Sean Eom - Academia.edu (original) (raw)

Papers by Sean Eom

Decision Support Systems, Aug 1, 1990

Human systems management, Jun 1, 1989

This research examines the current state of multiple criteria decision making (MCDM) model-embedd... more This research examines the current state of multiple criteria decision making (MCDM) model-embedded decision support systems (MCDSS). A comprehensive bibliography of 203 specific DSS applications was extracted from over 1100 English-language journals published in the last 18 years (1971–April 1988). The survey identified 26 MCDM model-embedded DSS (13% of the 203 DSS applications). Approximately 30% of the MCDSS applications (8 out of 26 applications) were developed to support strategic decisions; about 70% support tactical and operational levels of decisions. The integration pattern over the last two decades was also analysed. During the 1970s only two MCDM model-embedded DSS were developed; however, the number of MCDSS models has increased proportionately during the 1980s.

Interfaces, Jun 1, 1990

A survey of specific DSS applications published between 1971 and April 1988 indicates the develop... more A survey of specific DSS applications published between 1971 and April 1988 indicates the development of a wide variety of DSS applications in many different fields. Despite two decades of cooperative efforts by practitioners and theoreticians to develop specific DSSs, many goals in the DSS field remain unfulfilled. The critical issue is to implement a system that integrates organizational decision making vertically (among strategic, tactical, and operational levels) and horizontally (among many functional fields at the same level) to coordinate and manage conflicts among the various subunits of the organization.

The purpose of this research is to develop a goal programming based decision support system (DSS)... more The purpose of this research is to develop a goal programming based decision support system (DSS) for global financing strategies of the multinational corporation. The global financing decision is characterized by the uncertain foreign exchange rates and uncertain political conditions of each operating host country. Because of the nature of the uncertainty involved in the decision process, operations research (OR)/management science (MS) models themselves do not always provide satisfactory solutions for the financing problems of an MNC. On the other hand, managerial judgement alone is also inadequate because of the complexity, size of the decision variables, and numerous and often conflicting objectives of global financing strategies. Therefore, the effectiveness of global financing decisions may only be improved by simultaneous use of managerial judgement and a well designed DSS. The proposed DSS can assist financial managers of the MNC in their strategic, financial planning process through broadening the focus of managers and extending limits on managers as information processors. The global financing decision requires three consecutive stages of interactions between managers and the proposed DSS. The first stage is primarily concerned with the adjustments of the cost of capital and the consolidated firm\u27s financial structure (selection of an appropriate adjusted cost of capital). In the second stage, the financing decision process is analyzed to identify and select a best priority structure. In the third and final stage, with the information derived about the fixed cost of financing and priority structure, managers can incorporate their what if types of managerial queries in their decision making process. The DSS allows MNC financial managers to choose an appropriate global financing strategy to satisfy multiple, and often conflicting management goals and to effectively analyze the trade-offs among costs, foreign exchange risks, political risks, and managerial motivation goals

Inter-Organizational Information Systems in the Internet Age

An IOS is an information and management system that transcends organizational boundaries via elec... more An IOS is an information and management system that transcends organizational boundaries via electronic linkages with its trading partners. The electronic linkage is established by information and communication technologies. Extranets1 are the core technology for building IOSs. There are a host of other technologies that serve as the infrastructure for managing IOSs. They include coordination technologies, monitoring technologies, filtering and negotiating technologies (intelligent agents), and decision-making and knowledge-management technologies. In this chapter, a brief overview of each of these technologies is presented.

Encyclopedia of Information Systems, 2003

Decision Sciences Journal of Innovative Education, Apr 1, 2016

ABSTRACTA stream of research over the past decade that identifies predictors of e‐learning succes... more ABSTRACTA stream of research over the past decade that identifies predictors of e‐learning success suggests that there are several critical success factors (CSFs) that must be managed effectively to fully realize promise for e‐learning. Grounded in constructivist learning theories, this study advances previous work on CSFs in university online education. Structural equation modeling is applied to examine the determinants of students’ satisfaction and their perceived learning outcomes in the context of university online courses. The independent variables of motivation (intrinsic and extrinsic), student self‐regulation, dialogue (instructor‐student, and student‐student), instructor, and course design are examined as potential determinants of online learning outcomes. A total of 372 responses from students who have completed at least one online course at a university in the Midwestern United States were used to examine the structural model. Findings indicate that instructor‐student dialogue, student‐student dialogue, instructor, and course design significantly affect students’ satisfaction and learning outcomes. However, both extrinsic student motivation and student self‐regulation have no significant relationship with user satisfaction and learning outcomes. Finally, intrinsic student motivation affects learning outcomes but not user satisfaction. The findings suggest that course design, instructor, and dialogue are the strongest predictors of user satisfaction and learning outcomes.

International Journal of Information Management, 1990

International Journal of Decision Support System Technology

To increase healthcare quality, early illness prediction helps patients prevent potentially life-... more To increase healthcare quality, early illness prediction helps patients prevent potentially life-threatening health issues before it is too late. Artificial intelligence is a rapidly evolving area, and its applications to diabetes, a worldwide epidemic, have the potential to revolutionize the way diabetes is diagnosed and managed. A total of six supervised machine learning algorithms based on patient data were used and compared to predict the diagnosis of diabetes mellitus. For experiments, the Pima Indians Diabetes Database was used, and their missing values were carefully handled by different techniques. For random train-test splits, the Random Forest classification algorithm achieved an accuracy rate of 92 percent. This model outperforms other state-of-the-art approaches due to the application of a combination of techniques for dealing with missing values (the mixture of imputing missing values techniques) that is proposed. With this approach, the models of this manuscript achiev...

E-Learning and Digital Media

This paper examines the impact of using mobile devices, the pivotal element of a student-centered... more This paper examines the impact of using mobile devices, the pivotal element of a student-centered ecosystem, on the learning process and learning outcomes from a system’s view in which mobile technologies are considered a critical success factor to facilitate the dialogue and self-regulatory learning processes, thereby enhancing e-learning outcomes. We synthesize the disparate literature to develop an elevated model. A total of 323 valid and unduplicated responses from students who have completed at least one online course at a Midwestern university in the U.S. were used to examine the structural model, using SmartPLS v. 3.3.2. The results of this study show that the use of mobile devices positively affects student-instructor and student-student dialogues. It also facilitates the self-regulation process, which in turn positively affects the learning outcomes. Integrating mobile technology enables educational institutions to design and build distance learning systems that allow stude...

2019 IEEE 21st Conference on Business Informatics (CBI)

There are few empirical studies that directly investigate the relationships between mobile device... more There are few empirical studies that directly investigate the relationships between mobile device usage and four constructs (student-student dialogue, student-instructor dialogue, self-regulation, and learning outcomes) in university online education. Structural equation modeling is applied to examine the effects of mobile device usage on students' perceived learning outcomes. A total of 350 valid unduplicated responses from students who have completed at least one online course at a university in the Midwest USA were used to examine the structural model. The results of this study showed that mobile device usage affect student-instructor dialogue positively and facilitate the self-regulation process, which in turn positively affect the learning outcomes.

Journal of Media Literacy Education

Several attributes of students, the primary participants of e-learning systems, have been major s... more Several attributes of students, the primary participants of e-learning systems, have been major subjects of intense research over the past decade (Bitzer and Janson 2014). Structural equation modeling is applied to examine the effects of intrinsic motivation, extrinsic motivation, student-student dialogue, and studentinstructor dialogue on e-learners’ perceived learning outcomes in the context of university online courses. A total of 372 valid, unduplicated responses from students who have completed at least one online course at a university in the Midwest were used to examine the structural model. The results indicated that intrinsic and extrinsic motivation exhibit higher positive correlations with student-student (SS) dialog and self-regulated learning strategy. Moreover, self-regulated learning strategies, SS dialog, and SI dialog positively affect the e-learners’ perceived learning outcomes. However, extrinsic student motivation shows a positive correlation with SS dialog, but ...

International Journal of Mobile and Blended Learning, 2021

Mobile devices, primarily cell phones, smartphones, and tablet PCs, have gradually been introduce... more Mobile devices, primarily cell phones, smartphones, and tablet PCs, have gradually been introduced into the university campus and online education over the past few decades. Does the use of mobile devices in distance learning motivate students and affect the learning process? These are important questions that were raised more than a decade ago, but they are still unanswered. This research aims to answer these vital questions. A total of 323 valid and unduplicated responses from online students at a Midwestern university in the U.S. were used to examine the structural model, using SmartPLS v. 3.3.2. This study shows that the use of mobile devices positively affects the students' intrinsic and extrinsic motivation to learn, which in turn positively affects the cognitive learning process variables (student-student dialogue, student-instructor dialogue, and metacognitive self-regulated learning processes). Furthermore, the learning process variables positively affect the perceived ...