TY - GEN
T1 - Exploring Corporate Social Responsibility Through Sport
T2 - 10th International Conference on Information Management, ICIM 2024
AU - Abdul Rahman, Sumaya
AU - Rizvi, Syeda Warisha Fatima
AU - Anagnostopoulos, Christos
AU - Menezes, Brenno
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024/7/18
Y1 - 2024/7/18
N2 - Corporate social responsibility (CSR) is becoming increasingly important across industries, including sport. This paper explores CSR through sport using generative AI techniques. The case study here is Qatar, which has heavily invested in hosting major sporting events and leagues aligning with its National Vision 2030. This study examines the annual reports of banks listed on the Qatar Stock Exchange for the years 2008–2022. The reports are presented in PDF format, with each document spanning approximately 80 to 100 pages. Given the substantial volume of data, manual analysis proves to be exceedingly challenging. To effectively analyze their CSR practices in sports, the study employs advanced NLP techniques to systematically extract, process, and analyze textual data. The main significance of our research lies in an automated retrieval of information from documents using GPT-3.5 Turbo from OpenAI as our Large Language Model (LLM) and LangChain as our framework. The generative AI approach demonstrates the capacity to provide a diagnostic tool of how Qatari listed companies currently utilize sport for their CSR agendas. It can inform approaches to optimize the companies’ CSR strategies through sport and acts as a proof-of-concept for exploiting modern AI techniques in CSR research and several other topics as well.
AB - Corporate social responsibility (CSR) is becoming increasingly important across industries, including sport. This paper explores CSR through sport using generative AI techniques. The case study here is Qatar, which has heavily invested in hosting major sporting events and leagues aligning with its National Vision 2030. This study examines the annual reports of banks listed on the Qatar Stock Exchange for the years 2008–2022. The reports are presented in PDF format, with each document spanning approximately 80 to 100 pages. Given the substantial volume of data, manual analysis proves to be exceedingly challenging. To effectively analyze their CSR practices in sports, the study employs advanced NLP techniques to systematically extract, process, and analyze textual data. The main significance of our research lies in an automated retrieval of information from documents using GPT-3.5 Turbo from OpenAI as our Large Language Model (LLM) and LangChain as our framework. The generative AI approach demonstrates the capacity to provide a diagnostic tool of how Qatari listed companies currently utilize sport for their CSR agendas. It can inform approaches to optimize the companies’ CSR strategies through sport and acts as a proof-of-concept for exploiting modern AI techniques in CSR research and several other topics as well.
KW - Corporate social responsibility (CSR)
KW - Generative Pre-Training (GPT)
KW - Information Retrieval
KW - LangChain
KW - Large language model (LLM)
KW - Sport
UR - https://www.scopus.com/pages/publications/85200495523
U2 - 10.1007/978-3-031-64359-0_31
DO - 10.1007/978-3-031-64359-0_31
M3 - Conference contribution
AN - SCOPUS:85200495523
SN - 9783031643583
T3 - Communications in Computer and Information Science
SP - 398
EP - 415
BT - Information Management - 10th International Conference, ICIM 2024, Revised Selected Papers
A2 - Li, Shuliang
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 8 March 2024 through 10 March 2024
ER -