Background
Gloria Jones is a representative of restaurant supply sales. She is functioning in a vast ‘Metropolitan area’ and calls on numerous number of restaurant owners in the city. Gloria Jones would like to make her own café one day. She has substantial savings from her ‘Restaurant supply business’ and has recently gone over few financial figures with her banker. The banker and she both are agreeing that she has enough capital for getting significant investment. With the help of proper marketing strategy, good management, good service and strong financial information, a new restaurant could be a great business to start up.
Gloria Jones recruits a research firm for assisting an informed business decision. In previous studies, it is almost seen that business decisions are the analytical outputs that are often used by the business teams to circulate the real-time business processes. To derive the ‘informed business decisions’, the team of analysis provide the analytic output successfully in a decidedly integrated environment to enable an organization for delivering the forward-looking analytics (Sullivan & Verhoosel, 2013). Actually, the critical issues are resolved by ‘Informed business decisions’ that exposes the vital information to drive a right decision (Luthans & Youssef, 2004). The task is accomplished through statistical modeling and behavioral analysis in case of operational research.
The research team has collected data for assessing the feasibility of opening in an Italian-inspired Café. It is very important from the end of entrepreneurs who start new restaurants that might overestimate the market-size of the locality that might account a tough competition from the established restaurants. Therefore, it is very much necessary to know how to maintain the feasibility of a new restaurant. The customer satisfaction and loyalty are very much crucial from the end of the restaurant owners. Having profitable restaurant business is undoubtably the goal of Gloria Jones. In other words, profitability matters and it is crucial to occur the passion and commitment of business enterers (Conforti et al., 2013). It would be a life-changing achievement of Gloria Jones to establish a successful business. Constructing a lasting, profitable business is feasible with a restaurant.
At first, a simple random sample of 2000 customers were chosen. 400 responders responded out of the 2000 customers. The survey responses are tabulated in the data set tagged as ‘Gloria Jones_Data.xlx’. The data analysis is exploratory and deductive in nature. The deductive approach generally starts with a hypothesis. A deductive approach aims to test the hypothetical theory. On the other hand, an exploratory process provides the insights or situation of the researchers. Generally, the data collection processes for exploratory data analysis are experiments, group discussions, random sampling or interviews. However, in this report, the data is collected with the help of simple random sampling method aiming a target population.
Random Sampling Method
The data analysis is quantitative in nature. In this data, a total of 11 variables are present. The ‘totspent’ and ‘avprice’ are numerical in nature. Year of born is nominal in nature. Rest of all the variables are nominal. These variables are transformed into numerical values. The analysis is executed with the help of ‘MSExcel’.
While the financial data is obtained, the information is analyzed and it provide a projected number for a newly launched restaurant. Various types of tests are organized to investigate the survival and profitability of the restaurants. Using the information gathered from data-collection, the business would create a better understanding of the restaurant market in metropolitan area. The study was executed with the help of actual analysis of the target market to the unique financial aspects of the city. The study indicates the opening of a new sit-down restaurant that would be feasible in cosmopolitan sector to have a more accurate desirability of the restaurant market.
The data is secondary, hence the data analysis is ‘Secondary data analysis’. Secondary data analysis determines the research question, locates the data, evaluate the relevance of the data and assess the credibility of the data. Such type of analysis is majorly used in sociological research (Trzesniewski, Donnellan & Lucas, 2011). It includes an individual utilizing the data that someone else collected for own purposes. Secondary data is gathered for the purposes of a prior study to pursue a research interest described in research work (Lowrance, 2003). Although, the research with secondary data is not always reliable and valid for the proper confirmation of actual ground scenario, the researcher has undertaken the secondary data to estimate the planning and strategies of newly opened restaurants.
Hypothesis 1:
Null Hypothesis (H0): The average expenditure for a meal of potential patrons is $15.
Alternative Hypothesis (HA): The average expenditure for a meal of potential patrons is not equal to $15.
Hypothesis 2:
Null Hypothesis (H0): The average likeliness of patrons for males and females are equal to each other.
Alternative Hypothesis (HA): The average likeliness of patrons for males and females are unequal.
Null Hypothesis (H0): The average monthly food expenditure for all patrons is $200.
Alternative Hypothesis (HA): The average monthly food expenditure for all patrons is not equal to $200.
Hypothesis:
Null Hypothesis (H0): The average monthly food expenditures for postcode 2 and postcode 3 are equal to each other.
Alternative Hypothesis (HA): The average monthly food expenditure for postcode 2 is greater than average monthly food expenditure for postcode 3.
Data Analysis Techniques
Hypothesis:
Null Hypothesis (H0): The averages of likelihoods of various income levels are equal to each other.
Alternative Hypothesis (HA): There exists at least one inequality in the averages of likelihoods of various income levels.
Hypothesis:
Null hypothesis (H0): There is no linear significant association between the dependent variable (totspent) and independent variables (avprice, gender and age).
Alternative hypothesis (HA): There exists a linear significant association between the dependnet variable (totspent) and independent variables (avprice, gender and age).
- The one-sample t-test is a member of the t-test family. One-sample t-test is generally used for establishing he statistical difference between a sample mean and a known or hypothesized value of the average in the population. This typical application undertakes one-sample t-test for testing a sample against a pre-defined value (Cressie, 1980). It tests a sample against an expected value, common sense or expectations. Hence, this testing is an appropriate test to compare the difference between average of one variable and a ‘pre-determined’ mean.
- The two-sample t-test helps to compare whether the difference of averages between two groups is actually significant or not. Because, the difference could be caused due to random chance. However, for performing two-sample t-test, it should be ensured that both samples must be normally distributed taken from a single population (Lehmann, 1952).
- The one-way ANOVA (analysis of variance) is utilized for determining whether there exist any statistical significant differences between the averages of two or more than two independent or unrelated groups (Tamhane, 1977). However, one-way ANOVA is an omnibus test statistic which fail to state that which test-statistic is different from other (Christensen, 2002).
- With the help of multiple linear regression method, two or more independent variables are utilized for estimating the value of a dependent variable. The multiple linear regression helps to find the linear and significant association between response variable and the predictor variables (Neter et al., 1996).
In the first query, the researcher is eager to test the average of pay for a meal of potential patrons; hence, one-sample t-test is applied in this regard. The difference in the willingness to pay between genders (males and females) is calculated with the help of males and females. In second query, to test the average expenditure on food per month, one-sample two test is applied as it is dependent up on only one variable. In the third query, to compare the averages of monthly spend of the potential patrons, one-way ANOVA is needed. However, only two zip-codes of potential patrons are available in this context. Therefore, the researcher has used two sample t-test to find the difference between potential patrons instead of one-way ANOVA. In the fourth query, it is to be investigated whether the likelihood of patronize of the café differs between various different income levels or not. The test of equality of means that is ANOVA test is applied here. To find the statistical significant association between ‘Average amount of expenditure on food’ (dependent variable) and pays for meals, gender and age (independent variables) are accomplished with the help of multiple linear regression.
The average expenditure for a meal of potential patrons is equal to $15. Males and females have not difference in case of average expenditure for a meal of potential patrons. The monthly food expenditure of all sampled observations is much lesser than $200. The different area and different income of the people bring the variation about expenditure of meal. The people of higher income have capability to expense more money for foods and drinks. Specially, food likeliness varies as per the location of the café. Finally, ordered meal expenditure of the samples put a significant impact on total food expenditure.
Capital planning and development are the major concepts of coffee business. Owning and operating of cafe business take lots of work and profound dedication. A future research is the willingness and attitude to learn and grow. Maintaining the passion, a potential behavior and prospect of Gloria Jones are properly intrigued in the analysis. The accessibility of adequate possibilities, customer profile, direct and indirect competition as well as market area are beneficiary for further business.
Hypotheses Testing
Pricing plays an important role in any business. It undertakes the suitable pricing strategies. The pricing causes the positive or negative implications that surely control market share. The clever pricing techniques could help to get more bookings. The price of a meal is a significant factor that enhances or decreases the total monthly food expenditure. Besides, high earning people obviously have a better food-habit. The people who earn lesser, prefer to choose the foods of low cost. The consumption of food also varies with respect to region, gender and potential of patronization. The food-habit of the customers is a significant factor about profitability of the restaurant. The more value of a meal is, the more total amount is utilized in food expenditure.
Besides capital management, the factors like income, locality and patronization are going to be the defining factors of restaurant management and strategy-making. Therefore, the restaurant maintaining authority must take the strategies for the expansion of restaurant business according to the foresaid effecting factors. For restaurateurs who wish to optimize their revenues and return on investment, should control the management of cost. As per Adams (2010), one restaurant investor must make sure about that they are working with the minimum number of vendors to enhance all of the food and pay attention to drop sizes causing random amount of difference in cost. Some aspects that are needed the restaurant managers to control costs are- equipment management, negotiation of service cost and partnership management (Ahmed, Takeda & Thomas, 1999).
Opening a food restaurant or cafe takes a lot of time and effort to be successful. One of the most crucial aspect is to know at the time of assessing the target market for a restaurant. Food-restaurants aim the people who are in a hurry, require in-expensive meal, superb facilities and good services (Scarborough, 2016). The location of a restaurant influences the amount of income and therefore, a good location for a cafe is needed. Not only that, the appropriate capital strategy, but also planning, co-ordination and profitability impact the profitability and success of a cafe (Wiig, 1997). For determining the feasibility of a restaurant, a projected earning must be made to figure out expenses and profits.
It is often observed that a ‘Brand new restaurant’ faces its own set of scopes and challenges while entering into a competitive market. Not only that, restaurant business enhancement and planning could be vastly ‘Demanding’ especially at the pre-opening phase (Deloof, 2003). The restaurant manager and restaurant authority invest their money in the enhancement and expansion entirely. The planning for restaurant project marketing and distribution strategies should be taken aiming a fully staffed restaurant. During the course of measurements, the interim budgets, planning and market dynamics would increase the restructure of strategy and approach. The newly opened restaurants focus on public relations, revenue management strategy, sales policy and systematic distribution. The combination of cafe operations, capital management, finance and cost control improve the advanced strategical decisions. Not only that, the cost management, sales and capital are required to begin a rationalizing process in advance. Gloria Jones should always keep those things in mind for a prospered café business.
Factors Affecting Profitability
References:
Ahmed, A. S., Takeda, C., & Thomas, S. (1999). Bank loan loss provisions: a reexamination of capital management, earnings management and signaling effects. Journal of accounting and economics, 28(1), 1-25.
Aiken, L. S., West, S. G., & Reno, R. R. (1991). Multiple regression: Testing and interpreting interactions. Sage.
Berger, J. O., & Delampady, M. (1987). Testing precise hypotheses. Statistical Science, 317-335.
Christensen, R. (1987). One-Way ANOVA. In Plane Answers to Complex Questions (pp. 57-69). Springer, New York, NY.
Christensen, R. (2002). One-way ANOVA. In Plane Answers to Complex Questions (pp. 91-105). Springer, New York, NY.
Cohen, P., West, S. G., & Aiken, L. S. (2014). Applied multiple regression/correlation analysis for the behavioral sciences. Psychology Press.
Conforti, R., De Leoni, M., La Rosa, M., & Van Der Aalst, W. M. (2013, June). Supporting risk-informed decisions during business process execution. In International Conference on Advanced Information Systems Engineering (pp. 116-132). Springer, Berlin, Heidelberg.
Cressie, N. (1980). Relaxing assumptions in the one sample t?test. Australian Journal of Statistics, 22(2), 143-153.
De Winter, J.C., 2013. Using the Student’s t-test with extremely small sample sizes. Practical Assessment, Research & Evaluation, 18(10).
Deloof, M. (2003). Does working capital management affect profitability of Belgian firms?. Journal of business finance & Accounting, 30(3?4), 573-588.
Edwards, A. L. (1976). An introduction to linear regression and correlation (pp. 33-46). San Francisco: WH Freeman.
Fleming, T. R. (1982). One-sample multiple testing procedure for phase II clinical trials. Biometrics, 143-151.
Heiberger, R. M., & Neuwirth, E. (2009). One-way anova. In R through excel (pp. 165-191). Springer, New York, NY.
Lehmann, E. (1952). Testing multiparameter hypotheses. The Annals of Mathematical Statistics, 541-552.
Lowrance, W. (2003). Learning from experience: privacy and the secondary use of data in health research. Journal of health services research & policy, 8(1_suppl), 2-7.
Luthans, F., & Youssef, C. M. (2004). Human, social, and now positive psychological capital management: Investing in people for competitive advantage.
Mason, C. H., & Perreault Jr, W. D. (1991). Collinearity, power, and interpretation of multiple regression analysis. Journal of marketing research, 268-280.
Neter, J., Kutner, M. H., Nachtsheim, C. J., & Wasserman, W. (1996). Applied linear statistical models (Vol. 4, p. 318). Chicago: Irwin.
Norušis, M. J. (2006). SPSS 14.0 guide to data analysis. Upper Saddle River, NJ: Prentice Hall.
Pedhazur, E. J., & Kerlinger, F. N. (1973). Multiple regression in behavioral research. New York: Holt, Rinehart and Winston.
Ross, A., & Willson, V. L. (2017). One-Way Anova. In Basic and Advanced Statistical Tests (pp. 21-24). SensePublishers, Rotterdam.
Scarborough, N. M. (2016). Essentials of entrepreneurship and small business management. Pearson.
Sullivan, M., & Verhoosel, J. C. M. (2013). Statistics: Informed decisions using data. New York: Pearson.
Tamhane, A. C. (1977). Multiple comparisons in model I one-way ANOVA with unequal variances. Communications in Statistics-Theory and Methods, 6(1), 15-32.
Trzesniewski, K. H., Donnellan, M., & Lucas, R. E. (2011). Secondary data analysis: An introduction for psychologists. American Psychological Association.
Wiig, K. M. (1997). Integrating intellectual capital and knowledge management. Long range planning, 30(3), 399-405.
Yuen, K. K. (1974). The two-sample trimmed t for unequal population variances. Biometrika, 61(1), 165-170.
Order an Essay Now & Get These Features For Free:
Turnitin Report
Formatting
Title Page
Citation
Outline
