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Disruptive Factors and Customer Satisfaction at Chain Stores in Karachi, Pakistan
Aamir RASHID(Aamir RASHID ),Rizwana RASHEED(Rizwana RASHEED ),Noor Aina AMIRAH(Noor Aina AMIRAH ),Asyraf AFTHANORHAN(Asyraf AFTHANORHAN ) 한국유통과학회 2022 유통과학연구 Vol.20 No.10
Purpose: This study aims to determine the relationship between disruptive factors and customer satisfaction at chain stores. Survey-based questionnaires were designed in the distribution technique to measure the findings in this study. Research design, data, and methodology: In terms of the sampling technique, the researchers adopted the simple random sampling technique with a total of 200 sample sizes. For the statistical method, the researchers applied multiple linear regression analysis to determine the potential factors that affect customer satisfaction at chain stores. The analysis of this study measured how product quality, pricing policies of chain stores, design and layout, responsiveness, and location of chain stores impart their roles in customer satisfaction. Results: This study found a significant relationship between the product quality and location of chain stores on customer satisfaction. In addition, the responsiveness, pricing policy, and physical design of chain stores impart an insignificant role in customer satisfaction. However, it is proven that the location of chain stores and product quality positively impact customer satisfaction. Conclusions: The study is geographically limited to the region of Karachi, Pakistan. Therefore, the findings may differ in the context of study implications in the other areas.
Auto-Encoder Variants for Solving Handwritten Digits Classification Problem
Muhammad Aamir,Nazri Mohd Nawi,Hairulnizam Bin Mahdin,Rashid Naseem,Muhammad Zulqarnain 한국지능시스템학회 2020 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.20 No.1
Auto-encoders (AEs) have been proposed for solving many problems in the domain of machine learning and deep learning since the last few decades. Due to their satisfactory performance, their multiple variations have also recently appeared. First, we introduce the conventional AE model and its different variant for learning abstract features from data by using a contrastive divergence algorithm. Second, we present the major differences among the following three popular AE variants: sparse AE (SAE), denoising AE (DAE), and contractive AE (CAE). Third, the main contribution of this study is performing the comparative study of the aforementioned three AE variants on the basis of their mathematical modeling and experiments. All the variants of the standard AE are evaluated on the basis of the MNIST benchmark handwritten digit dataset for classification problem. The observed output reveals the benefit of using the AE model and its variants. From the experiments, it is concluded that CAE achieved better classification accuracy than those of SAE and DAE.
The Effect of Demographic Characteristics on Job Performance: An Empirical Study from Pakistan
Sherbaz KHAN,Rizwana RASHEED,Aamir RASHID,Qamar ABBAS,Farhan MAHBOOB 한국유통과학회 2022 The Journal of Asian Finance, Economics and Busine Vol.9 No.2
This holistic research focused on the interactive relationship of different factors with a unique relationship with the dependent variable. The first research objective of the study was to identify the most significant factor that has an impact on Job performance while being mediated. The second objective was to see the moderating effect of gender on the relationship between transformation leadership and innovation on job performance. This research followed a purely quantitative research paradigm with a structured questionnaire to quantify the information collected from 96 respondents for the empirical analysis. For testing the research hypotheses, IBM SPSS version 24 and SmartPLS version 3.2.8 softwares were used to run the structural equation modeling to establish the causal relationship between the study variables. Most of the variables were found with a significant impact on job performance. Further, the hypotheses H3, H6, and H10 were rejected as these contributed insignificant towards the research model. This research was limited to specific educational institutions and businesses, and the timeframe was restrictive. The findings of this research can benefit policymakers and the operational side of various industries. Future research may consider the difference in gender in predicting employee engagement through leadership and innovation.