1. Literature review and proposition development (25 marks)
\nIn this section, you are required to write a literature review on 6-8 journal papers on executive
\ncompensation and sustainability reporting on addressing the climate related issues. Explain
\nhow the above literature help you to develop your research question that you would like to
\ninvestigate using qualitative data. State your research questions, propositions, or predicted
\npatterns clearly. (Approximately 600 words)
\n2. Qualitative Research Method and Research Design (25 marks)
\nThis section will include (1) contextualising your research question for investigation, (2) data
\nyou will observe, such as annual reports, sustainability reports, interview (video\/audio), social
\nmedia data (e.g. Twitter posts), and survey results, and how you collect the data, and (3) other
\nimportant considerations in the research design of this investigation. You are required to
\nexplain why you collected the data in that way and justify your methods in detail. You might
\ninclude some in-text references to research methods literature to help explain your choice of
\nmethod. (Approximately 600 words)
\nSample size required: at least 20 effective files of 20 listed companies in the UK and\/or EU.
\n3. Results (20 marks)
\nIn this section, you will report the observed pattern using charts, tables and\/or word clouds.
\nInterpret the pattern you find. State to which extend that your findings answer your research
\n2
\nquestions, or whether the findings support or reject your propositions or predicted patterns.
\n(Approximately 500 words)
\n4. Discussion of Results (15 marks)
\nIn the discussion you should comment on your results (Approximately 300 words), including:
\n\u2022 Summarise and find themes from your data analysis above.
\n\u2022 Highlight the interesting findings and discuss the implications to the relevant readers.
\n\u2022 To which extent your findings agree or disagree with the literature? Write your answers
\nto this question as the analysis of your results.
\n\u2022 Consider the accuracy and reliability of your results and especially address the
\nlimitations of your research (e.g., small sample size, limited access to data, etc.)
\n5. Appendix (15 marks)
\nIn this section, you are required to:
\n1. Copy and paste the raw analysis outcomes as you produced in NVivo to evidence the
\ndata analyses and results as you reported above. There is no formatting requirement for
\nthis section, as long as the data analysis process is clear to read. Marking on this section
\nwill be reflected in the above relevant sections. Please note that insufficient data
\nanalysis reported in this section will result in low marks on the above relevant sections,
\nas the research originality would be questionable.
\n2. Use AI language generator (e.g. ChatGPT) as research assistant where applicable to
\nhelp you to contextualise the question and the possible draft. However, any texts
\ngenerated by any AI language generator in your research and analysis process
\nare required to be submitted in an Appendix attached in this section, which are
\nnot included in the word count nor being marked. You need to show the improvement
\nin your submission, from the AI generated texts, to:
\n1) Include the page number of the referenced parts of the annual reports and
\nother references that you used;
\n2) Summarise or paraphrase the AI generated texts to evidence your learning;
\n3) Correct the AI generated texts with reference to the academic references or the
\nlatest information that is missing from the AI used database.
\n4) Improve the expression, being more relevant to the question context with
\nspecific examples and the appropriate application of principles or theories.
\nThe above improvement from AI references will be considered in the marking of the
\nabove four sections. Using AI generated contents without the efforts on contextualising
\nand improving them, as required above, will result in low marks or as the evidence of
\nplagiarism. <\/p>\n","protected":false},"excerpt":{"rendered":"
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