{'fraction_non_alphanumeric': 0.017251635930993457, 'fraction_numerical': 0.007138607971445568, 'mean_word_length': 5.276119402985074, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Corporate Sustainability Performance and Financial Performance: Empirical Evidence from Japan and India

Purpose The purpose of this article is to explore the disclosure of corporate sustainability (CS) practices and to examine the association between sustainability performance and financial performance in Asian context considering firms from India and Japan. Design/methodology/approach The present study is based on secondary data collected from annual reports and CS reports of 28 and 35 listed non-financial firms from India and Japan from 2009 to 2014. Content analysis (binary coding system) is employed to calculate the sustainability disclosure score based on Global Reporting Initiatives (GRI) framework. Market-to-book ratio is used to measure the financial performance. These scores are further used to examine the impact of CS performance on financial performance employing both the panel data model and logit regression model. Findings The study finds that the average level of disclosure is more in case of Japanese firm as compared to Indian firms. Using both the regression model, the study finds the influence of CS performance on financial performance is positive and significant for both the nations. However, the influence of CS performance is more in case of Japan than that of India. Moreover, the study also reveals that environmental factor is more dominating in influencing the financial performance in Japan. Whereas, in India, it is the social factor that dominates the financial performance. Originality/value It is the first comprehensive study in Asia analyzing the CS practices using both the panel data regression model and logit regression model.