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Social networking sites and social media serve as vital avenues of interaction among people all over the world as well as essential platforms for the distribution of information. Companies, like individuals, embrace social networks as a non-formal way to engage with their customers and suppliers, gather information about their online behavior, and offer personalized content. Both scholars and managers from various organizations looking to increase their competitive edge or market shares have an interest in the impact that social networks have on human behavior. Because of this, the current research seeks to investigate how users perceive a wide range of concepts related to using social networking sites, including fear of missing out (FoMO), social networking fatigue, information and communication overload, ubiquitous connectivity and peer communication, and privacy concerns. The authors also examined users’ intentions to continue using social networking sites despite the COVID-19 outbreak in order to ensure a more thorough understanding of the research topic. The findings aim to provide a better knowledge of how users see interaction on social networks in their daily lives as well as an overview of user perspectives on the terms mentioned above.

PurposeEven though social media (SM) has been explored in-depth, its role remains unclear regarding short- and long-term preventive attitudes in global health emergencies. To fill this gap, the Stimulus-Organism-Response framework aims to clarify the social media exposure mission in acknowledging risk perception and triggering preventive attitudes and behaviors toward COVID-19 and general vaccination.Design/methodology/approachThe authors conducted an explanatory-predictive study on 480 Romanian students, using partial least squares structural equation modeling, and performed model evaluation, multi-group, model selection, and importance-performance map analyses.FindingsThe study provides insights in understanding significant relationships and drivers explaining and predicting attitudes towards vaccines. The main relationships are between fear and risk perception; risk and preventive attitudes and behaviors; and vaccination degree and attitudes to vaccines. The most important factor is the vaccination degree and media exposure is the most performant.Practical implicationsDeveloping and applying regulations and communication strategies for quality mass information may positively increase attitudes toward vaccines by indirectly enforcing the main drivers.Social implicationsOrganizations, authorities, and opinion leaders must have a coherent supportive presence in media.Originality/valueThis study filled the literature gap by building a generic theoretical and empirical proven framework that investigates the mediated effect towards vaccines of all media types by COVID-19 experience and vaccination degree.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-11-2021-0621

PurposeThe current study aims to empirically analyze the influence of different information sources, together with the persuasiveness of recommender systems (RSs) on the consumer’s purchase intention (PI). It also expands the research on RSs from the point of view of consumer behavior and psychology, considering perceived usefulness and relevance. In addition, it analyzes how different types of personalized recommendations, along with non-personalized ones, influence PI.Design/methodology/approachThe proposed model has been validated using partial least squares structural equation modeling (PLS-SEM), based on the data collected from 597 online shoppers.FindingsThis study proves that both information search and RSs influence PI, being complementary rather than mutually exclusive. Recommender systems’ findings indicate that the PI is primarily influenced by the perceived relevance of RSs, the information provided by manufacturers and reviews. Moreover, only the influence of the perceived usefulness of personalized recommendations strongly affects PI. Conversely, non-personalized recommendations do not affect PI.Practical implicationsDevelopers should focus on increasing the perceived usefulness and relevance of RSs. Thus, they could adopt the hybridization of RSs with the aggregation of both personal shopping behavior and social network contacts. It should integrate information signals from multiple sources to include sentiment extracted from reviews or links to the manufacturer’s page. Furthermore, the recommendation of discounted products must be only for products preferred by customers, because only these influence the PI.Originality/valueThis research provides a structural model that examines together, for the first time, the influence on the PI of the main RSs and sources of information.

M‐learning is a trending field in educational organizations, companies, and also for individual study. However, in some regions the ampleness of the phenomenon is not quantifiable or comparable due to the lack of an adequate framework and reliable metrics. Our research intends to make a little light by assessing the degree of m‐learning adoption in students at a moment when face‐to‐face education moved suddenly online due to the COVID‐19 outbreak's rapid and unpredictable spread. A new model relying on the Unified Theory of Acceptance and Use of Technology (UTAUT) was built to investigate and explain relationships between constructs. It reveals the key factors affecting technology adoption by considering hedonic motivation a mediator instead of an exogenous variable as in UTAUT2. Based on an analysis of 311 higher education learners, the way how performance expectancy, effort expectancy, social influence, and facilitating conditions influence directly or indirectly the behavioral intention is researched. The analysis was conducted employing partial least squares structural equation modeling. The strongest relationship is between hedonic motivation and behavioral intention followed by the one between perceived effectiveness and hedonic motivation. Age, gender, and experience moderate the model's relationships. Research contributes to theory development by successfully adjusting the original UTAUT model. Results indicate that universities may offer learners an enjoyable m‐learning experience by activating social support groups and inserting gameplay elements into the learning system.

Selecting online the most suitable product or service can be a draining process, and the broad diversity of products on retailers’ websites is overwhelming. Following this, there has been a high interest in examining buyers’ motivations and recognizing the determinant factors that affect the decision-making process in the case of online shopping. Hence, in this article, we carried out an empirical study and we analyzed to what extent certain factors such as product features, confidence in the online shop, product reviews, product price, consumer evaluations, the trust of the sources, usability of the online shop, convictions and commitment to the brand, product notoriety, consumer loyalty programs, WOM, and eWOM influence the purchasing decision. The findings reveal that the most significant variables determining the acquisition decision are product features, confidence in the online shop, reviews of the product, product price, and evaluations of customers from trusted sources. The most relevant information resources are trustworthy websites that offer specialized product reviews and reliable websites that offer customer product reviews.

User decision intuition is challenging and complex, even if the user and product are known. Thus, recommending products is a management decision with high degree of incertitude. What if we are facing also the cold-start problem, like new products or visitors? This is a hot topic in recommender systems, tackled in variously, successfully or not. This perspective adds more incertitude to the existing uncertain scenario. Our philosophy is the shift from a user-centric view, hit by uncertainty, to a company-centric one taken in certainty circumstances, later to apply win–win approaches. We propose a multi-criteria algorithm -MRS OZ- for an ecommerce site RS that tackles the cold-start differently. It uses Onicescu method, being adapted according to Zipf’s Law, very popular in internet marketing. The paper opted for an exploratory research based on primary and secondary methods, consisting in literature review, 2-step survey addressed to 110 managers splat in 2 groups, and statistical analyses. The algorithm may substitute the human expertise on the given sample item list and criteria set. This work reveals that Onicescu method is suitable for recommender systems field, but relative inner category rankings and more domain related weight ratios strengthen the algorithm. Onicescu method has a wide applicability, but not for recommender systems. Also, the mixture with Zipf’s Law is completely experimental in research area.

Introduction. Enrollment of patients with cardiovascular disease in rehabilitation programs may contribute to implementation of a healthy lifestyle, including by promotion of a diet adequate for each patient’s profile. In this context, the current study is aimed at creating a traffic light system model allowing to obtain the development, innovation and diversification of menus and to improve the nutritional programs for this category of patients. Material and method. Based on the data provided by USDA Food Composition Databases, the composition in terms of different nutritive principles was determined for each ingredient and for each final menu. Comparison of menus depending on each nutritive principle, as well as comparison between menus and nutrient requirements according to indications for patients attending cardiovascular rehabilitation programs was made. Results. The traffic light system was developed, using color codes, comparing daily nutrient requirements with preparations’ content. Conclusions. The major benefit of the traffic light system would reside in the fact that starting from classic menus, an intervention on these can be achieved, and healthier, more nutritionally balanced models can be created, according to healthy nutrition principles. These new menus will be calorically and nutritionally adapted for patients attending cardiovascular rehabilitation programs. Key words: nutrition, rehabilitation, traffic light system

Background Diastolic dysfunction is traditionally believed to be the first subclinical manifestation of diabetic cardiomyopathy (DCM), leading to systolic dysfunction and then overt heart failure. However, in the last few years, several studies suggested that systolic subclinical dysfunction measured by speckle-tracking echocardiography (STE) may appear ahead of diastolic dysfunction. In this review, the main endpoint is to show whether subclinical myocardial systolic dysfunction appears ahead of diastolic dysfunction and the implication this may have on the evolution and management of DCM. Materials and methods We performed a search in PubMed for all relevant publications on the assessment of DCM by STE from 1 June 2015 to 1 June 2020. Results and conclusions The results illustrate that subclinical systolic dysfunction assessed by STE is present in early DCM stages, with or without the association of diastolic dysfunction. This could be a promising perspective for the early management of patients with DCM leading to the prevention of the overt form of disease.

The presented prototype for image acquisitions and processing (IPA) aims to create a maintenance road system with minimal cost, mounted on non-specialized vehicle, enabling image acquisition in various conditions. IPA has an important role in the proposed platform designed by PAV3M for intelligent management, monitoring and maintenance of pavements and roads. We have developed new image processing solutions, analysis methods and enhanced (more robust, efficient, dedicated) solutions for solving the specific problems related to pavement analysis using PCI standards for road crack detection and classification.

Introduction Recent statistics show an increase in the prevalence of the elderly population. The year 2012 was declared European Year for Active Ageing and Solidarity between Generations, and the European Commission launched campaigns like The Active and Assisted Living Joint Program (AAL JP). Rehabilitation in the elderly is a desideratum, but the problems of rehabilitation in the elderly are numerous. The aim of the study was to evaluate degree of acceptance/implementation of different technologies in Romania, of monitoring in the rehabilitation activity conducted at home. Material and methods the study comprised 154 persons with a mean age of 73.37 ± 7.33 years, of which 64 (41.6%) male and 90 (58.4%) female. All subjects completed a questionnaire regarding the living conditions and health status, about the degree of acceptance of intelligent technologies for monitoring current health status/reporting acute events. Results 18.2% used the Internet frequently, and the rest used it almost never or rarely. 71.9 % of patients agreed to wear a portable sensor (p=0.07 between men and women), 33.1% accepted videocam, 47.4% accepted a screen, 41.3% accepted living room monitoring, 68% sensor in the room on the wall and 69.1% accepted fall detection sensor. No significant differences were found regarding the acceptance vs rejection of personal sensors, living room monitoring, sensors in the room, fall detection sensors depending on the gender, income level, type of caregiving. Using of videocam and screen was influenced by type of care giving and income (p=0.002, p

The sudden COVID-19-induced transition from a physical university life to a virtual one was a painful one for many students. Social distancing measures mean more than a simple change from face-to-face to online education. This study investigates how different social aspects, such as the students’ psychological sense of community, social capital, and use of social media, facilitated the perceived social support during the transition to the COVID-19 lockdown. Our results not only underline social media’s role, but also indicate that the perceived social support, as well as the bonding and bridging social capital, were particularly relevant during the transition process. Our findings are aimed at organizational management by recommending actionable ways in which they could improve social support by organizing computer-supported social networks, social support predictors, and specialized interventions for students with less perceived social support. As such, the study provides unique insights into the COVID-19-induced lockdown situation among students, while offering a transition model that also generalizes to other settings.

Product recommendations are generally understood as data-driven – however, we argue that knowledge-driven management decisions may also play a role, especially in the cold start problem, which has been tackled with various degrees of success through a number of approaches. We hereby advocate an approach that captures managerial priorities in the act of recommendation building – i.e., the proposal is to complement the traditional customer-centric view (affected by uncertainty) with a machine-readable business-centric view. For this purpose, the paper reports on an engineered method for the “digital nudging” of recommendations - it starts by capturing a manager's priorities with diagrammatic means, which are further exposed as a Knowledge Graph to a recommender built on a modified version of the Onicescu method taking into consideration a business “utility” concept to influence decision-making. The research follows the Design Science methodology, resulting in a “method” artifact that tackles the cold start with the help of a (by-design) recommendation nudging mechanism. In terms of method engineering, the proposal orchestrates its ingredients into a coherent method with the help of (a) Agile Modeling Method Engineering, to setup up a diagrammatic tool for prioritization rules, (b) the Resource Description Framework, to capture the diagrammatic rules in knowledge graph form, and (c) the Onicescu multi-criteria decision method with modifications based on Zipf's Law. The evaluation was based on surveys with potential stakeholders, for the different steps of the method. The implications are that the notion of “digital nudging” can take a knowledge-driven form, engineered as an artifact that bridges the decision-makers' priorities (captured by diagrammatic means) with the customer-facing output (recommendations), instead of relying solely on the accumulated history of transactional data. This interpretation of digital nudging may be extended towards other “digital choice environments” where contextual decisions are called to influence the computational output.

Online stores currently use recommendation systems (RSs) quasi-universally to provide their customers with added value and increase their profits, thus reshaping the world of e-commerce. RSs, however, depend on the availability of e-commerce user data to be effective. Nevertheless, data privacy regulations are increasingly becoming more restrictive and e-commerce users more aware of and concerned about their data being collected, stored, and processed for RSs. On the other hand, in the RSs context, there is currently a very limited understanding of e-commerce users' attitudes toward data privacy and of these attitudes' antecedents. This study examines the influence that an RS's perceived usefulness has on e-commerce users in terms of a specific RS collecting, storing, and processing their data. In addition, this study investigates the extent to which RSs' overall relevance for users depends on their perceived usefulness and users' degree of consent. We conducted an online survey of 597 e-commerce users, thereafter analyzing the data by means of partial least squares structural equation modeling (PLS-SEM). The results indicate that an RS's perceived usefulness positively and significantly influences the extent to which users consent to the RS's provider collecting and storing their data, which in turn also impact RSs' overall relevance for e-commerce users. The findings have practical implications for e-commerce industry players, as well as for national and international authorities responsible for online data privacy regulations

Social networking sites (SNSs) are now ubiquitous communities for constant online interpersonal interactions that trigger symmetric or asymmetric effects on our everyday life. Recent studies advocate in favor of the significant role that SNSs have in promoting well-being and, more importantly, in disseminating reliable information during a global crisis, such as the current COVID-19 pandemic. Based on the growing importance of SNSs to the global framework, the main purpose of this study is to empirically assess the link between the use of symmetric social networks such as Facebook, or asymmetric social networks, like Instagram, and the level of satisfaction, employing the methodology of structural equation modeling. The results of the research validate the hypothesis that SNS activities increase the level of satisfaction, and therefore, that there is a direct link between the number of posts and comments and the level of satisfaction. Furthermore, based on the reversible and significant link between the level of satisfaction and the importance attributed to SNSs, the main conclusion of the study is that the higher the importance of the SNS, the greater the level of dissatisfaction experienced by users. Also, public activities on social networks positively affect social network satisfaction, while private activities have a direct negative relationship with the importance of social networks.

The aim of this paper is to present a holistic platform for pavement monitoring and maintenance management (PMMP). Our solution tries to exploit new technologies in order to design, integrate and implement an automated and portable visual road inspection system for traffic-speed pavement distress monitoring and analysis that can be installed on non-specialized vehicles. PAV3M developed based on solution based on PMMS requirements and recommendation made in EU programs as a modular decomposition based on business process analysis. For road monitoring and maintenance we opted for LCCA methods. Our solution includes Risk management module.

Background The genes associated with hypertension could be genetic risk factors for metabolic syndrome (MetS). Aim To determine the frequency of M235T and T174M-AGT, I/D-ACE and A1166C-AGTR1 in hypertensive patients with MetS and to evaluate the relationship between these polymorphisms and central obesity and dyslipidemia, respectively. Materials and methods We performed AGT, AGTR1 and ACE genotyping in 56 hypertensive women (24 with MetS) and 71 normotensive women using PCR-RFLP methods and PCR, respectively. Results Hypertensive patients carrying the mutated TT235, MM174 and DD genotypes had an 1.53 (p = 0.56), 1.78 (p = 0.52) and 1.28 (p = 0.78)-fold increased risk to develop MetS. Hypertensive carriers of both mutated TT235 and MM174 or TT235 and D/D or TT235 and CC + AC genotypes had an 8.15 (p = 0.04), 4.83 (p = 0.04) and 10.53 (p = 0.05)-fold increased risk to develop MetS. Hypertensive patients with MetS and TT, D/D or CC genotypes had higher body mass index compared to hypertensive patients without MetS (p ≤ 0.05 for all the genotypes). Hypertensive patients with MetS and TT235, MM174, D/D or CC1166 genotypes had higher triglyceride levels, lower HDL-cholesterol levels and higher waist circumference compared to hypertensive patients without MetS (p ≤ 0.05, except for the association between CC1166 and HDL-cholesterol level). Conclusions The effect of the T174M, I/D and A1166C polymorphisms on MetS may depend on the M235T polymorphism. Among hypertensive subjects with MetS, the presence of TT235, MM174, DD and CC1166 genotypes could be a risk factor for central obesity and dyslipidemia.

Terms as knowledge discovery or KnowledgeDiscovery from Databases (KDD), Data Mining (DM), ArtificialIntelligence (AI), Machine Learning (ML), Artificial Neuralnetworks (ANN), decision tables and trees, gain from day to day,an increasing significance in medical data analysis. They permitthe identification, evaluation, and quantification of some lessvisible, intuitively unpredictable, by using generally large sets ofdata. Cardiology represents an extremely vast and importantdomain, having multiple and complex social and humanimplications. These are enough reasons to promote theresearches in this area, becoming shortly not just national orEuropean priorities, but also world-level ones. The profoundand multiple interwoven relationships among the cardiovascularrisk factors and cardiovascular diseases – but still far to becompletely discovered or understood – represent a niche forapplying IT&C modern and multidisciplinary tools in order tosolve the existing knowledge gaps.This paper’s aim is to present, by emphasizing their absoluteor relative pros and cons, several opportunities of applying DMtools in cardiology, more precisely in endothelial dysfunctiondiagnostic and quantification the relationships between theseand so-called “classical” cardiovascular risk factors.

Web Mining aims to discover new information fromthe web links structures, the content pages and the way in whichthe users interact with the database. Current concern in thedomain of online applications deals with the possibility ofextracting knowledge, evaluating the user’s behavior andunderstanding what they desire, in order to anticipateimmediately their navigational behavior. In our experiment wewill apply techniques such as clustering and the associationrules, in order to discover new patterns among the existent data,with the purpose of making correlations and organizing the databy creating new groups of similar objects, in order to access andretrieve the data in accordance with the user’s preferences.

Cardiovascular diseases (CVD) represent a severe cause in mortality cases over the world. Early detection and foreseeing of future developments are essential goals for every cardiologist. For this reason, detecting and understanding several factors that trigger cardiovascular diseases is essential. As we have emphasized in eProcord spreadsheets (a 3-year research project that assessed the cardiovascular risk through medical and modeling methods and identified patterns for patients), Database Management Systems, statistical, and Machine-Learning soft-wares are tools which can be used in order to identify these factors through data analysis. This paper presents an approach towards concept data analysis of medical data using a mathematization of the classical notion of concept which has been implemented in the knowledge management suite ToscanaJ, grounded on the Conceptual Knowledge Processing paradigm. Starting from this premise, we propose a human centered method to investigate, represent, process and acquire knowledge from a medical database. This method offers a reasoning support for an expert centered visualization of previous collected data which facilitates a better understanding of cardiovascular risk assessment hypotheses, in order to ground a solid environment for these hypotheses. We use the visualization capabilities of Formal Concept Analysis in order to explore the knowledge encoded in these data and we give an overview on how the Conceptual Knowledge Processing methods can be used as a knowledge discovery tool for datasets related to cardiovascular diseases.

Due to increased competition in higher education environments, the universities adopted modern Information and Communication Technologies (ICT) with the aim of completing quality educational processes. They plan to use more efficiently the collected data, develop tools so that to collect and direct management information, in order to support managerial decision making. The collected data could be utilized to evaluate quality, perform analyses and diagnoses, evaluate dependability to the standards and practices of curricula and syllabi, and suggest alternatives in decision processes. Data mining (DM) and Decision Support Systems (DSS) are well suited technologies to provide decision support in the higher education environments, by generating and presenting relevant information and knowledge towards quality improvement of education processes and management.

Road pavement management and maintenance are responsible for a considerable amount of resources and energy consumption, with limited options to forecast due to the fact that projects have different sizes, are taking place in regions with various types of soil, landforms or types of distress. The objective of this paper is to offer a solution for risk management using Monte Carlo simulation by taking into consideration three key aspects for the management of any project: costs, time, quality and the most important road parameter, that may cause the failure of the project -type of road distress. As a module of PAV3M maintenance and management project, Risk3M application uses the Pavement Condition Index (PCI) and the Long-Term Pavement Performance Program (LTPP) in order to identify and classify risk based on the type of road distress, using a scale from 1 (very poor) to 5 (very good). The impact on costs (Rc), on risks depending on the time span (Rt) and on the overall quality of the project (Rq) where classified in a scale from 1 to 5. This paper uses Monte Carlo simulation procedures with evaluation of risk scales and type of road distress (PCI) and specifications of correlations between the simulated variables (Rc, Rt, Rq).