Collecting data using an appropriate sampling technique is a challenging task for a researcher to do. The researchers will be unable to collect data from all possible situations, which will preclude them from answering the study’s research questions in their current form. In light of the enormous number and variety of sampling techniques/methods available, the researcher must be knowledgeable about the differences to select the most appropriate sampling technique/method for the specific study under consideration. In this context, this study also looks into the basic concepts in probability sampling, kinds of probability sampling techniques with their advantages and disadvantages. Social science researchers will benefit from this study since it will assist them in choosing the most suitable probability sampling technique(s) for completing their research smoothly and successfully.

In the academic world, academicians, researchers, and students have already employed Large Language Models (LLMs) such as ChatGPT to complete their various academic and non-academic tasks, including essay writing, different formal and informal speech writing, summarising literature, and generating ideas. However, yet, it is a controversial issue to use ChatGPT in academic research. Recently, its impact on academic research and publication has been scrutinized. The fundamental objective of this study is to highlight the application of ChatGPT in academic research by demonstrating a practical example with some recommendations. Data for this study was gathered using published articles, websites, blogs, and visual and numerical artefacts. We have analyzed, synthesized, and described our gathered data using an "introductory literature review." The findings revealed that for the initial idea generation for academic scientific research, ChatGPT could be an effective tool. However, in the case of literature synthesis, citations, problem statements, research gaps, and data analysis, the researchers might encounter some challenges. Therefore, in these cases, researchers must be cautious about using ChatGPT in academic research. Considering the potential applications and consequences of ChatGPT, it is a must for the academic and scientific community to establish the necessary guidelines for the appropriate use of LLMs, especially ChatGPT, in research and publishing.

Purpose: Family entrepreneurship benefits women because of their economic, family, and social needs. But, as rural women, it is not much easy for them to maintain their family entrepreneurship successfully. Thus, this paper aims to explore the main challenges faced by rural women entrepreneurs in Bangladesh to survive their family entrepreneurship.Design/methodology/approach: This study is qualitative in nature, based on narrative inquiry. The purposive sampling technique was used as a part of a non-probability sampling method to collect the data from rural women entrepreneurs from three districts (Khulna, Shatkhira, and Sylhet) in Bangladesh engaged in family entrepreneurship. No new information was found after collecting the data from seven (07) respondents; thus, they were chosen as the final sample size.Findings: The findings show that rural women entrepreneurs faced primarily social and cultural, financial, and skill-related challenges, though they face other challenges to survive their family entrepreneurship. The attitude and perception of society toward women and their roles are at the root of social and cultural barriers. Researchers also found that financial challenges have a colossal impact on rural women and the other problem.Practical implications: Although entrepreneurial activities are essential for socio-economic development in these developing countries, this research adds to the existing information by highlighting the main challenges that rural women face when they want to be business owners and entrepreneurs.Originality/value: Research on rural women entrepreneurship in Bangladesh is limited and new. This study can provide an overview of the challenges faced by the rural women entrepreneurs and provide them with a blueprint for the development of women entrepreneurs in developing countries.

Rapid technological advances give rise to the development of New Technology-Based Firms (NTBFs) which are focused on technology-based business models. One of the concerns is the changing pace of technology adoption and its impact on shortening the life cycle of goods and services based on technology. Technology tends to create more integration among communities, businesses, goods, and services. This integration can be seen in the substantial increase of firms, especially NTBFs, striving to improve international relations. In this regard, addressing the sustainability dimensions in light of technological change is of universal interest. Some studies highlight the role of technology in terms of its environmental impacts, whereas others describe technology as the main driving force of businesses towards innovation, which can lead to sustainability. This paper aims to examine international NTBFs to determine the factors, key drivers, and uncertainties impacting sustainability-oriented innovations concerning their encounter with technology and ultimately plan future scenarios for international NTBFs based on sustainability-oriented innovations. The description of the scenarios shows the importance of innovation development in digital technologies and the expansion of international relations, which is realized by using digital platforms. In addition to accelerating knowledge and innovation development to gain more key partners, we would observe target market expansion, cost reduction, and sustainability growth for international NTBFs. Identifying drivers and uncertainties and the future-oriented approach of this research provide insight to International NTBFs owners into realizing sustainability-oriented innovation by increasing their awareness of the opportunities and interventions to build capacity at the conceptualization and project management levels.

Recent improvements in Natural Language Processing (NLP) have led to the creation of powerful language models like Chat Generative Pre-training Transformer (ChatGPT), Google’s BARD, Ernie which has shown to be very good at many different language tasks. But as language tasks get more complicated, having even more advanced NLP tool is essential nowadays. In this study, researchers look at how the latest versions of the GPT language model(GPT-4 & 5) can help with these advancements. The research method for this paper is based on a narrative analysis of the literature, which makes use of secondary data gathered from previously published studies including articles, websites, blogs, and visual and numerical facts etc. Findings of this study revealed that GPT-4 improves the model's training data, the speed with which it can be computed, the flawless answers that it provides with, and its overall performance. This study also shows that GPT-4 does much better than GPT-3.5 at translating languages, answering questions, and figuring out how people feel about things. The study provides a solid basis for building even more advanced NLP tools and programmes like GPT-5. The study will help the AI & LLM researchers, NLP developers and academicians in exploring more into this particular field of study. As this is the first kind of research comparing two NLP tools, therefore researchers suggested going for a quantitative research in the near future to validate the findings of this research.

Recent improvements in Natural Language Processing (NLP) have led to the creation of powerful language models like Chat Generative Pre-training Transformer (ChatGPT), Google’s BARD, Ernie which has shown to be very good at many different language tasks. But as language tasks get more complicated, having even more advanced NLP tool is essential nowadays. In this study, researchers look at how the latest versions of the GPT language model(GPT-4 & 5) can help with these advancements. The research method for this paper is based on a narrative analysis of the literature, which makes use of secondary data gathered from previously published studies including articles, websites, blogs, and visual and numerical facts etc. Findings of this study revealed that GPT-4 improves the model's training data, the speed with which it can be computed, the flawless answers that it provides with, and its overall performance. This study also shows that GPT-4 does much better than GPT-3.5 at translating languages, answering questions, and figuring out how people feel about things. The study provides a solid basis for building even more advanced NLP tools and programmes like GPT-5. The study will help the AI & LLM researchers, NLP developers and academicians in exploring more into this particular field of study. As this is the first kind of research comparing two NLP tools, therefore researchers suggested going for a quantitative research in the near future to validate the findings of this research.