Search results for: empirical analysis

Authors: Alimshan Faizulayev, Eralp Bektas, Abdul Ghafar Ismail, Bezhan Rustamov

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This paper performs empirical analysis on determinants of profitability in Islamic and Conventional Banks. The main focus of this study is to evaluate and measure of financial performance of Islamic banking firms operating in Egypt, Iran, Malaysia, Pakistan, Turkey, UAE in contrast to Conventional ones in those countries. To evaluate empirically performance of the banks, various financial ratios are employed. We measure performance in terms of liquidity, profitability, solvency, and efficiency. In this work, t-test, F-test, and OLS analysis are used to make hypothesis tests. Our findings reveal that there are similarities and differences in profitability determinants of Islamic and Conventional banking firms. The cost to revenue ratio has inverse relationship with profitability indicators in both banking systems. However, there are differences in financial performances between Conventional Banks and Islamic banks which are found in overall picture of all banks in terms of net income margin.

Keywords: Islamic banking, conventional banking, GDP growth, emerging market economies

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Authors: Tahani H. Alluhaybi, Leila Mejrissi

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In the present theoretical study, we investigated adiabatically the electronic structure of the (BaNa)+ by the use of the ab initio calculation. We optimized a large atomic GTO basis set for Na and Ba atoms. The (BaNa)+ molecular ion is considered a two-electron thank to a non-empirical pseudo-potentials approach applied to Ba and Na cores with the Core Polarization Potentials operator (CPP). Then, we performed the Full Configuration Interaction (FCI) method. Accordingly, we calculated the adiabatic Potential Energy Curves (PECs) and their spectroscopic constants (well depth De, transition energies Te, the equilibrium distances Re, vibrational constant ⍵e, and anharmonic constant ⍵exe) for 10 electronic states in Σ+ symmetry. Then we determined the vibrational level energies and their spacing, and the electric Permanent Dipole Moments (PDM).

Keywords: Ab initio, dipole moment, non-empirical pseudo-potential, potential energy curves, spectroscopic constants, vibrational energy

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Authors: Chante Van Tonder, Bart Bossink, Chris Schachtebeck, Cecile Nieuwenhuizen

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Small and Medium-Sized Enterprises (SMEs) play a key role in national economies around the world, being contributors to economic and social well-being. Due to this, the success, growth and competitiveness of SMEs are critical. However, there are many factors that undermine this, such as resource constraints, poor information communication infrastructure (ICT), skills shortages and poor management. The Fourth Industrial Revolution offers new tools and opportunities such as digital transformation and business model innovation (BMI) to the SME sector to enhance its competitiveness. Adopting and leveraging digital technologies such as cloud, mobile technologies, big data and analytics can significantly improve business efficiencies, value proposition and customer experiences. Digital transformation can contribute to the growth and competitiveness of SMEs. However, SMEs are lagging behind in the participation of digital transformation. Extant research lacks conceptual and empirical research on how digital transformation drives BMI and the impact it has on the growth and competitiveness of SMEs. The purpose of the study is, therefore, to close this gap by developing and empirically validating a conceptual model to determine if SMEs are achieving BMI through digital transformation and how this is impacting the growth, competitiveness and overall business performance. An empirical study is being conducted on 300 SMEs, consisting of 150 South-African and 150 Dutch SMEs, to achieve this purpose. Structural equation modeling is used, since it is a multivariate statistical analysis technique that is used to analyse structural relationships and is a suitable research method to test the hypotheses in the model. Empirical research is needed to gather more insight into how and if SMEs are digitally transformed and how BMI can be driven through digital transformation. The findings of this study can be used by SME business owners, managers and employees at all levels. The findings will indicate if digital transformation can indeed impact the growth, competitiveness and overall performance of an SME, reiterating the importance and potential benefits of adopting digital technologies. In addition, the findings will also exhibit how BMI can be achieved in light of digital transformation. This study contributes to the body of knowledge in a highly relevant and important topic in management studies by analysing the impact of digital transformation on BMI on a large number of SMEs that are distinctly different in economic and cultural factors

Keywords: business models, business model innovation, digital transformation, SMEs

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Authors: Ching-I Chen

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The continued rise of e-commerce is the main driver of the rapid growth of global online purchase. Consumers can nearly buy everything they want at one occasion through online shopping. The purchase behavior models which focus on single product category are insufficient to describe online shopping behavior. Therefore, analysis of multi-category purchase gets more and more popular. For example, market basket analysis explores customers’ buying tendency of the association between product categories. The information derived from market basket analysis facilitates to make cross-selling strategies and product recommendation system. To detect the association between different product categories, we use the market basket analysis with the multidimensional scaling technique to build an associated map which describes how likely multiple product categories are bought at the same time. Besides, we also build an inter-purchase time model for associated products to describe how likely a product will be bought after its associated product is bought. We classify inter-purchase time behaviors of multi-category products into nine types, and use a mixture regression model to integrate those behaviors under our assumptions of purchase sequences. Our sample data is from comScore which provides a panelist-label database that captures detailed browsing and buying behavior of internet users across the United States. Finding the inter-purchase time from books to movie is shorter than the inter-purchase time from movies to books. According to the model analysis and empirical results, this research finally proposes the applications and recommendations in the management.

Keywords: multiple-category purchase behavior, inter-purchase time, market basket analysis, e-commerce

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Authors: Nihal Kartaltepe Behram, Göksel Ataman, Dila Okçu

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With the development of foreign direct investments, the social, cultural, political and economic interactions between countries and institutions have become visible and they have become determining factors for the strategic structuring and market goals. In this context the purpose of this study is to investigate the effects of cultural distance and institutions on foreign direct investment choices in terms of location and investment model. For international establishments, the concept of culture, as well as the concept of cultural distance, is taken specifically into consideration, especially in the selection of methods for entering the market. In the researches and empirical studies conducted, a direct relationship between cultural distance and foreign direct investments is set and institutions and effective variable factors are examined at the level of defining the investment types. When the detailed calculation strategies and empirical researches and studies are taken into consideration, the most common methods for determining the direct investment model, considering the cultural distances, are full-ownership enterprises and joint ventures. Also, when all of the factors affecting the investments are taken into consideration, it was seen that the effect of institutions such as Government Intervention, Intellectual Property Rights, Corruption and Contract Enforcements is very important. Furthermore agglomeration is more intense and effective on the investment, compared to other factors. China has been selected as the target country, due to its effectiveness in world economy and its contributions to developing countries, which has commercial relationships with. Qualitative research methods are used for this study conducted, to measure the effects of determinative variable factors in the hypotheses of study, on the direct foreign investors and to evaluate the findings. In this study in-depth interview is used as a data collection method and the data analysis is made through descriptive analysis. Foreign Direct Investments are so reactive to institutions and cultural distance is identified by all interviews and analysis. On the other hand, agglomeration is the most strong determiner factor on foreign direct investors in Chinese Market. The reason of this factors, which comprise the sectorial aggregate, are not the strongest factors as agglomeration that the most important finding. We expect that this study became a beneficial guideline for developed and developing countries and local and national institutions’ strategic plans.

Keywords: China, cultural distance, Foreign Direct Investments, institutions

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Authors: Vijay Kumar Bajpai, Sudhir Kumar Singh

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In this study, the performance analyses of the twenty five coal-fired power plants (CFPPs) used for electricity generation are carried out through various data envelopment analysis (DEA) models. Three efficiency indices are defined and pursued. During the calculation of the operational performance, energy and non-energy variables are used as input, and net electricity produced is used as desired output. CO2 emitted to the environment is used as the undesired output in the computation of the pure environmental performance while in Model-3 CO2 emissions is considered as detrimental input in the calculation of operational and environmental performance. Empirical results show that most of the plants are operating in increasing returns to scale region and Mettur plant is efficient one with regards to energy use and environment. The result also indicates that the undesirable output effect is insignificant in the research sample. The present study will provide clues to plant operators towards raising the operational and environmental performance of CFPPs.

Keywords: coal fired power plants, environmental performance, data envelopment analysis, operational performance

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Authors: Saurabh Sharma, Yong Sun, Bruce Vernham

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Accurate prediction of NOx emission is a continuous challenge in the field of diesel engine-out emission modeling. Performing experiments for each conditions and scenario cost significant amount of money and man hours, therefore model-based development strategy has been implemented in order to solve that issue. NOx formation is highly dependent on the burn gas temperature and the O₂ concentration inside the cylinder. The current empirical models are developed by calibrating the parameters representing the engine operating conditions with respect to the measured NOx. This makes the prediction of purely empirical models limited to the region where it has been calibrated. An alternative solution to that is presented in this paper, which focus on the utilization of in-cylinder combustion parameters to form a predictive semi-empirical NOx model. The result of this work is shown by developing a fast and predictive NOx model by using the physical parameters and empirical correlation. The model is developed based on the steady state data collected at entire operating region of the engine and the predictive combustion model, which is developed in Gamma Technology (GT)-Power by using Direct Injected (DI)-Pulse combustion object. In this approach, temperature in both burned and unburnt zone is considered during the combustion period i.e. from Intake Valve Closing (IVC) to Exhaust Valve Opening (EVO). Also, the oxygen concentration consumed in burnt zone and trapped fuel mass is also considered while developing the reported model.  Several statistical methods are used to construct the model, including individual machine learning methods and ensemble machine learning methods. A detailed validation of the model on multiple diesel engines is reported in this work. Substantial numbers of cases are tested for different engine configurations over a large span of speed and load points. Different sweeps of operating conditions such as Exhaust Gas Recirculation (EGR), injection timing and Variable Valve Timing (VVT) are also considered for the validation. Model shows a very good predictability and robustness at both sea level and altitude condition with different ambient conditions. The various advantages such as high accuracy and robustness at different operating conditions, low computational time and lower number of data points requires for the calibration establishes the platform where the model-based approach can be used for the engine calibration and development process. Moreover, the focus of this work is towards establishing a framework for the future model development for other various targets such as soot, Combustion Noise Level (CNL), NO₂/NOx ratio etc.

Keywords: diesel engine, machine learning, NOₓ emission, semi-empirical

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Authors: Sertac Arslan, Sezer Kefeli

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In this paper, firstly supercavitating phenomenon and supercavity shape design parameters are explained and then drag force calculation methods of high speed supercavitating torpedoes are investigated with numerical techniques and verified with empirical studies. In order to reach huge speeds such as 200, 300 knots for underwater vehicles, hydrodynamic hull drag force which is proportional to density of water (ρ) and square of speed should be reduced. Conventional heavy weight torpedoes could reach up to ~50 knots by classic underwater hydrodynamic techniques. However, to exceed 50 knots and reach about 200 knots speeds, hydrodynamic viscous forces must be reduced or eliminated completely. This requirement revives supercavitation phenomena that could be implemented to conventional torpedoes. Supercavitation is the use of cavitation effects to create a gas bubble, allowing the torpedo to move at huge speed through the water by being fully developed cavitation bubble. When the torpedo moves in a cavitation envelope due to cavitator in nose section and solid fuel rocket engine in rear section, this kind of torpedoes could be entitled as Supercavitating Torpedoes. There are two types of cavitation; first one is natural cavitation, and second one is ventilated cavitation. In this study, disk cavitator is modeled with natural cavitation and supercavitation phenomenon parameters are studied. Moreover, drag force calculation is performed for disk shape cavitator with numerical techniques and compared via empirical studies. Drag forces are calculated with computational fluid dynamics methods and different empirical methods. Numerical calculation method is developed by comparing with empirical results. In verification study cavitation number (σ), drag coefficient (CD) and drag force (D), cavity wall velocity (U

Keywords: cavity envelope, CFD, high speed underwater vehicles, supercavitation, supercavity flows

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Authors: Helena Baffoe

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The US has developed numerous programs over the past 50 years to enhance the lives of those who suffer from mental health illnesses and substance abuse, as well as the effectiveness of their treatments. Despite these advances over the past 50 years, there hasn't been a corresponding improvement in American public attitudes and beliefs about mental health disorders and substance abuse. Highly publicized acts of violence frequently elicit comments that blame the perpetrator's perceived mental health disorder since such people are thought to be substance abusers. Despite these strong public beliefs and perception about mental disorder and substance abuse, concreate empirical evidence that entail this perception is lacking, and evidence of their effectiveness has not been integrated. A rich data was collected from Substance Abuse and Mental Health Services Administration (SAMHSA) with a hypothesis that people who are diagnosed with a mental health disorder are likely to be diagnosed with substance abuse using logit regression analysis and Instrumental Variable. It was found that depressive, anxiety, and trauma/stressor mental disorders constitute the most common mental disorder in the United States, and the study could not find statistically significant evidence that being diagnosed with these leading mental health disorders in the United States does necessarily imply that such a patient is diagnosed with substances abuse. Thus, the public has a misconception of mental health and substance abuse issues, and social workers' responsibilities are outlined in order to assist ameliorate this attitude and perception.

Keywords: mental health disorder, substance abuse, role of a social worker, evidence based research

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Authors: D. Sigirli

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The Receiver Operating Characteristic (ROC) curve is a commonly used statistical tool for evaluating the diagnostic performance of screening and diagnostic test with continuous or ordinal scale results which aims to predict the presence or absence probability of a condition, usually a disease. When the test results were measured as numeric values, sensitivity and specificity can be computed across all possible threshold values which discriminate the subjects as diseased and non-diseased. There are infinite numbers of possible decision thresholds along the continuum of the test results. The ROC curve presents the trade-off between sensitivity and the 1-specificity as the threshold changes. The empirical ROC curve which is a non-parametric estimator of the ROC curve is robust and it represents data accurately. However, especially for small sample sizes, it has a problem of variability and as it is a step function there can be different false positive rates for a true positive rate value and vice versa. Besides, the estimated ROC curve being in a jagged form, since the true ROC curve is a smooth curve, it underestimates the true ROC curve. Since the true ROC curve is assumed to be smooth, several smoothing methods have been explored to smooth a ROC curve. These include using kernel estimates, using log-concave densities, to fit parameters for the specified density function to the data with the maximum-likelihood fitting of univariate distributions or to create a probability distribution by fitting the specified distribution to the data nd using smooth versions of the empirical distribution functions. In the present paper, we aimed to propose a smooth ROC curve estimation based on the boundary corrected kernel function and to compare the performances of ROC curve smoothing methods for the diagnostic test results coming from different distributions in different sample sizes. We performed simulation study to compare the performances of different methods for different scenarios with 1000 repetitions. It is seen that the performance of the proposed method was typically better than that of the empirical ROC curve and only slightly worse compared to the binormal model when in fact the underlying samples were generated from the normal distribution.

Keywords: empirical estimator, kernel function, smoothing, receiver operating characteristic curve

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Authors: Rafael Tedrus Bento

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The analysis of proportionality as a test is raised as a basic foundation for the achievement of Fundamental Rights. We used legal dogmatics and empirical analysis to seek the expected results, from the reading of the RV Oakes trial by the Supreme Court of Canada. In cases involving freedom of expression, two tests are used to resolve disputes. The first examines whether, in fact, the case can be characterized as a violation of freedom of expression; the second assesses whether this violation can be justified by the reasonable limit clause. This test was defined in the RV Oakes trial by the Supreme Court of Canada, concluding with the Oakes Test, used worldwide as a proportionality test. Resulting is a proportionality between the effects of the limiting measure and the objective - the more serious the harmful effects of a measure, the more important the objective must be.

Keywords: Oakes, proportionality, fundamental rights, Supreme Court of Canada

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Authors: Tanusha Raniga, Michael Boecker, Maud Mthembu

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This paper considers the formal employment sector, human capacity development and economic capital of single-mother households’ as they strive to sustain livelihoods. This paper advances empirical data in the field of economic and social development policy. The correlation between educational level, human capacity development and economic self-reliance of single-mother households is considered. This paper presents empirical evidence obtained from qualitative in-depth interviews conducted with twenty-five single mothers who were working in the formal work sector in Hagen, Germany and two provinces, namely KwaZulu-Natal and Gauteng in South Africa. This is an underexplored research area as most of the international literature focuses on pathology and victimhood related to single-mother households. Instead, this paper presents the biographic profiles and discusses two key themes that emerged from the data analysis namely; formal and informal streams of income enhanced human capital development through access to further education and training opportunities. The women perceived these themes as facilitating factors which helped them sustain their households. The paper presents some suggestions for policymakers and social work practitioners to consider to improve support systems and avoid economic exclusion of single mothers who work within the first economy.

Keywords: single mothers, formal work sector, economic capital, human capital

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Authors: Chen Xi, Lao Xuerui, Li Junjie, Jiang Yike, Wang Hanwei, Zeng Zihao

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To further promote the development of smart cities, the microscopic "nerve endings" of the City Intelligent Model (CIM) are extended to be more sensitive. In this paper, we develop a pedestrian trajectory twin terminal based on the CIM and CNN technology. It also uses 5G networks, architectural and geoinformatics technologies, convolutional neural networks, combined with deep learning networks for human behaviour recognition models, to provide empirical data such as 'pedestrian flow data and human behavioural characteristics data', and ultimately form spatial performance evaluation criteria and spatial performance warning systems, to make the empirical data accurate and intelligent for prediction and decision making.

Keywords: urban planning, urban governance, CIM, artificial intelligence, convolutional neural network

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Authors: J. Erina, I. Erins

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The goals of the present research are to estimate Six Sigma implementation in Latvian commercial banks and to identify the perceived benefits of its implementation. To achieve the goals, the authors used a sequential explanatory method. To obtain empirical data, the authors have developed the questionnaire and adapted it for the employees of Latvian commercial banks. The questions are related to Six Sigma implementation and its perceived benefits. The questionnaire mainly consists of closed questions, the evaluation of which is based on 5 point Likert scale. The obtained empirical data has shown that of the two hypotheses put forward in the present research Hypothesis 1 has to be rejected, while Hypothesis 2 has been partially confirmed. The authors have also faced some research limitations related to the fact that the participants in the questionnaire belong to different rank of the organization hierarchy.

Keywords: six sigma, quality, commercial banking sector, latvian

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Authors: Monica Santi, Evita Puspitasari

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International Financial Reporting Standard (IFRS) has developed the principle based accounting standard. Based on this, IASB then eliminated the conservatism concept within accounting framework. Conservatism concept represents a prudent reaction to uncertainty to try to ensure that uncertainties and risk inherent in business situations are adequately considered. The conservatism concept has two ingredients: conditional conservatism or ex-post (news depending prudence) and unconditional conservatism or ex-ante (news-independent prudence). IFRS in substance disregards the unconditional conservatism because the unconditional conservatism can cause the understatement assets or overstated liabilities, and eventually the financial statement would be irrelevance since the information does not represent the real fact. Therefore, the IASB eliminate the conservatism concept. However, it does not decrease the practice of unconditional conservatism in the financial statement reporting. Therefore, we expected the earnings quality would be affected because of this situation, even though the IFRS implementation was expected to increase the earnings quality. The objective of this study was to provide empirical findings about the unconditional conservatism and the earnings quality before and after the IFRS adoption. The earnings per accrual measure were used as the proxy for the unconditional conservatism. If the earnings per accrual were negative (positive), it meant the company was classified as the conservative (not conservative). The earnings quality was defined as the ability of the earnings in reflecting the future earnings by considering the earnings persistence and stability. We used the earnings response coefficient (ERC) as the proxy for the earnings quality. ERC measured the extant of a security’s abnormal market return in response to the unexpected component of reporting earning of the firm issuing that security. The higher ERC indicated the higher earnings quality. The manufacturing companies listed in the Indonesian Stock Exchange (IDX) were used as the sample companies, and the 2009-2010 period was used to represent the condition before the IFRS adoption, and 2011-2013 was used to represent the condition after the IFRS adoption. Data was analyzed using the Mann-Whitney test and regression analysis. We used the firm size as the control variable with the consideration the firm size would affect the earnings quality of the company. This study had proved that the unconditional conservatism had not changed, either before and after the IFRS adoption period. However, we found the different findings for the earnings quality. The earnings quality had decreased after the IFRS adoption period. This empirical results implied that the earnings quality before the IFRS adoption was higher. This study also had found that the unconditional conservatism positively influenced the earnings quality insignificantly. The findings implied that the implementation of the IFRS had not decreased the unconditional conservatism practice and has not altered the earnings quality of the manufacturing company. Further, we found that the unconditional conservatism did not affect the earnings quality. Eventhough the empirical result shows that the unconditional conservatism gave positive influence to the earnings quality, but the influence was not significant. Thus, we concluded that the implementation of the IFRS did not increase the earnings quality.

Keywords: earnings quality, earnings response coefficient, IFRS Adoption, unconditional conservatism

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Authors: Yu Zhao, Min Zhang, Yuanqian Wang, Qiuyu Yu

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The prediction of aesthetics of apparel is helpful for the development of a new type of apparel. This study is to build the quantitative relationship between the aesthetics and its design parameters. In particular, women’s pants have been preliminarily studied. This aforementioned relationship has been carried out by statistical analysis. The contributions of this study include the development of a more personalized apparel design mechanism and the provision of some empirical knowledge for the development of other products in the aspect of aesthetics.

Keywords: aesthetics, crease line, cropped straight leg pants, knee width

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Authors: Hazel Pettifor, Charlie Wilson, David Mccollum, Oreane Edelenbosch

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Vehicle purchase is a technology adoption decision that will strongly influence future energy and emission outcomes. Global integrated assessment models (IAMs) provide valuable insights into the medium and long terms effects of socio-economic development, technological change and climate policy. In this paper we present a unique and transparent approach for improving the behavioural representation of these models by incorporating social influence effects to more accurately represent consumer choice. This work draws together strong conceptual thinking and robust empirical evidence to introduce heterogeneous and interconnected consumers who vary in their aversion to new technologies. Focussing on vehicle choice, we conduct novel empirical research to parameterise consumer risk aversion and how this is shaped by social and cultural influences. We find robust evidence for social influence effects, and variation between countries as a function of cultural differences. We then formulate an approach to modelling social influence which is implementable in both simulation and optimisation-type models. We use two global integrated assessment models (IMAGE and MESSAGE) to analyse four scenarios that introduce social influence and cultural differences between regions. These scenarios allow us to explore the interactions between consumer preferences and social influence. We find that incorporating social influence effects into global models accelerates the early deployment of electric vehicles and stimulates more widespread deployment across adopter groups. Incorporating cultural variation leads to significant differences in deployment between culturally divergent regions such as the USA and China. Our analysis significantly extends the ability of global integrated assessment models to provide policy-relevant analysis grounded in real-world processes.

Keywords: behavioural realism, electric vehicles, social influence, vehicle choice

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Authors: Eti Mizrahi, Gizem İntepe

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The geographical location of Turkey that stretches from Asia to Europe and Russia to Africa makes it an important logistics hub in the region. Although logistics is a developing sector in Turkey, the stock market representation is still low with only two companies listed in Turkey’s stock exchange since 2010. In this paper, we use the daily values of these two listed stocks as a benchmark for the logistics sector. After modeling logistics stock prices, an empirical examination is conducted between the existing logistics indices and these stock prices. The paper investigates whether the measures of logistics stocks are correlated with newly available logistics indices. It also shows the reflection of the economic activity in the logistics sector on the stock exchange market. The results presented in this paper are the first analysis of the behavior of logistics indices and logistics stock prices for Turkey.

Keywords: forecasting, logistic stock exchange, modeling, Africa

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Authors: S. B. Provost, Susan Sheng

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An alternative bivariate density estimation methodology is introduced in this presentation. The proposed approach involves estimating the density function associated with the marginal distribution of each of the two variables by means of the saddlepoint approximation technique and applying a bivariate polynomial adjustment to the product of these density estimates. Since the saddlepoint approximation is utilized in the context of density estimation, such estimates are determined from empirical cumulant-generating functions. In the univariate case, the saddlepoint density estimate is itself adjusted by a polynomial. Given a set of observations, the coefficients of the polynomial adjustments are obtained from the sample moments. Several illustrative applications of the proposed methodology shall be presented. Since this approach relies essentially on a determinate number of sample moments, it is particularly well suited for modeling massive data sets.

Keywords: density estimation, empirical cumulant-generating function, moments, saddlepoint approximation

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Authors: Silvia Lorenzo, Hongmian Gong

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With the influx of research assessing the economic impact of the global financial crisis of 2007-8, a spatial analysis based on empirical data is needed to better understand the spatial significance of the financial crisis in New York, a key international financial center also considered the origin of the crisis. Using spatial statistics, the existence of financial clusters specializing in credit and securities throughout the New York metropolitan area are identified for 2000 and 2010, the time period before and after the height of the global financial crisis. Geographically Weighted Regressions are then used to examine processes underlying the formation and movement of financial geographies across state, county and ZIP codes of the New York metropolitan area throughout the 2000s with specific attention to tax regimes, employment, household income, technology, and transportation hubs. This analysis provides useful inputs for financial risk management and public policy initiatives aimed at addressing regional economic sustainability across state boundaries, while also developing the groundwork for further research on a spatial analysis of the global financial crisis.

Keywords: financial clusters, New York, global financial crisis, geographically weighted regression

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Authors: Roman Asatryan

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The decision-making processes in Corporate Social Responsibility (CSR) among firms in general and airlines in particular have to do with the benefits that accrue through those investments. The crux of the matter is not whether to invest in CSR or not, but rather, how firms can quantify the benefits derived from such investments. This paper analyzes the cost benefit adjustment strategies for firms in the airline industry in their CSR strategy adoption and implementation. The adjustment strategies identified will enable firms in the airline industry to have a basis for determining the worth of such CSR investments. This paper discusses the cost and benefit analysis model in order to understand the ways airlines can reduce costs and increase returns on CSR, or balance the cost and benefits. The analysis from this study points to the fact that economic concepts especially the CBA are useful, though they are not without challenges. The challenge arises when it is problematic to express the real impact of the externality in monetary terms. The use of rational maximization of the gains may seem to be a rather optimistic goal mainly because of environmental variability, perceptual uncertainty, and imperfect knowledge about the potential externality. This paper concludes that the CBA model gives a basic understanding of the motivations for investing in intangible assets like CSR. Consequently, it sets the tone for formulating relevant hypothesis in empirical studies in investment in CSR in particular and other intangible assets in business operations.

Keywords: cost-benefit analysis, corporate social responsibility, airline industry

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Authors: Claudeline D. Cellan

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Development is multifaceted, and efforts to hasten growth in all these facets have been gaining traction in recent years. Thus, producing a composite index that is reflective of these multidimensional impacts captures the interests of policymakers. The problem lies in going through a mixture of theoretical, methodological and empirical decisions and complexities which, when done carelessly, can lead to inconsistent and unreliable results. This study looks into index computation from a different and less complex perspective. Borrowing the idea of archetypes or ‘pure types’, archetypal analysis looks for points in the convex hull of the multivariate data set that captures as much information in the data as possible. The archetypes or 'pure types' are estimated such that they are convex combinations of all the observations, which in turn are convex combinations of the archetypes. This ensures that the archetypes are realistically observable, therefore achievable. In the sense of composite indices, we look for the best among these archetypes and use this as a benchmark for index computation. Its straightforward and simplistic approach does away with aggregation and substitutability problems which are commonly encountered in index computation. As an example of the application of archetypal analysis in index construction, the country data for the Human Development Index (HDI 2017) of the United Nations Development Programme (UNDP) is used. The goal of this exercise is not to replicate the result of the UNDP-computed HDI, but to illustrate the usability of archetypal analysis in index construction. Here best is defined in the context of life, education and gross national income sub-indices. Results show that the HDI from the archetypal analysis has a linear relationship with the UNDP-computed HDI.

Keywords: archetypes, composite index, convex combination, development

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Authors: Maya Irjayanti

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Local creativity utilization has been a strategic investment to be expanded as a creative industry due to its significant contribution to the national gross domestic product. Many developed and developing countries look toward creative industries as an agenda for the economic growth. This study aims to identify the role of local potential for creative industries from various empirical studies. The method performed in this study will involve a peer-reviewed journal articles and conference papers review addressing local potential and creative industries. The literature review analysis will include several steps: material collection, descriptive analysis, category selection, and material evaluation. Finally, the outcome expected provides a creative industries clustering based on the local potential of various nations. In addition, the finding of this study will be used as future research reference to explore a particular area with well-known aspects of local potential for creative industry products.

Keywords: business, creativity, local potential, local wisdom

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Authors: Hyun-Woo Cho

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For ensuring the safety of a manufacturing process one should quickly identify an assignable cause of a fault in an on-line basis. To this end, many statistical techniques including linear and nonlinear methods have been frequently utilized. However, such methods possessed a major problem of small sample size, which is mostly attributed to the characteristics of empirical models used for reference models. This work presents a new method to overcome the insufficiency of measurement data in the monitoring and diagnosis tasks. Some quasi-measurement data are generated from existing data based on the two indices of similarity and importance. The performance of the method is demonstrated using a real data set. The results turn out that the presented methods are able to handle the insufficiency problem successfully. In addition, it is shown to be quite efficient in terms of computational speed and memory usage, and thus on-line implementation of the method is straightforward for monitoring and diagnosis purposes.

Keywords: data analysis, diagnosis, monitoring, process data, quality control

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Authors: Andreea Halunga, Chris D. Orme, Takashi Yamagata

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This paper proposes a heteroskedasticity-robust Breusch-Pagan test of the null hypothesis of zero cross-section (or contemporaneous) correlation in linear panel-data models, without necessarily assuming independence of the cross-sections. The procedure allows for either fixed, strictly exogenous and/or lagged dependent regressor variables, as well as quite general forms of both non-normality and heteroskedasticity in the error distribution. The asymptotic validity of the test procedure is predicated on the number of time series observations, T, being large relative to the number of cross-section units, N, in that: (i) either N is fixed as T→∞; or, (ii) N²/T→0, as both T and N diverge, jointly, to infinity. Given this, it is not expected that asymptotic theory would provide an adequate guide to finite sample performance when T/N is "small". Because of this, we also propose and establish asymptotic validity of, a number of wild bootstrap schemes designed to provide improved inference when T/N is small. Across a variety of experimental designs, a Monte Carlo study suggests that the predictions from asymptotic theory do, in fact, provide a good guide to the finite sample behaviour of the test when T is large relative to N. However, when T and N are of similar orders of magnitude, discrepancies between the nominal and empirical significance levels occur as predicted by the first-order asymptotic analysis. On the other hand, for all the experimental designs, the proposed wild bootstrap approximations do improve agreement between nominal and empirical significance levels, when T/N is small, with a recursive-design wild bootstrap scheme performing best, in general, and providing quite close agreement between the nominal and empirical significance levels of the test even when T and N are of similar size. Moreover, in comparison with the wild bootstrap "version" of the original Breusch-Pagan test our experiments indicate that the corresponding version of the heteroskedasticity-robust Breusch-Pagan test appears reliable. As an illustration, the proposed tests are applied to a dynamic growth model for a panel of 20 OECD countries.

Keywords: cross-section correlation, time-series heteroskedasticity, dynamic panel data, heteroskedasticity robust Breusch-Pagan test

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Authors: Rui Du

Abstract:

This paper attempts to assess the impact of a major sporting event —the Super Bowl on the local economies. The identification strategy is to compare the winning and losing cities at the National Football League (NFL) conference finals under the assumption of similar pre-treatment trends. The stock market performances of companies headquartered in these cities are used to capture the sudden changes in local economic activities during a short time span. The exogenous variations in the football game outcome allow a straightforward difference-in-differences approach to identify the effect. This study finds that the post-event trends in winning and losing cities diverge despite the fact that both cities have economically and statistically similar pre-event trends. Empirical analysis provides suggestive evidence of a positive, significant local economic impact of conference final wins, possibly through city image enhancement. Further empirical evidence shows the presence of heterogeneous effects across industrial sectors, suggesting that city image enhancing the effect of the Super Bowl participation is empirically relevant for the changes in the composition of local industries. Also, this study also adopts a similar strategy to examine the local economic impact of Super Bowl successes, however, finds no statistically significant effect.

Keywords: Super Bowl Participation, local economies, city image enhancement, difference-in-differences, industrial sectors

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Authors: Olakunle Felix Adekunle

Abstract:

Innovation has become the key to firm success, and entrepreneurial leadership displayed by the top management team fundamentally drives innovation in firms. This paper discusses findings from an empirical study of factors influencing the success of firms operating in developing economies in an industry where science drives the pace of change. We find that success of firms in such industries depends on thriving innovation activity that in turn is primarily driven by effective entrepreneurial leadership of the top management team. The paper presents the dimensions of entrepreneurial leadership and its linkage to innovation and firm success in the form of testable propositions. Finally, a preliminary theory of firm success in industries where science drives the pace of change is also inducted from this empirical study and presented.

Keywords: leadership, innovation, firms, economic, efficiency, industry, resources

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Authors: Babek Erdebilli

Abstract:

The aim of this study is to develop and describe a new methodology for the Multi-Criteria Decision-Making (MCDM) problem using IFE (Elimination Et Choix Traduisant La Realite (ELECTRE) model. The proposed models enable Decision-Makers (DMs) on the assessment and use Intuitionistic Fuzzy Numbers (IFN). A numerical example is provided to demonstrate and clarify the proposed analysis procedure. Also, an empirical experiment is conducted to validation the effectiveness.

Keywords: multi-criteria decision-making, IFE, DM’s, fuzzy electre model

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Authors: Chen Xi, Liu Xuebing, Lao Xueru, Kuan Sinman, Jiang Yike, Wang Hanwei, Yang Xiaolang, Zhou Junjie, Xie Jinpeng

Abstract:

To further promote the development of smart cities, the microscopic "nerve endings" of the City Intelligent Model (CIM) are extended to be more sensitive. In this paper, we develop a pedestrian trajectory twin terminal based on the CIM and CNN technology. It also uses 5G networks, architectural and geoinformatics technologies, convolutional neural networks, combined with deep learning networks for human behavior recognition models, to provide empirical data such as 'pedestrian flow data and human behavioral characteristics data', and ultimately form spatial performance evaluation criteria and spatial performance warning systems, to make the empirical data accurate and intelligent for prediction and decision making.

Keywords: urban planning, urban governance, CIM, artificial intelligence, sustainable development

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Authors: Yan Torres, Fernanda Simoes, Francisco Petrucci-Fonseca, Freddie-Jeanne Richard

Abstract:

The non-invasive samples are an alternative of collecting genetic samples directly. Non-invasive samples are collected without the manipulation of the animal (e.g., scats, feathers and hairs). Nevertheless, the use of non-invasive samples has some limitations. The main issue is degraded DNA, leading to poorer extraction efficiency and genotyping. Those errors delayed for some years a widespread use of non-invasive genetic information. Possibilities to limit genotyping errors can be done using analysis methods that can assimilate the errors and singularities of non-invasive samples. Genotype matching and population estimation algorithms can be highlighted as important analysis tools that have been adapted to deal with those errors. Although, this recent development of analysis methods there is still a lack of empirical performance comparison of them. A comparison of methods with dataset different in size and structure can be useful for future studies since non-invasive samples are a powerful tool for getting information specially for endangered and rare populations. To compare the analysis methods, four different datasets used were obtained from the Dryad digital repository were used. Three different matching algorithms (Cervus, Colony and Error Tolerant Likelihood Matching - ETLM) are used for matching genotypes and two different ones for population estimation (Capwire and BayesN). The three matching algorithms showed different patterns of results. The ETLM produced less number of unique individuals and recaptures. A similarity in the matched genotypes between Colony and Cervus was observed. That is not a surprise since the similarity between those methods on the likelihood pairwise and clustering algorithms. The matching of ETLM showed almost no similarity with the genotypes that were matched with the other methods. The different cluster algorithm system and error model of ETLM seems to lead to a more criterious selection, although the processing time and interface friendly of ETLM were the worst between the compared methods. The population estimators performed differently regarding the datasets. There was a consensus between the different estimators only for the one dataset. The BayesN showed higher and lower estimations when compared with Capwire. The BayesN does not consider the total number of recaptures like Capwire only the recapture events. So, this makes the estimator sensitive to data heterogeneity. Heterogeneity in the sense means different capture rates between individuals. In those examples, the tolerance for homogeneity seems to be crucial for BayesN work properly. Both methods are user-friendly and have reasonable processing time. An amplified analysis with simulated genotype data can clarify the sensibility of the algorithms. The present comparison of the matching methods indicates that Colony seems to be more appropriated for general use considering a time/interface/robustness balance. The heterogeneity of the recaptures affected strongly the BayesN estimations, leading to over and underestimations population numbers. Capwire is then advisable to general use since it performs better in a wide range of situations.

Keywords: algorithms, genetics, matching, population

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