Anitas Kitchen Restaurant Customer Survey Summary - Research Paper Example

Name of institution School title Course Title Anita’s kitchen restaurant customer survey Student name Executive summary The study investigates the marketing efforts and what need to be focused on in the expansion of Anita’s restaurant to additional locations. The study begins by identifying the correct location for the study, then collection of the data through questionnaires. The primary goal of using the questionnaires is to determine customer preferences and to know their satisfaction levels and improvements to be made if any. The study employed a quantitative research design approach. 427 participants residing in Birmingham were considered for the study. The study took a period of 14 days where Primary data was collected through the use of questionnaires and later coded and captured in IBM SPSS statistical tool for analysis; the findings formed the basis of study conclusions, limitations, and future proposals. Therefore, the data that was collected was comprehensive and factual. Due to the increase of competition in the restaurant marketing, there is the need for effective marketing strategies and customer trust and understanding. What this report provides is a compilation of data that was collected and analyzed with the aim of knowing customer preferences and satisfaction requirements. Therefore, this report’s intent is to help Anita in her five-year expansion plan and also the limitations of the research design used and suggestions for a better design. The first chapter presents the introduction part and the general context of the background study as well as the objective of the research. Chapter 2 contains the data information, how it was collected and the methods used to analyze the data. Chapter 3 presents the results and conclusions as well as the limitations and proposals for future research. Results from the data analyzed are displayed in the Appendix part. Introduction Background of the problem Over the past years, there has been the need for preference customer understanding as a marketing strategy. Restaurants have been competing in winning customers that have led to many food businesses developing unique strategies for winning customers. Prior research is one of the effective ways to increase sells since sales plan rely heavily on market research. Market research is helpful in knowing strengths, weaknesses, threats and opportunities of competitors as well as knowing the current market position. Marketing is a skeptic channel, data driven, and customer centric that measures the market forces and how responding to customer interests and need is important. Also, marketing is a way that one positions their business reputation to match market needs and wants. This report gives general information on Anita’s restaurant research and statistical presentation and analysis associated with customers’ satisfaction levels. This paper's intent is to document the research pattern and all the corresponding quantitative technique used in the statistical analysis process. This report is designed to serve as a template for describing statistical methods and analysis for future reference by business owners who want a new market for their services. This paper is based on a market research concept for a restaurant that provides a wide array of traditional ethnic foods. Aim of the research The primary purpose of this study was to find out the characteristics of a restaurant that makes a customer satisfied. Other objectives • To identify customer satisfaction level • To know if there is a relationship between satisfaction and the other variables. • To measure the loyalty of clients on Anita’s restaurant and her array of ethnic foods. • To ascertain if the target market has correctly been defined • To identify the challenges experienced in Birmingham restaurant industry. Research question What are features in the restaurant can drive customer satisfaction? What needs to be improved in the restaurant to attract more customers? Research design This chapter provides an in-depth analysis of the methods and principles underlying this research. Data and sample population are also reviewed in this chapter. Data and sample population The data was collected in Birmingham in the UK, and the desired audience were customers who dine in restaurants around the target location. The sample of 427 customers was a suitable example since a sample is relatively a small group used to represent a large population. The reason for sampling as a method of data collection is because we need to save costs, limited human resources and to save time. Accessing the whole population may be impractical, expensive and sometimes inaccurate. Questionnaires data collection method was used, that was given to each member of a dining party; the questionnaires were self-administered. The questionnaire is the best tool used for statistical collection since interprets the views of a group of people from a target population. Structured questionnaires were used in the study since they are the most appropriate for a quantitative study. Questionnaires can be issued to respondents through various channels; post-office, email, face to face or telephone. In Anita’s research, questionnaires were issued to respondents through face to face in which they were to respond and give back their response. The aim of using the questionnaires was to make sure that the replies were straight forward and held the subject f the research. Methods The method employed in this research paper was the quantitative research design. The quantitative research design is used to quantify the problem by generating the data numerically to be usable in statistics. This method uses quantitative data to formulate facts and uncover facts in research. This study quantified variables such as attitudes, behaviors, and opinions. While statistics consists of a body of methods used 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. The research involved a total of 427 questionnaires that were filled in a period of 14 days. A successful research requires a large number of respondents as stated by (castellan, 2010). The data was then edited for accuracy, plenitude, and consistency, and then it was recorded in Microsoft excel and later transferred to IBM SPSS for analysis. A simple linear regression method was used to generate an analysis of variance (ANOVA) that showed if there is a difference in means of the variables. A Pearson correlation function was also utilized in the study to find out if there is the significant relationship between the variables. With the help of SPSS, frequency tables, basic statistics, variances, means, percentages, charts, and curves were generated to analyze the respondents’ measure of different aspects of the study as detailed in the data collection tools. Data presentation This chapter primarily presents and interprets the results the researcher found. Pie charts, tables, and graphs were used to submit the data to facilitate easy comparison of continuous data sets, ensure quick analysis and help present the data in a visually appealing way. Furthermore, the analyzed information was organized and categorized concerning shared themes and presented in tables. Age and gender The respondents were of mixed gender, with male dominating the number of respondents; 61% of the respondents were male while 39% were female. The pie-chart below displays the distribution. Chart 1: Gender distribution The age bracket was between 18 years and over 60 years, a large number of the respondents lived between 35-49 years, about 48%, with respondents of over 60 years having the lowest respondents (that’s about 2.5% of the total population sample). The bar graph below represents the age distribution of the respondents. Figure 2: Age against the response level Satisfaction level One of the intentions of the report was to find the satisfaction standards of the customers. Questionnaires consisted mainly of questions relating to satisfaction level, one of the questions was “whether one was satisfied with Anita’s restaurant”. Customers were to choose the degree of satisfaction that was on a scale of 1-7 with the lowest level being “not satisfied at all” and the highest level being “very satisfied”. The responses were captured and recorded in IBM SPSS for analysis. The chart displayed below represents the satisfaction standards of the respondents; Figure 3: satisfaction level chart The level of the highest satisfaction rate was 4(represented 37.5%), which represented the average satisfaction; while the lowest level was three that represented respondents who were not satisfied (8.5%). Analysis of multiple linear regression was conducted to satisfaction is related to the other variables. The study was to determine if satisfaction rate is dependent on the other variables (income, food taste, prices e.t.c) . After the analysis had been done using SPSS, the results were displayed in the appendix part of the report. Results The descriptive statistics table presents the means and standard deviation of the sample population. As Gledhill, Abbey, and Schweitzer (2008) asserts, uncertainties associated with the measuring of sample population should not be related to normal distribution but the p-value, the test statistic to test the null hypothesis at the significant level. The multiple regression tables are based on the correlation table, which measures the relationship between the other variables and satisfaction. For this analysis, a null hypothesis was formulated that was to test if there is a relationship between satisfaction level and the other variables against the alternate hypothesis that assumed there was no significant relationship between the variables. A Pearson correlation analysis was performed and the relationship was found to be significant; Pearson’s r(398,N=427)= 0.90, p< 1. We, therefore, accept the null hypothesis since alpha at 5% at 95% significant level is greater than our p-value and conclude that there is an important relationship between satisfaction level and the other variables. There is a high positive relationship between fresh food and satisfaction level as displayed in the results of the Pearson correlation analysis. Also, the strongest negative correlation is between those who avoid fried food and the satisfaction level. The ANOVA analysis tests if a difference in the means of the variables is significant. SPSS was used to run the data to determine if there is a difference in the means and the results were displayed in the ANOVA table in the appendix section. Analysis of variance is a method used to test the difference between two or more means of variables. A null hypothesis that the means are same against the alternate hypothesis that means are different was formulated. An ANOVA analysis was generated to test the difference; F(399,N=400)= 140.7068, p> 0.05. Our significant p-value is less than alpha 0.05 at 95% significant level we, therefore, accept the null hypothesis. And conclude that the means are different. Conclusion The analyzed results clearly show that the degree of customer satisfaction is dependent on the other variables (income, price, etc.). Also, the Pearson correlation analysis shows how a large number of variables have positive relationships with satisfaction; as the other variables also increase satisfaction level increases. Anita needs to focus on the variables with the negative correlation to improve them. Limitations and proposals for future research Weaknesses in the design and analysis The use of quantitative data research has numerous disadvantages and challenges that the researchers went through. For instance, the development of the standard questions can lead to misinterpretation and bias where the data does not represent the view of the respondents rather it views on them. Concerning this, the answers to the questions may not necessarily reflect how the respondents feel but may just be the closest match. The results of this study are mathematical descriptions instead of detailed narrative that makes them limited. Also, the data collected is precise and sometimes superficial. The data is collected using questionnaires, and surveys are known to be standardized, this makes it difficult to explain points that respondents might have misinterpreted. The research was restrictive since it heavily relied on a quantitative method of data collection. The survey process encountered low response rate, and studies are not proper in circumstances of low literacy respondents. Proposals for future results Questionnaires need to be piloted to a small group of colleagues or family members to avoid misinterpretation of the questions of the respondents. Most of the questions regarding the relationship between the variables should be taken into consideration. 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). Glasow, P. A 2005, fundamentals of survey research methodology. Retrieved January, 18, 2013. Gledhill, S. E, Abbey, J. A & Schweitzer, R 2008, Sampling methods: methodological issues involved in the recruitment of older people into a study of sexuality. 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