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The rapid evolution of artificial intelligence (AI) is revolutionizing organizational operations and altering competitive landscapes. This study examines the influence of organizational resources on AI adoption in recruitment, focusing on their role in achieving competitive advantage through effective implementation.

This research utilizes a cross-sectional quantitative approach, applying partial least squares structural equation modeling (PLS-SEM) to data from 290 human resource (HR) professionals. It is grounded in the resource-based view (RBV) and dynamic capability framework (DCF).

The results reveal that HR competencies and open innovation significantly influence dynamic capabilities, which are essential for AI integration, supported by financial support and information technology (IT) infrastructure. These capabilities enable effective AI adoption, leading to a competitive advantage.

The cross-sectional data in this study captures the current landscape of AI adoption in recruitment, providing a snapshot of the present scenario in a rapidly evolving technological environment.

This study offers HR professionals and managers strategic guidance on effectively integrating AI into recruitment processes. By enhancing HR competencies, fostering collaboration and ensuring sufficient financial and infrastructural support, organizations can navigate AI adoption challenges and secure a competitive advantage in a rapidly evolving technological landscape.

The adoption of AI in recruitment can reduce biases, enhance diversity and improve fairness through standardized assessments. However, as AI technologies evolve, continuous human oversight is essential to ensure ethical use and to modify AI systems as needed, further reducing biases and addressing societal concerns in AI-driven recruitment processes.

This research introduces a novel framework that underscores the importance of integrating human expertise with advanced technological tools to ensure successful AI implementation. A key contribution is that HR professionals not only facilitate AI integration but also ensure accuracy, accountability and configure the most suitable AI tools for recruitment by collaborating with AI developers to meet the specific needs of the organization.

We aim to examine two issues. First, we intend to identify the best performing expected return proxies. Second, we investigate whether the expected return proxies for individual stocks can track the corresponding realized returns during extremely good or extremely bad times of the economic environment related to business conditions, stock market valuation and broad market performance.

We construct four sets of expected return proxies, including: (1) characteristic-based proxies; (2) standard risk-factor-based proxies; (3) risk-factor-based proxies that allow betas to vary with firm characteristics and (4) macroeconomic-variable-based proxies. First, we estimate expected returns for individual stocks using newly developed methods and evaluate the performance of these expected return proxies based on the minimum variance criterion of Lee et al. (2020). Second, we regress expected return proxies and realized returns on indicator variables that capture the extreme phases of the economic environment. Then we compare the estimated coefficients from these two sets of regressions and see if they are similar in magnitude via formal hypothesis testing.

We find that characteristic-based proxies and risk-factor-based proxies that allow betas to vary with firm characteristics are the two best performing proxies. Therefore, it is important to allow betas to vary with firm characteristics in constructing expected return proxies. We also find that model-based expected return proxies do a reasonably good job capturing actual returns during extremely bad and extremely good phases of business cycles measured by leading economic indicators, consumer confidence and business confidence. However, there is a large gap between the adjustment of model-based expected returns and realized returns during extreme episodes of stock market valuation or broad market performance.

We examine four types of expected return proxies and use the newly developed methodology as in Lee et al. (2020) to see which one is the best. In addition, we document whether model-based expected returns from individual stocks adjust partially or fully to keep pace with actual returns in response to changing economic conditions. No prior studies have examined these two issues.

The purpose of this paper is to systematically review how agency and stakeholder theories are integrated within corporate governance and environmental disclosure practices in the UAE, highlighting their relevance and adaptation to a distinct economic and regulatory environment.

Using a comprehensive qualitative methodology, this study synthesises a broad spectrum of existing theoretical and empirical research to explore the dynamics of corporate governance mechanisms regarding environmental sustainability. This approach enables a detailed examination of how agency theory’s focus on principal–agent relationships complements stakeholder theory’s broader view of corporate responsibilities.

This research uncovers significant insights into corporate conduct and responsibility, emphasising the need to balance shareholder objectives with broader stakeholder interests. It identifies key challenges in this integration, such as managing the complexities and potential conflicts between different stakeholder demands. The findings underscore the crucial role of specialised governance mechanisms, like board characteristics and committees, in enhancing environmental transparency and accountability.

This study contributes to the academic discourse by shedding light on the interplay between corporate governance frameworks and environmental disclosure practices within the UAE. It offers fresh insights into applying established theories in a non-Western context. These insights are precious for academics, practitioners and policymakers interested in refining corporate governance and promoting environmental responsibility. The practical implications drawn from the findings empower stakeholders to implement effective strategies that can enhance a firm’s reputation, legitimacy and long-term viability.

This study examines whether and how the age-inverse relationship between the chief executive officer (CEO) and the top management team (TMT) affects corporate misconduct in China’s construction industry. Drawing on social identity theory, we propose that the age-inverse relationship in CEO–TMT may diminish their social identity and further decrease the likelihood of corporate misconduct in construction firms.

Using a longitudinal dataset of firms in China’s construction industry covering the years 2003 through 2021, this study adopted a logit regression model with fixed effects.

The results show that the age-inverse relationship in CEO–TMT is negatively related to corporate misconduct. Further investigations suggest that performance feedback moderates the relationship between the age-inverse relationship in CEO–TMT and corporate misconduct. Firms with an age-inverse relationship between CEOs and TMTs are more likely to engage in fraudulent behavior when performance is above aspirations and less likely to commit fraud when performance is below aspirations.

The sample of this study is limited to China’s construction firms. Drawing on social identity theory, this study explores the relationship between the age-inverse relationship and corporate misconduct in China’s construction industry, which enriches the antecedents of corporate misconduct and contributes to CEO–TMT interface research within construction firms.

This study provides a guideline for construction firms on how to regulate and reduce misconduct. It will offer insights into human resource arrangements within the management of construction firms in an aging context.

Considering that few studies explore fraudulent behavior of construction companies at the upper echelon level, this study focuses on a novel and new antecedent (i.e. age-inverse relationship in CEO–TMT) and its boundary conditions. The findings extend the research on corporate misconduct and strategic leadership in the construction industry.

This paper aims to assess the efficiency of emulsified essential oils in glycerol as eco-friendly antimicrobial and plasticized agents added to the biopolymer of gelatin for lining historical oil paintings on canvases.

Cedar oil, cinnamon oil and their mixtures were emulsified in glycerol and incorporated into gelatin adhesive as green biocides and plasticizers. Physical, biological, chemical and mechanical tests were conducted on experimental mock-ups to assess the gelatin-based adhesive formulations for the reinforcement of canvas supports. Scanning electron microscope, colorimetric measurements, antimicrobial activity test, attenuated total reflection-Fourier transform infrared spectroscopy, tensile strength and elongation tests were carried out on the mock-ups before and after the artificial aging.

The formulations of gelatin-based adhesive with cinnamon and cinnamon-cedar mixture emulsified in glycerol proved their efficiency on the antimicrobial activity test, chemically delaying the decomposition of gelatin and accordingly providing compatible mechanical properties. Gelatin-based adhesive with emulsified cinnamon oil showed a slight yellowing that was quite improved with the mixture of the cinnamon-cedar-based adhesive formulation.

This study promotes a green approach to lining historical oil paintings by developing green formulations from bio-based origins that minimize the shrinkage and microbial infection of gelatin for lining paintings.

This paper aims to use three proxy variables – initial public offerings, trading volume and business confidence index (BCI) to construct an investor sentiment index both for the USA and China, taking into account the challenging periods of the COVID-19 pandemic and the Russo-Ukrainian conflict.

Based on monthly data for a period from January 2009 to June 2022, this paper uses data of BCI, consumer confidence index (CCI), gross domestic product, trading volume and Fama and French (1993) factor data; linear regression of single and multifactor model; and EGARCH-M model for analyzing the effect of investor sentiment on stock market return and volatility, both in the USA and China.

The empirical results indicate the suitability of BCI over CCI as a measure of investor sentiment, both in the USA and China. The results indicate that investor sentiment has a significant positive effect on the excess returns in the stock market in both countries. Moreover, the effect of investor sentiment is higher in China than it is in the USA. Such an effect of investor sentiment is significant and fluctuates asymmetrically in the short run but loses its significance in the long run. Optimistic investor sentiment has a larger effect on the stock market volatility in the USA, while the pessimistic investor sentiment has a larger effect in the Chinese stock market.

This paper focuses on finding a more suitable proxy for investor sentiment from BCI or CCI. This paper also contributes by including both optimism and pessimism in explaining the stock return and volatility in both markets. The overall findings are important for understanding investor behavior in different market conditions.

This paper aims to investigate the sentiment connectedness among 10 European stock markets between January 2020 and July 2022, associating such connectedness with the level of the geopolitical risk index.

For this purpose, a time-varying parameter vector autoregressive connectedness framework is used.

Results show a high degree of sentiment connectedness. Overall, the sentiments of Portugal, France, the Netherlands, Spain, Germany and Italy are net transmitters of shocks while those of Poland, Sweden, Norway and Romania are net receivers. Additional evidence indicates that when geopolitical risks increase, the sentiment connectedness tends to decrease. However, the reverse holds under extremely high levels of geopolitical risks.

Overall, this study provides some significant contributions to the literature. First, to the best of the authors’ knowledge, this is among the first few studies to examine the dynamic connectedness among stock market sentiment across countries. This issue needs special consideration for European countries because of their close geographical distance and strong integration due to the European Union’s co-development strategies. Second, the association of sentiment connectedness with geopolitical risk is examined for the first time. This is even more meaningful in the context of growing geopolitical risks stemming from the Ukraine war, which could affect international financial markets.

Online portfolio selection sequentially allocates wealth among a set of assets and aims to maximize the investor’s cumulative return in the long run. Various existing algorithms in the finance and accounting area adopt an indirect approach to exploit one asset characteristic through the channel of assets’ expected return and thus cannot fully leverage the power of various asset characteristics found in the literature. This study aims to propose new algorithms to overcome this issue to enhance investment performance.

We propose a parameterized portfolio selection (PPS) framework, which directly incorporates multiple asset characteristics into portfolio weights. This framework can update parameters timely based on final performance without intermediate steps and produce efficient portfolios. We further append L1 regularization to constrain the number of active asset characteristics. Solving the PPS formulation numerically, we design two online portfolio selection (OLPS) algorithms via gradient descent and alternating direction method of multipliers.

Empirical results on five real market datasets show that the proposed algorithms outperform the state of the arts in cumulative returns, Sharpe ratios, winning ratios, etc. Besides, short-term characteristics are more important than long-term characteristics, and the highest return category is the most important characteristic to improve portfolio performance.

The proposed PPS algorithms are new end-to-end online learning approaches, which directly optimize portfolios by asset characteristics. Such approaches thus differ from existing studies, which first predict returns and then optimize portfolios. This paper provides a new algorithmic framework for investors’ OLPS.

The purpose of this study is to investigate the influence of IP (Intellectual Property) on the intention for premium consumption of Generation Z, and to construct a theoretical model of IP on the premium consumption of Generation Z. Based on the results of the study, it provides better marketing suggestions to merchants, and is an expansion of previous research on the consumption behavior of Generation Z.

This paper contains two empirical tests and one experimental analysis. First, this study crawl over 5,000 pieces of Generation Z’s consumption data from Poizon, an e-commerce platform and exclusive trending community for Generation Z. Second, this study designs a two-group online experiment to collect 292 valid data from members of the Generation Z. The authors use Stata software for multiple linear regression, t-tests, and ANOVA to test the hypotheses.

The results of the data analysis show that IP has a significant positive effect on the premium consumption intention of Generation Z, and the limited release strategy positively moderates the effect. Self-image congruence and social identification play mediating role in the influence of IP on Generation Z’s premium consumption.

First, this study finds a link between IP and commodity premiums, which is the first study to explore the effect of IP on commodity price changes. Second, this study is the first to examine the marketing science value of IP using a combination of empirical tests and experimental analysis. These fill research gaps. Finally, the mechanism of IP’s influence on Generation Z’s premium consumption is revealed, enriching the literature on Generation Z’s consumption behavior.

The purpose of this paper is to explore how variations in management’s tone within management’s discussion and analysis (MD&A) sections of 10-K reports can serve as an indicator of tax avoidance and highlight the complex relationship between such linguistic shifts and the tax avoidance decisions within firms.

The paper uses a textual analysis approach to identify linguistic cues in MD&A sections of 10-K filings related to tax avoidance, going beyond traditional quantitative measures. The study uses differences in negative word occurrences in MD&A to measure management’s tone change and examines various measures of tax avoidance. The sample covers the period from 1993 to 2017 and comprises all firms with 10-K filings available on EDGAR, totaling over 30,000 firm-year observations.

The findings indicate a complementary relationship between tax avoidance and other drivers of firm performance. When firms have more negative management’s tone, they are less willing to engage in tax avoidance and vice versa. The study’s approach with management’s tone change provides a different and statistically significant improvement in model fit for detecting tax avoidance.

This paper provides actionable insights for detecting tax avoidance through the analysis of management’s tone in corporate disclosures, offering a new tool for researchers, investors and tax authorities. It highlights the importance of linguistic cues as indicators of tax avoidance behavior, complementing traditional financial metrics.

The paper contributes to the literature by using management’s tone change as a time-varying factor to explain tax avoidance behavior. It uncovers a larger set of linguistic cues in MD&A that can be used to detect tax avoidance. This research provides a complementary approach to traditional quantitative tax avoidance measures and offers insights into the overall relationship between tax avoidance and firm performance, going beyond one-dimensional measures typically used in prior literature.