Content area
Purpose
“Metaverse” has become a buzzword in the Chinese stock market. However, it remains unclear whether a firm's metaverse-related announcements will elicit positive stock market reactions. Whether and how stakeholder reactions are influenced by a firm's metaverse-related readiness also needs to be further explored. This study aims to discuss the aforementioned objective.
Design/methodology/approach
The authors derived a set of factors based on readiness theory and business ecosystem literature and extend them into the context of the metaverse. The authors used a sample of 642 Chinese listed firms in 2021 to investigate the hypotheses through the event study.
Findings
The study’s findings show that metaverse coverage induces a positive stock market reaction, but it is subject to three moderating effects. The authors introduce the novel concepts of IT readiness, ecosystem readiness and digital infrastructure readiness as the moderators. Stakeholders perceive metaverse announcements as overhyped, and stock prices do not fluctuate significantly after a metaverse announcement when the listed firms are not ready to embrace the metaverse.
Originality/value
This study is one of the first that introduces the event study method into the metaverse research, and it reveals that different levels of readiness influence stakeholders' evaluations and reactions to corporate metaverse coverage. This provides empirical evidence on metaverse development in China from the stock market's perspective.
1. Introduction
Before 2021, the word “metaverse” seemed to exist only in cyberpunk science fiction. Stephenson (1992) used this word in his book Snow Crash to depict a virtual three-dimensional universe inhabited by millions of people whose avatars could interact with each other. However, it was not until 2021 that the concept of the metaverse took the public, especially the stock market, by storm. In March 2021, the gaming platform Roblox was the first to include the concept of the “metaverse” in its initial public offering prospectus. Its stock ended up being valued at an eye-watering $69.50 apiece, giving this gaming platform a market capitalization of $38.26 billion on the first day of going public. Roblox's success on the New York Stock Exchange brought the metaverse into public view and set off a wave of investment interest in the concept in the global stock market. The stock price of the chipmaking firm Nvidia leaped after unveiling the metaverse software platform, and shares of social media giant Facebook edged up after Chief Executive Officer Mark Zuckerberg announced his decision to rebrand the firm with the new name “Meta.” The new stock ticker “MVRS” demonstrated investor optimism about these new initiatives.
However, what is interesting is that the performance of metaverse stocks was more impressive in the Chinese stock market than the US stock market, where this concept originated. Investors have sought metaverse firms in China since the third quarter of 2021. By the end of January 4, 2022, the closing price of the Metaverse Concept Index in China reached CNY1,353, up by 33.40% from CNY 1,014 on September 2, 2021 (Pan, 2022). During this period, interest in the metaverse concept grew exponentially, and it was included in the roadmaps of many listed firms in the Shenzhen Stock Exchange and Shanghai Stock Exchange. However, some stock analysts and investors worry that the metaverse is overhyped in the Chinese stock market. Many listed firms use this metaverse concept to attract public attention but might not be ready to embrace it. They lack the readiness to deliver or enable the metaverse (Austin, 2022; Nath, 2022). Organizational readiness is defined as the availability of the needed resources for implementing information technology successfully (Iacovou et al., 1995). Although organizational readiness has been viewed as a critical precursor to successful implementation and been widely researched in different contexts, such as blockchain adoption assessment in the health care sector (Balasubramanian et al., 2021), artificial intelligence (AI), big data analytics (Raguseo, 2018), e-commerce (Molla and Licker, 2005) and enterprise resource planning implementation (Chwelos et al., 2001), few studies have investigated the metaverse readiness of listed firms in China. This study investigates the relationships between stock price and readiness of firms who claim that their next strategic move is related to the metaverse in the Chinese stock market. Under what circumstances does a listed firm's metaverse announcement positively affect its stock price? Under what circumstances do investors perceive metaverse announcements as overhyped? And under what circumstances do stock prices not fluctuate significantly after a metaverse announcement?
To answer these questions, we put forward four hypotheses based on stakeholder perspective and readiness theory. We argue that firms that issue metaverse announcements elicit a positive response from stakeholders, which leads to an increase in stock price. First, corporate metaverse coverage sends a positive signal to external audiences, reflecting the firm's willingness to invest in, develop and implement cutting-edge technologies and demonstrating its confidence and availability of sufficient resources to develop new businesses. Second, investors in the Chinese stock market are interested in positive information or news. The metaverse is regarded as a growing market in 2021 and has attracted significant attention from investors. Thus, we expect firms' stock prices to improve in the short term after the release of metaverse coverage.
Furthermore, we argue that the impact of corporate news related to the metaverse on stock returns varies with the organizational readiness of the metaverse, which is an essential factor for stakeholders to evaluate corporate metaverse operations. First, the adoption of and investment in critical technologies (e.g. virtual reality [VR], augmented reality [AR], and AI) are significant in the readiness for the metaverse. Firms with better technology readiness are more likely to gain positive responses from investors in the stock market after they publicly announce initiatives related to the metaverse. Second, ecosystem readiness reflects the availability of established trading partners and complementors of focal firms to ensure the implementation and use of critical technologies that support the metaverse. We argue that the availability of ecosystem support is essential for external audiences to evaluate a focal firm's readiness for carrying on metaverse businesses. Finally, we believe that regional digital infrastructure can facilitate the absorption and adoption of technology and knowledge spillover for focal firms, making the stock market respond positively after a corporate metaverse announcement.
By conducting the event study methodology in a sample of 642 Chinese listed firms, our study contributes to the literature regarding two aspects. One aspect is that we examine the impact of metaverse coverage on cumulative abnormal returns using the event study methodology. To the best of our knowledge, our study is one of the first to empirically investigate the stock market response to metaverse news based on secondary data analysis. More importantly, we integrate readiness theory into the business ecosystem literature in the context of metaverse studies. Our study reveals that different levels of readiness influence stakeholders' evaluations of and reactions to corporate metaverse coverage, providing empirical evidence on metaverse development in China from the stock market's perspective.
2. Theoretical background and hypotheses development
2.1 What is the metaverse?
Since its emergence, the metaverse and its meaning have constantly evolved, with vastly diversified participants describing them, differently, using terms such as second life (Sanchez, 2007), 3D virtual world (Dionisio et al., 2013), life logging (Bruun and Stentoft, 2019) and an embodied version of the internet (Xu et al., 2022). The solipsis white paper defined the metaverse as “a massive infrastructure of inter-linked virtual worlds accessible via a common user interface (browser) and incorporating both 2D and 3D in an immersive internet” (Frey et al., 2008). Lee (2021) called the metaverse “an immersive 3D virtual environment, a true virtual artificial community in which avatars act as the user's alter ego and interact with each other.” Ning et al. (2021) stated the metaverse is a new type of internet application and social form that combines many different emerging technologies. Mystakidis (2022) argued the metaverse as a post-reality universe, a multiuser environment that blends physical reality and digital virtuality. It provides an immersive experience based on AR technology, constructs a mirror image of the real world based on digital twin technology, establishes an economic system drawing on blockchain technology and seamlessly fuses the virtual and real worlds into economic, social and identity systems, allowing each user to produce content and edit the world (Barrera and Shah, 2023; Dwivedi et al., 2022). Based on these studies, we summarize seven key envisioned and commonly agreed-upon characteristics of the metaverse that distinguish it from incumbent digital innovations (see Table 1).
Although there is a lack of a strict uniform definition of the metaverse, it seems that the public and investors have formed a perceptual cognizance of the metaverse's characteristics from science fiction, films, media publicity and listed firms' annual reports and roadmaps. The metaverse is dubbed as an evolving paradigm of the ongoing internet revolution (Barrera and Shah, 2023). It is anticipated that the metaverse will provide businesses operating in various industries with a revenue potential of $1 trillion (CB Insights Research, 2022). The market for the metaverse is expected to increase at a compound annual growth rate (CAGR) of 13.1%, from $478.7 billion in 2020 to $783.3 billion in 2024 (Bloomberg Intelligence, 2021). However, the metaverse is only a hyped concept that is far from changing reality without the development and implementation of supporting digital technologies, ecosystems and digital infrastructures. According to readiness theory, “readiness for change” is a precursor to the successful implementation of complex changes (Weiner, 2009). The successful adoption of new concepts requires preparing the entire organization and ecosystem for the forthcoming change. The following section analyzes technology readiness, ecosystem readiness and infrastructure readiness that enable the aforementioned characteristics of the metaverse to become reality. In other words, whether firms are capable of embracing and reaping the commercial benefits of the metaverse depends on whether they already have or are on the way to possessing such readiness.
2.2 Market reaction to the metaverse
The concept of the metaverse exploded in 2021 in the Chinese stock market, with several firms announcing their metaverse-related initiatives. It was also deemed as the first year of China's metaverse. A report in Securities Times documented this astonishing phenomenon: Whenever a listed firm announces a product or project related to the metaverse concept, its stock price rockets up. For example, the listed firm ZhongQingBao (300,052)'s stock price rose 147% from September 6 to September 16 in 2021.
In management research, the media is viewed as an essential external stakeholder for firms (Tang and Tang, 2016). Media plays a vital role in corporate impression management and diffusion of corporate legitimacy information (Jeong and Kim, 2019). To evaluate a focal firm, stakeholders will collect information through multiple sources, including corporate announcements and media coverage (Bansal and Clelland, 2004). Thus, the media can significantly affect how stakeholders access a corporation. Some studies have regarded media coverage as an indicator of corporate legitimacy, and they have measured corporate legitimacy by calculating the amount of positive or negative coverage (Deephouse and Carter, 2005). Further, the media is a disseminator of corporate information. It can influence stakeholders' judgments of whether a corporation carries out its promised actions (Bednar, 2012), which can be reflected in its stock performance. For instance, Wang and Ye (2015) investigated the media coverage of stakeholders' impacts on firm value in China.
In China, investors' chasing behavior of positive news is quite common (Wang et al., 2004). This phenomenon makes the market reaction (i.e. stock volatility) to positive news more than to an equal degree to negative news. Stock liquidity in the Chinese stock market is limited because of institutional constraints, so Chinese investors are inclined to make short-term investments and seek positive information (Tao et al., 2017). Additionally, the number of institutional traders in the Chinese stock market is limited, and Chinese retail investors lack trading experience. As a result, retail investors have less rational trading behavior as well as a preference for listed firms with positive news (Black, 1986). News and announcements can alleviate information asymmetry between firms and external stakeholders, thus boosting corporate short-term stock returns. The ability to generate expected revenue for firms and to send positive signals to external audiences, such as tax policy announcements (Doidge and Dyck, 2015) and merger and acquisition announcements (Zhang et al., 2020), usually characterizes positive coverage. As a concept that has recently received much attention in the field of science and technology, the metaverse is also one of the critical players in future technological development. Stakeholders are more likely to focus on listed firms that issue metaverse coverage, perceiving such firms to have the potential for innovation and the ability to adapt to future technology trends (Aharon et al., 2022).
Furthermore, developing the metaverse requires substantial investment in research and development, which in turn requires significant resources (Jia et al., 2019). Stakeholders may interpret listed firms' metaverse announcements as positive signals as well as corporate executives' confidence in their future growth in the technology sector. Such coverage also signals to external audiences that the firm has substantial resources and capabilities to launch new businesses (Aharon et al., 2022). As a result, stakeholders are more likely to perceive metaverse coverage issued by listed firms as a positive signal.
In summary, for Chinese listed firms, making metaverse announcements can generate positive market reactions by eliciting favorable corporate judgments from external stakeholders. Therefore, we hypothesized the following:
Coverage of the metaverse by Chinese listed firms results in a positive stock market reaction.
2.3 Readiness for the metaverse
Despite the metaverse eliciting a positive market reaction, other factors still influence stakeholders' assessments of a firm's metaverse media coverage. In China, many listed firms gain the attention of stakeholders by deliberately emphasizing the relevance of their business to the metaverse. Market participants may also judge the readiness of a firm to conduct metaverse-related business. Therefore, we put forward several dimensions of readiness to identify marginal conditions for the metaverse to elicit a positive market reaction.
According to readiness theory, readiness is an essential precursor to complex organizational change and transformative development (Lokuge et al., 2019). Although the literature has highlighted the importance of different types of readiness of an organization to enable and deliver digital innovation, such as cultural readiness, strategic readiness, cognitive readiness and resource readiness, no prior studies have focused specifically on organizational readiness for the metaverse. Based on our review of metaverse literature, which emphasizes a long-term vision for an endless, seamless, interconnected universe where people live their virtual lives the same way they live their physical lives (Dwivedi et al., 2022; Wang et al., 2022; Xu et al., 2022), we propose that the most important types of readiness that influence an organization's entry into metaverse business development are IT readiness, ecosystem readiness and digital infrastructure readiness.
2.4 The moderating role of IT readiness
IT readiness is the readiness of an organization's digital fundamentals to develop innovative activities (Helfrich et al., 2011). The advancement and integration of a variety of digital fundamentals catalyze the development of the metaverse. Contributing factors include the availability, ability and compatibility of existing hardware, software, networks, applications and other information and communications technology (ICT) resources that facilitate the new technology (Kiberu et al., 2021). To actualize the spatial convergence of the virtual and real worlds, AR and its advanced variants, mixed reality (MR), holographic technologies and brain-computer interface (BCI) are used as the technological foundations. AR overlays the virtual information on the device's screen at a location determined by the detected object (e.g. two-dimensional, three-dimensional, global positioning system, somatosensory, facial and other detected objects) and allows users to interact with the virtual information (Cheng et al., 2022). Using a highly developed and idealized VR system, VR gives consumers a truly immersive experience that makes them feel like they are in a real world. MR is a novel visualization environment that merges actual and virtual worlds. In the new viewing environment, physical and digital objects cohabit and interact in real time. A holographic image is a recording and reproduction method that uses optical means to provide a true three-dimensional representation of an item. It is the product of the marriage of computer and electronic image technologies, and it employs coherent light interference to capture the amplitude and phase information of the light wave and obtain all the object's information, such as its shape and size. Users may see a holographic image with their own eyes from various angles without wearing any portable gadgets. BCI encodes and decodes brain signals throughout the process of brain activity by properly recognizing brain signals users may use for operations such as gaming and typing. BCI links the human neural world to the external physical world by decoding individual brain signals into instructions recognized by computer equipment, allowing the virtual and real worlds to merge in space (Zhang et al., 2019).
AI algorithms (e.g. machine learning, deep learning and reinforcement learning) are also the key to connecting the virtual and real worlds. The three AI components, data, algorithms and computing power, are critical to the metaverse's foundation and evolution. The metaverse may securely and freely engage in social and economic activities beyond the confines of the physical world by using AI technology (Ning et al., 2021). Users can experience the same visual and aural adventures as in the actual environment by using computer vision, intelligent voice, natural language processing and other technologies. Because user data are digitized, gathered and stored in the metaverse, a critical issue and technology bottleneck is how to store such vast amounts of data. Traditional data storage solutions often have a centralized design, which necessitates sending all data to a data center. With such a large volume of data, incredibly high storage capacity is required, which is often highly costly. Furthermore, sensitive information may be contained in such data, raising concerns of privacy leakage. Blockchain, as a distributed database, is another foundational technology to address these challenges. Users using blockchains may collaborate to produce data blocks and verify and record transactions. Blockchain-based data storage solutions are scalable and flexible in addition to being better at data storage. Each user can be both a data requester and supplier. Furthermore, the data are encrypted and moved to an anonymous node for storage, increasing data security. The location of data is recorded by all nodes in blockchains, allowing data owners to easily access their data (Lee et al., 2021). Therefore, the investment, innovation and adoption of the critical technologies mentioned above are vital for firms who aim to enhance their IT readiness for the metaverse.
IT readiness can be reflected in the annual reports of listed firms. By providing more information on digitalization in their reporting, listed firms are able to increase communication with stakeholders about their metaverse development efforts (Zimmerman and Zeitz, 2002), thereby increasing external audiences' perceptions of corporate readiness. Stakeholders can perceive firms disclosing more information about these technological foundations of the metaverse in their annual reports as having some readiness to develop metaverse businesses, which enhances the credibility and social acceptance of announcements related to metaverse initiatives. Additionally, extensive disclosure of information about these underlying technologies can alleviate the information asymmetry between firms and stakeholders about the viability of metaverse businesses (Amiram et al., 2016). Such behavior helps firms maintain a positive image and innovate and upgrade their technology. Therefore, we posit that firms with high IT readiness would achieve a stronger positive market reaction in the Chinese stock market than firms with low IT readiness after they announced metaverse-related initiatives.
The positive market reaction to metaverse coverage is stronger for firms with better IT readiness.
2.5 The moderating role of ecosystem readiness
The metaverse is an ecosystem-wide transformation that integrates a variety of new technologies and requires value co-creation among users, individual firms and industry partners (e.g. sensor networks, intelligent hardware producers, virtual service providers and physical service providers) to realize its full potential. The transition from a set of independent virtual worlds to an integrated network of 3D virtual worlds relies on multi-stakeholder and multi-source platform supports and spans a complex range of industries, organizations and interests (Lee et al., 2021). This makes an ecosystem perspective relevant when predicting organizations' adoptions of the metaverse. Moore (1993) introduced the concept of the business ecosystem to argue that innovation does not result from a single firm but takes alliance and collaboration efforts. In this context, he defined the business ecosystem in view of the natural ecosystem as constituting organizations and their interacting environments. In ecosystem research, the emphasis is on understanding the interaction between interdependent but legally autonomous actors creating and commercializing innovations that benefit end users. Often, these innovations fail if there is insufficient coordination within the ecosystem (Jacobides et al., 2018). Iansiti and Richards (2006) defined an IT ecosystem as “the network of organizations that derives the delivery of information technology products and services.” Gawer and Cusumano (2014) further noted that an ecosystem must solve an important problem within an industry; it must be easy to connect to while increasing in value when more users and complementors join it. Iansiti and Levien (2004) suggested that the business community as a whole has a shared fate, and each member's performance depends on the ecosystem's overall performance. These studies indicate that firms should not be regarded as players in a single industry, but as a member of a business ecosystem that transcends industries. A metaverse ecosystem typically comprises focal organizations, trading partners, technology vendors, customers and governments. Each actor in the ecosystem plays a role in the focal organization's engagement with the metaverse. The value of technologies (particularly those driven by network effects) can be maximized only when many trading partners are using them (Iacovou et al., 1995). Lokuge et al. (2019) highlighted the distinct roles of multiple software and hardware vendors' networked partner engagement as important facets of partnership readiness for digital innovation. Additionally, this intra-industry knowledge spillover effect can significantly contribute to the innovative activities of firms developing metaverse businesses (Montoro-Sánchez et al., 2011), thus enhancing their ecosystem readiness. Barnes III and Xiao (2019) developed an extended model for the adoption of blockchain based on the Technology-Organization-Environment framework (Depietro et al., 1990), which also considers ecosystem-related factors.
Influenced by the conceptual views of Barnes III and Xiao (2019) as well as by business ecosystem theory (Jacobides et al., 2018), we define ecosystem readiness as the availability of established trading partners and complementors to satisfy the implementation and use of critical technologies (e.g. AI, blockchain and AR) that support the metaverse. With the metaverse being a complex, interdependent technology integration and a collaborative innovation characterized by decentralized systems (Xu et al., 2022), a single firm is not adequate to support its implementation. Not only organizational internal technology development but also an external network of actors comprising a supportive ecosystem for metaverse development drive the metaverse. Thus, we posit that the availability of ecosystem-wide support will be a critical consideration in investors' judgment of organizations' readiness to develop the metaverse. Stakeholders may perceive firms with high ecosystem readiness as having lower costs of transition into the metaverse. Therefore, stakeholders react more positively to the metaverse media coverage of such firms.
The positive market reaction to metaverse coverage is stronger for firms with better ecosystem readiness.
2.6 The moderating role of digital infrastructure readiness
Private actors and their capabilities are subject to and embedded within urban policy and governance settings (Beckert, 1999). The metaverse is not only one virtual world platform, video game, or tool but rather an interconnected network of experiences, devices, tools and infrastructure. The metaverse requires computing and processing infrastructure that can support both big data flows and low latency (Lee et al., 2021). City-wide sensors, 5G, cloud infrastructure and edge infrastructure will power high-resolution metaverse applications, such as immersive worlds or games, by supporting reliable, flexible and lag-free networks for connected devices. For individual organizations to be able to engage in the metaverse, they require ease of access to stable and affordable digital infrastructure and to network services, devices and applications. Globally, the evolution of “smart city” regulations has resulted in a boost in the development of digital infrastructure. Public investment in digital infrastructure is viewed as the key platform for measuring the operation and administration of cities and governments within smart cities. The digital underpinnings of the contemporary city are becoming a more pervasive aspect of urban life. The “instrumentation” of urban infrastructure using sensor technologies enables the diffusion of information processing into the material areas of cities. Smart infrastructure services in cities, powered by dispersed sensors, offer real-time performance data and enable an enhanced metaverse experience (Allam et al., 2022; Bibri, 2022). In this context, attempts to comprehend the significance of the “digital city” have involved the hybridization of urban policy between, on the one hand, incentivizing the location and retainment of technology firms and personnel, and, on the other hand, conveying digitized and smart public services (Hollands, 2020; Wiig, 2015).
We expect firms in regions with better digital infrastructure to have more organizational readiness to develop the metaverse. Stakeholders are more likely to respond positively to metaverse coverage of such firms. For example, ZhongQingBao experienced a rapid increase in stock price following its metaverse coverage. Many market participants argued that Shenzhen, the center of science and technology innovation in China, can provide infrastructural support for its development of the metaverse. Thus, stakeholders are more friendly to the development of metaverse business of firms located in regions with better digital infrastructure.
Digital infrastructure brings spillover effects. Existing studies have found that knowledge spillover effects are an important source of regional innovation activity and technological development (Fritsch and Franke, 2004). Well-established digital infrastructure can facilitate information sharing and exchange among organizations in the region (Liu et al., 2021). This spillover effect facilitates firms to absorb technology and knowledge better, which helps them make progress in metaverse technology (Wang and Wu, 2016). Therefore, firms in regions with better digital infrastructure are more likely to have access to spillover resources in those regions. Moreover, digital infrastructure can alleviate spatial and temporal barriers and reduce transaction costs (Tang et al., 2021). Well-established digital infrastructure reduces information gathering and communication costs for firms, enabling them to receive more information about the metaverse. Hence, we argue that stakeholders are likely to perceive firms in regions with developed digital infrastructure as having more readiness in terms of the metaverse.
The positive market reaction to metaverse coverage is stronger for firms with better digital infrastructure readiness.
3. Methodology
3.1 Sample and data collection
Our initial sample consists of all listed firms in the Shenzhen Stock Exchange and Shanghai Stock Exchange during 2021. In 2021, the concept of the metaverse exploded and gained the attention of firms and investors, so 2021 became known as the original year of the metaverse in China. To investigate the market reaction to the announcement of the metaverse, we identified a sample of Chinese listed firms with metaverse coverage by conducting a keyword search from newspaper sources. We used the Chinese keyword “元宇宙” (i.e. metaverse) to search for headlines in several news sources. We also searched for news articles and announcements on the Datago database, which contains more than 15 million news articles published in 1,154 domestic and foreign newspapers since 1998, such as People's Daily. After collecting the news reports, we checked whether they mentioned a listed firm based on the date the news was published and whether they mentioned the firm's full name on that date, the stock abbreviation, or the stock code that matched the specified format. For multiple instances of media coverage in 2021, we retained news articles with the earliest publicized dates (Feng et al., 2020).
Stock prices and data related to firm characteristics were derived from the China Stock Market and Accounting Research (CSMAR) database. The CSMAR database covers information on Chinese stock trading and financial statements of Chinese listed firms and is widely used in management studies (Marquis and Qian, 2014). After combining two data sources and deleting missing values, the final sample consisted of 642 news reports from 642 listed firms in 2021. Table 2 displays the sample distribution by geography (i.e. province). It shows that sample firms from all 31 Chinese provinces were distributed unevenly, with the top five most distributed provinces accounting for 56.5% of the total.
3.2 Variables
3.2.1 Cumulative abnormal returns
We used cumulative abnormal returns (CARs) to examine the market reaction to the metaverse coverage. This variable has been widely used in finance and information science research to reflect the change in a firm's stock price during the occurrence of the event (Black and Kim, 2012; Im et al., 2001). First, we translated calendar days into trading days and defined news report dates as event days (Day 0). Second, we followed previous studies to use a seven-day event window that included three trading days prior to the event day (Day −3) to three trading dates post the event day (Day 3) (Feng et al., 2020). We also employed four event windows as robust checks: (−1,0), (−1,1), (−2,2) and (−1,5). Meanwhile, we used 210 days before the event date (Day −210) to 10 days before the event date (Day −10) as the estimation window for the event study method. Third, we calculated abnormal return (AR) based on a standard market model (Brown and Warner, 1985), which is presented as follows:where is the abnormal return of firm i on day t. is the daily stock return, measured by the daily individual stock return after considering the reinvestment of cash dividends. is the market return, measured by the daily market return after considering the reinvestment of cash dividends from the Shanghai Stock Exchange and Shenzhen Stock Exchange. Next, and are the coefficients obtained by ordinary least squares (OLS) regression of market returns on individual stock returns. Furthermore, cumulative abnormal returns are measured by summing the average AR for the event period (Tao et al., 2017).
3.2.2 Moderating variable
To investigate the moderating role of focal firms' readiness, we included multiple levels of readiness variables in the research model.
IT readiness (firm level). Following Mutula and Van Brakel's (2006) study, we measured IT readiness based on an organization's ICT and information strategy or policy. Previous studies have often used content analysis of annual reports of listed firms to explore corporate strategies and policies (Bowman, 1984). Thus, IT readiness was measured by the frequency with which words about digitalization appeared in those annual reports. First, we downloaded the reports through the CSMAR database and the official websites of firms. Second, we searched for keywords related to digitalization (e.g. AI, big data, blockchain, cloud computing and machine learning) in the annual report and counted their occurrences. Finally, we used the natural logarithm value of word frequency as the IT readiness of a firm.
Ecosystem readiness (industry level). The availability of established trading partners and complementors reflects the ecosystem readiness of focal firms for developing and implementing the metaverse. Some industries such as ICT provide more mature ecosystem-level supports for focal firms to develop critical technologies (e.g. AI, blockchain and VR) for the metaverse. Thus, we used a dummy variable to measure Ecosystem readiness, which was 1 if a firm belonged to an industry related to ICT (e.g. internet and related services; telecommunications, radio and television and satellite transmission services; software and information technology services), and 0 otherwise.
Digital infrastructure readiness (regional level). The quality of ICT infrastructure is an important factor in assessing the IT readiness of the regional environment (Mutula and Van Brakel, 2006). In this study, we used the natural logarithm value of smart city scores to measure Digital infrastructure readiness. Smart city scores were taken from the 2020 China Smart City Development Level Evaluation Report, with higher scores representing a better quality of regional informatization construction. The score is calculated based on a comprehensive evaluation system comprising six primary indicators: digital foundation, smart governance, smart livelihood, digital economy, digital citizen and smart environment as well as 15 secondary indicators and 41 tertiary indicators. Each indicator is assigned a specific value, and the total value of the indicators is 100 points.
3.2.3 Control variables
We included serval firm-level variables as the controls. Following previous studies, we included firm size, which was measured by the natural logarithm value of total assets, to avoid the potential effect of announcements on stock performance (Liu et al., 2018; Xia et al., 2016). Firm age was measured as the establishment year of firms. We also controlled for several corporate financial statement variables to alleviate the effect of corporate characteristics on abnormal returns. Debt ratio was measured by the proportion of total liabilities to total assets. Financial performance was measured by return on assets. We also included cash flow, measured as net cash flows from operating activities.
3.3 Estimation method
We used event study to investigate the impact of metaverse announcements on stock returns. Specifically, we calculated the mean value of cumulative abnormal returns in the event period and t-test statistics to determine whether Hypothesis 1 was supported or not.
To analyze how multiple-level readiness factors might affect CARs, we conducted an OLS regression absorbing province fixed effect. The regression model was as follows:where , , and are the coefficients of the moderating variables to test Hypotheses 2–4. Controls is the set of control variables, and is the error term.
4. Results
4.1 Event study
Table 3 provides the descriptive statistics of CARs of 642 Chinese listed firms during the metaverse coverage dates. CARs were positive with values of 0.6%, 2% and 1.6% for the (−1,1), (−2,2), and (−3,3) event windows, respectively. All of them are significant at the 1% level. The results show that, on average, metaverse coverage by Chinese listed firms generated a positive market reaction by producing positive stock returns to the shareholding of listed firms, thus supporting Hypothesis 1.
4.2 Hypothesis testing
We first report the descriptive statistics and correlations of all the variables in the regression model. As Table 4 shows, the mean value of cumulative abnormal returns is 0.018, suggesting a positive market reaction to the metaverse coverage. The correlations between the dependent variable and other variables were less than 0.5. We also calculated the variance inflation factors (VIFs). The maximum VIF was 2.30 with an average of 1.45, which is below than the cutoff point of 10. Therefore, multicollinearity did not have a serious impact on the estimation results (Liu et al., 2021).
Table 5 reports the moderating analysis results estimated for CARs in Model 1–5. We added control variables in Model 1 and moderating variables in Models 2–4 to test Hypotheses 2-4. Hypothesis 2 predicts a positive moderating role of IT readiness. As shown in Model 2, the coefficient on IT readiness was positive and significant (), indicating that the positive effect of metaverse coverage on stock market reaction is strengthened for firms with better IT readiness. In terms of the substantive effect, we found that doubling the frequency of digitalization words appearing in the annual reports of listed firms can lead to a 1.6% increase in the positive effect of metaverse coverage on stock market reaction, thus supporting Hypothesis 2.
Hypothesis 3 predicts a positive moderating role of ecosystem readiness. As shown in Model 3, the coefficient on ecosystem readiness was positive and significant (), suggesting that the market responded more positively to firms belonging to metaverse-related industries. Similarly, we found the substantive effect that firms belonging to an industry related to ICT can lead to a 7.0% increase in the positive effect of metaverse coverage on stock market reaction, thus supporting Hypothesis 3.
Hypothesis 4 predicts a positive moderating role of digital infrastructure readiness. As shown in Model 4, the coefficient of digital infrastructure readiness was positive but not significant (), suggesting that regional digital infrastructure development does not affect the market reaction to corporate metaverse coverage. Therefore, Hypothesis 4 does not receive support.
4.3 Robustness check
Because not all firms issued news reports related to the metaverse, there were significant missing values for the dependent variable in our study sample. Therefore, firms that issue metaverse coverage may vary systematically from those that do not resulting in sample selection bias. To address this issue, we used a two-stage Heckman selection model as an approach to estimate the CARs. In the first stage, we conducted a probit model to estimate whether firms issue metaverse coverage. We also included province- and industry-level metaverse coverage, measured as the proportion of issued metaverse coverage within the same industry classification and province, as an additional variable to meet the exclusion restriction of the Heckman two-stage model. Based on the estimation results of the first stage, we calculated the inverse Mills ratio and included it at the second stage of the Heckman model.
The results of the two-stage models are shown in Table 6, in which Models 1–3 are the results of the first stage and Models 4–8 are the results of the second-stage OLS regression model with the inverse Mills ratio added. As shown in Models 4–8, the coefficients on moderating variables were consistent with the main finding in Table 5, indicating the robustness of our results.
5. Discussion
We used a sample of Chinese listed firms in 2021 to investigate the impact of corporate metaverse coverage on stock returns through event study methodology. We found that metaverse coverage induces a positive market reaction. Cumulative abnormal returns of corporate stocks increase following the issuance of metaverse coverage. Because the metaverse attracts the attention of investors chasing positive news and short-term investments, it evokes positive reactions from stakeholders. Metaverse coverage also sends a positive signal to stakeholders regarding sufficient corporate resources, which results in an increase in the corporate stock price. Additionally, there are multiple dimensions of readiness moderating stakeholder responses to corporate metaverse coverage. We found that firms with better IT readiness and ecosystem readiness are more likely to gain credibility and social acceptance for their metaverse coverage and thus have better stock performance. However, we also found that digital infrastructure readiness does not statistically have a significant moderating effect on the relationship between metaverse coverage and stock returns. Most likely because the majority of the listed firms in our research sample are from developed regions of China, the gap in digital infrastructure between these regions is low and not statistically significant (which can be seen in Table 2).
5.1 Theoretical contributions
Metaverse became a buzzword in 2021, attracting widespread attention from the technology and investment sector. Especially in the context of China, it remains unclear whether a focal firm's metaverse-related announcements will elicit positive stock market reactions. Thus, the main contribution of this study is that it was the first to investigate the impact of the metaverse on Chinese stock market reactions in the context of media coverage. In contrast to previous studies that focused on the definition (Sparkes, 2021), framework, and technical foundation of the metaverse (Lee et al., 2021), we investigated the positive effect of the metaverse on corporate stock performance through a finance field method (i.e. event study). Our results enrich the relevant literature on the metaverse (Ning et al., 2021) while extending the application of the event study method to metaverse research (Binder, 1998).
Second, another contribution of our study is to link the readiness theory and business ecosystem literature to metaverse studies. Despite significant academic interest in organizational readiness, the previous literature has primarily focused on assessing firms' digital readiness (Aboelmaged, 2014; Molla and Licker, 2005), paying scant attention to factors contributing to stakeholders' evaluation of the development of metaverse businesses. By introducing the concept of metaverse readiness in multiple dimensions, we argued that a positive stakeholder response to the corporate development metaverse also relies on readiness. Meanwhile, we clarified the boundary conditions of the relationship between metaverse coverage and stock returns, which broadened our understanding of business in the metaverse.
Third, our findings on the different types of IT readiness, ecosystem readiness and digital infrastructure readiness enrich the understanding of the specific effects and differences along the readiness dimensions in organizational readiness theory and strengthen the results quantitatively. Current qualitative studies provide a general conceptualization framework to measure organizational readiness for digital innovation, but they lack an extension of particular types of innovation and their effectiveness on the adoption dimensions (Lokuge et al., 2019). This study further distinguishes the differences among dimensions of readiness in influencing the stakeholders' assessment of a firm's metaverse media coverage, contributing to further clarify systematic differences between readiness dimensions and provide valid empirical assessment methods in a new application scenario.
5.2 Practical implications
First, our investigation of the relationship between the metaverse and stock returns may be of interest to investors. Because the metaverse is an emerging concept, investors need reliable studies to determine whether the metaverse has a positive impact on corporate value. Moreover, stakeholders would like an easily understood and perceived framework to assess firms' readiness to develop the metaverse. Our study provides empirical evidence on how external audiences evaluate corporate metaverse businesses.
Second, we provide managerial implications for firms to conduct metaverse business by revealing the heterogeneous impact of different dimensions of organizational readiness on stakeholders' assessment of metaverse development. Our empirical results show that public firms with better IT and ecosystem readiness for metaverse businesses are more likely to be favored by investors, and increasing technology investment and choosing the right partners will be important conditions for firms to develop the metaverse. In addition, higher levels of digital infrastructure development do not induce additional positive stakeholder responses to the metaverse, which provides new lessons for corporate location decisions of metaverse businesses.
5.3 Limitations and future studies
Although we used strict theoretical and empirical analytical frameworks, our study still has some limitations. First, our study lacks generalizability. We verified the positive response of the Chinese stock market to the metaverse using a sample of Chinese listed firms. However, the Chinese political and market systems' specificities may limit our results' generalizability (Tsui, 2007). Moreover, the majority of the firms in our sample are from developed regions (e.g.eastern coastal China), and these firms receive significant attention from stakeholders. Future studies could collect data from additional sources (e.g. other regions and other types of firms) to investigate the impact of metaverse coverage on firm value. Second, we used newspaper sources to measure media coverage. We did not include other media, such as the internet and social media. However, the role of digital media in distributing corporate information is becoming increasingly important in China (Tang and Tang, 2016). Future studies could include media coverage from multiple sources to investigate the impact of media on firms.
This study received financial support from the Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China [2022RW033].
Characteristics of metaverse
| Characteristics | Meaning |
|---|---|
| Perpetual | There are no operations such as “shutdown” or “restart” in metauniverse. Users can use devices to freely connect with metauniverse anywhere in the world at any time, ensuring the user experience is continuous. The meta-universe will not stop or be reset but will continue to develop indefinitely in an open source and open way. Every participant in the metaverse is not only the “user” of the metaverse but also the “creator” who ensures the sustainable development of the metaverse |
| Boundless | Metaverse is an endless space, and there are no restrictions on the number of participants who can use it at the same time, the number of types of activities that can be carried out, and the number of industries that can be entered. Each participant can not only buy and use the content created by others, such as virtual identity and NFT (non-homogeneous token) but also create their own. In this mode, the boundary of the metaverse will be continuously expanded |
| Immersive | Beyond 2D interactions, the Metaverse could be “experienced” in ways that enable users to engage with other users in a manner that is possible in the real world. Users are able to feel psychologically and emotionally immersed in an alternate reality since the virtual area is realistic enough to satisfy their expectations |
| Shared | Thousands of users are expected to be able to coexist in a single server session, similar to the actual world, rather than being segregated into several virtual servers. With users having access to the Metaverse and being able to immerse themselves at any time and place, the lifelike interaction of users is shared worldwide, i.e. an action may affect any other user, just as it would in an open environment, and not only for users at a single server |
| Scalable | The scalability of the Metaverse is the capability of the Metaverse to continue operating effectively under all circumstances. The scenarios include a rise in concurrent users or avatars, scene complexity, and user or avatar interaction methods. Scalability of the Metaverse might be defined as the capability to expand in size or scale up as the number of users or demand grows without compromising the user experience or overall system efficiency |
| Interoperable | Similar to the actual world, our belongings in one virtual world should not lose value when smoothly transferred to another virtual world, even if they are built by distinct organizations. Therefore, no one entity or firm wholly “owns” the Metaverse. Metaverse permits interoperability across systems representing distinct virtual universes |
| Decentralized | Data processing work is distributed on multiple devices instead of relying on a single central server. Each unit device is a mini central processing unit that can interact independently with other nodes. Therefore, even if one of the primary nodes crashes or is attacked, other servers can operate normally, and users can continue to transmit and access data |
Sample distribution by geography
| Province | N of sample firms | Ave. CAR | Ave. smart city score |
|---|---|---|---|
| Guangdong | 115 | 0.009 | 75.405 |
| Beijing | 91 | 0.038 | 76.070 |
| Shanghai | 59 | 0.013 | 80.280 |
| Zhejiang | 55 | 0.028 | 74.561 |
| Jiangsu | 43 | 0.022 | 68.451 |
| Sichuan | 30 | 0.010 | 69.378 |
| Shandong | 29 | −0.001 | 64.694 |
| Hunan | 27 | 0.001 | 61.226 |
| Fujian | 23 | 0.002 | 64.888 |
| Hubei | 21 | 0.038 | 66.056 |
| Anhui | 19 | 0.022 | 61.349 |
| Henan | 13 | −0.010 | 58.583 |
| Chongqing | 12 | 0.011 | 68.830 |
| Shaanxi | 11 | 0.010 | 65.200 |
| Shanxi | 11 | 0.047 | 55.300 |
| Jiangxi | 10 | −0.041 | 58.042 |
| Liaoning | 9 | 0.028 | 61.073 |
| Tianjin | 8 | −0.041 | 68.850 |
| Yunnan | 8 | −0.013 | 58.059 |
| Hainan | 7 | 0.051 | 55.922 |
| Hebei | 7 | −0.052 | 52.623 |
| Tibet | 6 | −0.016 | 45.250 |
| Xinjiang | 6 | 0.110 | 43.990 |
| Guangxi | 5 | 0.022 | 55.185 |
| Gansu | 4 | 0.032 | 60.350 |
| Inner Mongolia | 3 | −0.037 | 56.620 |
| Guizhou | 3 | 0.037 | 60.147 |
| Heilongjiang | 2 | −0.028 | 58.080 |
| Jilin | 2 | −0.014 | 59.890 |
| Ningxia | 2 | 0.051 | 46.250 |
| Qinghai | 1 | 0.098 | / |
| Total | 642 |
Cumulative abnormal returns (CARs) for Chinese listed firms
| Variable | N | Mean | S.D. | t-Stat | % Positive |
|---|---|---|---|---|---|
| CARs (−1,0) | 642 | 0.003 | 0.046 | 1.501 | 55.76% |
| CARs (−1,1) | 642 | 0.006 | 0.054 | 3.012 | 57.79% |
| CARs (−2,2) | 642 | 0.020 | 0.084 | 5.983 | 58.26% |
| CARs (−3,3) | 642 | 0.016 | 0.103 | 3.896 | 59.97% |
| CARs (−1,5) | 642 | 0.007 | 0.101 | 1.744 | 52.49% |
Descriptive statistics and correlation matrix
| Variables | Mean | S.D. | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) |
|---|---|---|---|---|---|---|---|---|---|---|---|
| (1) CARs | 0.018 | 0.102 | 1.000 | ||||||||
| (2) Firm size | 23.514 | 2.069 | −0.034 | 1.000 | |||||||
| (3) Firm age | 21.399 | 6.126 | −0.001 | 0.280 | 1.000 | ||||||
| (4) Debt ratio | 0.488 | 0.226 | −0.013 | 0.597 | 0.271 | 1.000 | |||||
| (5) Financial performance | 0.031 | 0.082 | −0.195 | 0.079 | −0.090 | −0.358 | 1.000 | ||||
| (6) Cash flow | 11.500 | 16.906 | 0.026 | 0.071 | −0.029 | −0.170 | 0.269 | 1.000 | |||
| (7) Ecosystem readiness | 0.141 | 0.348 | 0.239 | −0.271 | −0.177 | −0.312 | −0.067 | 0.015 | 1.000 | ||
| (8) IT readiness | 2.691 | 1.219 | 0.173 | 0.026 | −0.096 | −0.061 | −0.004 | −0.039 | 0.383 | 1.000 | |
| (9) Digital infrastructure readiness | 4.262 | 0.138 | 0.004 | 0.102 | −0.036 | 0.016 | −0.012 | 0.039 | 0.060 | 0.170 | 1.000 |
Note(s): N = 491; correlations greater than |0.09| are significant at the 0.05 level
Estimation for the CARs
| Variables | DV: CARs (−3, 3) | ||||
|---|---|---|---|---|---|
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |
| Firm size | 0.003 | 0.002 | 0.004 | 0.004 | 0.003 |
| (0.003) | (0.003) | (0.003) | (0.003) | (0.003) | |
| Firm age | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | |
| Debt ratio | −0.055* | −0.051 | −0.024 | −0.063* | −0.041 |
| (0.026) | (0.029) | (0.027) | (0.029) | (0.031) | |
| Financial performance | −0.318*** | −0.350*** | −0.267*** | −0.301*** | −0.292*** |
| (0.057) | (0.062) | (0.057) | (0.062) | (0.068) | |
| Cash flow | 0.000 | 0.001 | 0.000 | 0.000 | 0.000 |
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |
| IT readiness | 0.016*** | 0.010* | |||
| (0.004) | (0.004) | ||||
| Ecosystem readiness | 0.070*** | 0.050** | |||
| (0.013) | (0.015) | ||||
| Digital infrastructure readiness | 0.019 | −0.008 | |||
| (0.057) | (0.059) | ||||
| Province dummies | Controls | Controls | Controls | Controls | Controls |
| Constant | −0.035 | −0.051 | −0.082 | −0.134 | −0.058 |
| (0.059) | (0.063) | (0.058) | (0.257) | (0.267) | |
| Adj R-squared | 0.041 | 0.080 | 0.083 | 0.027 | 0.091 |
| F-value | 1.78** | 2.38*** | 2.61*** | 1.45* | 2.38*** |
| Observations | 639 | 559 | 639 | 557 | 491 |
Note(s): Standard errors are in parentheses; ***p < 0.001, **p < 0.01, *p < 0.05
Robustness check using two-stage Heckman selection model
| Variables | First-stage Heckman model | Second-stage Heckman model | ||||||
|---|---|---|---|---|---|---|---|---|
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 | |
| Firm size | 0.333*** | 0.310*** | 0.253*** | −0.007 | −0.007 | −0.006 | −0.007 | −0.006 |
| (0.020) | (0.023) | (0.027) | (0.004) | (0.004) | (0.004) | (0.004) | (0.004) | |
| Firm age | −0.001 | 0.000 | −0.005 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| (0.004) | (0.005) | (0.006) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | |
| Debt ratio | −0.505** | −0.515** | −0.650** | −0.056 | −0.049 | −0.029 | −0.056 | −0.029 |
| (0.149) | (0.181) | (0.208) | (0.030) | (0.030) | (0.030) | (0.030) | (0.030) | |
| Financial performance | −0.402 | −0.190 | −0.251 | −0.334*** | −0.322*** | −0.286*** | −0.334*** | −0.288*** |
| (0.261) | (0.316) | (0.355) | (0.066) | (0.066) | (0.066) | (0.066) | (0.066) | |
| Cash flow | −0.002 | −0.001 | 0.002 | 0.000 | 0.001 | 0.000 | 0.000 | 0.000 |
| (0.002) | (0.002) | (0.002) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |
| IT readiness | 0.113*** | 0.075* | 0.012** | 0.007 | ||||
| (0.028) | (0.031) | (0.004) | (0.004) | |||||
| Ecosystem readiness | 0.172 | 0.084 | 0.056*** | 0.046** | ||||
| (0.102) | (0.112) | (0.014) | (0.015) | |||||
| Digital infrastructure readiness | 0.437 | 0.574 | 0.010 | −0.014 | ||||
| (0.363) | (0.406) | (0.058) | (0.058) | |||||
| Province industry-level metaverse | 4.040*** | |||||||
| (0.211) | ||||||||
| Inverse Mills ratio | −0.056*** | −0.051*** | −0.052*** | −0.056*** | −0.049*** | |||
| (0.009) | (0.009) | (0.009) | (0.009) | (0.009) | ||||
| Province dummies | Controls | Controls | Controls | Controls | Controls | Controls | Controls | Controls |
| Constant | −8.367*** | −10.067*** | −9.900*** | 0.278*** | 0.228*** | 0.215*** | 0.234 | 0.257 |
| (0.417) | (1.655) | (1.861) | (0.083) | (0.083) | (0.083) | (0.270) | (0.266) | |
| LR χ2 | 494.09*** | 339.21*** | 896.58*** | |||||
| Log likelihood | −1,626.275 | −1,178.453 | −899.770 | |||||
| F-value | 2.840*** | 3.100*** | 3.930*** | 2.760*** | 3.240*** | |||
| Adj R-squared | 0.132 | 0.126 | 0.333 | 0.114 | 0.130 | 0.142 | 0.112 | 0.145 |
| Observations | 4,733 | 3,062 | 3,062 | 491 | 491 | 491 | 491 | 491 |
Note(s): Standard errors are in parentheses; ***p < 0.001, **p < 0.01, *p < 0.05
© Emerald Publishing Limited.