Genetically Modified Foods †Friend or Foe Essay

In 1998 the first genetically modified (GM) food was approved for public consumption. Since then GM foods have become part of the world’s food supply and are produced in several countries. While horror stories in the 90s promised dire consequences for introducing GM foods to the populace most of those problems have failed to arise as promised. Some scientists say that GM foods are completely safe and the proof might be that we are all still here to debate the point. GM foods are not labeled in the United States and chances are that most Americans have already eaten GM foods. Still, how much is known about the GM foods that Americans are unknowingly feeding to their families? Is managing to survive the experiment the only yardstick we should use to measure risk? Genetically modified foods might be dangerous and more testing is desperately needed to avoid health hazards. While the FDA and their scientists say that GM foods are safe, the U. S. government is already aware that there have been problems with GM foods. Even before genetic modification became the industry it is today there were problems linked with hormonally enhanced foods. Small changes in our food supply can cause large results. Of course, the problems are just a small percentage of the whole. In 1998 Harvard Medical School released a study (as cited by Larsen, 1998, Â ¶ 1) showing evidence that a product known as Recombinant Bovine Somatotropin (rBST) increased the chances of humans developing cancer. Bovine Somatotropin is a hormone produced by cattle which is also known as Bovine Growth Hormone. The Recombinant status means it was synthetically produced using recombinant DNA technology. The synthetic chemical is injected into cows to stimulate milk production. Milk cows in the United States and England were once treated with this chemical but England banned its use after the link between rBST and cancer was shown (Larsen, 1998). The Federal Drug Administration (FDA) says that the chemical is safe and not only approves of its use but does not allow labeling of the products that come from the cows that are injected with rBST (Epstein, 1996; FDA Consumer, 1999). Of secondary concern when dealing with rBST injected cattle is the worry of infection. The more milk a cow produces the more likely it becomes that she will suffer from udder inflammation. This inflammation is regularly treated with antibiotics to which the cows are developing a resistance to over time. Not only can this resistance be passed along to the humans who drink the milk but humans can also have allergic reactions to the antibiotic traces left in the milk (Epstein, 1996). In 1989 approximately 5000 individuals became suddenly ill. This illness was later traced back to a health food supplement that had been created using GM enhanced bacteria. Of those 5000 people, 37 later died and 1500 were permanently disabled. The toxin which caused the problem was present in only 0. 01% of the product. One percent is below the level that would have caused concern or a halt of production. In 1996 a company created a B2 vitamin to be sold with GM bacteria and the FDA approved it as long as any contaminants were not found at greater than 0. 01%. With that standard in place the 1989 toxin problem would not be detected even if it happened today (Antoniou, 1996, Â ¶ 5-6). While the FDA does set the standards there is very little actual oversight of the biotech companies. As of 1992 (as cited by Whitman, 2000) the FDA policy is that biotech companies may voluntarily ask for a consultation with the FDA. The consultation is not compulsory and even if used the company does not have to follow the FDA recommendations. The United States Department of Agriculture (USDA) has the power to quarantine crops that are a danger but the biotech companies do not require a permit from the USDA as long as their product meets a short set of standards created to ensure the safety of the crop itself. To put it simply, the FDA is responsible for food safety and the USDA is responsible for plant and crop safety (Whitman, 2000, Â ¶ 32-35). The FDA sets the requirements that GM foods must meet to be declared safe. The main requirement for safety is that the modified food being judged is substantially equivalent to the original non-modified food (Physicians and Scientists for the Responsible Application of Science and Technology [PSRAST], 2006). For example, if a biomed modified potato is found to still be substantially equivalent to a regular potato then no further testing is needed. The theory is that being substantially equivalent gives them the same level of safety. For a food to be judged substantially equivalent it must be similar on several points, which are chosen by the manufacturers themselves. There must be no overt difference between the GM food and the non-GM food in regard to taste, appearance, and several points selected by the manufacturer in the areas of chemical composition and nutritional composition. The only other test required is to do an analysis looking for allergen markers. If the computers find no reason to believe that the product can cause allergies then the product is approved. Human testing is never required (PSRAST, 2006, Â ¶ 20-25). If genetically altering foods is an inherently safe procedure then the above tests are a perfectly logical way to test GM foods. If the foods are as unsafe as some claim then it is a dangerous policy for the biotech companies and the U. S. government to decide upon. In 1994 the FDA stated that modified foods were as safe as their non-modified counterparts and policy decisions have been based on that statement. The government believes so strongly in the safety of GM foods that they do not require labeling of any kind to differentiate GM foods from non-modified food sources (Whitman, 2000, Â ¶ 38-43). Since there is no way to differentiate GM from non-GM products there is no way for Americans to know if they are eating GM foods. In 2003 six countries produced 99% of the transgenic crops, also known as GM crops, sold in the world. Of these six countries the United States sold, by far, the largest percentage of these crops (James, 2003). The chart below lists the acreage of these crops by millions. Figure 1 Obviously, not all is doom and gloom when looking at the above figures. Although biotechnology can do harm it can also help the world, maybe. According to Raney, Pingali, T. R, & R. R. in 2007 a new variety of rice named Golden Rice was modified to produce beta-carotene. The rice was developed specifically to help the starving and poor in third world countries who become ill from vitamin A deficiencies (p. 108). Three servings of Golden Rice a day will provide an adult with 10% of their daily requirement of Vitamin A. While this does not seem earth shattering it shows a company attempting to use biotech to help others. Of course, even assuming the FDA is right and the problems caused by GM foods are an aberration there is the USDA’s bailiwick to ponder. Are the crops safe for the biosphere itself? That is a difficult question to answer, as well. Just like the food safety issue there are people on both sides of this argument who are convinced that they are right. On one side are the scientists who fully believe that the creation of GM foods cannot harm the biosphere and on the other are the scientists who believe that cross pollination will cause problems. According to the Department of Soil and Crop Sciences at Colorado State University (2004) a list of recommended separation distances for GM crops was released by the USDA. According to the USDA if the separation distance is maintained and divider crops are planted then the risk for migration or cross pollination is minimal. Divider plants are tall plants that will block the flow of pollen from wind caused migration. With these precautions in place biosphere damage is supposed to be minimal. A photo taken by Percy Schmeiser and provided by The Nature Institute in 1994 shows that even if the worry of cross pollination or plant migration is overblown it is not an unproven phenomena. The field in the picture was planted with wheat in 1999. In the year 2000 they allowed it to lie fallow, in layman’s terms they did not plant anything so to regenerate the soil. They sprayed the soil twice with a weed killer known as Round Up but somehow an herbicide resistant strain of canola plants migrated into the field. The bushes in the below picture are all a GM crop that was never planted by the farmer. No one is sure how it appeared in the field (Holdrege, 2004, Â ¶ 11). Figure 2 Even discounting the possibility of seed migration via accident or wind there is always the chance of cross pollination. With cross pollination one plant can pollinate or breed another plant via insect help or wind that it was not scheduled to pollinate. In this way a plant type that was supposed to be non-GM can be infected with GM genes without the farmer or company being aware of the problem. This has happened before to rice crops that were sold to Europe from the U. S. and caused the temporary halt of rice exports to certain companies in Europe. The rice in question was not approved for human consumption and no one is sure how it appeared either in the field or the food supply (Vogel, 2006). Besides cross pollination and migration one other crop issue needs to be addressed. Monsanto has produced crop plants that either target the RNA in insects to kill off their larvae, are tolerant of herbicides like Round Up to kill off weeds, or produce pesticides of their own to kill predatory insects (Whitman, 2000, Â ¶ 4-5; Webb, 2007). While these functions are beneficial to farmers in that they save money and protect the crops, there are some concerns with these changes. There is always the possibility of cross breeding or cross contamination affecting a species for which these changes were not intended. There is also the chance that the insect killing modifications will kill off non-pest insects like butterflies. Lastly, there is a chance that plants that produce pesticides will be toxic to the humans or animals that ingest it (Whitman, 2000, Â ¶ 18-22). While opinions still vary on GM food safety, what becomes obvious is that there are more questions than answers. More testing and more rigorous safety and control laws are needed to protect the populace from unmeant harm. While GM foods can be a boon to the world they can just as easily become a curse. Disease, poisonings, and even dangers to the biosphere itself are just some of the risks we currently run. The best way to safeguard our future is to demand that congress takes our safety seriously. References Antoniou, M. (1996). Is GM food devoid of DNA safe. Retrieved January 21, 2008, from http://www. purefood. org/ge/noDNA. htm Department of Soil and Crop Sciences at Colorado State University. (2004). Concerns about current farming practices. Retrieved January 28, 2008, from http://cls. casa. colostate. edu/TransgenicCrops/croptocrop. html Epstein, Samuel S. (1996). Unlabeled milk from cows treated with biosynthetic growth hormones: a case of regulatory abdication. International Journal of Health Services, 26(1), 173-185. Holdrege, C. (2004). The trouble with genetically modified crops. Retrieved January 15, 2008, from http://www.natureinstitute. org/pub/ic/ic11/gmcrops. htm James, C. (2003). Preview: Global status of commercialized transgenic crops: 2003. Ithica,NY: International Service for the Acquisition of Agri-biotech Applications [ISAAA]. Larsen, H. (1998). Milk and the cancer connection. Retrieved December 27, 2007, from http://www. vvv. com/healthnews/milk. html Physicians and Scientists for Responsible Application of Science and Technology [PSRAST]. (2006). Inadequate safety assessment of GE foods. Retrieved January 18, 2008, from http://www. psrast. org/subeqow. htm Raney, T. , Pingali, P. , T. R. , & P. P. (2007, September). Sowing a gene revolution. Scientific American, 297(3), 104-111. Retrieved December 7, 2007, from EBSCOhost database. Safety of rbST Milk Affirmed. (1999, May). FDA Consumer, 33(3), 4. Retrieved January 23, 2008, from EBSCOhost database. Vogel, G. (2006, September). Tracing the transatlantic spread of GM rice. Science, 313(5794), 1714. Webb, S. (2007, November 10). Silencing pests. Science News, 172(19), 292. Retrieved December 7, 2007, from EBSCOhost database. Whitman, B. (2000). Genetically modified foods: harmful or helpful. Retrieved January 23, 2008, from http://www. csa. com/discoveryguides/gmfood/overview. php.

Education and Mark Twain Tags

â€Å"Live as if you were to die tomorrow. Learn as if you were to live forever. †Ã‚   ? Mahatma Gandhi tags:  carpe-diem,  education,  inspirational,  learning 38,294 people liked it like â€Å"I have never let my schooling interfere with my education. †Ã‚   ? Mark Twain tags:  education 11,482 people liked it like â€Å"You can never be overdressed or overeducated. † ?  Oscar Wilde tags:  education,  fashion 6,803 people liked it like â€Å"You educate a man; you educate a man. You educate a woman; you educate a generation. †Ã‚   ? Brigham Young tags:  education,  feminism,  men,  women 3,833 people liked it like â€Å"The world is a book and those who do not travel read only one page.    ? Augustine of Hippo tags:  allegory,  books,  broad-mindedness,  classic,  education,  imagery,  travel,  world 3,650 people liked it like â€Å"Education is the most powerful weapon which you can use to change the world. â⠂¬ Ã‚   ? Nelson Mandela tags:  change,  education 2,344 people liked it like â€Å"Whatever the cost of our libraries, the price is cheap compared to that of an ignorant nation. †Ã‚   ? Walter Cronkite tags:  education,  ignorance,  intelligence,  libraries 2,006 people liked it like â€Å"Education is the ability to listen to almost anything without losing your temper or your self-confidence.    ? Robert Frost tags:  education 1,846 people liked it like â€Å"When you know better you do better. † ?  Maya Angelou tags:  education,  intelligence,  knowledge,  wisdom 1,709 people liked it like â€Å"The past has no power over the present moment. † ?  Eckhart Tolle tags:  education,  inspirational,  life,  philosophy,  truth,  wisdom 1,564 people liked it like â€Å"Education: the path from cocky ignorance to miserable uncertainty. †Ã‚   ? Mark Twain tags:  education 1,267 people liked it like â€Å"Intelligence plus character-that is the goal of true education. †Ã‚   ? Martin Luther King, Jr. tags:  education ,160 people liked it like â€Å"The task of the modern educator is not to cut down jungles, but to irrigate deserts. †Ã‚   ? C. S. Lewis tags:  education 1,111 people liked it like â€Å"The best thing for being sad,† replied Merlin, beginning to puff and blow, â€Å"is to learn something. That's the only thing that never fails. You may grow old and trembling in your anatomies, you may lie awake at night listening to the disorder of your veins, you may miss your only love, you may see the world about you devastated by evil lunatics, or know your honour trampled in the sewers of baser minds.There is only one thing for it then — to learn. Learn why the world wags and what wags it. That is the only thing which the mind can never exhaust, never alienate, never be tortured by, never fear or distrust, and never dream of regretting. Learning is the only thing for you. Look what a lot of things there are to learn. †Ã‚   ? T. H. White,  The Once and Future King tags:  curiosity,  depression,  education,  learning,  teaching 1,045 people liked it like â€Å"If you want to get laid, go to college. If you want an education, go to the library. †Ã‚   ? Frank Zappa tags:  education,  sex 1,013 people liked it ike â€Å"Give a girl an education and introduce her properly into the world, and ten to one but she has the means of settling well, without further expense to anybody. †Ã‚   ? Jane Austen tags:  education,  women 980 people liked it like â€Å"Education without values, as useful as it is, seems rather to make man a more clever devil. †Ã‚   ? C. S. Lewis tags:  character-development,  education,  ethics 834 people liked it like â€Å"Eragon looked back at him, confused. â€Å"I don't understand. † â€Å"Of course you don't,† said Brom impatiently. â€Å"That's why I'm teac hing you and not the other way around. †Ã‚   ?Christopher Paolini,  Eragon tags:  education,  humor 828 people liked it like â€Å"I am not a teacher, but an awakener. † ?  Robert Frost tags:  carpe-diem,  education,  inspirational,  learning,  mentoring 819 people liked it like â€Å"In real life, I assure you, there is no such thing as algebra. †Ã‚   ? Fran Lebowitz tags:  algebra,  education,  humor,  mathematics 775 people liked it like â€Å"[Kids] don't remember what you try to teach them. They remember what you are. †Ã‚   ? Jim Henson,  It's Not Easy Being Green: And Other Things to Consider tags:  childhood,  education,  learning,  teaching 773 people liked it like Ideally, what should be said to every child, repeatedly, throughout his or her school life is something like this: ‘You are in the process of being indoctrinated. We have not yet evolved a system of education that is not a system of indoctrinatio n. We are sorry, but it is the best we can do. What you are being taught here is an amalgam of current prejudice and the choices of this particular culture. The slightest look at history will show how impermanent these must be. You are being taught by people who have been able to accommodate themselves to a regime of thought laid down by their predecessors.It is a self-perpetuating system. Those of you who are more robust and individual than others will be encouraged to leave and find ways of educating yourself — educating your own judgements. Those that stay must remember, always, and all the time, that they are being moulded and patterned to fit into the narrow and particular needs of this particular society. †Ã‚   ? Doris Lessing,  The Golden Notebook tags:  conformity,  education,  feminism,  knowledge-power,  quip,  school 740 people liked it like â€Å"Study without desire spoils the memory, and it retains nothing that it takes in. †Ã‚   ? Leo nardo da Vinci ags:  education,  schooling,  university 734 people liked it like â€Å"Prejudices, it is well known, are most difficult to eradicate from the heart whose soil has never been loosened or fertilised by education: they grow there, firm as weeds among stones. †Ã‚   ? Charlotte Bronte,  Jane Eyre tags:  education,  prejudice 700 people liked it like â€Å"The mind is not a vessel to be filled, but a fire to be kindled. †Ã‚   ? Plutarch tags:  curiosity,  education,  mentoring,  mind,  thinking 669 people liked it like â€Å"Without education, we are in a horrible and deadly danger of taking educated people seriously. †Ã‚   ? G. K.Chesterton tags:  education,  ignorance,  indoctrination 667 people liked it like â€Å"Often, it’s not about becoming a new person, but becoming the person you were meant to be, and already are, but don’t know how to be. †Ã‚   ? Heath L. Buckmaster,  Box of Hair: A Fairy Tal e tags:  education,  growth,  life,  self-acceptance,  self-realization 664 people liked it like â€Å"You know, sometimes kids get bad grades in school because the class moves too slow for them. Einstein got D's in school. Well guess what, I get F's!!! †Ã‚   ? Bill Watterson tags:  calvin-and-hobbes,  comic,  education,  funny,  school 662 people liked it like The educated differ from the uneducated as much as the living differ from the dead. †Ã‚   ? Aristotle tags:  education 625 people liked it like â€Å"Try not to have a good time†¦ this is supposed to be educational. †Ã‚   ? Charles M. Schulz tags:  education,  humor 589 people liked it like * Home * Authors * Topics * Quote of the Day * Pictures * Top of Form Bottom of Form Authors:  A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z ————————â €”———————- Education Quotes * Grid  List * * Prev * 1 * 2 * 3 * 4 * .. * 40 * Next * Share220309 Education is the most powerful weapon which you can use to change the world.Nelson Mandela Change,  Powerful,  Weapon It is the mark of an educated mind to be able to entertain a thought without accepting it. Aristotle Mind,  Without,  Thought A liberal education is at the heart of a civil society, and at the heart of a liberal education is the act of teaching. A. Bartlett Giamatti Society,  Heart,  Act An education isn't how much you have committed to memory, or even how much you know. It's being able to differentiate between what you know and what you don't. Anatole France Between,  Able,  Memory My mother said I must always be intolerant of ignorance but understanding of lliteracy. That some people, unable to go to school, were more educated and more intelligent than college professors. Maya Angelou Mother,  Scho ol,  Ignorance Education is an admirable thing, but it is well to remember from time to time that nothing that is worth knowing can be taught. Oscar Wilde Time,  Nothing,  Remember Ads by Google Church Online Tired of your life? Find hope at Church Online westside-family. churchonline. org Education is the ability to listen to almost anything without losing your temper or your self-confidence. Robert Frost Without,  Anything,  LosingEducation is not the filling of a pail, but the lighting of a fire. William Butler Yeats Fire,  Lighting,  Filling Develop a passion for learning. If you do, you will never cease to grow. Anthony J. D'Angelo Learning,  Passion,  Grow An investment in knowledge pays the best interest. Benjamin Franklin Knowledge,  Best,  Interest In the first place, God made idiots. That was for practice. Then he made school boards. Mark Twain God,  School,  Made The roots of education are bitter, but the fruit is sweet. Aristotle Sweet,  Bitte r,  Roots Education is the key to unlock the golden door of freedom.George Washington Carver Freedom,  Door,  Key Education is not preparation for life; education is life itself. John Dewey Life,  Itself The only person who is educated is the one who has learned how to learn and change. Carl Rogers Change,  Person,  Learned Education is the best friend. An educated person is respected everywhere. Education beats the beauty and the youth. Chanakya Beauty,  Best,  Friend He who opens a school door, closes a prison. Victor Hugo School,  Door,  Prison I spent three days a week for 10 years educating myself in the public library, and it's better than college.People should educate themselves – you can get a complete education for no money. At the end of 10 years, I had read every book in the library and I'd written a thousand stories. Ray Bradbury Money,  Myself,  End Education is a progressive discovery of our own ignorance. Will Durant Ignorance,  Discov ery The only thing that interferes with my learning is my education. Albert Einstein Learning,  Interferes It is a thousand times better to have common sense without education than to have education without common sense. Robert Green Ingersoll Better,  Without,  SenseA human being is not attaining his full heights until he is educated. Horace Mann Human,  Until,  Full Education is a better safeguard of liberty than a standing army. Edward Everett Better,  Liberty,  Army No one has yet realized the wealth of sympathy, the kindness and generosity hidden in the soul of a child. The effort of every true education should be to unlock that treasure. Emma Goldman Sympathy,  Kindness,  True An educated person is one who has learned that information almost always turns out to be at best incomplete and very often false, misleading, fictitious, mendacious – just dead wrong.Russell Baker Best,  Person,  Learned —————— Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€- Share with your Friends Share Everyone likes a good quote – don't forget to share. ————————————————- Popular Authors Abraham Lincoln Albert Einstein Buddha C. S. Lewis Dalai Lama Eleanor Roosevelt Helen Keller John F. Kennedy Khalil Gibran Marilyn Monroe Mark Twain Martin Luther King, Jr. Maya Angelou Mother Teresa Oscar Wilde Ronald Reagan Socrates Thomas Jefferson William Shakespeare Winston Churchill More authors * Grid  List * * Prev * 1 * 2 * 3 * 4 .. * 40 * Next ————————————————- Popular Topics Love Quotes Life Quotes Friendship Quotes Motivational Quotes Inspirational Quotes Success Quotes Funny Quotes Wisdom Quotes More topics —————â€⠀Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€- Get Social with BrainyQuote BrainyQuote Desktop BrainyQuote Mobile ————————————————- Site Home Quote of the Day Topics Authors Pictures Professions Birthdays ————————————————- Social BQ on Facebook BQ on Twitter BQ on Pinterest BQ on Google+ ———————————————— Syndication Quote Feed Art Quote Feed Funny Quote Feed Love Quote Feed Nature Quote Feed ————————————————- About Us Our Story Inquire Advertise Submit Privacy Terms AdChoices Copyright © 2001 – 201 3 BrainyQuote ®          BookRags Media Network ————————————————- Sharing Successful! Share again! You've successfully shared using Po. st! Welcome to po. st! Powered by Po. stPrivacy Read more at  http://www. brainyquote. com/quotes/topics/topic_education. html#z1aYzpTLODgGBg7Q. 99

ASDA Employee Motivation Research

ASDA Employee Motivation Research Research Project â€Å"To determine some vital roles of leadership in   improving the sense of motivation in employees in   the retail organisations of UK- A study on ASDA†. Task 1 Understand how to formulate a research specification Part 1: You have to create a research proposal in a subject of your interest. In doing this, you have to formulate and record possible research project outline specifications (AC1.1); Identify the factors that contribute to the process of research project selection ( AC1.2); Undertake a critical review of key references (AC 1.3) ;Produce a research project specification (AC1.4). Introduction Business organisations are an integral part of nation’s economy in today’s globalised set of environment where all industrial sectors are largely concerned about a timely achievement of their stipulated goals and objectives. In context to which, they are hereby required to operate with a strategic arrangement of their action plans to duly accomplish their stipulated targets on time. It is with reference to yet another prime concept of realism in today’s set of business where an establishment is comprised of two vital set of bodies entitled as employees and employers (Alfalfa-Luque, Marin-Garcia and Medina-Lopez, 2015 ). Wherein, the employees are direct in charge of their respective set of employers where they are with a through accountability of guiding them. This in turn has breakdown the preceding role of employers where they are mainly positioned at two vital profiles of leaders and managers in the organisation. Both these parties are required to perform a similar set of responsibility by thoroughly guiding their respective set of employees and generate a prior sense of encouragement in them. It is mainly with respect to carry out there assigned tasks on time for an eventual achievement of their organisational goals and objectives. In order to achieve this research, I will start by: To analyse the fact ual concept of leadership in retail organisations of UK To discover the adopted tools of motivation in ASDA To identify the relationship between the tact of leadership and motivation in ASDA To recommend some principle strategies to enhance the motivation level of employees with some profound tactics of leadership Background of the study The present survey is based upon a configured purpose of exploring the function of leadership to enhance the sense of motivation in the deputed employees of retail organisations in UK. For which, a renowned retail enterprise named ASDA has been taken into consideration for it where it is a subsidiary supermarket of Wal-Mart as its parent company ( Cadden, Marshall and Cao, 2013 ). With a foundation year of near about 68 years, it is currently headquartered at Leeds that is in the West Yorkshire of England. It is evident to deal into grocery products along with general merchandise commodities and fiscal services as well. As per a recent tran scription of the year 2016, ASDA is depicted to be extended in around 630 distinct locations at a global level. Herein, it is also ascertained to employ total 180, 000 number of workers to operate at its widespread locations. The current study has hereby focussed to interpret the adopted procedures of ASDA to motivate their deputed set of employees by aligning it to their applied tact of leadership at the workplace. Leaders in ASDA are evident to play a crucial role in handling the work of their respective set of teams ( Kim and Brymer, 2011 ).

My reflection Assignment Example | Topics and Well Written Essays - 250 words - 1

My reflection - Assignment Example been considered the traditional family, the source brings into light changing family setups and the contentious issues that surround the definition of the traditional family. Handel et al. (2011) elaborate the extent to which different agencies of socialization impact child behavior. At home and in school for example, children’s behavior are molded to conform with certain rules. Parents and teachers play a significant role in correcting errant behavior and guiding children to adopt socially acceptable ones, as opposed to those that are considered negative. Like the other agencies of socialization, peer influence greatly shape the way children develop. As peers, for example, children engage in such activities as play and collaboration against parents and teachers to resist some of the directions given to them by the significant others. By engaging in different activities as peers, children get to learn interpersonal skills much as they learn emotional regulatory ability mostly from their parents. In general, Handel et al. Paint a broad, albeit vivid picture of how the school, family and peer group affect children’s

Women, the Hammurabi Code Essay Example | Topics and Well Written Essays - 1000 words

Women, the Hammurabi Code - Essay Example he following rules might broadly be challenged these days but their legitimacy in the distant past was considered as obvious (The Code of Hammurabi, 2010): (1) the sexuality of women should be given up to guarantee legality; (2) the assets of the family should be managed by the male members; and (3) women, particularly divorcees and widows needed the help of society (http://www.wsu.edu/~dee/MESO/CODE.HTM). Most women were supposed to marry and raise a family. A reasonable set of love poems would indicate that women did enjoy some authority, but marriage, theoretically, was fixed by their brothers or fathers. As stated in the Hammurabi Code, an agreement was required to perform a marriage. According to the few that exist, the agreements stated primarily what would occur if the matrimony ended through abandonment or divorce, or death, but other passages might let off each from the prenuptial obligations of the other or even oblige the bride to work as a slave to her husband’s mother (Meyers, 1991). The major article to be discussed was the amount of the bride price: If a woman who lived in a man’s house made an agreement with her husband, that no creditor can arrest her, and has given a document therefore: if that man, before he married that woman, has a debt, the creditor cannot hold the woman for it. But if the woman, before she entered the man’s house, had contracted a debt, her creditor cannot arrest her husband therefore (151). It is well-known that unsophisticated societies made use of bride price to pay off the family of the bride for labor loss and Israelite culture limited it to a symbolic amount as a sign of engagement. The bride price in Babylonia constantly became piece of the dowry (Meyer, 1991). The bride price, depending on socioeconomic standing, could embody a considerable wealth transfer, possibly several hectares of land and house—however, at the lower socioeconomic hierarchy it might have been little more than a piece of home decoration

Height and the Weight of the Mother, with structure Lab Report

Height and the Weight of the Mother, with structure - Lab Report Example The taller mother’s with an Q2 average 1.68 tend to weigh more that the mother’s with Q1 of 1.58 at 50.57Kgs. The average mother’s weighs around 58 kgs and the average BMI of 22. The age of mother’s is around 28Kgs. As the age increases, the height increases and the weight increase too. Taller mothers have a higher BMI than the shorter, thus the height of the mother has an effect on the weight of the mother and has a direct correlation with the BMI. Age does not affect the height of the mothers. There is a moderate correlation between BMI and gestation days. Mother’s with a high BMI tend to have longer gestation period. The average gestation day of a mother is 279 days and with an average of 22 BMI. The BMI effect on gestation period can be traced to weight factor; thus mother’s who have a higher BMI have a probability of having longer gestation period. Further, taller mother’s have a higher BMI; thus the probability of the taller mother’s having longer gestation period is

Singapore Prosperity Case Study Example | Topics and Well Written Essays - 250 words

Singapore Prosperity - Case Study Example Singapore has ultimately risen above all odds to become one of the world’s Economic power houses. It has been used as a bench mark of economic analysis across different parts of the world. However, of great importance is to focus on the several aspects that have contributed to this splendid success in almost all spheres of the Singaporean society. After forty years of economic reconstruction, Singapore has attracted the interests of not only economic analysts but also global investors that do not hesitate to invest in the island nation.Economic growth occurs at the predisposition of a conducive investment environment. The government definitely has a hand for the attainment of this conducive environment. This brings us to the analysis of the government structure and how its policies in one way or the other has contributed to Singapore’s spectacular economic growth. The people’s Action party has been greatly accredited of transparency, accountability and effective management not to forget sound policies that have been put in place to foster remarkable growth. Having only been ruled by just one party since independence, an aspect of stability has been initiated in the Singaporean political system which extremely puts great focus to economic growth rather than political destructions. It has developed policies that encourage domestic trade without much interference to the several foreign investors that have played a key role in its immense growth. Leadership transition has been peaceful playing a great role in stability making it a high affinity destination for investors. Singapore’s effective policies and able leadership was witnessed during the climax of the worst ever global recession that even shook economic giants such as the United States. The prime minister fostered bank lending of which the government also contributed without much interference to its GDP.Within no time after the introduction of this measures, Singapore bounced back to its economy fact living other economic titan states in the turmoil. Contrary to the common perspective that the private sector has got leverage over public enterprise, Singapore has stood the test of time to prove otherwise. It goes against the popular notion and treads its own economic course which baffles both its admirers and Critiques

Business Law and the Accounting Profession Research Paper

Business Law and the Accounting Profession - Research Paper Example It governs all transactions in the land including business. Law is often associated with ethics. Ethics is the field of study that aims to encourage all people to act in accordance with the law. It focuses more on one's morality, the ability to decide in a rightful manner taking into consideration any possible consequences of the decision. It is crucial for law and ethics to unite, but unfortunately at times it is not the case. Sometimes an individual must consider doing an unlawful act to do something that is ethical, it sounds complicated, but it happens in reality especially in handling business transactions (Beatty and Samuelson 16). The day to day dilemmas in business led to the formulation of business laws that seek to promote the value of law and ethics in the globally progressing business environment. Sources of Business Law In spite of the existence of business laws there are still some professionals who opt to perform their duties through unethical means. Perhaps this wrong ful act has contributed to the growing disbelief of people towards the information that is being shown in financial statements. The violation could either be intentional, that is when the accountant voluntarily made the violation or unintentional, if there is no willingness to do the wrongful act (e.g., when the employer threatens an employee to do something unethical for the sake of the company). Accounting regulations were established to govern professional accountants into practicing ethical behavior. The Securities and Exchange Commission (SEC) is the government agency tasked to promulgate regulations that are set to be followed by organizations particularly the accountants who are most of the time susceptible to trouble. The Securities Exchange Act of 1934 made possible the creation of SEC to formulate and impose securities law. In 2002 former U.S. President George W. Bush passed the Sarbanes-Oxley Act or the Public Accounting Reform and Investor Protection Act. The law was est ablished to ensure the credibility of financial statements. Due to the increasing number of fraud related issues in companies, the government finally realized the need to reform the laws that govern organizations and protect the right of investors (Peck 11). Issues in the Accounting Profession There have been recent scandals that involved accounting professionals. This type of profession is never easy to handle and sometimes it places an accountant's reputation at stake. In most cases, the cause of complications in the accounting profession is in the making of financial statements. Financial statement misrepresentation is strictly prohibited. Although that rule is clearly emphasized, many seem to disregard it. The violation is avoidable if companies will consciously make an effort to adhere to the said policy. If unluckily an accountant is caught doing this wrongful act, then there is no possible way to get out of this mess. Whatever the case may be, an accountant will still be held liable because an offense has been committed and the harm has already been done. As in the case of a criminal act a suspect is convicted if the commission of a crime is proven beyond reasonable doubt, so a suspect is not yet considered a criminal unless there is enough evidence. Say for example a company that is struggling financially. The only way to solve the problem is to manipulate the figures in the financial report so that investors will remain faithful to the company. The accountant is left with no other choice, but to do what the employer says. There are two possible consequences to this act. The first consequence is that if the manipulation will push through there are chances of reviving the company and so

APPLIED DATA MANAGEMENT Essay Example | Topics and Well Written Essays - 1750 words

APPLIED DATA MANAGEMENT - Essay Example Focusing on this aspect, the research aims at addressing data management problem of S&R Consultants. For about 30 years, the organisation has grown steadily in home (i.e. Australia) and overseas nation. However, with the increase in business issues regarding data management frequently becomes a great challenge for the S&R Consultants. In such condition, the use of web content management system (WCMS) would be beneficial for S&R Consultants. However, as it is a new technology, WCMS is characterised by several non-technical issues, which must be considered before implementing the new technology. Purpose of the Research Based on the use of WCMS in S&R Consultants, the research intends to explore how it can be proved beneficial for the organisation. Besides the purpose of undertaking the research is to generate features and facilities of WCMS, which can fulfil the requirements of S&R, to recommend certain criteria to compare and choose apparent WCMS, to provide recommendations to the boa rd of directors of S&R Consultants about steps required to select and implement WCMS and about website management policies for proper integration of website into the business operations. Rationale for Selecting WCMS The rationale for selecting WCMS is that it can allow S&R Consultants to rapidly react with the business situation and to make important materials available on website. Besides, WCMS is much effective in updating latest information without assistance of technical experts. In S&R Consultants, WCMS might assist in making different workflows more efficient than before, because it can provide timely publication of contents and also ensure that those contents are satisfactorily revised before publication (The Government of the Hong Kong Special Administrative Region, 2008). Target Audience of WCMS In order to ensure that the WCMS is kept relevant, there must be clear idea about the target audience. Target audience can help to understand the key prerequisites of WCMS system an d therefore can support S&R Consultants to make decisions regarding useful contents. Besides, target audience also allow for developing proper investment strategy for development of website (Perriss & et. al., 2006). Since, the website of S&R will be a private website the key audiences will be the existing clients and other prospects who desire to obtain services from the organisation. Features and Facilities of WCMS WCMS can provide the following features to S&R Consultants: Document Management System: WCMS is intended to manage and store internal organisational data and corporate information. It helps in document workflow and interpretation of data. Electronic publishing: WCMS tools are developed for supporting online publishing of news and other reports, which are essential for key stakeholders. WCMS enable simple workflow and quick publication of contents. E-commerce facility: WCMS also authorises e-commerce through enabling online shopping and electronic relationship management for S&R Consultants (Browning & Lowndes, 2001). WCMS provides three core facilities for S&R Consultan

Web 2.0 †Simplifying the Complicated Essay Example for Free

Web 2.0 – Simplifying the Complicated Essay Ever since its birth, Web 2. 0 has become a tempting topic of small talk everywhere technology is discussed. In most cases, those small talks can lead to lengthy discussions, and even debates. The word, to begin with, took time before settling with a definitive description. The processes and characteristics are also repeatedly debated about. If there is anything more intriguing with Web 2. 0, it is the power that it gives website visitors. It empowers web users to become part of the creation of a site, to associate themselves with a specific brand or service, or even with mere ideas. It creates a pool of knowledge which fosters collective intelligence, which becomes useful for all users. It also creates new uses for old applications, enabling them to be recreated and popularized. Introduction Except with enough interest on technology, Web 2. 0 is more of a jargon than a utility for many. What majority of internet users do not realize is that they are already using it even without them knowing. Web 2. 0 has suddenly taken over the whole computing industry. The takeover was so sudden that even experts agree that this has become the way computing today should be—or will be. The confusion is forgivable. For many, Web 2. 0 is a complicated thing. The more experts try to simplify it, the more complicated it becomes. To begin with, it is extremely difficult to at least define the term. There is also a lot of confusion as to where the Web 2. 0 begins; and where Web 1. 0 ends. Moreover, there is the question if the latter ends where the former begins. To better understand the word, it takes an analysis of the bits and pieces that make it up. We begin with the internet. (MacManus, 2005; Ding, 2007) The internet is designed to share and give out information. It began as an avenue for data creators and owners to send their information to others, often specific recipients. In review, it has become effective to what it is aimed at. The internet has become a way to make information available for others. The whole point of this information process is the need for knowledge. 55% of richness all over the world comes from knowledge. Peter Druker even said that the most production increase happens with the increase of knowledge. Today, communication has become more than just a one-on-one process. With information coming from sources of all directions, intelligence has become collective. The internet is one viable process of communication that cannot be underestimated. Without any initial help from bigger companies, the internet reaches one billion users. It should be noted that information is different from knowledge—and knowledge is different from intelligence. Information only becomes knowledge when a user processes it. In the same way, intelligence is a collection of knowledge. Thus, a single piece of information is not knowledge until used, while a single piece of knowledge cannot be called intelligence unless other knowledge is grouped with it. (Idehen, 2004; Bates, 2005) Communication enabled a big shift for knowledge. Where before knowledge is from one point to another (1 to 1), with communication it has become one point to several receiving points (1 to n). Thus, it can be said that there is an increase in the efficiency of knowledge sharing through communication. Yet, the web age has also contributed more to this. Thus, the Web 1. 0 and Web 2. 0. In Web 1. 0, everybody is creating information (n to n). In response, everybody can access information too (n to n). Instead of a linear process, there is a cycle of information sharing happening to all involved—the senders and receivers of information who are also receivers and senders in their respective rights. Web 1. 0 became an alternative way to communicate because of this efficiency that it offered everybody. (Barefoot, 2006) It does not only get information, it also gives information in return. Web 2. 0 – Simplifying the Complicated aims to present how Web 2. 0 works. It creates a bird’s eye view of the definitions. It also consults the processes of how Web 2. 0 is taking the web and its users. Effects, both good and bad, will also be analyzed. In the end, there is an attempt to conclude: is Web 2. 0 hype or a formation that is here to stay? Literature Review The word Web 2. 0 was coined by Dale Dougherty in 2004. (Anderson, 2007) Since then, it has become a widely-used word. Experts are talking about Web 2. 0 as if it is a new revolution. Indeed, it is. The word Web 2. 0 has been used and abused by many. Singel (2005) quoted Tim O’Reilly who defined the term as the framework of participation for the purpose of information. Schindler (2007) agreed to this statement, defining Web 2. 0 as the collaborative internet. However, Boutin (2006) claimed that definitions to the word may change, depending on who is using the term. Thus, no single definition can be associated with it. Almost every netizen is already using the Web 2. 0 in one way or another. Anderson (2007) identified that the Web 2. 0 is the use of collective information, where data is the chief element, among others. Thus, blogging, forums, tagging, and all other forms of publishing over the internet for public use may be regarded as Web 2. 0. Without one knowing it, Web 2. 0 is right before their very eyes. There have been questions, thus, if Web 2. 0 is hype, or a fad that will soon pass. Singel (2005) stated that the first conference held for Web 2. 0 was sold out despite the steep price tag. Schonfeld (2006) dismissed the idea of it being a publicity spin off. In fact, he claimed that the Web 2. 0 is the current name of the internet game. Despite the arguments, Web 2. 0 definitely has its positive effects. Companies view it as a way to save on marketing costs and improve customer relations. Businesses agree that the Web 2. 0 will impact their operations positively. These benefits, however, have corresponding detriments. (Serious business: Web 2. 0 goes corporate 2007) Keen (2006) stated that the Web 2. 0 setup enables users to publish just about anything within the limits, and so it can give way to anyone who wants to be a writer or performer. Thus, the entertainment industry may be threatened. Singel (2005) dismissed this. He quoted John Batelle who affirmed that despite the openness of Web 2. 0 for a new breed of entertainers, talent is one question that separates the real from the web-based. On the other hand, Harris (2006) expressed concerns to the loss of privacy because of the limitless sharing through Web 2. 0. Even mere personal files that get inside the Web 2. 0 is shared in a way, and he activities of users included, proving that privacy may in fact be sacrificed. Kantor (2006), however, stressed that copyright and limits will still be enforced, so that there is no need to worry about privacy. Despite the different views on Web 2. 0, it has become a fact that it is the way computing is currently being done. Internet users are getting the benefits, and are more than willing to participate. Internet companies are making waves for new Web 2. 0 applications and services. Businesses are turning their keys to include Web 2. 0 in their online presence. All these show that Web 2. 0 is definitely an issue worth discussing.

Death of a parent: Effects on children

Death of a parent: Effects on children Death of a parent: effects on children Thesis: Apparently, the death of a parent can be a dramatic experience for all members of the family, particularly for children, and can often have both short-term and extensive effects on the children. Even if the effects of parents death are heartbreaking, to live healthy and balanced life, members of this type of family must cope the reality and go on with their lives, leaving the fear and emptiness behind. Audience: Doctor Costa Purpose: To show how difficult is for children to cope with the death of a parent. When we think of a family, we most often visualize that family must have children and two parents. Nowadays, this is often not an example in many families throughout the world; single parent families are increasing dramatically. No matter how hard single parent try, he/she cannot replace the natural demand of a child for both of parents. There are several causes of the rise of single parenting across the globe. This essay will concentrate on the death of a parent. Apparently, the death of a parent can be a dramatic experience for all members of the family, particularly for children, and can often have both extensive and short-term effects on the children. Even if the effects of parents death are heartbreaking, to live healthy and balanced life, members of this type of family must cope the reality and go on with their lives, leaving the fear and emptiness behind. If family lost one of the parents, this affected perhaps children in a same level (or much more) as a mother/father that been left behind. One of the most common short-term effects on the children is the fear. This fear could â€Å"drag† children to melancholy and lose of self-esteem. Children are incapable and completely helpless of surviving alone, as a result, they might have great fear of insecurity. Consequently, children might practice a devastating fear of the unfamiliar, fear of not acknowledging what the future might hold, and where they might live, and fear of being left alone in the world. As an example, after my uncles death, we could see the fear in the eyes of my all five young cousins. We could indeed â€Å"see† how their souls were broken; one could read the sorrow in their eyes. It took some period for my aunt to cope with tragedy and give hope for her children with the aid of other family members and the district society. It can be dense for a widower parent to build acceptance of this event and assist the child in having a pleasant and balanced life. The second, extensive effect is the feeling and living with emptiness. As life goes along, perhaps a widower parent and his/her children leave the fear behind and (deep in their heart) never let go of pain and sorrow. This tragedy could create a great impact of emptiness in children, which might leave a â€Å"gap† in their spirit forever. I assume that the emotional part of the childrens world is entirely divided apart with this emptiness. This feeling can take away the happiness of childhood and worse of all; emptiness could create emotional isolation within the childrens personality. Their pain and sorrow might forever engrave in a hidden place of their remembrance. Children carry on searching for the lost parent for an extended period, even until they became parents themselves. Perhaps one method of filling this emptiness can be the creation of fresh happy memories. Finally, in families where a parent died, it difficult to accept the circumstance that nothing is going to be the same; however, children in these families are in great deal of challenge. I believe, after sometime children might fight the fear by coping with reality and willing to commence a fresh beginning with the support of a parent, friend, or society. Moreover, it is not easy to fill the emptiness of their hearts and souls until the day of new happiness. To sum up, letting go of the fear, emptiness, pain and sorrow could allow children to look forward to happiness, understand, and accept the reality. Only then, joy can enter to their memories and guide them to start a new beginning.

Carbon Dioxide :: essays research papers

Carbon Dioxide is a colorless, odorless gas that occurs in small quantities in the earth's atmosphere naturally. The earth's ocean, soil, plants and animals release CO2. The formula of Carbon Dioxide is CO2. The CO2 molecule contains 2 oxygen atoms that each share 2 electrons with a carbon atom to form 2 carbon - oxygen double bonds. The atoms are arranged as so (OHT). This is called a 'linear molecule'. Carbon dioxide is commonly found as a gas and is never a liquid. It sublimes to a solid known as 'dry ice' which is used as a substitute for normal ice as it is a lot colder and doesn't melt. Humans and animals breathe out Carbon Dioxide, often referred to as the greenhouse gas, as a waste product. Plants take in this CO2 and use it to make food. This is called photosynthesis. During this process oxygen is released which is then breathed in by humans and animals. This procedure is repeated over and over and a natural balance is obtained. However this natural balance is disrupted by human activity. People of the world are putting more than 5.5 billion tons of CO2 into the atmosphere every year. 75% of this is caused from the burning of fossil fuels. These fuels are burnt all the time to run factories, power plants and vehicles. The main sources of CO2 emissions are electric utilities, residential buildings, industry and transportation. The other 25% is induced by the destruction of the world's forests. The reason for this is that there are less trees and plants to take in the CO2 but there is just as many, if not more, humans and animals to breathe it out. The amount of CO2 in a planet's atmosphere affects the temperature of the planet. As more and more CO2 builds up in the atmosphere, less heat can escape and the planet gets hotter. The CO2 traps radiation from the sun like a greenhouse. This is called global warming or the greenhouse effect. Global warming is becoming a serious problem and CO2 is the major cause. The earth is now warmer than it has been in 1000's of years. The amount of CO2 deposited in the earth's atmosphere from human activities is expected to double by the year 2050. It could possible increase by four in the future with developing countries, such as China, anxious to improve their standard of living.

Comparison of Mid-Term Break, The Field Mouse, and On My First Sonne Es

Comparison of Mid-Term Break, The Field Mouse, and On My First Sonne The above poems are written by 3 different people and on reading them they seem to be about very different things. But at heart, they are about death and the pain that appears afterwards. Seamus Heaney's Mid-Term Break is a memory of his four-year-old brother's death. Gillian Clarke's The Field Mouse is about death in a political conflict compared to a death in nature. Finally On My First Sonne by Ben Johnson is about the death of his son and the religious view of the situation. Both Heaney and Johnson's poems are about the death of a close loved one and how it is dealt with emotionally and in reality. On looking at the title of Heaney's poem, you almost immediately assume that is a happy one, possibly about what he spends his holidays doing. This of course is not the case. Unlike the other two poems, you do not know immediately who has died or even if there is a death. Throughout the poem he keeps us guessing what is happening. He gives us a clue and we have to piece it together like a detective putting a jigsaw puzzle together to solve a crime. Also the fact that it is a memory and he is talking about himself as a child shows how badly it would have affected him and his parents emotionally. Through Heaney talks about the reaction of all his family members to his brother's death, Johnson only talks about how his son's death affected him. You can see that since the deceased was his first son, that he is hit emotionally very hard and seems to blame himself, but at the same time consulates himself by thinking that he has gone to a better place. Line 5 'O, could I loose all father now. For why.' seems to indicate that he has lost a... ...connection that the rest of us probably would not. I feel the poem that really explains the situation well is Seamus Heaney's Mid-Term Break as it keeps the person in suspense over who has died, but delivers a shock at the end when we find out who it really is. This really mixes your emotions and unlike the other 2 makes you feel sorry for a death that happened over 20-30 years ago. I also feel it is better because it focuses on the actually death. while Healey does fill in these criteria. Clarke's poem compares the killing of a field mouse to the killings in the Bosnian War. Though this is clever, it does not show the bad things in the Bosnian War as in reality the killing of one field mouse cannot really be literally compared to the massive killings involving the Muslims. The above reasons are why I think overall Seamus Heaney's poem is the best.

Reality Vs. Fantasy Essay -- essays research papers

Renà © Descartes, author of â€Å"Meditation 1†, writes how he must erase everything he had ever learned and thought to be true and must â€Å"begin again from the first foundations† (222). One may ask how Descartes came to this conclusion. The answer is that of he â€Å"realized how many were the false opinions that in [his] youth [he] took to be true, and thus how doubtful were all the things that [he] subsequently built upon these opinions† (222). This change was to take place at the perfect time in Descartes life however, he wasted much time waiting for that moment Descartes decided to simply let go of it. He started questioning everything he ever believed in. Descartes raised one specific question: How does one justify being awake from dreaming? He gives an example stating â€Å"that I am here, clothed in my dressing gown, seated at the fireplace, when in fact I am lying undressed between he blankets!† (222). Descartes describes how a dream may feel so real, one might actually think their dream is in fact reality. He goes on further saying â€Å"plainly that there are no definite signs to distinguish being awake from being asleep that I am quite astonished, and this astonishment almost convinces me that I am sleeping† (222). This all lead to Descartes coming up with a theory that â€Å"perhaps we do not even have these hands, or any such body at all† (223). He started questioning the existence of God as well, wondering whether or not he existed or if the heavens and earth were actuall...

Air asia case study Essay

Awarding large government contracts to Bumiputra companies. 2. Requiring new listings on the Malaysia stock exchange to have an initial 30 per cent Bumiputra equity ownership. 3. The allocation of at least 30 per cent of government contracts for public and private works to Bumiputra contractors. 4. Requiring all private companies to offer employment opportunities to Bumiputras. 5. Ensuring that a minimum of 60 per cent of government procurements, contract work and other related projects be awarded to Bumiputra entrepreneurs. 6. Making government finance available for the exclusive use of Bumiputra business people. The Malaysian government claimed that the NEP fulfilled its goals since the nation was acknowledged as one of the ten fastest-growing economies in the world from 1970 to 1990, a period that coincided with the NEP’s implementation. This conclusion was in agreement with the research on Malaysian economic development3 conducted by the Harvard Institute for International Development (HIID) and Institute of Strategic and International Studies in Kuala Lumpur (ISIS Malaysia) (Snodgrass, 1996, p. 1). Despite this and the new policies that superseded the NEP since 1990, the affirmative action programme remains controversial. Indeed, many people believe that the NEP continues to define current government development policies in Malaysia. Critics of the NEP believe that the policy was only partially successful in, for example, reducing socio-economic disparity and encouraging the arrogance of Bumiputras (Anshar, 2008). Research by the Australian Government’s Department of Foreign Affair4 (2005, p. xiii) was also critical about the alleged business restrictions that the NEP encouraged – it criticised that these were counterproductive and may even have thwarted the development of a vibrant and resilient business community. 3 The research looks into the Malaysian economic development from 1970 to 1990. Malaysia: An Economy Transformed (2005). This report on the Malaysian business environment prepared by The Economic Analytical Unit (formerly the East Asia Analytical Unit) is part of the Department of Foreign Affairs and Trade and is responsible for publishing reports analysing major trade and economic issues of relevance to Australia. The Entrepreneurial Tony Fernandes If the NEP was restrictive of non-Malay entrepreneurship, how was it possible that Fernandes, a non Bumiputra could emerge as the most celebrated entrepreneur in Malaysia? My research suggests that the NEP did not stifle entrepreneurship and that Fernandes is not the only successful non Bumiputra business person in Malaysia. This is a complex debate, and my doctoral thesis seeks to address it in greater detail. But in this paper I will outline some of the considerations that need to be taken into account in explaining how and why Fernandes rose to become one of Malaysia’s millionaires. Fernandes was born on 30th April 1964 into a family that had no prior knowledge or experience of business; his father was a physician from Goa (India) and his mother was a music teacher of Malaccan-Portuguese descent. In other words, Fernandes came from an Indian-Malaysian family of professionals; the new middle class that emerged in Malaysia from the 1960s. Like many other middle class families, the Fernandes had sufficient wealth to send Fernandes to study in England. Fernandes, at the age of 12, went to London in 1976 to study at Epsom College and attended the London School of Economics in which he graduated in 1987 with a degree in accounting (BusinessWeek, 2009). In total, he spent some 11 years in London, a painful separation from his parents who could not afford to pay for his flights back to Malaysia. It was this experience, according to Brown5 (2010) that gave him an insight into the benefits of perhaps developing cheap international carriers. However, at this stage his career path did not take him into the airline business. Upon graduation from the London School of Economics Fernandes took the normal route of working in accounting jobs. Fernandes worked briefly at Virgin Communications, a television division of the Virgin Group of companies. What did Fernandes learn from Virgin? 5 Kevin Brown is a journalist for the Financial Times. He was appointed Asia regional correspondent for the Financial Times in September 2009, based in Singapore. Prior to this role, he was Asia news editor. Previously, he was the personal finance editor of the Financial Times. The main benefit was the experience of working in a global company, acquiring insights into the running of an international business, and developing an impressive resume which worked in his favour in being appointed to the position of Senior Financial Analyst at Warner Music International6 in London. At Warner, Fernandes showed strong business acumen. He started in 1989 as Senior Financial Analyst, and by 2001, when he resigned from Warner, he was the Vice President, ASEAN region. Within 12 years at Warner he was promoted four times; that is on average he was promoted every three years. Fernandes’ time at Warner Music was significant because it was during this period that Fernandes matured and transformed himself from being a mere accountant into a strategist with an analytical mind. Commentators such as Ionides7 (2004) believed that Fernandes’ ability to think strategically, and understand his environment from a macro perspective, was the reason why Fernandes felt compelled not to be part of Warner’s ill-fated merger with America Online Inc in 2001. This incident was said to be the catalyst for Fernandes’ decision to switch careers after 12 years with Warner. A word of caution is needed: the early history of Fernandes’ emergence as an entrepreneur is based on the business press and journals. As part of my doctoral work I will be examining these issues in greater detail, and therefore reserve the right to correct the narrative as it currently stands. 6 Warner Music International is part of the Warner Music Group which is the third-largest business group and family of record labels in the recording industry. Warner Music Group also has a music publishing arm called Warner/Chappell Music, which is currently one of the world’s largest music-publishing companies.

Pv Trade War Between the Us and China

Introduction International trade and competitive advantages in the costs of production in China have brought numerous opportunities for China’s exports but also generated challenges due to protectionism from its foreign competitors. Consequently, there have been numerous trade cases against China, including anti-dumping, anti-subsidy, in many economic sectors. The very current trade case involving China is the US accusing Chinese manufacturers of dumping photovoltaic (PV) panels in the US market and the Chinese government unfairly subsidizing its own solar industry.In fact, the US’s trade balances in polysilicon products between both the US and China, and the US and the world significantly deficit while China’s polysilicon cells and modules production has increased dramatically (The Kearney Alliance 2012). This essay claims that, the surge in PV exports does not necessarily mean that the Chinese government has subsidized its PV manufacturers illegally, and Chines e solar manufacturers’ low prices do not necessarily imply they are selling their PV products below the cost of production.Importantly, imposing such significant imports tariff is highly likely to undermine not only the bilateral trade between two countries but also long-term benefits of both countries. First, this essay provides an overview of the US-China PV trade case; then explains why China solar industry has been growing dramatically; and finally it analyses what the consequences might be if the US imposes a countervailing and antidumping tariff on China’s PV. BackgroundOn October 2011, seven US-based PV manufacturers headed by SolarWorld Industries America reported China on a double-anti case to US Department of Commerce (DOC) and US International Trade Organization (ITO). The seven manufacturers, which later formed Coalition for American Solar Manufacturing (CASM), accused China for dumping their PV module products to US market and giving a huge amount of expor t subsidy to this industry which in turn causing severe injuries to US PV manufacturers.Several investigations have been carried out by both DOC and ITC for this issue, as the coalition accused China government providing cash grants, heavily discounted resources, huge loans and credits, tax exemption, incentives and rebate and export grant insurance to the industry. In its final determination held on 10 October 2012, DOC proposed 18. 32 per cent to 249. 96 per cent of anti-dumping and 14. 78 per cent to 15. 97 per cent of countervailing duty.Further actions, including issuing or not issuing anti-dumping and countervailing duty orders, will be made after ITC final determination (US DOC 2012). Photovoltaic industry is a new emerging industry as a response to the threat of energy shortage and environmentally-unfriendly fossil fuel-based energy. Governments issued supportive policies, including giving significant account of subsidy considering higher production cost of this new energy i ndustry compared to that of conventional one.In case of China, the country issued a PV market policy in 2007 that included deployment, investment and research and development supports under the scheme of middle and long term program of renewable energy development set by National Development and Reform Commission (NDRC) targeting the energy of 300MWp by 2010 and 1. 8 GWp by 2020 of PV cells installed (Grau et al. 2011). This policy and its comparative advantage on labors result in excessive growth of China PV industries, making China’s world market share skyrocketing from 1 per cent in 2001, 5 per cent in 2005 to 50 per cent in 2010.In 2012, four of the top five PV producers are Chinese overtaking US manufacturers which occupy 27 per cent in 2006, decreased to 5 per cent in 2010 of the total world share (The Kearney Alliance 2012). Why has China’s PV grown so big so fast? There are a number of reasons why the PV industry in China has experienced tremendous growth withi n a short span of time. For instance, China produced about 1 per cent of the world’s solar cells in 2001, and by 2010 it produced nearly almost half (The Kearney Alliance 2012).The same rate of growth was achieved by Japan and Germany during their PV industry expansion; however the key difference is it took them twice as long (The Kearney Alliance 2012). First, such fast paced growth would not be possible without assistance from the government. The Chinese government has been providing many different kinds of assistance to the manufacturers to promote the growth of the PV industry in China. The government’s policy to boost the industry came in the form of loans, tax credits and grants.Additionally, some of the resources required for manufacturing of PV cells were subsidized or discounted to encourage manufacturers to produce more. In 2011, the Chinese government initiated a ‘Five-Year Plan’ to induce further growth of the PV industry well into the year 201 5. Second, it is estimated that help from the government allowed some Chinese manufacturers of PV cells to have somewhere between 18-30 per cent cost advantage over their US counterparts (The Kearney Alliance 2012).The government alone is not responsible for the cost advantage enjoyed by the Chinese manufacturers; scale and vertical integration, and labour costs constitute significant part of the cost advantage. The scale and vertical integration of some of the top tier Chinese manufacturers means that they gain cost advantages due economy of scale; larger factories can produce at a lower cost, and additionally they tend to own or control majority of the companies in the supply chain as well as distributions outlets thus allowing them to maximize profit from supply, production and distributions.Moreover, labour costs are relative cheap compared to the US, especially for unskilled labour, where China has approximately 80 per cent labour cost advantage over the US counterparts (The Ke arney Alliance 2012). Third, besides the assistance and cost advantages, some, if not all, Chinese manufacturers tend to offer trade credit, where solar power customers can purchase the panels without having to pay upfront and are given 60 days payment window to complete the deal.This provides tremendous financial benefit to the customers, as they will have some time for installation of the panels without paying upfront for the panels thus the cost of downtime during the installation is not born by the customers. Finally, growth of China’s PV industry is also due to the extreme projected growth of domestic demand. In 2010, Chinese domestic demand for solar power was only 3 per cent of the world’s demand, and by the end 2014 this is expected to increase to 26 per cent (EPIA 2011). Is Chinese government providing illegal subsidies? Are Chinese manufacturers dumping their products on the U.S. market? The US government accuses the Chinese government of providing the export subsidies, which according to WTO rules is illegal. However, the Chinese government claims that the subsidies, grants, loans and discounts given to the manufacturers are intended to promote the solar power industry and make it cost competitive with conventional power sources. It is worth noting that it’s not just Chinese government that provides subsidies, the US also provides substantial subsidies to its solar power industry albeit to a slightly lesser extent and lower amount in dollar terms.For instance, the US government does not provide land grants or discounts, and the total stimulus loan/loan guarantee is only US$1. 3 billion compared to US$30 billion from the Chinese government (Goodrich et al. 2011). The US Department of Commerce accuses Chinese manufacturers of dumping PV cells on the US market. According to the WTO (WTO, 2012), dumping occurs when a company exports a good to foreign market at a price less than the price it normally charges in its domestic market. T he US considers Chinese economy as non-market economy, thus the Chinese domestic price of PV cells cannot be determined directly from the Chinese market.Therefore, third or surrogate country needs to be chosen in order to determine the fair value of Chinese PV cells. The U. S Department of Commerce has chosen Thailand from a list of 6 countries as the surrogate country. This is unlikely to reflect an appropriate normal price for the Chinese PV since the costs of PV production in China is normally lower than those in Thailand. Possible consequences Both sides are currently still waiting for ITC's final determination. If an affirmative determination is made in late November that imports of PV cells from China, no matter being assembled into modules or not, leads to US omestic industry being or is threatened to be materially injured, Commerce will issue the Anti-Dumping and Countervailing duties order. Back when the preliminary determinations was announced earlier this year, in which t he DOC assessed countervailing duties ranging at a lower rate, most Chinese manufacturers breached a sight of relief and continue their business in U. S. as before. However, DOC's final determination assessed significant higher countervailing duties at 14. 78 per cent -15. 24 per cent, comparing to its 2. 9 per cent-4. 3 per cent in the preliminary (US DOC 2012), undoubtedly it will have a severe impact on China's manufacturers and global solar industry. As the subject of DOC and ITC's investigation is PV cells that are manufactured in China, Chinese firms could shift manufacture or directly purchase PV cells from other countries to avoid tariffs on modules made of Chinese cells. An ideal location is Taiwan, which is already a robust solar cell manufacturing market. Although it is 8 per cent higher than using its domestic produced cells, cells made in Taiwan still have a 10-22 per cent cost advantage than the ones in the US (Wesoff 2012).Not to mention its relative closeness to Chin a. However, using PV cells from other countries other than the US and assembles into PV modules is not a proper long-term strategy. The US could also initiate another investigation into Chinese PV modules assembled, using other countries' cells. Thus, this is only a transitional strategy for Chinese manufacturers before China's domestic demand for PV products picks up to ameliorate industry's excessive supply situation. On the other hand, the imposition of high countervailing and anti-dumping duties might also affect the U.S. solar industry. In 2011, manufacturing only contributed 24000, or 24 per cent of the total employment in the solar industry (The Solar Foundation 2011). Punitive tariffs against Chinese cells will lead to a price jump on PV cells and modules in the US market, it causes the cost of solar projects in the US to increase and the implementation and demand for solar products to decline, which ultimately transits into lower employment in other sectors in the PV indust ry. The Coalition for Affordable Solar Energy commissioned a study showing that a 50 er cent tariff will indeed boost employment in the cell and module manufacturing sector. However, this tariff jump would also result in a huge decrease in employment from slowing-down discretionary spending by solar buyers and an overall demand decrease in other sectors in the whole PV industry. The net impact on total employment would be 15 per cent -40 per cent decline in the US PV industry compared to its 2010 numbers (Berkman et al. 2012). This means the resurrection of the US cells and module manufacturers is at the cost of the rest and the vast majority of the US PV industry.Another potential outcome is that Chinese manufacturers could retaliate against imposed tariffs. The US currently still has a huge positive net export of polysilicon and PV manufacturing equipment to China. In 2011, China attributed to around 30 per cent of the US total net exports of polysilicon and 60 per cent of PV capi tal equipment (GTM 2012). To protest against imposed tariffs and duties, Chinese manufactures could ramp up their own production of polysilicon or turn to other countries to fill the gap, effectively cutting out the US firms in the solar supply chain.Conclusion In sum, Governments in most industrial countries including the US and China have been promoting clean energy technology in recent years. Among the world’s solar producers, China’s booming renewable energy industry, especially solar industry has dominated world solar markets and challenges American leadership. President Obama affirmed the US’s concern about clean energy technology: ‘†¦to make sure that we win the competition. I don’t want the new breakthrough technologies and the new manufacturing taking place in China and India’ (Morris et al. 012, p1). Meanwhile the subsidy to energy, including solar industry, has been successful in China (rapidly increase its market share of wor ld polysilicon production), the US policy subsidy on clean energy has not brought any expected result, even failure (i. e. bankruptcy of Solyndra–the California solar firm) (Robert et al. 2010). Trying to protect the domestic solar industry by preventing other country’s polysilicon exports is highly unlikely to be a wise and fair policy.In particular, countervailing and anti-dumping duties would result in a significant decline in exports of polysilicon and PV manufacturing equipment to China as well as a fall in employment. Indeed, China could have several ways rather than bring the case to the WTO in responding to the trade barriers imposed by the US, but what the US needs to consider its long term benefit. The competitive price of Chinese solar as a cheap source of clean energy which potentially enhances the US economic growth, creates jobs for Americans and tackles with climate change.ReferenceBerkman, M, Cameron, L ; Chang, J 2012, ‘The employment impacts of proposed tariffs on Chinese manufactured photovoltaic cells and modules’, The Brattle Group, Washington, D. C. viewed 16 September 2012, . EPIA see European Photovoltaic Industry Association. European Photovoltaic Industry Association 2011, ‘Global market outlook for Photovoltaics until 2015’, viewed 12 Oct 2012, http://www. epia. org/index. php? eID=tx_nawsecuredl;u=0;file=fileadmin/EPIA_docs/publications/epia/EPIA-Global-Market-Outlook-for-Photovoltaics-until-2015. pdf;t=1351601058;hash=65fb67c830a17dc3384646f83c30e104Goodrich, A, James, T ; Woodhouse, M 2011, Solar PV manufacturing cost analysis: US competitiveness in a global industry, Stanford University, viewed 25 Oct 2012, ;lt; http://www. nrel. gov/docs/fy12osti/53938. pdf;gt;. Grau, T, Huo M ; Neuhoff, K 2011, ‘Survey of photovoltaic industries and policies in Germany and China’, Climate Policy Initiative, Berlin. GTM 2012, ‘U. S. Solar Energy Trade Assessment 2011: Trade Flows and Dom estic Content for Solar Energy-Related Goods and Services in the United States’, Greentech Media, Washington, D.C. Morris, AC, Nivola, PS ; Schultze, CL 2012, ‘Clean energy: revisiting the challenges of industrial policy’, The Brookings Institution, Washington, DC. Roberts, MJ, Lassiter, JB ; Nanda, R 2010, ‘US Department of Energy & Recovery Act Funding: bridging the â€Å"Valley of Death† ‘, Harvard Business School. The Solar Foundation 2011, ‘Nation Solar Jobs Census 2011’, viewed 12 October 2012, . The Kearney Alliance, 2012, ‘China solar industry and the US anti-dumping/anti-subsidy case’, China Global Trade.USDOC 2012, ‘Fact sheet: Commerce finds dumping and subsidization of crystalline silicon photovoltaic cells, whether or not assembled into modules from the People's Republic of China’, Department of Commerce, The United States of America, viewed 15 October 2012, . Wesoff, E 2012, ‘Breaking n ews: Commerce Dept. Chinese solar panel dumping verdict is now in’, Greentech Media, viewed 17 October 2012, . WTO see World Trade Organisation World Trade Organisation 2012, ‘Anti-dumping, subsidies, safeguards: contingencies, etc’, The World Trade Organisation, viewed 10 October 2012, http://www. wto. org/english/thewto_e/whatis_e/tif_e/agrm8_e. htm

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