Browsing by Author "Abir, Tanvir"

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A Global Study on the Correlates of Gross Domestic Product (GDP) and COVID-19 Vaccine Distribution
(Daffodil International University, 2022-02-10) Abir, Tanvir; Mamun, Abdullah Al; Zainol, Noor Raihani; Khanam, Mansura; Haque, Md. Rashidul; Milton, Abul Hasnat; Agho, Kingsley Emwinyore
This study aimed to explore the association between the GDP of various countries and the progress of COVID-19 vaccinations; to explore how the global pattern holds in the continents, and investigate the spatial distribution pattern of COVID-19 vaccination progress for all countries. We have used consolidated data on COVID-19 vaccination and GDP from Our World in Data, an open-access data source. Data analysis and visualization were performed in R-Studio. There was a strong linear association between per capita income and the proportion of people vaccinated in countries with populations of one million or more. GDP per capita accounts for a 50% variation in the vaccination rate across the nations. Our assessments revealed that the global pattern holds in every continent. Rich European and North-American countries are most protected against COVID-19. Less developed African countries barely initiated a vaccination program. There is a significant disparity among Asian countries. The security of wealthier nations (vaccinated their citizens) cannot be guaranteed unless adequate vaccination covers the less affluent countries. Therefore, the global community should undertake initiatives to speed up the COVID-19 vaccination program in all countries of the world, irrespective of their wealth.
Aligning Education with Market Demands: A Case Study of Marketing Graduates from Daffodil International University
(Scopus, 2024) Abir, Tanvir; Islam, Rakibul; Ullah, Anowar; Rahman, Siddiqur
This study conducted a comprehensive tracer analysis of 197 graduates from Daffodil International University’s Marketing bachelor program between 2019 and 2022. The main objective was to evaluate the program's alignment with labor market requirements and its effectiveness in equipping students with the necessary skills to navigate the complexities of the global market. A cross-sectional descriptive design was employed, utilizing a survey questionnaire as the primary data collection instrument. The target population consisted of graduates of the marketing program, selected through purposive sampling to ensure the inclusion of individuals with relevant experience. Data were analyzed using descriptive statistics to identify trends and percentages. Key findings revealed a significant gender disparity, with more male graduates than female, and high unemployment rates, which highlighted ongoing employment difficulties. While 75.5% of graduates affirmed the curriculum’s relevance to their professional roles, a gap was noted between the theoretical knowledge imparted and its practical application. The study suggests integrating comprehensive career preparation and extensive networking opportunities into the curriculum to mitigate employment barriers. Additionally, enhancing the curriculum to support entrepreneurial ventures is recommended. The findings emphasize the importance of ongoing curriculum revisions and the development of dynamic career support services to improve graduate employability and adapt to the evolving demands of the marketing profession. This research provides valuable insights for policymakers, curriculum developers, and educational researchers to enhance the relevance of higher education to the workforce, facilitating successful transitions into the labor market.
Aligning Education with Market Demands: A Case Study of Marketing Graduates from Daffodil International University
(Society for Research and Knowledge Management, 2024-08-15) Abir, Tanvir; Islam, Rakibul; Ullah, Anowar; Rahman, Siddiqur
This study conducted a comprehensive tracer analysis of 197 graduates from Daffodil International University’s Marketing bachelor program between 2019 and 2022. The main objective was to evaluate the program's alignment with labor market requirements and its effectiveness in equipping students with the necessary skills to navigate the complexities of the global market. A cross-sectional descriptive design was employed, utilizing a survey questionnaire as the primary data collection instrument. The target population consisted of graduates of the marketing program, selected through purposive sampling to ensure the inclusion of individuals with relevant experience. Data were analyzed using descriptive statistics to identify trends and percentages. Key findings revealed a significant gender disparity, with more male graduates than female, and high unemployment rates, which highlighted ongoing employment difficulties. While 75.5% of graduates affirmed the curriculum’s relevance to their professional roles, a gap was noted between the theoretical knowledge imparted and its practical application. The study suggests integrating comprehensive career preparation and extensive networking opportunities into the curriculum to mitigate employment barriers. Additionally, enhancing the curriculum to support entrepreneurial ventures is recommended. The findings emphasize the importance of ongoing curriculum revisions and the development of dynamic career support services to improve graduate employability and adapt to the evolving demands of the marketing profession. This research provides valuable insights for policymakers, curriculum developers, and educational researchers to enhance the relevance of higher education to the workforce, facilitating successful transitions into the labor market.
Deep Learning-Based Networks for Detecting Anomalies in Chest X-Rays
(Daffodil International University, 2022-07-23) Badr, Malek; Al-Otaibi, Shaha; Alturki, Nazik; Abir, Tanvir
X-ray images aid medical professionals in the diagnosis and detection of pathologies. They are critical, for example, in the diagnosis of pneumonia, the detection of masses, and, more recently, the detection of COVID-19-related conditions. The chest X-ray is one of the first imaging tests performed when pathology is suspected because it is one of the most accessible radiological examinations. Deep learning-based neural networks, particularly convolutional neural networks, have exploded in popularity in recent years and have become indispensable tools for image classification. Transfer learning approaches, in particular, have enabled the use of previously trained networks' knowledge, eliminating the need for large data sets and lowering the high computational costs associated with this type of network. This research focuses on using deep learning-based neural networks to detect anomalies in chest X-rays. Different convolutional network-based approaches are investigated using the ChestX-ray14 database, which contains over 100,000 X-ray images with labels relating to 14 different pathologies, and different classification objectives are evaluated. Starting with the pretrained networks VGG19, ResNet50, and Inceptionv3, networks based on transfer learning are implemented, with different schemes for the classification stage and data augmentation. Similarly, an ad hoc architecture is proposed and evaluated without transfer learning for the classification objective with more examples. The results show that transfer learning produces acceptable results in most of the tested cases, indicating that it is a viable first step for using deep networks when there are not enough labeled images, which is a common problem when working with medical images. The ad hoc network, on the other hand, demonstrated good generalization with data augmentation and an acceptable accuracy value. The findings suggest that using convolutional neural networks with and without transfer learning to design classifiers for detecting pathologies in chest X-rays is a good idea.
Detection of Heart Arrhythmia on Electrocardiogram using Artificial Neural Networks
(Daffodil International University, 2022-07-04) Badr, Malek; Al-Otaibi, Shaha; Alturki, Nazik; Abir, Tanvir
The electrocardiogram, also known as an electrocardiogram (ECG), is considered to be one of the most significant sources of data regarding the structure and function of the heart. In order to obtain an electrocardiogram, the contractions and relaxations of the heart are first captured in the proper recording medium. Due to the fact that irregularities in the functioning of the heart are reflected in the ECG indications, it is possible to use these indications to diagnose cardiac issues. Arrhythmia is the medical term for the abnormalities that might occur in the regular functioning of the heart (rhythm disorder). Environmental and genetic variables can both play a role in the development of arrhythmias. Arrhythmias are reflected on the ECG sign, which depicts the same region regardless of where in the heart they occur; thus, they may be seen in ECG signals. This is how arrhythmias can be detected. Due to the time limits of this study, the ECG signals of individuals who were healthy, as well as those who suffered from arrhythmias were divided into 10-minute segments. The arithmetic mean approach is one of the fundamental statistical factors. It is used to construct the feature vectors of each received wave and interval, and these vectors offer information regarding arrhythmias in accordance with the agreed-upon temporal restrictions. In order to identify the heart arrhythmias, the obtained feature vectors are fed into a classifier that is based on a multilayer perceptron neural network. In conclusion, ROC analysis and contrast matrix are utilised in order to evaluate the overall correct classification result produced by the ECG-based classifier. Because of this, it has been demonstrated that the method that was recommended has high classification accuracy when attempting to diagnose arrhythmia based on ECG indications. This research makes use of a variety of diagnostic terminologies, including ECG signal, multilayer perceptron neural network, signal processing, disease diagnosis, and arrhythmia diagnosis.
Human Rights Violations and Associated Factors of the Hijras in Bangladesh
(Daffodil International University, 2022-07-07) Amanullah, A. S. M.; Abir, Tanvir; Husain, Taha; Lim, David; Osuagwu, Uchechukwu L.; Ahmed, Giasuddin; Ahmed, Saleh; A Yazdani, Dewan Muhammad Nur; Agho, Kingsley E.
Background Hijras in Bangladesh face considerable discrimination, stigma, and violence despite the 2013 legislation that recognized Hijras as a third gender. There is a dearth of published literature describing the extent of human rights violations among this population and their associated factors. Methods A questionnaire was administered to 346 study participants aged 15 years and older, living in five urban cities of Bangladesh who self-identified as Hijra, in 2019. The six human rights violation indicators (Economic, Employment, Health, Education, Social and Civic and Political Right) assessed were categorized as binary. Associations between sociodemographic characteristics and the six human rights violations were tested using univariate and multivariate logistic regression. Results Human right violations including economic, educational, political, employment, health and social/civil right violations were reported in 73.3%, 59.3%, 58.5%, 46.4%, 42.7%, and 34.4% of the participants, respectively. Economic rights violations were associated with bisexuality (Adjusted odds ratios [AOR] 3.60, 95%CI: 1.57, 8.26) and not living with family (AOR 2.71, 95%CI: 1.21, 6.09), while Hijras who earned more than 10,000 Bangladesh Taka experienced higher odds of educational (AOR 2.77, 95%CI: 1.06, 7.19) and political rights violations (AOR 4.30, 95%CI: 1.06, 7.44). Living in Dhaka city was associated with a reduced odds for economic and political rights violation while experiencing violations of one human right could lead to violation of another in the Hijra community. Conclusion Human rights violations were common in Bangladesh Hijras, particularly the Bisexual Hijras. Media and educational awareness campaigns are needed to address the underlying roots of a violation. Programs focused on the families, young people and high-income earners of this community are needed in Bangladesh.
Modelling the Significance of Social Media Marketing Activities, Brand Equity and Loyalty To Predict Consumers' Willingness To Pay Premium Price for Portable Tech Gadgets
(Daffodil International University, 2022-08-27) Malarvizhi, Chinnasamy Agamudainambhi; Al Mamun, Abdullah; Jayashree, Sreenivasan; Naznen, Farzana; Abir, Tanvir
In order to sustain business operations during the COVID-19 pandemic, nearly all industries have to adopt online technology and social media marketing activities (SMMAs). Globally, portable tech gadgets are rapidly expanding, but empirical studies on SMMAs in relation to portable tech gadgets in Malaysia have remained scarce. Therefore, this study examined the elements of SMMAs and their influence on brand equity in terms of brand awareness (BBA) and brand image (BBI) as well as brand loyalty (BRL) and willingness to pay premium price (WPP) among Malaysian consumers of portable tech gadgets users. Five components of SMMAs, namely entertainment (ENT), interactivity (INT), trendiness (TRE), customisation (CUS), and electronic word-of-mouth (EWOM), were examined to understand how SMMAs influence BBA, BBI, BRL, and WPP. An online survey was conducted with 1332 Malaysian youths who used social media platforms maintained by portable tech gadget brands as their marketing strategies. The gathered data were evaluated using structural equation modelling. The study's results indicated the significant and positive effects of TRE, CUS, and EWOM on BBA and BBI. INT was revealed to have no significant impact on BBA and BBI. Furthermore, BBI and BBA partially mediated the relationships of the components of SMMAs with WPP. As for the theoretical underpinning, this study used the stimulus-organism-response (S-O-R) model to connect SMMAs (as stimuli), brand equity (as organism), and BRL and WPP (as responses). This study was the first to use the S-O-R model to explore the effects of SMMAs on BRL and WPP in this sector of portable tech gadgets. The study's findings can guide portable tech gadget brands in Malaysia in redesigning and developing the most efficient strategies of SMMAs, which should be tailored to maximise revenues, even during any crisis period (such as the COVID-19 pandemic) when physical marketing activities are deemed difficult.
Online Insurance Purchase Intention and Behaviour among Chinese Working Adults
(Springer Nature, 2022-07-30) Hayat, Naeem; Zainol, Noor Raihani; Abir, Tanvir; Mamun, Abdullah Al; Salameh, Anas A.; Mahshar, Munirah
The study examines the customers’ perception towards buying insurance online with factors taken from the theory of planned behavior like attitude, subjective norm, and perceived behavioral control among the Chinese working adults. The research collected cross-sectional survey-based data. The collected data analyzed with the structural equation modeling with SmartPLS 3.1. The study’s results offer empirical support that internet trustworthiness positively influences the attitude towards making purchases online, normative structure positively and significantly impacts the subjective norms towards internet purchase, and user internet self-efficacy significantly influences the perceived behavioral control for the internet purchase. The results confirm that the attitude and perceived behavioral control positively and significantly influence intention to purchase insurance online. Intention to purchase insurance online significantly predicts the behavior of purchasing insurance online. Current research establishes significant empirical evidence that the behavioral attitudinal beliefs build on the behavioral beliefs towards the making purchase over the internet. The subjective norms for internet purchase are not supportive, and consumers are finding less social support to buy financial products over the internet. Current work extends the theory of planned behavior with the behavior beliefs that formulates the attitudinal beliefs that leads to the development of the internet and purchase behaviors.
Predicting the Intention and Adoption of Near Field Communication Mobile Payment
(Daffodil International University, 2022-04-08) Malarvizhi, Chinnasamy Agamudainambi; Al Mamun, Abdullah; Jayashree, Sreenivasan; Naznen, Farzana; Abir, Tanvir
With the increasing use of mobile devices and new technologies, electronic payments, such as near field communication (NFC) mobile payments, are gaining traction and gradually replacing the currency-based cash payment methods. Despite multiple initiatives by various parties to encourage mobile payments, adoption rates in developing countries have remained low. The purpose of this research is to explore the prime determinants of NFC mobile-payment adoption intention and to develop a model of mobile payment adoption that includes perceived risk (PR) as one of the major elements by extending the UTAUT2 theory components. An online survey was used to acquire data from 370 NFC mobile payments users for the current study. To validate the components and their correlations, structural equation modelling (SEM) was implemented. According to the findings, performance expectancy (PE), hedonic motivation (HM), social influence (SI), and facilitating conditions (FC) have substantial impacts on the consumers’ intentions to adopt NFC mobile payments (INFC). Effort expectancy (EE) and PR were reported to have no considerable effects on the adoption intention. In addition, INFC is revealed to be a major mediator between the associations of the actual adoption of NFC mobile payment (ANFC) with PE, HM, and SI. The findings of the study would assist providers and marketers in better understanding of the consumers’ behavior, designing effective marketing strategies to enhance the consumers’ positive intentions, and achieving the mass adoption of NFC mobile payments in different environmental contexts.
Social Media Addiction and Emotions During the Disaster Recovery Period The Moderating Role of Post-COVID Timing
(Scopus, 22-10-20) Nur-A Yazdani, Dewan Muhammad; Abir, Tanvir; Qing, Yang; Ahmad, Jamee; Al Mamun, Abdullah; Zainol, Noor Raihani; Kakon, Kaniz; Agho, Kingsley Emwinyore; Wang, Shasha
Background: Social media addiction, a recently emerged term in medical science, has attracted the attention of researchers because of its significant physical and psychological effects on its users. The issue has attracted more attention during the COVID era because negative emotions (e.g., anxiety and fear) generated from the COVID pandemic may have increased social media addiction. Therefore, the present study investigates the role of negative emotions and social media addiction (SMA) on health problems during and after the COVID lockdown. Methods: A survey was conducted with 2926 participants aged between 25 and 45 years from all eight divisions of Bangladesh. The data collection period was between 2nd September- 13th October, 2020. Partial Least Square Structural Equation Modelling (PLS-SEM) was conducted for data analysis by controlling the respondents' working time, leisure time, gender, education, and age. Results: Our study showed that social media addiction and time spent on social media impact health. Interestingly, while anxiety about COVID increased social media addition, fear about COIVD reduced social media addition. Among all considered factors, long working hours contributed most to people's health issues, and its impact on social media addiction and hours was much higher than negative emotions. Furthermore, females were less addicted to social media and faced less health challenges than males. Conclusion: The impacts of negative emotions generated by the COVID disaster on social media addiction and health issues should be reconsidered. Government and employers control people's working time, and stress should be a priority to solve people's social media addiction-related issues.
Unveiling the determinants of online shopping: Insights from a developing nation
(Scopus, 2024) Fazal, Syed Ali; Alshebami, Ali Saleh; Abir, Tanvir; Hossain, Saif; Almamun, Abdullah; Seraj, Abdullah Hamoud Ali; Sobaih, Abu Elnasr E.
This study examined the factors influencing online purchases among consumers in Bangladesh, employing a modified version of the Technology Acceptance Model (TAM). Data from 353 individuals in Bangladesh revealed that perceived ease of use, social influence, security, convenience, trust, emotional experience, and functional experience significantly positively affect the intention to purchase online. Additionally, results show that the intention to purchase online significantly positively affects actual online purchases. Findings further highlighted that intention to make online purchases mediated the influence of perceived ease of use, social influence, security, convenience, trust, emotional experience, and functional experience over online purchases. The study provides significant practical recommendations to help businesses and consumers support online purchasing with diverse advantages.