Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.532991
Title: Market prediction for SMEs using unsupervised neural networks
Author: Walcott, Terry Hugh
Awarding Body: University of East London
Current Institution: University of East London
Date of Award: 2009
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Abstract:
The objective of this study was to create a market prediction model for small and medium enterprises (SMEs). To achieve this, an extensive literature examination was carried out which focused on SMEs, marketing and prediction; neural networks as a competitive tool for SME marketing; and clustering a review. A Delphi study was used for collating expert opinions in order to determine likely factors hindering SMEs wanting to remain business proficient. An analysis of Delphi responses led to the creation of a market prediction questionnaire. This questionnaire was used to create variables for analysis using four unsupervised algorithm. The algorithms used in this study were joining tree, k-means, learning vector quantisation and the snap-drift algorithm. Questionnaire data took the form of data collected from 102 SMEs. This led to the determination of 23 variables that could best represent the data under examination. Further analysis of each 23 variable led to the choice of respondents for case study analysis. A higher education college (HEC) and a private hire company (PHC) were chosen for this stage of the research. In case study one (1), analysis has discovered that HEC's can compete with Universities if they tailor their products and services to selected academic markets as opposed to entering all academic sectors. The findings suggest that if a HEC monitors the growth of its students and establishes the likely point of creating new courses they will retain students and not lose them to universities. Comparisons between the case HEC and rival HECs has demonstrated that there is a knowledge gap that currently exists between these institutions and by using post-modem marketing coupled with neural networks a competitive advantage will be realised. In case study two (2), a private hire company was investigated allowing for the interpretation of current markets for this firm by making existing operating areas more transparent. Therefore, knowledge barriers were discovered between telephonists and drivers, and the owner/manger and drivers. As such historical data was used for distinguishing the performance of drivers within this firm. In differentiating job times and driver performance our case organisation was better equipped for determining the times in which it is most busy. Therefore, being able to determine the amount of telephonists needed per shift and the likely busy periods in which this firm will operate. Analysis of all participating SMEs have revealed that: (1) these firms are more likely to fail in the first two years of operation generally, (2) successful SMEs are owned or managed by persons having prior management and or general business expertise, (3) success is normally attributed to experience gained as a result of working or managing a threatened firm in the past, (4) successful SMEs understand the importance of valuing the ethnicity held in their respective firms and (5) these firms are less likely to understand how technology can aid and sustain market growth generally. It seems market prediction in SMEs can be affected by employee performance and managerial ability to undertake predefined tasks. The findings suggest that there are SMEs that can benefit from market prediction. More importantly, the findings indicate the need to understand the SME for determining the types of intelligent systems that can be used for initiate marketing and providing marketing prediction generally. Several theoretical and practical implications are discussed. To this effect, SME owner/managers, researchers in academia, government and public SME organisations can learn from the results. Suggestions for future research are also presented.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.532991  DOI: Not available
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