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 Naïve 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.
| Identifer | oai:union.ndltd.org:nova.edu/oai:nsuworks.nova.edu:gscis_etd-1221 |
| Date | 01 January 2014 |
| Creators | Lui, Joseph Ping |
| Publisher | NSUWorks |
| Source Sets | Nova Southeastern University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | CEC Theses and Dissertations |
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