The Outsiders

The Outsiders by S. E Hinton, the author argues that heroism defines a person by their actions not by their background, history, or looks. The events at the church, the incidents that happened before the church and the aftermath all show the changes that happened over time that would eventually negate Ponyboys history and background and regard him as a hero.There are different points of heroism in the story and different forms of eroism in the story but for now we are going to look at the most important three, Cherry acting as a spy, Ponyboy rescuing the kids and what he was defined by before he was a hero. Ponyboy is very much defined as a hero after the events that happened at the church in which he rescued all those kids from death by grabbing them out of the burning church. He did out of courage and kindness and was praised as a hero for it. â€Å"Mrs.O'briant and I think you were sent straight from heaven. Or are you Just professional heroes or something? Sent from heaven? Had he gotten a good look at Dallas? â€Å"No were greasers† I said. I was too worried and scared to appreciate the fact that he was trying to be funny. mfou're what? † â€Å"Greasers you know like hoods, ID's. Johnny is wanted for murder, and Dallas has a record with the fuzz a mile long. â€Å"(95). The quote present here relates to the theme and explains the ambulance driver overlooking his past and still appreciating him as a hero.The author is putting emphasis on the heroism in the quote and that no matter what background heroes ome from, they are looked upon for their actions and the same applied for Ponyboy and what he did for those kids. The reason that he was looked upon as a hero was because of his actions, but what was he defined as before the church? He was a greaser, Just your average every day greaser who was looked upon as a hood, a thief, and Juvenile delinquent to society. â€Å"It was my pride. It was long and silky, Just like sodas only a little bit re dder.Our hair was tuff – we didn't have to use much grease on it. Our hair labeled us greasers too – it was our trademark. The one thing we were proud of. Maybe we couldn't corvairs or madras shirts, but we could have hair. † Ponyboys pride was his hair and that's what he considered himself to be defined by. It was a painful change for him to make when he had to cut it off. When Johnny told ponyboy that he is going to cut their hair ponyboy replied by stating the previous quote. Ponyboy could have been defined as a hero before the events at the church in a different way and from a different perspective.He could be a hero in Johnnys eyes or sticking with him ever since he killed the Socs Bob to him lying in his death bed. He is a hero for standing by his friends through the good and the bad. Although Ponyboy is the most notable hero in the story there are others who did other things such as Cherry acting as a spy for the Greases and giving them info. In the end al l of the different stories of heroism in the story all relate back to the main theme heroism is defined by your actions not by your background, history, or looks. The Outsiders By Pancakewaffe The Outsiders Search for Self Conflict arises between two incomparable social groups, resulting in tragic deaths. In the novel The Outsiders by S. E Hint, two separate gangs, the Soc and Greasers, are at constant contention. The Outsiders takes place In Oklahoma, the sass's. Hint uses the character, Pony Curtis to explain why It doesn't matter what social position you are In. The message she Is trying to get across to the readers Is you are your own person, and you don't have to be classified as anyone else but yourself.Throughout the novel Pony becomes more sophisticated, by learning to reaffirm is own values and sense of self. He progressively matures through the course of the novel in many different ways by experiencing things he would have never imagined going through. Pony never admired the girls that were Greasers but one night he found the girl of his dreams. Unfortunately, she was a Soc and she had different standards for her romantic companion. Pony knows his position in society but that never held him back from falling for the delectable Cherry Valance.Heartbreaking for him, she was more of the girl to fall for â€Å"bad-boys† unlike Pony. It wasn't Johnny's fault Bob was a booze-hound and Cherry went for boys bound for By the end of the novel, Pony finally comes to the realization of thinking of Cherry as more of a friend, rather than having romantic affections for her. He shows sensitivity and understanding by appreciating their differences and accepting her feelings towards him. A second way Pony has changed is by learning the consequences of his own mistakes and wrong doings.After Running away was always Pony supreme speculation on finding a place he felt acknowledgment, â€Å"Come on, Johnny, we're running )After coming home late, his older protective brother Dairy, who has taken over responsibility of Pony and Soda after their parents passed away, is very furious with him. As a result of his outrage, he ends up hitting Pony. Devastated, Pony runs away , and relies on one of the gang members to keep him up to date on what Is going on. He Is then stuck In an abandoned church starving for news to come from his confidant.As a result of his actions, Pony learns how big of an Impact his family and friends have n him. The biggest impact that changed Pony throughout the novel was the realization of his brother's feelings towards him. In the beginning of the novel, Pony is rebellious against his oldest brother Dairy, â€Å"Me and Dairy just didn't dig each other. â€Å"(p. 13)He felt like Dairy was only out to get after him, and that Pony was never exceptional enough. Even though Dairy is stricter than their parents, he was only trying to make the best of Pony, which he came to realize at the end. Dairy did care about me, maybe as much as he cared about Soda, and because he eared he was trying too hard to make something of me. â€Å"(p. 98)After trying to block Dairy out, Pony finally figured it out for himself after seeing the agony h e has put Dairy through.

Fiction Essay Instructions Essay

In Module/Week 3, you will write a 750-word (about 3–4-pages) essay that compares and contrasts 2 stories from the Fiction Unit. Before you begin writing the essay, carefully read the guidelines for developing your paper topic that are given below. Review the Fiction Essay Grading Rubric to see how your submission will be graded. Gather all of your information, plan the direction of your essay, and organize your ideas by developing a 1-page thesis statement and outline for your essay. Format the thesis statement and the outline in a single Word document using current MLA, APA, or Turabian style (whichever corresponds to your degree program). You have the opportunity to submit your thesis and outline by 11:59 p.m. (ET) on Monday of Module/Week 2 for instructor feedback. The essay is due by 11:59 p.m. (ET) on Monday of Module/Week 3 and must include a title page (see the General Writing Requirements), a thesis/outline page, and the essay itself followed by a works cited/referenc es page of any primary or secondary texts cited in the essay. Guidelines for Developing Your Paper Topic Chapter 39 in your textbook provides some helpful pointers for reading actively, taking notes, brainstorming, developing a clearly-defined thesis statement, preparing an outline, and writing a cogent fiction essay. Be sure that you have read the chapter before doing any further work for this assignment. Choose 2 of the following short stories to compare and contrast in your essay: â€Å"The Lottery† by Shirley Jackson â€Å"The Destructors† by Graham Greene â€Å"The Rocking-Horse Winner† by D.H. Lawrence â€Å"Young Goodman Brown† by Nathaniel Hawthorne â€Å"The Child by Tiger† by Thomas Wolfe â€Å"The Most Dangerous Game† by Richard Connell Also, make at least 1 of these elements of fiction the focus of your essay: Conflict/Plot/Structure Characterization Setting Theme/Authors’ Purposes Point of View Tone/Style/Irony/Symbol/Imagery If you need help focusing your essay, ask yourself questions that correspond to your chosen element(s). Conflict/Plot/Structure (This is not a summary of the stories) What are the basic conflicts, and how do these build tension, leading to major complicated incidents and climactic moment(s)? What are the ways in which each major character experiences conflict (either with self, with other characters, or with the social and/or physical environment)? How are the conflicts resolved? Do the protagonists succeed in achieving their goals? Who receives your deepest sympathy and why? Characterization Who are the main characters in the stories?  What are their outstanding qualities? Does the author give any indication as to how or why the character developed these qualities? What are the characters’ emotions, attitudes, and behaviors? What do these indicate to the reader about the character? Can the characters’ motivations be determined from the text? Setting Where and when do the stories take place (remember to include such details as geographic location, time of year, time period, if the setting is rural or urban, etc.)? Do the settings make the stories believable or credible? How does setting impact the plot of the story, and how would the plot be affected if the story took place in another setting? Are the characters influenced by their setting? How might they behave if they were in a different setting? What atmosphere or mood does the setting create (for example, darkness may create a mood of fear or unhappiness while light or bright colors may create one of happiness)? Is the setting or any aspect of it a symbol or does the setting express particular ideas? Does setting  create expectations that are the opposite of what occurs? Theme/Authors’ Purposes What is the major theme (or themes) of each story?  Are the themes of the stories similar or different?  How does the author convey the theme (or themes) to the reader? How do the stories’ themes relate to the authors’ purposes (some examples of author purposes are to entertain, to satirize, to realistically portray life’s problems, to analyze emotions and responses, and/or to communicate a moral message)? What unique style, techniques, or devices do the writers use to communicate their themes? Tone/Style/Irony/Symbol How would you describe the tone of the piece?  Does the tone correspond with the action occurring in the plot? What style does the author use (for example, one way an author might satirize is by including a lot of ironies, hyperbole, and unrealistic scenarios)? How might the story be different if the tone or style is changed? Does the writer use irony or symbols to communicate the message?

Describing an essay Example | Topics and Well Written Essays - 1000 words

Describing an - Essay Example This occurs in a case that there is grave danger facing an individual (Allyn 26). Mostly, when people experience fear, adrenalin kicks in allowing them to scamper extremely fast to safety. Arguably, no theory or ideology specifies on this reaction, but may be fear becomes the propellant for people to head to safer zones. However, fear is not anxiety as many would want to believe, but instead anxiety is a feeling of nervousness on an imminent event that is not necessarily associated to any form of danger. It is the uncertainty of the outcome of an event making it particularly different from fear. Therefore, the essence of this paper will be to describe a picture in relation to fear it evokes. Fear draws similar meaning to phobia, which was initially a Greek word. In essence, this gives meaning to the various forms of fear that are in existence. Incidentally, phobia is more clinical than fear as its use is in identifying and naming the fear that an individual has in order for them to a ccess treatment. For instance, the fear of water is aqua phobia while the fear of small spaces makes one to claustrophobic. Additionally, there is also the fear of death termed as Thanatobia and coulrophobia, which is the abnormal fear of clowns. These are just a few examples of existing fears, as the list is endless. However, there is no specific name given to the fear of scary looking faces, but the other fear closely related to coulrophobia is mask phobia that is the fear of masks. As per the picture, the woman in it appears one who provokes fear to anyone who views the picture for the first time. She appears to be a woman who has seen her fair share of unfortunate events in her life. For instance, the eyes are the windows to the soul hence hers appears evil and dark. Her eyes are not appealing although their color is unique. From another angle, she appears, as though she has evil spirits within her that makes fearful to associate with them. According to fictitious narratives, th ose possessed by evil spirits tend to camouflage their eyes to evoke fear among their viewers. Usually, a ray of light appears from their eyes that sometimes produces flames of fire. This is to mean that the eyes can reflect the nature of an individual and the availability of supernatural powers. Anyone who would meet her would fear having any form of confrontations with her because of her appearance. On the other hand, this woman’s face depicts suffering and the way of life subjected to her. It appears as though she has learnt not to trust anyone hence portraying the anger and rage that dwell within her. Essentially, the tormenting experiences that people go through make them build invisible walls around themselves due to the fear of going through the same experience again. Ideally, this woman might have experienced civil war and human suffering first hand. This experience appears to have hardened her in to not having a welcoming smile. Her face depicts distrust and a sharp contrast of a contented woman. Tentatively, the events in the lives of people reflect on their skin as a smooth skin, especially on the face, reflects a smooth life. In contrast, this woman’s face is rough and neglected. This means that she does not pay attention to detailed beauty to her facial appearance. Sequentially, her facial impressions make her the modest and rural kind of woman. This is mainly

Symbolic interactionism Essay Example | Topics and Well Written Essays - 750 words

Symbolic interactionism - Essay Example Our group known as the ‘The Saviour’ came up with the project of collecting clothes. Our strategy was to use all methods possible to collect many clothes to help the poor people in Senegal. To be effective in the meeting of our target, we divided the group into two. My team was given the task of placing donation boxes at various strategic points, giving out flyers to students in various colleges, and placing of posters to public notices. The posters had our contacts and activity. After two weeks of the activity the whole group came together to discuss on the progress. We all found out that the turnout was far below our expectations. According to symbolic interactionism theory, I expected my neighbors to appreciate my efforts to assist the poor through donations. However, that was not the case; the neighbors were seemingly getting a different symbolism from my efforts. I decided to meet with my team and discuss the way forward on how we could increase the awareness of our project in all our strategic places. We decided not to rule out possibility of miscommunication in our awareness efforts. As a team, we decided to purchase t-shirts printed with information regarding the project. At interval times, a person had to be at the strategic points we had placed the boxes and always carry with them a small mapping board with the group banner. Since our donation boxes were placed the bus terminal, the garden, school gates and the market place, the person was to talk to people around and create a good rapport about the group’s activity. In the next two weeks, we experienced a tremendous improvement in donations. This was perhaps possible because we were able to communicate our idea and convince people that we were a genuine group of youths dedicated to helping the poor in the developing countries. Language is an important concept of symbolic interaction. Therefore, we understood that different people perceive ideas differently. Our target was to colle ct several cargo tanks of clothes. We decided that each one of us would go from house to house in our respective neighborhood during our free time and weekends to distribute the remaining fliers. I visited most of the families in my neighborhood during weekends for a month. The response was encouraging because most neighbors embraced the idea and were happy about our project. Most of them gave out dozens of clothes and even promised tell others. Their appreciation and promises of cooperation was in accordance with the theory of symbolism interactionism. Thoughts are a concept of symbolic interactionism. Thought modifies the way a person perceives and understands a symbolism. We found out that placing the posters would not yield much as expected probably because people had gotten used to posters being used by scam artists. We decided to come up with alternative workable ideas of using posters. We placed the posters in each class notice board. We also visited some of the offices to al low us use their company’s staff notice board for a while. At first, most of the companies were afraid and thought we were a fake group but after we explained and defended our purpose, some of the companies permitted us. At the end of two weeks, we followed up and got a positive response from the employees, we collected several bunches of clothes from most companies. When the whole group met, we found out that our new strategy worked out very well. We shared the strategies we had used with the other team and the

The Importance of effective political communications and how to build Essay

The Importance of effective political communications and how to build and managed country reputation through effective political communications - Essay Example nd if the communication process is to be effective then the needs of the citizens to communicate on various issues in order to have them addressed must be fulfilled. This requires then a level of professionalism from both the political organisations and the journalist – the two institutions which are critical to the political communication system. McNamara (2010) definition of effective is worth mentioning here – ‘reaching the citizenry in such a way as to impact their views in a positive way.’ Numerous communication theories have been put forward in order to analyse communication in the political sphere. Grunig and Hunt (1984) has put forward four models of PR, three of which emphasise one way communication and a two-way symmetrical model which is the ideal situation for a democracy and thus for effective political communication,. This model employs communication for the purpose of negotiating with publics, resolving conflicts, and for the promotion of mutual understanding and respect between the organisation and its publics. Present day political communication seems to be lacking to a great extent in this regard. This paper looks at the work of a number of writers/researchers who have done work in the area of political communication. Each of them has put forward their theory on political communication. The ones which are of interest are introduced and critiqued separately. The paper also provides a comparative analysis of their work. Blumler and Gurevitch (1995) in their book ‘The Crisis of Public Communication’ looks at the issues affecting effective political communication. Blumler and Gurevitch (1995) attribute this crisis to four sources: the drive by the two political communication institutions – politicians and journalists, to understand the strategies used by each other in order to make adjustments in response to each others actions; and Blumler and Gurevitch (1995) link the communicators and the audience in a network of expectations

Craft and Cadbury horizontal merger as means of company growth Essay

Craft and Cadbury horizontal merger as means of company growth - Essay Example In 2010, 21 acquisitions have taken place till now and the total value is found to be 1.5 billion dollars. One of the most talked about merger and acquisition in the recent time is the Kraft and Cadbury horizontal merger. This report is all about this merger. The report tries to find out the relationship between the Kraft and Cadbury merger and the growth of both the company. Kraft foods Inc is one of the oldest and largest food and confectionary companies in the world. The company started its journey in the year 1903. Today the products of the company are consumed by billions of people in over 150 countries in the world. The company employs more than 100,000 people. Cadbury, on the other hand is one of the most popular confectionary company. The company is renowned for three different types of confectionary – chocolate, candy and gum. It started its journey in 18th century and it has its presence in 60 countries. The company employs almost 45,000 people. After the merger between these two giants (Kraft and Cadbury) Kraft foods have become the second leading food company in the world. It has also become the number one company in confectionary sector. Its total revenue is found to be almost 50 billion dollars. There are eleven brands; value of each of these is found to be 1 billion dollar. Moreover there are more than 70 brands, each of which is a 100 million dollars brand. It is found that 34% of Kraft’s middle and senior level management is women. Furthermore 30 leaders are found to be from 10 different nationalities (Kraft Foods. Building a global powerhouse). As a result it can be said that there is a diverse global team that is running Kraft Foods Inc. after its merger with Cadbury. In September, 2009 a proposal was made from Kraft foods Inc. to the Cadbury Inc.’s board regarding the merger of two companies. According to CNN, on September

Feedback and Evaluation Paper Essay Example | Topics and Well Written Essays - 1000 words

Feedback and Evaluation Paper - Essay Example It has a diverse workforce from all around the continent with recognized worldwide reputation for its innovation and leadership (CalPERS, 2012). The feedback mechanisms are a way to improve the services provided by the Calpers the Retirement agency for the state of California employees. Mostly, the problems are difficult, secrecy, and also pension programs tend to get boring with time. So, feedback is essential for this retirement agency of California as well. First of all, the scale of the problem should be recognized. Identifying the problem is the basic step from where feedback is generated. It is the feedback of the customers, as well as employees which pave the way for feedback mechanisms. The internal and external feedback mechanisms allow the company to prosper more successfully and enhance the atmosphere of the organization (State University, 2012). Firstly, the political leaders of California should realize the profundity of current financial deficits in the economy as what will the future implications will be. The company has found out that if pension systems even earn 7.5 to 7.75 percent on a yearly basis, CALPERS will only learn 73.5 to 75.3 correspondingly. The pension systems today cannot find their way out of this current scenario which is absolutely devastating. The debt of the state of California is rapidly increasing everyday as political instability continues to pertain in the region. Obviously, the retirement agencies like CALPERS are highly affected by such traumas. This situation is rather tragic and disappointing, but it is very costly for Retirement firms as well (CalPERS, 2012). Whenever the leaders do not act on the situation, the cost increases to the citizens and state and possibly to public workers to whom the state owe pension. This condition is comparable to an adverse loan amortization, which is a scenario where the owner of the house does not pay much for the principal owed and thus goes into an even shredder condition. The dela y which takes place in this situation over the period of next year can be figured out from any underfunded sum. The 6.2 percent rate of discount leads to yearly pitfalls for the CALPERS system and thus only $16.8 is estimated to be given to CALPERS, CalSTRS and UCRP combined. This problem has to be highlighted in the company (CalPERS, 2012) The feedback and evaluation programs are very important for CaLPERS. For this purpose, direct administration of Associate Program or Program Evaluator is required who assists in the planning and conducting of field audits of employment records and payroll at the public agencies to testify that the agencies are enrolling employees in accordance with the Law set by the Public Employees Retirement Law. Also, the agencies have to report rewards as well in accordance to this law. This situation helps then go to different public agencies located all across the state of California. Furthermore, the Associate Program Evaluator or Program Evaluator may as sist in carrying out the reviews and internal audits of the company which includes the EDP operations, administrative controls, and internal accounting and several other programs (CalPERS, 2012). The fiscal analysis of this public firm extensions for funding and also various other special programs is done by the Program evaluator. The present findings are then presented to the general public and are highly expected to show official dress and manner, in addition to sticking to

Risks in the Oil Industry Dissertation Example | Topics and Well Written Essays - 11000 words

Risks in the Oil Industry - Dissertation Example From this study it is clear that while currency may be the central thorough-put of social organization, it is forms of energy that drive the social apparatus. Energy in the form of petroleum, or oil, constitutes the predominant energy form of the modern world. Despite growing concerns about environmental sustainability, and the oftentimes-tumultuous conditions and nations that must be traversed to obtain oil, for the conceivable future this resource is Earth’s primary energy source. The central importance of oil to modern society has necessitated considerable public and private resources are devoted to its procurement. In addition to procurement infrastructure, financial markets have emerged for the sale and speculation on future prices. Oil is a complex resource that must be understood from multi-dimensional perspectives.This paper highlights that  oil is procured mainly from drilling methods, including offshore reserves. There are a variety of grades of oil, referred to as benchmarks, a fact that has necessitated differing terminology. To a large degree crude oil benchmarks emerged with the first futures contract on oil in 1983. Today there are three primary benchmarks: West Texas Intermediate (WTI), Brent Blend, and Dubai Crude. West Texas Intermediate is most prominently used in the United States. WTI is also recognized as the highest grade of oil and generally trades at a premium to the Brent Blend and Dubai Crude, although as will be later demonstrated this is a highly volatile market.

Motivational Effects of Technology in Music Education Essay

Motivational Effects of Technology in Music Education - Essay Example This report stresses that one of the main issues facing music education is the technological gap between the teachers and the real world. This is because most of the teachers are from another generation of learning and hence they did not acquire technological skills and knowledge in their training. In order to avoid this problem, music teachers and curriculum developers should integrate technology in their learning activities. It is important to note that in this context technology is a purveyor of many benefits which have virtually transformed the music world. Therefore, technology inclusion in the school system is not an imposition but rather a necessity. This paper makes a conclusion technology in music education has many benefits ranging from ease of study accruing from repetitive teaching tasks by computers to unlimited availability and accessibility of learning materials. Music as a subject is not very popular as compared to other disciplines such as engineering. There have been attempts by some countries to reform their music curriculum in a bid to appeal to more students . For instance, Scotland has implemented major reforms in its music schools including establishing technologically-enhanced music rooms for their schools . Technology seems to be the only solution to a seemingly unviable field of study . Integrating technology in a formal learning setting is bound to affect the involved parties in various ways . The effects of technology in education have already been studied in other curriculum.

Philosophy of Social Science Essay Example | Topics and Well Written Essays - 1000 words

Philosophy of Social Science - Essay Example (Gordon, 3) The considering of this second aspect engendered by social science, connects it to ethics, to the study of values. Dealing with the concept of "philosophy of social science", the same author defines it as "the study of how we are able to know whether our notions or theories about empirical phenomena are true or false." (3) Another definition is that the philosophy of social science is the study of the logic and the methods of social sciences. This second definition appears in the Cambridge Dictionary of Philosophy. According to the same source the problems it deals with are those connected to answering questions like: "What are the criteria of a good social explanation", "How are the social sciences distinct from the natural sciences", "Is there a distinct method for social research", "Through what sorts of empirical procedures are social science assertions to be evaluated", "Are there irreducible social laws", "Are there causal relations among social phenomena", "Do social facts and regularities require some form of reduction to facts and regularities involving only the properties and actions o f individuals" One of the problems the philosophy of social science is concerned with is that regarding methodology... dividuals having to conform to a model or pattern offered by society, nowadays conceptions emphasize on the importance of respecting one's individuality, conception based on the idea that all human beings are free to choose their own and personal way of life. As a consequence of individualization, a new method of social science research appeared: the biographical method.Extensively discussed in The Turn to Biographical Methods in Social Science: Comparative Issues and Examples, the biographical method is new to the field of the social science methodology. The explanation the authors give in regard to the fact that social science researchers rejected this method until recently, lies in the fact that "modeling themselves on the natural scientists", they "set out to construct models of body and mind which described uniformities and regularities, and which enabled human behavior to be understood "objectively", that is in terms of its abstracted common attributes" (Chamberlayne, Bornat an d Wengraf, 36). The authors mention the fact that, although biography is commonly regarded today as a written work, it is, in fact, a production of face to face oral communication. The definition provided by the same researchers connects it to the social integration aspect: "The work of orienting the temporal process of the individual's life and of social change." (115) Through biography, meaning by telling their life, people integrate themselves in a context and realize that they belong to a structure, they reconstruct themselves. The biographical method was attacked, researchers warning about its inadequacy and supporting their position by two main reasons: the possibility of having to do with what was called "a neurotic narrator", or a person who invents the facts presented as

Supply chain Essay Example for Free

Supply chain Essay INTRODUCTION The operation plays key role in firms because it affects operation managers understand their customers and translate their customers needs into performance objectives. In turn, the performance objectives (and especially the relative importance of each one) influence the overall operations strategy of the business. (Slack et al., 2010,) The reason is that managers can based on performance objectives to do decision because it is reflection of corporation strengths and weaknesses. This essay will describe the Toyota Motor Corporation performance around five-performance objective: quality, speed, dependability, flexibility and cost. Then following discussion of how Toyota uses techniques in operation management to achieve the five objectives. Based on analysis, some suggestions for this firm will shows in the conclusion. FIVE PERFORMANCE OBJECTIVES IN TOYOTA As most successful car manufacturer in the world, Toyota is a few automobile companies that able to be stockless production system by its unique operational management systems, the most famous Toyota Production System (TPS) and Just-In-Time (JIT). Further, in terms of five performance objectives, which Toyota did quite well in the overall circumstances, especially in the cost, speed and flexibility areas. Toyota cooperation was chosen to analysis because its significant performances and great operations management. Organizations can respond to the performance objectives, but the real challenge is to offer better quality, speed, dependability and flexibility at lower costs than the competition. (Slack et al., 2007) Also, the aim and objective of Toyota is making low-cost, high-efficiency, high-quality production to maximize customer satisfaction and keeping strong competitiveness. (Toyota, 2011) QUALITY Quality can be defined as specification of a product or service, also meaning high specification and must satisfy your customers by providing  error-free goods and services, which are fit for their purpose. (Pycraft, 2000) The external affect of good quality within in operations is that the customers without (or less) complain leading to customers more likely to consume again because the customer feel has received real value for money. This brings in more revenue for the company. For internal influence, conformance quality is high that generally means that cost is saved, dependability increases and speed of response increases. If an operation is continually correcting mistakes, it finds it difficult to respond quickly to customers requests. (Slack et al., 2010) SPEED Speed indicates the time between the beginning of an operation process and its end. (Greasley, 2010) More specific, that means the elapsed time between a customer asking for a product or service and getting it with a satisfactory condition. (Slack et al., 2010) Externally affect of speed is important because it helps to respond quickly to customers and resulting customers return with more business. The internal side have much to do with cost reduction. Usually, faster throughput of information (or customers) will mean reduced costs via reduces the need to manage transformed resources as they pass through the operation and helps to overcome internal problems by maintaining dependability. DEPENDABILITY In terms of dependability, is usually means being on time, in other words, keeping delivery promised to the customer that receive their products or services on time. (Slack et al., 2010) Also, dependability is other half of total delivery performance along with delivery speed and always linked in some way. In externally influence, dependability as good thing for customers also enhances the company product or service in the market, or at least avoids customer complaints. Internally dependability has affect on cost via saving time, saving money directly and giving an organisation the stability that allows it to improve its efficiencies. Moreover, it prevents late delivery slowing down throughput speed. FLEXIBILITY Flexibility always means being able to change the operation in some way, vary or adapt the operations activity to cope with unexpected circumstance or to meet customer requirements gives a flexibility advantage to customers. (Slack et al., 2010) Flexibility has several specific types: Product or service flexibility; Mix flexibility; Volume flexibility and Delivery flexibility. For external affect, the different types of flexibility allow an operation to fit its products and services to its customers in some way, such as produce wide range and frequent new products or services. On other hand, Volume and delivery flexibility adjust its output levels and its delivery procedures in order to cope with unexpected changes. The internal influence is speeds up response, saves time (money) and helps maintain dependability. COST Produce goods and services at a cost, which enables them to be priced appropriately for the market while still allowing for a return to the organization. (Slack et al., 2010) not surprisingly, low cost is a universally attractive objective because lower cost means higher revenue and more competitiveness. If managed properly, high quality, high speed, high dependability and high flexibility can not only bring their own external rewards, they can also save the operation cost.(Greasley, 2010) FIGURE 1: INTEGRATION IN FIVE PERFORMANCE OBJECTIVES Reference: Slack el at, 2010. Chapter 2: _The strategic role and objectives of operations_, Operations management. From figure 1 and unique Toyota Production System, which shows the company five-performance objectives irrelative significantly with each other. As Toyotas mainstay, quality is the most emphasize element of this company. According to the relevant survey institutions that indicates Toyotas  vehicles consistently rank near the top in third-party customer-satisfaction. (Toyota, 2012) Excellent quality of Toyota not only prevents errors slowing down throughput speed, also avoid wasted time and effort, therefore saving cost. For instance, if there occurs many non-value-added activities that mean the company need use time, money and human resource to solve it and increase cost. Also, if quality is not qualified in the produce process then rework will drag on production speed. According to survey data from relevant agencies, the three giants in US, General Motors, Ford, Chrysler, parts procurement costs higher than Toyota 8%. Alternatively, Just-in-Time (JIT) stockless production system is extremely increasing Toyota productivity, which means the speed of production is fast. High speed helps Toyota maintaining dependability via zero (less) inventories because the company can respond to the market quickly. Toyota is a dependable company resulting by TPS (Toyota production system) with multi skilled worker that work as a team, and with control has allowed them to deliver products as promised. On the other hand, be more dependability can increase speed without late delivery and saving cost. In addition, For Toyota plant, flexibility means the ability to produce new products and offer wide range volume. During these years, Toyota has provided a wide range of options cars, such as SUV (Prado, RAV4), hybrid fuel (Prius,Yaris Hybrid) , commercial vehicle, sports car(GT86) and bus et al, also to meet environment and economic changes, Toyota was the earliest car maker to introduce hybrid fuel cars, with the commencement of Prius model. The high flexibility leading to Toyota brings frequent new products to the market to gain more competitiveness. Moreover, the speed from JIT system makes Toyota more flexibility in volume. TECHNICS IN TOYOTA Currently, the main competition between enterprises is from supply chain. The world famous TPS strategic management is also created by Toyota. It is called a variety of small amount of production of the unique market demand in Japan. From mass production of the highly formative changed to be a variety of small amount of production, to completely eliminate waste, improve production efficiency and cost competitive. For example, to increase  production, one person can do kinds of multi-skilled jobs (Kawada Makoto, 1993) Figure 2: TPS Structure JIT JIDOHKA According to Figure 2 indicates that TPS is comprised of two pillars, JIT and Jidoka (automation). Bicheno (1991) states that JIT aims to meet demand instantaneously, with perfect quality and no waste. Three key issues identified by Harrison (1992) as the core of JIT philosophy are: eliminate waste, involve everyone and continuous improvement (Kaizen). JIT approach, through rational design for make the product easy to produce and easy assembly. Essentially, just in time manufacturing consists of allowing the entire production process to be regulated by the natural laws of supply and demand. Customer demand stimulates production of a vehicle. In turn the production of the vehicle stimulates production and delivery of the necessary parts and so on. The result is that the right parts and materials are manufactured and provided in the exact amount needed. Under just in time the ultimate arbiter is always the customer. This is because activity in the system only occurs in response to customer orders. In order to support its JIT system, Toyota needs to ensure that the supplier in accordance with the cost, quality and timeliness, with on time delivery and production, stringent quality control system to help Toyota to reduce inventory, also minimize scrap production, and reduce non-value-added operations to increase quality, save cost and respond market immediately by decrease lead time. It also played an important role to ensure working capital is fully available that make the company be more flexibility to explore new production. (Kawada Makoto, 2004) Moreover, JIT emphasis on total preventative management (TPM), developed by total quality management (TQM). Toyota insists to regular maintenance activities, periodic inspection to equipment to avoid breakdowns and preventative repairs. Attention to the detection and control of each process, ensure found quality problems in a timely manner, immediately stop  produce until resolved. On other hand, TPM encourage all employees involved and use their knowledge to improve performance. (Greasley, 2010) In Japanese jidoka simply means automation. At Toyota it means automation with a human touch. The most visible manifestation of automation with a human touch is using andon cord situated in the line to intervene any abnormalities occur. KANBAN One system for implementing a pull system called a kanban production system. (Greasley, 2009) Kanban is the core in the JIT and it does not work without Kanban manage method. A kanban is simply a message. For example, in the assembly shop this message takes the form of a card attached to every component that is removed and returned when the component is used. The return of the kanban to its source stimulates the automatic re-ordering of the component in question. This system permits greater control over production as well as inventory via efficiency maximum. In Toyota process of produce, kanban is intended to convey information: what is needed when it is needed, and in the amount needed!(Lowson, 2002) The accurate number of parts allocation to avoid waste in the production process, thereby improving the manufacturing speed. KAIZEN / CONTINUOUS IMPROVEMENT Kaizen is the heart of the Toyota Production System. The day-to-day improvements that Members and their Team Leaders make to their working practices and equipment are known as kaizen. This is simply common sense since it is clear that inherent inefficiencies or problems in any procedure will always be most apparent to those closest to the process. The sense of continuous improvement also can influence Toyota performance, such as quality directly. (Iyer, 2009) Overall, TPS use JIT to eliminate inventory and develop close relationships with suppliers, eliminate all but value-added activities, reduce the number of job classes and build worker flexibility, apply Total Productive  Maintenance (TPM) to increase productivity and ensure quality, thereby obtain faster produce speed and improve productions; avoid waste time and resource to reduce cost; Regards to integration of these five performance objectives, faster speed and low cost resulting to Toyota becoming more dependability and have enough capital to be flexibility. When a company can respond the customer required immediately that means it could be depend. Refers to the flexibility, when Toyota have large amount capital then the firm could use on the production innovation to explore more cars types to offer the market and develop cars capability to increase quality of productions. (Greasley, 2010) CONCLUSION In conclude, Toyota has good performances on speed and quality, thereby promoting the dependability to the customer, also this firm did great on the flexibility because Toyota keep introducing new types of car. The interrelationship between these performances takes a significant benefit for save cost, for instance, faster speed means efficiency to save time and labor source to avoid waste. Although Toyota is extremely successful carmaker, but the recall events in 2009 caused great impacts for it. The reason is the company eagers to rapid expand to the market, the quality of management and personnel training has not been followed up, and then resulting series of parts defects. Moreover, in terms of excessive cost cutting, cause quality of components cannot guarantee. There have four suggestions for Toyota to developing performance: Firstly, Toyota in order to reduce expenditure in research and development via using general parts and components, but the manager have not realize technical innovation is the most important mean to reduce the cost of doing business, promote new products, the company should focus on technology innovation to be more efficiency and flexibility. Secondly, Toyota enterprises should pay more attention on After-sales service, the cost of these services is much lower than the recall and increase the sense of dependability to Toyota, such as Toyota car examination and driving lessons for free. Thirdly, Toyota need to strengthen the staff training to avoid the quality defects occurs in  the process of produce line. Thus, not only can ensure the cars quality and save cost without deal with the consequence from employees non-value-added activities. Finally, the company should increase more quality testing sections to examine new productions repeatedly. In general, the speed of produce car is quick resulting the production line easily ignore the nature of Toyota quality. Increase the quality inspection to insure Toyota manufacture superior products, which enhance the company production quality and dependability. As we known, quality is everything, if the company to seek a long term development then should be offer good products, not just focus on how to save cost thereby supply defective to the customer. REFERENCE: Iyer, A., 2009. _Toyota Supply Chain Management: A Strategic Approach to Toyotas Renowned System_.pp.95-99 Harrison,A. ,1992. Philosophy and core techniques, TIME†¬: Greasley, A., 2010. Lean Operations and JIT, _OPERATIONS MANAGEMTN_ pp. 348-365 T. Gabriel, J. Bicheno, J.E. Galletly, (1991) JIT Manufacturing Simulation, Industrial Management Data Systems, Vol. 91 Iss: 4, pp.3 7 Kawada Makoto.1993. _Why And How, Management Accounting_, Strategic Management Accounting Kawada Makoto.2004. Toyota System And Management Accounting. Publish: Central Economic, China. Lowson, R., 2002. Lean production and just-in-time, Strategic Operations Management. PP.457 Pycraft, M., 2000. _OPERATIONS MANAGEMENT_, pp. 48. Slack N., Chambers, S., Johnston, R., 2010. _OPERATIONS_ _MANAGEMENT._ Slack, N., 2007. _OPERATIONS, STRATEGY AND OPERATIONS STRATEGY,_ pp.24 Toyota, 2011. _Toyota Global Vision Mission Statement Announced._ Available at: Toyota, 2012. _Global sustainability reports_ [pdf] Available at:

Rain Water Harvesting As Water Scarcity Solution Environmental Sciences Essay

Rain Water Harvesting As Water Scarcity Solution Environmental Sciences Essay Availability of water is critical for ecosystem health and productivity, ensuring supply of a range of products and services, to benefit human well-being (e.g., GEO4, 2007; MA, 2005). Future pressures from climate change, growing population, rapid land use changes and already degraded water resource quality, may intensify water shortages in specific communities and exacerbate existing environmental and economic concerns (5). Population around the world today depends on the renewable resources of water for their water needs in industrial, agricultural and domestic sectors. But when these are withdrawals are greater than 20% of total renewable resources, water stress often is a limiting factor on development; withdrawals of 40% or more represents high stress. Similarly, water stress may be a problem if a country or region has less than 1,700 m3 yr-1 of water per capita (4). In 1990, approximately one-third of the worlds population lived in countries using more than 20% of their water r esources, and by 2025 about 60% of a larger total would be living in such stressed countries, in the absence of climate change largely because of population growth (6).IPCC in its Third Assessment Report predicts that increase of temperature between 1-2 °C would lead to decrease in water supply in regions already suffering from water scarcity such as the Mediterranean, southern Africa, and arid parts of central and south Asia affecting half a billion people. These areas will be further affected if the temperature increases 2-3  °C (1). With growing number of population belonging to the water stress areas of the world, it has become crucial for humans to find out alternative sources of water, proper management of the given resources and bring in technological changes to improve water use. Though centralized water management systems has a huge impact on our lives today, societies, government and citizens around the world are looking out of alternative resources to augment the available water resources. Rainwater harvesting, as one of such methods, is the accumulating and storing, of rainwater. Depending on local environmental conditions, water harvesting may provide a supplementary supply, an alternative supply or the only feasible improved supply. The current centralized water supply paradigm which is followed in all the cities of the world seems unsustainable and extremely high on energy consumption. In United States, about 4% of the U.S power generation is used for water supply to the population and electricity re presents approximately 75 percent of the cost of municipal water processing and distribution (19) (20). As an alternative paradigm for more sustainable water availability, harvesting rainwater, storing it in tanks, and recharging groundwater may be used to provide drinking water, water for livestock, water for irrigation or to refill aquifers. In rural areas, rainwater can be used to even supplement agricultural income through small horticultural projects and maintaining improved amount of livestock apart from developing the quality of life of rural women in many parts of the world who spends a considerable portion of their day- to- day life in collecting water for drinking and house hold purposes. In just one day, more than 200 million hours of womens time is consumed for the most basic of human needs collecting water for domestic use (21)( I still remember, the distance I used to travel to collect water from nearby reservoirs as kid visiting my village during holidays back in Ind ia). As the civil society is becoming more aware and sensitized regarding its potential, rainwater harvesting can also be scaled up to neighborhood and micro-watershed levels. More than one out of six people lack access to safe drinking water, namely 1.1 billion people, and more than two out of six lack adequate sanitation, namely 2.6 billion people (Estimation for 2002, by the WHO/UNICEF JMP, 2004). Rainwater collected from the roofs of houses, tents and local institutions, or from specially prepared areas of ground, can make an important contribution to drinking water. Rainwater systems are simple to construct from inexpensive local materials, and are potentially successful in most habitable locations. Roof rainwater can be of good quality and may not require treatment before consumption. Although some rooftop materials may produce rainwater that is harmful to human health, it can be useful in flushing toilets, washing clothes, watering the garden and washing cars; these uses amount to a significant amount of water used by a typical home. In many parts of the world, households and communities have augmented or substituted their household supplies with ra inwater for reasons of scarcity, salinity, quality of service and for risk substitution. While rainwater may not always provide a full-year round of supply, it enhances water security in the house and generally provides a good quality water. Historical development of rainwater harvesting Water has been important for the development of cultural complexity in human society during the Holocene and earlier (16). Human ancestors have always used aquatic resources to their benefit (18), as we see the earliest association of hominid ancestors with lakes and pools dating back to 6 and 7 m.y. ago (Upper Miocene) from northern Chad, Central Africa(19).Rainwater collection is one of the oldest means of collecting water for domestic purposes. Archaeological excavations document ancient rainwater harvesting in Mesoamerica, the Mediterranean, and the Orient (10). Historically, in Baluchistan (erstwhile India and now in Pakistan), evidence of simple stone-rubble structures for impounding rainwater dates back to the third millennium BC (8). Hundreds of years before the birth of Christ, rainwater collection were already a common technique throughout the Mediterranean and Middle East, used by Egyptians, Palestinians, Iranians, Iraqis, Yemenis, Greeks and Romans(9). In the Negev desert in Israel, tanks for storing runoff from hillsides for both domestic and agricultural purposes have allowed habitation and cultivation in areas with as little as 100mm of rain per year.. Water was collected from roofs and other hard surfaces and stored in underground tanks, or excavated reservoirs (cisterns) with masonry domes (9). In some parts of the Middle East, rainwater was collected from hard surface areas and channeled through vertical shafts to horizontal tunnels (qanars) that in turn led the water to underground reservoirs (22). In addition to the traditional rainwater harvesting techniques found in India, North Africa and the western Mediterranean, there are also examples from Thailand, China, Bangladesh, Nepal, Sri Lanka, Indonesia and the small islands in the Pacific. In sub-Saharan Africa, the collection of rainwater was (and is) practiced using small containers, in among others, most of Southern Africa, Ghana, Kenya and Tanzania. The earliest known evidence of the use of the technology in Africa comes from northern Egypt, where tanks ranging from 200-2000m3 have been used for at least 2000 years many are still operational today (7). Even in Western Europe, historical records show that in many places rainwater was the primary drinking water source for drinking water, the same applies to the Americas and Australia. In all three continents rainwater continues to be an important source for isolated homesteads and farms (11). Rainwater harvesting for domestic water use in modern day Though there is significant evidence of rainwater harvesting in the world historically, it was lost to peoples memory for sometime due to extensive water supply systems which came in place with the urbanization of the world. Potential for rainwater use is wide and there are many ways of capturing the rainwater runoff. In this paper, I would however like to focus more on the domestic usage of rainwater. Alternative sources of domestic water are becoming particularly important in urban areas of the world as urban population is rapidly increasing. Since 1950, the number of people living in urban areas has jumped from 750 million to more than 2.5 billion people. Currently, some 61 million people are added to cities each year through rural to urban migration, natural increase within cities, and the transformation of villages into urban areas(7). Due to the severe challenges of water stress and scarcity issues in the world today, these small stand alone techniques of water supply is becomi ng popular. Urbanization of the world has also changed the way houses are built worldwide and concrete roofing is providing good catchment areas closer to the domestic water users. Plastic and Ferro-cement tanks has also become a good alternative to earthen tanks as reliable, economic and durable means of water cisterns. Rapid urbanization of the cities around the world has also brought forward the faults of the water distribution systems in many parts of the world, especially in the developing countries where people have felt the need to become self sufficient in water supply within their means. As the quality and quantity of ground water is decreasing, rainwater is becoming an alternative source. Urbanization also is bringing together large number of people within smaller areas to live such has flats, apartments, residential complexes etc where rainwater harvesting is becoming a community based approach where the cost of implementation and the benefits are getting shared within th e members of the communities. Rainwater harvesting can be categorized in a number of different ways according to the type of catchment surface used and by implication the scale of activity. Essentially these are either rooftop, ground, or rock with rooftop being most suited to individual household or community water supply, while ground and rock being more geared towards agricultural irrigation. Conceptually, rainwater harvesting catchments can vary in size from the individual house to a river basin Figure Source -http://buildandrebuild.com/rainwater-harvesting-and-you/ Rooftop rainwater harvesting however is a very small percentage of the total rainwater run-off. But as a small scale and domestic activity, this is significant as the production; control and use of these sources are maintained and controlled by domestic users. For arid and semi arid countries, rain-water is often the most readily accessible water source at the community and household level, although distribution of rainfall during the year, and storage necessary for the dry months can provide a problem. Rainwater collected using existing structures has few negative environmental impacts compared to other technologies for water resources development. Rainwater is relatively clean and the quality is usually acceptable for many purposes with little or even no treatment. The physical and chemical properties of rainwater are usually superior to sources of groundwater that may have been subjected to contamination. Rainwater harvesting can co-exist with and provide a good supplement to othe r water sources and utility systems, thus relieving pressure on other water sources. Rainwater harvesting provides a water supply buffer for use in times of emergency or breakdown of the public water supply systems, particularly during natural disasters. Rainwater harvesting can reduce storm drainage load and flooding in city streets. Users of rainwater are usually the owners who operate and manage the catchment system, hence, they are more likely to exercise water conservation because they know how much water is in storage and they will try to prevent the storage tank from drying up.Rainwater harvesting technologies are flexible and can be built to meet almost any requirements. Construction, operation, and maintenance are not labour intensive (7).Rainwater harvesting system also produce beneficial externalities in reducing peak storm water runoff and associated processing cost. Rainwater harvesting as a sustainable water strategy Access of water according to the UN is officially defined as 20 lpd within a 1 km distance from ones dwelling. The UN considers this a minimal standard to which all countries, even low income ones, can aspire. This definition has been critiqued on two counts: (1) 1 km is a considerable distance, especially when carrying water, which is heavy. For many women and girls, who make up the great proportion of water carriers, fetching the family minimum could require several 1 km trips each way a significant barrier to actual access. (2) Climatic variations are not accounted for in the universal definition of access (15). Rainwater harvesting can significantly address this issue and become a sustainable water source across the climatic condition if the management systems are robust and the water collected can be channelized to recharge groundwater. For example, Jordan faces a huge water crisis. Results of a study show that a maximum of 15.5 Mm3/y of rainwater can be collected from roofs of residential buildings provided that all surfaces are used and all rain falling on the surfaces is collected. This is equivalent to 5.6% of the total domestic water supply of the year 2005. The potential for water harvesting varies among the governorates, ranging from 0.023ÃÆ'-106 m3 for the Aqaba governorate to 6.45ÃÆ'-106 m3 for the Amman governorate. The potential for potable water savings was estimated for the 12 governorates, and it ranged from 0.27% to 19.7% (13). Rainwater harvesting can also reduce the dependence on the centralized water supply systems. Mega-Cites worldwide are facing similar challenges of water scarcity and water stress like polluted freshwater resources, overexploited groundwater resources, insufficient or poorly maintained water supply infrastructure systems and insufficient technical and water management capacities (14).Small pockets of water resources within a city are more resilient and can draw on rainwater and groundwater, providing the city with greater flexibility in the face of water shortages, operational failures and natural disasters. History tells us that cultures do not give up until they have exhausted options for survival over the area they occupied for longer period. The Mayan civilization is a case in point, which developed around 3000 years ago in Mesoamerica, and faced recurrent droughts due to solar forcing before it collapsed due to climate deterioration towards the end of the Classic Period. Ancient reservoir technology developed by the Mayan people in the seasonally dry tropics of southern Maya lowlands reveals that rainwater storage was a major source of water supply during the dry season. Reservoirs were constructed, for example, in Tikal to cope with seasonal scarcity of water (16). Rainwater harvesting can also improve the situations of urban flooding. More land area around the world today is getting covered by asphalt and concrete as new roads are laid down to support increasing amount of transport use of urban population. This has lead to the lower seepage of surface water to replenish ground water resources. In the United States alone, pavements and other impervious surfaces cover more than 43,000 square miles-an area nearly the size of Ohio-according to research published in the 15 June 2004 issue of Eos, the newsletter of the American Geophysical Union. Collection of rainwater significantly reduces this stormwater to flow down the sewerage systems of a city. At times, this is effective in controlling urban flooding which happens when too much of water due to precipitation flows down the sewerage system which are not capable of handling the amount does not function properly. Evidences and policies of successful rainwater harvesting around the world- As the world tries out new methods to address the newer problems it face in solving natural resources scarcity issues and which in fact has been a significant factor for human civilization from time immemorial, rainwater harvesting experiments as a source of water is also happening worldwide. Currently there is no US agency that has focus on Rainwater Harvesting and states are rapidly doing their own thing. The H.R. 3598: Energy and Water Research Integration Act which has been passed by the House of Representative in December 2009 which is formulated to to ensure consideration of water intensity in the Department of Energys energy research, development, and demonstration programs to help guarantee efficient, reliable, and sustainable delivery of energy and water resources(32) may promote federal support in rainwater harvesting. Some states of Usa have significantly worked in promoting rainwater harvesting. In October of 2008, the city of Tucson, Arizona became the first municipality in the country to require developers of commercial properties to harvest rainwater for landscaping.   The new measure approved by a unanimous vote by the City Council requires that new developments meet 50% of their landscaping water requirements by capturing rainwater. The new rule went into effect on June 1, 2010. Arizona taxpayers who install a water conservation system after January 1, 2007, and before January 1, 2012, may take a one-time tax credit of 25% of the cost of the system (up to a maximum of $1,000). This can be claimed over multiple tax years, but no taxpayer can receive more than a total of $1000 in credits through this program. Builders are eligible for an income tax credit of up to $200 per residence unit constructed with a water conservation system installed (17). Some government grants in Arizona also have given the scope of funding rainwater harvesting projects within an amount of $5000 (25). The Cincinnati EPA office has instituted a program to give incent ives to homeowners for rain gardens or rain barrels to improve quality/timing of stormwater runoff, rather than promoting a central engineering solution. The City of Austin Water Conservation Program distributes over 250 rain barrels per month to homeowners at a subsidized cost, and provides rebates for the installation of approved cistern systems. Commercial/industrial properties can receive rebates up to $40,000 for the installation of rainwater harvesting and greywater systems. New commercial facilities must install a separate irrigation meter costing between $5,000 and $25,000 unless they can provide 100% of all outdoor water needs from alternate water sources such as rain, grey-water, and air conditioning condensate (26). With Clean River Rewards which is the storm-water utilitys discount program of Portland, helps ratepayers save money and work for clean rivers and healthy watersheds at the same time through storm-water management in individual properties. There is an 100perce nt discount on the onsite storm-water management charges because these actions helps protect the rivers, streams and the groundwater(27).Rainwater harvesting methods are used as sources of water supply in other parts in USA and more and more state governments are coming out to give this method a try. In California, the California Rainwater Capture Act of 2010, would authorize a landowner to install, maintain, and operate, on the landowners property, a rainwater capture system meeting specified requirements. The bill is also known as AB 1834 (35).In California however there is no tax credit given to the people in order to install rainwater harvesting equipments. In New Mexico however there is no mandatory law to install rainwater harvesting in individual houses, but there is a tax credit for NEW Green Buildings, which could include rainwater harvesting. For Build Green New Mexico Gold level, the maximum possible credit is $11,000.00 per house. The North Carolina Department of Environ ment and Natural Resources, Division of Soil and Water has implement Community Conservation Assistance Program has created a voluntary incentive based program for promoting rainwater harvesting and awareness generation educational programmes are in place, yet there is no tax incentives in place. Under this program the landowner may be reimbursed up to 75 percent of the pre-established average cost of the BMP (best management practices). Included in this program are Rainwater Harvesting Systems (36) (37) (38). Around the world, rain water harvesting has many success stories. In Singapore, rainwater harvesting is growing as rapid urbanization is inducing rapid water demand. In Changi Airport, rainwater is collected from the runways which are used primarily for non-potable functions such fire-fighting drills and toilet flushing. Such collected and treated water accounts for 28 to 33% of the total water used, resulting in savings of approximately S$ 390,000 per annum. In India, direct recharge of rainwater into the ground (40) resulted in groundwater level increases of up to 5 to 10 metres in just two years. Water scarcity problems in Indonesia, has made government introduce a regulation requiring that all buildings have an infiltration well. The regulation applies to two-thirds of the territory, including the Special Province of Yogyakarta, the Capital Special Province of Jakarta, West Java and Central Java Province. It was estimated that if each house in Java and Madura had its own infiltra tion well, the water deficit of 53% by the year of 2000 would be reduced to 37%, which translates into a net savings of 16% through conservation. UNICEF is working with communities in Alor in Indonesia and the communities has a very positive response towards this effort (39).In Tokyo, Japan rainwater and reclaimed waste water is used to address water demand in emergency cases. There are 850 facilities for rainwater use in Tokyo. Since reclaimed wastewater use has several benefits, a huge water volume has been utilized for various purposes such as washing; water-cooling, toilet flushing, waterway restoration and creation of recreational waterfront (30).There are many case studies and success stories, feasibility studies on rainwater harvesting methods and uses in the world today. An exhaustive list of all of them is beyond the scope of this paper. International organization for promoting sustainable environmental strategies like UNEP are growingly focusing on this method as to cater water needs of communities to attain the objectives of Millennium Development Goals. Poorer countries in Africa and Asia are experimenting on harvesting rainwater for various human uses for a long time now in order to answer some of the persistent water problems plaguing human lives in these continents. Evaluation of rainwater harvesting as a water resource- As rainwater harvesting is emerging in many regions of the world as a sustainable means of addressing short term and long term water scarcity, it is critical to understand the robustness of the system. Purity of rain water is in question when there are instances of acid rain all around the world. Growing air pollution in urban areas also pollute the rain before it falls and therefore rainwater harvesting requires treatment mechanism to make the water fit for human consumption. Rainfall intensity and the number of dry days preceding a rainfall event significantly affects the quality of run-off water from the catchment systems. Presence of fecal coli form and other microbiological contaminants, zinc concentration due to the material used in roofing are some of the shortfalls of rainwater harvesting (41). Household water management practices where rainwater is used as non-potable household use and the limited water supplied by the central water service system as potable water source can be a good alternative. Newer technological developments can easily solve these problems of contaminants in rainwater though it may significantly increase the cost of the water. Household level water catchment areas are often small and it is increasingly smaller when we think of urban areas. Moreover, as people around the world prefer to stay apartments, access to individual roofs for each water consumer is impossible. But this also gives the scope of community involvement and shares the cost. Small involvements like managing a rainwater catchment in a building can bring in greater differences in how people think about the water availability. It becomes educational and it brings in awareness which translates in how we look towards the way we use water in our daily lives. People understands solutions of the problems they face better than analysis of their problem, when solutions are within their reach, they implement them. In the evolution of human civilization, it can be studied tha t humans have addressed their needs in small measures which together as brought in changes in they we live out life today. Popularizing rainwater harvesting requires significant push by the governmental institution. Water till date is used as a free good in many parts of the world and people generally do not have the mental set up to invest for water services and thinks that it is the responsibility of the government. Interestingly in some states of USA like Utah, Colorado and Washington, catching rain water was against the law as it reduces the water catchment area for downstream users if water is taken in up stream. Rainwater harvesting was possible in these states if the individual user goes through the process of gaining a state water right. With the growing problems of water scarcity in these regions, governments are slowly taken small yet bold steps in legalizing rain water harvesting by domestic users. Colorado is taking baby steps towards legalizing rainwater collection. Senate Bill 80 was signed by the Governor on 4/22/09 and becomes law on July 1, 2009. It allows rural catchment (Senate Bill 80 ), but still has some hurdles for those that want to move forward (42). The Department of Ecology of the State of Washington, on October 12, 2009 issued an Interpretive Policy Statement clarifying that a water right is not required for rooftop rainwater harvesting (43).  In Utah, the state passed Senate Bill 32 in 2010 which permits rainwater catchment for maximum capacity of no more than 2,500 gallons. There are several other restrictions, but the state engineer must grant the permit if all the conditions are met. In countries, around the world especially India and China which are experiencing rapid industrial developments, rainwater harvesting is also becoming a feasible policy advice. In the 11 Five Year Plan of the Government of India, rainwater harvesting is taken into consideration where sources of groundwater are limited. The plan stress that restoration and building of tanks and other water bodies along with rainwater harvesting structures for recharge and for direct colle ction at community and household levels constitute an attractive option. The Central Government should support the states for tapping the maximum external assistance for this purpose, a part of the assistance could be shared by the Centre as decided in the case of the external assisted Water Bodies Restoration Programme wherein 25% grant of the project cost is passed on to the states (45). The Water law of the Peoples Republic of China was promulgated on Oct.1 2002, This is the law concerning the water resources in a national scope, which pointed out definitely: the national government encourages citizens to use rainwater and tiny salt water for the purposes of harvesting, exploitation and utilization in regions short of water resources.(46) For promoting the development of rainwater utilization, the National Construction Department announced the Chinese ecosystem residence technique valuation manual in 2001 and updated it three times in the following three years, each edition formu lating content about rain water utilization (47).

Description of a participatory action oriented course

Description of a participatory action oriented course PROGRAMME DESCRIPTION OF A PARTICIPATORY ACTION-ORIENTED PAOT COURSE Background We will be conducting a PAOT on work improvement in small enterprises (WISE) course over a one week period. The PAOT course is not a formal lecture, is interactive and participant centred. It is recognised that SMEs contribute significantly to the national economy and that they are huge employers. It is also recognised that however, they do not always have a preventive or safety culture. They do not employ OSH practitioners nor do the employees and employers alike receive formal OSH training. Hence the implementation of the WISE programme as one of the PAOT methodologies, whose aim is to improve working conditions/OSH in the workplace and productivity using simple, effective and affordable techniques that provide benefits to owners/employers, workers and the community. Facilitators will do preliminary work, send invitations to identified participants. Other significant persons will be also invited as the programme will detail. Target group and participants Two facilitators will provide guidance and steer the programme. Invitations will be extended to 30 participants drawn from the local informal small to medium scale enterprises. These will consist of largely the employees or owners who do day to day work and including their supervisors, managers or owners who do supervisory or managerial work. Invited important observers will include two members of the community local leadership, one official from The Ministry of Public Service, Labour and Social Welfare and one representative from the financial sponsor of material: ILO, Zimbabwe Decent Work Programme General and specific objectives General objective: Make participants become aware that investment in low cost permanent simple improvements results in more satisfied and productive workers, more satisfied mangers who, together with the workers, will ensure efficient safe workplaces, leading ultimately to a more successful sustainable business. Specific objectives (for the participants) Learn application of the checklist for the purpose of selecting priority workplace improvements in their SMEs in the local setting for, materials storage and handling, workstation, machine safety, control of dangerous substances, lighting, welfare facilities, industrial facilities and work organisation. Identify and focus on commonly encountered working conditions problems in the above mentioned areas. Point out the local and commonly available simple low cost workplace improvements for the identified problems. Link better working conditions to better productivity. Course outline and contents Dates:29 December 2014 to 2 January 2015 (five days) Venue: Local Community Hall Site Visit: A walking distance from the Hall, an SME that is into furniture making Facilitators:Dr B. Ziki and Mr D. Moyo Participants: 30 (split into 5 groups of six individuals) Course content: Will include the history of PAOT, concept of PAOT, its advantages, the WISE methodology, scope for improvement and emphasis on the tapping of local wisdom for low cost sustainable workplace improvements in the SMEs. Day 1 to 5: Will be guided by the above course content. Activities will include: The opening ceremony, introductions, orientation, workplace visit, checklist exercise, group discussion of checklist results, presentation of group results, technical sessions – one or two a day, implementation of improvements with an action plan, workshop evaluation and closing. Methodology Facilitators will do preliminary work, visiting SMEs, finding and taking pictures of good examples to be used for discussion. A spacious venue where island sitting (round table) arrangement is possible is chosen. It must also be near the visit site On the first day after the opening ceremony, the course outline is presented and soon after there will be a site visit to a chose workplace. The 30 participants are split into five groups of six each. Each group will complete a checklist. A spokesperson is chosen and after discussions, he or she will point out important observations and low cost sustainable suggestions for improvement. No negative criticism is allowed. A different aspect of the WISE programme is tackled each day. Facilitator gives an outline of the topic for discussion and provides good examples and allows participants to discuss on the topic. Last will be implementation of improvements with an action plan, workshop evaluation and closing of the workshop. Timetable Evaluation and follow-up Evaluation of the PAOT course is necessary to assess usefulness, effectiveness and areas that were good and those that need improvement. Participants are given evaluation forms which they fill in and immediately return. Feedback is given after all forms are looked at. Participants also must demonstrate assimilation of information and that they are ready to undertake self help actions to improve workplace conditions in their local settings. They are reminded to do checklists at their workplaces, identify priority areas that need improvement and draw action plans. Participants are encouraged to share experiences with each other and with their or fellow employees, as well as continue to improve even on improvements already made. They are then issued with certificates of attendance. A tentative calendar for follow-up visits by the facilitators at the participant’s workplaces is drawn up. It is recommended that this is done two to three months after the course is conducted to assess the participants self help, low cost, and local practical solutions suggested and implemented to improve working conditions. After a walk through and discussions, positive developments are praised and the discussion must stimulate the participant to remain interested in the PAOT methodology and its ideals. A small, inexpensive and clever (SIC) contest held anytime between two to twelve months is organised to show the group with the best SIC solutions to identified workplace condition/s needing priority attention. An achievement workshop can be planned for six months to a year after the PAOT course. Participants present on their achievements and sustainable improvements and the best presentation can be rewarded. References Learning modules A8.1 and 8.2 Participatory Action-Oriented Training. Ton That Khai, Tsuyoshi Kawakami and Kazutaka Kogi. 2011. An ILO publication. Roles of Participatory Action-oriented Programs in Promoting Safety and Health at Work. Safety and Health at Work. Safe Health Work 2012;3:155-65 An introduction to the WISE Program. Conditions of Work and Employment Programme. An ILO initiative.

Confucius Curry and a Mountain Dew Essay -- Philosophy

As Americans, we ridicule others based on their selection of clothing. We are snobby because of how much money we make or what we hold as an occupation. We chew with our mouths full of macaroni and curse when the soda machine is out of Pepsi. We could use some manners, or maybe just a reintroduction. Confucius thought is constructed on kindness and propriety, as well as holding the morally virtuous to be the ideal person. This philosophy exceedingly expresses value in benevolence, education, and the treatment of other people, but has hidden innuendos that would knock the petals off any flower child. In this reflection paper, I will dabble with how incorporating Confucius thought and practices would help in some areas of American society, but shun the validity of others. Money Over Everything The definition of the American Dream fluctuates from person to person, but can ultimately lead to a broad basis: With hard work and dedication, one can achieve success. And with success, comes happiness. We strive for happiness. In this journey, most come to understand that a college education is the key to becoming knowledgeable, and knowledge is important in becoming successful. Therefore, going to college can lead to success, right? Not a difficult concept to grasp. With the staggering rates of tuition bills and the dwindle of job availability, it would seem the path to success narrows each day. Those with money to cover these costs aren't usually too worried about their debts, seeing as they could squash them like ants. In America, our education is highly valued, but the value of education is incredibly too high. Confucius was not around for colleges and technical institutions, so the subject matter of his teachings did not include di... ...ring 2012 Edition), Edward N. Zalta  (ed.), URL = . 3.) Dawson, Miles. "Ethics of Confucius." . sacred-texts.com, 10/2007. Web. 17 Apr 2012. . 4.) . "Philosophy 312: Oriental Philosophy Main Concepts of Confucianism." Oriental Philosophy. N.p., 09/2000. Web. 17 Apr 2012. . 5.) Richey, Jefferey. "Gender and Sexuality." Religion Library: Confucianism. Patheos, 2012. Web. 17 Apr 2012. . 6.) Fader, Hallie. "The Chinese Legal Tradition." Rule of Law: The Story of Human Rights in World History. ORIAS, 07/2004. Web. April 17 2012. .

Whirlpool Essay -- GCSE Business Marketing Coursework

Whirlpool The world is experiencing a third wave in the economy and many changes are taking place. One of these changes is the growing corporation that decides to go global. Most U.S. companies, both large and small, are rapidly acknowledging the necessity of global marketing. The demand for foreign products in the fast-growing economies of Europe, South America, Asia, and Pacific Rim nations offer one example of the benefits of global thinking. One company that has adapted to this new economy by globalizing has been Whirlpool. In 1989, Whirlpool Corporation embarked on an ambitious global expansion with the objective of becoming the world market leader in home appliances. However, by the mid-1990s, serious problems had emerged in the company’s international operations. Whirlpool’s European profit fell by 50%, lost $70 million in Asia, appliance sales in Brazil plummeted by 25% although the company invested hundreds of millions of dollars to modernize operations. In response to these problems, Whirlpool began to question the problems and called for the global restructuring effort (Johansson, 2000). What went wrong with Whirlpool’s global strategy? Did Whirlpool have enough understanding of how to create a global strategy? Was the appliance industry more suited for regional than global? What are some key success factors in appliance industry that Whirlpool did not have? Was it possible for Whirlpool to identify the problems and reacted earlier? In this case study, I intend to answer all of these questions that are mentioned above regarding to the appliance industry and Whirlpool Global strategies. There are four separate sections in this paper- the first two questions are related to the appliance industry i... ... now reaches markets in more than 140 countries, leading the markets in both North America and Latin America. Whirlpool is now number three in Europe and the largest Western Appliance Company in Asia. References Babyak, Richard J, â€Å"Strategic Imperative,† Appliance Manufacturer, Feb. 1995. C. Quintanilla and J. Carlton, â€Å"Whirlpool Announces Global Restructuring Effort,† Wall Street Journal, 19, Sept. 1997:A3, A6 Janesurak, Joe, â€Å"South American Sales Co.: Linking the Americas, Europe,† Appliance Manufacturer, Feb. 1995 Johansson, Johny, â€Å"Globalization Headaches at Whirlpool† Global Marketing, 2000, p85 Vlasic, Bill and Zachary Schiller. â€Å"Did Whirlpool gone Too Far Too Fast?† Business Week, 24 June 1996. Weiss, David D. and C. Gross, â€Å"Industry Corner: Major Household Appliances in Western Europe,† Business Economics, Vol. 30, Issue 3, July 1995: 67.

Essay --

â€Å"Did you ever hear of the Great Potato Famine?† (Mallon, 2013) The Great Potato Famine was a seven year period of mass starvation in Ireland between 1845 and 1852, which killed between five hundred thousand and one point five million Irish. The Great potato famine killed millions of people from starvation. Additional people died once they migrated from Ireland because they ended up having to live in overcrowded work houses. The cause of the Great Potato Famine was due to an organism called the Phytophthera Infestans (The Free Dictionary, 2013). After reading this paper the reader will know everything there is to know about the Great Potato Famine including the main topics of the Great Potato Famine, which will be covered in detail. The main topics that will be covered are the migration of the potato blight to Ireland and the history of the potato, land consolidation and agriculture laws in Ireland, food exports in Ireland during the time of the famine, the potato dependency to the Irish and the Irish/ English relation at the time of the Great Potato Famine. This paper is being written to prove that the Great Potato Famine was the worst disease to happen to the Irish in the 1800s. The whole potato famine was caused by potato blight, which was stated above. However, the potato blight wasn’t always in Ireland. But, then again, neither was the potato. They both had actually migrated to Ireland. The potato was not native to Ireland. It was believed that Sir Walter Raleigh brought the potato to Ireland from the new world in about 1507. The potato was perfect for the Irish climate. Potatoes grow great in moist climates where it is not too hot. They are also very good at growing in higher elevation like in the mountains... ...Irish thought about the situation. Seeing now that the main topics of the Great Potato Famine have been covered, the reader of this paper should be very well educated on the migration of the Potato Blight to Ireland along with the history of the potato, land consolidation and agriculture laws in Ireland, food exports in Ireland during the time of the famine, the potato dependency to the Irish, and the Irish/ English relationship at the time of the Great Potato Famine. Now that the reader is educated on the topic, did this paper prove the thesis that the Great Potato Famine was the worst disease to happen to the Irish in the 1800s? While the reader is thinking about if this paper proved that the Great Potato Famine was the worst disease to happen to the Irish in the 1800s, also question if this disease could be the worst disease to happen to the Irish of all time?

How Exchange Rate Targeting Can Affect the Balance of Payment

1. Explain how exchange rate targeting by the central bank can affect the balance of payment position of a country (Hint: Consider the current and the capital accounts) Exchange rate targeting is whereby the exchange rate becomes the nominal anchor. The subject of the most favorable monetary regime for small open developing economies is still widely discussed. The advantages and disadvantages of different exchange rate regimes are far too many to be readily captured and used to come up with a specific regime that suits the needs of all. Real exchange rate is perhaps the most popular real target for developing economies.The main advantages of Exchange rate targeting are a)The nominal anchor of an exchange-rate target directly contributes to keeping inflation under control by tying the inflation rate for internationally traded goods to that found in the anchor country. b)The exchange rate can be directly observed i. e, with a fairly narrow band on a certain exchange rate, it is easy to determine whether the intermediate target is fulfilled c)An exchange-rate target provides an automatic rule for the conduct of monetary policy that helps mitigate the time-inconsistency problem. )An exchange-rate target has the advantage of simplicity and clarity, as it is easily understood by the public. The main advantages of Exchange rate targeting are a)Shocks that change interest rates in the anchor country lead to corresponding changes in interest rates in the target country. b)The targeting country is open to speculative attack on its currency whenever the anchor country pursues tight monetary policy. The close linkage of the exchange rate to the general price levels of the economies produce an economy wide importance of policy making since it affects the real income and wealth of those economies.One of the main objectives of the exchange rate based stabilizations is to improve the Balance of Payment (BOP) performance through international competitiveness. Devaluation or dep reciation of a country’s currency is aimed at gaining external competitiveness and BOP improvement in an economy. Exchange rate targeting is likely to impact on a nation’s BOP through various means which can be assessed through looking at the various approaches to BOP. In order for xchange rate targeting to be successful, it is vital that international financial support be availed in the form of an injection of foreign currency to increase the supply and perhaps match the demand for forex in the country. At the same time, the central bank should be building its foreign reserves. When the central bank has adequate reserves, then it can enter the forex market to influence the value of the dollar by buying or selling forex to affect liquidity conditions in the market. As investors gain confidence in the economy, foreign investment starts flowing into the country, increasing supply of forex.Also, as production increases due to a favourable market related exchange rate, exp orts will increase and so will be the inflow of forex. The main reason why the exchange rate continues to overshoot its real value is because, the central bank lacks the capacity to influence its value due to lack of adequate foreign reserves. Consider the elasticity approach to BOP. The elasticity approach emphasizes price changes as a determinant of a nation's balance of payments. The elasticity approach, therefore, considers the responsiveness of imports and exports to a change in the value of a nation’s currency.For example, if import demand is highly elastic, a depreciation of the domestic currency will cause a disproportional decline in the nation’s imports. The Marshall-Lerner condition, states that a currency devaluation will only lead to an improvement in the balance of payments if the sum of demand elasticity for imports and exports is greater than one An upwards shift in the value of a nation's currency relative to others will make a nation's exports less co mpetitive and make imports cheaper and so will tend to correct a current account surplus.The main advantage of manipulating exchange rates is that, if output is traded internationally, changes in exchange rates will have a powerful effect on Aggregate demand. According to Marshal Lerner condition, devaluation currency leads to improvement in the balance of payments if the sum of import and export elasticity’s is greater than one. A weak exchange rate leads to reduction in price of exports and increase the price of the imports. As such, quantity demanded will increase and quantity of imports demanded will decrease. This will increase the current account balance and hence a country remains competitive.

Prison Reform Essay

Reform was a major issue in early 19th century America because it was a time when more middle-classed Americans were able to devote time to social causes and issues that they saw that concerned them. One of these important social movements was prison reform, and how men and women in prison were treated. In the early 1800’s the United States was regarded as having the best penal system in the world. This is why during the early 1800’s Alexis de Tocqueville was sent from France to the United States to study the penal system there. What he was coming here to study was how the system of prisons had been reformed already by Americans. Many individuals, in particular religious advocates who took up the cause of prison reform, had established themselves as the voice of prisoners. They felt that while prisoners needed to do time for their crimes and be punished accordingly, they also had the right to have good conditions within the prisons themselves. These people had a goal of creating prisons that were conducive to not only punishing individuals, but reforming them became an important part of their time in prison. These religious reformers felt that prisoners should be reformed to become good citizens and, if they never left jail, then at least they could be religious individuals.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The successes of these reformers were that they did create a great amount of interest in reform. Many new prisons began to be built that would be more conducive to reforming prisoners. These successes were shortly lived, however, because sadly the prisons did not go up as quickly as the prisoners were going in and therefore old prisons that were not good conditions were still in use, and many prisoners still languished in them, in even worse conditions than before.

Cluster Analysis

Chapter 9 Cluster Analysis Learning Objectives After reading this chapter you should understand: – The basic concepts of cluster analysis. – How basic cluster algorithms work. – How to compute simple clustering results manually. – The different types of clustering procedures. – The SPSS clustering outputs. Keywords Agglomerative and divisive clustering A Chebychev distance A City-block distance A Clustering variables A Dendrogram A Distance matrix A Euclidean distance A Hierarchical and partitioning methods A Icicle diagram A k-means A Matching coef? cients A Pro? ing clusters A Two-step clustering Are there any market segments where Web-enabled mobile telephony is taking off in different ways? To answer this question, Okazaki (2006) applies a twostep cluster analysis by identifying segments of Internet adopters in Japan. The ? ndings suggest that there are four clusters exhibiting distinct attitudes towards Web-enabled mobile telephony adoption. In terestingly, freelance, and highly educated professionals had the most negative perception of mobile Internet adoption, whereas clerical of? ce workers had the most positive perception.Furthermore, housewives and company executives also exhibited a positive attitude toward mobile Internet usage. Marketing managers can now use these results to better target speci? c customer segments via mobile Internet services. Introduction Grouping similar customers and products is a fundamental marketing activity. It is used, prominently, in market segmentation. As companies cannot connect with all their customers, they have to divide markets into groups of consumers, customers, or clients (called segments) with similar needs and wants.Firms can then target each of these segments by positioning themselves in a unique segment (such as Ferrari in the high-end sports car market). While market researchers often form E. Mooi and M. Sarstedt, A Concise Guide to Market Research, DOI 10. 1007/978-3-642-1 2541-6_9, # Springer-Verlag Berlin Heidelberg 2011 237 238 9 Cluster Analysis market segments based on practical grounds, industry practice and wisdom, cluster analysis allows segments to be formed that are based on data that are less dependent on subjectivity.The segmentation of customers is a standard application of cluster analysis, but it can also be used in different, sometimes rather exotic, contexts such as evaluating typical supermarket shopping paths (Larson et al. 2005) or deriving employers’ branding strategies (Moroko and Uncles 2009). Understanding Cluster Analysis Cluster analysis is a convenient method for identifying homogenous groups of objects called clusters. Objects (or cases, observations) in a speci? c cluster share many characteristics, but are very dissimilar to objects not belonging to that cluster.Let’s try to gain a basic understanding of the cluster analysis procedure by looking at a simple example. Imagine that you are interested in segment ing your customer base in order to better target them through, for example, pricing strategies. The ? rst step is to decide on the characteristics that you will use to segment your customers. In other words, you have to decide which clustering variables will be included in the analysis. For example, you may want to segment a market based on customers’ price consciousness (x) and brand loyalty (y).These two variables can be measured on a 7-point scale with higher values denoting a higher degree of price consciousness and brand loyalty. The values of seven respondents are shown in Table 9. 1 and the scatter plot in Fig. 9. 1. The objective of cluster analysis is to identify groups of objects (in this case, customers) that are very similar with regard to their price consciousness and brand loyalty and assign them into clusters. After having decided on the clustering variables (brand loyalty and price consciousness), we need to decide on the clustering procedure to form our group s of objects.This step is crucial for the analysis, as different procedures require different decisions prior to analysis. There is an abundance of different approaches and little guidance on which one to use in practice. We are going to discuss the most popular approaches in market research, as they can be easily computed using SPSS. These approaches are: hierarchical methods, partitioning methods (more precisely, k-means), and two-step clustering, which is largely a combination of the ? rst two methods.Each of these procedures follows a different approach to grouping the most similar objects into a cluster and to determining each object’s cluster membership. In other words, whereas an object in a certain cluster should be as similar as possible to all the other objects in the Table 9. 1 Data Customer x y A 3 7 B 6 7 C 5 6 D 3 5 E 6 5 F 4 3 G 1 2 Understanding Cluster Analysis 7 6 A C D E B 239 Brand loyalty (y) 5 4 3 2 1 0 0 1 2 G F 3 4 5 6 7 Price consciousness (x) Fig. 9. 1 Scatter plot same cluster, it should likewise be as distinct as possible from objects in different clusters. But how do we measure similarity?Some approaches – most notably hierarchical methods – require us to specify how similar or different objects are in order to identify different clusters. Most software packages calculate a measure of (dis)similarity by estimating the distance between pairs of objects. Objects with smaller distances between one another are more similar, whereas objects with larger distances are more dissimilar. An important problem in the application of cluster analysis is the decision regarding how many clusters should be derived from the data. This question is explored in the next step of the analysis.Sometimes, however, we already know the number of segments that have to be derived from the data. For example, if we were asked to ascertain what characteristics distinguish frequent shoppers from infrequent ones, we need to ? nd two different c lusters. However, we do not usually know the exact number of clusters and then we face a trade-off. On the one hand, you want as few clusters as possible to make them easy to understand and actionable. On the other hand, having many clusters allows you to identify more segments and more subtle differences between segments.In an extreme case, you can address each individual separately (called one-to-one marketing) to meet consumers’ varying needs in the best possible way. Examples of such a micro-marketing strategy are Puma’s Mongolian Shoe BBQ (www. mongolianshoebbq. puma. com) and Nike ID (http://nikeid. nike. com), in which customers can fully customize a pair of shoes in a hands-on, tactile, and interactive shoe-making experience. On the other hand, the costs associated with such a strategy may be prohibitively high in many 240 9 Cluster Analysis Decide on the clustering variables Decide on the clustering procedureHierarchical methods Select a measure of similarity or dissimilarity Partitioning methods Two-step clustering Select a measure of similarity or dissimilarity Choose a clustering algorithm Decide on the number of clusters Validate and interpret the cluster solution Fig. 9. 2 Steps in a cluster analysis business contexts. Thus, we have to ensure that the segments are large enough to make the targeted marketing programs pro? table. Consequently, we have to cope with a certain degree of within-cluster heterogeneity, which makes targeted marketing programs less effective.In the ? nal step, we need to interpret the solution by de? ning and labeling the obtained clusters. This can be done by examining the clustering variables’ mean values or by identifying explanatory variables to pro? le the clusters. Ultimately, managers should be able to identify customers in each segment on the basis of easily measurable variables. This ? nal step also requires us to assess the clustering solution’s stability and validity. Figure 9. 2 illu strates the steps associated with a cluster analysis; we will discuss these in more detail in the following sections.Conducting a Cluster Analysis Decide on the Clustering Variables At the beginning of the clustering process, we have to select appropriate variables for clustering. Even though this choice is of utmost importance, it is rarely treated as such and, instead, a mixture of intuition and data availability guide most analyses in marketing practice. However, faulty assumptions may lead to improper market Conducting a Cluster Analysis 241 segments and, consequently, to de? cient marketing strategies. Thus, great care should be taken when selecting the clustering variables. There are several types of clustering variables and these can be classi? d into general (independent of products, services or circumstances) and speci? c (related to both the customer and the product, service and/or particular circumstance), on the one hand, and observable (i. e. , measured directly) and un observable (i. e. , inferred) on the other. Table 9. 2 provides several types and examples of clustering variables. Table 9. 2 Types and examples of clustering variables General Observable (directly Cultural, geographic, demographic, measurable) socio-economic Unobservable Psychographics, values, personality, (inferred) lifestyle Adapted from Wedel and Kamakura (2000)Speci? c User status, usage frequency, store and brand loyalty Bene? ts, perceptions, attitudes, intentions, preferences The types of variables used for cluster analysis provide different segments and, thereby, in? uence segment-targeting strategies. Over the last decades, attention has shifted from more traditional general clustering variables towards product-speci? c unobservable variables. The latter generally provide better guidance for decisions on marketing instruments’ effective speci? cation. It is generally acknowledged that segments identi? ed by means of speci? unobservable variables are usually more h omogenous and their consumers respond consistently to marketing actions (see Wedel and Kamakura 2000). However, consumers in these segments are also frequently hard to identify from variables that are easily measured, such as demographics. Conversely, segments determined by means of generally observable variables usually stand out due to their identi? ability but often lack a unique response structure. 1 Consequently, researchers often combine different variables (e. g. , multiple lifestyle characteristics combined with demographic variables), bene? ing from each ones strengths. In some cases, the choice of clustering variables is apparent from the nature of the task at hand. For example, a managerial problem regarding corporate communications will have a fairly well de? ned set of clustering variables, including contenders such as awareness, attitudes, perceptions, and media habits. However, this is not always the case and researchers have to choose from a set of candidate variable s. Whichever clustering variables are chosen, it is important to select those that provide a clear-cut differentiation between the segments regarding a speci? c managerial objective. More precisely, criterion validity is of special interest; that is, the extent to which the â€Å"independent† clustering variables are associated with 1 2 See Wedel and Kamakura (2000). Tonks (2009) provides a discussion of segment design and the choice of clustering variables in consumer markets. 242 9 Cluster Analysis one or more â€Å"dependent† variables not included in the analysis. Given this relationship, there should be signi? cant differences between the â€Å"dependent† variable(s) across the clusters. These associations may or may not be causal, but it is essential that the clustering variables distinguish the â€Å"dependent† variable(s) signi? antly. Criterion variables usually relate to some aspect of behavior, such as purchase intention or usage frequency. Gen erally, you should avoid using an abundance of clustering variables, as this increases the odds that the variables are no longer dissimilar. If there is a high degree of collinearity between the variables, they are not suf? ciently unique to identify distinct market segments. If highly correlated variables are used for cluster analysis, speci? c aspects covered by these variables will be overrepresented in the clustering solution.In this regard, absolute correlations above 0. 90 are always problematic. For example, if we were to add another variable called brand preference to our analysis, it would virtually cover the same aspect as brand loyalty. Thus, the concept of being attached to a brand would be overrepresented in the analysis because the clustering procedure does not differentiate between the clustering variables in a conceptual sense. Researchers frequently handle this issue by applying cluster analysis to the observations’ factor scores derived from a previously car ried out factor analysis.However, according to Dolnicar and Grâ‚ ¬n u (2009), this factor-cluster segmentation approach can lead to several problems: 1. The data are pre-processed and the clusters are identi? ed on the basis of transformed values, not on the original information, which leads to different results. 2. In factor analysis, the factor solution does not explain a certain amount of variance; thus, information is discarded before segments have been identi? ed or constructed. 3. Eliminating variables with low loadings on all the extracted factors means that, potentially, the most important pieces of information for the identi? ation of niche segments are discarded, making it impossible to ever identify such groups. 4. The interpretations of clusters based on the original variables become questionable given that the segments have been constructed using factor scores. Several studies have shown that the factor-cluster segmentation signi? cantly reduces the success of segmen t recovery. 3 Consequently, you should rather reduce the number of items in the questionnaire’s pre-testing phase, retaining a reasonable number of relevant, non-redundant questions that you believe differentiate the segments well.However, if you have your doubts about the data structure, factorclustering segmentation may still be a better option than discarding items that may conceptually be necessary. Furthermore, we should keep the sample size in mind. First and foremost, this relates to issues of managerial relevance as segments’ sizes need to be substantial to ensure that targeted marketing programs are pro? table. From a statistical perspective, every additional variable requires an over-proportional increase in 3 See the studies by Arabie and Hubert (1994), Sheppard (1996), or Dolnicar and Grâ‚ ¬n (2009). uConducting a Cluster Analysis 243 observations to ensure valid results. Unfortunately, there is no generally accepted rule of thumb regarding minimum sampl e sizes or the relationship between the objects and the number of clustering variables used. In a related methodological context, Formann (1984) recommends a sample size of at least 2m, where m equals the number of clustering variables. This can only provide rough guidance; nevertheless, we should pay attention to the relationship between the objects and clustering variables. It does not, for example, appear logical to cluster ten objects using ten variables.Keep in mind that no matter how many variables are used and no matter how small the sample size, cluster analysis will always render a result! Ultimately, the choice of clustering variables always depends on contextual in? uences such as data availability or resources to acquire additional data. Marketing researchers often overlook the fact that the choice of clustering variables is closely connected to data quality. Only those variables that ensure that high quality data can be used should be included in the analysis. This is v ery important if a segmentation solution has to be managerially useful.Furthermore, data are of high quality if the questions asked have a strong theoretical basis, are not contaminated by respondent fatigue or response styles, are recent, and thus re? ect the current market situation (Dolnicar and Lazarevski 2009). Lastly, the requirements of other managerial functions within the organization often play a major role. Sales and distribution may as well have a major in? uence on the design of market segments. Consequently, we have to be aware that subjectivity and common sense agreement will (and should) always impact the choice of clustering variables.Decide on the Clustering Procedure By choosing a speci? c clustering procedure, we determine how clusters are to be formed. This always involves optimizing some kind of criterion, such as minimizing the within-cluster variance (i. e. , the clustering variables’ overall variance of objects in a speci? c cluster), or maximizing th e distance between the objects or clusters. The procedure could also address the question of how to determine the (dis)similarity between objects in a newly formed cluster and the remaining objects in the dataset.There are many different clustering procedures and also many ways of classifying these (e. g. , overlapping versus non-overlapping, unimodal versus multimodal, exhaustive versus non-exhaustive). 4 A practical distinction is the differentiation between hierarchical and partitioning methods (most notably the k-means procedure), which we are going to discuss in the next sections. We also introduce two-step clustering, which combines the principles of hierarchical and partitioning methods and which has recently gained increasing attention from market research practice.See Wedel and Kamakura (2000), Dolnicar (2003), and Kaufman and Rousseeuw (2005) for a review of clustering techniques. 4 244 9 Cluster Analysis Hierarchical Methods Hierarchical clustering procedures are characte rized by the tree-like structure established in the course of the analysis. Most hierarchical techniques fall into a category called agglomerative clustering. In this category, clusters are consecutively formed from objects. Initially, this type of procedure starts with each object representing an individual cluster.These clusters are then sequentially merged according to their similarity. First, the two most similar clusters (i. e. , those with the smallest distance between them) are merged to form a new cluster at the bottom of the hierarchy. In the next step, another pair of clusters is merged and linked to a higher level of the hierarchy, and so on. This allows a hierarchy of clusters to be established from the bottom up. In Fig. 9. 3 (left-hand side), we show how agglomerative clustering assigns additional objects to clusters as the cluster size increases. Step 5 Step 1 A, B, C, D, EAgglomerative clustering Step 4 Step 2 Divisive clustering A, B C, D, E Step 3 Step 3 A, B C, D E Step 2 Step 4 A, B C D E Step 1 Step 5 A B C D E Fig. 9. 3 Agglomerative and divisive clustering A cluster hierarchy can also be generated top-down. In this divisive clustering, all objects are initially merged into a single cluster, which is then gradually split up. Figure 9. 3 illustrates this concept (right-hand side). As we can see, in both agglomerative and divisive clustering, a cluster on a higher level of the hierarchy always encompasses all clusters from a lower level.This means that if an object is assigned to a certain cluster, there is no possibility of reassigning this object to another cluster. This is an important distinction between these types of clustering and partitioning methods such as k-means, which we will explore in the next section. Divisive procedures are quite rarely used in market research. We therefore concentrate on the agglomerative clustering procedures. There are various types Conducting a Cluster Analysis 245 of agglomerative procedures. However, before we discuss these, we need to de? ne how similarities or dissimilarities are measured between pairs of objects.Select a Measure of Similarity or Dissimilarity There are various measures to express (dis)similarity between pairs of objects. A straightforward way to assess two objects’ proximity is by drawing a straight line between them. For example, when we look at the scatter plot in Fig. 9. 1, we can easily see that the length of the line connecting observations B and C is much shorter than the line connecting B and G. This type of distance is also referred to as Euclidean distance (or straight-line distance) and is the most commonly used type when it comes to analyzing ratio or interval-scaled data. In our example, we have ordinal data, but market researchers usually treat ordinal data as metric data to calculate distance metrics by assuming that the scale steps are equidistant (very much like in factor analysis, which we discussed in Chap. 8). To use a hierarchical c lustering procedure, we need to express these distances mathematically. By taking the data in Table 9. 1 into consideration, we can easily compute the Euclidean distance between customer B and customer C (generally referred to as d(B,C)) with regard to the two variables x and y by using the following formula: q Euclidean ? B; C? ? ? xB A xC ? 2 ? ?yB A yC ? 2 The Euclidean distance is the square root of the sum of the squared differences in the variables’ values. Using the data from Table 9. 1, we obtain the following: q p dEuclidean ? B; C? ? ? 6 A 5? 2 ? ?7 A 6? 2 ? 2 ? 1:414 This distance corresponds to the length of the line that connects objects B and C. In this case, we only used two variables but we can easily add more under the root sign in the formula. However, each additional variable will add a dimension to our research problem (e. . , with six clustering variables, we have to deal with six dimensions), making it impossible to represent the solution graphically. Si milarly, we can compute the distance between customer B and G, which yields the following: q p dEuclidean ? B; G? ? ? 6 A 1? 2 ? ?7 A 2? 2 ? 50 ? 7:071 Likewise, we can compute the distance between all other pairs of objects. All these distances are usually expressed by means of a distance matrix. In this distance matrix, the non-diagonal elements express the distances between pairs of objects 5Note that researchers also often use the squared Euclidean distance. 246 9 Cluster Analysis and zeros on the diagonal (the distance from each object to itself is, of course, 0). In our example, the distance matrix is an 8 A 8 table with the lines and rows representing the objects (i. e. , customers) under consideration (see Table 9. 3). As the distance between objects B and C (in this case 1. 414 units) is the same as between C and B, the distance matrix is symmetrical. Furthermore, since the distance between an object and itself is zero, one need only look at either the lower or upper non-di agonal elements.Table 9. 3 Euclidean distance matrix Objects A B A 0 B 3 0 C 2. 236 1. 414 D 2 3. 606 E 3. 606 2 F 4. 123 4. 472 G 5. 385 7. 071 C D E F G 0 2. 236 1. 414 3. 162 5. 657 0 3 2. 236 3. 606 0 2. 828 5. 831 0 3. 162 0 There are also alternative distance measures: The city-block distance uses the sum of the variables’ absolute differences. This is often called the Manhattan metric as it is akin to the walking distance between two points in a city like New York’s Manhattan district, where the distance equals the number of blocks in the directions North-South and East-West.Using the city-block distance to compute the distance between customers B and C (or C and B) yields the following: dCityAblock ? B; C? ? jxB A xC j ? jyB A yC j ? j6 A 5j ? j7 A 6j ? 2 The resulting distance matrix is in Table 9. 4. Table 9. 4 City-block distance matrix Objects A B A 0 B 3 0 C 3 2 D 2 5 E 5 2 F 5 6 G 7 10 C D E F G 0 3 2 4 8 0 3 3 5 0 4 8 0 4 0 Lastly, when working with metr ic (or ordinal) data, researchers frequently use the Chebychev distance, which is the maximum of the absolute difference in the clustering variables’ values. In respect of customers B and C, this result is: dChebychec ? B; C? max? jxB A xC j; jyB A yC j? ? max? j6 A 5j; j7 A 6j? ? 1 Figure 9. 4 illustrates the interrelation between these three distance measures regarding two objects, C and G, from our example. Conducting a Cluster Analysis 247 C Brand loyalty (y) Euclidean distance City-block distance G Chebychev distance Price consciousness (x) Fig. 9. 4 Distance measures There are other distance measures such as the Angular, Canberra or Mahalanobis distance. In many situations, the latter is desirable as it compensates for collinearity between the clustering variables. However, it is (unfortunately) not menu-accessible in SPSS.In many analysis tasks, the variables under consideration are measured on different scales or levels. This would be the case if we extended our set o f clustering variables by adding another ordinal variable representing the customers’ income measured by means of, for example, 15 categories. Since the absolute variation of the income variable would be much greater than the variation of the remaining two variables (remember, that x and y are measured on 7-point scales), this would clearly distort our analysis results. We can resolve this problem by standardizing the data prior to the analysis.Different standardization methods are available, such as the simple z standardization, which rescales each variable to have a mean of 0 and a standard deviation of 1 (see Chap. 5). In most situations, however, standardization by range (e. g. , to a range of 0 to 1 or A1 to 1) performs better. 6 We recommend standardizing the data in general, even though this procedure can reduce or in? ate the variables’ in? uence on the clustering solution. 6 See Milligan and Cooper (1988). 248 9 Cluster Analysis Another way of (implicitly) sta ndardizing the data is by using the correlation between the objects instead of distance measures.For example, suppose a respondent rated price consciousness 2 and brand loyalty 3. Now suppose a second respondent indicated 5 and 6, whereas a third rated these variables 3 and 3. Euclidean, city-block, and Chebychev distances would indicate that the ? rst respondent is more similar to the third than to the second. Nevertheless, one could convincingly argue that the ? rst respondent’s ratings are more similar to the second’s, as both rate brand loyalty higher than price consciousness. This can be accounted for by computing the correlation between two vectors of values as a measure of similarity (i. . , high correlation coef? cients indicate a high degree of similarity). Consequently, similarity is no longer de? ned by means of the difference between the answer categories but by means of the similarity of the answering pro? les. Using correlation is also a way of standardiz ing the data implicitly. Whether you use correlation or one of the distance measures depends on whether you think the relative magnitude of the variables within an object (which favors correlation) matters more than the relative magnitude of each variable across objects (which favors distance).However, it is generally recommended that one uses correlations when applying clustering procedures that are susceptible to outliers, such as complete linkage, average linkage or centroid (see next section). Whereas the distance measures presented thus far can be used for metrically and – in general – ordinally scaled data, applying them to nominal or binary data is meaningless. In this type of analysis, you should rather select a similarity measure expressing the degree to which variables’ values share the same category. These socalled matching coef? ients can take different forms but rely on the same allocation scheme shown in Table 9. 5. Table 9. 5 Allocation scheme for matching coef? cients Number of variables with category 1 a c Object 1 Number of variables with category 2 b d Object 2 Number of variables with category 1 Number of variables with category 2 Based on the allocation scheme in Table 9. 5, we can compute different matching coef? cients, such as the simple matching coef? cient (SM): SM ? a? d a? b? c? d This coef? cient is useful when both positive and negative values carry an equal degree of information.For example, gender is a symmetrical attribute because the number of males and females provides an equal degree of information. Conducting a Cluster Analysis 249 Let’s take a look at an example by assuming that we have a dataset with three binary variables: gender (male ? 1, female ? 2), customer (customer ? 1, noncustomer ? 2), and disposable income (low ? 1, high ? 2). The ? rst object is a male non-customer with a high disposable income, whereas the second object is a female non-customer with a high disposable income. Accord ing to the scheme in Table 9. , a ? b ? 0, c ? 1 and d ? 2, with the simple matching coef? cient taking a value of 0. 667. Two other types of matching coef? cients, which do not equate the joint absence of a characteristic with similarity and may, therefore, be of more value in segmentation studies, are the Jaccard (JC) and the Russel and Rao (RR) coef? cients. They are de? ned as follows: a JC ? a? b? c a RR ? a? b? c? d These matching coef? cients are – just like the distance measures – used to determine a cluster solution. There are many other matching coef? ients such as Yule’s Q, Kulczynski or Ochiai, but since most applications of cluster analysis rely on metric or ordinal data, we will not discuss these in greater detail. 7 For nominal variables with more than two categories, you should always convert the categorical variable into a set of binary variables in order to use matching coef? cients. When you have ordinal data, you should always use distance me asures such as Euclidean distance. Even though using matching coef? cients would be feasible and – from a strictly statistical standpoint – even more appropriate, you would disregard variable information in the sequence of the categories.In the end, a respondent who indicates that he or she is very loyal to a brand is going to be closer to someone who is somewhat loyal than a respondent who is not loyal at all. Furthermore, distance measures best represent the concept of proximity, which is fundamental to cluster analysis. Most datasets contain variables that are measured on multiple scales. For example, a market research questionnaire may ask about the respondent’s income, product ratings, and last brand purchased. Thus, we have to consider variables measured on a ratio, ordinal, and nominal scale. How can we simultaneously incorporate these variables into one analysis?Unfortunately, this problem cannot be easily resolved and, in fact, many market researchers s imply ignore the scale level. Instead, they use one of the distance measures discussed in the context of metric (and ordinal) data. Even though this approach may slightly change the results when compared to those using matching coef? cients, it should not be rejected. Cluster analysis is mostly an exploratory technique whose results provide a rough guidance for managerial decisions. Despite this, there are several procedures that allow a simultaneous integration of these variables into one analysis. 7See Wedel and Kamakura (2000) for more information on alternative matching coef? cients. 250 9 Cluster Analysis First, we could compute distinct distance matrices for each group of variables; that is, one distance matrix based on, for example, ordinally scaled variables and another based on nominal variables. Afterwards, we can simply compute the weighted arithmetic mean of the distances and use this average distance matrix as the input for the cluster analysis. However, the weights hav e to be determined a priori and improper weights may result in a biased treatment of different variable types.Furthermore, the computation and handling of distance matrices are not trivial. Using the SPSS syntax, one has to manually add the MATRIX subcommand, which exports the initial distance matrix into a new data ? le. Go to the 8 Web Appendix (! Chap. 5) to learn how to modify the SPSS syntax accordingly. Second, we could dichotomize all variables and apply the matching coef? cients discussed above. In the case of metric variables, this would involve specifying categories (e. g. , low, medium, and high income) and converting these into sets of binary variables. In most cases, however, the speci? ation of categories would be rather arbitrary and, as mentioned earlier, this procedure could lead to a severe loss of information. In the light of these issues, you should avoid combining metric and nominal variables in a single cluster analysis, but if this is not feasible, the two-ste p clustering procedure provides a valuable alternative, which we will discuss later. Lastly, the choice of the (dis)similarity measure is not extremely critical to recovering the underlying cluster structure. In this regard, the choice of the clustering algorithm is far more important.We therefore deal with this aspect in the following section. Select a Clustering Algorithm After having chosen the distance or similarity measure, we need to decide which clustering algorithm to apply. There are several agglomerative procedures and they can be distinguished by the way they de? ne the distance from a newly formed cluster to a certain object, or to other clusters in the solution. The most popular agglomerative clustering procedures include the following: l l l l Single linkage (nearest neighbor): The distance between two clusters corresponds to the shortest distance between any two members in the two clusters.Complete linkage (furthest neighbor): The oppositional approach to single linka ge assumes that the distance between two clusters is based on the longest distance between any two members in the two clusters. Average linkage: The distance between two clusters is de? ned as the average distance between all pairs of the two clusters’ members. Centroid: In this approach, the geometric center (centroid) of each cluster is computed ? rst. The distance between the two clusters equals the distance between the two centroids. Figures 9. 5–9. 8 illustrate these linkage procedures for two randomly framed clusters.Conducting a Cluster Analysis Fig. 9. 5 Single linkage 251 Fig. 9. 6 Complete linkage Fig. 9. 7 Average linkage Fig. 9. 8 Centroid 252 9 Cluster Analysis Each of these linkage algorithms can yield totally different results when used on the same dataset, as each has its speci? c properties. As the single linkage algorithm is based on minimum distances, it tends to form one large cluster with the other clusters containing only one or few objects each. We can make use of this â€Å"chaining effect† to detect outliers, as these will be merged with the remaining objects – usually at very large distances – in the last steps of the analysis.Generally, single linkage is considered the most versatile algorithm. Conversely, the complete linkage method is strongly affected by outliers, as it is based on maximum distances. Clusters produced by this method are likely to be rather compact and tightly clustered. The average linkage and centroid algorithms tend to produce clusters with rather low within-cluster variance and similar sizes. However, both procedures are affected by outliers, though not as much as complete linkage. Another commonly used approach in hierarchical clustering is Ward’s method. This approach does not combine the two most similar objects successively.Instead, those objects whose merger increases the overall within-cluster variance to the smallest possible degree, are combined. If you expect s omewhat equally sized clusters and the dataset does not include outliers, you should always use Ward’s method. To better understand how a clustering algorithm works, let’s manually examine some of the single linkage procedure’s calculation steps. We start off by looking at the initial (Euclidean) distance matrix in Table 9. 3. In the very ? rst step, the two objects exhibiting the smallest distance in the matrix are merged.Note that we always merge those objects with the smallest distance, regardless of the clustering procedure (e. g. , single or complete linkage). As we can see, this happens to two pairs of objects, namely B and C (d(B, C) ? 1. 414), as well as C and E (d(C, E) ? 1. 414). In the next step, we will see that it does not make any difference whether we ? rst merge the one or the other, so let’s proceed by forming a new cluster, using objects B and C. Having made this decision, we then form a new distance matrix by considering the single link age decision rule as discussed above.According to this rule, the distance from, for example, object A to the newly formed cluster is the minimum of d(A, B) and d(A, C). As d(A, C) is smaller than d(A, B), the distance from A to the newly formed cluster is equal to d(A, C); that is, 2. 236. We also compute the distances from cluster [B,C] (clusters are indicated by means of squared brackets) to all other objects (i. e. D, E, F, G) and simply copy the remaining distances – such as d(E, F) – that the previous clustering has not affected. This yields the distance matrix shown in Table 9. 6.Continuing the clustering procedure, we simply repeat the last step by merging the objects in the new distance matrix that exhibit the smallest distance (in this case, the newly formed cluster [B, C] and object E) and calculate the distance from this cluster to all other objects. The result of this step is described in Table 9. 7. Try to calculate the remaining steps yourself and compare your solution with the distance matrices in the following Tables 9. 8–9. 10. Conducting a Cluster Analysis Table 9. 6 Distance matrix after ? rst clustering step (single linkage) Objects A B, C D E F G A 0 B, C 2. 36 0 D 2 2. 236 0 E 3. 606 1. 414 3 0 F 4. 123 3. 162 2. 236 2. 828 0 G 5. 385 5. 657 3. 606 5. 831 3. 162 0 253 Table 9. 7 Distance matrix after second clustering step (single linkage) Objects A B, C, E D F G A 0 B, C, E 2. 236 0 D 2 2. 236 0 F 4. 123 2. 828 2. 236 0 G 5. 385 5. 657 3. 606 3. 162 0 Table 9. 8 Distance matrix after third clustering step (single linkage) Objects A, D B, C, E F G A, D 0 B, C, E 2. 236 0 F 2. 236 2. 828 0 G 3. 606 5. 657 3. 162 0 Table 9. 9 Distance matrix after fourth clustering step (single linkage) Objects A, B, C, D, E F G A, B, C, D, E 0 F 2. 236 0 G 3. 06 3. 162 0 Table 9. 10 Distance matrix after ? fth clustering step (single linkage) Objects A, B, C, D, E, F G A, B, C, D, E, F 0 G 3. 162 0 By following the single linkage proce dure, the last steps involve the merger of cluster [A,B,C,D,E,F] and object G at a distance of 3. 162. Do you get the same results? As you can see, conducting a basic cluster analysis manually is not that hard at all – not if there are only a few objects in the dataset. A common way to visualize the cluster analysis’s progress is by drawing a dendrogram, which displays the distance level at which there was a ombination of objects and clusters (Fig. 9. 9). We read the dendrogram from left to right to see at which distance objects have been combined. For example, according to our calculations above, objects B, C, and E are combined at a distance level of 1. 414. 254 B C E A D F G 9 Cluster Analysis 0 1 2 Distance 3 Fig. 9. 9 Dendrogram Decide on the Number of Clusters An important question we haven’t yet addressed is how to decide on the number of clusters to retain from the data. Unfortunately, hierarchical methods provide only very limited guidance for making th is decision.The only meaningful indicator relates to the distances at which the objects are combined. Similar to factor analysis’s scree plot, we can seek a solution in which an additional combination of clusters or objects would occur at a greatly increased distance. This raises the issue of what a great distance is, of course. One potential way to solve this problem is to plot the number of clusters on the x-axis (starting with the one-cluster solution at the very left) against the distance at which objects or clusters are combined on the y-axis.Using this plot, we then search for the distinctive break (elbow). SPSS does not produce this plot automatically – you have to use the distances provided by SPSS to draw a line chart by using a common spreadsheet program such as Microsoft Excel. Alternatively, we can make use of the dendrogram which essentially carries the same information. SPSS provides a dendrogram; however, this differs slightly from the one presented in F ig. 9. 9. Speci? cally, SPSS rescales the distances to a range of 0–25; that is, the last merging step to a one-cluster solution takes place at a (rescaled) distance of 25.The rescaling often lengthens the merging steps, thus making breaks occurring at a greatly increased distance level more obvious. Despite this, this distance-based decision rule does not work very well in all cases. It is often dif? cult to identify where the break actually occurs. This is also the case in our example above. By looking at the dendrogram, we could justify a two-cluster solution ([A,B,C,D,E,F] and [G]), as well as a ? ve-cluster solution ([B,C,E], [A], [D], [F], [G]). Conducting a Cluster Analysis 255 Research has suggested several other procedures for determining the number of clusters in a dataset.Most notably, the variance ratio criterion (VRC) by Calinski and Harabasz (1974) has proven to work well in many situations. 8 For a solution with n objects and k segments, the criterion is given by: VRCk ? ?SSB =? k A 1 =? SSW =? n A k ; where SSB is the sum of the squares between the segments and SSW is the sum of the squares within the segments. The criterion should seem familiar, as this is nothing but the F-value of a one-way ANOVA, with k representing the factor levels. Consequently, the VRC can easily be computed using SPSS, even though it is not readily available in the clustering procedures’ outputs.To ? nally determine the appropriate number of segments, we compute ok for each segment solution as follows: ok ? ?VRCk? 1 A VRCk ? A ? VRCk A VRCkA1 ? : In the next step, we choose the number of segments k that minimizes the value in ok. Owing to the term VRCkA1, the minimum number of clusters that can be selected is three, which is a clear disadvantage of the criterion, thus limiting its application in practice. Overall, the data can often only provide rough guidance regarding the number of clusters you should select; consequently, you should rather revert to pr actical considerations.Occasionally, you might have a priori knowledge, or a theory on which you can base your choice. However, ? rst and foremost, you should ensure that your results are interpretable and meaningful. Not only must the number of clusters be small enough to ensure manageability, but each segment should also be large enough to warrant strategic attention. Partitioning Methods: k-means Another important group of clustering procedures are partitioning methods. As with hierarchical clustering, there is a wide array of different algorithms; of these, the k-means procedure is the most important one for market research. The k-means algorithm follows an entirely different concept than the hierarchical methods discussed before. This algorithm is not based on distance measures such as Euclidean distance or city-block distance, but uses the within-cluster variation as a Milligan and Cooper (1985) compare various criteria. Note that the k-means algorithm is one of the simplest n on-hierarchical clustering methods. Several extensions, such as k-medoids (Kaufman and Rousseeuw 2005) have been proposed to handle problematic aspects of the procedure. More advanced methods include ? ite mixture models (McLachlan and Peel 2000), neural networks (Bishop 2006), and self-organizing maps (Kohonen 1982). Andrews and Currim (2003) discuss the validity of some of these approaches. 9 8 256 9 Cluster Analysis measure to form homogenous clusters. Speci? cally, the procedure aims at segmenting the data in such a way that the within-cluster variation is minimized. Consequently, we do not need to decide on a distance measure in the ? rst step of the analysis. The clustering process starts by randomly assigning objects to a number of clusters. 0 The objects are then successively reassigned to other clusters to minimize the within-cluster variation, which is basically the (squared) distance from each observation to the center of the associated cluster. If the reallocation of an object to another cluster decreases the within-cluster variation, this object is reassigned to that cluster. With the hierarchical methods, an object remains in a cluster once it is assigned to it, but with k-means, cluster af? liations can change in the course of the clustering process. Consequently, k-means does not build a hierarchy as described before (Fig. . 3), which is why the approach is also frequently labeled as non-hierarchical. For a better understanding of the approach, let’s take a look at how it works in practice. Figs. 9. 10–9. 13 illustrate the k-means clustering process. Prior to analysis, we have to decide on the number of clusters. Our client could, for example, tell us how many segments are needed, or we may know from previous research what to look for. Based on this information, the algorithm randomly selects a center for each cluster (step 1). In our example, two cluster centers are randomly initiated, which CC1 (? st cluster) and CC2 (second clu ster) in Fig. 9. 10 A CC1 C B D E Brand loyalty (y) CC2 F G Price consciousness (x) Fig. 9. 10 k-means procedure (step 1) 10 Note this holds for the algorithms original design. SPSS does not choose centers randomly. Conducting a Cluster Analysis A CC1 C B 257 D E Brand loyalty (y) CC2 F G Price consciousness (x) Fig. 9. 11 k-means procedure (step 2) A CC1 CC1? C B Brand loyalty (y) D E CC2 CC2? F G Price consciousness (x) Fig. 9. 12 k-means procedure (step 3) 258 A CC1? 9 Cluster Analysis B C Brand loyalty (y) D E CC2? F G Price consciousness (x) Fig. 9. 13 k-means procedure (step 4) epresent. 11 After this (step 2), Euclidean distances are computed from the cluster centers to every single object. Each object is then assigned to the cluster center with the shortest distance to it. In our example (Fig. 9. 11), objects A, B, and C are assigned to the ? rst cluster, whereas objects D, E, F, and G are assigned to the second. We now have our initial partitioning of the objects into two c lusters. Based on this initial partition, each cluster’s geometric center (i. e. , its centroid) is computed (third step). This is done by computing the mean values of the objects contained in the cluster (e. . , A, B, C in the ? rst cluster) regarding each of the variables (price consciousness and brand loyalty). As we can see in Fig. 9. 12, both clusters’ centers now shift into new positions (CC1’ for the ? rst and CC2’ for the second cluster). In the fourth step, the distances from each object to the newly located cluster centers are computed and objects are again assigned to a certain cluster on the basis of their minimum distance to other cluster centers (CC1’ and CC2’). Since the cluster centers’ position changed with respect to the initial situation in the ? st step, this could lead to a different cluster solution. This is also true of our example, as object E is now – unlike in the initial partition – closer to t he ? rst cluster center (CC1’) than to the second (CC2’). Consequently, this object is now assigned to the ? rst cluster (Fig. 9. 13). The k-means procedure now repeats the third step and re-computes the cluster centers of the newly formed clusters, and so on. In other 11 Conversely, SPSS always sets one observation as the cluster center instead of picking some random point in the dataset. Conducting a Cluster Analysis 59 words, steps 3 and 4 are repeated until a predetermined number of iterations are reached, or convergence is achieved (i. e. , there is no change in the cluster af? liations). Generally, k-means is superior to hierarchical methods as it is less affected by outliers and the presence of irrelevant clustering variables. Furthermore, k-means can be applied to very large datasets, as the procedure is less computationally demanding than hierarchical methods. In fact, we suggest de? nitely using k-means for sample sizes above 500, especially if many clusterin g variables are used.From a strictly statistical viewpoint, k-means should only be used on interval or ratioscaled data as the procedure relies on Euclidean distances. However, the procedure is routinely used on ordinal data as well, even though there might be some distortions. One problem associated with the application of k-means relates to the fact that the researcher has to pre-specify the number of clusters to retain from the data. This makes k-means less attractive to some and still hinders its routine application in practice. However, the VRC discussed above can likewise be used for k-means clustering an application of this index can be found in the 8 Web Appendix ! Chap. 9). Another workaround that many market researchers routinely use is to apply a hierarchical procedure to determine the number of clusters and k-means afterwards. 12 This also enables the user to ? nd starting values for the initial cluster centers to handle a second problem, which relates to the procedureâ €™s sensitivity to the initial classi? cation (we will follow this approach in the example application). Two-Step Clustering We have already discussed the issue of analyzing mixed variables measured on different scale levels in this chapter.The two-step cluster analysis developed by Chiu et al. (2001) has been speci? cally designed to handle this problem. Like k-means, the procedure can also effectively cope with very large datasets. The name two-step clustering is already an indication that the algorithm is based on a two-stage approach: In the ? rst stage, the algorithm undertakes a procedure that is very similar to the k-means algorithm. Based on these results, the two-step procedure conducts a modi? ed hierarchical agglomerative clustering procedure that combines the objects sequentially to form homogenous clusters.This is done by building a so-called cluster feature tree whose â€Å"leaves† represent distinct objects in the dataset. The procedure can handle categoric al and continuous variables simultaneously and offers the user the ? exibility to specify the cluster numbers as well as the maximum number of clusters, or to allow the technique to automatically choose the number of clusters on the basis of statistical evaluation criteria. Likewise, the procedure guides the decision of how many clusters to retain from the data by calculating measures-of-? t such as Akaike’s Information Criterion (AIC) or Bayes 2 See Punji and Stewart (1983) for additional information on this sequential approach. 260 9 Cluster Analysis Information Criterion (BIC). Furthermore, the procedure indicates each variable’s importance for the construction of a speci? c cluster. These desirable features make the somewhat less popular two-step clustering a viable alternative to the traditional methods. You can ? nd a more detailed discussion of the two-step clustering procedure in the 8 Web Appendix (! Chap. 9), but we will also apply this method in the subseque nt example.Validate and Interpret the Cluster Solution Before interpreting the cluster solution, we have to assess the solution’s stability and validity. Stability is evaluated by using different clustering procedures on the same data and testing whether these yield the same results. In hierarchical clustering, you can likewise use different distance measures. However, please note that it is common for results to change even when your solution is adequate. How much variation you should allow before questioning the stability of your solution is a matter of taste.Another common approach is to split the dataset into two halves and to thereafter analyze the two subsets separately using the same parameter settings. You then compare the two solutions’ cluster centroids. If these do not differ signi? cantly, you can presume that the overall solution has a high degree of stability. When using hierarchical clustering, it is also worthwhile changing the order of the objects in y our dataset and re-running the analysis to check the results’ stability. The results should not, of course, depend on the order of the dataset. If they do, you should try to ascertain if any obvious outliers may in? ence the results of the change in order. Assessing the solution’s reliability is closely related to the above, as reliability refers to the degree to which the solution is stable over time. If segments quickly change their composition, or its members their behavior, targeting strategies are likely not to succeed. Therefore, a certain degree of stability is necessary to ensure that marketing strategies can be implemented and produce adequate results. This can be evaluated by critically revisiting and replicating the clustering results at a later point in time. To validate the clustering solution, we need to assess its criterion validity.In research, we could focus on criterion variables that have a theoretically based relationship with the clustering variabl es, but were not included in the analysis. In market research, criterion variables usually relate to managerial outcomes such as the sales per person, or satisfaction. If these criterion variables differ signi? cantly, we can conclude that the clusters are distinct groups with criterion validity. To judge validity, you should also assess face validity and, if possible, expert validity. While we primarily consider criterion validity when choosing clustering variables, as well as in this ? al step of the analysis procedure, the assessment of face validity is a process rather than a single event. The key to successful segmentation is to critically revisit the results of different cluster analysis set-ups (e. g. , by using Conducting a Cluster Analysis 261 different algorithms on the same data) in terms of managerial relevance. This underlines the exploratory character of the method. The following criteria will help you make an evaluation choice for a clustering solution (Dibb 1999; Ton ks 2009; Kotler and Keller 2009). l l l l l l l l l l Substantial: The segments are large and pro? able enough to serve. Accessible: The segments can be effectively reached and served, which requires them to be characterized by means of observable variables. Differentiable: The segments can be distinguished conceptually and respond differently to different marketing-mix elements and programs. Actionable: Effective programs can be formulated to attract and serve the segments. Stable: Only segments that are stable over time can provide the necessary grounds for a successful marketing strategy. Parsimonious: To be managerially meaningful, only a small set of substantial clusters should be identi? ed.Familiar: To ensure management acceptance, the segments composition should be comprehensible. Relevant: Segments should be relevant in respect of the company’s competencies and objectives. Compactness: Segments exhibit a high degree of within-segment homogeneity and between-segment h eterogeneity. Compatibility: Segmentation results meet other managerial functions’ requirements. The ? nal step of any cluster analysis is the interpretation of the clusters. Interpreting clusters always involves examining the cluster centroids, which are the clustering variables’ average values of all objects in a certain cluster.This step is of the utmost importance, as the analysis sheds light on whether the segments are conceptually distinguishable. Only if certain clusters exhibit signi? cantly different means in these variables are they distinguishable – from a data perspective, at least. This can easily be ascertained by comparing the clusters with independent t-tests samples or ANOVA (see Chap. 6). By using this information, we can also try to come up with a meaningful name or label for each cluster; that is, one which adequately re? ects the objects in the cluster.This is usually a very challenging task. Furthermore, clustering variables are frequently unobservable, which poses another problem. How can we decide to which segment a new object should be assigned if its unobservable characteristics, such as personality traits, personal values or lifestyles, are unknown? We could obviously try to survey these attributes and make a decision based on the clustering variables. However, this will not be feasible in most situations and researchers therefore try to identify observable variables that best mirror the partition of the objects.If it is possible to identify, for example, demographic variables leading to a very similar partition as that obtained through the segmentation, then it is easy to assign a new object to a certain segment on the basis of these demographic 262 9 Cluster Analysis characteristics. These variables can then also be used to characterize speci? c segments, an action commonly called pro? ling. For example, imagine that we used a set of items to assess the respondents’ values and learned that a certain segm ent comprises respondents who appreciate self-ful? lment, enjoyment of life, and a sense of accomplishment, whereas this is not the case in another segment. If we were able to identify explanatory variables such as gender or age, which adequately distinguish these segments, then we could partition a new person based on the modalities of these observable variables whose traits may still be unknown. Table 9. 11 summarizes the steps involved in a hierarchical and k-means clustering. While companies often develop their own market segments, they frequently use standardized segments, which are based on established buying trends, habits, and customers’ needs and have been speci? ally designed for use by many products in mature markets. One of the most popular approaches is the PRIZM lifestyle segmentation system developed by Claritas Inc. , a leading market research company. PRIZM de? nes every US household in terms of 66 demographically and behaviorally distinct segments to help ma rketers discern those consumers’ likes, dislikes, lifestyles, and purchase behaviors. Visit the Claritas website and ? ip through the various segment pro? les. By entering a 5-digit US ZIP code, you can also ? nd a speci? c neighborhood’s top ? ve lifestyle groups.One example of a segment is â€Å"Gray Power,† containing middle-class, homeowning suburbanites who are aging in place rather than moving to retirement communities. Gray Power re? ects this trend, a segment of older, midscale singles and couples who live in quiet comfort. http://www. claritas. com/MyBestSegments/Default. jsp We also introduce steps related to two-step clustering which we will further introduce in the subsequent example. Conducting a Cluster Analysis 263 Table 9. 11 Steps involved in carrying out a factor analysis in SPSS Theory Action Research problem Identi? ation of homogenous groups of objects in a population Select clustering variables that should be Select relevant variables that potentially exhibit used to form segments high degrees of criterion validity with regard to a speci? c managerial objective. Requirements Suf? cient sample size Make sure that the relationship between objects and clustering variables is reasonable (rough guideline: number of observations should be at least 2m, where m is the number of clustering variables). Ensure that the sample size is large enough to guarantee substantial segments. Low levels of collinearity among the variables ?Analyze ? Correlate ? Bivariate Eliminate or replace highly correlated variables (correlation coef? cients > 0. 90). Speci? cation Choose the clustering procedure If there is a limited number of objects in your dataset or you do not know the number of clusters: ? Analyze ? Classify ? Hierarchical Cluster If there are many observations (> 500) in your dataset and you have a priori knowledge regarding the number of clusters: ? Analyze ? Classify ? K-Means Cluster If there are many observations in your datas et and the clustering variables are measured on different scale levels: ? Analyze ? Classify ?Two-Step Cluster Select a measure of similarity or dissimilarity Hierarchical methods: (only hierarchical and two-step clustering) ? Analyze ? Classify ? Hierarchical Cluster ? Method ? Measure Depending on the scale level, select the measure; convert variables with multiple categories into a set of binary variables and use matching coef? cients; standardize variables if necessary (on a range of 0 to 1 or A1 to 1). Two-step clustering: ? Analyze ? Classify ? Two-Step Cluster ? Distance Measure Use Euclidean distances when all variables are continuous; for mixed variables, use log-likelihood. ? Analyze ? Classify ?Hierarchical Cluster ? Choose clustering algorithm Method ? Cluster Method (only hierarchical clustering) Use Ward’s method if equally sized clusters are expected and no outliers are present. Preferably use single linkage, also to detect outliers. Decide on the number of clu sters Hierarchical clustering: Examine the dendrogram: ? Analyze ? Classify ? Hierarchical Cluster ? Plots ? Dendrogram (continued) 264 Table 9. 11 (continued) Theory 9 Cluster Analysis Action Draw a scree plot (e. g. , using Microsoft Excel) based on the coef? cients in the agglomeration schedule. Compute the VRC using the ANOVA procedure: ? Analyze ?Compare Means ? One-Way ANOVA Move the cluster membership variable in the Factor box and the clustering variables in the Dependent List box. Compute VRC for each segment solution and compare values. k-means: Run a hierarchical cluster analysis and decide on the number of segments based on a dendrogram or scree plot; use this information to run k-means with k clusters. Compute the VRC using the ANOVA procedure: ? Analyze ? Classify ? K-Means Cluster ? Options ? ANOVA table; Compute VRC for each segment solution and compare values. Two-step clustering: Specify the maximum number of clusters: ? Analyze ? Classify ? Two-Step Cluster ?Numbe r of Clusters Run separate analyses using AIC and, alternatively, BIC as clustering criterion: ? Analyze ? Classify ? Two-Step Cluster ? Clustering Criterion Examine the auto-clustering output. Re-run the analysis using different clustering procedures, algorithms or distance measures. Split the datasets into two halves and compute the clustering variables’ centroids; compare ce