Food Quality Comparison of Multinational Food-Chains - Research Paper Example

Running Head: FOOD QUALITY COMPARISON OF FOOD-CHAINS Food Quality Comparison of Multinational Food-Chains [Name of the Writer] [Name of the Institution] Food Quality Comparison of Multinational Food-Chains Introduction In this paper there is a discussion on the quality of food and here is a comparison of quality at different fast food restaurants. Assessing the health consequences of food consumption habits has emerged as an important topic of inquiry in recent years (Oakes 2005; Seiders and Petty 2004; Verbeke 2006; Wansink and Huckabee 2005). From an evolutionary perspective, the easy availability of food presents a unique quandary for developed countries and societies. On the one hand, it has virtually obliterated problems resulting from malnourishment. On the other hand, it has generated problems stemming from the over consumption of food. For example, obesity is growing at an unprecedented rate, not just in the Canada, where nearly two thirds of the population are overweight or obese, but in other developing nations as well (Seiders and Petty 2004). To many consumers and consumer activists, organizations (e.g., lobbyists for grocery manufacturers) and restaurants (e.g., McDonald’s, Hardee’s) that are interested in promoting unhealthy eating habits are to blame for consumers’ propensity to over consume (Brownell and Horgen 2003; Nestle 2003). Consequently, lawsuits have been filed against fast-food companies such as McDonald’s (Parloff 2003). Attorneys representing these companies have argued that consumers are aware of the ingredients in their offerings and that they understand the weight and health consequences of over consumption. In their view, consumers are ultimately responsible for the negative consequences of their own actions. Obesity has been designated as a global epidemic by the World Health Organization since 1998. It is one of the most common chronic illnesses in North America and its frequency is steadily rising. The American Obesity Association (AOA) defines obesity as a Body Mass Index (BMI) of 30, while a BMI of 25 is defined as overweight in adults. The United States has become widely notorious for its high obesity rates. The AOA states that currently 64.5% of adult Americans and 45.6% of American children are categorized as overweight or obese. This alarming trend is also being observed in other westernized countries, including Canada. According to a National Population Health Survey by Statistics Canada, 41% of Canadian adults and 37% of Canadian children currently fit into this category. It is a startling fact that over the last 15 years the number of obese children in Canada has tripled. Obesity is becoming a crisis in the North American society. Obesity increases one's risk of many life threatening conditions. Many social and economic factors have contributed to this increasing health problem. There are many health concerns caused by this excess of body fat. They include hypertension, heart disease, arthritis, cancer, and diabetes. Obesity also often leads to psychological struggles with depression and low self-esteem. Many of these medical conditions that were once almost exclusive to adults are now becoming increasingly common in children. Both the Canadian and U.S. governments spend billions of dollars each year to treat these ailments. Individuals who are obese as children will likely be obese as adults, therefore given this now recognized trend; it is likely that we will only see an increase in the costs resulting from obesity. The purpose of this research study is to compare the quality of fast food at different multinational food chains. Methodology Participants The population of interest for this study includes randomly selected students from the University McMaster in Hamilton, Ontario. It is projected that the sample consisted of 50 participants. For the purpose of study a form of non-probability sampling used. As explained by Torchim (2001), in purposive sampling, the researcher samples with a purpose in mind from one or more specific and predefined groups, believed to be representative of the larger population of interest. Trochim noted that one of the benefits of purposive sampling is that it can be very useful for situations in which the researcher wants to reach a targeted group that otherwise might not be readily available. Instrument The instrument used in this study is the questionnaire consists of two demographic questions and the other eight questions presented in a multiple-choice format. The rating scale for the majority of the questions in the survey is the Likert Scale. The Likert scale is chosen for its ability to express the degree of favorableness of the items on the questionnaires. When using the Likert scale each response is given a numerical score to reflect its degree of attitudinal favorableness and the scores may be totaled to measure the respondent’s attitude (Cooper and Schindler, 2003). Data Collection The survey method is used as the means by which data is collecting within the study. Prior to initiating the data collection phase of the study, permission to conduct the survey would be obtained from appropriate management staff at university. Data collection commenced once approval granted. Questionnaires and the cover letter explaining the purpose of the survey and anonymity associated with participation in the survey distributed to participants and collected from them upon completion of the questionnaire. A well design questionnaire is critical for achieving the purposes of survey research. Additionally, concerns with validity (i.e. the degree to which an instrument measures what it was intended to measure) and reliability (i.e. the degree to which an instrument provides measurements that are consistent and accurate) must always be at the forefront of the questionnaire selection/construction process. For the purposes of the study, a questionnaire is located and used for which validity and reliability have already been established. Reliability and Generalizability For the research study to be accurate, its finding must be reliable and valid. Reliability refers to the property of a measurement instrument that causes it to give similar results for similar inputs. There are four methods of measuring reliability: Test-retest Multiple Forms Inter-rater Split-half The test-retest technique is to administer the test, instrument, survey or measurement to the same group of people at different points in time. All reliability estimates are usually in the form of correlation coefficient, so here all we do is calculate the correlation coefficient between two scores on the same group and report it as your reliability coefficient. The multiple forms technique is simply the scrambling or mixing up of questions on the survey; it’s a more rigorous test of reliability. Inter-rater reliability is most appropriate when we use assistants top do interviewing on content analysis. To calculate this type of reliability, all we do is report the percentage of agreement on the same subject between your raters, or assistants. Split half reliability is estimated by taking half of the test, instrument, or survey, and analyzing that half as if it were the whole thing. There are different variations of this technique, one of the most common being called Cronbach’s Alphs (a frequency reported reliability statistic) which correlates performance on each item with overall score. Reliability is basically the proportion of the variance in the scores contributed by true differences among people. Reliability = Generalizability theory, error is separated into pieces, each of which can be estimated. In generalizability theory, we have multiple error terms that might correspond to things. This theory is the statistical theory of measurement that expands classical test theory to include multiple sources of error and explicitly connects measurement operations to the purpose of measurement. Validity Validity is the degree to which an instrument measures what it was intended to measure; there are four good methods of estimating validity: Face Content Criterion Construct Face validity is the least statistical estimate (validity overall is not as easily quantified as reliability) as it is simply an assertion on the researchers part claiming that they have reasonably measured what they intended to measure. It is essentially a “take my word for it” kind of validity. Usually, a researcher asks a colleague or expert in the field vouch for the items measuring what they were intended to measure. Content validity goes back to the ideas of conceptualization and operationalization. If the researcher has focused into closely on only one type or narrow dimension of our construct or concept, then it is conceivable that other indicators were over looked. In such a case, the study lacks content validity. Content validity is making sure that you have converted all the conceptual space. There are different ways to estimate it, but one of the most common is a reliability approach where you correlate scores on one domain or dimension of a concept on your pretest with scores on that domain or dimension with the actual test. Another way is to simply look over your inter item correlations. Criterion validity is using some standard or benchmark that is known to be a good indicator. A researcher might have devised a police cynicism scale, for example, and they compare their Cronbach’s α, to the known Cronbach’s α of say, Neiderhoffer’s cynicism scale. There are different forms of criterion validity: Concurrent Validity is how well something estimates actual day/day behavior; Predictive Validity is how well something estimates some future event or manifestation that has not happened yet. The later type is commonly found in criminology. Suppose you are creating a scale that predicts how and when juveniles become mass murderers. Few established predictive validity, you would have to find atleast one mass murderer, and investigate if the predictive factors on your scale, re-prospectively, effected them earlier in life. With criterion validity, you are concerned with how well your items are determining your dependent variable. Construct validity is the extent to which your items are tapping into the underlying theory or model of behavior. Its how well the items hang together (Convergent Validity) or distinguish different people on certain traits or behaviors (Discriminant Validity). It’s the most difficult validity to achieve. You have to both do years and years of research and find a group of people to test that have the exact opposite traits or behaviors you are interested in measuring. Data Analysis In order to process and organize the data, a database is created by using a computer data base software program, (i.e., SPSS). The questionnaire selected has a scale response format, which will allow for a number to be assigned for which response given for each item. For each respondent, the numerical response provided for each item as well as descriptive data obtain during survey will be then entered into the database during the data-coding phase of the study. Within the study following the recommendations of Torchim (2001) statistical analysis are conducted through a progression or series of analysis using the SPSS. During the initial phase descriptive statistics is use to describe and summarize the data obtain within the study. The second phase of analysis is focused on the use of multivariate analysis to determine the relationship between specific items. This is conducted through the use of cross tabulation of nominal data between selected variables in the study. Statistical significance is to be set at an alpha level of 0.05; ANOVA is used to examine the variation among the data. Data Analysis and Results In this section we are doing analysis on the gathered data by applying different statistical tools. Table 1 Age Frequency Percent 20-23 20 40 23-26 17 34 26-29 13 26 Total 50 100 Graph 1 Here the graph shows that 40% of the participants of this study belong to the age group 20-23 years, there were 34% who has age of 23-26 years and the remaining 26% participants had age from 26 to 29 years. Table 2 Gender Count Percent Male 27 54 Female 23 46 Total 50 100 Graph 2 Here the pie chart shows that there were 54% male participants within the sample and 46% remaining participants were female in this study. Table 3 I like to go: Frequency Percent KFC 13 26 McDonald’s 9 18 Taco Bell 5 10 Burger King 11 22 Wendy’s 12 24 Total 50 100 Graph 3 Here the graph shows that from the selected sample most of the participants like KFC to go for meal, then Wendy’s, it is to be observed that there were few participants who preferred McDonald’s and Taco Bell to go for meal. Table 4 I am totally satisfied with services of: Frequency Percent KFC 13 26 McDonald’s 9 18 Taco Bell 5 10 Burger King 11 22 Wendy’s 12 24 Total 50 100 Graph 4 The pie chart shows that most of the respondents satisfied with the service of KFC and Wendy’s and then they were satisfied with the services of Burger King. Table 5 Quality food helps to increase the satisfaction level of customer on the restaurant. Frequency Percent Strongly Agree 15 30 Agree 21 42 Neutral 5 10 Disagree 7 14 Strongly Disagree 2 4 Total 50 100 Graph 5 Here we have the response of the question regarding quality of food plays an important role in improving the satisfaction level of customer on the restaurant, there were 72% participants who were agreed with this statement, 10% had neutral comments and there were 18% respondents who disagreed with the statement. Table 6 I feel that Burger King is better than Taco Bell Frequency Percent Strongly Agree 17 34 Agree 16 32 Neutral 9 18 Disagree 7 14 Strongly Disagree 1 2 Total 50 100 Graph 6 In order to compare the liking of burger King and Taco Bell when the question asked to the respondents then there were 66% participants who agreed that Burger king is better than taco Bell, 18% had neutral comments and there were 16% participants who were not agreed with this statement. Table 7 KFC has a good taste as compare to McDonald’s Frequency Percent Strongly Agree 17 34 Agree 16 32 Neutral 9 18 Disagree 7 14 Strongly Disagree 1 2 Total 50 100 Graph 7 Here the cones show that from the respondents there were 74% respondents who liked the taste of KFC more as compare to McDonald’s 14% didn’t give any particular comments and there were only 12% participants who disagreed with this statement. Table 8 Food at Wendy’s has good taste. Count Percent Strongly Agree 12 24 Agree 22 44 Neutral 8 16 Disagree 7 14 Strongly Disagree 1 2 Total 50 100 Graph 8 Here we have doughnut that give us the percentages of responses, we get to know by this that there were 68% participants who were satisfied with the taste of Wendy’s, 16% had neutral comments and there were 16% participants who were not satisfied with the taste of Wendy’s. Table 9 Gender 1. I usually visit the fast food restaurant   Strongly Agree Agree Neutral Disagree Strongly Disagree Total Male 14 26 4 6 4 54 Female 16 12 6 4 8 46 Total 30 38 10 10 12 100 Here the cross tabulation is between the gender and the responses regarding the visit of fast food restaurants. We observe that as compare to females the ratio of males is more and they visit fast food restaurants more than females. Here we are proving the hypothesis that: Is there any significant effect of age on visiting the fast food restaurants? Table 10: ANOVA SUMMARY  Groups Count Age 50 Fast Food Visit 50 Source of Variation SS df MS F P-value Age 4.05 2 2.03 1.25 0.297 Error 76.45 47 1.63   Total 80. 50 49 As we know that if we have P-value <  then we reject our hypothesis, from ANOVA table of age and fast food visit we get the P-value = 0.297 that is greater than  = 0.05, so here we don’t reject our hypothesis and conclude that there is no significant effect of age on visits of fast food restaurant. Reliability Statistics Cronbach’s Alpha No. of Items 0.252 2 Standardize item Cronbach’s alpha is computed only if inter-item statistics are specified, here we have two variables the age and the visiting of fast food restaurants and the coefficient of 0.252 reported for these items is an estimate of the true alpha, which in turn is a lower bound for the true reliability. Table 11 Age 4. For me the most impressive aspect of food is:    Quality Taste Presentation Variety Authenticity Total 20-23 7 6 2 2 3 20 23-26 5 5 3 4 0 17 26-29 5 4 1 0 3 13 Total 17 15 6 6 6 50 Here we have the cross tabulation between Age and responses of participants regarding the impressive effect of food. Tale shows that most of the respondents respond that the quality and taste are the main impressive aspects of food, the other goes with the presentation of food, variety in menu and authenticity of restaurant. Here we are proving the hypothesis that: Is there any significant effect of gender on determining the impressive aspect of food? Table 12: ANOVA SUMMARY  Groups Count Gender 50 Impressive Food Aspect 50 Gender: For me the most impressive aspect of food is: Source of Variation SS df MS F P-value Gender 1.46 1 1.46 0.76 0.388 Error 92. 32 48 1.92 Total 93.78 49       Here we have ANOVA table of age and determination of impressive aspect of food. From this ANOVA table we get to know that here we have P-value = 0.388 that is greater than  = 0.05, so here we don’t reject our hypothesis and conclude that there is no significant effect of gender on determination of impressive aspect of food. Reliability Statistics Cronbach’s Alpha No. of Items 0.149 2 Here we have two variables the gender and the impressive aspect of food and the coefficient of 0.149 reported for these items is an estimate of the true alpha, which in turn is a lower bound for the true reliability. Discussion The purpose of this research study was to compare the quality of multiple fast food chains in Canada, from the analysis it as come to our knowledge that as compare to females, males visit fast food restaurants more. It has come to know that people like KFC and Wendy’s more as compare to other fast food centers, because they are satisfied with the services provided in these restaurants. Along with it we find that people prefer the taste and quality of food more as compare to menu, or presentation or the authenticity of the restaurant, when we compared the respondents responses regarding McDonald’s and KFC, we find that KFC is more liked by people as compare to McDonald’s, it was also noticed that people like the taste of Burger King more as compare to Taco Bell. The ANOVA give us conclusion that there is a significant effect of age on visiting the fast food chains; it was also found that there is a significant effect of gender on the impressive aspect of food. Conclusion In this study we find that people are going with the healthy food, they prefer to go at that restaurant where they are sure to get the food that is tasty as well as they prefer the quality food. Healthiness is one of the most frequently mentioned reasons behind food choices in all countries (Lappalainen et al., 1998). Individuals are considered responsible for their own state of health and diseases are seen more and more as consequences of a consumer’s own behavior rather than as a result of the environment (Ogden, 1998). Diet may strongly influence an individual’s risk of obesity, cardiovascular disease, cancer or other lifestyle-related diseases. Traditionally, the healthiness of food has been associated with nutritional factors such as fat, fiber, salt and vitamin contents. When Finnish consumers sorted thirty-two foods into categories according to their perceived healthiness, the most important descriptions used by the consumers were obtaining beneficial compounds, the importance of nutrients, reduced fat content and general health effects of foods. (Roininen et al., 2000). In addition to traditional healthiness, food may contain single components that may have a positive impact on our well-being. Products that are claimed to have special beneficial physiological effects in the body are usually called functional foods (Roberfroid, 1997; Bellisle et al., 1998; Hardy, 2000; Kwak & Jukes, 2001). References Bellisle, F., Blundell, J.E., Dye, L. et al. (1998). Functional food science and behavior and psychological functions. British Journal of Nutrition, 80, 173–193. Brownell, Kelly D. and Katherine Battle Horgen (2003), Food Fight: The Inside Story of the Food Industry, America’s Obesity Crisis, and What We Can Do About It. New York: McGraw-Hill. Ecology of Food Nutrition, 39, 61–81. Hardy, G. (2000). Nutraceutical and functional foods: introduction and meaning. Nutrition, 16, 688–697. Kwak, N.-S. & Jukes, D.J. (2001). Functional foods. Part 2: the impact on current regulatory terminology. Food Control, 12, 109–117. Lappalainen, R., Kearney, J. & Gibney, M. (1998). A pan European survey of consumer attitudes to food, nutrition, and health: an overview. Food Quality and Preference, 9, 67–78. Nestle, Marion (2003), Food Politics: How the Food IndustryInfluences Nutrition and Health. Berkeley: University of California Press. Oakes, Michael E. (2005), “Beauty or Beast: Does Stereotypical Thinking About Foods Contribute to Overeating?” Food Quality and Preference, 16 (July), 447–54. Ogden, J. (1998). Health Psychology, A Textbook. Trowbridge: Open University Press. Parloff, Roger (2003), “Is Fat the Next Tobacco?” Fortune, (September), 50–54. Roberfroid, M.B. (1997). The European concept of functional food. In: Biotechnology in the Food Chain (edited by K. Poutanen). Pp.161–170 Roininen, K., La¨ hteenma¨ ki, L. & Tuorila, H. (2000). An application of means-end chain approach to consumers_orientation to health and hedonic characteristics of foods. Seiders, Kathleen and Ross D. Petty (2004), “Obesity and the Role of Food Marketing: A Policy Analysis of Issues and Remedies,” Journal of Public Policy & Marketing, 23 (Fall), 153–69. Verbeke, Wim (2006), “Functional Foods: Consumer Willingness to Compromise on Taste for Health?” Food Quality and Preference, 17 (1–2), 126–31. Wansick and Mike Huckabee (2005), “De-Marketing Obesity,” California Management Review, 47 (Summer), 6–18. Appendix Questionnaire Fast Food Comparison Gender Age 1. I usually visit the fast food restaurant Strongly Agree Agree Neutral Disagree Strongly Disagree 2. I like to go: KFC McDonald’s Taco Bell Burger King Wendy’s 3. I am totally satisfied with services of: KFC McDonald’s Taco Bell Burger King Wendy’s 4. For me the most impressive aspect of food is: Quality Taste Presentation Variety Authenticity 5. Quality food helps to increase the satisfaction level of customer on the restaurant. Strongly Agree Agree Neutral Disagree Strongly Disagree 6. I feel that Burger King is better than Taco Bell Strongly Agree Agree Neutral Disagree Strongly Disagree 7. KFC has a good taste as compare to McDonald’s Strongly Agree Agree Neutral Disagree Strongly Disagree 8. Food at Wendy’s has good taste. Strongly Agree Agree Neutral Disagree Strongly Disagree Read More

Many of these medical conditions that were once almost exclusive to adults are now becoming increasingly common in children. Both the Canadian and U.S. governments spend billions of dollars each year to treat these ailments. Individuals who are obese as children will likely be obese as adults, therefore given this now recognized trend; it is likely that we will only see an increase in the costs resulting from obesity. The purpose of this research study is to compare the quality of fast food at different multinational food chains.

Methodology Participants The population of interest for this study includes randomly selected students from the University McMaster in Hamilton, Ontario. It is projected that the sample consisted of 50 participants. For the purpose of study a form of non-probability sampling used. As explained by Torchim (2001), in purposive sampling, the researcher samples with a purpose in mind from one or more specific and predefined groups, believed to be representative of the larger population of interest.

Trochim noted that one of the benefits of purposive sampling is that it can be very useful for situations in which the researcher wants to reach a targeted group that otherwise might not be readily available. Instrument The instrument used in this study is the questionnaire consists of two demographic questions and the other eight questions presented in a multiple-choice format. The rating scale for the majority of the questions in the survey is the Likert Scale. The Likert scale is chosen for its ability to express the degree of favorableness of the items on the questionnaires.

When using the Likert scale each response is given a numerical score to reflect its degree of attitudinal favorableness and the scores may be totaled to measure the respondent’s attitude (Cooper and Schindler, 2003). Data Collection The survey method is used as the means by which data is collecting within the study. Prior to initiating the data collection phase of the study, permission to conduct the survey would be obtained from appropriate management staff at university. Data collection commenced once approval granted.

Questionnaires and the cover letter explaining the purpose of the survey and anonymity associated with participation in the survey distributed to participants and collected from them upon completion of the questionnaire. A well design questionnaire is critical for achieving the purposes of survey research. Additionally, concerns with validity (i.e. the degree to which an instrument measures what it was intended to measure) and reliability (i.e. the degree to which an instrument provides measurements that are consistent and accurate) must always be at the forefront of the questionnaire selection/construction process.

For the purposes of the study, a questionnaire is located and used for which validity and reliability have already been established. Reliability and Generalizability For the research study to be accurate, its finding must be reliable and valid. Reliability refers to the property of a measurement instrument that causes it to give similar results for similar inputs. There are four methods of measuring reliability: Test-retest Multiple Forms Inter-rater Split-half The test-retest technique is to administer the test, instrument, survey or measurement to the same group of people at different points in time.

All reliability estimates are usually in the form of correlation coefficient, so here all we do is calculate the correlation coefficient between two scores on the same group and report it as your reliability coefficient. The multiple forms technique is simply the scrambling or mixing up of questions on the survey; it’s a more rigorous test of reliability. Inter-rater reliability is most appropriate when we use assistants top do interviewing on content analysis. To calculate this type of reliability, all we do is report the percentage of agreement on the same subject between your raters, or assistants.

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