Success Factors for High-Technology SMEs: A Case Study from Australia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The act of establishing a successful small or medium-sized enterprise (SME) is a daunting one in any sector of industry or commerce. For those seeking to establish a small technology-based company, the challenges are even more numerous and complex (Litvak 1992). Litvak argues that the technology-based industry and marketplace are characterized by long lead times from basic research to industrial application, short lead times in commercialization, and accelerated obsolescence under global competitive pressures from new product and process innovations. Market opportunities are often short-lived, and technological break-throughs can quickly wipe out prior success. Clearly, for the managers of such companies, finding a means to compete and succeed in such a turbulent environment is a huge concern. Also, from a governmental policy standpoint, it is important that these firms succeed, given the contribution they can make to a technically advanced and innovative economy. The purpose of this article is to review the literature on success factors for high-technology SMEs and to report a case study of a successful, young, high-technology SME located in Perth, Australia. This study was carried out as part of an ongoing larger survey concerning success factors for high-technology SMEs in Australia and the UK. Literature Review A large amount of research was carried out in UK universities in the early 1990s in an attempt to identify success factors for SMEs (see Storey 1992 for an overview). Other contributors include Macrae (1992), who describes the characteristics of high and low growth SMEs in Scotland. Theng and Boon (1996) explore factors contributing to the failure of SMEs in Singapore. Beamish, Craig, and McLellan (1993) compare the characteristics of SME exporters in Canada and in the UK. The work of Rothwell and Zegfeld (1982) on product innovation has also been influential, with this strand of research being continued by Romano (1990), who identifies factors that impact product innovation to influence small business success. Other research concerns the characteristics and strategies of high-tech SMEs. Shearman and Burrell (1988) discuss the nature of new technology-based firms and their capacity for generating employment. Covin, Prescott, and Slevin (1990) describe the effects of technological sophistication on strategic profiles, structures, and the performance of organizations. Forrest (1990) addresses the business environment with a study on the importance of strategic alliances to small technology-based firms. Weinstein (1994) carried out a comparative study of market definition in small versus larger technology-based companies. There has also been useful research devoted to identifying success factors for high-technology SMEs. Some of this research is based on case studies. Examples include: Bouwen and Steyaert (1990) on the organizing processes in young, entrepreneurial firms; Martin et al. (1991), who present a case study of a small business developing artificial intelligence applications; Latona and LaVan (1993), who record the implementation of an employee involvement program in a small, emerging high-technology firm; Price and Chen (1993) who discuss how a Total Quality Management system can be tailored for a small, high-technology company; and finally, Pearson, Bracker, and White (1990) who discuss links between operations management activities and the high growth of small electronics firms. While this sort of research is insightful, its case study methodology limits its generalizability. The research efforts which are most relevant to determining success factors for high-technology SMEs are those of Ackroyd (1995), Litvak (1 992), and Covin, Prescott, and Slevin (1990); each involves survey research. Ackroyd (1995) identified the characteristics of small, successful information technology firms in northwest England. Ackroyd's survey classified almost one hundred such firms into three categories. …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it