Enhancing Motivation and Work Performance of the Salespeople: The Impact of Supervisors’ Behavior

This study examines the importance of authoritarian and positive achievement motivation behavior of the supervisors in enhancing salespeople’s motivation and work performance. Survey data were collected from all 105 sales employees in two retail organizations. Supervisors’ ratings were collected on all 105 of the salespersons. All hypotheses were tested using regression analysis and Pearson correlations controlling for background variables of gender, marital status, and ethnicity. The findings indicated that to the extent that supervisors engaged in positive motivational behaviors, salespersons’ intrinsic motivations were increased, which, in turn, increased their performance. Implications and a number of recommendations are discussed

Factors Affecting Access to Finance of Small and Medium Enterprises (SMEs) of Bangladesh

This study addresses the issues that impede SMEs of Bangladesh in obtaining finance from the financial institutions. To accomplish our goals we collected data from a sample of 86 SMEs to investigate the problems and suggest policy recommendations. We collected data by directly interviewing the respondents with the help of a questionnaire for self-guidance. We also used secondary data for this purpose. Findings revealed that the size and age of the firms, education and skills of the owners, and unfavorable credit terms such as high interest rates, lack of collateral security, corruption by bank officials etc. are some of the biggest hurdles that SMEs in Bangladesh face in getting loans from financial institutions. The limitation is that the study has included one city in its sample population. A nationwide study where there are SMEs in large numbers could lead to better results for this kind of study. The implication is that small businesses, as opposed to the larger ones, face funding obstacles and are discriminated by the financial institutions in granting loans. Built on current literatures and research on SMEs, specially in regard to their ability to access finance, this study captures the information relating to problems identified by SMEs in having access to finance and suggesting the ways and means needed for strengthening the SMEs access to finance for their expansion and growth

Scaling a convolutional neural network based Flower counting application in a distributed GPU cluster

Taking advantage of modern data acquisition techniques, the researchers of P2IRC located at the University of Saskatchewan developed an application to monitor the status of the flower growth during different phases of the blooming period and the yield prediction of canola crops. Though the application could predict the near accurate number of flowers in a few scenarios, its inability to function under challenging situations such as misinterpreting sun reflection or dust along the roadside as flowers have motivated the researchers to find an alternative approach of counting flowers. In addition to being a more accurate version, another goal is for the new application to be faster to infer the number of flowers and scalable in distributed environments. Putting these goals in mind, in this thesis, a Convolutional neural network (CNN) based flower counting application is developed and evaluated taking inspiration from two other previous works where CNN was used for counting heads in dense crowds and predicting the number of bacterial cells from medical imagery. In addition to that, the application addresses the performance and the accuracy goals previously mentioned. Two challenges of using the neural network are (a) the training needs a large volume of data to converge to a low error and (b) the training is computationally expensive and it takes longer time to complete. To address the first challenge, experiments were run with both "ground truth" estimated using a modified version of the previous flower counter, and ground truth from manual annotation. To address the problem of long training time, two distributed versions of the proposed application were created based on two different distributed architectures called Parameter Server and Ring-AllReduce. Moreover, a detailed explanation of the proposed CNN's architecture along with its memory footprints and GPU utilization is also organized as an in-depth case study to help trace the model's memory consumption during training. From different sets of experiments, the new flower counter application is observed more accurate than its previous version and both implementations of its distributed versions successfully reduced the total completion time as a result of being linearly scalable when more workers are added to run the training. The Ring-AllReduce version performed slightly better than the Parameter Server, but the differences were not substantial

Relationship between Sustainable Environmental Practices and Sustainable Value Creation: A Conceptual Study on Readymade Garments Industry of Bangladesh

Because of the increasing level of industrial contamination the demand for sustainable products and raw materials aligned with the ecological pollution has been on the rise in this horizon. This study theoretically explores the impact of sustainable environmental practices on financial performance of Ready-made Garment (RMG) companies of Bangladesh following the growing literature linking corporate sustainable environmental performance with revenue development and profitability. Various sustainable environmental practices have diverse effects on firm’s performance, as the literature review suggests. From literature review this paper reports four key areas (community, employee, environment and governance) of sustainable practices for sustainable financial performance that would provide a useful and practical perspective on how companies can explore the sustainable agenda. Finally this paper concludes that companies should integrate environmental, social and governance issues to capture value in terms of revenue development and profitability for the companies. We recommend that the theoretical model that we have derived from literature review needs to validated and tested by future research

Prevalence and Incidence of Health Risk Factors Among Adolescent Girls

Heart Disease has many different risk factors, including hypertension (high blood pressure) and high cholesterol. Research has shown that hypertension can be identified as early as adolescence, and preventative measures can be instilled in this age. However, the relationship between hypertension, high cholesterol, race, and income have not been studied in detail among adolescents. The present study analyzed the effects of different health factors on blood pressure and cholesterol levels in Caucasian and African-American girls ages 9-21 years old, the prime of adolescence. The dataset was part of a larger study completed by the National Heart, Blood, and Lung Institute and included 2,379 girls from the ages of 9-21 years. The data were analyzed using R- Statistical Software using logistic regression, t-tests for mean, multinomial regression, analysis of variance, and chi-square test of independence. There were significant differences between Caucasian and African-American girls for several different health factors. Caucasian girls were found to have lower systolic and diastolic blood pressures, but African-American girls were found to have higher high density lipoprotein (HDL) levels. Caucasian girls were found to have significantly higher triglyceride levels then African-American girls. Further, income, body mass index, systolic, and diastolic blood pressure were found to be highly related to race. Income and systolic blood pressure were also found to be highly related. As systolic blood pressure increased, the odds of being African-American increased. This was also found to be true in regard to body mass index. Many risk factors seem to have their roots in childhood, and this information may lead to better prevention techniques. Future studies should also investigate these health factors in adolescent boys

Multimodal neural machine translation for low-resource language pairs using synthetic data

In this paper, we investigate the effectiveness of training a multimodal neural machine translation (MNMT) system with image features for a lowresource language pair, Hindi and English, using synthetic data. A threeway parallel corpus which contains bilingual texts and corresponding images is required to train a MNMT system with image features. However, such a corpus is not available for low resource language pairs. To address this, we developed both a synthetic training dataset and a manually curated development/test dataset for Hindi based on an existing English-image parallel corpus. We used these datasets to build our image description translation system by adopting state-of-theart MNMT models. Our results show that it is possible to train a MNMT system for low-resource language pairs through the use of synthetic data and that such a system can benefit from image features

The Elements For Sustainable E-Business Modelling: A 3D Approach

The sustainability of the business is a global contemporary issue. E-business modelling is another already established term as it converts technology into economic value. Although e-business modelling and sustainability of the business are the two significant global trends now but still there is no common perception about the elements that are essential for a sustainable e-business model. Surprisingly, none of the approaches even consider sustainability as a major element while modelling e-business. Therefore, after completing extensive literature review on e-business modelling and sustainability of the business we carefully identify and determine the required elements for a sustainable e-business model in this paper. We also clarify the significance for selecting these elements. The elements are three dimensional (3D) and selected from customer value area, business value area, and process value area so that the modelling elements preserve the interests of all stakeholders while upholding the sustainability