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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.

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.

The number of mobile users has grown rapidly and continues to grow globally. In the literature, countless studies have been conducted on the influence of mobile devices on everyday life. To bring our contribution in this field, we conducted an empirical study with students from the Romanian university environment. The study investigated the influence of mobile devices, the way of communication, the categories of applications used and the frequency of use. Moreover, a series of correlations between variables have been tested, and the most relevant and statistically significant are presented in the article.

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

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.

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.

Collaborative tagging has become a very popular way of annotation, thanks to the fact that any entity may be labeled by any individual based on his own reason. In this paper we present the results of the case study carried out on the basis of data gathered at different time intervals from the social tagging system developed and implemented on Întelepciune.ro. Analyzing collective data referring to the way in which community members associate different tags, we have observed that between tags, links are formed which become increasingly stable with the passing of time. Following the application of methodology specific to network analysis, we have managed to extract information referring to tag popularity, their influence within the network and the degree to which a tag depends upon another. As such, we have succeeded in determining different semantic structures within the collective tagging system and see their evolution at different stages in time. Furthermore, we have pictured the way in which tag rec-ommendations can be executed and that they can be integrated within recommendation sys-tems. Thus, we will be able to identify experts and trustworthy content based on different cat-egories of interest.

Recommender systems were created to guide the user in a personalized way to interesting resources and to help users cope with the problem of information overload. A system’s ability to adapt to the users’ needs is based on gathering user-generated collective intelligence. In this paper, we present WSNRS, the system proposed for recommending content within social networks. The main goal of the system is to identify and filter the recently published valuable resources while taking into account the interactions and the relationships the user has within social structures. The interactions are logged and aggregated in order to determine the trust scores between users. Using the scores obtained, one can identify the types of relationship established between users; the scores will then be integrated into an adaptive global model used for recommending resources. Our approach presents several advantages over classic CF-based approaches and content-based recommendations regarding cold start, scalability and serendipitous recommendations. We will illustrate this with a case study that we made using data provided by the implementation of the system in a real online social network.

Personalization systems based upon the analysis of users' surfing behavior imply three phases: data collection, pattern discovery and recommendation. Due to the dimension of log files and high processing time, the first two phases are achieved offline, in a batch process. In this article, we propose Wise Recommender System (WRS), an architecture for adaptive web applications. Within this framework, usage data is implicitly obtained by the data collection submodule. This allows for the extraction of usage data, online and in real time, by using a proactive approach. For the pattern discovery, we efficiently used association rule mining among both frequent and infrequent items. This is due to the fact that the pattern discovery module transactionally processes users' sessions and uses incremental storage of rules. Finally, we will show that WRS can be easily implemented within any web application, thanks to the efficient integration of the three phases into an online transactional process.

The aim of the present paper is to analyze the user’s behavior in Web 2.0 through comparing statistical data from a Web 1.0 site and an online community from Web 2.0. In order to emphasize the difference between the two concepts, we have taken into consideration the following: new vs. returning visitors, visitor loyalty, length as well as the depth of the visit to the site. Analyzing the data, one can observe that the visitor loyalty, their lengthy visits to the site and the pages they view are much higher for the site built on the principles of Web 2.0, compared to the classical sites. Likewise, we developed and implemented a collaborative tagging system that allows every registered user to publish, save and label the content with the help of tags. Therefore, this can help in building a model that not only offers customized content but also adapts the site to user needs by providing a recommendation system and aggregate collective intelligence.