Study on Key Aroma Compounds and Its Precursors of Peanut Oil Prepared with Normal- and High-Oleic Peanuts

High-oleic acid peanut oil has developed rapidly in China in recent years due to its high oxidative stability and nutritional properties. However, consumer feedback showed that the aroma of high-oleic peanut oil was not as good as the oil obtained from normal-oleic peanut variety. The aim of this study was to investigate the key volatile compounds and precursors of peanut oil prepared with normal- and high-oleic peanuts. The peanut raw materials and oil processing samples used in the present study were collected from a company in China. Sensory evaluation results indicated that normal-oleic peanut oil showed stronger characteristic flavor than high-oleic peanut oil. The compounds methylpyrazine, 2,5-dimethylpyrazine, 2-ethyl-5-methylpyrazine and benzaldehyde were considered as key volatiles which contribute to dark roast, roast peanutty and sweet aroma of peanut oil. The initial concentration of volatile precursors (arginine, tyrosine, lysine and glucose) in normal-oleic peanut was higher than in high-oleic peanut, which led to more characteristic volatiles forming during process and provided a stronger oil aroma of. The present research will provide data support for raw material screening and sensory quality improvement during high-oleic acid peanut oil industrial production.

The Impact of Online Reviews on the Information Flows and Outcomes of Marketing Systems

Online reviews are changing the way that consumers shop and firms respond to consumer feedback. Viewed more broadly, online reviews are a type of information flow altering the functioning of marketing systems at the micro, meso, and macro levels. A systematic review of the past two decades of research shows great attention to the impact of online reviews on information flows, as well as the nuances of micro-and meso-level efficiency outcomes. However, there is scant consideration for the effectiveness related outcomes of online reviews (such as customer well-being, distributive justice, and externalities). Through a macromarketing lens, online reviews are an information flow with the potential to change well-being outcomes for all stakeholders, rather than just a tool to be exploited by firms or consumers. A theoretical framework and a series of questions are presented for future research on how online reviews and more generally information flows between actors may impact the efficiency and effectiveness of a marketing system.

'MONUMEN KREWENG' 3D ANIMATION VIDEO AS A VISUAL BRANDING STRATEGY FOR JURON TOURIST VILLAGE

The development of tourist destinations in this digital era focuses on using technology to develop successful tourism recovery during the pandemic era. We use this principle to develop a tourism strategy using 3D animated videos to introduce Juron Village as a tourist village. Specifically, this study analyzes the visual strategy of a 3D animation video entitled "Monumen Kreweng" using Marty Neumeier's visual branding analysis. The findings provide insight into the animated video's visual branding strategies, including differentiation, collaboration, innovation, validation, and cultivation. The differentiation is found in the work of the Kreweng monument as the focal point of attention and local wisdom, followed by validation through the use of social media platforms to solicit consumer feedback on visual branding. Finally, the cultivation concept encourages various stakeholders to participate in realizing Juron Village's tourism branding.

A model who looks like me: Communicating and consuming representations of disability

Diversity in the fashion industry, it seems, is on the rise, with recent efforts poised to address the exclusion of people with disabilities. Based on a content analysis of editorials, advertising campaigns, and 213 online consumer comments between 2014 and 2019, we examine how diversity is showcased: speci!cally, whether images of disability serve to challenge or reinforce negative stereotypes. We !nd that market logics constrain the use of models with disabilities and shape their posturing in advertisements and fashion images. While consumers respond favorably to these images, demanding disability be more regularly and prominently featured, they are often responding to images that are sanitized and na ̈ıvely conceived. Nonetheless, we show how consumer feedback interacts with the production process, which in turn can challenge market logics, providing opportunities for increased representation. We shed light on how cultural representations re"ect, shape, and challenge broader sociocultural norms and values.

Symptomatic relief from at-home use of activated Bifidobacterium infantis EVC001 probiotic in infants: results from a consumer survey on the effects on diaper rash, colic symptoms, and sleep

The gut microbiome during infancy is directly involved in the digestion of human milk, development of the immune system, and long-term health outcomes. Gut dysbiosis in early life has been linked to multiple short-term ailments, from diaper dermatitis and poor stooling habits, to poor sleep and fussiness, with mixed results in the scientific literature on the efficacy of probiotics for symptom resolution. Despite the growing interest in probiotics for consumer use, observed symptomatic relief is rarely documented. This study aims to evaluate observed symptomatic relief from at-home use of activated Bifidobacterium infantis EVC001 in infants. Consumer feedback was collected over a 2-year period via a 30-day post-purchase online survey of B. infantis EVC001 (Evivo®) customers. Outcome measures included observed changes in diaper rash, symptoms of colic, and sleep behaviours in infants fed B. infantis EVC001. A total of 1,621 respondents completed the survey. Before purchasing B. infantis EVC001, the majority of respondents visited the product website, researched infant probiotics online, or consulted with their doctor or other healthcare professional. Of the participants whose infants had ever experienced diaper rash, 72% (n=448) reported improvements, and 57% of those reported complete resolution of this problem. Of those who responded to questions about gassiness/fussiness, naptime sleep, and night-time sleep behaviours, 63% (n=984), 33% (n=520), and 52% (n=806) reported resolution or improvements, respectively. Although clinical data regarding probiotic use are often inconclusive for symptom resolution, home use of B. infantis EVC001 in infants improved diaper rash, gassiness/fussiness, and sleep quality within the first week of use in a significant number of respondents who engaged in a voluntary post-purchase survey. These outcomes may be a result of the unique genetic capacity of B. infantis EVC001 to colonise the infant gut highlighting the importance of strain selection in evaluating the effects of probiotic products.

A model who looks like me: Communicating and consuming representations of disability

Diversity in the fashion industry, it seems, is on the rise, with recent efforts poised to address the exclusion of people with disabilities. Based on a content analysis of editorials, advertising campaigns, and 213 online consumer comments between 2014 and 2019, we examine how diversity is showcased: specifically, whether images of disability serve to challenge or reinforce negative stereotypes. We find that market logics constrain the use of models with disabilities and shape their posturing in advertisements and fashion images. While consumers respond favorably to these images, demanding disability be more regularly and prominently featured, they are often responding to images that are sanitized and naïvely conceived. Nonetheless, we show how consumer feedback interacts with the production process, which in turn can challenge market logics, providing opportunities for increased representation. We shed light on how cultural representations reflect, shape, and challenge broader sociocultural norms and values.

Developing an Automatic System for Classifying Chatter About Health Services on Twitter: Case Study for Medicaid

Background The wide adoption of social media in daily life renders it a rich and effective resource for conducting near real-time assessments of consumers’ perceptions of health services. However, its use in these assessments can be challenging because of the vast amount of data and the diversity of content in social media chatter. Objective This study aims to develop and evaluate an automatic system involving natural language processing and machine learning to automatically characterize user-posted Twitter data about health services using Medicaid, the single largest source of health coverage in the United States, as an example. Methods We collected data from Twitter in two ways: via the public streaming application programming interface using Medicaid-related keywords (Corpus 1) and by using the website’s search option for tweets mentioning agency-specific handles (Corpus 2). We manually labeled a sample of tweets in 5 predetermined categories or other and artificially increased the number of training posts from specific low-frequency categories. Using the manually labeled data, we trained and evaluated several supervised learning algorithms, including support vector machine, random forest (RF), naïve Bayes, shallow neural network (NN), k-nearest neighbor, bidirectional long short-term memory, and bidirectional encoder representations from transformers (BERT). We then applied the best-performing classifier to the collected tweets for postclassification analyses to assess the utility of our methods. Results We manually annotated 11,379 tweets (Corpus 1: 9179; Corpus 2: 2200) and used 7930 (69.7%) for training, 1449 (12.7%) for validation, and 2000 (17.6%) for testing. A classifier based on BERT obtained the highest accuracies (81.7%, Corpus 1; 80.7%, Corpus 2) and F1 scores on consumer feedback (0.58, Corpus 1; 0.90, Corpus 2), outperforming the second best classifiers in terms of accuracy (74.6%, RF on Corpus 1; 69.4%, RF on Corpus 2) and F1 score on consumer feedback (0.44, NN on Corpus 1; 0.82, RF on Corpus 2). Postclassification analyses revealed differing intercorpora distributions of tweet categories, with political (400778/628411, 63.78%) and consumer feedback (15073/27337, 55.14%) tweets being the most frequent for Corpus 1 and Corpus 2, respectively. Conclusions The broad and variable content of Medicaid-related tweets necessitates automatic categorization to identify topic-relevant posts. Our proposed system presents a feasible solution for automatic categorization and can be deployed and generalized for health service programs other than Medicaid. Annotated data and methods are available for future studies.

Developing an Automatic System for Classifying Chatter About Health Services on Twitter: Case Study for Medicaid (Preprint)

BACKGROUND The wide adoption of social media in daily life renders it a rich and effective resource for conducting near real-time assessments of consumers’ perceptions of health services. However, its use in these assessments can be challenging because of the vast amount of data and the diversity of content in social media chatter. OBJECTIVE This study aims to develop and evaluate an automatic system involving natural language processing and machine learning to automatically characterize user-posted Twitter data about health services using Medicaid, the single largest source of health coverage in the United States, as an example. METHODS We collected data from Twitter in two ways: via the public streaming application programming interface using Medicaid-related keywords (Corpus 1) and by using the website’s search option for tweets mentioning agency-specific handles (Corpus 2). We manually labeled a sample of tweets in 5 predetermined categories or <i>other</i> and artificially increased the number of training posts from specific low-frequency categories. Using the manually labeled data, we trained and evaluated several supervised learning algorithms, including support vector machine, random forest (RF), naïve Bayes, shallow neural network (NN), k-nearest neighbor, bidirectional long short-term memory, and bidirectional encoder representations from transformers (BERT). We then applied the best-performing classifier to the collected tweets for postclassification analyses to assess the utility of our methods. RESULTS We manually annotated 11,379 tweets (Corpus 1: 9179; Corpus 2: 2200) and used 7930 (69.7%) for training, 1449 (12.7%) for validation, and 2000 (17.6%) for testing. A classifier based on BERT obtained the highest accuracies (81.7%, Corpus 1; 80.7%, Corpus 2) and F₁ scores on consumer feedback (0.58, Corpus 1; 0.90, Corpus 2), outperforming the second best classifiers in terms of accuracy (74.6%, RF on Corpus 1; 69.4%, RF on Corpus 2) and F₁ score on consumer feedback (0.44, NN on Corpus 1; 0.82, RF on Corpus 2). Postclassification analyses revealed differing intercorpora distributions of tweet categories, with political (400778/628411, 63.78%) and consumer feedback (15073/27337, 55.14%) tweets being the most frequent for Corpus 1 and Corpus 2, respectively. CONCLUSIONS The broad and variable content of Medicaid-related tweets necessitates automatic categorization to identify topic-relevant posts. Our proposed system presents a feasible solution for automatic categorization and can be deployed and generalized for health service programs other than Medicaid. Annotated data and methods are available for future studies. CLINICALTRIAL

Newsvendor Decisions with Two-Sided Learning

A substantial collection of work in operations management considers settings where a firm faces uncertain demand that depends on parameters that are ex ante unknown but can be learned by observing historical data. This work typically assumes that future demand is unaffected by the firm’s learning process. However, in the new era of social media, it is increasingly the case that the information used by the firm to gauge future demand (for example, sales and stockouts or consumer feedback) is now also observable to the consumers and may influence their purchase decisions. In this paper, we consider a newsvendor model where a product of ex ante unknown value is sold in an environment where learning is “two-sided” in that both the firm and the consumers learn the product’s value over time by observing the same information. The analysis establishes a consequential insight: When learning is two-sided, the value of information is often negative for the firm; as a result, the firm’ optimal stocking quantity is often lower than that under one-sided learning. Moreover, under certain conditions that we identify, we show that the optimal stocking quantity can be even lower than the critical fractile policy, in stark contrast to the recurring prescription found in existing literature of “stocking more” in the presence of learning. Additional results and numerical experiments suggest that the loss for the firm from failing to account for the two-sidedness of the learning process can be significant. This paper was accepted by Charles Corbett, operations management.