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How do informal lending institutions affect entrepreneurship? This paper aims to investigates the role of formal and informal credit market institutions in the decision to become an entrepreneur over the life cycle.

The author developed a dynamic Roy model in which a decision to become an entrepreneur depends on the access to formal and informal credit markets, nonpecuniary benefits of entrepreneurship, career-specific entry costs, prior work experience, education, unobserved abilities and other labor market opportunities (salaried employment and nonemployment). Using detailed Russian panel microdata (the Russia longitudinal monitoring survey) and estimating a structural model of labor market decisions and borrowing options, the author assesses the impact of the development of informal and formal credit institutions.

The expansion of traditional (formal) credit market institutions positively impacts all workers’ categories, reduces the share of entrepreneurs who borrow from informal sources and incentivizes low-type entrepreneurs to switch to salaried employment. The development of the informal credit market reduces the percentage of high-type entrepreneurs who borrow from formal sources. In the case of default, a higher value of the social network or higher costs of losing social ties demotivate low-type entrepreneurs to borrow from informal sources. The author highlights the practical implications of estimates by evaluating policies designed to promote entrepreneurship, such as subsidies and accessibility regulations in credit market institutions.

This study contributes to the literature in several ways. Unlike other studies that focus on individual characteristics in the selection for self-employment [Humphries (2017), Hincapíe (2020), Gendron-Carrier (2021), Dillon and Stanton (2017)], the paper models labor and borrowing decisions jointly. Previous studies discuss transitions between salaried employment and self-employment, taking into account entrepreneurial earnings, wealth, education and age, but do not consider the availability of financial institutions as a driving factor for the selection into self-employment. To the best of the author’s knowledge, this paper shows for the first time that the transition from salaried employment to self-employment is standard and consistent with changes in access to financial institutions. Another feature of this study is incorporating both types of credit markets – formal and informal. The survey by the European Central Bank on the Access to Finance of Enterprises (2018) shows 18% of small and medium enterprise in EU pointed funds from family or friends. Therefore, the exclusion from consideration of informal credit markets may distort the understanding of the role of the accessibility of credit markets.

This paper aims to address the safety concerns of path-planning algorithms in dynamic obstacle warehouse environments. It proposes a method that uses improved artificial potential fields (IAPF) as expert knowledge for an improved deep deterministic policy gradient (IDDPG) and designs a hierarchical strategy for robots through obstacle detection methods.

The IAPF algorithm is used as the expert experience of reinforcement learning (RL) to reduce the useless exploration in the early stage of RL training. A strategy-switching mechanism is introduced during training to adapt to various scenarios and overcome challenges related to sparse rewards. Sensor inputs, including light detection and ranging data, are integrated to detect obstacles around waypoints, guiding the robot toward the target point.

Simulation experiments demonstrate that the integrated use of IDDPG and the IAPF method significantly enhances the safety and training efficiency of path planning for mobile robots.

This method enhances safety by applying safety domain judgment rules to improve APF’s security and designing an obstacle detection method for better danger anticipation. It also boosts training efficiency through using IAPF as expert experience for DDPG and the classification storage and sampling design for the RL experience pool. Additionally, adjustments to the actor network’s update frequency expedite convergence.

This paper is a response to the failure of construction firms to use sufficient attention to their working capital management (WCM) practices, resulting in operational challenges, and leading to the collapse of firms in most developing countries. Hence, this study aims to explore the empirical perspective of WCM practices among large building construction firms (LBCFs) in Ghana, to help achieve the Sustainable Development Goal 9.

The study collected primary data through structured survey questionnaires from LBCFs in Ghana. The CEOs/Directors, General Managers and Accountant/Finance of LBCFs in Ghana formed the unit of analysis based on a simple random sampling technique. Mean score, standard deviation and one-sample t-test were used to perform the empirical analysis of the study.

According to this study's empirical results, LBCFs appear to have effective WCM practices in place. This was evidenced in the surveyed responses which indicate that the sector’s WCM practices sound good based on the mean scores and statistically significant as the t-values > 1.664. Notably, LBCFs in Ghana pay their suppliers early to reduce the fear of adverse effect of late payments on their credit history, making them conservative in their approach toward financial management.

This is a pioneering paper in a developing country like Ghana, highlighting the significance of gaining an in-depth understanding of WCM practices among LBCFs. The findings of this study are expected to provide valuable information to industry players toward ensuring WCM efficiencies and can serve as a solid foundation for further empirical studies.

Product returns are a major challenge for e-businesses as they involve huge logistical and operational costs. Therefore, it becomes crucial to predict returns in advance. This study aims to evaluate different genres of classifiers for product return chance prediction, and further optimizes the best performing model.

An e-commerce data set having categorical type attributes has been used for this study. Feature selection based on chi-square provides a selective features-set which is used as inputs for model building. Predictive models are attempted using individual classifiers, ensemble models and deep neural networks. For performance evaluation, 75:25 train/test split and 10-fold cross-validation strategies are used. To improve the predictability of the best performing classifier, hyperparameter tuning is performed using different optimization methods such as, random search, grid search, Bayesian approach and evolutionary models (genetic algorithm, differential evolution and particle swarm optimization).

A comparison of F1-scores revealed that the Bayesian approach outperformed all other optimization approaches in terms of accuracy. The predictability of the Bayesian-optimized model is further compared with that of other classifiers using experimental analysis. The Bayesian-optimized XGBoost model possessed superior performance, with accuracies of 77.80% and 70.35% for holdout and 10-fold cross-validation methods, respectively.

Given the anonymized data, the effects of individual attributes on outcomes could not be investigated in detail. The Bayesian-optimized predictive model may be used in decision support systems, enabling real-time prediction of returns and the implementation of preventive measures.

There are very few reported studies on predicting the chance of order return in e-businesses. To the best of the authors’ knowledge, this study is the first to compare different optimization methods and classifiers, demonstrating the superiority of the Bayesian-optimized XGBoost classification model for returns prediction.

Whistleblowers are individuals who detect and report misconduct in an organization. They help to mitigate organizational misbehavior and resulting damages effectively and relatively quickly. Whistleblower protection has not been systematically required in the European Union (EU), leaving many large organizations unregulated. This study aims to get in-depth insights into how unregulated organizations design, handle and view whistleblowing with the advent of a novel EU Whistleblowing Directive.

The authors conducted 17 semistructured interviews with a diverse group of organizations headquartered in Germany and inductively analyzed them following Grounded Theory. Linking the Grounded Theory to the legal endogeneity model, they developed seven perspectives that help to explain how organizations view whistleblowing.

In trying to make sense of the role of whistleblowing in the organization’s governance, organizations and their managers assume different perspectives. These perspectives guide their approach to whistleblower protection in the context of evolving regulation with little regulatory guidance. Perspectives vary in the degree of supporting whistleblowing regulation, from viewing whistleblowing as a natural, everyday governance tool to denying it and fearing denunciation. Most organizations exhibit several perspectives.

Little is known about day-to-day whistleblowing practices from the perspective of organizations. The authors fill this research gap by providing initial evidence on how organizations approach whistleblowing and the EU Whistleblowing Directive. Identifying organizations’ perspectives may help us understand how ineffective or noncompliant whistleblowing systems emerge and how organizations can improve.

This study aims to examine how financial analysts evaluate other comprehensive income (OCI) information with a focus on the information content and economic substance of OCI gain and loss.

This study conducted a 2 × 2 between-subject experiment by manipulating profitability (net profit or net loss) and OCI (OCI gain or loss). A total of 103 equity research analysts participated in the experiment.

The results show that when the company suffers a net loss, the presence of unrealized gain in OCI appears to cause concern for analysts, in that they assigned a lower valuation to the OCI gain company than the OCI loss company. However, in the cases where the company is profitable, analysts appeared to respond to the direction of OCI (i.e. gain or loss) and incorporated the directional information in their valuation judgment.

The experimental results complement prior archival research on OCI valuation. This study extends prior work on OCI’s decision usefulness, improves understanding of the impact of OCI on firm valuation and contributes to the ongoing debate about whether OCI is viewed as a performance measure. The findings indicate that the effect of OCI gains or losses is most pronounced when the company experiences a loss. During such instances, analysts may interpret a combination of net loss and OCI gain as a potential indicator of earnings management opportunities. Consequently, they may perceive it as a signal of deteriorating future financial performance.

The purpose of this study is to explore how the South African government has used accounting technologies to manage the pervasive issue of racial inequality.

Premised on Foucault’s notion of governmentality, we conducted a qualitative case study. Publicly available archival data are used to determine the extent to which accounting techniques have helped to shape policy responses to racial inequality.

We show that accounting techniques and calculations give visibility to the problems of government and help design a programme to solve racial inequality. The lived experiences and impacts of racism in the workplace have been problematised, turned into statistics, and used to rationalise the need for ongoing government intervention in solving the problem. These processes underpin the development of the scorecard system, which measures the contributions firms have made towards minimising racial inequalities.

This study augments the existing body of Foucauldian literature by illustrating how power dynamics can be counteracted. We show that in governmental processes, accounting can exhibit a dual role, and these roles are not always subordinate to the analysis of political realities. The case of B-BBEE reveals the unintended consequences of utilising accounting to control the conduct of individuals or groups.

We explore the impact of equity liquidity on a firm’s dynamic leverage adjustments and the moderating impacts of leverage deviation and target instability on the link between equity liquidity and dynamic leverage in the UK market.

In applying the two-step system GMM, we estimate our model by exploring suitable instruments for the dynamic variable(s), i.e. lagged values of the dynamic term(s).

Our analyses document that a firm’s equity liquidity has a positive impact on the speed of adjustment (SOA) of its leverage ratio back to the target ratio in the UK market. We also demonstrate that the positive relationship between liquidity and SOA is more pronounced for firms whose current position is relatively close to their target leverage ratio and whose target ratio is relatively stable.

This study provides important implications for both firms’ managers and investors. Particularly, firms’ managers who wish to increase the leverage SOA to enhance firms’ value need to give great attention to their equity liquidity. Investors who want to evaluate firms’ performance could also consider their equity liquidity and leverage SOA.

We are the first to enrich the literature on leverage adjustments by identifying equity liquidity as a new determinant of SOA in a single developed country with many differences in the structure and development of capital markets, ownership concentration and institutional characteristics. We also provide new empirical evidence of the joint effect of equity liquidity, leverage deviation and target instability on leverage SOA.

Unmanned aerial vehicles (UAVs), known for their exceptional flexibility and maneuverability, have become an integral part of mobile edge computing systems in edge networks. This paper aims to minimize system costs within a communication cycle. To this end, this paper has developed a model for task offloading in UAV-assisted edge networks under dynamic channel conditions. This study seeks to efficiently execute task offloading while satisfying UAV energy constraints, and validates the effectiveness of the proposed method through performance comparisons with other similar algorithms.

To address this issue, this paper proposes a task offloading and trajectory optimization algorithm using deep deterministic policy gradient, which jointly optimizes Internet of Things (IoT) device scheduling, power distribution, task offloading and UAV flight trajectory to minimize system costs.

The analysis of simulation results indicates that this algorithm achieves lower redundancy compared to others, along with reductions in task size by 22.8%, flight time by 34.5%, number of IoT devices by 11.8%, UAV computing power by 25.35% and the required cycle for per-bit tasks by 33.6%.

A multi-objective optimization problem is established under dynamic channel conditions, and the effectiveness of this approach is validated.

While size asymmetry in buyer–supplier relationships has been studied in non-disruption contexts, this research explores how supplier size influences positive and negative supply chain disruptions. Anchoring on the commitment-trust theory (CTT), we explore buyer commitment as a mediating variable and examine how buying firms' mediated power usage depends on different supplier sizes and types of supplier-induced disruptions.

Through two scenario-based behavioral experiments, we discover different patterns in buyers' use of mediated power, contingent on the types of supplier-induced disruptions.

In negative disruptions, buyers prefer more mediated power with large suppliers to control uncertainties, using reward or coercive power strategies. In positive disruptions, we find opposite results, indicating different buyers' perceptions and actions are contingent on both the supplier size and the types of disruptions. These findings underscore the complex interplay between supplier size, buyer commitment and mediated power strategies, revealing that disruption type significantly shapes buyer responses.

This paper extends the CTT framework by considering new antecedents and outcomes. We also provide a more comprehensive understanding of buyer behavior when facing positive and negative supplier-induced disruptions. Our study has limitations. Through vignette-based behavioral experiments, there is a risk that scenarios may not accurately represent real-life situations and that decision-making dynamics could be oversimplified. Future research should incorporate nuanced measurements and conduct additional qualitative research for a comprehensive understanding.

This study enriches the understanding of the buyer-supplier relationship by expanding the CTT framework for a more comprehensive picture. We also offer nuanced insights into size dynamics and disruption types, emphasizing tailored strategies in supply chain management. The findings underscore the importance of understanding these nuances to employ tailored strategy in a business-to-business (B2B) context, as mediated power is contingent on multiple factors.