The biggest challenge currently facing many companies or firms that are pioneers in their specific industries is reduction in sales (Anthony & Johnson, 2008). This is attributed to a number of factors such as advances in technology, change in consumer behavior, attributes and preferences as well as new market entries. These factors have overwhelmed the pioneer firms making their advantage in experience ineffective (Gamble, Thompson, Strickland, & John, 2010). However, the largest challenge facing these firms is lack of data. The challenge of not having enough data cuts across all the factors that lead to the reduction in sales. Availability of data is a key aspect in determining the causes and remedies to reduction in sales (Ireland, Hoskisson, & Hitt, 2008), (Farris & Neil, 2010).
Hubbard Foods Limited is a New Zealand based company that produces breakfast products. The products produced and sold by the Hubbard Foods Limited are; Muesli, Porridge, Thank Goodness Gluten Free, Light and Right, Outward Bound, Cereals and Bran, with each of the products having several varieties. The company has been in existence since it was founded by Dick Hubbard in 1989. The Hubbard Food Limited can therefore be said to be among the pioneer companies in the food industry in New Zealand having been in existence for the past 29 years.
Hubbard Foods Limited being a pioneer company in the food industry is faced with reduction in sales just like other pioneer companies globally. This proposed research therefore aims at collecting and analyzing data to help understand the causes of the reduction in the sales of Hubbard Foods Limited. The proposed research will also provide Hubbard Foods Limited with suggestions and recommendations to assist in increasing the sales of the company.
Opportunity Identification And Research Purpose
Hubbard Foods Limited is faced with a reduction in the sales of its products. The purpose of the research is to investigate this sales problem faced by the company. The results from the analysis in the research will provide Hubbard Foods Limited with sufficient information for them to make informed decisions that have statistical backing. These decisions will enable the company to return to profit making as a result of increased sales.
The main objective of the research will be;
Other general objectives that will be investigated in the research will be to;
The research will focus on two research questions:
The research will consider two hypotheses for the research questions:
H0: Hubbard Sales are not affected by product type, product price and the city where the product is sold.
H1: Hubbard Sales are affected by product type, product price and the city where the product is sold.
H0: Hubbard Sales are not affected by consumer attributes and preferences.
H1: Hubbard Sales are affected by consumer attributes and preferences.
The research will make use of both the primary and secondary data sources. The primary source for the data that will be collected and analyzed in the research will be an online survey. The questionnaire will be in the form of a questionnaire in google forms.
The secondary data will be collected from four different sources. These secondary sources will be:
The research will apply a quantitative research approach. Three quantitative research techniques are going to be applied. These are: Survey, Quasi Experimental Research (Casual Comparison Research) and Regression Analysis.
A survey can be described as a statistical process that involves the collection or gathering of quantitative information on various aspects of the elements in a population (McNabb, 2008). The research applies the survey technique in the data collection stage. The technique will be conducted in the form of questionnaires issued in an online survey. The questionnaires will be sent to the emails of the respondents as google forms’ links. The respondents will be expected to fill the questionnaires and submit the filled google forms.
The responses from the survey will form the data variables that will be used as either independent or dependent variables in the analysis stage of the research.
The Quasi Experimental Research (casual comparative research) is a research techniques that compares the attributes of different categories of a given data variable in a dataset (Babbie, 2010). This technique singles out the categorical data variable of interest and then records the attributes (with respect to a numerical data variable in the same dataset) for comparative analysis (Brians, 2011).
In the research, the Quasi Experimental Research is going to be applied in the following cases;
Regression analysis is a statistical analysis tool that represents the relationship between variables in the form of an equation (Galit, Peter, Inbal, Patel, & Kenneth, 2018). The variable of interest, Dependent variable, forms the subject of the equation while the remaining variables, independent variables, form the explanatory components on the right-hand side of the equation. The general form a regression model line is:
The y is the dependent variable, the x represents the independent variable, i represents the observation and j represent the specific independent variable (Tri & Jugal, 2015).
In the research, the regression analysis is going to be applied in the following case; establishing what are the factors that affect the sales of products produced by Hubbard Foods Limited. Here the Regression Analysis will be carried out twice as explained below;
The conclusions from the two above regression analysis will give both the producer and consumer perspective on the relationship that Sales has with other variables.
This research will also apply the use of graphs to represent the nature of the various data variables in both datasets.
The data that will be used for the analysis in the research will come from both the primary and secondary sources. The data from the secondary source will be collected as explained in INFORMATION SOURCES above.
The survey technique will be used for the collection of data from the primary source. The survey is a data collection technique that gathers the quantitative information of elements in a population (McNabb, 2008). The survey will be conducted in the form of questionnaires issued in an online survey. The questionnaires will be sent to the emails of the respondents as google forms’ links. The respondents will be expected to fill the questionnaires and submit the filled google forms. The responses from the survey will for the data variables that will be used as either independent or dependent variables in the analysis stage of the research.
Conducting an online survey is the most effective method of collecting information from the customers of Hubbard Foods Limited. The options of observation and experimentation are non-applicable for the context of Hubbard Foods Limited. The online survey allows the respondents to fill in the questionnaires at their own comfort. This method is also cost effective as opposed to conducting a physical survey involving physical data collection from consumers.
Probability Sampling Methods
The research will apply the Cluster Random Sampling Technique. The Cluster Random Sampling is a probability sampling technique in which the data collected is divided into a number of groups, clusters, from these groups samples are then collected randomly (Ahmed, 2009). In the research, the annual report dataset from the Hubbard Foods Limited official website (Hubbards Foods Limited, 2018) is going to be divided into clusters from which the samples are then going to be collected.
The research will apply the Quota Sampling Technique. The Quota Sampling is a non-probability sampling technique in which the actual population attribute proportion is considered in the collection of samples (Dillman, Smyth, & Christian, 2009). The technique involves the identification of an attribute in the population such as gender, the proportions of this attribute in the population is noted and becomes a guide in the proportions considered for the sampling (Freedman, 2009).
In the research, the Quota Sampling Technique is applied to the dataset from the online survey carried out. The information on the gender composition of New Zealand will be collected from (New Zealand Government, 2018). This composition will then be applied into collecting the samples for analysis.
In determining the sample size, we will consider the information on the population obtained from (New Zealand Government, 2018) on the New Zealand. We will then use the Cochran’s Sample Size Formula. In this formula we use the population size, together with the standard confidence interval of 95% and a margin of error of 1% to obtain the sample size. The equation for the formula is given below:
Z is obtained from the Z tables, a represents the gender for which the size is being calculated, p represents the proportion of the gender being considered in the population, q = p – 1 and e is the margin of error.
|
DATE |
ACTIVITY |
BUDGET |
|
October 1ST 2018 |
Collecting Data from the Secondary Sources |
N/A |
|
October 1st 2018 – October 31st 2018 |
Carrying Out Online Survey |
10 Dollars’ worth of voucher per respondent. |
|
November 1st 2018 – November 30th 2018 |
Data Entry, Data Analysis and Data Reporting. |
5 Dollars per day |
|
December 3rd |
Report Presentation |
N/A |
Sampling Errors
The research is exposed to the following sampling errors:
The research is exposed to the following non-sampling errors:
References
Ahmed, S. (2009). Methods in Sample Survey. Johns Hopkins Bloomberg. , 1-14. New York: John Hopkins Bloomberg.
Anthony, S. D., & Johnson, M. W. (2008). Innovator’s Guide to Growth. Havard Business School Press.
Babbie, E. R. (2010). The Practice of Social Research 12th edition. Belmont, CA: Wadsworth Cengage.
Barbara, I., & Susan, D. (2014). Introductory Statistics. OpenStax CNX.
Berinsky, A. J. (2008). Survey Non- Response. In W. Donsbach, & M. W. Traugott, The SAGE Handbook of Public Opinion Research (pp. 309-312). Thousand Oaks, CA: Sage Publications.
Brians, C. L. (2011). Empirical Political Analysis: Quantitative and Qualitative Methods. Boston, MA: Longman.
Burns, N., & Grove, S. K. (2009). The Practice of Nursing Research: Appraisal, Synthesis and Generation of Evidence. 6th Edition. St Louis, MO: Saunders Elsevier.
Cortes, C., & Mohri, M. (2014). Domain Adaptation and Sample Bias Correction Theory and Algorithm for Regression. Theoretical Computer Science , 103-126.
Dillman, D. A., Smyth, J. D., & Christian, L. M. (2009). Internet, Mail and Mixed-Mode Surveys: The Tailored Design Method. San Francisco: Jossey-Bass.
Farris, P. W., & Neil, B. T. (2010). Marketing Metrics. New Jersey: Pearson Education.
Freedman, D. A. (2009). Statistical Models: Theory and Practice (1st ed.). London: Cambridge University Press.
Galit, S., Peter, B. C., Inbal, Y., Patel, N. R., & Kenneth, L. C. (2018). Data Mining for Business Analytics (1st ed.). New Delhi: John Wiley & Sons, Inc.
Gamble, A. A., Thompson, J., A.J, S., & John, E. (2010). Crafting and Executing Strategy . Boston: McGraw-Hill Irwin.
Groves, R., Fowler, F., Couper, M., Lepkowski, J., Singer, E., & Tourangeau, R. (2009). Survey Methodology. New York: John Wiley & Sons.
Hubbard Foods Limited. (2018, September 1). What We Make. Retrieved from Hubbards: www.hubbards.co.nz
Hubbards Foods Limited. (2018, September 1). All About Us. Retrieved from Hubbards: www.hubbards.co.nz
Ireland, R. D., Hoskisson, R., & Hitt, M. (2008). Understanding Business Strategy: Concepts and Cases. Cengage Learning.
Lance, P., & A, H. (2016). Sampling and Evaluation: A Guide To Sampling for Program Impact Evaluation. Measure Evaluation, 6-8. London.
McNabb, D. E. (2008). Research Methods in Public Administration and Non-Profit Management. Armonk, NY: M.E. Sharpe.
New Zealand Government. (2018, September 1). Population & Society. Retrieved from data.govt.nz: www.data.govt.nz
New Zealand Government. (2018, September 1). Regions-nz. Retrieved from New Zeland Now: www.newzealandnow.govt.nz
O’Neil, C., & Schutt, R. (2013). Doing Data Science. London: O’Reily.
Saris, W. E., & Gallhofer, I. N. (2014). Design, Evaluation and Analysis of Questionnnaires for Survey Research. Hoboken: Wiley.
Tri, D., & Jugal, K. (2015). Select Machine Learning Algorithms Using Regression Models. 2015 IEEE Conference.
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