Unified External Stakeholder Engagement and Requirements Strategy
- URL: http://arxiv.org/abs/2409.05019v1
- Date: Sun, 8 Sep 2024 08:20:57 GMT
- Title: Unified External Stakeholder Engagement and Requirements Strategy
- Authors: Ahmed Abdulaziz Alnhari, Rizwan Qureshi,
- Abstract summary: This study proposes a framework for early stakeholder identification and continuous engagement throughout the project lifecycle.
The framework addresses common organizational failures in stakeholder communication that lead to project delays and cancellations.
- Score: 3.199782544428545
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Understanding stakeholder needs is essential for project success, as stakeholder importance varies across projects. This study proposes a framework for early stakeholder identification and continuous engagement throughout the project lifecycle. The framework addresses common organizational failures in stakeholder communication that lead to project delays and cancellations. By classifying stakeholders by influence and interest, establishing clear communication channels, and implementing regular feedback loops, the framework ensures effective stakeholder involvement. This approach allows for necessary project adjustments and builds long-term relationships, validated by a survey of IT professionals. Engaging stakeholders strategically at all stages minimizes misunderstandings and project risks, contributing to better project management and lifecycle outcomes.
Related papers
- CROSS: A Contributor-Project Interaction Lifecycle Model for Open Source Software [2.9631016562930546]
Cross model is a novel contributor-project interaction lifecycle model for open source software.
It explains a range of archetypal cases of contributor engagement and highlights research gaps, especially in EoS/offboarding scenarios.
arXiv Detail & Related papers (2024-09-12T17:57:12Z) - Confronting Project Conflicts into Success: a Complex Systems Design Approach to Resolving Stalemates [0.0]
In today's complex projects development, stakeholders are often involved too late.
A purely associative and a-priori design-supported approach integrates both system's reality and stakeholder's interests.
The state-of-the-art Preferendus is deployed to co-creatively generate a best-fit-for-common-purpose solution.
arXiv Detail & Related papers (2024-09-02T07:44:43Z) - Constraining Participation: Affordances of Feedback Features in Interfaces to Large Language Models [49.74265453289855]
Large language models (LLMs) are now accessible to anyone with a computer, a web browser, and an internet connection via browser-based interfaces.
This paper examines the affordances of interactive feedback features in ChatGPT's interface, analysing how they shape user input and participation in iteration.
arXiv Detail & Related papers (2024-08-27T13:50:37Z) - The Impossibility of Fair LLMs [59.424918263776284]
The need for fair AI is increasingly clear in the era of large language models (LLMs)
We review the technical frameworks that machine learning researchers have used to evaluate fairness.
We develop guidelines for the more realistic goal of achieving fairness in particular use cases.
arXiv Detail & Related papers (2024-05-28T04:36:15Z) - AI Sustainability in Practice Part One: Foundations for Sustainable AI Projects [0.46671368497079174]
AI projects are responsive to the transformative effects as well as short-, medium-, and long-term impacts on individuals and society.
This workbook is the first part of a pair that provides the concepts and tools needed to put AI Sustainability into practice.
arXiv Detail & Related papers (2024-02-19T22:52:14Z) - Unveiling Diversity: Empowering OSS Project Leaders with Community
Diversity and Turnover Dashboards [51.67585198094836]
CommunityTapestry is a dynamic real-time community dashboard.
It presents key diversity and turnover signals that we identified from the literature.
It helped project leaders identify areas of improvement and gave them actionable information.
arXiv Detail & Related papers (2023-12-13T22:12:57Z) - Exchange-of-Thought: Enhancing Large Language Model Capabilities through
Cross-Model Communication [76.04373033082948]
Large Language Models (LLMs) have recently made significant strides in complex reasoning tasks through the Chain-of-Thought technique.
We propose Exchange-of-Thought (EoT), a novel framework that enables cross-model communication during problem-solving.
arXiv Detail & Related papers (2023-12-04T11:53:56Z) - CORec-Cri: How collaborative and social technologies can help to
contextualize crises? [0.0]
In this paper, we investigate how collaborative and social technologies help to contextualize crises.
We define CORec-Cri (Contextulized Ontology-based Recommender system for crisis management) based on existing work.
arXiv Detail & Related papers (2023-10-03T15:29:37Z) - Participation Interfaces for Human-Centered AI [6.85316573653194]
This paper introduces interactive visual "participation interfaces" for Markov Decision Processes (MDPs) and collaborative ranking problems as examples restoring a human-centered locus of control.
arXiv Detail & Related papers (2022-11-15T18:57:34Z) - Collaborative Intelligence: Challenges and Opportunities [80.22863657331622]
The paper surveys the current state of the art in CI, with special emphasis on signal processing-related challenges in feature compression, error resilience, privacy, and system-level design.
arXiv Detail & Related papers (2021-02-13T01:24:05Z) - Leveraging traditional ecological knowledge in ecosystem restoration
projects utilizing machine learning [77.34726150561087]
Community engagement throughout the stages of ecosystem restoration projects could contribute to improved community well-being.
We suggest that adaptive and scalable practices could incentivize interdisciplinary collaboration during all stages of ecosystemic ML restoration projects.
arXiv Detail & Related papers (2020-06-22T16:17:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.