The COVID-19 pandemic spread rapidly across the world, generating a major health, social and economic crisis [1]. Various practices including social distancing, adoption of protective measures lockdowns, curfews [2] and extended home quarantine [3]; [4] As a result, COVID-19 has led to the expansion of home-based routines including distance learning and the digital workspace [5]. Individual resilience captures the person’s potential to cope with stressful situations [6] Coping with the extensive limitations during the pandemic accented the role of New Media communication. New Media platforms have been extensively used to the pandemic crisis [7] providing a wide range of resources including access to networkable and interactive resources (websites, multimedia, video games, CD-ROMS, DVDs, and social media). All these resources are available in real time and at relatively low cost (Wang et al, 2020) promoting their role in the process of increased resilience among individuals) [8]. Indeed, New Media platforms have been used extensively for work [9] education [10] health [11] and leisure [12]. Nonetheless, differences in the use of New Media reflecting various factors such as including education, gender, age, geographic location, employment status, and race [13] affect the process of resilience. These factors are associated with the concept of vulnerability [14] The concept of vulnerability captures the quantity of static assets [15] available to individuals, manifest in socioeconomic characteristics such as job continuity, flexible working conditions, and childcare responsibilities [16] A deterioration of employment status during the pandemic is especially problematic because it can manifest in the individual’s physical and mental deterioration [17] [18] Surprisingly, the evidence of the role of New Media in shaping the outcome of the resilience/vulnerability situation evident during the pandemic is limited [19] Thus, it is important to examine the extent to which New Media use has affected individual resilience during the COVID-19 pandemic [20] considering variations in vulnerability. In order to do so in this study, we proceed in two stages. First, we focus on the role of New Media. Second, we consider the role of variations in vulnerability [21]. Third, we control for a set of socioeconomic variations [22] we employ a wide definition of New Media including the following types: (a) general websites, (b) social media sites (Facebook, YouTube, and more), (c) online platforms (e-mail, WhatsApp, and ZOOM). We examine resilience in terms of the self-reported overall physical and mental state of individuals [23]. We identify vulnerability in terms of (a) deterioration of employment status during the pandemic and (b) perceived threat from COVID 19 restrictions. We apply an interdisciplinary model to predict variations in resilience considering (a) variations in New Media use (b) socioeconomic factors and (c) vulnerability factors [24].

Resilience in terms of a process of access to resources [24]. This process involves the attainment of a stable trajectory of healthy functioning following a highly adverse event often perceived as a crisis [25] Navi and colleagues (2022) report that lower individual resilience contributes to the prediction of depression, anxiety and stress (Braun-[26] and increase wellbeing and resilience [27]. Advancing the concept of resilience at the community level often examined public responses to crises, including natural disasters such as the 2004 tsunami and Hurricane Katrina [28] because they were shown to build more resilient communities over time” [29]. Indeed, the role of New Media in the process of resilience became evident during the COVID-19 pandemic providing a valuable pool of resources assisting individuals to cope with uncertainty experienced and restore lost resources [30]. Facebook, for example, decrease the detrimental effects of stress [31] and enhance experiences of well-being [32] Other online platforms, such as ZOOM, enable access to online meetings so that individuals are able to continue work-related activities and tasks [33]. Yet, the evidence of various types of New Media contributes to the process of resilience is limited and mostly focusing on social media effects [34] neglecting the impact of New Media variations in use and individual vulnerabilities [35].

New Media and Resilience

The functionality of New Media has indeed the potential to optimize personal gains and solve wellbeing concerns [36]. First, New Media advanced the creation of online communities among those who are “remote” from the perspective of physical social space. Second, greater use of online communities expands individuals’ alternatives for entering into additional and more extended social interactions. Third, these platforms generate and strengthen close ties with individuals of similar interests [37] and dissolve barriers caused by separation in time and space [38]. Fourth, these platforms eventually deepen a sense of identification and solidarity [39]. Nevertheless, some recent studies [40] also suggest that during COVID-19 individuals may have developed cyberchondria defined as the excessive use of online platforms to seek information and support [41]. This is especially evident among vulnerable individuals who experience the restrictions of COVID-19 as a severe crisis [42]. For such individuals, the need to restore/replace lost resources and assets becomes an essential element in reducing vulnerability [43] and increasing resilience [44]. The Resilience Portfolio Model [45] makes an important contribution in this direction. The model examines how individuals seeking a planned outcome choose between different ways of coping with life’s disruptions in order to restore balance following crisis-related disturbances. To that end, individuals may choose between different New Media activities to promote well-being. Indeed, technology adoption models such as the Uses and Gratifications Theory (UGT) suggest that individuals use online platforms and social networks to satisfy personal needs [46]. In fact, individuals actively and purposefully choose media sources based on their motivations personal inclinations and personal experiences [47] A higher level of gratification increases the perceived functionality of New Media, especially when accessing the sources of information may generate a beneficial reaction to the restrictions caused by COVID-19 [48]. Indeed, according to the Uses and Gratifications (U&G) theory individuals use online platforms and social networks to satisfy personal needs [49] but according to the Activation theory the context and content of use reflects the extent to which individuals take “conscious” actions to achieve their goals [50]. Evidence indicates for example that “passive” Facebook users are more likely to report lower mental health [51]. Similarly, some platforms are based on written interaction (Twitter, Tumblr) and considered as less effective, whereas other platforms such as Instagram, focusing on photo-sharing, are associated with a better mental health [52]. Indeed, according to the Social Diversification Hypothesis (hereafter SDH) the potential that New Media technology to provide tangible benefits [53] shapes individuals’ differences of New Media use [54, 55] These in turn affect potential of adapting to new daily routines during the pandemic [56, 57] and may protect against the detrimental effects of stress and experiences of threat [58-62] An important set of factors related to variations in the use of New Media are sociodemographic characteristics. Age plays an important role since young adults who are more likely to use online platforms they also experience negative effects, such as loneliness, anxiety, and lack of sleep [63-66] increased symptoms of depression [67, 67], anxiety and psychological distress Gender differences in New Media use reveal that women are likely to spend more time on Facebook than men, mainly to maintain interpersonal relationships and avoid loneliness. Female users are also more likely than male users to disclose personal information online, due to their tendency toward an expressive and open style of communication. Similarly, while both males and females access New Media, substantive differences exist in the particular devices they use reflecting personal limitations and expectations. Not surprisingly, differences reflecting the social environment of users influences their perceptions of possibilities of actions available in a technology design. Not surprisingly, gender is also positively associated with individual resilience. Men report higher personal resilience than women do whereas women were shown to exhibit more psychological impairment than men, including higher anxiety levels. Education is important as well in that it is positively associated with the skills necessary for using the internet. People with more years of education also have better cognitive skills and social support systems that facilitate more effective risk assessment. People who are less educated also have a lower sense of personal control. Income is also important because of the costs involved in the acquisition and use of ICT and mobile devices. Income is negatively associated with stress and a higher income lowers the likelihood of changes in occupational status and increase resilience. Marital status determines whether an individual has support during stressful life events. Married or cohabitating individuals exhibit lower psychological distress and diminished loneliness, providing a protective factor during the COVID-19 pandemic. Presence of children in the household is negatively associated with resilience, since ongoing childcare responsibilities may lead to greater mental unrest. These factors may all contribute to the generation of vulnerability.

Vulnerability During COVID-19

Vulnerability refers to the static conditions that may harm one’s sense of stability. Such conditions may include various factors, among them job continuity, flexible working conditions, product and services deliveries, and accommodating family and childcare responsibilities that in turn affect individuals’ potential to cope with the outcomes of COVID-19. These static factors accentuate the importance of individual differences regarding the way New Media is used and the way individuals estimate their potential to cope with the challenges of COVID-19. Personal characteristics, such as job position and household size produce different resources and assets leading to different vulnerabilities. Unwelcome changes in employment status, for example, rise uncertainty and vulnerability, whereas steady employment increases greater mental health. Research also demonstrates that while both men and women access New Media, substantive differences exist in the particular devices they use. We drawing upon this evidence and formulate the following hypotheses:

H1: Variations in New Media use generate variations on resilience during COVID-19

H2: Vulnerability factors shape variations in resilience controlling for New Media use

H3: Socio-demographic variations will shape variations in resilience during COVID-19.

H4: New Media use will increase resilience during COVID-19, after controlling for variations in vulnerability and socio-demographic variations.

Sample: The American Trends Panel (ATP) is a national, probability-based, online panel of adults living in households in the United States. Ipsos Public Affairs (“Ipsos”) conducted the Wave 64 survey of the ATPpanel from March 19 through March 24, 2020 on behalf of the Pew Research Center. In total, 11,537 ATP members (both English-speaking and Spanish-speaking survey takers) completed the Wave 64 survey. The overall target population was persons age 18 and over living in the US, including Alaska and Hawaii. Due to inclusion of the variable measuring employment change following COVID-19, the sample used in this study comprises 3639 individuals, 44.2 percent women. The questionnaire was developed by the Pew Research Center (2018) in consultation with Ipsos and was tested on both PC and mobile devices

Measures: Resilience: the sum of the reported points evaluated for the following items: In the past 7 days, how often have you a) felt nervous, anxious, or on edge? b) felt depressed? c) felt lonely? d) had trouble sleeping? Respondents answered on the following scale: 1=Not at all; 2=Somewhat; 3=quite often; 4=very often (Cronbach Alpha=.758). New Media use: Have you done any of the following? a) searched online for information about the coronavirus (via search engines, specific websites, or YouTube); b) used social media to share or post information about the coronavirus; c) used the internet or email to connect with doctors or other medical professionals; d) used social media (WhatsApp, Facebook Messenger) to communicate with others about COVID-19-related issues; e) used video calling or online conferencing services like Zoom or WebEx to attend a work or study meeting. Respondents answered 1=yes, used and 0=no, did not use. Vulnerability: (1) Social distancing restrictions: When you think about some of the steps taken to deal with the coronavirus outbreak, which of the following do you think were necessary or unnecessary: a) restricting international travel; requiring most businesses other than grocery stores to close; b) asking people to avoid gathering in groups; cancelling major sports and entertainment events; c) closing schools; limiting restaurants to carry-out orders only 1=necessary; 2=unnecessary (Cronbach Alpha=.798). (2) Employment deterioration: Because of the coronavirus outbreak, I lost my job (was fired) (1=yes). Socioeconomic variations: Age groups: 18-29; 30-49; 50-64; 65+. Gender: 1=Male. Educational level: less than high school, high school, postsecondary, academic. Marital status: never married and not in a relationship; never married and in a relationship (but not living with a partner); living with a partner; married; divorced/separated/widowed without new relationship. Household income categories: lower than $30,000; $30,000-$75.000; $75,000+ Presence of children under the age of 18 living in household served as a dichotomous variable (1=yes).

Findings

First, we examine the relationships between the study’s variables (Table 1).

Table 1: Correlation matric for examined variables.

The findings indicate that there are no collinearity effects between the examined variables. More importantly, all examined types of New Media exhibited a positive relationship with resilience, with the exception of the insignificant effect of social media. The correlation estimates provide information about the relationship between the examined variables. Clearly, in most cases the use of New Media is positively related to resilience. First, accessing information about COVID-19 (r= .118) as well as messaging to a similar extent (r= .116) and texting to a lower extent (r= .063) are significantly and positively related to resilience. Yet, no significant relation was found between using video/Zoom/WebEx platforms and resilience (r=-.003). The results indicate that individuals use diverse New Media resources, apparently to fulfill different needs and attain various goals. Next, in Table 2 we ran a three-step regression stepwise analysis to examine the relative effect (R Square) of the three sets of variables on resilience. Including the basic model (Step 1- Socioeconomic effects), the extended model (Step 2 -vulnerability factors), and the full model (Step 3- New Media use) controlling for socioeconomic (Step 1) and vulnerability (Step 2) effects.

Table 2: Model summary estimates predicting resilience.

The first step assesses the effect of socioeconomic variables which according to the social diversification hypothesis, are the most likely to shape the use of New Media and hence affect resilience. Introduction of the socioeconomic variables accounts for a significant portion of the explained variation in resilience (R² =.248). In the second step we inserted the vulnerability factors in the analysis, including level of perceived hindrance and deterioration of employment status following the emergence of the correlation estimates for COVID-19. These factors raise the prediction of resilience (R=.270) accounting for an additional 2% of the explained variance in reliability. A significant addition to the explained variance predicting resilience is revealed when we add the New Media variables (R² =.287). The full model entailed a set of New Media variables including information access, messaging, texting, social media and Zoom use. New Media use improves significantly the basic model including sociodemographic effects (R²==.248) the vulnerability effects (R²==.270) in the prediction of resilience. The significant differences between the models from Step 1 to Step 2 and Step 3 corroborate the study’s research question regarding the factors associated with resilience variations. Next, we present the direct effects of the study’s socioeconomic variables, vulnerability factors and New Media use on resilience (Table 3).

Table 3: Regression coefficients for socioeconomic characteristics, vulnerability factors and new media use predicting resilience.

Socioeconomic Effects and Resilience

In the first step, we include socioeconomic variables. According to the social diversification hypothesis, these variables are most likely to shape the use of New Media and hence affect resilience. Considering the importance of socioeconomic effects, the findings indicate that older individuals (B=-.406), male gender (β=?.781), and married/cohabitating individuals (B=?.516) report a decrease in resilience, while a higher income (B=.243) significantly increases resilience. The significant impact of these variables on resilience testifies to the importance of controlling the effect of socioeconomic variations, as suggested by the social diversification hypothesis (Rosenberg et al., 2020). Next, we added the set of variables related to vulnerability factors.

Vulnerability Factors and Resilience

In Step 2 we introduced the vulnerability factors, including level of perceived hindrance caused and deterioration of employment status following the emergence of COVID-19. The findings reveal that a deterioration in employment status (i.e., being laid off and/or on unpaid leave) (β=-.379) along with perceiving that the limitations and restrictions are not necessary (β=-.163) serve as a negative background for resilience. The negative impact of both these factors testifies to the importance of adopting a crisis perspective in analyzing resilience during COVID-19 and increases our understanding of the effect of vulnerability factors on resilience during the COVID-19 crisis. At the final step, we introduced variables related to New Media use.

New Media Uses and Resilience

Step 3 reveals that the impact of New Media is impressive as well. Searching online for information about COVID-19 (via search engines, specific websites, or YouTube) has a positive effect on resilience (B=.530), as does the use of social media to share or post information about the coronavirus (B=.371). Using e-mail or messaging services (i.e. WhatsApp, Facebook, Messenger) to communicate with others about COVID-19-related issues also increases resilience (B=.247). Nonetheless, the use of video calling or online conferencing services such Zoom or WebEx to attend a work or study meeting has no effect whatsoever. Clearly, the findings testify to the importance of using New Media in order to improve resilience during COVID-19.

The global crisis caused by COVID-19 has brought individuals face-to-face with new health, economic, and social stressors. Drawing upon existing studies focusing on community resilience we considered how New Media shapes individuals’ mental and physical resilience in the midst of the crisis of COVID-19. First, we examined the impact of socioeconomic variations and showed that differences in resilience may stem from variations in age, gender and income supporting the importance of drawing upon the social diversification hypothesis (Mesch., 2016). Second, the additional inclusion of vulnerability factors indicates that the level of affordances is also an important factor in determining resilience, since the deterioration of employment status and a higher perception that restrictions were unnecessary contribute to lower resilience following the emergence of COVID-19. A large number of factors related to age, gender and income reflect the vulnerability of certain social groups, such as older and low-income groups thatdefine the potential for resilience (Kim & Kim, 2017). We therefore conclude that controlling for socio-demographic vulnerability provides an additional set of factors pointing to the quality and quantity of assets. Third, introducing the impact of New Media has a major effect as well because it significantly improves the prediction of resilience. Most of the included New Media variables—information access, messaging, texting, social media and Zoom use—contribute significantly to resilience but not necessarily to improving individuals’ wellness. This indicates that differences between New Media sources play a significant role in the potential to generate variations in resilience. These results assess our hypotheses concerning the effect of variations in use of New Media on resilience. Moreover, this study enables to support the Social Diversification hypothesis considering the importance of vulnerabilities. As a result we highlight that the diversity of New Media use during the pandemic has enabled individuals to become resilient while coping with the crisis caused by restrictions and limited access to resources. More important though we revealed that using New Media was not equally effective to all individuals alike because they confronted specific conditions that increased their sense of crisis and reduced their potential to become resilient. We can therefore conclude that New Media use provides a limited potential to cope with the crisis of the pandemic.

Limitations and Recommendation for Future Studies

The results indicate that people have a hard time adjusting to the limitations imposed by the COVID-19 pandemic. The present data do not capture the full effects of using Zoom platform, which by now has become the single and most widespread means of replacing face-to-face communication. Future studies should focus on the way changes in resilience reflect the process of adopting new norms for connecting via New Media platforms. Such an assessment will be of great value in understanding, planning and implementing institutional practices and policies in our information-based society to achieve higher resilience and lower vulnerability in times of crisis. Clearly, we need to develop conceptual models in order to assess how various factors of vulnerability affect resilience in times of crisis.

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