Online vs. Offline Movie Rental: A Comparative Study of Carbon Footprints
Bibliographic record
Abstract
Introduction The increasingly cogent evidence that the environmental sustainability of our planet is rapidly deteriorating due to human actions has made the concept of Sustainability a key focus of humankind. Nonetheless, in order to assess the impact of various factors and complex underlying dynamics on Sustainability, we often need some quantitative and qualitative frameworks and models to make objective decisions (Velasquez, Ahmad and Bliemel, 2009; Velasquez, 2003). This paper employs quantitative approaches to compare the environmental impact of Online and Offline Movie Disc Rental services. In order to achieve as much Eco-Efficiency and Eco-Efficacy as possible in the modern business environment, the companies must continuously employ innovative processes to bring positive changes in the triple bottom line (Velasquez, Ahmad and Bliemel, 2009). In the current information age, it is possible to create new business models, value propositions, and more efficient and competitive firms. Based on the available technology infrastructure, an organization is able to communicate with its internal and external stakeholders in a seamless flow of information, and the Internet plays a major role in this digital integration. Indeed, the Internet has become the infrastructure of choice for E-Commerce because it offers businesses an easier and low cost way to link with customers and other businesses. Moreover, it is the Internet that helps companies to reduce their transaction costs (Laudon and Laudon, 2007). Indeed, it has been argued by many researchers and practitioners that the Internet and E-Commerce can help in realizing a sustainable development for human beings and the environment (The Climate Group, 2008). However, such arguments lack scientific evidence in most cases. Nevertheless, the trend is most certainly having shoppers move towards online shopping. Indeed, online retailing is growing tremendously worldwide. For instance, US online retail reached US$175 billion in 2007 and is projected to grow to US$335 billion by 2012 (Forrester, 2008). Similarly, Canadian online retail reached US$12.9 billion in 2007 and is projected to grow to US$22.2 billion by 2012 (eMarketer, 2008). Moreover, the online services in Canada are growing rapidly; the amount of US$12 million was spent in online Movie Rental service in Canada in 2006. By 2011 the number is expected to surpass US$285 million (Pricewaterhouse Coopers LLP, 2007). There are various underlying causes for such a strong trend towards online retailing. For instance, it has been argued that the Web channel has been relatively successful because it is a destination for consumers to find low prices and it is perceived to be more convenient than shopping in stores (Forrester, 2008). Based on the USA experiences, the growth of online shopping and services is driven by two major factors: first, by the growing rate of fast Internet connections which allow more interaction and faster buying process for the Online customers; and second, for the extensive and effective marketing strategies used specially by some of the larger online corporations (Punkett Research, 2009). In the worldwide ranking, Canada belongs to the top 15 in Internet usage (Computer Industry Almanac Inc., 2007). In 2008, Canada had 28 million Internet users, which is almost two-thirds of all Canadians (Internet World Stats, 2009). Considering the current economic crisis, the last three household items from the list of ten that Canadians would cut are the home Internet, Movie Rentals, and cell phone. However, the first seven that they will cut to their budget are big ticket events, Movie going, DVD buying, magazine subscription, cable/satellite TV extras, video game buying, and home phone. These results, from a survey conducted by Solutions Research Group in Canada and the USA, showed that during a recession households prioritize, almost like heating and water, the relative new services of home Internet, Movie Rentals and cell phone (Robertson, 2008). …
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".