Abstract
The use of artificial intelligence (AI) is quite usual in our life by now and increasing day by day (Latinovic, & Chatterjee, 2022). No doubt it has offered a large number of benefits but has also made a significant progress on raising more questions to be solved (Gevaert et al., 2021). But, to answer the related questions and move further in the right direction, it is important to understand some basic aspects related to AI and GPT. I am pleased to simplify the inference for those who would like to understand this area for further usage. One of the abilities of AI is natural language processing (NLP) and it has many use cases in management. It is a subfield of AI that deals with the interaction between computers and human language. It involves using computational techniques to enable computers to understand, analyse and generate human language. NLP is used in a wide range of applications such as sentiment analysis in investment, chatbots in customer relationship management, decision support system in retail and operations, legal, architecture, transportation and many more (Chowdhary, 2020). It involves various techniques such as statistical modelling, machine learning, and deep learning to build algorithms that can process, analyse, and generate natural language. Large language models (LLMs) are a type of NLP technology that has been gaining a lot of attention in recent years. LLMs are AI models that have been trained on massive amounts of text data, enabling them to generate human-like language and perform a wide range of language processing tasks (Schwenk, 2007). LLMs are usually trained on massive datasets, and learn patterns and structures of language without being explicitly programmed. This training process allows LLMs to generate coherent and contextually appropriate text in response to prompts. Some examples of LLM are ChatGPT by Open AI and Bard by Google.
After the hype cycle of metaverse, recently, ChatGPT has captured attention in multiple forums, townhalls and debate groups. I see this as a progression from non-fungible tokens (NFTs), which is an extension of blockchain. The reason of ChatGPT hype may be that it is a tool from Open AI which is being offered in freemium model. However, my finance colleagues may relate it to the stock market prices recently experienced by Goggle and Microsoft. From a user’s perspective, you can access it using web application. As the name suggest ChatGPT is a Generalized Pre-trained Transformer (GPT), and it is pre-trained on the existing data set. The tool is trained on a dataset which is at least two years old. Hence, latency is one of the shortcoming of the GPTs. Second, a GPT is as good as the data it is trained upon (Shen et al., 2023). We should be cautious at the time of using GPT and even ChatGPT also suggests doing so. There have been LLM tools in use since last fifteen years but the adoption of ChatGPT has been phenomenal. May be it can be interesting for my marketing area colleagues to think upon how users started comparing or combining cloud, mobile and internet systems. ChatGPT has the potential to disrupt many industries such as services industries, healthcare, finance, education and Information technology. Use of chatbot based automation is not a distant dream now. More over ChatGPT has waged a new war in the area of information search.
Role of AI and GPT in Academic Practices
There is a big debate in education industry about use of ChatGPT in education. One thing is sure, we cannot stop students from using ChatGPT and we should not be doing so also. ChatGPT will change the pedagogy in management education. For example, formative assessments (including assignment reports) to evaluate how someone is learning from the material throughout a course is not a promising idea now (Kung et al., 2023). It is time to define guidelines to use ChatGPT in education. Possible use of ChatGPT in planning of course material is: (a) Generating lesson plans; (b) Summarizing content; and (c) Creating assessments. But there are some serious drawbacks for using ChatGPT such as: (a) Limited understanding of the context; (b) Lack of creativity; (c) Dependence on the quality of the data; and (d) Dependence on language proficiency. In a nutshell in present capability ChatGPT has potential to assist a teacher as well as student but should be used with caution.
In a recent tweet, one of the researcher claims that after integration of ChatGPT in Bing, it has the capability to perform literature review and identify the research gap also. The role of an academician will witness a sea change in the coming year. In future, literature review may be done by a chatbot and researcher can focus more on evidence synthesis based on the identified literature. One of the jobs of the researcher will be to cross verify the evidence provided by ChatGPT. Another possible role of researcher is to present the evidence based on some framework so that knowledge is presentable. A researcher should also be aware about bias due to unavailability of data at time of training of these GPTs. For example, ChatGPT is trained for literature available before 2021 and written in English. In present form ChatGPT does not give us clue about the source of information it is presenting, which creates a doubt in the mind of the researcher. In traditional approach we can make an exclusion criteria that we will be using literature from reputed peer reviewed journals only. Scholars can use ChatGPT as a starting point for exploration of idea but they need to visit reliable databases such as SCOPUS, Web of Science, PubMed, and others, to do the actual research. ChatGPT can help you in rephrasing and sentence building which advance tools such as Grammarly already do. But one thing is sure using ChatGPT judiciously will reduce the time on manuscript writing.
Some Research Area Related to use of ChatGPT in Management are as Follows
AI tools and socialism
Capacity of AI tools in predicting behaviors
Potential Chatbot based automation.
Ethics and AI in management
Human–AI collaboration in management
AI and supply chain management
AI based information and bias.
Use of AI in training and development
User experiences on combined setting of cloud, internet and mobile
Knowledge Structure and Landscape in GPT world
Challenges to companies not capitalizing on AI float
Influence of AI hype on Stock Prices
To summarize, ChatGPT is tool which presents opportunity as well as threat in many areas related to management. There is a hype around it and perhaps we are eager to take a massive leap in future. Big tech companies such as Google and Microsoft are in hurry because there reputation is at stake. The situation has put people using the information from these tools at risk. No doubt ChatGPT is future, but we should be cautious till these products get matured.
With this, I am pleased to introduce volume 12.1 of FIIB Business Review. The contributions included in this issue are summarized as below:
Perspective
Predicting human behaviour correctly has been an ongoing quest for researchers. In the article titled ‘Back from the Future: Mediation and Prediction of Events Uncertainty through Event-Driven Models (EDMs) in Human Behavioural Research’, Samuel Ogbeibu and James Gaskin introduce prediction to estimate EDMs and recommend that researchers employ the segmentation mediation approach when estimating EDMs, thus further adding to theory development in event based prediction studies.
Case
In the sole contribution in this section, ‘Gyan Fresh: Digital Transformation of Dairy Business with Resilience and Technology Innovation’, Anubhav Mishra and Anuja Shukla highlighted on how companies can create disruptions and bring in innovation through the case of Gyan Diary which not only expanded their business, but also retained consumers and streamlined the distribution channels in face of challenges.
Review
In the review section, the first contribution is literature review titled ‘A Systematic Literature Review on Personal Financial Well-Being: The Link to Key Sustainable Development Goals 2030’. In this article, Ifra Bashir and Ishtiaq Hussain Qureshi provide a comprehensive overview of the current state of academic research on financial well-being, an important UN SDG Goal to tackle poverty and bring inclusion. The article contributes to theoretical development of research into financial well-being and can be used by managers to manage the potential predictors and outcomes of financial well-being effectively. In the next contribution titled, ‘Understanding the Microfinance’s Capital Structure: Does It Alter Its Business Model?’ by Nyamugira Biringanine Alexis, the author examine the implementation of standard finance theory to explain the change in the microfinance’s capital structure using systematic literature review. The findings indicate that the pecking order theory is applicable in the microfinance industry with respect to some specification while the industry’s business model evolves aligning with the life-cycle theory. Lastly, the impact of capital structure within the sector affirms the predictions of the profit-incentive theory.
Research
In the first contribution of the section, ‘Impact of Microfinance on Economic, Social, Political and Psychological Empowerment: Evidence from Women’s Self-help Groups in Kashmir Valley, India’, Shagufta Tariq Khan, Mohd Abass Bhat and Mohi-Ud-Din Sangmi examine the impact of microfinance on economic, social, political and psychological aspects of women empowerment using quasi-experimental design. The findings support that microfinance has strengthened women’s empowerment when it comes to the economic, social, political and psychological aspects.
In the next contribution, ‘Impact of Merger and Acquisition on Financial Performance: Evidence from Construction and Real Estate Industry of India’, Isha Gupta, T. V. Raman and Naliniprava Tripathy examine the impact of mergers and acquisitions (M&A) on the financial performance of the construction and real estate industry. The findings reveal that the Indian construction and real estate companies financial performance has improved overall for the acquiring firms during the post-M&A period implying that that M&A will improve synergy during the post-M&A period because of the consolidation of two firms’ resources. In the third contribution of this section, ‘Factors Influencing the Performance of Commercial Banks: A Dynamic Panel Study on India’, Sreemanta Sarkar and Debdas Rakshit investigates the determinants of commercial banks’ performance in India with special reference to the macroeconomic factors. The study provides useful insights for the development of banking sector in India. The positive influence of the growth of national income on banking sector’s performance indicates that adequate planning is needed to channelize the growth potential towards expanding the lending and deposit activities of the banking sector.
In the last offering in this section, ‘Do Board Quality and Promoters’ Holdings Affect Firm Performance? Evidence from Small and Medium-sized Enterprises’, Shweta Mehrotra, Birajit Mohanty and Tanushree Sharma, examined quality of SME board and its effects on the performance of SMEs in India using regression model on 68 BSE-listed SMEs. The findings indicate that SMEs with a highly concentrated ownership structure demonstrate better performance. Also, levered firms were found to perform substantially better. The study revealed that appointing independent directors and female directors merely to comply with norms but not in true spirit, can reduce a firm’s performance. The findings has implications for the escalation of governance standards through bringing more clarity to and streamline policy and disclosure in SMEs.
I am highly thankful to all the contributors, reviewers, Editors and Editorial Board Members and my teammates for their continued support and contribution. We hope you found the offerings in this issue useful. Please share your feedback to