The Outsourcing Industry Philippines Health And Social Care Essay

The Outsourcing Industry Philippines Health And Social Care Essay The outsourcing industry is currently a growing trend in the Philippines providing employment opportunities for many young professionals. The Philippine outsourcing industry has grown 46% annually since 2004 (Rivette, 2010) and is currently representing 21% of the $7.2 billion of total Business Process Outsourcing (BPO) revenues worldwide. With the increase in BPO employment opportunities, more and more young Filipino professionals are applying for and working as call center agents. Approximately 400,000 Filipinos are already employed as call center agents (Rivette, 2010) and with a growth rate of 46% annually, it can be estimated that another 200,000 Filipinos will be joining this work force next year. However, despite the economic benefits of the expansion of BPO in the Philippines, an increase in work-related diseases in call center companies have also been reported. The most researched work-related disease in call centers in the Philippines is on sexually transmitted infections, particularly HIV-AIDS. According to the study done by the UP Population Institute (2010), 20% of male call center agents are commercial sex workers while 14% of them give payment in exchange for sex. The study also showed that 1/3 of call center agents have had casual sex in the last 12 months. These statistics validate the increase in risky sexual behavior among call center agents in the Philippines. However, increase in risky sexual behavior is only a part of the lifestyle of most call center agents. Other poor lifestyle choices observed among call center agents is their patronage of fast food, smoking, consumption of alcohol, increased caffeine intake, decreased sleep, and decrease physical inactivity. Besides poor lifestyle choices, the nature of their work also predisposes them to stress and disturbances in their sleeping pattern. All of these factors predispose them to health problems particularly hypertension, obesity, and diabetes. A number of studies have already been condu cted on the incidence of sexually transmitted diseases and call center agents in the Philippines but there are currently no studies yet on the incidence of other diseases among call center agents. This study would like to bridge this information gap because knowledge on the development of other diseases like hypertension and diabetes are also as important as knowledge on the increased transmission of STIs among call center agents.    In this study, the researchers would like to explore the association between the development of Diabetes Mellitus Type II among call center agents in the Philippines. As mentioned above, call center agents and their lifestyle predisposes them to developing diabetes. The researchers would like to address the problem of potentially developing Diabetes Mellitus because of the long-term complications of this disease on the quality of life. The researchers would want to specifically address Type II Diabetes Mellitus for the basic reason that this type of Diabetes develops primarily because of lifestyle factors. The researchers believe that knowledge on the association between call center agents and the development of Diabetes Mellitus Type II is highly significant because of the health implications of this disease and its potential to be prevented. II. Significance of the Study The increasing trend of call center agencies in the country provides job opportunities to the increasing supply of graduates in the country. Being employed as a call center agent in a call center agency is assumed to increase the risk of predisposition to different disease entities because of the radical lifestyle changes one undergoes. With the increasing number of employed call center agents, there is therefore an increase in the number of people who are at risk of acquiring diseases. Few literature deals with call center agents that discusses the acquisition of certain diseases secondary to their occupation. This study aims to increase the fund of literature with regard to this. Diabetes Mellitus, Type II is a chronic and debilitating disease. Also, as said, this is a life-long disease. Once a person acquires this disease, he or she will forever be predisposed to the co-morbidities and effects of the disease; which in turn, will decrease ones number of productive life years.   Prevention is the most cost-efficient approach when targeting populations. If the results of this study will show an association between being a call center and acquiring Diabetes Type II, we would be able to address the gap in knowledge with regards to the association of being an employed call center agent and acquiring Diabetes Mellitus, Type II. Also this would provide additional data for policy makers to address measures with regards to the prevention of this disease. III. Scope of Limitations   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The study will only include employees in call centers in Ortigas, Philippines. The study will be done for a period of 5(?) years and will only determine if an individual will develop Type II Diabetes Mellitus (DM) or not. The study will not quantify the degree and severity of the disease upon diagnosis. Fasting blood glucose (FBG) will be used in the diagnosis of DM, as it is the most reliable and convenient test for identifying DM in asymptomatic individuals (Fauci et al, 2008) and part of the guidelines used by the American Association of Clinical Endocrinologists (AACE Diabetes Mellitus Clinical Practice Guidelines Task Force, 2007). Individuals will be counted as cases if diagnosed with Type II DM through the course of the study. Cases will be provided with appropriate interventions (non-pharmacological, referral).   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚     Ã‚  The study will exclude those who have the following at the start of the study: Type II DM, history of Diabetes in the immediate family, body mass index (BMI) above or below the normal value as per Asian standard, and more than or equal to 30 years of age. These exclusion criteria are the factors that can be controlled in selecting the individuals within the population that may predispose them to be identified as cases. IV. Review of Related Literature Call Center Industry According to a review done by OMaley (2008), the Philippines has been a major player in the outsourcing industry over the past ten years. Six major factors were identified to be the reasons why the Philippines participate radically in the said industry. One is the increasing government support for information technology investment despite the erratic political climate. Second is the continuous pooling of college graduates with good English communication skills and proficiency. It was stated in the review that 75% of the total population in the Philippines (according to a United Nations data) speak English fluently with a 94% literacy rate which gives a relative advantage in the industry as compared to other countries. Third is high knowledge about Information and Communications Technology (ICT). Fourth is the easy establishment of a reliable and reasonably priced telecommunication infrastructure. Fifth are the low costs but high quality locations of call center agencies. And lastly, sixth, the increasing trends of outsourcing globally. In that same article written by OMaley, it was said that the Philippines consistently ranks among the top five Business Process Outsourcing (BPO) locations globally. This shares a five-year-compounded annual growth rate of 38%. The Philippine BPO system was also coined as the major player in the growth of the service sector in the country. The Philippines plays a major role in supplying the demand for more call center agents as an effect of the global trending of outsourcing worldwide. According to the Philippine National Statistic Office (2010), call center activities ranked first among all BPO activities covering almost half of the total industry with 219 (48%) call center establishments.    With the increasing number of call center agencies, it is logical to say that there is also an increasing need for call center agents to work for such industry. Call center activities employ majority of the workers among all BPOs. In 2008, call center agencies employed about 150,000 workers (Philippine National Statistics Office, 2010). There are about 400,000 Filipinos who are currently employed as call center agents according to Rivette (2010). Call Center Agents According to  a policy provided by the Employment and Immigration Department of the Government of Alberta (2008), call center agents are the ones who respond to questions and inquiries, build customer relationships, resolve customer problems and provide information about company policies, products and services over the phone and via electronic communication. Working conditions from one call center to another may differ. According to that same policy, call center agents usually work indoors but in a rather open environment to decrease privacy. Further, managers are allowed to record and monitor the conversations of an agent and his or her customer. Working shifts also differ from one agency to another. Some agencies provide services 24-hours a day, seven days a week. Lifestyle of Call Center Agents and Associated Health Risk Factors Because of the nature of their work, call center agents usually live a lifestyle that may put them at risk for development of certain diseases. First, call center workers remained in a static sitting position 95% of the time (Rocha, 2005) which makes them prone to physical inactivity that may lead to obesity. Development of obesity is of significance because it is a risk factor for the development of Diabetes Mellitus Type II according to the AACE Diabetes Mellitus Clinical Practice Guidelines Task Force of 2007. Second, call center workers are exposed to a highly stressful environment. Call center workers identified call-time pressures i.e., having to process a customer call within a specific number of seconds as having the strongest relationship to job stress (Di Tecco et al, 1992). Another study identified having to deal with difficult customers as the most significant source of job stress in 54.0% of call center agents handling inbound services and 54.4% of call center agents handling outbound services (Lin et al, 2010). High levels of stress can lead to increased cortisol levels in the body which is of significance because of its effects on body metabolism. Abnormalities in body metabolism can lead to metabolic problems such as stress-induced obesity which may give rise to hypertension, hyperlipidemia, and hyperglycemia (Andrews, 2002). Third, the usual diet of call center agents is high in cholesterol and fat and low in fiber which puts them at risk for dyslipidemia and hypercholesterolemia. In a study conducted by the UP Population Institute, they identified the usual lifestyle choices of young professionals in Metro Manila and Metro Cebu. They studied the economic, social and health status of 929 young professionals less than 35 years old working at call centers and non call centers. The study revealed that there is a high level of consumption of chips, burgers, fries and fried chicken among the workers and a few number consume instant noodles and street food regularly. It was found out that fried chicken was the most popular food choice among Business Process Outsourcing (BPO) workers with 78% saying that they consume it regularly. Chips were the next most popular food choice with 54% saying they consume it regularly, followed by fries at 53% and burgers at 49%. High caffeine intake was also reported in 2/3 of a ll young professionals drinking coffee daily. However, the study pointed out that call center workers drank more coffee than non-call center workers. Call center workers drank 2.3 cups of coffee daily while non call center workers drank 1.7 cups daily. Tea intake was also reported where 1/4 of all call center workers drank tea while only 1/5 of non-call center workers drank tea. The study also revealed that 50% of all young workers drink soda daily at an average of 1.5 bottles or cans daily. The study also explored leisure activities of call center agents. Based on the UP Population Institute survey, 72% of call center agents said that their most common leisure activity is drinking compared to partying (62%) or videoke gimmicks (59%). The study said that overall there is a very high level of current drinking among workers, 85% for call center agents and 87% for non-call center agents. Fatty food and consumption of alcohol can increase triglyceride and cholesterol levels which is a risk factor for the development of diabetes (AACE, 2007). Fourth, sleep deprivation is common among call center agents. In the same study, they also found out that instead of the recommended 8 hours of sleep, call center agents only get 6.2 hours of sleep each day. Sleep deprivation can lead to metabolic disturbances and hormonal changes causing obesity (Merck) and consequently diabetes. Fifth, due to fatigue and lack of sleep, call center agents resort to smoking to cope with stress. They reported that 43% of call center employees smoke while only 21% of non call center agents smoke. A call center agent who smokes usually consumes 9 sticks a day on average. Smoking is a known risk factor for the development of atherosclerosis leading to hypertension and cardiac disease. Since hypertension and cardiac disease are risk factors for the development of Diabetes Mellitus Type II (AACE, 2007), smoking may then predispose an individual in developing diabetes. Diseases Associated with Call Center Employees An increase in the turnover, absenteeism, and occupational diseases in call center employees resulted from lack of modernization of processes and organizational planning in call centers in Brazil (Rocha et al, 2005). A focused group investigation conducted in a call center employed with 200 individuals observed the presence of complaints of muscular pain, stomach aches, sleep alterations and irritability (Westin in Rocha et al, 2005). Work-related muscular disorders were found to be highly prevalent among the female than male call center employees, specifically on the neck/shoulder region (43%) and on the wrists/hands region (39%). It was observed that a combination of high demands and lack of work control among the female call center employees   reflect a highly stressful job that predispose them to the increased risk of having musculoskeletal disorders (Theorell in Rocha et al, 2005). The limitations of the study done by Rocha et al (2005) are that the analyses were limited to on e call center linked to a bank, cross-sectional design, small sample size, and symptom-based diagnosis (such as pain, numbing, dizziness, tingling sensation, stiffening, burning sensation). In a study done by dErrico et al (2010), the presence of musculoskeletal symptoms in the same region was assessed using the following inclusion criteria to preserved the specificity of the outcome, although it likely decreased its sensitivity: a) presence of musculoskeletal symptoms (pain, burning, stiffing, numbness or tingling) at any time during the last 28 days and b) consultation to a physical and or self-medication because of the symptoms. Also, the presence of any disease known to be associated with musculoskeletal disorders such as hypertension, diabetes, systemic lupus erythematosus, gout, thyroid diseases, rheumatoid arthritis), previous injuries in the last five years, leisure physical activity, body mass index, smoking, marital status, educational level, gender, and age class were explored as potential confounders of the association between workplace factors and musculoskeletal symptoms. It was found in this study that 45% of workers reported musculoskeletal symptoms wher ein neck (39%) symptoms were the most prevalent, followed by the shoulder (22%), handwrist (10%), and elbow (4%). Neck/shoulder symptoms were associated with low job control, elevated noise, poor desk lighting and impossibility to lean back while sitting. Elbow/hand-wrist symptoms were associated with short intervals between calls, insufficient working space, lack of forearm support, job insecurity, and long seniority in the industry. Other study that reported the presence of musculoskeletal symptoms among call center employees were done by Halford and Cohen (2003) wherein computer use factors and individual psychosocial factors were significantly associated with self-reporting of musculoskeletal disorder symptoms. Sudhashree et al (2005) stated in a column letter that the call center industry in India ranked high for attrition due to health reasons such as sleeping disorders (83%), voice loss (8.5%), ear problems (8.5%), digestive disorders (14.9%) and eye sight problems (10.6%). Burnout stress syndrome, which includes chronic fatigue, insomnia, and complete alteration of biological rhythm of the body are routine cause for sickness absenteeism. Chronic level of stress also affects other systems of the body such as the cardiovascular and endocrine. In a study done by Lin et al (2010) in a bank call center in Taiwan, call center employees have had prevalent complaints of musculoskeletal discomfort, eye strain, hoarseness, and sore throat. Also, it was found that those who perceived higher job stress had significantly increased risk of multiple health problems, including eye strain, tinnitus, hoarseness, sore throat, chronic cough with phlegm, chest tightness, irritable stomach or peptic ulcers, and musculoskeletal discomfort. In the Philippines, there are no studies about the health risks and occupational diseases associated among call center employees. However, there is a report of a rise in the number of Filipinos infected with Human Immunodeficiency Virus (HIV) and includes the call center employees (Ruiz, 2010). Diabetes Mellitus,Type II Type II Diabetes Mellitus and Epidemiology   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Diabetes mellitus (DM) is a group of metabolic disorders wherein there is an increase in blood sugar (hyperglycemia) resulting from absolute or relative deficiency of insulin, or both. There are many classifications of this disease entity based on the pathologic process that leads to hyperglycemia. In Type II DM, hyperglycemia resulted from a range of predominantly insulin resistance with relative insulin deficiency to a predominantly insulin secretory defect with insulin resistance (Fauci et al, 2008). It usually occurs among the older age group (> 30 years old) but there is an increasing diagnosis in the younger group (Tidy, 2009). Most symptoms of diabetes appear very late in the stage of the disease. A lot of diabetics do not have symptoms when their blood sugars are elevated for the first time (National Objectives for Health, 2005).   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   There is a dramatic increase in the prevalence of Diabetes Mellitus worldwide, from ~30million cases in 1985 to 177 million in 2000. Type II DM is increasing more rapidly because of increasing obesity and reduced activity levels as countries become more industrialized, as in the case of many developing countries in Asia (Fauci et al, 2008). A nationwide prevalence survey in the Philippines by the Department of Health showed that four (4.1%) out of one hundred Filipinos are diabetics, and the prevalence was higher in urban (6.8%) than in rural (2.5%) areas. The World Health Organization estimates that there will be a doubling of prevalence of diabetes in Southeast Asia every five to ten years. Using this as assumption, the prevalence of diabetes in the Philippines is around 8 to 16 percent (National Objectives for Health, 2005). Also, the death rate in diabetes has risen from 4.3 per 100,000 population in 1984 to 7.1 per 100,000 population in 1993. It is important to note that there is underreporting of deaths due to diabetes, as shown by local studies, because of misclassification as deaths due to cardiovascular or renal disease both of which are chronic complications of DM (National Objectives for Health, 2005; Fauci et al, 2008). Type II Diabetes Mellitus Risk factors and Diagnostics According to the American Association of Clinical Endocrinologists (AACE) Medical Guidelines for Clinical Practice for the Management of Diabetes Mellitus (AACE Diabetes Mellitus Clinical Practice Guidelines Task Force, 2007), there are several risk factors to developing prediabetes and Diabetes Mellitus. Such risk factors are (a) family history of diabetes, (b) cardiovascular disease, (c) overweight or obese state, (d) sedentary lifestyle, (e) Latino or Hispanic, Non-Hispanic black, Asian American, Native American, or Pacific Islander ethnicity, (f) previously identified impaired glucose tolerance or impaired fasting glucose, (g) hypertension, (h) increased levels of triglycerides, low concentrations high-density lipoproteins cholesterol, or both, (i) history of gestational diabetes, (j) history of delivery of an infant with a birth weight > 9 pounds, (k) polycystic ovary syndrome, and (l) psychiatric illness. To diagnose Diabetes Mellitus, any one of the three criteria is sufficient in diagnosis the patient according to the AACE. These criteria are: (a) symptoms of diabetes such as polyuria, polydipsia, unexplained weight loss and casual plasma glucose concentration of greater than or equal to 200 mg/ dL, (b) fasting plasma glucose concentration of greater than or equal to 126 mg/ dL, and (c) 2-hour postchallenge glucose concentration of greater than or equal to 200 mg/ dL during a 75-gram oral glucose tolerance test.    Diabetes Mellitus Prevention A study done by the Diabetes Prevention Program (DPP) showed that intensive changes in lifestyle, quantified as diet and exercise for 30min/day five times/week in individuals with impaired glucose tolerance (IGT) delayed the development of Type II DM by 58%. (Harrisons, 2008). It was also found out that Metformin slowed down the progression or halted the development of Type II DM by 31% compared to placebo. People with a strong predisposition to diabetes due to family history or impaired glucose tolerance or impaired fasting glucose (IFG), are strongly advised to maintain a normal BMI and engage in regular exercise. According to the recent ADA Consensus panel, individuals with IFG and IGT who are at a high risk for progression to diabetes (age 35 kg/m2, family history of diabetes in the first-degree, elevated triglycerides, reduced HDL, hypertension, or A1C > 6.0%) could be appraised for Metformin treatment but not other medications. Acute complications of DM The acute complications of diabetes are diabetic ketoacidosis (DKA) and hyperglycemic hyperoslomar state (HHS). Both disorders are associated with absolute or relative insulin deficiency, volume depletion, and acid-base abnormalities. These may lead to serious complications if not promptly remedied. Diabetic Ketoacidosis The usual signs and symptoms of DKA are   nausea and vomiting, hyperglycemia, hypotension, Kussmaul respirations, fruity oder on the patients breath, excessive thirtst, and polyuria. DKA is characterized by hyperglycemia, ketosis, and metabolic acidosis that is accompanied by secondary metabolic abnormalities. Hyperglycemic Hyperosmolar State HHS may usually be seen in an elderly individual with Type II DM, with symptoms of polyuria, weight loss, and lessened oral intake that preceded mental confusion or coma. Physical examination shows profound dehydration and hyperosmolarity with concomitat hypotension, tachycardia, and altered mental state. In contrast to DKA, HHS does not present with nausea, vomiting, abdominal pain and Kussmaul signs. Chronic complications of DM The chronicity of the disease brings about systemic involvement that affects multiple organ systems. Complications may be divided into nonvascular and vascular complications. Nonvascular complications include gastroparesis, skin changes, and cataracts. Vascular complications can be further subdivided into micro and macrovascular. Microvascular changes, which result from long standing hyperglycemia include retinopathy, neuropathy, and nephropathy. Macrovascular changes include coronary artery disease and peripheral arterial diseases. (NIkki, Ill send you my draft. di ko lam kung tama. i Cant do the framework here.) Figure 1.Conceptual Framework V. Objectives   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   With the nature of the work and environment in a call center industry, the study aims to determine if working in a call center predisposes an individual to the development of Type II diabetes mellitus (DM). Specifically, it aims: a.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  To determine the incidence of Type II Diabetes Mellitus within the period of study. b.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  To determine the etiologic factors associated with the development of Type II Diabetes Mellitus. VI. References AACE Diabetes Mellitus Clinical Practice Guidelines Task Force (2007). American association of clinical endocrinologists medical guidelines for clincial practice for the management of diabetes mellitus. Endocrine Practice. 13:3-68 Andrews, R.C., O. Herlihy, D.E.W. Livingstone et al. (2002). Abnormal cortisol metabolism and tissue sensitivity to cortisol in patients with glucose intolerance. The Journal of Clinical Endocrinology 87 (12): 5587-5593. Di Tecco, D., Cwitco, G., Arsenault, A., Andre, M. (1992). Operator Stress and Monitoring Practices. Appl Ergon 23, 147-53. dErrico, A., Caputo, P., Falcone, U., Fubini, L., Gilardi, L., Mamo, C., Migliardi, A., Quarta, D., and Coffano, E. (2010). Risk factors for upper extremity musculoskeletal symptoms among call center employees. Journal of Occupational Health. 52:115-124. Employment and Immigration. (2008). Alberta Occupational Profiles: Call Centre Agent. Government of Alberta. Retrieved September 10, 2010 from   http://alis.alberta.ca/occinfo/Content/RequestAction.asp?aspAction=GetHTMLProfileformat=htmloccPro_ID=71002991 Fauci, AS., Braunwald, E., Kasper DL., Hauser, SL., Longo, DL., Jameson, JL.., and Loscalzo, J. (2008). Harrisons Principles of Internal Medicine. 17th ed.   USA: The McGraw-Hill Companies, Inc. Halford, V., and Cohen, HH. (2003). Technology use and psychosocial factors in the self-reporting of musculoskeletal disorder symptoms in call center workers. Journal of Safety Research. 34(2):167-173 Lin, YH., Chen, CY., HONG, WH., and Lin YC. (2010). Perceived job stress and health complaints at a bank call center: comparison between inbound and outbound services. Industrial Health. 48:349-356 Merck Manuals Online Medical Library (2010). Obesity. Retrieved September 11, 2010 from http://merck.com/mmhe/sec12/ch156/ch156a.html National Objectives for Health. (2005). Retrieved 9 September 2010 from http://www2.doh.gov.ph/noh/3-2-3.pdf National Statistics Office. (2010). 2008 Annual Survey of Philippine Businesss and INdustry: Business Process Outsourcing Activities. Manila Philippines. Retrieved September 10, 2010   from http://www.census.gov.ph/data/sectordata/aspbi08_bpotx.html OMaley, R. (2008). Special Report Call Centres in the Philippines. Retrived September 10, 2010 from: www.callcentrehelper.com/special-report-in-the-philippines-2231.htm Rivette, D. (2010). The Emerging Philippine Value Proposition. Trestle Group Consulting. Retrieved September 11, 2010 from http://www.bpap.org/bpap/publications/ TG_SDS_PhilippineValueProposition_March2010%5B1%5D(2).pdf Rocha, LE., Glina, DMR., Marinho, MdF., and Nakasato, D. (2005). Risk factors for musculoskeletal symptoms among call center operators of a bank in Sà £o Paulo, Brazil. Industrial Health. 43:637-646 Ruiz, J. (2010). HIV cases soar among Filipino yuppies, call center workers. ABS-CBN News. Retrieved 10 September 2010 from http://www.abs-cbnnews.com/lifestyle/01/27/10/hiv-cases-soar-among-filipino-yuppies-call-center-workers Sudhashree, VP., Rohith, K. and Shrinivas, K. (2005). Issues and concerns of health among call center employees. Indian Journal of Occupational and Environment Medicine. 9 (3): 129-132 Tidy, C. (2009). Diabetes mellitus. Philippine Medics. Retrieved 10 September 2010 from http://www.philippinemedics.com/diabetes-mellitus/ UP Population Institute (2010). Lifestyle, Health Status and Behavior of Young Workers in Call Centers and Other Industries : Metro Manila and Metro Cebu. Retrieved 11 September 2010 from http://www.abs-cbnnews.com/lifestyle/08/05/10/call-center-workers-diet-fast- food-caffeine-and-alcohol

Hannibal Barca and the Carthaginian Campaign Essay

Hannibal Barca is the famous Carthaginian general, who is especially renowned for his successful campaign against Rome during the Second Punic War in 218 BC. Hannibal won some of the most famous victories against a numerically superior Roman army in Roman battlefields, notably the Battle of Cannae, which is universally considered as a masterpiece of military strategy and ranks among greatest military achievements in history (Gabriel, 2001). The innovative use of strategy and resources and capitalizing on enemy’s slightest weakness to turn into decisive victory for himself had been unique characteristics of Hannibal’s leadership that has earned him place in annals of great military leaders of history. Even today, many military schools still teach Hannibal’s military strategy, specially his placement of forces and improvisation in attack maneuvers. Hannibal Barca (247 BC-183 BC) Hannibal rose to his fame during the Second Punic War (218-201 BC) during which he established himself as one of the most brilliant strategists and tactician of the war seen by ancient world. If analyzed from the overall view of leadership, there are very few generals even in modern times who can compete with Hannibal. Hannibal was not only extremely proficient in military techniques and innovations but he was also excellent in understanding the delicate balance between military and political power. He was also very apt in exercising directed will and personal leadership-indeed, it was his sheer personal presence and force that motivated soldiers under him for 16 years in a foreign land. A study of Hannibal’s style of campaign is highly relevant even from modern perspective. Further, the Second Punic War introduced the concept of strategic endurance and tactical engagement, which still form the basis of military strategy. In these senses, the Hannibal’s campaign in the Second Punic War is watershed event in the military history of the West (Gabriel, 2001) . Historians still debate about the exact causes that inspired Hannibal to muster Carthaginian Ships and lead the army to Italy on an inordinately long and, in the end, deliberately unaccomplished campaign. While in more than one ways, Hannibal continued to injure, wound and dent the Roman pride to the degree where Romans were afraid to send an army against him, Hannibal never did actually sack the Rome or take control of the Empire (Gabriel, 2001). It is suggested that Hannibal’s chief motivation was to neither to humiliate Rome for their victory in first Punic war, nor to settle any personal score, but rather a more prudent vision of checking the expansionist ambition of Rome and keep Carthage secure in the only feasibly way-by attacking the Rome itself. Rome of the third century B. C.  E. was still on the way to power and glory that it would acquire a century later. At this time Rome was largely a land power while Carthage had emerged as the richest and most powerful trading nation due to its control over sea and its access to market of Sicily, Corsica and Spain. But the expanding Roman interests soon brought Sicily in their purview, leading to direct conflict with Carthage (Gabriel, 2001). The strategic position of Sicily had placed it in such position that while it marked as a check over Roman expansion, its loss would translate into a direct threat over Carthage. This conflict of interests led to the first Punic War in 261 BC where Rome and Carthage were locked in a 20 years long war, bitterly fought by each side. Despite suffering huge casualties, Rome won by 241 BC and Carthage suffered heavy losses. Its major markets were annexed by Rome, its trading fleet was reduced and it was subjected to heavy indemnity. Faced with prospects of financial ruin, the state stood at the verge of civil war when it was rescued by its most able general Hamilcar Barca, father of Hannibal Barca. Hannibal was born in 247 BC and he grew up while closely watching his father’s style of leadership and military tactics (Gabriel, 2001). It can be said that defeat of first Punic War was one of the motivating factors for young Hannibal Barca, who quickly rose through military ranks to command the forces of Carthage. At this time, most of the fighting units were primarily composed of tribal mercenaries who only valued chieftains who could lead them to victory and subsequent plunder. Therefore, Hannibal’s rise among these soldiers in itself is a testimony to his formidable reputation as a brilliant young tactician, competent to deliver victory even in most adverse of the situations (Gabriel, 2001). The Second Punic War (218BC-201 BC) The Punic Wars are recognized as the harbinger of modern style of warfare, which is dependent more on strategy, skill and technique than numerical supremacy. They marked an important shift from the earlier one-day affairs where the fates of empires were often settled in a single engagement. The Second Punic War lasted for 16 years, during which Rome hardly ever won a single engagement; however, it maintained its tenacious grip over the empire without collapsing until it gathered sufficient strength to achieve victory (Gabriel, 2001). The war also established the important of political will and social organization as decisive elements towards victory. Eventually, Rome’s victory started the era of political and strategic resource gathering that ultimately led to creation of the Roman Empire. However, these results came much later on. At the time of Hannibal’s campaign, Rome was still a very strong nation-state with ample economic resources, manpower and competent generals with large legions of armies under their command. On the other hand, when Hannibal started his campaign his resources were severely restrained. After discounting all the forces required to secure Carthaginian mainland, Hannibal was left with only 40,000 men and 8–10,000 horse, mostly Africans and Numidians, from Carthage itself. The rest would have to be raised from friendly Iberian tribes. By comparison, Rome had a reservoir of 250,000 foot and 23,000 horses, which it could gather in any instant of war. Including the forces of its allies, the Roman swelled to Drawing swelled to 700,000 foot and 70,000 horse, an army that was even larger than Napoleon’s Grand Army that invaded Russia in 1812 (Gabriel, 2001). With these difficulties in sight, Hannibal was well aware that he could not win a war of attrition or a direct battle against Rome. His only route to success lied through a prolonged campaign where he hoped to defeat Roman army in separate encounters and thus alienate Roman allies, who would no longer see Rome as a significant power. This strategy was dangerous because Hannibal would be directly leading his army to play against Roman strength in ground war. Further, with Roman control over sea routes, the campaign would be required to be self sustaining for its entire period as no help could be reached from Carthage if the troops were entrapped by Roman army (Gabriel, 2001). Added to this multiplicity of difficulties was the fact that the entire campaign was to take place on Italian lands, where Roman generals had better advantage in understanding the weather and terrain. By 218 BC, Rome was itself preparing for a double assault under its two generals, Publius Cornelius Scipio and Sempronius Longus. Scipio was to attack Spain with a force of 24000 thousand soldiers and 1500 horses while Sempronius was preparing to invade Africa with 36000 men and 1800 horses. He started his invasion in May 218 BC, with strength of 50,000 men 9000 horses, and 37 elephants, hoping to recruit the Celtic and Gallic tribes en route during the campaign (Gabriel, 2001). He had to face some hostility from local tribes but after crushing them ruthlessly in a six week campaign he led his forces through Alps. Records show that Hannibal started his crossing with almost 60,000 men and 37 elephants and by the time he crossed the Alps, only 23000 men and horses and 10 elephants were left alive, though barely in fighting condition. This was a terrible setback to his campaign plans, but he did not let despair sink in (Morris. 1937). His sudden and completely unexpected descent by Alps had indeed taken the Roman Senate by surprise and thrown many of their military plans into haywire. Both Scipio and Longus were called from their planned invasion to counter impending threat of Hannibal. The Roman generals were indeed somewhat overconfident, having to operate within their own country lands. Further, they had remarkable degree of vanity, anger, impetuousness and ego-elements which Hannibal used dexterously to his advantage in drawing them to battle (Tony.  1992). Battles of Trebia, Lake Trasimene and Cannae. Hannibal’s forces had won a number of small skirmishes and minor battles against the pursuing army of Scipio which had given them confidence and also support of a large number of native tribes. Even some of the Celtic contingents within Scipio army revolted, killed Roman soldiers and joined Hannibal’s forces. This alerted the Roman general who then stationed his army over a hill near river Trebia, awaiting Longus and his army, to jointly take upon the Hannibal’s army that was resting across other side of the River (Tony. 1992). When Longus joined Scipio, the Roman contingent swelled to an impressive degree, far outnumbering Hannibal’s troops. However, even under these circumstances, Scipio urged caution and asked Longus to wait for winters and further reinforcement before beginning the battle. Semponius Longus was instead in favor of a quick action and quick glory. Hannibal provided further provocation to him as small part of his troops attacked Roman legions repeatedly, challenging them for war. Longus took the bait and ordered his troops to cross the Trebia river for a direct showdown against Hannibal’s army(Tony. 1992). However, unknown to him, Hannibal had concealed an elite force of 2000 cavalry under the banks of river, who were ready to spring a trap to Roman army. Further, Hannibal’s forces were well rested and had a definite action plan against their enemies. A 40000 strong Roman and allied army crossed the river Trebia to engage with Hannibal’s 30000 troops on a cold December morning. As the battle started, the hidden units of Hannibal attacked, taking them completely unawares and causing great disarray and confusion in the Roman columns. This confusion, along with strategic marshalling of Hannibal, cost Romans heavily (Tony.  1992). More than 30,000 of their soldiers died and rest fled to safety, handing Hannibal his first great victory of the campaign. His losses were minimal in comparison, which boosted the spirit of his army and drew more native tribes to him. Battle of Lake Trasimene The defeat prompted a change in of command in Roman army and senate appointed Cnaeus Servilius and Gaius Flaminius as counsel of wars to block Hannibal’s invasion to Rome. Hannibal found Flaminius not much different from Longus and therefore decided to lure him to battle using the same strategy that he used at Trebia. Hannibal ordered his troops to burn countryside, towns, villages and slaughter livestocks, but prevented them from taking directly on the Roman armies. The tactics was to enrage the generals, trick them into making a mistake and then destroy the Roman army at the place that offered Hannibal’s troop maximum advantage.. Flaminius fell for the these tricks and he decided to pursue Hannibal’s army through the valley besides lake Trasimene. He mistook 6000 of Hannibal’s troop as his entire army and entered the valley with 15000 of his force to defeat the Roman tormentor. But the full strength of Hannibal’s 30000 strong army was hiding in forest, under the veil of a thick fog, so that the entire Roman army went past them without taking any cognizance of their presence! At the right moment, Hannibal ordered the attack, which completely routed the Roman army. The battle lasted two hours during which 15000 Roman soldiers were killed at cost of 1500 men in Hannibal’s army (Gabriel, 2001). Battle of Cannae Hannibal’s victory in battle of lake Tresimene sent waves of fear through Roman empire. In just two years he had defeat four of the best Roman counsels and caused more than 50,000 casualties. Rome realized for the first time that it was up against one of its most formidable foe and to counter the challenge, it placed the command of battle in hands of Quintus Fabius, who was a very competent commander with acute understanding of military as well as political affairs (Daly. 2002). Fabius made a correct strategic assessment of the situation and concluded that in the end of war, Rome’s domestic advantage, its superior alliance relation and its vast resources would lead to its victory against Hannibal. Therefore, he did not show any hurry in marching to the battlefield and apprehending the culprit. He very well knew that time was working in Rome’s favor (Daly. 2002) The military policy he started was in accordance with this understanding and it was aimed at containing Hannibal rather than defeating him. For more than a year, Fabius policy paid dividends as he strengthened defenses, retained the alliances and by refusing to engage Hannibal in a direct conflict, denied him any opportunity of a victory. Fabius was fighting the true war of attrition, which would have destroyed Hannibal’s army (Gabriel, 2001). But the mood in Rome was favoring war and they viewed Fabius working style as too cautionary. The senate replaced Fabius by L. Aemilius Paulus and C. Terentius Varro as generals of war. These generals immediately fell for the bait of war that Fabius was deliberately avoiding in summer of 216 BC a 86000 strong Roman army under generalship of Varro took to field against Hannibal’s 45000 strong force near the village of Cannae (Daly. 2002). Varro made two crucial errors. First he positioned the river Aufidus on his right flank, which denied his soldiers any space of maneuver and secondly he completely ignored the strong Carthaginian cavalry. Hannibal, in his usual display of brilliance kept his strongest units at flanks and weakest at the center. Varro took the bait and his army pushed deep inside the Carthaginian formation, where they were trapped in a pincer movement by Hannibal’s superior strong force (Daly. 2002). It was akin that they were trapped in a V shaped formation with no route to escape. The battle was over within few hours and its end, 52000 Roman soldiers were laying dead, and 5000 were taken prisoner. Hannibal’s forces had suffered 8000 losses. The combined casualty was around 60000, making it one of the bloodiest battles fought (Gabriel, 2001). Further Campaigns Cannae was a great victory for Hannibal, and it marked culmination of his three years of war efforts where he had incapacitated more than 20 percent of entire Roman population that was capable of entering military. However, it is said, that the terrible sight after battle of Cannae had affected Hannibal deeply and despite the fact that there was no hindrance to his journey to Rome, he refused to take the coveted road, earning him censure and criticism from his own generals (Gabriel, 2001). Post the defeat of Cannae, Rome re-mobilized its army and within two years, it numbers had swelled back to 200,000 men under arms. However, it had learned from the mistakes and instead of attacking Hannibal directly, it played on Fabian strategy of tiring him, denying him an opportunity of waging a direct battle. This tactics worked successfully and by 210 BC, was Hannibal had been contained in southern Italy, while Roman armies won victories in Greece and Spain. True, Hannibal was still out of their reach and every effort to touch him resulted in a defeat for the Roman troops, but overall the Roman grip had greatly strengthened (Gabriel, 2001). By 204 B. C. E. Scipio launched a campaign against Africa, which threatened Carthage itself. This prompted the state politicians to negotiate with Rome which led to recall of Hannibal and his armies from Italy. Hannibal’s last battle was with Scipio on the African plain near the small village of Zama where he was as defeated, and thus ended the military career of one of the greatest generals of the ancient world/ Conclusion Of all the adversaries that Rome faced in its long history, Hannibal Barca is indelibly etched as its greatest foe and for very concrete reasons. In the entire history of Rome, no other general had single handed ravaged the empire to the degree that Hannibal managed, staying virtually undefeated through his 16 years long campaign, while outsmarting best of the Roman generals and strategist. It’s the testimony of Hannibal’s enterprise however, that his most authentic biography is given by none other than Roman historians. . His campaign against Rome produced some of the finest military strategy and thinking that ancient world had seen, or for that matter even the modern world has seen. Like all military leaders he was cruel and ruthless, but only to the degree where these traits served to meet the objective of his campaign. His very decision to abandon the route of Rome in wake of the slaughter at the Battle of Cannae shows the finer elements of his character.

Characteristics of a Good Performance Measurement System...

What do you believe are the key characteristics of a good performance measurement system? The first key characteristic of a good performance measurement system is that it cares about optimization rather than suboptimization. Under a good performance measurement system, an organization is viewed as a whole system which each part interrelated and interdependent. When making decisions, employees would take other departments into account and make sure their decisions is good to the whole company. Let me imagine the example of engine and transmission, if the company adopts a better performance measurement system, that is, management measures the overall results departments make together rather than the results each department make, I’m†¦show more content†¦Therefore, a good performance measurement system should measure what they can provide for customers, and the satisfaction of customers. The third key characteristic of a good performance measurement system is that it ca n find problems immediately, have people responded to it rapidly and inspire employees to raise problems. For example, Toyota designs the control in the work itself, so whenever a problem comes up, people can find it out. People at Toyota value problems solving, they go to the place where problem occurs to see by themselves. While watching and observing, they also ask why. Useful feedbacks usually come up when asking why and they can help employees do better jobs. Through such problems finding--problems solving--feedbacks way help enterprise works better. The fourth key characteristic of a good performance measurement system is that it can help build good teamwork spirits of employees. Under a good performance measurement system, Cooperation is more welcomed than competition. 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What is factor analysis - Free Essay Example

Sample details Pages: 9 Words: 2824 Downloads: 5 Date added: 2017/06/26 Category Mathematics Essay Type Dissertation Level High school Did you like this example? 1.0 DEFINITION OF FACTOR ANALYSIS Factor analysis (FA) refers to a latent structure approach that can be used to analyze interrelationships among a large number of variables by explaining the underlying unobservable variables (latent variables) that are reflected in the observed variables (manifest variables) known as factors. With FA, the researcher can first identify the separate dimensions of the structure and then determine the extent to which each variable is explained by each dimension.   Once these dimensions and the explanation of each variable are determined, the summarization and reduction of data can be achieved. In summarizing the data, FA describes the underlying dimensions of data in a much smaller number of items than the original variables. Don’t waste time! Our writers will create an original "What is factor analysis?" essay for you Create order It examines the pattern of correlations (or covariances) between the observed measures. Data reduction can be achieved by calculating scores for each underlying dimension and substituting them for the original variables. FA is an interdependence technique where variates (factors) are formed to maximize their explanation of the entire variable set. These groups of variable would represent dimensions within the data which the researcher needs to label them. Basically, there are two types of FA, exploratory and confirmatory. The first analysis is used to discover the nature of the construct that influence a set of response and latter, test a specified set of constructs is influencing responses in a predicted way. Data summarization The goal of data summarization is achieved by defining a small number of factors that adequately represent the original set of variables. Data reduction Data reduction is achieved by identifying representative variables from a much larger set of variables for use in subsequent multivariate analyses or creating an entirely new set of variables whilst retaining the nature and character of the original variables. Data reduction relies on the factor loadings and uses them a s a basis for either identifying variables for subsequent analysis with other techniques or making estimates of the factor themselves (factor scores or summated scales), which then replace the original variables in subsequent analysis. Factor analytic technique is run according to their purpose either an exploratory or confirmatory perspective. Many researchers consider using the Exploratory Factor Analysis (EFA) when they are searching for structure among a set of variables or as a data reduction technique. EFA technique does not set any a priori constraints on the estimation of the components or the number of components to be extracted compared to the C onfirmatory Factor Analysis (CFA). CFA is used to confirm what is expected on the basis of pre-established theory. 2.0 PURPOSE OF FACTOR ANALYSIS The primary purpose of FA is to discover simple patterns in the pattern of relationships amongst variables by defining the underlying structure in a data matrix. This could be done by data summarization) and reduction. 3.0 HISTORY OF FACTOR ANALYSIS FA was pioneered in 1904 by psychologist, Charles Spearman, who hypothesized that the enormous variety of test of mental ability (measures of mathematical skill, vocabulary, verbal skills and others) could be explained by one underlying factor of general intelligence he called g.FA was developed to analyze the test scores of g so as to determine if g is made up of a single underlying general factor or of several more limited factors measuring attributes like mathematical ability. Raymond Cattell expanded the Spearman g test by using a multi factor theory to explain intelligence. He also developed several mathematical methods such as Cree Test and similarity coefficient.   His statistical methods led to an improved version of factor analyses by statistician. 4.0 PRINCIPAL COMPONENT (PCA) VERSUS FACTOR ANALYSIS (FA) There are many debates amongst statistician on the different of Principal Component and FA. A distinct different is Principal Component assumes that responses are measured based on the underlying factors whist the latter are based on the measured responses. Principal component analysis is used when the objective is to summarize most of the original information (variance) in a minimum number of factors for prediction purposes. In contrast, FA is used primarily to identify underlying factors or dimension that reflect what the variables share in common. Principal components are defined as linear combinations of measurement, that contain small proportions of unique variance and in some instances, error variance whilst FA considers only the common or shared variance, assuming that both the unique and the error variance are not of interest in defining the structure of the variables. PCA produces an orthogonal transformation of the variables without taking into consideration of u nderlying model whilst FA is based on a proper statistical model and is more concern with explaining the covariance structure of the variables than with explaining the variance (Chatfield, 1980). The calculation of PC scores is straightforward whilst the calculation of factor scores is more complex and a variety of methods can be used. Looking at the practical perspective, principal component analysis is most appropriate when the primary concern is data reduction focusing on the minimum number of factors needed to account for the maximum portion of the total variance represented in the original set of variables. FA is most appropriate when the primary objective is to identify the latent dimension or construct represented in the original variables. 5.0 STEPS IN FACTOR ANALYSIS 5.1 TEST ASSUMPTIONS 5.1.1 FA is robust to assumptions of normality If the variables are normally distributed, then the solution is enhanced. To check normality, .. 5.1.2 Measure the sampling adequacy of sample size There are many proposed sample size for FA. Guilford (1954) recommended that the sample size should be at least 200 whilst Hair, Black, Babin Anderson (2010) stated that the minimum is to have at least five times as many observation as the number of variables to be analyzed and the more acceptable size would have 10:1 ratio. Comrey and Lee (1992) provided the following guidance in determining the adequacy of sample size: Table 1: Determining the Adequacy of Sample Size Sample Size Indication 100 Poor 200 Fair 300 Good 500 Very good 1,000 or more Excellent 5.1.3 All variables must be must be suitable for correlational analysis. The sample is identified homogeneous with the respect to the underlying factor structure. It is inappropriate to treat a subset of items as a set of items known to differ in FA such as gender, where it will mislead the representation of the unique structure of each group. There are various ways to quantify the degree of intercorrelations amongst the variables such as the Measure of Sampling Adequacy (MSA). The index ranges from 0 to 1 when each variable is perfectly predicted without error by other variables.   If MSA value falls below 0.50, researcher should identify variable for deletion to achieve an overall value of 0.50. According to Hair et al. (2010) can be interpreted as the followings: Table 2: Measure of Sampling Adequacy (MSA). Measure Of Sampling Adequacy Indication 0.8 or above Meritorious 0.7 or above Middling 0.60 or above Mediocre 0.5 or above Miserable Below 0.5 Unacceptable Another method of determining the appropriateness of FA is the Bartlet test of sphericity and Kaiser-Myer-Oikin (KMO), a statistical test for the presence of correlations among the variables that indicates the significant status of the correlation matrix among at least some of the variables. KMO should indicates more than 0.5. The factor analyst must ensure that the data matrix has sufficient correlations to justify the application of FA. The anti image correlation matrix can be used to indicate whether the data matrix is suitable for FA. It is based on the correlation matrix of unpredicted variables using multiple regression. FA should not be performed when anti image correlation is less than 0.5 due to the lack of sufficient correlation with other variables. 6.0 SELECT TYPE OF ANALYSIS 6.2. 1EXTRACTION In FA, the researchers group variables by their correlations, such that in a group (factor) have high correlations with each other. It is important to understand how much variables variance is shared with olther variables in that factor versus what cannot be shared. The total variance of any variable os composed of its common, unique and error variances. As a variable is more highly correlated with one of more variables, the commune variable known as communalities increases. 6.2.2 ROTATION This important tools refers to the movement of the reference axes of the factors from the origin to some other position. The ultimate effect of rotating the factor matrix is to redistribute the variance from earlier factors to later ones to achieve a simpler, theoretically more meaningful pattern. There are two ways of rotation, either orthogonal factor rotation or oblique factor rotation. In orthogonal factor rotation, the axes rotation is maintain at 90 degrees compared to oblique factor rotation. The major orthogonal approaches are Varimax, Quartimax and Equimax. The Varimax method encourages the detection of factors each of which is related to few variables, on the other hand, Quartimax seeks to maximize the variance of the squared loadings for each variables and tend to produce factors with high loadings for all variables. Equimax is a solution of compromise between Varimax and Quartimax. For Oblique factor rotation, Oblimin, Promax, Orthoblique, Dquart, Doblimin and Ort hoblique has been developed. Oblimin allows factors to covary and to correlate with each other. The researcher need to choose either orthogonal or oblique factor rotation based on the particular needs of a given research problem. However, Hair et al (2010) suggested that Orthogonal Rotation method is preferred when the research goal is data reduction to either a smaller number of variables or a set of uncorrelated measures for subsequent use in other multivariate techniques. Where as the oblique rotation methods are best suited to the goal of obtaining several theoretical meaningful factors or construct. The Significance of Factor Loadings Factor loadings indicatehow strongly a measured variable is correlated with a factor. A 0.30 loadings translates to approximately 10 percent explanation and a 0.50 loadings indicates that 25 percent of the variance is accounted for by the factor. Using practical significance of factor loadings, Hair et al. (2010) proposed the followings (for sample size of 100 or above): Table 3: Significance of Factor Loadings Factor Loadings Indication  ± 0.30 to 0.49 Meets the minimal level for interpretation of structure  ± 0.50 or greater Practically significant Exceed 1.7 Indicative of well defined structure Comrey Lee (1992) also proposed practical significance of factor loading as below: Table 4: Significance of Factor Loadings Factor Loadings Indication More than 0.70 Excellent Less than 0.63 Very good Less than 0.55 Good Less than 0.45 Fair Less than 0.32 Poor In relation to the table above, Hair et al (2010) provide guidelines for identifying significant factor loandings based on sample size as below: Table 5: Guidelines for Identifying Significant Factor Loadings Based on Sample Size Factor Loadings Sample Size Needed for Significant a 0.30 350 0.35 250 0.40 200 0.45 150 0.50 120 0.55 100 0.60 85 0.65 70 0.70 60 0.75 50 a Significance is based on a 0.5 significance level (ÃŽÂ ±), a power level of 80 percent, and standard errors assumed to be twice those conventional correlation coefficients. Source: Computation made with SOLO Power Analysis, BDMP Statistical Software, Inc. 1993 Assess the Communalities of Variable Communalities measures the percent of variance in a given variable explained by all the factors joint and may be interpreted as the reliability of the indicator. Communalities is used to indicate any variables that are not adequately accounted for by the factor solution. Variables with communalities less than 0.50 are considered of not having an acceptable level of explanation and researchers may then need to extract more factors to explain the variance. 6.3 DETERMINE NUMBER OF FACTORS There are number of methods to determine the optimal number of factors. Latent root Criterion/Kaiser Criterion. The latent root criterion or also known as Kaiser Criterion states that factors having latent roots or eigenvalues of the correlation matrix that are greater than 1 are considered significant. Eigenvalue refers to amount of variance explained by each principal component to each factor. Hair et all (2010) suggested that using eigenvalue for establishing a cut off is most reliable when the number of variables is between 20 and 50. Scree Test Criterion. The Cattell scree test is derived by plotting the latent roots against the number of factors in their order of extraction and the shape of the resulting curve is used to evaluate the cutoff point. From the Scree test, as one moves to the right, toward later components, the eigenvalues drop, The Cartell Scree test states to drop all other components after the one starting the elbow (a point after which the rem aining eigenvalues decline in approximately linear fashion. Variance Criterion Variance Criterion is an approach to ensure practical significance for the derived factors in which the cumulative percentages of the variance extracted by successive factors. Hair (2010) proposed that it is uncommon to accept a solution that accounts for 60 percent of the total variance as a satisfactory solution. 6.4 NAME AND DEFINE FACTORS As the variables become correlated and group together, the researchers need to label the group that can represent each group of variables as accurate as possible. 6.5 ANALYSE INTERNAL RELIABILITY Reliability is an indicator to measure internal reliability. The rationale for internal consistency is that the individual items or indicators of the scale should all be measuring the same construct and highly correlated. There are two diagnostic measures of reliabilities, either to look at the item-to-total correlation and inter item correlation or the reliability coefficient. If the researcher choose the first method, the item-to-total correlations should exceed 0.50 and inter item correlation exceed 0.30. Using reliabilities coefficient, Zikmund, Babin, Carr Griffin (2010) provide guideline in determining reliabilities as in Table below: Table 6: Coefficient alpha (ÃŽÂ ±) to Determine Reliabilities Coefficient alpha (ÃŽÂ ±) Indication Between 0.80 to 0.95 Very good Between 0.70 to 0.80 Good Between 0.60 to 0.70 Fair Below 0.60 Poor 7.0 EXPLANATORY FACTOR ANALYSIS USING STATISTICAL PACKAGE FOR SOCIAL SCIENCE (SPSS) Correlation Matrix att1 att2 att3 att4 att5 att6 att7 att8 att9 att10 att11 att12 att13 att14 att15 att16 Correlation att1 1.000 .664 .250 .435 .490 .315 .378 .328 .574 .336 .575 .338 .176 .436 .379 .560 att2 .664 1.000 .383 .506 .444 .456 .345 .260 .525 .316 .468 .414 .320 .533 .480 .674 att3 .250 .383 1.000 .457 .210 .321 .216 .054 .217 .206 .231 .225 .429 .425 .314 .296 att4 .435 .506 .457 1.000 .351 .352 .336 .240 .415 .352 .405 .416 .331 .558 .439 .529 att5 .490 .444 .210 .351 1.000 .210 .318 .194 .303 .216 .603 .330 .188 .296 .238 .352 att6 .315 .456 .321 .352 .210 1.000 .358 .128 .379 .475 .329 .290 .276 .421 .311 .486 att7 .378 .345 .216 .336 .318 .358 1.000 .256 .373 .344 .332 .320 .175 .333 .265 .397 att8 .328 .260 .054 .240 .194 .128 .256 1.000 .348 .209 .215 .128 .128 .200 .231 .265 att9 .574 .525 .217 .415 .303 .379 .373 .348 1.000 .437 .368 .383 .203 .492 .398 .609 att10 .336 .316 .206 .352 .216 .475 .344 .209 .437 1.000 .366 .296 .181 .325 .289 .419 att11 .575 .468 .231 .405 .603 .329 .332 .215 .368 .366 1.000 .338 .176 .382 .333 .445 att12 .338 .414 .225 .416 .330 .290 .320 .128 .383 .296 .338 1.000 .186 .377 .266 .386 att13 .176 .320 .429 .331 .188 .276 .175 .128 .203 .181 .176 .186 1.000 .391 .233 .318 att14 .436 .533 .425 .558 .296 .421 .333 .200 .492 .325 .382 .377 .391 1.000 .428 .579 att15 .379 .480 .314 .439 .238 .311 .265 .231 .398 .289 .333 .266 .233 .428 1.000 .559 att16 .560 .674 .296 .529 .352 .486 .397 .265 .609 .419 .445 .386 .318 .579 .559 1.000 Anti-image Matrices att1 att2 att3 att4 att5 att6 att7 att8 att9 att10 att11 att12 att13 att14 att15 att16 Anti-image Covariance att1 .399 -.141 -.001 -.012 -.051 .047 -.042 -.058 -.112 -.005 -.120 .029 .048 -.002 .014 -.016 att2 -.141 .366 -.057 -.013 -.054 -.080 .030 -.010 -.004 .051 .016 -.060 -.030 -.024 -.045 -.102 att3 -.001 -.057 .647 -.135 -.004 -.062 -.023 .075 .021 -.001 .006 .016 -.189 -.074 -.066 .063 att4 -.012 -.013 -.135 .519 -.027 .021 -.017 -.052 .011 -.046 -.024 -.097 -.019 -.107 -.057 -.047 att5 -.051 -.054 -.004 -.027 .570 .040 -.068 -.010 .010 .034 -.224 -.063 -.038 .021 .028 .009 att6 .047 -.080 -.062 .021 .040 .605 -.090 .041 -.010 -.183 -.039 -.004 -.033 -.046 .017 -.060 att7 -.042 .030 -.023 -.017 -.068 -.090 .719 -.089 -.024 -.061 -.002 -.075 .008 -.015 .001 -.035 att8 -.058 -.010 .075 -.052 -.010 .041 -.089 .817 -.102 -.032 .002 .053 -.055 .016 -.054 .017 att9 -.112 -.004 .021 .011 .010 -.010 -.024 -.102 .482 -.099 .039 -.071 .022 -.072 -.013 -.095 att10 -.005 .051 -.001 -.046 .034 -.183 -.061 -.032 -.099 .647 -.082 -.040 -.006 .023 -.014 -.029 att11 -.120 .016 .006 -.024 -.224 -.039 -.002 .002 .039 -.082 .491 -.025 .020 -.029 -.034 -.015 att12 .029 -.060 .016 -.097 -.063 -.004 -.075 .053 -.071 -.040 -.025 .711 .006 -.038 .011 .004 att13 .048 -.030 -.189 -.019 -.038 -.033 .008 -.055 .022 -.006 .020 .006 .737 -.094 .016 -.040 att14 -.002 -.024 -.074 -.107 .021 -.046 -.015 .016 -.072 .023 -.029 -.038 -.094 .501 -.026 -.064 att15 .014 -.045 -.066 -.057 .028 .017 .001 -.054 -.013 -.014 -.034 .011 .016 -.026 .632 -.125 att16 -.016 -.102 .063 -.047 .009 -.060 -.035 .017 -.095 -.029 -.015 .004 -.040 -.064 -.125 .362 Anti-image Correlation att1 .897a -.369 -.002 -.026 -.107 .095 -.079 -.101 -.256 -.009 -.271 .055 .089 -.005 .028 -.042 att2 -.369 .911a -.118 -.030 -.118 -.169 .059 -.019 -.008 .105 .037 -.118 -.058 -.055 -.095 -.280 att3 -.002 -.118 .865a -.234 -.006 -.099 -.034 .104 .037 -.001 .011 .024 -.274 -.130 -.104 .131 att4 -.026 -.030 -.234 .939a -.050 .037 -.027 -.079 .023 -.079 -.047 -.159 -.031 -.209 -.100 -.107 att5 -.107 -.118 -.006 -.050 .875a .069 -.106 -.015 .018 .056 -.423 -.098 -.058 .039 .047 .021 att6 .095 -.169 -.099 .037 .069 .907a -.136 .058 -.019 -.293 -.072 -.007 -.049 -.083 .027 -.127 att7 -.079 .059 -.034 -.027 -.106 -.136 .950a -.117 -.041 -.089 -.003 -.106 .011 -.025 .002 -.070 att8 -.101 -.019 .104 -.079 -.015 .058 -.117 .894a -.162 -.044 .003 .069 -.071 .024 -.075 .032 att9 -.256 -.008 .037 .023 .018 -.019 -.041 -.162 .921a -.177 .080 -.121 .036 -.147 -.023 -.229 att10 -.009 .105 -.001 -.079 .056 -.293 -.089 -.044 -.177 .901a -.145 -.059 -.008 .041 -.022 -.061 att11 -.271 .037 .011 -.047 -.423 -.072 -.003 .003 .080 -.145 .883a -.042 .034 -.057 -.060 -.036 att12 .055 -.118 .024 -.159 -.098 -.007 -.106 .069 -.121 -.059 -.042 .944a .009 -.063 .016 .008 att13 .089 -.058 -.274 -.031 -.058 -.049 .011 -.071 .036 -.008 .034 .009 .887a -.154 .023 -.078 att14 -.005 -.055 -.130 -.209 .039 -.083 -.025 .024 -.147 .041 -.057 -.063 -.154 .946a -.047 -.150 att15 .028 -.095 -.104 -.100 .047 .027 .002 -.075 -.023 -.022 -.060 .016 .023 -.047 .943a -.262 att16 -.042 -.280 .131 -.107 .021 -.127 -.070 .032 -.229 -.061 -.036 .008 -.078 -.150 -.262 .922a a. Measures of Sampling Adequacy(MSA) KMO and Bartletts Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .914 Bartletts Test of Sphericity Approx. Chi-Square 2491.010 df 120 Sig. .000 Communalities Initial Extraction att1 .601 .617 att2 .634 .606 att3 .353 .526 att4 .481 .514 att5 .430 .645 att6 .395 .360 att7 .281 .278 att8 .183 .164 att9 .518 .598 att10 .353 .308 att11 .509 .576 att12 .289 .274 att13 .263 .320 att14 .499 .550 att15 .368 .356 att16 .638 .682 Extraction Method: Principal Axis Factoring. Total Variance Explained Factor Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative % dimension0 1 6.452 40.324 40.324 5.959 37.243 37.243 3.346 20.915 20.915 2 1.340 8.373 48.697 .833 5.206 42.449 2.150 13.438 34.353 3 1.062 6.639 55.336 .582 3.637 46.086 1.877 11.733 46.086 4 .951 5.942 61.278 5 .841 5.253 66.531 6 .756 4.727 71.257 7 .656 4.101 75.359 8 .643 4.017 79.376 9 .577 3.608 82.985 10 .528 3.298 86.283 11 .499 3.118 89.401 12 .421 2.633 92.033 13 .389 2.431 94.464 14 .348 2.176 96.640 15 .302 1.889 98.529 16 .235 1.471 100.000 Extraction Method: Principal Axis Factoring. Factor Matrixa Factor 1 2 3 att16 .797 att2 .778 att1 .725 -.301 att14 .702 att9 .696 -.324 att4 .688 att11 .643 -.333 att15 .581 att6 .569 att5 .562 -.401 .410 att10 .526 att12 .522 att7 .519 att3 .487 .441 .308 att13 .412 .345 att8 .352 Extraction Method: Principal Axis Factoring. a. 3 factors extracted. 18 iterations required. Rotated Factor Matrixa Factor 1 2 3 att9 .732 att16 .710 .359 att1 .567 .525 att2 .555 .391 .382 att10 .498 att6 .464 .366 att15 .462 .344 att7 .424 att8 .370 att12 .361 att3 .709 att13 .545 att14 .470 .544 att4 .403 .527 att5 .770 att11 .341 .655 Extraction Method: Principal Axis Factoring. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 6 iterations. Factor Transformation Matrix Factor 1 2 3 dimension0 1 .717 .515 .470 2 -.113 .751 -.650 3 -.688 .412 .597 Extraction Method: Principal Axis Factoring. Rotation Method: Varimax with Kaiser Normalization.