# An Empirical Study on Digital Feedback Behavior of Young People in COVID-19 with Health Belief Model and UTAUT Model

Authors: Dan Lu; Huhuang Lin
Journal: Mobile Information Systems
 (2022)

---

## Abstract

During COVID-19 prevention and control, many middle-aged and elderly people lack the skills and knowledge of healthy online transmission, resulting in inadequate prevention and control measures. So as young people who are familiar with the rules of the internet transmit health information, they also reverse educate their parents on novel coronavirus prevention and control, helping them to bridge the digital information gap between generations. In this paper, the health belief model (HBM) and the integrated technology acceptance and use model (UTAUT) were combined to establish a model to explore the factors influencing the digital feedback behavior of young groups in the context of COVID-19. The results show that the willingness of digital feedback has a certain influence on digital feedback behavior. Perceived susceptibility, perceived severity, perceived benefit, perceived obstacle, self-efficacy, performance expectation, community influence, and convenience also affect the intention of feedback.


---

## Body

## 1. Introduction

During the COVID-19 pandemic, digital technologies such as electronic health codes and online vaccination reservations have become important skills for online health communication. In today’s Web 2.0 era, marginalized middle-aged and elderly groups are excluded by the tide of world information and gradually become “digital refugees.” Compared with the young group, the middle-aged and elderly groups have relatively closed access to information. Due to the low acceptance of health information in their cognitive system, many middle-aged and elderly people lack the ability to distinguish health rumors and even show resistance before epidemic prevention and control, which not only exposes the lack of health literacy, but also intensifies the spread of the epidemic. In order to help the middle-aged and older groups adapt to life in the postepidemic era and bridge the digital divide with the middle-aged and older groups, the young group is constantly trying to impart scientific health knowledge and skills to the middle-aged and older groups. This process results in the phenomenon of digital feedback. In view of the process of online health communication, receivers need to have high health literacy and master digital media skills, so the middle-aged and elderly often need reverse education and guidance from the young groups in order to learn correct health information and master scientific health skills. Therefore, digital feedback in online health communication is vital in intergenerational communication, and its application is quite common.In this study, the health belief model and the UTAUT model were introduced into the context of network health communication to explain the factors influencing the digital feedback behavior, so as to explore the factors influencing the digital feedback behavior and willingness under sudden public health events, and the influencing reasons. In addition, it also provides some theoretical basis for the field of digital feedback and puts forward feasible suggestions on how to optimize the phenomenon of digital feedback.


## 2. Theory

### 2.1. Health Belief Model

The health belief model (HBM) was originally born in a social science study of the U.S. Health Service Department [1]. In the 1950s, the U.S. Public Health Service offered free or low-cost testing for diseases such as tuberculosis, cervical cancer, and infectious diseases. But the public does not accept and adopt such screening tests [2]. In order to study how to make the public accept disease prevention testing, proposed the health belief model and found that individuals’ actions to disease prevention testing were strongly influenced by their self-perceived possibility of suffering from disease and self-efficacy [3]. The health belief model can be divided into three levels: demographic variables, perception, and action cues. Demographic variables include gender, youth, and education. The perceived level is divided into two variables: perceived threat and behavioral evaluation [4].Among the perceived threat variables are perceived susceptibility and perceived severity. Perceived susceptibility is defined as an individual’s subjective assumption that he or she has a disease or health problem [5]. Perceived severity is defined as an individual’s subjective perception of the degree of harm caused by a certain disease, including physical pain and death, and social consequences such as family, work, and interpersonal reputation [5]. If the individual’s perceived susceptibility is high, the individual will adopt healthy behaviors to prevent disease. If the perceived severity is high, it will also increase the probability of taking healthy behaviors and avoid health problems.Behavioral evaluation variables include perceived benefits, perceived barriers, and self-efficacy. Perceived benefit is defined as the subjective judgment that adopting a certain health behavior or attitude can bring positive effects to the individual [6]. Perceived barriers are defined as individuals’ belief that adopting a certain health behavior or attitude will face various obstacles, the most common manifestations of which are time-consuming, costly, and difficult to adopt [6]. The probability of adopting healthy behaviors will be increased if individuals feel more benefit in the process of health communication. On the contrary, individuals with more sensory barriers in the process of health transmission will limit the acceptance of health behaviors. Self-efficacy refers to an individual’s belief in his or her ability to perform certain healthy behaviors and achieve satisfactory health outcomes [7]. The higher the self-efficacy, the more likely the individuals are to engage in health interventions to prevent disease. Behavioral cues, including social media influence and suggestions from others, are often considered as influencing factors in the health belief model [8]. As the correlation between action cues and this study is not strong, the author does not include them in this research model.At present, the health belief model has gradually become one of the important theoretical structures to study health information transmission and health behavioral change. After the birth of the health belief model in 1950s, the health belief model was considered to be in the essence of an individual’s exploration of disease and health behavioral intention, which could be used as the theoretical basis for government health institutions to publicize medical health-related knowledge [8]. Nowadays, as the most typical social psychological model to interpret health communication behavior, the health belief model has been widely used to study the health behavior or willingness of various groups, prevention, and control of cervical cancer and other health communication fields. Schafer et al. used the health belief model to predict fat in the diets of married couples [9]. Lin et al. integrated the health belief model with the conceptual framework of emotional and communication factors to explore college students’ attitudes and responses to HIV [10]. Houlden et al. proposed a health misinformation intervention that relies on the health belief model to study how public health can respond to the misinformation crisis beyond COVID-19 [11]. Shahsavari et al. applied a health belief model to study the prevention of female genital warts in southern Iran [12].The research content of Chinese scholars is often carried out in all aspects of Chinese people’s healthy life, such as the sick population, which also plays a constructive contribution to the pluralistic and creative development of the health belief model. Yu and Sun were the first in China to use the health belief model to discuss AIDS health education and AIDS prevention and protection measures [13]. Based on the health belief model, Wang et al. studied how to mobilize residents’ subjective initiative in the management of chronic diseases at the grassroots level, so as to improve residents’ cognitive ability of chronic diseases [14]. Ren and Li used the health belief model to investigate the influence of opinion leaders on public health belief and vaccination intention. In addition, it is found in the study that the perception of novel coronavirus interest has a mediating effect on opinion leaders’ influence on the public’s intention to vaccinate against COVID-19. This mediating effect test can enrich the research on the COVID-19 vaccine spread model and health transmission effect to a certain extent [15]. At present, the research scope of the health belief model is increasing, but there are few studies that combine the health belief model with digital feedback. Therefore, this study takes it as an innovation point to explore the causes of influencing digital feedback behavior.


### 2.2. UTAUT Model

The UTAUT model was born in 2003. It was jointly proposed by Venkatesh et al. [16]. As a classical model of audience technology acceptance, the UTAUT model is essentially integrated on the basis of eight previous related theories. The eight related theories and models are the theory of reasoned action (TRA), theory of planned behavior (TPB), technology acceptance model (TAM), task-technology fit (TTF), innovation diffusion theory (IDT), motivational model (MM), model of PC utilization (MPCU), and social cognition theory (SCT) [17].The original predecessor of the UTAUT model is the theory of reasoned action (TRA) by Fishbein et al. in 1975, which is also one of the early authoritative theories on the user reception model [18]. The theory of reasoned action focuses on the study of what factors and how to act on individual behaviors and attitudes. The model proposes that an individual’s behavioral intention affects user behavior, while attitudes and nonobjective norms caused by individual behavior results and their own norms affect an individual’s behavioral intention. The model of TRA is simple and clear, but it neglects one point: under the condition of irrationality, it is difficult for individuals to accept the influence of their own subjective intention and is easy to be stimulated by other external incentives. Therefore, the theory of reasoned action cannot explain the situation of people in the irrational state, which is also its shortcoming. In 1991, Ajzen Icek proposed the theory of planned behavior (TPB) based on the above TRA [19]. This model adds the variable of “perceived behavioral control.” Ajzen believes that individuals will be influenced by environmental resources, ability opportunities, and other aspects besides attitude and subjective norms, which are all called perceived behavioral control [20]. The higher the individual’s perceived behavioral control degree is, the stronger the individual control belief is, the more convenient conditions are perceived, and thus, the greater the behavioral intention is, so as to promote the progress of using behaviors.Both TRA, the original predecessor of the UTAUT model, and other classical behavior theories have been used as universal theories to explore the acceptance and use of user behavior. But with the rapid development of internet technology today, they are no longer suitable for exploring the adoption of emerging technologies.Therefore, the UTAUT model selected in this study is a theoretical model with high explanatory power in information technology, which can explain the behavior of individuals’ adoption of emerging technologies. The UTAUT model takes use behavior as dependent variable, use willingness as independent variable, performance expectation, effort expectation, community influence, and convenience as key dimensions, and gender and age as control variables [21].In the research of foreign scholars on the UATUT model, most of the topics focus on healthcare services, technology use behavior of the elderly, and other topics. In terms of health and medical services, Holtz and Krein took the UTAUT model as the research object to explore the feelings of hospital nurses on the implementation of the EMR system [22]. The research results highlight the importance of the EMR deployment process for nurses, which is also helpful for the realization of future healthcare technologies. Yousef et al. used the adapted UTAUT model to predict patients’ willingness to use electronic health tools such as personal health records, aiming to encourage patients to carry out better health and well-being services [23]. In terms of new media emerging services, Van Schaik discussed the application of the UTAUT model in higher education student websites, and the research conclusion verified the influence of intrinsic motivation on performance expectations [24]. Haron et al. conducted a study based on the UTAUT model and concluded that all four dimensions of the model had an impact on the process of using MOOC in learning, which was also conducive to the public understanding of students’ adoption of MOOC and other media technologies in public universities and the field of online learning [25]. Mueller et al. combined with the UTAUT model investigated whether the SmartCards tablet computer system would increase the use of digital communication and affect users’ loneliness, autonomy, cognitive ability, and happiness [26]. The final conclusion of the study is that seniors are very much able and curious to use modern digital devices if the interface and hardware are adjusted to their needs and capabilities without being stigmatized. The use of modern communication services and the World Wide Web can promote the contact of seniors with their (younger) relatives. Quaosar et al. combined the UTAUT model to study the factors influencing the willingness of the elderly to use mobile medical services [27]. The findings of this study may become beneficial for the governments, policymakers, and healthcare service providers in developing countries.Domestic scholars also actively use the UTAUT model in combination with various industries to further explore and research. In terms of linguistics, Wang studied the influencing factors of users’ willingness to choose online English courses based on the UTAUT model and flow experience [28]. This study provides a reliable theoretical basis for the marketing practice of online English learning platform, which can help online English learning platform improve service quality, improve users’ willingness to continue using, and enhance the potential value of the platform. In terms of news communication, Li combined with the UTAUT model studied the influencing factors of the WeChat Moments’ use behavior of the elderly group, and the conclusion showed that the most critical factors affecting the use behavior were willingness to use and digital feedback [29]. On the theoretical basis of the UTAUT model, Zhu once revealed the phenomenon of bottom-up mobile phone technology dissemination in young people’s families, namely, “technology feedback,” and discussed its occurrence mechanism and social impact. The research shows that “technology feedback” effectively bridges the gap between generations, improves the new media literacy of the older generation, and realizes the transformation of family power structure from one-way authority to two-way authority, but does not fundamentally change the status between parents and children [30]. In terms of trade economy, Tian studied online shopping intention and behavior of fresh agricultural products on the basis of the UTAUT model, and the conclusion shows that performance expectation and effort expectation play an important role in online shopping intention [31].


### 2.3. Digital Feedback

The concept of digital feedback originally comes from cultural feedback, and the concept of cultural feedback comes from postfigurative culture [32]. In 1970, scholar Margaret Mead puts forward the concept of postsimile culture and defined it as a reverse socialization process in which the young group transmits cultural knowledge to the old group and teaches them correct ideas and values [33]. In 1988, Zhou introduced this concept to China and formally proposed the concept of cultural feedback. He believed that in the process of rapid cultural development, the young group should teach the old group the cultural knowledge of the new era and carry out reverse cultural inheritance [34]. Nowadays, cultural feedback has gradually evolved into digital feedback. Digital feedback refers to the teaching behavior of the young group to the old group about the access, use, and accomplishment of information technology [33].At present, with the rapid evolution of media technology, the use of electronic media has already infiltrated into individual life in an all-round way. The categories of digital media are becoming more and more diversified, and the performance and usage modes are becoming richer and richer. In the rapid changes of The Times, young people have mastered digital media with ease, but marginalized middle-aged and elderly people are excluded by the tide of world information, trapped in the complex rules of the internet, and gradually become “digital refugees.” In order to make the middle-aged and elderly groups adapt to the changes of the new era, it is necessary for the young group to carry out digital feedback, teach them to learn digital information skills, and cultivate the digital literacy and media literacy of the middle-aged and elderly groups, so that the middle-aged and elderly groups can comfortably survive in the virtual space and in the real world. As Zhu puts forward in his research, he believes that the one-way education process between the younger generation and the elder generation has been gradually replaced by the two-way education process in the aspects of mobile information technology, mobile phone use, internet buzzwords, and so on [35]. Zhou et al. regard digital access feedback, digital skill feedback, and digital literacy feedback as the three major elements of digital feedback, and point out that the most common field of digital feedback is family feedback [33]. The digital feedback of children to parents within the family and the traditional teaching of parents to children not only drive the vitality of intergenerational interaction, but also help to bridge the digital divide between generations and strengthen family relations [36].In foreign studies, Kiesler et al. found that the young group has become the hub between families and internet professionals, and provides internet skills’ education and guidance for family members, which means that the young group plays an indispensable role in digital feedback [37]. Lüders and Brandtzæg investigated and studied individuals in Norway who did not use SNS [38]. The research conclusion shows that individuals who are interested in SNS but do not use it believe that SNS can enhance their relationship with their relatives, but the lack of digital skills restricted by SNS makes it difficult for them to master SNS by themselves. This also emphasizes the importance of young groups supporting and teaching the elderly to learn digital media. Kuusimäki et al. studied the number of digital feedback encouraged by teachers in Finnish national schools, so as to observe parents’ satisfaction with school policies and enhance the close cooperation among children, guardians, and schools [39]. In contrast, domestic scholars have also published a lot of research on digital feedback, mainly focusing on short videos, digital divide, smartphone use, and other aspects. Zhou puts forward that in terms of the adoption and application of emerging technologies, there is a significant digital divide between the young group and the elderly group, and the differences in age and educational background between generations all affect the digital divide and feedback to varying degrees [40]. Zheng and Xu conducted interviews with young people born after 1995 who used short videos such as Douyin and their parents, and found that the most direct reason for the digital feedback behavior of young groups was the needs of middle-aged and elderly groups such as elders. Digital feedback by young groups could also eliminate the highest authority of parents in the family, and promote the establishment of an equal and harmonious relationship between the young and the elderly [41]. Zhao and Gao take villages in Shanxi Province as a case study and use questionnaire surveys and interviews on the basis of participatory observation. In addition, the intergenerational interaction of the elderly in Zhongshangda village was investigated from parental and offspring generations, and the phenomenon of access feedback, skill feedback, and concept feedback existed in the use of short videos by the elderly in rural areas. In this study, the characteristics and functions of short video feedback for the elderly in Zhongshangda village were further discussed. It was found that digital feedback in the process of using short video for the elderly in Zhongshangda village had the characteristics of coexistence of active feedback and appeal feedback, women were more likely to accept feedback, and the feedback was not thorough [42]. During in-depth interviews with 6 families, Wang et al. found that the improvement of digital literacy and the establishment of the bidirectional active communicative family interaction model are conducive to promoting digital feedback and bridging the digital divide [43].


### 2.4. Network Health Communication

The concept of health communication originated from the Stanford Heart Disease Prevention Program of American medical scientist Faquel and communication scientist McBee in 1972 [44]. This experiment shows that the use of innovation diffusion theory and other strategies can greatly improve the public’s health literacy and health communication skills. Subsequently, with the efforts of many scholars, health communication gradually developed into an independent discipline. In 1994, Rogers defined health transmission as any transmission of a health problem [45]. This definition brought health communication closer to the field of communication, and the concept has persisted to this day. With the rapid technological development of The Times, the media of health communication gradually evolved from oral communication to network communication. Shen believes that network health communication is the process of using network technology to spread health knowledge to the recipients and promote their healthy development level [46]. The characteristics of network communication, such as the large amount of information, strong interactivity, high popularity rate, and strong professionalism of some PUGC, provide opportunities and challenges for the development of network health communication. At present, the common network health communication channels are mainly health portal websites, health science microblog, health WeChat public account, and so on. Online health communication plays an important role in COVID-19 and other health emergencies. However, online health communication still needs to address issues such as privacy and professionalism. It is a top priority to give full play to the advantages of internet technology and effectively combine health communication.At present, although there are many research studies on health communication by scholars at home and abroad, there are few research studies on the issues related to online health communication. As of May 23, 2022, a total of 122 academic journals and dissertations were retrieved in China National Knowledge Infrastructure (CNKI) by using “online health communication” as the subject term. When “network health communication” and “digital feedback” were searched in CNKI, only 7 academic journals and dissertations were retrieved. It can be seen that although there are many research studies on network health communication, there are few research studies from the perspective of digital feedback. In addition, online health communication has gradually penetrated into people’s daily life. During the period of COVID-19, online health communication has also become an important way for the government to publicize health policies and grasp social situation. Based on this point, this study combines network health communication with digital feedback and takes it as the innovation point of this study.In the study of foreign scholars, Michael and Cheuvront proposed the theoretical basis of persuasive public health intervention by using the internet, and pointed out that if the persuasive health communication by using the internet and its coverage is continuously expanded, it can provide a more effective channel for changing health behaviors based on the internet [47]. Paige et al. studied the impact of e-health literacy on perceived trust in online health communication channels and sources, and found that older adults with low e-health literacy had higher trust in Facebook, but lower perceived trust in online support groups [48]. Bora et al. explored whether internet videos could be a useful source of health information in global public health emergencies, using the case of YouTube videos during the 2015-2016 Zika virus pandemic. The results show that a large number of videos are misleading, so it is necessary to organize and authenticate health information content on social media platforms [49]. Horváth et al. mainly discussed the advantages and disadvantages of online health communication channels, such as podcasts and video channels, and specifically studied the potential of social media in health education and communication, providing certain help for the public to understand health information on different social media platforms [50]. Chinese scholars mainly focus on the impact of social media platforms and technologies on health communication. Wang puts forward suggestions on how to control the disorder of online health communication, which have important practical value for the regulation of health network space and the development of online health communication in the future [51]. Sun and Kang took the community content of Baidu Post Bar for depression as a case study to explore the importance of online communities in obtaining psychological comfort and seeking social support for groups suffering from diseases, and to provide constructive suggestions for intervention methods of online health communication [52]. Liao explored the general rules of public health transmission of major infectious diseases based on Sina Weibo data samples, taking the online transmission of Ebola virus disease outbreak in West Africa in 2014 as an example. From the perspective of Weibo transmission of Ebola epidemic, this paper puts forward three inspiring suggestions of “close to mobile terminal,” “fragmented sharing,” and “positive energy collaboration” in public health transmission of major infectious diseases [53].Based on the review of previous studies, this study started from the perspective of the health belief model and the integrated technology acceptance and use model, so as to explore the influencing factors of digital feedback behavior of young groups in the context of COVID-19. This study attempts to answer the following questions, that is, to explore the influencing mechanism among the following groups of factors: the relationship between intention and behavior of feedback and its significance, the influence of dimension and intention of feedback under perceived threat variables and their significance, the influence of dimension and feedback intention under behavioral evaluation variables and their significance degree, and the influence of the dimension under the variable of ascending intention of digital feedback and the existence of feedback intention and its significance.


## 2.1. Health Belief Model

The health belief model (HBM) was originally born in a social science study of the U.S. Health Service Department [1]. In the 1950s, the U.S. Public Health Service offered free or low-cost testing for diseases such as tuberculosis, cervical cancer, and infectious diseases. But the public does not accept and adopt such screening tests [2]. In order to study how to make the public accept disease prevention testing, proposed the health belief model and found that individuals’ actions to disease prevention testing were strongly influenced by their self-perceived possibility of suffering from disease and self-efficacy [3]. The health belief model can be divided into three levels: demographic variables, perception, and action cues. Demographic variables include gender, youth, and education. The perceived level is divided into two variables: perceived threat and behavioral evaluation [4].Among the perceived threat variables are perceived susceptibility and perceived severity. Perceived susceptibility is defined as an individual’s subjective assumption that he or she has a disease or health problem [5]. Perceived severity is defined as an individual’s subjective perception of the degree of harm caused by a certain disease, including physical pain and death, and social consequences such as family, work, and interpersonal reputation [5]. If the individual’s perceived susceptibility is high, the individual will adopt healthy behaviors to prevent disease. If the perceived severity is high, it will also increase the probability of taking healthy behaviors and avoid health problems.Behavioral evaluation variables include perceived benefits, perceived barriers, and self-efficacy. Perceived benefit is defined as the subjective judgment that adopting a certain health behavior or attitude can bring positive effects to the individual [6]. Perceived barriers are defined as individuals’ belief that adopting a certain health behavior or attitude will face various obstacles, the most common manifestations of which are time-consuming, costly, and difficult to adopt [6]. The probability of adopting healthy behaviors will be increased if individuals feel more benefit in the process of health communication. On the contrary, individuals with more sensory barriers in the process of health transmission will limit the acceptance of health behaviors. Self-efficacy refers to an individual’s belief in his or her ability to perform certain healthy behaviors and achieve satisfactory health outcomes [7]. The higher the self-efficacy, the more likely the individuals are to engage in health interventions to prevent disease. Behavioral cues, including social media influence and suggestions from others, are often considered as influencing factors in the health belief model [8]. As the correlation between action cues and this study is not strong, the author does not include them in this research model.At present, the health belief model has gradually become one of the important theoretical structures to study health information transmission and health behavioral change. After the birth of the health belief model in 1950s, the health belief model was considered to be in the essence of an individual’s exploration of disease and health behavioral intention, which could be used as the theoretical basis for government health institutions to publicize medical health-related knowledge [8]. Nowadays, as the most typical social psychological model to interpret health communication behavior, the health belief model has been widely used to study the health behavior or willingness of various groups, prevention, and control of cervical cancer and other health communication fields. Schafer et al. used the health belief model to predict fat in the diets of married couples [9]. Lin et al. integrated the health belief model with the conceptual framework of emotional and communication factors to explore college students’ attitudes and responses to HIV [10]. Houlden et al. proposed a health misinformation intervention that relies on the health belief model to study how public health can respond to the misinformation crisis beyond COVID-19 [11]. Shahsavari et al. applied a health belief model to study the prevention of female genital warts in southern Iran [12].The research content of Chinese scholars is often carried out in all aspects of Chinese people’s healthy life, such as the sick population, which also plays a constructive contribution to the pluralistic and creative development of the health belief model. Yu and Sun were the first in China to use the health belief model to discuss AIDS health education and AIDS prevention and protection measures [13]. Based on the health belief model, Wang et al. studied how to mobilize residents’ subjective initiative in the management of chronic diseases at the grassroots level, so as to improve residents’ cognitive ability of chronic diseases [14]. Ren and Li used the health belief model to investigate the influence of opinion leaders on public health belief and vaccination intention. In addition, it is found in the study that the perception of novel coronavirus interest has a mediating effect on opinion leaders’ influence on the public’s intention to vaccinate against COVID-19. This mediating effect test can enrich the research on the COVID-19 vaccine spread model and health transmission effect to a certain extent [15]. At present, the research scope of the health belief model is increasing, but there are few studies that combine the health belief model with digital feedback. Therefore, this study takes it as an innovation point to explore the causes of influencing digital feedback behavior.


## 2.2. UTAUT Model

The UTAUT model was born in 2003. It was jointly proposed by Venkatesh et al. [16]. As a classical model of audience technology acceptance, the UTAUT model is essentially integrated on the basis of eight previous related theories. The eight related theories and models are the theory of reasoned action (TRA), theory of planned behavior (TPB), technology acceptance model (TAM), task-technology fit (TTF), innovation diffusion theory (IDT), motivational model (MM), model of PC utilization (MPCU), and social cognition theory (SCT) [17].The original predecessor of the UTAUT model is the theory of reasoned action (TRA) by Fishbein et al. in 1975, which is also one of the early authoritative theories on the user reception model [18]. The theory of reasoned action focuses on the study of what factors and how to act on individual behaviors and attitudes. The model proposes that an individual’s behavioral intention affects user behavior, while attitudes and nonobjective norms caused by individual behavior results and their own norms affect an individual’s behavioral intention. The model of TRA is simple and clear, but it neglects one point: under the condition of irrationality, it is difficult for individuals to accept the influence of their own subjective intention and is easy to be stimulated by other external incentives. Therefore, the theory of reasoned action cannot explain the situation of people in the irrational state, which is also its shortcoming. In 1991, Ajzen Icek proposed the theory of planned behavior (TPB) based on the above TRA [19]. This model adds the variable of “perceived behavioral control.” Ajzen believes that individuals will be influenced by environmental resources, ability opportunities, and other aspects besides attitude and subjective norms, which are all called perceived behavioral control [20]. The higher the individual’s perceived behavioral control degree is, the stronger the individual control belief is, the more convenient conditions are perceived, and thus, the greater the behavioral intention is, so as to promote the progress of using behaviors.Both TRA, the original predecessor of the UTAUT model, and other classical behavior theories have been used as universal theories to explore the acceptance and use of user behavior. But with the rapid development of internet technology today, they are no longer suitable for exploring the adoption of emerging technologies.Therefore, the UTAUT model selected in this study is a theoretical model with high explanatory power in information technology, which can explain the behavior of individuals’ adoption of emerging technologies. The UTAUT model takes use behavior as dependent variable, use willingness as independent variable, performance expectation, effort expectation, community influence, and convenience as key dimensions, and gender and age as control variables [21].In the research of foreign scholars on the UATUT model, most of the topics focus on healthcare services, technology use behavior of the elderly, and other topics. In terms of health and medical services, Holtz and Krein took the UTAUT model as the research object to explore the feelings of hospital nurses on the implementation of the EMR system [22]. The research results highlight the importance of the EMR deployment process for nurses, which is also helpful for the realization of future healthcare technologies. Yousef et al. used the adapted UTAUT model to predict patients’ willingness to use electronic health tools such as personal health records, aiming to encourage patients to carry out better health and well-being services [23]. In terms of new media emerging services, Van Schaik discussed the application of the UTAUT model in higher education student websites, and the research conclusion verified the influence of intrinsic motivation on performance expectations [24]. Haron et al. conducted a study based on the UTAUT model and concluded that all four dimensions of the model had an impact on the process of using MOOC in learning, which was also conducive to the public understanding of students’ adoption of MOOC and other media technologies in public universities and the field of online learning [25]. Mueller et al. combined with the UTAUT model investigated whether the SmartCards tablet computer system would increase the use of digital communication and affect users’ loneliness, autonomy, cognitive ability, and happiness [26]. The final conclusion of the study is that seniors are very much able and curious to use modern digital devices if the interface and hardware are adjusted to their needs and capabilities without being stigmatized. The use of modern communication services and the World Wide Web can promote the contact of seniors with their (younger) relatives. Quaosar et al. combined the UTAUT model to study the factors influencing the willingness of the elderly to use mobile medical services [27]. The findings of this study may become beneficial for the governments, policymakers, and healthcare service providers in developing countries.Domestic scholars also actively use the UTAUT model in combination with various industries to further explore and research. In terms of linguistics, Wang studied the influencing factors of users’ willingness to choose online English courses based on the UTAUT model and flow experience [28]. This study provides a reliable theoretical basis for the marketing practice of online English learning platform, which can help online English learning platform improve service quality, improve users’ willingness to continue using, and enhance the potential value of the platform. In terms of news communication, Li combined with the UTAUT model studied the influencing factors of the WeChat Moments’ use behavior of the elderly group, and the conclusion showed that the most critical factors affecting the use behavior were willingness to use and digital feedback [29]. On the theoretical basis of the UTAUT model, Zhu once revealed the phenomenon of bottom-up mobile phone technology dissemination in young people’s families, namely, “technology feedback,” and discussed its occurrence mechanism and social impact. The research shows that “technology feedback” effectively bridges the gap between generations, improves the new media literacy of the older generation, and realizes the transformation of family power structure from one-way authority to two-way authority, but does not fundamentally change the status between parents and children [30]. In terms of trade economy, Tian studied online shopping intention and behavior of fresh agricultural products on the basis of the UTAUT model, and the conclusion shows that performance expectation and effort expectation play an important role in online shopping intention [31].


## 2.3. Digital Feedback

The concept of digital feedback originally comes from cultural feedback, and the concept of cultural feedback comes from postfigurative culture [32]. In 1970, scholar Margaret Mead puts forward the concept of postsimile culture and defined it as a reverse socialization process in which the young group transmits cultural knowledge to the old group and teaches them correct ideas and values [33]. In 1988, Zhou introduced this concept to China and formally proposed the concept of cultural feedback. He believed that in the process of rapid cultural development, the young group should teach the old group the cultural knowledge of the new era and carry out reverse cultural inheritance [34]. Nowadays, cultural feedback has gradually evolved into digital feedback. Digital feedback refers to the teaching behavior of the young group to the old group about the access, use, and accomplishment of information technology [33].At present, with the rapid evolution of media technology, the use of electronic media has already infiltrated into individual life in an all-round way. The categories of digital media are becoming more and more diversified, and the performance and usage modes are becoming richer and richer. In the rapid changes of The Times, young people have mastered digital media with ease, but marginalized middle-aged and elderly people are excluded by the tide of world information, trapped in the complex rules of the internet, and gradually become “digital refugees.” In order to make the middle-aged and elderly groups adapt to the changes of the new era, it is necessary for the young group to carry out digital feedback, teach them to learn digital information skills, and cultivate the digital literacy and media literacy of the middle-aged and elderly groups, so that the middle-aged and elderly groups can comfortably survive in the virtual space and in the real world. As Zhu puts forward in his research, he believes that the one-way education process between the younger generation and the elder generation has been gradually replaced by the two-way education process in the aspects of mobile information technology, mobile phone use, internet buzzwords, and so on [35]. Zhou et al. regard digital access feedback, digital skill feedback, and digital literacy feedback as the three major elements of digital feedback, and point out that the most common field of digital feedback is family feedback [33]. The digital feedback of children to parents within the family and the traditional teaching of parents to children not only drive the vitality of intergenerational interaction, but also help to bridge the digital divide between generations and strengthen family relations [36].In foreign studies, Kiesler et al. found that the young group has become the hub between families and internet professionals, and provides internet skills’ education and guidance for family members, which means that the young group plays an indispensable role in digital feedback [37]. Lüders and Brandtzæg investigated and studied individuals in Norway who did not use SNS [38]. The research conclusion shows that individuals who are interested in SNS but do not use it believe that SNS can enhance their relationship with their relatives, but the lack of digital skills restricted by SNS makes it difficult for them to master SNS by themselves. This also emphasizes the importance of young groups supporting and teaching the elderly to learn digital media. Kuusimäki et al. studied the number of digital feedback encouraged by teachers in Finnish national schools, so as to observe parents’ satisfaction with school policies and enhance the close cooperation among children, guardians, and schools [39]. In contrast, domestic scholars have also published a lot of research on digital feedback, mainly focusing on short videos, digital divide, smartphone use, and other aspects. Zhou puts forward that in terms of the adoption and application of emerging technologies, there is a significant digital divide between the young group and the elderly group, and the differences in age and educational background between generations all affect the digital divide and feedback to varying degrees [40]. Zheng and Xu conducted interviews with young people born after 1995 who used short videos such as Douyin and their parents, and found that the most direct reason for the digital feedback behavior of young groups was the needs of middle-aged and elderly groups such as elders. Digital feedback by young groups could also eliminate the highest authority of parents in the family, and promote the establishment of an equal and harmonious relationship between the young and the elderly [41]. Zhao and Gao take villages in Shanxi Province as a case study and use questionnaire surveys and interviews on the basis of participatory observation. In addition, the intergenerational interaction of the elderly in Zhongshangda village was investigated from parental and offspring generations, and the phenomenon of access feedback, skill feedback, and concept feedback existed in the use of short videos by the elderly in rural areas. In this study, the characteristics and functions of short video feedback for the elderly in Zhongshangda village were further discussed. It was found that digital feedback in the process of using short video for the elderly in Zhongshangda village had the characteristics of coexistence of active feedback and appeal feedback, women were more likely to accept feedback, and the feedback was not thorough [42]. During in-depth interviews with 6 families, Wang et al. found that the improvement of digital literacy and the establishment of the bidirectional active communicative family interaction model are conducive to promoting digital feedback and bridging the digital divide [43].


## 2.4. Network Health Communication

The concept of health communication originated from the Stanford Heart Disease Prevention Program of American medical scientist Faquel and communication scientist McBee in 1972 [44]. This experiment shows that the use of innovation diffusion theory and other strategies can greatly improve the public’s health literacy and health communication skills. Subsequently, with the efforts of many scholars, health communication gradually developed into an independent discipline. In 1994, Rogers defined health transmission as any transmission of a health problem [45]. This definition brought health communication closer to the field of communication, and the concept has persisted to this day. With the rapid technological development of The Times, the media of health communication gradually evolved from oral communication to network communication. Shen believes that network health communication is the process of using network technology to spread health knowledge to the recipients and promote their healthy development level [46]. The characteristics of network communication, such as the large amount of information, strong interactivity, high popularity rate, and strong professionalism of some PUGC, provide opportunities and challenges for the development of network health communication. At present, the common network health communication channels are mainly health portal websites, health science microblog, health WeChat public account, and so on. Online health communication plays an important role in COVID-19 and other health emergencies. However, online health communication still needs to address issues such as privacy and professionalism. It is a top priority to give full play to the advantages of internet technology and effectively combine health communication.At present, although there are many research studies on health communication by scholars at home and abroad, there are few research studies on the issues related to online health communication. As of May 23, 2022, a total of 122 academic journals and dissertations were retrieved in China National Knowledge Infrastructure (CNKI) by using “online health communication” as the subject term. When “network health communication” and “digital feedback” were searched in CNKI, only 7 academic journals and dissertations were retrieved. It can be seen that although there are many research studies on network health communication, there are few research studies from the perspective of digital feedback. In addition, online health communication has gradually penetrated into people’s daily life. During the period of COVID-19, online health communication has also become an important way for the government to publicize health policies and grasp social situation. Based on this point, this study combines network health communication with digital feedback and takes it as the innovation point of this study.In the study of foreign scholars, Michael and Cheuvront proposed the theoretical basis of persuasive public health intervention by using the internet, and pointed out that if the persuasive health communication by using the internet and its coverage is continuously expanded, it can provide a more effective channel for changing health behaviors based on the internet [47]. Paige et al. studied the impact of e-health literacy on perceived trust in online health communication channels and sources, and found that older adults with low e-health literacy had higher trust in Facebook, but lower perceived trust in online support groups [48]. Bora et al. explored whether internet videos could be a useful source of health information in global public health emergencies, using the case of YouTube videos during the 2015-2016 Zika virus pandemic. The results show that a large number of videos are misleading, so it is necessary to organize and authenticate health information content on social media platforms [49]. Horváth et al. mainly discussed the advantages and disadvantages of online health communication channels, such as podcasts and video channels, and specifically studied the potential of social media in health education and communication, providing certain help for the public to understand health information on different social media platforms [50]. Chinese scholars mainly focus on the impact of social media platforms and technologies on health communication. Wang puts forward suggestions on how to control the disorder of online health communication, which have important practical value for the regulation of health network space and the development of online health communication in the future [51]. Sun and Kang took the community content of Baidu Post Bar for depression as a case study to explore the importance of online communities in obtaining psychological comfort and seeking social support for groups suffering from diseases, and to provide constructive suggestions for intervention methods of online health communication [52]. Liao explored the general rules of public health transmission of major infectious diseases based on Sina Weibo data samples, taking the online transmission of Ebola virus disease outbreak in West Africa in 2014 as an example. From the perspective of Weibo transmission of Ebola epidemic, this paper puts forward three inspiring suggestions of “close to mobile terminal,” “fragmented sharing,” and “positive energy collaboration” in public health transmission of major infectious diseases [53].Based on the review of previous studies, this study started from the perspective of the health belief model and the integrated technology acceptance and use model, so as to explore the influencing factors of digital feedback behavior of young groups in the context of COVID-19. This study attempts to answer the following questions, that is, to explore the influencing mechanism among the following groups of factors: the relationship between intention and behavior of feedback and its significance, the influence of dimension and intention of feedback under perceived threat variables and their significance, the influence of dimension and feedback intention under behavioral evaluation variables and their significance degree, and the influence of the dimension under the variable of ascending intention of digital feedback and the existence of feedback intention and its significance.


## 3. Research Hypothesis and Model Construction

### 3.1. Research Hypothesis

Gu defined the feedback behavior as three forms: active communication type, restricted guidance type, and expected feedback type [54]. Active communication refers to the form in which the young group and the elderly group accompany each other and discuss information and channels in the network health communication, and the feedback initiative is the strongest. Restricted guidance refers to that the young group restricts and screens the media information that the elderly group is exposed to, so as to guide the elderly group to adapt to the form of online health communication, and its feedback initiative is relatively general. The form of expectation feedback is contrary to active communication. It means that the young group takes actions to meet the problems raised by the older group, and its feedback initiative is the weakest. According to the research verified by Gu, the intention of regal feeding affects regal feeding behavior. Therefore, the following hypotheses are proposed in this study:Hypothesis 1.
(H1): The intention of feedback will have no significant effect on the active communicative feedback behavior.Hypothesis 2.
(H2): The intention of feedback will have no significant effect on the restricted guidance feedback behavior.Hypothesis 3.
(H3): The intention of feedback will have no significant effect on the expected feedback behavior.
Janz and Becker divided the health belief model into two parts, in which the part of perceived threat was divided into two dimensions of perceived susceptibility and perceived severity [4]. Perceived threat aims to study young people’s perception of the severe consequences of contracting COVID-19, and their perception of adverse effects on themselves and society. According to Yan’s research, it has been verified that perceived susceptibility and perceived severity have a certain effect on willingness to use [55]. Therefore, the two are included in this paper to investigate the influencing factors of feedback intention. The following hypotheses are proposed in this study:Hypothesis 4.
(H4): Perceived susceptibility will have no significant effect on the intention of feedback.Hypothesis 5.
(H5): Perceived severity will have no significant effect on the intention of feedback.
Janz and Becker divided the behavioral evaluation part of the health belief model into three dimensions: perceived benefit, perceived barriers, and self-efficacy [4]. The behavioral evaluation aims to study the self-assessed benefits, barriers, and abilities of young people in adopting digital feedback behavior. Yan showed that perceived gain, perceived barriers, and self-efficacy had certain effects on willingness to use [55]. Therefore, these three factors are included in this paper to investigate the influencing factors of feedback intention. The following hypotheses are proposed in this study:Hypothesis 6.
(H6): Perceived benefit will have no significant effect on the intention of feedback.Hypothesis 7.
(H7): Perceived barriers will have no significant effect on the intention of feedback.Hypothesis 8.
(H8): Self-efficacy will have no significant effect on the intention of feedback.
The improvement intention of digital feedback is to study whether respondents are willing to obtain health communication knowledge and information through network channels and whether they are willing to improve the technical ability of network health communication. Gu’s research shows that the variables of the integration technology acceptance and use model have a certain effect on the intention of feedback [54]. Therefore, this paper included these four factors in the study of influencing factors of feedback intention and proposed the following hypotheses:Hypothesis 9.
(H9): Performance expectation will have no significant effect on the intention of feedback.Hypothesis 10.
(H10): Efforts to expect will have no significant effect on the intention of feedback.Hypothesis 11.
(H11): Community influence will have no significant effect on the intention of feedback.Hypothesis 12.
(H12): Convenience conditions will have no significant effect on the intention of feedback.


### 3.2. Model Construction

This model establishes an action chain of “stimulus influence—feedback intention—feedback behavior choice.” According to Janz and Becker’s research, which divided the health belief model into two variables, perceived threat and behavioral evaluation, and Gu’s research on digital feedback to improve the willingness to the integrate technology acceptance and use model, and Yan’s research conclusions, this paper incorporated the health belief model and the UTAUT model into the research framework. Based on available research results, this study proposed a conceptual model (Figure1).Figure 1
Schematic diagram of this research model.


## 3.1. Research Hypothesis

Gu defined the feedback behavior as three forms: active communication type, restricted guidance type, and expected feedback type [54]. Active communication refers to the form in which the young group and the elderly group accompany each other and discuss information and channels in the network health communication, and the feedback initiative is the strongest. Restricted guidance refers to that the young group restricts and screens the media information that the elderly group is exposed to, so as to guide the elderly group to adapt to the form of online health communication, and its feedback initiative is relatively general. The form of expectation feedback is contrary to active communication. It means that the young group takes actions to meet the problems raised by the older group, and its feedback initiative is the weakest. According to the research verified by Gu, the intention of regal feeding affects regal feeding behavior. Therefore, the following hypotheses are proposed in this study:Hypothesis 1.
(H1): The intention of feedback will have no significant effect on the active communicative feedback behavior.Hypothesis 2.
(H2): The intention of feedback will have no significant effect on the restricted guidance feedback behavior.Hypothesis 3.
(H3): The intention of feedback will have no significant effect on the expected feedback behavior.
Janz and Becker divided the health belief model into two parts, in which the part of perceived threat was divided into two dimensions of perceived susceptibility and perceived severity [4]. Perceived threat aims to study young people’s perception of the severe consequences of contracting COVID-19, and their perception of adverse effects on themselves and society. According to Yan’s research, it has been verified that perceived susceptibility and perceived severity have a certain effect on willingness to use [55]. Therefore, the two are included in this paper to investigate the influencing factors of feedback intention. The following hypotheses are proposed in this study:Hypothesis 4.
(H4): Perceived susceptibility will have no significant effect on the intention of feedback.Hypothesis 5.
(H5): Perceived severity will have no significant effect on the intention of feedback.
Janz and Becker divided the behavioral evaluation part of the health belief model into three dimensions: perceived benefit, perceived barriers, and self-efficacy [4]. The behavioral evaluation aims to study the self-assessed benefits, barriers, and abilities of young people in adopting digital feedback behavior. Yan showed that perceived gain, perceived barriers, and self-efficacy had certain effects on willingness to use [55]. Therefore, these three factors are included in this paper to investigate the influencing factors of feedback intention. The following hypotheses are proposed in this study:Hypothesis 6.
(H6): Perceived benefit will have no significant effect on the intention of feedback.Hypothesis 7.
(H7): Perceived barriers will have no significant effect on the intention of feedback.Hypothesis 8.
(H8): Self-efficacy will have no significant effect on the intention of feedback.
The improvement intention of digital feedback is to study whether respondents are willing to obtain health communication knowledge and information through network channels and whether they are willing to improve the technical ability of network health communication. Gu’s research shows that the variables of the integration technology acceptance and use model have a certain effect on the intention of feedback [54]. Therefore, this paper included these four factors in the study of influencing factors of feedback intention and proposed the following hypotheses:Hypothesis 9.
(H9): Performance expectation will have no significant effect on the intention of feedback.Hypothesis 10.
(H10): Efforts to expect will have no significant effect on the intention of feedback.Hypothesis 11.
(H11): Community influence will have no significant effect on the intention of feedback.Hypothesis 12.
(H12): Convenience conditions will have no significant effect on the intention of feedback.


## 3.2. Model Construction

This model establishes an action chain of “stimulus influence—feedback intention—feedback behavior choice.” According to Janz and Becker’s research, which divided the health belief model into two variables, perceived threat and behavioral evaluation, and Gu’s research on digital feedback to improve the willingness to the integrate technology acceptance and use model, and Yan’s research conclusions, this paper incorporated the health belief model and the UTAUT model into the research framework. Based on available research results, this study proposed a conceptual model (Figure1).Figure 1
Schematic diagram of this research model.


## 4. Research Design

### 4.1. Definition and Measurement of Concepts

This study selected nine research variables: perceived susceptibility, perceived severity, perceived benefit, perceived barrier, self-efficacy, performance expectation, effort expectation, community influence, and convenience. In the formulation of the scale, the maturity scale of domestic and foreign scholars was referred to, and the corresponding adjustments were made according to the actual research background of COVID-19 and the digital feedback of the research content.In this study, the measurement of feedback behavior mainly refers to the scale of family digital skills’ use by Livingstone et al. [56] and Xiang [57], and sets items such as “I will actively share the health aspects worth attention with my parents.” The measurement of feedback intention mainly refers to Zhu’s research scale on the interaction of mobile phone technology feedback, intergenerational communication, and family education methods among college students’ families [30], and sets items such as “I hope parents can receive more health information through my guidance.” The measurement of the perceived threat and behavioral evaluation mainly refers to the research scale of Yan’s study on mobile medical service for ventilated patients [55], and sets items such as “I think digital feedback can make the elders know various inducing factors of the incidence of COVID-19.” The improvement intention of digital feedback was measured by referring to Gu’s scale of feedback phenomenon in online health communication [54], and questions such as “in order to make parents fit in with others, I will help parents learn online health communication skills” were set. The above scales have passed the reliability and validity tests, and have a certain scientific nature and stability. In addition to the items related to demographic variables, Likert 5-point scales were used for all other items.


### 4.2. Data Collection

The questionnaire of this study was chosen to be published online, and the questionnaire scale was edited and distributed on the platform questionnaire survey platform “questionnaire star.” The questionnaire was sent to WeChat “Moments,” WeChat “Group Chat,” Weibo, Douban, Xiaohongshu, and other social media platforms in by snowball sampling to expand the scope of sample collection to improve the fraction of coverage of the research results. The questionnaire began to be distributed and collected on March 14, 2022, and was stopped on March 19. A total of 342 valid questionnaires were finally collected. The questionnaire began to be distributed and collected on March 14, 2022, and was stopped on March 19. A total of 342 valid questionnaires were finally collected. The questionnaire began to be distributed and collected on March 14, 2022, and was stopped on March 19. A total of 342 valid questionnaires were finally collected.In the 342 valid samples, men and women accounted for 50.9% and 49.1%, respectively. The age-group was mainly 15–24 years old, accounting for 47.7% of the total number of respondents. The majority of respondents in the sample are those with junior college or bachelor’s degree, accounting for 79.5% of the total number of respondents. In addition, those with an average monthly income of 1,001 to 5,000 yuan accounted for 72.5 % of the total number of respondents. It can be seen that the vast majority of the samples in this study are young groups in the generationZ, and people with junior college or bachelor’s degree are in the majority, whose average monthly income is concentrated in 1001–5000 yuan.


## 4.1. Definition and Measurement of Concepts

This study selected nine research variables: perceived susceptibility, perceived severity, perceived benefit, perceived barrier, self-efficacy, performance expectation, effort expectation, community influence, and convenience. In the formulation of the scale, the maturity scale of domestic and foreign scholars was referred to, and the corresponding adjustments were made according to the actual research background of COVID-19 and the digital feedback of the research content.In this study, the measurement of feedback behavior mainly refers to the scale of family digital skills’ use by Livingstone et al. [56] and Xiang [57], and sets items such as “I will actively share the health aspects worth attention with my parents.” The measurement of feedback intention mainly refers to Zhu’s research scale on the interaction of mobile phone technology feedback, intergenerational communication, and family education methods among college students’ families [30], and sets items such as “I hope parents can receive more health information through my guidance.” The measurement of the perceived threat and behavioral evaluation mainly refers to the research scale of Yan’s study on mobile medical service for ventilated patients [55], and sets items such as “I think digital feedback can make the elders know various inducing factors of the incidence of COVID-19.” The improvement intention of digital feedback was measured by referring to Gu’s scale of feedback phenomenon in online health communication [54], and questions such as “in order to make parents fit in with others, I will help parents learn online health communication skills” were set. The above scales have passed the reliability and validity tests, and have a certain scientific nature and stability. In addition to the items related to demographic variables, Likert 5-point scales were used for all other items.


## 4.2. Data Collection

The questionnaire of this study was chosen to be published online, and the questionnaire scale was edited and distributed on the platform questionnaire survey platform “questionnaire star.” The questionnaire was sent to WeChat “Moments,” WeChat “Group Chat,” Weibo, Douban, Xiaohongshu, and other social media platforms in by snowball sampling to expand the scope of sample collection to improve the fraction of coverage of the research results. The questionnaire began to be distributed and collected on March 14, 2022, and was stopped on March 19. A total of 342 valid questionnaires were finally collected. The questionnaire began to be distributed and collected on March 14, 2022, and was stopped on March 19. A total of 342 valid questionnaires were finally collected. The questionnaire began to be distributed and collected on March 14, 2022, and was stopped on March 19. A total of 342 valid questionnaires were finally collected.In the 342 valid samples, men and women accounted for 50.9% and 49.1%, respectively. The age-group was mainly 15–24 years old, accounting for 47.7% of the total number of respondents. The majority of respondents in the sample are those with junior college or bachelor’s degree, accounting for 79.5% of the total number of respondents. In addition, those with an average monthly income of 1,001 to 5,000 yuan accounted for 72.5 % of the total number of respondents. It can be seen that the vast majority of the samples in this study are young groups in the generationZ, and people with junior college or bachelor’s degree are in the majority, whose average monthly income is concentrated in 1001–5000 yuan.


## 5. Data Analysis

### 5.1. Reliability and Validity Analysis

In this study, SPSS23.0 statistical software was used for reliability and validity detection. Reliability analysis judged the reliability of measurement results by observing Cronbach’s alpha. Validity analysis tests KMO value and Bartlett’s sphericity to determine the extent to which the measured results can truly reflect variables. The overall reliability of this study is 0.956, and the reliability of the fractal dimension is greater than 0.7, indicating that the scale has good reliability and high stability. The overall validity of this study was 0.988, and the validity of the fractal dimension was greater than 0.8. The Bartlett sphericity sig. value was 0.000, indicating good scale validity. The reliability and validity test results of each variable are shown in Table1.Table 1
Reliability and validity test results of each variable.
VariableItemCronbach’s alpha coefficientKMOFeedback behavior120.7360.972Willingness of feedback40.7580.841Perceived threat60.9480.969Behavioral evaluation70.8000.962Improvement intention of digital feedback120.9650.980In this study, exploratory factor analysis was conducted on all the above variables. The principal component analysis was adopted to extract the common factor of each variable, and orthogonal rotation was carried out by using the maximum variance method. The results show that the factor load of each variable is above 0.6, which proves that the scale of each variable in this study has good validity.


### 5.2. Descriptive Statistics

Descriptive statistics are statistical methods used to describe, interpret, and analyze data. The data information collected in quantitative research is very large and complicated. It is the main task of descriptive statistics to describe these data with simple and clear statistics. The number of cases is the total number of valid samples collected. After calculating the average value in descriptive statistics, if the average value is greater than 3, it proves that most respondents hold a positive attitude toward this question; if the average value is less than 3, it is vice versa. Standard deviation is mainly used to measure the difference between respondents on a certain question. The larger the standard deviation is, the greater the difference between respondents’ answers on this question is; the smaller the standard deviation is, the vice versa. The descriptive statistical results of each variable are shown in Table2.Table 2
Descriptive statistics for each variable.
VariableNumber of casesAverageStandard deviationFeedback behavior3423.200.461Willingness of feedback3423.521.018Perceived threat3423.601.097Behavioral evaluation3423.290.603Improvement intention of digital feedback3423.561.055As shown in Table3, the average values of the main variables in the study are all above 3. Except for feedback behavior and behavioral evaluation, the standard deviations of other variables are all above 1, which proves that most respondents have a positive attitude toward the main variables in the study. In terms of feedback behavior and behavioral evaluation, the mean value is slightly higher than 3, indicating that respondents have a strong identification attitude toward the questions set by the two, and that respondents adopt feedback behavior and are strongly influenced by behavioral evaluation. In terms of the promotion intention of feedback intention, perceived threat, and digital feedback, the mean value tends to be 4, which proves that the respondents hold a strong identification attitude toward the three items set by the respondents, and the respondents are strongly influenced by the promotion intention of feedback intention, perceived threat and digital feedback. In addition to feedback behavior and behavioral evaluation, there was a small difference in the responses of respondents to other items considering their own actual situation.Table 3
Correlation analysis results of perceived threat, behavioral evaluation, and digital feedback intention to improve and feedback intention.
Willingness of feedbackPerceived threatBehavioral evaluationImprovement intention of digital feedbackWillingness of feedbackPearson’s correlation significance1Perceived threatPearson’s correlation significance0.886∗∗0.0001Behavioral evaluationPearson’s correlation significance0.794∗∗0.0000.823∗∗0.0001Improvement intention of digital feedbackPearson’s correlation significance0.895∗∗0.0000.931∗∗0.0000.816∗∗0.0001p∗<0.05. ∗∗p<0.005.


### 5.3. Correlation Analysis

In this study, correlation analysis was used to describe the linear relationship between continuous variables. Pearson’s correlation coefficient is used to describe the linear correlation between continuous variables. Table3 shows the correlation analysis results of perceived threat, behavioral evaluation, and the promotion intention of digital feedback with the intention of feedback.According to the test results in Table3, the p values of significance between variables were all 0.000, and their values were less than 0.05. This proves that the dimensions under the perceived threat variable are significantly correlated with the intention of feedback, the dimensions under the behavioral evaluation variable are significantly correlated with the intention of feedback, and the dimensions under the variable of the intention to promote digital feedback are significantly correlated with the intention of feedback.The correlation analysis is also used to test the relationship between feedback behavior and feedback intention, and the test results are shown in Table4.Table 4
Correlation analysis results of feedback intention and feedback behavior.
Willingness of feedbackExpected feedback typeActive communication typeRestricted guidance typeWillingness of feedbackPearson’s correlation significance1Expected feedback typePearson’s correlation significance0.693∗∗0.0001Active communication typePearson’s correlation significance0.869∗∗0.0000.728∗∗0.0001Restricted guidance typePearson’s correlation significance0.791∗∗0.0000.804∗∗0.0000.806∗∗0.0001According to the above, feedback behavior can be divided into three forms: expected feedback, active communication, and restricted guidance. Therefore, the author tested the correlation between the intention of feedback and the three types of feedback behaviors. The test results showed that the significantP values of feedback intention and expected feedback, active communication, and restricted guidance feedback behavior were all 0.000, and their values were less than 0.05. This proves that there is a significant correlation between the intention of feedback and the expected feedback, the active communication, and the restricted guidance feedback.


### 5.4. Regression Analysis

According to the above results, there are significant correlations between the regressive intention and the regressive behavior, the perceived threat variable and the regressive intention, the behavioral evaluation variable and the regressive intention, and the variable of the ascending intention of digital regressive feeding and the regressive intention. Therefore, based on its linear relationship, this study uses SPSS23.0 to conduct linear regression analysis and an in-depth study on the influence relationship between variables.First, the linear regression between the intention of feedback and the three types of feedback behaviors was tested. In the regression analysis of feedback intention and active communicative feedback behavior, the standardized coefficient beta value was 0.869, and the significantp value was 0.000, proving that there was a significant positive relationship between feedback intention and active communicative feedback behavior. So H1 was not valid. In the regression analysis of feedback intention and restricted-directed feedback behavior, the standardized coefficient beta value was 0.791, and the significant p value was 0.000, proving that there was a significant positive relationship between feedback intention and restricted-directed feedback behavior. So H2 was not valid. In the regression analysis of feedback intention and expected feedback behavior, the standardized coefficient beta value was −0.693, and the significant p value was 0.000, which proved that there was a significant negative relationship between feedback intention and expected feedback behavior. So H3 was not established.Second, the linear regression of the dimensions under the perceived threat variable and the intention of feedback was tested. In the regression analysis of perceived susceptibility and intention of feedback, the beta value of the standardized coefficient was 0.49, and the significantp value was 0.000, proving that there was a significant positive relationship between perceived susceptibility and intention of feedback. So H4 was not valid. In the regression analysis of perceived severity and intention of feedback, the beta value of the standardized coefficient was 0.43, and the significant p value was 0.000, proving that perceived severity and intention of feedback had a significant positive relationship. So H5 was not valid.Third, the linear regression between the dimensions of behavioral evaluation variables and feedback intention is tested. In the regression analysis of perceived benefit and intention of feedback, the beta value of the standardized coefficient was 0.845, and the significantp value was 0.000, proving that there was a significant positive relationship between perceived benefit and intention of feedback. So H6 was not valid. In the regression analysis of perceived barriers and feedback intention, the standardized coefficient beta value was 0.053, and the significant p value was 0.000, proving that there was a significant positive relationship between perceived barriers and feedback intention. So H7 was not valid. In the regression analysis of self-efficacy and feedback intention, the standardized coefficient beta value was 0.008, and the significant p value was 0.000, proving that there was a significant positive relationship between self-efficacy and feedback intention. So H8 was not valid.Finally, the linear regression of the dimensionality under the variable of the ascending intention of digital feedback was tested. In the regression analysis of performance expectation and feedback intention, the beta value of the standardized coefficient is 0.212, and the significantp value is 0.000, which proves that there is a significant positive relationship between performance expectation and feedback intention. So H9 was not valid. In the regression analysis of effort expectation and feedback intention, the standardized coefficient beta value was 0.057, and the significant p value was 0.000, proving that there was a significant positive relationship between effort expectation and feedback intention. So H10 was not valid. In the regression analysis of community influence and feedback intention, the beta value of standardization coefficient was 0.387, and the significant p value was 0.000, proving that there was a significant positive relationship between community influence and feedback intention. So H11 was not established. In the regression analysis of convenience conditions and feedback intention, the beta value of standardization coefficient was 0.299, and the significant p value was 0.000, proving that there was a significant positive relationship between convenience conditions and feedback intention. So H12 was not established.


## 5.1. Reliability and Validity Analysis

In this study, SPSS23.0 statistical software was used for reliability and validity detection. Reliability analysis judged the reliability of measurement results by observing Cronbach’s alpha. Validity analysis tests KMO value and Bartlett’s sphericity to determine the extent to which the measured results can truly reflect variables. The overall reliability of this study is 0.956, and the reliability of the fractal dimension is greater than 0.7, indicating that the scale has good reliability and high stability. The overall validity of this study was 0.988, and the validity of the fractal dimension was greater than 0.8. The Bartlett sphericity sig. value was 0.000, indicating good scale validity. The reliability and validity test results of each variable are shown in Table1.Table 1
Reliability and validity test results of each variable.
VariableItemCronbach’s alpha coefficientKMOFeedback behavior120.7360.972Willingness of feedback40.7580.841Perceived threat60.9480.969Behavioral evaluation70.8000.962Improvement intention of digital feedback120.9650.980In this study, exploratory factor analysis was conducted on all the above variables. The principal component analysis was adopted to extract the common factor of each variable, and orthogonal rotation was carried out by using the maximum variance method. The results show that the factor load of each variable is above 0.6, which proves that the scale of each variable in this study has good validity.


## 5.2. Descriptive Statistics

Descriptive statistics are statistical methods used to describe, interpret, and analyze data. The data information collected in quantitative research is very large and complicated. It is the main task of descriptive statistics to describe these data with simple and clear statistics. The number of cases is the total number of valid samples collected. After calculating the average value in descriptive statistics, if the average value is greater than 3, it proves that most respondents hold a positive attitude toward this question; if the average value is less than 3, it is vice versa. Standard deviation is mainly used to measure the difference between respondents on a certain question. The larger the standard deviation is, the greater the difference between respondents’ answers on this question is; the smaller the standard deviation is, the vice versa. The descriptive statistical results of each variable are shown in Table2.Table 2
Descriptive statistics for each variable.
VariableNumber of casesAverageStandard deviationFeedback behavior3423.200.461Willingness of feedback3423.521.018Perceived threat3423.601.097Behavioral evaluation3423.290.603Improvement intention of digital feedback3423.561.055As shown in Table3, the average values of the main variables in the study are all above 3. Except for feedback behavior and behavioral evaluation, the standard deviations of other variables are all above 1, which proves that most respondents have a positive attitude toward the main variables in the study. In terms of feedback behavior and behavioral evaluation, the mean value is slightly higher than 3, indicating that respondents have a strong identification attitude toward the questions set by the two, and that respondents adopt feedback behavior and are strongly influenced by behavioral evaluation. In terms of the promotion intention of feedback intention, perceived threat, and digital feedback, the mean value tends to be 4, which proves that the respondents hold a strong identification attitude toward the three items set by the respondents, and the respondents are strongly influenced by the promotion intention of feedback intention, perceived threat and digital feedback. In addition to feedback behavior and behavioral evaluation, there was a small difference in the responses of respondents to other items considering their own actual situation.Table 3
Correlation analysis results of perceived threat, behavioral evaluation, and digital feedback intention to improve and feedback intention.
Willingness of feedbackPerceived threatBehavioral evaluationImprovement intention of digital feedbackWillingness of feedbackPearson’s correlation significance1Perceived threatPearson’s correlation significance0.886∗∗0.0001Behavioral evaluationPearson’s correlation significance0.794∗∗0.0000.823∗∗0.0001Improvement intention of digital feedbackPearson’s correlation significance0.895∗∗0.0000.931∗∗0.0000.816∗∗0.0001p∗<0.05. ∗∗p<0.005.


## 5.3. Correlation Analysis

In this study, correlation analysis was used to describe the linear relationship between continuous variables. Pearson’s correlation coefficient is used to describe the linear correlation between continuous variables. Table3 shows the correlation analysis results of perceived threat, behavioral evaluation, and the promotion intention of digital feedback with the intention of feedback.According to the test results in Table3, the p values of significance between variables were all 0.000, and their values were less than 0.05. This proves that the dimensions under the perceived threat variable are significantly correlated with the intention of feedback, the dimensions under the behavioral evaluation variable are significantly correlated with the intention of feedback, and the dimensions under the variable of the intention to promote digital feedback are significantly correlated with the intention of feedback.The correlation analysis is also used to test the relationship between feedback behavior and feedback intention, and the test results are shown in Table4.Table 4
Correlation analysis results of feedback intention and feedback behavior.
Willingness of feedbackExpected feedback typeActive communication typeRestricted guidance typeWillingness of feedbackPearson’s correlation significance1Expected feedback typePearson’s correlation significance0.693∗∗0.0001Active communication typePearson’s correlation significance0.869∗∗0.0000.728∗∗0.0001Restricted guidance typePearson’s correlation significance0.791∗∗0.0000.804∗∗0.0000.806∗∗0.0001According to the above, feedback behavior can be divided into three forms: expected feedback, active communication, and restricted guidance. Therefore, the author tested the correlation between the intention of feedback and the three types of feedback behaviors. The test results showed that the significantP values of feedback intention and expected feedback, active communication, and restricted guidance feedback behavior were all 0.000, and their values were less than 0.05. This proves that there is a significant correlation between the intention of feedback and the expected feedback, the active communication, and the restricted guidance feedback.


## 5.4. Regression Analysis

According to the above results, there are significant correlations between the regressive intention and the regressive behavior, the perceived threat variable and the regressive intention, the behavioral evaluation variable and the regressive intention, and the variable of the ascending intention of digital regressive feeding and the regressive intention. Therefore, based on its linear relationship, this study uses SPSS23.0 to conduct linear regression analysis and an in-depth study on the influence relationship between variables.First, the linear regression between the intention of feedback and the three types of feedback behaviors was tested. In the regression analysis of feedback intention and active communicative feedback behavior, the standardized coefficient beta value was 0.869, and the significantp value was 0.000, proving that there was a significant positive relationship between feedback intention and active communicative feedback behavior. So H1 was not valid. In the regression analysis of feedback intention and restricted-directed feedback behavior, the standardized coefficient beta value was 0.791, and the significant p value was 0.000, proving that there was a significant positive relationship between feedback intention and restricted-directed feedback behavior. So H2 was not valid. In the regression analysis of feedback intention and expected feedback behavior, the standardized coefficient beta value was −0.693, and the significant p value was 0.000, which proved that there was a significant negative relationship between feedback intention and expected feedback behavior. So H3 was not established.Second, the linear regression of the dimensions under the perceived threat variable and the intention of feedback was tested. In the regression analysis of perceived susceptibility and intention of feedback, the beta value of the standardized coefficient was 0.49, and the significantp value was 0.000, proving that there was a significant positive relationship between perceived susceptibility and intention of feedback. So H4 was not valid. In the regression analysis of perceived severity and intention of feedback, the beta value of the standardized coefficient was 0.43, and the significant p value was 0.000, proving that perceived severity and intention of feedback had a significant positive relationship. So H5 was not valid.Third, the linear regression between the dimensions of behavioral evaluation variables and feedback intention is tested. In the regression analysis of perceived benefit and intention of feedback, the beta value of the standardized coefficient was 0.845, and the significantp value was 0.000, proving that there was a significant positive relationship between perceived benefit and intention of feedback. So H6 was not valid. In the regression analysis of perceived barriers and feedback intention, the standardized coefficient beta value was 0.053, and the significant p value was 0.000, proving that there was a significant positive relationship between perceived barriers and feedback intention. So H7 was not valid. In the regression analysis of self-efficacy and feedback intention, the standardized coefficient beta value was 0.008, and the significant p value was 0.000, proving that there was a significant positive relationship between self-efficacy and feedback intention. So H8 was not valid.Finally, the linear regression of the dimensionality under the variable of the ascending intention of digital feedback was tested. In the regression analysis of performance expectation and feedback intention, the beta value of the standardized coefficient is 0.212, and the significantp value is 0.000, which proves that there is a significant positive relationship between performance expectation and feedback intention. So H9 was not valid. In the regression analysis of effort expectation and feedback intention, the standardized coefficient beta value was 0.057, and the significant p value was 0.000, proving that there was a significant positive relationship between effort expectation and feedback intention. So H10 was not valid. In the regression analysis of community influence and feedback intention, the beta value of standardization coefficient was 0.387, and the significant p value was 0.000, proving that there was a significant positive relationship between community influence and feedback intention. So H11 was not established. In the regression analysis of convenience conditions and feedback intention, the beta value of standardization coefficient was 0.299, and the significant p value was 0.000, proving that there was a significant positive relationship between convenience conditions and feedback intention. So H12 was not established.


## 6. Conclusions

### 6.1. Causes and Countermeasures of Feedback Behavior Affected by Feedback Intention

The digital back-feeding process in the family is not only affected by the parent-child relationship between the young group and the old group, but also affected by the back-feeding intention of the young group. Digital feedback is a process of intergenerational interaction in which young people can flexibly switch between positive and negative roles [33]. Therefore, the willingness of young groups to actively or passively feedback, that is, to choose active communicative feedback behavior, or to limit guidance, expected feedback behavior, will directly affect the process and effect of feedback behavior. For example, the people with the least initiative in feedback choose the expected feedback behavior, which verifies the “feedback effect” theory of psychology. The younger group acts only when the older group demands it, and the process is one-way rather than two-way. If the young group does not get feedback from the older group after feedback, it will gradually reduce its initiative of feedback feeding and also increase the possibility of choosing the expected feedback behavior next time. In addition, the health skills learned by the older group under the expected feedback behavior tend to be more limited. As Xu puts forward, such a feedback process will cause the problem of low feedback quality, and even if the communication frequency is improved, it is difficult to effectively enhance the efficiency of digital feedback [58].How to correctly use the intention of feedback to guide feedback behavior scientifically and efficiently to solve this problem can be used in the following two ways. First, geographical advantages should be made use of to give play to the timeliness of digital feedback. In the context of COVID-19, many families are required to stay at home or work from home due to policies. In this case, they can make up for the time they do not often communicate with their elders, and timely communicate with them to discuss the situation of health emergencies and the status quo and prevention and control methods of COVID-19 virus, so as to establish a common space of significance, and integrate health communication and digital feedback into every aspect of your life. Second, the intergenerational cognition and attitude should be changed and should be given play to the communication of digital feedback. The most basic factor in the process of digital feedback is that the young group and the old group should have the same attitude and willingness to accept/impart the health communication content. However, most middle-aged and elderly groups are constrained by traditional thinking and coupled with habitual information avoidance attitude, which leads to the phenomenon that the elderly group rejects the digital feedback of the young group from time to time. On the social level, WeChat and other social media can open more free online psychological counseling activities for middle-aged and elderly groups. Community committees and other relevant organizations can also recruit volunteers to communicate with middle-aged and elderly groups face to face. In order to guide the elderly group to actively accept the knowledge of network health communication, improve the dialogue mechanism with the young group at home, and promote the digital feedback in family health communication to get better results.


### 6.2. Causes and Countermeasures of Feedback Intention Affected by Perceived Threat

Young people who are familiar with the rules of the internet can quickly get frontline information about the epidemic, such as confirmed cases, and learn health information through popular science accounts. The severity of the COVID-19 situation has also aroused young people’s attention to the epidemic, and their interest in prevention and control measures and desire to learn. In this process, their perceived susceptibility and perceived severity gradually increase. For the need of “self-realization” in Maslow’s hierarchy of needs theory, young people often reverse persuade their parents to improve their media literacy and cognition of media skills, adapt to an intelligent lifestyle, and improve their physical and mental development.However, how to make parents and other older groups truly understand the seriousness of the COVID-19 epidemic and the necessity of prevention and control of the epidemic, and how to make parents truly integrate into the digital age from their own consideration and improve the problem need the joint efforts of the young group. First, young people should learn to put themselves in other people’s shoes in the process of feedback, so as to resonate with older people on the COVID-19 issue. In daily life, young people can use media that are of interest to older people, such as television, newspapers, and radio, to actively inform their elders about the development of COVID-19 and bridge the information technology access, skills, and literacy gap [33]. Second, young people should actively communicate with their elders to discuss ways to prevent and control the epidemic, so as to understand the real reasons why elderly people resist the prevention methods. For example, they should know that they do not wear masks because they are too stuffy and easily out of breath, and help elderly people to solve practical problems with appropriate medicine.


### 6.3. Causes and Countermeasures of Feedback Intention Affected by Behavioral Evaluation

It is a process of value judgment for young groups to choose whether to carry out digital feedback. Estimation of the harvest of digital feedback, obstacles, and their ability to achieve feedback effect all affect the young group’s decision on the intention of feedback. Due to the COVID-19 pandemic, young people’s energy is at a premium in order to save time and money to cope with work and study, as the economic situation and employment situation have worsened. Young people in the working and learning stages tend to weigh their own life and input into the digital feedback process. If the perceived benefit is not equal to the perceived obstacle, or the degree of self-efficacy is not high, it will have a certain impact on the extent of the young group’s feedback intention.Therefore, to deal with the relationship between feedback intention and behavioral evaluation is also the key to affect the feedback intention. In terms of young groups, as the receivers of health knowledge, young groups should control their pace of life and the energy input of digital feedback, try to combine digital feedback into their lives, and improve self-efficacy and perceived benefits. In terms of the government and media, it is relatively difficult for the nonmedical public to understand some health knowledge about medical professionals. Considering the urgent need for COVID-19 knowledge among the elderly, health information disseminators should try their best to convert obscure technical terms into easy-to-understand knowledge for the public, or use analogies and visualizations to help the public understand COVID-19 knowledge, and promote secondary transmission in digital feedback.


### 6.4. Causes and Countermeasures of Feedback Intention Affected by Improvement Intention of Digital Feedback

Young groups need to undergo a series of reactions before adopting digital feedback, and the final choice is determined by individual cognition and the surrounding environment. The attitude of the young group to digital feedback greatly affects the adoption and effect of digital feedback behavior. The higher the willingness of young groups to improve digital feedback, the more actively they will invest in the practice of learning and improving digital feedback ability, the more likely they are to adopt digital feedback behavior in the future, and the final effect of digital feedback will be more effective. However, the willingness of digital feedback to improve is mainly affected by the subjective will of young people, and the willingness to improve depends on the individual’s cognitive system and judgment of the surrounding environment.Therefore, it is still an important issue for the public to deal with the relationship between the intention of feedback and the intention of digital feedback. First, at the family level, family members should actively create an online learning environment suitable for the elderly group. Young groups timely improve the internet access equipment of the elderly group and add network traffic, to ensure the convenience and speed of online learning for the elderly group. Second, at the social level, all sectors of society should make concerted efforts to pay attention to the digital needs of middle-aged and elderly groups. The government can also arrange various communities to regularly organize young and elderly groups to participate in lectures or competitions on digital technology, so as to promote the importance of digital feedback between parents and children. This can also close the interpersonal relationship with the neighbors, so as to form a community influence, and enhance the attention of the young and old groups to digital feedback. News media and all walks of life should give full play to the mobility and stability of digital feedback in the social field [58], pay attention to the physical and mental health of middle-aged and elderly groups, help them constantly adapt to the development and needs of technology in the new era, and avoid the middle-aged and elderly being marginalized and even forgotten in the tide of technology. News media need to comprehensively and rationally report the current situation of digital feedback under the COVID-19 pandemic, so that more public can understand and consider adopting digital feedback. Through these measures, we can solve the problem of the use of digital technology for the elderly group, help the elderly group transition from digital refugees to digital immigrants, and constantly adapt to the development and needs of the new era of technology.


## 6.1. Causes and Countermeasures of Feedback Behavior Affected by Feedback Intention

The digital back-feeding process in the family is not only affected by the parent-child relationship between the young group and the old group, but also affected by the back-feeding intention of the young group. Digital feedback is a process of intergenerational interaction in which young people can flexibly switch between positive and negative roles [33]. Therefore, the willingness of young groups to actively or passively feedback, that is, to choose active communicative feedback behavior, or to limit guidance, expected feedback behavior, will directly affect the process and effect of feedback behavior. For example, the people with the least initiative in feedback choose the expected feedback behavior, which verifies the “feedback effect” theory of psychology. The younger group acts only when the older group demands it, and the process is one-way rather than two-way. If the young group does not get feedback from the older group after feedback, it will gradually reduce its initiative of feedback feeding and also increase the possibility of choosing the expected feedback behavior next time. In addition, the health skills learned by the older group under the expected feedback behavior tend to be more limited. As Xu puts forward, such a feedback process will cause the problem of low feedback quality, and even if the communication frequency is improved, it is difficult to effectively enhance the efficiency of digital feedback [58].How to correctly use the intention of feedback to guide feedback behavior scientifically and efficiently to solve this problem can be used in the following two ways. First, geographical advantages should be made use of to give play to the timeliness of digital feedback. In the context of COVID-19, many families are required to stay at home or work from home due to policies. In this case, they can make up for the time they do not often communicate with their elders, and timely communicate with them to discuss the situation of health emergencies and the status quo and prevention and control methods of COVID-19 virus, so as to establish a common space of significance, and integrate health communication and digital feedback into every aspect of your life. Second, the intergenerational cognition and attitude should be changed and should be given play to the communication of digital feedback. The most basic factor in the process of digital feedback is that the young group and the old group should have the same attitude and willingness to accept/impart the health communication content. However, most middle-aged and elderly groups are constrained by traditional thinking and coupled with habitual information avoidance attitude, which leads to the phenomenon that the elderly group rejects the digital feedback of the young group from time to time. On the social level, WeChat and other social media can open more free online psychological counseling activities for middle-aged and elderly groups. Community committees and other relevant organizations can also recruit volunteers to communicate with middle-aged and elderly groups face to face. In order to guide the elderly group to actively accept the knowledge of network health communication, improve the dialogue mechanism with the young group at home, and promote the digital feedback in family health communication to get better results.


## 6.2. Causes and Countermeasures of Feedback Intention Affected by Perceived Threat

Young people who are familiar with the rules of the internet can quickly get frontline information about the epidemic, such as confirmed cases, and learn health information through popular science accounts. The severity of the COVID-19 situation has also aroused young people’s attention to the epidemic, and their interest in prevention and control measures and desire to learn. In this process, their perceived susceptibility and perceived severity gradually increase. For the need of “self-realization” in Maslow’s hierarchy of needs theory, young people often reverse persuade their parents to improve their media literacy and cognition of media skills, adapt to an intelligent lifestyle, and improve their physical and mental development.However, how to make parents and other older groups truly understand the seriousness of the COVID-19 epidemic and the necessity of prevention and control of the epidemic, and how to make parents truly integrate into the digital age from their own consideration and improve the problem need the joint efforts of the young group. First, young people should learn to put themselves in other people’s shoes in the process of feedback, so as to resonate with older people on the COVID-19 issue. In daily life, young people can use media that are of interest to older people, such as television, newspapers, and radio, to actively inform their elders about the development of COVID-19 and bridge the information technology access, skills, and literacy gap [33]. Second, young people should actively communicate with their elders to discuss ways to prevent and control the epidemic, so as to understand the real reasons why elderly people resist the prevention methods. For example, they should know that they do not wear masks because they are too stuffy and easily out of breath, and help elderly people to solve practical problems with appropriate medicine.


## 6.3. Causes and Countermeasures of Feedback Intention Affected by Behavioral Evaluation

It is a process of value judgment for young groups to choose whether to carry out digital feedback. Estimation of the harvest of digital feedback, obstacles, and their ability to achieve feedback effect all affect the young group’s decision on the intention of feedback. Due to the COVID-19 pandemic, young people’s energy is at a premium in order to save time and money to cope with work and study, as the economic situation and employment situation have worsened. Young people in the working and learning stages tend to weigh their own life and input into the digital feedback process. If the perceived benefit is not equal to the perceived obstacle, or the degree of self-efficacy is not high, it will have a certain impact on the extent of the young group’s feedback intention.Therefore, to deal with the relationship between feedback intention and behavioral evaluation is also the key to affect the feedback intention. In terms of young groups, as the receivers of health knowledge, young groups should control their pace of life and the energy input of digital feedback, try to combine digital feedback into their lives, and improve self-efficacy and perceived benefits. In terms of the government and media, it is relatively difficult for the nonmedical public to understand some health knowledge about medical professionals. Considering the urgent need for COVID-19 knowledge among the elderly, health information disseminators should try their best to convert obscure technical terms into easy-to-understand knowledge for the public, or use analogies and visualizations to help the public understand COVID-19 knowledge, and promote secondary transmission in digital feedback.


## 6.4. Causes and Countermeasures of Feedback Intention Affected by Improvement Intention of Digital Feedback

Young groups need to undergo a series of reactions before adopting digital feedback, and the final choice is determined by individual cognition and the surrounding environment. The attitude of the young group to digital feedback greatly affects the adoption and effect of digital feedback behavior. The higher the willingness of young groups to improve digital feedback, the more actively they will invest in the practice of learning and improving digital feedback ability, the more likely they are to adopt digital feedback behavior in the future, and the final effect of digital feedback will be more effective. However, the willingness of digital feedback to improve is mainly affected by the subjective will of young people, and the willingness to improve depends on the individual’s cognitive system and judgment of the surrounding environment.Therefore, it is still an important issue for the public to deal with the relationship between the intention of feedback and the intention of digital feedback. First, at the family level, family members should actively create an online learning environment suitable for the elderly group. Young groups timely improve the internet access equipment of the elderly group and add network traffic, to ensure the convenience and speed of online learning for the elderly group. Second, at the social level, all sectors of society should make concerted efforts to pay attention to the digital needs of middle-aged and elderly groups. The government can also arrange various communities to regularly organize young and elderly groups to participate in lectures or competitions on digital technology, so as to promote the importance of digital feedback between parents and children. This can also close the interpersonal relationship with the neighbors, so as to form a community influence, and enhance the attention of the young and old groups to digital feedback. News media and all walks of life should give full play to the mobility and stability of digital feedback in the social field [58], pay attention to the physical and mental health of middle-aged and elderly groups, help them constantly adapt to the development and needs of technology in the new era, and avoid the middle-aged and elderly being marginalized and even forgotten in the tide of technology. News media need to comprehensively and rationally report the current situation of digital feedback under the COVID-19 pandemic, so that more public can understand and consider adopting digital feedback. Through these measures, we can solve the problem of the use of digital technology for the elderly group, help the elderly group transition from digital refugees to digital immigrants, and constantly adapt to the development and needs of the new era of technology.


## 7. Lack of Research

In this paper, the research on digital feedback in the context of COVID-19 has certain timeliness, and the quantitative research on digital feedback behavior and willingness of young groups has been carried out to broaden the research methods and perspectives of digital feedback research. However, there are still many limitations and deficiencies in this study.First, studies are underrepresented. The sample range of this study has certain limitations. Most of the samples come from Guangdong and Fujian provinces, so it is difficult to collect samples from all provinces in China, which will have a certain influence on the data. Second, the depth and breadth of research can be expanded. Limited by the author’s energy and writing ability, the research on feedback behavior is only limited to the effect of the influencing factors, but failed to study how to make young groups pay attention to digital feedback, and how to give full play to the advantages of digital feedback to bridge the generational gap.To sum up, further adjustments can be made to the above problems in this study. It is hoped that future research can explore the influencing factors of digital feedback behavior from a more specific and comprehensive perspective, and strive to provide more constructive suggestions for the national active aging policy.



---

*Source: 9095003-2022-09-21.xml*