Identifying The Product Line With The Highest Sales For Hubbard Foods Limited

Research Objectives

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).

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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.

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The main objective of the research will be;

  • Establishing what are the factors that affect the sales of products produced by Hubbard Foods Limited.

Other general objectives that will be investigated in the research will be to;

  • Determine which product line has the highest sales among the Hubbard Foods Limited production lines.
  • Compare the prices and sales of the different product lines of the Hubbard Foods Limited production lines.
  • Determine which city in New Zealand has the highest sales for the Hubbard Foods Limited products.
  • Determine which product line among the Hubbard Foods Limited production lines has the high sales in which of the cities in New Zealand.
  • Compare the sales of the Hubbard Foods Limited products in the different cities in New Zealand.
  • Determine the favorite or most popular product among the customers of the Hubbard Foods Limited.
  • Determine the gender composition and sales per gender of the Hubbard Foods Limited customers.
  • Determine the average monthly spending per person on Hubbard Foods Limited products.
  • Determine the average frequency of purchase per person of the Hubbard Foods Limited products per month.
  • Determine the average monthly expenditure percentage ratio between the monthly expenditure on Hubbard Foods Limited products and the overall monthly expenditure on breakfast products.

The research will focus on two research questions:

  1. Do product type, product price and city where a product is sold affect the sales of the products from Hubbard Foods Limited?
  2. How are the sakes of Hubbard Foods Limited affected by consumer attributes and preferences?

The research will consider two hypotheses for the research questions:

  1. The sales at Hubbard Foods Limited are affected by product type, product price and the city where the product is sold. Mathematical representation is given below;

Opportunity Identification and Research Purpose

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.

  1. The sales at Hubbard Foods Limited are affected by consumer attributes and preferences. Mathematical representation is given below;

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 Hubbard Foods Limited official website (Hubbards Foods Limited, 2018). This source is going to provide information on the sales of the Hubbard Foods Limited. Having access to the annual report of the company will give data on price by unit of each of the product, the different product types and their sales, the sales for different periods and different cities in New Zealand.
  • The Hubbard Foods Limited official website (Hubbard Foods Limited, 2018). This source is going to provide information on the different production lines on the Hubbard Foods Limited production.
  • The New Zealand Data Portal (New Zealand Government, 2018). This source will provide information on the population of New Zealand as well as the gender composition of the general population of New Zealand.
  • The New Zealand Government (New Zealand Government, 2018). This source will give information on the regions and cities in New Zealand.

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;

  • Comparing the prices and sales of the different product lines of the Hubbard Foods Limited production lines. Here the different categories of the Product Type variable are going to be compared with respect to the Sales variable. Also the categories of the Product Type variable will be compared with respect to the Price per Unit Variable. The dataset for this analysis will be from the annual report from the Hubbard Foods Limited official website (Hubbards Foods Limited, 2018).
  • Comparing the sales of the Hubbard Foods Limited products in the different cities in New Zealand. Here the different cities under the City variable will be compared with respect to the Sales variable. The dataset for this analysis will be from the annual report from the Hubbard Foods Limited official website (Hubbards Foods Limited, 2018).
  • Determining the sales per gender of the Hubbard Foods Limited customers. Here the categories of the Gender variable are going to be compared with respect to the Monthly Expenditure Variable. The dataset for this analysis will be from the online survey carried out.

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).

Research Questions

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;

  • Regression Analysis on the annual report dataset from the Hubbard Foods Limited official website (Hubbards Foods Limited, 2018). In this analysis, the Sales variable will be the dependent variable while the independent variables will the Product Type, Product Price and City. The analysis will aim at establishing the nature of the relationship between the Sales of products from Hubbard Foods Limited and, the Type of product, Price of Product and the City in which the Product is sold.
  • Regression Analysis on the dataset from the online survey carried out. In this analysis, the Monthly Expenditure variable will be the dependent variable while the independent variables will be the Price, Product Type, City, Product Variety and Gender.

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).

Research Hypotheses

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:

  • Selection Error: This is the error that occurs when respondents decide for themselves whether to take part in the survey or not (Cortes & Mohri, 2014). This error is solved by having a contract agreed to by the target respondents prior to the survey. This has been done in this research using the INFORMATION SHEET (HUBBARD FOODS LIMITED RESEARCH SURVEY)and CONSENT FORM (HUBBARD FOODS LIMITED RESEARCH SURVEY).
  • Sample Frame Error: This is a sampling error in which the wrong population is considered for sampling (Burns & Grove, 2009). In order to solve this error the population should be keenly chosen. In this research only customers of Hubbard Foods Limited are emailed the link for the online survey, thus minimizing the chances of Sample Frame Errors.
  • Population Specification Error: This is a sampling error that may occur when the population to be surveyed is not clear (Saris & Gallhofer, 2014). The research is faced with this errors since the person purchasing the product might not be the one consuming it. However, the research makes the assumption that the buyer is most likely the consumer or may have similar preferences as the consumer.
  • Sampling Error: This is a sampling error that occurs when there number of respondents is too low to be considered as representative of the population (Groves, et al., 2009). In this research this will be solved by targeting a large number of participants for the survey.

The research is exposed to the following non-sampling errors:

  • Response Error: This is the error that occurs when there is an incorrect submission and subsequent recording of data in a survey (Lance & A, 2016). To avoid this, this research applies the use of the PRE-TEST QUESTIONNAIRE DESIGN.
  • Non-Response Error: This is an error that occurs when the respondents partially fill the questionnaires or completely leave the questionnaire blank (Berinsky, 2008). In this research, the error is solved by an online survey. The questionnaire can only be submitted once all the questions have been answered.
  • Analysis Error: This is the error that occurs during the analysis stage of a research, when the wrong analysis tool is used (O’Neil & Schutt, 2013). To avoid this error, the analysis process in this research is well outlined and described. The analysis tools are also proven to be efficient for the required analysis
  • Estimation Error: This is error that occurs when the estimate used in the analysis of data is not representative of the actual parameter (Barbara & Susan, 2014). In order to avoid this error, the research applies the use of two different datasets, one from the producer and another from the consumer. This therefore reduces the chances of the estimation error occurring.

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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