Timely or Detailed? The Impact of Information Disclosure

on Citizen Co-production

Declarations

of Conflicting Interests

The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

This study was supported by the National Key R&D Program of China (Grant No. 2023YFC2308700), National Natural Science Foundation of China (Grant No. 72304154), and the Humanities and Social Science Fund of Ministry of Education of China (Grant No. 23YJCZH129). We would like to thank the anonymous referees and the editors.

Data Availability

The replication materials of this study (including the data sets) are available at BaiDu Yun Dataverse (https://pan.baidu.com/s/17ZNp8guWtnNl1sbyhlQt5w; password: wa0b).

Conflict of Interest

The authors have no conflicts of interest to declare.

Ethical Approval

This study has obtained ethical approval from the Research Ethics Committee of Tianjin University, with the approval number TJUE2025-H-S-049.

The approval date is January 2022 (initially approved by the College of Management and Economics in 2022, and the approval number was updated and re-granted by Tianjin University, the superior unit, in 2025). This approval covers all procedures involving the recruitment of participants, data collection, and data processing.

The research team strictly adheres to the ethical standards of the Institutional Review Board, the Declaration of Helsinki and its subsequent revisions, or equivalent ethical guidelines.

Consent Statement

Prior to collecting data from participants between June and November 2022, informed consent was first obtained from all participants. The method for obtaining consent was as follows: A written explanation was attached to the front page of both online questionnaires and offline paper questionnaires, which detailed the purpose of the study and promised to ensure the confidentiality and security of personal data. If a participant proceeded to complete the questionnaire, this act was deemed as their consent to participate; if a participant abandoned filling out the questionnaire, this was regarded as their refusal to participate. Meanwhile, participants were informed that they had the right to withdraw from the study at any time without incurring any consequences.

Abstract

During public health crisis, effective information disclosure is crucial for the government to guide citizens' participation in social recovery. In this paper, we focus on the influence of the timeliness and detail of information disclosure on citizens’ co-production behavior. We divide citizens’ co-production behavior into online information concern behavior and offline pandemic compliance with preventive behavior. Through behavioral experiments and regression modeling, this study quantitatively analyzed more than 5,800 pieces of data from 293 prefecture-level cities and 158 citizens’ experimental data. The results indicate that more timely and detailed information disclosure has a more significant effect on citizens’ co-production behavior. However, due to constraints on government human resources and time, it is recommended to adopt a general content and timely disclosure policy for information concerning behavior. A detailed content but non-timely disclosure policy is recommended for citizen compliance with preventive behavior. Findings have important implications for governmental public health crisis management.

Keywords:

Information Disclosure

Co-production

Public Health Crisis

1༎Introduction

Information disclosure is a paramount obligation of the government, with its fundamental purpose to enhance the transparency of government work (Padeiro et al., 2021). Disclosure of government information enables citizens to provide constructive feedback and make more effective decisions about resource allocation, ultimately promoting public welfare (Huang et al., 2021a; Shambaugh G E, 2017). During public health crisis, governments are frequently required to provide more timely and accurate information to reduce panic and enhance citizen cooperation (Baekkeskov and Rubin, 2017; Fu et al., 2020). Information disclosure can provide timely and accurate information related to public health crisis, thus changing people’s attitudes and enhancing their cooperation (Wu et al., 2022). However, there is still a lack of detailed academic research on information disclosure during public health crisis, with most existing studies focusing on financial disclosure or the disclosure of daily government work (Cuny, 2016). The few studies in this field focus on whether disclosure is made (Wu et al., 2022), but there is still a lack of research on the detailed forms of disclosure. Given the lack of government resources and time limits during public health crises, achieving timely and detailed disclosure can be challenging. Therefore, governments often need to choose between timeliness and level of detail when disclosing information. How information is disclosed with varying content and timing can have different effects on guiding citizens’ behaviors. It is crucial to explore ways for the government to effectively disclose information that promotes citizens’ engagement in pandemic preventive behaviors. These studies can improve the government’s crisis management and facilitate citizens’ joint response to the crisis.

Co-production behavior of citizens is an important research topic in studying the joint response of citizens and the government to crises. This paper aims to investigate how information disclosure promotes citizens' co-production behavior during public health crises. These crises highlight the need for governments to effectively, rapidly, and widely disseminate accurate and reliable information (Padeiro et al., 2021; Mori et al., 2021). Co-production is broadly defined as a variety of activities in which state actors and citizens collaborate to generate benefits (Nabatchi et al., 2017; Bovaird, 2007). During public health crisis, citizens' co-production behavior refers to sharing information and actively cooperating with preventive measures (Cheng et al., 2020). This behavior aims to enhance the quality of public services and improve crisis prevention and control. Notably, in situations where social service resources are limited and public health crisis have a wide-ranging impact, the successful implementation of various prevention measures is challenging without citizen cooperation (Steen and Brandsen, 2020; Cheng et al., 2020). Therefore, the co-production behavior of citizens has important research value. Current research on the impact of citizen co-production predominantly focuses on unified categories (Bovaird et al., 2014), and some studies categorizing individual co-production and collective co-production (Bovaird et al., 2014). During public health crises, social networks play a significant role in policy formulation. Citizens can increase public understanding the crisis’s situation through online attention and dissemination of information. This helps to raise public awareness and participation, thereby having a positive impact on the entire society. Therefore, it can be regarded as a form of co-production behavior. At the same time, compliance with offline behavior is also a form of co-production behavior, because citizens' compliance with measures can help reduce the risk of crises transmission and protect their health and safety. This behavior has important positive effects on the entire community, achieving the goal of co-production. Hence there are multiple types of citizens’ co-production behaviors, mainly comprising online and offline actions. These include citizens’ engagement in online activities such as following and sharing pandemic-related information, as well as offline behaviors like adhering to pandemic prevention measures and actively participating in volunteer services. However, there is currently insufficient research on the classification of co-production behavior and the extent to which online and offline behaviors are influenced by information disclosure. The degree to which co-production behaviors of different types are affected may not be the same. If co-production behaviors are studied according to a unified category, it may lead to incorrect analysis of the factors that affect them and result in erroneous guidance of citizen behavior. Targeted categorization studies can provide more effective foundations for government policy formulation. Citizens’ co-production behavior plays a crucial role in government crisis management, particularly during the outbreak of public health crisis (Li, 2020). These crises highlight the need for governments to effectively, rapidly, and widely disseminate accurate and reliable information (Padeiro et al., 2021). Hence, disclosing information during critical crisis can effectively promote government information dissemination and enhance citizen cooperation and support in emergency management (Wu et al., 2022). The COVID-19 pandemic provides a timely opportunity to study the effects of different forms of information disclosure during a crisis.

Given the research gap in the literature, this paper aim to make practical and academic contributions in the following three areas. Firstly, our study will focus on the impact of information disclosure on citizens' different categories co-production behavior. Previous research on citizens' co-production behavior has generally categorized it as a unified behavior where citizens and the government jointly produce benefits (Nabatchi et al., 2017; Kang and Van Ryzin, 2019). However, citizens’ co-production behavior during public health crisis can be classified into two types. One type involves concern and dissemination of government-disclosed information (Chatfield and Reddick, 2018). The other type involves complying with preventive measures, such as wearing masks and regular disinfection (Wu et al., 2022). The reasons for these two types of behavior and the degree to which they are influenced by information disclosure may differ. For example, when the content of government information disclosure is general, people may fail to understand the severity of the pandemic and may be less willing to cooperate with preventive measures. Therefore, studying co-production behavior as a whole may provide ineffective guidance. Our study aims to examine these two categories of co-production behavior separately to provide better recommendations for guiding specific citizen behaviors. Secondly, this study focuses on the effects of different forms of information disclosure. Currently, many governments engage in information disclosure, but new challenges have emerged regarding the specific operational aspects of disclosure. A simple “yes or no” answer regarding information disclosure does not provide sufficient guidance for the government. Addressing these questions requires further research to explore whether different forms of information disclosure lead to varying impacts. When studying information disclosure forms, the most common aspects to consider are the level of detail in the disclosed content and the timeliness of disclosure (Liu et al., 2020; Kim and Wertz, 2013). In-depth research on different forms of information disclosure can provide valuable recommendations for governments to improve their information disclosure practices. This can help governments increase their credibility and promote citizens’ co-production behavior by providing them with detailed and actionable guidance. Thirdly, this study employs regression analysis and experimental methods for quantitative research. Previous co-production studies have mainly relied on qualitative methods (Bovaird, 2007; Voorberg et al., 2015), and the measurement of citizens' co-production has often relied on self-reported data (Bovaird et al., 2014; Parrado et al., 2013). Nevertheless, the research based on self-reported data may overstate the actual effect of the relationship. This study utilizes panel data and experimental data to investigate citizens' co-production behavior during public health crisis, providing more reliable research findings.

2. Theoretical Background

2.1 Citizens' Co-production

The term “co-production” was first introduced by Percy in 1978 (Percy, 1978). In the following decades, scholars further explored the role of citizens in designing, implementing, and overseeing public policies and services, recognizing the significance of citizen participation for public service delivery and social development (Osborne et al., 2021; Brandsen et al., 2018). Co-production has been defined in previous research as various activities where state actors and citizens work together to generate benefits (Nabatchi et al., 2017). During public health crisis, there is an increased demand for citizen co-production in society. On one hand, a public health emergency encourages us to shoulder responsibilities collectively and maintain social stability(Van Eijk and Steen, 2016). On the other hand, stimulating public self-reliance and encouraging citizens to provide voluntary assistance is essential for governments to allocate their limited resources and energy to support more vulnerable individuals (Haug, 2023). Co-production becomes more apparent than ever during public health crises, and it holds significant implications for government crisis management (Bovaird et al., 2014). Public health crisis requires temporary and intensive citizen involvement and compliance (Cheng et al., 2020). Amid these situations, the social adherence to public health policies, such as maintaining social distancing and wearing masks, can be considered as a significant co-production endeavor (Wu et al., 2022).

Citizens' co-production behaviors comprise two important aspects, which are also two significant objectives for government information disclosure, the dissemination of accurate information and the encouragement of citizens to comply with preventive behaviors (Zhang et al., 2022). During the outbreak of a public health emergency, social unrest, lockdowns, and strict quarantines hinder information communication, making information exchange even more crucial (Baekkeskov and Rubin, 2017). In this situation, government information disclosure can provide more credible information about public health crisis and facilitate citizens' experience sharing, strengthening social support through their online networks, thereby fostering citizens' co-production (Meijer, 2011). Therefore, the first type of citizens' co-production behavior is online engagement, search, and sharing of information which called online information concern behavior. In addition to information exchange, governments will announce a series of measures to control the spread of health crisis after the outbreak. For example, during the COVID-19 pandemic, people were encouraged to maintain social distancing. At this time, when the government discloses relevant information on public health crisis, including the number of confirmed cases and travel records, it can enhance citizens’ risk perception, leading them to voluntarily comply with the prescribed measures and promote co-production. Therefore, another important aspect of citizens' co-production behavior is offline compliance with preventive behaviors and participation in volunteering for pandemic prevention and control which called offline compliance with preventive behaviors.

2.2 Information Disclosure and Citizens’ Co-production

Government information disclosure not only contributes to improving the government's image (Mutula and Wamukoya, 2009) and enhancing the level of government management (Badrul et al., 2016) but also increases the accuracy and rationality of public decision-making and behavior(Zhong and Duan, 2018; Wang et al., 2025; Wei and Zhang, 2025). During a public health emergency, society can fall into a state of chaos due to the severity and suddenness of the crisis. Normal communication channels between the government and citizens become disrupted. A scarcity of accurate and reliable information becomes a major challenge, hindering social stability and impeding citizens’ co-production (Lee and Na, 2024). False information and rumors often emerge in times of crisis (Baekkeskov and Rubin, 2017; Freelon and Wells, 2020; Lazer et al., 2018). For example, during the early stage of the COVID-19, the lack of timely and transparent information led to the spread of numerous rumors on social media (Fu et al., 2020). In times of emergency, citizens tend to become fearful and anxious (Fu et al., 2020), making it challenging to engage them effectively in co-production efforts. They may even tend to believe false information and take inappropriate actions, resulting in harmful consequences. Additionally, delayed information disclosure can negatively influence citizens’ evaluation of the government. For instance, the restrictions on information disclosure policy in Turkey were associated with a 26% decrease in citizens’ confidence in the accuracy of government-released information (Crepaz and Arikan, 2021). During a public health emergency, effective information disclosure can effectively pacify public emotions, enhance crisis response capabilities, mitigate the adverse impacts of the crisis (Zhong and Duan, 2018), and ultimately improve citizen co-production (Huang et al., 2021a).

Government information disclosure plays a crucial role in citizens' co-production. Public health crisis is typically widespread and affect entire nations (Huang et al., 2021b). During such crises, rumors and various pieces of information spread rampantly throughout society (Song et al., 2024). Citizens and communities heavily rely on government-provided information to enable their participation in crisis recovery (Wu et al., 2022). Government information disclosure promotes co-production in two main ways. First, when social order is disrupted and reliable information is in high demand (Chatfield and Reddick, 2018), timely information disclosure by the government can reduce social uncertainty and public panic about the unknown (Wang and Kapucu, 2008), enabling rational cooperative behavior. Without such information, some initiatives from non-governmental organizations and well-intentioned citizens may unintentionally impede the recovery of the crisis. Second, as a significant information provider, the government can establish unified guidelines and action plans so as to enhance public disease prevention skills and behaviors, facilitate citizen learning during public crises, and strengthen their willingness to take unified action (Fu et al., 2020; Lazer et al., 2018).

When government delivers information, two important factors that influence its effectiveness are the level of detail in information disclosure (Kim and Wertz, 2013) and the timeliness of the disclosure (Liu et al., 2020; Kim and Wertz, 2013). Following the outbreak of a public health emergency, general information disclosure may lead to greater panic among citizens. In the case of the COVID-19, if only the current number of confirmed cases (e.g., 120 cases) is announced without providing further details, citizens may become more concerned about whether there are confirmed cases in their neighborhood. This can result in panic purchase and reluctance to leave homes. On the other hand, detailed information disclosure, containing more information, can effectively clarify the specific circumstances, alleviate people’s panic, and encourage more engagement in co-production behaviors, thereby contributing more to society. Therefore, the level of detail in information disclosure is an essential factor influencing citizen co-production behavior. We propose the hypothesis:

H1a

Detailed information disclosure has a more significant impact on citizens’ online information concern behavior in co-production compared to general information disclosure.

H1_b

Detailed information disclosure has a more significant impact on citizens’ offline compliance with preventive behavior in co-production compared to general information disclosure.

The timeliness of information disclosure is another important influencing factor, and generally, more timely information disclosure is believed to have a better impact on the public (Pang et al., 2021; Mori et al., 2021). If the government delays information disclosure for a significant period after an emergency occurs, it may lead to citizens’ doubting the government’s capabilities, resulting in decreased trust and unwillingness to contribute to crisis management and social stability. Moreover, in the absence of timely information, citizens may turn to “internet influencers” or rely on hearsay for information, but such information cannot guarantee accuracy and may contribute to the spread of rumors and confusion regarding the credibility of information (Padeiro et al., 2021). When the public believes in misinformation, their actions based on it may have adverse effects. Therefore, this study hypothesizes:

H2a

Timely information disclosure has a more significant impact on citizens’ online information concern behavior in co-production compared to non-timely information disclosure.

H2b

Timely information disclosure has a more significant impact on citizens’ offline compliance with preventive behavior in co-production compared to non-timely information disclosure.

Additionally, there is an interaction between the timeliness and the level of detail in information disclosure, which governments often need to consider simultaneously in the process of information disclosure due to factors such as human resources and time (Huang et al., 2021a; Crepaz and Arikan, 2021; Kim and Wertz, 2013). In the aftermath of the pandemic outbreak, the government often needs to choose between information disclosure that is detailed but not timely enough and information disclosure that is general but timely. Which type of information disclosure can have a more significant impact on citizens’ co-production behavior is also one of the focuses of this paper’s research. Therefore, this paper hypothesizes that:

There is a significant interaction between the timeliness and level of detail in information disclosure.

3. Methods

3.1 Study 1: Experiment Research

Experimental design and subjects

This study selected prevention policies in China during COVID-19 to validate hypotheses mentioned earlier. China was selected as the research background because COVID-19 initially emerged in Hubei Province, China, and subsequently spread to other regions of the country. Being the first and one of the most severely impacted nations, China provides an ideal setting for testing our hypothesis. In addition, the nature of China as a nation determines that the citizens have a stronger dependence on government, and the influence of non-governmental organizations is relatively weak. The government has a stronger binding and guiding role on citizens. Chinese government policies have a significant role in disseminating official information and providing guidance to the public. This factor further enhances the research's suitability for verifying our hypothesis.

The hypothesis was validated through an experiment that adopted a 2 × 2 between-subjects experimental design. The independent and dependent variables are detailed in Table 1.

The experimental simulation adopted in this study is based on the context of government information disclosure in Tianjin, China. Consequently, the sample primarily consisted of residents from this region who were affected by the Tianjin epidemic in 2022 and had experienced epidemic prevention and control measures. We published recruitment information through official organizations in various communities in Tianjin. By employing a random recruitment approach, participants were selected from all six districts of Tianjin, ensuring their representativeness and suitability for the experiments. All recruited participants were involved in the experiment offline. Recruiters signed confidentiality agreements and participated voluntarily, and the recruitment and experimental processes were approved by ethics committees. Upon completion of the recruitment process, a total of 165 participants were enlisted, comprising 44.85% males and 55.15% females. After conducting a careful manipulation test, 158 subject responses were deemed valid and included in subsequent analyses.

Experiment task and process

The experimental participants were randomly divided into four distinct groups, which included the general disclosure group plus non-timely group, general disclosure group plus timely group, detailed disclosure group plus non-timely group, and detailed disclosure group plus timely group. Each participant was assigned to only one of these groups and received a unique set of experimental materials, which they were required to complete independently. After finishing the assigned task, participants were requested to return the materials immediately and then answer related questions. The experimental materials used in this study consisted of four separate sections. Within the task background section, subjects were informed that on January 8th, 2022, the Omicron variant of COVID-19 first emerged in China, leading to a new outbreak of the pandemic in Tianjin. Participants were then instructed to imagine being present in Tianjin and experiencing the outbreak firsthand. Following the outbreak, the Tianjin Municipal Government promptly carried out an investigation and disclosed related information about the pandemic through their official account, “Jinyun”. Disclosure information was divided into two parts: specific data and descriptions of cases’ travel records for the detailed disclosure group, and only basic data for the general disclosure group. A detailed summary of the specific information for both groups can be found in Appendix C. Timely information disclosure emphasizes the importance of "updating information within 10 hours of an outbreak". Conversely, non-timely information disclosure emphasizes "disclosure of information within 3 days of the outbreak".

After reading the relevant materials, participants were asked to answer questions related to the dependent and control variables used in this study. The question regarding to the dependent variable Y₁ was, “To what extent did you pay attention to information dissemination related to the Tianjin pandemic? (7-point scale, a higher number indicate higher levels of concern).” This question aimed to measure citizens’ online information concern behaviors in relation to co-production behaviors. The question regarding to the dependent variable Y₂ was “To what extent were you willing to voluntarily comply with pandemic preventive behaviors, such as actively maintaining social distancing, participating in anti-epidemic volunteer services and so on? (7-point scale, a higher number indicate higher levels of compliance).” This question aimed to measure citizens’ offline compliance with preventive behavior in relation to co-production behaviors. Participants were then required to provide demographic information. Descriptive statistics for the variables can be found in Table 2.

Manipulation check

To evaluate the effectiveness of manipulating two independent variables in the experiment, participants were required to answer questions related to the manipulation of disclosure content and disclosure timeliness. Specifically, they were asked, “What do you think is the level of detail included in the information disclosure? (General or Detailed)” and “Is the timeliness of the information disclosure timely or non-timely?” The results of the responses from 158 out of 165 residents who participated in the experiment indicate that the manipulation was successful.

Repetition of Experiment

To further validate the reliability of the experimental results, we conducted a round of repeated experiments. This round involved online random recruitment of participants, totaling 258 individuals. In this phase, we recognized that the previous measurement of online co-production behavior primarily focused on the level of information attention, which has certain limitations. Therefore, we introduced a measurement for the issue of information forwarding and sharing, represented as Y1 for " To what extent did you share and publicize information dissemination related to the Tianjin pandemic". For measuring offline co-production behavior, we took into account the relatively stringent epidemic prevention requirements in China during the pandemic, such as mandatory mask-wearing when going outside. The question in Y21 is “To what extent are you willing to voluntarily comply with pandemic prevention behaviors, such as keeping a social distance, actively disinfecting, and volunteering to fight the pandemic? (on a 7-point scale, with higher numbers indicating higher levels of compliance)”. Behaviors in this category are mainly derived from citizens' spontaneous behaviors. The question for Y22 is “To what extent are you willing to voluntarily comply with pandemic prevention behaviors, such as wearing a mask in public places, following home isolation requirements, etc.? (on a 7-point scale, with higher numbers indicating higher levels of compliance)”. This type of behavior is partly due to governmental requirements. The results of this experiment were consistent with those of the first round, thereby confirming the validity of Study 1. Detailed results are shown in Appendix D and E.

3.2 Study 2: Regression Analysis

Although the experimental study in Study 1 validated the significant impact of Information Disclosure Detail Level and Information Disclosure Timeliness on citizens' Co-Production behavior, it did not determine whether the impact was positive or negative. Additionally, Study 1 primarily focused on the Tianjin region, and the experimental method used in the study had sampling errors and subjective biases in answering questions. Therefore, we further validate the hypotheses through Study 2. Study 2 adopts regression analysis and investigates the information disclosure practices of all provinces and cities in China, which can provide further verification for the hypotheses.

The selected data for this regression analysis includes information disclosed by various provinces and cities in China between January 2020 and the end of February. Firstly, when COVID-19 first broke out in January 2020, China was the first country facing the pandemic with little prior experience to draw from. In comparison, subsequent countries had learned from China’s experience and adapted to deal with the pandemic. This discrepancy may have influenced citizen’s behaviors in cooperating with the government and accessing information from other sources. Secondly, provinces in China have varying degrees of autonomy in governance, resulting in varying degrees of information disclosure practices (Huang et al., 2021a). This research context can provide valuable insights in better analyzing the relationship between government information disclosure and human behavior. Hence, the selection of data from different provinces will help facilitate a comprehensive study of the topic at hand. Therefore, this paper focuses on government information disclosure and human behavior in 293 prefecture-level cities in mainland China from January 16 to February 29, 2020- the most intensive period of COVID-19 spread (Wu et al., 2022).

Variable Settings

Information Disclosure. Starting from January 20th, 2020, municipal Chinese governments at various levels have taken timely measures to release information to the public. The public information included the latest updates on the pandemic situation and the confirmed cases’ travel records. This information was primarily published on WeChat Official Accounts and Weibo. We referred to previous literature’s classifications to divide the information into general disclosure and detailed disclosure. The information on the number of daily confirmed cases is considered general information disclosure, whereas the information on commuting routes is classified as detailed information disclosure (Fu et al., 2020; Liu et al., 2020). Additionally, the information was sorted into three categories based on its release time: within 12 hours, 12–24 hours, and more than 24 hours, to represent the timeliness of information disclosure. We collected epidemic information released by various cities in China from January 16th to February 29th, 2020 (the outbreak period). This information was primarily published on WeChat Official Accounts and Weibo. The disclosed information includes the disclosure time, the number of confirmed cases, commuting routes of confirmed cases, etc.

Co-production behavior. Traditional research methods relied on subjective indicators such as citizens’ cooperative willingness and attitudes (Liu et al., 2020). However, subjective attitudes and willingness may not always accurately measure citizens’ co-production behavior. In this study, we rely on real-time data that reflect the citizens’ actual behaviors, such as the number of individuals who consciously maintain social distance and wear masks. However, collecting accurate real-time data is challenging, and some actions might not represent voluntary behavior due to China’s strict prevention policies. Therefore, this paper considers the role of social networks in citizens’ co-production behavior. Previous studies on public crisis have used online behavioral indicators, such as the number of share and reading on WeChat, as well as web browsing data, to measure citizen behavior (Wu et al., 2022; Chatfield and Reddick, 2018). Therefore, this paper employed two main indicators to measure citizens’ co-production behavior. The first is the “sharing times” and “reading times” of government-disclosed information, which is measured online information concern behavior in co-production. It is used to gauge the government-citizen interaction and level of online contact. The second indicator is the frequency of citizen search queries on pandemic preventive behaviors, obtained from Baidu Index, the most popular search engine in mainland China (Wu et al., 2022; Huang et al., 2021a). We collected daily search data from prefecture-level cities manually, focusing on keywords “wearing masks” and “sterilize,” in order to gauge citizens’ offline compliance with preventive behavior in co-production. The index was calculated by assigning weights to the search frequencies of the keywords “wearing masks” and “sterilize” on both personal computers and mobile phones, with a higher value indicating more frequent and intensive search queries.

The selection of this indicator to measure citizens’ offline compliance with preventive behaviors was based on several significant reasons. Firstly, online searching shows how individuals efficiently seek and absorb information related to “wearing masks” and “sterilize”. It essentially reflects citizens’ adherence to preventive behaviors (Wu et al., 2022). A low volume of search queries indicates little attention being given to preventive measures. It is difficult to imagine that individuals would demonstrate high cooperation and compliance with preventive behaviors if they show little concern for “wearing masks” and “sterilize.” On the contrary, a high search query volume indicates that citizens have devoted more time and effort to search for relevant information, displaying increased awareness of preventive behaviors and potentially being better prepared to undertake necessary preventive actions (Bento et al., 2020). Previous studies have found that individuals who pay more attention to COVID-19 online are more likely to show higher levels of social engagement (Wu et al., 2022). This finding provides further evidence supporting the reliability and relevance of using search queries as an indicator for citizen co-production. Secondly, the Chinese government implements relatively strict control measures during public health crisis, such as city blockades. Some offline compliance with preventive behaviors is mandated by the government, online search queries regarding compliance with preventive behaviors during public health crisis provide a clearer picture of the voluntary nature of co-production (Meijer, 2011).

Variable Control. To reduce the differences among various prefecture-level cities, this study employed corresponding control variables. Per Capita Gross Regional Product (

$\:\text{P}\text{c}\_\text{G}\text{R}\text{P}$

) is the extent of economic development for a city. Urban Resident Population (

$\:{\text{P}\text{o}\text{p}}_{i}$

) measures the resident population of a city. Local General Public Budget Revenue (

$\:{\text{F}\text{i}\text{s}\text{c}\text{a}\text{l}}_{i}$

) measures a city’s financial situation. Number of Beds of Hospitals (

$\:{\text{H}\text{o}\text{s}\text{p}\text{i}\text{t}\text{a}\text{l}\text{s}\_\text{B}\text{e}\text{d}\text{s}}_{i}$

) measures the medical resources of a city. Number of Employees Joining Basic Medical Care System (

$\:{\text{E}\text{m}\text{p}\text{l}\text{o}\text{y}\text{e}\text{e}\text{s}}_{i}$

) measures the social security situation for a city. References for selection of control variables Huang et al. (2021a).

Models and Research Methods

In this study, we employed a negative binomial regression model to measure the impact of government disclosure on citizens’ co-production behavior. Negative binomial regression models are commonly used to evaluate interventions for infectious diseases like H1N1 and HIV (Huang et al., 2021a; McLaughlin et al., 2019). By controlling for pre-existing conditions such as local economy, population, financial status, and medical resources, this model allows us to measure the effect of government information disclosure on citizens’ co-production behavior during the pandemic outbreak. The overall design of our model was as follows:

$\:\text{log}\left(\text{Y}\right)=\:{\beta\:}_{0}+{\beta\:}_{1}{\text{D}\text{i}\text{s}\text{c}\text{l}\text{o}\text{s}\text{u}\text{r}\text{e}\_\text{T}\text{i}\text{m}\text{e}}_{i,t-1}+{\beta\:}_{2}{\text{D}\text{i}\text{s}\text{c}\text{l}\text{o}\text{s}\text{u}\text{r}\text{e}\_\text{C}\text{o}\text{n}\text{t}\text{n}\text{t}}_{i,t-1}\:+{\beta\:}_{3}{\text{P}\text{o}\text{p}}_{i}{+\beta\:}_{4}{{\text{P}\text{c}\_\text{G}\text{R}\text{P}}_{i}+\beta\:}_{5}{\text{H}\text{o}\text{s}\text{p}\text{i}\text{t}\text{a}\text{l}\text{s}\_\text{B}\text{e}\text{d}\text{s}}_{i}{+\beta\:}_{6}{\text{E}\text{m}\text{p}\text{l}\text{o}\text{y}\text{e}\text{e}\text{s}}_{i}+{\beta\:}_{7}{\text{F}\text{i}\text{s}\text{c}\text{a}\text{l}}_{i}+{\epsilon\:}_{i,t}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:（1）$

Among them, the dependent variable Y is the co-production behavior of citizens, including the following four aspects. Y₁(Share) refers to the number of likes and retweets after the government releases the corresponding information disclosure content through WeChat or Weibo. Y₂(Readings) means the view times of citizens after the government releases the corresponding information disclosure content through WeChat or Weibo. Y₃(Search for “wearing masks”) is the search data for “wearing masks” in Baidu Index. Y₄(Search for “sterilize”) is the search data for “sterilize” in Baidu Index.

The independent variable

$\:{\text{D}\text{i}\text{s}\text{c}\text{l}\text{o}\text{s}\text{u}\text{r}\text{e}\_\text{T}\text{i}\text{m}\text{e}}_{i,t-1}$

refers to the time when the government discloses information, where the information disclosure within 12 hours 　represents 1, the information disclosure within 12–24 hours is 2, and the information disclosure more than 24 hours is 3. The independent variable

$\:{\text{D}\text{i}\text{s}\text{c}\text{l}\text{o}\text{s}\text{u}\text{r}\text{e}\_\text{C}\text{o}\text{n}\text{t}\text{n}\text{t}}_{i,t-1}$

refers to the level of detail of the government’s information disclosure, the detailed disclosure represents 2, and the general disclosure 1.

Due to the different outbreak times of each city, some cities will not disclose any information when there is no pandemic spread in the city. Therefore, the researchers excluded the data that did not disclose information. Finally, A total of 5800 data were collected from 293 cities in this study, whose descriptive statistics are shown in Table 3. We carried out the maximum-likelihood estimation for the negative binomial model. The result shows that the absolute value of the O value is greater than 1.96, and the p-value is less than 0.05, indicating that negative binomial regression is appropriate (Appendix A and B).

Table 3. Descriptive statistics of main variables

4.Results

4.1 Study 1: Experiment Results and Analysis

Analysis of Variance (ANOVA)

To evaluate the validity of the hypothesis, the study 1 conducted ANOVA analyses (Lei et al., 2022). The results of the ANOVA are presented in Table 4. Firstly, concerning the dependent variable Y₁, the study found significant main effects of information disclosure detail (F (1,154) = 4.046, p = 0.046 < 0.05), and disclosure timeliness (F (1,154) = 6.170, p = 0.014 < 0.05), as well as a significant interaction effect (F (1,154) = 9.029, p = 0.003 < 0.01). Secondly, with respect to the dependent variable Y2, the study observed significant main effects of information disclosure detail level (F (1,154) = 8.290, p = 0.005 < 0.01), and information disclosure timeliness (F (1,154) = 5.419, p = 0.021 < 0.05), as well as a significant interaction effect (F (1,154) = 5.604, p = 0.019 < 0.05).

Table 4. Analysis of Variance

Simple Main Effects Analysis

For the dependent variable Y₁ and Y₂ with interaction, the study 1 conducted a simple effect analysis which is presented in Table 5. For variable Y₁, first, in the case of non-timely information disclosure, the effect of the disclosure content is prominent (F (1,154) = 12.622, p = 0.001 < 0.01). Specifically, general disclosure (M = 6.41) has a more significant influence on the online information concern behavior than detailed disclosure (M = 5.63). Second, in the case of detailed information disclosure, the timeliness of disclosure has a significant effect (F (1,154) = 15.457, p < 0.001), and timely disclosure (M = 6.49) has a greater impact on citizens’ online information concern behavior than non-timely disclosure (M = 5.63). Third, the effect of disclosure content is not significant in the case of timely information disclosure (F (1,154) = 0.492, p = 0.484 > 0.05). Fourth, when the content of the disclosure is general, the timeliness of the disclosure has no significant effect (F (1,154) = 0.132, p = 0.717 > 0.05). Fifth, a further comparison between detailed non-timely disclosure and general timely disclosure shows that the effect of “detailed but non-timely” disclosure (M = 5.63) is lower than that of “general but timely” disclosure (M = 6.33) in influencing citizens’ online information concern behavior. This finding suggests that if the purpose of the disclosure is to disseminate information and encourage people to pay attention to the information, it is more recommended to adopt a general but timely method.

Regarding the variable Y₂, first, in the case of timely information disclosure, the effect of the information disclosure detail level is significant (F (1,154) = 13.719, p < 0.001). The results show that detailed disclosure (M = 6.42) has a greater influence on the offline compliance with preventive behaviors during the pandemic compared to general disclosure (M = 5.33). Second, in the case of detailed information disclosure, the timeliness of disclosure has a significant effect (F (1,154) = 11.311, p = 0.001 < 0.01), with timely disclosure (M = 6.42) having a higher impact on the offline compliance with preventive behaviors during the pandemic than non-timely disclosure (M = 5.45). Third, in the case of non-timely information disclosure, the effect of the disclosure content is not significant (F (1,154) = 0.131, p = 0.717 > 0.05), indicating that if the disclosure is not timely, detailed content or general content influence is not significant. Fourth, the effect of timeliness of disclosure is not significant when general information is disclosed (F (1,154) = 0.001, p = 0.978 > 0.05), indicating that if general information is disclosed, whether it is timely has little impact. Fifth, the effect of detailed non-timely disclosure (M = 5.45) is higher than that of general timely disclosure (M = 5.33). If disclosure is to comply with and publicize pandemic preventive behaviors in co-production behaviors, detailed but not timely methods are advised.

Table 5: Simple Main Effects Analysis

4.2 Study 2: Regression Result and Analysis

The negative binomial regression results were shown in Table 6, with (1) and (2) representing whether there is a control variable to reduce its impact. The results showed that for the dependent variable Y₁, information disclosure timeliness has a significant negative effect (β=-0.730; p < 0.001), whereas information disclosure detail level has a significant positive effect (β = 0.977; p < 0.001). With the dependent variable Y₂, information disclosure timeliness has a significant negative effect (β=-0.740; p < 0.001), and information disclosure detail level has a significant positive effect (β = 0.930; p < 0.001). For the dependent variable Y₃, information disclosure timeliness has no significant effect (β = 0.057; p > 0.05), while information disclosure detail level has a significant positive effect (β = 0.112; p < 0.001). With the dependent variable Y₄, information disclosure timeliness has a significant negative effect (β=-0.122; p < 0.001), and information disclosure detail level has a significant positive effect (β = 0.262; p < 0.001).

After conducting regression analysis and experimental tests, our findings show that: (1) Information disclosure timeliness has a significant negative impact on online information concern behavior in co-production, and H_2a is supported. Additionally, information disclosure detail level has a significant positive impact on behavior, H_1a is supported. (2) There is a significant interaction between information disclosure timeliness and information disclosure detail level with regards to online information concern behavior and H₃ is supported. In the case of non-timely information disclosure, general disclosure has a more significant impact on behavior. In contrast, in the case of detailed information disclosure, the impact of timely disclosure is more significant than non-timely disclosure. (3) “General but timely” disclosure has a more significant impact on online information concern behavior than “detailed but non-timely” disclosure. (4) Regarding offline compliance with preventive behavior in co-production, information disclosure timeliness has a significant negative impact, and H_2b is supported. However, when the regression analysis only verifies the dependent variable search for “wearing masks”, the impact of information disclosure timeliness is not significant. This indicates that the impact of timeliness on some behaviors of pandemic prevention compliance is not evident and requires further exploration. Information disclosure detail level has a significant positive impact, and H_1b is supported. (5) There is a notable interaction between information disclosure timeliness and information disclosure detail level when studying offline compliance with preventive behavior, and H₃ is supported. The impact of information disclosure on behavior differs depending on the timing and level of detail. Specifically, we found that for timely information disclosure, detailed disclosure has a more significant impact on behavior, whereas for detailed information disclosure, the impact of timely disclosure is more significant than non-timely disclosure. (6) “Detailed but non-timely” disclosure has a more significant impact on offline compliance with preventive behavior than “general but timely” disclosure.

5. Discussion and Conclusion

5.1 Key Findings

In conclusion, the research findings presented in this paper revealed the following noteworthy outcomes. Firstly, the effective management of public health crisis necessitates timely and comprehensive dissemination of information in order to encourage citizen’ involvement in co-production behavior. Secondly, the impact of information disclosure varies depending on the type of co-production behavior exhibited by citizens. For online information concern behavior, detailed and timely information disclosure is the most effective policy. However, due to limitations in government human resources and time, achieving detailed and timely information disclosure may be challenging. In such cases, a “general but timely” disclosure policy may be adopted, and “detailed but non-timely” disclosure is not recommended. Thirdly, “detailed and timely” information disclosure is the most effective policy for promoting citizens’ offline compliance with preventive behavior, followed by “detailed but non-timely “information disclosure policies.

5.2 Theoretical Implications

This study primarily contributes to the theoretical literature in the following three aspects: First, it breaks through the traditional static classification paradigm of citizen co-production behavior, innovatively deconstructing co-production behavior into online information concern behavior and offline pandemic compliance with preventive behavior. This framework allows for the examination of the impact of information disclosure forms on different co-production behaviors. It provides a new theoretical tool for understanding the complexity of citizen participation in the context of public health crisis. Second, the study develops an integrated analysis method that combines "objective data - experimental data." At the regression analysis level, it analyzes over 5,800 data points from 293 prefecture-level cities, revealing the correlation between information disclosure characteristics and group behavior patterns. At the subjective data analysis level, it utilizes behavioral experiments to collect decision-making data from 158 citizens and conducts variance analysis of the interactions among independent variables. This mixed-method research design bridges group behavior patterns with individual decision-making mechanisms, providing a replicable cross-level analysis framework for public administration research. Third, from the perspective of behavioral public administration, this study uncovers the matching mechanism between information disclosure forms and citizen co-production behavior types. By constructing a two-dimensional policy matrix of "timeliness × detail," it finds that online information concern behavior is more sensitive to timeliness, while offline pandemic compliance with preventive behavior requires more detailed information. Based on this, it proposes differentiated policy combinations of "timely but not detailed disclosure" and "detailed but not timely disclosure," offering a theoretical basis for governments to develop tiered crisis communication strategies.

5.3 Practical Implications

In terms of practical significance, we primarily proposes effective government information disclosure strategies and recommendations. Firstly, the results have shown that information disclosure significantly impacts citizens’ co-production behavior (Wu et al., 2022). However, the effect of information disclosure varies for different co-production behaviors. When the purpose of government information disclosure is to encourage the public to pay more attention and spread accurate information related to the pandemic, the best approach is to disclose information that is “detailed and timely.” However, if there is a lack of time and human resources, it is recommended that the government adopt a “general but timely” form of information disclosure to release information promptly. Even if the information is not yet comprehensive, the timely information can quickly allow the correct information disclosed by the government to have a more significant impact on public opinion (Padeiro et al., 2021). The incompleteness of information will draw people’s continuous attention, driving them to seek out more information.

Secondly, it is essential to acknowledge that resource constraints and time limitations within the government make it challenging to balance timely and detailed information disclosure simultaneously (Crepaz and Arikan, 2021). Sometimes the government is unable to adopt a “detailed and timely” form of information disclosure due to a lack of time and human resources. In this situation, studies have revealed that when the aim of government information disclosure is to encourage citizens to voluntarily follow pandemic prevention policies, it is recommended to adopt a “detailed but not timely” approach to information disclosure. This is because the level of detail in the information disclosure is more significant to citizens. By providing detailed information on the actual situation of the pandemic, the severity of the outbreak, and the travel records of confirmed cases, citizens can have a personal understanding of the gravity of the pandemic and be more likely to comply with prevention policies.

Thirdly, our study’s findings suggest that the government’s information disclosure policy is crucial in promoting citizens’ co-production behavior during public health crisis. However, it is important for the government to adjust its information disclosure policy to cater to different purposes, especially in the early stages of an outbreak when timely information is crucial for controlling rumors and emphasizing the role of official information (Huang et al., 2021a). During the critical phase of the pandemic, the government should focus on promoting citizens’ compliance with prevention measures and encourage voluntary services (Wu et al., 2022). These two scenarios require targeted measures and should be studied separately. In accordance with recommendations one and two, different information disclosure policies should be implemented at different time points to promote specific types of co-production behaviors. This approach will ensure that information disclosure policies are tailored to the unique local conditions and characteristics of the population and implemented diligently.

5.4 Conclusion and Future Study

During times of public health crisis, the dissemination of information by the government can significantly influence citizens’ contribution to the collective efforts. This study specifically aims to analyze the interplay between different forms of information disclosure and citizens’ co-production behavior. We have identified that the interplay between the timeliness and level of detail in information disclosure has varying impacts on different aspects of citizens’ co-production behavior. Specifically, timely but general information disclosure tends to have a larger impact on online information concern behavior, while detailed but non-timely information disclosure is more effective in promoting offline compliance with preventive behavior. Our finding can offer practical recommendations for authorities to enhance information disclosure policies during emergency situations, specifically regarding public health crisis, and consequently improve the quality of government crisis management. In addition, this paper also contributes theoretically. The research extends the definition of citizen co-production behavior, categorizing it into online and offline behaviors, and investigates the influencing factors of each behavior separately. Furthermore, this paper explores specific forms of information disclosure and their relationship to co-productive behavior, enriching the model of information disclosure research.

However, this paper has several limitations. Firstly, the classification of co-production behavior in this study is limited to online and offline behavior categories. A more comprehensive classification could lead to more effective measures being proposed. Secondly, the study only focuses on residents of Tianjin, China, and therefore the findings may not be generalizable to other regions or countries. In future studies, data should be collected from a larger and more diverse sample to enhance external validity. Thirdly, in this study, we utilized regression analysis to examine the data, primarily measuring offline co-production behavior through metrics obtained from Baidu searches. However, using online data to assess offline behavior has certain limitations. In the future, we aim to seize the opportunity to conduct extensive field experiments during public health emergencies to measure actual participation more accurately. What`s more, the research in this article focuses on the context of public health crisis. However, government information disclosure is a necessary policy in many other situations. Therefore, further research is necessary to address the limitations of this study and fill the gaps in our knowledge of this important topic.

Electronic Supplementary Material

Below is the link to the electronic supplementary material

Supplementary Material 1

Author Contribution

H.D. primarily designed the topic and structure of the paper and provided theoretical support.D.L. and C.Z. conducted the experimental study collection and data analysis. All authors co-authored the main manuscript text and reviewed the manuscript. The authors declare no potential conflicts of interest with respect to the research, authorship, and publication of this article.

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