In the stories of F. Scott Fitzgerald, everyone in the end experiences defeat. To what extent is this statement true? Essay

In â€Å"Flappers and Philosophers†, the ideas of defeat and humiliation are presented throughout the collection. The author produces a profound exploration into the lives of the female protagonists who depend on popularity and sexuality as sources of power. It also deals with the ramifications of being an outsider during that period of time and how defeat is nearly always the result from the need for acceptance into those societies. Although many of the characters suffer the negative consequences of their actions in their respective societies, some do achieve victory, but at a price. To understand the characters in the story we must first deal with the social, cultural and historical contexts. These short stories were written between the world wars and among the interwar boom, before the Great Depression. This era was very important of women, young people, the arts and creative industries. The blossoming of youth culture also contributed to the rise of â€Å"flapperdom†. Before this period, we had the Victorian era, where women were subjected to immense conservatism, and their emotions and opinions were unjustly oppressed. Women were expected to cover up and know their place in the social hierarchy. â€Å"Flapperdom† introduced the thought of independence and non-conformity. The epitome of this idea would be the character of Ardita in â€Å"The Offshore Pirate†. She is an allegory that represents a whole shift in the mode of behaviour, a kind of liberation into something that is more dangerous, alive and sexy. In many of the short stories the female protagonist does suffer from defeat as shown in the short story â€Å"The Ice Palace†. Sally Carrol is a Southern Belle, who wishes to become a flapper. She does not succeed in this matter, as she is forced to return to her home after a trial run in the North. The author has used two distinct sematic fields to suggest the binary opposition between the North and the South. The South is warm and bright, with â€Å"the sunlight dripp[ing] over the house light golden paint.† The central motif in the paragraph of the short story is liquid light and that gives the reader a feeling of warmth and laziness. The two worlds are juxtaposed when the author describes the setting of the North. The rigid nature of the lexical choice in part III of the story shows how Sally Carrol is a microcosm of the South transplanted into the hostile nature of the North. â€Å"It was very cold† and Sally Carrol â€Å"slid uncomfortably† into her clothes and she â€Å"stumbled† up to the diner. The semantic field of ice is used here as seen in the words â€Å"sliding† and â€Å"slippery†. This gives the reader the impression that Sally Carrol is very awkward in this setting and not in control as a flapper should be. Sally Carrol judges things by how long it has been around, not in terms of monetary value. The difference in value systems between the North and the South makes it impossible for Sally Carrol to integrate into the Northern society, a society fuelled by money. Therefore Sally Carrol has the hallucination of Margery Lee, who is a personification of the old South. Sally Carrol experiences defeat and cannot follow her dreams of living as a flapper because of the restrictions in her abilities to adapt to a new environment. However, one may look at it the other way and say she has actually succeeded in living the life she is most suitable for – the Southern life. This is evident in the lexical shift in part VI, where the surroundings are smothered in the â€Å"wealth of golden sunlight†. This parallels part I of the story provides a certain unity that enables the reader to feel like the Southern life is where Sally Carrol belongs. In â€Å"Bernice Bobs Her Hair†, Bernice deals with the problem of being an outsider to an elite society full of successful people. She is seduced into â€Å"flapperdom† by Warren, who initiated the process by flirting with her. The remark that she had an â€Å"awfully kissable mouth† made her excited enough to change into someone who is obsessed with superficiality. She becomes an artificial flapper, a shadow of Marjorie. The struggle for acceptance is evident through the fact that even though the ideas underpinning their belief system is from two different paradigms, Bernice still learns to grasp onto that vibrant and fleeting youth. Stripped from her original values, and seduced into the world of â€Å"flapperdom†, Bernice only superficially assimilates into the elite society but fails at the end when her bluff is called. However, through this defeat, one may argue that she has actually achieved more. Liberating herself from the shackles of the Victorian era, she started to exhibit the traits of a flapper when she cuts off Marjorie’s hair. Not only did this provide an intense climax to the short story, it also explains that although the character experiences defeat, the resultant victory can compensate for that. Although on the surface â€Å"The Cut-glass Bowl† is a story about an enormous glass punch bowl ruining the life of Eveyln Piper, when you look at it closely, the story traces the deterioration of her â€Å"flapperdom† and her marriage to a prosperous hardware dealer whose business declines over several years. The author foreshadows this deterioration at the beginning of the text when he says that â€Å"the struggle for existence began†. â€Å"The bonbon dish lost its little handle†; a â€Å"cat knocked the little bowl off the sideboard†; â€Å"the wine glasses succumbed to leg fractures†. Throughout the story, an idea of superficiality comes into play. The cut-glass bowl is a conceptual metaphor that not only embodies the character Evelyn, but also outlines the features of her marriage. It is â€Å"as hard as† she is and â€Å"as beautiful and as empty and as easy to see through.† Her marriage is empty as she has to see someone else other than her husband to relight her fire. Even her words contain nothing, as they are in italics suggesting a false and unnecessary emphasis. Evelyn lives in a society obsessed with external beauty and superficiality. The author finds it necessary to describe the â€Å"beautiful† Mrs Harold Piper with her â€Å"young, dark eyes†. Evelyn experiences defeat as she is married and no longer able to capture that fleeting youth and vibrant energy that formed the basis of her existence. She fails to retain her physical beauty in a society where it is valued the most. Again, towards the end, she gains a sense of victory when she destroys the cut-glass bowl that has destroyed her life. Even here, the author stresses how vulnerable and helpless Eveyln is as she struggles to smash the bowl. The lexical choice here is in the sematic field of difficulty and uneasiness. She had to â€Å"tighten her arms† and her muscles were â€Å"tauted†. Her energy was â€Å"desperate† and â€Å"frantic†, and her effort was â€Å"mighty† and â€Å"strained†. The author cleverly uses the juxtaposition of the size of the bowl with the size of Evelyn to accentuate the difficulty she experiences. Furthermore, the repetition of the phrase: â€Å"she must be quick – she must be strong† shows that although she does suffer defeat, victory and liberation can be achieved through great difficulty. In his short stories, F Scott Fitzgerald has made sure everyone at the end suffers some kind of defeat. This is achieved by Ardita in â€Å"The Offshore Pirate† being humiliated, Sally Carrol in â€Å"The Ice Palace† returning to her hometown, Bernice in â€Å"Bernice Bobs Her Hair† bobbing her hair, and Evelyn in â€Å"The Cut-glass Bowl† losing the essence that used to define her as a flapper. However, the author has also made us realise that in life, even though we may experience defeat and humiliation, we must look within ourselves and find that personal victory that will surpass the negative feelings of loss.

Groupon Case

9-511-094 REV: JUNE 13, 2011 SUNIL GUPTA RAY WEAVER DHARMISHTA ROOD Gro oupon n e as of On November 29, 2010, the technology industry wa buzzing with rumors o Google’s bid for Group pon, a two-y year-old web bsite that pro omoted daily deals offeri y ing deep dis scounts from local merch hants. Google reportedly offered at lea $3 billion, eclipsing a r e o ast rival $2 billion bid from Y Yahoo. And as Groupon re a esisted, Goog quickly rai gle ised its offer t as much as $6 billion. 1 to s Ind dustry expert and financ analysts were sharply divided on Google’s mo and Grou ts cial w y ove upon’s poten ntial value. A multibillion A n-dollar valua ation for a com mpany that is in a busines with virtua no s ss ally barrie to entry an is younger than my tod ers nd r ddler is absurd Forrester Research reta analyst Su d,† ail uchitra Mulpuru said blun 2 David Kirkpatrick, a former Fort ntly. K tune magazin columnist, sniffed, â€Å"Gro ne oupon isn’t even a techn nology company, for goo odness’ sake. It’s a discou unter that ha appens to us the se Intern net. †3 Bu others rega ut arded the company highl emphasizi ly, ing its specta acular growt Forbes cro th. wned Group pon the â€Å"fast test growing company ev ver,†4 while m media indust veteran a try and journalist John t Battel marveled, â€Å"I’ve never seen anything like it—we since Goog And just as Google la lle s g ell, gle. t apped the Ye ellow Pages in a fraction of the time, Gr n roupon seems to be on trac to do the sa s ck ame to Googl 5 le. † At the end of a frantic week, Groupon surprised m t n many observe by rejecti ers ing Google’s offer. Shortl after, the company anno ly ounced that it had raised $ $950 million f from private investors, and was d rumored to be laying the groun ndwork for an initial publ offering. T events pr a lic The rompted a br roader debat about whether Silicon Valley—whic had recent seen very high valuati te V ch tly y ions for Face ebook, Twitte and Zyn er, nga—was sho owing signs of another b bubble. Jeff Clavier, man naging partn at ner SoftTe VC and a well-known angel inves ech n stor, predicte â€Å"There m not be a b implosion but ed, may big n, down the road there will be a bu n unch of blood and tears. †6 d Com mpany Origins Gr roupon, a por rtmanteau of the words â€Å"group† and â€Å" f â€Å"coupon,† gr rew out of Th Point, an o he online comm munity for col llective action The site hel n. ped people p propose and promote soci campaigns such ial s as com mpany boyco and chari fundraiser Each camp otts ity rs. paign’s creato specified i â€Å"tipping p or its point,† the pa articipation le evel that was required befo supporter were called to act. The ti ore rs d ipping featur was re _______ _______________ ____ ___________ ________________ _______________ _______________ _______________ ________________ ______ Professo Sunil Gupta an Ray Weaver an Research Asso ors nd nd ociate Dharmishta Rood prepared th case. The auth his hors thank Paul Bu utler for contribu uting to online dat collection. This case was develope from published sources. HBS case are developed solely as the basis f class ta c ed es for discussi ion. Cases are not in ntended to serve as endorsements, sources of primary d s data, or illustrations of effective or ineffective manageme s ent. Copyrig  © 2011 Presiden and Fellows of Harvard College. To order copies or request permission to reproduce ma ght nt H T n aterials, call 1-800-5 545-7685, write Ha arvard Business School Publishing, Bo oston, MA 02163, or go to www. hbsp o p. harvard. edu/edu ucators. This publica ation may not be d digitized, photoco opied, or otherwise reproduced, poste or transmitted, without the permis ed, w ssion of Harvard Bu usiness School. This document is authorized for use only by Boshen Wang in MACC 402 – Groupon taught by William Forster from August 2011 to December 2011. For the exclusive use of B. WANG 511-094 Groupon designed to allocate the community’s resources only when a campaign had broad support. It also gave advocates of a cause an incentive to recruit others. The Point was founded in 2007 by Andrew Mason, then a master’s student in public policy at the University of Chicago. When an investor approached him with a funding offer, Mason decided to drop out of school and focus on the project full-time. But with an audience too small for advertising to sustain it, The Point initially struggled to generate revenue. Then Mason noticed that many of the most popular campaigns banded consumers together to get volume discounts from retailers. He decided to try pre-arranging similar deals and promoting them on the site for commissions. 7 The experiment was such a success that in November 2008, it was spun off as a separate business, with 27year-old Mason its Founder and CEO. Two hallmarks of Groupon—a focus on local merchants and a self-imposed limit to a single promotion each day—were designed to cope with minimal scale and resources. Mason explained: Have a different [offer] but only one every day so our very small community will still be large enough so that if we channel it all into one thing we’ll be able to achieve the critical mass that we need in order to make a success†¦. That’s part of why we went local. It became possible to go around to the people in our office building for starters. We kicked it off with just 500 people that we got signed up on our mailing list. Sales representatives began pitching merchants across Chicago on the idea of promoting their businesses by selling aggressively discounted vouchers for services to Groupon’s customer base. In return, Groupon would take a cut of each sale. The concept resonated with both business owners and consumers, and Groupon quickly expanded to other cities , beginning with Boston, New York, and San Francisco. After six months, the company had run more than 100 deals and had acquired 60,000 email subscribers. 9 Running and Marketing Groupon Promotions Merchant Profiles and Sales To generate deals, Groupon initially relied on an inside sales team in Chicago that called on local merchants around the country, closing business over the phone and email. Over time, it also began building an outside sales force of account executives based in local markets, starting with large population centers and other cities in which its business had grown rapidly. Though Groupon featured a wide variety of businesses, some themes emerged. Services predominated, though deals for products were not uncommon, especially baked goods and other foods. There was a strong emphasis on leisure, entertainment and recreation (Table A), and occasionally on novel experiences such as helicopter tours and exotic car rentals. Utilitarian services were less popular. One early flop was pet daycare: â€Å"We learned over time that people don’t want to experiment with who is watching their animal,† a company spokesperson explained. 10 2 This document is authorized for use only by Boshen Wang in MACC 402 – Groupon taught by William Forster from August 2011 to December 2011. For the exclusive use of B. WANG Groupon 5 511-094 Table A e Merchan Category Mix nt M Category C Activities A Dining D Salon & Spa S Merchandise M Membership (e. g. Gym) M Tourism T Hotel H % of Deals 29% 28% 20% 15% 7% 1% 1% Source: Steve Carpenter, â€Å"What Makes Groupon Tick,† TechCrunch, Ma 2, 2010. Based on analysis of a deals run in Q 2010. s † ay d all Q1 A survey cond ducted by mar rketing servic firm Merc ces chantCircle fo ound that loca businesses faced al a vari iety of challen nges in reach hing customer For one th rs. hing, they had very tight b d budgets: more than e half of the 8,500 bu o usinesses surv veyed spent le than $2,50 on marketi annually. They often lacked ess 00 ing . he expertise to adopt new media and te a m echnologies (Figure A). Consequentl the lure of an ly, outsourced online promotion with no up-fro expense w compellin And comp w ont was ng. pared to tradi itional adver rtising, Group pon’s impact was relatively easy to obse w y erve and mea asure. Figur A re Local Business Mark B keting Budgets and Preferr Outl ets red Facebook or other social me edia pro? le Online yell low pages or local n news site Custom emails mer Blog Print yell low pages Direct mail D Print n newspaper 0% 10% 20% 30% 4 40% 50% 60% 70% Source: MerchantCircl Merchant Con le nfidence Index su urvey, February 2 2011. Prepa aring the De eal When a merch hant signed on for a prom o motion, it wo orked with G Groupon to d decide the sp pecific produ or service to be offered and its disco uct d ounted price, ordinarily at least 50% of list. As wit The , ff th Point’ social camp ’s paigns, the de was valid only if the nu eal umber of buy yers achieved a tipping poi set int by the merchant. Other terms included the voucher’s ex e O xpiration date and, in som cases, lim on e me mits 3 This document is authorized for use only by Boshen Wang in MACC 402 – Groupon taught by William Forster from August 2011 to December 2011. For the exclusive use of B. WANG 11-094 Groupon individual or total purchases. Groupon’s standard agreement was a 50/50 split of voucher revenues, but merchants sometimes negotiated better terms. Groupon’s editorial staff wrote advertising copy to promote each offer. Their descriptions were intended to be entertaining as well as informative, oft en striking an irreverent and offbeat tone. One early deal for a Swedish massage, for example, was accompanied by a FAQ that read: Q: Where is Sweden? A: Sweden is a moon colony where aliens have been teaching American astronauts advanced massage technique for hundreds of years. Q: What is so special about a Swedish massage? A: First of all, the technique comes from outer space. Second of all, it is very advanced. Finally, it relieves the body of lots of stress. Q: How big are the masseuses’ hands at Lincoln Park Massage? A: Good question, for hand size is extremely important in massage. They’re a size 25 on average. Q: That’s not like, disgustingly large monster hands, is it? A: No, that’s just a little above average for humans. Perfect for masseuses. Q: There’s gotta be a catch. Where is the fine print? A: There is no fine print. Here are the completely reasonable stipulations on today’s deal in totally normal size print†¦. The deal terms, ad copy and an accompanying photograph were then assembled for online presentation (see Exhibit 1 for an example, and Exhibit 2 for representative deals in selected cities). Groupon scheduled promotions according to merchant preferences, though it sometimes committed only to a launch window rather than a specific date. Running the Deal Consumers signed up at Groupon. com to get their city’s daily deals, and could request notifications via email, Facebook or Twitter feeds. Each deal was posted online at midnight; outgoing alerts followed in the early morning. Most Groupons were available for purchase for only 24 hours, and a virtual hourglass counted down the remaining time. The deal page also showed a running tally of vouchers sold throughout the day. Groupon processed consumers’ online transactions, then paid out the merchant’s cut of the revenue in three equal installments 5, 30, and 60 days later. Online accounts contained each subscriber’s available Groupons, which could be printed in advance or presented to the merchant on a smartphone. Groupons for online stores included a unique code to be entered at checkout. Although the vast majority of Groupons featured local businesses, national brands were occasionally promoted with deals that were coordinated across cities. Groupon ran its first such deal in August 2010 with The Gap, offering $50 worth of merchandise for $25. It was a huge hit, generating $11 million on sales of 445,000 units. 11 Subsequent offers from Nordstrom Rack and Barnes & Noble were even more popular. And in February 2011, a deal touting new routes on Virgin America sold out in eight minutes. 12 These big promotions generated buzz that increased Groupon’s brand awareness and motivated new customers to sign up. This document is authorized for use only by Boshen Wang in MACC 402 – Groupon taught by William Forster from August 2011 to December 2011. For the exclusive use of B. WANG Groupon 511-094 Groupon Subscribers Marketing Groupon to Consumers Many people first learned about Groupon when friends or family alerted them to deals through email and social media. T o encourage this word of mouth, Groupon gave customers $10 toward a future purchase for each referral. 13 It also set up an affiliate marketing program for bloggers and websites to earn commissions of up to 15% on referred traffic. 4 Facebook and Twitter were Groupon’s top referring sites, accounting for 44% and 8% of traffic respectively in January 2010. 15 Groupon also got attention for a contest in which one customer was challenged to â€Å"Live Off Groupon† for an entire year. This â€Å"Groupawn† would be provided an unlimited supply of Groupons for things to eat, do, and buy across America, but could not spend any cash. If successful, he would win a $100,000 prize. Several hundred hopefuls applied, and 28-year-old Chicagoan Josh Stevens was chosen as the winner. Stevens began the challenge in May 2010, posting updates and pictures in various social media along the way. 6 Over time, Groupon began supplementing these efforts with paid advertising, spending h eavily on Google AdWords and AdSense. And in February 2011, the company launched its first TV campaign with a Super Bowl ad, for which each spot cost a reported $3 million. 17 The campaign tried to capture Groupon’s quirky sense of humor, but became a lightning rod for controversy. Each spot featured a celebrity who first appeared to be promoting a social or political cause, then segued into a Groupon endorsement. Actor Timothy Hutton, for example, intoned: The people of Tibet are in trouble. Their very culture is in jeopardy. But they still whip up an amazing fish curry. And since 200 of us bought at Groupon. com, we’re getting $30 of Himalayan food for just $15 at a Himalayan restaurant in Chicago. Many people took offense, accusing Groupon of trivializing and exploiting the Tibetans’ plight. Although Groupon was the second-most mentioned Super Bowl advertiser in online discussions, much of the conversation was unflattering: negative sentiment spiked from 10% in January to 60% the day after the game. 18 One viewer tweeted, â€Å"Groupon seems to have achieved the unique feat of paying $3M to lose customers who previously loved them. 19 Mason initially defended the campaign as tongue-in-cheek, but when criticism persisted, he decided to pull the ads. 20 Consumer Response Groupon became very popular among a customer base that tended to be young, well-educated, unmarried, and relatively affluent. Over three-fourths of subscribers were women (Exhibit 3). Consumers enthused not only about the m oney Groupon saved them, but also about its convenience, variety, and other benefits. I Love Groupon! From my first purchase, I have been hooked. I have purchased several Groupons and have never had a problem redeeming them. The merchants have always been appreciative of my participation in the Deal. 21 I think I’ve gained weight trying all the restaurant and bakery deals! But that’s where the cycling and exercise deals come in, along with some pampering specials. I’m on a wellbalanced Groupon diet! 22 5 This document is authorized for use only by Boshen Wang in MACC 402 – Groupon taught by William Forster from August 2011 to December 2011. For the exclusive use of B. WANG 511-094 Groupo on Groupon al llows me to discover even d nts/activities in NYC—I’m a tourist in my hometow m wn 2 th hanks to this savvy group. 3 s Redem mptions typic cally began with a big su w urge in the f first month a after a deal w run, the was en declined to a fairly stea t ady, lower rat and finally spiked again shortly befo expiration (Figure B). te, y n ore n Figure B Typical Gro oupon Redem mption Pattern n Source: Me erchant Welcome Guide, downloa e aded from www w. groupon. com/p pages/day-of-yo our-feature. But so ome vouchers were never used. Buye sometime reported fe s r ers es eeling â€Å"Grou upon remorse e† when offe that had seemed irresi ers s istible became less appeal ling in retrosp pect. One Ch hicago residen nt recalled th hinking, â€Å"Wh doesn’t wa a Segway tour? † as he spent $160 for four tick ho ant y e kets, only to let them expire because he never got around using them. And a Boston wom with $25 in voucher h a g man 50 rs from vario group bu ous uying sites lam mented, â€Å"Ther just isn’t en re nough time in the day to d it all. I mad n do de a spreads sheet, and it’s so sad, the ey’re all com ming due. † Gr roupon didn’ disclose no ’t on-redemptio on rates, but various estim mates put the number betw e ween 10 and 30 percent. 4 Many state l laws, howeve er, stipulated that vouchers could be re d edeemed for their purchas price after e t se expiration, of ften for several years. Th remedy was explaine on Group his w ed pon’s websit though it was unclea how man te, t ar ny consumer were aware of it. rs e Value to Merchan t nts Positive Reactions R Many merchants he eartily endors Groupon for its ability to raise awa sed y areness, increa traffic, an ase nd 25 acquire ne customers Among them was Gerric Adachi, ow ew s. ck wner of Aiea G Grill in Portla and, Oregon:2 The concept is sheer genius The web-s e s. avvy, interac ctive format is so well th hought out forwar and backw rds wards. Who ever heard of acquiring 51 new, quali customers in one day e f 16 ity with no money up front? You were also righ about the G n w ht Groupon mem mber being a high grade custom mer, operatin at a sop ng phistication level far ab bove that o the typic of cal bargain hunter r/coupon cutter. Bill Ra aupp of San Diego Desserts concurred: â€Å"There is cle D : early no othe program th creates th er hat his brand aw wareness, with a positive direct effect to my bottom line. Groupo far outweighs any othe h d o m on er 26 advertisin or free adv ng vertising prog gram out there e. † 6 This document is authorized for use only by Boshen Wang in MACC 402 – Groupon taught by William Forster from August 2011 to December 2011. For the exclusive use of B. WANG Groupon 511-094 American Apparel ran a popular deal in which it offered $50 worth of clothing for $25, selling 133,000 vouchers. Afterwards, a company insider listed several positive effects. First, customers spent an average of $20 above the voucher’s face value when cashing in the deal. The promotion also attracted many new customers: â€Å"The killer was email address acquisition†¦. We converted approximately 25% of in store redemptions into signing up for our email list†¦ which is on track to generate an additional five to six figures in online revenue. † Finally, American Apparel negotiated a contract that gave Groupon â€Å"much much less than half† of the v oucher revenue. 27 Negative Reactions Despite such enthusiasm, Groupon’s effect on merchant profitability was hotly debated. One wellpublicized critique came from the owner of Posie’s Bakery and Cafe, who called using Groupon â€Å"the single worst decision I have ever made as a business owner†: I [told the Groupon sales representative] we would have to get at least 50% to cover our costs of product†¦. What I didn’t think clearly enough about was that that margin we mark up is what covers all of our other costs†¦ like staff, rent, utilities, etc. Our overhead is roughly $25,000/month, and this decision was about to make it so that we didn’t cover any of those other costs. [W]e met many, many wonderful new customers, and were so happy to have them join the Posie’s family. At the same time we met many, many terrible Groupon customers†¦ customers that didn’t follow the Groupon rules and used multiple Groupons for single transactions, and argued with you about it with disgusted looks on their faces, or who tipped based on what they owed (10% of $0 is zero dollars, so tossing in a dime was to them being generous). After three months of Groupons coming through the door, I started to see the results really hurting us financially. There came a time when we literally could not make payroll because at that point in time we had lost nearly $8,000 with our Groupon campaign. 28 U. S. Toy, a retailer in Kansas City, was also unsatisfied. It offered $20 worth of merchandise for $10, half of which went to Groupon. Customers snapped up 2,800 coupons, but managers became discouraged by their shopping patterns. Co-CEO Jonathan Freiden said, â€Å"It didn’t drive in new people, and the people that were coming in didn’t spend even our average sale. It was just sad. † He estimated that U. S. Toy lost money on three-quarters of the transactions, and that 90% of purchasers were existing customers. 29 Profit Drivers It became clear that the success of any particular daily deal depended on a ariety of factors, including the mix of new versus existing customers, upside spending at the time of redemption, and success in converting discount buyers into regular customers. Several surveys tried to measure these and assess merchant satisfaction, often with contrasting results (Table B). b a American Apparel’s gross profits averaged 53% of sales, accor ding to MSN Money. b Customer mix was not measured in any of these surveys. It was believed to vary widely, but a common assumption was that half of Groupon buyers were new customers. 7 This document is authorized for use only by Boshen Wang in MACC 402 – Groupon taught by William Forster from August 2011 to December 2011. For the exclusive use of B. WANG 511-094 Groupon Table B Merchant Surveys about Experiences with Groupon and Other Daily Deals Merchant Responses Not reported 80 150 1,568 Promotional Partner(s) Groupon Groupon and others Groupon Groupon and others Spending Over Face Value avg redeemer spent 60% over face value Not asked 41% of redeemers exceeded face value Not asked % of Redeemers Who Made 1+ Repeat Visit 22% 19% 25% Not asked % of Merchants Who Would Run Another Deal 95% 93% 57% 45% Source Groupon Yipit daily deal aggregator Rice University marketing prof. MerchantCircle Sources: www. grouponworks. com/why-groupon; Ian Sherr, â€Å"Online Coupons Get Smarter,† The Wall Street Journal, August 25, 2010; Jim Moran, â€Å"Local Social Commerce: The Explosion of Group Buying,† blog. yipit. com, August 19, 2010; Utpal Dholakia, â€Å"How Effective Are Groupon Promotions for Business? †, September 28, 2010; MerchantCircle Merchant Confidence Index survey, February 2011. One cartoonist, poking fun at business owners’ potential naivete, summed up the dilemma this way: while Groupon â€Å"may bring in lots of customers,† merchants might â€Å"lose money on every sale† (Exhibit 4). Groupon, however, argued that bad outcomes were rare: 95% of merchants it surveyed were satisfied with their Groupon experience, and 96% would recommend it to others. 30 And in August 2010, the company reported a waiting list of 35,000 businesses. 31 Aggressive Growth Encouraged by its early success, Groupon expanded rapidly, replicating its model in new markets. By the end of 2009, the company operated in about 30 U. S. and Canadian cities, and business seemed to be booming in nearly every location (see Exhibit 5 for a sample). In 2010, Groupon set its sights on foreign territories, primarily by acquiring companies that had copied its model in their home countries—first in Western Europe, then South America, then Asia and elsewhere. The pace of this expansion was perhaps unprecedented: a little more than two years after its founding, Groupon had operations in more than 500 markets in 43 countries (Table C). One media outlet marveled, â€Å"We can’t think of a company—ever—that is so aggressive about going international so big, so fast. 32 The urgency was driven in part by the sense that an early mover could establish a lasting advantage. But some observers questioned Groupon’s ability to leverage its brand and experience overseas. 8 This document is authorized for use only by Boshen Wang in MACC 402 – Groupon taught by William Forster from August 2011 to December 2011. For the exclusive use of B. WANG Groupon 511-094 Table C Groupon’s Expansion Date Nov 08 Mar 09 Jun 09 Sep 09 Dec 09 Mar 10 Jun 10 Sep 10 Dec 10 Mar 11 Countries 1 1 1 1 1 1 18 29 35 43 Cities 1 2 7 18 28 40 150 230 300 500 Subscribers (Worldwide) Savings to Datea (North America) 50k 700k 1. 7M 3M 6M 13M 50M 60M $4. 5M $18M $42M $100M $285M $400M $900M a Aggregate consumer savings on Groupons sold in North American markets. Aggregate worldwide savings were $1,800M as of March 2011. Source: Compiled from company press releases archived at www. groupon. com/press. These acquisitions were funded in part by several rounds of venture capital financing: $30 million in December 2009, $135 million in April 2010, and $950 million in January 2011. As a result of its efforts, Groupon’s revenue exploded from $33 million in 2009 to $760 million in 2010. 33 Still, significant upside remained: only 6. % of respondents to the MerchantCircle survey had run a Groupon pro motion, with another 13% planning to do so in the coming months. And the local advertising market was estimated at $100 billion in the U. S. alone. 34 Growing Pains Groupon’s growth created significant management challenges, not least of which was the training and integration of newly hired and acquired personnel. The company ended 2010 with over 4,000 employees, up from just 120 the year before. 35 By comparison, Facebook’s employee count was less than 100 after two years in business, and around 2,000 in early 2011. This environment occasionally contributed to service failures and other snafus. One Groupon in Yokohama, Japan marketed home delivery from a local restaurant of osechi, a traditional New Year’s meal. Demand overwhelmed the business, and many osechi sets arrived late or in poor condition. In response, Mason posted an apology on YouTube, conceding that his company had â€Å"really messed up. † Customers were given refunds and credited 5,000 yen toward future purchases. 36 Another incident involved an FTD Valentine’s Day promotion. When browsing FTD’s website to redeem their purchases, customers noticed flower arrangements with sale prices (for which their vouchers weren’t valid) below the Groupon discounted price. Some accused FTD of overstating its retail prices to make the offer seem more attractive. FTD and Groupon denied this and called the situation a misunderstanding. Again, refunds were offered to the affected customers. 37 Groupons also sometimes adversely affected a merchant’s regular patrons, as one customer expressed in an online vent: I go to a Sushi bar who has offered a Groupon promotion. When I arrive there, I encounter a busy, under-staffed, ran-out-of-menu items restaurant where my full price is subsidizing the half-off diners who have destroyed a perfectly fine business for the next few days after the 9 This document is authorized for use only by Boshen Wang in MACC 402 – Groupon taught by William Forster from August 2011 to December 2011. For the exclusive use of B. WANG 511-094 Groupon Groupon offer. It works for merchandise. For services, a Groupon success is a curse for regular customers/patrons. 8 To alleviate such problems, Groupon undertook a variety of measures, notably the expansion of its customer service organization to 1,000 employees. The company also rolled out new services to help merchants run promotions smoothly, including a capacity planning tool and a smartphone app for voucher verification and redemption. 39 Widespread Competition For all Groupon’s acclaim as a web darling—media had hailed it as â€Å"the next web phenomâ € 40 and â€Å"the it digital phenomenon of the moment†41—the operation was at its core remarkably simple. Groupon neither held inventory nor carried out fulfillment, relying instead on its merchant partners. Much of the technology required—email broadcasting, transaction processing, and a website that was fairly basic by Web 2. 0 standards—was mature and fairly cheap. In principle, nearly any organization with a customer database and a business sales function could offer its own daily deals. Accordingly, competitors sprang up in droves, numbering nearly 300 in the U. S. by early 2011. 42 Many closely imitated not only Groupon’s business model, but its look and feel as well (Exhibit 6). The largest of these rivals was Washington, D. C. -based LivingSocial. Although significantly smaller than Groupon, LivingSocial was also growing rapidly, and in January 2011 got a big boost by promoting $20 Amazon gift cards at half off. (Amazon had recently invested $175 million in the company. 43) Nearly 1. 2 million customers took the deal. By March, LivingSocial had 24 million subscribers, and was operating in more than 200 cities across 11 countries. 44 Established e-commerce properties also scrambled to participate in the daily deals phenomenon. Some of these served particular niches, such as OpenTable (restaurants), The Knot (wedding services), and Travelzoo (travel). A cottage industry of aggregators also emerged. These sites sourced no deals themselves, instead collecting and presenting a summary of others’ offers to earn commissions on referred traffic. But despite the onslaught, Groupon held a domestic market share of over 50%. 45 Some competitors tried to win merchants over by offering lower fees or leveraging other media. One of these was Double Take Deals, launched by Clipper Magazine, America’s largest distributor of local coupon magazines. The owner of Haydn Zug’s restaurant in Lancaster, PA, was persuaded. When I learned that Double Take Deals could pay out a higher percentage than Groupon,† he said, â€Å"I was intrigued. But when they offered me a free full-page ad in Clipper Magazine too, it sealed the deal. I knew that was something no one else could offer. †46 But perhaps the biggest threat came from the web heavyweights. Spurned by Groupon, Google began developing a competing service of its own cal led Google Offers. Industry experts expected Offers to promote deals that were relevant to a consumer’s current location, tying them to mobile phones and Google Maps (Exhibit 7). 7 Similarly, Facebook launched a location-aware product called Facebook Deals. It offered merchants a menu of promotions to suit different objectives, such as attracting new customers or encouraging repeat visits (Exhibit 8). Groupon 2. 0 To stay ahead of competitors, Groupon was developing a variety of innovations, sometimes collectively referred to as â€Å"Groupon 2. 0†. One of these was Groupon Stores, which enabled merchants 10 This document is authorized for use only by Boshen Wang in MACC 402 – Groupon taught by William Forster from August 2011 to December 2011. For the exclusive use of B. WANG Groupon 511-094 to set up virtual storefronts on Groupon’s website. From its store, each merchant could launch selfservice deals of its choosing, dictating the number and frequency of offers (Exhibit 9). Groupon took a commission of 10% of sales, rather than its customary 50%, on these promotions. Because this meant that consumers would have access to more than one deal at a time, Groupon created Deal Feed to collect and personalize each subscriber’s current offers. But the reception for these was lukewarm. One industry insider, noting that Groupon had quietly removed the â€Å"My Deal Feed† link from its navigation header, speculated that Groupon Stores was â€Å"dead on arrival. †48 A more ambitious initiative was Groupon Now. Its vision—similar to those of Google’s and Facebook’s new products—was to offer just-in-time, hyper-local promotions on GPS-equipped smartphones. Groupon Now featured a simple two-button interface: â€Å"I’m Hungry† and â€Å"I’m Bored. † The product was still in trials, but Mason made his ambitions clear: â€Å"It makes Google’s market look quite small if we get it right. It’s really tapping into the largest part of commerce in the U. S. —local. †49 Looking Ahead In a little more than two years, Groupon had transformed from an unknown startup into a global enterprise with 6,000 employees in more than 40 countries. A few months after Groupon turned down Google’s $6 billion offer, Bloomberg Businessweek reported that the company was in negotiations with bankers for an initial public offering that could value the company at $25 billion. 50 To some, Groupon appeared to be an unstoppable juggernaut. But detractors rattled off a laundry list of concerns. Could Groupon maintain the high fees it extracted from merchants? Would it be able to fight off Google, Facebook, and an army of copycats? Was the daily deals phenomenon a hot fad that would inevitably cool off? Some even questioned Groupon’s fundamental business model: did it deliver lasting value to merchants? Forrester’s Suchitra Mulpuru warned, â€Å"Everyone thinks this hyper growth is going to continue. If these merchants come to realize these consumers are not coming back, they’re not going to do more Groupons. And if they don’t do more Groupons the whole model falls apart. †51 Andrew Mason was acutely aware of these risks, laying them out in a 2010 year-end internal memo that cautioned his employees against complacency and challenged them to secure Groupon’s place among the great Internet businesses:52 Not only must we continue to beat the thousands of clones who lifted our idea and began at roughly the same time as we did, but now we must also beat the biggest, smartest technology companies in the world. They are coming HARD. If you feel a little like Frodo climbing Mount Doom, you can’t be blamed. Is it hopeless? How can we avoid the fate of the Internet darlings before us – Yahoo, MySpace, Friendster, AOL – that crashed as magnificently as they rose? Companies don’t lose to competitors – they lose to themselves. MySpace lost to itself, not Facebook. MySpace essentially handed Facebook the keys to the castle by devolving into a service that wasn’t delighting its customers. For whatever reason, it got stuck. It stopped innovating. By this time next year, we will either be on our way to becoming one of the great technology brands that define our generation, or a cool idea by people who were out executed and out innovated by others that were smarter and harder working. 11 This document is authorized for use only by Boshen Wang in MACC 402 – Groupon taught by William Forster from August 2011 to December 2011. For the exclusive use of B. WANG 511-094 Groupon o Exhibit 1 Example Daily Deal D Source: Gro oupon. com. 12 This document is authorized for use only by Boshen Wang in MACC 402 – Groupon taught by William Forster from August 2011 to December 2011. For the exclusive use of B. WANG Groupon 511-094 Exhibit 2 Deal Profiles for Groupons Run in Various Cities Median Deal Terms City Austin, TX Boston, MA Charlotte, NC Chicago, IL Kansas City, MO Omaha, NE Phoenix, AZ San Francisco, CA Tampa, FL Vancouver, BC Launch Date Sep 2009 Mar 2009 Oct 2009 Oct 2008 Nov 2009 Feb 2010 Aug 2009 Jun 2009 Sep 2009 Apr 2010 Voucher Price $43 $42 $32 $37 $31 $29 $33 $42 $28 $46 Retail Value $110 $109 $86 $94 $90 $85 $96 $106 $82 $116 Tipping Point 59 94 25 130 51 24 37 56 42 54 Months Valid 5. 7 7. 6 7. 7 7. 3 7. 4 6. 6 7. 1 8. 1 7. 7 6. 9 Source: Compiled by case writers from deals run 10/08 – 12/10, archived on Groupon. com and ThePoint. com. 13 This document is authorized for use only by Boshen Wang in MACC 402 – Groupon taught by William Forster from August 2011 to December 2011. For the exclusive use of B. WANG 511-094 Groupo on Exhibit 3 Groupon User Demogra U aphics Source: http p://www. group ponworks. com/w why-groupon/demographics. e 14 This document is authorized for use only by Boshen Wang in MACC 402 – Groupon taught by William Forster from August 2011 to December 2011. For the exclusive use of B. WANG Groupon 511-094 Exhib 4 bit A Cart toonist’s Take e Source: Tom Fishburn Marketoonist. com. ne, 15 This document is authorized for use only by Boshen Wang in MACC 402 – Groupon taught by William Forster from August 2011 to December 2011. For the exclusive use of B. WANG 511-094 Groupon o Exhibit 5 Quarterly Results in Sel R lected Cities Source: Com mpiled by case writers from arch w hived deal results on Groupon. co m and ThePoint s o t. com. 16 This document is authorized for use only by Boshen Wang in MACC 402 – Groupon taught by William Forster from August 2011 to December 2011. For the exclusive use of B. WANG Groupon 5 511-094 Exhib 6 bit Some Competing Daily Deals C D Sources LivingSocial. com, BuyWithM s: Me. com, and GiltCity. com. C 17 This document is authorized for use only by Boshen Wang in MACC 402 – Groupon taught by William Forster from August 2011 to December 2011. For the exclusive use of B. WANG 511-094 Groupon o Exhibit 7 Sample De from Goog Offers eal gle Source: http p://techcrunch. c com/2011/01/25 5/sneak-peak-google-offers/. o 18 This document is authorized for use only by Boshen Wang in MACC 402 – Groupon taught by William Forster from August 2011 to December 2011. For the exclusive use of B. WANG Groupon 511-094 Exhib 8 bit Facebo Deals ook Source: http://www. f facebook. com/deals/. e 19 This document is authorized for use only by Boshen Wang in MACC 402 – Groupon taught by William Forster from August 2011 to December 2011. For the exclusive use of B. WANG 511-094 Groupo on Exhibit 9 Groupon Stores S Source: http p://www. group pon. com/merchants/welcome. a 20 This document is authorized for use only by Boshen Wang in MACC 402 – Groupon taught by William Forster from August 2011 to December 2011. For the exclusive use of B. WANG Groupon 511-094 Endnotes Evelyn Rusli and Claire Miller, â€Å"Google Is Said to Be Poised to Buy Groupon,† DealBook, The New York Times, November 30, 1010, http://dealbook. nytimes. com/2010/11/30/google-is-said-to-be-close-to-buyinggroupon/? ref=grouponinc, accessed December 2010. Evelyn Rusli and Jenna Wortham, â€Å"Google Gambit for Groupon Raises Concern,† DealBook, The New York Times, November 30, 2010, http://dealbook. nytimes. com/2010/11/30/googles-gambit-for-groupon-raisesconcerns/? partner=rss&emc=rss, accessed December 2010. 3 Tiernan Ray, â€Å"Does Google’s Groupon Deal Make Sense? Barron’s, December 4, 2010, http://online. barrons. com/article/SB50001424052970204033804575645052537926526. html? mod=BOL_twm_col, accessed December 2010. 4 Christopher Steiner, â€Å"Meet the Fastest Growing Company Ever,† Forbes. com, August 30, 2010, http://www. forbes. com/forbes/2010/0830/entrepreneurs-groupon-facebook-twitter-next-webphenom_2. html, accessed February 2011. 2 1 John Battelle, â€Å"Thinking Out Loud: What’s Driving Groupon? † businessinsider. com, December 19, 2010, http://www. businessinsider. com/battelle-groupon-2010-12#ixzz1CGRvk7Sb, accessed February 2011. Jenna Wortham and Evelyn Rusli, â€Å"A Silicon Bubble Shows Signs of Reinflating,† DealBook, The New York Times, December 3, 2010, http://dealbook. nytimes. com/2010/12/03/a-silicon-bubble-shows-signs-ofreinflating/, accessed December 2010. Christopher Steiner, â€Å"Meet the Fastest Growing Company Ever,† Forbes. com, August 30, 2010, http://www. forbes. com/forbes/2010/0830/entrepreneurs-groupon-facebook-twitter-next-webphenom_2. html, accessed February 2011. 8 9 7 6 5 Ibid. â€Å"Groupon Saves Consumers More Than $1 Million in Less Than Six Months Chicagoans, Bostonians Save Big Using Daily Discount Website,† Globenewswire. om, April 22, 2009, http://www. globenewswire. com/ newsroom/news. html? d=163568, accessed February 2011. 10 â€Å"Daily Deals Dissected: Where the Popular Offers are and Who is Buying,† mint. com, December 24, 2010, http://www. mint. com/blog/trends/groupon-12142010/, accessed February 2011. 11 Wailin Wong, â€Å"Gap’s Gro upon Pulls in $11 Million,† ChicagoTribune. com, August 20, 2010, http://articles. chicagotribune. com/2010-08-20/business/sc-biz-0821-groupon-20100820_1_gender-and-zipcode-chicago-startup-coupon-site, accessed February 2011. 12 Owen Thomas, â€Å"Can Groupon Take to the Skies with its First Airline Deal? venturebeat. com, February 17, 2011, http://venturebeat. com/2011/02/17/groupon-virgin-america/, accessed February 2011. 13 â€Å"New on Groupon: Referral Rewards,† goupon. com, October 5, 2009, http://www. groupon. com/blog/ cities/new-on-groupon-referral-rewards/, accessed February 2011. 14 â€Å"Groupon: Collective Buying Power,† groupon. com, http://www. groupon. com/pages/affiliates, accessed February 2011. 15 Lindsay Steinbach, â€Å"Do You Groupon? † blog. compete. com, March 10, 2010, http://blog. compete. com/2010/ 03/10/do-you-groupon/, accessed February 2011. 16 â€Å"One Brave Soul. Living only off Groupons,† http://liveoffgroupon. com/about/, accessed February 2011. 17 Stuart Elliott, â€Å"Super Bowl Marketers Try to Score Points, Too, nytimes. com, January 31, 2011. http://www. nytimes. com/2011/02/01/business/media/01adcol. html? _r=1&scp=3&sq=groupon&st=cse, accessed February 2011. 18 â€Å"Like new customers? Then you’ll love Groupon,† grouponworks. com, http://www. grouponworks. com/, accessed February 2011. 21 This document is authorized for use only by Boshen Wang in MACC 402 – Groupon taught by William Forster from August 2011 to December 2011. For the exclusive use of B. WANG 511-094 Groupon Laurie Segall, â€Å"Groupon Spends Big on Controversial (Tasteless? ) Super Bowl Spots,† money. cnn. com, February 7, 2011, http://money. cnn. com/2011/02/06/technology/groupon_superbowl_ad/index. htm, accessed February 2011. Wailin Wong, â€Å"Groupon Pulls Controversial Tibet Ad,† chicagobreakingbusiness. com, February 11, 2011, http://chicagobreakingbusiness. com/2011/02/groupon-pulls-controversial-tibet-ad. html, accessed February 2011. 21 22 23 20 19 http://amplicate. com/love/groupon, opinion by User-7855g5, posted Jan 27 2011, accessed March 2011. http://www. roupon. com/press, accessed March 2011. Ibid. 24 Beth Teitell, â€Å"For Coupon Overreachers, a Chance to Recoup,† The Boston Globe, March 9, 2011, http://www. boston. com/lifestyle/articles/2011/03/09/market_for_groupon_remors e_allows_users_to_unload _coupons, accessed March 2011. â€Å"Too Much of a Good Thing? † groupon. com, September 16, 2010, http://www. groupon. co m/blog/cities/ too-much-of-a-good-thing, accessed February 2011. â€Å"What do you get with Groupon that you don’t get anywhere else? † grouponworks. com, http://www. grouponworks. com/why-groupon, accessed February 2011. William Wei, â€Å"American Apparel Source Raves about a $3 Million Groupon Deal—Reveals Sales Numbers that Will Erase ‘Ongoing Doubts about Groupon’,† BusinessInsider. com, December 14, 2010, http://www. businessinsider. com/american-apparel-groupon-2010-12, accessed March 2011. â€Å"Groupon in Retrospect,† posiecafe. com, September 11, 2010 http://posiescafe. com/wp/? p=316, accessed February 2011. Shira Ovide, â€Å"Groupon Merchant: ‘There’s a Flaw in their Business’,† DealJournal, WSJ. com, January 7, 2011, http://blogs. wsj. com/deals/2011/01/07/a-groupon-customer-speaks-why-groupon-didnt-work-for-me, accessed January 2011. 0 â€Å"What do you get with Groupon that you don’t get anywhere else? † grouponworks. com, http://www. grouponworks. com/why-groupon, accessed February 2011. 31 Rolfe Winkler, â€Å"Groupon Has a Chance to Cash This One In,† online. wsj. com, January 24, 2011, http://online. wsj. com/article/SB10001424052748703398504576100203631870050. html? KEYWORDS=groupon, accessed February 2011. 32 Pascal-Emmanuel Gobry, â€Å"Groupon Buys Up Competitors in Israel, South Africa, and India,† businesssinsider. com, January 11, 2011, http://www. businessinsider. com/groupon-snaps-up-more-internationalcompetitors-2011-1, accessed February 2011. 9 28 27 26 25 Michael Hickins, â€Å"The Groupon Frodo Memo,† WSJ Digits, February http://blogs. wsj. com/digits/2011/02/25/the-groupon-frodo-memo/, accessed March 2011. 33 25, 10, 2011, 2011, Bill Saporito, â€Å"The Groupon Clipper,† Time, February http://www. time. com/time/business/article/0,8599,2047215-1,00. html, accessed February 2011. 35 34 Michael Hickins, â€Å"Groupon Revenue Hit $760 Million, CEO Memo Shows,† The Wall Street Journal, February 26, 2011, http://online. wsj. com/article/SB10001424052748703408604576164641411042376. html? mod= WSJ_Tech_LEFTTopNews, accessed March 2011. 6  "Groupon CEO Apologizes to Japan Customers for ‘Osechi’ Mess-Up,† japantoday. com, January 18, 2011, http://www. japantoday. com/category/national/view/groupon-ceo-apologizes-to-japanese-customers-forosechi-mess-up, accessed February 2011. 37 Tim Krisher, â€Å"Groupon Users Furious about FTD Flower Deal,† HuffingtonPost. com, February 13, 2011, http://www. huffingtonpost. com/2011/02/13/groupon-ftd-deal_n_822360. html, accessed March 2011. 22 This document is authorized for use only by Boshen Wang in MACC 402 – Groupon taught by William Forster from August 2011 to December 2011. For the exclusive use of B. WANG Groupon 511-094 â€Å"Groupon CEO Apologizes to Japan Customers for ‘Osechi’ Mess-Up,† japantoday. com, January 18, 2011, http://www. japantoday. com/category/national/view/groupon-ceo-apologizes-to-japanese-customers-forosechi-mess-up, accessed February 2011. Kunur Patel, â€Å"Groupon Primes Itself to Become the Next Zappos,† AdAge Digital, March 1, 2011, http://adage. com/article/digital/groupon-primes-zappos/149141/, accessed March 2011. Christopher Steiner, â€Å"The Next Web Phenom,† forbes. com, September

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Comprehensive Mental Health Essay Example | Topics and Well Written Essays - 2000 words

Comprehensive Mental Health - Essay Example Diagnosis 3: Risk of serious malnutrition and existing malnutrition indicated by greater than 1 lb weight loss in a week, which is probably related to this current illness. The patient's feeling of heaviness of limbs, back, head, and aches in the same areas; loss of energy and fatigability, difficulty eating without the staff urging poses risks for further malnutrition. Gastrointestinal symptoms of dry mouth and somatic psychic gastrointestinal symptoms of depression manifested by wind, indigestion, diarrhoea, cramps, belching may aggravate the decreased appetite. Slight retardation at interview may indicate lassitude, and irritability may further aggravate loss of appetite, since it may represent inner tension representing feelings of ill-defined discomfort, edginess, inner turmoil, mental tension mounting to panic, dread or anguish that may lead to further loss of desire for food. Priority 1: Diagnosis 1: Risk for Injury related to hopelessness and impaired pro... ion manifested by suicide attempt in the current admission and sadness, suicidal thoughts, dejection, or episodes of weeping on assessment and feeling of rejection, despondence, self-reproach, and hopelessness indicated by expressions of feelings of discouragement, despair, pessimism about future, which cannot be dispelled. Also, current serious attempts of suicide and helplessness and worthlessness indicated by patient's statement on interrogation. Rationale of the Priority 1: Depressed individuals have negative evaluation of their worth, which often is unrealistic. They are known to have guilty preoccupations or ruminations about minor past failings, where always they blame themselves. It is very common for these individuals to misinterpret neutral or trivial everyday events as due to failure of his or her person. Their exaggerated sense of responsibility for untoward events makes them very commonly feel hopeless, helpless, worthless and powerless. Due to her previous history of paranoid schizophrenia, it would be natural to expect possibility of disorganized thought processes, such as circumstantial or tangential thinking. Although there is no evidence of such in examination or history, there is a high possibility of hallucinations and delusions. Any assessment of depression requires that the risk of self-harm or suicide be assessed. This is the first priority since patient safety if the first nursing priority. In this patien t, due to attempted suicide and related admission, this becomes a greater priority. In depressed patients, self-harm and suicide are very prevalent. Anyone showing symptoms of severe depression, especially psychomotor retardation and/or psychotic symptoms should be regarded as at high risk of suicide, as should anyone who has previously

LEADERSHIP Case Study Example | Topics and Well Written Essays - 750 words

LEADERSHIP - Case Study Example He came up with strategies to bring change in the organization without analysing what the existing culture of the organization is and it has been the major reason for failure. Harold was ineffective as a change agent and as a social architect for HTE. There are different reasons for him for considering him ineffective and the most important reasons include that he did not understand the organizational culture of HTE and implemented things without knowing what has been prevailing at HTE for years. If Harold has another chance to become the President of HTE, then it is important for him to first understand the organizational culture and how the work is performed at the company and then come up with strategies and recommendations. Also he needs to communicate the reasons for bringing change to make the change successful. Making strategies can be ineffective if it does not match with the organizational culture. Also vision is important for the company, however the execution is as important and therefore he should have planned things both in short term and in long term rather than targeting long term objectives only. Dr. Cook is a good example of transformational leadership. There are different characteristics found in how he manages the team that can be compared to transformational leader. Behaviours of a transformational leader are found in the leadership style of Dr. Cook as he sets his example as a role model that influences others. He articulated goals to make the tour a success, communicated high expectations and encouraged two-way communication that lead to high morale and increase in confidence, and all these behaviours resulted in achievement of goals of each and every individual. The leadership style of Dr. Cook can be considered as highly effective with respect to full range of Leadership Model. Highly effective leader would include the 4

Individual and society Essay Example | Topics and Well Written Essays - 1750 words

Individual and society - Essay Example This is concerned with where the idea o love originated, as well as its relationship to moral and morality systems (Carroll, 2009:557). The second of these aspects is individuals’ determination to achieve love. This is regardless of the personal and social costs. Mary Evans starts her arguments in the book by pointing out that unconditional love offers support and selfless care. In addition to this, she states that unconditional love may sometimes portray aspects of violent and abusive patterns. She in particular mentions that love can be separated from moral and morality expectations. This means that for the generations that have been given sexual freedom, there is every reason to suppose that love is fulfilled and immediate. For those people, living in the contemporary west, falling in love has however, never been easy. The author also looks at the current society in that it is richer as far as availability of goods is concerned than the previous societies. The rich live by reading newspapers and watching the television. Those individuals who have married and divorced many times still remarry. This world acknowledges that love can go and can still keep coming back. People do not acknowledge that sometimes life is better if lived with a ser ies of people instead of one lifelong partner. At the start of the twenty first century, people do not have to link marriage with love, or sex with marriage, or love with sex. The once close relationships of these ideas have been sabotaged by moral and social change. With the intention of improving the relationships between men and women, campaigners and reformers have fought for more broadminded agendas on contraception, divorce, and sexuality. It is however arguable that marriage should be build by sexual fulfillment, with no sexual inhibition. However, an argument that marriage should be happy commits people to expectations and assumptions that people

Arts and communication Essay Example | Topics and Well Written Essays - 1500 words

Arts and communication - Essay Example suous because it is here that the definition differs from a simple, nearly involuntary expression of shock or anger and the almost automatic directives of everyday communication. In order to be considered art, the form of expression must be capable of inciting an emotional response in some form of premeditated presentation. While the art itself may be to a large extent spontaneous, the display of it as such would qualify for the required element of premeditation while the encouragement to others of stopping and reflecting upon the emotions this display evokes elicits the sensuous response. Although art can be appreciated for its aesthetic values alone, it can also be put to work for a variety of other reasons, such as advocating a particular political or ideological concept. This is most often accomplished through mass media which is influenced by and influences political and corporate agendas. However, in this more fragmented society, in which socially isolated individuals become mo re susceptible to exterior manipulation, propaganda produced within the mass media has a repressing effect upon the mass population. This, in turn, has a repressing effect upon the art that is being produced as only those art forms that support the dominant conventions gain notice. This single viewpoint shared across all mediums is referred to as hegemony. However, there is another art form that subverts these restrictions. Graffiti, largely identified as an illegal art, has been used by artists as a means of expressing resistance to this hegemonic shift in countries around the world. The definition of graffiti changes depending upon who is providing the definition. Although often referred to as art, its artistic merits are frequently ignored or deemed unimportant, as is exemplified in the definition provided by the Dublin City Council (â€Å"What is Graffiti?†, 2007) in which only one of the five listed characteristics even mentions its artistic qualities: â€Å"Graffiti is

10 Facts on the Great Depression Essay Example for Free

10 Facts on the Great Depression Essay 1.Hoover reacted to growing despair by urging more voluntary action. 2.Hoover blamed the depression on international economic problems, and he was at least partially right. 3.In may 1931, the leading Austrian bank collapsed;by June , the German financial system. 4.Many World War I veterans lost their jobs during the Great Depression, and beginning in 1930, they lobied for the payment of their veterans’ bonuses, wich were not dure until 1945. 5.A bill passed congress in 1931, over Hoovers veto, allowing the veterans to borrow up to 50% of the bonuses due to them, but this did not satisfy the destitute veterans. 6.In May 1932, about 17,000 veterans marched onto Washington. Some took up residence in a ShantyTown called Bonus City that was located in the Anacostia flats outside the city. 7.In the Mid-June, the senate defeated the Bonus Bill, and most of the veterans were disappointed and resigned, and accepted a free railroad ticket home. 8. Several thousand remained, however, along with some wives and children, in the unsanitary shacks during the steaming summer heat. Among the were a small group of committed Communists and other radicals. 9. General Douglas MacArthur, the army chief of staff, ordered the army to disperse the veterans. He described the Bonus marchers as a â€Å"mob†¦animated by the essence of revolution.† With tanks, guns, and tear gas, the army routed veterans who 15 years before had worn the same uniform as their attackers. Two Bonus marchers were killed. And several others were injured. 10. Roosevelt’s caution and conservatism shaped the first New Deal. He did not promote socialism or suggest nationalizing the banks. He was even careful in authorizing public works projects to simulate the economy.

Image To Voice Converter Is Software Computer Science Essay

Image To Voice Converter Is Software Computer Science Essay Image to Voice converter is software or a device to recognize an image and convert it into human voice. The purpose of the conversion is to provide communication aid for blind people to sense what the object in their hand or in front of them. This converter is also suitable for children at the age of three until six years old for early education part. In this project converter, it consists of image processing and sound generation. For an image processing, it is a series of calculation techniques for analyzing, reconstructing, compressing, and enhancing images. When an object is inputting, an image will captured through scanning or webcam; analyze and manipulate of the image, accomplished using various specialized software applications such as MATLAB and output like a printer or a monitor. Image processing has several techniques, including template matching, KNN (K-Nearest Neighbour), thresholding and etc. For the template matching, it is a technique for finding small parts of an image to match with the template image; it is also used to identify printed characters, numbers, and other small, simple objects. KNN (K-Nearest Neighbour) is an algorithm that can work very well in practice and easy to understand. It is also a lazy algorithm that does not use the training data points to do any generalization. Besides, thresholding technique is one of the most important approaches to image segmentation. It is a non-linear operation that can converts a gray-scale image into a binary image. The purpose of image processing in this project is to analysis of a picture using techniques that can identify shades, colours and relationships that cannot be observed by the human eye. Besides that, an image processing is used to solve identification problems, i.e. in forensic medicine or in establishing weather maps from satellite photos. It assigns with images in bitmapped graphics form that have been scanned in or taken with digital cameras. For sound generation is to generate a sound through window sound library or play a wav file from computer. Problem Statement Nowadays, many visually impaired people still using blind mans stick to sense the road of the direction and object in front of them in this society. With just only a plain stick and a pair of covered eye, it is difficult for a human to get sense of their direction. Probably, they would not know what the objects around the people which had been blinded eye. As we can see the economy nowadays is getting worse, most of the people or family members were getting busy on their busy work life; they have no extra time to spend on the handicap people to give them a good care. In this case, for all the handicap people especially blind people, they have to get use to it on their living style. In order than that, this product is also available to help the small kids to improve the ability on distinguishing or differentiate the daily use objects. This is the reason why the product mentioned above was developed. Project Aim and Objective: The aim of this project is to develop an Image to Voice converter which able to recognize an image from the webcam and then convert it into sound by window sound library or wav file with good performance. To achieve the main objective of this project, there are sub-objectives need to be carry through as follows: To develop a unique image recognition algorithms for shapes and colours for real time application using MATLAB. To analyze the performance of the image recognition algorithm in term of accuracy and time processing. To develop an algorithm to convert recognized image to voice using MATLAB. To analyze the performance of image to voice conversion algorithm. Test the performance of the closed loop interface for the image and sound processing converter system. To develop Graphical User Interface (GUI) of the image to voice converter for case of user finding. Project Scope/Limitation The scope of this project is to construct a unique image to voice converter within a period of time at cost not to exceed RM200. Referring to this project, it consists of hardware which is webcam and software which is MATLAB. The system of this project is to capture an image using webcam, then recognize an image and generate a sound using MATLAB with several techniques. This product specially created for visually impaired people or to improve small kids learning capability. There was few limitation of this project which specified as follows: Shape limitation Colour limitation Resolution limitation Distance limitation Literature Review Image processing is a technique to convert an image into digital specification and go through some actions on it, so as to get an enhanced image or to collect some advanced information from it. It is a kind of signal exemption in which input is image, like video frame or photograph and output may be image or features related with that image. Frequently, image processing institution consist of treating images as two dimensional signals while applying already set signal processing techniques to them[1]. For the image recognition process can be divided into several algorithms which are image acquisition, image pre-processing, image segmentation, image representation and image classification. For the image acquisition, it is a digital image that captured by one or a few image sensors, such as various types of light-sensitive cameras, range sensors, tomography devices, radar, ultra-sonic cameras and etc. According to the type of sensor, the outcome of an image data is an generally two dim ensional image, a three dimensional capacity, or an image order. The pixel values usually correspond to strength of light in one or a few spectral bands, but can also be involved many physical measures, such as depth, absorption or reflectance of sonic or electromagnetic waves, or nuclear magnetic resonance. Image pre-processing is one of the algorithms that can increase the dependability of an optical inspection. This algorithm can be categorized into two categories which are image enhancement. Image enhancement requires intensifying the different features of images either for display or analysis targets. The enhancements techniques are edge enhancements, noise filtering, magnifying and sharpening an image. Several filter operations which increase or reduce certain image features allow an easier or faster evaluation. For examples, mean filter, median filter, wiener filter, and etc. With continuous use, an image will becomes degraded and has many errors. Image restoration is the process used to restore the degraded image. This process is also used to correct images read from different sensors that show up murky or out of focus[2]. Next, image segmentation is performed to assemble pixels into salient image areas, for example, areas corresponding to specific surfaces, objects, or inherent sections of objects. Segmentation could be used for object recognition, occlusion boundary estimation within motion or stereo systems, image density, image editing, or image database. The traditional image segmentation method can be divided into several techniques including gray threshold segmentation method, edge extraction method, regional growth method and split consolidation method and etc. Threshold technique was applied in this project. It is a technique that deals with gray-scale images. For the moment of the influence of noise or illumination, it can be assumed that the majority of pixels belonging to the objects will have a relatively low gray-level, whereas the background pixels will have a relatively high gray-level. For example, Black is represented by a gray-level of 0, and White by a gray-level of 255. Based on th is observation, we can divide the pixels in the image into two dominant groups, according to their gray-level. These gray-levels may serve as detectors to distinguish between background and objects in the image. On the other hand, if the image is one of smooth-edged objects, then it will not be a pure black and white image; hence this would not be able to find two distinct gray-levels characterizing the background and the objects. This problem intensifies with the existence of noise[3]. In order to overcome the ill influence of noise and shading, there are two methods that can solve this problem which are Otsu known as Global Threshold and Neighbourhood known as Adaptive Threshold. For the image representation, all information is commonly represented in binary. This is real of images as well as numbers and text. However, an important differentiation needs to be made between how image data is shown and how it is stored. Displaying includes bitmap representation while storing as a file includes many image formats, such as jpeg and png[4]. There are few techniques for image representation which are Roundness ratio known as Circularity, Fourier Descriptors and etc. The intent of the image classification procedure is to sort all pixels in a digital image into one of several land cover categories, or themes. This categorized data may then be used to deliver thematic maps of the land cover present in an image. Ordinarily, multispectral data are used to carry out the classification and truly the spectral pattern present within the data for each pixel is used as the numerical basis for categorization. The purpose of image classification is to determine and describe, as a distinct gray level or colour, the characteristics occurring in an image in terms of the object or kind of land cover these characteristics practically express on the ground[5]. The technique for this algorithm is using template matching and KNN (K-Nearest Neighbour). Table : Comparison of image sensors for image acquisition[6, 7] Types of Image Sensor Strength Weakness 1 Webcam allow face to face interaction low cost easy to use low resolution not portable no optical zoom lenses no auto-focus 2 Digital Camera high resolution portable with batteries has optical zoom lenses has auto-focus high operating speed less durability battery consumption faster high cost many complex function From the Table 1, it can be seen that both image sensors have its own strengths and weaknesses. This research will more focus on webcam due to this image sensor is using for this project. Webcam can be used to connect with computer to capture an image for image recognition. On the other hand, it is easy to use and cheaper compare with digital camera which is more complex and high cost. However, the megapixel of digital camera is higher than webcam. .. Table : Comparison of several types of filter for image pre-processing[2, 8] Types of filter Strength Weakness 1 Median filter more robust more smoothing provide good results memory consuming complex computation 2 Mean filter intuitive simple to use smoothing not good in sharpen images susceptible to negative outliers 3 Wiener filter short computation time controls output error straightforward to design results often too blurred spatially invariant From the Table 2, it can be seen that all filters have its own strengths and weaknesses. This research will focus on two types of filter which are median filter and mean filter. Median filter have been chosen for this project is because median filter is more robust on average than mean filter and so a not representative pixel in a neighbourhood will not influence the median value significantly. Since the median value needs to be the value of one of the pixels in the neighbourhood, the median filter does not establish new unrealistic pixel values when the filter straddles an edge. This is because of the median filter is better at preserving sharp edges than the mean filter. Also, median filter removes the noise level more than mean filter. Table : Comparison of threshold techniques for image segmentation [9, 10] Threshold Techniques Strength Weakness 1 Otsu fast ease of coding easy to use less sensitivity assumption of uniform illumination does not use any object structure or spatial coherence complex computation 2 Neighbourhood produce a good result less computation memory consumption time consumption sensitive From the Table 3, it can be seen that both techniques have its own strengths and weaknesses. Otsus method, named after its inventor Nobuyuki Otsu, is a global threhold that consists of many binarization algorithms[11]. This method involves iterating through all probable threshold values and computing a measure of propagates for the pixel levels each side of the threshold, i.e. the pixels that can be falls in background or foreground. The purpose is to find the threshold value where the total of foreground and background propagate is at its minimum. Neighbourhood which known as adaptive threshold is used to separate desirable foreground image objects from the background based on the difference in pixel intensities of each region. The differences between both methods were Otsu uses a histogram to threshold the image and the Neighbourhood method uses a histogram to threshold the pixels in a small region/neighbourhood around the pixel. In addition, Otsu methods suffer less errors occur t hat are caused by the sensitivity of the local algorithms to image noise compare with the Neighbourhood methods. Table : Comparison of the two techniques for image representation[12] Techniques of Image Representation Strength Weakness 1 Roundness Ratio very fast algorithm scale, position and rotation invariant high accuracy if image shape can be preserved properly after segmentation susceptible to errors if object shape is changed due to improper segmentation 2 Fourier Descriptor medium speed produce a good result low computation cost overcome the weak discrimination ability scale, position and rotation invariant difficult to obtain high order invariant moments cannot deal with disjoint shapes From the Table 4, it can be seen that both techniques have its own strengths and weaknesses. Roundness is defined in term of a surface of revolution like cylinder, cone or sphere where all marks of the surface alternated by any plane vertical to a common axis in case of cylinder and cone are equal in distance from axis. As the axis and centre do not exist, measurements have to be made with consultation to surfaces of the figures of revolution only. The circularity of the outline is to measuring roundness[12]. Fourier Descriptors are used to describe the feature of contour of shape. It was founded in the early sixties last century by Cosgriff and Fritzsche. According to the Fourier analysis theory, Fourier coefficients can be often generated by Fourier transformation. Lower frequency coefficients have the general shape of the signature, and higher frequency coefficients have the more information about the shape. As the harmonic amplitude and the phase angle can represent the Fourier D escriptor, and Fourier coefficients are usually normalized by dividing the first Fourier coefficient separately. Because there are some fast algorithms in computing the coefficient of Fourier series, many recognition systems in machine vision using these coefficients as shape features. Table : Comparison of several techniques for image classification [13-15] Techniques for Image Classification Strength Weakness 1 Template Matching easy to implement high degree of flexibility high accuracy of detection shape limitation computation speed susceptible to scaling and rotation 2 K-Nearest Neighbour easy to implement very effective improve accuracy improve run-time performance poor run-time performance if the training set is large very sensitive outperformed by more exotic techniques 3 Neural Network minimize energy function high accuracy easy to use unstable curse of dimensionality space consumption From the Table 1.5, it can be seen that all techniques have its own strengths and weaknesses. This research will focus on two techniques which are Template Matching and K-Nearest Neighbour. The standard template matching technique is known as simple mechanism, high accuracy of detection, and is used as a general model assessment and error estimation. Hence, it plays a very important role in image processing, and is commonly used in object detection and recognition. But the contradiction between rapidity and accuracy is exceptional. The main factors affecting rapidity are searching calculation, and operations of template matching. Appropriately decreasing positions and similarity computing precision can increase the speed of template matching obviously. That is becoming a focus in this field. Many studies focus on improving the searching algorithm, decreasing the matching times by decreasing the matching points on the template of images, which need to be detected so that rapidity is r ealized. The typical algorithms are pyramid algorithm, genetic algorithm and so on. Each matching operation is based on the template matching, thus it is necessary to pay attention to improving the computation speed of template matching fundamentally[14]. The intuition underlying Nearest Neighbour Classification is quite straightforward, examples are classified based on the class of their nearest neighbours, it is often useful to take more than one neighbour into account so the technique is more commonly referred to as K-Nearest Neighbour (KNN) Classification where k-nearest neighbours are used in determining the class. Since the training examples are needed at run-time, i.e. they need to be in memory at run-time; it is sometimes also called Memory-Based Classification. Because induction is delayed to run time, it is considered a Lazy Learning technique[13]. . Analysis on Similar Products and Paper Literatures Oral Image to Voice Converter by Takaaki HASEGAWA and Keiichi OHTANI[16]: In this paper, the authors propose a new speech communication system to convert oral image into voice, Image input Microphone. This system synthesizes the voice from only the oral image. This system provides high security and is not affected by acoustic noise, because actual utterance is not always necessary to input. Moreover, since the voice is synthesized without recognition, this system is independent of languages. Simulations to convert oral image to voice about Japanese five vowels are carried out as basic investigation. A vocal tract area function is estimated from the oral image, and PARCOR synthesis filter is obtained from the vocal tract area function. The PARCOR synthesis filter is driven by a pulse train. The performance of this system is evaluated by hearing tests of the synthesized voice. As a result, audible voice has been synthesized and the mean recognition rate of Japanese five vowels has been 91%. This paper describes a system to convert oral image into voice with considering humans lip-reading ability. In the proposed system, the voice is directly synthesized only from the oral image without recognition, and actual utterance is not always necessary to input. They use both the feature of a tongue and the feature of lips obtained from the oral image. Therefore this system is not affected by the acoustic noise, and simultaneously, it provides high security because of no utterance input capability. The system structure of this product is using a vocal tract area function which is equivalent to the transfer function of the vocal tract as a parameter. Indirect means synthesis via the vocal tract area function. The vocal tract area function is obtained from the PARCOR analysis of speech signals, and speech signals are synthesized by inverse processing of PARCOR analysis. Therefore if the vocal tract area function is estimated from oral image signals, they can convert the oral image to the corresponding voice. Human utters various voice by changing the vocal tract, and each articulator moves not independently but cooperatively in utterance, It is generally known that the information of articulation is obtained from lip-reading. Software Comparison Table below shows that the two comparison of the software between MATLAB and C++. Table : Comparison of software between MATLAB and C++[17] Types of Software Strength Weakness 1 MATLAB easy to learn fast numerical algorithms inexpensive software fast development slow processing complex computation 2 C++ mature standard large community fast complex computation difficult to debug low level programming From the Table 6, it can be seen that both types of software have its own strengths and weaknesses. MATLAB is software that has been widely used in image processing and computer vision community. Multiple image analysis function has been build into this software; it is very useful image analysis tools for end user. C++ is a standard template library (STL), computer graphics, and image processing. Based on C++ template mechanism, the library accepts all C++ build-in types as the image data, although certain functions are only valid to subset of build-in types. MATLAB has been selected due to the project analysis characteristic. MATLAB version R2010b will be used to analyze the image quality and performance in this project. Project Methodology This project has been divided into hardware and software. For the hardware section is the webcam as the input and speaker as the output. For the software section is using MATLAB to recognize image to sound with several image processing techniques. Block Diagram Webcam Image Segmentation (Thresholding) Image Acquisition (Acquire image) Image Preprocessing (Median filtering) MATLAB Image Representation (Roundness Ratio) Sound Generation (WAV file) Image Classification (Template Matching using KNN) Speaker Figure 1: Block diagram of Image to Voice converter. The block diagram shown in Figure 1 is the basic concept on the system interface that needed to be carried out. Base on the block diagram, first prepared a webcam. Then, capture the image in front of the webcam. After that, perform a median filtering in image pre-processing using MATLAB. It will filtered unwanted signal or noise inside the image. Next is image segmentation, referring to the literature review, the most suitable method is using Otsus method in thresholding techniques to convert grayscale image into binary image to do segmentation. Secondly, find the largest object and do the image representation using roundness ratio to calculate the ratio of the largest object to determine which one is the nearest to the template ratio. Next stage is image classification, using template matching with KNN techniques to find the small part of the image to match with the template image. After matching done, it will automatically generate a sound from the computer with WAV file. Flow Chart Start Acquire image from webcam Perform median filtering Colour Space Conversion Thresholding using Otsu Image labelling Find the largest object Image Representation -roundness ratio Template matching using KNN Is the image matched? No Yes Generate Sound Figure 2: Flow chart of Image to Voice converter. Based on Figure 2, before the beginning of image recognition, first, acquire an image in front of the webcam, and then the acquired image will go through image enhancement process to perform median filtering to filter some unwanted noise and sharpening the image. After that, the image will perform a colour space conversion which is convert the image colour space to another colour space, i.e. RGB, HSV, YCbCr and etc. The purpose of converting the colour space is to ensure that the converted image to be as same as the possible to the original image. Next, perform a threshold technique using Otsus method to calculating a measure of spread for the pixel levels each side of the threshold. The reason of doing this is to separate the objects from the background. Once the thresholding technique is done, perform a image labelling by taking the outside lines in the image and label them as occluding the background. After that, find the largest object and do the image representation using roundn ess ratio to calculate which object is similar to the template ratio. Then, perform a template matching techniques to find a match between the template and a portion of the image. The template that most closely matches the object is then found using the KNN method to do a matching system with the database image. If the data is matched, it will generate a sound automatically by using MATLAB to load the wav file from the computer or laptop. After that, it will repeat the procedure starting from the first step. If the data is unmatched, it wont generate a sound and it will go back to the first step and repeat the procedure again until the data is matched. Projects Method Median Filter Median filters are nonlinear rank-order filters based on replacing each element of the source vector with the median value, taken over the fixed neighbourhood of the processed element. These filters are widely used in image and signal processing applications. The purpose of median filtering is to removes impulsive noise, while keeping the signal blurring to the minimum[18]. Otsu Method Otsus method is a widely used method of segmentation, also known as the maximum infra-class variance method or the minimum inter-class variance method. This method involves iterating through all the possible threshold values and calculating a measure of spread for the pixel levels each side of the threshold, i.e. the pixels that either falls in foreground or background. The aim is to find the threshold value where the sum of foreground and background spreads is at its minimum[11]. Roundness Ratio/Circularity Roundness is defined as a condition of a surface of revolution like cylinder, cone or sphere where all points of the surface intersected by any plane perpendicular to a common axis in case of cylinder and cone. Since the axis and centre do not exist physically, measurements have to make with reference to surfaces of the figures of revolution only. For measuring roundness, it is only the circularity of the contour which is determined[12]. Template Matching The classical template matching method is charactered as simple mechanism, high accuracy of detection, and is used as a general model evaluation and error estimation. Therefore, it plays a very important role in image processing, and is widely used in object detection and recognition. It is a technique for finding small parts of an image to match with a database image[14]. K-Nearest Neighbour (KNN) K-Nearest Neighbour (KNN) is a branch of simple classification and regression algorithms. It can be defined as a lazy method. It does not use the training data points to do any generalization. Although classification remains the primary application of KNN, it can use to do density estimation also. Since KNN is non parametric, it can do calculation for arbitrary assignation[19]. Project Specification This project is divided into 3 main sections which are hardware, software and project estimate cost. Hardware The hardware was using for this project is Logitech HD Webcam C310, below is the basic requirement of the webcam: logitech-hd-webcam-c310.png Figure 3: Logitech HD Webcam C310[20] Windows Vista, Windows 7 (32-bit or 64-bit) or Windows 8 1 GHz 512 MB RAM or more 200MB hard drive space Internet connection USB 1.1 port (2.0 recommended) Software The software for this project is using MATLAB for image recognition and sound generation. Project Estimate Cost The estimate cost for this project is RM89 which was the Logitech HD Webcam C310, because this project was basically software based project and the software to be used is MATLAB from college engineering lab. Gantt Chart