Artificial Intelligence

Artificial Intelligence

Author: Priyal Vijay Shejwal

A brief historical overview of artificial intelligence research
This paper gives a brief timeline of the evolution of the field of Artificial Intelligence (AI), a field that is currently generating approximately sixty thousand research papers per year. This represents a 12.9% growth in output over the past five years, and is markedly higher than the growth seen across all research, which has grown at a rate of 2.3% per year in the same period. AI research is of interest around the world and this paper highlights many of the activities undertaken to stimulate this research in the USA, Canada, Mexico, Europe, and Asia.

Artificial Intelligence and its Application in Business Management
The paper aims to conduct an in-depth analysis of artificial intelligence applications in business management with a bibliometric investigation. Design/methodology/approach: (mandatory) The objectives are achieved by carrying out the research with the use of the VOSviewer software, developed at Leiden University’s Centre for Science and Technology Studies (CWTS), Leiden University, the Netherlands. This software allows conducting a literature review by generating, visualizing and analyzing bibliometric networks. The scope of the study is limited to data retrieved from the Scopus database obtained by three search queries. Findings: The work identifies the main topic areas related to the application of artificial intelligence application, particularly in business management. Originality/value There are lots of materials devoted to artificial intelligence though there is still a lack of materials related to AI technologies categorization and its application in certain areas. The paper points out how artificial intelligence technologies are adopted in business management. The paper also defines potential areas for research or which areas require future examination.

Artificial Intelligence in E-Commerce: A Bibliometric Study and Literature Review
This paper synthesizes research on artificial intelligence (AI) in e-commerce and proposes guidelines on how information systems (IS) research could contribute to this research stream. To this end, the innovative approach of combining bibliometric analysis with an extensive literature review was used. Bibliometric data from 4335 documents were analyzed, and 229 articles published in leading IS journals were reviewed. The bibliometric analysis revealed that research on AI in e-commerce focuses primarily on recommender systems. Sentiment analysis, trust, personalization, and optimization were identified as the core research themes. It also places China-based institutions as leaders in this researcher area. Also, most research papers on AI in e-commerce were published in computer science, AI, business, and management outlets. The literature review reveals the main research topics, styles and themes that have been of interest to IS scholars. Proposals for future research are made based on these findings. This paper presents the first study that attempts to synthesize research on AI in e-commerce. For researchers, it contributes ideas to the way forward in this research area. To practitioners, it provides an organized source of information on how AI can support their e-commerce endeavors.

The role of Artificial Intelligence in the procurement process: State of the Art and research agenda
Artificial intelligence (AI) is widely adopted in many areas, but it is still in its infancy in procurement, despite its potential. To map the state of the art of both research and practice and identify future research directions, this paper presents a mixed methodology exploratory study of the role of AI in the procurement process. The paper combines a systematic literature review, a mapping of the offerings of providers of AI-based procurement platforms and a focus group with procurement managers. Results map the functionalities of AI-based solutions throughout the procurement process, describe benefits and challenges to their adoption and identify future research directions. • This paper studies the role of Artificial Intelligence in the procurement process. • Through a multiple methodology, 8 directions for future research are suggested. • Results show the procurement activities impacted by AI in research and practice. • The paper identifies benefits and challenges from the adoption of AI in procurement. • The paper suggests theoretical lenses for future studies about AI in procurement.

Challenges of Financial Risk Management: AI Applications
This paper reviews different artificial intelligence (AI) techniques application in financial risk management. Motivation: Financial technology has significantly changed the business operations which required transformation of financial industry. The financial risk management needs to be restructured because the methods that have been used in the past became low effective. The artificial intelligence techniques proved their efficiency and contributed to fast, low-cost and improved financial risk management in both financial institutions and companies. Idea: The aim of this paper is to present a state of AI techniques application in financial risk management, as well as to point out the direction in which further application and development could be expected. Data: The analysis was conducted by reviewing various papers, books and reports on AI applications in financial risk management. Tools: The relevant literature systematization was used to provide answers to the question to what extent AI techniques (especially machine learning) could be used in managing financial risk management. Findings: Artificial intelligence largely improved the market risk and credit risk management through data preparation, modelling risk, stress testing and model validation. Artificial intelligence techniques can be useful in data quality assurance, text-mining for data augmentation and fraud detection. The financial technology will continue to affect the financial sector through requiring the adaption to new environment and new business models. Because of that, it could be expected that artificial intelligence will become part of the financial risk management framework. Contribution: This paper provides a review of artificial intelligence applications in market risk management, credit risk management and operational risk management. The paper identified the key AI techniques that could be used for financial risk management improvement because of financial industry transformation.

Sustainable Development – An Artificial Intelligence Approach
In mid 20s Carson (1963) raised the question of environmental issues in economics as one that should be discussed by practitioners and researchers. From that point, the environmental issues from the analysis of exhaustible and productive resources to its economic role and consequentially its impact, were examined. Environment affects must be considered as human activity-based processes which repercussions effect economy in general. Thus, the long-term decision-making processes in economy must take into account future generations as well. Furthermore, tourism as an economic activity is growing rapidly and one must take into consideration, that it is based on, primarily, natural environment and man-made resources. Degradation of basic tourism resources can lead to decrease of tourism demand. Therefore, the analysis of environmental impact on tourism, and vice versa, the impact of tourism on environment is crucial. With the development of information technology sector, new and innovative methods for analysis and forecasting have emerged such as artificial intelligence. The main hypothesis of this paper is to research how artificial intelligence can contribute to analysis and investigating environmental aspect of tourism. Therefore, the paper provides a theoretical overview of possible fields of artificial intelligence usage in sustainability, in the context of tourism development.

Extremes Of Acceptance: Employee attitude towards Artificial Intelligence
The purpose of this paper is to present paradoxical employee attitudes towards interacting with artificial intelligence (AI). Design/methodology/approach: This is a conceptual paper, which builds on prior research, especially on the widely accepted notion of not-invented-here attitudes in technology adoption. Findings: Many companies experience barriers in implementing AI owing to negative attitudes among their employees. This paper develops the concept of no-human-interaction attitudes, which describe employees’ preference to collaborate with real humans rather than having virtual colleagues. If they perceive a benefit from voluntarily using AI, however, many employees exhibit positive attitudes, leading to the concept of intelligent-automation attitudes. Jointly, these attitudes lead to the paradox that the same persons may have positive or negative attitudes to AI, depending on the particular situation. Firms need to address these attitudes because the interface of human and AI will be a key driver of competitive advantage in the future. Originality/value: The new concepts of negative and positive employee attitudes contribute to our understanding of firms’ success and problems in implementing AI. Moreover, the paradox of negative and positive attitudes among the same employees helps to reconcile partly diverging findings in extant studies. A thorough understanding of the roots of these employee attitudes, along with several examples, further provides immediate starting points for actively influencing these attitudes in practice.

International Financial Markets face to face with Artificial Intelligence and Digital Era
Economic development is the process by which a nation improves the economic, political and social well-being of its people. The research paper starts from the reality that financial markets play an important role in each economy. The research find out that, the inequalities in the levels of development of the regions have arisen from two main reasons: economic and social conditions; and, the level of implementation of artificial intelligence and digital finance – FinTech. Nowadays, investments and financial markets are moving to a next stage: artificial intelligence and secured financial services and transfers using digital financial system, Block chain. The research paper comes to present how artificial intelligence combine financial information with tech capabilities, accelerate digital transformation of finance to create a more safety business and economic environment, reducing human error.

Effectiveness of using the method of Artificial Intelligence in Maintenance of ICT System
Reliability of production systems in enterprises is increasingly dependent on the availability of ICT systems that supervise them. The research presented in this paper focuses on the improvement of the availability of ICT systems by identifying possibilities of further automation of their maintenance processes. After preliminary research, it has been concluded that the possibility of further automation by using artificial intelligence methods to support decision making regarding the improvement of ICT systems is worth considering. The main aim of the paper was to carry out research into the possibilities of using and implementing artificial intelligence to support decision making regarding the improvement of ICT systems.

Artificial Intelligence in FinTech: Understanding robo advisors adoption among customers
Purpose: Considering the increasing impact of Artificial Intelligence (AI) on financial technology (FinTech), the purpose of this paper is to propose a research framework to better understand robo-advisor adoption by a wide range of potential customers. It also predicts that personal and sociodemographic variables (familiarity with robots, age, gender and country) moderate the main relationships. Design/methodology/approach: Data from a web survey of 765 North American, British and Portuguese potential users of robo-advisor services confirm the validity of the measurement scales and provide the input for structural equation modeling and multisample analyses of the hypotheses. Findings: Consumers’ attitudes toward robo-advisors, together with mass media and interpersonal subjective norms, are found to be the key determinants of adoption. The influences of perceived usefulness and attitude are slightly higher for users with a higher level of familiarity with robots; in turn, subjective norms are significantly more relevant for users with a lower familiarity and for customers from Anglo-Saxon countries. Practical implications: Banks and other firms in the finance industry should design robo-advisors to be used by a wide spectrum of consumers. Marketing tactics applied should consider the customer’s level of familiarity with robots. Originality/value: This research identifies the key drivers of robo-advisor adoption and the moderating effect of personal and sociodemographic variables. It contributes to understanding consumers’ perceptions regarding the introduction of AI in FinTech.

Conclusion:
Artificial Intelligence is an existing area of research that has received significant attention in recent years. Based on my analysis research papers there are several key area of focus within the field. These include machine learning, natural language processing, computer vision, robotic, and decision making. Overall the research suggest that AI ha enormous potential to revolutionize many industries and fields, including healthcare, finance, education, and transportation. However there are also concerns about the potential negative consequences of AI, such as job displacement and ethical concern. Despite these challenges, the research demonstrate that AI is rapidly advancing field with endless possibilities for the future.

Reference:

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BAWACK, R. E. et al. Artificial intelligence in E-Commerce: a bibliometric study and literature review. Electronic Markets, [s. l.], v. 32, n. 1, p. 297–338, 2022. DOI 10.1007/s12525-022-00537-z. Disponível em: https://discovery.ebsco.com/linkprocessor/plink?id=4f55324d-0f59-3da9-9e0c-1dfe128e74a6. Acesso em: 12 maio. 2023.

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Submitted by:
Priyal Vijay Shejwal
Batch B
Roll No: 0222115
Kohinoor Business School

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