Researching and Writing a Dissertation Review

Researching and Writing a Dissertation Review

Table of Contents

Have we ever found ourselves staring at a blank screen, cursor blinking mockingly at us, wondering how on earth we’re going to make it through our dissertation? Fear not, fellow business students, because there’s a beacon of hope on the horizon! The book titled “Researching and Writing a Dissertation: An Essential Guide for Business Students” is here to bail us out.

Find your new Researching and Writing a Dissertation: An essential guide for business students on this page.

The Lifesaver Introduction

We all know that the beginning stages of any research are crucial. This book kicks off with a clear and engaging introduction, setting the stage perfectly. It breaks down the dissertation process into manageable chunks, which feels like having a friend guide us through a labyrinth, breadcrumb by breadcrumb.

Breaking Down the Basics

From the word go, this guide makes it clear that writing a dissertation doesn’t have to be akin to wrestling an octopus. It introduces us to the fundamental aspects and helps organize our chaotic thoughts. The structure gives us the confidence that we won’t miss anything crucial.

The Art of Research

Ah, research. That elusive beast. This section is gold. It offers insightful advice on both qualitative and quantitative methods. It’s like finally finding the Rosetta Stone for academic research.

Qualitative vs. Quantitative Methods

The book dives into the nitty-gritty of choosing between qualitative and quantitative research. We finally understand that it’s not just about numbers or narratives but about selecting the method that best fits our research question. Here’s a handy table that simplifies it for us:

Method When to Use Advantages Disadvantages
Qualitative Exploratory research, understanding concepts Deep insights, rich data Time-consuming, subjective
Quantitative Testing hypotheses, measuring variables Statistical analysis, larger samples Can miss context, less depth

Literature Review Mastery

The literature review section of the book is like a cozy library we never want to leave. It provides a step-by-step approach to finding and reviewing relevant literature, ensuring we build a solid foundation for our dissertation.

Finding Relevant Literature

We get practical advice on where to look for scholarly articles, books, and other resources. It’s like having a treasure map handed to us. We learn not just to skim through content but to evaluate and synthesize information effectively.

Crafting a Thesis Statement

Creating a thesis statement is often likened to embarking on a philosophical quest. This guide demystifies the process, making it feel more like assembling a satisfying cheese platter. We gain confidence in articulating our research focus succinctly and clearly.

Clear and Concise Thesis

Through tips and examples, the book helps us craft a thesis statement that’s not only clear and concise but also compelling. We’re taught to create a statement that acts as the spine of our dissertation.

Methodological Details

The book delves into the importance of methodology with the care of a maestro conducting a symphony. It ensures we understand why our chosen methods are appropriate, which is invaluable when we need to justify them in our dissertation.

Selecting the Right Methodology

Through practical examples and detailed explanations, we’re guided to choose methodologies that align perfectly with our research questions. It’s like finding shoes that fit just right instead of ones that pinch in all the wrong places.

Data Collection Techniques

Let’s not beat around the bush—data collection can be downright intimidating. The authors provide comprehensive coverage of various techniques from surveys to interviews, making the entire process feel a bit more palatable.

Effective Data Collection

We’re given tools and strategies to collect data effectively, whether it’s through sending out surveys or conducting interviews. It’s like having the ultimate toolkit for ensuring we gather robust and relevant data.

Analyzing the Data

This part of the book is akin to having a personal data analyst walk us through the steps. From organizing data to conducting the analysis, the authors make sure we hold the reins firmly and confidently.

Making Sense of Data

We learn different analytical techniques and how to apply them to our data. It provides clarity that turns raw data into meaningful insights, which is like uncovering a masterpiece from a block of marble.

Writing the Dissertation

Here’s where the rubber meets the road. Writing up our findings and crafting the dissertation is divided into clear, manageable sections. It’s reassuring to know we have a roadmap for the potentially overwhelming task ahead.

Structure and Flow

The guide breaks down the ideal structure for our dissertation, ensuring we know how to logically present our findings. It’s like having a recipe that ensures our cake rises perfectly.

Overcoming Writer’s Block

Ah, writer’s block, that cruel adversary. The book offers practical strategies to keep our writing process moving. It’s a bit like being handed a manual for slaying dragons or dissolving the metaphorical wall that tries to stall our progress.

Practical Writing Tips

From setting timelines to creating an outline, we’re given numerous tips to stay on track. These practices help turn the marathon of dissertation writing into manageable sprints, complete with water breaks and cheering crowds.

Importance of Referencing

If we thought referencing was just a tedious necessity, this book might shift our perspective. It explains the importance of getting our references right, not just to avoid plagiarism but to lend credibility to our work.

Citation Styles and Tips

Different citation styles, like APA, MLA, and Harvard, are clearly explained. It’s like attending a mini-masterclass on academic honesty, ensuring our bibliography is as polished as the rest of our work.

Editing and Proofreading

The unsung heroes of dissertation writing—editing and proofreading—get their due diligence here. The section ensures our dissertation is not only well written but also free from errors and inconsistencies.

Polishing Our Work

Practical tips on proofreading, from reading aloud to using software tools, are provided. It’s like receiving a pet grooming session for our dissertation, making sure it’s perfectly manicured before submission.

Support and Feedback

We all need a support system. The authors emphasize the importance of seeking feedback and maintaining a dialogue with our advisors. It’s comparable to having a pit crew during a race—they’re there to tune us up and keep us going.

Utilizing Advisors and Peers

We’re encouraged to engage actively with our advisors and peers. Through practical advice, the book helps us understand the value of constructive criticism and peer support to elevate the quality of our work.

See the Researching and Writing a Dissertation: An essential guide for business students in detail.

The Final Checklist

Wrapping everything up with a final checklist, the book ensures we haven’t missed a thing. It’s like the moment before takeoff, making sure every box is ticked before we soar.

Ensuring Completion

From formatting to double-checking references, the checklist covers everything. It’s a comprehensive guide ensuring our dissertation is airtight, ready for submission.

Our Final Thoughts

“Researching and Writing a Dissertation: An Essential Guide for Business Students” is more than just a book; it’s a lifeline. It’s reassuring, comprehensive, and incredibly user-friendly. It breaks down an often-daunting process into manageable parts, making us feel equipped to tackle our dissertation with confidence. It’s the epitome of what we needed but didn’t know we were looking for in our academic pursuit.

In conclusion, if we are business students teetering on the edge of dissertation despair, this guide is the safety net we’ve been hoping for. It’s packed with useful advice, structured insights, and practical tips that make the entire process less overwhelming and more achievable. Happy writing!

Learn more about the Researching and Writing a Dissertation: An essential guide for business students here.

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University Student Essentials
University Student Essentials

About Me

With 25 years of experience in healthcare IT implementation, Emmanuel began his career at the University of Pittsburgh Medical Center, working as an assistant manager for a billing system implementation. Over the years, he has explored various aspects of the healthcare IT domain, successfully implementing several laboratory information systems and electronic medical record (EMR) systems, such as Cerner Millennium and Epic EMR.

In 2005, Emmanuel shifted his focus to public health, working on bio-surveillance implementation for the Centers for Disease Control and Prevention (CDC). He contributed to the BioSense Data Provisioning Project and performed extensive analysis of HL7 messages in hospitals and healthcare facilities. Additionally, Emmanuel requirements analysis for the CDC BioSense Analysis, Visualization and Reporting (AVR) project and played a key role in publishing the Situational Awareness updates to the BioSense System Requirements Specification (SRS).

Over the past 11 years, Emmanuel has worked in the Middle East, implementing the Epic EMR system at Cleveland Clinic Abu Dhabi. As a multidisciplinary team member, he has taken on various roles, including SCRUM Master, Project Manager, Integration Engineer, and Platform Engineer. Concurrently working as an adjunct university faculty member, teaching graduate-level courses in Systems Life Cycle and undergraduate courses in Health Information Systems

From a technological standpoint, Emmanuel has designed, installed, and implemented complete hospital integration systems using Rhapsody Integration Engine, MS SQL Server, and Public Health Information Networks Messaging System (PHINMS). He has also developed over 10,000 interfaces some of which coded in Java and JavaScript.

In 2019, Emmanuel expanded his skill set and entered the field of digital marketing, quickly becoming a proficient Digital Marketing Strategist. He has since helped numerous clients develop robust digital marketing strategies for their businesses. His expertise encompasses Social Media Marketing, On-page and Off-page SEO, Google Ads, and Google Analytics. Additionally, he and a team have managed clients’ website development projects, ensuring that each site is optimized for SEO, further enhancing their online presence and performance.

Alongside their digital marketing expertise, Emmanuel has delved into the world of Affiliate Marketing, where Emmanuel and his team successfully managed and executed campaigns for a variety of clients. By identifying the right products and services to promote, Emmanuel and his team helped clients generate passive income streams and increase their overall revenue.

Their approach to Affiliate Marketing involves creating valuable content that educates and engages the target audience, while strategically incorporating affiliate links. Emmanuel and his team have experience working with multiple affiliate networks and platforms, ensuring optimal tracking and reporting of performance metrics. By staying up to date with the latest trends and best practices, Emmanuel and his team have been able to optimize affiliate campaigns for maximum results, fostering long-term partnerships and sustainable growth for their clients.

As an accomplished professional, Emmanuel holds dual Bachelor of Arts degrees in Linguistics and English, a Master of Science in Health Information Systems from the University of Pittsburgh, and a Ph.D. in Information Systems from Nova Southeastern University.

My Teaching History

Professor Bazile is a dedicated technology instructor and Adjunct Faculty professor, who began his teaching career in April 2000 at the Business Career Institute in Las Vegas, Nevada.

In 2001, he expanded his expertise by training nurses in the use of Electronic Medical Records (EMR) systems. His experience in both technology and healthcare led to his appointment as an Adjunct Faculty professor at the University of Phoenix in May 2008, where he has taught several graduate-level information technology and healthcare information systems courses.

Dr. Bazile has also developed an HL7 course, which he has taught at various healthcare facilities, drawing from his own book, “HL7: Introductory and Advanced Concepts,” currently available on Amazon. With a passion for teaching and a commitment to ensuring students get the most out of each course he teaches, Dr. Bazile is a valuable asset to both his students and the institutions he serves.

My Teaching Philosophy

My teaching philosophy as an Information Systems professor in healthcare is built on the concept that education should equip students to be confident and capable problem solvers who are prepared to traverse the complicated and ever-changing landscape of Healthcare IT.

In order to accomplish this, I prioritize the creation of a dynamic and engaging learning environment that encourages students to engage with course material and with one another. This involves employing a range of teaching approaches, such as lectures, seminars, and hands-on activities, to ensure that students learn in the manner that best matches their learning style.

I believe the reason we have Information Systems as a discipline is to allow students to apply technology to solve real world problems. If that is the case, both undergraduate and graduate students have to be challenged to incorporate their core academic courses with their matriculated subjects. As such, it is important that students enter their Junior and Senior years with a strong command of the core courses such as Programming, databases, networks, hardware and software, as they serve as the foundation upon which real-world solutions will be built.

I also believe in the importance of incorporating real-world examples and case studies into my courses, as this helps to connect abstract concepts to practical applications. Additionally, I encourage students to apply what they are learning to their own personal and professional goals, as this helps to make the material more meaningful and relevant to their lives.

I strive to foster a positive and supportive learning environment where all students feel comfortable asking questions and participating in class discussions. I believe that this is key to fostering a sense of community and ensuring that all students have the opportunity to succeed.

I have also taught online courses. I have found in an asynchronous learning environment it can be difficult to apply the Peer Teaching or Experiential Learning Pedagogical Approaches. However, I have found the Discovery Learning approach to works quite well. Along with a boost to students’ self-confidence, Discovery Learning in an online environment allows students to synthesize information, expand on existing concepts on their own, while experiencing a positive outcome through trial and error.

Ultimately, my mission as an educator, and a Healthcare IT Information Systems professor is to provide students with the knowledge, skills, and confidence they need to thrive and succeed in their careers and to be technological leaders. By creating a positive and supportive learning environment, incorporating real-world examples and case studies, and encouraging students to apply what they are learning to their own objectives; my hope is to inspire and empower all students to achieve their full potential.

Population Size:

A total of 310 responses were originally received. Any response containing missing data due to unclicked radio buttons or unchecked checkboxes were first reviewed, and, if justified, were omitted from analysis. For surveys with missing data, a total of 18 responses were removed. In order to address any issues with response-set, the data was downloaded into Microsoft Access and queries ran to identify responses that contained the same values for each question. A total of 16 responses were found to be qualified for removal. Another 18 were identified as outliers and removed leaving a total of 258 responses for the study analysis.

In order to assess multivariate outliers, the Mahalanobis distances were calculated and plotted against their corresponding Chi-Square distribution percentiles (Schmidt & Hunter, 2003). The resulting scatterplot is similar to a univariate normal Q-Q plot, where deviations from a straight line show evidence of non-normality. The data showed indications of moderate deviations from multivariate normality, as indicated by the concavity of the data points. There were no additional multivariate outliers or missing values in the data after the removal of 52 responses.

Descriptive Statistics

Frequencies and percentages were conducted for the demographics indicators, while means and standard deviations were calculated for the continuous indicators. For gender, there were 151 females (59%) and 107 males (41%) in the sample. For ethnicity, most participants were Caucasian (119, 46%), followed by African American (56, 22%). The two most populous education levels were Bachelor’s (90, 35%) and Master’s (62, 22%). The biggest proportion of the sample by age group was the 35-44 age group (101, 39%) followed by the 45-54 age group (59, 23%).

Analysis:

Confirmatory Factor Analysis and Composite Reliability

A CFA was conducted along with a reliability analysis to assess construct validity. Examination of modification indices and factor loadings indicated that CSE1, CSE5, CSE7, PC5, ATE1, ATE6, ATE8, PP5, and PP6 were all causing significant problems with the model parameters. The results of the last iteration of the CFA performed in R showed significantly improved fit, although still poor overall (χ2(545) = 2125.61, p < .001, CFI = 0.82, TLI = 0.81, RMSEA = 0.11). The high degrees of freedom indicate that a very large number of parameters are being estimated in this model.

Composite Reliability

For the full model, each construct had excellent reliability. The ATE latent construct had a composite reliability value of 0.89. The ORC construct had a composite reliability value of 0.94. The CSE latent construct had a composite reliability value of 0.85 and PC had a composite reliability value of 0.95. For PP and RES, the composite reliability scores were 0.80 and 0.96 respectively. These values indicate that the loadings for each construct were all directionally similar, and that the items in each construct show a high degree of consistency.

Cronbach’s Alpha

Cronbach’s alpha values were calculated for the items in each construct. The alphas for PC (α = 0.90), AXY (α = 0.94), and RES (α = 0.94) indicated excellent reliability. The alphas for CSE (α = 0.80), ATE (α = 0.88), and PP (α = 0.83) all showed good reliability. These values confirm the results of the composite reliability tests, and reiterate the high degree of reliability within each latent construct.

Partial Least Squares – Structural Equation Modeling

A partial least squares- structural equation modeling (PLS-SEM) was conducted to determine how well the data fit the proposed model, and discern whether significant relationships existed between the independent and dependent constructs. The full model showed AVE values of 0.53 for ATE, 0.69 for AXY, 0.44 for CSE, .72 for PC, .35 for PP, and 0.81 for RES. The high values for AXY, PC, and RES indicate that the amount of variance accounted for in the manifest variables is sufficiently high. The values for ATE, CSE, and PP indicate that some of the variance in the manifest variables is left unexplained.

Structural Model

Once the measurement model had been tested for model specification, the structural model was tested to determine if ATE, AXY, CSE, PC, and PP had a significant effect on RES. A path weighted model was calculated using 10,000 bootstrap samples in R. The results showed a pseudo R-squared value of 0.78. This indicates that approximately 78% of the variance in RES is explainable by the collective effects of CSE, PC, ATE, PP, and AXY.

Further examination of the effects indicated that AXY had a highly significant effect on RES (= 0.87, < .001). This indicates that a standard deviation increase in AXY increases the expected value of RES by 0.87 standard deviations. CSE did not have a significant effect on RES (= 0.02, = .423). Additionally, CSE (= 0.02, = .423), PC (= 0.05, = .334), ATE (= 0.00, = .983), and PP (= 0.03, = .407) did not have significant effects on RES. Table 11 outlines the results of the path estimates.

Correlation Analyses

Both Pearson and Spearman correlations were calculated on the composite scores. The results of the Pearson correlations indicated that CSE was significantly correlated AXY (= 0.22, < .001) and RES (= 0.21, < .001). The results also indicated that PC was significantly correlated with ATE (= -0.79, < .001), AXY (= 0.18, < .001), and RES (= 0.20, < .001). ATE was significantly correlated with AXY (= -0.19, < .001) and RES (= -0.19, < .001). AXY was significantly correlated with RES (= 0.85, < .001).

ANCOVA Analyses

An analysis of covariance (ANCOVA) was conducted to determine if a significant relationship existed between the AXY, PP, CSE, PC, ATE scores and RES controlling for Gender, Age, Ethnicity, Education, and Specialty. The overall model was found to be significant (F(63,194) = 53.39, < .001), with an R2 value of .95, indicating that 95% of the variance in RES was explained by the collective effect of the independent variables and covariates.

Since the overall model was found to be significant, the model’s covariates were assessed. The AXY (F(10,194) = 262.20, < .001), ATE (F(7,194) = 2.20, = .036), Years computers (F(1,194) = 5.71, = .018), and PC (F(12,194) = 2.00, = .026) scores were found to be significant, indicating that a significant amount of variance in RES is explained by AXY, ATE, and PC.

A path diagram depicting the results of the structural model.

Results

This research investigated Computer Self-Efficacy (CSE), Perceived Complexity (PC), Attitudes toward EMR Systems (ATE), Peer Pressure (PP), and Anxiety (AXY) to determine whether these constructs as individuals, or as a group, or coupled together with some other factors could significantly explain resistance to EMR systems. Quantitative examination of self-reported survey results was performed to understand the strength and significance of the relationships, while these relationships were investigated to test the strength of model fit.

the regression paths of the structural model were examined to test the hypotheses. Significance was determined using an alpha level of .05. The model had an overall R2 value of 0.78. This indicates that approximately 78% of the variability in RES can be accounted for by CSE, PC, ATE, PP, and AXY. Since the overall model was significant, the individual coefficients can be interpreted. Some of the hypotheses were supported by the results of this study, and some were rejected. The construction of a data model of the relationships in this study could not meet thresholds that would be evidence of a good fit of the relationships identified in the study.

The fifth hypotheses tested the influence of AXY on resistance to EMR systems. AXY was expressed to be significantly related to resistance (r=.87, p<.001). This finding supports the hypothesis that anxiety with the EMR system will lead to medical care professionals rejecting use of the system. Unlike the findings of the first four hypotheses, the findings of the current study support previous research. Angst and Agarwal (2009) indicated that AXY is a factor which is significantly related to the problem of EMR system resistance. Based on the empirical findings of previous research, the present research and conceptual propositions and conclusions in previously written scholarly articles, there is a great deal of support for the finding that AXY is significantly influenced by EMR resistance.

The findings of this research do not support all findings by previous researchers, and there are multiple relationships which had been established as being significant that were identified as being insignificant in the current research. Generally, because of the inconsistency of previous findings and the current study there may be elements related to the sample examined or other contextual factors which may contribute to the inconsistency that exists. Ultimately, it is suggested that there be further research done on the problem of resistance to EMR system use.

Ultimately the findings support a new take on the problem of EMR system resistance that may contribute to the ways in which scholars investigate the problem of EMR resistance in general. This may also help with the way practitioners approach EMR systems, and articulate value of the systems to medical professionals investing record-keeping systems in the workplace.