a phd research proposal example

a phd research proposal example

Exploring the Impact of Artificial Intelligence on Business Operations: A PhD Research Proposal

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1. Introduction

This research proposal presents a detailed outline plan for a PhD project. The proposal is positioned at the interface between a number of research areas, including information technology, operations management, operations research, and strategic management, and addresses a number of key issues in management with relevance to academics, professional practitioners, and policymakers. Artificial intelligence (AI) is advancing at a rapid pace, and increasingly it is being used by a variety of companies to help automate their business operations. The overarching aim of this project is to explore the extent to which AI can have an impact on business operations. In pursuit of this aim, the following research questions are suggested:

1. What is the current impact of AI on business operations? 2. What barriers do companies face when introducing AI into business operations? 3. What are the key performance drivers in AI-supported business operations?

The outcome of this research will be a comprehensive understanding of how AI is currently impacting business operations and how it can be best employed to improve business operation performance.

The report opens with a background explaining the motivation for this project. The key aims and objectives of the research are then outlined at the beginning of section 1. Research questions and a brief outline of the proposed research are also provided in the opening section. The structure and research contribution of the paper are summarized at the end of section 1. Section 2 presents a broad review of the current impact of AI on business operations and the ways in which the two are coupled. A complementary paper report on the actual use of AI in business operations is in the final editing stages of a double-blind review process. The paper is divided into six main sections, each of which feeds further knowledge into the answers to the research questions.

2. Literature Review

The literature review section will demonstrate an in-depth analysis and synthesis of relevant theories, research, and frameworks that have emerged from the study of the impact of artificial intelligence (AI) on business operations. It aims to create a comprehensive understanding of the current state of knowledge in the research field, and it will help in identifying gaps in the existing research literature that will be covered by my research. Furthermore, the literature review can suggest theoretical frameworks or conceptual models that can be used and developed in my research. It will also help build on existing research from different perspectives, hence the paper could reconstruct the different methodologies used to solicit a range of findings.

This proposal sets out to conduct a PhD exploring AI, norms, and sensemaking as it emerges within business contexts. The first chapter has introduced the research topic, its relevance, and has formulated research questions. Moreover, it has mapped the structure of the whole proposal and indicated the purpose of individual chapters. In particular, this proposal has introduced Daniel J. Boorstin’s travelogue as an inspiring metaphor to reveal the multifaceted and entangled impact of AI and to emphasize the need for closer attention to interdisciplinary cooperative research. The second chapter presents a comprehensive review of the relevant research literature, which served to highlight not only our critical point of departure but also the bald spots that offer research potential to this project. In particular, it focuses on the research contributions pertinent to the key underlying concepts and theoretical and empirical findings, highlighting the primary themes and also the contradictory evidence. The literature review has been organized thematically rather than a systematic review to better highlight different dimensions of the research topic. It then provides a thorough discussion of the other research programs and key influencing theories relevant to this proposal, thereby conducting an analysis of the study’s place and potential contribution to the existing research body.

3. Research Methodology

3.1 Research Design: A qualitative research approach will best undertake this investigation in a constructivist tradition. Data-driven phenomenological investigation is the most suitable research paradigm that illustrates the interpretation of direct experiences into a meaningful form to construct the social reality – here the interpretation would be the first-hand experience of the practitioners using AI, and the extraction of the best use-cases, and operational outcomes, their individual perception and retrospective insights into AI-induced transformation would form the meaningful form. As the result merits dealing with the ‘how’ and ‘why’ questions, a mixed approach would afford a full exploration, potential development, and implementation of the AI, via case studies. The following proposed research questions are both descriptive and explanatory, relating to processes, organizational outcomes, and individuals’ emotional and productivity-driven journey narratives.

3.2 Data Collection: In-depth semi-structured interviews (15-20) with business leaders will take place at their workplace or a site of their choosing. Conversations will be recorded and transcribed. The research, at this time, considers a limited number of project stakeholders, via change managers, human resources business partners, IT department staff, and the AI developers themselves. However, the research acknowledges that the social actors playing specific roles within the organization will probably contribute, in the case of the AI developers, by reflecting upon their societal roles, and AI more generally within their reflective narratives. The interview protocol to this point assumes the use of interpretative phenomenological analysis as the basis of data analysis.

3.3 Data Sources: Industry source data and direct inquiries will form the main sources of information. These will include online publications, company and industry reports, industry analysts, and business leaders.

3.4 Methods of Analysis: Multiple case studies will be utilized. Post-interview data analysis, proposed as interpretative phenomenological analysis, the use of the grounded theory techniques which includes open coding, axial coding, and selective coding, will provide the means to develop an in-depth and rich descriptive and interpretive analysis of the lived experiences of the interviewees.

3.5 Rationale: A qualitative research approach has been selected in order to explore the phenomena as experienced by organizational stakeholders, or sub-actors. The research will draw on the micro-level insights to explore and uncover the organizations’ changing of forms to better adapt, succeed or fail when preparing, introducing and later utilizing AI. The investigation will contribute to the understanding of one of the means by which AI, often defined as one of the key facilitators of change, impacts on and can shape an organization’s methods of work, strategies, design, and views of the future, internally and externally. The proposal cannot predict in advance what differences or changes in form using AI will resemble in practice; the uniqueness of the empirical phenomena, as well as the local nature of the proposal. The in-depth exploratory nature of the research requires detailed knowledge of the cases, thereby precluding the process of random sampling. Given this, a multistage, theoretical sampling procedure will be employed. As part of the sampling technique, a unique spin on the in-depth interviews will occur. The aim is to choose leading organizations which are seen by their peers as ‘leaders’ in their industry relative to the considered transformation project also in-depth. This decision will guide decision-makers, change practitioners, and those instrumental in business who are pivotal in the future success of their organizations. Research containment of sample size presents potential limitations in terms of the generalizability of results. This, whilst understood, is not necessarily problematic given the focus on exploratory narration. Practical and ethical considerations in choosing the AI lead and in conduct of the study are also addressed in detail.

4. Expected Contributions and Significance

The implications of this work are anticipated to make a significant contribution to the literature on artificial intelligence and business operations. Resulting from the three-phase research process, it is hoped that the third study will offer insights into how AI is affecting business operations and present an understanding of any perceived impacts on professional perception. This is expected to further illustrate the ongoing relationship between AI and digital labour. If the research finds a significant impact, it is expected that it will go on to offer additional research opportunities and allow the incumbent theory to be further developed. This, in turn, is anticipated to offer new contributions to the literature. Theoretically, in either instance, this work is expected to go some way to helping fill the identified research gap as well as develop sociology of work, digital labour, and AI and eBusiness theories. This work can help practitioners develop AI in a way where they can tap the huge potential for industries as well as the workforce working within these industries.

This research will also offer a number of practical implications for academic researchers, practitioners, and policymakers. It is anticipated that the findings from the three-phased research will present deeper insights into the growing dependence and influence of AI technology on business operations from the professional viewpoint. This can also aid departments working with big data and analytics for training and data management and will primarily be relevant in the domain of management, sociology, and digital work. In addition, this research is significant for society, the economy, and the environment. Sociologically, the research is important in showing the increasing reliance of companies on intelligent technologies and digital labour and has the potential to inform policy which can represent the workforce and manage the ethical implications of liberating individuals from work. Focusing on the environment, the research can help in identifying the degree to which AI technologies can support/disrupt global supply chains.

5. Conclusion

The research has demonstrated the significant gap in the literature on AI, which needs to be filled to support business professionals to create an adequate strategy for the next few years. There are about 2.5 quintillion bytes of data generated each day, and the business decisions based on facts are mostly supported by AI-enabled solutions. In this context, the unprecedented global impact of the recent pandemic has helped businesses to leverage these technologies, and this trend will continue for the upcoming years. However, there is a lack of understanding of AI operations and its full potential in strategic and operational management. This research fills that gap and proposes to accomplish research objectives related to exploring the AI operations and AI impact on businesses.

The findings of this exploratory research that demand multiple intellectual contributions would be to understand the capabilities, the applications, and the business AI opportunities. The research study has recognized that AI operations represent the combination of human intelligence, collective experiences, and perspectives of the company to support decision-making that ensures strategic planning and real-time operational agility for innovative and value-enhancing solutions. Moreover, the current AI potential is costly consumptive in many AI environments where the classic return on investment would not endorse managers to engage. The opportunities exist in the capacity to recognize valuable meaning and to enable AI operations to support creative HRM, behavioral and cognitive value addition, and the development of new business models.

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