Does GRI Sustainability Reporting Pay Off? An Empirical Investigation of Publicly Listed Firms in China

Abstract

The Global Reporting Initiative (GRI) guidelines have emerged as an important instrument used by firms to structure the content of sustainability reporting (SR). This development has led to the question of whether the elaboration of GRI SR is beneficial to a firm’s financial performance. In this study, building on signaling theory, we carry out an empirical investigation of the impact of GRI SR on firm profitability and the factors moderating that impact. Drawing from the China Stock Market and Accounting Research (CSMAR), the WIND Economic, and the Chinese Research Data Services Platform (CNRDS) databases, we identified a sample of 122 listed firms with GRI SR in China. We then employed an event study method to compare the firms following GRI SR with a set of matched firms reporting sustainability without following the GRI guidelines. The results show that GRI SR significantly increases firm profitability. Moreover, firms with local political ties reap more benefits from GRI SR, while the moderating effect of central political ties is not significant. Surprisingly, the performance impact of GRI SR is negatively correlated to the firm’s internationalization level.

Keywords

event study financial performance Global Reporting Initiative (GRI)sustainability

Over the last decade, an increasing number of companies have moved toward monitoring the impact of their activities on the social and natural environments, thus sustainability reporting¹ (SR) has become a mainstream practice (Boiral, Heras-Saizarbitoria, & Brotherton, 2017; Fonseca, McAllister, & Fitzpatrick, 2014). According to a survey by KPMG (2017), most of the world’s biggest companies now integrate non-financial data to their annual financial reports (78 percent), suggesting that they believe corporate responsibility information is relevant for investors. Corporate social responsibility (CSR) has been taken seriously in China since when China was admitted into the WTO in 2001 (Gao, 2009). In recent years, under the mounting pressure from governments and the society, there has been a considerable growth in Chinese organizations to implement and report social responsibility behaviors (K. Wang & Li, 2015; L. Wong, 2009).

Among the different standards for organizing SR, Global Reporting Initiative (GRI) guidelines have become one of the most widely adopted ones worldwide (Brown, Jong, & Levy, 2009; Christofi, Christofi, & Sisaye, 2012). They help companies to determine what to report and how to report the information in SR (Sutantoputra, 2009) and provide a unified standard for SR, allowing for the comparison of information between various organizations (Diouf & Boiral, 2017). GRI SR could partially help to address the issue of “selective reporting” used by some companies for greenwashing, where firms reveal a subset of private information to create a misleadingly positive public impression (Marquis, Toffel, & Bird, 2016). Although GRI guidelines have these advantages, the number of Chinese firms that adopt GRI still remains below the international level (Dong, Burritt, & Qian, 2014). We believe that one of the reasons may be related to the unclear economic benefits of GRI in practice in China.

Previous research has found a direct positive relationship between SR and financial performance (Huang & Lien, 2012; H. Kang & Liu, 2014), while the results for the adoption of GRI guidelines in SR are still controversial (Nikolaeva & Bicho, 2011). Some scholars claim that firms adopting GRI guidelines have better stock market performance (Willis, 2003) and environmental performance (Alonso-Almeida, Llach, & Marimon, 2014). Other scholars are more skeptical. Goel and Cragg (2005) argue that GRI is only used for shaping reporting processes rather than as a management tool and that it is very general and contains many indicators that are not used by companies. Implementing GRI guidelines is complex, time-consuming, and costly due to the difficulty of collecting information for a large number of indicators (Lozano, 2006; Lozano & Huisingh, 2011; Luken & Stares, 2005). Furthermore, it proves difficult to compare the sustainability performance of different firms based on qualitative/content analysis of GRI reports (Boiral & Henri, 2017). Hence, it is not yet clear what benefits are achieved through GRI reporting (Goel & Cragg, 2005). To the best of our knowledge, there is no study that attempts to examine the financial impact of GRI reporting compared with non-GRI reporting.

Motivated by this background, our study aims to explore the link between the adoption of GRI guidelines in SR and a firm’s financial performance building on signaling theory. Specifically, we seek to answer the following research questions:

Research Question 1: What is the impact of the adoption of GRI guidelines in SR on Chinese firms’ profitability?

Research Question 2: What are the factors affecting the relationship between the adoption of GRI and a firm’s profitability for Chinese firms?

We focus on China for three reasons. First, China is a global manufacturing hub and has become the largest energy consumer in the world (L. Zhang, Wang, & Fung, 2014). Second, due to severe environmental and social problems in China and criticism from overseas, CSR has been taken seriously and there has been an increasing trend in reporting sustainability in China in recent years (K. Wang & Li, 2015). Third, there are few studies on SR in China.

To answer the two research questions, we carry out an event study analysis based on 122 firms in China and compare GRI-reporting firms with non-GRI-reporting firms by controlling for industry type, firm size, and preadoption performance. We compare changes in return on assets (ROA) of each sampled firm with those of a portfolio of control firms. We find that there is a significant improvement in ROA due to the adoption of GRI guidelines. Moreover, from cross-sectional analyses, we determine that the performance improvement is significantly correlated with firms’ ties with the local government, but not with firms’ ties with central government. Interestingly, firms with a higher level of internationalization appear to benefit less from the adoption of GRI guidelines.

We make the following contributions to the literature on SR. First, our article is among the earliest to analyze the impact of GRI SR financial performance by comparing GRI-reporting companies with non-GRI-reporting companies. Second, it extends signaling theory to a SR context by investigating the relationship between GRI SR (strong signal) and financial performance. Third, we further find that signals in a SR context have different strengths; the performance effects of signals strength is moderated by the signaling environment represented by political ties and firm internationalization level. Fourth, we identify some moderating variables specific to the Chinese context.

The article is organized as follows. Initially, we discuss the relevant literature and develop the research hypotheses. We then describe the sample and the event study method. Next, we present the tests of the hypotheses and the results and discuss them. Finally, we summarize the conclusions, explain the limitations of the study, and propose opportunities for future research.

Background and Research Hypotheses

GRI SR and Firm Profitability

GRI guidelines represent the most widely recognized international standards for reporting on social and environmental issues (Villiers & Marques, 2016). The number of firms adopting GRI SR has significantly increased in the last few years. However, with regard to the benefits of GRI adoption, most articles only use qualitative descriptions, such as increased transparency, credibility, and comparability in reporting. Research provides limited compelling evidence to show that companies adopting GRI are more likely to become profitable. There is also still an ongoing debate on the relationship between GRI adoption and financial performance (Belkhir, Bernard, & Abdelgadir, 2017).

We only find three quantitative studies comparing the different performance of GRI-reporting and non-GRI-reporting firms. J. Lee and Maxfield (2016) examine the impact of reporting type on corporate social and financial performance. They find that reporting according to the GRI framework has a more comprehensive impact on environmental and financial performance than general CSR reporting. The industry-specific analysis conducted by Bernard, Abdelgadir, and Belkhir (2015) shows that only the utilities industry exhibited a dramatic decrease in emissions intensity when they compare GRI-reporting and non-GRI-reporting companies. Belkhir et al. (2017) also find that the correlation between GRI reporting and carbon emissions performance is not significant when comparing GRI- and non-GRI-reporting companies.

Focusing on these three articles, the first one only selects large corporations in the Fortune 500 companies (which may have some bias) and does not compare GRI-reporting with non-GRI-reporting companies. The second and third articles are co-authored by the same groups of authors and use the same dataset. They compare GRI and non-GRI reporting by choosing 40 sampled firms and 24 control firms and mainly focus on the effects of the GRI’s adoption on CO₂ emissions. In sum, a need exists for large-scale quantitative studies to empirically test the effects of GRI adoption compared with non-GRI adoption on firm profitability.

Signaling Theory in SR

In this article, we apply signaling theory to analyze the relationship between GRI reporting and financial performance. Signaling theory suggests that the more informed party tries to credibly convey information about itself to the less informed party to reduce information asymmetry (Spence, 1973). The whole information process is composed of “sender > signal > receiver” (Connelly, Certo, Ireland, & Reutzel, 2011). It is difficult for external parties to know company’s sustainability-related practices. To reduce the information asymmetry, the company (sender) can proactively release their SR (signals) to the relevant parties, including customers, suppliers, the government, and other stakeholders (receivers). The reporting would provide visible signals of the company’s attitudes, management practices, standings, and intents on environmental and social issues, thus creating an avenue to increase overall firm reputation. After receiving the signal, the stakeholders can assume that the company runs well and is committed to sustainability issues (Corazza, Scagnelli, & Mio, 2017). Those firms engaging in environmental protection, such as pollution reduction and recycling, can receive grants and incentives (Branco & Rodrigues, 2006), such as taking part in the annual election of the Top 100 Green Companies in China. In addition, an increasing number of investors and rating agencies integrate sustainability issues into their decisions (Hahn & Lülfs, 2014). These are all ultimately beneficial to firm performance. However, when the information is unfavorable or there is no congruence between the expectations of the society and a corporation’s activities and actions, the legitimacy gap will arise (Dowling & Pfeffer, 1975).

Lindblom (1994) identifies four legitimation strategies (educate stakeholders, change perception, distract attention, and change expectation) for an organization to deal with legitimacy threats to make the value system of the organization congruent with that of society. Adopting GRI in their CSR reports can help the firm educate stakeholders about their intention to enhance social performance, and try to change stakeholders’ perception concerning some negative events. However, when the negative news outweighs the good news, firms tend not to signal (Mahoney, Thorne, Cecil, & Lagore, 2013). In this case, they tend to use the distraction strategy, to distract attention away from such negative news by emphasizing more positive actions (Lindblom, 1994). Moreover, even though the firms transmit some negative signals to the public such as information of some corporate social irresponsibility in the reports, previous studies have found that the positive CSR news in the report can minimize the negative outcomes of the corporate social irresponsibility news in the same report (Groening & Kanuri, 2018). Peloza (2006) and Eisingerich, Rubera, Seifert, and Bhardwaj (2011) state that the prior positive level of CSR has an insurance-like effect. Therefore, we argue that in general SR tends to send positive signal to customers, suppliers, governments, and other stakeholders (Hou, Liu, Fan, & Wei, 2016; Su, Peng, Tan, & Cheung, 2016).

Two constructs derived from signaling theory are important and relevant to our study, namely the signaling strength and the signaling environment (Connelly et al., 2011; Ndofor & Levitas, 2004). Signaling strength refers to the power or influence driving the signal (Saxton, Gomez, Ngoh, Lin, & Dietrich, 2017). A weak signal is ambivalent, while a strong signal is explicit and can lead to positive stakeholder reactions (Suazo, Martínez, & Sandoval, 2011). As Levy, Brown, and de Jong (2010) point out, GRI is one of the most important tools (i.e., strong signal) for managing corporate sustainability efforts, assessing and protecting corporate reputation, and enhancing brand values. Reports based on GRI guidelines are generally considered to improve the quality of information, showing stronger signals to receivers than do non-GRI reporting. Although there might be some problems in SR such as insufficient and low credibility (Tilt, 1994), due to the fact that GRI is a set of guidelines rather than a regulation or a standard (with third party audit) (Nielsen & Thomsen, 2007), GRI adoption can be used to legitimize the actions of the firm and make the signal of CSR commitment stronger. The benefits of GRI adoption are confirmed by the Governance & Accountability Institute, which concludes that companies using the GRI framework consistently achieve average contextual element scores (e.g., supply chain, human rights, and environment) higher than companies not using the GRI for their reporting (Governance & Accountability Institute, 2017). Schadewitz and Niskala (2010) also argue that there is a significant relationship between market value and GRI reporting. The positive association between future performance and CSR expenditures is due to the signaling value of CSR expenditures (Lys, Naughton, & Wang, 2015). Accordingly, we develop the following hypothesis:

Hypothesis 1 (H1): Due to the strong positive signal conveyed, GRI SR has a significant positive effect on firm profitability.

Factors Affecting the Relationship Between GRI SR and Firm Profitability

There are some studies that have focused on the moderators between CSR and firm performance. Internal moderating factors include corporate governance (Javed, Rashid, & Hussain, 2017), the CSR engagement strategy (Tang, Hull, & Rothenberg, 2012), the differentiation strategy (S. Lee & Jung, 2016; Yuen, Thai, & Wong, 2017), outside investment (S. Lee & Jung, 2016), and firm size and organizational form (Cui, Liang, & Lu, 2013; Hou et al., 2016). External moderating factors include the economic environment (Hou et al., 2016; S. Lee, Singal, & Kang, 2013; Sánchez, Sotorrío, & Diez, 2015; Su et al., 2016), the national culture (Miras-Rodríguez, Carrasco-Gallego, & Escobar-Pérez, 2015), institutional environments (Xie, Jia, Meng, & Li, 2017), stakeholders’ CSR awareness (Rhou, Singal, & Koh, 2016), and the industry setting (Hou et al., 2016; Melo & Garrido-Morgado, 2012; Youn, Hua, & Lee, 2015).

Unlike these studies, we consider the factors moderating the relationship between GRI SR and firm profitability in our article. According to signaling theory, although GRI SR sends positive signals to stakeholders, the strength of such signals may vary in different environments (Montiel, Husted, & Christmann, 2012). In this section, we seek to explore whether the different domestic and foreign institutional environments can enhance or reduce the strength of GRI signals. Taking into account the context of China, we consider political ties as a proxy of the domestic environment (because the government plays a substantive role in steering the economy) and use the internationalization level to describe a firm’s degree to tap into foreign markets.

Political ties

Most organizations in China are government controlled, either by being regularly associated with the government or having an executive with personal connections with the government (Q. Liu, Tang, & Tian, 2013). Unlike Western countries, the Chinese government plays a leading role in constructing China’s market economy because formal market institutions are still underdeveloped (Anderson, Chi, & Wang, 2017). In China, the Communist Party has a stable control over the country and no large scale of political and social turmoil has happened during the past decades (Lin, 2011).

Political ties and connections with government officials play a pivotal role in China (Zhou & Poppo, 2010). Firms rely on personal interactions and social networks, rather than on formal contracts and arm’s-length transaction (Sheng, Zhou, & Li, 2011), to improve a firm’s political legitimacy (Suchman, 1995). Earlier studies on government-business links illustrate that political ties are a valuable asset for firms (Faccio, 2006; Li, Poppo, & Zhou, 2008; Siegel, 2007). We recognize that political ties can potentially affect the impact of GRI SR on performance in two different ways. First, firms with political ties have more chances to keep in touch with government officials and political ties lead to be higher visibility to the government even though they are not state-owned, and thus receive greater pressure from the government to conduct CSR (Cowen, Ferreri, & Parker, 1987). Hence, to fulfill politically oriented goals, they have to better satisfy the demands of stakeholders to prevent the risks associated with irresponsibility. They have therefore more incentives to invest in CSR practices and report them completely and normally (Jia & Zhang, 2013). Second, a unique feature of the Chinese market is the government’s heavy involvement in resource allocation (Carey, Liu, & Qu, 2017). Thus, firms with political ties can have easier access to limited resources (e.g., land, bank loans, and subsidies), the increased possibility of avoiding fines or taxes and obtaining credit grants, and the protection from external competitors (Anderson et al., 2017; Luo & Chen, 1997; Xin & Pearce, 1996). Furthermore, Chinese governments generally devise industry development plans and set regulatory policies to guide economic activities. Hence, firms with political ties have privileged access to policy and aggregate industrial information (Hillman, Zardkoohi, & Bierman, 1999). As a result, they would have higher bargaining power and tend to enjoy a better reputation in China. Political ties will therefore enhance the reputation of a firm based on its GRI guidelines adoption. According to signaling theory, the effect of political ties is more likely to foster support from various stakeholders, which benefits companies’ financial performance in the long run.

Political ties can be divided into two levels: ties with the national government (central ties) and ties with subnational governments, such as provinces, municipalities, and towns (local ties) (Anderson et al., 2017; Zheng, Singh, & Mitchell, 2015). Central governments have broader nationwide authority and responsibilities and potentially greater access to resources (Zheng et al., 2015), while local governments’ authority, responsibility, and expenditures are limited to their specific jurisdictions, creating greater specificity, focus, and responsiveness to local needs (Qian & Roland, 1998; Trounstine, 2009). These differences in interdependence, responsiveness, and resources between local and central governments lead us to consider them as separate moderators. Hence, the following hypotheses can be developed:

Hypothesis 2a (H2a): Central political ties moderate the relationship between GRI SR and firm profitability such that the higher the level of central political ties, the stronger the effect of GRI SR on firm profitability.

Hypothesis 2b (H2b): Local political ties moderate the relationship between GRI SR and firm profitability such that the higher the level of local political ties, the stronger the effect of GRI SR on firm profitability.

Internationalization level

A firm’s internationalization level refers to the extent to which it depends on foreign markets rather than domestic markets for sales (Chakrabarty & Wang, 2012). The level of internationalization has been widely used in studies related to business ethics (Garegnani, Merlotti, & Russo, 2015). We argue that a firm’s internationalization level is a contextual factor that potentially affects the relationship between GRI SR and financial performance for the three reasons detailed below.

First, higher internationalization levels lead to higher visibility of firms and greater exposure to various stakeholders (Aguilera-Caracuel, Guerrero-Villegas, Vidal-Salazar, & Delgado-Márquez, 2015; Christmann, 2004; J. Kang, 2012). This might in turn lead firms to increase their CSR activities to protect their reputation (Attig, Boubakri, Ghoul, & Guedhami, 2016). Hence, facing institutional pressures from both domestic and foreign countries would lead these firms to pay more attention to sustainable practices and reporting (Agle, Mitchell, & Sonnenfeld, 1999; Cheung, Kong, Tan, & Wang, 2015).

Second, firms operating in different markets can redistribute the costs and benefits of CSR investments among such markets. These firms have therefore a greater economic incentive to invest in sustainable practices and reporting than domestic-focused firms (McWilliams & Siegel, 2001). Cruz and Boehe (2010) also argue that the standardization of CSR practices worldwide would reduce costs and enable the firms to reproduce some sustainable activities in various places without incurring additional program development costs.

Third, some researchers propose the learning-by-exporting concept, which means exporting firms would increase their knowledge base by learning from their involvement in foreign markets (Martins & Yang, 2009; Vendrell-Herrero, Gomes, Mellahi, & Child, 2016). These firms are in fact exposed to new/different ideas from various national contexts (Ayuso, Roca, Arevalo, & Aravind, 2016). By frequently interacting with foreign agents, customers, suppliers, competitors, and collaborators, they would be exposed to know-how and technologies not available in domestic markets (Dimitratos, Amorós, Etchebarne, & Felzensztein, 2014; Salomon & Shaver, 2005). Hence, such experience can help Chinese internationalized firms to further develop a set of best sustainable practices, to learn better methods, and to decrease the costs of collecting information for GRI indicators.

Christmann and Taylor (2006) state that the achievement of higher sustainability standards represents for Chinese firms a competitive advantage to compete in the global market. Better CSR performance could in fact enable them to attract more business from developed economies (Cheung et al., 2015). Hence, we develop the following hypothesis:

Hypothesis 3 (H3): Internationalization level moderates the relationship between GRI SR and firm profitability such that the higher the level of internationalization, the stronger the effect of GRI SR on firm profitability.

Method

Database

Our sample consists of firms publicly listed on the A-share markets of the Shanghai and Shenzhen Stock Exchanges that have published GRI sustainability reports² for at least three continuous years³ during the period 2008-2016. We chose 2008 as the starting year because the Chinese government issued guidelines encouraging state-owned enterprises to act in a responsible way toward their stakeholders and the environment at that time. The securities regulator also issued guidelines to address the interests of stakeholders and promote sustainable development in the same year (Cheng, Lin, & Wong, 2016).

To test our research hypotheses, we use three databases: the China Stock Market and Accounting Research (CSMAR) database (http://www.gtarsc.com), the WIND Economic (WIND) data base (http://www.wind.com.cn), and the Chinese Research Data Services Platform (CNRDS) database (http://www.cnrds.com). These databases have been used in recent studies and the former two are widely regarded as the most authoritative data sources in China (McGuinness, Vieito, & Wang, 2017; Sun, Peng, & Tan, 2017). CNRDS is an open platform providing high standard Chinese business research data. CSMAR has been used to identify GRI SR companies; WIND has been used to obtain data on financial performance, while some control variables are obtained from CNRDS.

Event Study Method

We adopt the event study methodology to measure GRI SR adoption effects on firm profitability. We use the ROA—calculated as operating income/total assets—which has been recommended by Barber and Lyon (1996) as the best overall measure of profitability performance as our dependent variable.

We define the first year of GRI adoption as the event year⁴ (year t). To isolate the performance effects of GRI SR adoption from the effects of external factors, we examine the abnormal performance of each adopting firm. As the time taken to prepare the report is at most a whole year t, in year t − 1, the firms are free from the impact of GRI SR. Therefore, we set the base year of our event study at t - 1 and measure the change over the next 2 years (i.e., t + 1 and t + 2) using both year-on-year changes and changes over multiple-year periods.

Then, we match each sampled GRI-reporting firm with a set of non-GRI-reporting firms based on specific matching criteria. As we want to see the benefit of GRI adoption in SR, we control for other variables aside from the GRI. Hence, we compare firms that have GRI reporting with those that have non-GRI reporting (non-GRI indicates SR in which the organization discloses information on its economic, environmental, social, and governance performance but without referencing to GRI guidelines or GRI standards).

Barber and Lyon (1996) suggest that the selection of the control sample of each firm of the event study should be based on a combination of three criteria: industry, pre-event performance, and firm size. The matching steps are as follows: Step 1: The matched firms should have the same China Securities Regulatory Commission (CSRC) code, 33%-300% of the sample firms’ total assets⁵ and 90%-110% of ROA in year t − 1, no GRI reports from t – 3 to t − 1 (because if they had GRI reporting during these years, it could affect the following event window), and non-GRI reporting from year t to t + 2 (to control for variables other than GRI). Step 2: If no firm is matched in Step 1, we use only the letter in the code, 33% – 300% of the sample firms’ total assets and 90%-110% of ROA in year t − 1, have no GRI reports from t – 3 to t – 1, and have non-GRI reporting from year t to t + 2. Step 3: If no firm is matched in Step 2, we use 33% to 300% of the sample firms’ total assets and 90% to 110% of ROA in year t − 1, they must have no GRI reports from t-3 to t-1, and they must have non-GRI reporting from year t to t + 2.⁶

We first obtain 152 firms that adopted GRI for at least three continuous years during the period 2008-2016 from the CSMAR database. In the process, we exclude financial services and real estate companies because their data are very different from that of other firms (H. Kang & Liu, 2014) and those issuing B shares (subscribed, bought, and sold in foreign currencies) or H shares (denominated in the Hong Kong dollar) (Casalin, Pang, Maioli, & Cao, 2017). There are 122 GRI-reporting firms remained. We present the sample firms’ first adoption year and industries in Table 1. The most frequent adoption year is 2010. The sampled firms comprise 39 unique CSRC code and 11 industries; manufacturing is the most important sector.

Table 1.

The Sample Firms’ First Adoption Year and Industries.

Year	Number	Letter in the code	Industry	Number
2008	12	A	Agriculture, forestry, livestock farming, fishery	2
2009	9	B	Mining	8
2010	33	C	Manufacturing	60
2011	27	D	Utilities	14
2012	20	E	Construction	11
2013	10	F	Wholesale and retail	8
2014	11	G	Transportation	13
Total	122	I	Information transmission, software and information technology service	3
		L	Leasing and commerce service	1
		N	Water conservancy, environment and public facilities management	1
		R	Culture, sports and entertainment	1
		Total		122

Of the 122 sample firms, we drop seven firms that had insufficient financial data in the base year t – 1. We further exclude nine observations that could not be matched with any non-GRI sustainability reports firms, and finally, 106 firms remained. On average, each sample firm is matched with 2.8 control firms. We then estimate the abnormal ROA of the sample firms compared with the control firms, using the following formulas:⁷

{AP}_{(t + i)} = {PS}_{(t + i)} - {EP}_{(t + i)},

{EP}_{(t + i)} = {PS}_{(t + j)} + ({PC}_{(t + i)} - {PC}_{(t + j)}),

where $AP$ means abnormal performance; $EP$ means expected performance; $PS$ means actual performance of sample firms; $PC$ means median performance of control firms; $t$ means the first year of adoption of the GRI sustainability report; $i$ means ending year of comparison (i = 0, 1 or 2); and $j$ means starting year of comparison (j = −1, 0, or 1).

We obtain the ROA data from the WIND database, covering the period 2006-2016. Next, we have to test whether this abnormal performance differed significantly from zero. The commonly used tests in event studies are the paired-sample t test, the Wilcoxon signed ranks (WSR) test and the sign test. The first parametric t test is generally valid when the abnormal performance follows a normal distribution. Barber and Lyon (1996) put forward that non-parametric tests are normally more powerful than parametric tests in these kinds of studies. Hence, we first examine the data for normality by using the Kolmogorov–Smirnov test. If the abnormal performance is not normally distributed, the two non-parametric tests are used (Lo, Yeung, & Cheng, 2009). We also report the parametric t test to ensure that our findings are robust.

Furthermore, to ensure the robustness of our results to alternative measures, we conduct several sensitivity tests. In particular, we use the industry-adjusted ROA and return on sales (ROS) as the financial performance to repeat the event study, and use alternative measure for internationalization level to conduct the regression.

Cross-Sectional Analysis of Contextual Factors

To test the impact of the contextual factors on financial performance, we use the ordinary least squares (OLS) methodology to test H2 and H3, which is used in other studies such as Lo, Pagell, Fan, Wiengarten, and Yeung (2014) and Orzes, Jia, Sartor, and Nassimbeni (2017). The regression equation is the following:

\begin{matrix} Full model : {AP}_{k} = β_{0} + β_{1} ({ROA}_{t - 1}) + β_{2} ({FSize}_{k}) + β_{3} ({ISize}_{k h}) \\ + β_{4} ({Year}_{k}) + β_{5} ({Committee}_{k}) + β_{6} ({Ownership}_{k}) \\ + β_{7} ({ESS}_{k}) + β_{8} ({FL_Eastern}_{k}) + β_{9} ({FL_Western}_{k}) \\ + β_{10} ({FL_Inner}_{k}) + β_{11} (CSR {strengths}_{k}) \\ + β_{12} (CSR {concerns}_{k}) + β_{13} (Central {PTs}_{k}) \\ + β_{14} (Local {PTs}_{k}) + β_{15} (Internationalization {level}_{k}) + e_{k}, \end{matrix}

where k refers to the kth sample firm and h refers to the hth industry in which the kth firm operates.

The outcome ${AP}_{k}$ is taken from the analysis made in the previous paragraph, and it represents the abnormal ROA in the range between the year t – 1 and the year t + 2. We code political ties as the chairman’s or the senior managers’ current or past political experience, such as serving as government cadres, top officers, or holding membership in the People’s Congress or the Chinese People’s Political Consultative Conference (Faccio, 2006). Through the resumes or CV of both the chairman and senior managers from CSMAR, we separately code central ties and local ties as 1 if the chairman or the senior managers held any political appointment in the government currently or in the past and 0 otherwise. We then sum up to obtain the overall rating of a firm’s central and local ties. Internationalization level is measured as the ratio of foreign sales to total sales and indicates the extent to which a firm’s business comes from foreign versus domestic markets (Chakrabarty & Wang, 2012). Moreover, we also use an alternative measure—the percentage of overseas subsidiaries (the number of overseas subsidiaries/the total number of subsidiaries)⁸ (Jung, Lee, & Dalbor, 2016; Rhou & Koh, 2014) to conduct the sensitivity analysis.

To ensure the rigorousness of our model, we include eight control variables in the analysis. Because more profitable firms might have more resources to achieve higher profitability in the future, we control for their ROA in year t – 1 $({PP}_{k})$ . The relative impact of any improvement program is likely to be higher in small firms (Swink & Jacobs, 2012). Hence, we control the firm size, calculated as the natural logarithm of the total assets in the year t – 1 $({FSize}_{k})$ . One industry level control variable is also included. We use the mean number of total assets in the same sector in the year t – 1 $({ISize}_{k h})$ as industry size. We also include the first year of the GRI report adoption $({Year}_{k})$ , as a control for the differences between early adopters and followers (Lo et al., 2014). Given that the CSR engagement strategy has great impact on the relationship between CSR and firm performance, we control for the existence of an independent CSR committee within the company (Committee). We get the data from CNRDS database; if the firm has a CSR committee, the variable is equal to “1”; otherwise it is equal “0.”

We argue that the relationship between GRI SR and firm performance is influenced by political ties and control for the firm’s ownership (Ownership): state-owned (owned or controlled by central or local government) and non-state-owned firms. It is a dummy variable, if the firm is non-state-owned, the variable is equal to “1”; otherwise it is equal to “0.” Additional firm level controls include environmental sensitivity of the sector (ESS), “1” if the firm belongs to non-environmental sensitive industries, “0” if no (including oil exploration, paper and pulp, chemical and drugs, mining and metallurgy, iron and steel, and textiles) (Zou, Zeng, Zhang, Lin, & Shi, 2015). Finally, because different provinces in China may have different legal environment regulation and degrees of environmental pollution, we control the firm location (FL). According to C. W. Y. Wong, Miao, Cui, and Tang (2018), the economic zones of China are divided into four regions, namely Eastern China, Northeastern China, Inner China, and Western China. These data are collected from the WIND databases. We discard two firms that do not have foreign sales data; then, the regression analysis is based on a sample of 104 firms.

Furthermore, because our study is not making a distinction between greenwashing and credible reporting, controlling for firm’s social performance is necessary in the empirical examination. Following Rothenberg, Hull, and Tang (2017), we view CSR strengths and CSR concerns separately, and then derive the CSR strengths by adding the CSR strengths items in the CNRDS database and the CSR concerns variable by adding the CSR concerns items. The CNRDS database, similar to Kinder, Lydenberg, and Domini (KLD), provides annual data on a large cross-section of Chinese firms, comprising multiple measures of strengths and concerns for each of the six CSR dimensions (i.e., charity, corporate governance, diversity, employee relations, environment, and product). There are a variety of items to measure each dimension’s strengths and concerns, and each item is evaluated as either “1” (the firm has demonstrated this strength or concern) or “0” (otherwise). The scores are determined by third-party raters, who have expertise in CSR efforts and performance; however, they have no direct interest in the firms according to CNRDS. As a lot of firms had insufficient CSR data in year t – 1, we use therefore CSR strengths and CSR concerns scores in year t + 2 minus the according scores in year t to measure the change during the period. Hence, we also conduct parallel regression models for firms that had CSR performance data in year t and t + 2 (N = 100).

Results

We present the results beginning with a focus on the effects of the adoption of GRI guidelines on firm performance (ROA) on a year-on-year basis and for multiple-year periods. We then present the results on the contextual/moderating factors.

Overall Performance Effects

Given that our baseline period to establish control groups is year t – 1, we present the three yearly changes in abnormal performance starting with the change from year t – 1 to t and continuing through the change from year t + 1 to t + 2. By comparing the results across the annual and multiple-year periods, we can see whether the effects of GRI guidelines adoption on firm performance begin immediately or if they are lagged. To take into account the multiple-testing problem, the false discovery rate methodology by Benjamini and Hochberg (1995) was applied.

After conducting the Kolmogorov–Smirnov tests, we find the data are not normally distributed. Hence, we consider the WSR and the sign test when testing the hypotheses. Table 2 presents the results for abnormal changes in ROA on a year-to-year basis. Although the year-to-year changes in abnormal ROA for the sample firms are not significant, the change per firm over the 3-year (from t – 1 to t + 2) and 2-year (from t to t + 2) periods are significantly positive (H1 is supported). The average increase in abnormal ROA reaches 1.1 percentage points in total over the 3-year period from year t – 1 to t + 2.

Table 2.

Annual Abnormal Changes in ROA for Sample Firms.

From year	Median	%	M	p value(WSR)	p value(Sign test)	p value(t test)
All sample firms: N = 106
t – 1 to t	0.002	52.83	0.004	.317	.627	.256
t – 1 to t + 1	0.002	51.89	0.005	.315	.771	.294
t to t + 1	0.002	54.72	0.002	.302	.382	.627
t – 1 to t + 2	0.006	60.38	0.011	.002**	.041	.006**
t to t + 2	0.007	63.21	0.007	.012**	.009*	.063
t + 1 to t + 2	0.003	58.49	0.005	.098	.099	.242
Non-environmental sensitive industries: N = 86
t – 1 to t	0.002	54.65	0.005	.184	.450	.125
t – 1 to t + 1	0.005	56.98	0.010	.045*	.236	.058
t to t + 1	0.004	60.47	0.005	.024**	.067	.239
t – 1 to t + 2	0.010	68.60	0.015	.000***	.001***	.000***
t to t + 2	0.008	66.28	0.010	.001***	.004**	.016**
t + 1 to t + 2	0.003	58.14	0.005	.129	.161	.233

Note. ROA = return on assets; WSR = Wilcoxon signed rank.

, **, and *** denote significance at the .1, .05, and .01 levels, respectively (Benjamini & Hochberg, 1995 false discovery rate correction).

In addition, we repeat the analysis for a subsample consisting only of firms competing in non-environmental sensitive industries to see whether the performance improvement of GRI SR occurs also in such sectors.⁹ The most significant improvement appears in the t – 1 to t + 2 and t to t + 2 periods. The mean cumulative change in ROA in the period t – 1 to t + 2 is 1.5%, which is higher than the general level in all industries (1.1%).

Endogeneity

There may be other factors affecting changes in ROA that were not captured in the matching process. Hence, according to previous studies on the effect of other CSR standards on firm performances (Lo et al., 2014), we conduct the similar tests for the performance ROA over the periods t – 2 to t – 1, t – 3 to t – 1, and t – 4 to t – 1, to examine whether the impact of GRI SR on ROA during the event period (year t – 1 to t + 2) was actually driven by earlier performance gains. In Table 3, we can see that there was no significant change in these periods, which suggests that the causal relationship is not due to systematic bias in ROA prior to the adoption of GRI SR. Hence, our sample selection is robust, and the results are free from endogeneity issues.

Table 3.

Endogeneity Tests.

	Median	%	M
ROA (t – 2 to t – 1)	0.006 (.147)	0.563 (.284)	0.003 (.603)
ROA (t – 3 to t – 1)	0.003 (.615)	0.519 (.822)	−0.005 (.600)
ROA (t – 4 to t – 1)	−0.004 (.384)	0.435 (.336)	−0.004 (.417)

Note. The sample size are N = 87 (t – 2 to t – 1), N = 79 (t – 3 to t – 1), and N = 69 (t – 4 to t – 1), respectively (The sample size is smaller than in Table 2, because we deleted those firms that do not have ROA data in year t – 2, t – 3, and t – 4). Values of p are reported in parentheses. ROA = return on assets.

Factors Affecting the Relationship Between GRI SR and Firm Profitability

Table 4 provides descriptive statistics and a correlation matrix for the regression variables. Due to some correlation coefficients being slightly high, to verify the independence of each variable, we check their variance inflation factor (VIF). They are all between 1.095 and 5.016, suggesting that multicollinearity is not an issue.

Table 4.

Descriptive Statistics and Correlation of the Variables in Regression Analysis.

		M	SD	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)	(15)
a: N = 104
ROA _{t – 1}	(1)	0.06	0.05	1
Firm size	(2)	23.45	1.55	−.16	1
Industry size	(3)	22.90	0.98	−.28***	.59***	1
First adoption year	(4)	2011	1.60	−.03	−.23**	−.12	1
CSR committee	(5)	0.17	0.38	−.05	.06	.14	.02	1
Ownership (non-state owned)	(6)	0.26	0.44	.22**	−.45***	−.45***	.13	−.10	1
Environmental sensitivity of the sector	(7)	0.81	0.40	.13	−.10	−.22**	.16	.09	.12	1
Firm location: Eastern	(8)	0.68	0.47	−.09	.27***	.06	−.09	.09	.03	−.30***	1
Firm location: Western	(9)	0.16	0.37	.01	−.25**	−.14	.12	−.06	−.02	.31***	−.65***	1
Firm location: Inner	(10)	0.11	0.31	.13	−.08	.06	−.05	.01	−.06	.07	−.50***	−.15	1
Central political ties	(11)	1.67	1.73	−.11	.53***	.41***	−.11	.21^*	−.28***	−.05	.23**	−.26***	−.03	1
Local political ties	(12)	2.03	1.86	.12	−.30***	−.32***	.12	−.10	.16	−.19	.04	.06	−.19	−.33***	1
Internationalization level	(13)	12.41	21.39	−.09	.02	−.19	−.01	.05	.10	−.11	.19**	−.17	−.07	.07	−.04	1
b: N = 100
ROA _{t – 1}	(1)	0.06	0.05	1
Firm size	(2)	23.41	1.56	−.17	1
Industry size	(3)	22.87	0.95	−.28***	.59***	1
First adoption year	(4)	2011	1.58	−.01	−.22**	−.15	1
CSR committee	(5)	0.17	0.38	−.04	.05	.11	−.02	1
Ownership (non-state owned)	(6)	0.26	0.44	.21**	−.45***	−.44***	.16	−.09	1
Environmental sensitivity of the sector	(7)	0.82	0.39	.12	−.08	−.19	.13	.07	.10	1
Firm location: Eastern	(8)	0.68	0.47	−.10	.26***	.07	−.08	.08	.02	.29***	1
Firm location: Western	(9)	0.16	0.37	.02	−.25**	−.17	.12	−.05	−.01	−.29***	−.64***	1
Firm location: Inner	(10)	0.11	0.31	.13	−.07	.08	−.06	.01	−.06	−.09	−.51***	−.15	1
CSR strengths	(11)	1.46	3.92	.03	.12	−.03	.13	−.20	.02	−.01	.04	.07	−.12	1
CSR concerns	(12)	−0.07	0.66	.09	.07	−.08	−.12	−.07	.13	.15	.22**	−.16	−.16	.09	1
Central political ties	(13)	1.69	1.74	−.11	.54***	.43***	−.13	.19	−.29***	.02	.22**	−.25**	−.03	.03	−.06	1
Local political ties	(14)	2.06	1.87	.13	−.28***	−.32***	.12	−.09	.15	.20**	.07	.04	−.20**	.01	−.03	−.33***	1
Internationalization level	(15)	12.63	21.75	−.09	.025	−.20**	−.02	.04	.11	.11	.19	−.16	−.08	−.27***	−.11	.07	−.04	1

Note. ROA = return on assets; CSR = corporate social responsibility.

Significant at the .05 level. ***Significant at the .01 level.

Then we conduct some regression analysis to test the moderating effects (see Table 5). M1 is the control model that contains eight control variables. M2 includes the three contextual factors. We find that firm’s ownership as a control variable is significant, which shows that the non-state-owned firms would benefit more from the GRI adoption in their SRs. M3 and M4 are parallel regression models for firms that had the CSR performance data (N = 100). It is worth noticing that the CSR strengths’ coefficient is statistically significant, indicating that the improvement in CSR strengths has a positive effect on firm performance. The coefficients for central ties in M2 and M4 are both insignificant, so H2a is not supported. The results show that the coefficient of local ties is positive and significantly different from zero at the .01 significance level in M2 and .05 level in M4. Thus, H2b is supported. Although the coefficient for internationalization is significant, it is negative in M2. This fails to support H3.

Table 5.

Estimated Coefficients From Regression of Abnormal ROA Change From Year t – 1 to t + 2.

	M1	M2	M3	M4	M5	M6
ROA _{t – 1}	0.04	−0.01	0.04	0.00	0.02	0.02
Firm size	0.23*	0.34***	0.20	0.33**	0.34***	0.31**
Industry size	−0.19	−0.20	−0.20	−0.21	−0.17	−0.18
First adoption year	0.28***	0.27***	0.21**	0.20**	0.26***	0.20**
CSR committee	−0.01	0.03	0.01	0.04	0.02	0.04
Ownership (non-state owned)	0.19*	0.22**	0.21*	0.23**	0.20*	0.21**
Environmental sensitivity of the sector	0.22**	0.20**	0.22**	0.20**	0.19*	0.20**
Firm location: Eastern	0.04	0.07	0.02	0.05	0.06	0.04
Firm location: Western	0.21	0.20	0.16	0.16	0.20	0.17
Firm location: Inner	0.09	0.15	0.08	0.14	0.17	0.16
CSR strengths			0.21**	0.17*		0.20**
CSR concerns			−0.09	−0.10		−0.07
Central political ties		−0.08		−0.11	−0.11	−0.12
Local political ties		0.25***		0.24**	0.24**	0.24**
Internationalization level (Foreign sales/Total sales)		−0.17*		−0.15
Internationalization level (Number of overseas subsidiaries/Total number of subsidiaries)					−0.17*	−0.16*
F	2.92***	3.44***	2.83***	3.26***	3.50***	3.37***
R ²	.24	.33	.281	.368	.34	.38
Adjusted R²	.16	.24	.181	.255	.24	.26
N	104	104	100	100	104	100

Note. Standardized regression coefficients are reported. ROA = return on assets.

Significant at the .1 level. **Significant at the .05 level. ***Significant at the .01 level.

Sensitivity Analyses

To test the robustness of our results, we perform some sensitivity analyses with respect to our choices for financial performance and internationalization level.

First, to test the sensitivity of the calculated abnormal performance, we run the analyses using the industry-adjusted ROA (the difference between the firm’s ROA and the median ROA of the firms in the same two-digit CSRC code industry in the same year), because during the matching process, some were not matched with firms in the same industry (Step 2 and Step 3). Furthermore, we also use the ROS—calculated as operating income/total sales—as the financial performance. The similar outcomes of the tests (see Table 6) ensure that the results are not determined by the selected performance measure.

Table 6.

Annual Abnormal Changes in Industry-Adjusted ROA and ROS for Sample Firms (N = 106).

From year	Median	%	M	p value	p value	p value
From year	Median	%	M	(WSR)	(Sign test)	(t test)
Annual abnormal changes in industry-adjusted ROA
t – 1 to t	−0.003	0.387	−0.002	.054	.025*	.509
t – 1 to t + 1	0.002	0.528	0.003	.513	.627	.503
t to t + 1	0.001	0.528	0.000	.603	.627	.998
t – 1 to t + 2	0.007	0.623	0.006	.038	.015*	.212
t to t + 2	0.007	0.585	0.002	.082	.099	.571
t + 1 to t + 2	0.004	0.604	0.002	.029	.041*	.629
Annual abnormal changes in ROS
t – 1 to t	0.001	52.83	0.010	.190	.627	.123
t – 1 to t + 1	0.015	55.66	0.021	.092	.285	.045
t to t + 1	0.002	53.77	0.011	.390	.497	.204
t – 1 to t + 2	0.014	63.21	0.030	.004**	.009*	.005**
t to t + 2	0.008	41.51	0.020	.040	.099	.038*
t + 1 to t + 2	0.006	55.66	0.008	.107	.285	.366

Note. ROA = return on assets; ROS = return on sales.

, **, and *** denote significance at the .1, .05, and .01 levels, respectively (Benjamini & Hochberg, 1995 false discovery rate correction).

Second, because the results do not support H3, we use alternative measure for internationalization level to repeat the regression by using the percentage of overseas subsidiaries (the number of overseas subsidiaries/the total number of subsidiaries) and report the results in M5 and M6 in Table 5. We find that the coefficient for internationalization is still negatively significant, suggesting that the result is free from the chosen measure.

Discussion

The Relationship Between GRI Reporting and Firm Profitability

Our results indicate that the benefits of the adoption of GRI guidelines, representing a strong positive signal in SR, tend to more than compensate for associated costs and required investment. However, the effect on profitability may take longer to achieve and therefore needs to be observed by only examining longer periods. This result is consistent with the argument proposed by Mahapatra (1984) and Preuss and Barkemeyer (2011) that investment in CSR tends to be at least a medium to long-term commitment. Although GRI reporting has been less effective in achieving its original aspirations instilled by its founders, which is to empower non-governmental organizations (NGOs; Levy et al., 2010), and there are some difficulties (e.g., indicator contingency, ambiguous or incomplete information, data heterogeneity, and reporting opacity) in comparing the sustainability performance of different firms based on GRI reports (Boiral & Henri, 2017), it is still relatively successful in promulgating the practice of non-financial reporting and promote the firms’ financial performance in our sample.

Regarding the environmental sensitivity of the industry, the findings of this study suggest that firms in non-environmental sensitive industries significantly benefit from GRI SR. As pointed out by X. Liu and Anbumozhi (2009), firms belonging to highly environmental sensitive industries face more stringent regulation because they are more likely to damage the environment. In China, the government has enacted a series of laws and regulations for those firms to control environmental accidents (Zeng, Xu, Dong, & Tam, 2010). Hence, these firms have already disclosed more environmental information and made their SR more transparent than non-environmental sensitive firms (Fernandez-Feijoo, Romero, & Ruiz, 2014; Villiers & Marques, 2016). In this situation, paying attention on CSR practices and optimizing SRs for the non-environmental sensitive firms still have much space to improve, and adopting GRI is a significant option.

Signaling Environment

We have also identified two moderators of firms’ political ties and internationalization level, which form the signaling environment for SR.

Moderating effect of political ties

When we examine the moderating effect of political ties on the relationship between GRI reporting and financial performance, the role of local ties is significant, while a firm’s central ties are not. China is a complex transition economy, with multiple levels of governments (i.e., a central government and numerous local authorities; L. Wang, Sheng, Wu, & Zhou, 2017). This result further confirms that local governments are pivotal in the Chinese economy (Arnoldi & Villadsen, 2015) because they have substantial power and provide access to key resources, such as land, production facilities, and skilled labor, making ties with them more valuable than those with the central government (Arnoldi & Villadsen, 2015). Currently, local provincial governments have obtained substantial autonomy in establishing their own economic policies and GDP of many provinces (e.g., Guangdong, Shanghai) equals that of a medium-sized country (L. Wang et al., 2017). Interdependence with local governments leads to more specific formulations, the successful application of regulations, and more effective co-operation, which in turn improves the firm’s performance (Zheng et al., 2015). Walder (1995) also suggests that local government-controlled firms are often more financially healthy because they are not under budget constraints that would normally be expected of government-owned enterprises; therefore, support from the local government is very important (Luo, 2010). Hence, we establish and verify that in our context, the benefits of political ties are more salient at a local level than at the central level.

Moderating effect of internationalization Level

Although we put forward the positive moderating effect of the internationalization level, the empirical analysis shows an opposite result that presents a significant negative moderating effect. It has been acknowledged that by expanding abroad, firms can acquire substantial resources and exploit foreign market opportunities while improving their competitive advantage and enhancing performance (Bartlett & Ghoshal, 1989; Ma, Zeng, Shen, Lin, & Chen, 2016). However, some researches also state that companies with a high internationalization level may have higher transaction costs and be exposed to trade barriers (Peng & Chen, 2009; Y. Zhang, Li, Li, & Zhou, 2010). In particular, firms from emerging economies face more intensive societal and environmental regulatory requirements from host governments, global competitors, foreign customers, communities, NGOs, and the international media when entering foreign markets (Christmann, 2004; J. Kang, 2012; Ma et al., 2016).

This is the case for China. Chinese firms with a high internationalization level may lack the necessary knowledge and experience with sustainability. CSR issues may become their weaknesses with respect to overseas markets (Ma et al., 2016). In addition, they face pressures not only from stakeholders at home but also from those of the countries where they export their products/services (Cheung et al., 2015). The different stakeholders may have different CSR expectations, so these firms have to make more efforts to meet the different requirements (Cheung et al., 2015). At the same time, internationalization would bring in stronger policies as well as the implementation of stricter environmental laws and regulations than in China (Zhu, Sarkis, & Lai, 2012). Due to the fierce competition and difficulties of gaining legitimacy in global market, a lot of firms in the international markets have already reported GRI SRs to shift from “implicit” to more “explicit” CSR (Matten & Moon, 2008). Hence, Chinese firms doing GRI SR do not obtain significant comparative advantages.

On the contrary, firms focusing on domestic market have some advantages. Although environmental protection is important, economic growth is the primary goal in the mind of Chinese firms (Villiers & Marques, 2016). There are still few firms paying attention to CSR issues at home, and even fewer considering to organize their SRs by adopting GRI. Hence, firms whose businesses mainly focus on China would obtain significant advantages from adopting GRI to organize their reports, because this differentiates them from their competitors by showing their greater commitment to CSR issues.

Conclusion

Our study provides solid support for the hypothesis that the adoption of GRI guidelines in SR tends to produce significant abnormal benefits in firms’ financial performance. These benefits appear to be persistent over the time period from year t – 1 to t + 2. In addition, we find that the improvement of abnormal financial performance is contingent upon some important factors. We note in particular that companies with local political ties experience benefit more from the adoption of GRI guidelines. Firms’ internationalization level negatively moderates the relationship between the adoption of GRI and performance. In addition, the moderating effects of a company’s central political ties are not significant.

Contribution to Theory

We make a number of significant contributions to SR research and signaling theory. First, ours is the first empirical study that supports the positive relationship between GRI SR and firm financial performance by adopting an event study method based on a large sample. When compared with previous research on GRI (Belkhir et al., 2017; Bernard et al., 2015; J. Lee & Maxfield, 2016), our research expands the scope to all industries in A-share markets except for financial services and real estate companies.

Second, our study significantly extends signaling theory to an SR context. We suggest that signaling theory is a powerful theoretical framework that can reveal how GRI reporting contributes to financial performance. We argue that information asymmetry exists between the firm and the relevant parties (e.g., investors, the government, and other stakeholders) in terms of attitudes and practices regarding sustainability of a firm. GRI reporting can strengthen the signals from senders to the receivers avoiding the image of “selective reporting.” Previous research adopting signaling theory identifies only the causal link between signaling strength and a reduction of information asymmetry. Ours may be the first study that builds a link between signaling strength and financial performance in the context of SR.

Third, we extend signaling theory beyond the question of simply whether a GRI-reporting signal is related to financial performance and further find that the relationship between signal strength and financial performance is moderated by the signaling environment (i.e., political ties and internationalization level). Our study is the first to examine the moderating effects on the relationship between GRI SR and financial performance.

Moreover, previous CSR-related research does not consider political ties as contextual factors. We are therefore among the first to shed light on the signaling environment, which is of particular importance considering the status quo of CSR practices in China.

Contribution to Practice

This study makes a number of significant contributions to SR practice. First, the choice of the adoption of the GRI in SR is an organizational strategic investment that transfers positive and green signals to outside stakeholders, reduces information asymmetry, and builds a close connection with diverse stakeholders to gain their support. Managers can expect that the adoption will lead to increased abnormal financial performance in the long run. In China, governments and stakeholders are paying an increasing amount of attention to severe environmental and social issues; thus, it is now a good time for Chinese companies to take actions on sustainability-related practices and structure their SR well by using the new GRI standards. Second, the benefits of the adoption of GRI apply to all organizations but are stronger for those with local political ties. The contingent nature of the relationship between GRI adoption and financial performance may provide an important insight for practitioners and organizations in identifying and exploiting their motivational advantages.

Limitations and Future Research

Our study is subject to certain limitations. First, our sample consists of publicly listed firms in China, and it may be biased toward larger firms. The results for relatively small firms may be quite different. Given the difficulty in obtaining secondary data on small and medium-sized enterprises, other data collection approaches might be considered. Furthermore, our study is limited by its focus on China. Future research could extend to other countries to compare the results. Second, our study is limited to focusing on financial performance impacts over a 3-year time horizon. As time passes, future studies should be able to examine whether the effects persist over even longer periods. Moreover, there are several other performance effects that could be examined, such as social performance (employees’ wages, working hours, training, education, health and safety performance, and external stakeholders’ benefits) and environmental performance (emissions, eco-efficiency, wastes and recycling) (Lozano & Huisingh, 2011). Third, we explore the political ties and internationalization level as contextual factors. Future studies might consider other potentially important contextual factors, such as business ties (Luo, 1997), firms’ business and geographic diversification (Wood, Wang, Olesen, & Reiners, 2017), and marketing spending (Oh, Bae, Currim, Lim, & Zhang, 2016). Other industry-level factors might also be considered, such as industry competitiveness (Lo, Wiengarten, Humphreys, Yeung, & Cheng, 2013). Fourth, we only consider the situation of strong-positive signals and assume that in general the signal of GRI adoption can minimize the negative outcomes of corporate social irresponsibility news. Future research can consider the distinctions between what constitutes a strong versus a positive signal and how to handle the difference between strong-positive and strong-negative signals. In addition, the small sample size for non-foreign-owned firms and environmental sensitive industries and the unavailable data for firms’ international operating locations do not allow us to conduct separate tests considering these variables. Future studies—in particular based on primary data—could try to overcome these limitations.

Footnotes

Author Note

Lujie Chen is now affiliated to International Business School Suzhou, Xi’an Jiaotong-Liverpool University, Suzhou, China. E-mail: lujie.Chen@xjtlu.edu.cn

Declaration of Conflicting Interests

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

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Notes

ORCID iD

Fu Jia

Author Biographies

Yang Yang is a PhD candidate at the School of Management, Xiamen University, China. She is also an Academic Visitor at the University of Bristol (UK). Her research interests focus on sustainable supply chain management, corporate social responsibility, and supply chain learning. Her articles have appeared in such journals as International Journal of Production Research and Supply Chain Management: An International Journal.

Guido Orzes (PhD, University of Udine) is assistant professor in management engineering at the Free University of Bozen-Bolzano (Italy). He is also honorary research fellow at the University of Exeter Business School (UK) and visiting scholar at the Worcester Polytechnic Institute (USA). His research focuses on international sourcing and manufacturing and their social and environmental implications. He has published more than 50 scientific works on these topics in leading operations management and international business journals (e.g., International Business Review, International Journal of Operations and Production Management, International Journal of Production Economics, and Journal of Purchasing and Supply Management) as well as in conference proceedings and books. He is involved in various EU-funded research projects on global operations management and Industry 4.0 and is member of the board of the European division of the Decision Science Institute.

Fu Jia (PhD, Cranfield School of Management, UK) is a chair (full) professor of supply chain management and director of Sustainability and Resilience Theme at the University of York Management School, England. He is also a visiting professor to several universities including Zhejiang and Fudan Universities. He was previously director of Business, Nature and Value (BNV) Research Centre at Exeter and currently leads a number of projects funded by Newton Fund, ESRC, EPSRC, and industrial sources related to supply chain management and e-commerce both in the United Kingdom and in China. He has published extensively in leading international operations management (OM)/supply chain management (SCM) journals such as International Journal of Operations and Production Management, International Journal of Production Economics, Journal of Business Logistics, Journal of Operations Management, Journal of Purchasing and Supply Management, Journal of Cleaner Production, and Supply Chain Management: An International Journal, among others. He is an associate editor to International Food and Agri-business Management Review (IFAMR), International Journal of Operations and Production Management, and Journal of Purchasing and Supply Management.

Lujie Chen (PhD, Linköping University, Sweden) is associate professor in management at Xi’an Jiao tong-Liverpool University, China. Her expertise is in the areas of Operations and Supply Chain Management as well as operations strategy. Her research interests mostly focus on sustainable supply chain management and innovation, CSR reports, supply chain finance, network-based service and business model innovation, supply chain integration, digital supply chain, big data–based supply chain and logistics optimization. Her publications appear in leading international journals such as the Harvard Business Review, International Journal of Production Economics, International Journal of Production Research, Journal of Cleaner Production, and Journal of Purchasing and Supply Management, among others.

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