Robotic Process Automation as a Driver for Sustainable Innovation and Entrepreneurship

• URL: http://arxiv.org/abs/2403.00431v1
• Date: Fri, 1 Mar 2024 10:32:48 GMT
• Title: Robotic Process Automation as a Driver for Sustainable Innovation and Entrepreneurship
• Authors: Petr Prucha
• Abstract summary: This study investigates the extent to which technological innovation contributes to a more sustainable future and fosters entrepreneurship. Our research involved gathering data from the 300 largest companies in terms of market capitalization. Our findings revealed a statistically significant association between RPA and ESG ratings, indicating their interconnection.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Technological innovation plays a crucial role in driving economic growth and development. In this study, we investigate the extent to which technological innovation contributes to a more sustainable future and fosters entrepreneurship. To examine this, we focus on robotic process automation (RPA) highly relevant technology. We conducted a comprehensive analysis by examining the usage of RPA and its impact on environmental, social, and governance (ESG) factors. Our research involved gathering data from the 300 largest companies in terms of market capitalization. We assessed whether these companies used RPA and obtained their corresponding ESG ratings. To investigate the relationship between RPA and ESG, we employed a contingency table analysis, which involved categorizing the data based on ESG ratings. We further used Pearson's Chi-square Test of Independence to assess the impact of RPA on ESG. Our findings revealed a statistically significant association between RPA and ESG ratings, indicating their interconnection. The calculated value for Pearson's Chi-square Test of Independence was 6.54, with a corresponding p-value of 0.0381. This indicates that at a significance level of five percent, the RPA and ESG variables depend on each other. These results suggest that RPA, representative of modern technologies, likely influences the achievement of a sustainable future and the promotion of entrepreneurship. In conclusion, our study provides empirical evidence supporting the notion that technological innovations such as RPA have the potential to positively shape sustainability efforts and entrepreneurial endeavours.

Related papers

• Bridging the Gap: Integrating Ethics and Environmental Sustainability in AI Research and Practice [57.94036023167952]
  We argue that the efforts aiming to study AI's ethical ramifications should be made in tandem with those evaluating its impacts on the environment. We propose best practices to better integrate AI ethics and sustainability in AI research and practice.
  arXiv  Detail & Related papers  (2025-04-01T13:53:11Z)
• Empirical Analysis of Digital Innovations Impact on Corporate ESG Performance: The Mediating Role of GAI Technology [0.4532517021515834]
  This study investigates the relationship between corporate digital innovation and Environmental, Social, and Governance (ESG) performance. We use a comprehensive panel dataset of 8,000 observations from the CMARS and WIND database spanning from 2015 to 2023. Our findings reveal that digital innovation significantly enhances corporate ESG performance, with GAI technology adoption serving as a crucial mediating mechanism.
  arXiv  Detail & Related papers  (2025-03-31T12:34:02Z)
• Raising the Stakes: Performance Pressure Improves AI-Assisted Decision Making [57.53469908423318]
  We show the effects of performance pressure on AI advice reliance when laypeople complete a common AI-assisted task. We find that when the stakes are high, people use AI advice more appropriately than when stakes are lower, regardless of the presence of an AI explanation.
  arXiv  Detail & Related papers  (2024-10-21T22:39:52Z)
• ESG Sentiment Analysis: comparing human and language model performance including GPT [0.0]
  ESG has grown in importance in recent years with a surge in interest from the financial sector. The use of sentiment analysis to measure ESG related reputation has developed and with it interest in the use of machines to do so. Our study seeks to compare human performance with the cutting edge in machine performance in the measurement of ESG related sentiment.
  arXiv  Detail & Related papers  (2024-02-26T15:22:30Z)
• AI in ESG for Financial Institutions: An Industrial Survey [4.893954917947095]
  The paper surveys the industrial landscape to delineate the necessity and impact of AI in bolstering ESG frameworks. Our survey categorizes AI applications across three main pillars of ESG, illustrating how AI enhances analytical capabilities, risk assessment, customer engagement, reporting accuracy and more. The paper also addresses the imperative of responsible and sustainable AI, emphasizing the ethical dimensions of AI deployment in ESG-related banking processes.
  arXiv  Detail & Related papers  (2024-02-03T02:14:47Z)
• Open-sourced Data Ecosystem in Autonomous Driving: the Present and Future [130.87142103774752]
  This review systematically assesses over seventy open-source autonomous driving datasets. It offers insights into various aspects, such as the principles underlying the creation of high-quality datasets. It also delves into the scientific and technical challenges that warrant resolution.
  arXiv  Detail & Related papers  (2023-12-06T10:46:53Z)
• On the Opportunities of Green Computing: A Survey [80.21955522431168]
  Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades. The needs for high computing power brings higher carbon emission and undermines research fairness. To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
  arXiv  Detail & Related papers  (2023-11-01T11:16:41Z)
• On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Norms [56.119374302685934]
  There have been severe concerns over the trustworthiness of AI technologies. Machine and deep learning algorithms depend heavily on the data used during their development. We propose a framework to evaluate the datasets through a responsible rubric.
  arXiv  Detail & Related papers  (2023-10-24T14:01:53Z)
• Creating a Systematic ESG (Environmental Social Governance) Scoring System Using Social Network Analysis and Machine Learning for More Sustainable Company Practices [0.0]
  This project aims to create a data-driven ESG evaluation system that can provide better guidance and more systemized scores by incorporating social sentiment. Python web scrapers were developed to collect data from Wikipedia, Twitter, LinkedIn, and Google News for the S&P 500 companies. Machine-learning algorithms were trained and calibrated to S&P Global ESG Ratings to test their predictive capabilities.
  arXiv  Detail & Related papers  (2023-09-07T20:03:45Z)
• The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence [67.70415658080121]
  Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole. AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
  arXiv  Detail & Related papers  (2023-07-09T21:16:56Z)
• A Comparative Study of Machine Learning Algorithms for Anomaly Detection in Industrial Environments: Performance and Environmental Impact [62.997667081978825]
  This study seeks to address the demands of high-performance machine learning models with environmental sustainability. Traditional machine learning algorithms, such as Decision Trees and Random Forests, demonstrate robust efficiency and performance. However, superior outcomes were obtained with optimised configurations, albeit with a commensurate increase in resource consumption.
  arXiv  Detail & Related papers  (2023-07-01T15:18:00Z)
• Enhancing Artificial intelligence Policies with Fusion and Forecasting: Insights from Indian Patents Using Network Analysis [0.0]
  This paper presents a study of the interconnectivity and interdependence of various Artificial intelligence (AI) technologies. By analyzing the technologies through different time windows and quantifying their importance, we have revealed important insights into the crucial components shaping the AI landscape.
  arXiv  Detail & Related papers  (2023-04-20T18:37:11Z)
• Artificial Intelligence and Innovation to Reduce the Impact of Extreme Weather Events on Sustainable Production [1.290382979353427]
  unpredictability of extreme weather endangers sustainable production and life on land. Modern technologies such as Artificial Intelligent (AI), the Internet of Things (IoT), blockchain, 3D printing, and virtual and augmented reality (VR and AR) are promising to reduce the risk and impact of extreme weather. However, research directions on how these technologies could help reduce the impact of extreme weather are unclear.
  arXiv  Detail & Related papers  (2022-09-21T06:52:39Z)

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.