This study informs the academic and policy debate on the policy effectiveness of exchange rate interventions on exchange rate levels and volatility. Using a constructed data set comprising daily data on exchange rates, monetary policy fundamentals, exchange rate intervention dates and magnitudes of those interventions as well as financial news speculation of such interventions, we empirically estimate the policy effectiveness of Bank of Japan interventions in the exchange rate over the 12-year period between 2010 and 2022. This allows us to investigate the policy effectiveness of a variety of exchange rate interventions, or news of exchange rate interventions, across different time-horizons. We find that policy interventions in the yen exchange rate are more effective over short-horizons than long-horizons, more effective when the policy objective is a competitive devaluation of the yen rather than a revaluation, and more effective at influencing the level of the yen against major world currencies other than the US dollar. In fact, for the yen-dollar rate, we find that policy interventions may have the unintended consequences of weakening the yen (when the policy intention is to strengthen it) and increasing volatility in the yen-dollar exchange rate.

This paper tries to understand economic, social and legal implications of the introduction and usage of MediSearch (AI search engine) in the Indian healthcare context. Discussing the economic ramifications, the paper highlights the potential for cost savings, the influence on healthcare accessibility, and the shifts in traditional medical paradigms. On the social side, the study explains ability of AI based platforms to bridge healthcare disparities, with a potential for enhancing general health literacy among the general population. From a legal standpoint, study highlights the concerns related to data privacy, regulatory issues, and possible malpractice implications. With the integration of these perspectives, the study also explains opportunities, challenges and future of MediSearch from the Indian health perspective.

Technology development in the agricultural sector is important in the development of Thailand’s economy. The purpose of this research was to study the approach of guidelines for future agricultural technology development to increase productivity in the Agricultural sector in order to develop a structural equation model. The research applied mixed-methodology. Qualitative research by in depth interview from 9 experts and focus group with 11 successful businesspersons for approve this model. The quantitative data gather from firm, in the 500 of agricultural sector by using questionnaire, using statistical tests of descriptive analysis, inferential analysis, and multivariate analysis. The research found guidelines for future agricultural technology development to increase productivity in the Agricultural sector composed of 4 latent. The most important item of each latent were as following: 1) Agrobiology Technology (= 4.41), in important item as choose seeds that for disease resistance and tolerate the environment to suit the cultivation area, 2) Environmental Assessment (= 4.37),, in important item as survey of cultivated areas according to topography with geographic information system, 3) Agricultural Innovation (= 4.30), in important item as technology reduces operational procedures, reduce the workforce and can reduce operating costs, and 4) Modern Management Systems (= 4.13), in important item as grouping and manage as a cooperative to mega farms. In addition, the hypothesis test found that the difference in manufacturing firm sizes. Medium and Small size and large size revealed overall aspects that were significantly different at the level of 0.05. The analysis of the developed structural equation model found that there was in accordance and fit with the empirical data and passed the evaluation criteria. Its Chi-square probability level, relative Chi-square, the goodness of fit index, and root mean square error of approximation were 0.062, 1.165, 0.961, and 0.018, respectively.

Employee retention promotes positivity in an organization and improves employers’ brand value. As the human resource department operates with the objective of improving employees’ contribution towards the organization, meaningful work is an important topic in the core areas of human resource development (HRD), such as employee involvement, motivation, and personal development. Not only salary, benefits, working environment, and status but also the factors that determine whether you enjoy going to work every day are whether you believe that your work makes a meaningful contribution. In HRD, meaningful work comes to the forefront through a connection with a high level of commitment. Thus, this study aims to establish the relationship between meaningful and purposeful jobs affecting employee retention and the mediating factors of person organization fit (POF) and person job fit (PJF). A cross-sectional study involving a survey methodology was used to collect data from 150 white-collar employees working in the IT, banking, textile, and multinational companies in Bangladesh. The results indicate that job meaningfulness has a positive relationship with employee retention (p-value = 0.031) and both the mediating factors of PJF (p-value = 0.040) and POF (p-value = 0.028). The results also indicate that while POF positively influences employee retention (p-value = 0.019), PJF has no significant influence on employee retention (p-value = 0.164). Thus, promoting employee job meaningfulness and purpose in the workplace may represent an opportunity for organizations to improve employee engagement and retention.