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Define the Management question;

Define the Research question(s);

Give (at least) 2(two) objectives.

1.Complete a literature search and write 5(five) annotations (less than half a page each). Be aware that you will need to add a small literature review into your final proposal (assignment 2) so keep track of other reference material that you find while you are doing your searches.


  • Data Mining,
  • Surveys,
  • Quantitative Rating Scales.

3.Examine each of them for possible use in solving your research question. Use examples within your answers.

Provide an overview of

  • Questionnaires
  • Focus Groups
  • Qualitative rating scales

Explain how they may (or may not) be used in your research proposal;

  • Explain coding.  Provide examples.

Discuss Primary and Secondary data and their use in research.  Include at least 3 examples of each.

  • Primary Data
  • Secondary Data

Research design should have research ethics approval, therefore, an ethics form should be completed. Summarize the meaning of ethics and include a comment whether your research should, or should not, need ethics approval.

Describe Sampling, and then relate your answers to validity; representativeness; reliability.

  • Sampling
  • Validity
  • Representativeness
  • Reliability
Spark New Zealand is one of the major telecommunications companies in New Zealand which provides mobile networks and fixed line telephone services, in New Zealand. The company is also a major internet service provider and a major ICT provider to many businesses in New Zealand. The company was established in 1987 and became a public company in 1990, and has been offering its services and products to the New Zealand citizens since 1990.

Just like the other companies, Spark New Zealand faces some managerial problems and some other problems which limit its effectiveness in offering its services to the people. The main questions of our proposal aim to address some of the major problems faced by this company. Some of this information can be obtained from the available literature which discusses the major challenges faced by organizations. This knowledge urges us to come with some annotations can help us to get some of the required affirmation about this organization and the other organizations in general.


This source (book) gives a detailed introduction of good management of organizations and also gives some important hints about effective management of organizations. The authors of the book give different management strategies which the managers of organizations should apply for them to manage all the employees and the other stakeholders of the organizations well, and this will make their organizations successful. The content of this book is very important and can be used by the management staff of Spark New Zealand for them to know the best management strategies which they can apply in their operations to improve the performance of their organization.

This source (book) focusses on the management of large systems such as the systems found in organizations some of which are very large and complex. The book clearly states that there are many challenges associated with managing large systems and the managers must be very skilled and competent for them to manage these systems properly. For effective management of these large and complex systems, all the experts from different fields working in the organizations need to work together to manage these organizations effectively. The information of this source is very important to the management staff of Spark New Zealand as it advises them the importance of working together to solve the many challenges faced by large organizations which have very large and complex systems.

This source (journal) discusses the importance of understanding the organizational resources and how these resources can be used to explain the organizational problems, and then come up with some solutions which can be used to solve these problems. This journal gives the case study of the nonprofit sports organizations some of which have been able to understand their problems and use the resources they have to address most of these problems. This journal is very important in our research as it teaches the management staff of Spark New Zealand that they can use the resources they have to address some of the problems they face just like some of the nonprofits sports organizations in Germany do.

This source (journal) describes how incompetence and managerial problems delay rewards in crowdfunding projects. The incompetent leaders delay or fail to give the awards where necessary and at the right time and this makes most of the crowdfunding projects end up failing. This source clearly explains the negative impacts of incompetence leadership and other managerial problems on the expected outcomes, which means if there are incompetent leaders and other managerial problems in organizations, most of the projects of the organizations will end up failing. This information is very important to the management staff of Spark New Zealand, and they should do all that it takes to eliminate incompetent leaders and try as much as possible to solve its managerial problems which can make most of its projects to fail if not solved.

This source (book) explains in details how the managerial problems can be solved systematically. According to the book, there are some strategic steps which should be followed systematically and strictly to solve managerial problems. These steps mainly involve first understanding the managerial problems well and their sources and then developing and implementing the best solutions which can be used to solve the problems. After implementing the solutions, they should then be evaluated to see their performance, and if they did not perform as expected, they can be replaced by other better solutions. This source is very important and helps the management staff of Spark New Zealand to understand how they can effectively solve the management problems they face.

Data mining

Data mining is the process of examining and sorting large and complex pre-existing databases to generate new and more useful information or data (Shmueli, Bruce, Yahav, Patel, and Lichtendahl, 2017). Data mining is done using some useful data mining tools, and this process of data mining helps organizations to predict their future trends and thus implement the necessary measures which can help to improve their performance. Spark New Zealand can use data different data mining techniques to predict its future performance and know what the customers may need in the future, and by getting this information, the company can do the required modifications which will help to keep on performing well in the market. Again, this prediction and implementation of the measures which will help to improve the performance of the company will help it to address the problem of competition in the market which greatly affects its performance by robbing some of its customers.


Surveys are important in organizations and help many organizations to solve some of the problems they face. As already mentioned, Spark New Zealand faces managerial problems and other non-managerial problems which affect its performance greatly. One of the major problems which Spark New Zealand faces is stiff competition from other companies such as Vodafone which offer similar products and services. Surveys are very important in addressing such problems since they will help the company will collect the views and the perceptions of the customers about their products and services for it know what it needs to improve or add for it to perform better than the competitors (Callegaro, 2017). Therefore, the company should make use of surveys to understand its customers’ demands and implement them for it to compete well in the market and this will help to improve its overall performance.

Quantitative rating scales

Quantitative rating scales are types of numerical scales which are used by researchers or organizations when they want the respondents of the research to rate various products or services in the research. These rating scales are normally given some numerical values such as 0 to 5, 0 to 10, or any other numerical values where each numerical value represent a certain aspect of the research questions or issues being investigated. Quantitative rating scales help the researchers to understand the perceptions of the respondents about the issues or questions under investigation. Spark New Zealand can make use of quantitative rating scales to understand the customers’ perceptions about its products and services by looking at the different scores given to different products and services, and this will help the company to make the necessary improvements which will help to improve the rating scores of its products and services which will mean improved satisfaction to the customers.


A questionnaire is a set of printed or handwritten questions which are prepared to aid in the process of data collection in research (Brace, 2018). The questionnaire questions can be closed, or open-ended questions. In the closed questions, the respondents are restricted in their responses and are supposed to answer by either yes or no, true or false, or chose their responses from the set of answers given by the researchers. The open-ended questions, on the other hand, allow the respondents to give their answers and express themselves about the issues being investigated (Züll, 2016, pp.2-5). Questionnaires are very important and can be used in our research proposals to understand the major problems faced by Spark New Zealand. The questionnaires will be given to the staff of the organization and the customers of Spark New Zealand for them to explain various problems which they feel affect the organization. The management staff and the customers of Spark New Zealand are the most suitable respondents of the research since they are directly involved in the operation of the company, and so they know the major challenges or the problems which face the organization.

Focus groups

Focus groups are some groups made of a small number of participants (normally about 6 to 12 participants) who are selected from the company’s target market. The participants are selected from the company’s main customers, and these customers discuss with the company’s moderators to see what can be done to improve to improve the company’s products or services (Edley and Litosseliti, 2018, pp.6-9). Focus groups can be effectively used in our research proposal where Spark New Zealand will select a few of its loyal customers and engage them in the discussion about the company’s performance and request them to give some areas which they feel to be improved by the company for the customers to benefit from the services and products offered by the company. The focus groups are very important as they represent the customers in the decisions made by the company, and we know the customers are very important stakeholders who should be engaged in the company’s decisions for it to thrive well.

Qualitative rating scales

Qualitative rating scales are some special non-numerical scales used by researchers and organizations to help them to understand the respondents’ views and perceptions about their research questions or other issues being investigated. These scales normally use some comparative words such as good, very good, excellent, agree, strongly agree, among many other words which can be used to express the respondents’ perceptions or satisfaction on the issues being investigated. Qualitative rating scales can also be used in our research proposal to help the researchers to understand the respondents’ perceptions and views of the major challenges which affect the organization’s performance. The researchers can come with some proposed problems and come up with some qualitative rating scales which will help them to understand the respondents’ perceptions about those problems, and these results can be used by the organization to improve its products and services as suggested by the respondents.


In research, coding is an analytical process which involves categorizing the data obtained from the data collection to facilitate its analysis. Coding is very important as it helps the data to be categorized as required and transformed into the most appropriate forms which can be easily analyzed by the tools and the software to be used in analyzing the data (Woolf, 2017). Some of the major types (examples) of the coding process include source coding also called data compression, channel coding also called error control, cryptographic coding, and line coding. Data compression is done to remove redundancy from the raw data and thus enhance its transmission while error control is done to minimize the errors which may be in the data and thus enhance its processing.

Primary and secondary data in research

In research, primary data is the original or the first-hand data which is collected or observed directly from the field of research. Primary data may be obtained through some methods such direct observation from the field, direct interviews, direct questionnaires, or other methods which involve interacting directly with the respondents of the research (Fallon, 2016, pp.147-155). Some of the examples of primary data include recorded speeches, filled questionnaire forms obtained from the field of research, and different samples or other materials obtained from the field during the research. Primary data is used by the researchers who want to get accurate or specific results in their research, and so they prefer collecting the data from the field themselves other than using the data which was collected by other people (researchers) which may not be accurate or may have been tampered with by other people. Again, we have some kinds of data which keep on changing and using the data collected by other researchers may not give the required results.

Secondary data

Secondary data is the data which is used by people (researchers) who did collect it from the field but rather obtained it from some sources where it was recorded by the people who did the actual research (Johnson and Sylvia, 2018, pp.2-11). Secondary data simply means that the data was not obtained from the field but from other secondary sources where it was stored by the people who collected it. Examples of secondary data are census data stored in government websites, health data stored in some hospitals websites, and different data stored on websites of companies. Secondary data is mainly used by researchers when the actual process of data collection is really difficult and complex and faced with many challenges such as high financial costs and so much time needed, and this makes it quite hard or impossible to conduct the actual data collection process. It’s good to note that although using secondary may not give very accurate or specific results like the primary data, it helps to obtain satisfactory results which meet the demands of the researchers.

  1. Ethics in research

Ethics is the term used to describe the moral principles which govern the lives of the people for them to live peacefully with the other members of their societies. The data collection process needs ethics approval since it involves getting the data of the respondents some of which may be very sensitive data and getting such information may be against the ethics of different societies (Wong and Nather, 2016, pp.149-179). Before conducting the data collection process in this research, ethics approval is needed where an ethics form will be filled giving the details of the data collection process and assuring the people that the data collection process will observe the ethics of the society. The respondents will be assured that the data which will be collected in the research will be used for improving the quality of products and services offered by Spark New Zealand and will not be used for any other purposes and won’t be disclosed to the third parties who are not involved in the research.

  1. Sampling

Sampling is the technique or process of selecting representatives (samples) of a population with the aim of using these samples to study the characteristics of the whole population (Gentles, 2016). Sampling is very common in research where the researchers use some few samples or members of the population they wish to analyze and understand. Using the whole population can make the research process very bulky and tedious, and the researchers may not end up meeting the objectives of their research, and that’s why they prefer working with samples.


In research, validity is the term used to represent the soundness or the state of being logical and reasonable. Selecting a good sample to represent the whole population in the research helps to improve the validity of the results which means that the results obtained will be more logical and more reasonable and very close to the actual results (Jansson and Nordgaard, 2016, pp.9-16). This means that the features of the sample will be very close to the actual features of the whole sample, and so the sample will serve to represent the whole population well.


Representativeness is the term used to describe the effectiveness of the sample in representing the actual population. Representativeness can be used to define how accurately and effectively does the population reflects in the chosen sample (Du et al., 2017, pp.14-26). When the chosen sample gives many and correct details of the population it represents, this kind of study is said to have good representativeness.


Reliability is the quality or the degree of being trustworthy and consistent. In research, reliability is used to mean the likelihood of obtaining good and consistent results which can be used to make meaningful deductions and conclusions (Crowder, 2017). Selecting a good and an appropriate sample helps the researchers to obtain reliable results which can be used to make meaningful deductions and conclusions in the research.


Brace, I. (2018). Questionnaire design: How to plan, structure and write survey material for effective market research. Kogan Page Publishers.

Callegaro, M. (2017). Importance of Surveys in the Era of Big Data. London: Routledge.

Crowder, M. J. (2017). Statistical analysis of reliability data. London: Routledge.

Du, B., Wang, Z., Zhang, L., Zhang, L., Liu, W., Shen, J., & Tao, D. (2017). Exploring representativeness and informativeness for active learning. IEEE transactions on cybernetics, 47(1), 14-26.

Edley, N., & Litosseliti, L. (2018). Critical Perspectives on Using Interviews and Focus Groups. Research Methods in Linguistics, 195(3), 6-9.

Fallon, M. (2016). Primary Data Collection. In Writing up Quantitative Research in the Social and Behavioral Sciences (pp.147-155). Sense Publishers, Rotterdam.

Gentles, S. J. (2016). Sampling in Qualitative Research: Insights from a Systematic Overview of the Methods Literature. Sage

Heerkens, H., & van Winden, A. (2017). Solving managerial problems systematically. Noordhoff.

Hislop, D., Bosua, R., & Helms, R. (2018). Knowledge management in organizations: A critical introduction. Oxford University Press.

Jansson, L., & Nordgaard, J. (2016). Validity and Reliability. The Psychiatric Interview for Differential Diagnosis (pp. 9-16). Springer, Cham.

Johnson, E., & Sylvia, M. L. (2018). Secondary Data Collection. Clinical Analytics and Data Management for the DNP, 61(1), 2-11.

Sayles, L. R. (2017). Managing large systems: Organizations for the future. London: Routledge.

Schiavone, F. (2017). Incompetence and Managerial Problems Delaying Reward Delivery in Crowdfunding. Journal of Innovation Economics & Management, (2), 185-207.

Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons.

Wicker, P., & Breuer, C. (2013). Understanding the importance of organizational resources to explain organizational problems: Evidence from nonprofit sports clubs in Germany. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 24(2), 461-484.

Wong, J. L. Y., & Nather, A. (2016). Ethics for Research. In Planning Your Research and How to Write it (pp.149-179).

Woolf, S. (2017). An Active Learning Approach to Transcript Coding for Education Research (Doctoral dissertation, Tufts University).

Züll, C. (2016). Open-ended questions. GESIS Survey Guidelines, 3(2), 2-5.

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