HI6008 Research Design and Methodology Assignment Sample

• Subject Code : HI6008
• University : Holmes Institute My Assignment Services is not sponsored or endorsed by this college or university.
• Subject Name : Business Research and Design

Business Research

Table of Contents

Introduction..

Research methodology.

Research design.

Example questions.

Data analysis.

Summary of findings.

Conclusion.

References

Introduction to Business Research

The primary motive of the report is to analyze and examine the relevance of utilizing big data technology in corporations to gain competitive benefit. Big data is highly beneficial for increasing the effectiveness of occupational processes. Every company is requisite to use and execute big data technology to attain numerous benefits and bring competitiveness in the industry. Big data analytics denotes to the usage of advanced analytic technique against data sets that involve structured as well as unstructured data from various sources. It will focus on the potential benefit of applying big data technology and evaluate its contribution to the attainment of competitive advantage. Big data analytics is significant for the companies to improve business operations, strategies, increase profit and reduce costs. The research aims to determine the importance of implementing big data technology for the company to achieve different benefits. Based on the research problem, big data technology should be employed by companies to bring competitiveness in the industry. Different data and findings will be discussed in the report to evaluate the significance of using big data analytics. Businesses are facing issue with the presence of numerous datasets which led to the increased usage of big data technology into the business processes.

Research Methodology

Research Design

The research methodology refers to the approach as well as the process in which particular research is conducted in a specific way (Adnan & Akbar 2019). It is highly significant to decide on the research methodology before the research takes place. The primary, as well as secondary sources of data, are collected for the research. In this research, a mixed approach is used in which data collected in qualitative and quantitative form. Besides this, qualitative data refers to the data collected from the primary source in which a questionnaire is used. The quantitative data is gathered from the secondary sources and measurable in numeric terms. In this research, qualitative as well as a quantitative research methodology is adopted to discover the significance of using big data technology in the companies. The study depends on evidence collected through questionnaire and case study sources. Besides this, the questionnaire is proposed to be conducted with around 50 respondents from different groups.

Moreover, qualitative research methodology is an integral method of analyzing data which involve an in-depth interview, content analysis, focus group, observation and case study research (Ajayi et al. 2019). In addition, the outcomes of the qualitative research method are descriptive and can be easily obtained. On the other hand, quantitative research method mainly focuses on statistical and numerical analysis of data through surveys and questionnaire. In addition, a quantitative research method can be effectively used for verifying the correct hypothesis while qualitative research method is used to gain in-depth information about the topic. It is critically evaluated that the study controlled for the demographic variables relevant to the analytics managers which involve industry, education, age, and experience, size of the firm, education and gender to prevent any bias based on demographics. The questionnaire is determined as a good way of obtaining information from a large number of individuals. In this approach, the participants can share their views and opinions about the significance of introducing big data analytics in organizations. Diverse methods can be used for collecting data to acquire a thorough understanding of the usage of big data technology is gaining numerous benefits from the companies.

Moreover, big data analytics assist the organizations in harnessing their data and utilize data to identify innovative opportunities (Bag 2016). In addition, big data can bring efficiency as well as speed in the business operations which lead to an increase in the ability of the organization to work faster and acquire a competitive edge. Descriptive form of research design is planned to use to confirm that the data achieved would aid in answering the research questions more efficiently (Bradlow et al. 2017). Sample questions and data analysis are a significant part of the research design to conduct the research process and collect relevant data based on the topic.

Example Questions

A questionnaire is used as a quantitative method to acquire relevant information from a large number of persons (Yeo & Carter, 2017). 10 questions will be asked to 50 participants to assess the significance of utilizing big data analytics in the companies. Sample questions for the questionnaire are as follows:

• What do you think would be the biggest opportunity for utilizing big data technology in business?
• Do you have any big data initiatives in the planning of the business process?
• What is the key scope of using big data technology in the corporation?
• What do you think would be the data type for starting the usage of big data in the organization?
• How would you rate access to accurate data in business?
• How do you think big data would be helpful in business decision-making?
• How would you outline big data analytics based on your thinking?
• How do you think big data technology create value for the organization?
• Do you agree with the statement that big data offers business growth?
• Do you think big data assist the companies in understanding consumer behaviour?

However, the questions would be sent to all the participants to gather relevant and useful data regarding the need for introducing big data technology in the business to acquire significant benefits. It is expected that all the participants would respond to the questions appropriately without any bias. Moreover, the case study is the qualitative method used for the research, which allows investigation of the topic in the complexity and natural environment (Brock & Khan 2017). This methodology seems to be efficient for the study to identify and explore the answers to the research questions. The methodology has selected different companies that are highly innovative, respected and maintain competitive benefits in the digital era. After conducting meetings with project managers as well as researchers of big data analytics, information regarding organizations is gathered. A sample of 11 companies is selected for the collection of data for the research study. It is evaluated that both questionnaire and case study method of research used to gather and examine the data obtained from the sample respondents.

Data Analysis

Qualitative and quantitative methodologies are appropriate for the research. In this research, the data collected through questionnaire and case study, which are useful in obtaining useful data regarding the importance of using big data analytics in the business (Tian & Liu 2017). However, questions are sent to the sample participants through email to get information and allow them to state their opinions and feelings. In addition, an initial set of questions is assembled to gather data on the relevance of utilizing big data analytics in the companies. After considering a careful analysis of the responses, the questionnaire is considered to have been correctly filled out and responded appropriately, thus give a rate of response of 37.72%. Besides this, after initial mails, it is analyzed that all the companies are not involved in the case study analysis, and only 6 out of 11 companies are responding to the research analysis. Based on the responses, 25% of the participants agreed that big data technology is highly advantageous for companies in analyzing huge amounts of data to uncover different insights. In addition, it is analyzed that 25% of the respondents believed that the implementation of big data analytics within the business is significant for achieving advantages and attain a competitive position in the industry.

Furthermore, it is identified that 15% of the respondents approved that the usage of big data technology helps in fast and better decision-making in the business by analyzing various information immediately. Besides this, 20% of the participants agreed that big data technology significantly contributes to the attainment of competitive benefits by assisting the businesses in making smarter as well as efficient decisions while minimizing costs and improving productivity. On the other hand, 15% of the participants state the disadvantages of using big data because it collects numerous unstructured data, manipulates customer records and violates the principles of privacy. It is analyzed that most of the respondents positively support the use or execution of big data analytics within the business because it allows the organizations to take different advantages and gain innovative opportunities, which lead to effective operations and higher revenue (Thuy & Teuteberg 2019).

It is interpreted that big data analytics offers high value across all the businesses and examine collected information (Christina & Kelly 2017). Meanwhile, big data analytics can be observed as the key driver of competitive advantage for the companies. It is argued by the respondents that effective skills, along with expertise, are the critical resources necessary for managing big data analytics. In addition, the data interprets that big data assists the businesses in changing the whole core system in terms of selling processes and marketing that leads to change in the value proposition of various solutions (Dremel et al. 2018). Based on the responses, it is interpreted that big data create value to the organizations through five ways which involve developing transparency, allows firms to set up experiments, forms a detailed segmentation of consumers, support decisions made by hidden risks and improve the existing products as well as services. It is critically interpreted that most of the respondents agreed with the statement that big data technology helps businesses in understanding the behaviour of the consumers. 

Summary of Findings

It is expected that the focus on the use of big data technology would significantly evaluate the benefits and opportunities to the companies. To be specific, the research has used both qualitative and quantitative method for gathering relevant data based on the topic. Questionnaire and case study approaches are used effectively for collecting data from the selected participants. The research specifies that most of the respondents agreed with the application of big data technology in the business. Some of the respondents define big data analytics as to the process of extracting relevant information by examining various forms of data (Droll et al. 2017). Few of the participants define big data analytics as the advanced analytic technique utilised for discovering market trends, hidden patterns and customer preferences for making effective decisions. Many people defined big data analytics as to the complicated procedure of examining and evaluating different data sets to gain useful information (Muhammad et al. 2019).

Besides this, it is found that the execution of big data analytics is essential for innovative companies to gain different advantages (Popovič et al. 2018). It is analyzed that implementing big data technology would allow the businesses to cut costs, increasing operational efficiency and enhance competitiveness (Hazen et al. 2018). Meanwhile, big data technology also helps the corporations in increasing sales and loyalty of customers. It is also evaluated that utilizing big data analytics assist in ensuring that the organizations employ the right employees and allow to compete with the other businesses successfully. It is expected that all the respondents support the use and implementation of big data analytics, but a few participants state that big data analytics examines unstructured data and manipulate records of customers as well as violate the principles of privacy. It demonstrates that big data technology may also contain limitations which would create difficulty in the implementation of the technology in the companies (Kache & Seuring 2017). It is evaluated that big data technology can be considered as the key procedure of extracting relevant information by analyzing as well as examining various forms of data sets (Rana, 2019).

Moreover, it is found that big data analytics is widely used in diverse top business sectors such as education, transportation, healthcare, energy, banking, sports, communication, manufacturing and insurance (Li et al. 2018). The technology is widely required for businesses so that they could make informed decisions as well as improve the operational effectiveness diversely. It is realized that companies can achieve significant value from unstructured data and reduce the barriers of handling the technology. The major findings evaluated from the research that big data technology aids organizations in gaining competitive advantages in the competitive industry. It also allows the companies to achieve a leading position in the market and understand the significance of big data quickly (Simon 2019). The outcomes of collected data confirm that emphases on the applicability of using big data analytics are the major point for identifying as well as determining the benefits of the technology for the corporations. The technology has found to be positively associated with the competitiveness of the firms in the relevant industry. It is critically evaluated that flexibility in technology enable the firms to recognize the valuable data and successfully implement big data analytics within the business (Mikalef et al. 2018).

It is found that the data collected from diverse participants are free from bias and each participant response to the questions asked during the research process effectively. The answer to the research questions is acquired properly from the respondents to evaluate the significance of using as well as implementing the big data analytics within the business. After interpreting the collected data, it is evaluated that most of the companies are utilizing big data technology to gain competitive benefits and achieve a competitive position in the industry (Simon & Krogstie 2017). Besides this, the outcomes obtained from the collected data have evaluated the high relevance of using big data technology in the businesses because of its potential benefits. However, big data analytics would be beneficial for reducing costs and obtain costs advantages from a large amount of data, which aid in making a quick business decision (Mawed & Al-Hajj 2017).

Conclusion on Business Research

The paper has critically designed the research methodology to adopt data collection approaches and gather relevant data to acquire an answer to the research questions. It is recognized that big data analytics is significant for companies to accomplish competitive advantages and obtain a unique position in the market. Both qualitative and quantitative research approaches are used to gather useful data from the sample participants. A set of 10 questions are formulated in the form of a questionnaire, and around 50 participants are selected to respond to the questions. A case study approach is also adopted, which involve 11 companies but only six companies respond to the research process and state the importance of utilizing or applying big data analytics in the businesses. Besides this, the limitations of the research are that it does not evaluate adequate information about the topic based on the uses and challenges during implementation. It is evaluated that the companies can implement the technology by taking key steps and increase their competitiveness in the industry. The data collected has reflected that most of the respondents agreed with the significant application of big data analytics in the businesses and defined the technology in different forms. Hence, it is an effective process of examining a large amount of information and used in various business sectors.

References for Business Research

Adnan, K. & Akbar, R. 2019, "An analytical study of information extraction from unstructured and multidimensional big data", Journal of Big Data, vol. 6, no. 1, pp. 1-38.

Ajayi, A., Oyedele, L., Davila Delgado, J.M., Akanbi, L., Bilal, M., Akinade, O. & Olawale, O. 2019, "Big data platform for health and safety accident prediction", World Journal of Science, Technology and Sustainable Development, vol. 16, no. 1, pp. 2-21.

Bag, S. 2016, "Fuzzy VIKOR approach for selection of big data analyst in procurement management", Journal of Transport and Supply Chain Management, vol. 10, no. 1.

Bradlow, E.T., Gangwar, M., Kopalle, P. & Voleti, S. 2017, "The Role of Big Data and Predictive Analytics in Retailing", Journal of Retailing, vol. 93, no. 1, pp. 79-95.

Brock, V. & Khan, H.U. 2017, "Big data analytics: does organizational factor matters impact technology acceptance?", Journal of Big Data, vol. 4, no. 1, pp. 1-28.

Christina O’Connor & Kelly, S. 2017, "Facilitating knowledge management through filtered big data: SME competitiveness in an agri-food sector", Journal of Knowledge Management, vol. 21, no. 1, pp. 156-179.

Dremel, C., Wulf, J., Maier, A. & Brenner, W. 2018, "Understanding the value and organizational implications of big data analytics: the case of AUDI AG", Journal of Information Technology Teaching Cases, vol. 8, no. 2, pp. 126-138.

Droll, A., Khan, S., Ekhlas, E. & Tanev, S. 2017, "Using Artificial Intelligence and Web Media Data to Evaluate the Growth Potential of Companies in Emerging Industry Sectors", Technology Innovation Management Review, vol. 7, no. 6, pp. 25-37.

Hazen, B.T., Skipper, J.B., Boone, C.A. & Hill, R.R. 2018, "Back in business: operations research in support of big data analytics for operations and supply chain management", Annals of Operations Research, vol. 270, no. 1-2, pp. 201-211.

Kache, F. & Seuring, S. 2017, "Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management", International Journal of Operations & Production Management, vol. 37, no. 1, pp. 10-36.

Li, L., Ting, C., Hao, T. & Yu, T. 2018, "Customer demand analysis of the electronic commerce supply chain using Big Data", Annals of Operations Research, vol. 268, no. 1-2, pp. 113-128.

Mawed, M. & Al-Hajj, A. 2017, "Using big data to improve the performance management: a case study from the UAE FM industry", Facilities, vol. 35, no. 13, pp. 746-765.

Mikalef, P., Pappas, I.O., Krogstie, J. & Giannakos, M. 2018, "Big data analytics capabilities: a systematic literature review and research agenda", Information Systems and eBusiness Management, vol. 16, no. 3, pp. 547-578.

Muhammad, S.S., Tsui, E., Irfan, I., Shujahat, M., Mosconi, E. & Murad, A. 2019, "Value creation through big data application process management: the case of the oil and gas industry", Journal of Knowledge Management, vol. 23, no. 8, pp. 1566-1585.

Popovič, A., Hackney, R., Tassabehji, R. & Castelli, M. 2018, "The impact of big data analytics on firms’ high value business performance", Information Systems Frontiers, vol. 20, no. 2, pp. 209-222.

Rana, S. 2019, "Moving in the Realm of Big Data: Using Analytics in Management Research and Practices", FIIB Business Review, vol. 8, no. 1, pp. 7-8.

Simon, E.B. & Krogstie, J. 2017, "The core enabling technologies of big data analytics and context-aware computing for smart sustainable cities: a review and synthesis", Journal of Big Data, vol. 4, no. 1, pp. 1-50.

Simon, E.B. 2019, "Data-driven smart sustainable urbanism: the intertwined societal factors underlying its materialization, success, expansion, and evolution", GeoJournal, pp. 1-26.

Thuy, D.O. & Teuteberg, F. 2019, "The role of business analytics in the controllers and management accountants’ competence profiles", Journal of Accounting & Organizational Change, vol. 15, no. 2, pp. 330-356.

Tian, X. & Liu, L. 2017, "Does big data mean big knowledge? Integration of big data analysis and conceptual model for social commerce research", Electronic Commerce Research, vol. 17, no. 1, pp. 169-183.

Yeo, A.C. & Carter, S. 2017, "Segregate the wheat from the chaff enabler: will big data and data analytics enhance the perceived competencies of accountants/auditors in MALAYSIA?", Journal of Self-Governance and Management Economics, vol. 5, no. 3, pp. 28-51.

Remember, at the center of any academic work, lies clarity and evidence. Should you need further assistance, do look up to our Business Research Assignment Help