Exploration of Process and Competitive Factors of Entrepreneurship in Digital Space: A Multiple Case Study in Iran
Bibliographic record
Abstract
The purpose of this study is exploration of the process and competitive factors of entrepreneurship in digital space in Iran.In the last decades, the development and advancement of information and communication technologies (ICTs) and the business innovations related to them have defined a new economy which is known as “digital economy”. Establishing and running businesses in this digital space means carrying out a kind of electronic commerce by exploiting the internet and other electronic networks. The new digital economy provides exceptional opportunities for many entrepreneurs to create new ventures in different business areas according to electronic commerce models. Given that researchers in Iran have not studied digital entrepreneurship and there is a great interest in digital entrepreneurship, further research is needed on this subject. According to a research in Canada by Carrier, Raymond, and Eltaief (2004) on cyber entrepreneurship, we used the same method and studied five cases that work in digital entrepreneurship in Iran. In this paper, we first review the literature of digital entrepreneurship- digital entrepreneurship framework that include a typology of digital ventures and their characteristics, characteristics of digital entrepreneurs, and the distinctions between digital entrepreneurship and traditional one. Then we surveyed five digital entrepreneurs in Iran to explore the process of their entrepreneurship and competitive elements applied by them.The most notable contribution of our research is our focus on the process of this type of entrepreneurship and the steps which were used by entrepreneurs. Unlike the traditional forms of entrepreneurship, the entrepreneurs in cyber space in Iran did not examine their feasibility of their projects. And also the identification of a business opportunity created business ideas. The other contribution of our research is entrepreneurs’ competitive elements. We found 10 factors as the internet-based entrepreneurs’ competitive elements in Iran. Also we found interesting results about characteristics of internet-based entrepreneurs.This paper provides an overview of internet-based entrepreneurship in Iran. According to collected information, we propose the process of internet-based entrepreneurship and its competitive elements, and establish a basis for future research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".