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Articles 1 - 12 of 12
Full-Text Articles in Business
Customer Experience Quality With Social Robots: Does Trust Matter?, Sanjit K. Roy, Gaganpreet Singh, Saalem Sadeque, Richard L. Gruner
Customer Experience Quality With Social Robots: Does Trust Matter?, Sanjit K. Roy, Gaganpreet Singh, Saalem Sadeque, Richard L. Gruner
Research outputs 2022 to 2026
Although service providers increasingly adopt social robots, much remains to be learned about what influences customers' experiences with robots. To address this issue, this study investigates the relationships among customer equity drivers (i.e., value equity, brand equity and relationship equity), trust in social robots, and trust in service providers. Specifically, we hypothesize that customer equity drivers influence trust in social robots and trust in service providers. We also propose that customer equity drivers influence customer experience quality in the context of social robots and that trust in social robots and trust in service providers mediate these relationships. The study used …
Implementing Artificial Intelligence In Forecasting The Risk Of Personal Bankruptcies In Poland And Taiwan, Tomasz Korol, Anestis K. Fotiadis
Implementing Artificial Intelligence In Forecasting The Risk Of Personal Bankruptcies In Poland And Taiwan, Tomasz Korol, Anestis K. Fotiadis
All Works
Research background: The global financial crisis from 2007 to 2012, the COVID-19 pandemic, and the current war in Ukraine have dramatically increased the risk of consumer bankruptcies worldwide. All three crises negatively impact the financial situation of households due to increased interest rates, inflation rates, volatile exchange rates, and other significant macroeconomic factors. Financial difficulties may arise when the private person is unable to maintain a habitual standard of living. This means that anyone can become financially vulnerable regardless of wealth or education level. Therefore, forecasting consumer bankruptcy risk has received increasing scientific and public attention. Purpose of the article: …
Dynamic Interplay Of Operations And R&D Capabilities In U.S. High-Tech Firms: Predictive Impact Analysis, Jooh Lee, He-Boong Kwon
Dynamic Interplay Of Operations And R&D Capabilities In U.S. High-Tech Firms: Predictive Impact Analysis, Jooh Lee, He-Boong Kwon
Rohrer College of Business Departmental Research
This study presents a unique analytic process in measuring two complementary capabilities (operations and R&D) and assessing their comparative impact on firm performance, represented by Return on Sales (ROS) and Tobin's Q. First, as a new approach, the combined data envelopment analysis (DEA)-artificial neural network (ANN) measures both capabilities by using data from U.S. high-tech firms. Then, the joint OLS multiple regression (MR)-ANN model investigates the individual effect of these capabilities and further explores their relative influence and the synergistic interplay on ROS and Tobin's Q. At its core, this study not only proposes an innovative approach to quantifying capabilities, …
Forecasting Locational Marginal Prices In Electricity Markets By Using Artificial Neural Networks, Kim Jay R. Rosano, Allan C. Nerves
Forecasting Locational Marginal Prices In Electricity Markets By Using Artificial Neural Networks, Kim Jay R. Rosano, Allan C. Nerves
Journal of Economics, Management and Agricultural Development
Electricity price forecasting is an important tool used by market players in decision-making and strategizing their participation in the electricity market. In most studies, market-clearing price is forecasted as it gives an aggregated overview of system price. However, locational marginal price (LMP) gives better outlook of the price particular to the customer location in the electrical power grid. This study utilizes Artificial Neural Networks to forecast weekday LMP of generator and load nodes. Various inputs such as historical prices and demand, and temporal indices were used. Using data for selected nodes of the Philippine Wholesale Electricity Spot Market, forecast Mean …
Improving Stock Trading Decisions Based On Pattern Recognition Using Machine Learning Technology, Yaohu Lin, Shancun Liu, Haijun Yang, Harris Wu, Bingbing Jiang
Improving Stock Trading Decisions Based On Pattern Recognition Using Machine Learning Technology, Yaohu Lin, Shancun Liu, Haijun Yang, Harris Wu, Bingbing Jiang
Information Technology & Decision Sciences Faculty Publications
PRML, a novel candlestick pattern recognition model using machine learning methods, is proposed to improve stock trading decisions. Four popular machine learning methods and 11 different features types are applied to all possible combinations of daily patterns to start the pattern recognition schedule. Different time windows from one to ten days are used to detect the prediction effect at different periods. An investment strategy is constructed according to the identified candlestick patterns and suitable time window. We deploy PRML for the forecast of all Chinese market stocks from Jan 1, 2000 until Oct 30, 2020. Among them, the data from …
Uncertainty Due To Infectious Diseases And Stock–Bond Correlation, Konstantinos Gkillas, Christoforos Konstantatos, Costas Siriopoulos
Uncertainty Due To Infectious Diseases And Stock–Bond Correlation, Konstantinos Gkillas, Christoforos Konstantatos, Costas Siriopoulos
All Works
We study the non-linear causal relation between uncertainty-due-to-infectious-diseases and stock–bond correlation. To this end, we use high-frequency 1-min data to compute daily realized measures of correlation and jumps, and then, we employ a nonlinear Granger causality test with the use of artificial neural networks so as to investigate the predictability of this type of uncertainty on realized stock–bond correlation and jumps. Our findings reveal that uncertainty-due-to-infectious-diseases has significant predictive value on the changes of the stock–bond relation.
A Comparison Of Artificial Neural Networks And The Statistical Methods In Predicting Mba Student’S Academic Performance, Ojoung Kwon, Harry Hui Xia, Serin Zhang
A Comparison Of Artificial Neural Networks And The Statistical Methods In Predicting Mba Student’S Academic Performance, Ojoung Kwon, Harry Hui Xia, Serin Zhang
Journal of International Technology and Information Management
MBA has become one of the most popular and vital professional degrees internationally. The MBA program admission process’s essential task is to choose the best analysis tools to accurately predict applicants’ academic performance potential based on the evaluation criteria in making admission decisions. Prior research finds that the Graduate Management Admission Test (GMAT) and undergraduate grade point average (UGPA) are common predictors of MBA academic performance indicated by graduate grade point average (GGPA). Using a sample of 250 MBA students enrolled in a state university with AACSB accreditation from Fall 2010 to Fall 2017, we test and compare the effectiveness …
Evaluation Of Herd Size Management Strategies, Colson A. Tester
Evaluation Of Herd Size Management Strategies, Colson A. Tester
Graduate Theses and Dissertations
This thesis is comprised of two studies examining the effects of price signal based herd size management strategies on profitability of cow-calf operations. Herd size management strategies were evaluated across the previous two cattle cycles, 1990-2014, using a fixed land resource and included a variety of production scenarios. These scenarios varied in terms of stocking rates, fertilizer applications rates, and calving season. Each scenario was also analyzed both with and without weather effects on forage production. Weather effects were simulated using a production index derived from satellite imagery across the observed 25-year period. Three herd size management strategies: i) constant …
The Synergistic Effect Of Environmental Sustainability And Corporate Reputation On Market Value Added (Mva) In Manufacturing Firms, Jooh Lee, He-Boong Kwon
The Synergistic Effect Of Environmental Sustainability And Corporate Reputation On Market Value Added (Mva) In Manufacturing Firms, Jooh Lee, He-Boong Kwon
Rohrer College of Business Departmental Research
This study is an attempt to explore the predictive effect, in terms of operational capability, of a large manufacturing firm’s environmental greening efforts carried out in the interest of sustainability, and of the firm’s reputation for social responsibility. Through both a traditional and a new, innovative approach, this study investigates the potential synergistic effect of environmental sustainability and the improvement of corporate reputation on a firm’s market performance in terms of shareholders’ equity value (market value added, or MVA) when taken together with the firm’s other key differential business factors. The findings of this study provide notable implications that establish …
Forecasting Traditional Vs Blended Retirement System For Individual Service Members, Kevin M. Dwyer
Forecasting Traditional Vs Blended Retirement System For Individual Service Members, Kevin M. Dwyer
Theses and Dissertations
Starting January 1, 2018, the Department of Defense new Blended Retirement System (BRS) will go into effect. Military members with less than twelve years of service will have the option to either remain in the current High 3 Retirement System or opt into the BRS. This decision will have a lasting impact on their lives well beyond their military careers. With this in mind, we have developed a Decision Support System that will enable service members to compare the two retirement choices in terms of annual and total lifetime expected value. There were three phases to the development of the …
Forecasting Nigerian Stock Market Returns Using Arima And Artificial Neural Network Models, Godknows M. Isenah, Olusanya E. Olubusoye
Forecasting Nigerian Stock Market Returns Using Arima And Artificial Neural Network Models, Godknows M. Isenah, Olusanya E. Olubusoye
CBN Journal of Applied Statistics (JAS)
The study reports empirical evidence that artificial neural network based models are applicable to forecasting of stock market returns. The Nigerian stock market logarithmic returns time series was tested for the presence of memory using the Hurst coefficient before the models were trained. The test showed that the logarithmic returns process is not a random walk and that the Nigerian stock market is not efficient. Two artificial neural network based models were developed in the study. These networks are TECH (4-3-1) and TECH (3-3-1)whose out-of-sample forecast performance was compared with a baseline ARIMA (3,0,1) model. The results obtained in the …
Poster Session A: Use Of Neural Networks In Multi-Sensor Fusion For Remote Sensing Applications, Engr. S. M. Haider Aejaz
Poster Session A: Use Of Neural Networks In Multi-Sensor Fusion For Remote Sensing Applications, Engr. S. M. Haider Aejaz
International Conference on Information and Communication Technologies
Remote sensing encounters different types of objects with similar spectral signatures. Multi-sensors form the solution of the problem with spectral different parts of the spectrum and the resulting information is then processed using digital signal processing techniques. Artificial neural networks provide another method for processing this information. The research describes how neural networks may be used to classify objects on the basis of their spectral response to different frequencies.