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This paper explores the relationship between earnings management and firms' value through the moderating effect of the missing elements – corporate social responsibility (CSR) disclosure and state ownership in Russian companies. The main argument of the paper is that CSR disclosure can be used as a mitigating mechanism to weaken the negative relationship between earnings manipulation and market value. Additionally test whether state ownership is an important moderating factor in this relationship are conducted as state has always played an important role in the emerging Russian market.

The hypotheses are tested on panel data for 223 publicly listed Russian firms for the period 2012–2018. A number of robustness tests are used to check the obtained results for consistency. Following previous research GMM method is employed to address endogeneity concerns.

Supported by stakeholder theory, it is observed that firms that disclosed more CSR information experience a weaker negative relationship between earnings management and market value because investors and other stakeholders positively evaluate a positive CSR image. This negative effect of earnings management on market value is even weaker for state-owned companies as market participants appreciate involvement of state-owned companies in CSR activities and place greater expectations on these firms to be responsible without clear understanding whether these actions are “window dressing” for this type of companies or not.

The study results provide new insights into the relation between earnings management, firm's value, CSR disclosure and state ownership in emerging-market firms. The paper highlight the importance of considering country-specific factors, such as state ownership, while analysing the market reaction on CSR disclosure and earnings management since the institutional peculiarities may help to explain differences in the obtained results.

This study analyses the literature on artificial intelligence (AI) and its implications for the agri-food sector. This research aims to identify the current research streams, main methodologies used, findings and results delivered, gaps and future research directions.

This study relies on 69 published contributions in the field of AI in the agri-food sector. It begins with a bibliographic coupling to map and identify the current research streams and proceeds with a systematic literature review to examine the main topics and examine the main contributions.

Six clusters were identified: (1) AI adoption and benefits, (2) AI for efficiency and productivity, (3) AI for logistics and supply chain management, (4) AI for supporting decision making process for firms and consumers, (5) AI for risk mitigation and (6) AI marketing aspects. Then, the authors propose an interpretive framework composed of three main dimensions: (1) the two sides of AI: the “hard” side concerns the technology development and application while the “soft” side regards stakeholders' acceptance of the latter; (2) level of analysis: firm and inter-firm; (3) the impact of AI on value chain activities in the agri-food sector.

This study provides interpretive insights into the extant literature on AI in the agri-food sector, paving the way for future research and inspiring practitioners of different AI approaches in a traditionally low-tech sector.