Saptarshi Chakma, Gourab Chakma, Neingmraching Chowdhury Nani
Asian Journal of Economics, Business and Accounting, 12(4), 2019
Saptarshi Chakma
Asian Finance & Banking Review
Saptarshi Chakma
Abstract
Nowadays, understanding and predicting revenue trends is highly competitive,
in the food and beverage industry. It can be difficult to determine which aspects
of everyday operations have the most impact on income, especially for coffee
shops. Transactional and behavioral data are readily available, but numerous
small businesses lack the data-driven models necessary to convert these insights
into predictions that can be put into action. By using linear regression
techniques to forecast daily income based on important business parameters,
this study seeks to close the gaps. In order to investigate feature distributions
and correlations, exploratory data analysis performed including statistical
summaries, box plots, and scatter plots with regression lines. A correlation
study determines the most important parameters and are “Number of Customers
Per Day”, “Average Order Value”, and “Marketing Spend Per Day”. Secondary
elements like location foot traffic, the number of employees and operating
hours come after this. A linear regression model is trained using these
characteristics, yielding an R2 score of 0.89, a Mean Absolute Error (MAE) of
244.13. The model’s efficacy is validated by comparing actual and projected
revenue. This method provides a useful foundation for forecasting revenues
and making well-informed decisions in small-scale retail businesses, such as
coffee shops.
Keywords
Revenue, Data Analysis, Linear Regression, Business, Coffee House
Saptarshi Chakma, Gourab Chakma and Rishita Chakma
Abstract: Nowadays, recommendation systems are crucial in e-commerce. They deliver timely and relevant product suggestions to users. A well-crafted recommendation system can increase sales and create value for both buyers and sellers. In this research, we examine a large dataset containing sales data, product information, and customer contacts to gather statistical insights. We then introduce a collaborative filtering approach enhanced with data based on recency. Exploratory data analysis (EDA) techniques help identify relationships among variables, using key statistical tools and measures. To better understand consumer behavior, we generate grouped statistics such as purchasing trends by product category and customer age. The results of this research support the development of a collaborative filtering recommendation engine that incorporates recency weighting to improve product suggestions for online retail platforms.
Saptarshi Chakma*, Gourab Chakma
Abstract
Inclusive leadership involves actively fostering an environment where every employee feels valued and integral to the organization’s targets. This study aims to illustrate the positive impact of inclusive leadership practices on generating enhanced organizational performance, fostering a positive organizational culture, and improving employee engagement and job satisfaction. By quantitatively examining the impact of inclusive leadership, the study provides actionable insights for organizations pursuing sustainable growth. A survey was conducted using a five-point Likert scale to evaluate leadership inclusiveness and its related outcomes. Using data from 100 employees, a Structural Equation Model (SEM) was developed and analyzed with SmartPLS 4 to examine the relationships between inclusive leadership and organizational outcomes. The study shows that inclusive leadership significantly boosts organizational performance by positively impacting key areas like organizational culture, employee engagement, and job satisfaction. The findings highlight that effective inclusive leadership not only improves individual employee experiences but also contributes to overall organizational success. Based on these findings, organizations are advised to invest in inclusive leadership development programs and implement practices that ensure fair opportunities for all employees.
Keywords
Inclusive Leadership (IL), Organizational Culture (OC), Employee Engagement (EE), Job Satisfaction (JS), Organizational Performance (OP), PLS-SEM
