Faster? Softer? Or More Formal? A Study on the Methods of Enterprises’ Crisis Response on Social Media

Abstract

Algorithmic recommendation mechanisms of social media platforms, viral diffusion of user-generated content (UGC), and real-time public opinion pressures are fundamentally deconstructing the traditional corporate crisis response paradigm that used to rely on one-way statements and delayed reactions. This compels enterprises to elevate their crisis response standards and construct new response frameworks. Based on an empirical analysis of 3,135,675 social media dissemination data points from 94 corporate crisis incidents, this study explores effective crisis response patterns for enterprises through three dimensions: response timing, methods, and content. The key findings indicate that traditional crisis response timelines prove inadequate for social media scenarios, whereas intervention during the ascending phase of dissemination significantly curtails crisis propagation cycles. Beyond formal statements, informal responses demonstrate equivalent mitigation effects, with combined formal–informal approaches yielding optimal outcomes. The comparative analysis of four content strategies (downplaying, supporting, denying, and reframing) reveals differentiated impacts on dissemination volume and duration, highlighting an inherent trade-off between these parameters. This research contributes to the crisis management theory in social media contexts while providing actionable guidance for enterprises to establish systematic crisis response methodologies. The results emphasize temporal sensitivity in response deployment, strategic content formulation, and multimodal communication integration.

1. Introduction

When a public crisis occurs in an enterprise, promptly making the right response decisions to sudden incidents allows for preventing the overinterpretation of uncertain events [1]. The impact of negative information can also be reduced by taking relevant measures. While striving to maintain the public’s original perception of the enterprise, efforts should focus on turning the crisis into an opportunity.

Due to the rapid growth of social media, its vast user base has made it a crucial platform for information dissemination. For enterprises, social media is an important channel for brand promotion and communication, as well as a high-incidence area for crisis communication. Social media has disrupted the traditional media-based crisis communication system, making the effectiveness of corporate response decisions more uncertain [2]. More precisely, the instantaneity and openness of social media have significantly increased the speed and scope of corporate crisis communication, making the demands for corporate crisis responses more critical. On the one hand, various crises exist. Thus, response methods based on a unified template are ineffective, and they may easily result in secondary crisis communication. On the other hand, determining the effective timing of corporate crisis response on social media is more difficult. Some corporate crises may simmer for one or two days before suddenly escalating, while others can rapidly spread, with information disseminating in an instant, similar to a virus [3]. The process from the exposure to the incident to the surge in large-scale attention often unfolds within a few hours, or even faster [4]. Public crises on social media have a broad impact, transcending geographical boundaries and rapidly spreading on a global scale, affecting audiences from diverse cultural and social backgrounds [5]. Therefore, the requirements for corporate crisis response have become higher.

However, the existing studies have problems dealing with these new characteristics. In fact, most of them focus on crisis communication in the traditional media environment, and they exhibit insufficient understanding of the unique laws of crisis communication in the social media environment. Although some studies have taken social media into consideration, their response strategies mainly consist of traditional crisis response theoretical methods, while ignoring the characteristics of social media crisis events and their complex impacts [6]. Therefore, studying the impact of enterprise crisis responses on crisis management and communication is of great theoretical and practical significance. Exploring effective applications of mathematical models in crisis communication management is also crucial.

This paper studies enterprise crisis responses and their impacts under the management of social media crisis communication. Based on the theoretical framework of crisis response while incorporating the division of time-domain response stages in cybernetics, a quantitative study was conducted on the response timing, content, and mode of enterprise crisis communication, as well as on their impacts on the volume and speed of social media crisis communication. This involved the use of 3,135,675 pieces of data related to 94 enterprise crisis incidents from 2016 to 2019. The obtained results demonstrate the invalidation of the response time and establish the concept of the response stage. In addition, they clarify the incompatibility of the response content strategies in the situational crisis communication theory in terms of the volume and time cycle of crisis communication. Moreover, the roles of formal statements and informal responses in enterprise crisis communication were identified.

In terms of theoretical contributions, this study represents a comprehensive quantitative exploration of traditional crisis response theory applied to social media platforms. By proposing novel conclusions across three dimensions—response timing, content, and methods—the research advances crisis communication management for enterprises in social media contexts, thereby contributing to the expansion of traditional crisis response frameworks to new platforms. The findings hold significant theoretical value. Practically, the study investigates innovative crisis response strategies grounded in the influencing factors of corporate crisis dissemination on social media. By identifying effective response methodologies under the pervasive influence of social media, this research provides actionable guidance for enterprises, demonstrating tangible practical implications. The dual focus on theoretical advancement and real-world applicability underscores its relevance to both academic discourse and organizational practice.

In summary, this paper studies the application of traditional crisis response theories on social media platforms and provides new suggestions. The obtained results provide practical guidance for enterprise crisis communication management.

2. Literature Review

2.1. Research Background

2.1.1. Differentiations in Enterprise Crisis Communication on Social Media

At present, social media is the main channel for enterprise crisis communication. Due to the information dissemination ecology of social media, enterprise crisis communication has significantly changed. More precisely, crisis communication has become faster and more significant. The widespread use of social media has enabled the rapid and broad dissemination of crises [7,8]. The ubiquitous accessibility of social media platforms enables individuals to voice opinions on corporate crises anytime and anywhere. The interactive nature of these platforms further facilitates information sharing among consumers, significantly lowering barriers to dissemination while amplifying its velocity. Compounding this effect, algorithm-driven content recommendation mechanisms endow social media with unparalleled information diffusion capabilities [9]. Coupled with the inherently viral propagation dynamics of digital platforms, negative narratives can reach millions or even tens of millions of users within mere hours. This phenomenon underscores an exponential escalation in the speed of public opinion dissemination, fundamentally altering the temporal constraints traditionally governing crisis communication. Pfeffer et al. [4] denoted this phenomenon as cyber storm. As a result, the traditional golden 8 h or even golden 1 h rules are no longer sufficient to deal with the rapid and explosive nature of crisis communication [2]. Corporations need to find more efficient ways to manage crisis communication on social media.

Secondly, the communication cycle is repetitive. When a sudden enterprise crisis occurs, corresponding public opinions emerge online, triggering widespread public sentiment. At that time, the public mainly discusses topics related to the crisis and processes information about the exposed incident. Before the enterprise responds, the public opinion usually follows the narrative set by the media and the public [10]. In the online spread of a crisis, if no new topics are introduced, the public’s discussion cycle typically lasts no longer than five days [11]. This indicates that without a response from the enterprise, the intensity of online public sentiment is naturally reduced. However, to maintain its social image, the enterprise often implements response strategies [1]. When the enterprise responds, it introduces a new topic, which sparks public attention and discussion, reigniting the intensity of online public sentiment [1]. Even if the enterprise believes that the crisis has been properly managed, the vast information storage and easy retrievability of social media may resurface past crisis incidents for re-discussion by users, potentially dragging the enterprise back into the whirlpool of public opinion [11]. In contrast to the era of traditional media, the attention of traditional media to crisis incidents often fades with the passage of time and the emergence of new events. However, social media makes the cycle of enterprise crisis communication more complex, repetitive, and unpredictable.

Moreover, the communicative subjectivity of ordinary users is prominent. Traditional studies on enterprise crisis communication focus on enterprise-led crisis management [12,13], and ordinary users are considered to be passive recipients. However, in contrast to mass media (e.g., television and newspapers), social media has a decentralized structure and social nature. More precisely, ordinary users are able to express their opinions online [14]. Individual consumers can become powerful influencers in decentralized enterprise crisis communication by actively sharing and posting information and comments related to the underlying crisis [15].

In conclusion, enterprise crisis communication on social media is characterized by rapidity, extensiveness, repetitive cycles, and user initiative, which make its impact more complex. When an enterprise crisis spreads on social media, the enterprise should make more careful decisions and consider many factors, such as user sentiment [1].

2.1.2. Factors Influencing Corporate Crisis Dissemination on Social Media Platforms

As social media emerges as the central arena for crisis propagation, research on its influencing factors has evolved to reflect multidimensional and dynamic characteristics. Recent empirical studies identify platform-specific features, key opinion leaders (KOLs), and crisis typology as the critical mechanisms shaping crisis dissemination. At the platform level, architectural distinctions—such as algorithmic curation mechanisms (e.g., Weibo’s trending lists)—create inherent propagation biases, leading to divergent dissemination outcomes for identical crises across platforms [16]. Furthermore, the proliferation of platform diversity has introduced cross-platform effects as an emergent property in crisis propagation dynamics [3]. KOLs serve as pivotal mediators in corporate crisis dissemination, capable of amplifying narratives or recalibrating public agendas [17]. The existing findings suggest that KOLs leverage authority to reframe crisis narratives [16], while their empathetic engagement accelerates the spread of crisis-related content. Consequently, organizations increasingly deploy KOL-driven agenda-countering strategies [18].

Beyond platform and KOL influences, crisis typology fundamentally dictates dissemination outcomes, with distinct crisis types exhibiting inherent propagation differentials [19]. The temporal context of a crisis outbreak—specifically, the “heat environment” shaped by social media’s real-time activity levels—further exerts critical leverage over dissemination trajectories.

While corporate crisis responses remain pivotal as intervention measures, their efficacy on social media diverges markedly from traditional media paradigms. Unlike legacy approaches, social media crisis response demands methodologies addressing cross-platform coordination, real-time adaptability (e.g., 18 April 2025 public opinion pressures), and algorithmic interactivity, necessitating novel theoretical and operational frameworks.

2.1.3. Enterprise Crisis Responses on Social Media

Crisis response is “what a company says” and “what a company does” after the occurrence of a crisis [1]. Inappropriate crisis responses result in negative consequences, while rapid and appropriate responses allow the company to effectively manage the crisis and minimize its damage. Since the era of traditional media, enterprise crisis response has been crucial for the management of crises. Most of the studies on crisis response are based on two main theories: Benoit’s image repair theory (IRT) and Coombs’ situational crisis communication theory (SCCT) [1,12]. The advent of the social media era has prompted scholars to explore effective corporate crisis response strategies on digital platforms, predominantly grounded in the institutional theory (IRT) or the situational crisis communication theory (SCCT). Representative frameworks include the social-mediated crisis communication model (SMCC) [20], which extends the SCCT framework by integrating social media affordances to propose novel response paradigms. Similarly, the interactive crisis communication model (ICCM), building on the SCCT principles, refines crisis management through Cheng’s classification of five core response typologies (base, denial, evasion, justification, concession) and 28 phase-specific strategies. This framework demonstrates enhanced applicability to contemporary corporate crisis propagation management on social media [21].

Beyond this, scholars have developed specialized frameworks for distinct crisis scenarios, such as the algorithmic accountability framework addressing AI-related crises [22], the ongoing accountability loop for managing persistent negative sentiment [23], and the psycho-strategic alignment theory, which bridges psychological dynamics with tactical response design [24].

Many studies then considered these frameworks to analyze and evaluate effective enterprise crisis response strategies on social media [25]. For instance, Li et al. [15] conducted a study on crisis situations and response strategies. Raithel and Hock conducted a study incorporating the theory of crisis response matching [26]. Many studies tackle the types and effectiveness of crisis response strategies.

These studies clearly identified crucial issues of crisis response from a strategic perspective, including the response time and response content [27]. However, at present, many crisis communication types exist, and it is difficult to generalize the evaluation of crisis responses [28]. The efficacy of unified “real-time adaptability” methodologies warrants rigorous validation, particularly as crisis typologies proliferate in complexity and scale. Furthermore, the cross-platform propagation of corporate crises on social media necessitates systematic frameworks for coordinating responses across multiple channels. Critical questions—such as how to architect multiplatform response frameworks, synchronize timing thresholds, and tailor content strategies to platform-specific affordances—have become pivotal to modern crisis management.

In addition, the types of enterprise responses are more diverse. Studies should be conducted on the effectiveness of these diverse changes, such as response methods and more diverse crisis events.

2.2. Research Framework and Hypotheses

This study sought to investigate effective corporate crisis response models applicable to contemporary social media platforms. Grounded in the situational crisis communication theory [1] and the interactive crisis communication model [21], the research advanced empirical exploration across three dimensions—response timing, methods, and content—to identify actionable patterns for managing crisis propagation in digital ecosystems. By employing a quantitative lens, the study aimed to uncover novel methodological frameworks for modern corporate crisis management, thereby extending the theoretical corpus of crisis response literature. The findings are positioned to refine adaptive strategies tailored to the dynamic, algorithm-driven nature of social media, while contributing actionable insights for both academic discourse and organizational practice.

2.2.1. Response Timing: Response Time and Stages

The crisis response timing is part of the enterprise crisis response problem (i.e., crisis response), mainly addressing the question of when to act and speak [1]. When an enterprise faces a crisis, it should respond as quickly as possible. When a crisis breaks out, information asymmetry and people’s uncertainty about the authenticity of its occurrence will negatively affect the enterprise [29]. The faster the processing of information, the higher its credibility, and the more likely its positive impact [30]. A late enterprise response will bring more uncertainties and uncontrollability, yielding negative results.

The studies on the timing of crisis response mainly tackle crisis timing strategies, which comprise two types: the pre-crisis and post-crisis timing strategies. The pre-crisis timing strategy is studied based on the theory of stealing thunder, and it is based on the self-disclosure strategy before the occurrence of the crisis [31]. The post-crisis timing strategy aims at identifying the convenient timing of an enterprise’s effective intervention and response. Many studies have been conducted on specific time periods, ranging from the traditional golden 24 h, to the golden 8 h, and then to the current golden 1 h [32]. They provided clear time boundaries to guide the public relations response behavior of enterprises.

However, in the era of big data on social media, the number of crisis incidents is continuously increasing, and their types are becoming more complex. In addition, the communication cycles of different crisis incidents are not consistent. For example, in a crisis incident related to the endorsed celebrity of an enterprise, the spread of the crisis may reach its peak within a dozen minutes, reaching the most serious stage of event development. On the contrary, an enterprise crisis incident caused by a general social problem may not reach its peak within a few days [33]. The various and complex cycles of enterprise crisis incidents make the establishment of a unified standard for a specific time point challenging. Some studies do not analyze the cycle of enterprise crisis incidents based on their time points, but rather rely on the enterprise crisis cycle stages. Fink proposed a four-stage analysis theory of crisis [34], believing that crisis communication can be divided according to different stages besides its classification according to the duration of the incident. He also divided the development of a crisis into four stages: prodromal, breakout, chronic, and resolution. Pearson and Mitroff also proposed a five-stage crisis management model for crisis management [35]. As for the response issue, some studies established the enterprise response stage hypothesis based on the cycle of enterprise crises.

Based on the aforementioned literature review, we believe that the measurement of response timing should be judged in combination with the communication cycle of the crisis incident, rather than only relying on a time point separated from the communication cycle. At this stage, when crises occur, due to the complexity of their types, the reasonable intervention timing for an enterprise should be selected around the communication stage of the incident, rather than a specific time node. Moreover, it has been shown that faster information processing yields a quicker spread of the incident end or transformation. This study analyzed the spread of crisis incidents in terms of the communication speed and volume. The following hypothesis was made:

H1. 

After a crisis incident occurs to an enterprise, its response intervention in the later stage has a significant positive impact on the spread of the underlying crisis.

H1a. 

After a crisis incident occurs to an enterprise, its response intervention in the later stage has a significant positive impact on the volume of the underlying crisis communication.

H1b. 

After a crisis incident occurs to an enterprise, its response intervention in the later stage has a significant positive impact on the speed of the underlying crisis communication.

2.2.2. Enterprise Crisis Response Modes

Yao deduced that the responses of enterprises should also take the specific response modes into consideration [36]. The timeliness and completeness of crisis responses are crucial. However, it is often difficult to simultaneously achieve them. Therefore, managers should make trade-offs based on the existing resources. When the rumor environment of a crisis remains stable or predictable, managers should evaluate the situation and provide the public, as much as possible, with complete information. When dealing with a complex situation where the relevant enterprise departments cannot provide sufficient crisis-related information, managers should take the timeliness issue into consideration.

Based on the actual response modes of enterprises on social media, some enterprises adopt a short, flat, and fast approach to prepare effective responses in an approachable way, while others use official documents for formal responses. Shao et al. argued that the evaluation of an enterprise’s crisis response requires judgment from many dimensions, such as discourse, spokesperson, and delivery method [37]. As for the content of crisis responses, enterprises can respond through clear and logical elaborations. They can also employ spokespersons having good credibility and image to respond, and use their personal reputation and credibility to convey information. This may make the public more receptive to the content of the response [37]. Therefore, the formal statement or quick reply will have a positive impact on the spread of enterprise crises as long as the public can accept the information. Based on the aforementioned results and the actual social media response forms of enterprises, this study proposed two response modes: formal statements and quick replies. It also explored the effective intervention timing of enterprise crisis response modes.

The following hypotheses were made:

H2. 

Different response modes of enterprises significantly affect the spread of their crises.

H2a. 

Formal statements of enterprises have a significant positive impact on the volume of their crisis communication.

H2b. 

Formal statements of enterprises have a significant negative impact on the speed of their crisis communication.

H2c. 

Quick replies of enterprises have a significant positive impact on the volume of their crisis communication.

H2d. 

Quick replies of enterprises have a significant negative impact on the speed of their crisis communication.

2.2.3. Content of Enterprise Crisis Responses

When a crisis occurs to an enterprise, its response has the same importance as the adopted response strategy. The rhetorical orientation of the crisis response of an enterprise mainly explores its ability to justify and persuade after the occurrence of a crisis. It also defuses the crisis and saves the image from the perspective of its discourse, aiming at repairing the reputation of the enterprise, reducing the negative impacts, and minimizing the occurrence of negative behaviors [1]. Different crisis response content systems have been proposed based on various crisis scenarios and objects. These systems are presented in

Table 1. Classification of different crisis response content systems.

No.	Classification of Crisis Response Strategies	Research Object	Reference
1	Excuse, justification, ingratiation, intimidation, apologize, factual distortion	Enterprise	Allen et al. [38]
2	Denial, evasion of responsibility, reduction of event offensiveness, corrective action, mortification	Organization	Benoit [12]
3	Denial, diminishment, rebuilding, bolstering	Organization	Coombs [1]
4	Denial, evasion of responsibility, rationalization, concession	Enterprise	Bradford et al. [39]
5	Shifting of blame, minimization, no comment, apology, compensation, corrective action	Organization	Lee [40]
6 *	Denial, evasion of responsibility, formal condolences, reduction of external attacks, acknowledgment/apology, corrective action, providing information and constructing new issues.	Enterprise	Huang et al. [14]
7	Denial, providing partial/inaccurate/delayed information, establishing open and accurate communication channels	Organization	Wilcox et al. [41]
8	Denial of responsibility, hedge responsibility, catering, apology/compensation, evoking sympathy	Organization	Ray [42]
9	Indicative information, adjusted information, internalized information	Organization	Sturges [43]
10	Foundation, denial, diminishment, rebuilding, reinforcement, and punishment	Organization	Jin et al. [20]

* Principal component analysis yielded 5 categories of crisis communication strategies: denial, evasion of responsibility, justification, concession, and diverting attention.

.

It can be deduced from the aforementioned literature review that Coombs’ situational crisis communication theory (SCCT) remains the most effective response content system for enterprise crises [27]. The four common categories of crisis response content (i.e., denial, diminishment, rebuilding, and bolstering) can effectively summarize the response content methods selected by enterprises when crisis incidents occur. In fact, a response content system based on the SCCT can affect the people’s acceptance of the enterprise during the underlying crisis. This affects their evaluation of the enterprise, which limits the spread of crises and reduces their negative impacts on the enterprise.

In the process of enterprise crisis response, various content methods have different effects on the spread of crises. The denial content is used to cut off the connection between the enterprise and the crisis. In addition, different responses can weaken the public’s attention to the crisis spread to a certain extent. The diminishment content is used to reduce the connection between the enterprise and the crisis by reducing the impact of the crisis as well as the volume and speed of communication. The rebuilding content is used to change the image of the enterprise in the crisis. The bolstering content allows the enterprise to find opportunities in the crisis [1]. The latter aim at repairing the enterprise’s image, and they may have different impacts on the volume and speed of crisis communication.

By analyzing the communication cycle, it can be deduced that various crisis response contents of enterprises can have different impacts on the spread of their crises in a wide range of scenarios. The following hypotheses were made:

H3. 

The response contents of enterprises significantly affect the spread of their crises.

H3a. 

The denial response content of enterprises has a significant negative impact on the volume of their crisis communication.

H3b. 

The rebuilding response content of enterprises has a significant negative impact on the volume of their crisis communication.

H3c. 

The diminishment response content of enterprises has a significant negative impact on the volume of their crisis communication.

H3d. 

The bolstering response content of enterprises has a significant negative impact on the volume of their crisis communication.

3. Design of the Study

3.1. Original Data

This study leveraged the proprietary dataset from Zhiwei Data’s Crisis Insights platform to capture raw corporate communication data and corresponding response metrics during crisis incidents. Zhiwei Data’s Crisis Insights are specially collected and sorted for enterprise crisis incidents. It is the largest enterprise crisis case database in China, recording almost ten thousand incidents every year [33] (https://crisis.zhiweidata.com/, accessed on 19 March 2025).

Chinese social media platforms were selected as the research focus due to their dual value in elucidating unique crisis propagation mechanisms and exploring universal applicability. First, China’s platforms—such as WeChat and Weibo, each with over one billion monthly active users—are deeply embedded in public daily life, positioning them as central arenas for crisis information diffusion and public opinion formation [44]. Unlike Western counterparts, Chinese social media exhibits distinctive “strong-tie network propagation” and “stratified interaction dynamics,” where crises rapidly proliferate through acquaintance networks (strong-tie connections) and interest-based communities. This compels organizations to adopt localized dialogic strategies to reconcile multifaceted stakeholder demands [45]. Consequently, focusing on Chinese platforms not only advances crisis management paradigms in non-Western contexts, but also provides a differentiated empirical sample for global scholarship on social media crisis propagation.

In this study, Chinese social media data from 2016 to 2019 were selected. In 2016–2019, enterprise crisis incidents rapidly spread on social media. Compared with years before 2016, the rapid development of social media after 2016 has made the spread of enterprise crisis incidents more influential. Studies have also been conducted on crisis communication management based on social media around the incidents in this period [46].

This study took 94 representative crisis incidents from 2016 to 2019 from Zhiwei Crisis Insights, then collected and sorted out the relevant data. The selected incidents were to meet the following criteria: belong to the key incidents of the year, be included in multiple domestic annual lists, with the influence index of Zhiwei Crisis Insights greater than 85, and spread on multiple platforms. These 94 incidents involved many enterprises, including Chinese enterprises and foreign-funded enterprises, as well as their data on Chinese social media platforms (e.g., the communication data on Weibo, WeChat, and online media platforms). Note that Weibo is the largest social media platform in China, while WeChat has the highest private domain communication ability. The online media platform consists of the websites operated by the media. In the 94 enterprise crisis incidents, the maximum number of article spreads was 67,431, and the total number of articles was 3,135,675. Zhiwei Crisis Insights mainly comprises original data about the incidents and the relevant attribute characteristic data during the spread of the incidents (e.g., the original article content of the incidents, the authors of the articles, and their interaction data).

Moreover, the earliest information about an incident on the Internet is considered to be its start. Based on Fink’s four-stage method, the end of an incident is the time when the heat of the entire incident continues without a natural heat increment, that is, the spread of the incident enters the long tail period.

3.2. Data Processing and Calculation

3.2.1. Data Description

The study used data from Zhiwei Crisis Insights for research. The Zhiwei Crisis Insights platform collects the original propagation data on social media platforms during corporate crisis events. The research required pre-processing of the original data from Zhiwei Crisis Insights. Firstly, the same data cleaning was performed for data with the same IP to ensure data validity. Since the experimental objects of the study were event data, there was no problem of missing values. The study needed to deal with outliers that were too high or too low in the experimental data. The study adopted the method of logarithmic transformation. Logarithmic transformation is a common data pre-processing method, especially suitable for datasets with right-skewed distribution (positive skewness) or extremely high values. Its core function is to reduce the skewness of data distribution by compressing the scale of the high-value area and expanding the scale of the low-value area, thereby weakening the interference of outliers in statistical analysis. Through logarithmic processing, this study effectively solved the problems of experimental data.

3.2.2. Dependent Variables

Based on the existing studies, we selected the volume and speed of communication during the spread of enterprise crises as dependent variables to analyze how they are impacted by responses.

Volume: based on the original data, the quantity of content related to enterprise crisis incidents on social platforms (e.g., the number of Weibo articles, WeChat articles, and media articles) was organized and counted to form the original value of the incident spread.

In terms of the speed of crisis incident spread, the rhythms of crisis incidents at various stages are inconsistent, and there will be deviations if they are reflected by a unified average spread speed. In this study, the results obtained by Yu et al. [33] were used to calculate the speed of crisis incident spread. This research modeled the propagation dynamics of corporate crisis events. To address the challenge that nonlinear crisis dissemination cannot be adequately captured by uniform parameters, the study proposed two novel metrics—time constant $P$ and decay coefficient $T$—to parameterize the evolution and attenuation processes of crises. By introducing $P$ and $T$, the framework expands crisis analysis beyond conventional dimensions (e.g., dissemination volume and propagation cycle), offering complementary analytical axes to reveal latent patterns in crisis dynamics. This approach enhances the granularity of crisis phase identification while providing operational metrics for real-time intervention efficacy assessment. In this research, enterprise crisis communication incidents can be calculated using the second-order time-domain response equation:

$$W_1(t)=\left\{\begin{array}{l}0, t<\tau \\ 1-{\displaystyle \frac{T{e}^{\frac{t-\tau }{T}}}{T-P}}+{\displaystyle \frac{P{e}^{\frac{t-\tau }{P}}}{T-P}}, t\ge \tau \end{array}\right.$$

(1)

The model includes an endogenous evolution model:

$$W_1(t)=\left\{\begin{array}{l}0, t<\tau \\ 1-e^{-{\displaystyle \frac{(t-\tau )}{P}}}, t\ge \tau \end{array}\right.$$

(2)

where $P$ is the time constant for the spread and evolution of the crisis incident used to represent the speed at which the crisis spread rises in this incident; $T$ represents the decay coefficient of the crisis incident spread, which indicates the decay speed of the crisis spread in this incident [33]; $W_1\left(t\right)$ and $W\left(t\right)$ represent the cycle of the social media corporate communication cycle; $t$ represents the time in which the corporation’s first response occurs; and $\tau$ represents the coefficient of events.

$P$ and $T$ jointly affect the speed of the spread of the enterprise crisis incident. They serve as effective evaluation metrics for the spread speed of the enterprise crisis incident. This study combined these parameters to analyze the impact of the response timing on the speed of the spread and its overall value. The latter reflects the final total amount of the spread, while $P$ and $T$ represent its speed. The combination of the two can reflect the overall situation of the spread.

3.2.3. Independent Variables

We constructed characteristic variables regarding the overall response mode, response time, and response content of the enterprise:

• Timing of the Enterprise Crisis Response

This study first defined the time point of the first response and the time stage in which it occurred:

The time point of the enterprise’s first response ($inter\_time$) represents the time when the enterprise makes its first response after a crisis incident. The time point of the first response is mainly used for comparative studies on the timing issue of the response.

The time period of the enterprise’s first response $(inte{r}_{stage})$ represents the time period of the life cycle of the crisis incident in which the enterprise makes its first response after a crisis incident.

Based on the time period in which the enterprise’s response occurs, and relying on the study of Yu et al. [33] as well as the stage division method of the first-order time-domain response, communication incidents are divided into different stages. The definitions of the upswing, decline, and long tail periods of crisis incidents are based on Fink’s definition of crisis stage division. They are defined in combination with the characteristics of timely crisis communication [34].

The time of entering the upswing period $t_{in\_r}$ is defined as the time point when the crisis incident enters the upswing period of the event development. It can be expressed as follows:

$$t_{in\_r}=t_{inc}=\tau +0.105P$$

(3)

The time of entering the decline period $t_{in\_re}$ is defined as the time point when the crisis incident enters the decline period of the event development. It is expressed as follows:

$$t_{in\_re}=t_{in\_r}+t_r=\tau +2.305P$$

(4)

The time of entering the long tail period is defined as the time point when the crisis incident enters the long tail period of the event development. It can be expressed as follows:

$$t_{in\_lt}=\tau +3P$$

(5)

In addition, based on the specific time point of the response, the response stages are defined as follows: $inter\_stage1$, $inter\_stage2$, and $inter\_stage3$.

2.

Enterprise Crisis Response Modes

The overall response mode of the enterprise: based on the previously mentioned literature review, this study divided the response modes of enterprises into two types: informal response (denoted by $resp$) and formal statement (denoted by $stat$). Formal statements refer to official responses issued by corporations through verified social media accounts, typically structured as standardized official correspondence (e.g., press releases, policy announcements). Informal responses, by contrast, encompass non-official engagement tactics such as humorous or colloquial interactions via corporate accounts or employee-generated posts on personal or semi-official platforms. For these two variables, a binary classification method was adopted. That is, they were represented by 0 and 1, respectively.

3.

Content of Enterprise Crisis Responses

The theoretical analysis, the categories of crisis response content in Coombs’ SCCT crisis response strategy, and the actual response content of enterprises in incidents were adopted to organize the enterprise crisis response content as follows: denial, diminishment, rebuilding, and bolstering. Guided by the situational crisis communication theory (SCCT) definitions of deny, diminish, rebuild, and reinforce, trained coders systematically evaluated each corporate response. Coding classifications were derived through a tripartite analysis of contextual factors (e.g., crisis stage, stakeholder salience), semantic content (explicit claims, rhetorical framing), and affective tone (empathy, defensiveness, neutrality). Since the enterprise response content often involves multiple aspects, it is not considered a single variable. Each variable adopted a binary classification method. That is, if the enterprise response contained content of the corresponding category, the coder would assign it a 1. Otherwise, it was coded as 0. In addition, if there was no response, the corresponding category of content was considered not to be included.

3.2.4. Other Control Variables

Results of the existing studies show that the spread of crisis incidents is related to the brand health of enterprises in normal situations. If an enterprise is highly concerned about daily life, it is more likely to attract people’s attention in the case of crisis occurrence, and vice versa. This study used the proportion of the discussion volume of an enterprise crisis incident at the time of its occurrence in the overall discussion volume on social media to reflect the attention of people at the beginning of the incident. It also considered it as a control variable.

The proportion of the discussion volume at the beginning of the incident ($start\_vol\_rate$) represents the proportion of the spread volume of an enterprise crisis incident at the time of its occurrence to the overall discussion volume on social media.

The descriptive statistical analysis of the data is shown in

Table 2. Descriptive statistics of the numerical variables.

	Mean	Std. Dev.	Min	Max
$Volume$	33,358.24	8924.83	161.00	67,431.00
$P$	28.15	19.48	4.46	109.31
$T$	17.30	19.79	0.00	109.30
$inter\_time$	13.14	11.44	0.00	47.50
$stat\_vol\_rate$	2.08%	401.00%	0.01%	33.66%
$KOL$	45.23	26.99	7.00	140.00

and

Table 3. Descriptive statistics of the categorical variables.

Lable	0	1
$resp$	16 (17.02%)	78 (82.98%)
$stat$	46 (48.94%)	48 (51.06%)
$inter\_time$	77 (81.91%)	17 (18.09%)
$inter\_stage$	83 (88.3%)	11 (11.7%)
$resp\_deny$	78 (82.98%)	16 (17.02%)
$resp\_diminish$	78 (82.98%)	16 (17.02%)
$resp\_rebuild$	76 (80.85%)	18 (19.15%)
$resp\_bolster$	78 (82.98%)	16 (17.02%)
$stat\_deny$	68 (72.34%)	26 (27.66%)
$stat\_diminish$	55 (58.51%)	39 (41.49%)

below.

Moreover, according to an existing study on social media [7], the key users ($kol$) often play a crucial role in the spread of enterprise crisis incidents, and they can affect the overall communication. Therefore, this study considered the number of the key communication users in a crisis incident as a control variable. The specific dimensions and definitions are given below.

The number of the key users ($kol$) is the number of the key users who participate in the electronic word-of-mouth communication in a crisis incident, that is, the users whose followership ranks in the top 20% on their respective platforms.

Finally, the type of the incident, initial occurrence time period, and initial occurrence platform are organized as defined below:

(1)

$e\_tim{e}_i$ represents the i^th time period in the initial occurrence’s time period of the enterprise. Based on the national unified time period division standard, time periods are divided into four stages: 1–8 am, 8 am–1 pm, 1–7 pm, and 7 pm–1 am. In this paper, the time period 1–8 am was set as the reference.

(2)

$typ{e}_j$ represents the $j^{\mathrm{t}\mathrm{h}}$ type of the incident type. The incident types are divided into several categories: product attributes, marketing services, corporate values, illegal and criminal activities, sensitive issues, internal corporate management, and corporate strategies. The product attributes category was considered to be the reference. More precisely, product attribute incidents are the enterprise crisis incidents caused by product quality and other issues. Marketing service incidents are the enterprise crisis incidents caused by improper marketing publicity and marketing activities of the enterprise. Corporate value incidents are the enterprise crisis incidents triggered by many factors, including insufficient corporate ESG, corporate social responsibility, and corporate annual reports of low quality. Illegal and criminal incidents are the enterprise crisis incidents caused by criminal cases, such as tax evasion and tax fraud involving the enterprise, or those involving employees, including prostitution and bribery. Sensitive issue incidents are the enterprise crisis incidents triggered by issues related to ideology in the daily operation. Internal corporate management incidents are the enterprise crisis incidents caused by non-criminal cases and non-ideological issues, such as excessive work, layoffs, and salaries of internal employees. Corporate strategy incidents are the enterprise crisis incidents triggered by enterprise acquisitions, asset sales, etc.

(3)

$platfor{m}_k$ represents the platform where the crisis incident first occurred, while setting the online media platform as the reference.

These variables are summarized in

Table 4. The features used in the study.

Feature	Definition	Source
$Volume$	Overall volume value of the dissemination of crisis events	Zhiwei Crisis Insights, $\mathrm{l}\mathrm{n}$
$P$	Time constant of the dissemination evolution of crisis events	Calculated, $ln$
$T$	Decay coefficient of the dissemination of crisis events	Calculated, $ln$
$resp$	Indicates whether the enterprise has an informal response	Encoding
$stat$	Indicates whether the enterprise has an official statement	Encoding
$inter\_time$	Time of the first intervention of the enterprise	Zhiwei Crisis Insights, $\mathrm{l}\mathrm{n}$
$inter\_stage$	Time stage of the first intervention of the enterprise	Calculated
$resp\_deny$	Indicates whether the content of the informal response of the enterprise contains negative content	Encoding
$resp\_diminish$	Indicates whether the content of the informal response of the enterprise contains downplaying content	Encoding
$resp\_rebuild$	Indicates whether the content of the informal response of the enterprise contains reshaping content	Encoding
$resp\_bolster$	Indicates whether the content of the informal response of the enterprise contains supportive content	Encoding
$stat\_deny$	Indicates whether the official statement of the enterprise contains negative content	Encoding
$stat\_diminish$	Indicates whether the official statement of the enterprise contains downplaying content	Encoding
$stat\_rebuild$	Indicates whether the official statement of the enterprise contains reshaping content	Encoding
$stat\_bolster$	Indicates whether the official statement of the enterprise contains supportive content	Encoding
$stat\_vol\_rate$	Proportion of the reputation volume at the beginning of the event	Zhiwei Crisis Insights
$kol$	Number of the key users	Top 20% on every platform, $\mathrm{l}\mathrm{n}$
$type$	Type of the crisis event	Zhiwei Crisis Insights Encoding
$platform$	The platform where the crisis event first occurred	Zhiwei Crisis Insights
$e\_time$	The time period when the crisis event first occurred	Zhiwei Crisis Insights

.

3.2.5. Content Coding

In this paper, several aspects, such as formal responses and statements, response content, and types of crisis incidents, required cooperation with the content-coding work. Note that the entire coding process was performed by two coders. One of them holds a doctoral degree in the field of crisis communication management, and the other has 8 years of industry experience in enterprise crisis management. Fleiss’ kappa measurement was used to evaluate and report the level of consistency among the coders. The inter-rater reliability of the coders reached 92%, which meets the requirements of content analysis for the reliability among coders [47].

3.3. Research Model

This study employed MATLAB 2023b for computational processing of variables and utilized Stata 18 to execute regression modeling. We used a regression model to conduct separate studies on three types of hypotheses. The ordinary least squares (OLS) method was adopted to fit the aforementioned equations. For continuous variables, data processing was performed according to the data situation. In the equation, the coefficients were used to represent the intervention impact of control variables on the spread of enterprise crises. These control variables included the proportion of the word-of-mouth volume at the beginning of the incident ($start\_vol\_rate)$, the number of the key users $(kol)$, the type of the crisis incident $(type)$, the $platform$, and the initial occurrence time period $(e_{time})$. Note that ${\beta }_0$represents the intercept term. By combining these variables, the impacts of the response timing, response mode, and response content of enterprise crises on the communication effect were analyzed.

• Study on the Response Timing

This section studies the impact of the response time points on the spread of crises in enterprise crisis incidents. This reflects the communication situation of enterprises in crisis incidents through the evolution time constant, decay coefficient, and total spread volume:

$$\mathit{ln}P={\beta }_0+{\beta }_{11}·\mathit{ln}inter\_time+(controls)·\gamma +\epsilon$$

(6)

$$\mathit{ln}T={\beta }_0+{\beta }_{11}·\mathit{ln}inter\_time+(controls)·\gamma +\epsilon$$

(7)

$$\mathit{ln}Volume={\beta }_0+{\beta }_{11}·\mathit{ln}inter\_time+(controls)·\gamma +\epsilon$$

(8)

Afterwards, based on the analysis of the first response time point, a study was conducted on the time stage of the enterprise’s crisis response:

$$\mathit{ln}P={\beta }_0+{\beta }_{12}·\mathit{ln}inter\_stage+(controls)·\gamma +\epsilon$$

(9)

$$\mathit{ln}T={\beta }_0+{\beta }_{12}·\mathit{ln}inter\_stage+(controls)·\gamma +\epsilon$$

(10)

$$\mathit{ln}Volume={\beta }_0+{\beta }_{12}·\mathit{ln}inter\_stage+(controls)·\gamma +\epsilon$$

(11)

2.

Enterprise Crisis Response Modes

This section analyzes the impact of the response strategy on the spread of enterprise crises. The informal responses and formal statements of enterprises were considered as research variables.

$$\mathit{ln}P={\beta }_0+{\beta }_2·resp+{\beta }_3·stat+(controls)·\gamma +\epsilon$$

(12)

$$\mathit{ln}T={\beta }_0+{\beta }_2·resp+{\beta }_3·stat+(controls)·\gamma +\epsilon$$

(13)

$$\mathit{ln}Volume={\beta }_0+{\beta }_2·resp+{\beta }_3·stat+(controls)·\gamma +\epsilon$$

(14)

3.

Content of Enterprise Responses

This section analyzes the impact of the content of enterprise responses during crisis incidents on the spread of the underlying crisis on social media. These three equations were used to gradually explore the kind of content strategies adopted in crisis responses that more effectively intervened in the spread of crises:

$$\begin{array}{ll}\ln P={\beta }_0+{\beta }_{31}& ·resp\_deny+{\beta }_{32}·resp\_diminish+{\beta }_{33}·resp\_rebuild+{\beta }_{34}\\ & \cdot resp\_bolster+{\beta }_{41}·stat\_deny+{\beta }_{42}·stat\_diminish+{\beta }_{43}\\ & \cdot stat\_rebuild+{\beta }_{44}·stat\_bolster+\left(controls\right)·\gamma +\epsilon \end{array}$$

(15)

$$\begin{array}{ll}\ln T={\beta }_0+{\beta }_{31}& ·resp\_deny+{\beta }_{32}·resp\_diminish+{\beta }_{33}·resp\_rebuild+{\beta }_{34}\\ & ·resp\_bolster+{\beta }_{41}·stat\_deny+{\beta }_{42}·stat\_diminish+{\beta }_{43}\\ & ·stat\_rebuild+{\beta }_{44}·stat\_bolster+\left(controls\right)·\gamma +\epsilon \end{array}$$

(16)

$$\begin{array}{ll}\ln Volume={\beta }_0& +{\beta }_{31}·resp\_deny+{\beta }_{32}·resp\_diminish+{\beta }_{33}·resp\_rebuild+{\beta }_{34}\\ & ·resp\_bolster+{\beta }_{41}·stat\_deny+{\beta }_{42}·stat\_diminish+{\beta }_{43}\\ & ·stat\_rebuild+{\beta }_{44}·stat\_bolster+\left(controls\right)·\gamma +\epsilon \end{array}$$

(17)

4. Obtained Results

4.1. Results and Analysis of the Timing of Corporate Response

Based on Equations (6)–(11), this paper studied the impact of the response timing to a crisis event on the crisis communication. Besides the division of the enterprise crisis communication stages, it studied the impact of the time period of response on the crisis communication. This was performed by analyzing the impacts of different corporate response times. The obtained results are shown in

Table 5. Impact of the timing of the enterprise’s response to a crisis.

	$\mathit{P}$		$\mathit{T}$		$\mathit{V}\mathit{o}\mathit{l}\mathit{u}\mathit{m}\mathit{e}$
$inter\_time$	0.112		0.122		−0.039
$inter\_stage1$		−0.273		−0.904 **		0.083
$inter\_stage2$		−0.058		−0.416		0.116
$inter\_stage3$		0.774		−0.153		0.089
Internal management	0.021	0.030	0.122	−0.329	0.447	0.443
Enterprise value	−0.404	−0.295	−0.461	0.254	0.583	0.545
Strategic actions	0.369	0.389	0.159	−0.929	0.192	0.197
Sensitive issues	0.068	0.130	−0.941	−0.119	1.109 ***	0.976 ***
Marketing services	−0.242	−0.059	−0.150	−0.407	0.261	0.387
Criminal offenses	−0.042	0.016	−0.694	0.710	0.819	0.744
Wechat	0.402 **	0.296 *	0.500	−0.070	0.364	0.322
Weibo	−0.035	−0.030	0.085	−0.111	0.304	0.440 *
8 am–1 pm	0.047	0.058	−0.222	0.127	−0.161	−0.336
1–7 pm	0.101	0.004	−0.276	0.562	−0.507 *	−0.505 *
7 pm–1 am	−0.048	0.024	0.526	0.045	−0.415	−0.336
$start\_vol\_rate$	−0.058	−0.100 **	−0.293	−0.279 ***	0.019	0.029
$kol$	0.458 ***	0.564 ***	−0.260 ***	1.104 ***	1.577 ***	1.575 ***
$cons$	1.031 *	1.053 *	0.926 ***	−1.243	1.134	0.959
$N$	78	94	−1.340	94	78	94
$R^2$	0.281	0.269	78	0.373	0.680	0.648
$\mathrm{Adjusted}{R}^2$	0.121	0.117	0.353	0.242	0.609	0.575
$F$	1.757	1.767	2.450	2.858	9.559	8.859

Note: * p < 0.1, ** p < 0.05, *** p < 0.01.

.

It can be seen from the first row of Table 5 that the specific response time point of a company’s crisis response does not significantly affect the corporate crisis communication in a statistical sense. This demonstrates that in various and complex corporate crisis events, the time point of the first crisis response ($inter\_time$) does not affect the corporate crisis communication. That is, at the current stage, the traditional concept of the golden n hours does not affect the communication in corporate crisis events. On the contrary, various communication stages where different companies respond have different impacts on the corporate crisis communication. Compared with the choice of not intervening, when a company intervenes and responds during the incubation period, the decay coefficient of crisis communication decreases by 90.4% ($p<0.01$).

In an environment where crisis events frequently occur, it is difficult to find an effective and unified intervention time node. Nevertheless, there exist effective stages for intervention. The corporate crisis response that intervenes during the incubation period has a different impact on the crisis event compared with the choice of not intervening (or intervening at later stages). At that time, the response can shorten the cycle of corporate crisis communication.

4.2. Results and Analysis of the Manner of Corporate Response

Table 6. Impact of the types of responses made by enterprises after crisis occurrence.

	$\mathit{P}$	$\mathit{T}$	$\mathit{V}\mathit{o}\mathit{l}\mathit{u}\mathit{m}\mathit{e}$
$resp$	−0.234	−0.138	0.134
$stat$	0.087	−0.607 **	0.294
Internal management	−0.031	−0.471	0.384
Enterprise value	−0.155	0.009	0.569
Strategic actions	0.264	−1.025 *	0.107
Sensitive issues	0.054	−0.143	0.951 ***
Marketing services	−0.162	−0.354	0.330
Criminal offenses	−0.388	0.649	0.551
Wechat	0.213	−0.081	0.283
Weibo	−0.043	−0.253	0.394 *
8 am–1 pm	−0.052	−0.059	−0.299
1–7 pm	−0.052	0.455	−0.483 *
7 pm–1 am	−0.023	−0.157	−0.276
$start\_vol\_rate$	−0.116 ***	−0.262 ***	0.025
$kol$	0.475 ***	0.936 ***	1.518 ***
$cons$	1.388 ***	−0.681	1.055 *
$N$	94	94	94
$R^2$	0.266	0.359	0.657
$\mathrm{Adjusted}{R}^2$	0.125	0.236	0.591
$F$	1.888	2.919	9.946

Note: * p < 0.1, ** p < 0.05, *** p < 0.01.

shows the impact of the types of responses made by enterprises after the occurrence of a crisis. It can be seen that, in a crisis event, the informal response is less effective than the formal statement. In addition, for enterprises that adopt a formal statement, the decay index ($T$) of a crisis event in crisis communication is 0.607 higher ($p<0.05$) than that of those that do not respond. In other words, a formal statement by an enterprise is more conducive to the removal of crisis communication.

4.3. Results and Analysis of the Content of Corporate Crisis Response

The impact of the content of corporate crisis responses on the crisis communication was studied. The obtained results are presented in

Table 7. Impact of the content of enterprise crisis response.

	$\mathit{P}$	$\mathit{T}$	$\mathit{V}\mathit{o}\mathit{l}\mathit{u}\mathit{m}\mathit{e}$
$resp\_deny$	0.110	−0.155	0.391
$resp\_diminish$	−0.446 **	−0.025	0.654 **
$resp\_rebuild$	0.038	0.130	−0.346
$resp\_bolster$	−0.140	−0.018	−0.449 *
$stat\_deny$	0.231	0.660 *	−0.184
$stat\_diminish$	0.110	−0.072	−0.073
$stat\_rebuild$	0.423 **	−0.042	−0.354
$stat\_bolster$	−0.116	−0.644 *	0.587 **
Internal management	−0.149	−0.501	0.408
Enterprise value	−0.473	−0.223	1.163
Strategic actions	0.145	−1.464 **	0.279
Sensitive issues	−0.135	−0.062	1.238 ***
Marketing services	−0.184	−0.459	0.313
Criminal offenses	−0.653	0.830	0.684
Wechat	0.320 *	−0.107	0.105
Weibo	−0.115	−0.382	0.522 **
8 am–1 pm	0.024	0.100	−0.504 **
1–7 pm	0.037	0.632	−0.659 ***
7 pm–1 am	0.006	0.080	−0.470 *
$start\_vol\_rate$	−0.114 ***	−0.251 ***	−0.005
$kol$	0.369 ***	0.868 ***	1.677 ***
$cons$	1.649 ***	−0.696	0.650
$N$	94	94	94
$R^2$	0.345	0.395	0.722
$\mathrm{Adjusted}{R}^2$	0.154	0.218	0.641
$F$	1.808	2.237	8.903

Note: * p < 0.1, ** p < 0.05, *** p < 0.01.

.

By synthesizing informal responses and formal statements, if an informal response adopts a reduction strategy, it decreases the overall communication cycle (−0.446, $p<0.05$). However, it increases the magnitude of corporate crisis communication (0.654, $p<0.05$). In general, an adopted supportive strategy results in a one-sided decrease in the magnitude of corporate crisis communication (−0.449, $p<0.1$). As for formal statements, if a denial strategy is adopted, the decay coefficient significantly increases (0.660, $p<0.1$), which results in prolonging the communication cycle of the crisis event. An adopted reshaping strategy extends the cycle of crisis communication (0.423, $p<0.05$). A supportive strategy significantly decreases the decay coefficient (−0.644, $p<0.05$) and increases the magnitude of crisis communication (0.587, $p<0.05$). These results demonstrate that various response strategies have different effects, and enterprises should often develop appropriate response methods based on their own needs.

Moreover, the obtained results show that, although various response strategies have different impacts, it is difficult to simultaneously reduce the magnitude and the cycle of crisis event communication. Response strategies of enterprises often exhibit an opposing tension between the communication cycle and the magnitude.

4.4. Results and Analysis of Other Control Variables

This study identified the roles of the type of crisis event, initial release platform and time period, proportion of the initial public sentiment volume of the crisis event, and the number of the key users involved in its communication. Except for the event type, the initial release platform, and the time period, the key user variables in Table 5, Table 6 and Table 7 are significant at the 1% level. This indicates that the number of the key users can significantly increase the evolutionary time constant of crisis communication, and also increase the decay coefficient, which results in prolonging the communication cycle of the crisis event.

Furthermore, an increase in the number of the key users can significantly increase the magnitude of corporate crisis communication. The proportion of the public sentiment volume at the beginning of the crisis event was also studied. The obtained results show that, compared with a lower proportion, a higher proportion is associated with a shorter communication cycle when a crisis event breaks out. The proportion of the public sentiment volume at the beginning of the crisis event often reflects the position of the public sentiment volume of the enterprise on social media before the occurrence of the crisis. It also indirectly reflects the health level of the public sentiment. A high brand health level before a crisis event often leads to a quicker end of crisis communication.

5. Conclusions

The development of social media brought a qualitative change in corporate crisis management. It also reduced the controllability of corporate communication when dealing with crisis events. Corporate crisis response is an important module in corporate crisis management. It has gradually evolved new laws in terms of social media commercial communication. In the social media environment, corporate crisis communication events are becoming more diverse and complex. A careless enterprise response may lead to counterproductive effects, and sometimes trigger a secondary crisis. The development of a more effective methodology for corporate crisis response is crucial in the current corporate operation and management.

In this paper, we conducted a study on corporate response issues in the context of corporate crisis communication on social media. Based on 3,135,675 pieces of real communication data of corporate crisis events from 2016 to 2019, effective corporate crisis response methods were explored from a quantitative perspective. The new changes, with respect to the existing theoretical framework of crisis response, were also explored from two aspects: the timing and content of the response. In addition, the new corporate response issue of the response method that emerged along with social media was studied. As for the timing of the response, the obtained results show that the specific time point of crisis response is ineffective. Thus, the concept of corporate crisis response intervention at different time stages of crisis response was proposed. As for the content of the response, the impacts of four types of content (downplaying, supporting, denying, and reshaping) on crisis communication were studied. The obtained results demonstrate that it is difficult to balance the communication volume and time cycle. It is shown that in the social media environment, enterprises should develop a dynamic response system based on actual needs rather than adopt a unified response method. Finally, in terms of the response method, informal responses and formal statements were compared, which allowed clarifying the effectiveness of formal statements.

The main contribution of this paper consists in systematically conducting a quantitative study on corporate crisis response issues based on the theoretical framework of crisis response studies and in exploring new corporate crisis response strategies on social media platforms. The influencing factors of corporate social media crisis communication were then explored from three crisis response directions (response timing, content, and method), and effective corporate crisis response methods in the context of social media were analyzed, providing practical guidance for enterprises.

It can be concluded that the time point of corporate crisis response is invalid. The concept of the corporate crisis response stage is a new version of the traditional golden n hours theory. It also verifies the conclusion obtained by Yao et al. [36]: “the time point of crisis response is not the faster the better, and the speed of crisis response needs to consider the quality of the response”. To expedite the attenuation of the dissemination volume during corporate crisis propagation on social media, organizational intervention is most effective when deployed in the latent phase of the incident. The study’s core theoretical innovation lies in proposing a phase-driven intervention framework that supersedes conventional timepoint-triggered decision logic, thereby aligning response strategies with the dynamic, nonlinear progression of digital crisis cycles.

Moreover, in terms of the response method issue, based on the actual corporate applications, informal responses and formal statements were proposed, and quantitative results are provided for the qualitative hypotheses on the discourse and release methods proposed by Shao et al. [37]. This study paves the way for the combination of informal responses and statements. Finally, in terms of the response content, it was demonstrated that the crisis response strategy based on Coombs’ SCCT should be further refined to deal with social media [27], and a detailed analysis should be conducted on multiple types of objectives and dimensions in social media communication.

The findings partially challenge the validity of monolithic crisis response methodologies, underscoring the necessity for organizations to develop customizable, context-sensitive crisis communication architectures. Crucially, by integrating artificial intelligence (AI) and machine learning capabilities to establish predictive monitoring frameworks, enterprises can empirically quantify dissemination metrics—including propagation volume, lifecycle phase, and decay indices. This data-driven approach, when coupled with multimodal, dynamically adaptive response systems, empowers organizations with enhanced organizational agency during crisis propagation.

This paper also proposes some valuable directions for future studies on corporate crisis response. First, this study significantly expanded the research trajectory of social media crisis communication management by introducing three pivotal dimensions: response timing, methods, and content. Subdivided inquiries within these domains—such as multi-phase response sequencing, lexical density of communications, and frequency-intensity trade-offs—warrant rigorous empirical scrutiny to assess their impact on the established frameworks such as the social-mediated crisis communication (SMCC) model and the interactive crisis communication model (ICCM). The proposal of the crisis response phase can also guide various types of research on the impact of the crisis response phase. Second, in terms of the overall crisis response strategy, new feasible perspectives will emerge in the timing, method, and content of corporate crisis response. The impacts of these relevant strategies should be further analyzed. Third, this paper considered the communication volume and time of corporate crisis communication on social media as dependent variables. Future studies can more effectively enrich the dependent variables of corporate crisis communication and evaluate the impacts of crucial variables, such as corporate brand reputation and corporate sales volume. Fourth, future research should investigate how to construct crisis response systems that optimize the trade-off between the dissemination volume and propagation cycles, particularly through dynamic calibration of response intensity and content saturation thresholds. Fifth, the mediating role of control variables—such as the interplay between organizational responses and stakeholder–KOL collaborative networks—warrants deeper exploration. At the practical level, this study proposed to quantify the process and stages of crisis event dissemination. Therefore, combining artificial intelligence and machine learning to construct faster emotional assessment and integrate it into the response system is also a practical issue worth exploring in the future.