simulation model concluded applied carbon tax B u s i n e s s F i n a n c e
Please respond to the following two post . In each response please include at least one citation .
Data Analysis Processes
For the purpose of my doctoral research I found case study the better choice. My problem statement reads: Some Canadian politicians have claimed the impact of revenue-neutral carbon tax will hurt the economy greatly and Liu et al. (2018) simulation model concluded applied carbon tax has negative impact on economic growth. In contrast, studying the impact of supply chain integration where restrictive government regulations such as carbon tax exist, Xu et al. (2020) highlighted, regulations in forms of subsidizing low carbon products for consumers and imposing tax on high carbon product manufacturers will not affect social welfare. The general business problem is politically supported corporations in different industries complain that a carbon tax raise will cause economic losses for the society. The specific business problem is leaders of large corporations lack knowledge of strategies to safeguard the environment and society.
In order to refer to the business problem, as mentioned earlier, I found case study and qualitative analysis, the better choice. Walden University Office of Research and Doctoral Services (2021) provided a checklist with references to the importance of appropriate data analysis process for the research design. Mallette and Saldaña (2019) called data collection an elusive task through which the researcher is required to have a holistic understanding of the qualitative analysis. Among the major strategies, Yin (2018) highlighted, relying on theoretical propositions and working on data from the ground up pinpoints the significance of developing a rational process for the research. Qualitative research requires the researcher to develop an inductive analysis and verbal exchange coding as Saldaña (2016) underlined. This approach can help researchers to deal with the pile of data they face after going through the interview sessions. Coding as referred to by Saldana (2015) is a representation tool to capture a datum’s primary content. Therefore, through coding the collected data, researchers enable themselves to manage and develop a meaningful and organized database to refer to for future references.
For the purpose of the sample interview, I chose a similar topic to my doctoral research only at a smaller scale hoping that I can prepare myself for my doctoral research. Nowell et al. (2017) introduced thematic analysis as a base for research method referring to the fact that researchers need to demonstrate a credible and systematic approach towards collecting, organizing, and analyzing data. Furthermore, Saunders et al. (2019) framed thematic analysis as a flexible and accessible approach to analyse qualitative data and highlighted the importance of coding and then categorizing the codes into major categories in order to provide a meaningful understanding of the pile of collected data. After conducting interviews, I figured creating thematic frames out of categorized codes will help me navigate through the content of the interviews easily. I am certain coding, categorization, and thematic analysis will help me create a better understanding of my collected data and will help me through data analysis accordingly.
Briefly describe the business problem identified for your DBA Doctoral Prospectus.
Saunders et al. (2015) describe a business problem to develop valid knowledge to support an organization problem-solving. My business problem discusses the challenges companies have with hiring and retaining successful leaders. Many organizations have been successful at hiring and prospering with effective leaders; however, the success factors used to make good hires are unknown. I believe identifying these factors could contribute to effective hiring and retaining effective leaders.
Explain which data analysis process is most appropriate to your DBA Doctoral Study, providing a rationale for your choice using supportive scholarly examples.
Walden University (n.d.-b) describes the data analysis process as a critical factor for doctoral students. Data analysis requires a doctoral student to utilize competency and engage within the first or second paragraph with dept details (Walden University, n.d.-b). According to Yin (2018), there are five analytical techniques: Pattern Matching, Explanation Building, Time-Series Analysis, Logic Models, and Cross-Case Synthesis. I chose to use Pattern Matching, Explanation Building, and Cross-Case Synthesis. Pattern Matching will allow me to pinpoint the patterns by identifying “how is” and “why is” a previous or current organization has successfully hired and retained leaders. The Explanation Building will allow me to further my details by explaining the causal sequence of the “how is” and “why is” some outcomes have happened (Saunder et al., p. 178, 2015). Lastly, the Cross-Case will assist with analyzing multiple-case studies, which I plan to use to further my reasonings.