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This study aims to analyse whether the presence of supply chain complexity (SCC) influences firms to improve their supply chain (SC) resilience and SC robustness capability. This study also examines an important paradox: whether investing in both exploitation and exploration practices is conflicting or complementary to enabling SC resilience and robustness in the presence of SCC.

The authors used a survey-based approach to collect 242 useful responses from SC professionals of Pakistani firms, an important emerging economy context. The data were analysed with covariance-based structural equation modelling to statistically validate the model.

The analysis reveals several key findings: the presence of SCC has a direct, positive influence on SC resilience and SC robustness; while exploitation practices only partially mediate the nexus between SCC and SC resilience, they fully mediate the relationship between SCC and SC robustness; while exploration practices partially mediate the nexus between SCC and SC resilience, they do not mediate the relationship between SCC and SC robustness and SCC has a significant influence on SC resilience and SC robustness sequentially through exploitation and exploration (i.e. one after the other).

These findings help to reconcile the exploitation versus exploration paradox in cultivating SC resilience and SC robustness in the presence of SCC. The findings assist SC managers in determining how to deploy their limited resources most effectively to enhance SC resilience and SC robustness while facing SCC.

The authors devise and empirically validate a unique framework that demonstrates how the presence of SCC works as a stimulus to build SC resilience and SC robustness.

In recent years, the COVID-19 pandemic has become one of the most impactful disruptions which has imposed high levels of uncertainty on supply chains around the world. Supply chain integration (SCI) is highly recommended as an underlying mechanism that can facilitate the development of resilience and robustness as two dynamic capabilities. They can in turn positively influence firm performance and success during the disruptive conditions of COVID-19 era. The study aims to examine whether SCI as an enabler of resilience and robustness can improve firm performance during COVID-19 pandemic.

A theoretical model is developed to elaborate the relationship between SCI dimensions, resilience and robustness and firm’s operational and financial performance during the COVID-19 pandemic. A survey method is then used to empirically examine the model using a sample of 94 companies in the food industry in the province of Tehran, Iran, during the COVID-19 pandemic. This study makes several contributions. It provides a novel theoretical model on the relationship between SCI, resilience and robustness and firm performance and tests this model in a less-studied yet critical context (i.e. Iranian food industry) and during a disruptive era (i.e. COVID-19 pandemic).

The results support the positive effect of three SCI dimensions of internal, product and process integration on operational and financial performance during corona virus pandemic. Furthermore, internal and process integration have positive effects on resilience. Internal, product and process integration have positive effects on robustness. In addition, resilience mediates the effects of internal and product integration on both operational and financial performance, whereas robustness mediates the effect of internal and product integration on financial performance.

This study was conducted in the Province of Tehran. To test and generalize the results, it is recommended to conduct this study in other places and countries.

These results highlight the importance of SCI dimensions as vital enablers of resilience and robustness and their consequent impact on firm’s performance during the COVID-19 pandemic.

Many popular simultaneous localization and mapping (SLAM) techniques have low accuracy, especially when localizing environments containing dynamically moving objects since their presence can potentially cause inaccurate data associations. To address this issue, the proposed FADM-SLAM system aims to improve the accuracy of SLAM techniques in environments containing dynamically moving objects. It uses a pipeline of feature-based approaches accompanied by sparse optical flow and multi-view geometry as constraints to achieve this goal.

FADM-SLAM, which works with monocular, stereo and RGB-D sensors, combines an instance segmentation network incorporating an intelligent motion detection strategy (iM) with an optical flow technique to improve location accuracy. The proposed AS-SLAM system comprises four principal modules, which are the optical flow mask and iM, the ego motion estimation, dynamic point detection and the feature-based extraction framework.

Experiment results using the publicly available RGBD-Bonn data set indicate that FADM-SLAM outperforms established visual SLAM systems in highly dynamic conditions.

In summary, the first module generates the indication of dynamic objects by using the optical flow and iM with geometric-wise segmentation, which is then used by the second module to compute the starting point of a posture. The third module, meanwhile, first searches for the dynamic feature points in the environment, and second, eliminates them from further processing. An algorithm based on epipolar constraints is implemented to do this. In this way, only the static feature points are retained, which are then fed to the fourth module for extracting important features.

The prerequisite for most traditional visual simultaneous localization and mapping (V-SLAM) algorithms is that most objects in the environment should be static or in low-speed locomotion. These algorithms rely on geometric information of the environment and restrict the application scenarios with dynamic objects. Semantic segmentation can be used to extract deep features from images to identify dynamic objects in the real world. Therefore, V-SLAM fused with semantic information can reduce the influence from dynamic objects and achieve higher accuracy. This paper aims to present a new semantic stereo V-SLAM method toward outdoor dynamic environments for more accurate pose estimation.

First, the Deeplabv3+ semantic segmentation model is adopted to recognize semantic information about dynamic objects in the outdoor scenes. Second, an approach that combines prior knowledge to determine the dynamic hierarchy of moveable objects is proposed, which depends on the pixel movement between frames. Finally, a semantic stereo V-SLAM based on ORB-SLAM2 to calculate accurate trajectory in dynamic environments is presented, which selects corresponding feature points on static regions and eliminates useless feature points on dynamic regions.

The proposed method is successfully verified on the public data set KITTI and ZED2 self-collected data set in the real world. The proposed V-SLAM system can extract the semantic information and track feature points steadily in dynamic environments. Absolute pose error and relative pose error are used to evaluate the feasibility of the proposed method. Experimental results show significant improvements in root mean square error and standard deviation error on both the KITTI data set and an unmanned aerial vehicle. That indicates this method can be effectively applied to outdoor environments.

The main contribution of this study is that a new semantic stereo V-SLAM method is proposed with greater robustness and stability, which reduces the impact of moving objects in dynamic scenes.

Autonomous mobile robots (AMRs) play a crucial role in industrial and service fields. The paper aims to build a LiDAR-based simultaneous localization and mapping (SLAM) system used by AMRs to overcome challenges in dynamic and changing environments.

This research introduces SLAM-RAMU, a lifelong SLAM system that addresses these challenges by providing precise and consistent relocalization and autonomous map updating (RAMU). During the mapping process, local odometry is obtained using iterative error state Kalman filtering, while back-end loop detection and global pose graph optimization are used for accurate trajectory correction. In addition, a fast point cloud segmentation module is incorporated to robustly distinguish between floor, walls and roof in the environment. The segmented point clouds are then used to generate a 2.5D grid map, with particular emphasis on floor detection to filter the prior map and eliminate dynamic artifacts. In the positioning process, an initial pose alignment method is designed, which combines 2D branch-and-bound search with 3D iterative closest point registration. This method ensures high accuracy even in scenes with similar characteristics. Subsequently, scan-to-map registration is performed using the segmented point cloud on the prior map. The system also includes a map updating module that takes into account historical point cloud segmentation results. It selectively incorporates or excludes new point cloud data to ensure consistent reflection of the real environment in the map.

The performance of the SLAM-RAMU system was evaluated in real-world environments and compared against state-of-the-art (SOTA) methods. The results demonstrate that SLAM-RAMU achieves higher mapping quality and relocalization accuracy and exhibits robustness against dynamic obstacles and environmental changes.

Compared to other SOTA methods in simulation and real environments, SLAM-RAMU showed higher mapping quality, faster initial aligning speed and higher repeated localization accuracy.

This study aims to the development of the scale of supply chain performance measures (SCPMs), food supply chain resilience (FSCS) and sustainable corporate performance (SCP) in small- and medium-sized enterprises (SMEs) in an emerging market. Based on this purpose, the study examines the relationships between SCPMs and SCP by exploring the mediating role of FSCS in emerging markets.

Based on a comprehensive literature review on the SCPMs, FSCS and SCP, the author evaluates the nexus of these constructs on disruptions during the COVID-19 pandemic emergency in an emerging market. The article follows a quantitative approach. A total of 567 valid responses from managers at senior and middle levels were received and used for data analysis. The Smart PLS version 3.3.2 was employed to analyse Structural Equation Modelling (SEM) to investigate the relationships between constructs and latent variables.

This study provides some theoretical contributions to expand the extant literature on the domain of SCPMs. First, the findings determine that multidimensional measures of flexibility, diversity, agility, inventory efficiency, redundancy and robustness are appropriate for measuring food SC performance in disruptions during the COVID-19 emergency. Besides, this study enriches the existing literature on SC disruption by providing extensive empirical evidence on SCPMs in disruptions during the COVID-19 emergency. Finally, this research provides an integrated empirical model that explores the link between the identified food SCPMs to FSCS and SCP.

The contributions may be of interest to business practitioners, business leaders and academics. In addition, this study provides empirical evidence to demonstrate that food SC performance, as measured by these measures, is strongly related to the firm's food supply chain resilience. This is the novel contribution of this study to the current literature on food SC management. Furthermore, this study provides further empirical evidence demonstrating the partial mediating role of the firm's food supply chain resilience in the nexus between food SC performance and SCP. The unique contribution of this study is an extension of the body of knowledge of SC management literature from a comprehensive approach by providing a proven set of performance measures of SC management to which it can drive SC resilience and SCP for food manufacturing SMEs in an emerging economy that hardly found in the current literature.

This paper aims to assess the impact of digital financial innovation on financial system development in Common Market for eastern and Southern Africa (COMESA). This paper evaluates the dynamic relationship between digital financial innovation measures and financial system development using time series data from COMESA countries for the period 1997–2019.

A dynamic autoregressive distributed lag model (ARDL) was adopted and the mean group (MG), pooled mean group (PMG) and dynamic fixed effect (DFE) of the model were estimated to evaluate the short- and long-run impact. In addition, the dynamic generalized method of moments (DGMM) was adopted for a robustness check. The Hausman test results show PMG to be the most consistent and efficient estimator, while the coefficient of lagged dependent variable of different GMM is less than the fixed effect coefficient, and, as such, suggests system GMM is the most suitable estimator. Data for the study were sourced from World Bank Development Indicator (WDI, 2020), World Governance Indicator (WGI, 2020) and World Bank Global Financial Development Database (GFD, 2020).

The result shows that digital financial innovation significantly impacts financial system development in the long run. As such, the evidence revealed that automated teller machines (ATMs), point of sale (POS), mobile payments (MP) and mobile banking are significant and contribute positively to financial system development in the long run, while mobile money (MM) and Internet banking (INB) are insignificant but exhibit positive and inverse relationship with financial development respectively. Further investigation revealed that institutional quality and a stable macroeconomic environment including their interactive term are significantly imperative in predicting financial system development in the COMESA region.

Researchers recommend a cohesive and conscious policy that would checkmate the divergence in the short run and suggest a common regional innovative financial strategy that could be pursued to incentivize technology transfer needed to promote financial system development in the long run. More so, plausible product and process innovations may be adapted to complement innovative institutions in the different components of the COMESA financial system.

Digital financial innovation services if well managed increase the inherent benefits in financial system development.

To the best of the authors’ knowledge, this paper presents new background information on digital financial innovation that may stimulate the development of the financial system, particularly in the COMESA region. It also exposes the relevance of digital financial innovation, institutional quality and stable macroeconomic environment as well as their interactive effect on COMESA financial system development.

Omnidirectional mobile robots with a special type of wheel structure can realize flexible motion with all three degrees of freedom in a plane. But the driving method brings large disturbance, which affects motion accuracy and stability. This study aims to improve the motion control accuracy of the omnidirectional mobile platform with MY3 wheels (MY3-OMR), a new fuzzy active disturbance rejection control (FADRC) method with adaptivity is proposed.

Based on the basic mechanical structure and drive characteristics of MY3-OMR, the dynamics model of the system is established. The linear active disturbance rejection control (LADRC) system is designed to reduce the interference of nonlinear factors in this dynamics model. A fuzzy controller is introduced to realize the online adjustment of the parameters of the LADRC, which further improves the anti-disturbance performance of the system.

The control method proposed in this paper is compared and analyzed with other methods by simulation and experiment. Results show that the proposed method has better tracking and robustness, which effectively improves the control accuracy of trajectory tracking of MY3-OMR.

A FADRC method with adaptivity is proposed by combining fuzzy control and LADRC. The motion accuracy and anti-interference ability of the MY3-OMR are improved by this control method, which lays a foundation for the subsequent application of MY3-OMR.

Earnings management (EM) plays a vital role in risk management. This paper aims to investigate the impact of real earning management (REM) on credit risk.

This paper measures the credit risk by the expected default frequency of Kealhofer, McQuown and Vasicek model. This paper uses data from 2011 to 2020 of Pakistani manufacturing listed firms. This paper applies the fixed effect to analyze the results and generalized methods of moments to handle the heterogeneity issue.

This paper finds that the impact of REM on corporate credit risk is positive and significant and that of sales manipulation is negative and significant. This paper also reports similar outcomes of the robustness test using dynamic panel regression.

The findings of this study may help managers to modify the EM strategy to minimize corporate credit risk. Furthermore, the findings of this study are important for investors to enhance their understanding of firms’ accounting information, REM activities and cash flow patterns. It further suggests the manager should consider credit risk as an important factor while practicing REM.

This study aims to examine the direct relationship between board gender diversity (BGD) and financial performance and the moderating role of corporate social responsibility (CSR) in the said relationship.

Using data collected from the Thomson Reuters Eikon ASSET4 database from 42 UK financial institutions listed in the ESG index for the period 2005–2019, this study used multivariate regression analysis on panel data to test the effect of BGD on financial performance and estimate the moderating effect of CSR between them. Moreover, to control the endogeneity problem, the authors conducted an additional analysis by testing the dynamic dimension of the data set through the generalized moment method.

The empirical results show that BGD is positively related to financial performance and that BGD increases firm performance with the moderating effect of CSR. Regarding the endogeneity problem, the existence of continuity between financial institution performances over time is demonstrated.

The current paper sheds light on the importance of BGD in improving firm performance and the moderating role of CSR in strengthening the relationship between BGD and firm performance, thereby contributing to the agency theory, the resource dependency theory and the stakeholder theory. Therefore, regulators and policymakers in the UK can use the outcomes of this study to enforce the representation of female directors on boards to enhance the financial performance of financial institutions. Moreover, the findings could be useful for regulatory bodies to encourage financial institutions to practice CSR activities and disclose them in their annual reports.

To the best of the authors’ knowledge, this is the first study investigating the moderating role of CSR on the relationship between BGD and financial performance in the context of the financial sector. It is also the first study documenting that CSR reinforces the relationship between gender-diverse boards and financial institutions' performance. This study fills a research gap as it expands the existing literature that has generally focused on the impact of BGD on financial performance and has not reached similar results.