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The 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.

The proposed model has been validated using partial least squares structural equation modeling (PLS-SEM), based on the data collected from 597 online shoppers.

This 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.

Developers 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.

This 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.

Even 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.

The 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.

The 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.

Developing and applying regulations and communication strategies for quality mass information may positively increase attitudes toward vaccines by indirectly enforcing the main drivers.

Organizations, authorities, and opinion leaders must have a coherent supportive presence in media.

This 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.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-11-2021-0621

This paper investigates if the existing degree of students' acceptance and use of mobile or m-learning may face the online shift determined by SARS-CoV-2. Based on the extended unified theory of acceptance and use of technology (UTAUT2), a new comprehensive model, SD-UTAUT (social distancing-UTAUT), is developed to better understand relationships between the original constructs, plus personal innovativeness (PI) and information quality (IQ). It identifies the key factors affecting behavioral intention (BI) and use by examining the influence of revaluated hedonic motivation (HM) and learning value (LV) importance as mediators.

The paper opted for an exploratory study involving 311 learners, using partial least squares structural equation modeling (PLS-SEM).

SD-UTAUT can be a new m-learning model in higher education. It has high predictive power and confirmed 15 out of 16 hypotheses. The most powerful relationship is between performance expectancy (PE) and HM. IQ affected LV the most, since HM the behavioral use (BU). HM impacts the use behavior (UB) more than LV, but habit (HT) affects it the most.

Because of the pandemic context, output may lack generalizability and reproducibility.

To improve usage, staff must provide better support, course creators emphasize the objectives and competencies and developers integrate innovation. The joy and pleasure of m-learning use may stimulate the LV through interesting and interactive content, like incorporating gamification.

The model set-up and circumstances are previously unseen. SD-UTAUT confirms ten new hypotheses and introduces the student's grade point average (GPA) as a moderator.

The peer review history for this article is available at https://publons.com/publon/10.1108/OIR-01-2021-0017

This study aims to identify the roles that privacy experiences and social media use play in influencing privacy-protection behaviors. As social media use expands in terms of the number of users and functionality; it is important to understand social media user privacy-protection behaviors and the users’ psychological underpinnings driving those behaviors. Among these, perceptions are the users’ evaluation of their privacy concerns and data sharing benefits inherent in social media use which influence the users’ behaviors to protect their privacy.

To research these issues, a theoretical model and hypotheses were developed, based on self-efficacy theory. The theoretical model was empirically tested using 193 questionnaire responses collected from students enrolled in business courses at a medium-sized university in the western USA. All the respondents reported that they routinely use social media. The empirical analysis was performed using structural equations modeling in PC SAS version 9.4, procedure Calis.

The estimation of the paths in the structural model indicates that privacy concerns positively influence social media users’ protection behaviors while the perceived benefits of data sharing negatively influence protection behaviors. Privacy experience positively influences privacy concerns. Alternatively, social media use positively influences social media self-efficacy and perceived usefulness, which, in turn, have meaningful influences on data sharing benefits.

Previous findings about the effect of self-efficacy on protection behaviors has been inconclusive. This study adds some clarity. Specifically, the findings suggest that the effect depends upon the foci of self-efficacy. While higher self-efficacy with respect to using privacy-related features of a specific technology tends to lead to greater privacy concerns, higher self-efficacy with respect to the more general technology (e.g. social media, computer) seems to affect protection behaviors through perceived benefits. Further, the results of this study offer conclusions about the roles that privacy experiences, social media use and perceived social media benefits play in affecting protection behaviors.

During the coronavirus disease 2019 (COVID-19) pandemic, ubiquitous social media has become a primary channel for information dissemination, social interactions and recreational activities. However, it remains unclear how social media usage influences nonpharmaceutical preventive behavior of individuals in response to the pandemic. This paper aims to explore the impacts of social media on COVID-19 preventive behaviors based on the theoretical lens of empowerment.

In this paper, survey data has been collected from 739 social media users in China to conduct structural equation modeling (SEM) analysis.

The results indicate that social media empowers individuals in terms of knowledge seeking, knowledge sharing, socializing and entertainment to promote preventive behaviors at the individual level by increasing each person's perception of collective efficacy and social cohesion. Meanwhile, social cohesion negatively impacts the relationship between collective efficacy and individual preventive behavior.

This study provides insights regarding the role of social media in crisis response and examines the role of collective beliefs in the influencing mechanism of social media. The results presented herein can be used to guide government agencies seeking to control the COVID-19 pandemic.

This conceptual study investigates the adoption of education technology (EdTech) products among college students, focusing on identifying the key factors influencing the adoption process within educational institutions. Technology integration in education has rapidly gained prominence, with EdTech offering innovative solutions to enhance teaching and learning experiences. However, understanding the determinants that affect EdTech adoption remains critical for its successful implementation and impact. This paper aims (1) to identify the factors influencing the adoption of EdTech by college students (2) to create a conceptual model that shows the connections between the elements that lead to college students adopting EdTech.

The research employed a mixed-methods approach, combining qualitative data analysis and conceptual modeling to achieve the objectives. The underlying knowledge required to create a qualitative data gathering tool was obtained through a thorough literature analysis on innovation dissemination, educational psychology and technology adoption. College students, teachers and administrators participated in semi-structured interviews, focus groups and surveys to provide detailed perspectives on their attitudes about and experiences with EdTech. The Scopus and Web of Science databases are searched for relevant information in an organized manner in order to determine the factors influencing the adoption of EdTech. Second, an extended version of the technology adoption model is adopted to develop a qualitative data-based conceptual framework to analyze EdTech adoption in the Indian context.

Overall, by highlighting the critical components that emotionally influence college students' adoption of EdTech products in educational institutions, this course adds to the body of information already in existence. The conceptual framework model serves as a roadmap for educational stakeholders seeking to leverage EdTech effectively to enrich the learning environment and improve educational outcomes. By recognizing the significance of the identified factors, academic institutions can make informed decisions to foster a climate conducive to successful EdTech integration.

A comprehensive conceptual framework model was developed based on qualitative data analysis to illustrate the interrelationships between the identified factors influencing EdTech adoption. This model presents a valuable tool for educational institutions, policymakers and EdTech developers to comprehend the complex dynamics of implementing these technological solutions.

The findings of this study demonstrated a number of important variables that affect the uptake of EdTech products in educational settings. These factors encompassed technological infrastructure, ease of use, perceived usefulness, compatibility with existing academic practices, institutional support, financial constraints and individual attitudes towards technology. Additionally, the research explored the significance of institutional preparation for embracing technological advancements as well as the influence of socio-cultural elements.