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Scholars have underscored the importance of organizational authenticity, but it is unclear how it influences the links among market strategy, and nonmarket strategy (NMS) and firm performance. This study addresses this gap in the literature.

A survey of 294 managers in firms based in the United States investigates configurations among competitive strategy (e.g. cost leadership or differentiation), political and social nonmarket strategy (NMS), authenticity, and firm performance.

Cost leaders tend to engage in political nonmarket strategy (PNMS), but the interaction does not necessarily improve firm performance. Differentiators are more likely to pursue social nonmarket strategy (SNMS) and perform better, but neither market-nonmarket strategy configuration is inherently optimal.

The results support market-nonmarket strategy configurations but do not prescribe optimal combinations. However, the sample is cross-sector and employs self-reports for firm performance.

Political and social authenticity can enhance firm performance, but nonmarket activity can compromise a firm’s ability to be politically and socially authentic. Authenticity can drive performance, but a firm’s nonmarket activity can compromise its ability to be politically and socially authentic. Firms should view a prospective loss in authenticity as a potential cost of nonmarket activity.

This paper investigates how a firm’s emphasis on market (competitive) strategies, political and social nonmarket strategies, and political and social authenticity impact financial and non-financial performance. It also tests the veracity of two market-nonmarket configurations, cost leadership with political NMS and differentiation with social NMS.

This chapter discusses the evolution of online trading, its application in various market structures, and its benefits and potential concerns. Computers were first used in electronic communication networks among brokers and dealers to make trades and for informational purposes. Online brokers became popular with retail investors as the internet spread. Online trading comes with various trading protocols and order types. It enables traders to automate trading decisions and process data more easily using charting tools and customized programs connected to the broker's infrastructure. Electronic trading allows for greater centralization but can also be accompanied by market fragmentation. Market regulation has affected market structure and is still evolving. Centralization allows for more competitive prices and reduces search costs. Decentralized markets could cope better with asymmetric information.

Recent international business research finds that state-owned multinational enterprises (SOMNEs) invest relatively more in politically risky host countries than do privately-owned multinational enterprises (MNEs). This study aims to investigate theoretically and empirically whether state ownership mitigates the impact of host-country political risk on subsidiary economic risk.

The authors link theoretical arguments on state ownership to arguments from non-market strategy literature to outline mechanisms whereby state ownership can buffer subsidiaries from political risk, weakening the link between host-country political risk and earnings volatility in subsidiaries. Using a data set on Norwegian MNEs’ foreign subsidiaries across almost two decades, the authors test this prediction using both matching methods and panel regressions.

While standard panel regressions provide empirical support only for the infrastructure sector and for the highest political risk contexts, nearest-neighbour matching models – comparing only otherwise similar private- and SOMNE subsidiaries using the full sample – reveal more general support for the political risk mitigation hypothesis.

The study presents the first comprehensive analysis of whether state ownership can mitigate the effect of political risk on subsidiary economic risk.

Arthroscopic osteochondroplasty is a minimally invasive procedure that has been used to treat femoroacetabular impingement syndrome, leading to significant improvements in patients’ clinical outcomes and quality of life. However, some studies suggest that inadequate bone resection can substantially alter hip biomechanics. These modifications may generate different contact profiles and higher contact forces, increasing the risk of developing premature joint degeneration. To improve control over bone resection and biomechanical outcomes during arthroscopic osteochondroplasty surgery, this study aims to present a novel system for measuring femoroacetabular contact forces.

Following a structured design process for the development of medical devices, the steps required for its production using additive manufacturing with material extrusion and easily accessible sensors are described. The system comprises two main devices, one for measuring femoroacetabular contact forces and the other for quantifying the force applied by the assistant surgeon during lower limb manipulation. The hip device was designed for use within an arthroscopic environment, eliminating the need for additional portals.

To evaluate its performance, the system was first tested in a laboratory setup and later under in-service conditions. The 3D printing parameters were tuned to ensure the watertighness of the device and sustain the intraoperative fluid pressures. The final prototype allowed for the controlled measurement of the hip contact forces in real-time.

Using additive manufacturing and readily available sensors, the present work presents the first device to quantify joint contact forces during arthroscopic surgeries, serving as an additional tool to support the surgeon’s decision-making process regarding bone resection.

The purpose of this paper is to clarify the influence of low earth orbit space environment on the wear mechanism of TC4 alloy material and crank rocker mechanism.

In this study, friction experiments were carried out on TC4 alloy friction discs and crank rocker mechanisms, both before and after exposure to atomic oxygen and proton irradiation. Nanoindentation, grazing incidence X-ray diffraction (GIXRD), and X-ray photoelectron spectroscopy were employed to systematically characterize alterations in mechanical properties, surface phase, and chemical composition.

The results show that the wear mechanism of TC4 alloy friction disc is mainly adhesive wear in vacuum environment, while the wear mechanism of crank rocker mechanism includes not only adhesive wear but also abrasive wear. Atomic oxygen exposure leads to the formation of more oxides on the surface of TC4 alloy, which form abrasive particles during the friction process. Proton irradiation will lead to a decrease in fatigue performance and an increase in hardness on the surface of TC4 alloy, thus causing fatigue wear on the surface of TC4 alloy, and more furrows appear on the crank rocker mechanism after proton irradiation. In the three environments, the characteristics of abrasive wear of the crank rocker mechanism are more obvious than those of the TC4 alloy friction disc.

These results highlight the importance of understanding the subtle effects of atomic oxygen and proton irradiation on the wear behavior of TC4 alloy and provide some insights for optimizing its performance in space applications.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-02-2024-0051/

Research on the political connections of multinational enterprises’ (MNEs’) subsidiaries in emerging host countries has been growing. The purpose of this paper is to integrate institutional and resource dependence theories to argue that MNEs-subsidiaries are likely to develop fewer formal (i.e. board-level) political connections when operating in welfare-state monarchies as compared to in host countries with developmental-state democratic systems. Furthermore, this paper argues that MNE-subsidiaries develop formal political connections to a greater extent in industries where religion influences the development of products and services considerably. Finally, the extent of developing formal political connections varies by the scale of the MNEs’ investment (or subsidiary density) in the host market.

The paper tests its hypotheses on a sample of foreign-owned subsidiaries operating in Saudi Arabia and Egypt. The data was collected by combining information from Bureau Van Dijk’s Orbis database with company websites and other secondary sources. The final sample consisted of 156 observations – 70 MNEs-subsidiaries operating in Saudi Arabia, and 86 in Egypt.

The findings confirm that foreign subsidiaries are likely to develop fewer formal political connections in a welfare-state monarchy as compared to in a developmental-state democratic system. Furthermore, formal political connections are more significant in industries that are impacted by the influence of religion – such as the financial industry in Arab countries. Finally, the extent of using political connections varies by the scale of the MNEs’ investment in the host market – that is, with a greater scale of investment (or higher subsidiary density), formal political connections are greater.

The paper contributes theoretically by explaining that a combination of institutional heterogeneity and its associated resource dependence conditions between MNEs and host governments influence MNE-subsidiaries' political connections. The paper tests its hypotheses in an emerging Arab context, which is characterized by both autocratic and semi-democratic political settings, and which makes the integration of institutional and resource dependence theories useful in explaining how MNE-subsidiaries navigate local complexities in this region.

Based on the strong feature representation ability of the convolutional neural network (CNN), generous object detection methods in remote sensing (RS) have been proposed one after another. However, due to the large variation in scale and the omission of relevant relationships between objects, there are still great challenges for object detection in RS. Most object detection methods fail to take the difficulties of detecting small and medium-sized objects and global context into account. Moreover, inference time and lightness are also major pain points in the field of RS.

To alleviate the aforementioned problems, this study proposes a novel method for object detection in RS, which is called lightweight object detection with a multi-receptive field and long-range dependency in RS images (MFLD). The multi-receptive field extraction (MRFE) and long-range dependency information extraction (LDIE) modules are put forward.

To concentrate on the variability of objects in RS, MRFE effectively expands the receptive field by a combination of atrous separable convolutions with different dilated rates. Considering the shortcomings of CNN in extracting global information, LDIE is designed to capture the relationships between objects. Extensive experiments over public datasets in RS images demonstrate that our MFLD method surpasses the state-of-the-art methods. Most of all, on the NWPU VHR-10 dataset, our MFLD method achieves 94.6% mean average precision with 4.08 M model volume.

This paper proposed a method called lightweight object detection with multi-receptive field and long-range dependency in RS images.

This study aims to compare traffic sign (TS) and obstacle detection for autonomous vehicles using different methods. The review will be performed based on the various methods, and the analysis will be done based on the metrics and datasets.

In this study, different papers were analyzed about the issues of obstacle detection (OD) and sign detection. This survey reviewed the information from different journals, along with their advantages and disadvantages and challenges. The review lays the groundwork for future researchers to gain a deeper understanding of autonomous vehicles and is obliged to accurately identify various TS.

The review of different approaches based on deep learning (DL), machine learning (ML) and other hybrid models that are utilized in the modern era. Datasets in the review are described clearly, and cited references are detailed in the tabulation. For dataset and model analysis, the information search process utilized datasets, performance measures and achievements based on reviewed papers in this survey.

Various techniques, search procedures, used databases and achievement metrics are surveyed and characterized below for traffic signal detection and obstacle avoidance.

The purpose of this work is to present an approach for autonomous detection of eye disease in fundus images. Furthermore, this work presents an improved variant of the Tiny YOLOv7 model developed specifically for eye disease detection. The model proposed in this work is a highly useful tool for the development of applications for autonomous detection of eye diseases in fundus images that can help and assist ophthalmologists.

The approach adopted to carry out this work is twofold. Firstly, a richly annotated dataset consisting of eye disease classes, namely, cataract, glaucoma, retinal disease and normal eye, was created. Secondly, an improved variant of the Tiny YOLOv7 model was developed and proposed as EYE-YOLO. The proposed EYE-YOLO model has been developed by integrating multi-spatial pyramid pooling in the feature extraction network and Focal-EIOU loss in the detection network of the Tiny YOLOv7 model. Moreover, at run time, the mosaic augmentation strategy has been utilized with the proposed model to achieve benchmark results. Further, evaluations have been carried out for performance metrics, namely, precision, recall, F1 Score, average precision (AP) and mean average precision (mAP).

The proposed EYE-YOLO achieved 28% higher precision, 18% higher recall, 24% higher F1 Score and 30.81% higher mAP than the Tiny YOLOv7 model. Moreover, in terms of AP for each class of the employed dataset, it achieved 9.74% higher AP for cataract, 27.73% higher AP for glaucoma, 72.50% higher AP for retina disease and 13.26% higher AP for normal eye. In comparison to the state-of-the-art Tiny YOLOv5, Tiny YOLOv6 and Tiny YOLOv8 models, the proposed EYE-YOLO achieved 6–23.32% higher mAP.

This work addresses the problem of eye disease recognition as a bounding box regression and detection problem. Whereas, the work in the related research is largely based on eye disease classification. The other highlight of this work is to propose a richly annotated dataset for different eye diseases useful for training deep learning-based object detectors. The major highlight of this work lies in the proposal of an improved variant of the Tiny YOLOv7 model focusing on eye disease detection. The proposed modifications in the Tiny YOLOv7 aided the proposed model in achieving better results as compared to the state-of-the-art Tiny YOLOv8 and YOLOv8 Nano.