Predicting the Continuance Intention to Use Anti-COVID Mobile App for Crisis Response: Evaluating User Appraisal and Emotion

Introduction

The recent corona virus outbreak, otherwise known as Covid-19, which is apparently different from the previous related virus like the Severe Acute Respiratory Syndrome (Li et al., 2020) started in December 2019 in Wuhan, a popular city in the Hubei Province of China. Ever since then, the virus has been a major public health crisis that has caused a great deal of damage to countries across the world. A total of over 110 million infections have been recorded across >200 countries and territories with over 2.4 million deaths (Worldometers, 2021). The Covid-19 pandemic being a huge shock to the world (Chinazzi et al., 2020) has forced so many countries to adopt several innovative technologies toward dealing with the global health challenges. As a result of this unforeseen circumstance and high rate of infections (Mo et al., 2020), there was a total lockdown and quarantine measures were initiated around the world in order to avoid spread of the virus (Srinivasa Rao & Vazquez, 2020). The World Health Organization by way of policy directives have also ensured the need for people to adhere strictly to preventive measures aimed at reducing the rate of infection (Brooks et al., 2020). This according to Velicia-Martin et al. (2021) has brought about intense preparation should the next stage of the coronavirus pandemic surface and also to ensure that health care facilities are not overwhelmed.

Historical facts have revealed that global health crisis like the Covid-19 has the capacity to stimulate various forms of technological innovations (Tencent, 2020). The integration of technological innovation in curbing the spread of coronavirus is of paramount importance because the traditional means have proven difficult in terms of preventing the spread of the virus (Altmann et al., 2020), information dissemination and contact tracing, among others.

The use of technology by crisis management experts enables them in the process of making effective decisions whenever crisis arises (Kourti et al., 2019). There have been several ways, and some are still emerging, by which national and international governments across the world are identifying persons who are COVID-19 positive. Some of these ways are via blockchain technology used in ensuring that different location is being put under surveillance (Nguyen et al., 2021), the integration of “Smart Cities” to keep a close watch on people who are carriers of the virus (Allam & Jones, 2020), and the use of “immunity passports” (Agol et al., 2005).

Several countries have identified technological solution as a significant measure in the combat against the pandemic (Kelion, 2020). A technological innovation like an anti-covid mobile app is therefore of crucial value at this critical time. Covid-19 tracing app which can be categorized under anti-covid mobile app is one of the technological innovations that have been deployed in the fight against the pandemic. In the study of Velicia-Martin et al. (2021), they found out that its users acknowledged the usefulness of Covid-19 tracing app and the ease of its usage. In a similar study, Covid-19 tracing app was found to be of tremendous benefits to its users by way of helping to track positive cases and also helping the users take precaution regarding their movements from one location to another (Altmann et al., 2020).

For instance, in the recent past, the Bluetooth technology was used to develop the STOP COVID APP by the French government (Bradford et al., 2020). There is the Corona-Warn-App in Germany, Radar Covid in Spain, NHS APP in the United Kingdom, TraceTogether in Singapore, The Shield in Israel, Coronalert in Belgium etc. (European Commission, 2020). All these apps are playing a huge role in the process of ensuring that the pandemic is defeated.

Since the outbreak of the coronavirus in China, the Chinese government through its several government agencies has taken a lot of decisive actions in ensuring the mitigation of the spread of the virus. So many ANTI-COVID APPs have been developed that are used my majority of the Chinese population purposely to access latest information about the movements of infected persons and also to ensure that others don’t expose themselves to infected persons (Suh & Li, 2021). These ANTI-COVID APPS have become very instrumental to its users as it enables them to identify whether they have gotten exposed to an infected persons or not, which then make them embrace safety measures such as self-isolation and medical treatments. China’s coronavirus mobile health code app is a good example out of many anti-covid mobile app, used in strengthening the surveillance system in the fight against coronavirus outbreak in China (Feuer, 2020). The App is used to monitor the movement of people and inform them if they have been in close contact with an infected person. The China National Health Commission describes a close contact has an individual who has been exposed to someone who has been infected with the virus or who is likely to have been infected. However, despite the numerous advantages of the anti-covid app to public health, there have been various observations as it relates to privacy concern (Suh & Li, 2021). This is one of the major drawbacks of this anti-covid mobile app. Data which is personal to its users is required. This information may include their age, gender, zip code, weight, height as well as information about whether the user has an underlying disease or not (Mayor, 2020). It is also evident according to the studies of Kaspar (2020) that a large number of people are so much interested in using these apps despite the privacy concerns. It is also a known fact that the Chinese population has adopted the use of anti-covid mobile app for their day-to-day activities.

Some past studies have applied technological acceptance model (TAM) in studying health information systems (Cho et al., 2014; Olver & Selva-Nayagam, 2000; Whitten et al., 2005) like China’s coronavirus mobile health code app. In addition, the findings have revealed those factors that affect the willingness of people to adopt and make use of an APP which has the capacity to notify them anytime they are exposed to an infected person (Velicia-Martin et al., 2021). Most of the past studies have also applied the technological acceptance model in unraveling those factors in different context (Beldad & Hegner, 2018; Marangunić & Granić, 2015; Velicia-Martin et al., 2021), however this study decides to apply the cognitive appraisal theory because so much remains unknown about how the cognitive appraisal of anti-covid mobile app (China’s coronavirus mobile health code app) can influence the emotional condition of its users and how the emotional condition of users influence their continuance intention to use the app. In specific terms, it is important to note that this study addresses a relatively unresearched area regarding the use of technology artifacts, specifically anti-COVID mobile apps like the China’s coronavirus mobile health code app, during public health crises. By examining the relationship between appraisals, emotions, and continuance intention to use, this study provides valuable insights into how individuals are motivated to continue using the app. This information can be used by policymakers and developers to improve the effectiveness and usability of such apps during future outbreaks.

This study uses the cognitive appraisal theoretical framework to investigate how perceived threats and perceived opportunities related to China’s coronavirus mobile health code app impact users’ emotions and their intention to continue using the app during the pandemic. It also explores the role of emotions as a mediator in this relationship. This study is divided into sections on context, theoretical background, methodology, analysis, results, discussion, implications, limitations, and future research direction.

Context: China’s Coronavirus Mobile Health Code App

The health code app was developed by the Chinese government in its effort to contain the spread of COVID-19 infection in the country. Platforms like Alipay and WeChat, each having billions of users host the health code service and help it run effectively and efficiently. The users of this app are assigned certain color code based on their travel record and health status, including a QR code meant to be checked and scanned by authorities when the need arises (Davidson, 2020). This app gives information in real time whether the user poses a risk of infecting others with the virus and performs surveillance function of monitoring users’ movement and vaccination status. Figure 1 contains the screenshot of the health code service on WeChat and Alipay (Yang et al., 2021).

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Figure 1.

Shows the photo of health code mini app on both WeChat (left) and Alipay (right) platforms (Yang et al., 2021).

In addition, it is important to note that the Chinese health code system was formally hosted on its own app before it was been integrated has a mini app on popular Chinese app like Alipay and WeChat (Zhou et al., 2021). The health code was first launched on February 11, 2020, in Hangzhou municipal through a project led by the local government supported by Ant Financial, a close company with Alibaba company. In China, the use of the health code system is based on the prerogative of the national and provincial authorities. In other words, the app is specific to each city or province; nevertheless, users with the green color code are allowed to travel with no restriction. Users with the yellow color code are meant to be in isolation while those with the red color code are labeled as being Covid-19 infected and are supposed to be quarantined.

The health code app has gone a long way in helping to stem the tide of Covid-19 infection in China. This is one of the major reasons for China’s success in curbing the spread of the virus. Other countries have also adopted the model. There are now several anti-covid mobile apps that are used for different purposes in combating the virus. For instance, countries such as USA, Russia, Switzerland, Hungary, India etc. have all developed one form of anti-covid mobile app or the other.

People have also raised different concerns about the health code app in China especially as it has to do with privacy. Another concern focuses on lack of transparency as it relates to the operation of the app and the way peoples’ information are being stored (Davidson, 2020). Many users have been reported to have complained about being designated as “red” while there was no reason for such “red” designation. People have therefore tagged this as erroneous and have complained about reliance on internet connections which can be prone to occasional errors.

Theoretical Background

The cognitive appraisal theory opines that peoples’ emotions undergo the process of constant changes and that emotions are derived as a result of observation and cognitive assessment of societal events (Bartsch et al., 2008). The appraisal of the society or events have been found to exert influence on emotions and the process of assessing the society has a correlation with emotions (So et al., 2019). Information technology plays an important role in managing crisis and disaster by way of ensuring that the public get easy access to information at the right time about the risk and hazard involved. An appraisal of information technology by users is correlated with the kind of emotion that is derived from using such information technology (Suh & Li, 2021). The cognitive appraisal theory has been used in past studies to investigate the way people assess the use of information technology and disaster management as well as how it influence their emotions (Oh et al., 2021). The cognitive appraisal theory has been identified in many studies as the theory of emotion because the theory provides adequate understanding about how peoples’ emotions are aroused through the process of appraisal (Myrick, 2017). For instance, there is a strong possibility that the usage of anti-covid mobile app might have different influence or effects on its users during the covid-19 pandemic. On the one hand, it might have positive effects on the users by making them become more conscious of the need to adhere to preventive measures because of the information that is been disseminated through the App. On the other hand, it might have negative effects on its users because of disseminating personal information which could be about carriers of the virus. In a situation whereby the effect is positive, then it can be categorized has opportunity and when the effect is negative, it can be categorized as threat (Chattopadhyay et al., 2001). It is equally important to know that information technology has many aspects or sides to it, therefore the appraisal of certain type of IT by users could be either positive (opportunity) or negative (threat), depending on its importance to the users (Beaudry & Pinsonneault, 2005). The emotional experience attached to the usage of information technology explains the mental state of its users which drives behavioral intention (Beaudry & Pinsonneault, 2010)

Appraisal as Correlates of Emotion

Past researchers have revealed that the cognitive appraisal theory is largely focused on emotions owing to the fact that it provides a concrete understanding about how peoples’ emotions is being aroused by appraisal (Myrick, 2017). According to Stein et al. (2015) emotions are “reactions to situational events that are appraised to be relevant to a person’s needs, goals or concerns.” There have been various emotional frameworks developed by past researchers which are applied in different contexts. With regards to this study, we adopted the emotional framework developed by Beaudry and Pinsonneault (2010). According to the framework, emotion attached to IT usage is classified as follows: loss, deterrence, challenge, and achievement.

Loss emotion as one of the classes of emotion consists of anger, dissatisfaction, frustration, and disgust (Beaudry & Pinsonneault, 2010). IT users do have an emotion that depict loss when they perceive certain treat that seems insurmountable which is associated with the IT (Beaudry & Pinsonneault, 2010). In the studies of Suh and Li (2021), their findings revealed that some users of anti-covid mobile app experience anger, dissatisfaction, frustration, and disgust whenever people are been stigmatized and condemned as a result of their personal data been made public. This according to the study was because of perceived threat. In the same study, it was revealed that some anti-covid mobile app users perceived the app as beneficial because it provided them with up-to-date information about the virus and helped them take preventive steps. This was identified as been caused by perceived opportunity in the study. In addition, an appraisal of anti-covid mobile app by users is multifaceted with either a positive or negative effect on loss emotion. It is therefore logical to state that the greater the perceived threat by the user, the greater the loss emotion while the greater the perceived opportunity by the user, the lesser the loss emotion. It is on this basis that the following hypotheses are formulated:

Deterrence emotion consists of anxiety, worry, fear, and distress (Beaudry & Pinsonneault, 2010). The deterrence emotion arises when there is an appraisal of a situation or an event which is likely to bring about negative consequences. In other words, these types of emotions are developed when an IT event is perceived as threats which its consequences can be controlled by users (Suh & Li, 2021). Within the context of this study, we assumed the possibility of deterrence emotion when users of anti-covid mobile app feel that the app is a threat. Part of what gives rise to deterrence emotion could be the persistent messages that users get about the need to adhere to safety precautions. Users often get worried, fearful, anxious, and distressed when they are consistently bombarded with warning notifications about the virus as well as information about the movements of infected persons. On the other hand, some users of anti-covid app could see the information been disseminated as advantageous to them as they are able to keep abreast of the dangers related to the virus and the need for them to avoid visiting locations where infected persons have visited to prevent risk of contracting the virus. So, the users of the anti-covid app could have different appraisals of the app either as perceived threat or perceived opportunity. It is on this basis that the following hypotheses are formulated:

Challenge emotion according to Beaudry and Pinsonneault (2010) consists of excitement, hope, anticipation, arousal and flow. These challenge emotions are activated because of an appraisal of a situation that can be controlled by people and that which its opportunity has a high possibility of leading to a positive result. In the context of this study and based on past studies reviewed, it is an established fact that some users of anti-covid mobile app like the contact tracing app develop challenge emotion when they appraise the app and realized they have substantial control over its positive outcomes. For example, in the study of Suh and Li (2021), they found out that some users of the contact tracing app have a firm believe that the usage of the app would lead to positive outcomes which is the prevention of spread of the virus. The results also highlighted the fact that users of contact tracing apps support the use of the apps in the fight against covid-19 and also acknowledge the fact that IT plays a significant role in ensuring that public health crisis is well controlled. On the contrary, the same study revealed that some users found the app usage a threat because it exposes their information which may be humiliating to carriers of the virus. It is therefore on this basis that the following hypotheses are formulated:

Achievement emotions consist of happiness, satisfaction, joy and pleasure (Beaudry & Pinsonneault, 2010). The achievement emotions arise when there is an appraisal of a situation or an event which is likely to bring about positive consequences. Previous studies have revealed that the users of contact tracing app derived achievements emotions such as satisfaction, joy, happiness and pleasure by using the app (Suh & Li, 2021). The users can receive loads of information about the virus as well as make informed decisions regarding their safety which makes them satisfied. However, the same study revealed that there are some users of the contact tracing app who do not see the app as that which can bring about opportunity but only threat. This group of users does not see the anti-covid app as that which can bring about achievement emotions. It is on this basis that the following hypotheses are developed:

Emotions as Correlates of Continuous Intention

According to the cognitive appraisal theory, emotion is being aroused as a result of certain appraisal which then leads to action tendency or intentions to behave in certain ways (Kessler & Schmidt-Weitmann, 2021). Some past studies have found out that emotions have the capacity to influence people to take certain action or behave in certain ways (Wen-Hai et al., 2019).

Loss emotion has the capacity to exert a negative influence on users behavioral intention by making them to ignore issues that are critical to them (Liang et al., 2019). Studies in the past have revealed that the various dimensions of loss emotion which consist of anger, dissatisfaction, frustration, and disgust (Beaudry & Pinsonneault, 2010) have the capacity to bring about negative IT consequences and dissatisfaction (Zeelenberg & Pieters, 2004) as well as the lack of intention to continue usage (Bougie et al., 2003). In addition, when users develop loss emotion about IT, they tend to attach less significance to IT (Lerner et al., 2007). Based on extant literatures reviewed, the following hypothesis is developed:

Deterrence emotions such as anxiety, worry, fear, and distress arise when there is an appraisal of a situation or an event which is likely to bring about negative consequences (Beaudry & Pinsonneault, 2010). Just like loss emotion, deterrence emotions also have the capacity to exert a negative influence on user’s behavioral intention. There is high possibility for IT users with deterrence emotions to deter themselves from certain circumstance which can result in their disinterest for IT (Beaudry & Pinsonneault, 2010). Since anti-covid mobile app is stimulated by information technology, we therefore develop the following hypothesis:

Challenge emotions such as excitement, hope, anticipation, arousal and flow are activated as a result of an appraisal of a situation that can be controlled by people and that which its opportunity has a high possibility of leading to a positive result (Beaudry & Pinsonneault, 2010). Findings in extant literatures have revealed that there is a positive relationship between challenge emotions and users intention to use IT (Markus et al., 2004). In the context of this study, it is assumed that users of anti-covid mobile app with challenge emotion would attach much importance to the app because they are able to access loads of information about the virus through it and will be able to prevent themselves from contracting the virus. It is on this basis that the following hypothesis is developed:

Achievement emotions such as happiness, satisfaction, joy and pleasure are activated when there is an appraisal of a situation or an event which is likely to bring about positive consequences (Beaudry & Pinsonneault, 2010). In addition, people experience the various dimensions of achievement emotions when certain types of IT are perceived to meet their needs and expectations. Nevertheless, it is important to point out that some users might not see the need for more usage of IT simply because they have limited control over the effects it exerts on them. For instance, users of the anti-covid mobile app would be delighted and filled with satisfaction when the information they acquire from the anti-covid mobile app is advantageous to them. However, it is also possible for the users of anti-covid mobile app to have the feeling that the anti-covid app is only fulfilling their information needs without them having enough control over the circumstances surrounding them. This means that those achievement emotions in general would bring about more positive outcomes than negative outcomes which make users develop more interest in the usage of the App. Past studies have found achievement emotions to have positive relationship with the kind of attitude users have toward the usage of IT (Kim et al., 2007). It is due to this insight that we proposed the following hypothesis within the context of our study:

Congnitive appraisal and continuance intention

The cognitive appraisal theory has stated that an appraisal of a particular situation or event such as IT can bring about the activation of certain emotional responses. This emotional response could either be positive or negative as revealed in past studies (Stein et al., 2015). It is possible for users of anti-covid mobile app to find the App very useful and beneficial to them simply because they can receive frequent updates about the virus which they believe is meant to prevent further escalation of the virus. This can be seen as perceived opportunity. On the other hand, users of anti-covid mobile app may find the usage of the app as a means by which government invade their privacy through overwhelming surveillance. This can be seen as perceived threat. The users of anti-covid mobile app can therefore appraise the App in diverse ways which can influence their intentions to continue using the App. It is in furtherance to these insights that the following hypotheses are developed:

H13: There is a negative association between perceived threat by users of anti-covid mobile app (China’s coronavirus mobile health code app) and their intention to continue using the app.

H14: There is a positive association between perceived opportunity by users of anti-covid mobile app (China’s coronavirus mobile health code app) and their intention to continue using the app.

Based on the cognitive appraisal model presented in Figure 2, it is clear there is interrelationship in form of direct and indirect relationship between cognitive appraisal, emotion, and continuous intention to use anti-covid mobile app. To fully explore the cognitive appraisal—emotion—intention pathways, we therefore propose a mediation test to examine the mediating role of emotion (challenge emotion, achievement emotion, loss emotion, deterrence emotion) in the relationship between cognitive appraisal (perceived threat and perceived opportunity) and continuous intention to use anti-covid mobile app (China’s coronavirus mobile health code app). It is against this background that we developed the following hypotheses pivoted around the mediating role of emotion (challenge emotion, achievement emotion, loss emotion, deterrence emotion).

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Figure 2.

Illustrates the hypothesized relationships between the variables and the proposed path model.

H15: Emotion such as (challenge emotion, loss emotion and deterrence emotion) will significantly mediate the relationship between perceived threat and continuous intention to use anti-covid mobile app (China’s coronavirus mobile health code app) while achievement emotion will not significantly mediate the relationship between perceived threat and continuous intention to use anti-covid mobile app (China’s coronavirus mobile health code app).

H16: Emotion such as (challenge emotion, and loss emotion) will significantly mediate the relationship between perceived opportunity and continuous intention to use anti-covid mobile app (China’s coronavirus mobile health code app) while (achievement emotion and deterrence emotion) will not significantly mediate the relationship perceived opportunity and continuous intention to use anti-covid mobile app (China’s coronavirus mobile health code app).

Materials and Methods

Participants and Process

It is mandatory for people to use the health code mini app in China. People have it on their mobile phone. The reason for this is to help facilitate the fight against the spread of the virus. The health code app is widely used in China because it plays major role in the process of eradicating the virus. Students from the University of Science and Technology of China, who have the health code app running on their smartphones were selected using convenience sampling method during the fall semester of 2020 to test the proposed research model. This group was selected because they are likely to have a higher level of awareness and compliance with the health code app, which is relevant to the study. Students who did not have the health code app installed on their smartphones were excluded from the data collection. Additionally, students who did not respond to the questionnaire or provided incomplete responses were also excluded from the data analysis. The questionnaire was in English, but a translated Chinese version was developed so that those who do not understand English could be able to answer the questions. The justification for adopting the convenience sampling method in selecting students from the University of Science and Technology of China is because the university is in the city of Hefei, which is the capital of Anhui Province of China. The city is also known as one of the “natural hubs of communication” and in the forefront of leading scientific research. An online survey was adopted whereby the questionnaire was sent to various WeChat platforms which include different personal contacts and chat groups on WeChat. The questionnaires were accompanied by a written statement stating that the responses of research participants were confidential and were for the sole purpose of research. A total of 450 questionnaires were shared and the total responses received were 420. A total of 416 (92.4%) questionnaires were found to be useful after eliminating questionnaires with incomplete responses. This was then used in the process of further analysis. The demographic report is presented in Table 1. However, since convenience sampling method, which is used to select the sample, is not random sampling method and may not provide a representative sample. As such, caution should be exercised when generalizing the findings of the study to the larger population of students in China or the general population in China.

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Table 1.

Descriptive Information of Samples Demographic Characteristics.

Variables	Classes	Frequency (%)
Gender	Male	258 (62%)
	Female	158 (37.9%)
Age	18–25	194 (46.6%)
	26–33	118 (28.4%)
	34 and above	104 (25%)
Education	Undergraduate	201 (48.3%)
	Master	110 (26.4%)
	PhD	65 (15.6%)
	Postdoctoral	40 (9.6%)
Marital status	Single	314 (75.4%)
	Married	102 (24.5%)

Analytic Strategy

After gathering the data, two major statistical packages were adopted namely SPSS 22 and AMOS 21 to analyze the data. The exploratory factor analysis (EFA) was conducted by using SPSS 22 to confirm the reliability and validity of measurement model. In addition, AMOS 21 was used to examine the structural equation modeling, confirmatory factor analysis and path testing (Hair et al., 2014). Structural equation modeling was found to be suitable in this regard because the current study focuses on the relationship between evaluations in multiple dimensions. This section of the study is therefore partitioned into two parts. The first part addressed the reliability and validity of measurement items used in this study and fitness indices of the measurement model while the second part addressed the structural equation modeling and the test of hypotheses.

Results

Reliability and Validity

The principal component analysis method and the varimax rotation in SPSS 22 were adopted in conducting the exploratory factor analysis (EFA) and used to access the factor loadings of measurement items used in this study. As recommended by Hair et al. (2014), a suppressed value of 0.50 was adopted and the factor loadings for measurement items were identified to be above 0.70 without cross-loading effects. This therefore indicates that the measurement items used in this study were adequate and appropriate for the data in relation to the survey. In addition, Kaiser-Meyer-Olkin measure of sampling adequacy (KMO) result was 0.943 closer to 1 which indicates the sampling was adequate (Kaiser, 1974). Bartlett’s test of sphericity X² (227) = 6,755, p < .001 was also conducted which indicates that the structure of correlation was adequate for factor analyses. All the factors’ eigenvalue were found to be greater than 1.

Furthermore, factor 1 was comprised of 3 items scored on a 5-point Likert scale that explained 58% of the variance with factor loadings from 0.709 to 0.844. Factor 2 was also comprised of 3 items scored on a 5-point Likert scale that explained 65% of the variance with factor loadings from 0.839 to 0.873. Factor 3 was comprised of 4 items scored on a 5-point Likert scale that explained 63% of the variance with factor loadings from 0.788 to 0.982. Factor 4 was also comprised of 4 items scored on a 5-point Likert scale that explained 68% of the variance with factor loadings from 0.886 to 0.781. Factor 5 was comprised of 6 items scored on a 5-point Likert scale that explains 70% of the variance with factor loadings from 0.880 to 0.891. Factor 6 was comprised of 4 items scored on a 5-point Likert scale that explained 66% of the variance with factor loadings from 0.880 to 0.833. Lastly, factor 7 was also comprised of 4 items scored on a 5-point Likert scale that explained 66% of the variance with factor loadings from 0.824 to 0.842.

The Cronbach reliability and validity for the constructs were also calculated through Cronbach alpha (CA), Cronbach reliability (CR), and the average variance extracted (AVE). Table 2 presents the results for the values of CA, CR, and AVE which are all within acceptable (Fornell & Larcker, 1981b; Hair et al., 2014; Wu & Chen, 2017). As shown in Table 2, the AVEs were found to be above 0.50 which is the minimum threshold for convergent validity of constructs and were also greater than the outer correlation of constructs thereby representing appropriate discriminant validity. The results from the HTMT analysis also demonstrate adequate discriminant validity because the values are below 0.85 (Voorhees et al., 2016). Table 3 contains the results of the HTMT test.

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Table 2.

Convergent Validity and Reliability.

Constructs	Items	FL	CA	CR	AVE
Perceived threat	PT1	0.709	0.887	0.85	0.65
	PT2	0.864
				PT3	0.844
Perceived opportunity	PO1	0.839	0.894	0.88	0.70
	PO2	0.805
				PO3	0.873
Challenge emotion	CE1	0.788	0.933	0.94	0.78
	CE2	0.944
				CE3	0.813
				CE4	0.982
Achievement emotion	AE1	0.886	0.879	0.91	0.72
	AE2	0.822
				AE3	0.897
				AE4	0.781
Loss emotion	LE1	0.880	0.938	0.95	0.75
	LE2	0.900
				LE3	0.752
				LE4	0.865
				LE5	0.902
				LE6	0.891
Deterrence emotion	DE1	0.880	0.901	0.93	0.76
	DE2	0.907
				DE3	0.863
				DE4	0.833
Continuance intention	CI1	0.824	0.855	0.94	0.80
	CI2	0.775
				CI3	0.798
				CI4	0.842

Note. FL = factor loading; CA = Cronbach alpha; CR = composite reliability; AVE = average variances extracted.

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Table 3.

Heterotrait–Monotrait Ratio (HTMT) Matrix Discriminant Validity.

Constructs	PT	PO	CE	AE	LE	DE	CI
PT	-
PO	0.401	-
CE	0.352	0.622	-
AE	0.559	0.372	0.508	-
LE	0.333	0.588	0.497	0.650	-
DE	0.566	0.415	0.634	0.598	0.758	-
CI	0.542	0.744	0.386	0.555	0.638	0.411	-

Note. PT = perceived threat; PO = perceived opportunity; CE = challenge emotion; AE = achievement emotion; LE = loss emotion; DE = deterrence emotion; CI = continuance intention.

To examine the fitness indices of the measurement model, a confirmatory factor analysis using AMOS 24 was conducted. The fitness indices used in this study include: GFI, AGFI, NFI, IFL, TLI, RMSEA, and CFI. GFI and AGFI represent the absolute indices, NFI, IFL, TLI represent the relative indices, RMSEA and CFI represent the non-centrality indices (Beckett et al., 2017). All the fitness indices fall within the standard range. CMIN/df is 2.145. The GFI and AGFI are 0.932 and 0.964 respectively, which are above the minimum value of 0.90 as recommended by Fornell and Larcker (1981a). The NFI is 0.934, IFL is 0.942 and TLI is 0.977, which are all above the minimum threshold of 0.95. The RMSEA is 0.059 and CFI is 0.981 which are in line with the postulation of Anderson and Gerbing (1988) that RMSEA must be <0.08< and CFA >0.90. In all, the results show valid model fitness.

The results for the means, standard deviations and bivariate correlations between the constructs used in this study are presented in Table 4.

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Table 4.

Descriptive Statistics and Correlations.

Variables	Mean	SD	1	2	3	4	5	6	7
1. PT	4.15	1.538	(0.806)
2. PO	4.07	1.647	0.048	(0.837)
3. CE	3.25	0.412	0.275**	0.405**	(0.883)
4. AE	3.88	0.576	0.171	0.334**	0.248	(0.849)
5. LE	4.11	0.877	0.448**	−0.103*	0.027	0.039	(0.866)
6. DE	3.59	1.153	0.468**	0.481	0.042	0.024	0.320	(0.872)
7. CI	3.97	1.559	−0.108*	0.329**	0.417**	0.356	−0.186**	0.178	(0.894)

Note. The values for Fornell-Larcker discriminant validity are the ones in parentheses. SD = standard deviation; PT = perceived threat; PO = perceived opportunity; CE = challenge emotion; AE = achievement emotion; LE = loss emotion; DE = Deterrence emotion; CI = continuance intention.

**

Correlation is significant at the .01 level (two-tailed), *correlation is significant at the .05 level (two-tailed).

Evaluation of Structural Model

After observing good and valid model fitness in the confirmatory factor analysis (CFA) to examine the structural equation model, the path analysis was subsequently conducted by using AMOS 22 in order to test the hypotheses. The results show that perceived threat exerts significant positive influence on loss emotion ((β = .379***, t = 3.638, p ≤ .001), deterrence emotion ((β = .357***, t = 3.795, p ≤ .001) and challenge emotion (β = .135*, t = 3.349, p ≤ .05). However, while perceived opportunity was found to exert significant positive influence on achievement emotion (β = .365***, t = 7.543, p ≤ .001) as well as challenge emotion (β = .283***, t = 4.206, p ≤ .001), it exerts significant negative influence on loss emotion (β = −.239***, t = −5.442, p ≤ .001).

Challenge and loss emotion were the only emotion variables that have significant influence on continuance intention to use anti-covid mobile app. While challenge emotion exerts significant positive influence on continuance intention to use anti-covid mobile app (β = .318***, t = 3.471, p ≤ .001), loss emotion was found to exert significant negative influence on continuance intention to use anti-covid mobile app (β = −.355***, t = −5.120, p ≤ .001)

Lastly, the analysis revealed that perceived opportunity has a significant positive influence on continuance intention to use anti-covid mobile app (β = .394***, t = 6.839, p ≤ .001), while perceive threat was found to have a significant negative influence on continuance intention to use anti-covid mobile app (β = −.240, t = −5.894, p ≤ .001). The detailed presentation can be found in Table 5.

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Table 5.

Results of Hypotheses Testing.

Path (direct path)	Estimate (β)	t-statistics	Hypothesis	Inference
PT→ LE	.379***	3.638	H1	Supported
PO→ LE	−.239***	−5.442	H2	Supported
PT→ DE	.357***	3.795	H3	Supported
PO→DE	.227 (ns)	0.186	H4	Rejected
PT→CE	.135*	3.349	H5	Rejected
PO →CE	.283***	4.206	H6	Supported
PT→ AE	.234 (ns)	0.244	H7	Rejected
PO→ AE	.365***	7.543	H8	Supported
LE→ CI	−.355***	−5.120	H9	Supported
DE→ CI	.210 (ns)	0.138	H10	Rejected
CE→ CI	.318**	3.471	H11	Supported
AE→ CI	.320 (ns)	0.269	H12	Rejected
PT→ CI	−.240	−5.894	H13	Supported
PO→ CI	.394***	6.839	H14	Supported

Note. ns = not significant; PT = perceived threat; PO = perceived opportunity;

CE = challenge emotion; AE = achievement emotion; LE = loss emotion; DE = deterrence emotion; CI = continuance intention.

*

<.05, **<.01, ***<.001.

Mediation Analysis

The mediation analysis was conducted using the bootstrapping approach by PROCSS macro for SPSS. According to the theoretical framework used in this study, the mediation model contains single mediation pathways. The pathways are as follows: (perceived threat → challenge emotion →continuous intention); (perceived threat → achievement emotion → continuous intention); (perceived threat → loss emotion → continuous intention); perceived threat → deterrence emotion → continuous intention); perceived opportunity → challenge emotion → continuous intention); (perceived opportunity→ achievement emotion → continuous intention); (perceived opportunity → loss emotion → continuous intention); (perceived opportunity → deterrence emotion → continuous intention). Table 6 contains results of the mediation analysis. According to Brown (1997), there is a mediating effect when the independent variable (exogenous variable) and the endogenous (dependent variable) pathways are been linked by a mediator. It is based on this assertion that the mediation analysis was conducted.

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Table 6.

Analysis of Indirect Effects.

Pathways	Beta (β)	CI	p-value	Mediate	Inference
PT → CE → CI	−.45	[−0.055, 0.722]	***	Yes	Accepted
PT → AE → CI	.33	[0.048, 0.931]	.443	No	Accepted
PT → LE → CI	.25	[0.012, 0.065]	***	Yes	Accepted
PT → DE → CI	.15	[0.075, 0.255]	***	Yes	Accepted
PO → CE → CI	.18	[0.018, 0.054]	***	Yes	Accepted
PO → AE → CI	.61	[0.042, 0.056]	.132	No	Accepted
PO → LE → CI	−.27	[−0.015, 0.039]	***	Yes	Accepted
PO → DE → CI	.54	[0.044, 0.083]	.138	No	Accepted

Note: ***p < .005, **p < .001, and *p < .01.: PT = perceived threat; PO = perceived opportunity; CE = challenge emotion; AE = achievement emotion; LE = loss emotion; DE = deterrence emotion; CI = continuance intention.

The results indicate that challenge emotion (β = −.45, p < .005; CI [−0.055, 0.722]), loss emotion (β = .25, p < .005; CI [0.012, 0.065]) and deterrence emotion (β = .15, p < .005; CI [0.075, 0.255]) significantly mediate the relationship between perceived threat and continuous intention to use anti-covid mobile app (China’s coronavirus mobile health code app) while achievement emotion (β = .33, p = .443; CI [0.048, 0.931]) did not significantly mediate the relationship between perceived threat and continuous intention to use anti-covid mobile app (China’s coronavirus mobile health code app). Thus, these results are in support of H15.

In addition, the results show that challenge emotion (β = .18, p < .005; CI [0.018, 0.054]), and loss emotion (β = −.27, p < .005; CI [−0.015, 0.039]) significantly mediate the relationship between perceived opportunity and continuous intention to use anti-covid mobile app (China’s coronavirus mobile health code app) while achievement emotion (β = .61, p = .132; CI [0.042, 0.056]) and deterrence emotion (β = .54, p = .138; CI [0.044, 0.083]) did not significantly mediate the relationship perceived opportunity and continuous intention to use anti-covid mobile app (China’s coronavirus mobile health code app). Thus, these results are in support of H16.

Discussion

As stated in the Introduction Section, the major objectives which this study seek to address is to identify how cognitive appraisals such as perceived threat and perceived opportunity that users observed about anti-covid mobile app influence their emotions about anti-covid mobile app during the pandemic. It is also to identify the influence of users’ emotional states in influencing continuance intention to use anti-covid mobile app. It is to reveal the direct influence of perceived threats and perceived opportunity on continuance intention to use anti-covid mobile app. Lastly, it is to find out the mediating role of emotion in the relationship between cognitive appraisal and continuous intention to use anti-covid mobile app.

Results in this study revealed that users’ perceived threat about anti-covid mobile app increases loss emotion such as anger, dissatisfaction, frustration, and disgust whenever people are being stigmatized and condemned because of their personal data been made public. Results also revealed that perceived threat causes an increase in deterrence and challenge emotions. This simply means that when users of anti-covid mobile app perceive a particular threat about the app which could be the persistent messages that users get about the need to adhere to safety precautions, they tend to feel anxious, worried, fearful, and distressed. Challenge emotion too is also activated by perceived threat which includes excitement, hope, anticipation, arousal, and flow.

In addition, perceived opportunity was found to cause an increase in achievement emotions which include (happiness, satisfaction, joy and pleasure) and challenge emotions which include (excitement, hope, anticipation, arousal and flow). In other words, users of the anti-covid mobile app were found to have the opportunity to receive loads of information about the virus as well as make informed decisions regarding their safety which led to positive outcomes and made them satisfied. On the other hand, perceived opportunity was found to reduce loss emotion, meaning that users of anti-covid mobile app don’t develop anger, dissatisfaction, frustration, and disgust whenever they are confronted with the advantageous aspect of the app.

Among all the four emotion categories, loss emotion stands out, as it exerts the strongest negative influence on continuance intention to use anti-covid mobile app by its users. In other words, when users of an anti-covid mobile app get angry, dissatisfied, frustrated, and disgusted because of their negative experience with the app, it causes a reduction in the level of continuance intention to use the app. On the contrary, findings in the study show that users of anti-covid mobile app tend to continuously make use of the app whenever they develop challenge emotions like excitement, hope, anticipation, arousal, and flow about the app.

Interestingly, results also show that negative appraisal of anti-covid mobile app that makes users identify the app as a threat, was found out to have a detrimental effect on their continuance intention to use the app. In other words, users’ continuance usage of anti-covid mobile app is adversely affected whenever users perceive threat about the app. In contrast, it is revealed that perceived opportunity leads to an increase in continuance usage of anti-covid mobile app by its users.

Lastly, challenge emotion, loss emotion and deterrence emotion all play significant mediating role in the relationship between perceived threat and continuous intention to use anti-covid mobile app (China’s coronavirus mobile health code app) while achievement emotion did not significantly mediate the relationship between perceived threat and continuous intention to use anti-covid mobile app (China’s coronavirus mobile health code app). Furthermore, challenge emotion and loss emotion both played significant mediating role in the relationship between perceived opportunity and continuous intention to use anti-covid mobile app (China’s coronavirus mobile health code app) while achievement emotion and deterrence emotion both did not significantly mediate the relationship perceived opportunity and continuous intention to use anti-covid mobile app (China’s coronavirus mobile health code app).

Theoretical and Practical Implications

This study provides some theoretical implications that are quite important according to the findings revealed in this study. First, this study adds to the body of existing knowledge in crisis, risk, and disaster management by explaining the relationship between users’ cognitive appraisals while using the anti-covid mobile app with emotion and continuance intention to use the app. The anti-covid mobile app becomes very useful in getting information about the virus and is used to trace the movement of carriers or potential carriers of the virus based on a centralized database system which is done to prevent the spread of the virus. Frequent data update and contact tracing through apps play a crucial role during public health crisis like COVID-19 (Cohen et al., 2020). However, this study revealed that despite the benefits attached to using anti-covid mobile apps, some individuals still see the app as detrimental because of the possible harm that it might inflict on their lives.

Studies focusing on how emotion influences the usage of information technologies have been quite scares and suggestions have been made for more studies to be conducted in this regard (McGrath, 2006; Stein et al., 2015). In this study, contribution toward emotional dimensions was achieved. The emotional framework developed by Beaudry and Pinsonneault (2010) was tested empirically to examine its utility within the context of this study. The study of Suh and Li (2021) asserted that some past studies have used this emotional framework by focusing on a specific emotional class or dimension. This current study, however, categorized emotion into various classes or dimensions by extensively presenting a comprehensive and empirical insight about the role which emotion plays in influencing continuance intention to use anti-covid mobile app.

In practical terms, it is very clear that access to timely information plays tremendous role during the period of public health crisis like Covid-19 (Pan, 2020). However, findings in this study have revealed that the implication can be either positive or negative. Anti-covid mobile apps help in providing loads of data to its users about various circumstances surrounding the virus. This information spread via this app can be quite excessive and users may not be able to control the impact it has on them which can give rise to negative emotions like loss emotion such as anger, dissatisfaction, frustration, and disgust.

Based on the foregoing, it is therefore imperative for designers of IT such as the anti-covid mobile app to integrate more functions into the app in such a way that users will be able to exercise more control over its usage. And lastly is the privacy concern, designers of anti-covid mobile app must ensure that too much information about users of the app is not disseminated to the public. It is equally important for policy makers to initiate policies that will address privacy concerns of users of anti-covid mobile apps.

Limitations and Future Research Directions

Despite the theoretical and practical implications that this study generated, some limitations that future research scholars can work on as research directions are identified. First, the data used for this study were gathered from just one big university in China which could affect generalizability of its findings. It is suggested that future studies should explore more samples from more countries with different demographics. In addition, since the severity of the virus during a particular time could affect emotions and subsequent continuance intention to use anti-covid mobile app by the users. It is therefore important for future studies to factor in the time periods. This can be achieved by gathering data at different periods of time. It is believed that if these suggestions are put into consideration by future scholars, the generalizability of these findings will be more strengthened.

Second is the cross-sectional research design that was adopted in conducting this study which makes the connections between constructs that are statistically supported to be correlational in nature. It is imperative for future studies to adopt experimental or longitudinal research designs to test the causal relationship of constructs.

Lastly, the data used in this study were collected from just one source which is from the users of anti-covid mobile app, and the constructs were also measured based on their perceptions. Despite this, there were no serious issues regarding common method bias in this study based on the analysis conducted. However, it is suggested that future studies should endeavor to collect data from multiple sources which could be based on log data as it relates to some functions to solve issues relating to common method bias.

Conclusion

The critical position which IT occupies in crisis management has been presented in several past studies and it cannot be overemphasized. The cognitive appraisal theory served as the theoretical framework upon which the research model was developed. The research model reveals that the anti-covid mobile app (China’s coronavirus mobile health code app) used in the process of curbing the spread of the coronavirus gives users the opportunity to experience the peculiar interrelationship between social and technological environments that contain both positive and negative impacts of IT that bring about perceived threats and opportunities. This study highlights the fact that the appraisal or assessment of the anti-covid mobile app (China’s coronavirus mobile health code app) by users exerts influence on users’ emotional response which then determines their continuance intention to use the App. In addition, to ingrain the culture of continuance usage of the anti-covid mobile app at this time of Covid-19 pandemic, it is suggested that the consciousness of users need to be stimulated and sustained. Policy makers are also expected to formulate and implement policies that will address controversial issues regarding privacy as it relates to anti-covid mobile apps.