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This study aims to explore how a change in the staffing configuration of foreign subsidiaries affects subsidiary performance by focusing on staffing localization.

The relationship between localization and subsidiary performance is analyzed from the perspective of human capital. Hypotheses are tested using a panel data set of foreign direct investment by Japanese multinational enterprises.

The analysis demonstrates that localization has a positive effect on subsidiary performance when subsidiaries can access a pool of competent local managers in the host country. It also shows that when competent local managers are highly available, localization has a positive effect on subsidiary performance under high cultural distance. In comparison, when the availability of competent local managers is limited and cultural distance is high, localization has a negative effect on subsidiary performance.

Using human capital theory, this study theorizes how localization, which is a change in the configuration of human capital toward a reliance on local-specific human capital, enhances subsidiary-specific advantages. It introduces the effects of changes in the configuration of human capital over time, into studies on subsidiary staffing. In addition, from a different viewpoint than previous studies, this study proposes one possible path where human capital leads to organizational performance. Specifically, it shows that a change in the configuration of human capital affects subsidiary-specific advantages, which eventually impacts subsidiary performance.

The purpose of this paper is to propose a localizability-based particle filtering localization algorithm for mobile robots to maintain localization accuracy in the high-occluded and dynamic environments with moving people.

First, the localizability of mobile robots is defined to evaluate the influences of both the dynamic obstacles and prior-map on localization. Second, based on the classical two-sensor track fusion algorithm, the odometer-based proposal distribution function (PDF) is corrected, taking account of the localizability. Then, the corrected PDF is introduced into the classical PF with “roulette” re-sampling. Finally, the robot pose is estimated according to all the particles.

The experimental results show that, first, it is necessary to consider the influence of the prior-map during the localization in the high-occluded and dynamic environments. Second, the proposed algorithm can maintain an accurate and robust robot pose in the high-occluded and dynamic environments. Third, its real timing is acceptable.

When the odometer error and occlusion caused by the dynamic obstacles are both serious, the proposed algorithm also has a probability evolving into the kidnap problem. But fortunately, such serious situations are not common in practice.

To check the ability of real application, we have implemented the proposed algorithm in the campus cafeteria and metro station using an intelligent wheelchair. To better help the elderly and disabled people during their daily lives, the proposed algorithm will be tested in a social welfare home in the future.

The localizability of mobile robots is defined to evaluate the influences of both the dynamic obstacles and prior-map on localization. Based on the localizability, the odometer-based PDF is corrected properly.

The purpose of this paper is to fill a gap in the literature by examining the link between managerial attitudes regarding localization practices and firm behavior. The paper compares manager perceptions and understanding of their firm's web localization practices to the actual localization on their firm's web sites.

Two phases of empirical research were completed: a survey of localization attitudes for 65 Fortune 500 managers and a content analysis of localization practices of 27 German web sites. After reviewing descriptive statistics, a series of hierarchical regressions were performed to determine if responses to the localization survey predicted localization efforts, as coded by web site content.

The paper finds that multinational enterprise managers do consider localization to be important and that a managerial focus on localization, and on some of the important localization processes for web content, can lead to more localization of that content.

The paper reveals the localization norms within the industry. Managers often spend the resources necessary to localize with total standardization being rare. They are reluctant to outsource or let local partners have autonomy and want to incorporate some standardized content. Finally, managerial concern with the challenges associated with translation, internationalization, and quality assurance was linked to increased web content localization.

There is a body of literature stressing the importance of localization, but there is little to no exploration of the link between managerial attitudes toward localization and their firm's localization activity. The paper begins to address this gap.

This paper aims to extend the traditional conceptualization of localization, which has largely focused on recruitment, by examining “employability” and “retention” as crucial determinants of localization success.

Using survey data from local (Emirati) employees in private sector organizations in the United Arab Emirates (UAE), the authors develop a holistic model of localization and test the relationships between stereotypes, organizational socialization, employability and retention outcomes, using Smart-PLS.

Among the main findings are the significant influence of stereotypes on organizational socialization (OS) experiences of Emirati employees, with a negative relationship between “work ethics stereotypes” and perceptions of OS experiences. Strong evidence is also found for the indirect effects of OS experiences on retention of Emirati employees, mediated by employability.

This study contributes to the literature on localization by examining the relationships between key variables not examined together in previous research. In terms of limitations, the cross-sectional nature of the study prevents the identification of direction of causality among these relationships. Since employee integration, as part of localization efforts, is a gradual process, future research should include longitudinal studies.

Employability has emerged as a significant challenge for organizations and governments working to implement successful localization initiatives. This study’s findings highlight several opportunities for organizational and governmental policy interventions to support successful localization.

The authors’ holistic model extends localization literature by providing evidence for the role of stereotypes and employability as key constructs to be examined along with OS experiences and retention.

To analyse the scope of localization strategies and corresponding benefits of these strategies to subsidiaries of international companies in the automobile industry in Thailand.

The authors have adopted the case research method to investigate localization strategies of subsidiaries of seven companies (Toyota, Hino, Honda, Isuzu, DaimlerChrysler, BMW, and Auto Alliance) as well as 14 of their dealers and suppliers in Thailand. The information was gathered by conducting in‐depth multiple interviews with 120 local and expatriate employees at various levels in the organizations; by referring to annual reports, policy documents and internal reports of these companies; and by observation during plant visits.

Contrary to the belief that international companies implement localization strategies to simply match the local market environment, it was found that these companies implement a wide range of localization strategies to achieve multiple benefits. The paper identifies nine areas of localization: localization of strategic decision making; building and exploiting the local knowledge pool; deployment of local human resources; localization of R&D; localization of products; use of local supplier networks; adaptations to manufacturing processes; local deployment of subsidiary profits; and localization of corporate image. These localization strategies are not just based on the principle of “cost‐based localization” but are based on “value‐based localization.” These strategies work in tandem and create value through a system of multiple benefits, such as managements' ability to comprehend and deal with uncertainty in the operating environment; make informed decisions to respond to challenges in developing efficient local assembly and marketing systems; cost reduction; higher degree of commitments by local employees; product customization and acceptance; and greater brand equity and image as a good corporate citizen.

Based on concrete illustrations of seven companies, this study identifies nine distinct areas for planning and implementing localization strategies and their corresponding benefits. The managers of subsidiaries can benefit by focusing their localization efforts in these areas to gain maximum advantage from host country context and then translate these advantages into a competitive international strategy.

CEOs of subsidiaries in emerging markets can learn how to build and harness local advantages for global competitiveness by implementing a wide range of localization strategies.

This paper aims to explore the key risk factors affecting the Personnel Localization Management of international construction projects under the major public emergencies represented by the novel coronavirus pneumonia pandemic (hereinafter COVID-19) and how the public emergency affected the Personnel Localization Management from three levels: staff turnover rate, the number of different personnel, the salary and performance of workers. The paper also helps to enhance the construction enterprises' response capacity of major public emergencies and provides a comprehensive framework of optimization strategies for the Personnel Localization Management of international construction projects (hereinafter projects).

The main research method of this paper is the case study, and ten representative international construction projects are selected for case study in China construction enterprises (hereinafter CCE). And this study used the failure mode and effects analysis (FMEA) and comparative analysis to find out all potential risk factors under the COVID-19 and analyze how the epidemic affects the Personnel Localization Management of projects which based on the primary data from 10 projects obtained through in-depth interviews and the secondary data from China First Metallurgical Group and Central South Construction Group's Overseas Enterprise.

The findings show that the outbreak of the major public emergencies not only greatly increased eight risk factors but also directly led to an increase in staff turnover rate. Meanwhile, the numbers of Chinese and local managers and workers are all affected, and an increase in the number and the salary performance of local workers can be reduced, to a certain extent, to the cost-to-output ratio of the projects. The findings would help construction enterprises better cope with Personnel Localization Management and enhance the response capacity of major public emergencies.

This study will broaden researchers' horizons regarding “Personnel Localization Management under major public emergencies” and “risk factors of Personnel Localization Management in an international context.” Furthermore, construction enterprises looking for a better mechanism of Personnel Localization Management can benefit from research findings and lessons learned from the authors' case study during or before an outbreak of major public emergency. Lastly, the framework of optimization strategies for Personnel Localization Management can be used both for research purposes and practice issues in international construction projects.

The findings from the authors' case study offer the direction for international construction enterprises in China and other countries to formulate effective measures, strengthen overseas business and establish a crisis management mechanism for Personnel Localization Management under major public emergencies, and the findings provide emergency plans for projects to improve the public crisis handling capacity and respond to major public emergencies such as the COVID-19.

This study analyzes the impact of the COVID-19 on the Personnel Localization Management of international construction projects from the perspective of personnel. This study provides a theoretical reference for the international construction industry to actively respond to major public emergencies. Besides, the research is conducive to improving the emergency response mechanism in the construction industry, and further promoting the high-quality and globalized development of international construction.

This study provides other researchers with a comprehensive understanding of the risk factors affecting the Personnel Localization Management of projects under the COVID-19 and insight for further research on localization management, risk management, and project management.

Self-localization of an underwater robot using global positioning sensor and other radio positioning systems is not possible, as an alternative onboard sensor-based self-location estimation provides another possible solution. However, the dynamic and unstructured nature of the sea environment and highly noise effected sensory information makes the underwater robot self-localization a challenging research topic. The state-of-art multi-sensor fusion algorithms are deficient in dealing of multi-sensor data, e.g. Kalman filter cannot deal with non-Gaussian noise, while parametric filter such as Monte Carlo localization has high computational cost. An optimal fusion policy with low computational cost is an important research question for underwater robot localization.

In this paper, the authors proposed a novel predictive coding-biased competition/divisive input modulation (PC/BC-DIM) neural network-based multi-sensor fusion approach, which has the capability to fuse and approximate noisy sensory information in an optimal way.

Results of low mean localization error (i.e. 1.2704 m) and computation cost (i.e. 2.2 ms) show that the proposed method performs better than existing previous techniques in such dynamic and unstructured environments.

To the best of the authors’ knowledge, this work provides a novel multisensory fusion approach to overcome the existing problems of non-Gaussian noise removal, higher self-localization estimation accuracy and reduced computational cost.

Localization of the nodes is crucial for gaining access of different nodes which would provision in extreme areas where networks are unreachable. The feature of localization of nodes has become a significant study where multiple features on distance model are implicated on predictive and heuristic model for each set of localization parameters that govern the design on energy minimization with proposed adaptive threshold gradient feature (ATGF) model. A received signal strength indicator (RSSI) model with node estimated features is implicated with localization problem and enhanced with hybrid cumulative approach (HCA) algorithm for node optimizations with distance predicting.

Using a theoretical or empirical signal propagation model, the RSSI (known transmitting power) is converted to distance, the received power (measured at the receiving node) is converted to distance and the distance is converted to RSSI (known receiving power). As a result, the approximate distance between the transceiver node and the receiver may be determined by measuring the intensity of the received signal. After acquiring information on the distance between the anchor node and the unknown node, the location of the unknown node may be determined using either the trilateral technique or the maximum probability estimate approach, depending on the circumstances using federated learning.

Improvisation of localization for wireless sensor network has become one of the prime design features for estimating the different conditional changes externally and internally. One such feature of improvement is observed in this paper, via HCA where each feature of localization is depicted with machine learning algorithms imparting the energy reduction problem for each newer localized nodes in Section 5. All affected parametric features on energy levels and localization problem for newer and extinct nodes are implicated with hybrid cumulative approach as in Section 4. The proposed algorithm (HCA with AGTF) has implicated with significant change in energy levels of nodes which are generated newly and which are non-active for a stipulated time which are mentioned and tabulated in figures and tables in Section 6.

Localization of the nodes is crucial for gaining access of different nodes which would provision in extreme areas where networks are unreachable. The feature of localization of nodes has become a significant study where multiple features on distance model are implicated on predictive and heuristic model for each set of localization parameters that govern the design on energy minimization with proposed ATGF model. An RSSI model with node estimated features is implicated with localization problem and enhanced with HCA algorithm for node optimizations with distance predicting.

This paper aims to demonstrate a cloud-based version of the improved Monte Carlo localization algorithm with robust orientation estimation (IMCLROE). The purpose of this system is to increase the accuracy and efficiency of indoor robot localization.

The cloud-based IMCLROE is constructed with a cloud–client architecture that distributes computation between servers and a client robot. The system operates in two phases: in the offline phase, two maps are built under the MapReduce framework. This framework allows parallel and even distribution of map information to a cloud database in pre-described formats. In the online phase, an Apache HBase is adopted to calculate a pose in-memory and promptly send the result to the client robot. To demonstrate the efficiency of the cloud-based IMCLROE, a two-step experiment is conducted: first, a mobile robot implemented with a non-cloud IMCLROE and a UDOO single-board computer is tested for its efficiency on pose-estimation accuracy. Then, a cloud-based IMCLROE is implemented on a cloud–client architecture to demonstrate its efficiency on both pose-estimation accuracy and computation ability.

For indoor localization, the cloud-based IMCLROE is much more effective in acquiring pose-estimation accuracy and relieving computation burden than the non-cloud system.

The cloud-based IMCLROE achieves efficiency of indoor localization by using three innovative strategies: firstly, with the help of orientation estimation and weight calculation (OEWC), the system can sort out the best orientation. Secondly, the system reduces computation burden with map pre-caching. Thirdly, the cloud–client architecture distributes computation between the servers and client robot. Finally, the similar energy region (SER) technique provides a high-possibility region to the system, allowing the client robot to locate itself in a short time.

Precise vehicle localization is a basic and critical technique for various intelligent transportation system (ITS) applications. It also needs to adapt to the complex road environments in real-time. The global positioning system and the strap-down inertial navigation system are two common techniques in the field of vehicle localization. However, the localization accuracy, reliability and real-time performance of these two techniques can not satisfy the requirement of some critical ITS applications such as collision avoiding, vision enhancement and automatic parking. Aiming at the problems above, this paper aims to propose a precise vehicle ego-localization method based on image matching.

This study included three steps, Step 1, extraction of feature points. After getting the image, the local features in the pavement images were extracted using an improved speeded up robust features algorithm. Step 2, eliminate mismatch points. Using a random sample consensus algorithm to eliminate mismatched points of road image and make match point pairs more robust. Step 3, matching of feature points and trajectory generation.

Through the matching and validation of the extracted local feature points, the relative translation and rotation offsets between two consecutive pavement images were calculated, eventually, the trajectory of the vehicle was generated.

The experimental results show that the studied algorithm has an accuracy at decimeter-level and it fully meets the demand of the lane-level positioning in some critical ITS applications.