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Markets for free from foods have undergone extensive growth as consumers attempt to manage their health in increasingly novel ways. This research explores the making of consumer perceptions about the health of gluten-free foods.

This research employs qualitative methods including in-depth interviews with consumers of gluten-free foods and content analysis of online consumer comments.

Findings illustrate how consumers leverage personal responsibility, social commentary and political criticism in ways that forge essential connections with traditional medical authority. In particular, consumers blend diverse views together by expressing reverence, positioning complementarity and framing temporality.

This research highlights the productive role of consumers in shaping what constitutes health-related concerns and widens the scope of explanatory factors beyond product- and individual-level differences. This research is set in the context of gluten-free foods and draws on interview data from a single set of consumers. Future research could consider other free from markets including, for example, soy-free foods and corn-free foods, both of which implicate some of the most common ingredients in food products and potential regional differences both within and outside of North America.

This research offers insights into the marketing of gluten-free foods and free from foods in general, specifically the participation of consumers in legitimising the need for these foods on the basis of health.

I weave together multiple streams of work across disciplines including food marketing, contested illnesses and institutional logics to further our understanding of the dynamic nature of contemporary markets for free from foods.

This study aims to examine the impact of managerial use of motivating language on employee absenteeism, turnover intention, job satisfaction and job performance for employees from three nations: India, the USA and Vietnam.

Data is collected from 614 employees working in India, the USA and Vietnam. A variance-based partial least squares structural equation modeling technique is used to test the hypotheses. In addition, a statistical test is used to examine the statistical differences in the results across the three nations.

The findings are consistent with the motivating language theory, in that managerial use of motivating language can be an effective strategy in motivating employees. Specifically, motivating language is found to significantly decrease employee absenteeism and turnover intention, as well as significantly increase job satisfaction and performance across the three nations. The effect sizes indicate that, across all samples, motivating language has a medium effect for all employee outcomes, except absenteeism, which is shown to have a small effect size. Moreover, the results indicate that employees in different cultures perceive and interpret the leader’s use of motivating language in different ways. Whereas motivating language may receive greater success in promoting workers’ job performance in eastern cultures, it is also more effective in retaining employees in western cultures.

The study adds to the literature in three major ways. First, it provides evidence for two understudied relationships: motivating language and absenteeism and motivating language and turnover intention. Second, it assesses the generalizability of the motivating language theory by investigating data from India, the USA and Vietnam. Finally, this paper offers a statistical comparison of the three samples to analyze how the relationship between motivating language and worker outcomes differ among the three samples.

The paper aims to sketch out the scenario of money laundering (ML) in Vietnam and the harm ML may cause to the country.

This paper first, based on the general concept of ML, scrutinizes actual and potential ML in Vietnam as well as ML threat to Vietnam. The typical cases of predicate offences, which were associated with ML activity, will be provided to illustrate the fact. Then a brief of Vietnam's response to ML will be examined.

ML has actually occurred since early in Vietnam. The potential for ML in Vietnam is substantial and poses growing harm to Vietnam in both the respects of economy and security. Although Vietnam has the primary legal framework of AML and set out AML countermeasures, the implementation has been hindered by several factors.

This paper would attract the attention of people who are concerned about ML in Vietnam.

The purpose of this paper is to develop non-leaching and eco-friendly antimicrobial waterborne polyacrylates with excellent antibacterial properties by grafting antibacterial vinyl monomer, glycidyl methacrylate (GMA) modified polyhexamethylene guanidine hydrochloride (PHMG).

PHMG of different molecular weights were modified by GMA to synthesize antibacterial vinyl monomer, GMA-modified PHMG (GPHMG). Different content and molecular weights of GPHMG were used to synthesize antimicrobial waterborne polyacrylates through emulsion polymerization.

The addition of GPHMG gained by modifying PHMG showed little influence on thermal stability of the films, but decreased the glass transition temperature(Tg). Meanwhile, the tensile strength decreased, while the breaking elongation increased. The antibacterial properties of the antibacterial films with different GPHMG contents were studied, when GPHMG content was around 0.9 Wt.%, antibacterial films showed excellent antibacterial activity (antibacterial rate >= 99.99 per cent). When weight content of GPHMG in the films remained constant, antibacterial property of films increased first and then decreased with the increase of molecular weight of GPHMG. The structural antibacterial polymer film had more perdurable antibacterial activity than the blended one.

The grafting efficiency of GPHMG to antimicrobial waterborne polyacrylates could be further improved.

Antimicrobial waterborne polyacrylates with excellent antibacterial properties can be used to antibacterial coating and adhesive.

The antibacterial properties of films with different molecular weight of GPHMG were studied, and the durability and stability of antibacterial properties between structural antimicrobial films and blended antimicrobial films were also investigated by ring-diffusion method.

Machine learning (ML) techniques are increasingly important in enabling business-to-business (B2B) companies to offer personalized services to business customers. On the other hand, humans play a critical role in dealing with uncertain situations and the relationship-building aspects of a B2B business. Most existing studies advocating human-ML augmentation simply posit the concept without providing a detailed view of augmentation. Therefore, the purpose of this paper is to investigate how human involvement can practically augment ML capabilities to develop a personalized information system (PIS) for business customers.

The authors developed a research framework to create an integrated human-ML PIS for business customers. The PIS was then implemented in the energy sector. Next, the accuracy of the PIS was evaluated using customer feedback. To this end, precision, recall and F1 evaluation metrics were used.

The computed figures of precision, recall and F1 (respectively, 0.73, 0.72 and 0.72) were all above 0.5; thus, the accuracy of the model was confirmed. Finally, the study presents the research model that illustrates how human involvement can augment ML capabilities in different stages of creating the PIS including the business/market understanding, data understanding, data collection and preparation, model creation and deployment and model evaluation phases.

This paper offers novel insight into the less-known phenomenon of human-ML augmentation for marketing purposes. Furthermore, the study contributes to the B2B personalization literature by elaborating on how human experts can augment ML computing power to create a PIS for business customers.

The paper aims to develop a particle matter (PM_2.5) prediction model for open-plan office space using a variety of data sources. Monitoring of PM_2.5 levels is not widely applied in indoor settings. Many reliable methods of monitoring PM_2.5 require either time-consuming or expensive equipment, thus making PM_2.5 monitoring impractical for many settings. The goal of this paper is to identify possible low-cost, low-effort data sources that building managers can use in combination with machine learning (ML) models to approximate the performance of much more costly monitoring devices.

This study identified a variety of data sources, including freely available, public data, data from low-cost sensors and data from expensive, high-quality sensors. This study examined a variety of neural network architectures, including traditional artificial neural networks, generalized recurrent neural networks and long short-term memory neural networks as candidates for the prediction model. The authors trained the selected predictive model using this data and identified data sources that can be cheaply combined to approximate more expensive data sources.

The paper identified combinations of free data sources such as building damper percentages and weather data and low-cost sensors such as Wi-Fi-based occupancy estimator or a Plantower PMS7003 sensor that perform nearly as well as predictions made based on nephelometer data.

This work demonstrates that by combining low-cost sensors and ML, indoor PM_2.5 monitoring can be performed at a drastically reduced cost with minimal error compared to more traditional approaches.

Every day, small and big incidents happen all over the world, and given the human, financial and spiritual damage they cause, proper planning should be sought to deal with them so they can be appropriately managed in times of crisis. This study aims to examine humanitarian supply chain models.

A new model is developed to pursue the necessary relations in an optimal way that will minimize human, financial and moral losses. In this developed model, in order to optimize the problem and minimize the amount of human and financial losses, the following subjects have been applied: magnitude of the areas in which an accident may occur as obtained by multiple attribute decision-making methods, the distances between relief centers, the number of available rescuers, the number of rescuers required and the risk level of each patient which is determined using previous data and machine learning (ML) algorithms.

For this purpose, a case study in the east of Tehran has been conducted. According to the results obtained from the algorithms, problem modeling and case study, the accuracy of the proposed model is evaluated very well.

Obtaining each injured person's priority using ML techniques and each area's importance or risk level, besides developing a bi-objective mathematical model and using multiple attribute decision-making methods, make this study unique among very few studies that concern ML in the humanitarian supply chain. Moreover, the findings validate the results and the model's functionality very well.

This study aims to develop a regression-based machine learning model to predict housing price, determine and interpret factors that contribute to housing prices using different data sets available publicly. The significant determinants that affect housing prices will be first identified by using multinomial logistics regression (MLR) based on the level of relative importance. A comprehensive study is then conducted by using SHapley Additive exPlanations (SHAP) analysis to examine the features that cause the major changes in housing prices.

Predictive analytics is an effective way to deal with uncertainties in process modelling and improve decision-making for housing price prediction. The focus of this paper is two-fold; the authors first apply regression analysis to investigate how well the housing independent variables contribute to the housing price prediction. Two data sets are used for this study, namely, Ames Housing dataset and Melbourne Housing dataset. For both the data sets, random forest regression performs the best by achieving an average R² of 86% for the Ames dataset and 85% for the Melbourne dataset, respectively. Second, multinomial logistic regression is adopted to investigate and identify the factor determinants of housing sales price. For the Ames dataset, the authors find that the top three most significant factor variables to determine the housing price is the general living area, basement size and age of remodelling. As for the Melbourne dataset, properties having more rooms/bathrooms, larger land size and closer distance to central business district (CBD) are higher priced. This is followed by a comprehensive analysis on how these determinants contribute to the predictability of the selected regression model by using explainable SHAP values. These prominent factors can be used to determine the optimal price range of a property which are useful for decision-making for both buyers and sellers.

By using the combination of MLR and SHAP analysis, it is noticeable that general living area, basement size and age of remodelling are the top three most important variables in determining the house’s price in the Ames dataset, while properties with more rooms/bathrooms, larger land area and closer proximity to the CBD or to the South of Melbourne are more expensive in the Melbourne dataset. These important factors can be used to estimate the best price range for a housing property for better decision-making.

A limitation of this study is that the distribution of the housing prices is highly skewed. Although it is normal that the properties’ price is normally cluttered at the lower side and only a few houses are highly price. As mentioned before, MLR can effectively help in evaluating the likelihood ratio of each variable towards these categories. However, housing price is originally continuous, and there is a need to convert the price to categorical type. Nonetheless, the most effective method to categorize the data is still questionable.

The key point of this paper is the use of explainable machine learning approach to identify the prominent factors of housing price determination, which could be used to determine the optimal price range of a property which are useful for decision-making for both the buyers and sellers.