Conjoint Analysis Research Articles

Hybrid teaching and learning: A conjoint analysis of student preferences in online and onsite scenarios

AbstractBackgroundThis research paper presents a cross‐sectional study that examinefs the preferences of students for hybrid teaching and learning scenarios. Unlike previous studies that merely describe hybrid scenarios, this research prioritizes them, offering evidence‐based findings for informed policy decisions.MethodsThe data collection method involved eight choice‐based tasks using the conjoint analysis technique conducted with ‘Sawtooth’ software. The study surveyed students at a mid‐sized university across four departments in Central Switzerland. The sample analysed in this article comprised 319 respondents from the Lucerne School of Business.Results and ConclusionsOur survey found that students' own location during a teaching session played a significant role in determining participation preferences, followed by that of the lecturer. Factors that influenced students' preferences whether to attend onsite or online include workload, didactical format, perceived level of difficulty, student residence, and semester. Forms of hybrid collaboration and student–student interaction did not significantly influence student preferences.TakeawaysThe value of this study lies in its evidence‐based findings for specific hybrid scenarios, which can provide useful insights for policymakers in degree programs and faculty in hybrid classrooms. This study is one of only a few to use conjoint analysis with such a high respondent rate to determine student preferences for hybrid attendance.

Correlation between carcinoembryonic antigen (CEA) expression and EGFR mutations in non-small-cell lung cancer: a meta-analysis.

The purpose of this meta-analysis was to investigate the relationship between serum carcinoembryonic antigen (CEA) expression and epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC). Databases such as PubMed, Cochrane, EMBASE and Google Scholar were systematically searched to identify studies assessing the association of serum CEA expression with EGFR mutations. Across 19 studies, 4168 patients were included between CEA expression and EGFR mutations odds ratio (OR) conjoint analysis of correlations. Compared with CEA-negative NSCLC, CEA-positive tumors had an increased EGFR mutation rate (OR = 1.85, 95% confidence interval: 1.48-2.32, P < 0.00001). This association was observed in both stage IIIB/IV patients (OR = 1.60, 95% CI: 1.18-2.15, P = 0.002) and stage I-IIIA (OR = 1.67, 95% CI: 1.01-2.77, P = 0.05) patients. In addition, CEA expression was associated with exon 19 (OR = 1.97, 95% CI: 1.25-3.11, P = 0.003) and exon 21 (OR = 1.51, 95% CI: 1.07-2.12, P = 0.02) EGFR mutations. In ADC pathological type had also showed the correlation (OR = 1.84, 95% CI: 1.31-2.57, P = 0.0004). This meta-analysis indicated that serum CEA expression was associated with EGFR mutations in NSCLC patients. The results of this study suggest that CEA level may play a predictive role in the EGFR mutation status of NSCLC patients. Detecting serum CEA expression levels can give a good suggestion to those patients who are confused about whether to undergo EGFR mutation tests. Moreover, it may help better plan of the follow-up treatment.

Increasing graduates’ interest in B2B sales: how to dispel lay beliefs, fight stigma, and create a profession of choice

Given the pressing talent shortage and the need for well-educated recruits in business-to-business (B2B) sales, understanding how to increase university graduates’ low interest in this profession is vital to sales departments’ competitiveness. To tackle this issue, we adopt a mixed-method approach to investigate the reasons for and measures against graduates’ low interest in B2B sales. First, we develop a comprehensive, theory-based framework to understand the root causes of graduates’ low interest in B2B sales (Study 1). This framework uncovers specific beliefs discouraging graduates from pursuing B2B sales, including negative perceptions of job characteristics, the profession’s prevailing stigma, and negative career success expectations. Second, comparing graduates’ and B2B salespeople’s assessments of B2B sales job characteristics (Study 2), we reveal that graduates hold lay beliefs about B2B sales lacking intrinsic and social job characteristics. As a basis for a solution, we identify the types and combinations of sales exposure required to dispel lay beliefs and create a favorable but realistic picture of B2B sales. Third, combining text analysis of B2B sales labor market communication and conjoint analysis of graduate application drivers (Studies 3a and 3b), we develop guidelines to better align communication and job structures with graduates’ needs.

Transcriptome and metabolome profiling provides insight into the regulatory network of fruit coloration in Coffea arabica L

Coffee is the most popular beverage in the world with exceptional flavor and taste. Fruit color is known as the prime quality attribute that influences the nutritional quality and health benefits of coffee. The molecular mechanism underlying the diversity of fruit coloration is still unclear in C. arabica. A network of key genes and metabolites involved in coffee fruit coloration was constructed. The analysis of transcriptome and metabolic was performed by using six contrast-colored fruits. The results revealed that the differential metabolites and genes were enriched in the anthocyanin biosynthesis and carotenoid biosynthetic pathways in C. arabica fruits. In total, 31 differentially accumulated anthocyanins, 34 differentially accumulated carotenoid and 75 differentially expressed genes were identified. In addition, the difference in composition and concentration of carotenoids influenced the diversity of fruit coloration. The results of combined metabolome and transcriptome data showed a strong relationship between differentially expressed genes and accumulated metabolites. After conjoint analysis of transcriptome and metabolome, we found 13 anthocyanidins associated DEGs had a strong linkage with nine compounds and 16 carotenoids annotated DEGs displayed a strong connection with nine carotenoid compounds. This study identified the expression of key genes and the abundance of metabolites, and biosynthesis pathways, which provided the regulatory mechanism of fruit coloration in C. arabica. This study further supported the analysis for genetic control of fruit color in C. arabica.

An integrated bi-objective optimization model accounting for the social acceptance of renewable fuel production networks

Renewable liquid fuels produced from biomass, hydrogen, and carbon dioxide play an important role in reaching climate neutrality in the transportation sector. For large-scale deployment, production facilities and corresponding logistics have to be established. However, the implementation of such a large-scale renewable fuel production network requires acceptance by citizens. To gain insights into the structure of efficient and socially accepted renewable fuel production networks, we propose a bi-objective mixed-integer programming model. In addition to an economic objective function, we consider social acceptance as a second objective function. We use results from a conjoint analysis study on the acceptance and preference of renewable fuel production networks, considering the regional topography, facility size, production pathway, and raw material transportation to model social acceptance. We find significant trade-offs between the economic and social acceptance objective. The most favorable solution from a social acceptance perspective is almost twice as expensive as the most efficient economical solution. However, it is possible to strongly increase acceptance at a moderate expense by carefully selecting sites with preferred regional topography.

Deciphering the role of extrachromosomal circular DNA in adipose stem cells from old and young donors

BackgroundThe functional impairment of adipose stem cells (ASCs) during aging limits their clinical transformation. Studies have shown that extrachromosomal circular DNAs (eccDNAs) are associated with tumor progression and cell aging, but the roles of eccDNAs in ASCs remain unknown.MethodWe conducted Circle sequencing (Circle-seq) to identify eccDNAs in ASCs isolated from young and old donors. The differentially expressed eccDNAs were calculated, annotated and validated via polymerase chain reaction.ResultsThousands of eccDNAs were identified and comprehensively characterized. Most of them were GC-rich, < 1000 base pairs in size, and were enriched on chromosome 19 and 17 with a high density of Alu elements and genes, 2 kb upstream/downstream of genes and satellites. In total, 3025 eccDNAs were differentially expressed among the two ASC groups. Conjoint analysis of the Circle-seq results and previous RNA-seq results revealed that 73 eccDNAs and 55 genes exhibited the same differential expression between the two groups. KEGG and GO analyses revealed that genes encoding differentially expressed eccDNAs were enriched for cell adhesion, cellular senescence and TGF-β receptor signaling pathway. We also found that aged ASCs exhibited loss of eccDNAs, including CAMK2G (chr10: 75577899-75578176), TRABD2B (chr1: 48305638-48307008) and TRABD2B (chr1: 48305425-48307091).ConclusionIn this study, we elucidated the first eccDNA profile relating to ASCs and demonstrated that three eccDNAs are lost in aged ASCs, which may be potential biomarkers of stem cell aging and valuable targets for stem cell rejuvenation.

What drives researcher preferences for chemical compounds? Evidence from conjoint analysis.

We investigated the attributes and attribute levels that affect researcher preferences for chemical compounds. We conducted a conjoint analysis on survey data of Korean researchers using chemical compounds from the Korean Chemical Bank (KCB). The analysis estimated the part-worth utility for each attribute's level, calculated relative importance of attributes, and classified user segmentation with different patterns. The results show that the structure database offers the highest part-worth utility to researchers, followed by high new functionality, price, screening service, and drug action data provided only by the KCB. Notably, researchers view the offer of a structured database and high new functionality as more important than other attributes in decision-making about research and development of chemical compounds. Furthermore, the results of segmentation analysis demonstrated that researchers have distinct usage patterns of chemical compounds: researchers consider structure database and high new functionality in cluster 1; and high new functionality and price in cluster 2, to be the most appealing. We discussed some policy and strategic implications based on the findings of this study and proposed some limitations.

Undercutting Transit? Exploring Potential Competition Between Automated Vehicles and Public Transportation in the United States

Automated vehicles (AVs) have the potential to dramatically disrupt current transportation patterns and practices. One particular area of concern is AVs’ impacts on public transit systems. If vehicle automation enables significant price decreases or performance improvements for ride-hailing services, some fear that it could undercut public transit, which could have significant implications for the environment and transportation equity. The extent to which individuals adopt automated transportation modes will drive many system-level outcomes, and research on public preferences for AVs is immature and inconclusive. In this study, we used responses from an online choice-based conjoint survey fielded in the Washington, D.C. metropolitan region (N = 1,694) in October 2021 to estimate discrete choice models of public preferences for different automated (ride-hailing, shared ride-hailing, bus) and nonautomated (ride-hailing, shared ride-hailing, bus, rail) modes. We used the estimated models to simulate future marketplace competition across a range of trip scenarios. Respondents on average were only willing to pay a premium for automated modes when a vehicle attendant was also present, limiting the potential cost-savings that AV operators might achieve by removing the driver. Scenario analysis additionally revealed that for trips where good transit options were available, transit remained competitive with automated ride-hailing modes. These results suggest that fears of a mass transition away from transit to AVs may be limited by people’s willingness to use AVs, at least in the short term. Future AV operators should also recognize the presence of an AV attendant as a critical feature for early AV adoption.

Differential expression and implication of m6A methylation in mice with experimental myocardial infarction

Objective: To define differentially expressed N6-adenylate methylation (m6A) genes in the myocardial tissue of mice with myocardial infarction (MI) and explore its potential impact on the pathological process of MI. Methods: The random number table method was used to divide the eighteen SPF C57BL/6J male mice aged from 8 to 10 weeks into MI group (MI group, n=9) and control group (control group, n=9). Modified m6A genes from the myocardial tissue were detected via methylated RNA immunoprecipitation with the next generation sequencing (MeRIP-seq). We explored methylation modified characteristics, verified mRNA expression and m6A modified level by bioinformatics analysis, qPCR and MeRIP-qPCR. Results: The Heatmap revealed that 901 differentially modified m6A genes between MI and control group, of which 537 genes were upregulated, and 364 genes were downregulated. The principal component analysis affirmed that two groups could be distinguished significantly in terms of m6A gene modification. The characteristic sequence of m6A modification was GGACU and mainly concentrated in the coding sequence. According to the conjoint analysis with RNA-seq and MeRIP-seq, 119 genes expressed simultaneous m6A modification difference and mRNA expression difference. The Venn diagram exhibited the positive and negative correlation between m6A modification and mRNA expression. Besides, the GO enrichment analysis indicated that the genes with m6A differential modification in MI group were mainly involved in heart development and other processes. qPCR verified that Gbp6 was up-regulated, while Dnaja1 and Dnajb1 were down-regulated. MeRIP-qPCR revealed that the m6A modification level of Hspa1b was downregulated. Conclusion: Myocardial infarction induces differential modification of m6A in the mice model. In addition, the genes with m6A modification may be affected by methylation related enzymes, thus participating the pathogenesis of MI by regulating apoptosis and inflammation.

Size matters! Using conjoint analysis to uncover public preferences for design optimisation in road infrastructure EIAs

The planning and realisation of road infrastructure projects is often societally controversial because of the impacts and the difficult trade-offs between alternative project designs. Neither in current literature nor in Environmental Impact Assessment (EIA) practice do we find any clear method to engage the public in integral trade-off choices. This paper aims to uncover public preferences about integrated road infrastructure projects, not by focusing directly on the – often contested – alternative project designs but by focusing on the size of the impacts. This paper examines the preferences for combinations of impacts and the corresponding orders of magnitude. We do so by employing Conjoint Analysis (CA), a well-established method in consumer studies which, to date, has rarely been used in infrastructure planning. CA systematically estimates public preferences for (1) the relative importance of selected impacts and (2) the corresponding Willingness-to-Pay and Willingness-to-Accept. We used the total range in sizes of impacts of three existing EIAs for infrastructure projects and asked 600 citizens to trade off the impacts involved. First, we find that public and policy preferences do not necessarily align. The CA results show that loss of ‘forest and green structures’ and ‘agricultural land’ are considered to be the most important impacts. Conversely, in each of the projects, travel time gain is regarded as the least important impact. Second, the order of magnitude of the impacts seems crucially important: the greater the magnitude of the impact, the higher its relative importance. Third, when comparing our results with the alternatives in the original EIAs, it appears that the project alternative with the highest weighted public preference required an optimised project design. We find that our systematic approach to uncover public preferences at this stage in the EIA-process can contribute to further design optimisation of the project. This stage is often overlooked in the process while it is the last EIA stage before the final approval decision is made, but the first time that the forecasted size of the impacts of the different alternative designs of a project are available and presented. Such an approach may enhance the development of alternatives that are more widely accepted by the communities and that thus potentially alleviate or even prevent social contestation.

Like parent, like child: how attitudes towards immigrants spill over to the political inclusion of their children

ABSTRACT Across Europe, citizenship is traditionally attributed at birth through descent only. As immigrant populations grow, policy-makers have come under pressure to extend citizenship rights to the children of immigrants born in the country. While such inclusive measures often counter political opposition, public attitudes on this question remain remarkably underexplored. In this study, we report on the results of an original choice-based conjoint survey experiment designed to examine which parental attributes affect respondents’ willingness to grant citizenship to newborns. We implement the survey experiment in Italy, where over one million children do not have Italian citizenship, yet reform proposals have so far been unsuccessful. In line with our pre-registered expectations, we find that respondents are more likely to support birthright citizenship for children born to parents who are economically, legally and socially integrated in society. These attitudes vary little by political background, education and age-category of respondents. Our findings suggest that incorporating immigration-related conditionality in birthright citizenship proposals is key to convincing sceptical publics of the legitimacy of such measures.

A paradigm shift in Precambrian research driven by big data

The enigmatic nature of the Precambrian era poses ongoing challenges for researchers seeking to uncover its secrets. With the exponential growth of scientific data, there exists a vast repository of information through which these mysteries can be explored. However, this data is often derived from fragmented investigations of geological phenomena within specific disciplinary domains and limited spatiotemporal boundaries, resulting in untapped potential for extracting undiscovered knowledge. To address this limitation, the field of big data science provides a foundation for multidisciplinary research on the geological evolution of the Precambrian. By harnessing the power of large-scale data integration and analysis, valuable insights into this pivotal era in Earth's history can be revealed. This paper offers a comprehensive overview of big data types in geoscience and elucidates common analysis methods. Through a case study focused on the North China Craton (NCC), we demonstrate the application of conjoint analysis to a dataset comprising rock and mineral geochemistry data. Employing local singularity analysis and wavelet analysis on zircon age frequency and Hf isotopic time series, we reveal a persistent long-term periodicity of 800–500 million years since 3.5 billion years ago. This finding indicates a co-evolution between the NCC and the global supercontinent cycle dating back to the Archean period. To investigate the formation of the NCC, we employ machine learning-based crustal thickness evolution reconstruction, which highlights arc formation during subduction as the primary factor, with regional mantle activities playing a secondary role in the convergence. Combining spatio-temporal evolution analysis of magmatic intensity and εHf(t) values, we infer that the development of the NCC primarily resulted from a prolonged process of accretion targeting the Eastern Block (the primary continent nuclei) through the incorporation of diverse arc massifs. This examination of the NCC serves as an example of how data collection, processing, and utilization can enhance our understanding of formational and evolutionary processes, signaling a paradigm shift in Precambrian research driven by the integration of big data.

Integrative physiological, transcriptomic and metabolomic analysis reveals how the roots of two ornamental Hydrangea macrophylla cultivars cope with lead (Pb) toxicity

Lead (Pb) soil contamination has caused serious ecological and environmental issues. Hydrangea macrophylla is a potential Pb-contaminated soil remediation plant, however, their Pb stress defense mechanism is largely unknown. Here, the physiology, transcriptomic and metabolome of two H. macrophylla cultivars (ML, Pb-sensitive cultivar; JC, Pb-resistant cultivar) under Pb stress were investigated. The results demonstrated that JC performed superiorly, with activities of the antioxidant enzymes superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) were 1.25, 2.84, and 1.67 times higher than those of ML after Pb treatment, respectively, and the amount of soluble sugar in JC increased by 231.34 % compared with that in ML. The electrical conductivity (EC) value of the root exudates of JC was 43.71 % lower than that of ML under Pb stress. The non-targeted metabolomics analysis revealed 193 metabolites grouped into nine categories. Pb stress-induced differential expression of the 37 metabolites, among which the major metabolites up-regulated in ML were organic acids, while in JC, these were carbohydrates, fatty acids, organic acids and lipids. The transcriptomic analysis revealed that Pb exposure induced 1075 and 1314 differentially expressed genes (DEGs) in JC and ML, respectively. According to the functional annotation results, hub genes were primarily enriched in carbohydrate metabolism, root growth, and plant resistance to external stresses. A conjoint analysis of the two omics indicated that the cutin, suberine and wax biosynthesis pathway in JC played an essential role in Pb detoxification. These findings clarify the resistance mechanism of H. macrophylla to Pb stress and open up a new avenue for breeding H. macrophylla Pb-resistant cultivars.

Homocysteine and Lp-PLA2 levels: Diagnostic value in coronary heart disease.

Coronary heart disease (CHD) is the leading cause of mortality worldwide. Identifying effective diagnostic markers and understanding risk factors is crucial for prevention and management. This study aimed to investigate the levels of homocysteine (Hcy) and lipoprotein-associated phospholipase A2 (Lp-PLA2) in human plasma and their roles in the diagnosis and prognosis of CHD. A retrospective study was conducted on 232 patients with CHD, divided into Acute Myocardial Infarction, unstable angina pectoris, and stable angina pectoris groups, and a control group of 75 healthy adults. Blood samples were analyzed for serum Hcy and Lp-PLA2 levels using the cycling enzyme method and ELISA method, respectively. Statistical analyses were performed to evaluate the risk factors, and diagnostic efficacy was assessed using receiver operating characteristic (ROC) curves. No significant differences in age and sex were observed between the study and control groups, whereas marked disparities in risk factors such as obesity, hypertension, diabetes, and hyperlipidemia were noted. Significant differences in serum Hcy and Lp-PLA2 levels were identified among the CHD subgroups. Univariate and multivariate logistic regression analyses revealed that Hcy, Lp-PLA2, hypertension, and hyperlipidemia were significant risk factors for CHD. The combined diagnostic Area Under the Curve (AUC) for Hcy and Lp-PLA2 was found to be higher than that when using them individually. This study identified the elevation of Hcy and Lp-PLA2 levels as independent risk factors for CHD, and their conjoint analysis significantly enhanced clinical diagnostic efficacy. These findings provide valuable insights for CHD diagnosis, treatment, and prevention, highlighting the importance of these markers in CHD management.

Analysis of medium-frequency oscillation based on bifurcation theory and joint modelling of SVG and direct-drive wind turbine

With the expansion of wind power clusters in power system, medium and high frequency oscillations in wind farm area are preliminarily emerging. At present, linear analysis such as impedance analysis are mainly used to analyse the grid-connected oscillation of renewable energy generation. However, the explanation of the oscillation mechanism is not intuitive and does not take into account the nonlinearity of converter. Considering the influence of external loop control of SVG and direct-drive wind turbine on the medium-frequency characteristics of the parallel system, a reduced-order nonlinear model under voltage time scale is established. The conjoint analysis of Jacobi matrix, bifurcation diagram and phase space trajectory are applied to study the influence rule of machine-side output power and short-circuit ratio on the oscillation behaviour of the parallel system. In addition, the oscillation evolution process is analysed from the dynamic standpoint. It is found that the system operates on the limit-loop state when the medium-frequency oscillation occurs, which reveals the mapping relationship between the oscillation amplitude and the limit-loop trajectory. The experimental results ultimately validate the theoretical analysis conducted on the StarSim-HIL (hardware-in-the-loop) experiment platform.

Role of Artificial Intelligence in Project Efficiency Mediating with Perceived Organizational Support in the Indian IT Sector

This study investigates the influence of demographic variables such as experience and age on the project efficiency of the IT sector. The study employed a quantitative methodology by collecting data from 380 responses from respondents working in various IT organizations. The data was further processed and analysed using SPSS software. Conjoint analysis is used to identify the attributes that are important to employees and classify each attribute into its own level. Discriminant analysis is used to find the association between the demographic characteristics of the respondents and employee status. The results of this study lay the groundwork for future research on artificial intelligence adoption in emerging nations, and they show a notable relationship between enhancing project efficiency in the IT industry. The researchers found that the age of the employees has a significant impact on project efficiency. Moreover, this study shows that IT workers under the age of 30 have the largest influence on project efficiency, representing a substantial demographic cohort in the organization. In addition, this research expands on these findings by indicating that individuals under the age of 30 with less than 5 years of experience are highly motivated to investigate AI opportunities and effectively use them in their job.

ANALISIS PREFERENSI KONSUMEN TERHADAP GULA SEMUT AREN DI PULAU LOMBOK

This study aims to: (1) Describe consumer buying behavior towards Crystals Palm Sugar from point of purchase, purchase frequency, purchase amount and purchase purposes on Lombok Island, (2) Analyze consumer preferences on Crystals Palm Sugar attributes on Lombok Island.&#x0D; The method used is the descriptive method. The unit of analysis in this study was the consumers of crystals palm sugar on the island of Lombok. Determination of the sample area using purposive sampling with the consideration that West Lombok Regency is an crystals palm sugar production location and sale location and the city of Mataram is a crystals palm sugar production retail outlet. The number of respondents in this study was 50 respondents that were determined by the accidental sampling technique. The types of data used are qualitative data and quantitative data. The data sources used are primary data and secondary data. The data analysis used was an interval scale evaluation and it was processed by conjoint analysis with the help of the SPSS version 25 application.&#x0D; The results of this study indicate that: (1) The purchasing behavior of crystals palm sugar consumers in Lombok Island is that they prefer to buy at the production site, twice a month, the number of purchases is &gt; 1000 gr in a month, and the purpose of the purchase is for consumption. (2) Crystals palm sugar attributes that are consumer preferences based on utility value are original flavor 0.095, IDR15,000-IDR25,000 price 0.357, permanent bag packaging type 0.180, BPOM label 0.590, halal label 0.855, and 1000gr size 0 .332. The crystals palm sugyar attributes that respondents considered the most when choosing crystal palm sugar on Lombok Island in sequence, starting from most considered to least considered, were halal label value 23,599, price 21,068, BPOM label value 17,002, packaging type value 16,511, size value 14,601, and last one is the aroma attribute with an importance value of 7.219.

Integrative proteomic and metabonomic profiling elucidates amino acid and lipid metabolism disorder in CA-MRSA-infected breast abscesses.

Bacterial culture and drug sensitivity testing have been the gold standard for confirming community-acquired methicillin-resistant Staphylococcus aureus (CA-MRSA) infection in breast abscess with a long history. However, these tests may delay treatment and increase the risk of nosocomial infections. To handle and improve this critical situation, this study aimed to explore biomarkers that could facilitate the rapid diagnosis of CA-MRSA infection. This study for the first time applied label-free quantitative proteomics and non-targeted metabonomics to identify potential differentially expressed proteins (DEPs) and differentially expressed metabolites (DEMs) in breast abscess infected with CA-MRSA compared to methicillin-susceptible S. aureus (MSSA). The two omics data were integrated and analyzed using bioinformatics, and the results were validated using Parallel Reaction Monitoring (PRM). Receiver operating characteristic (ROC) curves were generated to evaluate the predictive efficiency of the identified biomarkers for diagnosing CA-MRSA infection. After using the above-mentioned strategies, 109 DEPs were identified, out of which 86 were upregulated and 23 were downregulated. Additionally, a total of 61 and 26 DEMs were initially screened in the positive and negative ion modes, respectively. A conjoint analysis indicated that the amino acid metabolism, glycosphingolipid biosynthesis, and glycerophospholipid metabolism pathways were co-enriched by the upstream DEPs and downstream DEMs, which may be involved in structuring the related network of CA-MRSA infection. Furthermore, three significant DEMs, namely, indole-3-acetic acid, L-(-)-methionine, and D-sedoheptulose 7-phosphate, displayed good discriminative abilities in early identification of CA-MRSA infection in ROC analysis. As there is limited high-quality evidence and multiple omics research in this field, the explored candidate biomarkers and pathways may provide new insights into the early diagnosis and drug resistance mechanisms of CA-MRSA infection in Chinese women.

Exploring which factors impact restaurant willingness-to-pay by menu course

ABSTRACT While hospitality researchers have examined what impacts willingness-to-pay in restaurants, they have yet to determine if willingness-to-pay varies by menu course. The impact of sustainability messages and historical information on willingness-to-pay has also not been explored. This study used choice-based conjoint analysis to examine the effects of food quality, service quality, ambiance, sustainability information, historical information, and price on willingness-to-pay for three different courses in a restaurant: appetizer, entrée, and dessert. The choice of menu items and prices was based on an examination of casual dining restaurants in the U.S. The results indicate that while food quality, ambiance, and service quality were significant for all three courses, there were some interesting differences by course. While food quality was consistently the highest-rated antecedent, demographic information significantly changed the value of other antecedents for appetizers. For desserts, the uncommon menu item was the most chosen item. The sustainability and historical information messages were not significant, indicating that consumers may be experiencing information overload.

How to design a value-based Chatbot for the manufacturing industry: An empirical study of an internal assistance for employees

With regard to AI as a key technology, this scientific paper deals with the identification of user drivers on the purchase decision of a cooperative AI (as explainable AI—XAI), as well as the analysis of the willingness to pay in the context of value-based pricing. Besides the economic dimension with regard to usefulness and usability of the system, the focus is mainly on the (innovative) explainable character. The analysis is carried out by a choice-based conjoint analysis (CBC) using the example of an intelligent assistance system for employees that supports internal business processes and workflows in business organizations. For this purpose, fictitious purchase offers were created under which decision-makers in manufacturing business organizations in Germany made simulated purchase decisions. The analysis shows that the target group attach great utility value to transparency in the sense of explanatory content, in addition to a high degree of interactivity and a high level of reliability.