Improving Customer Service in Quick Service Restaurants in China - Research Proposal Example

? Identifying Six Sigma Key Ingredients (KIs) for Improving Service in Quick Service Restaurants (QSR) in China Table of Contents Table of Contents 2 Chapter 1: Research Background 3 Chapter 2: Research Question and Research objectives 4 Chapter 3: Literature Review 5 Six Sigma Key Ingredients (KIs) 5 Statistical Concept 7 Chapter 4: Conceptual Framework & Hypothesis 8 Chapter 5: Research Methodology 10 Chapter 6: Research Timeline 10 Reference List 12 Chapter 1: Research Background In 1987, the concept of Six Sigma was being initiated in Motorola in order to reduce frequency of faults in designed product and improving the quality of production system. In last 26 years, the concept of Six Sigma has not been adopted by many product manufacturing companies like POSCO, Hyundai, Samsung etc but also some service companies have successfully implemented Six Sigma which is an important division under Statistical Process Control (SPC) (Cho et al., 2011). According to Byrne (2003) and Harry (2000), Six Sigma can be classified as proven technique to improve quality of manufacturing process or reduce errors in manufacturing sector. Cho et al. (2011) found that people have common misconception regarding Six Sigma because most of the people believe Six Sigma can only be implemented in manufacturing sector but in real case, key ingredients of Six Sigma decide context of its applicability. Theoretically, it can be said from the argument of Cho et al. (2011) is that Six Sigma as part of Process Control (SPC) can be used in service industries like restaurants & hospitality sector, hospitality sector, banking services etc. However, in real business world scenario, use of Six Sigma in service sector is pretty scarce and even scholars like Crom (2000) argued that Six Sigma model is only developed for product driven sector. So, what is the actual problem in using Six Sigma in service sector? To answer this question, one has to understand the conjectural and intangible nature of quality in case of service organizations. Gronroos (1984, p. 37) defined service quality as “The consumer compares his expectations with the service he perceives he has received, i.e. he puts the perceived service against the expected service. The result of this process will be the perceived quality of service.” It is evident from the mentioned line that service quality is being judged as par the expectation of customers and magnitude of service gap might be influenced by perception of a customer. Ladhari (2009) rightly argued that service quality cannot be easily measured due to four characteristics of services such as intangibility, heterogeneity, perishability and inseparability. According to Markovic (2006), in most of the service sectors like Quick Service Restaurants (QSR), banks, hospitals etc, customers has to consume the offering at the place of production. In case of production system, faults in output can be quantified but is possible to quantify service output which is intangible in nature? For Chakrabarty and Tan (2007), the answer to the question is no while Sulek (2004) stated that it is possible to implement Statistical Process Control (SPC) in order to improve quality of services. Consideration of research works of Cho et al. (2011) reveals the fact that one can measure service quality by identifying Six Sigma Key Ingredients (KIs). It is evident from the above discussion that significant amount of confusion exist regarding the use of Statistical Process Control (SPC) to measure service quality. Such level of arguments and existing confusion among scholars has influenced the researcher to take up this study and select the research topic named as “Identifying Six Sigma Key Ingredients (KIs) for Improving Customer Service in Quick Service Restaurants (QSR) in China.” Fact is that, apart from research work of Zhen (2011), very few research scholars ever tried to identify Six Sigma Key Ingredients (KIs) as part of Statistical Process Control (SPC) in case of food service industry. According to limited knowledge of the researcher, before this study, little amount of research work has been done in order to identify Six Sigma as Statistical Process Control (SPC) can be used to improve service quality in Quick Service Restaurants (QSR). Therefore, significant amount of literature gap exists in the topic and aim of the research is fulfil certain portion of this gap. Although, scholars like Cho et al. (2011) and Zhen (2011) tried to understand application Six Sigma Key Ingredients (KIs) in service sector but they relied more on theoretical approaches to solve a numeric problem. Another thing is that their research works can be considered as pretty much confused when it comes to separating Six Sigma ingredients from success factors. In such context, the researcher has selected customer service as the primary coordinate of the study in order to get proper direction for probable Six Sigma ingredients in service Quick Service Restaurants (QSR). Chinese QSRs have been selected as study subject due to two reasons, 1- growing demand for QSRs in China and 2- existing problems regarding poor customer service in QSRs in China (My Decker Capital, 2010). It is not the fact that this research paper will have only academic value for fulfilling gap in the literature but findings of this research can have commercial value too. For example, research findings can help QSRs in China to improve service quality by improving customer service responsiveness for food products. Based on the above arguments, following research questions have been developed. Chapter 2: Research Question and Research objectives One thing has to be remembered that use of Six Sigma in improving food safety in Quick Service Restaurants (QSR) is not a widely acknowledged practice rather eminent scholar like Chakrabarty and Tan (2007) found it non-coherent in using Six Sigma in service sector. So, it will be wrong to assume the fact that Six Sigma can be used in improving customer service quality in Quick Service Restaurants (QSR) before conducting this research. Rather, primary objective of the research paper will be to verify the fact that whether Six Sigma can be in QSRs or not. Question 1: Is the Six Sigma system applicable in improving customer service responsiveness in Quick Service Restaurants (QSR)? Question 2: What Six Sigma Key Ingredients (KIs) can be identified for Improving Customer Service in Quick Service Restaurants (QSR) in China? Question 3: How Six Sigma Key Ingredients (KIs) can improve customer service in Quick Service Restaurants (QSR) in China? Chapter 3: Literature Review Six Sigma Key Ingredients (KIs) Antony (2006) tried to understand the meaning of the term Sigma before providing definition for Six Sigma. According to Antony (2006), Sigma denotes standard deviation (?) from expected service quality. According to Antony (2006), Six Sigma is a management philosophy which helps companies to establish high quality objectives, collect data regarding the process and analyze the process data and then take necessary measures to reduce frequency of errors in the process. According to Harry (2000), different types of benefits are associated with implementation of Six Sigma processes such as 1- half of the cost reduction, 2- lead cycle time improvement, 3- decrease chance of errors in manufacturing the product, 4- less waste of materials, 5- fulfilling customer requirements in responsive manner and 6- increase in customer satisfaction. Cho et al. (2011) used Six Sigma DMAIC (defines, measures, analyzes, improves, and controls) to implement the model to measure service quality instead of using Six Sigma DMADV (defines, measures, analyzes, designs, and verifies) model. Fundamentally, Six Sigma DMADV model is suitable for measuring quality of new product design hence the model is not accurate when it comes to measuring service quality. In case of six sigma approach, control charts are being drawn in order to measure deviation in quality output. In simple words, SLupper or upper quality limit (not more than 3? deviation from process mean) and SLlower or lower quality limit (not less than 3? deviation from process mean) are being selected in order to prepare control chart for Six Sigma process (Yilmaz and Chatterjee, 2000). The term “Six Sigma” has been arrived due to the fact that distance between SLupper and process mean always remain with 6th order standard deviations. In Six Sigma process, 3.4 parts per million defects are allowed in the process or only or 3.4 times the process is being allowed to produce defect out of 1 million times. Although, Yilmaz and Chatterjee (2000) found it difficult to adjust Six Sigma model in service sector but Antony (2006) argued that Six Sigma process can be applied repetitive service processes like invoicing, customer data processing, customer feedback collecting etc. Hoerl and Snee (2003) found that average defect rate in service process never exceeds is 3.5 sigma quality level or performance level of 99.38 per cent can be achieved for service processes. In such context, Hoerl and Snee (2003) identified three benefits of implementing Six Sigma in service sector, 1- decrease in frequency of errors in service processing, 2-performance of the service can be measured through data driven statistical analysis and 3- increase in responsiveness in service process delivery. Although, these research scholars argued that Six Sigma can be successfully implemented in service sector but most of their study was based on assumptions and theories. Therefore, the question regarding practical application of Six Sigma in improving service quality remains unanswered. Therefore, this study has decided to dig deep on mathematical explanation of Six Sigma as Statistical Process Control (SPC) in order to find the root of the problem regarding application of Six Sigma in service sector. In the above section, nature of Six Sigma has been discussed but there was not much discussion about Six Sigma Key Ingredients (KIs). Cho et al. (2011) defined Six Sigma Key Ingredients (KIs) are the factors which ensure successful outcome of Six Sigma implementation. Cho et al. (2011) identified Six Sigma Key Ingredients like belt system, training, leadership, project management skill, innovation efforts, project competition skills etc. Nature of key ingredients changes with change in objective for implementing Six Sigma process. For example, when the objective is to improving customer service in Quick Service Restaurants (QSR) then key customer centric key ingredients should be selected. Customer centric key ingredients such as quality of the food, customer waiting time, customer query handling, repeat purchase etc can be included as key ingredients in Six Sigma model for increasing decreasing quality gap in customer service in QSRs. Statistical Concept It has been found by the researcher that most of the studies regarding Six Sigma relied only on descriptive statistical concepts like mean, median, mode, standard deviation etc to prepare Six Sigma framework or analyze data gathered through Six Sigma approach. In such context, Zomerdijk and de Vries (2007) found that Kolmogorov-Smirnov goodness-of-Fit Test suits quite well with Six Sigma quality model. According to Zomerdijk and de Vries (2007), Kolmogorov-Smirnov goodness-of-Fit Test plays vital role in determining the quality gap in the customer centric service processes like Quick Service Restaurants (QSR), banks, hospitals etc. Although, Zomerdijk and de Vries (2007) supported the use of goodness-of-Fit Test for measuring customer service quality gaps but till date, very few researchers tried to use Kolmogorov-Smirnov goodness-of-Fit Test in developing Six Sigma Key Ingredients (KIs). The statistical model can be briefed in context to customer Services in Quick Service Restaurants (QSR). P [CIPi] = total customer input in food service process O [CIPi] = total frequency of service encounter/customer Aj[R] = service requesting activities/customer O (Aj[R]) = at particular service log, total frequency of service requesting activities of all customers P [CIPi] = O [CIPi]/?j O(Aj[R])*Aj[R]; i=1........n (Rigdon and Basu, 2000) In such context, the study will use P [CIPi] as customer engagement index in the Six Sigma Model. Using Kolmogorov–Smirnov (K–S) goodness-of-fit test will help the researcher to compare the mean difference between expected cumulative quality distribution function ^F(x) (read it as F(x) hat) and estimated cumulative quality distribution function Fn (x). Fn(x) = I(x) = service quality mean value less than or equal to process mean/ n= total frequency of customer encounter in QSR..... (Rigdon and Basu, 2000) Now, probable customer service quality gap will be Dn= sup (x) (sup) {|Fn(x)- ^F(x)|} Generally, Dn+ (greater than the expected customer service quality in QSR) = Maxi=1....n{i/n- ^F(xi) while Dn- (lower than the expected customer service quality in QSR)= Maxi=1.....n {^F(xi) – i-1/n} (Evans, Drew and Leemis, 2008). Figure 1: Probable Goodness-of-Fit Plot for Customer Service Quality (Source: Evans, Drew and Leemis, 2008, p. 1398) Chapter 4: Conceptual Framework & Hypothesis There will be three primary hypotheses in the paper and they can be stated in the following manner. Hypothesis 1: Six Sigma systems are applicable in improving customer service responsiveness in Quick Service Restaurants (QSR) in China. Hypothesis 2: Implementation of customer centric Six Sigma Key Ingredients (KIs) can improve customer service in Quick Service Restaurants (QSR) in China. Hypothesis 3: Goodness-of-Fit test can be synchronized with Six Sigma Key Ingredients (KIs) to measure quality gap Quick Service Restaurants (QSR) in China. It will not be right to identify customer centric key ingredients before conducting the research because customer centric key ingredients of Six Sigma model varies significantly as part geo-demographic position of the QSRs. Therefore, most of key ingredients of proposed Six Sigma model will be adjusted during the course of the research. In such context, following model can be used as conceptual framework. Chapter 5: Research Methodology Saunders, Lewis and Thornhil (2007 and 2009) stated that researchers can conduct research by using either qualitative or quantitative research methods. In case of qualitative research methods, research topics are being analyzed in non-numeric manner. However, as the research topic is purely statistical in nature and research questions need measurable and quantifiable answers, therefore, the researcher will use quantitative research methodology to conduct the research. In such context, secondary data will be collected from variety of sources like industry reports, academic journals, books, online academic articles, magazines etc while questionnaire survey will be used to gather primary data. There will be two parts in research process, the researcher will conduct open ended interview with quality managers or Six Sigma managers of 5 QSRs in China in order to identify customer driven Key Ingredients in quality model. After identifying key indicators, the researcher will design close ended questionnaire (consisting 15 to 20 questions, multiple choice questions, dichotomous question, likert scale questions will be present in the questionnaire) in order to conduct primary survey on customers. Social networking site community for Chinese QSRs will be used to find customer population who more or less frequently visit QSRs in China. Simple random sampling will be used as part of probabilistic sampling to select sample population to conduct close ended questionnaire survey. Simple random sampling is being used in order to assign equal probability of selection to every Chinese QSR community members. As part of ethical consideration, purpose of the researcher will be discussed with sample population prior conducting the research and identity of the respondents will not be revealed at any part of research. Collected data will be analyzed by using statistical tests like multivariate analysis, Goodness-of-Fit test and factor analysis. SPSS (Statistical Package for Social Sciences) will be used for data analysis. Chapter 6: Research Timeline Table 1: MS Project Gantt chart for Research Activities Task Name Duration Month Identifying the research problem 3 weeks 1st Developing Research Proposal 4 weeks 1st Collecting secondary data to prepare literature review 3 weeks 2nd Developing literature review and questionnaire for survey 4 weeks 2nd Conducting survey and gathering primary data 4 weeks 3rd Analyzing gathered data and preparing the research report 4 weeks 4th Revising the submitted report 2 weeks 5th Submitting the final report 4 weeks 5th Reference List Antony, J., 2006. Six sigma for service processes. Business Process Management Journal, 12(2), pp. 234-248. Byrne, G., 2003. Ensuring optimal success with Six Sigma implementations. Journal of Organization Excellence, 22(2), pp. 43-50. Chakrabarty, A. and Tan, K., 2007. The current state of six sigma application in services. Managing Service Quality, 17(2), pp. 191-208. Cho, J. H., Lee, J. H., Ahn, D. G. and Jang, J. S., 2011. Selection of Six Sigma key ingredients (KIs) in Korean companies. The TQM Journal, 23(6), pp. 611-628. Crom, S., 2000. Implementing Six Sigma in Europe. Quality Progress, October, pp. 73-5. Evans, D. L., Drew, J. H. and Leemis, L. M., 2008. The Distribution of the Kolmogorov–Smirnov, Cramer–von Mises, and Anderson–Darling Test Statistics for Exponential Populations with Estimated Parameters. Communications in Statistics—Simulation and Computation, 37, pp. 1396–1421. Gronroos, C., 1984. A service quality model and its marketing implications. European Journal of Marketing, 18(4), pp. 36-44. Harry, M. J., 2000. Six Sigma: A breakthrough strategy for probability. Quality Progress, May, pp. 60-4. Hoerl, R. W. and Snee, R. D., 2003. Leading six sigma. Englewood Cliffs, NJ: Prentice-Hall. Ladhari, R., 2009. A review of twenty years of SERVQUAL research. International Journal of Quality and Service Sciences, 1(2), pp. 172-198. Markovic, S., 2006. Expected service quality measurement in tourism higher education. Nase Gospodarstvo, 52(1/2), pp. 86-95. My Decker Capital., 2010. Chain & Restaurants Industry in China. [pdf] My Decker Capital. Available at [Accessed 18 November 2013]. Rigdon, S. and Basu, A. P., 2000. Statistical methods for the reliability of repairable systems. New York: John Wiley & Sons. Saunders, M., Lewis, P. and Thornhil, A., 2007. Research methods for business students. 3rd ed. New Jersey: Pearson Education. Saunders, M., Lewis, P. and Thornhil, A., 2009. Research methods for business students. 4th ed. New Jersey: Pearson Education. Sulek, J., 2004. Statistical process control in services. International Journal of Services Technology and Management, 5(5/6), pp. 522-31. Yilmaz, M. R. and Chatterjee, S., 2000. Six sigma beyond manufacturing – a concept for robust management. IEEE Engineering Management Review, 28(4), pp. 73-80. Zhen, Y., 2011. Food safety and lean six sigma model. Missouri: Department of Safety Science University of Central Missouri. Zomerdijk, L. G. and de Vries, J., 2007. Structuring front office and back office work in service delivery systems. International Journal of Operations & Production Management, 27(1), pp. 108-31. Read More