ASSESSMENT RISIKO PADA WARALABA COFFEE SHOP (STUDI KASUS: ASSESSMENT DUA WARALABA COFFEE SHOP DI JAKARTA)
Bibliographic record
Abstract
ASSESSMENT RISIKO PADA WARALABA COFFEE SHOP (STUDI KASUS: ASSESSMENT DUA WARALABA COFFEE SHOP DI JAKARTA) Rizkia Natasya ABSTRAK Penelitian ini difokuskan hanya pada dua waralaba coffee shop di Jakarta. Dengan pertimbangan mengambil satu contoh waralaba coffee shop asing dan satu contoh waralaba coffee shop lokal. Penelitian ini bertujuan untuk mengetahui risiko yang ada didalam sistem bisnis kedua waralaba coffee shop tersebut. Penelitian ini menggunakan pendekatan kualitatif dengan menerapkan metode House of Risk yang mengacu pada dasaran AS/NZS 4360: 2004 dimana terdapat dua fase yaitu fase pengidentifikasian risiko (risk identification) yang terbagi menjadi 3 bagian yaitu identifikasi risiko, analisis risiko dan evaluasi risiko yang memiliki 5 tahap serta fase penanganan risiko (risk treatment) yang memiliki 2 tahap. Teknik analisis yang digunakan adalah kualitatif melalui brainstorming dan wawancara kepada 4 franchisee dan 4 manager cafe dari dua waralaba coffee shop tersebut dengan total 8 responden dan semi-kuantitatif dengan pengisian angket bobot konsekuensi dan kemungkinan. Hasil penelitian ini menyatakan bahwa risiko terberat dalam bisnis waralaba kedua coffee shop ini bagi franchisee yaitu persaingan dengan kompetitor, hubungan dengan franchisor, lokasi yang tidak tepat dan sumber daya manusia. Dan terdapat satu agen risiko terberat yang paling banyak menimbulkan risiko yaitu pemilihan lokasi yang tidak tepat. Kata Kunci: waralaba, manajemen risiko, House of Risk RISK ASSESSMENT IN FRANCHISE COFFEE SHOP (CASE STUDY: ASSESSMENT OF TWO FRANCHISE COFFEE SHOP IN JAKARTA) Rizkia Natasya ABSTRACT This study focused only on two franchise coffee shop in Jakarta. With consideration to take one example of foreign coffee shop franchises and one example of a local coffee shop franchise. This study aims to determine the risks that exist within the business systems both of the coffee shop franchise. This study used a qualitative approach by applying House of Risk which refers to the standard AS / NZS 4360: 2004 where there are two phases which identifies the risk (risk identification) is divided into 3 parts, risk identification, risk analysis and risk assessments have 5 stages and phase of risk management (risk treatment) which has 2 stages. The analysis technique used is qualitatively through brainstorming and interviews to 4 franchisee and 4 manager of the two franchises cafe coffee shop with a total of 8 respondents and semi-quantitative questionnaire filling weight and possible consequences. Results of this study stated that the most weightest in coffee shop franchise for franchisee is competitive with competitors, relationship with franchisor, not exactly location and human resources. And the most weightest agent risk that show the most risk,is is not appropriate choice the location. Keywords: franchise, risk management, House of Risk DAFTAR PUSTAKA Ambadar, J., M. Abidin, a Y. Isa (2007). Membeli dan Menjual Franchise. Jakarta: Yayasan Bina Karya Mandiri. Amrullah, A. M. (2013). Panduan Menyusun Proposal Skripsi Tesis a Desertasi. Jakarta: Smart Pustaka. AS/NZS 4360: 2004. (2004). Australian/New Zealand Standard Risk Management. Sydney: Standards Australia International Ltd. Astuti, D. (2005). Kajian Bisnis Franchise Makanan Indonesia. Jurnal Manajemen Kewirausahaan, 83-98. Bekefi, T., J. Marc a K. Yuthas (2008). Management Accounting Guideline Managing Opportunities and Risks. Canada: The Society of Management Accountants of Canada (CMA Canada), the American Institute of Certified Public Accountants, Inc. (AICPA) and The Chartered Institute of Management Accountants (CIMA). Beresford, L. (1995). Seeing Double, Enterpreur. Berg, H. P. (2010). Risk Management : procedures, methods and Experience . Electronic Journal Reliability : Theory and Application, 79-95. Bisio, R. (2011). The Educated Franchisee Second Edition. Minneapolis: Bascom Hill Publishing Group. Black, H. C. (1990). Black Law's Dictionary 6th ed. St Paul: West Publishing, Co. Bogdan, R. C., a S. K. Biklen (1992). Qualitative research for education. An introduction to theory and methods. Boston: MA: Allyn a Bacon. Bruce R. Barringer a D. R. Ireland (2008). Enterpreneurship: Successfully Launching New Ventures. New Jersey: Pearson Prentice Hall. Chappel, C. (2014). The Executive Guide to Enterprise Risk Management. United Kingdom: Palgrave Macmillan. Comcover, A. G. (2009, October 26). Comcover Information Bulletin. Retrieved August 2015, from Australian Goverment Department of Finance Website: http://www.finance.gov.au/sites/default/files/IB_Issue32_Oct09_1.pdf Comcover, A. G. (2010, August). AS/NZS ISO 31000: 2009. Retrieved July 2015, from Australian Goverment Department of Finance Website: http://www.finance.gov.au/sites/default/files/COV_216905_Risk_Management_Fact_Sheet_FA3_23082010_0.pdf Embargoed, Countries (2012). Coffee Consumptions Statistics. Retrieved April 2015, 25, from Embargoed Countries Blogspot: http://embargoed-countries.blogspot.com/2012/12/coffee-consumption-statistics.html European, Franchise Federation (2003). European Code of Ethics for Franchising.Retrieved April 2015, from European Franchise Federation Website: http://www.franchise-fff.com/base-documentaire/finish/206/327.html Excelso, Coffee (2013, February). Franchising. Retrieved July 2015, from Excelso Coffee Website: http://www.excelso-coffee.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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; 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".