Model Assisted Statistics and Applications

This research explores the impact of budget deficits on inflation in Sri Lanka from 1990 to 2022, examining both long-term and short-term effects. The study uses statistical methods like unit root tests and econometric models, including the Autoregressive Distributed Lag (ARDL) model and the Error Correction Model (ECM), to assess the reliability and stability of these relationships. Inflation is the dependent variable, with budget deficits as the primary independent variable, and money supply, interest rates, and unemployment rates as secondary variables. Findings indicate an inverse relationship between budget deficits and long-term inflation, while money supply and unemployment rates positively affect inflation. In the short term, previous year’s money supply, budget deficit, and unemployment rates are negatively correlated with inflation. However, the previous year’s budget deficit and the current year’s unemployment rate significantly increase short-run inflation. Interest rates do not significantly impact inflation in either the short or long term. This study, through historical data analysis and econometric techniques, aims to clarify these impacts, enhancing understanding of Sri Lanka’s macroeconomic dynamics and aiding policymakers in sustainable economic management.

To meet the needs of the growing population, the extraction capacity has outdone the regeneration capacity of renewable sources. So, the adoption of sustainable methods to generate energy should be seriously taken into consideration. However, the energy sector is facing numerous hurdles in implementing sustainable methods of producing clean energy. Existing research has identified a few factors that hinder the execution of sustainable energy development in South Asian Low and middle income countries (LMICs), but lacks a systematic investigation and is unable to decipher any causal relationship between the factors and their importance. This paper identifies the key factors that are being faced by the energy sector in the achievement of sustainable energy using existing literature and uses a Fuzzy Decision-making trial and evaluation laboratory (DEMATEL) to quantify the cause-and-effect relationship between the challenges. The results found were classified into financial factors, Operational Factors, and technical factors that were the key factors that act as a hurdle in the accomplishment of sustainable energy.

Understanding relationships between stress, resilience, mental wellbeing, and task-performance is critical for success in today’s sustainable workplaces. Thus, we aimed to analyse and develop a management framework to deal with this criticality. Inspired by Salutogenesis theory – prioritizing positive variables over the absence of negative ones, our emphasis was on resilience and mental wellbeing for stress management and improving task performance. Data from 445 employees was collected by a survey instrument employing standardised scales. Reliability and validity of constructs were established through the measurement model, while the structural model tested the strength of the relationships. Low stress and high resilience were identified as having a strong effect on mental wellbeing, which in-turn improved task-performance. This study highlights that resilience and mental wellbeing, in addition to stress management, significantly improves task performance for sustainable workplaces.

Analytic Hierarchy Process (AHP) is a unique tool which can help in improvising the usage of machine learning in attaining organizational effectiveness. True, machine learning has emerged as one of the most important tools in enhancing organizational effectiveness through improved strategic decision-making vis-à-vis key performance indicators. It has redefined the way companies can create, and measure value added, and experiences generated for the end users of their products, services, and other offerings. Machine learning algorithms are being leveraged for making more predictive and prescriptive key performance indicators which ultimately contribute towards optimizations of business processes and overall improvement in the competitiveness of the organizations. It also helps the organizations in attaining excellence in execution of strategic decisions through almost accurate predictive insights on various management functions related to HR, marketing, finance, and operations which in turn boost stakeholder satisfaction. In this study, the authors have developed an analytic hierarchy process framework based on review of 166 peer-reviewed research papers to determine how the organizations can priorities management functions and their attributes coupled with machine learning applications for higher levels of efficiencies. Insights from this article may help the practicing managers in prioritizing use of machine learning in management functions for optimizing results and improving overall organizational effectiveness.

The paper delves into the critical significance of incorporating Artificial Intelligence (AI) into Human Resource (HR) functions. It extensively explores the multifaceted challenges encountered by organizations during AI implementation in HR, with a particular focus on the vital aspect of employee understanding and acceptance. To elucidate these challenges faced in adopting AI technologies, this study undertakes a comprehensive exploration of the obstacles. This paper adopts a two-phased methodology to explore the critical significance of integrating AI into HR functions and the multifaceted challenges organizations encounter during this implementation. The first phase entails an extensive literature review, delving into the myriad challenges organizations face as they navigate the adoption of AI in HR. In the second phase, industry experts provide ratings and rankings to help us grasp the critical challenges based on industry priorities. The paper acknowledges the evolving nature of jobs and the consequential increase in employment opportunities as technology reshapes the employment landscape.

This paper critically reviews the role of dairy farming policies and interventions required by the different government agencies and their associations. This article explains the divergence in formulation and implementation of dairy development policies and schemes (DDPS) in the Indian dairy sector. This paper is based on qualitative research representing a diverse range of epistemological, theoretical, and disciplinary perspectives. Review of Literature is to access databases from peer-reviewed journals and published survey reports of statutory and non-statutory bodies in the dairy farming sector. Dairy farmers/dairy entrepreneurs/milk producers are the respondents and information gathered through detailed telephonic interviews and audio recordings due to COVID-19 outbreak. Sample size ranges from 5–10 responses from stakeholders involved in dairy farming in Milk Supply Chain (MSC). Thematic Analysis (TA) is used for identifying, analyzing and interpreting patterns of text and its meaning (‘themes’) using NVIVO 10 software.

Benefit of this research are also academicians, researchers and agriculture scientists for further research in the field of dairy farming and MSC.

This paper develops a fair understanding about risks and challenges among small-medium size farmers in effective implementation of dairy development programmes and schemes.

Dairy producers,processors and enterprises are able to formulate better marketing strategies for future market expansion of desi cow milk.

Grounded Theory (GT), an iterative process based on the perception and concerns of respondents gathered from initial data is a novelty of this research. Study is only limited to qualitative approaches with small sample sizes. There is a need to investigate other statistical methods to validate the research.