Competition in Restaurant Business - Research Paper Example

Name of institution School title Course Title Anita’s kitchen restaurant customer survey Student name Executive summary This research empirically aims to focus on marketing efforts and expansion of Anita’s restaurant in additional locations. The investigation will be based on a quantitative approach to data collection via the use of questionnaires. The objective of the questionnaires is to know customer preferences and to identify their satisfaction levels and what need to be done to improve the levels. The research took a total of 14 days, with the data and results received validating the discussions in this report. Therefore, the data that was collected is comprehensive, transparent and factual. As competition in restaurant business gets intense, there is a need for extensive research to find out customers needs and the satisfaction they need. This report is an analysis of data that was collected in Birmingham city to find out what customers prefer and what will be required in the expansion process. The aim of this report is to help Anita to develop a five-year plan for her business, with a consideration of expanding the restaurant. Also, this report suggests marketing strategies and disadvantages for research design and suggestions for a better design. The first chapter contains the introduction part that describes the general context and background of the study, and also explains what data is available and the problem to be studied. Also, this chapter explains the objectives and the research question. Chapter 2 explains the data and how it was obtained and the methodology part; it describes the statistical techniques and tools used in the analysis. The 3rd chapter gives the main results and conclusions. The last part contains the limitations and proposals for future research. Overview of the study background Prior research is an effective way to increase sales since sales plan relies heavily on market research. Based on market research one can know strengths, weaknesses, opportunities and threats of their competitors. Also, it helps one to know the current market position, strengths, and weaknesses. Stremersch & Verhoef (2005) asserts that marketing should be an agnostic channel, data driven and customer centric that measures results that leverage the marketplace at large; responding to customers feedback and needs or interest. Also, to that marketing is a way of positioning your business reputation such that it matches the chosen market needs and wants (Marian, 2003). This report summarizes statistical presentation and analysis of results associated with customers preferences study. The intention of this report is to document the research pattern, and all the corresponding qualitative and quantitative techniques used during the statistical analyzes process. Also, this report is designed to serve as a template for describing statistical techniques and analysis future reference by business owners who want a new market for their services. This research is base on a restaurant concept that provides a wide array of popular ethnic foods. One of the basic marketing strategies s to undertake research before Research question What are the features of the restaurant that drive customer satisfaction? How can they be improved? Objectives • The main intent of this report is to find out the characteristics that makes a customer satisfied. • To find out if there are problems with the food, r the environment in which the restaurant lies • To identify the level of customer satisfaction. • To ascertain if the target market has been correctly defined. • To identify if there are subgroups who are loyal to Anita’s restaurant. Research design Data and sample population The desired target audience was customers who live in Birmingham and are used to dining in any restaurants around the target location. The sample was a convenience sample, as defined by Gledhill, Abbey & Schweitzer (2008) that a sample is a relatively small group used to represent the large population. The reason for using a small group is to save labor, time and unnecessary costs. One data collection method was used in the research; the questionnaires approach. Questionnaires were given randomly to one member of each dining party; the questionnaires were self-administered. According to Gledhill, Abbey & Schweitzer (2008), questionnaires are tools that are used in the collection of data in an investigative study. Structured questionnaires were used in the study since they are the most appropriate for a quantitative study. Glasow(2005), asserts that questionnaires can be issued to respondents through several channels, post-office, email, face to face, telephone. In this study face to face, a method was used for distribution of questionnaires. The research involved simple and direct questions that respondents were expected to fill by themselves at their own free time. The aim of using questionnaires was to achieve straight forward response without change of subject under research just like Gledhill, Abbey & Schweitzer (2008) explains,” questionnaires are suitable when researchers intend to collect straightforward and absolute information.” Methods Researchers used two primary approaches, namely quantitative approach and qualitative approach. A quantitative approach was used to generate data in this study while, in the qualitative approach, numerical data is generated before it is transformed into statistical data to be analyzed, presented and interpreted. As Castellan (2010), states quantitative approach quantifies variables such as behaviors and attitudes and results from the sampled population. Firestone (1987) defines quantitative approach as a technique for collecting data numerically and analyzing them statistically to explain a situation. While statistics consists of a body of methods for collecting and analyzing data, statistics can also be used for getting information from numerical and categorical data as well as finding answers to various questions( Agresti & Finlay, 2007). This research involved a total of 427 questionnaires that were filled in a period of 14 days. According to Castellan (2010), a research is more successful when a researcher involves a large number of respondents. The data was edited for completeness, accuracy, and consistency, and then was recorded in Microsoft excel and later transferred to IBM SPSS for analysis. The sample size that was used was 427; the same number of questionnaires was filled. A simple linear regression was used to generate an analysis of variance (ANOVA) that showed if there is a difference in means of the variables. Also, a Pearson correlation was also generated to determine the relationship between the variables, or if there is no link between the variables. Data presentation Gender and age respondents The respondents who were sampled included male and female genders, with the male holding 60% while female 40% as shown in chart 1.0 below. Figure 1: Gender distribution The age of the respondents ranged from 18 years to over 60 years, a large number of the respondents (48%) were aged between 35-49years, with over 60 years old recording the lowest respondents( about 2.6% of the total sampled population). The bar graph below represents the age distribution. Figure 2: Age versus the response level Satisfaction level According to the research, one of the objectives was to find out the satisfaction level. Questionnaires were prepared, and the question whether customers were satisfied was asked, customers were to choose between “not satisfied at all” and “Highly satisfied.” Respondents’ responses were recorded and captured in SPSS for analysis. The chart below represents the satisfaction level of the respondents. Figure 3: satisfaction level chart A high percentage of the respondents were satisfied (37.5%) with Anita’s services, with only 8.5% of the sampled population not satisfied. Analysis of multiple linear regressions was conducted to determine if satisfaction is related to the other variables. The study is to focus if the satisfaction level of customers depends on the independent variable (income, party person, prices, etc.). Analysis of data was done using SPSS, and the results recorded n the Appendix section of the report. Results The first table shows the descriptive statistics, which displays the means, and standard deviations as well as the sample size. The multiple regression tables are based on the correlation table. It measures the relationship between satisfaction and the other variables. The formulated null hypothesis that was being tested was that there is a relationship between the other variables and satisfaction level against the alternate hypothesis, there no significant relationship between satisfaction level and the other variables. We performed a Pearson correlation analysis and the relationship was found to be significant; Pearson’s R (398, N=400) = 0.90, p< 1. Hollander and Wolfe (1999), assert that uncertainties associated with the measuring of sample population should not be associated with normal distribution but the p-value, and the test statistic to test the null hypothesis at significance level. We, therefore, accept the null hypothesis since our significant p-value is less the alpha level 0.05 at 95% confidence level. We, therefore, conclude that there is a significant relationship between satisfaction level and the other variables. There is a high positive relationship between fresh food and the satisfaction level and the highest negative relationship between satisfaction and those who avoid fried food. The ANOVA analysis tests if differences between the means of the variables exist. IBM SPSS was used in the analysis of the data, and an ANOVA model generated to ascertain if a difference exist between the means of the variables. Online statistic education (2015), states that Analysis of variance is a statistical method used to test the difference between two or more means. A null hypothesis was generated and it stated the means are against the alternate hypothesis that the means are not different. An ANOVA analysis was performed to test the difference; F (399, N=400) = 140.707, p< 0.05. Since our significant p-value is less than alpha 5% at 95% confidence level, we accept null hypothesis conclude that the means are different. Conclusion From the analyzed results, we can see that the level of satisfaction of customers is dependent on the other variables. From the Pearson’s correlation, most of the variables have positive relationships with satisfaction, meaning when for instance the prices of the restaurants food increase the level of customer’s satisfaction. Anita needs to focus on the variables that have a negative correlation and find a way in which they can be improved. Limitations and proposals for future research Weakness in the design and analysis The use of qualitative approach has many disadvantages. A qualitative approach is subjective and uncontrolled since most of the themes directed by literature rather than the researcher. Furthermore, qualitative approach lacks criteria by which to judge the trustworthiness of results. Since data was collected through questionnaires, and questionnaires are standardized, it is not possible to explain points that respondents might have misinterpreted. Some of the respondents were unwilling to answer the questions since they think they will not benefit from the study. Also, questionnaires happen after events. Therefore, respondents might forget. Since report measures were used, common –method variance and response consistency methods may have biased the reported results. The study was restrictive since it relied heavily on a quantitative methodology of data collection. Since the survey method was used, various disadvantages were encountered. There was a low response rate, also surveys is not proper in the circumstances of low literacy respondents. Also, it is impossible to probe respondents’ hence limited sampling and respondent availability. Proposals for future results Most questions regarding the relationship between the variables and satisfaction should be taken into consideration. Given the size of the study, this research is tentative. Questionnaires need to be piloted to a small group of colleagues or friends or members of one family to avoid misinterpretation of the questions by the respondents. Appendix Regression Descriptive Statistics Mean Std. Deviation N X22 -- Satisfaction 4.65 .955 400 X1 -- Try New And Different Things 5.27 .908 400 X2 -- Party Person 3.99 .876 400 X3 -- People Come to Me 5.51 1.313 400 X4 -- Avoid Fried Foods 3.12 1.308 400 X5 -- Likes to Go Out Socially 3.94 .928 400 X6 -- Friends Come to Me 5.34 1.228 400 X7 -- Self-Confident 3.97 1.072 400 X8 -- Eat Balanced, Nutritious Meals 3.60 1.210 400 X9 -- Buy New Products 5.68 .951 400 X10 -- Careful About What I Eat 3.43 1.104 400 X11 -- Try New Brands 5.34 .809 400 X12 -- Friendly Employees 4.27 .908 400 X13 -- Fun Place to Eat 3.96 .859 400 X14 -- Large Size Portions 4.51 1.313 400 X15 -- Fresh Food 5.76 1.194 400 X16 -- Reasonable Prices 4.34 1.228 400 X17 -- Attractive Interior 4.05 .996 400 X18 -- Excellent Food Taste 5.31 1.088 400 X19 -- Knowledgeable Employees 4.74 .952 400 X20 -- Proper Food Temperature 4.57 1.104 400 X21 -- Speed of Service 5.34 .809 400 X23 -- Likely to Return 4.35 1.039 400 X24 -- Likely to Recommend 3.46 .930 400 Descriptive Statistics Mean Std. Deviation N X25 - Frequency of Patronage of Anita's Kitchen Restaurant 2.00 .746 400 X26 -- Price 2.34 1.143 400 X27 -- Food Quality 1.60 .742 400 X28 -- Atmosphere 3.33 .729 400 X29 -- Service 2.75 1.025 400 X30 - Distance Travelled 1.84 .855 400 X31 - Advertising Recall .35 .477 400 X35 -- Income 2.89 1.445 400 X33 -- Number of Children at Home 1.83 .874 400 Correlations X22 -- Satisfaction X1 -- Try New And Different Things X2 -- Party Person Pearson Correlation X22 -- Satisfaction 1.000 .406 .452 X1 -- Try New And Different Things .406 1.000 .229 X2 -- Party Person .452 .229 1.000 X3 -- People Come to Me .309 .044 .401 X4 -- Avoid Fried Foods -.543 -.197 -.300 X5 -- Likes to Go Out Socially -.060 -.014 .193 X6 -- Friends Come to Me .321 .016 .406 X7 -- Self-Confident .082 .066 .224 X8 -- Eat Balanced, Nutritious Meals -.392 -.133 -.351 X9 -- Buy New Products .411 .616 .377 X10 -- Careful About What I Eat -.530 .004 -.193 X11 -- Try New Brands .274 .880 .149 X12 -- Friendly Employees .406 1.000 .229 X13 -- Fun Place to Eat .462 .230 .939 X14 -- Large Size Portions .309 .044 .401 X15 -- Fresh Food .558 .180 .277 X16 -- Reasonable Prices .321 .016 .406 X17 -- Attractive Interior .384 .205 .593 X18 -- Excellent Food Taste .409 .140 .365 X19 -- Knowledgeable Employees .432 .636 .416 X20 -- Proper Food Temperature .530 -.004 .193 X21 -- Speed of Service .274 .880 .149 X23 -- Likely to Return .752 .303 .502 ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 205.627 1 205.627 518.094 .000b Residual 157.963 398 .397 Total 363.590 399 2 Regression 240.440 2 120.220 387.556 .000c Residual 123.150 397 .310 Total 363.590 399 3 Regression 260.599 3 86.866 334.003 .000d Residual 102.991 396 .260 Total 363.590 399 4 Regression 274.905 4 68.726 306.104 .000e Residual 88.685 395 .225 Total 363.590 399 5 Regression 280.112 5 56.022 264.414 .000f Residual 83.478 394 .212 Total 363.590 399 6 Regression 282.635 6 47.106 228.677 .000g Residual 80.955 393 .206 Total 363.590 399 7 Regression 284.928 7 40.704 202.842 .000h Residual 78.662 392 .201 Total 363.590 399 8 Regression 287.990 8 35.999 186.183 .000i Residual 75.600 391 .193 Total 363.590 399 9 Regression 291.465 9 32.385 175.115 .000j Residual 72.125 390 .185 Total 363.590 399 10 Regression 293.215 10 29.321 162.075 .000k Residual 70.375 389 .181 Total 363.590 399 11 Regression 294.697 11 26.791 150.883 .000l Residual 68.893 388 .178 ANOVAa Model Sum of Squares df Mean Square F Sig. 11 Total 363.590 399 12 Regression 295.794 12 24.649 140.707 .000 Residual 67.796 387 .175 Total 363.590 399 13 Regression 295.620 11 26.875 153.412 .000 Residual 67.970 388 .175 Total 363.590 399 14 Regression 297.019 12 24.752 143.888 .000 Residual 66.571 387 .172 Total 363.590 399 15 Regression 298.465 13 22.959 136.079 .000 Residual 65.125 386 .169 Total 363.590 399 16 Regression 298.034 12 24.836 146.616 .000 Residual 65.556 387 .169 Total 363.590 399 17 Regression 298.951 13 22.996 137.326 .000 Residual 64.639 386 .167 Total 363.590 399 18 Regression 299.642 14 21.403 128.858 .000 Residual 63.948 385 .166 Total 363.590 399 19 Regression 299.309 13 23.024 138.255 .000 Residual 64.281 386 .167 Total 363.590 399 a. Dependent Variable: X22 -- Satisfaction Residuals Statisticsa Minimum Maximum Mean Std. Deviation N Predicted Value 3.14 6.27 4.63 .868 410 Residual -1.247 .945 .002 .401 410 Std. Predicted Value -1.735 1.871 -.015 1.002 410 Std. Residual -3.055 2.316 .005 .984 410 a. Dependent Variable: X22 -- Satisfaction Model Summaryt Model R R Square Adjusted R Square Std. Error of the Estimate 1 .752a .566 .564 .630 2 .813b .661 .660 .557 3 .847c .717 .715 .510 4 .870d .756 .754 .474 5 .878e .770 .767 .460 6 .882f .777 .774 .454 7 .885g .784 .780 .448 8 .890h .792 .788 .440 9 .895i .802 .797 .430 10 .898j .806 .801 .425 11 .900k .811 .805 .421 12 .902l .814 .808 .419 13 .902m .813 .808 .419 14 .904n .817 .811 .415 15 .906o .821 .815 .411 16 .905p .820 .814 .412 17 .907q .822 .816 .409 18 .908r .824 .818 .408 19 .907s .823 .817 .408 Model Summaryt Model R R Square Adjusted R Square Std. Error of the Estimate 1 .752a .566 .564 .630 2 .813b .661 .660 .557 3 .847c .717 .715 .510 4 .870d .756 .754 .474 5 .878e .770 .767 .460 6 .882f .777 .774 .454 7 .885g .784 .780 .448 8 .890h .792 .788 .440 9 .895i .802 .797 .430 10 .898j .806 .801 .425 11 .900k .811 .805 .421 12 .902l .814 .808 .419 13 .902m .813 .808 .419 14 .904n .817 .811 .415 15 .906o .821 .815 .411 16 .905p .820 .814 .412 17 .907q .822 .816 .409 18 .908r .824 .818 .408 19 .907s .823 .817 .408 References Agresti, A & Finlay, B 1997, Statistical Methods for the Social Sciences, 3th Edition. Prentice Hall, 1997. Castellan, C. M 2010, Quantitative and qualitative research: A view for clarity. International journal of education, 2(2). Firestone, W. A 1987, Meaning in method: The rhetoric of quantitative and qualitative research. Educational researcher, 16(7), 16-21. Glasow, P. A 2005, fundamentals of survey research methodology. Retrieved January, 18, 2013. Gledhill, S. E, Abbey, J. 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