Your dissertation needs to include suggestions for future research. Depending on requirements of your university, suggestions for future research can be either integrated into Research Limitations section or it can be a separate section. You will need to propose 4-5 suggestions for future studies and these can include the following:   1. Building upon findings of your research. These may relate to findings of your study that you did not anticipate. Moreover, you may suggest future research to address unanswered aspects of your research problem.   2. Addressing limitations of your research. Your research will not be free from limitations and these may relate to formulation of research aim and objectives, application of data collection method, sample size, scope of discussions and analysis etc. You can propose future research suggestions that address the limitations of your study.   3. Constructing the same research in a new context, location and/or culture. It is most likely that you have addressed your research problem within the settings of specific context, location and/or culture. Accordingly, you can propose future studies that can address the same research problem in a different settings, context, location and/or culture.   4. Re-assessing and expanding theory, framework or model you have addressed in your research. Future studies can address the effects of specific event, emergence of a new theory or evidence and/or other recent phenomenon on your research problem.   My e-book, The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance offers practical assistance to complete a dissertation with minimum or no stress. The e-book covers all stages of writing a dissertation starting from the selection to the research area to submitting the completed version of the work within the deadline. John Dudovskiy  

Research supervisors play a great role in the success of your project; therefore, it is important to establish appropriate and beneficial relationships with your supervisor from the beginning of your research process. Dealing with supervisor is a critical success factor impacting the outcome of the project In some universities you are able to choose your dissertation supervisor. If this is the case with your university, then you should give preference to more experienced supervisor in your research area. You must have realistic expectations. Do not expect your supervisor to immerse in your work in great details. The responsibility of your supervisor is to direct you towards the right path no to complete research on your behalf. Do not expect your supervisor to immerse in your work in great details Adhering to the following principles can increase the quality of your interactions with your supervisor significantly making your task of producing a quality dissertation much easier:   1. Take time to prepare for each meeting with your supervisor. You will have only a limited number of meetings with your supervisor and you must strive to make the most of each meeting. It is critically important to address each point mentioned by your supervisor during the last meeting.   2. Respect the time of your supervisor. Supervisors are usually dealing with a number of students writing dissertation simultaneously, and most of them have teaching classes as well. You must prepare questions for your supervisor in advance, and refrain asking unintelligent questions such as if you need to put course title on the cover of your work or asking questions about submission date which can be found on dissertation handbook. Asking such questions is a waste of time for you and your supervisor and it will most likely annoy your supervisor.   3. Communicate…

Research methods refer to the systematic techniques used to collect, analyse, and interpret data in order to answer research questions. In business research, they include decisions about research philosophy, approach, design, data collection, sampling, analysis, and ethical considerations. On this page: What are Research Methods? Research Philosophy Types of Research Research Approach Research Design Data Collection Methods Sampling Data Analysis Methods Ethical Considerations Component Purpose Example Research Philosophy Defines view of knowledge Positivism, Interpretivism Research Approach Guides logic of study Deductive, Inductive Research Design Overall plan of study Exploratory, Conclusive Data Collection Gathering information Surveys, Interviews Sampling Selecting participants Random, Purposive Data Analysis Interpreting data Statistical analysis, Thematic analysis Ethics Ensuring responsible research Consent, confidentiality Research methods at a glance   What Is Research Methods? Research methods is a broad term. While methods of data collection and data analysis represent the core of research methods, you have to address a range of additional elements within the scope of your research. The most important elements of research methodology expected to be covered in business dissertation at Bachelor’s, Master’s and PhD levels include research philosophy, types of research, research approach, methods of data collection, sampling and ethical considerations. These decisions are interconnected and must be logically consistent with one another. For example, the research philosophy influences the research approach, which in turn affects the research design, data collection methods, and data analysis techniques. Understanding how these elements relate to each other helps ensure that the research process is coherent and methodologically sound. The main components of research methodology commonly addressed in business dissertations are discussed below.   Research Philosophy Research philosophy is associated with clarification of assumption about the nature and the source of knowledge. All studies are based on some kind of assumptions about the world and the ways of understanding the world. There is no…

Research validity in surveys relates to the extent at which the survey measures right elements that need to be measured. In simple terms, validity refers to how well an instrument as measures what it is intended to measure. Reliability alone is not enough, measures need to be reliable, as well as, valid. For example, if a weight measuring scale is wrong by 4kg (it deducts 4 kg of the actual weight), it can be specified as reliable, because the scale displays the same weight every time we measure a specific item. However, the scale is not valid because it does not display the actual weight of the item. Research validity can be divided into two groups: internal and external. It can be specified that “internal validity refers to how the research findings match reality, while external validity refers to the extend to which the research findings can be replicated to other environments” (Pelissier, 2008, p.12). Moreover, validity can also be divided into five types:   1. Face Validity is the most basic type of validity and it is associated with a highest level of subjectivity because it is not based on any scientific approach. In other words, in this case a test may be specified as valid by a researcher because it may seem as valid, without an in-depth scientific justification. Example: questionnaire design for a study that analyses the issues of employee performance can be assessed as valid because each individual question may seem to be addressing specific and relevant aspects of employee performance.   2. Construct Validity relates to assessment of suitability of measurement tool to measure the phenomenon being studied. Application of construct validity can be effectively facilitated with the involvement of panel of ‘experts’ closely familiar with the measure and the phenomenon. Example: with the application…

Ethical considerations in research refer to the principles and standards that ensure the protection of participants, the integrity of data, and the honesty of the research process. They guide how researchers collect, analyse, and report data in a responsible and transparent manner. On this page: Ethical Considerations in the Use of AI Core Principles of Ethical Considerations When to Address Ethical Considerations   Ethical Considerations in the Use of AI The increasing use of AI tools such as ChatGPT in academic research introduces new ethical considerations that must be carefully addressed. Most common ethical risks of AI use include plagiarism or unintentional duplication and over-reliance on AI-generated content. Moreover, inclusion of inaccurate or fabricated information and the lack of transparency in the research process is also risks AI use can cause in your work. When using AI in your dissertation, you should: Ensure that AI-generated content is not presented as your own original thinking without modification Use AI tools as support tools, not as substitutes for critical analysis Verify the accuracy and reliability of any information generated by AI Follow your university’s guidelines regarding AI usage and disclosure Clearly acknowledge the use of AI tools where required AI can assist your research (which you should acknowledge), but you remain fully responsible for the accuracy, originality, and integrity of your work   Core Principles of Ethical Consideration Ethical considerations are one of the most important parts of any research project and must be clearly addressed in your dissertation. Ethical research in business studies involving primary data collection is based on the following nine core principles: No harm to participants – physical, psychological, or emotional Informed consent – participants must fully understand the study Voluntary participation – no pressure or coercion Confidentiality and anonymity – identities must be protected Honesty and transparency…

Quota sampling method is a non-probability sampling and it can be defined as a sampling method of gathering representative data from a group. Application of quota sampling ensures that sample group represents certain characteristics of the population chosen by the researcher. Quota sampling can be divided into two groups: controlled and uncontrolled. Controlled quota sampling involves introduction of certain restrictions in order to limit researcher’s choice of samples. Uncontrolled quota sampling, on the other hand, resembles convenience sampling method in a way that researcher is free to choose sample group members according to his/her will. The main difference between quota and stratified sampling can be explained in a way that in quota sampling researchers use non-random sampling methods to gather data from one stratum until the required quota fixed by the researcher is fulfilled. Accordingly, the quota is based on the proportion of subclasses in the population. Application of Quota Sampling: an Example Let’s assume your research objective is to evaluate the impact of cross-cultural differences on employee motivation in Virgin Media in the UK. You need to assess the effectiveness of employee motivational tools taking into account gender differences among the workforce. Quota sampling can be applied in the following manner: 1. Dividing the population into specific groups. Virgin Media employees in the UK as the sampling frame need to be divided into the following five groups according to their cultural background: European Asian (India) Asian (China) Black (African) Other 2. Calculating a quota for each group. You supervisor confirms that in order to achieve research objectives, 30 representatives from each group and the total sample size of 150 respondents would be appropriate. 3. Determining specific condition(s) to be met and quota in each group Both genders, males and females need to be represented equally in your sample group. This is an important condition that has to…

Qualitative data analysis refers to the process of interpreting non-numerical data such as interviews, observations, and documents to identify patterns, themes, and meanings. It is used to understand how and why people think, behave, or experience a particular phenomenon. On this page: When to use qualitative data analysis Categories of qualitative data analysis Qualitative data analysis steps Method Purpose Key Feature Content analysis Categorise and summarise data Coding and classification Narrative analysis Interpret stories and experiences Focus on individual cases Discourse analysis Analyse language and communication Context and meaning Framework analysis Structured thematic analysis Multi-stage process Grounded theory Develop new theory Iterative and inductive Qualitative data analysis methods at glance   When to Use Qualitative Data Analysis Qualitative data analysis is most appropriate when your research aims to explore meanings, experiences, or complex social phenomena. You should use qualitative data analysis if: Your research focuses on understanding behaviours, perceptions, or experiences You are using interviews, focus groups, or observations The research problem is complex or context-dependent You are following an inductive or interpretivist approach You aim to generate new insights or theories Use qualitative data analysis when you want to understand why something happens, not just how often it happens   Categories of Qualitative Data Analysis Qualitative data refers to non-numeric information such as interview transcripts, notes, video and audio recordings, images and text documents. Qualitative data analysis can be divided into the following five categories: 1. Content analysis. This refers to the process of categorizing verbal or behavioural data to classify, summarize and tabulate the data. 2. Narrative analysis. This method involves the reformulation of stories presented by respondents taking into account context of each case and different experiences of each respondent. In other words, narrative analysis is the revision of primary qualitative data by researcher. 3. Discourse analysis.…