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The present study aims to investigate the impact of internal control effectiveness on supply chain management efficiency (SCME) and capital allocation efficiency for companies listed in the Tehran Stock Exchange (TSE). In addition, it investigates the mediating role of supply chain management efficiency in the relationship between internal controls and capital allocation efficiency.

The data about 191 companies in 2014–2022 were examined. The sales per inventory ratio was used to calculate SCME. The present study also applied the Generalized Method of Moments (GMM) for endogeneity concerns.

The results showed that internal control effectiveness has a significant positive effect on SCME. Moreover, internal control effectiveness and SCME significantly positively affect capital allocation efficiency. SCME has a mediating role in the relationship between internal control effectiveness and capital allocation efficiency. These findings remained robust even after several robustness tests. In addition, this study tested the results' robustness by dividing data into the pre-COVID-19 and post-COVID-19 years. The previous results were also confirmed according to the robustness test of COVID-19.

Challenges in the supply chain often hinder capital allocation efficiency. In addition, enterprises should try to establish strong internal controls to ensure SCME. Therefore, the relationship between internal control effectiveness, SCME and capital allocation efficiency is complex and underscores the importance of robust internal controls in optimizing resource allocation within organizations. Interestingly, this topic has not been extensively researched in accounting and business research, and there is a lack of empirical evidence on these effects. Consequently, this study aims to fill the gap and identify potential opportunities for new research directions.

This paper takes stock of past research in international management, zeroing in on the location, organization, and capabilities for innovation in a multinational enterprise. It then reviews current realities and identifies emergent trends of multinational enterprise innovation to outline avenues for future research. It puts forward the need for further exploration of issues, such as emerging markets as an innovation context and emerging multinationals' knowledge creation approaches, as well as the particularities of structuring for open innovation and capabilities for global knowledge sharing.

Environmental, social and governance (ESG) performance has received significant attention around the world. Could robust ESG performance become a new advantage for supporting companies’ outward foreign direct investment (OFDI) in emerging markets? Prior studies have not articulated the nexus between ESG performance and OFDI. This paper aims to conduct both theoretical and empirical work to clarify the effect, especially the mechanisms of ESG performance on companies’ OFDI.

Using the data of A-share listed companies in China from 2010 to 2020, this paper empirically tests the effect and the mechanisms of ESG performance on companies’ OFDI.

Firstly, robust ESG performance increases the likelihood of companies engaging in OFDI and also augments the scale of such investments. Within the realm of ESG, environmental performance, social performance and governance performance all play important roles in fostering OFDI. Secondly, strong ESG performance promotes OFDI by enhancing the competitive edge and alleviating financial constraints. Also, environmental performance, social performance and governance performance individually contribute to supporting competitiveness and mitigating financial constraints. Thirdly, the effect of ESG performance on OFDI is particularly pronounced for companies targeting developed countries, those operating in heavily polluting sectors and those with significant institutional investor presence.

This study advances the applicability of the stakeholder theory in the realm of firm internationalization. Moreover, the findings of this paper provide new strategies for promoting the OFDI of companies in emerging market economies.

The purpose of this study is to develop a hybrid algorithm for segmenting tumor from ultrasound images of the liver.

After collecting the ultrasound images, contrast-limited adaptive histogram equalization approach (CLAHE) is applied as preprocessing, in order to enhance the visual quality of the images that helps in better segmentation. Then, adaptively regularized kernel-based fuzzy C means (ARKFCM) is used to segment tumor from the enhanced image along with local ternary pattern combined with selective level set approaches.

The proposed segmentation algorithm precisely segments the tumor portions from the enhanced images with lower computation cost. The proposed segmentation algorithm is compared with the existing algorithms and ground truth values in terms of Jaccard coefficient, dice coefficient, precision, Matthews correlation coefficient, f-score and accuracy. The experimental analysis shows that the proposed algorithm achieved 99.18% of accuracy and 92.17% of f-score value, which is better than the existing algorithms.

From the experimental analysis, the proposed ARKFCM with enhanced level set algorithm obtained better performance in ultrasound liver tumor segmentation related to graph-based algorithm. However, the proposed algorithm showed 3.11% improvement in dice coefficient compared to graph-based algorithm.

The image preprocessing is carried out using CLAHE algorithm. The preprocessed image is segmented by employing selective level set model and Local Ternary Pattern in ARKFCM algorithm. In this research, the proposed algorithm has advantages such as independence of clustering parameters, robustness in preserving the image details and optimal in finding the threshold value that effectively reduces the computational cost.

Asteroids have the characteristics of noncooperative, irregular gravity and complex terrain on the surface, which cause difficulties in successful landing for conventional landers. The purpose of this paper is to study the trajectory tracking problem of a multi-node flexible lander with unknown flexible coefficient and space disturbance.

To facilitate the stability analysis, this paper constructs a simplified dynamic model of the multi-node flexible lander. By introducing the nonlinear transformation, a concurrent learning-based adaptive trajectory tracking guidance law is designed to ensure tracking performance, which uses both real-time information and historical data to estimate the parameters without persistent excitation (PE) conditions. A data selection algorithm is developed to enhance the richness of historical data, which can improve the convergence rate of the parameter estimation and the guidance performance.

Finally, Lyapunov stability theory is used to prove that the unknown parameters can converge to their actual value and, meanwhile, the closed-loop system is stable. The effectiveness of the proposed algorithm is further verified through simulations.

This paper provides a new design idea for future asteroid landers, and a trajectory tracking controller based on concurrent learning and preset performance is first proposed.

The purpose of this paper is to examine the underlying mechanisms influencing the social entrepreneurial intentions of individuals in China, adopting social capital theory, bottom-up and people-based approach. The interrelated effects of intellectual capital (social community trust, social capital bonding and social participation) on social innovation tendency were measured.

We recruited 502 Chinese individuals by utilising a reliable survey platform in China. This study used structural equation modelling as an analytical approach to examine the influence of social capital on social innovation and social entrepreneurship intention.

The findings showed that social innovation tendencies mediate the relationship between social community trust, social capital bonding and social participation and the social entrepreneurial intentions of individuals. The findings support the tested hypotheses that social innovation tendencies are the key mechanism to translate into stronger social entrepreneurial intentions. An iterative framework emphasising transparency and open collaboration among stakeholders, which are vital for fostering social entrepreneurial intentions, was proposed.

This is a novel empirical study to apply social capital theory to the field of social enterprises in the Chinese context using structural equation modelling, bottom-up, people-based approach and iterative model. The findings offer valuable practical insights for entrepreneurial and SME practices to foster social entrepreneurship through a people-based approach, emphasising the importance of fostering trust, strengthening social bonds, and encouraging active social participation.

An empirical study investigated the antecedents to perceived usefulness (PU) and its consequences in the context of smoking cessation online health communities (OHCs).

To validate a research model for perceived informational support, perceived emotional support and perceived esteem support, the authors conducted a partial-least-squares analysis of empirical data from an online survey (N = 173) of users of two smoking cessation OHCs. The proposed model articulates these as antecedents to PU from a social support perspective, and knowledge sharing and continuance intention are expressed as consequences of PU.

The empirical study identified that the PU of smoking cessation OHCs is influenced by perceived emotional support and perceived esteem support, and perceived informational support indirectly affects PU via these factors. In turn, PU exerts a positive influence on both knowledge sharing and continuance intention. Also, knowledge sharing positively affects continuance intention.

The study contributes to scholarship on users' postadoption behavior in the context of smoking cessation OHCs by disentangling the antecedents to PU from a social support perspective and pinpointing some important consequences of PU. The research also has practical implications for managing smoking cessation OHCs.

This study aims to investigate the impact of audit partners’ narcissism on the readability of audit reports for companies listed on the Tehran Stock Exchange (TSE). It examines the effects of narcissism among both lead and review audit partners on the clarity of audit reports, considering the regulatory requirements and auditing practices within the Iranian financial reporting context.

This paper analyzed 2,691 firm-year observations from TSE-listed companies spanning 2011–2023, using ordinary least squares regression. Readability of audit reports was assessed using the FOG index, with the size of partners’ signatures serving as a proxy for narcissism.

The findings indicate a significant negative relationship between increased narcissism and audit report readability; higher levels of narcissism correspond with elevated FOG index scores. Narcissism in lead partners notably diminishes readability more than that of review partners. This pattern holds across various robustness checks, including alternative readability metrics, variations in auditor engagement complexity, auditor specialization, subsets of qualified audit reports and considerations for endogeneity. Audit reports for economically significant clients tend to be clearer, suggesting a preference for reputation management over yielding to client pressure. Although no direct link was established between partners’ quality and readability, a positive relationship exists between audit firm rank and partners’ narcissism. Furthermore, interactions between auditor and CEO narcissism increase report complexity, especially in contentious negotiation scenarios. Despite regulatory advancements such as International Auditing Standard 701, its moderating effects were found to be inconsequential, highlighting the persistent influence of narcissism on audit report outcomes.

This research expands the understanding of how auditor personality traits, particularly narcissism, affect audit outcomes. By exploring the influence of narcissism on report readability within the Iranian context, this study fills a notable gap in the literature on emerging markets and non-Western reporting environments, providing valuable insights into global audit practices.

This chapter aims at shedding light upon how transforming or detrending a series can substantially impact predictions of mixed causal-noncausal (MAR) models, namely dynamic processes that depend not only on their lags but also on their leads. MAR models have been successfully implemented on commodity prices as they allow to generate nonlinear features such as locally explosive episodes (denoted here as bubbles) in a strictly stationary setting. The authors consider multiple detrending methods and investigate, using Monte Carlo simulations, to what extent they preserve the bubble patterns observed in the raw data. MAR models relies on the dynamics observed in the series alone and does not require economical background to construct a structural model, which can sometimes be intricate to specify or which may lack parsimony. The authors investigate oil prices and estimate probabilities of crashes before and during the first 2020 wave of the COVID-19 pandemic. The authors consider three different mechanical detrending methods and compare them to a detrending performed using the level of strategic petroleum reserves.