Abstract

This study investigated the determinants of financing decisions of firms from the Middle East and North Africa (MENA) region, particularly the effect of public corruption on financing decisions and the effect of disorder following the Arab Spring on public corruption-financing structure relationship. The study encompasses a total of 800 business entities from 14 different countries, spanning the time frame of 2005–2018. Data is analyzed through the application of static fixed effects and dynamic GMM-System models. Results indicate that large, tangible companies prefer debt, whereas profitable companies with more room for growth choose equity. Less corruption is associated with increased corporate debt. The use of debt decreased because of the Arab Spring, and corruption’s effect on leverage became weaker. The speed of adjustment to the target leverage is comparatively slow for book leverage, while it is significantly greater for market leverage. These outcomes are consistent with the pecking order behavior resulted from increasing information asymmetry, but the tradeoff theory has some support as well. The implications of this study entail the need for enhancing investor protection, strengthening control measures, increasing transparency, and fostering the overall growth of the financial system to facilitate enterprises’ use of debt financing, particularly in the post-Arab Spring era. This paper provides fresh empirical evidence demonstrating the effect of the Arab Spring on capital structure and on the relationship between country corruption and capital structure in the MENA region. The paper also expands the body of research on capital structure and corruption across countries by providing empirical findings from a region that has been relatively overlooked in previous scholarly works.

Introduction

Lack of a perfect capital market necessitates the need for capital structure (CS) policy [1, 2, 3, 4, 5]. The choice of debt and equity is important not only for stable corporate financial health but also for the role it plays in economic growth [6,7]. Since Modigliani and Miller’s [8] slice-of-the-cake argument, the literature has identified many firm-specific CS determinants [6,9] as well as macroeconomic factors [7,10, 11, 12]. Country corruption facets, such as weak regulations and inconsistent policies at the macro level, may affect financing decisions as they influence agency conflicts of interest, the risk of financial fraud, and resource waste at the business level [10,13]. Such consequences are exacerbated if accompanied by weak disclosure policies that enhance the information asymmetry problem and increase the cost of capital [14]. Consequently, financial market activity reduces as a result of investors’ low confidence.

Public corruption is a potential determinant of CS that is largely ignored. A common link is through asymmetric information as a result of ineffective management transparency, monitoring, and law enforcement [15]. This information gap is also caused by manager-shareholder agency conflicts [16]. Because of poor ruling systems and regulations, emerging countries are deemed more corrupt than developed countries [17]. Non-financial enterprises are less regulated compared with financial firms, and thus they are more exposed to corruption [18].

Conclusions

This study investigates the CS practices of non-financial companies operating in the MENA region. Country-level corruption and the Arab Spring’s effect on CS is analyzed through the application of static and dynamic models. Country corruption has an effect on CS because of the significant agency problems and information asymmetry that are associated with corrupt practices. The developing countries in the MENA region are more susceptible to corruption because of inadequate governance systems and regulations, which make them a suitable choice for this study.

Our contribution to the literature on capital structure is threefold. The study expands the research on CS across countries in a geographic area that has been relatively overlooked in previous scholarly works. Our second contribution is emphasizing the significance of enhancing institutional environments in developing countries to facilitate firms’ access to external finance, particularly when the level of corruption in a country is found to have a significant effect on the decisions that corporations make about their CS. Finally, addressing the effect of the Arab Spring, a period of political instability, on the CS decisions is the first instance in which such an analysis has been conducted. The findings of this research are likely to be significant to other developing countries and regions and carry major implications for emerging economies that share similar attributes.

Abstract

To what extent does the collapse of a commercial bank spread contagion across cryptocurrency markets? How do markets behave around bankruptcy if digital assets remain stuck within the bank and cannot be withdrawn? We use a BEKK model to examine contagion effects across major digital assets during the Silicon Valley Bank (SVB) collapse period in early March 2023. We find evidence of contagion across major stablecoins and Bitcoin. We also examine the price action when nearly all withdrawals at SVB were prohibited. We find substantial abnormal movements in stablecoin cumulative returns and traded volumes, indicating a “flight to safety” from less to more authoritative and trusted stablecoins. The implications for practitioners and policymakers are discussed.

Introduction

Cryptocurrencies are gradually gaining traction in trading and becoming a notable segment of the financial landscape, providing new investment avenues. However, their inherent volatility continues to hinder their broader acceptance. Among cryptocurrencies, a subset known as stablecoins, which is intended to maintain a “stable” peg to a reference currency, is crucial to the market as it allows traders to work around the volatility issue and keep money in a form equal to US dollars (De Blasis et al., 2023). Access to those digital assets is typically given by new financial technologies (also called Fintech) such as digital wallets or Centralized cryptocurrency Exchanges (CEXs), with the aim of decentralizing finance through innovative offers of financial services. However, due to the high costs associated with the trading of cryptocurrencies in US dollars, the impossibility of using US dollars on cryptocurrency exchanges,1 and the simplicity and speed of transferring those virtual assets between exchanges, stablecoins occasionally trade at a premium2 to the underlying asset they imitate (De Blasis et al., 2023). The recent bankruptcy of Silicon Valley Bank (SVB), the largest commercial bank for nearly half of all venture-backed tech startups in Silicon Valley,3 in March 2023 tied up $3.3 out of $40 billions of stablecoins in its financial statement, making this exogenous shock a significant event to study.

Thus, the aim of this research is to examine the impact of bank failure on the stability of stablecoin markets. Specifically, we examine the question of whether halting access to digital assets through the bankruptcy procedure of a traditional finance institution spills over contagion effects across those innovative instruments. Answering this question is important as the March 2023 collapse of SVB, together with the ensuing instability in a number of major stablecoins, demonstrated the importance of the interlinks between traditional and digital financial systems. The results of this research are therefore relevant to policymakers in both the traditional and modern financial systems interested in avoiding the risk of financial contagion, as well as investors willing to defend their savings and balance their portfolios against market uncertainty.

Conclusion

This study examined the extent to which the collapse of a commercial bank spread contagion across cryptocurrency markets and how those markets behave around the failure when asset withdrawals are halted. We use a BEKK-GARCH multivariate model to test for financial contagion around the Silicon Valley Bank collapse across multiple digital assets. Arguably, this is the second-largest bank run after the global financial crises experienced in history. We find evidence of volatility spillover effects across major stablecoins and Bitcoin, with a “flight to safety” from less to more stable virtual currencies.

Hence, this study provides important evidence of the ways the traditional and cryptocurrency financial systems are interlinked. Arguably, it links centralized and decentralized finance, showing the ways in which spillovers can occur between what seem to be separate realms of finance. Academics, practitioners, and policymakers alike interested in destabilizing risk in the digital finance ecosystem should pay attention to the connections with traditional finance and thus to the results of this research. A limitation of this study is that it focuses on the most liquid stablecoins and cryptocurrencies. Future studies may, therefore, further explore the links between centralized and decentralized finance, including stablecoins and other means of digital value exchange.

Abstract

Corporate bankruptcy and financial distress prediction is a topic of interest for a variety of stakeholders, including businesses, financial institutions, investors, regulatory bodies, auditors, and academics. Various statistical and artificial intelligence methodologies have been devised to produce more accurate predictions. As more researchers are now focusing on this growing field of interest, this paper provides an up-to-date comprehensive survey, classification, and critical analysis of the literature on corporate bankruptcy and financial distress predictions, including definitions of bankruptcy and financial distress, prediction methodologies and models, data pre-processing, feature selection, model implementation, performance criteria and their measures for assessing the performance of classifiers or prediction models, and methodologies for the performance evaluation of prediction models. Finally, a critical analysis of the surveyed literature is provided to inspire possible future research directions.

Introduction

Corporate bankruptcy prediction (BP) and financial distress prediction (FDP) have been a field of investigation by researchers around the world for nearly a half century. However, increasing attention has been paid to this evergreen subject since the Global Financial Crisis of 2008 (Alaminos et al., 2016), as predictions can have a significant impact on the decisions and returns of various stakeholders (Alam et al., 2021).

Corporate bankruptcy and financial distress events are not desirable (Gordon, 1971), as their legal and financial costs are prohibitive (Weiss, 1990). In addition, these events increase the expected costs for financial institutions, such as banks, to hedge against the risk of these events happening (Alnassar & Chin, 2015). To reduce exposure to risk and catch early warning signs, stakeholders including investors, bankers and governments are proactively looking for solutions to effectively analyze and predict corporate bankruptcy and financial distress events. The earliest study in terms of modern bankruptcy prediction can be traced back to 1932 when Fitzpatrick (1932) presented a successful way to distinguish between failed and healthy companies by analyzing 20 pairs of companies’ accounting ratios. Since the 1960s, several accounting-based statistical and probabilistic models have been proposed to predict corporate bankruptcy and financial distress (e.g., Beaver, 1966; Altman, 1968; Ohlson, 1980; Zmijewski, 1984; Zavgren, 1985). In addition, several studies focused on the identification of bankruptcy and financial distress drivers2 (e.g., Altman, 1968; Liang et al., 2016; Tobback et al., 2017; Tinoco et al., 2018; Mai et al., 2019), on one hand, and others focused on the design of methodologies for selecting such drivers (e.g., Tsai, 2009; Lin et al., 2014; Tian & Yu, 2017; Uthayakumar et al., 2020; Kou et al., 2021), on the other hand. Furthermore, a stream of literature investigated the causes of the under-performance of bankruptcy and financial distress prediction models such as data sample imbalance (e.g., Le et al., 2018; Veganzones & Séverin, 2018; Zoričák et al., 2020, Shen et al., 2020). In the era of the rise of machine learning and artificial intelligence technologies, the prediction of bankruptcy and financial distress has been lifted to a whole new level, as more and more new methodologies managed to improve prediction accuracy through the design features of new models (e.g., Ouenniche & Tone, 2017; Ouenniche et al., 2018a, 2018b, 2018c, 2019; Hosaka, 2019; Matin et al., 2019; Yuan et al., 2022), the type of implementation decisions such as over-sampling and under-sampling (e.g., Le et al., 2018; Sun et al., 2020; Shen et al., 2020; Du et al., 2020), and bagging and boosting (e.g., West et al., 2005; Zięba et al., 2016; Jones & Wang, 2019; Chen et al., 2020). On the other hand, another stream of research focused on reducing the computational requirements of prediction models or methods using parallel implementations that take advantage of today’s powerful computers, or to be more specific, GPUs (e.g., Huang & Yen, 2019; Le et al., 2019).

Conclusions and future research directions

The topic of corporate bankruptcy and financial distress prediction has attracted the attention of many researchers for several decades and continues to evolve with more and more advanced prediction methodologies and issue fixation solutions being proposed. This study contributes to this domain of research by providing an up-to-date state-of-the-art review, classification and critical analysis of the current BP and FDP literature, where six major research streams are identified and discussed, namely, the definition and coding of bankruptcy and financial distress; design of new prediction models/classifiers or new application of existing ones; design of new drivers or evaluation of existing ones; design or evaluation of feature selection methods; design of methodologies for the performance evaluation of prediction models; and issues affecting the performance of prediction models and related solutions. By painting the landscape of research and analyzing each research stream, this study would serve as a guide for researchers who intend to explore this field of study and/or contribute to its development.

Our analysis of the surveyed papers revealed a clear trend in terms of prediction methodologies of bankruptcy and financial distress, where more emphasis is put on advanced machine learning and artificial intelligence models such as ensemble learning models. Ensemble learning models and other methodological advances, such as DEA- and MCDA-based prediction methods, have contributed to further pushing the boundaries of research. Overall, it is fair to conclude that there is no single methodology that is better than the others, as each methodology has its strengths and weaknesses, some of which are design-related, and others are implementation decisions related, which have made the design and implementation of new ensemble models more popular.

Abstract

The purpose of this research is to examine the effect of voluntary integrated reporting on information asymmetry in European and Asian firms and investigate size as a moderator variable to this relationship. Using a final sample of 94 firms in Europe and Asia that published integrated reports in 2016, the Ordinary Least Square is then performed to analyze the data on quarterly basis. The quarterly analysis is used to look at the relevance of accounting information decline as the time lag increases. The results show that there is an insignificant relationship between integrated reporting quality and information asymmetry which is captured by spread. In addition, the insignificant effect of size to moderate this relationship is also found. These results are supported by additional analysis. This research contributes to the existing debate about whether integrated reporting affects the market, particularly information asymmetry. To the best of the authors’ knowledge, this is the first study to examine the effect of integrated reporting quality on the market on a quarterly basis.

Introduction

Integrated reporting (IR) in recent years has received great public attention particularly after the formation of the International Integrated Reporting Council (IIRC) and the issuance of IR iguidelines (Adams, 2017; Zhou et al., 2017). Despite the critique arising that IR could be yet the tool for corporate impression (Melloni et al., 2017), the IIRC (2013, p. 3) stated that IR can improve “the quality of information available to providers of financial capital to enable a more efficient and productive allocation of capital.” Integrated reporting aims to reduce the clutter of sustainability report by disclosing aspects they consider to be material and useful about the value-creating activities in the short, medium, and long-term into a concise and coherent report (Zhou et al., 2017; IIRC, 2013). Integrated reporting offers a broader picture of the new firm by shifting away from standalone sustainability or social responsibility reports towards an integrated report that delivers a comprehensive picture of long-term performance from various perspectives, business model, and value creation (Burke and Clark, 2016). Hence, the voluntary information disclosure is a way to mitigate the information asymmetry problem and lead to better business transparency (Brammer and Pavelin, 2006).

Nevertheless, empirical evidence on the IR benefit is still sparse (De Villiers et al., 2014). Previous studies conducted by Zhou et al. (2017) and Vitolla et al. (2020) found an inverse relationship between alignment of integrated reports and information asymmetry, measured by analysts’ forecast errors, as well as cost of equity capital. Other studies conducted by Maria et al. (2017), Akker (2017), Martinez, 2016a, Martinez, 2016b, Lee and Yeo (2015) found that IR offers more relevant information. Thus the alignment of corporate reporting with the integrated report is negatively associated with the asymmetric information. Barth et al. (2001) also suggested the evidence that IR quality reduces information asymmetry as captured in a lower bid-ask spread. On the contrary, Stubbs and Higgins (2014) found evidence that IR did not affect a decrease of the level of information asymmetry. They argued that the information provided is not required by the capital providers to make the investment decisions. Accordingly, the limited evidence and inconsistent results found about the market responds to the information available in the integrated reports (Mervelskemper and Streit, 2016).

Conclusions

This research examined the existence of a negative relationship between voluntary integrated reporting and information asymmetry for 94 firms in Europe and Asia in 2017 with publishing an integrated report in 2016. The results show that there is an insignificant relationship between the integrated reporting quality and information asymmetry which is captured by the spread. There is no significant effect as well for size effect to moderate the relationship between the integrated reporting quality and information asymmetry. Those results are also supported by additional analyses.

These findings could be relevant for some aspects of the literature especially in the research design where this study used a continuous variable based on detailed content analysis instead of a dummy variable to measure the integrated reporting quality. Other innovations of this study are that it investigated the IR effect on a quarterly instead of an annual basis. From a practical perspective, as the significant effects are not yet observed, this result can provide the companies with insight on whether to implement the integrated reporting immediately or not. Investors who are interested in the integrated reporting also can consider this finding in their decision making. Finally, the IIRC as the regulator body can conduct some improvements to see how the integrated reporting is proceeding, particularly in the voluntary context. As the IR concept is relatively a new area for corporate reporting in the world, the governments are given the consideration and insight as well whether to encourage the companies in their countries to adopt this trend immediately or not.

Abstract

This study investigates how voluntary International Financial Reporting Standards (IFRS) adoption impacts information asymmetry in the stock market. We analyze data from firms in Japan, where local accounting standards have substantially converged with IFRS but are more rules-based than principles-based IFRS. These different implementation approaches allow us to investigate how increased accounting flexibility influences recognition and measurement practices, attenuating the effects of accounting standard differences on the level of accounting rule existence or non-existence. We find that information asymmetry increases after voluntary IFRS adoption, which is driven by small- and medium-sized IFRS adopters’ decrease in earnings quality after voluntary IFRS adoption. Additional analyses reveal that firms’ reporting incentives and accounting resources influence these changes. These results suggest that IFRS’s increased accounting flexibility in recognition and measurement practices worsens the information environments of firms with weak incentives to commit to transparent financial reporting or fewer accounting resources (i.e., small- and medium-sized firms).

Introduction

As International Financial Reporting Standards (IFRS) are intended to provide investors with information that enables them to make better investment decisions, information asymmetry in stock markets is a critical potential consequence of IFRS adoption. IFRS adoption worldwide is either voluntary or mandatory. Since the 2005 mandatory adoption for firms listed in EU countries, extensive research has been conducted on the effects of mandatory IFRS adoption. However, voluntary IFRS adoption effects are relatively under-researched, especially in recent years (De George et al., 2016, Leuz and Wysocki, 2016). Understanding the effects of voluntary adoption is also important because a non-trivial number of countries either allow voluntary IFRS adoption or prohibit IFRS adoption (Song and Trimble, 2022). As of 2019, 11 % (22) of 195 countries/territories permit IFRS adoption and 6 % (11) presently prohibit it, although the latter could permit voluntary adoption in the future. This study attempts to further our understanding of how voluntary IFRS adoption affects information asymmetry in stock markets.

Prior research provides mixed evidence on the stock market consequences of voluntary IFRS adoption. Early studies on this topic focus on Germany, where local accounting standards are substantially different from IFRS. These studies provide evidence suggesting that voluntary adopters exhibit lower information asymmetry in the stock market compared to non-adopters (Gassen and Sellhorn, 2006, Leuz and Verrecchia, 2000). However, in international comparisons, Daske et al. (2008) use two proxies for differences between local accounting standards and IFRS and report inconsistent liquidity effect results. Specifically, they observe no change in liquidity after voluntary IFRS adoption in countries with small differences between local generally accepted accounting principles (GAAP) and IFRS, but a decrease in liquidity in countries with IFRS convergence processes. Therefore, how voluntary IFRS adoption affects information asymmetry in stock markets in countries whose accounting standard differences from IFRS are relatively small remains unclear. This study aims to extend this line of research.

Conclusions

Prior research suggests that voluntary IFRS adoption positively affects information asymmetry in stock markets when local accounting standards significantly differ from IFRS and is viewed as a commitment to transparent financial reporting. However, other research obtains inconsistent results for voluntary IFRS adoption effects on information asymmetry in countries with small accounting differences between local standards and IFRS. We looked closer at Japan, where local accounting standards have been converged to IFRS but have a different implementation approach (i.e., small accounting rule differences but large implementation approach differences). We investigated the effect of voluntary IFRS adoption on the stock market (i.e., changes in information asymmetry in the stock market proxied by bid-ask spread) as well as its potential reason (i.e., changes in earnings quality proxied by the absolute value of discretionary accruals). We also examined whether this effect varies according to firm size, which represents firms’ reporting incentives and accounting resources. Our study’s findings and incremental contributions to the literature are as follows.

First, we find an increase in the bid-ask spread after voluntary IFRS adoption. This result suggests that, on average, voluntary IFRS adoption worsens information asymmetry in Japan’s stock markets. Second, we observe deterioration in bid-ask spread only for smalland medium-sized voluntary adopters. Third, we examine changes in IFRS adopters’ earnings quality before and after IFRS adoption to explore a reason for the increased bid-ask spread. We find that small- and medium-sized firms report larger discretionary accruals after voluntary IFRS adoption. These results suggest that the increase in information asymmetry in the stock market results, at least partially, from the decreased earnings quality of small- and medium-sized firms after voluntary IFRS adoption. Finally, we provide some evidence suggesting that firms’ reporting incentives and limited accounting resources deteriorate earnings quality and information asymmetry at the time of voluntary IFRS adoption.

Abstract

Start-up firms play a major role in the economy, with B2B start-ups often developing new technologies and offerings to business customers that incumbent firms do not provide. However, little is known about the cost-effectiveness of start-up firms’ investments in marketing. We integrate insights from managerial interviews with signaling theory to posit how conducting systematic marketing affects start-up firm valuation, and how the combination of the firm’s primary customer-type (B2B versus B2C) and development stage (early versus late) moderates that effect. Our empirical analysis reveals that marketing investments affect firm valuation positively for some start-ups and negatively for others depending on the noted moderators. In particular, we find that investments in systematic marketing by early-stage B2B start-ups increase firm valuation, yet more than half of early-stage B2B start-up firms choose not to invest in systematic marketing, apparently believing such investments will not pay off.

Introduction

Business-to-business (B2B) start-up firms are major drivers of economic growth as they produce new technologies and offerings that incumbent firms cannot or are reluctant to provide (Dörner, Flötotto, Henz, & Strålin, 2021). In aggregate, start-up firms are responsible for 10% of all worldwide job growth between 2017 and 2021(Wijngaarde, 2021), while B2B focused start-ups make up 71% of reported start-up sales in the US Census Bureau’s 2015 Annual Survey of Entrepreneurs (U.S. Census Bureau, 2017). However, despite the importance of start-up firms, it is difficult for either B2B or business-to-consumer (B2C) start-up firms to succeed: only about 50% of start-up firms survive for five or more years (U.S. Bureau of Labor Statistics, 2016).

Allocating resources to marketing could potentially help a start-up increase its likelihood to succeed since focusing on marketing reduces ambiguity about the firm, its value proposition, and its offerings (e.g., Acar, Dahl, Fuchs, & Schreier, 2021). Further, a start-up that allocates significant resources to marketing is signaling to investors that the firm is focusing on its customers and is creating and delivering value to them (Halberstadt et al., 2021). Yet, start-up firms are 60 times more likely to report being resource constrained than to report possessing sufficient resources for their development (Evans & Jovanovic, 1989; Wasserman, 2012). That resource constraint limits what functions start-ups can allocate scarce resources (O’Toole & McGrath, 2018), which, in turn, impacts start-up firms’ actions (Fernhaber & Patel, 2012) and ultimately their survival and performance (Dencker & Gruber, 2015).

Consequently, for start-up firms, to conduct systematic marketing — that is, following Moorman and Day’s (2016) definition of a systematic marketing organization based on four “C’s”, e.g., capabilities, configuration, (human) capital, and culture, operating on an on-going basis — often requires directing scarce resources from other aspects of their business. As a result, as confirmed through our multi-method approach that involved interviews, secondary data, and a follow-up survey, many start-ups do not report conducting systematic marketing, but instead report either not conducting any marketing or only conducting marketing on an ad hoc and opportunistic basis.1 One (typical) interview response about marketing’s role was “Marketing?? We don’t do any real marketing…we have much more important issues than marketing to focus on.” A 2015 Capital One survey of small business owners supports this observation, reporting that 76% of owners face marketing challenges, 64% feel they are unable to effectively market their businesses, and 39% report that their firms have not executed any marketing initiatives in the past six months (Capital One, 2015).

Discussion

This research investigates whether and when conducting systematic marketing benefits start-up firms, an important topic that has been rarely addressed in the marketing literature. We develop a conceptual model of the antecedents and consequences of conducting systematic marketing that includes the interactions of two firm characteristics – type of customer and stage of development– to determine when conducting systematic marketing should be more or less beneficial to startup firms. We test our conceptual model using a rich dataset on start-up firms provided by Equidam, an online start-up firm valuator, through a wide range of robustness tests, and with an analysis of an independent survey of start-up firms. In each of these analyses, we find strong support for our conceptual model and associated hypotheses. One key finding challenges current practice: while we find that conducting systematic is more beneficial to early-stage B2B start-up firms than to any other category of firm, such firms are the least likely to conduct systematic marketing.

Abstract

The paper examines whether common ownership along supply chains (i.e., vertical shareholding) is associated with better firm performance during supply chain shock. We find that supplier-customer dyads with vertical shareholding report higher combined monthly returns since the outbreak of the COVID-19 pandemic. The dedicated common investors have the most positive effects, followed by the common quasi-indexers.

Introduction

As overlapping institutional ownership becomes more prevalent, it has been heatedly debated whether it plays a positive role in market competition and corporate governance (e.g., Azar et al., 2018; Edmans et al., 2019; Schmalz 2021; Koch et al., 2021; Bebchuk and Hirst 2022). Most studies focus on its effects on industry rivals (i.e., horizontal shareholding). Compared to industry rivals, firms along the supply chain network have more collaborative and interdependent relationships. Hence, overlapping institutional ownership of supplier-customer dyads (i.e., vertical shareholding)1 may have distinct effects, especially in response to disruptive events that could jeopardize collaboration.

We utilize the COVID-19 pandemic as an exogenous supply chain shock to investigate whether vertical shareholding is associated with stock performance. Supply chain disruptions can be devastating to both upstream and downstream firms (e.g., Carvalho et al., 2021) and significantly exacerbate interfirm conflicts as opportunism and information asymmetry increase. Economic theories predict that cross-ownership can address inherent interfirm conflicts, such as the holdup problem (e.g., Klein et al., 1978; Grossman and Hart 1986). Institutional investors simultaneously holding shares of economically connected firms have incentives to prevent either contracting party from making a move that will hurt the value of their portfolios. Hence, they can influence the management of both upstream and downstream firms and thereby encourage information sharing and balance the bargaining power in supply chain contract renegotiations. For example, institutional investors can influence the supplier firms to prioritize the co-owned customer firms during supply shortages (Sheffi 2020).

Conclusion

We show that supply chain dyads with vertical shareholding have significantly better aggregate stock market performance since the COVID-19 pandemic. The effects vary with the incentives of common institutional shareholders. To our knowledge, we are one of the first empirical studies to show that vertical shareholding mitigates negative consequences of supply chain shocks. Our evidence sheds light on the debates over the roles common institutional investors play in interfirm relationships and supply chain risk management.

Abstract

This study examines the role of gamification elements in engaging suppliers of sharing economy platforms in corporate social responsibility (CSR) activities. We expected that engaging business owners in CSR would improve the brand equity of these platforms by leveraging the business owners’ brand loyalty and brand association. The statistical population of the study is owners of Iranian accommodation who use sharing economy platforms. The size of the sample was 323 accommodation owners. The hypotheses of this study were tested using Smart PLS-3. The study confirmed all the research hypotheses and showed that engaging business owners in social responsibility activities using gamification elements on sharing economy platforms can lead to an increase in the brand equity of the sharing economy platforms. These results will reinforce the synergy between the platforms and their users by making both parties aware of it.

Introduction

The sharing economy (SE) is where customers and business owners (suppliers) are brought together by an online platform. It is a means by which individuals may leverage their assets, such as their cars, their property and, of course, their time so that these assets may be utilized more thoroughly. This increases the efficiency of society as a whole while either increasing the wealth of participating business owners, or increasing their work flexibility, or both (Kuhzady et al., 2021). Advocatesof the SE have often painted a picture of a utopia where business owners will be set free from the shackles of employment while their customers would be able to take advantage of the lower prices (Belk, 2007). Well known examples of SE platforms are Uber in the ride sharing (taxi substitute) field and Airbnb in the accommodation sharing (hotel substitute) field, though there are also imitators of these platforms which have different degrees of market presence in different parts of the world.

However, the vision presented by SE tends to place its emphasis on the freedom of individuals, whether customers or business owners, while ignoring the fact that one of the reasons for the lower costs and prices of the SE compared with traditional businesses is the lack of regulation designed to counteract some negative consequences. Criticisms of the SE approach (Kuhzady et al., 2021) have included negative social and environmental impacts (Ma et al., 2018), limited safety and security (Birinci et al., 2018) and lack of certainty caused by unknown parties (Hossain, 2020) that contradict the vision of SE platforms of being sustainable and socially responsible. Consumers demand social responsibility from SE platforms and therefore business owners must also adopt sustainability and corporate social responsibility (CSR) principles in their businesses (Mi and Coffman, 2019). Consequently, sustaining the SE requires the engagement of all stakeholders where platforms, consumers and business owners are stakeholders in making a sustainable and responsible SE.

Discussion and conclusion

The study examined the role of gamification elements in engaging accommodation owners in CSR activities and enhancing their experience on SE platforms and how it leads to enriching brand equity through mediating effects of accommodation owners’ satisfaction and trust that leads to brand loyalty and brand association.

ads to brand loyalty and brand association. The gamification elements have a significant effect on engaging accommodation owners in CSR activities on SE platforms based on selfdetermination theory. Analysis of the collected data showed that gamification can have a significant and strong effect on engaging accommodation owners in CSR activities on accommodation sharing platforms. This is consistent with previous research on the power of gamification. Researchers have shown that gamification can be used to transfer complex information and promote green behaviour among users (Souza et al., 2020). In this study, it was shown that gamification elements can be used, not only for customers, but also for the accommodation owners and can engage them in behaving sustainably and responsibly. Analysing the collected data indicates a significant effect of gamification elements on the experience of accommodation owners on SE platforms. This also is consistent with previous studies which indicate that gamification can activate hedonic emotions and ultimately enhance the user experience (Hsu and Chen, 2018). It was also concluded that this hedonic experience is not limited to customers but also includes the business owners.

Abstract

The application of big data technology to global tax management is becoming increasingly widespread. China has been implementing increasingly mature technologies for tax governance using big data systems in recent years. By collecting data through web scraping on the earliest implementation times of big data tax administration in various provinces of China, we explore the relationship between big data tax administration and corporate bank credit in emerging markets. Our results show that big data tax administration enhances firms’ ability to obtain bank loans. Mechanism tests indicate that big data tax administration improves the quality of corporate information disclosure, facilitating access to bank credit loans. We find that big data tax administration improves the corporate financing environment, enhancing the efficiency of resource allocation in the credit market.

Introduction

The digital economy has expanded rapidly around the world. Driven by continuous upgrades in Internet functionality and the widespread application of big data, significant changes are occurring in governments, corporate business models and in people’s daily lives (Chen and Srinivasan, 2024). Taxation departments provide a good example of such changes, as big data technology is expanding the traditional auditing model. “Big data tax administration” combines big data with tax auditing; it involves acquiring big data from Internet platforms and integrating and comparing multiple sources of data (Bassey et al., 2022). Its implementation, which has become a new trend in national tax governance, reflects the modernization of national governance capabilities and systems within the tax system and significantly enhances the efficiency of governments’ tax collection (Canares, 2016). A key question is whether, looking beyond strict tax enforcement, modernized tax governance can guide firms to improve the quality of their information disclosure, thereby enhancing the efficiency of resource allocation in capital markets? Examining this question yields insights relevant to governments worldwide who are implementing governance based on big data technology.

Bank loans are an important financial resource and whether they are allocated in a timely and appropriate manner is an important issue from both theoretical and practical perspectives. An extensive body of research discusses the various factors that influence firm credit, including corporate characteristics and the policy environment. However, few studies explore whether innovations and modernizations within the tax governance system can improve the financing environment for firms. Studying the effects and mechanisms of big data tax administration on corporate bank credit enhances the understanding of the social effects of modernized governance and provides empirical evidence for the motivational impact of “tax administration with data” on firms.

Conclusions and implications

The widespread adoption and application of the Internet and big data have reduced the cost of information flow, and the multi-party storage and sharing of big data has gradually become a new form of social capital. The use of big data by tax authorities not only improves the capabilities and efficiency of tax collection but also promotes the rational allocation of financial capital at the national level and facilitates enterprise financing. We investigate the effects of big data tax administration on corporate credit and find that implementing big data tax administration (1) has a positive impact on corporate bank credit; (2) improves the quality of corporate information, thereby promoting corporate credit and (3) has a more pronounced effect on enterprises with stronger (vs. weaker) financial constraints. The conclusions of our study are beneficial because they enhance academic understanding of the economic consequences of big data tax administration, and also have practical significance for tax authorities actively seeking external forces to improve tax collection procedures. Based on our theoretical and empirical analyses, we put forward the following policy recommendations.

First, the government should enhance cooperation between tax authorities and third-party online platforms, synchronously strengthening tax administration under the “delegation, management and service” framework. We show that by enhancing information collecting capabilities, big data tax administration improves the quality of corporate information disclosure, thereby optimizing corporate financing efficiency. In the context of the digital transformation of enterprises and the increasing application of big data in society, tax inspection departments should make full use of the data at the societal level, engaging in cooperation with online platforms and online assessment agencies. By utilizing the correlation between multi-party data and the data provided by enterprises, tax inspection departments can strengthen their verification of corporate information. This will help eliminate the corporate practices of inflating projects and manipulating information disclosure, and encourage and guide firms to improve their information quality.

Abstract

This study examines the relationship between Bitcoin market dynamics and user activity on the r/cryptocurrency subreddit. The purpose of this research is to understand how social media activity correlates with Bitcoin price and trading volume, and to explore the sentiment and topical focus of Reddit discussions. We collected data on Bitcoin’s closing price and trading volume from January 2021 to December 2022, alongside the most popular posts and comments from the subreddit during the same period. Our analysis revealed significant correlations between Bitcoin market metrics and Reddit activity, with user discussions often reacting to market changes. Additionally, user activity on Reddit may indirectly influence the market through broader social and economic factors. Sentiment analysis showed that positive comments were more prevalent during price surges, while negative comments increased during downturns. Topic modeling identified four main discussion themes, which varied over time, particularly during market dips. These findings suggest that social media activity on Reddit can provide valuable insights into market trends and investor sentiment. Overall, our study highlights the influential role of online communities in shaping cryptocurrency market dynamics, offering potential tools for market prediction and regulation.

Introduction

The rapid growth and adoption of cryptocurrencies have attracted significant attention from investors, researchers, and policymakers. Among these digital assets, Bitcoin (BTC) [1] stands out due to its market dominance and influence on the broader cryptocurrency ecosystem. Understanding the factors that drive Bitcoin’s price and trading volume is crucial for stakeholders across various sectors. One such factor is the sentiment and activity within online communities, where discussions and opinions about cryptocurrencies are actively shared.

Social media platforms and online forums play a vital role in shaping the market dynamics of cryptocurrencies [2]. These platforms provide real-time information, opinions, and sentiment that can influence investor behavior and market trends. Several studies have explored the relationship between social media activity and cryptocurrency markets, highlighting the significance of sentiment analysis and user engagement in predicting price movements and trading volumes.

While previous studies have examined the relationship between social media sentiment and cryptocurrency markets, our research distinguishes itself by focusing on the specific community of r/cryptocurrency on Reddit. We leverage a combination of sentiment analysis and topic modeling on a comprehensive dataset of posts and comments, providing a detailed view of how online discussions evolve and correlate with Bitcoin market movements. By examining both sentiment and thematic content over time, our study offers nuanced insights into the interplay between social media activity and cryptocurrency market dynamics.

Results

4. Results
4.1. Bitcoin Market Data
Figure 1 illustrates the closing price of Bitcoin (BTC) over a two-year period, from 1 January 2021 to 31 December 2022. The data reveal significant volatility, characteristic of the cryptocurrency market. Notably, BTC reached an all-time high in November 2021, approaching USD 68,000, before undergoing a substantial correction throughout 2022. This decline reflects broader market trends, including macroeconomic factors and changes in investor sentiment. The closing price provides a critical foundation for understanding market dynamics and investor behavior during this period.

Abstract

Abstract
The rapid expansion of mobile broadband networks and the proliferation of Internet of Things (IoT) applications have substantially increased data transmission and processing demands. However, the application domains of IoT-enabled models often face resource limitations, requiring rapid responses, low latency, and large bandwidth, surpassing their inherent capabilities. To address these challenges, we propose a fishnet approach-based packet scheduling and resource allocation system, termed Fishnet-6G, to optimize network resource allocation in the proposed 6G networks. Initially, we constructed a Sierpinski Triangle-based network in a 6G-IoT environment, enhancing device connectivity. We utilize the Quantum Density Peak Clustering (QDPC) algorithm to perform clustering for IoT devices, establishing Cluster Head (CH) and Substitute CH (SUB CH) based on actual metrics. Furthermore, traffic prediction is achieved through two processes, grouping, and fair queue status, using the Improved Deep Deterministic Policy Gradient (IMPDDPG) algorithm with a variable sampling rate, resulting in well-organized packet scheduling. Subsequently, we perform optimal packet scheduling by employing the Willow Catkin Optimization (WCO) algorithm, and the scheduled packets are managed within a Fishing Net Topology to reduce energy consumption and system complexity. Finally, we allocate the scheduled packets to the desired resource blocks using the Bayesian Game-Theoretic Approach (BGTA). The proposed approach is implemented using Network Simulator-3.26, and the performance of the Fishnet-6G model is evaluated based on time, transmission rate, energy efficiency, average throughput, latency, and Packet loss rate. Numerical analysis demonstrates that Fishnet-6G outperforms existing approaches across these metrics, showcasing its effectiveness in addressing the challenges of 6G-IoT networks.

Introduction

The rapid expansion of IoT devices has led to notable progress in data generation, contributing significantly to the growth of linked objects inside the IoT systems. It is essential to highlight that this development is experiencing a sustained increase as many objects and devices are interconnected in IoT applications [1,2]. As 5G networks continue to improve and expand, concerns arise about their capacity to support the constantly evolving needs of emerging IoT applications fully [3]. The impending era of sixth-generation (6G) mobile communication is marked by the widespread adoption of IoT devices and cellular networks, contributing to a substantial increase in energy consumption and network traffic [4]. The ever-growing demands of intelligent and self-sufficient systems challenge their capabilities and spark the evolution towards the 6G-IoT vision [5]. By 2025, it is projected that there will be over 25 billion IoT devices, putting immense pressure on current multiple access techniques and necessitating the development of B5G wireless systems. In light of this uncertainty, attention has turned towards the potential of 6G wireless technologies to overcome these limitations and propel the transformation of existing networks [6]. Envisioned as a vital component in the development of sustainable smart cities, the 6G-IoT vision incorporates cutting-edge elements such as intelligent edge computing, health analytics, and multidimensional design technology, with a firm emphasis on critical features such as scalability, wireless multi-access, personalized AI, machine learning, cyber security, blockchain, and augmented sensing [3,[6], [7], [8], [9]]. The strategic placement of small cells in a 6G system optimizes coverage and data transmission efficiency, while sensor nodes collect essential information communicated to IoT devices, enabling diverse applications like home automation and healthcare, showcasing the network’s sophistication [10,11]. Network architecture and administration must mitigate complexities in the 6G network to provide exemplary performance. The construction of a 6G network presents numerous advantages, encompassing enhanced capacity, extensive coverage, cost-effectiveness, load balancing, and energy efficiency [12].

Conclusion

This study proposes a fishnet technique to overcome the significant issues related to packet scheduling and resource allocation in the context of the 6G-IoT environment. The primary objective of this study is to implement the Sierpinski triangle-based network construction in a 6G-IoT environment to mitigate the issues of excessive energy consumption and communication overhead. The edge server utilizes QDPC to provide clustering for IoT devices. The suggested study aims to group IoT devices based on relevant metrics. This clustering process involves the identification of cluster heads and sub-cluster heads, which are executed by an edge server. Subsequently, the process of traffic prediction is conducted through two distinct stages, namely grouping and fair queue status assessment, employing the IMPDDPG algorithm with variable sampling rate. Packet scheduling is executed by considering the relevant metrics and employing the WCO algorithm. Following the establishment of a schedule, the management of packets is conducted inside the fishing net architecture, resulting in a reduction in energy consumption, complexity, and overall process weight. The allocation of scheduled packets to the requested resource blocks is accomplished using BGTA, while taking into consideration the operational indicators. The proposed approach was tested using Network Simulator-3.26, and its effectiveness was assessed by comparing its performance to existing methods taking into account various permanence metrics, including time, transmission rate, energy efficiency, average throughput, latency, and packet loss rate. The efficacy of our methodology is examined by quantitative analysis, which substantiates that our technique surpasses existing methodologies across all metrics. The future work will consider cases high availability in cases of Base Station failures and massive IoT V2X environment to support the next generation of wireless networks.

Abstract

We estimate the effect of global supply chain disruptions on inflation for a panel of 28 European countries. Adverse shocks have a stronger and more persistent effect than favorable shocks.

Introduction

Disruptions to global supply chains, e.g. due to natural disasters and lockdowns, are considered a major driver of the drastic increase in inflation in Europe and other advanced economies after the COVID-19 pandemic. The evidence (Carrière-Swallow et al., 2023, Burriel et al., 2023, Ascari et al., 2023, Laumer, 2023, Khalil and Weber, 2022, Finck and Tillmann, 2023, Finck et al., 2023, Liu and Nguyen, 2023, Elsayed et al., 2023, De Santis, 2023) suggests that supply chain shocks indeed have a significant effect on inflation.

More recently, supply bottlenecks eased and indices of supply chain conditions (Benigno et al., 2022) are back at pre-pandemic levels. However, inflation remains high. The Economist (2023) asks: “Supply chains are back to normal. Why is inflation still so high?” This note provides one potential answer to this question. We estimate local projections for a panel of 28 European economies between 2010 and 2023 in order to quantify the effect of a supply chain disruption on headline inflation, core inflation and producer price inflation. The supply chain shock we use is the purified change in the Global Supply Chain Pressure Index (Benigno et al., 2022). Importantly, we allow positive, i.e. restrictive, and negative, i.e. expansionary, shocks to have different absolute effect sizes.

Conclusions

This note showed that the inflationary effects of disruptions to global supply chains are asymmetric. An increase in supply chain stress strongly contributes to the increase of inflation as seen after the COVID-19 pandemic. However, an easing of supply chain pressure contributes much less to the decline of inflation. Hence, our results provide one potential answer to the question asked by The Economist (2023).

Of course, asymmetric supply chain shocks are just one potential explanation of persistent inflation rates despite an easing of supply chain conditions. A tendency to de-globalize supply chains could raises costs even as shipping conditions normalize. Inflation could also remain elevated due to second-round effects of the initial spike in energy prices and costs of intermediate goods on wages. In addition, a shortage of labor post-Covid might also prevent inflation from returning to low levels. Finally, the sectoral shift of demand during the pandemic from services to goods is still visible in the data, which causes inflationary pressure in the presence of frictions.

Abstract

In a sample of 1,097 European merger and acquisitions (M&A) deals, we find that the targets exhibiting higher levels of accruals earnings management (AEM) relate to lower ownership stakes offered by acquirers. Real earnings management (REM) measures do not relate to ownership decisions. This research contributes to broadening our knowledge about accounting information’s role in the intricacies of M&A negotiations.

Introduction

The ownership decision refers to how much equity acquirers aim to get from targets in mergers and acquisitions (M&A). The objective of this study is to examine how the manipulation of the target’s financial statements affects this decision.

Manipulated accounting figures generate uncertainty, and highly uncertain settings are associated with minority deals, which leads to expect that higher target’s earnings management (EM) relates to lower ownership stakes. However, the bidder may be unable to detect the manipulation, or it may be easier or more convenient to adjust other terms of the deal to overcome the overpayment risk.

We examine 1097 European deal announcements between 1990 and 2021, showing that higher discretionary accruals (DA), our proxy of accruals-based earnings management (AEM), are associated with lower ownership stakes and higher odds of minority deals. This suggests that, on average, acquirers are conservative regarding the equity stake they bid for when the target’s financial statements are opaque, making difficult the assessment of the potential synergies from the transaction.

We also find that real earnings management (REM) proxies do not correlate with the ownership decision. This is consistent with acquirers having more difficulties in understanding REM than AEM. Indeed, REM models are proven to be less powerful than AEM models (Nguyen et al., 2023). Alternatively, this result supports the thesis that REM is less pervasive than AEM (Bagnoli and Watts, 2000).

Conclusion

We add the target’s EM to the pool of non-controlling deal drivers (Drees et al., 2013; Liao, 2014; Povel and Sertsios, 2014; Soloveva et al., 2021). We find that buyers prefer to proceed cautiously and bid for small equity stakes and/or carry out non-controlling deals when they detect upward AEM in the target’s financial statements. This provides new insights into the usefulness of financial statements in the due diligence of M&A to avoid potential negative consequences of the transaction. Considering AEM might help acquirers make better-informed ownership decisions when dealing with overvaluation. Our evidence is consistent with prior research suggesting that the firms involved in non-controlling deals create wealth by aligning incentives and improving the information flow (Drees et al., 2013).

Our evidence is consistent with prior research suggesting that the firms involved in non-controlling deals create wealth by aligning incentives and improving the information flow (Drees et al., 2013). Moreover, our results are in line with recent evidence that, despite its relatively scarcity in the past, toeholds are useful when performing difficult takeovers, such as is the case in hostile deals, tender offers, or multiple bidders contests (Dai et al., 2021). Our results are relevant considering that takeovers are sophisticated transactions which parties have many terms to negotiate. Moreover, our findings are more than timely for practitioners in the current context of M&A, plagued by uncertainty and the looming threat of a recession, since rather than avoiding value-driven deals due to the overvaluation risk, they may use minority acquisitions strategically to mitigate the risks. However, this also poses an interesting question. If acquirers have many tools to prevent the overpayment risk, why are there M&A that destroy value? Very and Schweiger (2001) claim that acquirers usually lose money in M&A because of problems concerning the reliability of the target’s financial information and the integration process. Given our findings and those of other studies, showing that acquirers adjust the terms of the deal to mitigate the uncertainty derived from the target’s financial statements, future research should delve deeper into the integration phase of M&A to cast light on this question.