This is the assignment ofLiterature review (Systematic mapping study) & Research planand the maximum word count for the whole assignment is 4000 words.No need to write dissertation. Only want,...

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This is the assignment of

Literature review (Systematic mapping study) & Research plan

and the maximum word count for the whole assignment is 4000 words.No need to write dissertation. Only want, Literature review (Systematic mapping study) & Research plan.

Assignment description:

Assignment 3 consists of two parts. First, you conduct a literature review in the form of systematic mapping study and second create a research plan based on it.

Part 1 -Initially, you need to identify and write about a research problem you wish to investigate (you can choose a topic provided in the list below in Assignment 3 description – Part 1). By arguing with (and referencing) the academic literature you read, the research problem will become clearer and more delimited, allowing you to finally formulate a research question that forms the basis of your systematic mapping study.

Part 2
- Having reviewed the selected literature and gained deeper insight, you might need to refine your research question for the empirical research plan you then need to develop subsequently. Therein, you need to describe what kind of empirical study you will conduct and clearly delimit the scope in topic and time.

Assignment 3 – Part 1 instructions:

For the first part of this assignment (systematic mapping study), you need to:

Pick one topic from the list below to perform part 1:

o Project management in large-scale IT projects

o Cost/effort estimation and project planning in IT project management

o Use of AI/ML in IT project management

o Hybrid project management of IT projects

o Or any topic of your choice
Answered 15 days AfterDec 26, 2023

Answer To: This is the assignment ofLiterature review (Systematic mapping study) & Research planand the...

Deblina answered on Jan 11 2024
13 Votes
Literature Review        2
Table of Contents
In recent years, the proliferation of Artificial Intelligence (AI) and Machine Learning (ML) technologies has revolutionized numerous industries, redefining traditional approaches to problem-solving, decision-making, and resource optimization. Within the realm of Information Technology (IT), these innovations have emerged as transformative tools with substantial potential for reshaping project management methodologies and practices. The dynamism and complexity inherent in IT projects demand adaptive frameworks and cutting-edge methodologies to ensure efficiency, timely delivery, and optimal resource utilization. AI and ML stand as promising assets, offering capabilities to analyze vast datasets, predict potential outcomes, automate routine tasks, and enhance decision-making processes. However, the seamless integration of AI/ML into IT project management r
emains a critical challenge, marked by the absence of standardized methodologies and frameworks.
Despite the evident potential, the absence of established guidelines or frameworks poses significant hurdles in harnessing the full potential of AI/ML within IT project management. This deficiency translates into challenges related to suboptimal resource allocation, inadequate risk mitigation strategies, and difficulties in predicting project timelines accurately. Moreover, the variability in AI/ML adoption across different sectors of IT project management further accentuates the complexity. Software development projects might require distinct AI-driven strategies compared to infrastructure deployment initiatives or data science endeavors. This heterogeneity amplifies the need for tailored methodologies to suit specific project domains, emphasizing the urgency for structured frameworks that accommodate diverse project requirements.
This study seeks to delve into the integration of AI/ML technologies within the domain of IT project management, aiming to address the overarching research problem: the lack of standardized methodologies or frameworks hindering the seamless incorporation of AI/ML tools.
By comprehensively examining the existing research landscape, this investigation endeavors to identify the prevailing gaps, challenges, and opportunities in the utilization of AI/ML in IT project management. Moreover, this exploration aims to synthesize and evaluate the diverse methodologies and frameworks proposed in academic literature to guide effective integration strategies.
Research Problem Description
Within the intricate landscape of Information Technology (IT) project management, a significant challenge arises concerning the effective allocation of resources by harnessing the potential of Artificial Intelligence (AI). This problem encapsulates the complexities inherent in streamlining resource utilization across diverse IT project domains, often resulting in inefficiencies, missed deadlines, and budgetary constraints. This problem holds profound relevance in the domain of computer and systems sciences due to its multidimensional impact. The integration of AI into resource allocation methodologies within IT projects requires a deep understanding of algorithmic models, data analytics, decision support systems, and human-computer interaction paradigms.
The complexity of modern IT projects demands dynamic resource allocation strategies that consider multiple variables, including project scope, team expertise, task dependencies, and evolving requirements. AI offers the potential to enhance this process by leveraging predictive analytics, machine learning algorithms, and optimization techniques. However, the lack of standardized AI-driven resource allocation frameworks presents a significant bottleneck.
The relevance of this problem reverberates through the realms of computer and systems sciences due to its multidimensional implications. The integration of AI into resource allocation methodologies within IT projects necessitates a profound understanding of algorithmic design, data analytics, decision support systems, and human-computer interaction paradigms. This issue aligns with various sub-disciplines within computer science: from algorithm design aimed at dynamically allocating resources based on project parameters and team capabilities, to leveraging machine learning and predictive analytics to forecast resource needs and identify potential bottlenecks. Furthermore, the design of human-computer interaction interfaces plays a pivotal role in creating intuitive AI-driven systems, facilitating seamless interactions for project managers to make informed resource allocation decisions. Moreover, the development of decision support systems empowered by AI is integral to aiding project managers in leveraging data-driven insights for resource optimization. The complexity of this problem necessitates a multidisciplinary approach, amalgamating computer science theories, data analytics techniques, and system design principles to create adaptive AI-driven resource allocation models. Resolving this issue holds paramount significance not only in theoretical constructs but also in practical applications, directly impacting the efficiency, success, and innovation in IT project management. Addressing this challenge can catalyze the development of novel methodologies, optimizing resource allocation and elevating the performance of IT projects, thereby making substantial contributions to both academic research and industry practices in computer and systems sciences.
Research Question
"How can AI-driven resource allocation frameworks be developed and implemented to optimize resource utilization in diverse IT project domains, addressing the challenges of efficiency, deadlines, and budget constraints?"
This research question is pivotal as it directly addresses the core challenge of inefficient resource allocation in IT project management, offering a focused approach toward leveraging AI for improvement. By exploring the development and implementation of AI-driven resource allocation frameworks, this inquiry aims to unravel the intricacies of adapting AI technologies to diverse project domains. Answering this question holds the promise of providing structured methodologies that dynamically allocate resources based on project parameters, team capabilities, and evolving requirements. Consequently, this could potentially mitigate inefficiencies, reduce project delays, and alleviate budgetary constraints by enabling project managers to make informed resource allocation decisions based on data-driven AI insights. Ultimately, the solution to this research question could significantly contribute to enhancing the overall efficiency and success rates of IT projects.
Motivation for Conducting a Review
Conducting a systematic mapping study in the domain of AI-driven resource allocation in IT project management is crucial for several reasons. Despite the increasing relevance and adoption of AI technologies, there's a notable scarcity of comprehensive studies consolidating the existing literature on this specific topic. While individual research papers address facets of AI-enabled resource allocation, a consolidated review is pivotal to synthesize and evaluate the diverse methodologies, frameworks, and challenges identified across multiple studies. Furthermore, as technology and practices evolve rapidly, there might be a multitude of new insights and developments in this field. Thus, a review will provide an updated and holistic view, identifying gaps, trends, and emerging paradigms, thereby laying the groundwork for future research initiatives. The absence of recent secondary studies specifically focusing on AI-driven resource allocation in IT project management underscores the urgency and significance of conducting a systematic mapping review in this area.
Search Strategy
The search strategy aims to retrieve peer-reviewed articles and conference papers published within the last 10 years in English, focusing on AI, resource allocation, and IT project management. The search string ("AI" OR "Artificial Intelligence") AND ("resource allocation" OR "resource management") AND ("IT project management" OR "information technology projects") will be utilized across prominent databases, including IEEE Xplore, ACM Digital Library, Scopus, and Web of Science. The search areas encompass titles, abstracts, keywords, and full-text content to ensure comprehensive coverage. This approach intends to capture a diverse range of articles discussing AI applications, methodologies, frameworks, challenges, and outcomes pertaining to resource allocation within IT projects. By incorporating these criteria and databases while examining various sections of the articles, the search aims to retrieve a relevant and comprehensive set of peer-reviewed literature focusing on the intersection of AI and resource allocation in the domain of IT project management.
Inclusion Criteria
Inclusion criteria encompass peer-reviewed articles and conference papers that specifically address AI-based resource allocation within the domain of IT project management. The selected articles should delve into methodologies, frameworks, challenges, and applications pertaining to the integration of AI in resource allocation strategies within IT projects. This criterion ensures that the retrieved literature contributes directly to the understanding of AI's role in optimizing resource allocation and addresses the complexities of IT project management.
Articles meeting the inclusion criteria will specifically address AI-driven resource allocation within the context of IT project management. These articles should offer insights into methodologies, frameworks,...

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