Search results for: corporate credit rating prediction

Authors: Mohammad A. R. S. Almutairi

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This study will provide the introduction that constitutes the problem cornerstone of legal disputes of disclosure and transparency under Kuwaiti Capital market authority Law No. 7 of 2010. It also will discuss the reasons for the emergence of corporate governance and its purposes in the Capital Market Authority Law in Kuwait. In addition, it will show the legal disputes resulting from the unclear concept of disclosure and interest and will discuss the main reasons in support of the possible solution. In addition, this study will argue why the Capital Market Authority Law in Kuwait needs a clear concept and a straight structure of disclosure under section 100. This study will demonstrate why a clear disclosure is led to a better application of the law. This study will demonstrate the fairness in applying the law regarding the punishment against individual, companies and securities market. Furthermore, it will discuss added confidence between investors and the stock market with a clear concept under section 100. Finally, it will summarize arises problem and possible solution.

Keywords: corporate governors, disclosure, transparency, fairness

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Authors: Qiming Zhang, Youda Ye, Qinxue Jiang

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Accurate prediction of external flowfield and aero-heating at the wall of hypersonic vehicle is very crucial for the design of aircrafts. Unstructured/hybrid meshes have more powerful advantages than structured meshes in terms of pre-processing, parallel computing and mesh adaptation, so it is imperative to develop high-resolution numerical methods for the calculation of aerothermal environment on unstructured/hybrid meshes. The inviscid flux scheme is one of the most important factors affecting the accuracy of unstructured/ hybrid mesh heat flux calculation. Here, a new hybrid flux scheme is developed and the approach of interface type selection is proposed: i.e. 1) using the exact Riemann scheme solution to calculate the flux on the faces parallel to the wall; 2) employing Sterger-Warming (S-W) scheme to improve the stability of the numerical scheme in other interfaces. The results of the heat flux fit the one observed experimentally and have little dependence on grids, which show great application prospect in unstructured/ hybrid mesh.

Keywords: aero-heating prediction, computational fluid dynamics, hybrid meshes, hybrid schemes

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Authors: Prateep Wajeetongratana

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The objective of this research is to examine the effects of internal audit innovation on firm performance effectiveness influences of financial report reliability, organizational process improvement, and risk management effectiveness. This paper drew upon the survey data collected from 400 employees survey conducted at Nonthaburi province, Thailand. The statistics utilized in this paper included percentage, mean, standard deviation, and regression analysis. The findings revealed that the majority of samples were between 31-40 years old, married, held an undergraduate degree, and had an average income between 10,000-15,000 baht. And also the results show that auditing integration has only influence on financial report reliability. Moreover, corporate risk evaluation has effect on firm performance by risk management effectiveness and control self-assessment has effect influence on firm performance by organizational process improvement and risk management effectiveness as well.

Keywords: corporate risk evaluation, firm performance effectiveness, internal audit innovation, marketing management

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Authors: Eko Budi Santoso

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There is a growing interest in Corporate Social Responsibility (CSR) issues in developing countries such as Indonesia. Firms disclose their CSR activities, and some provide assurance to gain recognition as socially responsible firms. However, several of those socially responsible firms involve in tax scandals and raise a question of whether CSR disclosure is used to disguise firm misconduct or as a reflection of socially responsible firms. Specifically, whether firms engage in CSR disclosure and its assurance also responsible for their tax matters. This study examines the association between CSR disclosure and tax aggressiveness and the role of sustainability reporting assurance to the association. This research develops a modified index according to global reporting initiatives to measure CSR disclosure and various measurement for tax aggressiveness. Using a sample of Indonesian go public companies issued CSR disclosure, the empirical result shows that there is an association between CSR disclosure and tax aggressiveness. In addition, results also indicate sustainability reporting assurance moderate those association. The findings suggest that stakeholder in developing countries should examine carefully firms with active CSR disclosure before label it as socially responsible firms. JEL Classification: M14

Keywords: CSR disclosure, tax aggressiveness, assurance, business ethics

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Authors: Rehan Waheed, Abdul Shakoor

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Distortion produced during welding of thin metal plates is a problem in many industries. The purpose of this research was to study distortion produced during welding in 2mm Mild Steel plate by simulating the welding process using Finite Element Analysis. Simulation of welding process requires a couple field transient analyses. At first a transient thermal analysis is performed and the temperature obtained from thermal analysis is used as input in structural analysis to find distortion. An actual weld sample is prepared and the weld distortion produced is measured. The simulated and actual results were in quite agreement with each other and it has been found that there is profound deflection at center of plate. Temperature dependent material properties play significant role in prediction of weld distortion. The results of this research can be used for prediction and control of weld distortion in large steel structures by changing different weld parameters.

Keywords: welding simulation, FEA, welding distortion, temperature dependent mechanical properties

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Authors: Abdelkader Hocine, Abdelhakim Maizia

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The exponential growth of reinforced fibers composite materials use has prompted researchers to step up their work on the prediction of their reliability. Owing to differences between the properties of the materials used for the composite, the manufacturing processes, the load combinations and types of environment, the prediction of the reliability of composite materials has become a primary task. Through failure criteria, TSAI-WU and the maximum stress, the reliability of multilayer tubular structures under pressure is the subject of this paper, where the failure probability of is estimated by the method of Monte Carlo.

Keywords: composite, design, monte carlo, tubular structure, reliability

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Authors: Farah Al-Shatti, Elsayed El-Khamisi, Nabel Suleiman

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The study aimed at identifying the relationship between self-injury behavior and social skills among children with mild intellectual disability (ID) in the state of Kuwait. The sample of the study consisted of 65 males and females with ID; their ages ranged between 8 to 12 years. The study used a measure for rating self-injury behavior designed by the researcher; and a measure for rating social skills was designed. The results of the study showed that there was an increase in the percentages of the two dimensions of the self-injury behavior for children with ID; the self-injury behavior by child’s own body was higher than the self-injury behavior by environmental tools, additionally the results showed that there were statistically significant differences between males and females on the dimensions and total scorer of self-injury scale favor the males, and there were statistically significant differences between them on the dimensions of the social skills and total score favor the females, It also indicated that there was statistically significant negative relationship between the dimensions of the self-injury and the dimensions of the social skills for children with intellectual disability.

Keywords: mild intellectual disability, self injury behavior, social skills, state of Kuwait

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Authors: Ghizlane Barzi, Zineb Bamousse

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In an increasingly volatile global economic context characterized by recurring crises, the financial function of companies is called upon to play a strategic role not only in resource management but also in organizational resilience. The emergence of financial technologies (fintech) offers innovative tools capable of transforming this function by enhancing the efficiency of financial processes and increasing companies' ability to adapt and overcome economic shocks. However, despite the rapid rise of fintechs and their growing adoption by companies, there remain uncertainties regarding the real impact of these innovations on the financial resilience of organizations. Indeed, how do fintech-driven innovations transform the financial function, and to what extent does this transformation contribute to strengthening the financial resilience of companies in the face of contemporary crises? This research aims to explore these questions by examining the interrelationships between the financial function, fintech innovations, and corporate resilience, in order to identify optimization levers that could be adopted for better financial risk management.

Keywords: finance, financial function, fintech, resilience, innovation

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Authors: Sylwia Frydrych, Jörg Prokop, Michał Buszko

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This study investigates the complex relationship between corporate ESG (Environmental, Social, Governance) performance and stock price risk within the European market context. By analyzing a dataset of 435 companies across 19 European countries, the research assesses the impact of both combined ESG performance and its individual components on various risk measures, including volatility, idiosyncratic risk, systematic risk, and downside risk. The findings reveal that while overall ESG scores do not significantly influence stock price risk, disaggregating the ESG components uncovers significant relationships. Governance practices are shown to consistently reduce market risk, positioning them as critical in risk management. However, environmental engagement tends to increase risk, particularly in times of regulatory shifts like those introduced in the EU post-2018. This research provides valuable insights for investors and corporate managers on the nuanced roles of ESG factors in financial risk, emphasizing the need for careful consideration of each ESG pillar in decision-making processes.

Keywords: ESG performance, ESG factors, ESG pillars, ESG scores

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Authors: Jiabei Zhang

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Online programs developed for preparing qualified teachers have significantly increased over the years in the United States of America (USA). However, no online graduate programs for training adapted physical education (APE) teachers for children with significant developmental disabilities are currently available in the USA. The purpose of this study was to develop an online master’s degree program for the preparation of APE teachers to serve children with significant developmental disabilities. The characteristics demonstrated by children with significant developmental disabilities, the competencies required for certified APE teachers, and the evidence-based positive behavioral interventions (PBI) documented for teaching children with significant developmental disabilities were fully reviewed in this study. An online graduate program with 14 courses for 42 credit hours (3 credit hours per course) was then developed for training APE teachers to serve children with significant developmental disabilities. Included in this online program are five components: (a) 2 capstone courses, (b) 4 APE courses, (c) 4 PBI course, (d) 2 elective courses, and (e) 2 capstone courses. All courses will be delivered online through Desire2Learn administered by the Extended University Programs at Western Michigan University (WMU). An applicant who has a bachelor’s degree in physical education or special education is eligible for this proposed program. A student enrolled in this program is expected to complete all courses in 2.5 years while staying in their local area. This program will be submitted to the WMU curriculum committee for approval in the fall of 2018.

Keywords: adapted physical education, online program, teacher preparation, and significant disabilities

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Authors: David Abad, M. Encarnación Lucas-Pérez, Antonio Minguez-Vera, José Yagüe

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We examine explicitly the relation between the gender diversity on corporate boards and the levels of information asymmetry in the stock market. Based on prior evidence that suggests that the presence of women on director boards increases the quantity and quality of public disclosure by firms, we expect firms with higher gender diversity on their boards to show lower levels of information asymmetry in the market. Using a Spanish sample for the period 2004-2009, proxies for information asymmetry estimated from high-frequency data, and a system GMM methodology, we find that the gender diversity on boards is negative associated with the level of information asymmetry in the stock market. Our findings support legislative changes implemented to increase the presence of women on boards in several European countries by providing evidence that gender diverse boards have beneficial effects on stock markets.

Keywords: corporate board, female directors, gender diversity, information asymmetry, market microstructure

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Authors: Okoh Charity N.

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This study investigated the predictive power of preschoolers’ involvement in indoor and outdoor learning activities on their social learning skills in Niger state, Nigeria. Two research questions and two null hypotheses guided the study. Correlational research design was employed in the study. The population of the study consisted of 8,568 Nursery III preschoolers across the 549 preschools in the five Local Education Authorities in Niger State. A sample of 390 preschoolers drawn through multistage sampling procedure. Two instruments; Preschoolers’ Learning Activities Rating Scale (PLARS) and Preschoolers’ Social Learning Skills Rating Scale (PSLSRS) developed by the researcher were used for data collection. The reliability coefficients obtained for the PLARS and PSLSRS were 0.83 and 0.82, respectively. Data collected were analyzed using simple linear regression. Results showed that 37% of preschoolers’ social learning skills are predicted by their involvement in indoor learning activities, which is statistically significant (p < 0.05). It also shows that 11% of preschoolers’ social learning skills are predicted by their involvement in outdoor learning activities, which is statistically significant (p < 0.05). Therefore, it was recommended among others, that government and school administrators should employ qualified teachers who will stand as role models for preschoolers’ social skills development and provide indoor and outdoor activities and materials for preschoolers in schools.

Keywords: preschooler, social learning, indoor activities, outdoor activities

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Authors: Rashini Maduka, C. R. Wijesinghe, A. R. Weerasinghe

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Drug-drug interactions (DDIs) can happen when two or more drugs are taken together. Today DDIs have become a serious health issue due to adverse drug effects. In vivo and in vitro methods for identifying DDIs are time-consuming and costly. Therefore, in-silico-based approaches are preferred in DDI identification. Most machine learning models for DDI prediction are used chemical and biological drug properties as features. However, some drug features are not available and costly to extract. Therefore, it is better to make automatic feature engineering. Furthermore, people who have diabetes already suffer from other diseases and take more than one medicine together. Then adverse drug effects may happen to diabetic patients and cause unpleasant reactions in the body. In this study, we present a model with a graph convolutional autoencoder and a graph decoder using a dataset from DrugBank version 5.1.3. The main objective of the model is to identify unknown interactions between antidiabetic drugs and the drugs taken by diabetic patients for other diseases. We considered automatic feature engineering and used Known DDIs only as the input for the model. Our model has achieved 0.86 in AUC and 0.86 in AP.

Keywords: drug-drug interaction prediction, graph embedding, graph convolutional networks, adverse drug effects

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Authors: Synthia Atas Sari

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The purpose of this paper is to examine the impact of reward system which determine by government over the work of Board of Commissioners to implement good corporate governance in Indonesian state-owned enterprises. To do so, this study analyzes the adequacy of the remuneration, the job attractiveness, and the board commitment and dedication with the remuneration system. Qualitative method used to examine the significant features and challenges to the government policy over the remuneration determination for the board of commissioners to their roles. Data gathered through semi-structure in-depth interview to the twenty-one participants over nine Indonesian stated-owned enterprises and written documents. Findings of this study indicate that government policies over the remuneration system is not effective to increase the performance of board of commissioners in implementing good corporate governance in Indonesian stated-owned enterprises due to unattractiveness of the remuneration amount, demotivate active members, and conflict interest over members of the remuneration committee.

Keywords: reward system, board of commissioners, stated-owned enterprises, government policy

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Authors: Jiao Sun, Li Pan, Shijun Liu

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Recommender systems have been widely used in contemporary industry, and plenty of work has been done in this field to help users to identify items of interest. Collaborative Filtering (CF, for short) algorithm is an important technology in recommender systems. However, less work has been done in automobile recommendation system with the sharp increase of the amount of automobiles. What’s more, the computational speed is a major weakness for collaborative filtering technology. Therefore, using MapReduce framework to optimize the CF algorithm is a vital solution to this performance problem. In this paper, we present a recommendation of the users’ comment on industrial automobiles with various properties based on real world industrial datasets of user-automobile comment data collection, and provide recommendation for automobile providers and help them predict users’ comment on automobiles with new-coming property. Firstly, we solve the sparseness of matrix using previous construction of score matrix. Secondly, we solve the data normalization problem by removing dimensional effects from the raw data of automobiles, where different dimensions of automobile properties bring great error to the calculation of CF. Finally, we use the MapReduce framework to optimize the CF algorithm, and the computational speed has been improved times. UV decomposition used in this paper is an often used matrix factorization technology in CF algorithm, without calculating the interpolation weight of neighbors, which will be more convenient in industry.

Keywords: collaborative filtering, recommendation, data normalization, mapreduce

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Authors: Ravija Gunawardana, Banuka Athuraliya

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Machine learning has emerged as a powerful tool for disease diagnosis and prediction. The use of machine learning algorithms has the potential to improve the accuracy of disease prediction, thereby enabling medical professionals to provide more effective and personalized treatments. This study focuses on developing a machine-learning model for disease prediction using symptoms and X-ray images. The importance of this study lies in its potential to assist medical professionals in accurately diagnosing diseases, thereby improving patient outcomes. Respiratory diseases are a significant cause of morbidity and mortality worldwide, and chest X-rays are commonly used in the diagnosis of these diseases. However, accurately interpreting X-ray images requires significant expertise and can be time-consuming, making it difficult to diagnose respiratory diseases in a timely manner. By incorporating machine learning algorithms, we can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The study utilized the Mask R-CNN algorithm, which is a state-of-the-art method for object detection and segmentation in images, to process chest X-ray images. The model was trained and tested on a large dataset of patient information, which included both symptom data and X-ray images. The performance of the model was evaluated using a range of metrics, including accuracy, precision, recall, and F1-score. The results showed that the model achieved an accuracy rate of over 90%, indicating that it was able to accurately detect and segment regions of interest in the X-ray images. In addition to X-ray images, the study also incorporated symptoms as input data for disease prediction. The study used three different classifiers, namely Random Forest, K-Nearest Neighbor and Support Vector Machine, to predict diseases based on symptoms. These classifiers were trained and tested using the same dataset of patient information as the X-ray model. The results showed promising accuracy rates for predicting diseases using symptoms, with the ensemble learning techniques significantly improving the accuracy of disease prediction. The study's findings indicate that the use of machine learning algorithms can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The model developed in this study has the potential to assist medical professionals in diagnosing respiratory diseases more accurately and efficiently. However, it is important to note that the accuracy of the model can be affected by several factors, including the quality of the X-ray images, the size of the dataset used for training, and the complexity of the disease being diagnosed. In conclusion, the study demonstrated the potential of machine learning algorithms for disease prediction using symptoms and X-ray images. The use of these algorithms can improve the accuracy of disease diagnosis, ultimately leading to better patient care. Further research is needed to validate the model's accuracy and effectiveness in a clinical setting and to expand its application to other diseases.

Keywords: K-nearest neighbor, mask R-CNN, random forest, support vector machine

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Authors: Asra Yousuf, Ajaykumar Tannirkulum

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Inferring rural-urban migration trends can help design effective policies that promote better urban planning and rural development. In this paper, we describe how machine learning algorithms can be applied to predict internal migration decisions of people. We consider data collected from household surveys in Tamil Nadu to train our model. To measure the performance of the model, we use data on past migration from National Sample Survey Organisation of India. The factors for training the model include socioeconomic characteristic of each individual like age, gender, place of residence, outstanding loans, strength of the household, etc. and his past migration history. We perform a comparative analysis of the performance of a number of machine learning algorithm to determine their prediction accuracy. Our results show that machine learning algorithms provide a stronger prediction accuracy as compared to statistical models. Our goal through this research is to propose the use of data science techniques in understanding human decisions and behaviour in developing countries.

Keywords: development, migration, internal migration, machine learning, prediction

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Authors: Libor Zachoval, Daire O Broin, Oisin Cawley

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E-learning platforms, such as Blackboard have two major shortcomings: limited data capture as a result of the limitations of SCORM (Shareable Content Object Reference Model), and lack of incorporation of Artificial Intelligence (AI) and machine learning algorithms which could lead to better course adaptations. With the recent development of Experience Application Programming Interface (xAPI), a large amount of additional types of data can be captured and that opens a window of possibilities from which online education can benefit. In a corporate setting, where companies invest billions on the learning and development of their employees, some learner behaviours can be troublesome for they can hinder the knowledge development of a learner. Behaviours that hinder the knowledge development also raise ambiguity about learner’s knowledge mastery, specifically those related to gaming the system. Furthermore, a company receives little benefit from their investment if employees are passing courses without possessing the required knowledge and potential compliance risks may arise. Using xAPI and rules derived from a state-of-the-art review, we identified three learner behaviours, primarily related to guessing, in a corporate compliance course. The identified behaviours are: trying each option for a question, specifically for multiple-choice questions; selecting a single option for all the questions on the test; and continuously repeating tests upon failing as opposed to going over the learning material. These behaviours were detected on learners who repeated the test at least 4 times before passing the course. These findings suggest that gauging the mastery of a learner from multiple-choice questions test scores alone is a naive approach. Thus, next steps will consider the incorporation of additional data points, knowledge estimation models to model knowledge mastery of a learner more accurately, and analysis of the data for correlations between knowledge development and identified learner behaviours. Additional work could explore how learner behaviours could be utilised to make changes to a course. For example, course content may require modifications (certain sections of learning material may be shown to not be helpful to many learners to master the learning outcomes aimed at) or course design (such as the type and duration of feedback).

Keywords: artificial intelligence, corporate e-learning environment, knowledge maintenance, xAPI

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Authors: Queen Suraajini Rajendran, Sai Hung Cheung

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Statistical down scaling models are required to connect the global climate model outputs and the local weather variables for climate change impact prediction. For reliable climate change impact studies, the uncertainty associated with the model including natural variability, uncertainty in the climate model(s), down scaling model, model inadequacy and in the predicted results should be quantified appropriately. In this work, a new approach is developed by the authors for statistical classification, statistical down scaling and uncertainty assessment and is applied to Singapore rainfall. It is a robust Bayesian uncertainty analysis methodology and tools based on coupling dependent modeling error with classification and statistical down scaling models in a way that the dependency among modeling errors will impact the results of both classification and statistical down scaling model calibration and uncertainty analysis for future prediction. Singapore data are considered here and the uncertainty and prediction results are obtained. From the results obtained, directions of research for improvement are briefly presented.

Keywords: statistical downscaling, global climate model, climate change, uncertainty

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Authors: S. Sundarasen, I. Ibrahim

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This study examines whether the legal origin of a country alters the association between corruption and corporate performance in the East Asia and South East Asia Region. A total of 18,286 companies from 14 countries in the East Asia and South East Asia Region are tested using Generalized Least Square (GLS) panel and pool data analysis with the cross-section being the income level. The data is further analyzed in terms of high income, upper middle income and low-income countries within the East and South Asia region. The empirical results indicate that legal origin positively moderates the relationship between a country’s corruption level and firm performance. As for the sub-analysis, legal origin positively moderates only in the high and upper middle-income countries. As for the low-income countries, no significance is documented in both the common and civil law.

Keywords: corruption, performance, legal origin, East Asia and South East Asia Region

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Authors: N. Amruth Raj, Suja S. Nair

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The last six decades have borne witness to a radical change in the private sectors relationship with both the state and civil society. Firms have been increasingly called upon to adopt strategies beyond the financial aspects of their operations and consider the social and environmental impact of their business activities. In this context, many companies have modified their policies and activities and engaged into Corporate Social Responsibility (CSR) especially on Rural development in India. At the firm level, CSR is implemented through various practices, which aim to enhance the company’s social and environmental performance and may cover various topics. Examples of CSR practices are abundant in Andhra Pradesh relevant literature. For instance, in India especially at Andhra Pradesh companies like Amara Raaja requires from its suppliers to prohibit child labour, Nagarjuna Cements applies a series of programs for reducing its CO2 emissions, LANCO group of Industries addresses health and safety issues in the workplace whereas GVK works limited has adopted a series of policies for addressing human rights and environmental abuse related to its operations.

Keywords: CSR, limitations, need, objectives, rural development

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Authors: Cristian Păuna

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After the widespread release of electronic trading, automated trading systems have become a significant part of the business intelligence system of any modern financial investment company. An important part of the trades is made completely automatically today by computers using mathematical algorithms. The trading decisions are taken almost instantly by logical models and the orders are sent by low-latency automatic systems. This paper will present a real-time price prediction methodology designed especially for algorithmic trading. Based on the price cyclicality function, the methodology revealed will generate price cyclicality bands to predict the optimal levels for the entries and exits. In order to automate the trading decisions, the cyclicality bands will generate automated trading signals. We have found that the model can be used with good results to predict the changes in market behavior. Using these predictions, the model can automatically adapt the trading signals in real-time to maximize the trading results. The paper will reveal the methodology to optimize and implement this model in automated trading systems. After tests, it is proved that this methodology can be applied with good efficiency in different timeframes. Real trading results will be also displayed and analyzed in order to qualify the methodology and to compare it with other models. As a conclusion, it was found that the price prediction model using the price cyclicality function is a reliable trading methodology for algorithmic trading in the financial market.

Keywords: algorithmic trading, automated trading systems, financial markets, high-frequency trading, price prediction

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Authors: Man Fung Ho, Lap So, Jiaqi Zhang, Yuheng Zhao, Huiyang Lu, Tat Shing Choi, K. Y. Michael Wong

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Nowadays, a tremendous amount of data is available in the transportation system, enabling the development of various machine learning approaches to make short-term latency predictions. A natural question is then the choice of relevant information to enable accurate predictions. Using traffic data collected from the Taiwan Freeway System, we consider the prediction of short-term latency of a freeway segment with a length of 17 km covering 5 measurement points, each collecting vehicle-by-vehicle data through the electronic toll collection system. The processed data include the past latencies of the freeway segment with different time lags, the traffic conditions of the individual segments (the accumulations, the traffic fluxes, the entrance and exit rates), the total accumulations, and the weekday latency profiles obtained by Gaussian process regression of past data. We arrive at several important conclusions about how data should be refined to obtain accurate predictions, which have implications for future system-wide latency predictions. (1) We find that the prediction of median latency is much more accurate and meaningful than the prediction of average latency, as the latter is plagued by outliers. This is verified by machine-learning prediction using XGBoost that yields a 35% improvement in the mean square error of the 5-minute averaged latencies. (2) We find that the median latency of the segment 15 minutes ago is a very good baseline for performance comparison, and we have evidence that further improvement is achieved by machine learning approaches such as XGBoost and Long Short-Term Memory (LSTM). (3) By analyzing the feature importance score in XGBoost and calculating the mutual information between the inputs and the latencies to be predicted, we identify a sequence of inputs ranked in importance. It confirms that the past latencies are most informative of the predicted latencies, followed by the total accumulation, whereas inputs such as the entrance and exit rates are uninformative. It also confirms that the inputs are much less informative of the average latencies than the median latencies. (4) For predicting the latencies of segments composed of two or three sub-segments, summing up the predicted latencies of each sub-segment is more accurate than the one-step prediction of the whole segment, especially with the latency prediction of the downstream sub-segments trained to anticipate latencies several minutes ahead. The duration of the anticipation time is an increasing function of the traveling time of the upstream segment. The above findings have important implications to predicting the full set of latencies among the various locations in the freeway system.

Keywords: data refinement, machine learning, mutual information, short-term latency prediction

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Authors: Ade Oyedijo

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This paper reports the findings of a study on the strategic and organizational antecedents and effects of two opposing pay patterns used by some manufacturing companies in Lagos Nigeria with particular reference to the behavioural correlates of the pay strategies considered. The assumed relationship between pay strategies and some organizational correlates such as business and corporate strategies and firm size was considered problematic in view of their likely implications for employee motivation and citizenship behaviour and firm performance. The survey research method was used for the study. Structured, close ended questions were used to collect primary data from the respondents. A multipart Likert scale was used to measure the pay orientations of the respondent firms and the job and organizational involvement of the respondent employees. Utilizing hierarchical linear regression method and "t-test" to analyze the data obtained from 48 manufacturing companies of various sizes and strategies, it was found that the dominant pattern of employee compensation in the sampled manufacturing companies. The study also revealed that the choice of a pay strategy was strongly influenced by organizational size as well as the type of business and corporate level strategies adopted by afirm. Firms pursuing a strategy of related and unrelated diversification are more likely to adopt the algorithmic compensation system than single product firms because of their relatively larger size and scope. However; firms that pursue a competitive advantage through a business level strategy of cost efficiency are more likely to use the experiential, variable pay strategy. The study found that an algorithmic compensation strategy is as effective as experiential compensation strategy in the promotion of organizational citizenship behaviour and motivation of employees.

Keywords: compensation, corporate strategy, business strategy, motivation, citizenship behaviour, algorithmic, experiential, organizational commitment, work environment

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Authors: Nada Azhar

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The aim of the paper is to investigate the effect of corporate social responsibility (CSR) CSR on the National Commercial Bank (NCB) in Saudi Arabia. In order to achieve this, a case study was made of the CSR activities of this bank from the perspective of its branch managers. The NCB was chosen as it was one of the first Saudi banks to engage in CSR and currently has a wide range of CSR initiatives. A qualitative research method was used. Open-ended questionnaires were administered to eighty branch managers of the NCB, with fifty-five usable questionnaires returned and twenty managers were interviewed as part of the primary research. Data from both questionnaires and interviews were analysed using qualitative content analysis. Six themes emerged from the questionnaire findings were used to develop the interview questions. These themes are the following: Awareness of employees about CSR in the NCB; CSR activities as a type of investment; Government and media support; Increased employee loyalty in the NCB; Prestige and profit to the NCB; and View of CSR in Islam. This paper makes a theoretical contribution in that it investigates and increases understanding of the effect of CSR on the NCB in Saudi Arabia. In addition, it makes a practical contribution by making recommendations which can support the development of CSR in the NCB. A limitation of the paper is that it is a case study of only one bank. It is therefore recommended that future research could be conducted with other banks in Saudi Arabia, or indeed, with a range of other types of firm within the financial services area in Saudi Arabia. In this way, the same issues could be explored but with a greater potential generalisability of findings of CSR within the Saudi Arabian financial services industry. In addition, this paper takes a qualitative approach and it is suggested that future research be carried out using mixed methods, which could provide a greater depth of analysis.

Keywords: branch managers, corporate social responsibility, national commercial bank, Saudi Arabia

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Authors: Y. Tahat, T. Dunne, S. Fifield, D. Power

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Information about a firm’s usage of Financial Instruments (FIs) plays a very important role in determining its financial position and performance. Yet accounting standard-setters have encountered problems when deciding on the FI-related disclosures which firms must make. The primary objective of this paper is to review the extant literature on FI disclosure. This objective is achieved by surveying the literature on: the corporate usage of FIs; the different accounting standards adopted concerning FIs; and empirical studies on FI disclosure. This review concludes that the current research on FI disclosure has generated a number of useful insights. In particular, the paper reports that: FIs are a very important risk management mechanism in ensuring that companies have the cash available to make value-enhancing investments, however, without a clear set of risk management objectives, using such instruments can be dangerous; accounting standards concerning FIs have resulted in enhanced transparency about the usage of these instruments; and FI-related information is a key input into investors’ decision-making processes. Finally, the paper provides a number of suggestions for future research in the area.

Keywords: financial instruments, financial reporting, accounting standards, value relevance, corporate disclosure

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Authors: Gebrehiwot H., Giorgis Bahita

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Firms in Africa experience less financial market in comparison to other emerging and developed countries, thus lagging behind the rest of the world in terms of innovation and growth. Though there are different factors to be considered, underdeveloped financial systems take the lion's share in hindering firm innovation and growth in Africa. Insufficient capacity to innovate is one of the problems facing African businesses. Moreover, a critical challenge faced by firms in Africa is access to finance and the inability of financially constrained firms to grow. Only little is known about how different sources of finance affect firm innovation and growth in Africa, specifically the formal and informal finance effect on firm innovation and growth. This study's aim is to address this gap by using formal and informal finance for working capital and fixed capital and its role in firm innovation and firm growth using firm-level data from the World Bank enterprise survey 2006-2019 with a total of 5661 sample firms from 14 countries based on available data on the selected variables. Additionally, this study examines factors for accessing credit from a formal financial institution. The logit model is used to examine the effect of finance on a firm’s innovation and factors to access formal finance, while the Ordinary List Square (OLS) regression mode is used to investigate the effect of finance on firm growth. 2SLS instrumental variables are used to address the possible endogeneity problem in firm growth and finance-innovation relationships. A result from the logistic regression indicates that both formal and informal finance used for working capital and investment in fixed capital was found to have a significant positive association with product and process innovation. In the case of finance and growth, finding show that positive association of both formal and informal financing to working capital and new investment in fixed capital though the informal has positive relations to firm growth as measured by sale growth but no significant association as measured by employment growth. Formal finance shows more magnitude of effect on innovation and growth when firms use formal finance to finance investment in fixed capital, while informal finance show less compared to formal finance and this confirms previous studies as informal is mainly used for working capital in underdeveloped economies like Africa. The factors that determine credit access: Age, firm size, managerial experience, exporting, gender, and foreign ownership are found to have significant determinant factors in accessing credit from formal and informal sources among the selected sample countries.

Keywords: formal finance, informal finance, innovation, growth

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Authors: Radhika Ralhan

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With the formalization of 2030, Global Agenda for Sustainable Development nations have instantaneously geared up their efforts towards the implementation of a comprehensive list of global goals. The criticality of Sustainable Development Goals (SDGs) is imperative, as it will define the course and pace of development for the next 15 years. This development will entail transformational shifts towards a green and inclusive growth. Leadership, investments and technology will constitute as key ingredients of this transformational shift and governance will emerge as a one of the most significant driver of the global 2030 agenda. Corporate Governance is viewed as one of the key force to accelerate the momentum of SDGs and initiate these transformational shifts. Many senior level leaders have reinstated their conviction that adopting a triple bottom line approach will play an imperative role in transforming the entire industrial sector. In the Indian context, the above occurrence bears an intriguing facet, as the framing of SDGs in the global scenario coincided with the emergence of mandatory Corporate Social Responsibility (CSR) Rules in India at national level. As one of the leading democracies in the world, India is among few countries to formally mandate companies to spend 2% from their CSR funds under Section 135 of The New Companies Act 2013. The overarching framework of SDGs correlates to the areas of CSR interventions as mentioned in the Schedule VII of Section 135. As one of the legitimate stakeholders, business leaders have expressed their commitments to their respective governments, to reorient the entire fabric of their companies to scale up global priorities. This is explicitly seen in the case of India where leading business entities have converged national government priorities of Clean India, Make in India and Skill India by actively participating in the campaigns and incorporating these programmes within the ambit of their CSR policies. However, the CSR Act has received mixed responses with associated concerns such as the onus of doing what the government has to do, mandatory reporting mechanisms, policy disclosures, personnel handling CSR portfolios etc. The overall objective of the paper, therefore, rests in analyzing the discourse of CSR and the perspectives of Indian Inc. in imbibing the principles of SDGs within their business polices and operations. Through primary and secondary research analysis, the paper attempts to outline the diverse challenges that are being faced by Indian businesses while establishing the business case of sustainable responsibility. Some of the principal questions that paper addresses are: What are the SDG priorities for India Inc. as per their respective industry sectors? How can corporate policies imbibe the SDGs principles? How can the global concerns in form of SDGs align with the national CSR mandate and development issues? What initiatives have been undertaken by the companies to integrate their long term business strategy and sustainability? The paper will also reinstate an approach or a way forward that will enable businesses to proceed beyond compliance and accentuate the principles of responsibility and transparency within their operational framework.

Keywords: corporate social responsibility, CSR, India Inc., section 135, new companies act 2013, sustainable development goals, SDGs, sustainability, corporate governance

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Authors: Peter Crow

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Boards and directors have been subjects of much scholarly research and public interest over several decades, more so since the succession of high profile company failures of the early 2000s. An array of research outputs including information, correlations, descriptions, models, hypotheses and theories have been reported. While some of this research has shed light on aspects of the board–performance relationship and on board tasks and behaviours, the nature and characteristics of the supposed board–performance relationship remain undetermined. That satisfactory explanations of how boards influence company performance have yet to emerge is a significant blind spot. Yet the board is ultimately responsible for company performance, in accordance with the wishes of shareholders. The aim of this paper is to explore corporate governance and board practice through the lens of strategic management, and to take tentative steps towards a new conception of corporate governance. The findings of a recent longitudinal multiple-case study designed to explore the board’s involvement in strategic management are reported. Qualitative and quantitative data was collected from two quasi-public large companies in New Zealand including from first-hand observations of boards in session, semi-structured interviews with chief executives and chairmen and the inspection of company and board documentation. A synthetic timeline framework was used to collate the financial, board structure, board activity and decision-making data, in order to provide a holistic perspective. Decision sequences were identified, and realist techniques of abduction and retroduction were iteratively applied to analyse the multi-year data set. Using several models previously proposed in the literature as a guide, conjectures were formed, tested and refined—the culmination of which was a provisional model of how boards can influence performance via strategic management. The model builds on both existing theoretical perspectives and theoretical models proposed in the corporate governance and strategic management literature. This paper seeks to add to the understanding of how boards can make meaningful contributions to value creation via strategic management, and to comment on the qualities of directors, social interactions in boardrooms and other circumstances within which influence might be possible given the highly contingent relationship between board activity and business performance outcomes.

Keywords: board practice, case study, corporate governance, strategic management

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Authors: Marian Sorin Ionescu, Olivia Negoita, Cosmin Dobrin

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This scientific communication reports and discusses learning models adaptable to modern business problems and models specific to digital concepts and paradigms. In the PAC (probably approximately correct) learning model approach, in which the learning process begins by receiving a batch of learning examples, the set of learning processes is used to acquire a hypothesis, and when the learning process is fully used, this hypothesis is used in the prediction of new operational examples. For complex business models, a lot of models should be introduced and evaluated to estimate the induced results so that the totality of the results are used to develop a predictive rule, which anticipates the choice of new models. In opposition, for online learning-type processes, there is no separation between the learning (training) and predictive phase. Every time a business model is approached, a test example is considered from the beginning until the prediction of the appearance of a model considered correct from the point of view of the business decision. After choosing choice a part of the business model, the label with the logical value "true" is known. Some of the business models are used as examples of learning (training), which helps to improve the prediction mechanisms for future business models.

Keywords: machine learning, business models, convex analysis, online learning

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