Bibliographic record
Abstract
Learning outcomes Through a discussion of the case, students will be able to conduct “Pros and Cons” analysis for entering a new market. Conduct “SWOT” analysis for entering a new market. Explain how to create a Blue Ocean Market Space, by implementing the concept of value-innovation. Demonstrate the role of “strategy canvas” and “The four action framework” in creating Blue Ocean Market Space. Case overview/synopsis This case describes a situation in which Vivek Vyas (Vyas) and Vimal Popat (Popat) first generation entrepreneurs starts their venture shradhanajali.com in June 2011. The monthly revenues range in between INR 75,000 and INR 80,000. Shradhanjali.com has garnered customers from major parts of India, USA, Canada, UK and Africa. It was in 2019 when Vyas and Popat co-founders of Shradhanjali.com were in their office at Rajkot, Gujarat reading an article in financial express, which had a mention of India’s 10 most wacky startups which used technology and internet to get closer to users. One of the startups reported by financial express was offering people to book cremation for the funeral of the deceased loved ones. Looking at the article Popat thought to enter a new market space by mid of 2020, where new offering to customers to book online pujas across temples in India on the birth and death anniversaries of their loved ones. The purpose of this case is to provide an opportunity for the participants to make use of management tools such as Pros and Cons; and SWOT analysis to decide whether shradhanjali.com should penetrate more into this world of e-commerce and offer online puja service to their customers. However, the two co-founders are undecided whether to add online puja service in their service portfolio as they do now know the way forward to convert the idea of online puja into a viable business? Participants need to take into consideration the data given for shradhajali.com and make assumptions and resolve the dilemma through which Vyas and Popat are going through. Complexity academic level The case involves various issues with first generation online startup in strategic management field such “Pros and Cons” analysis and challenges faced during the new market creation phase. Thus, this case can be used for covering multiple perspectives related to blue ocean strategy (e.g. application of strategy canvas and the four action framework). Supplementary materials Teaching notes are available for educators only. Subject code CSS 11: Strategy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.041 | 0.010 |
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".