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    1. QUESTION

    research proposal, 2500 word count
    ASSIGNMENT INSTRUCTIONS
    Assessment Individual coursework: research proposal
    Assessment code: 011
    Academic Year: 2019-2020
    Trimester: 2
    Module Title: Research Methods and Ethics
    Module Code: MOD006472
    Level: 7
    Module Leader: Tom Farnsworth
    Weighting: 100% (fine graded)
    Word Limit: 2,500
    This excludes bibliography and other items listed in rule 6.75 of the Academic Regulations:

    Assessed Learning Outcomes 1, 2, 3

    Submission Deadline :
    This assignment must be received by no later than 14:00 on 8th May 2020

    WRITING YOUR ASSIGNMENT:
    • This assignment must be completed individually.
    • You must use the Harvard referencing system.
    • Your work must indicate the number of words you have used. Written assignments must not exceed the specified maximum number of words. When a written assignment is marked, the excessive use of words beyond the word limit is reflected in the academic judgement of the piece of work which results in a lower mark being awarded for the piece of work (regulation
    6.74).
    • Assignment submissions are to be made anonymously. Do not write your name anywhere on your work.
    • Write your student ID number at the top of every page.
    • Where the assignment comprises more than one task, all tasks must be submitted in a single document.
    • You must number all pages.

     

     

    ASSIGNMENT QUESTION

    Research proposal

    The Research Proposal is a highly structured document which will explain in outline terms the plan for your proposed research project. Most of the problem solving involved in creating this plan will be the same for everyone, but how you solve these problems will differ greatly based on many factors such as your topic, the availability of academic literature about it, your research strategy and methodology, and the availability of data.

    You are strongly advised to stick closely to the following outline structure and use the marks available as a rough guide to how much detail you should include in each section.

    Task
    Your task for this assignment is to produce a Research Proposal of no more than 2,500 words.

    Please read carefully the table overleaf which contains the assessment criteria (what the marker expects to see), shows how marks will be allocated, and gives some points to consider.

     

    ASSESSMENT CRITERIA
    Section Assessment criteria / points to consider Marks available
    Title Give your proposed research a title which covers the main characteristics of the project: topic/problem, aim, scope, key concepts/variables of the study 5
    (1) Introduction Research topic
    Explain the topic and the key issues you wish to address through research.

    Research problem
    What problem are you trying to solve? This could come from a real-world issue you have seen or experienced in management practice, or from a gap or argument in the academic literature you have investigated. It will probably be appropriate to reference a few key sources which illustrate the problem.

    Research questions/hypotheses
    These are the conceptual ‘instruments’ that will guide your research project. Generally speaking, qualitative work will use questions; quantitative work will set hypotheses to confirm or reject through analysis of data. Three or four questions or hypotheses are sufficient for most Masters-level research.

    5

     

    5

     

     

    5
    (2) Literature evaluation Here you have three tasks:
    (i) critically evaluate the state of the academic literature about your topic using ideas such as the research maturity cycle;
    (ii) identify important authors, theories/models, schools of thought;
    (iii) explain the importance/relevance of selected literature to your own project
    You should critically examine no fewer than ten items of relevant academic literature. You do not have to describe their arguments, findings or contributions in detail. Your focus here should be on explaining the importance of the literature to your own research project.

     

    30
    (3) Research design Here you should explain:
    (i) The research strategy you have chosen, and why. Are you using a primary or secondary data strategy, or a systematic literature review strategy? Why?
    (ii) The research methods you will use. Are you using qualitative (e.g. unstructured interviews or surveys), quantitative (e.g. corporate financial data, closed-ended questionnaires)? Are you using a single method or ‘mixed methods’?
    (iii) The techniques you will use for gathering and processing data (or literature in the case of systematic review).
    (iv) The process of your research project. Ideally you should give an outline project plan showing how the project will be feasible within 12-16 weeks.

     

    40
    (4) Formatting and referencing You should aim to meet the following expectations in terms of formatting and referencing:
     Correct and thorough use of Harvard referencing. This includes in-text references and an alphabetized bibliography.
     Appropriate and consistent choice of visual elements, including correct selection of graphs and tables in relation to their explanatory purpose
     Legible and consistent presentation of data in graphs and tables including clearly labelled and denominated axes
     Organized and logical approach to structure, including table of contents, list of figures and tables, numbered pages, numbered sections and subsections, bibliography.
     Spelling, punctuation and grammar (SPG): UK English consistent with good practice in business and management applications.

     

     

     

    10

     

     

    This needs a relevant 10-15 research articles about
    Artificial Intelligence impact to Radiology such as MRI scan for detection and early diagnosis of diseases, What are the current challenges of AI in cardiac imaging and What are the future opportunities of AI in Radiology and to promote a new interesting advance research in the field.

    Research proposal

     

    SID: Type your student ID number here

    Word count:
    (Excluding text in abstracts; data; tables; figures; diagrams; in-text citations; footnotes/endnotes used for reference purposes and kept within reasonable limits; references; appendices. Per ARU Assessment Regulations 12, 2019, §6.83). Type word count here

    Academic honesty: [By submitting this assignment, I declare that] I understand that the piece of work submitted will be considered as the final and complete version of my assignment of which I am otherwise the sole author. I understand both the meaning and consequences of plagiarism and that my work has been appropriately attributed unless otherwise stated. I have not knowingly allowed another to copy my work.

    Assignment deadline: Type deadline date here

    Assignment: 011
    Module: MOD006472
    Module leader: Tom Farnsworth
    Module lecturer Type your lecturer’s name here
    Semester/Trimester: 2
    Academic year: 2019-20

    Table of contents
    Table of contents 2
    List of figures 3
    List of tables 4
    (1) Introduction 5
    (2) Literature evaluation 6
    2.1. Section 1 heading 6
    2.1.1 Subsection 1.1 heading 6
    (3) Research design 7
    3.1. Research strategy 7
    3.2 Methodology 7
    3.3 Constraints 8
    3.4 Research philosophy 8
    3.5 Outline project plan 8
    References 9

    List of figures

    No table of figures entries found.

    List of tables

    No table of figures entries found.

     

    9.1 Reading list
    Bryman, A., Bell, E., and Harley, B. 2018. Business Research Methods. 5ed. Oxford: Oxford University Press.
    Supplementary texts:
    Easterby-Smith, M., Thorpe, R., Jackson, P. R., and Jaspersen, L. J. 2018. Management and Business Research. 6ed. London:
    SAGE.
    Saunders, M., Lewis, P., and Thornhill, A. 2015. Research Methods for Business Students. 7ed. Harlow: Pearson.
    Reference texts:
    Field, A. 2013. Discovering Statistics Using SPSS. 4ed. Thousand Oaks, CA: SAGE.
    Flick, U., von Kardoff, E., and Steinke, I. [eds.]. 2004. A Companion to Qualitative Research. Thousand Oaks, CA: SAGE
    Hair, J. F., Celsi, M. W., Money, A. H., Samouel, P., and Page, M. J. 2003. Essentials of Business Research Methods. Chichester:
    Wiley
    Hair, J. F., Tatham, R. L., Anderson, R. E., and Black, W. 2006. Multivariate data analysis. Upper Saddle River, NJ: Pearson
    Prentice Hall.
    Patton, M. Q. 1990. Qualitative Evaluation and Research Methods. Thousand Oaks, CA: SAGE.
    Classic books and articles:
    Alvesson, M. 2010. Interpreting Interviews. Thousand Oaks, CA: SAGE
    Angrosino, M. 2007. Doing Ethnographic and Observational Research. Thousand Oaks, CA: SAGE
    Creswell, J. W. and Plano-Clark, V. 2011. Designing and Conducting Mixed Methods Research. London: SAGE.
    de Bono, E. 2016. Lateral Thinking: a Textbook of Creativity. London: Penguin Life
    Eisenhardt, K. M. 1989. ’Building theories from case study research.’ Academy of Management Review. 14.4, pp532-550.
    Flynn, B.B., et al. 1990. ‘Empirical research methods in operations management.’ Journal of Operations Management. 9.2.
    pp250-284.
    Glaser, B. G., and Strauss, A. L. 2009. The Discovery of Grounded Theory: Strategies for Qualitative Research. Piscataway, NJ:
    Transaction Publishers.
    Hacking, I. 2012. ‘Introductory essay’ in Kuhn, T. S. The Structure of Scientific Revolutions. 50th anniversary edition. Chicago, IL:
    Chicago University Press.
    Hitt, M. A.,et al. 2007. ‘Building theoretical and empirical bridges across levels: Multilevel research in management.’
    Academy of Management Journal. 50.6. pp1385-1399.
    Holden, M. T., and Lynch, P. 2004. ‘Choosing the appropriate methodology: understanding research philosophy.’ The
    Marketing Review. 4.4, pp397-409.
    Israel, M., 2014. Research Ethics and Integrity for Social Scientists: Beyond Regulatory Compliance. Thousand Oaks, CA: Sage
    Oliver, P. ‘Questions of Ethics,’ in Oliver, P. 2010. Understanding the Research Process. London: SAGE. Ch8, pp122-133.
    Meredith, J. 1993. ‘Theory building through conceptual methods.’ International Journal of Operations & Production
    Management. 13.5. pp3-11.
    10
    Mingers, J. 2004. ‘Real-izing information systems: critical realism as an underpinning philosophy for information systems.’
    Information and Organization. 14.2. pp87-103.
    O’Dochartaigh, N. 2012. Internet Research Skills. 3ed. Thousand Oaks, CA: SAGE
    Oppenheim, A. N. 1992. Questionnaire Design, Interviewing and Attitude Measurement. London: Bloomsbury Publishing.
    Pawson, R., Greenhalgh, T., Harvey, G. and Walshe, K., 2005. ‘Realist review – a new method of systematic review designed
    for complex policy interventions.’ Journal of Health Services Research and Policy. 10, pp.21-34
    Ridley, D. 2012. The Literature Review: a Step by Step Guide for Students. 2ed. Thousand Oaks, CA: SAGE
    Sandberg, J., and Alvesson, M. ‘Ways of constructing research questions: gap-spotting or problematization?.’ Organization.
    18.1.
    Tranfield, D., Denyer, D. and Smart, P. 2003. ‘Towards a methodology for developing evidence‐informed management
    knowledge by means of systematic review.’ British Journal of Management, 14(3), pp.207-222.
    Wallace, M. and Sheldon, N., 2015. ‘Business research ethics: participant observer perspectives.’ Journal of Business Ethics,
    128(2), pp.267-277.
    Yin, R. K. 2013. Case Study Research: Design and Methods. Thousand Oaks, CA: SAGE 
    (1) Introduction

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    (2) Literature evaluation

    2.1. Section 1 heading

    2.1.1 Subsection 1.1 heading

    (3) Research design

    3.1. Research strategy

    What does the literature and the nature of your research questions tell you about where your work should be positioned in terms of the research maturity cycle (exploratory/descriptive/explanatory)?

    Will you adopt an inductive or deductive approach? Why?

    What is the nature of your research questions? Are they suitable for empirical investigation (i.e. by gathering and analysing data) or conceptual investigation? (i.e. through some kind of structured or systematic literature review)?

    If you are doing empirical work (recommended) will you be using primary or secondary data? Justify this in terms of constraints, practicality, access issues.

    3.2 Methodology

    What kind of methodology are you using?

    Conceptual
    Systematic literature review (SLR)
    Realist review (RSLR)
    Metanarrative review

    Empirical
    Secondary data
    Single descriptive case study (profile an organization or institution)
    Multi-case comparative case study (compare organizations or institutions)
    Inferential statistics to test hypotheses (i.e purely quantitative data)
    Other secondary methodology

    Will you be using a single method (definitely recommended, especially for conceptual projects) or do you have a good reason for multiple methods? If multiple methods are used, are they
     Multi-methods – each method addresses the same research questions, or
     Mixed-methods – different research questions are addressed by different methods
    If using multiple methods, will they be arranged in series or in parallel, and why?

    If you are doing empirical work, you may need to consider sampling in this section – sample size, how determined; sampling method, choose and justify.

    3.3 Constraints

    What constraints are you facing? (time, cost, access to participants, lack of relevant literature, lack of practitioner or ‘grey’ literature etc)

    3.4 Research philosophy

    Use Creswell’s ‘worldview elements’ (ontology, epistemology, axiology and rhetoric) to explain your philosophical stance. You need to write something about each of these elements and it needs to be coherent and consistent with the strategic and methodological choices you have made, and the constraints you face.

    3.5 Outline project plan

    Create a top-level Gantt chart or set of milestones covering key activities for a four-month project. This must be consistent with the choices you have made in the methodology section.

    References

     

     

    Keywords: Artificial Intelligence, MIR Scan, Radiology, diagnostics Medicine

    Example of Research Proposal

    Title

    The effectiveness of performance management strategy in a multicultural environment in the UK retail industry: a systematic literature review.

    Introduction
    Research topic
    Performance management, as defined by Armstrong (1995) is an analytical process through which a company can improve its performance through the management and development of the team’s and individual employees’ performance.
    This process, as argued by Armstrong (1995) will, therefore, be achieved by the chain of performance appraisal and pinpointing of objectives, through which the management can support the team during their professional growth.
    Given the constant search by organizations to find competitive advantage capable of making it emerge from an increasingly saturated market, it was realized, as suggested by Barney (Barney, 1995), that the company able to obtain this advantage will be the one able to make the best use of its resources, making them unique and inimitable. According to Barney and Wright (1998), human resources are strongly part of these, making crucial for the organization to have efficient human resource management.
    Due to this end, numerous studies have been conducted to highlight a relationship between human resource management and corporate performance. They are demonstrating how this connection is mainly based on two main factors. From one hand, as argued by Barney (1995), we see how deployment of human resources is a powerful tool for achieving competitive advantages. On the other hand, how the effectiveness of this deployment depends on how these performance management strategies are combined and put into practice (MacDuffie, 1995).
    As further argued by Hofstede (1994), manager during performance management will need to deal with cultural differences, which will make performance management techniques appropriate in some countries and inappropriate in others.
    Therefore, given the increase in immigration in the UK, as shown by a research carried out by ONS which reports an increase in immigration to the UK with a strong percentage of migrants mostly from non-European countries (Figure 1.1), however, it is immigration that comes from the European Union to mark the most significant growth:
    Figure 1.1 Immigration to the UK by citizenship, 2006 to 2015 (YE December 2015)

    Source: (ONS, 2016).

    With a prevalence of citizens from the EU15 area (figures 1.2).
    Figure 1.2 EU immigration to the UK, 2006 to 2015 (YE December 2015)

    Source: (ONS, 2016)
    ONS summarized the main reasons for this immigration growth in the chart below (figira1.3):

     

     

    Figure 1.3 Long-Term International Migration estimates of immigration to the UK, by main reason for migration, 2006 to 2015 (YE December 2015)

    Source: (ONS, 2016)
    Highlighting how the main reason for this immigration is the need for employment, where the majority 61% from come from UE citizen (ONS, 2016). This trend highlight how the work environment in the UK is inevitably acquiring a multicultural connotation.
    Research problem
    Taking into consideration all the aspects mentioned above, the intent of this research will be to conduct a systematic literary review of the literature already present in this field with the aim of analyzing how an organization can obtain competitive advantages through the deployment of multicultural human resources, and specifically how such practices can be adopted in such environment without losing effectiveness.
    Research questions
    The following questions will be used to guide this research and answer that question:
    • What is performance management strategy and how impact the performance in the UK retail industry.
    • How can performance management strategies be used to improve employee performance and engagement in the UK retail industry.
    • How performance management strategy can be implemented in a multicultural environment?
    • How does this impact organizational performance?

    Literature evaluation

    As described in section three, to conduct this research I will proceed with a systematic literature review focused on the analysis of the three literature domains: performance management, human resource and corporate performance, and human resource management in a multicultural environment.
    The number of articles dealing with the human resources deployment, performance management practices, and their relation with the corporate performance is vast and is developed mainly between 1990 and 2010 as reflected from the table below:
    Table 3.1 Relevant literature
    Dimension Authors
    Performance Management (Ariyachandra & Frolick, 2008), (Chubb, et al., 2011), (Elias & Scarborough, 2004), (DeNisi, 2011), (Fletcher & Williams, 1996), (Hartog, et al., 2004), (Mitchell, et al.,
    2000), (Pulakos, 2004), (Trkman, 2010)
    Human resource management and corporate performance (Barney, 1995), (Barney & Wright, 1998), (Batt, 2002),
    (Becker & Gerhart, 1996), (Backer & Huselid, 1998),
    (Cully, et al., 1999), (Delaney & Huselid, Academy of
    Management Journal), (Delery & Doty, 1996),
    (Dyer & Reeves, 1995), (Guest, 1997), (Guest, et al., 2000), (Huselid, 1995), (Legge, 1995), (Patterson, et al., 1997), (Pfeffer, 1994), (Wood, 1999).

    Diversity management (Appelbaum, et al., 1998), (Bhadury, et al., 2000),
    (Groeschl & Doherty, 2000), (Harung & Harung, 1995),
    (Herbig & Genestre, 1997), (Higgs, 1996), (Cox &
    Blake, 1991), (Chandaa, et al., 2009), (Seymen , 2006)
    ,

     

    The performance management domains will help us to answer the first and the third questions.
    Pulakos (2004) defined a practical approach to this topic by defining performance management systems as a valid tool in decisions on remuneration, promotion, employee development, and reduction in the workforce.
    Emphasizing how will be essential for a successful PMS focus on one factor at a time. As evidenced by the same Pulakos (2004), PMS aimed at employee development will generate outputs such as mentoring, training, or in any case activities aimed at developing the employee’s skills. While PMS focused on decision-making will affect administrative HR actions. Defining also a process model, dividing the PMS practice through five steps. Figure 3.2: Performance management Process

    Source: (Pulakos, 2004)

    On Hartog, et al. (2004), the concept of PM is associated with the company challenge to improve the organization performance through the definition, measuring, and stimulation of employee performance. Thus linking the practice of PM with the analysis of tools such as performance appraisal. This study also presents a model for PM combining insight from strategic HRM and psychology, incorporating in this model employees perceptions and the role of direct supervision.

    On Fletcher & Williams (1996) will instead associate the PMS with the positive employee attitude. Where quantitative study conduct on nine organization shows how make employees understand their role within the organization, creating a shared vision of the company’s goals and purpose can improve the employee and organization performance.

    On Mitchel, et al. (2000) , we also see the link between PMS and the motivation theory emphasized. Heading what motivates people, shifting attention not only to the practical aspect of increasing performance, but directing the focus on behaviour. In particular, why people want to select a particular goal, emphasizing how the assignment of particular goals and tasks will be able to generate improvements in employee performance.

    The second dimension will aim to answer the second part of the first research question, trying to give a more detailed view of the actual connection between human resource practice and its impact on corporate performance. Most of the literature in this field use a quantitative approach where putting in relation the practices used by the organization and the real impact on the organization performance.

    As anticipated in the introduction, many organizations to gain competitive advantage have begun to analyse both internal and external factors to the company itself. Creating in this way, two different currents of thought, on the one hand, those who maintain the actual value brought by human resources in terms of competitive advantages and the other hand those who do not. The study carried out by Barney & Wright (1998) using the VRIO framework to evaluate the role of HRM in the development and sustainable competitive advantage, can be considered a fundamental starting point through which to search for this link.

    In its study Batt (2002), it has been evaluating relations between performance practice, quite rates, and sales growth. In particular, this study shows how to present human resource incentives as high pay and job security, employee participation in decision making.

    The study conducted by Becker & Gerhart (1996) also highlights the growing product-market competition, and the consequent research by companies to improve productivity by reducing costs. By placing the labour cost as for many companies, the primary cause of such costs, many organizations see reducing employment as a valid strategy to achieve this result. This study starts to feel the need to analyse if such a strategy can create value for the company or merely a cost cut. Moreover, showing the role played by human resource management, and above all how the use of the right practices aimed at developing and improving resource efficiency can represent a more effective way to create a competitive tool able to improve the performance of the organization.

    Much of the literature developed around this topic also shows a growing number of evidence to support the association between a high commitment human resource management practices and organizational performance. The study conducted by David Guest (1997) argues that to provide a real explanation for this association, Will be necessary to increase the theoretical and analytic framework of three key areas: the nature of human resources and in particular on human resource practices, the nature of organizational performance and the link between HRM and performance.

    The third domain will aim to individualize literature capable of analyzing and explaining the effectiveness of management diversity in particular on cultural diversity, evaluating which are the main implementations in the management of these resources and how these activities can increase corporate performance. The literature in this field has a fragmentary relevance due to a generic approach in terms of defining diversity. Specifically, most of the literature on this topic examines factors such as gender, ethnicity, culture, and religion. Therefore, sometimes presenting arguments that are not relevant to the research. Focusing also on the managerial practices conducted to avoid problems of a discriminatory nature rather than aimed at improving corporate performance.

    In Cox and Blake (1991), through the analysis of several factors directly impacted by management diversity, they tried to explain how diversity management practice can generate competitive advantages. Finally suggesting how to increase the capacity of such practices.

    Chandaa, et al. (2009) review the literature in this field with the focus on managing diversity, examining the practices of human resource diversity management in the organization and developing a framework of human resource diversity management at the strategic, tactical and operational level.

    Seymen (2006), considered the “cultural diversity management” by exploring the literature in this topic by analyzing the different points of view and approaches in this field. While Harung and Harung (1995), the focus is instead on how the creation of “unity in diversity” is a fundamental factor in enhancing organizational performance.

    In the research conducted on this literature at the time, it was noted that the use of certain types of strings is essential for identifying relevant literature. In particular, in literature searches concerning performance management strategies, the use of the word style results to produce more relevant results. Regarding the second domain, literature is vast, and it is possible to drill many articles by merely using keywords like human resource management and performance. The third domain seems to be the one with the most problems. For the research of this literature more than the use of the multicultural keyword it was preferred to use the keyword diversity, but as anticipated such research besides producing relevant articles, unfortunately, brings with it as results also articles focused on discrimination. Since this term is not relevant, we prefer to put this word among the exclusion terms for this domain.
    Will also be proposed during the scoping study to identify more exclusive word to narrow don the three domains. The set of inclusion and exclusion words identified in this initial phase are represented in the following table:
    Table 3.3 Search String

    Research design

    Research philosophy
    To refine the strategy adopt for this research the following consideration will be take in consideration:
    Ontology: The present research will provide from an ontology point of view a deductive approach, through which previous works will be analysed to support the objective assumption.
    Epistemology: The purpose of this research will be to analyse in a descriptive way the literature already present in this field to highlight how certain managerial practices can generate a competitive advantage in a given context.
    Axiology: A neutral approach to this topic will be conducted. Trying to prevent researcher own personal values from inflating the evaluation of the literature examined.
    Methodology: The process used for this research, as already anticipated in the previous section, will be a systematic process, consistent with the standards of these types of methodology.
    Rhetoric: From the rhetorical point of view, this research aims to contribute to the academic debate, but at the same time give a practical contribution to the practices of this management approach.

    Research Strategy
    Given the vastness of the literature already present in this field, it was decided to proceed, to answer the questions posed by this research, with a systematic literature review.
    This methodology, due to its predisposition to provide a transparent and replicable analysis, is able to eliminate those that are the limitations of traditional researches which are often only limited by the knowledge of the author himself or of the limited literature taken into consideration (Mallet, et al., 2012).
    This research methodology born as a result of the development in the process of revision in the literature already present. Due to its scientific and systematic approach was often used in the field of medical sciences. However, this approach can be extended and applied in management fields (Tranfield, et al., 2003).
    Moreover, due to this scientific approach as argue to Tranfield, et al. (2003), it is useful for be use in the analysis of studies conducted with quantitative methods. For this natural predisposition in the synthesis of quantitative studies makes it a useful tool for the present research.
    A systematic review differs from traditional research, as previously mentioned, from its scientific and transparent approach, and from its purpose as argued by Cooper and Hedges (1994) to be replicable by other researchers, who by applying the same criteria should be able to obtain similar results. In detail, the planning of a Systematic literature review will consist of the following stages:
    1. Planning the review.
    2. Selection.
    3. Analysis.
    In the first stage of this methodology, it may be necessary to conduct a scoping study to understand the actual size of the literature present in this field, and in case it will be necessary limit the research area. Like, for example, specific time limits, geographical restrictions, and type of business.
    In particular, as regards the present research, it was decided to limit the research to organizations operating in England and specifically in the retail sector.
    Subsequently, through the identification of keywords, search terms and exclusion criteria (defined during the scoping study phase). Will be set up a rigorous handling system of the literature obtained through database searches such as Emerald and Ebsco, which not using search algorithms capable of producing research based on the chronology history and research behavior previously conducted, will be able to produce replicable research. However, a possible challenge present in the systematic research according to Mallet, et al. (2012), research through institutional sites can affect the objectivity of the research process, potentially losing a large number of relevant sites, thus distorting the review process.
    Once this process has defined, the evaluation of the literature in this field will be divided into two steps: selection and analysis. Where in the first we will focus on the creation of three research domains, placing in the same context of studies, while in the second we will deal with a critical evaluation of the studies examined to identify the primary purpose that they address.
    In detail, to find the most relevant articles for our systematic review, research terms such as “performance management”, “human resource management”, “corporate performance”, and “multicultural” will be used in Boolean research, combine with the terms such as “and”, “or”, to produces relevant results. Subsequently, keyword searches will be conducted, used as a term of inclusion and exclusion, within the titles, abstract and body of the articles found to determine their relevance. All items that comply with these inclusion criteria will then be taken into consideration and reported in detail to allow reproduction from other researchers.
    Regarding the articles that will not meet these criteria both in the title and in the abstract will be rejected. Reporting the number of researches taken into consideration and excused at each stage with the relative reasons for exclusion. Furthermore, taking advantage of the possibility of discussing with the research supervisor, the possibility of taking or not in consideration sources considered ambiguous.

    The articles considered relevant will then be resumed and analysed in-depth through a critical examination to highlight the main result that they address. This evaluation will decide whether this source will be chosen or not to be part of the systematic review.
    All articles selected through these process will be afterward will analyse individually to identify the study presented by a research proposal or result linked to our research field.
    Finally, to produce a synthesis of the analysed literature, conclusions will be formalized through the use of an aggregation process with which the different researches and results will be compared, producing a possible answer to the present research.

    Defining of literature domain
    In planning this type of research, as described in the previous section, we will proceed to the identification of three search domains. Through which will be constructed the schema with which the literature will be included or excluded. In detail, considering the title suggested in section 1, the following domains were extracted: Performance management, corporate performance, and multicultural workforce (Figure 4.1).

    Figure 4.1

    1. Performance management

    2. Corporate performance

    3. Multicultural

    The effectiveness of performance management strategy in a multicultural environment in the UK retail industry

    Context
    Source: Own creation.

    The intersection of these domains will lead the identification of the area of interest where the useful literature will be found (Figure 4.2).
    These domains will also be limited by the “UK retail industry” context. However, as the necessary number of relevant literature in this context was not found in this initial phase, it is proposed that if the problem persists during the scoping study to widen this restriction to a broader context.

     

     

    Project plan timetable
    The research project will run in sixteen weeks from the start of the scoping study in the beginning of September to the final submit in the beginning of January. The time of each step require from this research method are summarize on the table in figure 4.3:
    Figure 4.3 Gantt chart timetable

    Source: own creation.

    References
    Appelbaum, S., Shapiro, B. & Elbaz, D., 1998. The management of multicultural group conflict. Team Performance Management, 4(5), pp. 211-234.
    Ariyachandra, T. & Frolick, M., 2008. Critical success factors in business performance management—Striving for success. Information Systems Management, 25(2), pp. 113-120.
    Backer, B. & Huselid, M., 1998. High performance work systems and firm performance: a synthesis of research and management implication. Research in Personnel and Human Resource Management, Volume 16, pp. 53-101.
    Barney, J., 1995. Looking inside for competitive advantage. Academy of Managemet Executive, Volume 9, pp. 49-61.
    Barney, J. & Wright, P., 1998. On becoming a strategic partner: the roles of human resources in gaining competitive advantage. Human Resource Management, Volume 37, pp. 31-46.
    Batt, R., 2002. Managing customers services: human resouce practices, quit rates, and sales grow. Academy of Management Journal, Volume 45, pp. 587-597.
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    an example attached

    I sent you the details and instructions with template and example.

    Also the Research Topic I am interested in proposing

    the Research topic I am interested ‘ Artificial Intelligence Impact on Radiology ‘

    it’s all in the file I sent. Also include reading list.
    read and understand the assignment

     

 

Subject Writing a proposal Pages 24 Style APA

Answer

 

Introduction

The role of artificial intelligence (AI) in deep learning has made a tremendous impact on image recognition activities. As espoused by Hosny, Parmar, Quackenbush, Schwartz, and Aerts (2018), various methods, including the variational autoencoders and convolutional neural networks, have significantly changed the medical image and analysis field. Traditional radiology activities involved trained physicians using their medical knowledge and vision to understand the medical images. These approaches were characterised by various challenges, including possibility of errors and timely diagnostics. Information and communication technologies (ICT) in the healthcare sector has transformed the quality and safety of healthcare delivery. According to Sogani, Allen, Dreyer, and McGinty (2020), AI in radiology has altered the way images are collected and interpreted.

The importance of AI in radiology is to address the rising cases and needs of imaging. According to Smith-Bindman, Miglioretti, and Larson (2008), the need for radiology exams has almost doubled in a decade. This is attributed to the rising cases of the aged population and complex issues. The impacts of imaging technologies and advancements are to establish new approaches to completing the imaging activities and promote the efficiency of the practice. Another apt use of ICT in imaging technologies is to address the rising need for data storage systems for the evaluations. Digital technologies, according to Sogani et al. (2020), have advanced radiology practices and improved the worklist flow.

Although AI in radiology has demonstrated significant transformation in the sector, there are still skeptical individuals who argue based on technology replacing the responsibilities of human radiologists. Notably, AI and radiologists should not be perceived as mutually exclusive. Rather, society should acknowledge the importance of AI in augmenting the radiologists’ care rather than replacing individuals. The role of AI should be recognised as essential in promoting the quality of care and making informed decisions, including the healthcare challenges and management approaches. Similar to other technologies in the healthcare sector, AI in radiology is not a replacement for the human role. Instead, it is a collection of machine learning tools, algorithms, and neural networks, which will alter how services are delivered (Tang et al., 2018). Besides promoting the early detection and diagnosis of illnesses, AI should be examined based on the impacts in the future and the opportunities. In addition, the challenges of the technologies in the healthcare practice should be explored, including the concern of replacing human radiologists.

Research Problem

The impacts of AI in medicine are associated with enhancing diagnostic accuracy. According to Thrall et al. (2018), there are four core categories of AI in radiology. These include optimising the worklist, pre-analysis of the cases in applications that involve high volume, and those that may be challenging for the human radiologist to evaluate due to fatigue, extracting details from images that may be invisible to the human eye, and enhancing the quality of the images. Despite these advancements, there are several challenges associated with AI in radiology. Notably, these drawbacks are circumstantial and are related to the huge technological changes replacing the role of human technologies. According to Obermeyer and Emanuel (2016), machine learning will replace the majority of the work that was done by human radiologists and pathologists involved in human anatomy. Also, there have been concerns regarding the machine accuracy exceeding that of humans. In the next 5-10 years, it is anticipated that machine learning will end the thriving capacity of human radiologists.

There are also intrinsic issues associated with AI. These include the validation of the results, establishing if the processing speeds are sufficient for clinical practice, and determining the exact population that a specific program is valid. Both the intrinsic and extrinsic challenges surrounding AI in radiology are the foundations of this research. Besides, AI programs are specific to cases, which implies that a broad source of truth is required to validate the discoveries (Prevedello et al., 2019). Concerning the generalisation of the findings, clinical research issues have attempted to use the findings in one population for inclusivity, which is a significant concern. The trade-off between the generalisation programs and accuracy levels for the patient population is the foundation of future studies on AI in radiology. Also, the tolerance in using AI for imaging different populations based on ethnicity, gender, and race diversity remains unknown and can result in incorrect results. Therefore, it is imperative to explore the future and opportunities of AI in radiology, including the mitigation of the aforementioned challenges.

Research Questions

Three questions guide this research, which includes

  1. What are the present challenges of AI in cardiac imaging?
  2. What are the future opportunities for AI in radiology?

iii.           What are the advanced research areas on AI in radiology?

Literature Review

Artificial Intelligence is among the remarkable progress in activities involving image recognition. The ICT technology has transformed the role of radiologists, who traditionally relied on medical knowledge and their vision to make decisions regarding the images. In an opinion article by Hosny et al. (2018), the excel of AI in imaging is reflected in the automatic recognition of the complex patterns in the data and providing quantitative analysis of the radiographic features rather than qualitative. This implies that AI plays an essential role in transforming the healthcare sector, including providing quality and safe care. In the last decade, AI has enhanced the healthcare sector through deep learning, speech recognition, and caption generation. In a white paper by Tang et al. (2018), it is revealed that over the next decade, AI in radiology will result in various functions, including enhancing the value, quality, and depth of the radiology in population health. It will also transform the radiologist’s workflow. Thrall et al. (2018), in a literature analysis, argues against the role of AI in population health. According to the literature, among the challenges and pitfalls of AI in radiology is generalising the data to other populations of different ethnicities. This demonstrates one of the challenges of AI in radiology.

Challenges of AI in Radiology

Radiology is characterised as effective, although there are several downsides that should be considered in promoting smooth transition. The literature analysis by Liew (2018), revealed that besides the numerous applications and advantages of AI in radiology, there are also moral, privacy, ethical, and safety concerns related to the ICT. The issues of safety and ethics are reflected in the non-maleficence principle. Notably, the primary concern in the use of AI is to avoid doing harm to the patients. This implies that the AI systems should be validated to be accurate, safe, and infallible prior to being used on individuals. The issue of privacy is based on the access to protected health information in either cloud-based storage and on-site, which pose a risk to the individual’s privacy. Raymond Geis et al. (2019), through a statement paper on the radiology technologies, shared similar findings on ethics and confidentiality of AI in radiology. According to the statement, the use of AI in clinical context should be aimed at promoting the patient’s wellbeing, minimise harm, and ensure that the harms and benefits are distributed in a just manner among the stakeholders.

The impacts of AI in shouldering the workload to radiologists are impaired by deep-learning limitations. The literature review by Yasaka and Abe (2018) revealed that the health care sector should recognise the limitations of deep learning, including the calculations to interpret data. Also, there are compromises in the AI strategies, including the decision trees. The implications of this study are the importance of technical investigators in developing the explainable AI that has deep learning for interpreting the models. Besides the interpretation issues, deep learning models, including machine learning, are subjected to overfitting and may not demonstrate a consistent performance during data analysis. The solution to this challenge is to accompany the image data with valid reference labels, including survival time, pathological evaluation, and clinical diagnosis. To address these challenges, collaboration is vital between deep learning and radiologists. In an opinion statement by Paul, Hui, and Ting (2018), the interpretation of the AI data should be complemented by radiologists’ evaluation of the quality of the datasets. Notably, radiologists are crucial players in examining the quality of the findings in the deep learning and rejecting data that has inadequate quality.

Challenges of AI in Cardiac Imaging

The application of AI in cardiac imaging has significant clinical impacts. Two of the key factors that have contributed immensely to this application is the Second Annual Data Science and the insights from the cardiovascular magnetic resonance (CMR) in the 2017 annual scientific session (Petersen, Abdulkareem, & Leiner, 2019). The literature analysis by Petersen et al. (2019) revealed that AI applications in cardiac imaging are reflected in performing image segmentation and classifying the image orientation. The challenges of imaging technologies are also attributed to false discovery risks. According to Shrestha and Sengupta (2018), issues of sample size data and high dimensions are a concern in the imaging technologies. There are also concerns regarding the identification of patterns. In their editorial, Shrestha and Sengupta (2018) revealed that there is a need for larger populations to determine the effectiveness of the algorithms in examining heart failure. This implies that there are still several aspects that need to be considered in the future. Notably, the impacts of AI in cardiac imaging are vast and have enhanced the imaging processes, although further studies are required to validate the patterns. 

The Future and Opportunities of AI in Radiology

The present limitations of AI, including generalisation are the foundations of future applications of AI in radiology. In the literature review by Savadijev et al. (2019) to demystify the AI-driven image interpretation, it was revealed that deep learning has produced pressure on the transition from the hypothesis-driven approach to data-driven research. This implies that AI has brought new opportunities in radiology, but the foundation research approaches remain, including the clinical reality and the effects on patient care. On the other hand, future approaches should examine the diverse challenges affecting the people while avoiding issues such as generalisation. Presently, a common issue is expectation that AI will establish an issue in the image. Therefore, issues such as misinformation without profound analysis are inherent and which could result in patient harm. Another issue that should be addressed in the future is establishing the exact manner in which AI can aid radiologists. As noted by Kahn (2017), through literature analysis, among the predictions about AI is that they will replace radiologists in the next 5-10 years alongside other interpreters. Future strategies should, therefore, focus on establishing how AI can be used by radiologists to make critical decisions, while addressing errors, such as omission.

Research Gap

The role of artificial intelligence in radiology has transformed the sector through promoting the quality and safety of service delivery, including interpretation of images. According to Hosny et al. (2018), AI has excelled in recognition of complex patterns and providing a quantitative evaluation of the radiographic features. Through deep learning, AI has improved how images are collected and interpreted. However, there are still several gaps that should be examined regarding AI in radiology. These include the fear that ICT technologies will replace radiologists. In addition, there are issues concerning the generalisation of the data to the populations (Thrall et al. 2018). It is, therefore, imperative to further examine the challenges associated with AI in radiology to promote the efficiency of the technology. In Cardiac imaging, Petersen et al. (2019) note that there are concerns regarding false discovery risks and the need for vast data to verify the patterns. This study, therefore, aims at exploring the challenges in the use of AI in cardiac imaging, future opportunities in radiology, and advanced research areas on AI in radiology.

Methodology

This study aims at evaluating the challenges and opportunities of AI in radiology, including cardiac imaging. The research is centred on three study questions, which will also involve an evaluation of advanced research areas on AI in radiology. The research will involve a systematic review strategy. The selection of a systematic review strategy is based on examining the current applications of AI in radiology and the potential areas for future research. Besides, this research entails a research question, which will be answered by a collection and summary of the empirical evidence befitting the eligibility criteria. An inclusion and exclusion criteria will be used in this regard, including the availability of the studies in full, limitation of 5 years since publication, and the presence of the studies in English.

Research Philosophy

Ontology

This research is centred on the interpretivism ontology, which acknowledges that the world is dynamic and complex and is experienced by people differently. In this study, the interpretation of different people regarding the role of AI in radiology is different, including how radiologists think and feel about the role of AI in cardiac imaging.

Epistemology

This research is aimed at establishing the beliefs, values, understandings, and reasons surrounding AI in radiology. The interpretivism approach is centred on the role of knowledge in providing meaning to people’s lives. The theories in this regard are sensitive to the context and approximate the truth.

Axiology

The research is value bound, and it will involve examining the effectiveness and challenges surrounding AI in radiology. The research is conducted by an individual from the healthcare sector, thus the study will be subjective.

Rhetoric

This study will entail an elaboration in the literature to examine the challenges and effectiveness of AI in radiology and the areas of future development. Among the aspects examined in the rhetorical context are the impacts of the AI, which is alleged to replace the role of radiologists.

Research Method

This study will employ the qualitative method, systematic review design. The studies for the review will be obtained using inclusion and exclusion criteria. The inclusion elements will involve the relationship with the topic of study, the availability of the study in English, and a publication from 2015. The exclusion elements will involve studies with the abstracts only and the peer-reviewed articles. The selection of the qualitative research approach is based on the interpretivism philosophy, which is characterised by the different interpretation of the obtained information.

Data Collection and Analysis

The sources for the systematic review are obtained from various databases, including CINAHL, Cochrane, EMBASE, and MEDLINE. The search will involve keywords and phrases, after which the inclusion and exclusion criteria will be used to filter the sources. The analysis will be informed by the research objectives, which will form the foundation of the analysis topics. The evaluation of the literature will be examined from three areas, including the present challenges of the AI in cardiac imaging, future opportunities of the AI in radiology, and advanced research of the AI in radiology.

Research Plan

The research will take 14 weeks, which will be categorised according to the Gantt chart in table 1 below. The main activities include the proposal, introduction, literature review, search for the sources, analysis, discussion and conclusions, compilation of the research, and final presentation.

Task/Dates

Start Dates

End Date

April 30

May 16

June 1

June 16

July 1

July 16

August 1

August 15

Proposal

 

 

 

 

 

 

 

 

Introduction

 

 

 

 

 

 

 

 

Literature Review

 

 

 

 

 

 

 

 

Collection of Sources

 

 

 

 

 

 

 

 

Analysis

 

 

 

 

 

 

 

 

Conclusion and Recommendations

 

 

 

 

 

 

 

 

Compilation and Presentation

 

 

 

 

 

 

 

 

Table 1: Project Plan

 


 

References

Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. (2018). Artificial intelligence in radiology. Nature Reviews Cancer18(8), 500-510.

Kahn Jr, C. E. (2017). From images to actions: opportunities for artificial intelligence in radiology, 285(3).

Liew, C. (2018). The future of radiology augmented with artificial intelligence: a strategy for success. European journal of radiology102, 152-156.

Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. The New England journal of medicine375(13), 1216.

Paul, H. Y., Hui, F. K., & Ting, D. S. (2018). Artificial intelligence and radiology: collaboration is key. Journal of the American College of Radiology15(5), 781-783.

Petersen, S. E., Abdulkareem, M., & Leiner, T. (2019). Artificial intelligence will transform cardiac imaging–opportunities and challenges. Frontiers in cardiovascular medicine6, 133.

Prevedello, L. M., Halabi, S. S., Shih, G., Wu, C. C., Kohli, M. D., Chokshi, F. H., … & Flanders, A. E. (2019). Challenges related to artificial intelligence research in medical imaging and the importance of image analysis competitions. Radiology: Artificial Intelligence1(1), e180031.

Raymond Geis, J., Brady, A. P., Wu, C. C., Spencer, J., Ranschaert, E., Jaremko, J. L., … & van den Hoven van Genderen, R. (2019). Ethics of artificial intelligence in radiology: Summary of the joint European and North American multisociety statement. Radiology293(2), 436-440.

Savadjiev, P., Chong, J., Dohan, A., Vakalopoulou, M., Reinhold, C., Paragios, N., & Gallix, B. (2019). Demystification of AI-driven medical image interpretation: past, present and future. European radiology29(3), 1616-1624.

Shrestha, S., & Sengupta, P. P. (2018). Imaging Heart Failure with Artificial Intelligence: Improving the Realism of Synthetic Wisdom.

Smith-Bindman, R., Miglioretti, D. L., & Larson, E. B. (2008). Rising use of diagnostic medical imaging in a large integrated health system. Health affairs27(6), 1491-1502.

Sogani, J., Allen Jr, B., Dreyer, K., & McGinty, G. (2020). Artificial intelligence in radiology: the ecosystem essential to improving patient care.

Tang, A., Tam, R., Cadrin-Chênevert, A., Guest, W., Chong, J., Barfett, J., … & Poudrette, M. G. (2018). Canadian Association of Radiologists white paper on artificial intelligence in radiology. Canadian Association of Radiologists’ Journal69(2), 120-135.

Thrall, J. H., Li, X., Li, Q., Cruz, C., Do, S., Dreyer, K., & Brink, J. (2018). Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. Journal of the American College of Radiology15(3), 504-508.

Yasaka, K., & Abe, O. (2018). Deep learning and artificial intelligence in radiology: Current applications and future directions. PLoS medicine15(11), e1002707.

 

 

 

 

Appendix

Appendix A:

Communication Plan for an Inpatient Unit to Evaluate the Impact of Transformational Leadership Style Compared to Other Leader Styles such as Bureaucratic and Laissez-Faire Leadership in Nurse Engagement, Retention, and Team Member Satisfaction Over the Course of One Year

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