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This study delves into how corporations make decisions about influencer marketing. Specifically, it examines the differences between human influencers, who carry the risk of scandals, and virtual influencers, a new and unpredictable realm, regarding their integration with social media platforms.

Using game theory and empirical data, the study explores crucial factors in influencer marketing, including influencer quality, reputation repair costs and the probability of R&D failures.

This study suggests that companies favor human influencers when the risk of scandal is low. However, competing companies switch to virtual influencers at different intervals as this risk increases. The costs, likelihood of scandals and competition intensity all play a role in a company's decision-making regarding technology management. Additionally, a higher chance of R&D failure can motivate a company to invest more in R&D to gain a competitive advantage over rivals that may suffer failures.

This study provides insights into how corporations manage social media influencer marketing in the digital age. It contributes to marketing theory and technology management decisions by offering a fresh perspective on the relationship between corporate reputation and influencer marketing strategy.

This study offers valuable perspectives into a relatively uncharted area of marketing strategy. It employs game theory and empirical analysis to introduce a fresh method of comprehending the dynamics of influencer marketing, its impact on corporate reputation management and its interaction with social media.

The purpose of this paper is to garner a deeper understanding of the site of influence of aspects of risk management for tax practitioners.

The research design is twofold. Phase one consisted of a wide-scale international survey with 1,061 tax experts across 59 jurisdictions. In phase two, the authors followed up with 68 semi-structured interviews with tax practitioners working in 11 different countries.

The findings recognise the importance of the firm as a significant “site of influence” for tax practitioners in shaping their risk appetite in their tax work. The firm eclipses other influences of risk such as professional body oversight, public interest and demographic markers such as gender and career stage. The authors show that firm is significant, irrespective of size of firm.

This work has practical implications as the findings highlight the importance of oversight of professional service firms by both the professional accountancy bodies and revenue authorities. The findings may have impact on the ethical training and guidance for trainee accountants in terms of an increased awareness on the employing firm as a site of influence for tax practitioners.

This research is important as it adds to the significant body of work on firm socialisation and highlights the important role that the firm holds in moderating (or exacerbating) the risk appetite of tax practitioners, which has significant implications in terms of pushing the boundaries of tax aggressive behaviours. The work aims to recognise the important role that tax practitioners can have in moderating aggressive tax practice, and, thus, reducing tax inequalities and shaping a better world of “Reduced Inequalities” (SDG10).

Prior research from transaction costs economics argued that central firms perform better because they have superior access to information to discipline their alliance partners. Central firms may also, however, face higher costs and risks of unintentional learning and weaken their competence through structural inertia. We propose that these costs and risks are influenced by the learning capacities of the firms in the network and can explain different outcomes for focal firm performance.

To test our predictions, we use instrumental variable–generalized method of moments estimation techniques on 15,517 firm-year observations from equity alliance portfolios in the global food industry across a 21-year window.

We find support for our predictions and show that the relationship between network degree centrality and firm performance is negatively influenced by partners’ learning capacity and positively influenced by focal firms’ learning capacity, while firms with low network degree centrality benefit less from their learning capacity.

Future developments in transaction cost economics may consider partner and focal firms’ learning capacity as moderators of the network degree centrality – firm performance relationship.

In alliance decisions, managers must consider that the combination of high network degree centrality and partners’ learning capacity can lead to high costs, risks of unintentional learning, and structural inertia, all of which have negative consequences for performance. In concentrated industries where network positions are controlled by a few large firms, policymakers must acknowledge that firms may face substantial barriers to collaboration with learning-intensive firms.

This study is the first to develop and test a comprehensive transaction cost analysis of the central firm’s unintended knowledge flows and structural inertia in alliance networks. It is also the first to incorporate theoretically and empirically the hazards of complex and unintended information flows on the relationship of network degree centrality to performance in equity alliance portfolios.

This paper examines the impact of Central Bank Digital Currencies (CBDCs), regulated stablecoins and tokenized traditional assets on the cryptocurrency market, following the guidelines set by the Basel Committee. This study aims to analyze the implications for secure storage, cross-border transfers and necessary investments.

The paper uses a policy analysis approach to assess the potential effects of the Basel Committee’s regulations on CBDCs, regulated stablecoins and tokenized traditional assets. It explores their impact on the cryptoasset market, strategies of central and commercial banks, payment systems and risk management.

The adoption of CBDCs, regulated stablecoins and tokenized traditional assets is expected to grow rapidly in the coming years. It raises concerns about secure storage, cross-border transfers and required investments. Central banks are likely to introduce CBDCs and authorize stablecoin issuance, aiming for efficient monetary policies and risk management. Basel III regulations may lead to asset tokenization by banks, reducing asset size and increasing fee-based income.

This paper provides insights into the potential impact of the Basel Committee's regulations on CBDCs, regulated stablecoins and tokenized traditional assets. It contributes to the understanding of the evolving cryptoasset market and the strategies of central and commercial banks in adopting these technologies. The findings offer valuable information for policymakers, regulators and market participants in navigating the changing landscape of digital assets.

Electric vehicle adoption (EVA) drives sustainability by significantly reducing carbon emissions and reliance on fossil fuels. Despite EVA’s notable advantages from existing literature and its evolving nature, a gap persists in evaluating EVA research. This research presents a systematic literature review, offering insights into the current state of EVA advancements.

This study amalgamates various factors influencing EVA and elucidates their associations, fostering sustainable transportation. To evaluate progress in this domain, we adopt the Theory-Context-Characteristics-Methodology (TCCM) framework, systematically assessing the theories, contextual factors, characteristics and methodologies employed in EVA research to support efficient decision-making.

The study reveals 18 theories, prominently including the theory of planned behavior, innovation diffusion theory, technology acceptance model and UTAUT. The study identifies diverse factors such as perceived risk, effort expectancy, social norms, performance expectancy, government policy, personal norms, attitude, perceived behavioral control, subjective norms, demographics and ecological knowledge as pivotal in shaping attitudes and intentions toward electric vehicle adoption. Furthermore, structured equation modeling emerges as the predominant methodology, while including alternative approaches enriches the methodological landscape, contributing to a more comprehensive understanding of the factors driving EV adoption.

The insights gained from this research can inform policymakers, manufacturers and researchers, ultimately contributing to the global transition towards more sustainable transportation solutions.

This research’s cardinal contribution lies in developing an integrated theoretical framework, a novel approach that offers a structured and holistic perspective on the multifaceted determinants of EVA. This framework not only illuminates the intricate relationships among these variables but also opens up exciting avenues for future research.

This research investigates whether focal firms employ strategic supply chain information disclosure, focusing on the concealment of supplier and customer identities, as part of their supply chain environmental risk management strategies (supplier sustainability risk and customer loss risk, respectively).

Using a panel dataset of Chinese listed firms from 2009 to 2019 and utilizing the suppliers’ environmental punishment of peer firms (peer events) as an exogenous shock and employing ordinary least squares (OLS) estimation, this study conducts a regression analysis to test how focal firms disclose the identities of their suppliers and customers.

Our results indicate that focal firms prefer to hide the identities of their suppliers and customers following the environmental punishment of peer firms’ suppliers. In addition, supplier concentration weakens the effect of withholding supplier identities, whereas customer concentration strengthens the effect of hiding customer identities. Mechanism analysis shows that firms hide supplier identities to avoid their reputation being affected and hide customer identities to prevent the deterioration of customers’ reputations and thus impact their market share.

Our study reveals that reputation spillover is another crucial factor in supply chain transparency. It is also pioneering in applying the anonymity theory to explain focal firms’ information disclosure strategy in supply chains.

This study examines how potentially irresponsible banking operations affect organisational reputation. A moderated mediation model is applied to explain how major aspects of social irresponsibility affect the relationship between consumer awareness of allegedly irresponsible operations, blame and bank reputation. The empirical context is the Swiss franc mortgage crisis that affected the banking industry in most Central and Eastern European countries.

The research study uses data collected from a large survey (N = 1,000) conducted among Polish bank consumers, including those with mortgage loans in Swiss francs. To test the proposed model, the authors use Hayes' process macro.

The findings show that blame fully mediates the effects of corporate social irresponsibility (CSI) awareness on organisational reputation. Three facets of social irresponsibility moderate this relationship. Specifically, the perceived harm and intentionality of corporate culprits cause people to be more likely to blame a bank for the difficulties posed by indebted consumers. At the same time, the perceived complicity of consumers in misselling a mortgage reduces the level of blame and its subsequent adverse effects on bank reputation.

Although a strong reputation is crucial in the financial industry, few studies have attempted to address reputational risk from a consumer perspective. This study helps to understand how potentially irresponsible selling of a financial product can adversely affect a bank's reputation.

In response to multiple disruptions, the purchasing supply management (PSM) function in construction supply chain management (CSCM) has gained prominence due to stakeholder pressures, dynamic market conditions and the need to adhere to complex sustainability, safety and health regulations and standards. However, there is a noticeable absence of empirical research on measuring and mitigating PSM vulnerabilities, especially considering the distinct challenges faced by large engineer-to-order project-oriented manufacturers. To address these issues, the purpose of this study is to develop and test a novel method to assist companies in construction supply chains in assessing and managing risks associated with sustainable procuring and sourcing materials.

Grounded in the literature gaps on construction PSM and a real case supply chain, the research uses the design science research (DSR) approach to develop an integrated method for assessing PSM strategies in this sector. The method integrates three essential purchasing dimensions: supply risks, profit impact and sustainability risks of materials, supported by nine subdimensions. Empirical validation took place within a multinational European construction company based in Denmark.

Findings from the supplier–buyer relationships confirmed that the developed method allows for the identification of the key components that significantly impact supplier–buyer relationships, profitability and sustainability. The research further suggests that construction supply chain managers and purchasing practitioners can use the proposed method to evaluate PSM, thus enabling them to make more informed decisions.

Through the utilization of the proposed artifact, construction companies can take a more proactive approach to address PSM uncertainties, thereby enhancing their competitiveness in dynamic construction supply chains.

The research contributes to bridging the theory and practice, providing valuable assistance for construction companies assessing and managing the PSM and supply risks within global construction value chains. This paper provides original insights for the CSCM, aiding in adopting competitive PSM strategies to improve overall supply chain performance.

This paper aims to analyse the influence of board characteristics on corporate reputation.

In total, 128 Brazilian publicly traded companies from Refinitiv Eikon were analysed between 2016 and 2020. The dependent variable was corporate reputation, whereas the independent variables were board size, gender diversity, board independence and audit committee presence. Multivariate analysis was used.

The results presented empirical evidence that board members can impact corporate reputation. Findings showed that board size, gender diversity and independence positively influence Brazilian companies’ corporate reputation. Conversely, an audit committee had no significant impact on corporate reputation.

The paper presents a contribution to the significance of board members in shaping a company's corporate reputation, using the signalling theory and the resource-based view (RBV) theory.

Regarding practical implications, this work provides subsidies for managers to value board characteristics because they directly reflect on corporate reputation and competitive advantage, leading to more sustainable performance.

The research findings highlight that a diverse board encourages the organisation to improve its workforce, human rights, relations with the community and responsibility for manufactured products.

The relationship between board characteristics and corporate cooperation is poorly established in the literature. Furthermore, the results prove the RBV theory in an emerging context. Similarly, the signalling theory proved helpful in improving Brazilian firms’ corporate reputation.

Grounded in the stakeholder theory and signaling theory, this study aims to broaden the research agenda on environmental, social and governance (ESG) investing by uncovering public sentiments and key themes using Twitter data spanning from 2009 to 2022.

Using various machine learning models for text tonality analysis and topic modeling, this research scrutinizes 1,842,985 Twitter texts to extract prevalent ESG investing trends and gauge their sentiment.

Gibbs Sampling Dirichlet Multinomial Mixture emerges as the optimal topic modeling method, unveiling significant topics such as “Physical risk of climate change,” “Employee Health, Safety and well-being” and “Water management and Scarcity.” RoBERTa, an attention-based model, outperforms other machine learning models in sentiment analysis, revealing a predominantly positive shift in public sentiment toward ESG investing over the past five years.

This study establishes a framework for sentiment analysis and topic modeling on alternative data, offering a foundation for future research. Prospective studies can enhance insights by incorporating data from additional social media platforms like LinkedIn and Facebook.

Leveraging unstructured data on ESG from platforms like Twitter provides a novel avenue to capture company-related information, supplementing traditional self-reported sustainability disclosures. This approach opens new possibilities for understanding a company’s ESG standing.

By shedding light on public perceptions of ESG investing, this research uncovers influential factors that often elude traditional corporate reporting. The findings empower both investors and the general public, aiding managers in refining ESG and management strategies.

This study marks a groundbreaking contribution to scholarly exploration, to the best of the authors’ knowledge, by being the first to analyze unstructured Twitter data in the context of ESG investing, offering unique insights and advancing the understanding of this emerging field.