Recent orders
Big data (100) promotion
Audio:Introduction (50):
Amazon Kindle is a sequence of mobile electronic gadgets designed to allow readers to browse, read, download, or buy books for their consumption. The range of products provides by Kindle business is created and marketed by Amazon, for customers to read e-books through its e-reader (Dudley, 2019). For it is one of the products of industry 4.0 in the 21st century and developing, hence needs current digital business analysis to understand the trends.
Big data (100): promotion
Amazon Kindle can make an accurate suggestion before making sales. According to research done by Simon Peter Rowberry of the University of Stirling, the promotion process involves turning useful information from the data of the most preferred books through Business Intelligence (BI). It then highlights the words and notes in customers’ books and analyzes the feedback of Kindle and on social media (Wills, 2018). This process contributes to big data, a central project of modern science and business, which is used in the digital business to analyze and visualize the information (Breur, 2016). Amazon is widely known as a pioneer. To analyze data more accurately, Amazon developed Amazon RedShift in 2013, to manage a data warehouse in the cloud (Watson, 2014). The redshift is a part of Amazon Web Services (AWS), also used in an online social game, Zynga (Watson, 2014). The Redshift was useful to the Amazon company in the introduction stage of the game (Watson, 2014). This can inflect more concept of business intelligence, especially for businesses with a lot of data, which help in identifying more target market. Amazon’s application of big data will be emulated and promoted soon by various e-commerce enterprises as it becoming a trend.
Technological product (100): product
The products and product forms also changed. It is sent from Amazon Kindle to the e-reader mobile app. Both order and delivery occur virtually. Kindle uses a piece of technology equipment, through which virtual products are sent to the e-readers in a minute (Norman, 2009). This method is more developed, having improved technology sense, including fictitious, a unique code to identify each product. The equipment is updated periodically with a changing trend. For example, the idea of Kindle began in 2004, with the name of Fiona (Newton, 2019) and updated from 1.0 to Kindle 10.0. Also, there have been e-commerce developments such as VR, AR, and XR, which depicts future changes in e-reading.
Shopping website (no physical evidence) (100): place
Amazon Kindle uses a website to provide its products to customers. The website can be considered as an online store since it allows people to search, choose, and buy books they want through the web browser (Dudley, 2007). It was started by Jeff Bezos in 1994 as a simple form, to give people a fire-new way to buy books and receive them through logistics (Byers, 2007). However, with the emergence of digital disruption that slows down the process, there has been the development of more forms of shopping website, Kindle cloud is one of it. As one of the giant competitors in the market of content selling, Amazon, Google, and Microsoft tried to digitalize as many as books they can. The content is scanned and sent into the cloud (Grafton, 2015), and then be sent to customers’ equipments from the cloud in seconds after payment (Kowalczyk, 2019). This process does not need any physical evidence, but only a sum of data and a channel to pass through the cloud. Therefore, there is no physical evidence of store or product, which shows a current trend of e-commerce in the digital business.
Blockchain (100): price
As people are buying, payment has to be made to complete the sale process. Kindle provides an online payment platform based on the blockchain concept. In 2008, the most popular method of block chain was the bitcoin, whereas it currently widely used for funds flow (Swan, 2015). The block chain is used to translate users’ funds into the business’ account with rail (Nofer et al., 2017). The payment mode is convenient for customers, with cost-friendly prices. Also, the paying process is secure as it uses cryptography, which protects data from the modification (Narayanan et al., 2016). In addition, the payment mode can easily be recorded and tracked by the users themselves. It can be guessed that as the industry leader, Amazon Kindle has arguably influenced the entire Internet economy, can this method will be used widely.
References
Breur, T. (2016). Statistical Power Analysis and the contemporary “crisis” in social sciences. Journal of Marketing Analytics, 4(2-3), pp.61-65.
Byers, A. (2007). Jeff Bezos. New York: Rosen Pub. Group.Dudley, B. (2007). The Seattle Times: Brier Dudley’s blog. [online] Web.archive.org. Available at: https://web.archive.org/web/20101221083337/http://blog.seattletimes.nwsource.com/brierdudley/2007/11/chatting_with_amazons_kindle_d.html [Accessed 23 Dec. 2019].
Grafton, A. (2007). Future Reading: Digitization and its discontents. The New Yorker.
Kowalczyk, P. (2019). What is Kindle cloud, exactly?. [online] Ebook Friendly. Available at: https://ebookfriendly.com/what-is-kindle-cloud-library-amazon/ [Accessed 2 Jan. 2020].
Narayanan, A. Bonneau, J. Felten, J, Miller, A. and Goldfeder, S. (2016). Bitcoin and Cryptocurrency Technologies. Network Security, 2016(8), p.4. R1
Newton, C. (2019). Inside the secret lab where Amazon is designing the future of reading. [online] The Verge. Available at: https://www.theverge.com/2014/12/17/7396525/amazon-kindle-design-lab-audible-hachette [Accessed 23 Dec. 2019].
Nofer, M., Gomber, P., Hinz, O. and Schiereck, D. (2017). Blockchain. Business & Information Systems Engineering, 59(3), pp.183-187. R3
Norman, D. (2009). THE WAY I SEE ITSystems thinking. interactions, 16(5), p.52.
Rowberry, S. (2019). The limits of big data for analyzing reading. Journal of Audience & Reception Studies, 16(1).
Watson, H. (2014). Tutorial: Big Data Analytics: Concepts, Technologies, and Applications. Communications of the Association for Information Systems, 34.
Wills, J. (2020). 7 Ways Amazon Uses Big Data to Stalk You. [online] Investopedia. Available at: https://www.investopedia.com/articles/insights/090716/7-ways-amazon-uses-big-data-stalk-you-amzn.asp [Accessed 1 Jan. 2020].
Swan, M. (2015). Blockchain: Blueprint for a new economy. Sebastopol, CA: O’Reilly Media, Inc.
A Proposal to Test the Validity of the Public Interest Inquiry Criteria Used by the Canadian International Trade Tribunal (CI
Proposal
Name of Student
Name of Institution
A Proposal to Test the Validity of the Public Interest Inquiry Criteria Used by the Canadian International Trade Tribunal (CITT) Focusing on the Circular Copper Tube Decision
Introduction
CITT is the Canadian body that determines whether the imposition of countervailing measures and anti-dumping duties should be reduced, maintained or eliminated. It reports to the Minister of Finance pursuant to the Special Import Measures Act (s.42). The Parliament enacted the provision following consumer concerns that full margin levies of dumping duty resulted to higher costs of products for consumers and adversely affected competition in Canada. The public interest test sprang in light of these concerns. However, the legislation provides no definition of “public interest” hence it is left to CITT’s discretion to determine the definition as witnessed in the Circular Copper Tube decision (2014).
Statement of Purpose
Because unfettered discretion could derail the objectives of CITT and erode public confidence in it, there is the need to test the validity of the criteria used by CITT to warrant public interest enquiries. The factors outlined in the Act will be weighed against internationally recognized standards to ascertain their credibility. Moreover, the Act gives room for CITT to take into account any other relevant factors, a situation that calls for scrutiny into the possible loopholes that might arise in such imprecise modes of operation.
Importance of the Study
The study will prove that although players in the industry see “public interest” as a concept deserving strong support, there are many underlying issues that should be taken into account by CITT in the determination of a public interest inquiry. It would go against procedural fairness and good public policy not to have specific criteria on determining public interest and how to allocate weight to the interests of both producers and other players in the industry. An ideal test should find that injury has been occasioned to a domestic industry by dumping, and there is the need for a compelling argument for not imposing measures in such cases due to the public interest consideration.
Criteria that merely list factors are unsatisfactory as whoever determines the need for public interest would need to adopt at a certain view on the importance of industry and exporter interests. Failing to address the issue would trigger intense lobbying by interested parties to include their views on each factor’s relative weight in the absence of an indication of the weight attributed to each factor in determining the respective interests of consumers and the producer.
Any criterion of inquiry must be founded upon the requirement that there is the need for compelling reasons not to roll out measures due to broader issues of public interest. The requirement is crucial as it maintains a degree of integrity for the industry and addresses unfair trade practices. The failure to give greater weight to industry needs that are discovered to be materially injured than the needs of other players would produce unacceptable results for the industry and substantially undermine the entire rationale for anti-dumping. In turn, this would mean that failing to impose measures is largely exceptional, which should not be viewed as a critique to the reduced application of public interest.
List of Jurisprudence
The World Trade Organization Anti-Dumping Agreement
Global Congress Declaration on Fundamental Public Interest Principles for International Intellectual Property Negotiations
CITT Decision – Beer Originating in or Exported from United States of America by or on behalf of Pabst Brewing Company, G. Heileman Brewing Company and the Stroh Brewery Company, their Successors and Assigns, for Use or Consumption in the Province of British Columbia [Opinion No.: PI-91-001]
General Agreement on Trade and Tariffs (Canada membership, 1947)
North Atlantic Free Trade Area (Canada, United States, Mexico) – 1994
Most Favored Nation Treatment under GATT
Non-discriminatory Administration of Quantitative Restrictions – GATT
Qualifications for GATT Exceptions
Chapeau – GATT
Vienna Convention – Treaty Interpretation
Big Data & Artificial Intelligence in Marketing What are the Ethical Implications
-4572002628900Big Data & Artificial Intelligence in Marketing: What are the Ethical Implications?
Have you heard or did you hear about the Cambridge Analytica Data Scandal? This is a typical example of just how much is at stake when it comes to big data and artificial intelligence in Marketing. The advent of these new technologies has become the backbone of the success for a number of renowned multinational corporations including Google, Facebook, Instagram, YouTube, Amazon, Alibaba etc. As more organizations jump onto the big data craze and AI technology as marketing tools, certain questions arise. A review of ethical implications of the technology demands answering certain concerns on big data and AI based marketing strategies and decision-making processes used presently. Looking at Big data and AI intelligence we must consider; the negative ethical implications of this new technology, how far is too far?
Big data is now big business but has become a tool for manipulation. According to Saran, (2018), Big data has enabled companies to better target the changing needs of customers; developing relevant, rich and needs oriented content but this has come at a cost. Organizations like Google and Facebook are now generating huge chunks of their revenue from big data. Big data is a big business because of the high accuracy levels of collected data that have helped make major organizational decisions in real time. Through data harvesting, organizations are now able to understand the needs of their target customers recognize the changing needs of every customer and respond appropriately in real time. This has created the problem of privacy breaches and user manipulation based on their personal information. Because of this level of accuracy, data can be manipulated and used to target individual vulnerabilities considered as weaknesses for eases of manipulation in crucial decision-making aspects such as elections.
Another major ethical concern is the issue of selling private user data to third parties. Companies like Google and Facebook have been found to sell user data and information to third party organizations at a profit. This is a growing concern, because there is no telling what extents, these organizations go to obtain the data and how much of the private user data is being given out. Additionally, most users don’t really know when, why and how their data is harvested, what is harvested and how this data is used. Companies like Cambridge analytica was able to use private user information to create targeted advertisements, which helped dissuade the electorate to vote in certain patterns. There are many other instances of user privacy violations and undesired targeted advertising which point to just how serious this problem will become as more organizations, governments and the general population joint the online data harvesting and artificial intelligence technology bandwagon.
00Big Data & Artificial Intelligence in Marketing: What are the Ethical Implications?
Have you heard or did you hear about the Cambridge Analytica Data Scandal? This is a typical example of just how much is at stake when it comes to big data and artificial intelligence in Marketing. The advent of these new technologies has become the backbone of the success for a number of renowned multinational corporations including Google, Facebook, Instagram, YouTube, Amazon, Alibaba etc. As more organizations jump onto the big data craze and AI technology as marketing tools, certain questions arise. A review of ethical implications of the technology demands answering certain concerns on big data and AI based marketing strategies and decision-making processes used presently. Looking at Big data and AI intelligence we must consider; the negative ethical implications of this new technology, how far is too far?
Big data is now big business but has become a tool for manipulation. According to Saran, (2018), Big data has enabled companies to better target the changing needs of customers; developing relevant, rich and needs oriented content but this has come at a cost. Organizations like Google and Facebook are now generating huge chunks of their revenue from big data. Big data is a big business because of the high accuracy levels of collected data that have helped make major organizational decisions in real time. Through data harvesting, organizations are now able to understand the needs of their target customers recognize the changing needs of every customer and respond appropriately in real time. This has created the problem of privacy breaches and user manipulation based on their personal information. Because of this level of accuracy, data can be manipulated and used to target individual vulnerabilities considered as weaknesses for eases of manipulation in crucial decision-making aspects such as elections.
Another major ethical concern is the issue of selling private user data to third parties. Companies like Google and Facebook have been found to sell user data and information to third party organizations at a profit. This is a growing concern, because there is no telling what extents, these organizations go to obtain the data and how much of the private user data is being given out. Additionally, most users don’t really know when, why and how their data is harvested, what is harvested and how this data is used. Companies like Cambridge analytica was able to use private user information to create targeted advertisements, which helped dissuade the electorate to vote in certain patterns. There are many other instances of user privacy violations and undesired targeted advertising which point to just how serious this problem will become as more organizations, governments and the general population joint the online data harvesting and artificial intelligence technology bandwagon.
-10287001485900AI & BIG DATA IN MARKETING
00AI & BIG DATA IN MARKETING
-1028700-102870000