The sharing economy holds promise for the way we consume, work, and interact. However, consuming in the sharing economy is not without risk, as institutional trust measures (e.g. contracts, regulations, guarantees) are often absent. Trust between sellers and buyers is therefore crucial to complete transactions successfully. From a buyer ́s perspective, a seller ́s profile is an important source of information for judging trustworthiness, because it contains multiple trust cues such as a reputation score, a profile picture, and a textual self-description. The effect of a seller’s self-description on perceived trustworthiness is still poorly understood. We examine how the linguistic features of a seller’s self-description predict perceived trustworthiness. To determine the perceived trustworthiness of 259 profiles, 189 real buyers on a Dutch sharing platform rated their trustworthiness. The results show that profiles were perceived as more trustworthy if they contained more words (which could be an indicator of uncertainty reduction), more words related to cooking (indicator of expertise), and more words related to positive emotions (indicator of enthusiasm). Also, a profile’s perceived trustworthiness score correlated positively with the seller’s actual sales performance. These findings indicate that a seller’s self-description is a relevant signal to buyers, eventhough it is cheap talk (i.e. easy to produce). The results can guide sellers on how to self-present themselves on sharing platforms and inform platform owners on how to design their platform so that it enhances trust between platform users.
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In the course of our supervisory work over the years we have noticed that qualitative research tends to evoke a lot of questions and worries, so-called frequently asked questions (FAQs). This series of four articles intends to provide novice researchers with practical guidance for conducting high-quality qualitative research in primary care. By ‘novice’ we mean Master’s students and junior researchers, as well as experienced quantitative researchers who are engaging in qualitative research for the first time. This series addresses their questions and provides researchers, readers, reviewers and editors with references to criteria and tools for judging the quality of qualitative research papers. The first article provides an introduction to this series. The second article focused on context, research questions and designs. The third article focused on sampling, data collection and analysis. This fourth article addresses FAQs about trustworthiness and publishing. Quality criteria for all qualitative research are credibility, transferability, dependability, and confirmability. Reflexivity is an integral part of ensuring the transparency and quality of qualitative research. Writing a qualitative research article reflects the iterative nature of the qualitative research process: data analysis continues while writing. A qualitative research article is mostly narrative and tends to be longer than a quantitative paper, and sometimes requires a different structure. Editors essentially use the criteria: is it new, is it true, is it relevant? An effective cover letter enhances confidence in the newness, trueness and relevance, and explains why your study required a qualitative design. It provides information about the way you applied quality criteria or a checklist, and you can attach the checklist to the manuscript.
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The impact of perceived trustworthiness on love for news media brands needs further understanding, given that brand love brings significant business benefits. This article argues that perceived trustworthiness influences brand love for news media brands. More specifically, we explored the following: how much of the variance in brand love can be explained by the factors of perceived trustworthiness of a brand (integrity, benevolence, and ability), whether perceived trustworthiness relates differently to brand love in countries with different levels of trust in the media, and what is the relationship between political leaning and the perception of specific news media brands’ trustworthiness. The data were collected through an online survey in the Netherlands (N = 292) and Brazil (N = 239). Among the main findings, data indicated that brand love is influenced by perceived trustworthiness, and integrity is its best predictor. This study provides evidence of the importance of each factor of perceived trustworthiness for brand love, confirms the relation between political leaning and trust, and reveals the differences between these two countries.
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Trust between providers and consumers in the sharing economy are crucial to complete transactions successfully. From a consumer's perspective, a provider's profile is an important source of information for judging trustworthiness, because it contains multiple trust cues. However, the effect of a provider's self-description on perceived trustworthiness is still poorly understood. We examine how the linguistic features of a provider's self-description predict perceived trustworthiness. To determine the perceived trustworthiness of 259 profiles, real consumers on a Dutch sharing platform rated these profiles for trustworthiness. The results show that profiles were perceived as more trustworthy if they contained more words, more words related to cooking, and more words related to positive emotions. Also, a profile's perceived trustworthiness score correlated positively with the provider's actual sales performance. These findings indicate that a provider's self-description is a relevant signal to consumers, even though it seems easy to fake.
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We present a novel anomaly-based detection approach capable of detecting botnet Command and Control traffic in an enterprise network by estimating the trustworthiness of the traffic destinations. A traffic flow is classified as anomalous if its destination identifier does not origin from: human input, prior traffic from a trusted destination, or a defined set of legitimate applications. This allows for real-time detection of diverse types of Command and Control traffic. The detection approach and its accuracy are evaluated by experiments in a controlled environment.
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Citizens regularly search the Web to make informed decisions on daily life questions, like online purchases, but how they reason with the results is unknown. This reasoning involves engaging with data in ways that require statistical literacy, which is crucial for navigating contemporary data. However, many adults struggle to critically evaluate and interpret such data and make data-informed decisions. Existing literature provides limited insight into how citizens engage with web-sourced information. We investigated: How do adults reason statistically with web-search results to answer daily life questions? In this case study, we observed and interviewed three vocationally educated adults searching for products or mortgages. Unlike data producers, consumers handle pre-existing, often ambiguous data with unclear populations and no single dataset. Participants encountered unstructured (web links) and structured data (prices). We analysed their reasoning and the process of preparing data, which is part of data-ing. Key data-ing actions included judging relevance and trustworthiness of the data and using proxy variables when relevant data were missing (e.g., price for product quality). Participants’ statistical reasoning was mainly informal. For example, they reasoned about association but did not calculate a measure of it, nor assess underlying distributions. This study theoretically contributes to understanding data-ing and why contemporary data may necessitate updating the investigative cycle. As current education focuses mainly on producers’ tasks, we advocate including consumers’ tasks by using authentic contexts (e.g., music, environment, deferred payment) to promote data exploration, informal statistical reasoning, and critical web-search skills—including selecting and filtering information, identifying bias, and evaluating sources.
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Abstract Background: Frail older adults who are hospitalized, are more likely to experience missed nursing care (MNC) due to high care needs, communication problems, and complexity of nursing care. We conducted a qualitative study to examine the factors affecting MNC among hospitalized frail older adults in the medical units. Methods: This qualitative study was carried using the conventional content analysis approach in three teaching hospitals. Semi-structured interviews were conducted with 17 nurses through purposive and snowball sampling. The inclusion criteria for the nurses were: at least two years of clinical work experience on a medical ward, caring for frail older people in hospital and willingness to participate. Data were analyzed in accordance with the process described by Graneheim and Lundman. In addition, trustworthiness of the study was assessed using the criteria proposed by Lincoln and Guba. Results: In general, 20 interviews were conducted with nurses. A total of 1320 primary codes were extracted, which were classified into two main categories: MNC aggravating and moderating factors. Factors such as “age-unfriendly structure,” “inefficient care,” and “frailty of older adults” could increase the risk of MNC. In addition, factors such as “support capabilities” and “ethical and legal requirements” will moderate MNC. Conclusions: Hospitalized frail older adults are more at risk of MNC due to high care needs, communication problems, and nursing care complexity. Nursing managers can take practical steps to improve the quality of care by addressing the aggravating and moderating factors of MNC. In addition, nurses with a humanistic perspective who understand the multidimensional problems of frail older adults and pay attention to their weakness in expressing needs, can create a better experience for them in the hospital and improve patient safety.
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The user’s experience with a recommender system is significantly shaped by the dynamics of user-algorithm interactions. These interactions are often evaluated using interaction qualities, such as controllability, trust, and autonomy, to gauge their impact. As part of our effort to systematically categorize these evaluations, we explored the suitability of the interaction qualities framework as proposed by Lenz, Dieffenbach and Hassenzahl. During this examination, we uncovered four challenges within the framework itself, and an additional external challenge. In studies examining the interaction between user control options and interaction qualities, interdependencies between concepts, inconsistent terminology, and the entity perspective (is it a user’s trust or a system’s trustworthiness) often hinder a systematic inventory of the findings. Additionally, our discussion underscored the crucial role of the decision context in evaluating the relation of algorithmic affordances and interaction qualities. We propose dimensions of decision contexts (such as ‘reversibility of the decision’, or ‘time pressure’). They could aid in establishing a systematic three-way relationship between context attributes, attributes of user control mechanisms, and experiential goals, and as such they warrant further research. In sum, while the interaction qualities framework serves as a foundational structure for organizing research on evaluating the impact of algorithmic affordances, challenges related to interdependencies and context-specific influences remain. These challenges necessitate further investigation and subsequent refinement and expansion of the framework.
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Trustworthy data-driven prognostics in gas turbine engines are crucial for safety, cost-efficiency, and sustainability. Accurate predictions depend on data quality, model accuracy, uncertainty estimation, and practical implementation. This work discusses data quality attributes to build trust using anonymized real-world engine data, focusing on traceability, completeness, and representativeness. A significant challenge is handling missing data, which introduces bias and affects training and predictions. The study compares the accuracy of predictions using Exhaust Gas Temperature (EGT) margin, a key health indicator, by keeping missing values, using KNN-imputation, and employing a Generalized Additive Model (GAM). Preliminary results indicate that while KNN-imputation can be useful for identifying general trends, it may not be as effective for specific predictions compared to GAM, which considers the context of missing data. The choice of method depends on the study’s objective: broad trend forecasting or specific event prediction, each requiring different approaches to manage missing data.
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In this paper we present a system that generates questions from an ontology to determine a crisis situation by ordinary people using their mobile phone: the Situation Awareness Question Generator. To generate questions from an ontology we propose a formalization based on Situation Theory and several strategies to determine a situation as quickly as possible. A suitable ontology should comply with human categorization to enhance trustworthiness. We created three ontologies, i.e. a pragmatic-based ontology, an expert-based ontology and a basiclevel ontology. Several experiments, published elsewhere, showed that the basic-level ontology is most suitable.
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