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The effectiveness of corporate governance is a major factor in forecasting firm performance. We examine the relationships among cross-listing, corporate governance and firm performance for a sample of Chinese cross-listed companies. We show that cross-listed firms display higher overall quality of corporate governance compared to non-cross-listed firms. Consequently better corporate governance results in higher operating performance. Our results support the bonding hypothesis of cross-listing. Furthermore, we also illustrate that the cross-listing status encapsulates the higher quality of corporate governance that leads to higher operating performance. When forecasting performance of cross-listing companies, it is therefore important to recognize the substitute effect between cross-listing and corporate governance.

Acquisitions are complex and ambiguous events fraught with information asymmetries emphasizing market failure before an acquisition or organizational failure during integration. While often treated in isolation, market and organization failure are intertwined in acquisitions as integration planning starts before a deal is closed. Effective integration begins with a deep understanding of the target to be able to share assets and knowledge. However, acquiring firms currently have limited solutions to address information asymmetries. Most remedies primarily aim at market failure using due diligence and external advisors, leaving information asymmetry due to organizational failure primarily unattended. The authors develop a typology that leverages informal and formal social ties to address information asymmetries across the acquisition process that jointly considers market and organizational failure. The typology of this study combines existing research to develop how social ties with stakeholders influence acquisitions and can increase their success.

Social entrepreneurship has played a significant role in reducing unemployment and poverty, fixing other social issues and environmental concerns. Although there is an increasing concern on social entrepreneurship by the public sector, practitioner and scholars, there are still limited studies on predicting the intention to become a social entrepreneur. Thus, investigating social entrepreneurship intention using a systematic literature review (SLR) approach is crucial due to the lack of systematisation and categorisation in this field. Therefore, this study aims to conduct a SLR to identify the antecedents of social entrepreneurial intention (SEI) used by the previous research. In this sense, this chapter carries out a systematic review of the literature on social entrepreneurship intentions. The review is guided by the PRISMA Statement (Preferred Reporting Items for Systematic reviews and Meta-Analyses). After the identification and screening process, only 56 articles were qualified for further analysis. This SLR focused on articles that are using quantitative research and in the English language published in Scopus. Although there is no limitation in the timeline, the search string results found that the related articles were published between 2010 and 2020. From the thematic analysis, nine main themes were found. The themes are categorised based on the antecedents of SEI used by previous research. There are nine antecedents found: (1) perceived desirability and feasibility, (2) attitude, subjective norms and perceived behavioural control, (3) prior experience, (4) emotional factors, (5) self-efficacy, (6) personality, (7) support systems, (8) skills and competencies and (9) motivational factors. Further analysis of the themes has resulted in ten sub-themes. This chapter's contribution includes offering a clearer picture of the antecedents of social entrepreneurial intention that is still at its infancy stage. Additionally, this chapter managed to identify the research gaps and proposed future research agenda.

Dynamic predictions of financial distress of the firms have received less attention in finance literature rather than static prediction, specifically in Malaysia. This study, therefore, investigates dynamic symptoms of the financial distress event a few years before it happened to the firms by using neural network method. Cox Proportional Hazard regression models are used to estimate the survival probabilities of Malaysian PN17 and GN3 listed firms. Forecast accuracy is evaluated using receiver operating characteristics curve. From the findings, it shown that the independent directors’ ownership has negative association with the financial distress likelihood. In addition, this study modeled a mix of corporate financial distress predictors for Malaysian firms. The combination of financial and non-financial ratios which pressure-sensitive institutional ownership, independent director ownership, and Earnings Before Interest and Taxes to Total Asset shown a negative relationship with financial distress likelihood specifically one year before the firms being listed in PN 17 and GN 3 status. However, Retained Earnings to Total Asset, Interest Coverage, and Market Value of Debt have positive relationship with firm financial distress likelihood. These research findings also contribute to the policy implications to the Securities Commission and specifically to Bursa Malaysia. Furthermore, one of the initial goals in introducing the PN17 and GN3 status is to alleviate the information asymmetry between distressed firms, the regulators, and investors. Therefore, the regulator would be able to monitor effectively distressed firms, and investors can protect from imprudent investment.

While earlier acquisition research often focused on either the acquirer or the target side of analysis, recent work has increasingly emphasized the need to understand the dyadic interrelationship between the target and the acquirer. This review aims at synthesizing research progress in the area of target–acquirer interrelationships and understanding what questions remain unanswered. The author organizes this review into three dimensions of target–acquirer interrelationship: (a) their relative attributes (what both parties are relative to each other), (b) their connections (what both parties have with each other), and (c) their interactions (what both parties do to each other). Based on the review, the author then identifies critical research gaps and opportunities for developing a more comprehensive understanding of the interrelationship between the target and the acquirer in acquisitions.

In the last few years, information and communication technologies (ICTs) and social media have become increasingly relevant to politicians and political parties alike, often used to issue statements or campaigning, among others. At the same time, many citizens have become more involved in politics, partly due to the highly interactive and social environments that the social networking services (SNS) provide. Political events flow through these networks, influencing their users; such events, however, often start offline (outside the online platform) and are, therefore, hard to track. Event studies, a methodology often used in financial and economic studies, can be translated to social networks to help modeling the effect of external events in the network. In the present case, the event study methodology is applied to two sample cases: the tariff war between the United States and China, with multiple responses and retaliations from both sides, and the Brexit referendum. In both cases, the Twitter social networks that arise from users who discuss the respective subjects are analyzed to examine how political events shape and modify the network. Results show how event studies, combined with the possibilities offered by the ICTs both in data retrieval and analysis, can be applied to understand the effect of external political events, allowing researchers to quantitatively track, observe, and analyze the spread of political information over social network platforms. This is a first step toward obtaining a better understanding on how political messages are diffused over social networks and their effects in the network structures and behaviors.

The digital and material traceability of our interactions in organizations are nowadays the subject of very advanced analyses through tools known as social media analytics (SMA). As thinking (infrastructure), SMA tools constitute objects to think of our digitally mediated interactions with. It produces a substratum (a new meaning) that would not exist otherwise, and enacts different types of reasoning that hypothetically influence community managers’ or members’ sensemaking of digitally mediated interactions. This chapter proposes to look behind the curtain of charts and graphs, in order to highlight the performativity of the interactions between the different machines and the traces of our digitally mediated interactions. Drawing on a detailed analysis of the fabric of SMA, this chapter highlights the explanatory power of a communication perspective on types of reasoning enacted by thinking infrastructures. First, considering the SMA tool as an editorial enunciation allows us to see it as a process implying several beings (e.g. machines, humans and logs) that are not without consequences. Second, we show that these beings have different modalities of interactions with each other, and that these modalities of interactions influence the materiality of the digital traces of past interactions. Third, throughout the process, we demonstrate the fragility and variability of their materiality. Finally, faced with the rise of a technological deterministic discourse, which tends to portray the exploitation of our digital traces as an objective way of representing the collaborative practices that make up the organization, our research aims, on the contrary, to demonstrate their relativity.

We empirically investigate the forecasting ability of USD-INR exchange rate volatility models by considering Google Trends data. Within a multiple regression framework, we use historical volatility and liquidity measures to build our benchmark volatility model (Chandra & Thenmozhi, 2014). Moreover, we extend Bulut (2018) to incorporate indexes for 15 keywords (price-related, income-related, and liquidity-related) from Google Trends data into our benchmark volatility model to evaluate the forecasting ability of the models. Our results indicate that Google Trends data can improve volatility prediction and that among the groups of keywords that we consider, the price-related keywords have the best forecasting ability. Incorporating data on searches for “prices” into the model produces the highest reduction in the forecasting error: a 22.75% decrease compared to the level in the benchmark model. Hence, these empirical findings indicate that Google Trends data contain information that influences exchange rate movements.