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This study aims to evaluate the impact of the proposed CARICOM Octagon “High In” Warning Label (OWL), against four alternative Front-of-Package Nutrition Labels (FOPNLs): US “Facts Up Front” (FUF), UK Multi-Traffic Light (MTL), Mexican OWL and the Brazilian Magnifying Glass “High In” Warning Label, on respondents’ purchase intentions, perception of healthiness and understanding of nutritional information across and within food products.

In an online randomized control experiment, adults from eight CARICOM countries (n = 948) were randomly assigned to a control and four treatment FOPNL groups. Respondents were tasked to choose between four categories of mock products with three variations in healthfulness across and within products.

No statistically significant difference (p > 0.05) was found across FOPNL groups compared to the OWL on outcomes of purchase intentions and perception of healthfulness. Regarding the understanding of nutritional information, FUF performed the best, as participants were 1.76 times (p = 0.03) and 3.23 times (p = 0.00) more likely to correctly identify the products with the highest and lowest amount of sugar, respectively. Results were similar for products with the lowest sodium (odds ratio [OR] = 2.25, p = 0.00) and highest saturated fats (OR = 2.11, p = 0.00).

Some limitations of this study include the use of an online platform to conduct the experiment. Though this was the most cost-effective method of execution and presented many benefits, there were limitations to using this approach. Firstly, this approach may not entirely replicate the real world in store purchasing settings. Although online grocery shopping is becoming increasingly popular, in the Caribbean, most grocery purchases are made in stores. Furthermore, online surveys are more likely to lead to samples with higher educational and income levels than the average population (Bethlehem, 2010). The skewedness observed was not unique to this study and was common with similar published studies (Franco-Arellano et al., 2020; Packer et al., 2021; Talati et al., 2018). Nevertheless, all respondents were randomly assigned to groups, and it was confirmed that there were no systematic differences in the education and income levels of participants across the FOPNL groups.

Some CARICOM policy makers advocate for the use of “High In” warning labels to limit the intake of nutrients of concern (NOCs) and to encourage healthier eating habits among consumers. However, regional private sector stakeholders have expressed concern about the lack of sufficient research undertaken at a regional level, to inform the effectiveness of this model within CARICOM, and some have also expressed a preference for the use of other interpretative and reductive FOPNLs, already in use in the Region. The results of this study reveal that while interpretative FOPNLs like the Draft CARICOM Regional Standard, DCRS5 (OWL) can assist consumers in making healthier purchase decisions, it was outperformed by the MTL on perception of healthiness and by the FUF on the understanding of nutrient information. It was also noted that the DCRS5 (OWL) was more effective when choosing across products with distinct nutritional differences but performed poorly in assisting respondents with making healthy purchasing decisions when all the products contained NOCs above the relevant thresholds. This study’s findings highlight that the existing FOPNL schemes can be further enhanced for improved outcomes. This can be achieved by using a hybrid approach which includes both reductive and interpretative elements to allow for comparison across and within food products. The literature also suggests the use of colour and combining positive as well as negative elements to encourage ease of interpretation, improved understanding and healthier food choices.

A properly designed FOPNL can support consumers in making healthier food choices; however, it must be accompanied by measures to raise consumer awareness and increase the health literacy of the population to cause shifts in preferences and behavioural patterns over time. This must also be coupled with policies to make healthy food choices more affordable to the general population.

The results of this study revealed that FUF and MTL performed the best in assisting participants to correctly identify between products with the highest or lowest NOCs at the 5% significant level, and that the OWL performed poorly in assisting participants with making healthy purchasing decisions when all the products contained NOCs above the relevant thresholds.

The purpose of this paper is to develop a blockchain-based data capture and transmission system that will collect real-time power consumption data from a household electrical appliance and transfer it securely to a local server for energy analytics such as forecasting.

The data capture system is composed of two current transformer (CT) sensors connected to two different electrical appliances. The CT sensors send the power readings to two Arduino microcontrollers which in turn connect to a Raspberry-Pi for aggregating the data. Blockchain is then enabled onto the Raspberry-Pi through a Java API so that the data are transmitted securely to a server. The server provides real-time visualization of the data as well as prediction using the multi-layer perceptron (MLP) and long short term memory (LSTM) algorithms.

The results for the blockchain analysis demonstrate that when the data readings are transmitted in smaller blocks, the security is much greater as compared with blocks of larger size. To assess the accuracy of the prediction algorithms data were collected for a 20 min interval to train the model and the algorithms were evaluated using the sliding window approach. The mean average percentage error (MAPE) was used to assess the accuracy of the algorithms and a MAPE of 1.62% and 1.99% was obtained for the LSTM and MLP algorithms, respectively.

A detailed performance analysis of the blockchain-based transmission model using time complexity, throughput and latency as well as energy forecasting has been performed.

This paper aims to examine the impact of the COVID-19 pandemic on the private label food products, specifically assessing the stability and changes in factors influencing purchasing decisions, and comparing pre-pandemic and post-pandemic datasets.

The study employs the Extra Tree Classifier method, a robust quantitative approach, to analyse data collected from questionnaires distributed among two distinct consumer samples. This methodological choice is explicitly adopted to provide a clear classification of factors influencing consumer preferences for private label products, surpassing conventional qualitative methods.

Despite the profound disruptions caused by the COVID-19 pandemic, this research underscores the persistent hierarchy of factors shaping consumer choices in the private label food market, showing an overall stability in consumer behaviour. At the same time, the analysis of individual variables highlights the positive increase in those related to product quality, health, taste, and communication.

The use of online surveys for data collection may introduce a self-selection bias, and the non-probabilistic sampling method could limit the generalizability of the results.

Practical implications suggest that managers in the private label industry should prioritize enhancing quality control, ensuring effective communication, and dynamically adapting strategies to meet evolving consumer preferences, with a particular emphasis on quality and health attributes.

This study contributes to the existing body of literature by providing insights into the profound transformations induced by the COVID-19 pandemic on consumer behaviour, specifically in relation to their preferences for private label food products.

The purpose of this study is to examine the buyers’ preferences influencing the purchase of privately developed affordable housing in Kolkata and to determine whether unsold houses result from misalignment with these preferences.

The literature review and user-opinion survey identified 119 independent variables that indicate buyers’ preferences. A questionnaire survey of 383 households in affordable housing units from 32 housing complexes in Kolkata recorded buyers’ preferences and satisfaction against the independent variables grouped under five levels of characteristics. The product weights of variables derived from the rank sum method and percentage satisfaction give the Utility Score. Multivariate regression and univariate linear regressions were conducted to determine the significance of each Level of characteristics and each variable, identifying the significant variables that would affect the sale of affordable houses.

The multivariate regression analysis has indicated that 68.56% of the variation in the percentage of unsold houses was explained by the five utility scores, which affirms that misalignment with buyers’ preferences significantly affects the sale of privately developed affordable houses. Furthermore, building and neighbourhood-level utility show the highest significance as predictors, while city-level and miscellaneous utility have moderate significance, but housing complex-level utility lacks statistical significance.

This study addresses a research gap in privately developed affordable housing in Kolkata, enhancing understanding of buyer preferences in this segment.

The study aimed to investigate the relationship between the intention to avoid food waste (IAFW) and the use of food-sharing technologies, such as internet platforms and mobile applications. The study utilized a model based on the theory of planned behavior (TPB) as an extension of the unified theory of acceptance and use of technology (UTAUT2).

An online platform tool (Prolific), and online self-report questionnaires were used to gather empirical data on 309 individuals. These data were then analyzed using two-step structural equation modeling.

The model explained 76% of the variance in user adoption of food-sharing mobile applications and internet platforms, supporting seven out of the nine tested hypotheses. Effort expectancy, social influence and IAFW were found to be the significant determinants of the behavioural intention to use food-sharing mobile applications and internet platforms (BITA). IAFW partially mediated the relationship between perceived behavior control and BITA. Age played a moderator role between the adoption of food-sharing mobile applications and internet platforms. However, IAFW did not play a mediating role between environmental concerns and BITA. The facilitating condition construct had an insignificant impact on BITA.

The current study was affected by some limitations. First, the data may not be considered as statistically representative because they were gathered online. However, the varied sociodemographic backgrounds of the respondents would boost the reliability of the findings. However, it would be prudent to use caution when extrapolating these findings to other contexts and cultures. Second, environmental concerns and perceived behavior control related to the avoidance of food waste behavior, as well as other factors that affect technology acceptance, may alter with time. Data from cross-sections may cause difficulties in following such changes. Thus, we recommend that longitudinal research studies aimed at building on our findings should be conducted. A qualitative study may help gain deeper insights into the relationship between IAFW related behavior and the adoption of various technologies to share leftover food, thereby revealing further details regarding different perspectives held by various respondents.

The positive relationship between environmental concerns and IAFW underlines the significance of investing in this area to raise social awareness and public concern for environmental safety. Additional initiatives aimed at increasing public concern regarding environmental issues may increase the overall IAFW. Instead of concentrating on a single source pertaining to the avoidance of food waste, the government and policy regulations should focus on regulating and eliminating waste from all sources that generate waste. The adoption of technology to share leftover meals may be influenced by social factors. Increased advertising for food-sharing mobile apps and online platforms may persuade more users to join. Additionally, building additional platforms and mobile apps in these fields with friendlier interactions may improve the cyber environment, making it easier for people to use them. By providing information, tools and assistance to promote the reduction of food wastage, policymakers may create interventions that enhance public perception and behavior toward the reduction of food waste. In conclusion, the findings of our study indicated that the social impact and ease of use are important factors in determining the adoption of food-sharing technology. Cooperation with social influencers, policymakers and developers may lead to the development of user-friendly technology that may improve accessibility to food-sharing technology.

In order to encourage the adoption of food-sharing technology among various age groups, policymakers may create initiatives that take the specific requirements and traits of each group into consideration. Policymakers and governments may also create legislation and regulations that are tailored to guarantee food safety and health security for users of food-sharing technology, such as instructions for handling and storing food as well as safeguards against food fraud and contamination.

This study addressed practical issues related to managing and reducing household food wastage through social sharing via mobile applications and internet platforms. The proposed model, which integrated TPB with UTAUT2 in the context of food wastage and technology acceptance, contributes to the current body of knowledge.

The paper examines the role and significance of gastronomic manifestations in the context of destination branding, within the framework of image transfer mechanisms and the Associative Network Memory Model.

The research was conducted on a sample of 53 gastronomic events in the tourist destination of Vojvodina.

The results indicate that gastronomic manifestations image has a positive impact on the brand image and brand identity of the destination, as well as the destination's overall image. Furthermore, the study found that the food experience has a positive influence on the image of gastronomic events and the destination.

The study contributes to the advancement of research on tourist destination branding.

Small-to-medium-sized enterprises (SMEs) in e-commerce often invest in information technology (IT) to stay competitive. However, whether and how IT capability (ITC) translates into financial performance requires further research. This paper examines the role of ITC in enabling value proposition innovation (VPI) as an important mechanism that improves financial performance for Chinese e-commerce SMEs during the COVID-19 pandemic. We argue that ITC is critical for enabling innovation because it elevates SMEs’ understanding of changing customer needs, especially when SMEs operate on multiple e-commerce platforms (multihome).

We used partial least squares structural equation modeling (PLS-SEM) and tested the hypotheses that ITC mediated by VPI and moderated by multihoming increases the financial performance of e-commerce SMEs through a survey among 206 Chinese SMEs operating on Taobao.

We find that not only higher levels of ITC lead to better financial performance, but also that the effect is fully mediated by VPI. Moreover, the effect of ITC on innovation is enhanced when vendors operate on multiple platforms.

The study identifies VPI as an important mechanism through which SMEs can leverage their ITC to adapt, innovate and thrive in competition. Our work suggests that using technology to develop innovative ideas and identify opportunities (which are reflected in VPI) is key to success and that doing so is more likely when vendors multihome. Thus, this study contributes to the innovation literature by explicating a concrete link between ITC, multihoming, VPI and increased financial performance. Different e-commerce stakeholders, including SME owners, IT and service providers and e-commerce platforms, can benefit from the findings of this work.

This paper aims to analyze practices, processes and outcomes related to construction supply chain management (CSCM) in Brazil to identify opportunities and direct actions and strategies to improve the Brazilian sector through blockchain technology (BT).

The study collected empirical data through semi-structured interviews with consumers and distributors to identify how the CSCM functions today. The interviews were structured and analyzed around the following topics: processes, digitalization, quality and weakness of the activities performed. The opportunities for using BT in CSCM were identified by crossing these results with evidence in the bibliography.

The study identified deficiencies, gaps and good practices applied to information management in the sector. Also, it analyzed the features, drivers and barriers of BT from a practical perspective. The findings suggested that BT is feasible and promising, reducing costs and enhancing process transparency. The need to overcome cultural and economic barriers is a challenging task.

This study brings a new perspective on using BT in CSCM, relating it to the demands and particularities of the construction sector in Brazil. The study identifies a limited panorama of chronic CSCM concerns, given BT’s infinite opportunities. And it motivates a gradual transition toward installing a new technological culture based on BT that focuses on priority care for the sector’s chronic deficiencies. The results can guide practical actions for the successful insertion of BT in the CSCM, laying the foundations for significant future research.

This study aims to study the thermal identification issue by harvesting both solar energy and atmospheric thermal updraft for a solar-powered unmanned aerial vehicle (SUAV) and its electric energy performance under continuous soaring conditions.

The authors develop a specific dynamic model for SUAVs in both soaring and cruise modes. The support vector machine regression (SVMR) is adopted to estimate the thermal position, and it is combined with feedback control to implement the SUAV soaring in the updraft. Then, the optimal path model is built based on the graph theory considering the existence of several thermals distributed in the environment. The procedure is proposed to estimate the electricity cost of SUAV during flight as well as soaring, and making use of dynamic programming to maximize electric energy.

The simulation results present the integrated control method could allow SUAV to soar with the updraft. In addition, the proposed approach allows the SUAV to fly to the destination using distributed thermals while reducing the electric energy use.

Two simplified dynamic models are constructed for simulation considering there are different flight mode. Besides, the data-driven-based SVMR method is proposed to support SUAV soaring. Furthermore, instead of using length, the energy cost coefficient in optimization problem is set as electric power, which is more suitable for SUAV because its advantage is to transfer the three-dimensional path planning problem into the two-dimensional.

Rare book collections are special, not only in terms of their physical appearance but also because of their historical significance and the information they contain. The purpose of this study is twofold: to identify the physical condition of rare book collections and to determine the main causes of damage to rare books collection that belongs to a museum library in Indonesia.

This research involved conducting a survey of the physical condition of the collection of rare books owned by a museum library in Indonesia. Supporting data was also obtained through interviews with one of the staff who served as the museum collection conservator. This study used random sampling to take samples from the collection, which consisted of 950 rare books, with total sample of 91.

The results obtained state that the condition of the existing rare book collection is classified as severely damaged. One of the causes of damage that can be addressed immediately is the cleaning regime: the collection and library space should be cleaned thoroughly and regularly so that dust and dirt in and around the rare book collection can be reduced.

This research was limited to physical identification, which can be done easily because it does not require various kinds of laboratory tests. It was a case study examining a single collection in a single museum library. The pool of books from which the samples were taken was therefore relatively homogenous. Therefore, it is hoped that further research can identify other factors and types of damage in more detail so that all damage to rare book collections can be identified and mitigated.

Research discussing the condition of rare book collections, especially for special libraries in museums in Indonesia, is still very limited. Detailed surveys of the physical condition of collections, especially rare book collections in museums, have rarely been discussed by previous research. The work will contribute to assessing the physical condition of rare book collections.