A Bayesian Approach to International Distributor Selection for Small and Medium-Sized Enterprises in the Software Industry

Autor/in	Lui, Joseph P.
Titel	A Bayesian Approach to International Distributor Selection for Small and Medium-Sized Enterprises in the Software Industry
Quelle	(2013), (123 Seiten) PDF als Volltext Verfügbarkeit Ph.D. Dissertation, Nova Southeastern University
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
ISBN	978-1-3037-2902-7
Schlagwörter	Hochschulschrift; Dissertation; Small Businesses; Computer Software; Bayesian Statistics; Selection; Classification; International Trade + Suchen Sie Ihr Suchwort? Thesis; Dissertations; Academic thesis; Kleingewerbe; Auslese; Classification system; Klassifikation; Klassifikationssystem; Trade; International relations; Handel; Internationale Beziehungen
Abstract	Identifying appropriate international distributors for small and medium-sized enterprises (SMEs) in the software industry for overseas markets can determine a firm's future endeavors in international expansion. SMEs lack the complex skills in market research and decision analysis to identify suitable partners to engage in global market entry. Foreign distributors hold responsibility to represent, sell, market, and add value to the software manufacturer's products in local markets. Identifying appropriate attributes to determine a suitable distributor is essential in assuring success in new export markets. Methods for partner selection have been addressed in the international marketing and information systems literature. Building on this literature, this dissertation develops an improved method for identifying suitable distributors in the SME software industry. The partner selection conundrum is modeled as a binary classification problem in that it involves predicting whether an alliance relationship will survive over a specific period. The challenge presented to researchers is not just the large number of variables involved in the selection process but also the inherent uncertainty in the decision making process. This study uses a Bayesian methodology for this classification task. A Naive Bayes (NB) classification model was developed factoring sixteen alliance attributes identified in the partner selection literature and validated by domain experts who scored the importance of these attributes. Thirty years of partnership data that contributed to relationship longevity trained the model and held-back data was used to validate the model. The NB classification model returned accurate predictions in both the group of foreign distributors that succeeded and failed to reach the relationship longevity threshold of five years. The study's contribution to the software SME business community and its practitioners was the identification of an improved methodology for predictive success. The approach employed a simple Bayesian prediction model utilizing key alliance attributes to help software SMEs identify potential foreign distributor partners who can sustain relationship longevity from which to build a strong business partnership. Keeping the methodology simple is critical for SMEs who struggle with an abundance of challenges to maintain their corporate viability in the market place. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] (As Provided).
Anmerkungen	ProQuest LLC. 789 East Eisenhower Parkway, P.O. Box 1346, Ann Arbor, MI 48106. Tel: 800-521-0600; Web site: http://www.proquest.com/en-US/products/dissertations/individuals.shtml
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01