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IRAN: Outreach to the West will face challenges
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DOI: 10.1108/OXAN-ES291129
ISSN: 2633-304X
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Aparna Sameer Dixit and Sunita Jatav
The principal aim of this research is to acquire a deeper understanding of the opinion held by the training and development (T&D) professionals, regarding the use of artificial…
Abstract
Purpose
The principal aim of this research is to acquire a deeper understanding of the opinion held by the training and development (T&D) professionals, regarding the use of artificial intelligence (AI) technology in the area of T&D. Particularly in response to the evolving needs of learners, the research aims to ascertain T&D professionals' perspective on the efficiency of AI in fostering T&D, while understanding the constraints and limitations associated with this technology.
Design/methodology/approach
The study is based on qualitative data. With the help of semi-structured interviews, qualitative data has been collected from 21 T&D professionals. Experts working with multinational corporations (MNCs) are selected as a study sample using a convenient sampling technique. Qualitative data were analysed using thematic analysis. Conclusions were drawn based on the results of thematic analysis.
Findings
The findings of the study have revealed a notable and rapid evolution in the requirements of learners, particularly during and post-COVID-19 period. AI-based technology has emerged as a significant contributor, offering learners distinct personalised experiences and enhanced convenience. However, the implementation of AI in training remains in its early stages and has not reached widespread adoption. The findings of the study also highlighted various challenges and limitations, while using AI-based technology for training. It has been found that AI complements rather than replaces the role of a physical trainer.
Originality/value
The originality of this study lies in the application of AI-based training for professional learners, from the perspective of the T&D practitioners working with MNCs in Maharashtra, India. Numerous studies that have recently been published, emphasise the areas in which AI technology can transform the T&D industry. Yet, there are currently very less studies that have attempted to understand the evolving needs of learners and support of AI-based training for the same, from the perspective of the T&D professionals working in Maharashtra, India.
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Kwaku Kyei Gyamerah and Francis Kamewor Tetteh
This paper aims to investigate the role of institutional quality in the relationship between mobile money and financial inclusion among Sub-Saharan Africa (SSA) from 2002 to 2022.
Abstract
Purpose
This paper aims to investigate the role of institutional quality in the relationship between mobile money and financial inclusion among Sub-Saharan Africa (SSA) from 2002 to 2022.
Design/methodology/approach
The paper uses annual data from SSA on a bundle of four financial inclusion variables, six institutional quality indicators (i.e. rule of law, government effectiveness, control of corruption, voice and accountability, regulatory quality and political stability) and total volume of mobile money transaction in a year. The two-stage least squares regression was used to validate the hypotheses. Also, the random effects model was also used to account for potential unobserved heterogeneity across countries in SSA.
Findings
The empirical results reveal that institutional quality and mobile money have direct impact on financial inclusion. Also, institutional quality plays a positive and significant contingency role in the relationship between mobile money and financial inclusion.
Originality/value
The study contributes to financial inclusion theory by providing multi-country empirical evidence to validate the theory in explaining mobile money’s role in expanding financial access. It also highlights the key insight from financial inclusion theory regarding the need for strong governance institutions for technology-enabled inclusion. By examining interactions between mobile money, institutions and financial inclusion across 15 African SSA economies, the study allows for more generalizable conclusions about contextual dependencies.
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