confidences onto PLCs; furthermore, this would also enhance the knowledge of forensic accounting in Malaysia.