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Summary of Previous Work
To study market situation (PESTEL & Five Forces) for McDonald's to enter Vietnam market.
To study investment decision of McDonald
To identify the success factors to open McDonald's franchise in Vietnam market.
Based on the above objectives, the following research questions are developed:
What are the opportunities for McDonald to enter Vietnam market?
How does McDonald process of decision?
What are the key factors that effect on McDonald's decision to enter Vietnam market?
According to Sekaran (2003), research design comprises a series of activities such as identify problems, data collection, analysis data, and make valid conclusions. It illustrates detailed in Figure 1.1.
From the objectives mentioned in chapter one, this study should address he following research questions.
What are the vital factors which promote Franchise in Vietnam?
What are the main factors impacts on the McDonald's decision?
How to enhancing the competitiveness of SMEs?
Purpose of study
There are three types of study. Firstly, the exploratory approach attempts to discover general information of research about a new area that is not well understood by the researcher. They have no information on previous knowledge of research issues or no information is available on how similar problems (Saunders, 2007). There are three principal ways of conducting exploratory research: a research of the literature; interviewing experts in the subject and conducting focus group interviews. Secondly, descriptive study aim to offer the researchers a profile which discusses relevant aspects of the phenomena of interest, these study are undertaken to describe the characteristics of the variables of interest. Lastly, hypotheses testing explain, understand the relationship between some variables in situation. It must be able to explain the phenomena under any set of conditions. In this study, data and information will be collected from the survey to identify factors that impact on the Franchising (Sekaran, 2003).
Types of investigation
Causal or correlation study can be used to find out answer in research (Sekaran, 2003). Causal Research explores the effect of one thing on another and more specifically, the effect of one variable on another. It is used to measure what influence a specific change will have on existing issues. While correlation studies can suggest that there is a relationship between two variables, they cannot prove that one variable causes a change in another variable. However, correlation studies describe the important variables that are associated with issues. This research will use correlation.
In this report, a survey will be conducted to find out (1)â€¦â€¦â€¦â€¦â€¦â€¦â€¦â€¦â€¦â€¦â€¦â€¦â€¦â€¦â€¦â€¦â€¦â€¦.
Extent of researcher interference
The extent of interference depends on whether a study undertaken is causal or co-relational (Sekaran, 2003). A co-relational study is conducted in the natural environment of the organization with minimum interference by the researcher with the normal flow of work. Whereas, there is some disruption to the normal flow of work in the system is minimal as compared to that caused during causal studies. The extent of interference in this case is minimal. In this study, people are the most important factors. Researcher wants to let people behave naturally which will reflect real thing.
A research can be done either in non-contrived or in and contrived settings (Sekaran, 2003). This study is conducted in all ranges of Franchising. Data is collected in a natural setting does have more accuracy and reflect real things.
Unit of analysis
Unit of analysis is the major entity which indicates the level of aggregation of the data gathered during the subsequent data analysis stage (Sekaran, 2003). In this research, the unit of analysis is a group of small medium enterprises belong various industry because problems focus on reflects of SMEs on both macro environment and micro factors in industry. Data will be collected from leader of each enterprise.
A study can be done in which data are gathered just once, perhaps over a period of days or weeks or months, in order to answer a research question. It is called cross-sectional design. Longitudinal means studying phenomena at more than one point in time and be used to know changes over time (Sekaran, 2003).
This survey will be completed by cross sectional, focused on finding relationships between variables at a specific point in time. Choosing cross sectional because time limited and just want to know the problems, not focus on the changes of problems. The major advantage of cross-sectional research is that data can be collected on many different kinds of enterprises in a relatively short period of time.
Sampling is the process of selecting a sufficient number of elements from the population, so that a study of the sample and an understanding of its characteristics would make it possible for us to generalize such properties or characteristics to the population elements (Thompson, 1992). The most important thing is choosing the best represent the population defined
Population refers to entire group of people, events, or things of interest that the researcher wishes to know (Sekaran, 2003). Population comprises total set of subjects that researcher wishes to learn more about certain behavior or specific characteristics. The population in this study consists of Franchisees and Franchisors which are operating in Vietnam. The survey will be conducted in two main areas which concentrate most of the Franchising in Vietnam (HoChiMinh and Hanoi). They are asked to participate actively into the questionnaire and fill up all the sections given.
To select sample there are many different methods of choosing respondents, a mathematical approach called "probability sampling" and "non-probability sampling" (Foreman, 1991). Stratified random sampling, belong to the probability sampling approach will be used in this research because there are many kind of participants involve in this process, each one have different characteristics and functions
With stratified random sampling, the population is first divided into a number of parts base on some characteristic, reflect the diversity of the population. Simple random samples are then selected from each stratum. When sub-populations vary considerably, it is easy to sample each subpopulation (stratum) independently. Stratified random sampling can also be used to improve population representativeness in a study.
Measurement and measures
Measurement is the process of assessing numbers to objects or observation. There are four major levels of measurement: nominal, ordinal, interval and ratio. (Robbins, 2004)
Nominal scale is really a list of categories to which objects can be classified. Nominal measures offer names or labels for certain characteristics. Stevens (1946)
Ordinal scale assigns values to objects based on their ranking with respect to one another. Names may be used like "bad", "medium", "good". Stevens (1946)
Interval scale: one unit on the scale represents the same characteristic being measured within the whole range of the scale. Interval scales measure the differences in the preferences. This does not have a "true" zero point, and evaluate the distance between any two pints on the scale.
Ratio scale: is like interval scales except they have true zero points. It measures a certain distance along the scale. This ratio hold true regardless of which scale the object is being measured in
In this study, researcher will integrate four types to measure (1) impact of macro environment on Franchising; (2) impact of micro factors in different industries on its Franchising; (3) awareness of Franchisees and Franchisors toward the roles of business environment on their business; (4) the desires of Franchise to the improving business environment.
Data - collection Method
Source of data
Data can be obtained from primary or secondary source. Primary data refer to information obtained firsthand by the researcher on the variables of interest for the specific purpose of the study. Primary data source includes responses to questionnaires, interview or observations. Secondary data refer to information gathered from previous works (Sekaran 2003). Secondary data involves statistical records, government document, journal, news, association. This study will rely on both primary and secondary data.
Data Collection Methods
Data collection methods are integral part of research design as shown in the shaded portion in the figure. Inaccurate data collection can impact the results of a study and ultimately lead to invalid results. There are several data collection methods, each with its advantages and disadvantages. Data can be obtained through some methods of collecting data such as interview, questionnaire, observation, and case study. The optimal data collection technique is selected only after the researcher has determined the purpose, the information sought and the basic research design method. Problem researched with the use of appropriate methods greatly enhance the value of the research (Phillips & Stawarski, 2008)
Figure 2.2: Data collection techniques and tools.
Primary data collection
An interview is a data-collection technique that involves oral questioning of respondents, either individually or as a group. Answers to the questions posed during an interview can be recorded by writing them down (either during the interview itself or immediately after the interview) or by tape-recording the responses, or by a combination of both. There are three types of interview which are structured interview, Semi-structured interview and In-depth interview. In this case, researcher will not use interview.
A questionnaire is a pre-formulated written set of questions to which respondents record their answers, usually within rather closely defined alternatives. Questionnaires are an efficient data collection mechanism when the researcher knows exactly what is required and how to measure the variables of interest. Questionnaires can be administered personally, mailed to the respondents, or electronically distributed (Neuman, 2000)
In this study, the information will be collected through questionnaire that distributed personally, email and direct to the enterprises leaders. Questionnaires often make use of checklist, rating scales and comment.
Questionnaire design is a logical process that can be divided into simple steps. Use the following steps to help you develop a valid, reliable, and effective instrument. (Phillips and Stawarski 2007)
There are two types of questionnaires: one is fixed response and another is open-ended questions. The fixed-response questions were given to interviewees, whereas the open-ended questions were asked to gain an in-depth understanding of their answers. The fixed-response questions covered five areas relating to respondents: (1) knowledge business environment; (2) main factors that impact on Franchising; (3) give comment to improve business environment ; and (4) background information (i.e., gender, education level, age, residence location, acreage of land owned, etc.).
Secondary data collection
Secondary data is data that has been previously gathered by someone before. Data can be accessed through the internet or perusal of recorded or published information from internal or external source to the organization.
There are several sources of secondary data, including books and periodicals government publications of economic indicators, census data, statistical abstracts, data bases, the media, annual reports of companies, case studies and other archival records. Secondary sources of data provide a lot of information for research and problem solving. Such data are as we have seen mostly qualitative in nature. Also included in secondary sources are schedules maintained for or by key personnel in organizations, the desk calendar of executives and speeches delivered by them. Much of such internal data though could be proprietary and not accessible to all.
In this case, data from Government's organization and Non-government organization (NGOs) is also important sources because, Vietnam is developing country, there are penalty of programs which are conducting to promote the development of Franchising.
Data analysis is process of ordering and organizing so that useful information can be extracted from it. Both quantitative and qualitative data analysis is used to analyze information that was obtained in the previous section. That will be used to answer the research question.
Getting Data Ready for Analysis
In order to make data analysis systematical and logical, some preparations should be done beforehand (Sekaran, 2003). The process is defined to six steps as editing data, handling blank responses, coding data, categorizing data, and entering data (figure 3.1).
Handling Blank Responses
To collate notes
To follow up respondents
To determine the validity
To handle the blank items
To transcribe the data from the questionnaire
To set up a scheme for categorizing the variable
To key the raw data into the computer
To create data file
Information is coded systematically
The accuracy of editing is proved
The questionnaire is quite credible
The process is simplified
Human errors are reduced
Measuring a concept with several items works wholly
The data are recorded safely
The data are ready for analysis
Source: (Sekaran, 2003)Figure 3.1 Ready for analysis
Feel for the data
We can acquire a feel for the data by checking the general trend and the spread. In other words, the range and the variance in the data will give the researchers a good idea of how the respondents react to the questionnaires and how good the items and measures are. If the respondents show very little variability, the particular question may be not properly worded and the respondents do not quite understand the intent of the question. In this study, all the items in the questionnaire will be tested by asking people to do it in advance. The feeling and understanding of each question is recorded to help the researchers make a general sense of what others think of the questions. Therefore, it is easy to know whether the understanding of the researchers and the respondents is in accordance.
Testing goodness of data
The reliability and validity of the measures can now be tested. The reliability of a measure is formed by testing for both consistency and stability; while the factorial validity can be established by submitting the data for factor analysis.
How well the items measuring a concept run together as a set.
How well a correlation between two similar forms of a measure obtains.
How well a correlation between the same test at two different time periods works.
How well the measure to differentiate individuals who are known to be different.
How well a correlation between two different sources responding to the same measure conducts.
How well an establishment is made when two different concepts are not correlated to each other?
Source: (Sekaran, 2003)
Quantitative data analysis
Example: Type of quantitative data.
For each type of measure or combinations of types of measure, there are several different techniques to analyze. For interval variables, we have a bigger choice of statistical techniques. This is the process of presenting and interpreting numerical data. To analysis quantitative data, we need to go through five steps.
Firstly, the raw data must be organized and processed to make them meaningful. All the responses to each question will be tabulated in a data sheet. Secondly, an amended version of the original question sheet will be made, and the new sheet will cover sample size and number of responses. Next, some descriptive statistics as well as frequency tables will be conducted. For example, sample size, maximum and minimum values, averages and measures of variation of the data about the average. Then pie chart, bar chart, line chart and/or other charts will be drawn according as the data information of each question. The last step is to analyze the charts separately and relate them with the context in this research (Matthew & Huberman, 1994)
Qualitative data analysis
Figure 3.2 Type of qualitative data
Qualitative Data Analysis (QDA) is process of moving from the qualitative data that have been collected into some form of interpretation of the people and situations we are investigating. According to Matthew & Huberman (1994), the finding from qualitative studies has a quality of "undesirability." Words, especially organized into incidents or stories, have a concrete, vivid, meaningful flavor that often proves far more convincing to a reader Data presents the real issues happening and its meaning. So data analysis and interpretation are compulsory things to do and it decides how successful a research is (Boyce, 2005)
The process of QDA usually involves two things, writing and the identification of themes. It can be analytic ideas or summary, description of the data. Description involves some level of interpretation. Coding into themes mean applying labels to phrases that indicate they are examples of some thematic idea.
The hypothesis testing consists of three main steps:
Based on types of quantitative data collected, select the appropriate statistical method for the test of interdependence amongst interdependent variables and dependent variables.
Select the statistical methods for hypothesis testing involving relationship of research variables.
Execute the test of relationships using the appropriate statistical method.
In this study, there are two hypotheses are developed on different scales as following:
Both macro and micro environment are factors that create opportunities for McDonald.
Key factors have effect on McDonald's decision
Hypotheses 1 can be stated in the null and alternative as follows:
H10: With the open policies of government to promote the Franchising that brought many opportunities to increasing the number of Franchising both in quality and quantity.
H1A: Because of lacking of cooperation in the implementing policies some macro environment factors (legal, economy factors) become the barrier to inhibit the Franchising.
H1B: Franchisees and Franchisors do not have enough long term strategies and resources, so they can't meet each other to implement the franchise.
Hypotheses 2 can be state in the directional and non-directional as follows:
H20: The relationship among macro environment, micro factors and McDonald's decision is the key factors.
The most meaningful explanation of a research may be data analysis and interpretation of results (Sekaran, 2003). To some degrees, interpretation decides how successful a research is. Not only does interpretation summarize the conclusion of data analysis, it but also gives illustration of how the data indicates the real business and issues (Boyce 2005). A good research cannot be short of an excellent interpretation of the results.