Search Results (30)

With the rise of omnichannel (OC) retailing in food e-retail, understanding how OC retailing and product quality attributes influence consumer purchasing behaviour and value perceptions is crucial for developing e-retail strategies and enhancing consumer services. This study examined their impacts on Chinese consumers’ purchases of Australian beef (brisket) through a discrete choice experiment in Beijing, Shanghai, Guangzhou and Shenzhen and analysed 872 valid responses using multinomial logit, random parameter logit, and latent class models. Our findings reveal that Chinese consumers prefer buying Australian brisket via OC apps and offline stores, paying approx. 44% and 134% more per 500 g, respectively, compared to self-operated e-commerce stores. Brand, manufacturer and origin traceability are key quality attributes, with additional paid for brisket manufactured and packaged in Australia (under Australian brands) and featuring the MLA “True Aussie Beef” label over QR codes. This study also identified four distinct consumer clusters: (i) premium shoppers, (ii) channel and traceability-oriented shoppers, (iii) omnichannel and price-oriented shoppers and (iv) tech-savvy and discerning shoppers, highlighting varying sensitivities to e-retail channels and product attributes. These findings offer strategic and actionable insights for Australian beef exporters and OC retailers seeking to optimise consumer engagement and value creation in China’s evolving e-retail landscape. Full article

In recent years, there has been a sharp increase in the number of online fraud cases. However, research on crime geography has paid little attention to online crimes, especially to the influencing factors behind their spatial distributions. Online fraud is closely related to people’s daily internet use. The existing literature has explored the impact of internet use on online crimes based on small samples of individual interviews. There is a lack of large-scale studies from a community perspective. This study applies the routine activity theory to online activities to test the relationship between online fraud alert data and the usage durations of different types of mobile phone users’ applications (apps) for communities in ZG City. It builds negative binomial regression models for analyzing the impact of the usage of different types of apps on the spatial distribution of online fraud. The results reveal that the online fraud crime rate and the online time spent on a financial management app share the most similar spatial distribution. While financial management, online education, transportation, and search engine app usages have a significant positive association with online fraud, the use of a financial management app has the greatest impact. Additionally, time spent on social media, online shopping and entertainment, and mobile reading apps have a significant negative association with online fraud. As not all online activities lead to cybercrime, crime prevention efforts should target specific types of apps, such as financial management, online education, transportation, and search engines. Full article

Forming a habit of practicing mindfulness meditation around the same time of day is one strategy that may support long-term maintenance and in turn improve physical and mental health. The purpose of this study was to identify common patterns in the time of day of meditation associated with long-term meditation app use to assess the importance of temporal consistency for maintaining meditation over time. App usage data were collected from a random sample of 15,000 users who had paid for an annual membership to a commercial meditation app in 2017. We constructed three measures of temporal consistency in the time of day of meditation sessions in order to categorize users into one of three behavioral phenotypes: Consistent, Inconsistent, or Indeterminate. Panel data models were used to compare temporal consistency across the three phenotypes. Of the 4205 users (28.0%) in the final analytic sample, 1659 (39.5%) users were Consistent, 2326 (55.3%) were Inconsistent, and 220 users (5.23%) were Indeterminate. Panel models confirmed that temporal consistency had contrasting relationships with meditation maintenance among these three phenotypes (p < 0.01). These findings revealed that temporal consistency was associated with meditation maintenance for less than half of app users, which suggests that other behavioral mechanisms in addition to temporally consistent habits can support meditation app use over time. This has important implications for researchers and policymakers trying to promote the maintenance of meditation and other complex health behaviors, such as increased physical activity and healthier diets. Full article

Over the last ten years, continuous attention has been paid to the use of digital games in vocabulary learning. Their effectiveness and availability have been widely discussed. However, the experiences of language learners and the underlying patterns of their engagement while using digital games for vocabulary learning remain underexplored. In order to fill this significant gap, this study aimed to examine the influential mechanism of English as a Foreign Language (EFL) learners’ flow experiences in digital game-based vocabulary learning (DGBVL). The sample consisted of 306 Chinese EFL learners who had DGBVL app usage experience, and data collection was based on a DGBVL flow experience instrument employed through an online platform. Structural equation modeling (SEM) was employed to assess the reliability and validation of the existing scale for various DGBVL apps. A multi-group analysis was then conducted, revealing that the influential mechanism was a process in which the effects of antecedents on outcomes could be mediated by flow experiences. In addition, the role of usage frequency was also explored, and three paths were found to differ across three usage frequency levels (i.e., seldom, sometimes, and always): the effect of balance of skill and challenge on enjoyment, the effect of enjoyment on satisfaction, and the effect of perceived learning on satisfaction. These findings provide new insights for the influential mechanism of flow experiences and will assist EFL learners in optimizing their learning outcomes in digital game-based vocabulary learning. Full article

For artificial intelligence (AI) to support nutrition care, high quality and accuracy of its features within smartphone applications (apps) are essential. This study evaluated popular apps’ features, quality, behaviour change potential, and comparative validity of dietary assessment via manual logging and AI. The top 200 free and paid nutrition-related apps from Australia’s Apple App and Google Play stores were screened (n = 800). Apps were assessed using MARS (quality) and ABACUS (behaviour change potential). Nutritional outputs from manual food logging and AI-enabled food-image recognition apps were compared with food records for Western, Asian, and Recommended diets. Among 18 apps, Noom scored highest on MARS (mean = 4.44) and ABACUS (21/21). From 16 manual food-logging apps, energy was overestimated for Western (mean: 1040 kJ) but underestimated for Asian (mean: −1520 kJ) diets. MyFitnessPal and Fastic had the highest accuracy (97% and 92%, respectively) out of seven AI-enabled food image recognition apps. Apps with more AI integration demonstrated better functionality, but automatic energy estimations from AI-enabled food image recognition were inaccurate. To enhance the integration of apps into nutrition care, collaborating with dietitians is essential for improving their credibility and comparative validity by expanding food databases. Moreover, training AI models are needed to improve AI-enabled food recognition, especially for mixed dishes and culturally diverse foods. Full article

In Japan, nutritional guidance based on food-recording apps and food frequency questionnaires (FFQs) is becoming popular. However, it is not always recognized that different dietary assessment methods have different nutritional values. Here, we compared the compatibility of dietary intake data obtained from an app with those obtained from FFQs in 59 healthy individuals who recorded information regarding their diet for at least 7 days per month using an app developed by Asken (Tokyo, Japan). The diurnal coefficient of variation in total energy and protein intake was 20%, but those for vitamins B₁₂ and D were >80%, reflecting the importance of 7 days of recording rather than a single day of recording for dietary intake analyses. Then, we compared the results of two FFQs—one based on food groups and one based on a brief self-administered diet history questionnaire—for 7 days, as recorded by the app. There was a correlation coefficient of >0.4 for all the items except salt. Regarding the compatibility between the app and FFQs, the percentage errors for total energy and nutrients were >40–50%, suggesting no agreement between the app and the two FFQs. In conclusion, careful attention should be paid to the impact of different dietary assessment methods on nutrient assessment. Full article

This research investigates the factors influencing user satisfaction and dissatisfaction in fitness mobile applications. It employs Herzberg’s two-factor model through text mining to classify Fitbit mobile app attributes into satisfiers and dissatisfiers. The Fitbit app was chosen due to its prevalence in the United States. The study analyzes 100,000 English reviews from the Fitbit app on the Google Play Store, categorizing attributes. It identifies three dissatisfying categories (functional, compatibility, paid services) and three satisfying categories (gratification, self-monitoring, self-regulation), comprising 25 sub-attributes. This classification offers in-depth insights into what drives user contentment or discontent with fitness apps. The findings contribute to the fitness app domain by applying text-mining and Herzberg’s model. Researchers can build upon this foundation, and practitioners can use it to enhance app experiences. However, this research relies on user reviews, often lacking comprehensive explanations. This limitation may hinder a profound understanding of the underlying psychological aspects in user sentiments. Nonetheless, this study takes strides toward optimizing fitness apps for users and developers. Full article

Due to the volatility of the markets and the ongoing crises (COVID-19, the Ukrainian war, etc.), investors are keen to exploit any potential chances to make profits. For this reason, the idea of harvesting data from cryptocurrency market users takes an innovative step. Potential investors in supply chain firms in the fertilizer industry need to know whether the observation of data originating from the cryptocurrency market is capable of explaining their stock price variation. The authors identify the innovative utilization of cryptocurrency markets’ user analytical data to model and predict the stock price of supply chain firms in the fertilizer industry stock price. The main aim of this research is to evaluate the contribution of cryptocurrency market big data as a predicting factor for the stock price of fertilizer market firms. Such a finding improves the knowledge and decision-making of potential investors in the fertilizer market. Moreover, this study seeks to highlight the benefits of utilizing cryptocurrency market big data for other financial purposes, apart from stock price prediction. The analytical data was derived from cryptocurrency websites and applications and was then processed through statistical analysis (correlation and linear regressions), Fuzzy Cognitive Maps (FCM), and Hybrid Modeling (HM) modeling. The hybrid model’s simulation showed that analytical data from the cryptocurrency markets tend to explain and predict the stock price of supply chain firms in the fertilizer industry. Such data refer to Bitcoin’s website organic keywords and traffic costs, as well as paid traffic costs from cryptocurrency trade websites/apps. A rise in Bitcoin and cryptocurrency trade websites’ organic and paid traffic costs tend to increase supply chain firms in the fertilizer industry’s stock prices, while Bitcoin’s website organic keywords variation decreases accordingly. Full article

Artificial intelligence (AI) is recently seeing significant advances in teledermatology (TD), also thanks to the developments that have taken place during the COVID-19 pandemic. In the last two years, there was an important development of studies that focused on opportunities, perspectives, and problems in this field. The topic is very important because the telemedicine and AI applied to dermatology have the opportunity to improve both the quality of healthcare for citizens and the workflow of healthcare professionals. This study conducted an overview on the opportunities, the perspectives, and the problems related to the integration of TD with AI. The methodology of this review, following a standardized checklist, was based on: (I) a search of PubMed and Scopus and (II) an eligibility assessment, using parameters with five levels of score. The outcome highlighted that applications of this integration have been identified in various skin pathologies and in quality control, both in eHealth and mHealth. Many of these applications are based on Apps used by citizens in mHealth for self-care with new opportunities but also open questions. A generalized enthusiasm has been registered regarding the opportunities and general perspectives on improving the quality of care, optimizing the healthcare processes, minimizing costs, reducing the stress in the healthcare facilities, and in making citizens, now at the center, more satisfied. However, critical issues have emerged related to: (a) the need to improve the process of diffusion of the Apps in the hands of citizens, with better design, validation, standardization, and cybersecurity; (b) the need for better attention paid to medico-legal and ethical issues; and (c) the need for the stabilization of international and national regulations. Targeted agreement initiatives, such as position statements, guidelines, and/or consensus initiatives, are needed to ensure a better result for all, along with the design of both specific plans and shared workflows. Full article

Inhalable particulate pollution has adverse effects on human health. Many studies have been conducted to investigate levels and risks of exposure to particulate pollution in public transit, but scant attention has been paid to variations in the pollution levels among different positions inside the cabin. Differences can be observed among different positions inside transit vehicles, like passenger density and activities, which may lead to differentiated exposure risks between different positions. Therefore, this study investigates the level of PM_2.5 concentrations and the spatiotemporal variations among three different positions (front, middle, and rear) inside the cabin during bus travel. The three positions represent the three areas inside the bus cabin, i.e., front area (FA), middle area (MA), and rear area (RA). We developed a portable monitoring system for in-cabin measurements, which consists of a mobile device with the designated app, a PM_2.5 collecting device, and a temperature-relative humidity monitor. Surveys were carried out on buses of a route in Shenzhen, China, which include inbound and outbound trips during both morning and evening peaks of two working days. Based on spatial and temporal information embedded in data collection devices, PM_2.5 concentration data were spatially and temporally referenced. Comparative and descriptive statistical analyses were employed to examine the differences and variations in PM_2.5 concentration among the areas inside the bus cabin. There are three major findings. First, for all of the surveyed trips, FA showed the highest and RA showed the lowest PM_2.5 concentration. Second, concerning the average pollution level of route segments along the routes, the PM_2.5 concentrations in the three cabin areas were higher during inbound trips than during outbound trips. Third, within route segments, PM_2.5 concentrations in FA and MA increased rapidly after the bus stopped at stops, and then gradually decreased, while in RA they were relatively stable. In general, a higher level of passenger density increased in-cabin PM_2.5 concentration. The findings of our study could provide insight into improving the bus in-cabin microenvironment, such as the seating arrangement for vulnerable passengers. Full article

As digitalization increasingly gains attention in the agriculture sector, many African youths are seizing the opportunity provided by digital technologies to engage in agriculture. While prior studies have started examining the intricacies of digitalization in agriculture, they have paid less attention to its implications for fostering institutions and inter-organizational synergies that can support and sustain the businesses of youth enterprises engaging with Africa’s agriculture. Against this backdrop, we undertook a qualitative case study to shed light on the development of a community of practice comprising youth enterprises engaging in agriculture through digitalization. Thus, we studied Africa Goes Digital (AFGD), a consortium of young African enterprises that rely on digital technologies and solutions to provide services to farmers across the African continent. The results showed that: (1) the Technical Centre for Agricultural and Rural Cooperation ACP-EU (CTA) played a pioneering role as a development agency in supporting enterprises with drones and training for agriculture, which led to the formation of AFGD; (2) two prevalent themes characterized the services offered by AFGD members; (3) AFGD brings members under one platform where they leverage WhatsApp for communication and knowledge sharing and Twitter to showcase success stories and form partnerships with each other to execute certain development projects with governments and development agencies; (4) the lack of regulations or the restrictive regulations on the use of drones are still problems that some members face, and the high costs of sensors and related drones are challenges for the members. Building on the results, we discuss the implications of digitalization for supporting African governments in meeting sustainability goals and conclude by describing the theoretical contribution of our study and promising future research directions. Full article

Currently, monitoring applications are being designed to provide comprehensive, flexible access. Monitoring applications are also required to function on several platforms (e.g., desktops and mobile phones). However, designers often have difficulty meeting these needs, owing to a lack of expertise in various fields. Here, we aim to convey a simple concept to achieve this goal. Visualization data on the system can be displayed and updated online using Google Sites, which can be accessed on several platforms. The data were displayed using dynamic image HTML embedded code. They were generated from the URL of an image stored in Google Drive. The image was taken from the monitoring system user interface created with the Windows Forms app and periodically uploaded to Google Drive. Therefore, using this concept, a cross-platform monitoring application (CPMA) can be created in a short time without complicated database processing or subscriptions to paid applications. Additionally, a messaging feature from the user platform was developed to enable messaging features between an operator at the machining site and another user/manager of the CPMA. This concept was applied to revisualize the collected data from the data acquisition system and the results of the predicted parameters from the main user interface through image uploadbased data visualization and to monitor two CNC lathe machines (with different sensors attached) at different locations. Full article

Reference evapotranspiration (ETo) is a key agrometeorological index for rational irrigation management. The standard method for ETo estimation, proposed by the FAO, is based on a complicated Penman–Monteith equation and requires many meteorological inputs, making it difficult for practical use by farmers. At present, there are many alternative simplified approaches for ETo estimation; most of them are directed at cutting the number of required meteorological inputs for calculation. Among them, special attention should be paid to the various temperature-based methods of ETo assessment. One of the temperature-based models for ETo computation was realized in the free mobile app ETo Calculator (Ukraine). The app gives Ukrainian farmers an opportunity to assess ETo values on a daily or monthly scale using mean air temperature, obtained through free online meteorological forecasts and archive services, as the only input. The objective of the study was to test the app’s accuracy compared to FAO-based calculations in five key regions of Ukraine, each representing a particular climatic zone of the country. It was established that the app provides relatively good accuracy of ETo estimation even in raw (not adjusted to wind speed and relative air humidity) runs. The results of the statistical comparison with the FAO-calculated values on the daily scale are as follows: R² within 0.82–0.87, RMSE within 0.74–0.81 mm, MAE within 0.60–0.70 mm, MAPE within 18.07–25.50%, depending on the region. The results of the statistical comparison with the FAO-calculated values on the monthly scale are: R² within 0.88–0.95, RMSE within 0.50–0.72 mm, MAE within 0.33–0.59 mm, MAPE within 8.96–24.08% depending on the region. The ETo Calculator (Ukraine) is a good alternative to the complicated Penman–Monteith method and could be recommended for Ukrainian farmers to be used for irrigation management. Full article

We conducted a qualitative study involving African migrants (n = 20) and service providers (n = 10) in South Australia to explore mental health stressors, access to mental health services and how to improve mental health services for African migrant populations. This paper presents the views and experiences of African migrants about the post-migration stressors they faced in resettlement that pose mental health challenges. The participants were recruited using the snowball sampling technique. To align with the COVID-19 pandemic protocol, the data collection was conducted using one-on-one online interviews through Zoom or WhatsApp video calls. Data analysis was guided by the framework analysis. The post-migration stressors, including separation from family members and significant others, especially spouses, imposed significant difficulties on care provision and in managing children’s attitudes and behavior-related troubles at school. African cultural practices involving the community, especially elders in care provision and disciplining children, were not consistent with Australian norms, compounding the mental health stressors for all involved. The African cultural norms, that do not allow young unmarried people to live together, also contributed to child–parent conflicts, enhancing parental mental stressors. Additionally, poor economic conditions and employment-related difficulties were post-migration stressors that the participants faced. The findings indicate the need for policy and intervention programs that address the above challenges. The provision of interventions, including social support such as subsidized or free childcare services, could help leverage their time and scheduled paid employment, creating time for effective parenting and improving their mental health and wellbeing. Future studies exploring what needs to be achieved by government and non-governmental institutions to support enhanced access to social and employment opportunities for the African migrant population are also recommended. Full article

Mobile phone apps can be a cost-effective way to provide decision support to farmers, and they can support the collection of agricultural data. The digitisation of agricultural systems, and the efforts to close the digital divide and to include smallholders, make data ownership and privacy issues more relevant than ever before. In Central and South American countries, smallholders’ preferences regarding data licenses and sharing have largely been ignored, and little attention has been paid to the potential of nonfinancial incentives to increase the uptake of digital solutions and participation by farmers. To investigate incentives for smallholder farmers to potentially use an agricultural advisory app in which they share their data, a Discrete Choice Experiment was designed. Based on a survey of 392 farmers in Mexico, preferences for attributes related to its usage were revealed using a conditional logit (CL) model. To explore heterogeneity, groups and profiles were explored through a latent class (LC) model. The CL model results revealed, for example, farmers’ positive preference to receive support at first use and access to training, while negative preference was found for sharing data with private actors. The LC identified three classes which differ in their preference for attributes such as the degree of data sharing. Furthermore, for example, a farmer’s connectedness to an innovation hub was found to be one of the significant variables in the class membership function. The main contribution of the study is that it shows the importance of nonfinancial incentives and the influence of data sharing on farmer preferences. Full article