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IEEE is a monthly peer-reviewed scientific journal introduced by the Institute of Electrical and Electronics Engineers (IEEE). IEEE and its members are inspiring the global community for a better tomorrow by appreciating people to publish their work in highly cited Journals. It is a trusted platform to get information on computing, engineering, and other technology-related information all over the world. Moreover, IEEE, until now, has published more than 339 Scopus indexed journals. All in all, almost all engineers and technologists dream of getting their work published in the Scopus Indexed Journal Published by IEEE.

A literature review aims to get insights related to the research problem and dive into the discussion. The structure of literature review is something that needs close attention. Without having a good structure, you cannot ace the milestones, and your literature review cannot stand out. The easiest way to structure literature review is using 5 Cs. These 5 Cs are comprised of cite, compare, contrast, critique, and connect. Each C has a particular set of attributes that help you make a perfect design to structure literature review. If any of the C is found to be missing in your literature review, I assure that you will have to resubmit it after

Abduction is a different form of scientific inference appropriate for quantitative research. Charles Peirce was an advocate of abductive approach in research. Peirce considered abduction approach the catalyst for theory discovery. It is the most inventive and flexible scientific logic compared to deduction and induction. Researchers must develop a hypothesis on probation. Researchers use abduction when they come across anomalies in relation to accepted hypotheses. Abduction is the term used to describe this innovative stride.

Defining objectives in a dissertation is a vital part of it. Why? Objectives provide structure and clarity of expectations from the dissertation. Also, the objectives are very much related to the data analysis of your dissertation. How? While collecting data for your dissertation and analysing that data for final results, you are actually moving towards achieving the pre-defined set of objectives. In this way, the definition of objectives plays an important in data analysis. This definition dictates the whole analysis process as your ultimate goal is to achieve the defined objectives once the analysis is completed.

This first topic relates to the field of marketing and customer retention through loyalty programs specifically. Every business desires that its customers do not leave its brand. So, in order to retent customers, businesses introduce different loyalty programs. Such programs aim at benefitting the customers more and keep them buying products. So, in this dissertation topic,

Data analysis is a practice that aims to make effective decisions after getting in-depth information related to any problem. The process of data analysis includes data collection, modelling and analysis. There are different methods of data analysis based on the type of research. The selection of a particular type of data analysis method is made based on the purpose of investigation. For example, the method of data analysis used for qualitative research is different from quantitative research. The methods used for quantitative research do not work well in qualitative research. Furthermore, the procedure of data analysis also has different steps that assist in finding the best results. As per its importance, this article aims to discuss different methods and procedure of data analysis in detail.

Data collection is the process of gathering information from a variety of sources in the most reasonable fashion that enables researchers to answer research questions, test formulated hypotheses, and evaluate research outcomes. For providing concrete assertion, the collection of the data that best represents a targeted population is necessary. The most important thing regarding data collection is to make sure that information-rich and credible data is collected to run statistical or non-statistical tests to make an educated decision related to research.

Basic research is theoretical and also called pure, fundamental research. Its major aim is to generate new ideas, findings, and theories or improve existing theories by using a specific methodology. There are many questions in the researcher's mind before conducting the research, such as what exactly basic research is? What is the relation between basic and theoretical research? Or is there any difference between them or not? In this article, you will know all the answers to the above questions. We are going to tell you in-depth about basic and theoretical research so you understand if there is any difference between basic and theoretical research or not.

In this modern world, different researches take place about different issues and topics related to every field of life. This is very important to make your research authentic, accurate and reliable. For this purpose, researchers use different methods and techniques to enhance the reliability of their research. Triangulation in research is one of those methods that you can use to achieve the goal of your research. Triangulation in research may help you perform more reliable and effective research. This article will provide you with the concept of triangulation in research, its importance and its different types, which will benefit you. If you want more authentic and effective research, read this article for more useful techniques.

Have you ever felt that your emotions, experiences, and personal perceptions about a phenomenon create problems in the research process? If yes, then you know that you are bracketing in qualitative research. Qualitative research is about analysing a phenomenon, and the variables in this study are your control. If you introduce a change in the variables other than the natural one, you have bracketed your research. Initially, the term bracketing was solely related to phenomenological things, but now this has expanded to other qualitative research types. It is most common in observation and interview methods of qualitative research.

The very first difference between causal & mechanistic analysis is because of their goals. The causal analysis has different variables that are linked with each other. When these variables find a relationship between them, there can be some causes and effects in the result. The goal of causal analysis is to work on the cause and effects of having a relation between several variables. From available variables, you need to see which one is dependent.