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The ongoing COVID-19 pandemic has led to several changes in academic teaching practices. Although educational digital technologies have been crucial during the initial phases of the pandemic, their forced adoption has led to negative consequences. In the present study, we aimed to integrate the Technology Acceptance Model theoretical framework (Davis, 1989) by exploring the effects of some possible factors that influence the willingness to adopt digital learning tools in the future when the pandemic is over. Among them, technostress was considered one of the external factors that could have adversely affected digital teaching technology adoption in the future. In contrast, the perception of technical support offered by the university was considered a potential protective factor. A total of 463 Italian university faculty completed an online questionnaire at the end of the first semester (a.y. 2020–21). The frequency of distance teaching technologies usage behavior was measured objectively by extracting teachers' activities from the University's e-learning databases. Key findings indicated that distance teaching technologies' frequency of use increased technostress, which in turn negatively impacted the perception of ease of use. The latter influences - both directly and indirectly through perceived usefulness - the intentions to adopt distance learning tools after the pandemic. Organizational support negatively predicted technostress. Implications to help public institutions develop functional strategies to cope with the technological changes brought by the pandemic are discussed.

Digital contact tracing apps have been introduced by governments as a strategy to limit the spread of the COVID-19 pandemic. Digital contact tracking is an alternative to traditional contact tracing performed by human tracers who have to reconstruct each contact an infected person had in the recent past by means of interviews. The Italian government proposed the Immuni digital contact tracking app as a solution. Immuni uses Bluetooth technology to anonymously register all close contacts a person had: if she tests positive for COVID-19 then all registered contacts are notified. The main aim of the paper is to propose a cluster analysis of some factors concerning the possible acceptance of the Immuni app to build behaviour profiles that explain and predict the possible behaviours of the respondents. The factors considered referred to three different pillars: the technological pillar, investigated by considering factors from the technology acceptance models family; the health pillar, where variables of the health belief model were used; and the sociopolitical pillar, where some values of the respondents were considered as possible barriers to or facilitators of the acceptance of this technology. As a result of the cluster analysis, three behavioural profiles were built: the ProApp profile, the Hesitant profile, and the AntiApp profile. The first is the profile grouping the respondents who intend to use the contact tracing app; the second is more about people who are favourable of the use of the app, but some issues such as privacy reduce the strength of their intention; the last profile is about people who are less favourable to use the app. We are confident that the behaviour profiles found would be useful to build more tailored communication campaigns to help promote the use of the app by managing factors that could either be facilitators or barriers.

Organizations are interested in finding new and more effective ways to promote the well-being of their workers, to help their workers manage work-related stress. New technologies (e.g., smartphones) are cheaper, allow more workers to be reached, and guarantee their anonymity. However, not all employees agree on the use of new technological interventions for the promotion of well-being. Consequently, organizations need to investigate technological acceptance before introducing these tools. By considering the technology acceptance model (TAM) framework, we investigate both the influence of workers’ perceived usefulness and ease of use on their intentions to use apps that help them managing work stress. Moreover, we contribute to the extension of this model by considering both personal (i.e., self-efficacy, personal innovativeness) and organizational (i.e., organizational support for innovation) variables. Our research involved 251 participants who completed an online self-report questionnaire. The results confirm the central hypothesis of the TAM and the influence of other variables that could influence acceptance of new technologies, such as apps that help manage work stress, and the intentions to use them. These results could help organizations ensure technological acceptance and usage by their workers, increasing the effectiveness of new technologies and interventions to promote well-being.