Answer

1. DBA Empirical Research

   Data Presentation and Interpretation

               Data analysis and interpretation process is the fundament of research work and dissertation writing. Data description refers to extraction of basic information of data by providing simple summaries about the dataset’s attributes and their measures. Measures like central tendency, dispersion or spread, variable distribution, and variable association overviews are assessed. Data interpretation refers to the reviewing of the analyses described with an intended purpose of arriving at informed conclusion based on predetermined hypotheses. Data interpretation assigns meaning to data and determines the significance and implications of the analyzed information. Data description and interpretation are complementary data analysis processes that augment research. The current research, being in the DBA level, it exploits data description and interpretation frameworks to reach its goals.

               Data description sets the stage for formulation of hypotheses and indication of the direction that the research is likely to take. Before this process, decision has to be made on the tool to use and the kinds of descriptions to draw from the data based on the intentions of the research. Data description raises inquiries that can be answered in the interpretation section. In describing variable distribution, the empirical analysis framework is decided. The purpose served by data descriptions inform advanced analyses of the data towards attaining the goals of the research and reaching conclusion. Though data description tends to be extremely subjective, it informs and expands interpretations.

                Interpretation and presentation of data is probably the most creative aspect of research that needs articulate, coherent and holistic consideration in the process of research, as the ultimate point. The importance of interpretation is unequivocal, hence calling for proper handling. Since there is a high likelihood of data arriving from multiple sources and tends to have haphazard ordering that needs description for understanding to ensure proper interpretation. The nature of interpretation varies from thesis to thesis in research, depending on the need of research and correlating to the nature of data being analyzed, hence the unambiguous importance of data description.

               Data interpretation is designed to help researchers make sense of numerical data that has been described that need consistency by baseline methods. Interpretation also raises challenges beyond what is describable, like selection of material to be used for drawing conclusion about the research study, establishment of significance of information and identifying plausible weaknesses and limitations of the materials used, and decision on how to present the finding and observations of the research.

               Therefore, data analysis and interpretation are concurrently important in research due to their complimentary nature to ensure holistic research in preparation of dissertation. In this sense, DBA thesis cannot be based only on data description but has to be complemented by interpretation which develops the thesis.

   Hypothesis

               In the process of testing the hypothesis of relationship between project management standards and the success of IT projects in government departments, variable association is checked and assessed. The association between project management standards as a variable and success of IT projects as the other variable articulates the relationship that guides conclusion in testing this hypothesis. In this case, the explanatory or independent factor or variable is ‘project management standards’ while the response or dependent variable is ‘Success of IT projects in the government departments’.

               Correlation tests the degree and direction of association between two variables. Correlation between these two variables will be the primary explanatory analysis to use to assess the hypothesis. Further, regression analysis which tests linear relationship’s significance and degree of effects, will be used to assess the linear effects of project management standards on success of IT projects.

               Since both variables are categorical, Spearman’s correlation measure rather than Pearson’s will be used to assess significance of association between the two variables. Further, logistic regression (regression model for predicting categorical variables) will be used to fit a model for the relationship between the two variables. The logistic function for success of IT based on project management standards is an articulate way to test the relationship hypothesis between these two variables. The returned F statistic will be used to make conclusion on the significance of the relationship.

   Qualitative Research and Content Analysis

               Inductive content analysis is the closest case to the current research. This is the method used to develop theory or model based on open or half-structured data, and identify themes by assessing the collected data. This method relies on inductive reasoning (formation of theory by extracting themes from raw data by use of repeated examination and comprehensive comparison).

               The primary model that leads the current research is relationship between project management standards and the success of IT projects in government departments. An induction of the theory that there is indeed a relationship between project management standards and success of IT projects in government departments is done through examination of raw data that will be collected to this course.

               This comparison is based on the fact that this is a qualitative research study that is geared towards deriving quantitative measures from non-numerical data. There is dearth of previous research of this kind to replicate, hence calling for intuitive examination of the model to confirm it to induction. The current study involves identification of key aspects in the area of interest (IT).

Answer

Assessing Risk and Reward

The Nissan Motor Company is one of the leading automobile makers in the world. The Japanese carmaker has primarily enjoyed a successful run, allowing it to enter various regional and international markets such as the United States. However, the changing business environment was not favorable to the company in 2019. Notably, the cooperation recorded losses amounting to 7.8%. The experience pushed the management into making tough decisions, requiring almost all of its North American workforce to go for unpaid leaves.

In late 2019, the company announced that the decline in sales necessitated a two-day unpaid leave for the North American workers. The stated days for the vacation were January 2 and 3^rd    (Chicago Tribune, 2019).  Notably, this move was a crucial decision for the company because of its conflicting impacts. Whereas on the positive side, it could help the firm minimize expenses, it threatened to affect the public perception of the company regarding employee welfare.

The rewards for the decision involved cutting expenses by not paying the workers on leave, which eventually would translate into reduced expenses. Another reward was that the decision could allow the company to optimize performance by evaluating employee performances then developing new milestones. However, on the low side, the company risked affecting its public image and brand name, especially in the North American market. As per Chakravorti (2010), the way an organization treats its employees influences the firm’s public perception. Thus, Nissan risked eliciting a negative public perception. With a distorted public image, the company could fail to revive its declining sales.

I would have advised the management of Nissan to utilize the Predictive Analytic perspective in determining the right decision to take. Ideally, the approach tries to predict what might happen in the future if particular decisions or actions are undertaken at the moment (Traymbak & Aggarwal, 2019). Looking at the situation at Nissan, the company needed to develop a goal such as increasing sales. After that, they would have made decisions aimed at realizing the set goal. In this regard, the predicted outcome could give the management an overview of whether more risks existed or significant rewards could be realized.

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