Intergenerational Information-Sharing Behavior During the COVID-19 Pandemic in China: From Protective Action Decision Model Perspective

Abstract

Information sharing plays an important role in the study of health communication and contributes to sustainable development goals. During the COVID-19 pandemic, elderly individuals in families could obtain COVID-19-related information via multiple channels. With respect to digital back-feeding, younger generations can share COVID-19 information with their elderly family members via face-to-face communication, video or phone calls, etc. In this paper, we aim to investigate the antecedents of intergenerational information-sharing behavior from young people to their elderly family members. On the basis of the protective-action decision model, we collected 409 valid questionnaires and then constructed a structural equation model. The influencing factors of COVID-19 information-sharing (CIS) behavior are divided into information level, intergenerational level, and motivation level. We found that source credibility, intimacy, response efficacy (RE), and altruism (ALT) have positive effects on CIS behavior. An indirect path exists between information severity, information usefulness, interaction degree, and the CIS through the mediation of RE and the ALT. Information and intergenerational levels could influence RE and ALT. These findings help us understand reciprocal behavior in the family and improve the digital well-being of elderly family members.

1. Introduction

The COVID-19 pandemic has significantly influenced complex socioecological systems around the world and caused irreversible behavioral changes in people’s daily lives. The progress of the United Nations Sustainable Development Goals (SDGs) for many counties was undermined by the COVID-19 pandemic, and some timelines for reaching targets have slowed down in different regions [1]. Concerns of whether the SDGs are appropriate have been raised [2,3] considering the huge impact of the pandemic on global health systems and well-being in all age groups.

Information sharing is an effective response to pandemic outbreaks and can achieve cooperation among countries [4]. During the COVID-19 pandemic, both governmental institutions and individuals took countermeasures to alleviate the influence of the virus and progress both towards the SDGs worldwide [5] and towards Healthy China 2030 in China [6]. We can see that health communication has helped promote sustainable behavior change [7]. Global health and digital inclusion became more important in this crisis, which is also the main framing of the SDGs [8,9]. Due to the COVID-19 lockdown, home quarantine, and health monitoring, people who stay at home rely on multiple outlets (e.g., television, radio, and social media) to obtain COVID-19 information. The government, media institutions, and platforms have cooperated to announce the pandemic status and prevention measures for the public in a timely manner [10,11]. This enabled them to share up-to-date and accurate information during times of crisis, which could reduce the infodemic and then realize digital inclusion [12]. It also enables people to have equal access to healthcare systems and maintains social stigmatization in a sustainable way [11,13]. Public attention and self-protection awareness have increased through this dissemination of COVID-19 information [14].

As the smallest unit of social action, individual information cognition and behavioral responses in families are beneficial for implementing epidemic-prevention policies [15]. As a supplement, COVID-19 information sharing from younger generations to their elderly family members is an important channel [16] that can promote healthy lives and help elderly members fight health emergencies, as mentioned in SDG Target 3. Elderly individuals can learn more about COVID-19 messages from younger generations through face-to-face communication, video and voice phone calls, and forwarding via instant messaging software. These trust-based and effective communication mechanisms further foster intergenerational solidarity and realize knowledge transfer in inclusive societies, as mentioned in SDG Target 16 and Target 17 [5].

Moreover, intergenerational information-sharing behavior contributes to reducing inequalities in digital literacy among elderly individuals, where more accurate health information is selected by the youth and then told to their elderly family members. Intergenerational information sharing not only provides healthy messages but also addresses digital divides by resolving the disparities between the youth and elderly populations in the context of the pandemic. Therefore, our research topic also involves the content of SDG Target 10 [5].

We can see that during the COVID-19 pandemic, elderly people are vulnerable to serious conditions and are less likely to obtain high-quality information via mobile services. Some measures, such as lockdown or home quarantine, increase their feelings of social isolation or loneliness [17]. In China, population aging has gradually become a trend and will enter the stage of moderate or even severe aging in the next 5–10 years. Moreover, the penetration rate of the internet and digital literacy is not sufficient among elderly groups, which will mean they lag behind in terms of information acquisition and aggravate the digital divide in society. As a result, the issues of digital inequalities and healthcare well-being are exacerbated and affect the progress of SDG targets (e.g., SDGs 3 and 10). In addition to mass media channels, as a supplement outlet for elderly people, it is important for them to obtain information through sharing from the younger generation. Therefore, intergenerational information-sharing behavior among family members is a significant issue in public health and even the SDGs. Intergenerational solidarity [18] and community resilience [12] are further enhanced.

In this paper, we used the protective action decision model (PADM) as the framework for the following reasons. As a global public health emergency, the COVID-19 pandemic is highly harmful to human health. Because of its high level of risk, individuals prefer to acquire and share relevant information through various channels to reduce uncertainty, which actually involves taking protective actions to address risks. In addition, COVID-19 information-sharing behavior is determined by individual decision-making processes, which are affected by objective information [19,20], intergenerational elements [21], and subjective behavioral motivation [22]. These dimensions correspond to the warning components, social context, and psychological processes of the PADM. Furthermore, existing researchers have shown that the formation of information-sharing motivation (or behavior) during a pandemic is not only a psychological cognitive process [23] but is also affected by information characteristics, the surrounding environment, altruism, etc. [19,20]. In addition, Guo et al. applied the concept of the information flow of the PADM and a heuristic systematic model to construct structural equation modeling and verified the impact of consumer information risk perception on personal decision-making [24]. However, the PADM was initially proposed as a behavioral framework, and the concrete variables are not specific to each element, which requires providing specific definitions and operational indicators for measurement in practice.

Considering that family members play a crucial role in information dissemination, few researchers have focused on intergenerational information-sharing behavior from youth to elderly family members during the COVID-19 pandemic, which will further benefit intergenerational solidarity and digital inclusion from a sustainability perspective. As digital natives, young people, especially university students, are more likely to engage in active health information sharing within the family because of their greater digital literacy and altruistic motives. In addition, university students are good at discovering and disseminating up-to-date information, which is useful for protecting family members’ health and contributing to sustainable public health in the community. In this study, according to the PADM, we construct a structural equation model and then divide the influencing factors into information, intergenerational, and motivation levels. This study aims to investigate the antecedents of COVID-19 information-sharing behavior from Chinese university students to their elderly family members. The research questions (RQs) are presented as follows:

• RQ1: What kind of COVID-19 information did Chinese university students share with whom in their family?
• RQ2: Which of these influencing factors affected COVID-19-related information-sharing behavior from Chinese university students to elderly family members, and how?

Although information-sharing behavior has been studied during the COVID-19 pandemic, for example, from the perspectives of information usefulness [25], intimacy [26], and altruistic relationships [27], few studies have focused on intergenerational information-sharing behavior. The contributions can be summarized as follows:

• First, with the PADM, an extended structural equation model was constructed to explore the motivation and behavior of university students to share COVID-19-related information with the elderly family members. Therefore, we divide the influencing factors into three dimensions, namely, the information level, intergenerational level, and motivation level, which correspond to the elements of predecision-making, decision-making, and behavioral response in PADM.
• Second, we reveal that source credibility, intimacy, response efficacy, and altruism have positive effects on COVID-19 information-sharing behavior and that the influence of source credibility on response efficacy is mediated by information usefulness. In addition, the mediating effects of response efficacy and altruism on information severity and the degree of interaction with respect to COVID-19 information sharing are revealed.
• These findings help us understand the antecedents of intergenerational family information-sharing behavior and better protect the health of family members when public health emergencies occur, especially by providing insights into the postpandemic era.

This paper contains the following structures: Section 2 presents theoretical concepts of the protective-action decision model, information sharing behavior, and intergenerational relationships. Section 3 puts forward the conceptual model and research hypothesis. Section 4 describes the data-collection process, measurement development, and demographic information. Section 5 elaborates on the results of the data analysis, i.e., the reliability and validity, and the hypothesis test. Section 6 discusses the major research findings. Also, the implications and limitations are presented. Finally, Section 7 summarizes this study.

2. Literature Review

2.1. Protective Action Decision Model

With the accelerated impact of information disorders, behaviors related to health information (e.g., seeking, adopting, and sharing) have attracted extensive attention. Many theories and models are used to illustrate these behaviors. The PADM explains individuals’ protective-action decisions regarding environmental hazards and disasters [28]. According to Lindell and Perry’s model diagram, the PADM includes three key dimensions: predecision-making, decision-making, and behavioral response. The predecision-making stage includes several key elements: environmental factors, information sources, warning messages, and receiver characteristics. The decision-making stage includes elements of threat perception and efficacy perception. Behavioral response refers to the behavior and measures taken in response to risk after psychological processes. The process of people taking action includes predecision, decision, and action stages. Predecisional processes are influenced by cues from environmental and social perspectives, information sources, and warning content. The decision processes comprised threat perceptions and participants’ opinions toward actions [29]. Subsequently, people take protective actions on the basis of their decisions [30].

To explain human behavior during disasters, the PADM is widely applied to infectious diseases (e.g., H7N9 [31]), natural disasters (e.g., fires [32]), and environmental protection (e.g., nuclear power plants [33]). Unlike ordinary disasters and diseases, people do not know how long it took to recover during the COVID-19 pandemic, which increases the degree of importance of taking protective measures. The perception of personal and social risks also affects people’s behavior and decision-making when dealing with the COVID-19 pandemic [34].

Warnings are transmitted from the source to the receiver via a channel, and its effects depend on the receiver’s characteristics [30]. The sources, channels, and message content are the essential factors determining how a warning impacts people’s disaster response [35], especially when people lack knowledge about disease. In public health emergencies, the use of information is important for disease prevention and treatment. Information characteristics positively impact on health information behavior. The types of warning sources differ from authorities, media, and individuals, which are judged by their credibility. A warning from a credible source is more likely to trigger motivation towards protection. The warning message content describes the degree of threat, severity, and usefulness of the message source. When these factors are included in the message content, the recipient is more likely to respond to risky behavior [36]. Renn and Levine reported that trust develops further and is more likely to generate protective actions only if the information is fair, precise, objective, and complete [37], which is a good explanation for information usefulness.

Environmental cues are defined as the behavioral responses of others, which can promote the process of predecision and protective motivation decisions [35]. Interaction with family members is vital in the process of warning dissemination and contributes to promoting adaptation to disaster warnings. Lindell and Perry confirmed that the family context forms a constraint on the implementation of protective action that affects protective-action decisions and implementation activities [38].

2.2. Information-Sharing Behavior

Information-sharing behavior contributes to information dissemination [39]. With the property of being voluntary, useful information will be shared with other people who have similar needs for common interests [40,41]. In the COVID-19 pandemic period, information sharing is crucial for health communication and management. Shared COVID-19-related messages can aid people in learning about the threats of this disease and then act accordingly [42]. The information-sharing behavior related to younger generations has been studied in various regions. For example, Sharma et al. compared information sharing and verification behaviors on social media between users with average ages of 29.5 and 32 years in India and the USA, respectively. They reported that cultural context and media literacy significantly influence the frequency and content of information sharing [43]. Laato et al. revealed the reason why unverified COVID-19 information was shared by people in Bangladesh—especially when the situation became severe, younger people were likely to share COVID-19 misinformation [44], as young people were found to be less concerned about COVID-19 infection [34].

The characteristics of information and their sources (or contents) collectively affect individual health information behavior, which has been verified by prior studies. Perceived trust and credibility in different types of information sources affect the sharing behavior of COVID-19 information [42]. Sharam et al. reported that information with positive polarity increased information sharing [43].

For individuals, psychological processes (e.g., cognition, emotion, and motivation) are inextricably linked to information-sharing behavior. While seeking COVID-19 information, perceived information severity significantly affects people’s feelings. Lv et al. reported that the factors of information seeking, emotion regulation, altruism, public engagement, and personal behavior habits affect COVID-19 information-sharing behavior in regular risk situations [45]. Xia et al. confirmed that altruism directly influences the validated COVID-19 information sharing on SNS [46].

Social influence is a collective characteristic of communities and families that determines the health and well-being of populations [21]. The link between social influence and health behavior has been investigated. Wong et al. showed that family well-being was related to individuals’ preventive behaviors to combat the COVID-19 pandemic. The factors of family health, harmony, and happiness as mediating variables can affect information-sharing behavior in families [47]. Lee et al. also reported that community participation and perceived cohesion are related to health information sharing among family members [21].

2.3. Family Intergenerational Relationships

Intergenerational relationship refers to the relationship between two adjacent generations. Its core is the parent–child relationship [48]. Intergenerational family relationships provide space for offspring to support their parents. For intergenerational solidarity in aging families, the elements of interaction patterns and frequency, resource exchange and support, consensus and common family norms are emphasized [49]. Reciprocating and assisting family members contributes to fostering healthy behaviors. Some researchers subsequently focused on intergenerational family communication and investigated the communication behavior between elderly parents and their adult children. Adult children are the first line of support and care for their elderly parents. Especially during the treatment of diseases, maintaining continuous communication and mutual assistance between generations in the family will promote a healthy life. Waites reported that meaningful communication from adult children benefits the health and well-being of the elderly [50]. These studies examined how intergenerational members in the family environment engage in healthy communication, and the impact of healthy communication.

3. Research Model and Hypothesis

Based on PADM, we aimed to investigate university students’ COVID-19 information-sharing behavior to assist elderly members in learning the latest news about the COVID-19 pandemic. The proposed conceptual model comprises information severity (IS), information usefulness (IU), source credibility (SC), intimacy (INT), interaction degree (ID), response efficacy (RE), altruism (ALT), and COVID-19 information sharing (CIS), which are shown in

Table 1. Definitions for each construct.

Construct	Definition	Reference
IS	The perception of university students regarding the severity, significance, and the magnitude of the threat contained in the COVID-19 information.	Rogers, 1975 [51], Witte, 1992 [52]
IU	The assessment of the informativeness, value, accuracy, veracity, timeliness, completeness, reliability, adequacy, and helpfulness of COVID-19 information.	Mckinney et al., 2002 [53]
SC	The extent to which a COVID-19 information source is perceived to be accurate, believable, competent, and trustworthy by university students.	Petty et al., 1981 [54]
INT	The degree of closeness between university students and the elder family members.	Schaefer et al., 1981 [55]
ID	The distance, times, and duration of daily communication, and the frequency of common activities of family members.	Bengtson, 1991 [49]
RE	The belief that sharing COVID-19 information is effective and has desired consequences.	Witte, 1992 [52]
ALT	The extent to which university students share COVID-19 information without the expectation of reciprocity and compensation, but to directly or indirectly benefit the elder family members.	VanLange et al., 1997 [56]
CIS	The process of university students providing COVID-19 information they obtained to the elder family members.	Mesmer et al., 2009 [57]

.

In the conceptual model, source credibility corresponds to the element of information sources, information severity and information usefulness correspond to the element of warning messages, intimacy and interaction degree correspond to the element of receiver characteristics, and altruism and response efficacy correspond to stakeholder perception and protective-action perception in the original model. The constructs of COVID-19 information sharing correspond to behavioral responses.

3.1. Information Severity

Information severity is the severity of information perceived subjectively by receivers that the threat will cause harm [51]. Information severity is a crucial component of health messages. When people encounter health information, the severity of the information allows them to evaluate health threats and consider possible responding measures to address such threats [58]. Thus, health behavior has been actively adopted. Previous studies shown that information severity affected a recipient’s cognition, attitude, and behavior. Matthias et al. analyzed three common information characteristics and reported that high-severity health information is more likely to have an impact on media exposure and the health behavior of recipients [59]. Mohammed et al. noted that a relationship exists between the severity of social media information delivery and health protection behavior for social media usage during the COVID-19 pandemic [19]. Sarah et al. reported that the severity of the information increases how frequently information is passed on to others [60]. Information severity and sharing efficacy interact in several ways. Witte and Allen confirmed that the more severe the information contained, the stronger the perception of risks and threats in information, and the stronger the individual’s perceived efficacy of sharing information [61]. On the basis of these viewpoints, we hypothesize the following.

Hypothesis 1 (H1a).

Information severity positively impacts COVID-19 information-sharing behavior among university students.

Hypothesis 1 (H1b).

Information severity positively influences the response efficacy of the COVID-19 information-sharing behavior among university students.

3.2. Information Usefulness

Information usefulness is a key variable in explaining how readers are aware that the received information is valuable [62]. The evaluation criteria includes accuracy, veracity, timeliness, completeness, and reliability [63]. Useful information is of utmost importance when health information is shared. Park et al. showed that perceived information usefulness is an important antecedent factor in information-sharing behaviors for the online investment communities [64]. The more useful the information is, the stronger the perception of the response efficacy of sharing information. Liu et al. reported that the perceived usefulness of COVID-19 information is an important incentive for information sharing. When social media users find information more useful, they have a more positive attitude and generate a higher expected reciprocation [20]. We can see that the more useful information is, the stronger people’s perception of response efficacy that sharing information may bring. Thus, we hypothesize the following:

Hypothesis 2 (H2a).

Information usefulness impacts COVID-19 information-sharing behavior positively among university students.

Hypothesis 2 (H2b).

Information usefulness positively impacts the response efficacy of COVID-19 information-sharing behaviors among university students.

3.3. Source Credibility

Source credibility appears to be one of the frequently referenced cues in health information-sharing behavior [54,65]. When faced with sudden public health emergencies, people often obtain information everywhere because of a lack of prior knowledge about the disease. Identifying credible sources from excessive information has become a problem that must be emphasized during the COVID-19 pandemic.

The source credibility of COVID-19 information has a significant effect on shaping personal attitudes and behaviors [42,44]. Yang et al. noted that individuals who perceive information about the pandemic on social media as believable are more likely to share information [66]. Lu et al. also investigated the impact of different information sources on COVID-19 information sharing against the background of the high flow of COVID-19, showing that people share information from these information sources, such as health professionals, academic institutions, and government agencies, because they are more credible [42].

A sense of credibility in news has been widely thought to affect the way people recognize and process information [67,68]. Lindell and Perry proposed that perceived information credibility is associated with people’s perceived efficacy of hazard adjustment in response to environmental disasters [29]. Nancy also reported that information sources not only affect individuals’ risk perception, perceived severity, and self-protection behavior under risk conditions but also influence the response efficacy of protective behavior [69].

In the information adoption model, source credibility is a key determinant of information usefulness [65]. Shen et al. suggested that the greater the credibility of contributors in Wikipedia is, the more useful the information presented on the page [70]. Luo et al. investigated the effects of different information sources on the usefulness perceptions of readers in third-party forums and reported that consumer eWOM source credibility positively affects information readers’ perceptions of information usefulness in third-party forums [62]. Shang et al. explored the health information-seeking and sharing behavior of elderly people via social media and reported that in information processing, the positive impacts of source credibility on health information sharing are fully mediated by perceived usefulness [71]. Thus, we hypothesize the following:

Hypothesis 3 (H3a).

Source credibility positively influences the COVID-19 information-sharing behavior among university students.

Hypothesis 3 (H3b).

Source credibility positively influences response efficacy of COVID-19 information-sharing behavior among university students.

Hypothesis 3 (H3c).

Source credibility positively influences university students’ perceptions of the usefulness of COVID-19 information.

3.4. Intimacy

Intimacy is considered a key aspect of social support and is characterized by behavioral interdependency, fulfillment of needs, and emotional attachment [72]. In particular, it has become the central factor in the development of youth. In intergenerational relationships among families, intimacy reflects closeness and openness when family members interact with others [73]. In addition, researchers have emphasized that family intimacy in a healthy relationship not only involves closeness but also includes an interplay between closeness (e.g., emotional sharing, connectedness) and autonomy (e.g., individuality) [74,75]. In health communication, a causal relationship between intimacy and attitudes (or healthy behaviors) among family members has gradually attracted increasing attention.

Harvey et al. evaluated the impact of intergenerational intimacy on healthy behavior on the basis of intergenerational family systems theory and reported a direct or indirect relationship between the quality of family intimacy and the promotion of healthy behavior [76]. Steijn et al. examined the relationship between information-sharing behavior and relationship development and reported that information sharing might normally be reserved only for close friends and family on social networking sites [77]. Moreover, with increasing intimacy, individuals are more tolerant of others. Aron suggested that intimacy reflects not only the perceptions of self versus others but also that those in intimate relationships tend to consider the interests of family members more than seeking any material or spiritual rewards, which indicates a stronger altruistic tendency [78]. Palmer showed that the principle of reciprocal altruism reinforced by kinship can explain information sharing among community fishermen [79]. We propose the following hypotheses:

Hypothesis 4 (H4a).

Intimacy positively influences COVID-19 information-sharing behavior among university students.

Hypothesis 4 (H4b).

Intimacy positively influences altruism in the COVID-19 information-sharing behavior of university students.

3.5. Interaction Degree

The family can be viewed as the smallest group unit in human life [80]. Family operation influences the formation of an individual’s character, values, and social adaptability. The interaction between family members is one of the conditions that ensures the normal operation of the family system [81]. Traditionally, assessments of family interaction consider all family members’ cohesion, adaptability, conflict, and flexibility, as well as the frequency of conversation and avoidance of communication [82]. Lee et al. reported that perceived cohesion, adaptability, and communication were significantly correlated with health information sharing among family members [21]. Therefore, we have included the degree of interaction in this study.

The influence of the degree of interaction on information sharing is outlined. Watzlawick et al. argued that the occurrence and meaning of health behavior are particularly dependent on its family interaction context [83]. Steijn et al. also indicated that intimate interactions between family members can affect individual information sharing on social networking sites in a unique manner [77]. Ma et al. confirmed that a good family interaction environment tends to increase the prosocial orientation of adolescents [84]. Sousa et al. directly reported the positive impact of family interaction on altruism [85]. Thus, we hypothesize the following:

Hypothesis 5 (H5a).

The interaction degree positively influences COVID-19 information-sharing behavior among university students.

Hypothesis 5 (H5b).

The interaction degree positively influences altruism in COVID-19 information-sharing behavior among university students.

3.6. Response Efficacy

Protection motivation theory (PMT) is commonly used to explain and predict personal health motivation and behaviors [86] and has been widely applied and confirmed in the field of infectious diseases [87,88]. As a component of coping appraisal in protection motivation theory, response efficacy implies one’s belief in its effectiveness in reducing the threat of taking the recommended action, which is an important condition for personal protective behavior [51]. The greater the response efficacy is, the greater the likelihood that individuals are prepared to take protective actions to eliminate risks [51]. In terms of information communication, studies have proven that response efficacy is an influential factor in information sharing. For example, Lee et al. conducted an experiment, and the results showed that the expected benefit of response efficacy influenced users’ intention to share their context information in social network services (SNSs) [89]. Shore et al. used protective motivation theory as a model to confirm that response efficacy can serve as a predictor for individuals to share information with the police [22]. Therefore, we hypothesize as follows:

Hypothesis 6.

The response efficacy positively influences their COVID-19 information-sharing behavior among university students.

3.7. Altruism

Altruism means voluntary actions that assist other people with expecting nothing in return [90]. Given its association with human cooperation and helping others, altruism is recognized as an essential motive for altruistic human behavior [91,92] and represents an important area of inquiry in the social sciences [93,94]. Subjectively, during the COVID-19 pandemic, sharing pandemic information to target credible communities and circles (e.g., family) is a prosocial behavior driven by altruism to help promote family members’ healthcare. Therefore, altruism was included in this study.

Some studies have proven that altruism is a predictor of information sharing. Li et al. reported that altruism has a positive effect on users’ health information sharing in a social question-and-answer community [26]. Apuke et al. demonstrated that altruism was the most significant factor that predicted fake-news sharing related to COVID-19 [23]. Sanghee et al. reported that altruism is a major motivation for social media users to share information [95]. Therefore, we hypothesize as follows:

Hypothesis 7.

Altruism among university students positively influences their COVID-19 information-sharing behavior.

We illustrate the proposed model and mark the assumptions in Figure 1.

4. Research Method

4.1. Data-Collection Procedure

To understand the Chinese university students’ motivations of COVID-19-related information-sharing behavior, we conducted an online questionnaire survey over the period of 14 March 2023 to 14 April 2023, which is a suitable time period and enables students to recall information-sharing behavior, because China adjusted the management policy of the COVID-19 pandemic from ’Category A management of Category B infectious diseases’ to ’Category B management of Category B infectious diseases’ on 8 January 2023. The university students who shared COVID-19 pandemic information with elderly family members in the past 3 months during the pandemic were recruited voluntarily. For the informed consent for participation, we described the research purpose, data usage scope, privacy, and anonymity at the beginning of questionnaire. If and only if the participants were fully informed and agreed to fill the questionnaire, they could click the next-page button to read and fill the items further. In this way, we obtained all participants’ consent in this study. All the measurement items were presented in Appendix A and a five-point Likert scale (1 = “strongly disagree” 5 = “strongly agree”) was used. To improve the quality and representativeness of the survey, data collection was divided into two stages: pre-testing and formal distribution. In the pre-test stage, we first recruited eight participants from four levels of college—undergraduate, graduate, and doctoral students—by purposive sampling. Then, we received 75 of 78 questionnaires back, as some items were revised. For the large-scale distribution, questionnaires were distributed through social media (such as WeChat and QQ) via the snowball sampling method, because we needed to recruit participants who share relevant information with their elderly family members. Qualifying questions were used to screen the target participants.

We used the following screening criteria: (1) screening questions; (2) the time to complete is greater than or equal to 120 s; (3) all item scores should not be exactly the same; and (4) there are no missing or obvious errors in the answers. Hence, 409 valid questionnaires out of 438 returned questionnaires remained, which is more than 10 times the largest number of paths [96]. The recovery rate was 93.38%. To confirm the adequacy of the sample size, we conducted a G-power analysis for structural equation models (SEMs) [97]. The required minimum sample size is 103. Our valid sample size (N = 409) is larger than this threshold, indicating that the sample size is statistically sufficient for the current research framework.

Using Harman’s single-factor test, the performance of the common method bias was obtained through SPSS 26. The results showed that the explanatory power of the first factor was 32.3%, below the critical criterion of 40% [98], indicating that no significant common method bias occurred.

4.2. Descriptive Analysis

Table 2. The demographic characteristics (N = 409).

Characteristic	Category	Frequency	Percentage
Gender	Male	209	51.1%
	Female	200	48.9%
Age	18–20	223	54.5%
	21–25	137	33.5%
	26–30	43	10.5%
	Above 30	6	1.5%
Education	College	156	38.14%
	Undergraduate	164	40.10%
	Master’s and above	89	21.76%
Region	Eastern areas	214	52.3%
	Middle areas	172	42.1%
	Western areas	23	5.6%
Types of COVID-19 shared information (multiple-choice)	Official announcement	249	60.9%
	Notification report	171	41.8%
	Science popularization	195	47.7%
	Prevention dynamic status	187	45.7%
	Character deeds	77	18.8%
Means of COVID-19 information sharing behavior (multiple-choice)	Verbal face-to-face	280	68.5%
	Telephone and voice	216	52.8%
	(including QQ and WeChat phone)
	Remote video call	91	22.2%
	Forward through social media	285	69.7%

shows demographic information. For gender, 51.1% of the respondents were male and 48.9% were female. In terms of age distribution, 54.5% of them were between the ages of 18 and 20, and 33.5% were between the ages of 21 and 25. In terms of education, 38.14% of the participants were in the college stage, 40.1% were undergraduates, and 21.76% were graduate students. Regarding regional distribution, the surveyed subjects were distributed in 23 provinces. As for China’s three major economic zones, 52.3% of the participants were from the east (such as Beijing, Shanghai, Shandong, Guangdong, etc.), 42.1% were in central areas (such as Shanxi, Hubei, Hunan, Jiangxi, etc.), and 5.6% were from western areas (such as Guizhou, Yunnan, Gansu, and Xinjiang). Therein, Shandong (16.4%), Guangdong (14.7%) and Jiangsu (8.3%) ranked as the top three places.

5. Data Analysis and Results

After the normality test, both the results of the Kolmogorov–Smirnov and Shapiro–Wilk tests showed that the significance level of all the constructs was less than 0.05 (p< 0.05), which means that variables were not normally distributed. Therefore, we used partial-least-square structural equation modeling (PLS-SEM) via SmartPLS 3.3.9 to estimate the proposed model. The measurement model was tested by PLS algorithm (maximum iterations set to 1000 and stop criterion ${10}^{-7}$). The structural model was tested by the bootstrapping procedure, with parameters of 5000 subsamples, bias-corrected and with an accelerated bootstrap, and two-tailed tests at 5% significance level. Blindfolding was carried out with the omission distance at 7.

5.1. Reliability and Validity

We analyzed the reliability and validity of the measurement model by calculating the reliability of the indicator’s consistency, as well as convergent and discriminant validity. Therein, the reliability of the internal consistency contains Cronbach’s alpha and composite reliability (CR), requiring a threshold value above 0.7. Factor loading is used to test for an indicator reliability that is greater than 0.7 [99]. Convergent validity is estimated through average variance extracted (AVE) that is higher than 0.5 [96]. For discriminant validity, the square root of AVE for every construction was larger than the below-correlation relationship with other latent variable.

Construction’s reliability and convergent validity are presented in

Table 3. Construct reliability and validity.

Construct	Items	Loadings	Cronbach’s Alpha	CR	AVE
IS	IS1	0.881	0.91	0.937	0.787
	IS2	0.888
	IS3	0.907
	IS4	0.874
IU	IU1	0.887	0.895	0.927	0.761
	IU2	0.883
	IU3	0.872
	IU4	0.847
SC	SC1	0.878	0.903	0.932	0.775
	SC2	0.874
	SC3	0.884
	SC4	0.885
INT	INT1	0.803	0.9	0.923	0.667
	INT2	0.825
	INT3	0.755
	INT4	0.796
	INT5	0.849
	INT6	0.867
ID	ID1	0.883	0.887	0.921	0.746
	ID2	0.844
	ID3	0.824
	ID4	0.902
RE	RE1	0.875	0.892	0.925	0.755
	RE2	0.846
	RE3	0.857
	RE4	0.896
ALT	ALT1	0.858	0.898	0.924	0.709
	ALT2	0.844
	ALT3	0.85
	ALT4	0.844
	ALT5	0.815
CIS	CIS1	0.857	0.917	0.938	0.752
	CIS2	0.841
	CIS3	0.889
	CIS4	0.867
	CIS5	0.881

. The values of Cronbach’s alpha (from 0.887 to 0.917) and CR (from 0.921 to 0.938) showed the reliability and internal consistency. AVE values (from 0.667 to 0.787) means relatively better convergence validity.

Table 4. Discriminant validity (Fornell–Larcker criterion).

	ALT	CIS	ID	INT	IS	IU	RE	SC
ALT	0.842
CIS	0.363	0.867
ID	0.383	0.315	0.864
INT	0.38	0.412	0.386	0.817
IS	0.359	0.295	0.362	0.355	0.887
IU	0.419	0.306	0.407	0.408	0.416	0.873
RE	0.344	0.341	0.358	0.365	0.432	0.424	0.869
SC	0.409	0.375	0.364	0.365	0.391	0.400	0.315	0.880

reveals the discriminant validity. It suggests that the square root of average variance values (in bold) is larger than all the correlation coefficients between the constructs (off-diagonal values). These constructs can be distinguished from each other.

5.2. Hypothesis Testing

The proposed hypothesis and path coefficients were examined by applying the bootstrapping procedure. The results of hypothesis tests are presented in

Table 5. Hypothesis test results.

Hypothesis	Path	Path Coefficient	T-Statistics	${\mathit{f}}^2$	p-Value	Result
H1a	IS→CIS	0.024	0.407	0.001	0.684	Not
H1b	IS→RE	0.282	5.373 ***	0.083	0.000	Supported
H2a	IU→CIS	0.005	0.076	0.000	0.939	Not
H2b	IU→RE	0.267	4.998 ***	0.074	0.000	Supported
H3a	SC→CIS	0.166	2.906 **	0.027	0.004	Supported
H3b	SC→RE	0.097	1.813	0.010	0.070	Not
H3c	SC→IU	0.400	8.793 ***	0.190	0.000	Supported
H4a	INT→CIS	0.220	3.923 ***	0.047	0.000	Supported
H4b	INT→ALT	0.273	5.498 ***	0.080	0.000	Supported
H5a	ID→CIS	0.062	1.088	0.004	0.277	Not
H5b	ID→ALT	0.278	5.538 ***	0.083	0.000	Supported
H6	RE→CIS	0.128	2.203 *	0.016	0.028	Supported
H7	ALT→CIS	0.133	2.385 *	0.017	0.017	Supported

*** p < 0.001, ** p < 0.01, * p < 0.05.

, where nine of the thirteen hypotheses are valid. The structural model is depicted in Figure 2. IS can positively influence RE ($\beta$ = 0.282, p < 0.001), but has an insignificant positive relationship with CIS ($\beta$ = 0.024, p = 0.684), indicating that H1b is verified and H1a is unproven. IU is similar to IS. IU has a positive association with RE ($\beta$ = 0.267, p < 0.001), so H2b is confirmed. However, it shows an insignificant effect on CIS ($\beta$ = 0.005, p = 0.939), indicating H2a is rejected. SC was positive related to CIS ($\beta$ = 0.166, p < 0.01) and IU ($\beta$ = 0.400, p < 0.001), which supports H3a and H3c. H3b ($\beta$ = 0.097, p = 0.070) was unproven hypothesis. At the social-context level, INT positively affected CIS ($\beta$ = 0.220, p < 0.001) and ALT ($\beta$ = 0.273, p < 0.001). Additionally, ID had positive impact on ALT ($\beta$ = 0.278, p < 0.001), but had not positive correlation with CIS ($\beta$ = 0.062, p = 0.277). Thus, H4a, H4b, H5b were confirmed, and H5a was not supported. Moreover, RE ($\beta$ = 0.128, p < 0.05) and ALT ($\beta$ = 0.133, p < 0.05) were positively correlated with CIS, indicating H6 and H7 were valid hypotheses.

The effect size ($f^2$) is explicated in

Table 6. Mediating relationship.

Indirect Path	Beta	SD	T-Statistics	p-Value	Confidence Intervals (2.5%)	Confidence Intervals (97.5%)
IS→RE→CIS	0.036	0.018	2.058 *	0.040	0.004	0.072
IU→RE→CIS	0.034	0.017	1.978 *	0.048	0.003	0.071
SC→IU→RE	0.108	0.024	4.357 ***	0.000	0.062	0.158
ID→ALT→CIS	0.037	0.017	2.178 *	0.029	0.006	0.073

*** p < 0.001, * p < 0.05.

. The values of 0.02, 0.15, and 0.35 imply weak, medium, and strong effect sizes, respectively [100]. The results showed that SC has strong effect size on IU; other hypotheses are relatively weak.

Bootstrapping was used to test the mediating effects, and the results are shown in Table 6. For the invalid hypotheses H1a, H2a, H3b and H5a, there exist the mediating variables. The relationship between IS and CIS (H1a) is moderated by the response efficacy of information sharing (p = 0.040 < 0.05). The relationship between IU and CIS (H2a) was also influenced by the mediation of response efficacy (p = 0.048 < 0.05). The influence of SC on RE (H3b) was significantly mediated by information usefulness (p = 0.000 < 0.001). Meanwhile, ID (H5a) positively impacted CIS through altruism (p = 0.029 < 0.05).

To assess the predictive and explanatory capacity of the proposed model representing its quality, the coefficient of determination (${\mathrm{R}}^2$) and predictive relevance (${\mathrm{Q}}^2$) were shown in

Table 7. The values of ${\mathrm{R}}^2$ and ${\mathrm{Q}}^2$.

Construct	${\mathbf{R}}^2$	${\mathbf{R}}^2$ Adjusted	${\mathbf{Q}}^2$
ALT	0.210	0.206	0.144
CIS	0.273	0.261	0.200
IU	0.160	0.158	0.119
RE	0.266	0.260	0.195

. The ${\mathrm{R}}^2$ measures the combined effects of exogenous variables [101]. CIS and RE indicate prediction accuracy. Meanwhile, the ${\mathrm{Q}}^2$ values of ALT, CIS, IU, and RE are all above zero, related exogenous constructs have a predictive correlation with the given endogenous construct [96].

The results of the model fit are listed in

Table 8. Model fit indicators.

Index	NFI	rms_theta	SRMR
Value	0.9	0.103	0.037
Threshold	0.9 < NFI < 1	<0.12	<0.08

. The values of Normed Fit Index (NFI), $\mathrm{rms}\_\mathrm{theta}$, and standardized root mean square residual (SRMR) indicate that the model fits well.

For the PLSPredict assessment as shown in

Table 9. PLSPredict assessment of manifest variables.

	PLS-SEM			LM
Items	RMSE	MAE	${\mathbf{Q}}_{\mathrm{Predict}}^{\mathbf{2}}$	RMSE	MAE
IU1	1.395	1.181	0.109	1.368	1.112
IU2	1.395	1.178	0.108	1.33	1.083
IU3	1.332	1.114	0.131	1.291	1.043
IU4	1.254	1.043	0.121	1.221	0.996
RE1	1.293	1.068	0.162	1.277	1.04
RE2	1.289	1.086	0.094	1.273	1.055
RE3	1.313	1.089	0.157	1.33	1.076
RE4	1.319	1.098	0.178	1.32	1.075
ALT1	1.202	1.023	0.179	1.196	0.998
ALT2	1.375	1.134	0.114	1.386	1.147
ALT3	1.293	1.051	0.113	1.277	1.023
ALT4	1.236	1.026	0.173	1.22	0.987
ALT5	1.268	1.047	0.114	1.266	1.025
CIS1	1.286	1.051	0.154	1.306	1.073
CIS2	1.258	1.071	0.16	1.297	1.09
CIS3	1.344	1.106	0.187	1.366	1.11
CIS4	1.25	1.047	0.172	1.265	1.041
CIS5	1.39	1.17	0.16	1.42	1.186

, we can see that those indicators $Q_{\mathrm{Predict}}^2>0$, which means that the model has predictive power. Then, indicators of root mean square error (RMSE) and Mean Absolute Error (MAE) were compared regarding to PLS-SEM and linear regression model (LM). Compared to the LM, the PLS-SEM analysis yields lower prediction errors in terms of RMSE and MAE for a minority of the indicators, which showed low predictive power [102].

6. Discussion

In this section, we discuss the factors influencing COVID-19 information-sharing behavior from three dimensions: information level as the warning components (information severity, information usefulness, source credibility), intergenerational level as the social context (intimacy, interaction degree) and motivation level as the personal psychological process (response efficacy and altruism).

We conducted a descriptive analysis of the shared types, channels, and target members shown in Table 2 and

Table 10. Comprehensive sorting results of objects of COVID-19 information-sharing behavior (N = 409).

	Parents		Grandparents and Maternal Grandparents		Relatives and Elders Within Three Generations		Others
Sort	Frequency	Ratio	Frequency	Ratio	Frequency	Ratio	Frequency	Ratio
1	370	90.5%	19	4.6%	20	4.9%	1	0.25%
2	12	2.9%	362	88.5%	24	5.9%	0	0%
3	14	3.4%	7	1.7%	345	84.4%	1	0.25%
4	13	3.2%	21	5.1%	20	4.9%	407	99.5%

. The most common type was official announcements (60.9%), which is a credible, authoritative, and reliable information type and has a guiding role in understanding the current and future situation of the COVID-19 pandemic. Science popularization related to personal life, health, and safety accounted for 47.7%. Science popularization provides protective measures for the public to help protect their lives during sudden public health events. The following types were prevention dynamic status and notification reports, with 45.7% and 41.8%, respectively. The types of COVID-19 information shared by university students align with the information needs and cognitive preferences of older people identified in existing studies. Shang et al. reported that elderly individuals tend to prioritize authoritative and practical information because of their lower digital literacy, as they rely more on offline communication or trusted sources for health-related information [71]. This information, which is closely related to daily life and work, can not only help the public understand local information that can directly impact individuals but also effectively meet the public’s needs to understand the overall epidemic prevention situation in society. We can see that university students intended to share accurate and up-to-date information with their elderly family members, which could improve the coverage of this authorities’ information for senior people and eliminate the knowledge gap. In this way, the potential health risk can be decreased and the health system with enough resilience can be strengthened to achieve SDG Targets 3.8 and 3.d [103].

In terms of the shared channel, because of socialization and mobility trends, the proportion of forwarding through social media ranked the highest, accounting for 69.7%. Social media become an indispensable tool for daily communication and information acquisition. However, traditional interpersonal communication channels, such as verbal communication and phone communication, have not been completely replaced. Oral communication still accounted for a larger proportion, for example, 68.5% and 52.8%, through verbal face-to-face communication and telephone calls, respectively. Family relationships are a type of strong connection with high levels of intimacy and interaction among members. Compared with text communication with weak-tie social members in cyberspace, university students intend to share COVID-19 information with the elders in the family via oral communication and forwarding. Only 22.2% of the participants chose remote video calls, which is a dialog process that requires both parties to participate, and they often talk about more intimate, personal, and emotional content rather than COVID-19 pandemic information.

Specific to the target member of sharing, when university students share information, the first person they share it with is their parents; the percentage reaches 90.5%, with only 0.25% of people ranking their parents last to share. Grandparents and maternal grandparents are often considered second, accounting for 88.5% of the total, followed by relatives and the elderly within three generations, with a proportion of 84.4%. This can be explained by parents, grandparents, and maternal grandparents always taking care of and accompanying university students with stronger intimacy and a high frequency of daily interaction. Compared with grandparents and maternal grandparents, parents have a smaller intergenerational gap and closer relationships with university students, making them more likely to engage in sharing behaviors. Therefore, RQ1 is answered.

As previously mentioned, SC, INT, RE, and ALT were positively associated with the CIS. For SC, a credible information source means greater trust in the recipients, which can enhance their perception of the authority, reliability and authenticity of information. The finding is consistent with the prior studies [70,71]. On the one hand, SC can eliminate uncertainty about the pandemic and alleviate anxiety. The information value is enhanced when sharing behavior occurs. On the other hand, credible information has a lower probability of errors and results in positive outcomes. Like the community leader in [12], university students play a critical role in distributing information, which depends on a credible source to further satisfy the needs of their elderly family members. Therefore, source credibility is a pivot in trust and effective communication mechanisms. This finding is also in line with a study showing that source trust could increase source-specific sharing intentions during the COVID-19 pandemic [42]. INT is a vital foundation for communication within the family system [104]. The higher the level of intimacy is, the better the relationship quality among family members, and the easier it is to cultivate trust and closeness, which lays a psychological foundation for them to maintain contact and communication. Information sharing is more likely to occur as a means of information communication.

RE and ALT are regarded as individuals’ internal motivations that can further influence the CIS. As a cognitive process, RE plays a direct role in determining whether COVID-19 information-sharing behavior occurs to reduce pandemic threat [22]. When a university student recognizes that shared information can reduce infection risk, sharing motivation can be promoted because of the common interests of family members. Especially for the elderly in the family, a digital gap still exists between the middle-aged and elderly groups. However, younger generations, as digital natives, have a stronger ability to seek, identify, understand, and process information. To some extent, they have a great say in helping elderly people understand emerging information. In this way, sharing information related to the COVID-19 pandemic for elderly family members is more likely to occur, which provides clues to bridging the digital gap in families.

In contrast to the studies in [19,20,87], we find no direct effects between IS, IU, ID, and CIS. For H1a (IS→CIS) and H2a (IU→CIS), IS and IU had no direct impact on the CIS. This may be because students’ information-sharing behavior is driven more by the perceived effectiveness of sharing (RE) than by the inherent attributes of the information itself. Consistent with the protective-action decision model, individuals tend to take protective actions (e.g., sharing information) on the basis of their judgment of action efficacy rather than merely relying on information characteristics. When the received COVID-19 information is more threatened (or risky) and a coping strategy is provided, the efficacy of shared information will improve, which is a key factor affecting actual information-sharing behavior [105]. This conclusion is similar to the inherent logic of the “danger control” program in the extended parallel process model [106]. Ahmed reported that usefulness is a key factor in generating behavior in specific environments, but individuals’ perceptions of the efficacy of behavior play a mediating role [107]. Therefore, IS and IU can positively affect the CIS through the mediation of RE.

For H5a (ID→CIS), the ID showed no direct link with the CIS. However, ID was positively correlated with CIS, which was mediated by altruism. In terms of the mediating role of altruism, when the degree of intimacy and interaction among family members is greater, university students tend to consider the interests and needs of family members rather than themselves in the process of accessing information. When they believe that this information exactly matches their needs, sharing occurs naturally in regular interactions. In family contexts, interaction frequency is more likely to shape emotional motivation rather than directly drive specific behaviors, which aligns with the finding that altruism mediates the ID–CIS relationship.

Moreover, For H3b (SC→RE), SC had no direct effect on RE. This could be attributed to the fact that the influence of source credibility on individuals’ perceived efficacy is often mediated by information usefulness (IU). As Shang et al. noted, users typically evaluate the usefulness of information first on the basis of source credibility and then form efficacy judgments [71]. Our study confirmed this indirect path (SC→IU→RE), indicating that source credibility affects RE through IU rather than directly. Since the sudden outbreak of the COVID-19 pandemic, cyberspace has been full of excessive information that makes it difficult to distinguish true from false [23]. Individuals suffer from rumors and fake news. These prior experiences make it difficult for them to trust specific information sources. When people receive highly reliable sources of information from official authorities and media institutions, they intend to carefully consider the content and then evaluate its usefulness on the basis of credible judgment of the source [42]. Therefore, RQ2 is answered.

6.1. Implications

These findings have several practical implications for the dissemination of pandemic information and the occurrence of prosocial behavior among university students. Providing risky and valuable information can psychologically enhance university students’ perceptions of necessity. At the governmental level, authoritative information from trust institutions can directly increase sharing behavior. For example, health authorities, as credible information sources, should convey accurate information to the public through various channels in a timely manner. The information needs of special groups (e.g., the elderly) should be considered due to the digital divide or inequality. Official institutions (e.g., health commissions, Centers for Disease Control) should establish a unified information-release platform with clear labeling of sources and timeliness, which can enhance the credibility of COVID-19 information.

Some intergenerational digital literacy programs have been initiated, such as Older Adults Technology Services (OATS) in [108] and digital literacy training by the National Digital Inclusion Alliance (NDIA) [109]. Policymakers, such as health commissions, should develop policies that target digital inclusion among senior people. Moreover, information sharing involves not only information transmission but also information interpretation. Cooperation among the government, telecom providers, and social media platforms needs to be enhanced to improve communication effectiveness through universal and affordable access, as mentioned in SDG indicator 9.c. For example, depending on the number of users, social media and other mobile outlets should try their best to diffuse authoritative information to cover users. Telecommunications companies are able to offer digital literacy workshops for seniors. The credible information needs to be highlighted and labeled, which could remind the users and distinguish misinformation.

At the media level, transferring timely, accurate, and clear information from official and mainstream media is important for promoting information flow during pandemics, which also contributes to pandemic prevention and control. In addition, authoritative media institutions need to ensure the objectivity of reported content and provide high-quality crisis-related information. The subscription service in platforms could be provided for the youth and then push lasting notifications without fees to family members through short messaging services or app messages. Media and educational institutions should strengthen digital literacy education among different age groups, and let them distinguish the true from false information. In this way, the corporate social responsibility of media institutions or platforms comes into play.

At the family level, family members should work together to create a healthy family environment and then enhance intergenerational intimacy and interaction, which can promote the occurrence of information sharing and protection behaviors. It would be better to encourage multi-channel communication, such as combining face-to-face conversations with video calls. In addition, sharing the needed information is necessary, such as prioritizing science popularization and prevention dynamics which are closely related to daily health. We can see that the reciprocal behavior among intergenerational family members is disclosed when a no-normalization crisis occurs. Maintaining harmonious family relationships will attract younger generations to participate in COVID-19 information-sharing behavior. In the postpandemic era, it is necessary to rethink how to mitigate harm from the future health crisis. For example, how to optimize the information distribution and diffusion toward different age groups. Intergenerational information sharing from youth to the elderly is still a valuable topic considering the digital divide and inequities in access to emerging technology. Exploring the factors that affect intergenerational information sharing can provide a reference for public health emergencies.

In terms of social sustainability implications, intergenerational information sharing not only promotes healthcare awareness but also fosters community resilience by reducing the infodemic and digital divides. This means that digital inclusion can further mitigate social inclusion risk for the elderly group. Through information sharing, the information flow is created in addition to the mass media channel, which contributes to sustaining social cohesion and achieving the SDGs.

6.2. Limitations

This study had several limitations. First, the external validity of the research results is relatively low. Snowball sampling and all questionnaires were distributed online. This convenience sample may limit the generalizability of the findings. Future research could use random sampling to validate these findings. Second, we only studied the younger–elder relationship as our research target in the family. However, family intergenerational relationships are not limited to youth and older generations but also include peer relationships. Researchers could focus on the younger generation to enrich the results of intergenerational information-sharing behaviors. Third, this study focuses on one-way information-sharing behavior from university students to elderly people but fails to capture the dynamic feedback mechanism (e.g., whether elderly people actively request specific information or provide feedback on shared content). This may limit the understanding of the reciprocal nature of intergenerational information interaction.

7. Conclusions

In this work, we have revealed the influencing factors of COVID-19 information-sharing behavior from younger generations (i.e., university students) to elderly family members. The response efficacy (RE) and altruism (ALT) at the motivation level and source credibility (SC) at the information level could directly impact COVID-19 information-sharing (CIS) behavior. Therefore, information severity (IS) and information usefulness (IU) at the information level positively affect RE. For ALT, intergenerational factors (i.e., intimacy and interaction degree) play important roles. Specifically, the indirectly influential mechanisms identified in this study enrich the theoretical understanding of the PADM. Information severity and usefulness affect sharing behavior through response efficacy, whereas the degree of interaction affects sharing behavior via altruism.

The study also confirmed that official announcements and science popularization are the most common information types, which provides a reference for optimizing health information dissemination strategies targeting elderly groups. University students like to share COVID-19 information with parents and grandparents. Moreover, oral communication and forwarding through social media applications are regarded as vital channels through which to share COVID-19 information, which suggests that combining digital tools with traditional interpersonal interactions can increase the effectiveness of intergenerational information transmission.

We aim to fill the gap in the study of family intergenerational information-sharing behavior during the COVID-19 pandemic. These findings help us understand how to promote pandemic information dissemination in families and the occurrence of prosocial behavior among university students, which will facilitate the achievement of sustainable development goals during or after the COVID-19 pandemic. In addition, understanding the psychological and social drivers of such behavior could offer actionable insights for improving elderly health information access and strengthening family cooperation.

In future work, it would be better to collect feedback from the elderly (e.g., their acceptance and utilization of shared information) to construct a bidirectional intergenerational information-sharing model. The epidemic-related information cognition and behavioral patterns of elderly individuals to whom this information is shared need to be further investigated, which is meaningful for elderly people and improves the efficiency of information sharing. Moreover, a cross-cultural comparative design could be employed to explore whether the mechanisms identified vary across collectivist and individualist cultures, which would increase the generalizability of our findings.