User Knowledge Research Articles

SME users in Industry 4.0 Study of the Metalworking Industry in Argentina

In the era of Industry 4.0, the success of the adoption of advanced technologies depends on how they are assimilated and incorporated into current production systems. This work focuses on analyzing the state of technological adoption in Small and Medium Enterprises (SMEs) in the metalworking sector in Argentina, analyzing the ICT products implemented in each of the functional areas of the company that maintain interaction with the people who operate them and identifying the knowledge and capabilities of real users for each functional area. For this purpose, an expanded InTICs®methodology was used to measure the degree of technological maturity of a productive company. The obtained results show that most of the companies surveyed present a medium level of technological maturity, with variations depending on their size. Microenterprises tend to register a basic level of maturity, while medium and large companies show greater adoption of advanced technologies. These results offer a detailed vision of the integration of Industry 4.0 technologies in Argentine metalworking SMEs, providing valuable information to identify users and the levels of interaction between people and the technologies they operate, as well as to analyze the required human capabilities and training strategies to promote digital transformation in this sector.

Mapping the evidence on patient-centred outcome measures used in orphan medicinal product reimbursement frameworks: Protocol for a scoping review

Background Patient-centered outcome measures (PCOMs) are tools designed to integrate patient perspectives by focusing on outcomes that matter the most to patients. Their importance is pronounced in rare diseases where patients face unique challenges. PCOMs and Orphan Medicinal Product (OMP) reimbursement frameworks are critical for addressing the challenges of rare diseases. Their integration helps ensure that treatments meet patient needs, while being accessible and economically sustainable. This scoping review aims to explore the evidence available on the relationship between PCOMs and OMP reimbursement frameworks. Methods This scoping review will adhere to the Preferred Reporting Items of Systematic Reviews extension for Scoping Review (PRISMA-ScR) and Joanna Briggs Institute guidelines, utilizing a structured six-step framework: (1) defining the research question, (2) locating relevant studies, (3) selecting studies, (4) extracting and organizing data, (5) synthesizing, summarizing, and presenting findings, and (6) engaging in consultation with knowledge users. Inclusion criteria were determined using the population-conceptual-context (PCC) framework. Searches will be conducted in CINAHL, Embase, MEDLINE (via PubMed), PsycINFO, SCOPUS, and the Web of Science. Grey literature will also be explored. Two reviewers will screen the titles and abstracts of the identified studies using a dual review process to ensure rigorous selection of relevant articles. The patterns, advances, gaps, evidence for practice, and research recommendation (PAGER) framework will be applied to summarize the findings. A panel of patient and public involvement (PPI) representatives will be engaged to validate the results and ensure alignment with patient-centered perspectives. Results The results will be reported according to the PRISMA-ScR checklist. Conclusions This scoping review will provide a comprehensive overview of the existing evidence on the relationship between PCOMs and OMP reimbursement frameworks. The findings have the potential to inform policy decisions, shape future research, and enhance the integration of patient-centered perspectives in reimbursement frameworks.

The impact of emotional cues on the information-gathering willingness of paid knowledge users

The impact of emotional cues on the information-gathering willingness of paid knowledge users

The influence of user knowledge and usage behaviour on decision-making and perceived reputation of streaming sites that use dark patterns

ABSTRACT In this paper, we examined how dark patterns (confirmshaming and trick-question), user knowledge, number of services owned and usage frequency impact users' decision-making and service reputation using a subscription-based streaming website as proof-of-concept. Overall, users perceived both patterns as manipulative. However, this negative perception did not adversely impact the perceived trustworthiness and credibility of the website. While in the confirmshaming condition, 68% of those without knowledge of dark patterns selected the expensive plan promoted by the service over the cheap (standard) plan, the reverse is the case among those with knowledge, 35% of whom selected the expensive (premium) plan. This finding indicates that as users become knowledgeable about dark patterns, they are more likely to reject the service-promoted choice, as 40% of knowledgeable users in the trick-question condition edited their initial choice, compared with 10% and 6% in the confirmshaming and control conditions, respectively. Moreover, low-frequency and low-services users in the trick-question condition were most likely to fall for the expensive plan. However, high-frequency and high-services users in the confirmshaming condition were most likely to fall for the expensive plan. The findings highlight the need to raise awareness about dark patterns to prevent unsuspecting users from making financial decisions against their best interest.

BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories.

Bayesian network modeling (BN modeling, or BNM) is an interpretable machine learning method for constructing probabilistic graphical models from the data. In recent years, it has been extensively applied to diverse types of biomedical data sets. Concurrently, our ability to perform long-time scale molecular dynamics (MD) simulations on proteins and other materials has increased exponentially. However, the analysis of MD simulation trajectories has not been data-driven but rather dependent on the user's prior knowledge of the systems, thus limiting the scope and utility of the MD simulations. Recently, we pioneered using BNM for analyzing the MD trajectories of protein complexes. The resulting BN models yield novel fully data-driven insights into the functional importance of the amino acid residues that modulate proteins' function. In this report, we describe the BaNDyT software package that implements the BNM specifically attuned to the MD simulation trajectories data. We believe that BaNDyT is the first software package to include specialized and advanced features for analyzing MD simulation trajectories using a probabilistic graphical network model. We describe here the software's uses, the methods associated with it, and a comprehensive Python interface to the underlying generalist BNM code. This provides a powerful and versatile mechanism for users to control the workflow. As an application example, we have utilized this methodology and associated software to study how membrane proteins, specifically the G protein-coupled receptors, selectively couple to G proteins. The software can be used for analyzing MD trajectories of any protein as well as polymeric materials.

A Human-Centered Multiperspective and Interactive Visual Tool For Explainable Machine Learning

Understanding why a trained machine learning model makes some decisions is paramount to trusting the model and applying its recommendations in real-world applications. In this article, we present the design and development of an interactive and visual approach to support the use, interpretation and refinement of ML models, whose development was guided by user's needs. We also present Explain-ML, an interactive tool that implements a visual multi-perspective approach to the support interpretation of ML models. Explain-ML development followed a Human-Centered Machine Learning strategy guided by the target (knowledgeable) users' demands, resulting in a multi-perspective approach in which interpretability is supported by a set of complementary visualizations under several perspectives (e.g., global and local). We performed a qualitative evaluation of the tool´s approach to interpretation with a group of target users, focused on their perspective regarding Explain-ML helpfulness and usefulness in comprehending the outcomes of ML models. The evaluation also explored users' capability in applying the knowledge obtained from the tool's explanations for adapting/improving the current models. Results show that Explain-ML provides a broad account of the model's execution (including historical), offering users an ample and flexible exploration space to make different decisions and conduct distinct analyses. Users stated the tool was very useful and that they would be interested in using it in their daily activities.

Perspectives on which health settings geriatricians should staff: a qualitative study of patients, care providers and health administrators

BackgroundWith a shortage of geriatricians and an aging population, strategies are needed to optimise the distribution of geriatricians across different healthcare settings (acute care, rehabilitation and community clinics). The perspectives of knowledge users on staffing geriatricians in different healthcare settings are unknown. We aimed to understand the acceptability and feasibility (including barriers and facilitators) of implementing a geriatrician-led comprehensive geriatric assessment (CGA) in acute care, rehabilitation, and community clinic settings.MethodsA qualitative description approach was used to explore the experience of those implementing (administrative staff), providing (healthcare providers), and receiving (patients/family caregivers) a geriatrician-led CGA in acute care, rehabilitation and community settings. Semi-structured interviews were conducted in Toronto, Canada. The theoretical domains framework and consolidated framework for implementation research informed the interview guide development. Analysis was conducted using a thematic approach.ResultsOf the 27 participants (8 patients/caregivers, 9 physicians, 10 administrators), the mean age was 53 years and 14 participants (52%) identified as a woman (13 [48%] identified as a man). CGAs were generally perceived as acceptable but there was a divergence in opinion about which healthcare setting was most important for geriatricians to staff. Acute care was reported to be most important by some because no other care provider has the intersection of acute medicine skills with geriatric training. Others reported that community clinics were most important to manage geriatric syndromes before hospitalization was necessary. The rehabilitation setting appeared to be viewed as important but as a secondary setting. Facilitators to implementing a geriatrician-led CGA included (i) a multidisciplinary team, (ii) better integration with primary care, (iii) a good electronic patient record system, and (iv) innovative ways to identify patients most in need of a CGA. Barriers to implementing a geriatrician-led CGA included (i) lack of resources or administrative support, (ii) limited team building, and (iii) consultative model where recommendations were made but not implemented.ConclusionsOverall, participants found CGAs acceptable yet had different preferences of which setting to prioritise staffing if there was a shortage of geriatricians. The main barriers to implementing the geriatrician-led CGA related to lack of resources.Clinical trial numberNot applicable.

Enhancing young people's pregnancy loss and fertility awareness and knowledge via schools: a way forward.

Pregnancy and infant loss, in the form of miscarriage, stillbirth or early neonatal death, occurs in 20-25% of all pregnancies. Despite its prevalence and associated physical and psychological impacts, there remains a lack of public awareness and understanding of pregnancy loss, including amongst people of reproductive age. Drawing on evidence from a preliminary review of peer-reviewed and grey literature, we make the case for enhancing pregnancy loss and (in)fertility awareness, specifically focusing on young people in second-level education. We situate our work within reproductive health and reproductive justice frames, recognizing the impact of social factors on people's reproductive lives, and the need for multi-level interventions to enable people to fully realize their reproductive rights and goals. Although schools provide an important setting to learn about and discuss topics relating to sexual and reproductive health-including pregnancy loss and fertility-current evidence suggests that this is not happening, despite young people's desire to engage in such conversations. While there are barriers to addressing sexual and reproductive health issues within schools (including lack of access to teacher training, continuing professional development, appropriate and engaging resource material, teacher discomfort and lack of confidence), it is important that interventions are developed in collaboration with all relevant knowledge users, including young people themselves. This will help to ensure that any interventions developed are relevant, acceptable, feasible and effective. Further research is needed to explore how education around pregnancy loss and fertility can be best delivered within school settings.

Leveraging E-Marketing for Enhancing Customer Knowledge Management: A Systematic Review of Tourism Strategies in the Saudi Arabian Context

Leveraging E-Marketing for Enhancing Customer Knowledge Management: A Systematic Review of Tourism Strategies in the Saudi Arabian Context

Digital Transformation of Rheumatology Care in Germany: Cross-Sectional National Survey.

In recent years, health care has undergone a rapid and unprecedented digital transformation. In many fields of specialty care, such as rheumatology, this shift is driven by the growing number of patients and limited resources, leading to increased use of digital health technologies (DHTs) to maintain high-quality clinical care. Previous studies examined user acceptance of individual DHTs in rheumatology, such as telemedicine, video consultations, and mHealth. However, it is essential to conduct cross-technology and continuous analyses of user acceptance and DHT use to maximize the benefits for all relevant stakeholders. This study aimed to explore the current acceptance, use, and preferences regarding DHTs among patients in rheumatology care in Germany. Rheumatology patients from 3 clinics in Germany were surveyed to understand their perspectives on DHTs. The survey included main themes, including acceptance, preferences, COVID-19's impact, potential, and barriers related to DHTs. The data were analyzed using descriptive statistics and correlation analysis. Out of 337 participants, 53% (179/337) reported using DHTs. Specific technologies included wearables (72/337, 21%), mHealth apps (71/337, 21%), digital therapeutics (32/337, 9%), electronic prescriptions (30/337, 9%), video consultations (15/337, 4%), and at-home blood self-sampling (3/337, 1%). Nearly two-thirds (220/337, 65%) found DHTs useful, and 69% (233/337) held a generally positive attitude toward DHTs. Attitudes shifted positively during the COVID-19 pandemic for 40% (135/337) of participants. Higher education was more prevalent among DHT users (114/179, 63.7%) compared with nonusers (42/151, 27.8%; P=.02). The main potential benefits identified were location-independent use (244/337, 72%) and time-independent use (216/337, 64%). Key barriers included insufficient user knowledge (165/337, 49%) and limited information on DHTs (134/337, 40%). Patient acceptance and use of DHTs in rheumatology is increasing in Germany. A prospective, standardized monitoring of digital transformation in rheumatology care is highly needed.

Revealing Interaction Dynamics: Multi-Level Visual Exploration of User Strategies with an Interactive Digital Environment.

We present a visual analytics approach for multi-level visual exploration of users' interaction strategies in an interactive digital environment. The use of interactive touchscreen exhibits in informal learning environments, such as museums and science centers, often incorporate frameworks that classify learning processes, such as Bloom's taxonomy, to achieve better user engagement and knowledge transfer. To analyze user behavior within these digital environments, interaction logs are recorded to capture diverse exploration strategies. However, analysis of such logs is challenging, especially in terms of coupling interactions and cognitive learning processes, and existing work within learning and educational contexts remains limited. To address these gaps, we develop a visual analytics approach for analyzing interaction logs that supports exploration at the individual user level and multi-user comparison. The approach utilizes algorithmic methods to identify similarities in users' interactions and reveal their exploration strategies. We motivate and illustrate our approach through an application scenario, using event sequences derived from interaction log data in an experimental study conducted with science center visitors from diverse backgrounds and demographics. The study involves 14 users completing tasks of increasing complexity, designed to stimulate different levels of cognitive learning processes. We implement our approach in an interactive visual analytics prototype system, named VISID, and together with domain experts, discover a set of task-solving exploration strategies, such as "cascading" and "nested-loop', which reflect different levels of learning processes from Bloom's taxonomy. Finally, we discuss the generalizability and scalability of the presented system and the need for further research with data acquired in the wild.

Adaptive recommendation method for teaching resources based on knowledge graph and user similarity

Adaptive recommendation method for teaching resources based on knowledge graph and user similarity

Adaptive recommendation method for teaching resources based on knowledge graph and user similarity

Adaptive recommendation method for teaching resources based on knowledge graph and user similarity

Supply Chain Sustainability in Disruption: SC Intelligence, Customer Knowledge Transfer, and the Mediating Role of IS/IT

Supply Chain Sustainability in Disruption: SC Intelligence, Customer Knowledge Transfer, and the Mediating Role of IS/IT

How frontline employees navigate the double-edged effects of customer knowledge

Knowledgeable customers can be both assets and liabilities to sales organizations. However, understanding how frontline employees (FLEs) can influence customers’ firm-specific knowledge and navigate its consequences remains ambiguous. Leveraging relational theories within a high-involvement business-to-consumer sales context, this study examines (1) the FLE-contingent drivers that shape the development of customer knowledge of the firm, and (2) the FLE-contingent impact of such knowledge. Findings show that customer knowledge of the firm enhances customer revenue but impairs FLE-owned loyalty. To buffer the negative knowledge-loyalty effect, FLEs need to show high level of competence. This study also theorizes two factors that influence the level of customer knowledge of the firm: customer multi-sourcing, which is believed to cause a reduction in customer knowledge, and FLE cross-selling behaviors, which is believed to drive an increase in customer knowledge. Results reveal that FLE competence presents a mixed set of moderating influences on the extent to which customers invest in firm-specific knowledge. Taken together, findings provide actionable insights into how FLEs navigate knowledgeable customers to drive customer revenue and FLE-owned loyalty.

The Influence of Income, Knowledge, and Time Period on Customer Decisions to Take Financing at PT. BPRS ADECO Langsa

When taking out murabahah financing at a BPRS, customers will always consider their income. Customer income is very important because it can determine the customer's ability to make installment payments. Apart from income, when taking out financing at a bank, customers can be influenced by other factors such as the customer's knowledge of a product. Apart from that, another factor that can influence a customer's decision to take out financing is the time period. This is because the financing payment period varies depending on the length of time agreed between the customer and the bank. Therefore, researchers are interested in conducting research on income, knowledge and time period on customers' decisions to take financing. The research method is quantitative with an associative approach. The sample using the limeshow formula of 100 people. The results of the research showed that: (1) Variables interested in conducting research on income, knowledge and time period on customer's decision to take financing. (2) The knowledge variable on customer decisions in taking financing at PT BPRS Adeco Langsa is 0.000 < 0.05 and tcount 5.697 > ttable 1.984. (3) The time period variable for the customer's decision to take financing at PT BPRS Adeco Langsa is 0.341 > 0.05 and tcount 0.957 < ttable 1.984. (4) The income, knowledge and term variables can be said to have a simultaneous influence on customer decision variables in taking financing at PT BPRS Adeco Langsa. With test results Fcount 34.058 > Ftable 2.696 and Sig. 0.000 < 0.05. The R2 value is 51.6% and the remaining 48.4% is influenced by other variables outside this research.

Analysis of the Architecture of Expert Systems for Cancer Diagnosis

The article presents an analysis of the architecture of expert systems for cancer diagnosis based on modern artificial intelligence approaches. The key components of such systems are considered, including the knowledge base, inference mechanisms, user interface, and integration with multi-omic medical data. A comparative analysis of architectural solutions used in modern expert systems is carried out, and their advantages and limitations for early diagnosis and monitoring of cancer are identified. Particular attention is paid to machine learning methods that provide automatic knowledge updating and improve diagnostic accuracy. The results obtained can be used for further development of efficient, self-learning expert systems aimed at improving the quality of medical diagnosis and monitoring of patients with cancer

A Study on Socio-Demographic Determinants of Digital Financial Literacy in India

Digital financial literacy has emerged as a key driver for fostering financial inclusion in India. As India has been witnessing rapid digitalization of financial services, digital financial literacy has become an imperative because of it’s transformative potential to spread awareness, knowledge and influence user behaviors towards adoption of digital financial services. The purpose of this study was to investigate relationships between various socio-economic and demographic factors and digital financial literacy variables in both rural and urban areas of India. The study relied on a mix of quantitative surveys and qualitative interviews from the respondents belonging to urban and rural areas spread across eight districts of India. The primary data collected underwent several statistical analyses, including the Chi-Square Test, Cramer’s V, correlation analysis, and multiple regression analysis to examine the relationships between socio-demographic factors and digital financial literacy. The study revealed a strong positive correlation between education, age and income with digital financial literacy, underscoring their substantial impact. In contrast, other socio-demographic factors like occupation and social stratification were found to have lesser influence on the digital financial literacy in view of their weaker correlations. The study revealed that digital financial literacy is gender neutral considering negligible correlation between gender and variables of digital financial literacy. The existence of digital divide between urban and rural areas was evident in the study as the rural population lags in digital financial literacy to their urban counterparts. Regression analysis further validates the predictive significance of age, education, and annual family income on digital financial literacy. The earlier research studies and available literature emphasize mainly supply side factors of digital financial literacy viz. measures taken by Governments, Regulators and Financial institutions. This study, focusing on demand side factors, put special emphasis on social and demographic variables and their degree of influence on digital financial literacy. Key findings emerged from the study identify the socio-demographic factors influencing digital financial literacy and offer insights for policy makers to identify gaps in financial education and design targeted strategies based on socio-demographic parameters for broadening digital financial literacy.

Designing Research to Produce Usable Knowledge from Archives that Have Significant Ethical and Privacy Constraints

Archives are sometimes imagined to be dusty repositories of historical documents. The considerations of care and ethical practice at the heart of qualitative research with active participants may not seem relevant. However, when archives contain sensitive information about people, living or no longer living, and those people cannot be consulted about the use of the materials, significant ethical issues arise. This paper offers innovative ways of balancing a desire to explore the treasures of a highly sensitive archive with rigorous attention to the rights and interests of those whose stories the archive holds. Based on research with a video archive of recorded therapy sessions left by Michael White, the co-originator of narrative therapy, my paper sets out a series of innovative responses to ethical considerations about the right to privacy, confidentiality, and anonymity. These include strategies to anonymize data while retaining context, re-presenting material in the archive so that it could be shared, creating a role for an advocate on behalf of people recorded in the archive, and using subjective learnings from the archive as a basis for collective practice-based research. This enabled the generation of research findings that are directly applicable to practitioners. The practices described make possible an “opening” of a previously locked archive. They also set out a possible pathway for others seeking to conduct research with highly sensitive archives.

Revisiting Information Cascades in Online Social Networks

It is widely believed that a user’s activity pattern in Online Social Networks (OSNs) is strongly influenced by their friends or the users they follow. Building on this intuition, numerous models have been proposed over the years to predict information propagation in OSNs. Many of these models drew inspiration from the process of infectious spread within a population. While this approach is definitely plausible, it relies on knowledge of users’ social connections, which can be challenging to obtain due to privacy concerns. Moreover, while a significant body of work has focused on predicting macro-level features, such as the total cascade size, relatively little attention has been given to the prediction of micro-level features, such as the activity of an individual user. In this study we aim to address this gap by proposing a method to predict the activity of individual users in an OSN, relying solely on their interactions rather than prior knowledge of their social network. We evaluated our results on four large datasets, each comprising over 14 million tweets, recorded on X social network across four different topics over several month. Our method achieved a mean F1 score of 0.86, with a best result of 0.983.