Does The Gender Pay Gap Actually Exist - 1221 Words

Does the Gender Pay Gap Actually Exist? Lecture Outline: The concept of a pay gap between female and male workers has been around for decades, this is because countless studies seek to justify why women earn less than men, and in many disciplines women are more easily subjected to discrimination in comparison to their male counterparts. This unit exposes many underlining factors that ultimately determine whether the gender pay gap truly exists in the contemporary Western hemisphere. The scholarly field that correlates to this unit is economics and gender studies. ï  ¶ Song of the day: Benny - Little Game (Official Video) ï  ¶ Video Link: https://www.youtube.com/watch?v=WNr3x1kVVEc ï  ¶ This song does a compelling job at†¦show more content†¦People are paid based on their human capital and since many women tend to enter fields such as psychology, nursing, and humanities they are likely to be paid less. While on the other hand, men tend to enter fields like engineering, therefore they are paid a higher wage that continues to grow higher annually. ï  ¶ Secondly, marital status and childbearing have a great impact on income. A vast majority of women take time off of work to have children, which subsequently results in them losing potential earnings during those years and after. The only discrimination that can be found among employers is when it comes to marriage and children. Married men with children pose less of a threat to their job status because they have their wives to take care of the children and housework. However, women with children are seen as a liability to employers because they have larger responsibilities in the household. On average women spend 167 hours engaging in non-paying work at home, such as cleaning and taking care of the children, while men only spend 100. ï  ¶ Lastly, society routinely encourages gender roles of the sexes, for example, boys are told to take interest in sports, cars, trucks, math, science, engineering, or to become doctors and lawyers. While girls are often encouraged to find interests in nursing, teaching, humanity courses, fashion, makeup, the colour pink, and

Key Players Decision Making in Terrorist Incidents Free Essays

Decision making is defined as â€Å"simply the act of choosing among available alternatives about which uncertainty exists† (Mingst, p. 457). However, decision making is not an easy process especially with the participiation of individuals within the government. We will write a custom essay sample on Key Players: Decision Making in Terrorist Incidents or any similar topic only for you Order Now    Individual elites or the â€Å"key players’† decisions and actions greatly affect the course of events as well as the situation of the country when one of various factors are present and one of which is during time of crisis like terrorist incidents (Mingst, p. 133). The decisions made by these elite individuals are influence by politics within the government which eventually affect their actions (Mingst p. 460). The effects of the choices that they make are not always advantageous as sometimes it have its adverse consequences in the situation of the ccountry. A good example to exemplified such idea is the aftermath of President George W. Bush’s decision to attack Iraq. Three imporatnt areas were badly affected by it, the country’s domestic cohesion, the international standing of the United States of America and the insecurity felt by those immediately affected by the decision. The domestic cohesion of the United States of America has been greatly affected by Pres. Bush’s decision . Many citizens especially the ones leaning towards the democratic side did not agree with his reasons of doing such in the name of U.S. battle against the war on terror. Moreover, the domestic consensus became much harder to achieve with the issue of personal gain. Opening   Iraq’s oil economy would greatly benefit the Bush family, being a member of one of the largest American oil company the Washington’s Carlyle Group (Chenoy, p. 23 ). A clear   indication of which is the drop in the approval ratings of the president after his speech that ordered the deployment of more troops in Iraq. Sixty percent of those who were surveyed expressed their disapproval in Pres. Bush’s performance (Iraq drives Bush’s rating to new low, 2006). Another area that is adversely afftected is the international standing of the country. Many states as well as international organizations are not in favor of the U.S. president’s course of action. This immediately put the country in a bad light especially its leader because the non-approval of these states emphasizes the illegitimacy of this action. A good example is the European Union (EU) that has been the country’s partner in economic activities but they didn’t support Bush’s action against Iraq. EU believes that America’s unilateral strategy would not benefit their multi-polar interest (Chenoy, p. 25 ). Lastly, is the effects of this decision to the citizens of Iraq as well as to the U.S. soldiers.  Ã‚   The war has left numerous casualties in terms of death tolls, damage to property, as well as the over all security of the country (Chenoy, p. 27). According to the Iraq Body Count Project (IBC), there were 947 civilian deaths reported between 1st and 27th of February this year (Enforced security: solution or stopgap?). The weight of an elite individual’s decisions and actions is greatly felt in crisis situation like terrorist incidents. The choices they make could have numerous effects in the country of their leadership and to others as well. Unfortunately, the politically driven decision of these â€Å"key players† could have adverse consequences in the lives of many people. Works Cited Chenoy, Anuradha. Demystifying Terrorism : a War Against Terror The Terror Of War, US   Ã‚  Ã‚   Hegemony Militarism. ARENA, 1997. â€Å"Enforced security: solution or stopgap?†. 28 February 2008. Iraq Body Count. 12 May 2008. https://www.iraqbodycount.org/analysis/beyond/enforced-security/. â€Å"Iraq drives Bush’s ratings to a new low†. 14 March 2006. CNN. 12 May 2008.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   ;http://edition.cnn.com/2006/POLITICS/03/13/bush.poll/;. Mingst, Karen. Essentials of International Relations. Norton, 2001. How to cite Key Players: Decision Making in Terrorist Incidents, Essay examples

Big Data in Supply Chain Management

Question: Discuss about the Big Data in Supply Chain Management. Answer: Introduction According to Dumbill (2013), a supply chain is a complex and dynamic network of the supply and demand. On the other hand, researchers Marz and Warren (2015) define the term supply chain as that particular system of operational activities, resources, data sets and organizations that facilitate the transportation of any service or product from the production facility to the customer At present, Amazon is considered as one of the largest cloud computing and electronic commerce US based multinational organizations. Headquartered in Seattle, in Washington, Amazon is not only considered as the largest internet retailers of the world but is also known widely for its well-managed supply chain and delivery systems. The following sections of the report would provide a detailed discussion of the manner in which Amazon utilizes big data tools in its supply chain process. The report would also shed light on the basic differences in between online and offline big data tools and the manner in which such tools might be chosen so as to gain optimized results. Big data: the differences between online and offline big data Researchers McAfee et al. (2012) define big data as the technical term that is used for depicting those large and complex volumes of information (both structure and unstructured), which are deemed unmanageable by traditional database systems. Thus, Big data can be considered as those initiatives and technologies that can be utilized for managing data sets far too diverse, massive and dynamic for conventional technologies. Researchers Wu et al. (2014) are of the opinion that Big data technologies can be categorized into two sections: Online Big data and Offline Big data. In the following section of the report, detailed discussions on these subdivisions would be provided: Online Big data: According to Chen, Chiang and Storey (2012) big data that is generated, managed, transformed and analyzed in real time, so as to support various ongoing operational activities and provide online services to users, is considered as online big data. Thus, online big data can be precisely described as the big data that is created and managed over the internet. Online Big data tools are characterized by the ability to generate new data sets, along with the production of dynamic outputs. Offline Big data: According to Chen, Mao, and Liu (2014) big data technologies that facilitate the analysis and management (needless to say transformation) of big data in batch processes are considered as Offline Big data technologies. One of the most remarkable characteristics of offline Big data is the fact that not only these technological tools lack the capability of generating new data sets but are also restricted the generation of static outputs like reports and dashboard updates. The differences between online and offline big data are: Sl. No Characteristics Offline Big Data Online Big Data 1. Mode of operation Offline, data managed in batch context (Lohr 2012). Over the internet 2. Capability of generating new data sets Not capable Capable 3 Nature of Output Static Dynamic 4. Application Latency Minimum latency, as the performance of the systems, must meet the end user expectations (Provost and Fawcett 2013). Slow response is accepted, as the outcomes of such applications are not directly utilized in the operational activities of the organization. 5 Examples Business intelligence tools, ETL applications, Data warehouses, etc NoSQL databases like MongoDB(Kim et al. 2014). Selecting the right Big Data application for achieving the desired outcomes It is a well-known fact that the Big data technologies that are currently available in the market typically belong to two categories: operational workloads that allow the real time interaction with data and analytical workloads that allow the complex analysis of business data in an offline mode (Gandomi and Haider 2015). Since making the correct choice of the big data application (for achieving the desired outcome) is one of the most difficult tasks associate with the incorporation of big data technologies within the operational infrastructure of an organization, a well- accepted strategy for doing the same has been outlined in the section below: Defining the problem in the appropriate manner Researchers Kaisler e al. (2013) are of the opinion that the very first step towards selecting the big data tool is to identify and define the problem that the business is currently experiencing, so as to make sure of the fact that Big Data technologies would indeed be capable of solving them. On the other hand, Demchenko et al. (2013) recommend that during his particular phase, the organizational heads should consider all the data silos that are available to them, irrespective of the significant extent some of them might appear to be typically resistant to any further analysis by any big data tool. Determine the outcomes and benefit of solving the problem Authors Chen, Chiang, and Storey (2012) are of the opinion that once the problem has been defined, it is important to identify the possible outcomes of solving the same. Researchers Marz and Warren (2015) on the other hand are of the opinion that ranking the expected benefits would also help in selecting the appropriate big data tool (that is, the tool that provides the majority of the highly the functionalities outcomes becomes the winner!). Defining the strategies for measuring the progress of the project According to Gandomi and Haider (2015), it is important to define the policies that would be utilized to measure the progress of the task (that is, finding solutions to business problems with the help of big data technologies). The identification of this strategy would facilitate the process of monitoring the functions of the selected big data tool. Considering the end users In the final phase of the selection process, it is important to identify the end users of the system and the functionalities that each of the big data technologies would provide to these users. Researchers Provost and Fawcett (2013) are of the opinion that big data technologies that are selected as a result of considerations made in step 2, 3 and 4 are more often than not found to be the most suitable ones. Big Data technologies Researchers Kim, Trimi and Chung (2014) have pointed out the following big data technologies that are currently available in the market: Predictive analytics: Those software and hardware that allow organizations to develop and deploy business models by analyzing the big data sources that are available to them are termed as predictive analytical tools (Kaisler et al. 2015). Databases: NoSQL databases used for storing graphs, documents, and key values. Search and discovery tools: Big data solutions also include tools that are capable of extracting information from large repositories of structure and unstructured data. Steam Analytics: Researchers Demchenko et al. (2013) are of the opinion that software tools capable of filtering, aggregating and analyzing big data coming from several online sources are termed as stream analytic tools and are to be considered as one of the most essential big data technologies. Data virtualization: According to Kim, Trimi and Chung (2014) data virtualization is defined as that particular technology that facilitates the delivery of information from several distributed sources, including big data sources supported by Hadoop, in real time frame. Business Impact of Big Data According to researchers Waller and Fawcett (2013), Big data technologies can be utilized to manage the core functionalities of any supply chain process, namely business planning and operation. The following section of the report provides a detailed discussion on how the utilization of big data technologies can benefit the above-mentioned functionalities. Business planning: Forecasting: According to Feki, Boughzala and Wamba (2016), each activity and element associated with the Supply Chain process generate huge volumes of data that can be utilized effectively by Big data tools so as to make accurate supply and demand forecasts. Marketing and sales intelligence: It is a well-known fact that order management applications and tools are quite useful in collecting large volumes of customer information. Appropriate big data technologies can be utilized to analyze these data sets, as a part of a customized market research, so as to as to identify the geographical regions or business domains in which business activities can be expanded (Sanders 2014). Operations: Crowdsourced package delivery systems have been one of the greatest contributions of big data technologies in supply chain management systems: this has primarily been because big data technologies allow the match between the available drivers and the delivery times requested by the customers (Erevelles, Fukawa and Swayne 2016). Operational efficiency: Big data technologies have made significant contributions in increasing the operational efficiency of supply chains. Operational activities, like that of monitoring the supply routes, capacity planning and responding to bottle neck situations in real time are some of the activities that can essentially be benefitted by the incorporation of Big data technologies (Waller and Fawcett 2013). Organizational Impact of Big Data Researchers Erevelles, Fukawa and Swayne (2016) are of the opinion that most retail organizations, the task of optimizing the retail and logistics chain are considered as one of the most difficult activities. However, the authors consider this very activity to be one of the greatest opportunities that can be taken for improving their services, all the while reducing their costs. In an attempt to grab the above mentioned opportunity, Amazon has introduced the concept of anticipatory shipping'- a method that allows the organization to start shipping even before the customer has placed the order (Whitmore 2014). In fact, in 2012, the enterprise had filed a patent application for the system, describing its functionalities as the method and system for anticipatory package shipping,and was awarded with the patent in the following year. Although the details of the said system (algorithm to be very precise) have not yet been revealed by the organization, the patent summary description of the same reveals it to be a system that allows the retailer to send packages to particular geographical regions, without any specific address. These particular attributes of the system have encouraged Leveling, Edelbrock and Otto (2014) to consider it as a significant improvement over traditional predictive analytical tools. The information available from the patent document indicates that Amazon utilizes the data available from the previous activities of a user to run the anticipatory shipping system (Cuthbertson, Furseth and Ezell 2015). These activities might include the time at which he/ she had logged in, the time that has been spent on reviewing the products available on the website, the links that have been clicked and the queries placed by the customer with the sales team. As for example, in case the local residents of Sydney order a lot of scarves in August, the local order fulfillment centers of the city would be stocked with scarves at this particular time of the year. However, the shipments would only be made when Amazon's shipping system receives the actual orders (and the delivery addresses) (Whitmore 2014). According to Leveling, Edelbrock and Otto (2014), the anticipatory shipping method demonstrates the finest manner in which Big Data technologies can be utilized in optimizing supply chain processes: in fact, the introduction of this particular system now allows Amazon to provide guaranteed 2-day deliveries, thus ensuring customer satisfaction. Conclusion Experts in the domain of information technology have often pointed out the fact that Big data and supply chain are a made for each other pair: partly due to the enormous amount of data generated by supply chain processes on a daily basis and partly due to the immense strength of Big data technologies in analyzing voluminous data and generating patterns from the same. The preceding sections of the report provide a detailed discussion on the big data technologies that are currently being utilized for the management of supply chain and logistics. The discussions provided in section 2 of the report sum up to the fact that, based on the mode in which the analysis of Big data collected from various sources is made, Big data technologies can be subdivided into two categories: online big data operating in real time domain and offline big data emphasizing on the batch processing of enterprise data. On the other hand, the discussions provided in section 3 of the report reveal that the selection of big data technologies to be utilized in an organization require to be made based on the benefits available to it. Section 4 and 5 of the report provide an in-depth discussion on the benefit of the utilization of Big data technologies in supply chain process. In the light of the discussions made in these sections, it can thus be concluded that the incorporation of Big Data technologies increases the efficiency of supply chain processes to a significant extent. References Chen, H., Chiang, R.H. and Storey, V.C., 2012. Business Intelligence and Analytics: From Big Data to Big Impact.MIS quarterly,36(4), pp.1165-1188. Chen, M., Mao, S. and Liu, Y., 2014. Big data: a survey.Mobile Networks and Applications,19(2), pp.171-209. Cuthbertson, R., Furseth, P.I. and Ezell, S.J., 2015. Amazon and Borders: From Sector Focus to Competence Focus. InInnovating in a Service-Driven Economy(pp. 130-144). Palgrave Macmillan UK. Demchenko, Y., Grosso, P., De Laat, C. and Membrey, P., 2013, May. Addressing big data issues in scientific data infrastructure. InCollaboration Technologies and Systems (CTS), 2013 International Conference on(pp. 48-55). IEEE. Dumbill, E., 2013. Making sense of big data.Big Data,1(1), pp.1-2. Erevelles, S., Fukawa, N. and Swayne, L., 2016. Big Data consumer analytics and the transformation of marketing.Journal of Business Research,69(2), pp.897-904. Feki, M., Boughzala, I. and Wamba, S.F., 2016. Big Data Analytics-enabled Supply Chain Transformation: A Literature. Gandomi, A. and Haider, M., 2015. Beyond the hype: Big data concepts, methods, and analytics.International Journal of Information Management,35(2), pp.137-144. Kaisler, S., Armour, F., Espinosa, J.A. and Money, W., 2013, January. Big data: issues and challenges moving forward. InSystem Sciences (HICSS), 2013 46th Hawaii International Conference on(pp. 995-1004). IEEE. Kim, G.H., Trimi, S. and Chung, J.H., 2014. Big-data applications in the government sector.Communications of the ACM,57(3), pp.78-85. Leveling, J., Edelbrock, M. and Otto, B., 2014, December. Big data analytics for supply chain management. In2014 IEEE International Conference on Industrial Engineering and Engineering Management(pp. 918-922). IEEE. Lohr, S., 2012. The age of big data.New York Times,11. Marz, N. and Warren, J., 2015.Big Data: Principles and best practices of scalable realtime data systems. Manning Publications Co.. McAfee, A., Brynjolfsson, E., Davenport, T.H., Patil, D.J. and Barton, D., 2012. Big data.The management revolution. Harvard Bus Rev,90(10), pp.61-67. Provost, F. and Fawcett, T., 2013. Data science and its relationship to big data and data-driven decision making.Big Data,1(1), pp.51-59. Sanders, N.R., 2014.Big Data Driven Supply Chain Management: A Framework for Implementing Analytics and Turning Information Into Intelligence. Pearson Education. Waller, M.A. and Fawcett, S.E., 2013. Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management.Journal of Business Logistics,34(2), pp.77-84. Whitmore, A., 2014. Could Information Technology Enable Efficient Central Economic Planning?.Available at SSRN 2524877. Wu, X., Zhu, X., Wu, G.Q. and Ding, W., 2014. Data mining with big data.IEEE transactions on knowledge and data engineering,26(1), pp.97-107.