Bibliographic record
Abstract
<p>1.1 Masters background As part of the “Masters in Advanced Technology Programme” each student had to select a high-tech start-up that they wanted to be involved in throughout the year. Each individual would bring value to the start up through their background and experience. The start-up I selected was an interactive robotic toy called “Auti”. Project champion, Helen’s envisioned goal was for the toy to help children with autism learn positive behaviours. Our team consisted of two main individuals not including the product champion (Please refer to Appendix A to learn more about the team, team dynamics etc.). My individual responsibility in terms of contribution to the team was to establish a strategic business plan, including a growth strategy for the project. Gaining funding is a critical part of any start-up’s growth (Ministry of Economic Development, 2007). Financial planning forces companies to think about their goals. A common goal most companies have is the goal to grow (Ross et al., 2002). 1.2 Objectives The objective of this study was to identify the best suited funding sources, which I could then recommend “Auti” implement in order to help the company become a feasible, sustainable business. In order to make the most appropriate recommendations, I had to become financially literate. A study done in Canada found that weak financial literacy may be one of the biggest reasons start-up businesses do not succeed (Intuit, 2013). 1.3 Research questions My thesis looks to answer three specific questions. Questions one and two are specific to my individual research conducted into the angel investment industry in New Zealand. 1) How do angel investors in New Zealand view the angel investment industry in New Zealand? 2) What do angel investors expect high-tech start-ups to have in place before they would consider investing? Thesis question three is related to the main theory of the thesis. 3) How relevant is the “pecking order capital structure” theory to high-tech start-up companies in New Zealand? 1.4 Contribution This thesis contributes to practice as well as theory. My interviews with angel investors are “practice led”, meaning that the research led to a new understanding about practice (Edmonds et al., 2006). In terms of my own research, a new understanding was formed on angel investment in New Zealand in 2014. Specifically, a common list of things angels throughout New Zealand look for in “high-tech” start-ups, before they would consider investing, was identified. The main theory within this thesis is to do with the “pecking order capital structure”, in relation to high-tech start-ups, therefore contributing to research done around the pecking order theory. 1.5 Thesis layout This thesis is a reflection of the two facets of research that I conducted. The two approaches used were action-based research and in-depth Interviews. Action-based research aims to contribute both to the practical concerns of people in an immediate problematic situation and to further the goals of social science at the same time (Gilmore et al., 1986). Action-based research, as mentioned in this thesis, looks into the process that was taken to find the best suited funding sources for our start-up, “Auti”. An in-depth interview was conducted with angel investors in New Zealand to get a better understanding of angel investment in New Zealand. Specific focus is put on “angel investment” in New Zealand as this is the preferred choice of start-up capital for “Auti”. The thesis begins with a literature evaluation. The first section will evaluate funding source literature that influenced us to select angel investment funding as something we wanted to get a better understanding of. Further angel investment literature will be evaluated, including the gap in literature that my individual research into angel investment fills. Research question three looks to see if our start-up, “Auti”’s capital structure follows the “pecking order capital structure”, therefore there will also be a section within the literature review chapter that will include my main findings on past research, which has been conducted around the world, looking into if high-tech start-ups, such as “Auti”, follow the “pecking order capital structure”. The definition of high-tech firms, also known as new technology based firms, is not clear, its application differs significantly depending on time, space, and authors (Laranja &Fontes, 1998; Fontes & Coombs, 2001). One way it has been defined by Little (1977) is “independent owned business established for not more than twenty-five years and based on the exploitation of an invention or technological innovation implying substantial technological risks”. Following the literature review chapter, my research methodology is described, specifically with regards to my individual research into angel investment in New Zealand, explaining what I did, why, and problems that I faced. The thesis then follows with main findings from my individual qualitative research into the angel investment industry in New Zealand. The thesis conclusion will have six main sections. Sections will cover whether or not my research supports the literature, what my research contributions are, and an implementation section (recommending start-up funding implications for “Auti”). As my individual research looked into the angel investment industry in New Zealand, a majority of the implementation will be specific to what the “Auti” team should do in respect to approaching angel investment in order to have a higher chance of gaining investment. My recommended start-up funding implications will then be compared to the pecking order capital structure to show that it follows that structure. A section will also look into the limitations that my research faced. The last section will be recommendations in terms of further research needed to be conducted in order to support my research conclusions.</p>
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; both teacher heads agree on what is shown here.
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".