Drivers of egovernment adoption amidst COVID-19 pandemic: The Information Adoption Model (IAM) approach

Abstract

The study scrutinized the behavioral adoption of informational e-government services among Chinese citizens in the midst of the COVID-19 pandemic. A structural equation modeling (SEM) technique was applied for the data analysis using Smart PLS 3.0 statistical software. The results show that information quality, information credibility, and ease of COVID-19 informational e-government services are significant in determining citizens’ perception of the usefulness of COVID-19 information shared on e-government platforms. Also, the study revealed that the perceived usefulness of COVID-19 informational e-government services was significant in predicting citizens’ intention to adopt and recommend COVID-19 informational e-government services. The theoretical and practical implications of these findings are interrogated further.

Keywords

e-government e-government services information adoption model COVID-19 China

Introduction

The novel coronavirus known as COVID-19 presented an unprecedented challenge in terms of the amount of information generated and disseminated on the COVID-19 pandemic. Accordingly, many reports indicate that the volume of information on the disease creates a situation where it is difficult for people to differentiate between what is factual and what is not about the COVID-19 (Dhar et al., 2020; World Health Organisation (WHO) 2020a). This situation did not only affect the people’s confidence in the actions to take but also their trust in government as well as in the management of information concerning COVID-19 (WHO, 2020a). It is important to note though that the use of ICT has been instrumental in the management of COVID 19 cases, especially for developing systems for information dissemination and contact tracing (Park et al., 2020). This application of ICT platforms by government to deliver service is known as e-government, which facilitates and makes public information more readily available. The result is that e-government enhances government efficiency and accountability (Alshomrani, 2012; Palvia and Sharma, 2007). Such adoption of ICT for government operations provides a means for citizens to enjoy uninterrupted public services (Fraga, 2002; Palvia and Sharma, 2007; Zhang and Hou, 2011).

The development of e-government can be categorized into four stages: transformation, transaction, interaction, and presence-stages. The presence stage has to do with the provision of simple information of government services in the form of brochures, whilst the interaction stage provides simple interactions between government and citizens for informational reactions (Di Maio, 2003). On the other hand, the transactional aspects provide enabled services such as payment of fees for online license renewals and payment of taxes or levies, whilst the transformation stage is the ultimate stage which has to do with the reinvention of how government functions are conceived and organized (Di Maio, 2003). These four stages or forms of e-government development can lead to the classification of e-government into two: transactional and informational (the emphasis of this study) e-government services. First, the informational aspect of e-government comprises the stages of simple web presence and interaction which enable governments to provide informational government services to the citizens, for instance, the provision of the COVID-19 pandemic information. The second classification, termed transactional e-government service, is made up of the transaction and transformation stages, which empower the government to achieve full integration of its functions and the automation of payment services or completion of financial transactions virtually.

The most important production element under the digital economy is information (Civelek, 2018) and particularly during COVID-19, to promote strategic information management (Alawneh et al., 2013; Civelek, 2019). Even more important is the adoption of e-government services specifically geared towards managing the COVID-19 pandemic. The questions are: whether citizens are aware of the e-government services, what factors affect their use of them, how useful they are to them, to what extent do they really use them and influence others within their networks to use them? These are questions addressed by the study and consequently enrich the e-government adoption literature as they relate to the informational aspect of e-government. Further, this is one of the few studies which have adopted the Information Adoption Model (IAM) as a theoretical foundation in explaining COVID-19 informational e-government services. The IAM, which was derived from the combination of the technology acceptance model (TAM) and Theory of Reason Action (TRA), is considered as more capable in explaining information adoption behavior compared to the use of the individual theories of TAM and TRA. As indicated by Wang et al., (2018), IAM is devised to elucidate how users are influenced by the nature of the information presented to them through a computer-mediated system. While TAM is used to determine behavioral adoption from the Internet technology acceptance perspective, the IAM interrogates it from the information adoption angle (Cheung et al., 2008; Wang et al., 2018). Therefore, the IAM was used in this study since it can provide suitable parameters in explaining the adoption of COVID-19 information on e-government systems.

In the sections that follow, we provide a brief background to the study, including a United Nations (UN) survey on COVID-19, after which we discuss the research framework and hypothesis development. The methodology is then explained and the findings are analyzed and in the final section, conclusions, the limitations of this study, and opportunities for further studies are given.

Research background

E-government and COVID-19

The WHO’s declaration of COVID-19 as a global pandemic was unprecedented in the history of the world, and a critical moment in our generation, with its equivalent occurring about a hundred (100) years earlier (UN, 2020). The phenomenon has claimed the lives of over 1.7 million people worldwide and about 80 million are currently being infected as of December 26, 2020 (WHO, 2020b). Indeed, no public health issue has had such a global impact as COVID-19 (UN, 2020; WHO, 2020b). Currently, it is unclear when the pandemic will end, creating economic, health, and social uncertainties in many countries, although vaccines have been developed and are being used in many countries. Such is the impact of the pandemic that the World Bank has estimated that about one-third of organizations worldwide have adopted and increased use of ICT enhanced platforms to manage their business processes and operations during the crisis (Paul and Wadhwa, 2020). The phenomenon is likely to cause permanent changes and redefinition of what is currently considered to be the workplace and the nature of jobs, and how various functions are performed. According to Dwivedi et al. (2020), many organizations have redefined and implemented systems that enable their employees to work from home permanently.

According to Hellewell et al. (2020), control of the transmission of infectious diseases like COVID-19 requires the implementation of systems for prompt identification of cases. The effectiveness of such measures usually depends on two important factors; the level of transmission at the onset of the symptoms and the number of secondary infections generated by each new infection. Hence, governments in many countries took steps to reduce the transmission and consequently death from COVID-19 by heeding the WHO’s recommended measures through contact tracing, and enforced social/physical distancing, isolation, and quarantine rules. However, the difficulty of contact tracing resulted in widespread community transmission, and mass infection in many countries (Whaiduzzaman et al., 2020). All over the world, however, the adoption and use of e-government are on the increase to ensure efficient and effective service for key stakeholders at all levels (Heeks, 2001; Ntulo and Otike, 2013; Pagani and Pasinetti, 2008; United Nations Department of Economic and Social Affairs (UNDESA, 2018), 2020). Also, many e-government solutions have been implemented specifically in response to the global pandemic (Danquah et al., 2019; Goh and Arenas, 2020). Many of the IT-based solutions include cell phones or mobile applications, not only for collecting data, but also to inform the citizenry about safety measures and precautions against the pandemic (Steinhubl et al., 2015; Danquah et al., 2019). Goh and Arenas (2020), have noted that IT applications like mobile apps and social networks are required in the management of the main stages of the COVID-19 crisis before, during, and after phases. During the onset of the disease, the focus of the government agencies is on control of information, released in such a manner as to manage citizens’ behavior and ensure compliance (Coombs, 2014; Rowe et al., 2020; Bavel et al., 2020; Walton, 2013). The pre-crisis stage usually involves preventive and preparatory measures. Various IT-based systems are used to provide information to citizens. The post-crisis stage entails the implementation of measures to equip institutions and citizens to deal with the aftermath of a crisis and future crisis (Institute of Public Relations, 2007). The IT systems are used to build capacity, analyze the pandemic, and predict future occurrences. The specific application of e-government systems for the management of the various stages of pandemic or health crisis (See Figure 1)

Figure 1.

E-government Systems for Crisis Management.

Privacy issues are usually raised regarding the use of various IT applications in the fight against COVID-19. For instance, (Sharma and Bashir, 2020) analyzed the use of fifty (50) COVID-19-related apps and how they access users’ personally identifiable information, to assess the levels of compliance with the right to privacy and protection of civil liberties. Likewise, The Department of Health in Australia, with support from the Digital Transformation Agency, has implemented COVID-Safe that enables government agencies to conduct contact tracing to stop the spread of COVID-19 (Watts, 2020). Similarly, Siriwardhana et al., (2020) have proposed the use of 5G and Internet of Things (IoT) related technologies to provide government responses to the COVID-19 pandemic, including; telehealth, contact tracing, and citizen education.

Research framework and hypothesis development

Technology Acceptance Model

The Technology Acceptance Model (TAM) developed over 30 years ago by Davis (1989) is one of the most recognized models in information systems and its related application is used to explain ICT adoption intention. It is based on the assumption that the adoption of new technology can be understood in terms of the relationships with the individual user’s attitudes, internal beliefs, and intentions. The TAM is therefore grounded on the basic logical argument of Fishbein’s (1975) Theory of Reason Action (TRA) and has received a lot of recommendations and application in various research studies such as e-government (Mensah and Adams, 2020; Puthur et al., 2020; Sulistyowati et al., 2020), e-commerce (Alves and Reis, 2020; Wongkhamdi et al., 2020), and e-health (Dahleez et al., 2020; Papa et al., 2020). However, TAM has also received some criticisms. First, is its inadequate and robust ability to explain the adoption intention as it fails to explore the interaction between intention and actual behavior (Ayeh, 2015; Bagozzi, 2007). The TAM depends only on the concept of usefulness with limited attention to networks or social relations (Riffai et al., 2012). Also, it is criticized for being ‘information system’ focused (Erkan and Evans, 2016) as compared to TRA, which looks at behavioral theories (Fishbein, 1975). The criticisms led to various attempts to introduce new theories, or improve the constructs to better predict the adoption behavior of any information technology application or system. For instance, Venkatesh et al. (2003) analysed TAM and eight related theories to develop the Unified Theory of Acceptance and Use of Technology (UTAUT), which identified performance expectancy, effort expectancy, social influence, and facilitating conditions as the direct determinants of usage intention and behaviour (Venkatesh et. al., 2003). The impact of the UTAUT constructs on usage intention and behavior were further posited to be moderated by gender, age, experience, and voluntariness of use (Venkatesh et. al., 2003). Many of the TAM criticisms have increasingly become redundant given the extensive modification and extension of the basic constructs.

This study adopted the TAM based on our strong conviction that many of the criticism of TAM are redundant because studies that often applied TAM do so through modifications, integrations, and extensions of the basic assumptions of TAM along with its constructs. These integrations, extensions, and modifications thus provide a new perceptive in explaining the adoption of new technologies through the new constructs or variables that are integrated, extended, and modified in the TAM theory. This, therefore, makes TAM still a relevant theory and consequently accounts for its current application in this study.

Information Adoption Model (IAM)

The TAM (see Figure 2) has served as a foundation for many other frameworks including the Information Adoption Model (IAM), which seeks to better explain the adoption behavior of individuals in terms of their intention and behaviors through dedicated technology-driven communication (Sussman and Siegal, 2003). The IAM could also be described as an updated version of the Elaboration Likelihood Model (ELM) model and simplifies the identification of the determinants of the persuasion process in a specific circumstance. Primarily, the ELM helps in understanding how individuals are affected during the communication process in producing a particular attitudinal change, and the mechanism forming the effectiveness of credible communication (Petty and Cacioppo, 2012). The ELM was put forth by (Petty and Cacioppo, 2012) to explain differences in influence outcomes in many diverse contexts and people. Thus IAM argues that individuals usually identify received information as useful if the information has high argument quality and is provided by credible sources, in which case, useful information increases individuals’ intention to adopt the information (Shen, et al., 2014).

Figure 2.

IAM Model.

Cheung, Lee, and Rabjohn, (2008) applied the model to explain adoption of online reviews in online communities, whereas Jin et al., (2009) adopted the theory to provide elucidation on important factors affecting continuous information usage in an online community. To the extent that this study sought to explore informational e-government services, IAM seems very appropriate.

Like the IAM, the ELM, made up of the peripheral and central paths, has to do with examining critically, analyzing, and evaluating messages to ascertain their authenticity (Sussman and Siegal, 2003). However, not every message is elaborated since some receivers fail to elaborate some messages. The central route is when recipients carefully examine the issues that are presented in the message while the peripheral route is a situation in which recipients apply basic decision rules to appraise the message other than undertaking a critical analysis of the content of the message (Petty and Cacioppo, 2012; Sussman and Siegal, 2003). The source credibility has a more peripheral influence, while argument quality has a core effect. The indicators of the IAM are shown in Figure 2 and have been applied in fields such as consumer behavior (Hussain et al., 2019; Li et al., 2020).

Information Quality (IA)

Information quality is derived from the quality of service, which is considered as a total assessment of the quality of services and information provided online and is identified by scholars as the major determinants of e-government services or their failure (Li and Shang, 2020; Santos, 2003; Srivastava, 2011). Information quality refers to the authenticity of the information generated from an information system (DeLone and McLean, 1992; Gorla et al., 2010). Information quality has four important dimensions: accuracy, completeness, consistency, and currency. Hence the dissemination of COVID-19 information that meets the information quality expectations of end-users in terms of its accuracy, completeness, consistency, and currency will be important in ensuring the usefulness of such information to the users. Prior studies have demonstrated that information quality has a direct significant impact on the usefulness of information (Erkan and Evans, 2016). Based on this assumption and evidence in the literature, we suggest that COVID-19 information quality provided through e-government will influence the usefulness of COVID-19 information (informational e-government). Accordingly, we hypothesize that:

H1: Information quality has a positive effect on the usefulness of informational e-government services (COVID-19 information).

Information Credibility (IC)

Source credibility refers to the individual’s perception of the credibility of the information and thus it provides information about the competence or expertise and trustworthiness of the information. Accordingly, the credibility of the information provided is vital in determining whether such information is assimilated or not (Huerta and Ryan, 2003; Rieh, 2010; (Hovland et al., 1953). Trustworthiness is a core factor in credibility assessment that seeks to question the perceived goodness and morality of the source (Fogg, 2002). On the other hand, expertise has to do with knowledge and competence of the source and it is vital to influence users’ perceptions of particular source credibility to produce timely and authentic information (Fogg, 2002; Rieh, 2010). Many other studies have produced results in support of these assertions (Erkan and Evans, 2016; Salehi-Esfahani et al., 2016). It thus follows that when users are convinced of the credibility of COVID-19 information disseminated on e-government platforms, it will determine their understanding of the usefulness of COVID-19 information. From the discussion above, it is expected that:

H2: Information credibility and usefulness of informational e-government services (COVID 19 information) are positively correlated.

Information Appropriateness (IA)

Information appropriateness usually has a positive impact on e-government adoption. One major challenge that has been identified during the COVID-19 pandemic is the non-timely dissemination to the public of relevant information about the virus. Hence this delay in the releasing of information, for instance about the number of people infected, cured, hospitalized, and where people can go to get needed medical attention is accounted as the major reason for escalating the number of COVID-19 infections world wide. E-government becomes the medium for governments and public sector works to disseminate information about the virus and thus become the vehicle through which the people could assess relevant and timely information on COVID-19. Hence the timely provision of COVID-19 informational e-government services is related to the usefulness of COVID-19 information offered on the e-government platform. Based on this argument, H3 was suggested.

H3: Information appropriateness is positively related to the adoption of informational e-government services (COVID 19 information).

Information Ease of Use (IEU)

Ease of use is the extent to which users feel that the use of any new information system will be effort-free (Davis, 1989; Davis et al., 1989). Information ease of use is the provision of information in such a manner that it can be easily assimilated by its users. The provision of informational e-government services for the dissemination of COVID 19 information should be delivered effectively to ensure that people can make good use of such information without having to go through any technical challenges. The design of the website interface, easy download and upload of information in terms of request for information and feedback, easy navigation of pages and content are important aspects of ease of use that influence the adoption of information, particularly the adoption of COVID 19 information. Prior studies have indicated that ease of use has a significant impact on the intention to adopt and on perceived usefulness (Mensah, 2016; Mensah et al., 2018). We thus propose that ease of use of COVID 19 information will positively influence its usefulness to the people concerned. Accordingly, H4 was put forward.

H4: Information ease of use is positively related to the usefulness of informational e-government services (COVID 19 information).

Information Usefulness (IU)

Usefulness is defined as the perception of the individual users towards a particular technology application that the use of such an application will empower them to accomplish their task efficiently (Davis, 1989; Davis et al., 1989). The usefulness of information is a vital contributor to information adoption and intention to use since users will be eager to adopt information that is meaningful and useful to them (Dahi and Ezziane, 2015; Erkan and Evans, 2016; Mensah, 2016; Soneka and Phiri, 2019). The use of informational e-government to produce useful COVID-19 information is crucial to the citizen’s adoption of COVID-19 information and its subsequent recommendation. Citizens who feel that COVID-19 information on informational e-government is useful, will in turn share that information to friends and colleagues to encourage them also to adopt. Consequently, H5 and H6 were proposed.

H5: Information usefulness is positively related to the intention to adopt informational e-government services (COVID-19 information).

H6: Information usefulness is positively related to the intention to recommend the adoption of informational e-government services (COVID-19 information).

Research model

The research model based on the arguments presented in the previous section is shown in Figure 3.

Figure 3.

Research Model.

Research methodology

The study adopted the survey approach as a means to gather the relevant data. The survey questionnaire is in two parts. The first section contained demographical information of the respondents, including age, gender, and education, while information about the constructs examined in this study is addressed in the second section. The constructs experimented with in this study were carefully selected based on a thorough review of the literature. The key constructs include information quality (Park et al., 2007; Sussman and Siegal, 2003), information credibility (Prendergast et al., 2010; Sussman and Siegal, 2003), information appropriateness (Lee et al., 2002), and information ease of use and usefulness (Bailey and Pearson, 1983; Davis, 1989; Sussman and Siegal, 2003), intention to recommend adoption and intention to adopt (Cheung et al., 2009; Oliveira et al., 2016; Sussman and Siegal, 2003).

Each construct was measured on a five (5) point scale from 1 = strongly disagree (SD) to 5= strongly agree (SA). Before data collection, the questionnaire was pre-tested and piloted and the results from that exercise were excluded from the data analysis. Feedback was used to modify some of the items in the questionnaire to avoid ambiguity. The constructs used in this study are presented in Appendix A.

The questionnaire survey was hosted online for effective administration and ease of completion by the respondents. It was administered to the university community (Jiangxi University of Science and Technology) via personal and group chats on WeChat platforms. The university community was selected for data collection because at the height of the COVID-19 pandemic educational institutions were among the most affected and as such it was eager to assimilate information relating to COVID-19. Similarly, the WeChat medium was adopted because it is the most used mobile social network in China, and also enables faster information dissemination and data gathering. The questionnaire was administered for about two months from May to July 2020, and a total of seven hundred and (716) responses were received. The 716 responses gathered were captured and analyzed with SPSS and Smart PLS through the use of Structural Equation Modeling (SEM) Techniques. Thorough checks were conducted on the validity and reliability measures of the data generated. The Smart PLS software is considered a non-parametric data analysis tool enabling researchers to examine complex models with many constructs that measure correlations between the variables and observed items (measurement model) and the linear regressions between the constructs (structural model) (Ramayah et al., 2017; Ringle et al., 2015). Smart PLS has been recommended for use when testing a research theoretical framework from a predictive view, and when the research goal is to explore the complexity of a theoretical model extension of recognized/validated concepts for theory development and exploratory research (Hair et al., 2019; Sander and Teh, 2014). Also, Smart PLS provides a path model that designates the relationship between constructs and indicators and has greater statistical supremacy for identifying statistically significant relationships in models (Sander and Teh, 2014).

Data analysis

Respondents profile

The respondents’ profile is indicated in Table 1. Many of the respondents were males, representing 56.3%, and the majority of the respondents were between the ages of 26-30 years old (29.5%), with many of them being undergraduate students (52.9%). similarly, about 40.90% of the respondents were students and teachers were 44.7%.

Table 1.

Respondents Profile.

Item	Description	Frequency	percentage
Gender	Male	403	56.3
Gender	Female	313	43.7
Age	18-25	112	15.6
	26-30	211	29.5
	31-40	146	20.4
	41-50	87	12.2
	51-60	101	14.1
	60+	59	8.2
Education	Bachelors	256	35.8
	Postgraduate	379	52.9
	others	81	11.3
Profession	students	293	40.9
	Teachers	320	44.7
	Teachers (non-teaching staff)	103	14.4

Measurement model

The results of the measurement model, which was used to assess the reliability and validity of the questionnaire instruments, are shown in Table 2. The indicators average variance extracted (AVE), composite reliability, Cronbach’s alpha, and factor loadings were used to assess the validity and reliability of the measurement model of our constructs. The convergent validity test was performed using the analysis of average variance extracted and factor loadings. It is recommended that to confirm the existence of convergent validity, the AVE values of each item should be greater than 0.50 (Fornell and Larcker, 1981; Sarstedt et al., 2014) and the factor loadings should be greater than 0.60 (Hair et al., 2012; Henseler et al., 2009).

Table 2.

Measurement Model Analysis.

Construct	Item	AVE	Composite reliability	Cronbach’s alpha	Loading
Information Quality (IQ)	IQ1	0.931	0.976	0.963	0.945
	IQ2				0.986
	IQ2				0.962
	IQ3				0.962
Information Credibility (IC)	IC1	0.936	0.978	0.966	0.965
	IC2				0.972
	IC2				0.966
	IC3				0.966
Information Appropriateness (IA)	IA1	0.883	0.958	0.933	0.977
	IA1				0.888
	IA2				0.888
	IA3				0.954
Information Ease of Use (IEU)	IEU1	0.936	0.978	0.966	0.963
	IEU1				0.959
	IEU2				0.959
	IEU2				0.980
	IEU3				0.980
Perceive Usefulness (PU)	IU1	0.950	0.983	0.974	0.975
	IU1				0.962
	IU2				0.962
	IU3				0.986
Intention to Recommend (ITR)	ITR1	0.968	0.989	0.983	0.985
	ITR1				0.991
	ITR2				0.991
	ITR3				0.976
Intention to Adopt (ITA)	IU1	0.917	0.971	0.954	0.990
	IU2				0.927
	IU3				0.954

As indicated in Table 2, the values of both the AVE and factor loadings met the criteria for convergent validity, and thus confirm the convergent validity of the model. Cronbach’s alpha and composite reliability (CR) were used to assess the reliability of the survey. The recommended and acceptance threshold value for Cronbach’s alpha and composite reliability is 0.70 (Henseler et al., 2009). Again as indicated in Table 2, the values recorded for both alpha and composite reliability met the accepted standards and therefore provide a good indication of the reliability of the instrument used for the study.

To further ensure the reliability and validity of the model used, a discriminate validity analysis was conducted. The results are shown in Table 3. It was done by using Fornell-Larcker and cross-loading principles. The Fornell-Larcker principles state that if the square roots of AVE are greater than the correlations between the variables then discriminant validity is confirmed. As illustrated in Table 3, the condition for discriminate validity to exist has been met which established the discriminate validity of the scales used in our study.

Table 3.

Discriminant Validity.

Items	IQ	IC	IA	IEU	PU	ITR	ITA
IQ	0.965
IC	0.784	0.967
IA	0.683	0.692	0.940
IEU	0.786	0.678	0.583	0.967
PU	0.773	0.772	0.739	0.863	0.975
ITR	0.533	0.631	0.606	0.673	0.796	0.984
ITA	0.684	0.581	0.568	0.632	0.773	0.750	0.958

Notes: Correlation of constructs and the square root of AVE (BOLD). Information Quality (IQ), Information Credibility (IC), Information Appropriateness (IA), Information Ease of Use (IEU), Perceive Usefulness (PU), Intention to Recommend (ITR), Intention to Adopt (ITA).

Structural model

Table 4 depicts the output of our structural model. The model is the second level analysis performed in SEM after the reliability and validity of the measurement indicators have all been confirmed and established. It is undertaken to test the hypotheses proposed in the study. As indicated in Table 4, all the proposed research hypotheses are statistically supported. Factors such as information quality (β = 0.559, p < 0.05) and information credibility (β = 0.251, p < 0.05) were found to be significant in determining the perceived usefulness of COVID 19 information. Accordingly, H1 and H2 were supported. In addition, information appropriateness (β = 0.347, p < 0.05) and information ease of use (β = 0.142, p < 0.05) were also positive and significant predictors of the perceived usefulness of information. Hence H3 and H4 were also supported. Finally, perceived usefulness of COVID 19 information shared through informational e-government was significant in determining both the intention to adopt (β= 0.673, p < 0.05) and intention to recommend the adoption of COVI9 information (β = 0.780, p < 0.05). Consequently, H5 and H6 were supported. The validated structural model with the estimation of partial least values is depicted in Figure 4.

Table 4.

Results of Hypotheses Tested.

Hypotheses		Path	T-value	Supported	R²
H1	IQ→PU	0.559	13.225	Yes	0.678
H2	IC→PU	0.251	7.883	Yes
H3	IA→PU	0.347	43.408	Yes
H4	IEU→PU	0.142	3.737	Yes
H5	PU→ITA	0.673	51.463	Yes	0.748
H6	PU→ITR	0.780	54.501	Yes	0.760

Note: Estimation of partial least squares (***p < 0.01,**p < 0.05,*p < 0.1). Information Quality (IQ), Information Credibility (IC), Information Appropriateness (IA), Information Ease of Use (IEU), Perceive Usefulness (PU), Intention to Recommend (ITR), Intention to Adopt (ITA).

Figure 4.

Validated Research Model.

Discussion

The COVID-19 pandemic was uncharted waters for many governments and people around the world. They were faced with many challenges in handling the issues surrounding the emergence of the COVID-19 pandemic, particularly when it came to the dissemination of information about COVID-19. E-government, therefore, provided a congenial environment for government and public sector agencies to share relevant COVID-19 information with the people. Particularly the use of informational e-government service was key and instrumental in the fight against the deadly pandemic. This study, therefore, attempted to examine the factors affecting the adoption of COVID-19 information shared through informational e-government systems. The results have demonstrated that information quality, information credibility, information appropriateness, and information ease of use were all significant factors influencing the perceived usefulness of COVID-19 information delivered through informational e-government services. Also, the results have established that the perceived usefulness of COVID-19 information through informational e-government service was significant in predicting, first the intention to adopt and secondly the intention to recommend the adoption of informational COVID-19 e-government services.

The significant impact of information quality and information credibility on the usefulness of informational COVID-19 e-government services are consistent with prior studies that also have demonstrated that these factors are positively related to the usefulness of information (Erkan and Evans, 2016; Hussain et al., 2019; Ngarmwongnoi et al., 2020; Pitasari and HH, 2020; Sun et al., 2019). This the quality and credibility of the COVID-19 information shared with people are critical in determining whether citizens will consider such information to be useful or not. Providing quality information on COVID-19 that is up to date, well organized, quick to retrieve, accurate, and relevant will help citizens to use such information to protect themselves from the deadly pandemic. Also during the COVID-19 period, there was mis information and fake news that was spread by ill-motived people and this created a credibility crisis for COVID-19 information provision. This may affect the usefulness or seriousness that people will attach to such information. This, therefore, makes it imperative for the government to provide credible COVID-19 pandemic information to the people.

Information appropriateness and information ease of use of informational COVID 19 e-government services support the findings of other researchers that both are significant determinants of the usefulness of information (Erkan and Evans, 2016). The timely provision of relevant COVID-19 information in addition to making sure that information is delivered in a way that users can assimilate and make use of it with less effort will impact positively on their perception of the usefulness of COVID-19 information shared through informational e-government. That is, providing information relating to, for instance, protecting oneself through practicing social distance, washing of hands, and noticing early signs of COVID-19 infection.

Lastly, our results corroborate the findings of other scholars that perceived usefulness predicts significantly both intention to adopt and to recommend (Erkan and Evans, 2016; Hussain et al., 2019). This implies that the decision of users to adopt or recommend informational e-government services related to COVID-19 will depend on the usefulness of the information provided. The adoption and application of COVID-19 information by society are instrumental in the fight against the virus, and the provision of useful COVID information to the people is a shared responsibility. The element of recommendation to others to adopt COVID-19 information is one of the very important findings of this study. This act of recommendation is vital, not only educating in people at a faster rate on the COVID-19 pandemic, but crucially reaching more people at the same time and thus contributing to the reduction of infections and deaths.

Theoretical implications

This is one of the few studies that have applied the Information Adoption Model (IAM) based on the Technology Acceptance Model (TAM) to understand the adoption behavior of informational e-government services in the context of COVID-19 information. In addition to confirming the core constructs in the IAM in predicting the usefulness and adoption of informational COVID-19 e-government services, this study has introduced new variables such as information appropriateness, information ease of use, and intention to recommend. These three variables are not included in the original model of IAM proposed by Sussman and Siegal, (2003) and thus appear to be a unique contribution to literature. Our study revealed that the newly introduced variables, along with the core constructs of IAM, jointly accounted for 67.8% of the variance towards the usefulness of COVID-19 informational e-government services. Also, it was demonstrated that the usefulness of COVID-19 information accounted respectively for 74.8% and 76.0% of the reasons for people to adopt and recommend informational COVID-19 e-government services.

Practical implications

The significant impact of both information quality and information credibility on perceived usefulness implies that e-government systems should be used to provide information about COVID 19 that is of high quality and credible. It demonstrated the seriousness and importance that the government and policymakers should attach to the quality and credulity of informational e-government services, particularly COVID-19 information. The developers and stakeholders must ensure that information provided through informational e-government services are accurate, complete, consistent, correct, and reliable. These attributes will drive users towards positive perception of the usefulness of COVID-19 informational e-government services. Any deficiency in the quality and credibility of the information on the e-government platform will cause users to cast doubts on the usefulness of such information and thus may affect negatively the measures instituted by the government to reduce the rate of infections and deaths from COVD-19.

Another important implication of our study is the direct significant impact of information appropriateness and information ease of use on the usefulness of COVID-19 informational e-government services. In a time of crisis, such as COVID-19, the timeliness and quicker provision of relevant data and information is crucial in addressing such a crisis. Stakeholders and practitioners must use informational e-government systems to provide appropriate COVID-19 information that meets the expectations of users/citizens. The provision of relevant COVID-19 information should be organized so that users can easily assimilate and make use of it. The informational e-government services should be designed to enable easier access in terms of download and smooth navigation between web pages.

Last but not the least, our study has empirically demonstrated that the perceived usefulness of information has a direct impact on the intention to use and recommend it. Stakeholders or policy implementers must strive to ensure that the factors of perceived usefulness such as information quality, information credibility, information appropriateness, and information ease of use are implemented and operationalized. These antecedents of perceived usefulness will encourage users to have positive attitudes to the usefulness of, COVID-19 informational e-government services. This will lead them not only to adopt information but also to recommend the adoption of such information to people in their social environment. Policymakers can depend on recommendations by users to promote the faster diffusion and assimilation of informational e-government services concerning COVID-19.

Conclusion

This study examined the behavioral characteristics that influence the adoption of e-government services by Chinese citizens in COVID-19 pandemic. We focused on the informational dimension of e-government and applied the Information Adoption Model (IAM) to explore the use of informational COVID-19 e-government services. The results have confirmed that the elements of perceived usefulness such as information quality, credibility, appropriateness, and ease of use were significant in predicting the perceived usefulness of COVID-19 informational e-government services. Also, the perceived usefulness of COVID-19 information was found to predict significantly both the intention to adopt and to recommend informational COVID-19 information. These results have important outcomes that empower government and policymakers to use e-government as a good policy option, not only to offer COVID-19 e-government during this challenging period in world history but for any crisis that may arise in the future.

Limitation and future research

This study has some limitations. First, the study only examines the behavioral adoption of COVID-19 information without looking at actual adoption situations. Secondly, the study was confined to a particular geographical setting, and thus if the approach and the methods used, are applied in other studies the results may be inconsistent with our study. Another limitation is that not all the elements determining the adoption of informational e-government services have been experimented with in this study. Future study is anticipated which examines the cost of the mobile data/bundle, privacy, and institutional trust on the adoption of COVID-19 informational e-government services.

Footnotes

ORCID iD

Isaac Kofi Mensah

Appendix A: Questionnaire Items

Information Quality (IQ)

IQ1: I think the information on e-government services (COVID-19 information) is understandable

IQ2: I believe that information on e-government services (COVID-19 information) is clear

IQ3: In totality, I think the information on e-government services (COVID-19 information) is of high quality.

Information Credibility (IC)

IC1: I think the information on e-government services (COVID-19 information) is convincing

IC2: I think the information on e-government services (COVID-19 information) is strong

IC3: I think the information on e-government services (COVID-19 information) credible and accurate.

Information Appropriateness (IA)

IA1: I think the information on e-government services (COVID-19 information) is timely

IA2: I think the information on e-government services (COVID-19 information) is rich and suitable

IA3: I think the information on e-government services (COVID-19 information) meets my needs on COVID 19 information.

Information Ease of Use

IEU1: I think the information on e-government services (COVID-19 information) is easy to digest.

IEU2: I have the needed skills to use the information on e-government services (COVID- 19 information).

IEU3: Access to information on e-government services (COVID-19 information) is flexible and less difficult.

Information Usefulness (IU)

IU1: I think the information on e-government services (COVID-19 information) useful to me

IU2: I think the information on e-government services (COVID-19 information) very informative

IU3: I think the information on e-government services (COVID-19 information) efficient and effective.

Intention to Recommend (ITR)

ITR1: I will recommend the adoption information on e-government services (COVID-19 information) to others.

ITR2: I plan to recommend the adoption information on e-government services (COVID-19 information) to my immediate family and friends

ITR3: I will frequently recommend the adoption information on e-government services (COVID-19 information).

Intention to Adopt (ITA)

ITA1: I will adopt information on e-government services (COVID-19 information).

ITA2: I will always adopt information on e-government services (COVID-19 information).

ITA3: I intend to adopt information on e-government services (COVID-19 information) in the future.

References

Alawneh

Al-Refai

Batiha

(2013) Measuring user satisfaction from e-Government services: Lessons from Jordan. Government Information Quarterly, 30(3), 277–288.

Alshomrani

(2012) A comparative study on United Nations e-government indicators between Saudi Arabia and USA. Journal of Emerging Trends in Computing and Information Sciences, 3(3), 411–420.

Alves

Reis

(2020) The Intention to Use E-Commerce Using Augmented Reality-The Case of IKEA Place. Paper presented at the International Conference on Information Technology and Systems.

Ayeh

(2015) Travellers’ acceptance of consumer-generated media: An integrated model of technology acceptance and source credibility theories. Computers in Human Behavior, 48, 173–180.

Bagozzi

(2007) The legacy of the technology acceptance model and a proposal for a paradigm shift. Journal of the Association for Information Systems, 8(4), 3.

Bailey

Pearson

(1983) Development of a tool for measuring and analyzing computer user satisfaction. Management Science, 29(5), 530–545.

Cheung

Lee

Rabjohn

(2008) The impact of electronic word-of-mouth: The adoption of online opinions in online customer communities. Internet Research.

Cheung

Luo

Sia

Chen

(2009) Credibility of electronic word-of-mouth: Informational and normative determinants of on-line consumer recommendations. International Journal of Electronic Commerce, 13(4), 9–38.

Civelek

(2018) Humans of Machine Age Management Strategies for Redundancy. Journal of Industrial Policy and Technology Management, 1(2).

10.

Civelek

(2019) Development phases of e-government.

11.

Coombs

(2014) Ongoing crisis communication: Planning, managing, and responding: Sage Publications.

12.

Dahi

Ezziane

(2015) Measuring e-government adoption in Abu Dhabi with technology acceptance model (TAM). International Journal of Electronic Governance, 7(3), 206–231.

13.

Dahleez

Bader

Aboramadan

(2020) E-health system characteristics, medical performance and healthcare quality at UNRWA-Palestine health centers. Journal of Enterprise Information Management.

14.

Danquah

Hasham

MacFarlane

Conteh

Momoh

Tedesco

AA.

… Weiss

(2019) Use of a mobile application for Ebola contact tracing and monitoring in northern Sierra Leone: a proof-of-concept study. BMC Infectious Diseases, 19(1), 810.

15.

Davis

(1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340.

16.

Davis

Bagozzi

Warshaw

(1989) User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35(8), 982–1003.

17.

DeLone

McLean

(1992) Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60–95.

18.

Dhar

Wani

Shiekh

(2020) Will trust survive the COVID pandemic? SAGE Publications Sage UK: London, England.

19.

Di Maio

(2003) Traditional ROI measures will fail in government. Sourced from.

20.

Erkan

Evans

(2016) The influence of eWOM in social media on consumers’ purchase intentions: An extended approach to information adoption. Computers in Human Behavior, 61, 47–55.

21.

Fishbein

(1975) i Ajzen, I (1975) Belief, Attitude, Intention, and Behaviour: An Introduction to Theory and Research: Addison-Wesley.

22.

Fogg

(2002) Persuasive technology: using computers to change what we think and do. Ubiquity, 2002 (December), 2.

23.

Fornell

Larcker

(1981) Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 39–50.

24.

Fraga

(2002) Trends in e-government how to plan, design, secure, and measure e-government. Paper presented at the Government Management Information Sciences (GMIS) Conference.

25.

Goh

Arenas

(2020) IT value creation in public sector: how IT-enabled capabilities mitigate tradeoffs in public organisations. European Journal of Information Systems, 29(1), 25–43.

26.

Gorla

Somers

Wong

(2010) Organizational impact of system quality, information quality, and service quality. The Journal of Strategic Information Systems, 19(3), 207–228.

27.

Hair

Risher

Sarstedt

Ringle

(2019) When to use and how to report the results of PLS-SEM. European Business Review.

28.

Hair

Sarstedt

Ringle

Mena

(2012) An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414–433.

29.

Heeks

(2001) Understanding e-governance for development: Institute for Development Policy and Management Manchester.

30.

Hellewell

Abbott

Gimma

Bosse

Jarvis

Russell

TW.

… Sun

(2020) Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. The Lancet Global Health.

31.

Henseler

Ringle

Sinkovics

(2009) The use of partial least squares path modeling in international marketing New challenges to international marketing (pp. 277–319): Emerald Group Publishing Limited.

32.

Hovland

Janis

Kelley

(1953) Communication and Persuasion: Psychological Studiesof Opinion Change, New Haven, CT: Yale UniversityPress. Hovland Communication and Persuasion: Psychological Studies of Opinion Change 1953.

33.

Huerta

Ryan

(2003) The credibility of online information. AMCIS 2003 Proceedings, 279.

34.

Hussain

Song

Niu

(2019) Consumers’ Motivational Involvement in eWOM for Information Adoption: The Mediating Role of Organizational Motives. Frontiers in Psychology, 10.

35.

Institute of Public Relations (2007) Crisis Management and Communications. Retrieved 2020/12/20, 2020, from https://instituteforpr.org/crisis-management-and-communications/

36.

Jin

X-L

Cheung

CMK

Lee

MKO

Chen

How to Keep Members Using the Information in a Computer-Supported Social Network. Computers in Human Behavior 25, 5 (2009), 1172–1181.

37.

Lee

Strong

Kahn

Wang

(2002) AIMQ: a methodology for information quality assessment. Information and Management, 40(2), 133–146.

38.

Ong

Ito

(2020) Credibility in Question: travel information adoption among Chinese consumers in Canada and Singapore Information and Communication Technologies in Tourism 2020 (pp. 79–91): Springer.

39.

Shang

(2020) Service quality, perceived value, and citizens’ continuous-use intention regarding e-government: Empirical evidence from China. Information and Management, 57(3), 103197.

40.

Mensah

(2016) Perceived Ease-of-Use and Intention to Use E-Government Services in Ghana: The Moderating Role of Perceived Usefulness. International Journal of Humanities and Social Sciences, 10(12), 3867–3872.

41.

Mensah

Adams

(2020) A Comparative Analysis of the Impact of Political Trust on the Adoption of E-Government Services. International Journal of Public Administration, 43(8), 682–696.

42.

Mensah

Vera

(2018) Factors determining the use of e-government services: An empirical study on Russian students in China. International Journal of E-Adoption (IJEA), 10(2), 1–19.

43.

Ngarmwongnoi

Oliveira

AbedRabbo

Mousavi

(2020) The implications of eWOM adoption on the customer journey. Journal of Consumer Marketing.

44.

Ntulo

Otike

(2013) E–Government: Its Role, Importance and Challenges. School of Information Sciences. MoiUniversity.

45.

Oliveira

Thomas

Baptista

Campos

(2016) Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61, 404–414.

46.

Pagani

Pasinetti

(2008) Technical and functional quality in the development of t-government services Electronic Government: concepts, methodologies, tools, and applications (pp. 2943–2965): IGI Global.

47.

Palvia

SCJ

Sharma

(2007) E-government and e-governance: definitions/domain framework and status around the world. Paper presented at the International Conference on E-governance.

48.

Papa

Mital

Pisano

Del Giudice

(2020) E-health and wellbeing monitoring using smart healthcare devices: An empirical investigation. Technological Forecasting and Social Change, 153, 119226.

49.

Park

D-H

Lee

Han

(2007) The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement. International Journal of Electronic Commerce, 11(4), 125–148.

50.

Park

Choi

(2020) Information technology–based tracing strategy in response to COVID-19 in South Korea—privacy controversies. Jama.

51.

Paul

Wadhwa

(2020) 2020 Year in Review: The impact of COVID-19 in 12 charts. Retrieved 2020/12/20, 2020, from https://blogs.worldbank.org/voices/2020-year-review-impact-covid-19-12-charts

52.

Petty

Cacioppo

(2012) Communication and persuasion: Central and peripheral routes to attitude change: Springer Science and Business Media.

53.

Pitasari

(2020) Factors influencing customers’ continued mobile app use intention from the internet service provider perspective: Information adoption model. Paper presented at the Contemporary Research on Business and Management: Proceedings of the International Seminar of Contemporary Research on Business and Management (ISCRBM 2019), 27-29 November, 2019, Jakarta, Indonesia.

54.

Prendergast

Siu Yin

(2010) Online word of mouth and consumer purchase intentions. International Journal of Advertising, 29(5), 687–708.

55.

Puthur

George

Mahadevan

(2020) Understanding citizen’s continuance intention to use e-government services: the case of the Indian railway e-ticket booking site. International Journal of Business Information Systems, 34(2), 183–203.

56.

Ramayah

Yeap

Ahmad

Halim

Rahman

(2017) Testing a confirmatory model of Facebook usage in SmartPLS using consistent PLS. International Journal of Business and Innovation, 3(2), 1–14.

57.

Rieh

(2010) Credibility and cognitive authority of information.

58.

Riffai

Grant

Edgar

(2012) Big TAM in Oman: Exploring the promise of on-line banking, its adoption by customers and the challenges of banking in Oman. International Journal of Information Management, 32(3), 239–250.

59.

Ringle

Da Silva

Bido

(2015) Structural equation modeling with the SmartPLS. Brazilian Journal Of Marketing, 13(2).

60.

Rowe

Ngwenyama

Richet

J-L

(2020) Contact-tracing apps and alienation in the age of COVID-19. European Journal of Information Systems, 1–18.

61.

Salehi-Esfahani

Ravichandran

Israeli

Bolden III

(2016) Investigating information adoption tendencies based on restaurants’ user-generated content utilizing a modified information adoption model. Journal of Hospitality Marketing and Management, 25(8), 925–953.

62.

Sander

Teh

(2014) SmartPLS for the human resources field to evaluate a model.

63.

Santos

(2003) E-service quality: a model of virtual service quality dimensions. Managing Service Quality: An International Journal.

64.

Sarstedt

Ringle

Smith

Reams

Hair

Jr (2014) Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers. Journal of Family Business Strategy, 5(1), 105–115.

65.

Sharma

Bashir

(2020) Use of apps in the COVID-19 response and the loss of privacy protection. Nature Medicine, 1–2.

66.

Shen

Zhang

Zhao

(2014, January) Understanding information adoption in online review communities: the role of herd factors. In 2014 47th Hawaii International Conference on System Sciences (pp. 604–613). IEEE.

67.

Siriwardhana

De Alwis

Gür

Ylianttila

Liyanage

(2020) The fight against the COVID-19 pandemic with 5G technologies. IEEE Engineering Management Review, 48(3), 72–84.

68.

Soneka

Phiri

(2019) A Model for Improving E-Tax Systems Adoption in Rural Zambia Based on the TAM Model. Open Journal of Business and Management, 7(2), 908–918.

69.

Srivastava

(2011) Is e-government providing the promised returns? Transforming Government: People, Process and Policy.

70.

Steinhubl

Marriott

Wegerich

(2015) Remote sensing of vital signs: a wearable, wireless “band-aid” sensor with personalized analytics for improved Ebola patient care and worker safety. Global Health: Science and Practice, 3(3), 516–519.

71.

Sulistyowati

Alrajawy

Yulianto

Isaac

Ameen

(2020) Factors Contributing to E-Government Adoption in Indonesia—An Extended of Technology Acceptance Model with Trust: A Conceptual Framework Intelligent Computing and Innovation on Data Science (pp. 651–658): Springer.

72.

Sun

Wang

Shen

Zhang

(2019) Bias effects, synergistic effects, and information contingency effects: Developing and testing an extended information adoption model in social QandA. Journal of the Association for Information Science and Technology, 70(12), 1368–1382.

73.

Sussman

Siegal

(2003) Informational influence in organizations: An integrated approach to knowledge adoption. Information Systems Research, 14(1), 47–65.

74.

United Nations (UN) (2020) United Nations Comprehensive Response to COVID-19: Saving Lives, Protecting Societies, Recovering Better. United Nations. Retrieved 2020/12/20, 2020, from https://www.un.org/other/afics/sites/www.un.org.other.afics/files/sg-pdf-20200916-un_comprehensive_response_to_covid-16_sep_2020_002_0.pdf

75.

United Nations Department of Economic and Social Affairs (UNDESA) (2018) UN E-Government Survey 2018, Gearing E-Government to Support Transformation Towards Sustainable and Resilient Societies. Retrieved 2020/12/23, 2020

76.

UNDESA (2020) UN E-Government Survey 2020, Digital Government in the Decade of Action for Sustainable Development With addendum on COVID-19 Response. New York. Retrieved 2020/12/23, 2020

77.

Van Bavel

Baicker

Boggio

Capraro

Cichocka

Cikara

… Druckman

(2020) Using social and behavioural science to support COVID-19 pandemic response. Nature Human Behaviour, 1–12.

78.

Venkatesh

Thong

(2012) Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, 157–178.

79.

Venkatesh

Morris

Davis

(2003) User acceptance of information technology: Toward a unified view. MIS Quarterly, 425–478.

80.

Walton

(2013) Scare tactics: Arguments that appeal to fear and threats (Vol. 3): Springer Science and Business Media.

81.

Wang

Shao

Tsai

Y-S

Jiang

Zhu

Wenmin

… Liang

(2018) Online Purchase Intention Based on TAM and IAM: A Literature Review. International Journal of e-Education, e-Business, e-Management and e-Learning, 8(2), 66–73.

82.

Watts

(2020) COVIDSafe, Australia’s Digital Contact Tracing App: The Legal Issues. Australia’s Digital Contact Tracing App: The Legal Issues (May 2, 2020) .

83.

Whaiduzzaman

Hossain

Shovon

Roy

Laszka

Buyya

Barros

(2020) A privacy-preserving mobile and fog computing framework to trace and prevent covid-19 community transmission. IEEE Journal of Biomedical and Health Informatics.

84.

WHO (2020a) Information dissemination during a global pandemic: Experiences from WHO. Retrieved October 16, 2020, 2020, from https://extranet.who.int/kobe_centre/en/news/UNU

85.

WHO (2020b) WHO Coronavirus Disease (COVID-19) Dashboard. Retrieved 2020/12/20, 2020, from https://covid19.who.int

86.

Wongkhamdi

Cooharojananone

Khlaisang

(2020) E-Commerce Competence Assessment Mobile Application Development for SMEs in Thailand. International Journal of Interactive Mobile Technologies, 14(11).

87.

Zhang

Hou

(2011) Government Process Management under electronic government and its application. Paper presented at the 2011 International Conference on E-Business and E-Government (ICEE).