Green consumers’ behavioral intention and loyalty to use mobile organic food delivery applications: the role of social supports, sustainability perceptions, and religious consciousness

AbstractConsumer behavior in the food industry has undergone significant changes in recent years, largely driven by growing consumer awareness of environmental, technological, religious, and social concerns. As a result, organic food has emerged as a popular alternative to conventionally produced food. Many emerging nations, including Bangladesh, promote its consumption due to its perceived health and safety benefits. Despite this growing trend, there remains a need for more understanding of consumer behavior, particularly concerning their motivations for continuous purchases toward mobile organic food delivery applications. In order to fill this knowledge gap, this study looks at how six indirect predictors (emotional support, informational support, environmental consciousness, religious consciousness, trust, and technological consciousness) affect customer loyalty through the intention to use organic food. This study employed a purposive sampling technique (i.e., judgmental sampling) and collected data from 386 respondents across three cities in Bangladesh. Data analysis was conducted using SmartPLS 3 software. The study found that all predictors, except for technological consciousness, significantly influenced behavioral intention, which, in turn, significantly influenced loyalty. Additionally, the study revealed that the five predictors, excluding technological consciousness, indirectly influenced loyalty through behavioral intention. The results of this study add to the existing literature on organic food by extending social support theory to include consumers' primary motivations, such as environmental, religious, technological, and social consciousness, as predictors of loyalty to use mobile organic food delivery applications. The study highlights the importance of sustainable food consumption in promoting environmental protection, ensuring social justice, creating economic success, and providing valuable insights for implementers looking to expand the organic food market. Graphical abstract</jats:p

An empirical recommendation framework to support location-based services

© 2020 by the authors. The rapid growth of Global Positioning System (GPS) and availability of real-time Geo-located data allow the mobile devices to provide information which leads towards the Location Based Services (LBS). The need for providing suggestions to personals about the activities of their interests, the LBS contributing more effectively to this purpose. Recommendation system (RS) is one of the most effective and efficient features that has been initiated by the LBS. Our proposed system is intended to design a recommendation system that will provide suggestions to the user and also find a suitable place for a group of users and it is according to their preferred type of places. In our work, we propose the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for clustering the check-in spots of the user's and user-based Collaborative Filtering (CF) to find similar users as we are considering constructing an interest profile for each user. We also introduced a grid-based structure to present the Point of Interest (POI) into a map. Finally, similarity calculation is done to make the recommendations. We evaluated our system on real world users and acquired the F-measure score on average 0.962 and 0.964 for a single user and for a group of user respectively. We also observed that our system provides effective recommendations for a single user as well as for a group of users

Advancements in AI-Enhanced OCT Imaging for Early Disease Detection and Prevention in Aging Populations

Optical Coherence Tomography (OCT) proves essential as an imaging modality for detecting early diseases especially by helping patients who age and face increased susceptibility to retinal and systemic conditions. The development of artificial intelligence technology now boosts OCT diagnostic features to identify conditions like diabetic retinopathy in addition to age-related macular degeneration and cardiovascular diseases at an early stage. This paper examines two main advancements in artificial intelligence for OCT imaging monitoring such as Google Health's Retinal Disease Predictor and AI systems used to evaluate cardiovascular risks. This research develops HealthSight AI which combines deep learning algorithms with real-time predictive analytics to detect multiple diseases in healthcare. Medical studies demonstrate how AI-enhanced OCT technology can transform preventive healthcare delivery through its clinical implementations. The integration of AI in OCT imaging holds vast prospective advantages yet operational hurdles stem from ethical matters and system adherence needs together with healthcare structure implementation barriers. The findings emphasize the necessity to develop additional research together with collaboration so AI-powered OCT imaging can reach broad clinical implementation

Understanding the Predictors of Rural Customers’ Continuance Intention toward Mobile Banking Services Applications during the COVID-19 Pandemic

The purpose of this study is to examine the antecedents of customers’ continuance intention to use mobile banking services applications (MBSAs) during the COVID-19 pandemic. Grounding on the Technology Acceptance Model, Theory of Planned Behavior, and Cognitive Load Theory, an integrated conceptual framework was proposed and tested incorporating psychological factors (i.e., cyberchondria, perceived anxiety) and situational factors (i.e., social distance, institutional support). Data were collected from 250 rural customers and analyzed with Structural Equation Modeling. The results showed that subjective norms, perceived ease of use, social distance, attitudes, cyberchondria, and institutional support influenced users’ continuance intention. Moreover, the results showed that perceived anxiety, subjective norms, perceived ease of use, and perceived usefulness influenced users’ attitudes. Besides, the findings suggested that attitudes mediate the influence of subjective norms, usefulness, ease of use, and social distance on users’ intention. This study is unique in terms of investigating pandemic-specific psychological and situational factors in explaining consumers’ continuance intention. Therefore, the service providers and professionals should be cautious in designing MBSAs so that consumers’ usage behaviors may not vary during an unprecedented situation (e.g., COVID-19). The theoretical and practical implications were discussed

Prevalence of Iron Deficiency Anemia and its Biochemical Parameters among the Selected School- going Under-priviledged Children in Dhaka City

Iron deficiency is a serious health complication particularly in developing countries, which is usually caused due to poor nutrition, genetic disorders and chronic infections. Compared to developed countries prevalence of anaemia in developing and underdeveloped countries is very high, and children are the ones which are mostly affected. In this paper an attempt has been made to study the prevalence of anaemia among some school-going children in Dhaka. An attempt has also been made to assess the severity of anaemia and iron status among the school-going underprivileged children by measuring serum iron (SI), serum TIBC and serum ferritin (SF) and explore a relationship between haemoglobin level and various parameters of iron nutrition. A substantial number of indicators have been used in determining the iron deficiency.Results obtained from the study show that two thirds of the study children are anaemic due to haemoglobin level below 12 gm/dl. However, majority of them had mild anaemia (haemoglobin level between 10.0 to 11.9 gm/dl) and only a few of them had moderate anaemia (haemoglobin level between 7.0 to 9.9 gm/dl). None of the study population had severe anaemia (haemoglobin level below 7.0 gm/dl). Results also show that only 10 of the study population (6%) were found to have significantly low serum iron, low serum ferritin and high serum iron binding capacity (TIBC) as compared to that of the students who had normal haemoglobin level.DOI: http://dx.doi.org/10.3329/jom.v14i2.19657 J Medicine 2013, 14(2): 130-134</jats:p

A novel extendable multilevel inverter for efficient energy conversion with fewer power components: Configuration and experimental validation

Multilevel inverters (MLIs) are widely used in various sectors of power electronics. Some major benefits of using MLI are the high-quality voltage output, harmonic reduction, and efficiency. However, MLIs also bear a range of disadvantages including an excessive number of components, high total standing voltage (TSV), and complicated control scheme. Thus, a novel cross-switched neutral point clamped (CSNPC) nine-level inverter is proposed. Many of the suggested MLIs use a large number of DC supplies and additional switches that are not fully utilized and generate high power loss. However, the proposed MLI topology has a smaller number of power electronic switches and DC supplies, leading to higher energy efficiency. Moreover, a simplified high frequency modulation technique, named hysteresis-band-based discontinuous pulse width modulation (DPWM) is devised to control the proposed MLI. This modulation scheme has effectively minimized capacitor voltage imbalance. Furthermore, the designed structure is extendable to a modular MLI, without any additional H-bridge circuit. To verify its benefits over other recent similar types of MLIs, a thorough comparison is presented for the number of components and power losses. The proposed MLI has showed 95.54% efficiency, and the modular CSNPC can achieve a total harmonic distortion (THD) of 6.43%.No Full Tex