Employee performance in the public sector is a crucial factor in determining the effectiveness of government services. However, low levels of motivation and non-participatory bureaucratic communication remain obstacles to improving employee performance. This study aims to analyze the influence of leadership style and communication style on employee performance, considering work motivation as a mediating variable. This study uses a quantitative approach with a survey method on 204 employees. Data analysis was conducted using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique using the SmartPLS 3 application. The results of the analysis indicate that leadership style and communication style have a positive and significant effect on employee motivation and performance, both directly and indirectly through motivation as a mediating variable. Motivation is proven to be a key factor that bridges the influence of these two variables on performance. This study reinforces the importance of participatory leadership style and open communication in building motivation and improving the performance of public sector employees. The practical implication is the importance of developing leadership capacity and a more inclusive communication system in government organizations.
Bangladesh has experienced continuous progress in the digital landscape, with a significant portion of the population now having access to online services. This rapid expansion has made online shopping more accessible and contributed to its growing popularity. This study aims to examine the consumer perceptions toward online shopping and identify the factors influencing consumer satisfaction in Sylhet city. Data were collected using purposive sampling from 150 respondents through a structured questionnaire. Descriptive statistics were used for demonstrating demographic characteristics of the respondents and to know the consumer perceptions on online shopping. Reliability analysis, factor analysis and multiple regression were employed to identify the significant factors that affect consumer satisfaction. The findings showed that most of the respondents became aware of online shopping through social media, purchased products from online occasionally and mainly used Facebook rather than another social platform. Factor analysis technique suggested that among the five identified factors product & service value is the most important factor affecting overall satisfaction of consumers. Furthermore, multiple regression analysis revealed that all the identified factors had a statistically significant influence on consumer satisfaction. This research finding gives valuable insight to online retailers on what aspect they should focus on to enhance consumer satisfaction.
This research introduces a dynamic, real-time and hybrid intelligent fuzzy Multi-Criteria Decision-Making (MCDM) framework for supplier evaluation in the uncertain logistics con-text. The proposed framework is based on fuzzy logic, dynamic entropy weighting, temporal Basic Unit-Interval Monotonic (BUM) aggregation, Dynamic Fuzzy TOPSIS and Adaptive Neuro-Fuzzy Inference System (ANFIS) optimization to enhance the accuracy, robustness and real-time responsiveness of the decision-making process. The framework dynamically changes the importance of each criterion as a result of the logistics conditions; and assigns greater weight to recent operational information through temporal weighting mechanisms. The fuzzy inference modelling is very useful to place the uncertainty and nonlinear decision relationship in control, and the optimization of ANFIS has improved its adaptive learning capability and prediction reliability. The proposed model was implemented and validated in MATLAB and Simulink environments with a dynamic logistics dataset, including the sup-plier evaluation criteria like shipping cost, lead time, reliability, cargo status, fulfillment ef-ficiency, and route risk. The experimental results yielded 97.0% prediction accuracy, a cor-relation coefficient of 0.933 and low prediction error, which indicated good learning capabil-ity and computational stability. In addition, the real-time Simulink implementation proved adaptive decision behavior in changing continuously logistics conditions. The results ob-tained show that the proposed hybrid intelligent framework is an efficient, scalable and reli-able solution for the applications of intelligent supplier selection and real-time logistics deci-sion making.
This study investigates the total factor productivity growth (TFPG) of India's organized manufacturing sector over 43 years (1980–2023), comparing pre- and post-liberalization periods. Using a gross output framework with three inputs (material, labour, capital) and a Translog index, the findings reveal negative TFPG in most disaggregated industries during the pre-reform era, a trend that largely persisted after liberalization. However, the sharpness of this negativity declined noticeably, and a positive shift emerged in the post-pandemic period (2016–17 to 2022–23), except in the chemical sector. Overall, aggregate manufacturing TFP showed pessimistic trends post-reforms. Despite this, Indian manufacturing remains vital for national economic growth, particularly outperforming selected countries during the COVID-19 pandemic. The results underscore the need for productivity-focused trade and industrial policies to help firms build competitive advantages and access export markets. This research offers novel insights into the long-standing debate on whether trade liberalization has significantly enhanced industrial performance in India's shifting policy landscape.
