Attachment Behaviours Essays - Attachment Theory, John Bowlby

Attachment Behaviours Why have psychologists stressed the importance of attachment behaviours in development? Many theorists agree that social contact early in a child's life is important for healthy personality development. This is the most important relationship of the child development period as it is from this that the child drives its confidence in the world. A break from this relationship is experienced as highly distressing and constitutes a considerable trauma (Schaffer 1964). Through frequent social and emotional exchanges with parents the infant not only defines itself, but also acquires a particular style and orientation that some researchers believe is carried over into later life (Sroufe 1978). Therefore, the relationship between an infant and its caregiver and its development is one that has generated much interest to developmental psychologists. John Bowlby (1958, 1968) put forward a comprehensive account of attachment and believed that the infant and mother instinctively trigger each other's behaviour to form an attachment bond. Attachment can therefore be defined as ' the ab ility to form focused, permanent and emotionally meaningful relationships with specific others' (Butterworth & Harris 1994). In child psychology, attachment is often restricted to a relationship between particular social figures and to a particular phenomenon thought to reflect unique characteristics of the relationship ( Santrock & Bartlett 1986). This essay will attempt to examine the role and importance of attachment behaviours in development. In Bowlby's view, there is a dyadic emotional regulation between the infant and the mother or caregiver. The infant has innate signals to elicit responses from the caregiver. Conversely, infant behaviour such as crying, cooing, smiling etc are elicited by the caregivers specific actions e.g. leaving the room or putting the infant down. Santrock and Bartlett (1986) found that 'the infant's behaviour is directed by the primary goal of maintaining the mother's proximity. The baby processes information about the mother's location and changes his behaviour based on this fact. Thus?instinct or a fixed pattern is the primary force for developmental change, but is transformed through social experience.' This reciprocal tie of mother and infant is a state that ensures care and protection during the most vulnerable period of development. This attachment to the mother has a clear biological survival value, explaining the significance of the mother-infant interaction within the overall framework of attachment behaviour. Sroufe (1991) supports this view, he maintains that attachment refers to a behavioural system, which is 'selected for its effect on the reproductive success of individuals in the environment in which they evolved.' Bowlby argued that different attachment behaviours, such as crying, following etc, are functionally related, in that all may lead to the same outcome - the caregiver-infant proximity (Sroufe 1991). Bowlby argues that attachment, is therefore a primary process, which is innate, and is mediated by social interchange. Here the visual channel plays an important role, i.e. through smiling and eye to eye contacts. Bowlby outlined four phases of the development of attachment as an integrated system of behaviours in infants: Phase 1:- Birth - 2/3 months The infant directs his attachment to human figures on an instinctual bias; all are equally likely to elicit smiling or crying because the infant is not discriminating. Phase 2:- 3-6 months The infant's attachment focuses on one figure, typically the primary caregiver. Phase 3:- 6-9 months The intensity of attachment to the mother or caregiver increases. Due to this and newly acquired motor skills, the infant now readily seeks the proximity to the caregiver. Phase 4:- 9-12 months The elements of attachment listed above become integrated into a mutual system of attachment to which both infant and mother contribute. Bowlby argued that communication between the infant and the caregiver takes the form of non verbal communication, this can be eye to eye contact, or face to face interaction. He went on to propose that the baby's smile is the essential catalyst that generates the infant-caregiver interaction. The interaction goes through positive feedback on both sides until it becomes a conversation of visually perceived gestures. Wright (1991) outlines the progress of this progression of 'smiling' in the development of attachment behaviours: Begins at birth: At first the smile is fleeting and incomplete. 4-5 weeks: The smile is now nearly complete and the trigger for the smile becomes more specific. 5-6 weeks: The smile response is now

Five Most Important Points for Each Chapter Essays

Five Most Important Points for Each Chapter Essays Five Most Important Points for Each Chapter Essay Five Most Important Points for Each Chapter Essay Name: Instructor: Course: Date: : Five Most Important Points for Each Chapter Chapter 1 An anti-bias curriculum helps children figure out the difference between fair and unfair treatment. It makes children have a strong self-esteem, and not feel superior or inferior about their social status. Children grow more comfortable with each other despite their differences. It enables children to think critically about how their peers feel when discriminated. Chapter 2 To create an anti-bias environment teachers must structure their teaching habits according to children’s needs. All activities should be integrated into the teaching system. All cultures should be treated as equal. Anti-bias topics that teach cultural diversity should be incorporated into the curriculum. Chapter 3 Little children are aware of the privileges bestowed upon a certain kind of people at a young age. Teachers must be able to shape and direct the ways through which little children think. Teachers should understand the various views children have concerning matters that deal with racism and sexism. Classroom decorations, books and activities should reflect diversity. Chapter 4 Children should be made to understand the fact that differences are good. Racial differences and similarities should be taught in such a way that children find them enriching. They should be made to know that there is no superior race. The learning environment should allow children to interact with their peers without feeling insecure. Children should interact with their peers from different backgrounds. Chapter 5 Young children are supposed to be taught about people with disabilities via widespread and interactive means in order to overcome their fear. Children should be taught to be empathetic so that they may focus on the person and not the disability. Children with disabilities are to be taught to use their other abilities and to develop skills that will help them deal with injustices. Chapter 6 Gender stereotyping creates an uneven cognitive development in both girls and boys. Boys are supposed to learn to embrace competition without feeling superior over girls. Girls have to learn that they are just as competent as boys are and can make similar choices. Children should to take up different roles. They should respect each other regardless of gender. Chapter 7 Teachers should help children to develop an identity based on their cultures. The class should focus on multicultural ways of teaching. Teachers should desist from using methods that portray one culture as inferior or superior. Children should be taught to appreciate the differences in culture and race. Children should celebrate their culture without being discriminated. Chapter 8 Children should be taught to be empathetic to diverse peoples. Teachers should instill a culture where children feel secure in their own identities. Children must be empowered with skills to identify unfair stereotypes. They should also be taught that bias causes pain to the victim. Children should learn to accept other people’s differences without stereotyping. Chapter 9 Children should be taught to defend themselves and others. They should be taught on ways to act when confronted with situations of bias. Children’s empathy should be cultivated for them to be able to stand up for others. Critical thinking on bias issues is necessary for activism. Chapter 10 Holiday activities should reflect the needs of the children; such activities should be culturally relevant. Teachers should take holidays as opportunities to teach about culture. Teachers should create appropriate holiday programs that reflect the needs of each child in the classroom. Children should identify with their culture and have an overall understanding of diversity through holidays. Chapter 11 Parents and other family members should be involved in anti-bias education. Involving parents depends on the creativity of the tutors. Parents’ ideas about bias issues should be analyzed. Educators should find out about the cultural backgrounds of children. Parents can be used as advisors especially on issues concerning culture. Chapter 12 Teachers should increase their awareness of different social and cultural contexts. They should analyze what they know about other peoples’ feelings towards diversity. A teacher should understand the disadvantages of unfair treatment. Teachers should talk with families about anti-bias issues. A teacher requires commitment and a change of attitude to be able to carry out anti-bias education.

Engineering - Quality Management Research Paper

Engineering - Quality Management - Research Paper Example Looking back to the early 1980s a revolution, which can be perceived as a not-so-quiet, has been in effect in the global business. This has been an ideas revolution involving the questions of how to do business. The revolution can be said to be largely spearhead by three individuals, that is, Joseph Juran, Philip Crosby as well as W. Edward Deming. Thesis Statement The purpose of this paper is to access the similarities and the differences from the works of Deming and Crosby in quality management. On similarities, the paper will address; Customer Requirement as an important standard, the responsible quality management, goal of Quality as well as management Perspective. On the aspect of differences, the paper will address; The Basic orientation to quality, the question of what is Quality, implementation’s chief elements, inspection/ Defect control, improvement Basis, management perspective and cost of quality Discussion Similarities Customer Requirement as an important standard According to the work by Deming, he is of the view that the presence of a consumer is brought about by a necessity and as such, it is the most vital aspect of a system of production, that is, where there are no consumers, what then is the need for production? Crosby’s work define quality as conforming to certain set of specifications, which have been stipulated by the management of an organization and not some concept of goodness full of vagueness. The specifications entailed in quality are not made arbitrary either (Baxter & MacLeod, 2008). As such, they ought to be set in accordance with the needs as well as wants of the customers. The responsible quality management team According to the work advanced by Deming, it advances that quality is designed in the boardroom. In his notion of quality, he says that ideas such as; sincerity, hard work, personal responsibility as well as decency, indeed are responsible for the changed management world. He says that it is unsubstantial to just perform a task to the best of your capacity. It is necessary that one becomes aware of what he is working on. Crosby work indeed shares the same notion (Baxter & MacLeod, 2008). According to him, improvement in quality ought to commence from the top. In order to come up with a manufacturing process, with no defect, or what he term as zero defects, then the management of any organization must then set the suitable atmosphere and the tone whereby the employees will easily follow. If the management fails to establish a production system with zero defects, then it is not closer enough to a quality product. Goal of Quality In both works, they advance that quality improvement is an unending process. In Deming’s work, it is suggested that in order to meet and exceed the needs of a customer, it is necessary that there are continuous improvements. In the same notion, Crosby points out that the continuous improvements must be enhanced and as such, it should be done by setting a production with zero defects (John & Barnes, 2006). Though both works share common grounds in regard to the quality management, the largest part they share are the differences on the notion of quality management. Management Perspective In both works, drawing of comparison is mentioned

Home work Assignment Example | Topics and Well Written Essays - 250 words

Home work - Assignment Example Technical writing has several characteristics, including having a specific purpose for writing, the audience written for, providing accurate information, and logical and linear organization (â€Å"Characteristics†). The mentioned characteristics are found in the given prose. The purpose of the prose is to inform readers in a matter-of-fact manner the characteristics and causes of fog, most probably written for academically-engaged individuals like science students. The prose provides scientific information necessary for the audience and not consisting opinions, as in technical writing. It is arranged in an organized manner, initially introducing fog, then progressing to its types and occurrences, finally concluding to how fog poses hazards to vehicle drivers’ ways to disperse fog. The presence of such technical writing features in the prose thus supports that the characteristics of the former can be applied to the latter. Reference Characteristics of technical writing. (n.d.).

Marketing Essay Example | Topics and Well Written Essays - 1000 words - 9

Marketing - Essay Example I feel so passionate about the motorbike particularly considering the shape which almost resembles the cheetah’s speed. The motorbike just seems comfortable and enjoyable to any rider who loves sporting. The Ducati monster would not be assumed by anyone who loves sports especially because of its unique sport nature. What interests me most is the styling of the motorbike which provides a comfortable feeling when the rider is exposed outdoors. The lovely color of the motorbike would actually attract attention of everyone who adores colors even if the person does not love motorbikes. The Ducati Monster motorbike makes me have a real natural taste of an animal ride thus prompting me to feel like it is actually a lifestyle I should adopt. The Ducati Monster motorbike seem to be the fastest motorbike from the advertisement and this makes me cherish it even more because I can ride faster than vehicles. The motorbike shape which looks like the Cheetah’s shape makes it look very flexible especially while moving past many vehicles. Motorbikes normally give me freedom and enjoyment I love more so when I bring things into focus while riding. I find it enjoyable to see nature around and appreciate the beauty of everything around me while riding. While on the motorbike, I really feel in control of almost everything around me. ... This shoe can really inspire and communicate a lot of information to the audience. The green color of the shoe simply makes it look very natural and nature friendly. The color makes it look very cool and comfortable to the wearer. The advertisement is very clear and the writings are too big to be avoided. I love the shoes because they are very comfortable especially while playing. I do cherish sports so much and any sportswear particularly from Nike Company. The company has a record of making some of the best brands of shoes and I am one of its loyal customers. Associating me with Nike Inc. is a good experience and privilege to me. I love being regarded as someone in the high status and that is exactly what I get from the shoes. These shoes’ comfort that is derived from their design and material used to manufacture it may really improve an athlete’s performance and reduce foot injury. These shoes are very flexible and simple thus enabling flexible performance as toes wi ll be able to stretch and clasp. The TOMS advertisement does not impress me but I have a friend called Sam who really loves stylish shoes. The guy is just crazy about fashion. He would always want to match the shoes with his clothes. He really enjoys going out with friends while in the latest fashion shoes or clothes. Sam loves recognition while in the group particularly if someone would just appreciate his attires. He is an individual who can take even 30 minutes thinking about what to wear for an occasion or on a weekend. These shoes are really the best fit for Sam and I have seen him with a few pairs of these kinds. The shoes actually match his casual wears such as jeans and linen trousers. The advertisement by TOMS can

Counterculture Analysis: Blackbeard

Counterculture Analysis: Blackbeard Zachariah Chiles Many groups have been established as countercultures throughout the course of history. However, what makes those groups actually be considered countercultures? Author W. LaVerne Thomas attempts to answer such a question in his book, a group [that] rejects the major values, norms, and practices of the larger society and replaces them with a new set of cultural patterns (Thomas). One group that significantly follows Thomass definition are the Blackbeard pirates. This group rejected the cultural patterns of the British monarchy to live their own cutthroat life of stealing, killing, and raping. To this day pirates are still a significant threat to those who tread international waters, and even those who live in third world countries. Before Blackbeard acquired his name, he was known as Edward Teach or Edward Thatch. As far as origin goes, not much is known about Thatch. However, it is recorded that he joined the British navy as a privateer during the Queen Annes War, and turned to piracy shortly after (Division of Archives and Historys Office of State Archaeology). Blackbeard began his pirating in 1713 under the Captain Benjamin Hornigold (Ossian). Once given a smaller ship by Hornigold and able to command his own crew as a captain, Blackbeard found the French slaver ship La Concorde. This esteemed ship would be known to many as the Queen Annes Revenge, La Concorde was big, fast, and powerful. With such a vessel, Blackbeard knew his men could cause more havocà ¢Ã¢â€š ¬Ã‚ ¦ (Woodard). In 1717, the two pirates were so deadly that the British monarchy offered both Hornigold and Blackbeard currency in exchange for putting down pirating. Hornigold accepted, whereas Blackbeard denied the offer, and continued ravaging the Caribbean on his esteemed Queen Annes Revenge. However, his time came to an end on November 22nd, 1718 when facing a British Royal Navy Contingent sent by Governor Alexander Spottswood. Blackbeard and his crew mainly raided ships for one thing, and that was gold. Everything they did was based upon how much loot they could take, and although he has died many years ago, his reputation and name still stands out in the history of pirating. Both the sociological perspective and the sociological imagination can be used to explain the actions of Blackbeard and his crew. According to author LaVerne Thomas, The sociological perspective helps you see that all people are social beings. It tells you that your behavior is influenced by social factors and that you have learned your behavior from others (Thomas). Many heard and saw the stories of Blackbeard and his ferocious crew. Because of this, many saw his actions and adopted them, to continue pirating and adapting Blackbeards techniques for more efficient plundering. His name alone put fear in the hearts of men, so many see that fear and want to become it; inspiring many to take up piracy and life on the seas. C. Wright Mills believes the sociological imagination is, the capacity to range from the most impersonal and remote [topics] to the most intimate features of the human self and to see the relationship between the two (Thomas). In other words, this describes the insight of how your social environment shapes you, and how you shape your social environment (Thomas). Blackbeard and his crews environment most likely included a poor social background, and the loss of a loved one. Many who are greedy and kill, have often grown up in these conditions. They surrounded themselves with murderers and thieves, and thus became murderers and thiev es themselves. They shaped their social environment by surrounding others with the same negative behavior, thus having new people join Blackbeards crew. The more people in his crew, means the more people that go out and tell the infamous story of Blackbeard, the cutthroat killer. Ethnocentrism is a large part of any culture. It is described as, [the] tendency to view ones own culture and group as superiorà ¢Ã¢â€š ¬Ã‚ ¦ (Thomas). Countercultures are subcultures, therefore Blackbeard and his crew is technically a subculture of the larger society the British monarchy. Blackbeard and his crew saw these norms as superior to the restricting life in the monarchy, and therefore ethnocentrism formed. Also, the British already having ethnocentrism, saw the opposing moral standards set by Blackbeards new found subculture, and rejected their views, making Blackbeard and his crew a counterculture. Many examples can be made as to why he and his crew is a counterculture. One such case is that there was no law against killing on Blackbeards ship, whereas it was outlawed in the British monarchy. Another similar case would be with stealing, where Blackbeard plundered and stole from other ships for loot, whereas such atrocities were against the law in the British monarchy. Cultural relativism can be defined as, the belief that cultures should be judged by their own standards rather than by applying the standards of another culture (Thomas). Looting, pillaging, and killing is what pirates know. These simple standards cannot be judged outside cultural beliefs without noticing the large moral negativity that follows. Blackbeard and his crew had no moral compass, so their actions should not be justified through the eyes of the British monarchy. From a logistical point of view, them being strong, picked on the weak in order to gain wealth and become stronger in the world. Although they may know what they do is morally unacceptable and goes against the laws of many larger societies, they followed their own standards and traditions and should not be judged outside of that. My counterculture Blackbeard and his crew, have many intriguing norms and standards that oppose that of many societies of that era as well as modern times. However, this does not excuse the actions of Blackbeard and his crew. Killing, stealing, and plundering all leave large marks on this world. Anywhere from crushing the economy of a British town to killing the last son of a lonely French mother, cultures that directly affect the larger societies in a negative manner should not exist. Cultures having opposing standards is completely fine, as long as the opposing standards does not actively contradict those of a larger society. Blackbeard and his crew have very free standards, however the deaths that have been caused forces me to disagree with the philosophy and norms of their counterculture. References Division of Archives and Historys Office of State Archaeology. Queen Annes Revenge Project. n.d. 12 3 2017. Ossian, Rob. The Pirate King. n.d. 12 3 2017. Thomas, W. LaVerne. Holt Sociology: The Study of Human Relationships. Holt, Rinehart and Winston, 2003. Woodard, Colin. The Republic of Pirates. New York: Houghton Mifflin Harcourt Publishing Company, 2007. Customer Segments in Retail Supermarket | Analysis Customer Segments in Retail Supermarket | Analysis CHAPTER 1 : INTRODUCTION BACKGROUND In todays dynamic retail environment, customers are offered with a tremendous range of choices and their loyalty is increasingly becoming transitory due to the severe impact of competitors actions on existing relationships (Reinartz and Kumar, 2000). This increased competition to satisfy the diverse needs of the customer, forces the traditional production and selling focus of the retailers towards customer relationships. In the context of retail supermarket, this has resulted in large investments in retail information systems to collect the shoppers data to understand the customer shopping behaviour (Brijs.T et al 2001). Several tools and technologies of data warehousing, data mining, and other customer relationship management (CRM) techniques are exploited to manage and analyse this data. Especially through data mining, simply means extracting knowledge from large amounts of data which helps the organisations to find the patterns and trends in their customers data, and then to drive improved customer relationships (Rygielski, Wang and Yen, 2002). According to Witten Frank, (2005), some data mining techniques include decision trees (DT), artificial neural networks (ANN), genetic algorithms (GA), association rules (AR), etc., are usually used to solve problems related with customers in various fields like engineering, science, finance and business. In retail supermarket domain, data mining can be applied to identify useful customer behaviour patterns from large amounts of customer and transaction data (Giudici Passerone, 2002). Consequently, the discovered information can be used to support better decision-making in retail marketing. Data mining techniques have been mostly adopted to make predictions and describe behaviours. During the past decade, there has been an array of significant developments in data mining techniques. Some of these developments are implemented in customized service (Chen et al, 2005) which is vital in retail markets to develop customer relationship. Therefore, this research focuses to provide customised service to distinct customer segments in retail supermarkets, by implementing data mining techniques with the help of data mining tools. Related Work Researchers proposed various approaches to mine sales transaction data of a retail supermarket to improve customer relationships. Previously, the customer behavioural variables such as (RFM) Recency-Frequency-Monetary variables are associated with demographic variables to predict customer purchase behaviour (Chen et al, 2005). Current research improved significantly, as Business Intelligence tools and advanced data mining algorithms are implemented to analyse the data in a much more reformed way. Liao et al, (2008), proposed a methodology based on Apriori and K-means algorithms to mine the customer knowledge from household customers for product and brand extension in retailing. Bottcher et al, (2009), presented an approach which aimed to mine the changing customer segments in dynamic market through deriving frequent itemsets as representations of customer segments at different points of time, which are then analysed for changes. Problem Definition Effective management of sales transaction data is as important as any other asset for a retail supermarket store. The sales transaction data usually contains great amount of information distributed through numerous transactions. This study focuses on applying data mining techniques to analyse the sales transaction data of a retail supermarket store and suggests recommendations to provide customised service to defined customer segments. This research specifically uses two data mining techniques namely clustering and association rule discovery. The research starts with identifying different customer segments based on their purchase frequencies, in order to find out the differences in their purchase behaviour. The definition of behaviour in retail supermarket domain covers different meanings. For example, retailers often distinguish between light, medium and heavy users or weekday or weekend customers etc (Brijs et al, 2001). In this research, the differences will be discovered by identifying frequently purchased items for each customer segment and comparing their combinations. The retailer may use this information to customize his offer towards those segments and also to further examine the underlying relation ships between those items for purposes of pricing, product placement or promotions. AIM OBJECTIVES The aim of this research is to provide customised service to defined customer segments in a retail supermarket, by implementing data mining techniques on sales transaction data with the help of data mining tools. OBJECTIVES To conduct a critical review of the literature and present the current research within the discipline. Obtain the customer sales transaction dataset, in order to apply the data mining algorithms. Based on the literature review, select the appropriate data mining approach to pre-process the dataset and to implement the algorithms on the pre-processed data. Analyse the results obtained from the data mining algorithms and propose recommendations to provide customised service. Draw conclusions, discuss the limitations of this research and suggest the areas of future research. Research Approach This research follows the quantitative methodology by obtaining the dataset and analysing the data with data mining tools. The dataset for analysis was obtained from ABC retail supermarket store, Canada, which was available online (http://www.statsci.org/datasets.html). The data required for this project is selected and loaded onto data mining tools SPSS (Statistical Package for the Social Sciences) and Weka, the tools selected for this research to mine the data. The data mining algorithms that are selected for this study are k-means algorithm for Clustering and Apriori algorithm for association rule mining, the reason behind the choice of these algorithms is justified in the literature review. These algorithms are implemented on the dataset with SPSS and Weka. The results obtained from these algorithms needs to be justified with the help of charts, tables and graphs. Microsoft Excel is used to plot the charts, tables and graphs. Finally, the recommendations are made based on the ana lysis of results. Dissertation Outline This chapter presents the essence of this dissertation, highlighting the aim and objectives of this research. The rest of this dissertation is structured as follows Chapter 2 provides a comprehensive literature review of different aspects relating to the research topic under study. Chapter 3 discuss in detail about the research methods and the data analysis techniques followed, in order to achieve the aim of this research. Chapter 4 presents the analysis of the results obtained from the application of data mining algorithms on the data and provides recommendations. Chapter 5 summarises the entire project and gives insights on limitations of this research and points out the areas of future research. CHAPTER 2 : LITERATURE REVIEW Introduction This chapter provides a critical review of literature addressing the application of data mining in retail supermarkets. It begins with an introduction to data mining, followed by its evolution and applications in todays business world. Then explore the role of data mining in retail supermarkets to improve customer relationships, followed by a discussion about the typical data mining approach. It also discusses the techniques and algorithms implied in this project and the reason for their choice. Data Mining: An Introduction The word mining means extracting something useful or valuable, such as mining gold from the earth (Lappas, 2007).The importance of mining is growing continuously, especially in the business world. Data mining is a process of finding interesting patterns in databases for decision-making. It is one of the fast growing and most prominent fields, which can provide a significant advantage to an organization by exploiting the vast databases (Rygielski, Wang and Yen, 2002). Finding patterns in business data is not new; traditionally business analysts use statistical approach. The computer revolution and huge databases ranging from few Giga Bytes to Tera Bytes changed this scenario. For e.g. companies like Wal-Mart stores huge amount of sales transaction data, which can be used to analyze the customer buying patterns and make predictions(Bose and Mahapatra, 2001). Data warehousing technology has enabled the companies to store huge amount of data from multiple sources under a unified schema. Data mining has been considered to be a tool of business intelligence for knowledge discovery (Wang Wang, 2008). Many people consider data mining as Knowledge Discovery from Data (KDD), but it is actually a part of the larger process called knowledge discovery which describes the steps that must be taken to secure the desired results (Han and Jiawei, 2006). Typical data mining process implicates various iterative steps; the first step is the selection of appropriate data from a single database or multiple source systems followed by cleaning and preprocessing for consistency. The data is then analyzed to find patterns and correlations in the data. This approach compliments the other data analysis techniques like statistics, OLAP (On-line analytical processing) etc, (Bose and Mahapatra, 2001). Every organization follows a different data mining and modelling process to achieve their business imperatives. The Evolution of data mining It all started with the need to store the data in computers and improve the access to it for decision-making. Today the technology enables the users to access and navigate the real time data. At the beginning of 1960s, the data was collected for the purpose of making simple calculations to answer the business questions like the total average revenue for a specific period of time. In 1980s 1990s the usage of data warehouses to store data in a structured format emerged, policies regarding the format of data to be used in an organization were implemented (Therling.K, 1998). The data warehouses extended to be multi-dimensional that facilitates the stakeholder to drilldown and navigate through the data. Nowadays, online analytic tools assist to retrieve the data real-time. Now computers can query data from past to until the current. In recent years many technologies like statistics, AI (Artificial Intelligence) and machine learning have been evolving as core sectors in data mining field(Rygielski, Wang and Yen, 2002). So these technologies combined with relational database systems with data integration provide potential knowledge from the data. Data mining applications Data mining can be implied in many fields depending on the aim of the company. Some of the main areas in todays business world where data mining is applied are as follows (Apte.C. et al, 2002): Finance Telecom Marketing Web analysis Insurance Retail Medicine Data mining for CRM in retail supermarkets Swift (2001) defined CRM as an Enterprise approach to understanding and influencing customer behaviour through meaningful communications in order to improve customer acquisition, customer retention, customer loyalty, and customer profitability. According to research by the American management association It costs three to five times as much to acquire a new customer than to retain the existing one and is especially evident in services sector (Ennew Binks, 1996). Therefore it is very important to create a good relationship with the existing and new customer rather than expanding the customer base. A large number of companies are adopting various tools and strategies to enhance a more effective CRM, in order to gain an in-depth understanding about their customers. Data mining is a powerful new technique, which helps the companies to mine the patterns, trends and correlations in their large amounts of customer, product, or data, to drive improved customer relationships. It is one of the well-known tools given to customer relationship management (CRM) (Giudici Passerone, 2002). In the context of retail supermarket these patterns not only assists the retailers to offer high quality products and service to their customers, but also helps them to understand the changes in customer needs. Data mining applications for CRM in retail supermarkets Data mining improves customer relationship in retail supermarket, which is a wide area of research interest. Depending on the retailers objective, there are various application areas in which data mining can be applied to enhance customer relationship management. Some of the major data mining applications in retail supermarket, identified from literature are as follows: Cross-selling (Brijs et al 1999, Feng and Tsang, 1999) Product recommendation (Shih and Liu 2005, Li et al 2009) Customer behaviour modelling (Baydar.C 2003, Cadez, 2001) Shelf space allocation (Chen and Lin 2007, Chen et al 2006) Catalogue segmentation (Ester et al,2004, Lin and Hong, 2006) Direct marketing (Bhattacharyya, 1999, Prinzie and Poel, 2005) Prize optimization (Chen et al 2008, Kitts and Hetherington, 2005) THE DATA MINING PROCESS Ivancsy Vajk, (2006), defined the three main stages involved in the data mining process which are: (i) preprocessing, (ii) pattern discovery, (iii) pattern analysis/interpretation. Preprocessing Famili .A, (1997), defined data preprocessing as all the actions taken before the actual data analysis process starts. It is essentially a transformation T that transforms the raw real world data vectors Xik, to a set of new data vectors Yij. Yij = T (Xik) Such that: Yij preserves the valuable information in Xik, Yij eliminates at least one of the problems in Xik and Yij is more useful than Xik. In the above relation: i=1 n where n = number of objects, j=1 m where m = number of features after preprocessing, k=1. . . l where l = number of attributes/features before preprocessing, and in general, m ? l. The most common data used for mining the purchase behaviour in retail supermarket is customer and transaction data (Giudici and Passerone, 2002). With a huge collection of customers sales transaction data available in the databases, it is necessary to pre-process the data and extract the useful information from it. In the context of retail supermarkets Pinto et al, (2006), suggested four key tasks in data preprocessing, they are data selection, data cleaning, data transformation, and data understanding. The first preprocessing task is data selection. Here the subset of the data is identified on which pattern discovery is to be performed. This task is especially helpful in solving the problem of large amounts of data through precisely evaluating and categorizing the data into much smaller datasets. Computational requirements necessary for data analysis and manipulation are also hugely reduced by preprocessing large datasets through data selection techniques like clustering or vector quantization (Famili .A, 1997). The second is data cleaning where basic operations include removing noise and handling missing data (Fayyad et al, 1996). Other issues regarding the data quality like errors and insufficient attributes which may complicate data analysis are also addressed in data cleaning. In most cases missing attribute values are replaced by attribute mean but traditionally, if more than 20% of attribute values are missing, the entire record is eliminated (Famili .A, 1997). To handle the outliers and noise data, techniques like binning (partitioning the sorted attribute values into bins), clustering and regression are applied. The next preprocessing task is data transformation. The application of each data mining algorithm requires the presence of data in a mathematically feasible format (Crone et al, 2006). Inaccuracies in the measurements of input or incorrect feeding of data to the data mining algorithm could cause various problems. Since, operations such as normalization, aggregation, generalization and attribute construction are performed. Normalization deals with scaling the attribute value into a specific range, whereas aggregation and generalization refers to the summary of data in terms of numeric and nominal attributes. Attribute construction handles the replacement or addition of new attributes based on the existing attributes (Markov.Z and Larose.T.D, 2007). Once issues regarding the data are solved and the data are prepared, understanding the nature of data would be useful in many ways. According to Famili .A, (1997), the majority of the data analysis tools have some limitations regarding the data characteristics; therefore, it is important to recognize these characteristics for appropriate setup of data analysis process. He further pointed out that techniques like visualization and principal component analysis are useful for better understanding the data. Pattern discovery Fayyad et al, (1996), defined that core of the process is the application of specific data-mining methods for pattern discovery and extraction. Pattern discovery is the key stage of the process in this research, which is where the data is mined. Once the data is pre-processed, and the irrelevant information is eradicated, it is then used for mining, using data mining techniques to discover patterns. However, it is not the intent of this paper to describe all the available algorithms and techniques derived from these fields. This research focuses on two main data mining methods that to helps to mine the data and find patterns. They are Clustering and Association. The reason behind choosing these rules is justified below. Clustering Clustering can be defined as a technique to group together a set of items having similar characteristics (Kuo et.al, 2002). In retail domain, cluster analysis is a common tool to segment the customers on the basis of their similarity on a chosen segmentation base or set of bases (Stewart.D.W and Girish.P., 1983). The actual choice for one or a combination of these bases largely depends on the business question under study (Wind, Y., 1978). Segmentation can be done on the basis of various variables/bases, such as 1) general or product-specific, and 2) observable or non-observable as classified by wedel M and Kamakura (2000). General bases for segmentation are independent of products, services or circumstances, whereas product-specific bases for segmentation are related to the product, the customer or the circumstances. Observable segmentation bases can be measured directly, whereas non-observable bases must be inferred. The combination of classification of segmentation bases is shown below. Twedt, D.W., (1967) as cited in Engel.J.F et.al, (1972), stated that the existence of huge amounts of transaction data in retail supermarket domain provides a great impetus for segmentation on the basis of purchase frequencies. Segmentation based on this divides customers into groups on their intensity of buying a product(s), such as light, medium and heavy buyers. According to Brijs.T, (2002), if customers are classified by their purchase frequency, these segments could then be treated differently in terms of marketing communication (pricing, promotion, product recommendation etc.) to achieve greater return of investment (ROI) and customer satisfaction. Therefore, in this research clustering is employed to segment the customers into various clusters on the basis of their similarity in purchase frequency. Several algorithms have been proposed in the literature for clustering, such as ISODATA, CLARA, CLARANS, ScaleKM, P-CLUSTER, DBSCAN, Ejcluster, BIRCH and GRIDCLUS (Kanungo.T. et al, 2002). It is not the objective of this research to use all these algorithms for clustering. However, as discussed earlier, k-means clustering algorithm would be used to cluster and its justification is given below. k-Means Clustering Algorithm The K-means has been considered as one of the most effective algorithms in producing good clustering results for many practical applications (Alsabti et.al, 1998). The main reason behind this is, when clustering is done for the purpose of data reduction, the goal is not to find the best partitioning, but simply needs a reasonable consolidation of N data points into k clusters, and, if necessary, some efficient way to improve the quality of the initial partitioning (Faber, 1994). Therefore, k-means algorithm proves to be very effective in data reduction and produces a good clustering output. The k-means algorithm clusters the data that are similar into various clusters namely Cluster 0, Cluster 1 to Cluster n (Kanungo et.al, 2002). Provided a set of n data points in real d dimensional space (Rd) and an integer k, the aim is to determine k points in Rd, called the centers, so as to minimize the mean squared distance from each data point to its nearest center. This measure is often called as squared-error distortion (Jain Dubes, 1988). The diagram below illustrates the standard k-means algorithm. It shows the results during two iterations in the partitioning of nine two-dimensional data points into two well separated clusters. Points in cluster 1 are shown in red, points in cluster 2 are shown in black; data points are denoted by open circles and reference points by filled circles. Clusters are indicated by dashed lines. The iteration converges quickly to the correct clustering; even there was a bad initial choice of reference points. Lloyds algorithm is another popular version for K-means clustering which requires about the same amount of computation for a single pass through all the data points, or a single iteration, like the standard K-means algorithm (Faber, 1994). Lloyds algorithm is similar to standard k-means algorithm, except when the cluster centroids are chosen as reference points in subsequent partition; the centroids are adjusted both during and after each partition. However, the k-means algorithm constantly updates the clusters and requires comparatively less iterations than Lloyds algorithm, thus, k- means algorithm is considerably faster. This is the key reason that leads to the selection of k-means algorithm, since it can group the customers which have similar purchase frequency into different clusters in less iterations. However, Faber, (1994), pointed two major drawbacks to this algorithm. Firstly, it is computationally inefficient for large datasets. Secondly- although the algorithm will always produce the desired number of clusters, the centroids of these clusters may not be particularly representative of the data. Association Rules Association rule discovery was proposed to find all rules in a basket data to analyze how items purchased by customer in a shop are related (Gery Haddad, 2003). The rule refers to the discovery of attribute value associations that occur frequently together within a given data set (Han Kamber, 2001). It is typically used for market basket analysis to discover rules of the form x% of customers who buy item A and B, also buy item C (Zaiane, 2001) and is an implication of the form (A, B) à ¨C. Some of the key definitions drawn from literature that characterize association rule technique are provided below (Agarwal, Imielinski and Swami, 1993). Itemset (i) Set of items that contain in a single transaction (e.g. {milk, sugar, curd}) Support (s) The support expresses the percentage of transactions in the data that contain both the items in the antecedent and the consequent of the rule. Confidence (c) Confidence estimates the conditional probability of B given A, i.e. P (B |A) and it can be calculated as Confidence (c) =s (A B) / s (A). Association rule discovery typically involves a two phased sequential methodology (Brijs T., 2002). Finding frequent itemsets The first phase involves looking for so-called frequent itemsets, i.e. itemsets for which the support in the database equals or exceeds the minimum support threshold set by the user. This is computationally the most complex phase because of the number of possible combinations of items that need to be tested for their support. Generating association rules Once all frequent itemsets are known, the discovery of association rules is comparatively straightforward. The general scheme is that, if ABCD and AB are frequent itemsets, then it can be calculated whether the rule AB à ¨ CD holds with sufficient confidence by computing the ratio confidence = s (ABCD) / s (AB). If the confidence of the rule equals or exceeds the minconf threshold set by the user, then it is a valid rule. For an itemset of size k, there are potentially 2k-2 confident rules. Association rules can help to discover frequently purchased combinations of products within a customer segment and provide customised service by promoting certain products or product combinations to the defined segments (Brijs T. et al, 2001). Therefore, in this research, frequent itemsets for each customer cluster will be generated and their combinations are compared to identify the differences in purchase behaviour to provide customised service. Traditionally, support and confidence are used in association rule discovery, but Aggarwal Yu, (1998), criticized this support-confidence framework for association rule discovery for the following main reasons. First of all, setting good values for the support and confidence parameters in association rule mining is critical. For example, setting the support threshold too low will lead to the generation of more frequent itemsets. But even if they would be statistically significant, their support is usually too low to have a significant influence. On the other hand, setting the support threshold too high increases the probability of finding insignificant relations and of missing some important associations between items. Further Agarwal Yu, (1998); Brin et al., (1998), as cited in Brijs.T,(2003), introduced the lift (also called interest) measure to overcome the disadvantage of confidence in not taking the baseline frequency of the consequent into account. Lift/Interest (l) Lift is computed as the confidence of the rule divided by the support of the right-hand-side (RHS). In other words, lift is the ratio of the probability that A and B occur together to the multiple of the two individual probabilities for A and B. Lift (l) = s (A B) / s (A).s (B) In order to perform predictive analysis, it is useful to discover interesting patterns in the given dataset that serve as the base for future trends. The best and most popular algorithm used for this analysis is called the Apriori algorithm (Varde et.al, 2004). Apriori Algorithm The Apriori algorithm was proposed by Agarwal et.al, (1994) (Varde et.al, 2004). The algorithm finds frequent items in a given data set using the anti-monotone constraint (Petrucelli et.al, 1999), as cited in Varde et.al, 2004). It works under the principle that all subsets of a frequent itemset must also be frequent. In other words, if at least one subset of an itemset is not frequent, the itemset can never be frequent anymore. This principle simplifies the discovery of frequent itemsets significantly because for some itemsets, it can be determined that they can never be frequent before checking their support against the data anymore. This is the key reason to select this algorithm, since the association rules for the items can be discovered more quickly and efficiently. Given a data set, the problem of association rule mining is to generate all rules that have support and confidence greater than a user-specified minimum support and minimum confidence respectively. Candidate sets having k items can be generated by joining large sets having k-1 items, and deleting those that contain a subset that is not large (where large refers to support above minimum support). Frequent sets of items with minimum support form the basis for deriving association rules with minimum confidence. For A à ¨ B to hold with confidence C, C% of the transactions having A must also have B. Though the algorithm is very efficient in association rule mining, it has certain drawbacks, found by Margahny Shakour, (2006). After discovering the 4-frequent itemsets this algorithm needs extra data structure and methods to process, since the further itemsets can be obtained by different ways. This method is fast only while handling small data. There are several tools available for clustering and association rule mining such as ARMiner, Clementine (SPSS), Enterprise Miner (SAS), Intelligent Miner (IBM), Decision Series (NeoVista). To mine association rules, WEKA is used, which is a collection of machine learning algorithms for data mining tasks and SPSS statistics 17.0 for clustering. WEKA is an open source software available online and very efficient in mining large datasets, where as SPSS statistics 17.0 is a statistical analysis package available at Brunel university computer labs. Pattern Analysis Pattern analysis means understanding the results obtained by the algorithms and drawing conclusions. This is the last phase in data mining process, where the uninteresting rules or patterns from the set found in the pattern discovery phase are filtered out (Cooley et.al, 2000). The uninteresting patterns are filtered out by applying appropriate methodologies on the results and produce some interesting statistical patterns. SUMMARY This chapter discussed the concept of data mining, its evolution and applications in todays business world. Then, it provided an overview regarding the role of data mining in retail supermarkets to improve customer relationships, followed by a discussion about the typical data mining approach. It also discussed the techniques and algorithms implied in this project and the reason for their choice. The following chapter will explain about the research approach followed in this dissertation. CHAPTER 3 : RESEARCH APPROACH Introduction This chapter will discuss about the research approach employed in this project. It starts with a discussion about the research and literature review methods, followed by the data collection and justification of data mining approach on the data. Research Methods The research approach depends upon the objectives and aim of the study, as it assists the researcher to elicit appropriate responses. Boyatzis (1998) defines research methods as taxonomic procedure used for problem solving where, first data is collected based on the research question, hypotheses are stated, data analysis is carried out using appropriate techniques, results are interpreted and conclusions are derived. According to Hussey et al (1997), research methods can be distinguished in two types they are Qualitative and Quantitative approach. Oates (2006) says that, quantitative research method is the data or evidence on numbers whereas qualitative research method includes all non-numeric. In this research, quantitative research methodology is used. Quantitative study makes use of the numeric data that has been collected from a group of people interested in the subject area which is then analysed and interprete

Surrendering Freedom for Peace of Mind :: Technology Technological Papers

Surrendering Freedom for Peace of Mind A glance back into history illustrates many eras that have come and gone which have left their mark on the world and its people. The industrial revolution changed the face of modern society and yet there is no comparison between its effect and that of the computer. Today, it is difficult to find an area of our lives that computer technology has not touched. The recent attempt by the longshoremen in California to strike was a prime example of the fear of computer technology that many feel. These men stated that they wanted guarantees that they would not lose their jobs as the freight industry becomes more and more computerized. Conversely, the shipping magnets are trying to compete in a world where the computer dominates the way freight is handled and they fear they are being forced to let progress pass them by. We were recently asked in class if anyone could identify a â€Å"computer free† part of our world. No one offered a suggestion since it is intuitive that the computer dominates all areas of our lives. Examples range from how we travel, the way our food is grown, what we eat, how we place restaurant orders, the size of food portions, the practice of medicine, how we shop, what we buy, how it gets to our home. There are countless other examples that could help to illustrate this point. Technologies that we could not even imagine a few short years ago are now common, accepted parts of our daily lives. We have passed through eras of information, networking and e-mail. Are we quickly headed to the point where we have lost all privacy and freedom? Are we moving to the era of personal invasion? Or are we already there? And more importantly, do we care? Have we been on the â€Å"slippery slope† so long that we have lost track of where we are headed and what we are leaving behind? One of the most troubling technologies being developed are highly precise tracking devices which can be used to detect the whereabouts of humans anywhere on earth. We are all familiar with the â€Å"teathers† that our probationers wear around their ankles. They are â€Å"free† to move around and live a relatively normal life since they can sleep at home, drive vehicles and maintain employment while being tracked by a teather officer. Surrendering Freedom for Peace of Mind :: Technology Technological Papers Surrendering Freedom for Peace of Mind A glance back into history illustrates many eras that have come and gone which have left their mark on the world and its people. The industrial revolution changed the face of modern society and yet there is no comparison between its effect and that of the computer. Today, it is difficult to find an area of our lives that computer technology has not touched. The recent attempt by the longshoremen in California to strike was a prime example of the fear of computer technology that many feel. These men stated that they wanted guarantees that they would not lose their jobs as the freight industry becomes more and more computerized. Conversely, the shipping magnets are trying to compete in a world where the computer dominates the way freight is handled and they fear they are being forced to let progress pass them by. We were recently asked in class if anyone could identify a â€Å"computer free† part of our world. No one offered a suggestion since it is intuitive that the computer dominates all areas of our lives. Examples range from how we travel, the way our food is grown, what we eat, how we place restaurant orders, the size of food portions, the practice of medicine, how we shop, what we buy, how it gets to our home. There are countless other examples that could help to illustrate this point. Technologies that we could not even imagine a few short years ago are now common, accepted parts of our daily lives. We have passed through eras of information, networking and e-mail. Are we quickly headed to the point where we have lost all privacy and freedom? Are we moving to the era of personal invasion? Or are we already there? And more importantly, do we care? Have we been on the â€Å"slippery slope† so long that we have lost track of where we are headed and what we are leaving behind? One of the most troubling technologies being developed are highly precise tracking devices which can be used to detect the whereabouts of humans anywhere on earth. We are all familiar with the â€Å"teathers† that our probationers wear around their ankles. They are â€Å"free† to move around and live a relatively normal life since they can sleep at home, drive vehicles and maintain employment while being tracked by a teather officer.

Concert Paper About Blue Man Group

Sarah DeMattio LA 321-801 Concert Paper 2/2/13 Though I had often heard of them growing up, I never really knew who, or what, Blue Man Group was. When my nineteenth birthday came along earlier this past January, my best friend decided that in celebration of my birthday, we would be solving our own age-long mystery about Blue Man Group. We were going to a show to see what exactly it was that these blue men did. My initial reaction was certainly less than satisfied when my best friend told me that she got us tickets to see Blue Man Group for my birthday.I remembered a discussion we had had a few months prior when we spoke avidly about how our parents always made references to something called â€Å"the blue man group† as we were growing up, and that we still to that day had no idea what it was or why it was being referenced in the first place. Though still not completely convinced, I bit my tongue, thanked my friend for the gesture, told her I could not wait to see the show, and calmed myself with the thought that at least finally, my questions of the smurph band would be answered. On January 6th, 2013, my friend Kaya and I made our way downtown.We had tickets to see the 8 p. m. show of Blue Man Group at the Astor Place Theater. Upon arrival to the venue, my reservations about the experience became even more acute; the theater looked like some kind of underground grunge dungeon I’ve heard about and warned of. We entered the theater lobby where I immediately noticed the concession and souvenir stand, because aside from the usual assortments of cookies, popcorns and soft drinks, the top recommended item to buy was a poncho. My suspicions instantly hit their zenith. â€Å"Kaya, what the heck did you bring me to?! Kaya laughed off my remark and once we were seated, elaborated about the ponchos. She told me that contrary to typical shows, where the closer your seat is to the stage in orchestra, the more expensive the seat becomes, Blue Man Group actuall y offers a discounted price for the first 5 rows of Orchestra. The â€Å"splash zone,† if you will. And that of course, explains the need for ponchos. Our seats happened to be the first row behind the last row of the â€Å"splash zone†, so obviously I spent the entire show holding my breath a little in fear that bodily fluids of any kind would make their way to me. They didn’t, thankfully.Blue Man Group consisted of three men that were all bald, blue, wore black clothing, and had extremely large mouth capacities. My favorite moment of the entire 105-minute show is hard to decide, both honestly and to my surprise. One of my top favorite and most impressed moments of the show was each time the men played their large bongo-type, garbage receptacles as drums. Two men played their own drums simultaneously while the third man stood in the middle of the two and squirted different colored liquids onto the drums, thus splashing colorful water everywhere and creating some kind of a rainbow waterfall.It was both visually fantastic and musically pleasing. The Blue Man Group’s abilities varied, but certainly never failed to impress. I cannot really describe what they played, or even did. They seem to have a knack for making instruments out of anything but instruments, and creating art with anything but art supplies. The combination of the sarcastic, poking-fun-at-society nature of the Blue Man Group along with their quirky abilities, talents, and looks, was what made the entire experience such a pleasantly surprising and enjoyable one.I am not a big fan of freaky looking characters that do odd things, spit into the audience, and chew Captain Crunch cereal in harmony, but for Blue Man Group, I made the exception and would again and again in a heartbeat. The show was unlike anything I’ve ever experienced. It is a different and interesting approach to having fun, and I am determined to make any other doubters in my life see what Blue Man Gro up is all about for themselves.

Free Essays on Patriarchy In Romeo & Juliet

in the scene threaten her disownment and possible death: An you’l... Free Essays on Patriarchy In Romeo & Juliet Free Essays on Patriarchy In Romeo & Juliet Patriarchal Politics in Fair Verona The imagery in Shakespeare’s Romeo and Juliet reflects and often supports the time period’s stereotypes of men and women and their certain function and responsibilities in society. Shakespeare’s figurative language throughout the play portrays women with the following traits in relationship to men; silence, obedience, sexual chastity, patience and humility. This patriarchal potency is the root of conflict in the play and ends up causing the â€Å"star cross’d lovers’† demise. The role of women in Verona is made clear early on in the play. In the first scene you witness a conversation between Sampson and Gregory, both Capulet kinsmen. When Sampson says â€Å"†¦therefore women, being the weaker vessels†¦Ã¢â‚¬Å" we see a definite distinction between who they consider inferior and superior. He then says, â€Å"I will be civil with the maids. I will cut off their heads.† To that he adds â€Å"or their maidenheads.† By saying this he assumes a patriarchal role of divine judge while comparing rape with execution, implying that either one would be a just punishment. This quote shows how important it was for a man in Verona to hold power over his inferiors, specifically women. The patriarchal power structure in the Capulet family, where Juliet’s father controls the action of each family member, places Juliet in an extremely vulnerable position. She is unable to speak of her true feelings or even vocalize her opinion on marriage. When her father enters late in Act 3 Scene 5 the plays central conflict is made obvious. By this time Romeo and Juliet have performed their marriage without any parental consent, which was an offense against her demanding father. After consummating this new union Juliet is brokenhearted and anxious after Romeo leaves the scene, she then has to deal with her father’s verbal lashing. His last words in the scene threaten her disownment and possible death: An you’l...

Fi515 Week 1 Quiz Essay Example

Fi515 Week 1 Quiz Essay Example Fi515 Week 1 Quiz Essay Fi515 Week 1 Quiz Essay Grade Details 1. Question :Money markets are markets for ______. Student Answer: foreign stocks consumer automobile loans U. S. stocks short-term debt securities long-term bonds Points Received:6 of 6 Comments: 2. Question :Which of the following could explain why a business might choose to organize as a corporation rather than as a sole proprietorship or a partnership? Student Answer: A. Corporations generally face fewer regulations. B. Corporations generally face lower taxes. C. Corporations generally find it easier to raise capital. D. Corporations enjoy unlimited liability. E. Statements C and D are correct. Instructor Explanation:The advantages of incorporation include unlimited life, easy transferability of ownership interest, limited liability, and ease of raising money in the capital markets. Regulations and double taxation are disadvantages of corporations. Points Received:0 of 6 Comments: 3. Question :Which of the following statements is true? Student Answer: One of the benefits of incorporating your business is that you become entitiled to receive unlimited liability. Sole proprietorships are subject to more regulations than corporations. Sole proprietorships do not have to pay corporate tax. All of the above answers are correct. None of the above answers are correct. Instructor Explanation:Sole proprietorships pay personal income tax, not corporate tax. The other statements are false. Corporations are subject to limited liability, but are subject to more regulations than the other forms of business organizations. Points Received:0 of 6 Comments: 4. Question :While other things are held constant, which of the following actions would increase the amount of cash on a companys balance sheet? Student Answer: The company repurchases common stock. The company pays a dividend. The company issues new common stock. : The company purchases a new piece of equipment. The company gives customers more time to pay their bills. Points Received:6 of 6 Comments: 5. Question :Kramer Corporation recently announced that its net income was lower than last year. However, analysts estimate that the companys net cash flow increased. What factors could explain this discrepancy? Student Answer: A. The companys depreciation expense increased. B. The companys interest expense declined. C. The company had an increase in its noncash revenues. D. Answers A and B are correct. E. Answers B and C are correct. Points Received:0 of 6 Comments: . Question :Which of the following statements is most correct? Student Answer:A. Actions that increase net income will always increase net cash flow. B. One way to increase EVA is to maintain the same operating income with less capital. C. One drawback of EVA as a performance measure is that it mistakenly assumes that equity capital is free. D. Answers A and B are correct. E. Answers A and C are correct. Instructor Explanation:EVA = EBIT(1-T) (after-tax cost of capital) (total capital). Therefore, if less capital is used with the same operating income, EVA will be increased. Points Received:0 of 6

Audit and Internal Control Essay Example | Topics and Well Written Essays - 1750 words - 1

Audit and Internal Control - Essay Example There are no hard and fast rules for auditing, which can be prescribed for all the countries. These rules can be different for different countries according to their needs and cultural settings. According to ICAEW (2002) with all the contrasts present in the rules and regulations of different countries emphasis is given to generic auditing principles of responsibility, accountability, transparency and fairness. "Inventory controls are designed to ensure the safe custody. Such controls include restriction of access documentation and authorisation of movements regular Independent inventory counting and review of Inventory condition." (BPP, 2008) Recording of Inventory: In order to effectively control the Inventory on the basis of book inventory it is important to segregate the duties of custody and recording of inventory. It is important to check if the pair of shoes are checked and recorded at their reception. Inventory issues are supported by appropriate documentation. It is also important to maintain Inventory records such as Inventory ledger, Bin cards and Transfer records. The physical counts of the inventory should be recon ciliated with the computed amounts. The transactions having high values should be analyzed. The inventory items should be divided into different divisions according to group, location, type, etc. The inventory age should be calculated by the date of receipt. The sales transactions should be checked according to the prices, quantities, extension and totals in the sales register. There is not any evidence of issuing the invoices to the customers. The sales transactions should also be checked according to the sequential numbers of blank invoices and regular sequential checks. The sample of the inventory movement records should be taken and cross checked with the goods received and dispatched according to the

Consumer Involvement in New Product Development Essay - 1

Consumer Involvement in New Product Development - Essay Example The paper tells that in the development of new products, customers participate to give new ideas to help create the products through communication. The process referred to as co-creation, employed by the developers, is important in the new product development sector. In this case, customers may come up with new goods or services or simply try to improve on what is already circulating in the market to fulfill their needs. These ideas shared by customers reach the intended producer through different avenues like the company website or through social media. Co-creation used this way is a symbiotic relationship in which the customers and the firms collude and make a product where the customer will enjoy interacting with the product and the producer will meet the customer’s needs. Producers nowadays use this mode of collaboration as a way to reduce time in the production process and ease their thinking and the uncertainty that comes with the new product, questioning whether the pro duct fits the market and carrying out surveys for gauging where most customers lie, either in favor or out of favor. The scope of co-creation is the extent to which an organization decides to involve the consumer in the development stages up to the post-launch stage. Threadless.com is a T-shirt manufacturing company high on the scope and they depend on co-creation for manufacturing their products. The consumers submit T-shirt designs online and the company employees and visitors to the site vote. The designer who wins gets a monetary compensation and retains the rights to the design. The co-creation process does not end there, but after launching the product. In the end, it serves as a marketing strategy.