ANALYZING CORPORATE EXPANSION TO INTERNATIONAL MARKETS: THE CASE OF GERMANY, UNITED KINGDOM, CANADA, MEXICO AND CHINA
Bibliographic record
Abstract
In this study, the authors utilized the Analytic Hierarchy Process (AHP) decision-making model to select the optimal market for international expansion for ABC Corporation located in Ohio[1]. The benefits of exporting to nine different countries: Germany, the United Kingdom, Canada, Mexico, Netherlands, China, United Arab Emirates, Australia and Brazil were analyzed. For the sake of more precise and in-depth research, preliminary studies performed on these nine countries were used to determine the top five markets: China, Mexico, Canada, Germany and the United Kingdom. Preliminary research included multiple factors about these nine countries. Market size, market growth rate, market consumption capacity, market intensity, market receptivity, commercial infrastructure, trade barriers, contribution margins, country risk and the growth rate of construction were the qualitative and quantitative criteria specifically considered. The importance of each criteria and sub-criteria were determined with export market experts and company decision makers. The AHP analysis enabled the authors to determine the best possible export market for the company by evaluating the data from China, Mexico, Canada, Germany and the United Kingdom. The robustness of the results was tested using sensitivity analysis. Sensitivity analysis results were then discussed with the decision makers. The best market was selected and alternative markets were presented with degrees of preference. Managerial implications of the study and future research directions will be discussed. [1] Company name has been disguised for confidentiality reasons. -This paper received “Best Student Achievement in International Business Award for Graduate Students”, Youngstown State University, Williamson College of Business, April 18, 2018. -Acknowledgement: This project allowed our group to become better researchers, taught us how to use AHP methodology in real - life decision making and allowed us to network with colleagues around the world. This was a fantastic experience for all of us and it will not be forgotten. Being able to represent Youngstown State University at the MCDM, 2017 Conference was an honor. We learned and did things that students cannot learn in the classroom. Working alongside Dr. Karpak allowed us to have a hands - on experience with the project and she was there when questions needed addressed. We feel that our research benefitted ABC and allowed them to gain a better understanding of what market they should export to. We are beyond grateful for this experience and glad that we were selected to go to Ottawa, Canada and to now be submitting our research to the IJAHP journal. The authors also thank the export expert Mr. Mousa Kassis, CGBP, Director, Ohio Small Business Development Center (SBDC) Export Assistance Network, Williamson College of Business Administration of Youngstown State University, for identifying ABC Company and giving his expert judgments on criteria evaluations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".