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This paper aims to examine the monitoring role of institutional investors in corporate decision-making by classifying financial institutions based on geographical proximity and investment horizon from 1980 to 2014.

By using unique data sets on firm and institution location and investor horizon measure (Gaspar et al., 2005), the authors categorize institutional investors into six proximity-horizon classifications. This method captures the heterogeneity of investors. The corporate decisions assessed include firm investment, financing, payout policy, misbehavior, takeover defenses and profitability.

Both geographical proximity and investment horizon are directly related to institutional investors' monitoring cost. As a result, the effectiveness of institutional monitoring may vary based on geographical proximity and investment horizon. This paper collectively examines both dimensions of financial institutions and provides evidence that institutional investors present different preferences for corporate policies. Given stronger information advantage, both local and nonlocal investors that are long-term oriented fulfill better roles in monitoring corporate decisions but from different perspectives.

Different from previous studies that treat institutional investors homogeneously, this paper provides empirical support that investors are indeed different in influencing firm policies.

To the authors’ best knowledge, this is the first study that classifies investors based on two dimensions, geographical proximity and investment horizon, and examines their joint effects on corporate policies. This proximity-horizon classification allows the authors to better disentangle the effects of institutional ownership structure on the monitoring outcomes.

The purpose of this paper is to propose a new algorithm for independent navigation of unmanned aerial vehicle path planning with fast and stable performance, which is based on pigeon-inspired optimization (PIO) and quantum entanglement (QE) theory.

A biomimetic swarm intelligent optimization of PIO is inspired by the natural behavior of homing pigeons. In this paper, the model of QEPIO is devised according to the merging optimization of basic PIO algorithm and dynamics of QE in a two-qubit XXZ Heisenberg System.

Comparative experimental results with genetic algorithm, particle swarm optimization and traditional PIO algorithm are given to show the convergence velocity and robustness of our proposed QEPIO algorithm.

The QEPIO algorithm hold broad adoption prospects because of no reliance on INS, both on military affairs and market place.

This research is adopted to solve path planning problems with a new aspect of quantum effect applied in parameters designing for the model with the respective of unmanned aerial vehicle path planning.

Despite recent organizational behavior studies have witnessed considerable progress in abusive supervision research; some demerits for both theory and methodology still remain in the past years. To clarify the current state of knowledge in the field, this study aims to analyze the current state of theories and methods on abusive supervision and provides a detailed future research agenda.

This paper conducted a literature review for both theory and methodology of the abusive supervision research using a content analysis of 134 publications.

For the theory part, this paper summarized the theories that had been applied to explain the relationship between abusive supervision and its consequences as well as antecedents. For the methodology part, this paper outlined some critical issues regarding country of origin, research design, measurement, analysis strategy and also summarized with a discussion of the relationship between methodological issues and article impact. Finally, this paper concluded by presenting an agenda for future abusive supervision research regarding both theory and methodology.

First, this paper summarizes the main theories, antecedents and consequences often used in abusive supervision research to allow scholars to carry out theoretically driven research investigating abusive supervision in the future. Second, through a content analysis of the methods sections of abusive supervision research in the samples (i.e. country of origin, research design, measurement and analytical procedures), this paper identified the potential reasons underlying the inconsistency in the conclusions of abusive supervision research and provide some guidance for future empirical studies. Third, based on the qualitative review, this paper provides an agenda for future research investigating abusive supervision by developing a content-specific theoretical framework to benchmark abusive supervision research against other research related to leadership and offers an accurate response to scholars’ criticisms of abusive supervision research.

This paper aims to investigate analytical solutions of natural frequencies and mode shapes of Euler-Bernoulli beams with step changes in the stiffness.

In this work, analytical solutions for a beam with a single discontinuity was performed. Subsequently, based on an effective matrix formulation, the closed-form expressions of the single discontinuity beam could be conveniently extended to stepped beams with multiple stiffness discontinuities.

The results of the study show that the natural frequency of the beam can be adjusted by the local stiffness variation, and step location plays a significant role in free vibration responses.

The effects of the stiffness of the segment and step location on the natural frequencies of the stepped beams under different boundary conditions were examined using the proposed analytical scheme. This study provides insights into the design of variable-stiffness beam structures with the capability to adjust natural frequencies.

This study is a pioneer in the academic literature to investigate the relationship between Valentine’s Day and stock market returns of major economies around the world.

Specific control variables for Valentine's Day are introduced to eliminate the potential influence of other effects.

The findings indicate that stock returns are higher on the days when Valentine's Day is approaching than on other days for most cases, showing “the Valentine Effect” in the stock market.

Unlike other holiday effects in the previous literature, the Valentine's Day effect cannot be explained by many conventional theories, such as tax-loss selling and the inventory adjustment hypothesis.

The Soybean Producer Subsidy Policy (SPSP), an agricultural support policy enacted in China within the past few years, is designed to optimise crop planting structure. This study analyses the impact of SPSP on the crop planting structure in terms of absolute and comparative incomes and elucidates the mechanisms involved.

Utilising balanced county-level panel data from 966 counties in China’s major soybean-producing regions, spanning from 2008 to 2021, we investigate the impacts of SPSP on crop planting structure by applying a difference-in-difference (DID) model.

The findings reveal several crucial insights. First, SPSP optimises the crop planting structure in Northeast China, primarily through an expansion in the area sown to soybeans and a simultaneous reduction in the area sown to maize. Second, the impacts of SPSP gradually strengthen over time but begin to weaken by 2021. Third, heterogeneity analysis indicates that the effects of SPSP are most pronounced in Eastern Inner Mongolia, followed by Heilongjiang, Jilin, and Liaoning. Finally, SPSP incentivises farmers to expand soybean sown areas by improving absolute rather than comparative incomes from soybean cultivation.

Addressing structural contradictions within China’s food supply chain necessitates the adjustment of support policies for different crops to mitigate market distortions. Establishing a holistic agricultural support system encompassing various crops could promote sustainable agricultural practices in the future.

Our findings are valuable for policy makers in China and globally who aim to establish support systems for regional linkages that include a variety of crops.

The purpose of this paper is to explore the role of FinTech companies in SCF. The application of digital technology in supply chain activities has facilitated the evolution of supply chain finance (SCF) to a new level. However, how financial technology (FinTech) companies promote this evolution has not been thoroughly examined.

This research used the multiple-case study approach and social network analysis method to explore how FinTech companies influence SCF networks.

The results reveal that FinTech companies play the crucial role of a signaling intermediary by facilitating interactions among relevant parties, accelerating the flow of information and reducing information asymmetry arising from data smog. Moreover, FinTech companies make SCF information networks more equitable and promote the performance of SCF.

This study deepens the conversation at the nexus of signal theory and SCF and provides managerial implications for alleviating information asymmetry between borrowers and lenders to solve the difficulty and high-cost problems of obtaining financing of small- and medium-sized enterprises.

Based on the perspectives of social capital, image motivation and motivation affordances, this paper explores the direct and moderation effects of different kinds of motivations (i.e. relationship-based motivation, community-based motivation and individual-based motivation) on users' continuous knowledge contributions in social question and answer (Q&A) communities.

The authors collect the panel data of 10,193 users from a popular social Q&A community in China. Then, a negative binomial regression model is adopted to analyze the collected data.

The paper demonstrates that social learning, peer recognition and knowledge seeking positively affect users' continuous contribution behaviors. However, the results also show that social exposure has the opposite effect. In addition, self-presentation is found to moderate the influence of social factors on users' continuous use behaviors, while the moderation effect of motivation affordances has no significance.

First, this study develops a comprehensive motivation framework that helps gain deeper insights into the underlying mechanism of knowledge contribution in social Q&A communities. Second, this study conducts panel data analysis to capture the impacts of motivations over time, rather than intentions at a fixed time point. Third, the findings can help operators of social Q&A communities to optimize community norms and incentive mechanisms.

For start-ups or growing firms, to effectively navigate the unpredictable nature of digital development and achieve superior innovative performance, it is crucial to have a workforce comprised of creative and innovative employees. Drawing upon the principles of social information processing theory, this study aims to investigate whether specific combinations of organizational internal and external environments, as well as work characteristics in the digital age, can foster a high level of employee innovative behavior.

By collecting a multilevel and multisource data set comprising 693 employees and 88 CEOs from 88 start-ups or growing firms, this study used fuzzy-set qualitative comparative analysis to examine the distinctive configurations associated with achieving a high level of employee innovative behavior.

The study found that six solutions enabled employees to innovate more effectively, but six solutions led to the absence of employee innovative behavior.

The findings of this study offer important theoretical and practical implications to motivate employee innovative behavior in Chinese enterprises.

First, this study contributes to the literature on employee innovative behavior by addressing the need to explore the impact of the digital context on promoting innovation among employees. Second, this study adds to the existing literature on employee innovation and entrepreneurship by examining multiple organizational contexts and their influence on innovative behavior. Third, this study makes a significant contribution to the field of employee innovative behavior by examining the macroenvironment surrounding digital transformation within enterprises and integrating both internal and external organizational factors.

Banks are the major suppliers of external funds for companies in China. The purpose of this paper is to examine whether Chinese banks exercise effective monitoring over borrowers in two lending decisions, including loan interest rates and loan renewals.

Using a sample of Chinese public industrial firms from 2000 to 2005, the authors perform multivariate regression analysis to investigate whether banks adjust their loan interest rates and consider loan renewal decisions in response to borrowers financial performance. The authors also examine these bank lending decisions before and after 2003, when the major banking reforms started to take place in China.

A negative relation was found between the loan interest rate spread and the financial performance of borrowers. However, a negative relation was found between loan renewals and the financial performance of borrowers, consistent with firms in financial difficulties being in need of more funding and hence more likely to get its bank loans renewed. Additionally, it was found that the factors banks consider when making loan decisions vary before and after 2003.

The authors' findings suggest that Chinese banks play a limited role in monitoring and disciplining borrowers through adjustments of loan interest rates, and that their loan renewal decisions for firms with poor financial performance highlight banks' financing, instead of monitoring role in this transition economy. These findings provide empirical evidence on bank governance in a transition economy dominated by state‐owned enterprises. The paper contributes to the literature by constructing an alternative loan renewal measure using financial statement information.