Analysis of How Tesla Creates Core Innovation and Capability to Sustain the Market
Bibliographic record
Abstract
Tesla as green technology is new concept to current market. Current conventional market is significantly comparative. As a new comer to existing difficulty to enter the market, the company demonstrates significant innovation in various ways. Thus, Tesla has different concept of management and marketing to the market place. The company demonstrates online strategy and vehicle’s IT connected system. To improve integration of technology, the main firm acquired solar panel and installing firms. The company’s pathway to manage the organization and approach to its consumers would bring significant understanding in terms of business and management. Global consumption for fossil fuel energy is around 82% and demand for the energy had been increased more than twice from 1971 to 2015. This phenomenon has significant impact on carbon dioxide emissions which had been skyrocketing from 1950. Carbon dioxide emission caused by transportation reached 25% in 2015 and among transportation emission, 28% was emitted from China and 17% was emitted from North America. In terms of saving the planet, introducing electric car seems to be remedy for worsened environment (IEA, 2017). Turning point that auto industry paid attention to electric vehicle was one of leading automobile maker, Volkswagen was hit by diesel emission scandal and fined for $ 15 billion. One of significant solutions to maintain in current market place was introducing electric power engine (Focus, 2017). Not only the world faced negative perspective on carbon dioxide emission which causing climate change and other harmful effects, but also the energy consumption for industrial activities caused major concern of depletion of oil reservoir (Bilbeisi & Kesse, 2012, p. 2017). Price of crude oil was peak point in 2014 and it was around $ 96.2 per barrel. Years later, the price was plunged as around $ 56 per barrel in 2018. However, anticipation of the World Bank for the price of crude oil is gradually increasing from 2016 to 2030. High price of petrol can be one of reasons for shifting from conventional engine power to electric power engine (TheWorld Bank, 2018). Carbon dioxide problem and high price of gasoline are tremendous forces to consider other alternative resources to power the engine. There is trial to set recharging stations for electric vehicles, supported by the International Economics Forum of the Americas in Montreal, Canada (DUNN, 2016). As above indicated, some of researchers in these fields suggest that big auto producers such as GM, Toyota and some others to shift their manufacturing line from conventional engine to electric car
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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 | 0.001 | 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 | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".