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Through an empirical inquiry into manufacturing joint ventures companies in Vietnam setting, this paper aims to examine the relationships among knowledge sharing and its antecedents such as organisational culture, ethics, and human resources localization.

An analysis of data returned from a questionnaire survey among middle level managers in these manufacturing joint ventures companies was conducted via analysis of variance and structural equation modelling.

The study findings display the correspondence between control culture and ethics of justice. Flexibility culture, on the other hand, tends to nurture ethics of care, which in turn positively impact localization of intellectual capital. The influence of intellectual capital localization on knowledge sharing is also discerned.

The study offers insight into the linkage pattern of knowledge sharing and its antecedents, organisational culture, ethics, and human resources localization, in manufacturing joint venture companies in a Vietnam business context.

The purpose of this study was to explore the relationship between localization (Omanization) practices and financial performance in Oman.

Firms listed in the Muscat Securities Market were surveyed. Data were obtained from 73 firms. Financial performance data (average ratio of market value to book value) were obtained from published records.

Results indicated that localization practices were related to financial performance after controlling for size, type of firm, average price earnings ratio of the industry and Omanization levels.

The measure of localization did not specify the level at which Omanization practices are focused on. This is a limitation of this study, and future research must measure localization practices for different levels in the organization.

From a practical perspective, the results of this study suggest that organizations in the Arabian Gulf can enhance their performance by implementing systematic localization human resource management practices. The authors believe that this study makes a significant preliminary contribution to the understanding of localization practices and financial performance in the Arabian Gulf region.

These results are encouraging for managers who argue for integrating locals into the workforce rather than engaging in localization practices for public relations purposes. Sincere localization efforts develop local human capital.

Study was conducted in the Sultanate of Oman, an Arabian Gulf country. To the authors’ knowledge, this is the first study of localization practices and financial performance in the Arabian Gulf. This study therefore contributes to and extends the growing literature on localization practices in the Arabian Gulf in general and Oman in particular.

The purpose of this paper is to empirically analyze what effects the headquarters’ (HQ) business strategy and corporate culture, the local network embeddedness of the foreign subsidiary, and HQ-subsidiary communication have on the staff localization of foreign subsidiaries. The authors carry out empirical analysis on how localization of foreign subsidiaries ultimately affects the performance of foreign subsidiaries.

This study is an empirical analysis on the determinants of staff localization and the relationship between staff localization and corporate performance. In this study, the five hypotheses were proposed and tested using survey data. The authors randomly selected a total of 800 companies as subjects and conducted a survey. The final 222 survey data including HQs and subsidiaries were used for empirical analysis. The statistical analyses such as reliability test, factor analysis and regression were used.

This study shows that there was a higher level of staff localization by the foreign subsidiary when the investment goal was market-oriented investment, the Korean foreign subsidiary had stronger local network embeddedness and there was better HQ-subsidiary communication. In addition, the relationship between localization and subsidiary performance shows an inverted U-shape. Such results will give various implications to companies.

The research that takes a multilayered consideration on factors of the HQ, subsidiaries, and the HQ-subsidiary relationship is rare. To overcome such limitations, this study carried out a survey in order to find more in-depth decision factors. Specifically, this study analyzed the effects of three large aspects of investment goals and corporate culture from the aspect of the HQ, local network embeddedness from the aspect of foreign subsidiaries, and the level of HQ-subsidiary communication from the aspect of HQ-subsidiary relations, and how they affect staff localization.

The purpose of this paper is to describe a general purpose localization system, GRAIL. GRAIL provides real‐time, adaptable, indoor localization for wireless devices.

In order to localize as diverse a set of devices as possible, GRAIL utilizes a centralized, anchor‐based approach. GRAIL defines an abstract data model for various system components to support different physical modalities. The scalable architecture of GRAIL provides maximum flexibility to integrate various localization algorithms.

The authors show through real deployments that GRAIL functions over a variety of physical modalities, networks, and algorithms. Further, the authors found that a centralized solution has critical advantages over distributed implementations for handling privacy concerns.

A key contribution of this system is its universal approach: it can integrate different hardware and software capabilities within a single localization framework. The deployment of such a system in academic and research environments allows researchers to explore issues beyond algorithms and investigate effects in real deployments.

The purpose of this paper is to analyze the drivers that influence subsidiaries of international companies in the automobile industry in Thailand and how automobile companies pursue localization in response to these drivers.

Using case research method, examined seven leading automobile manufacturers – Toyota Motor (Thailand); Hino Motors (Thailand); Honda Automobile (Thailand); Isuzu Motors (Thailand); BMW (Thailand); DaimlerChrysler (Thailand); and Auto Alliance (Thailand) – as well as 14 of their dealers and suppliers in Thailand. In total 120 Thai and expatriate managers were interviewed.

Extending the knowledge body from existing research, this study found that there are four drivers for automobile manufacturers to adopt localization strategies. Those are host country characteristics, industry characteristics, company characteristics, and market characteristics. The results show that automobile manufacturers react to drivers by localization of their decision making, building and exploiting knowledge pool of local suppliers and distributors, increasing numbers of Thais at the management level while decreasing the number of expatriates, increasing R&D activities locally, localizing products, increasing usage of local suppliers, adapting manufacturer processes, reinvesting at subsidiary, and localizing corporate image. However, these vary in degree from company to company. Localization strategies produce benefits that go beyond allowing automobile manufactures to compete within the local situation, also enabling them to overcome challenges and use their successes to transform the parent company and other subsidiaries and eventually contribute to the parent company's globalization strategy.

Managers in subsidiaries may first implement localization strategies to cope with driver factors to mitigate risks and uncertainty. By adopting localization, managers should not focus only on short term benefits to gain local advantages in host countries, but these advantages at the subsidiary should be transmitted to the parent company and other subsidiaries to build a competitive international strategy.

CEOs of subsidiaries in emerging markets can learn what drivers influence localization strategies and how to cope and create local advantages for global competitiveness by implementing wide range of localization strategies.

Localization is a fundamental problem in wireless sensor networks. In many applications, sensor location information is critical for data processing and meaning. While the global positioning system (GPS) can be used to determine mote locations with meter precision, the high hardware cost and energy requirements of GPS receivers often prohibit the ubiquitous use of GPS for location estimates. This high cost (in terms of hardware price and energy consumption) of GPS has motivated researchers to develop localization protocols that determine mote locations based on cheap hardware and localization algorithms. The purpose of this paper is to present a comprehensive review of wireless sensor network localization techniques, and provide a detailed overview for several distance‐based localization algorithms.

To provide a detailed summary of wireless sensor network localization algorithms, the authors outline a tiered classification system in which they first classify algorithms as distributed, distributed‐centralized, or centralized. From this broad classification, the paper then further categorizes localization algorithms using their protocol techniques. By utilizing this classification system, the authors are able to provide a survey of several wireless sensor network localization algorithms and summarize relative algorithm performance based on the algorithms' classification.

There are numerous localization algorithms available and the performance of these algorithms is dependent on network configuration, environmental variables, and the ranging method implemented. When selecting a localization algorithm, it is important to understand basic algorithm operation and expected performance. This tier‐based algorithm classification system can be used to gain a high‐level understanding of algorithm performance and energy consumption based on known algorithm characteristics.

Localization is a widely researched field and given the quantity of localization algorithms that currently exist, it is impossible to present a complete review of every published algorithm. Instead, the paper presents a holistic view of the current state of localization research and a detailed review of ten representative distance‐based algorithms that have diverse characteristics and methods. This review presents a new classification structure that may help researchers understand, at a high‐level, the expected performance and energy consumption of algorithms not explicitly addressed by our work.

Over the past two decades, building information modeling (BIM) has been promoted as one of the most disruptive innovations across the global architecture, engineering and construction (AEC) community. Nevertheless, despite its widely propagated benefits, BIM adoption in various localities is not progressing excitingly. BIM as an innovation developed from a presumed, general context may not fit well with the specific regulatory, social and cultural settings of a locality. This study aims to tackle the lukewarm local BIM adoption by developing a deployment framework for BIM localization.

Drawing upon the diffusion of innovation (DoI) theory, a longitudinal case study is designed and conducted by engaging closely with a top cost consultancy company in Hong Kong for forty-one months.

The findings refuted the “one-size-fits-for-all” view to use a standardized BIM for international users. Rather, an organization needs to undergo a series of localization works to integrate global BIM in its specific local context. The deployment framework outlines the BIM dimensions (i.e. technology, process and protocol) and the mechanisms (i.e. configuration, coupling and reinterpretation) of BIM localization that go through the three “A”s (i.e. analysis, adaption and assimilation) process under a firm's network and contextual factors.

This study improves our understanding of sluggish BIM adoption by attributing it to the gap between general BIM development and the local, unique BIM use context. Proposing the deployment framework, the study also offers a handy tool for prospective executives to localize BIM and harness its power in their respective organizations and localities.

Robot localization in dynamic, cluttered environments is a challenging problem because it is impractical to have enough knowledge to be able to accurately model the robot’s environment in such a manner. This study aims to develop a novel probabilistic method equipped with function approximation techniques which is able to appropriately model the data distribution in Markov localization by using the maximum statistical power, thereby making a sensibly accurate estimation of robot’s pose in extremely dynamic, cluttered indoors environments.

The parameter vector of the statistical model is in the form of positions of easily detectable artificial landmarks in omnidirectional images. First, using probabilistic principal component analysis, the most likely set of parameters of the environmental model are extracted from the sensor data set consisting of missing values. Next, we use these parameters to approximate a probability density function, using support vector regression that is able to calculate the robot’s pose vector in each state of the Markov localization. At the end, using this density function, a good approximation of conditional density associated with the observation model is made which leads to a sensibly accurate estimation of robot’s pose in extremely dynamic, cluttered indoors environment.

The authors validate their method in an indoor office environment with 34 unique artificial landmarks. Further, they show that the accuracy remains high, even when they significantly increase the dynamics of the environment. They also show that compared to those appearance-based localization methods that rely on image pixels, the proposed localization strategy is superior in terms of accuracy and speed of convergence to a global minima.

By using easily detectable, and rotation, scale invariant artificial landmarks and the maximum statistical power which is provided through the concept of missing data, the authors have succeeded in determining precise pose updates without requiring too many computational resources to analyze the omnidirectional images. In addition, the proposed approach significantly reduces the risk of getting stuck in a local minimum by eliminating the possibility of having similar states.

Localization is one of the critical issues in Ocean Internet of Things (OITs). The existing research results of localization in OITs are very limited. It poses many challenges due to the difficulty of deploy beacon accurately, the difficulty of transmission distance estimation in harsh ocean environment and the underwater node mobility. This paper aims to provide a novel localization algorithm to solve these problems.

This paper takes the ship with accurate position as a beacon, analyzes the relationship between underwater energy attenuation and node distance and takes them into OITs localization algorithm design. Then, it studies the movement regulation of underwater nodes in the action of ocean current, and designs an Energy-aware Localization Algorithm (ELA) for OITs.

Proposing an ELA. ELA takes the ship with accurate position information as a beacon to solve the problem of beacon deployment. ELA does not need to calculate the information transmission distance which solves the problem of distance estimation. It takes underwater node movement regulation into computation to solve the problem of node mobility.

This paper provides an ELA based on the relationship between propagation energy attenuation and node distance for OITs. It solves the problem of localization in dynamic underwater networks.

The purpose of this paper is to describe a novel application of the recently introduced concept from computer vision to self‐localization of a walking robot in unstructured environments. The technique described in this paper enables a walking robot with a monocular vision system (single camera) to obtain precise estimates of its pose with regard to the six degrees of freedom. This capability is essential in search and rescue missions in collapsed buildings, polluted industrial plants, etc.

The Parallel Tracking and Mapping (PTAM) algorithm and the Inertial Measurement Unit (IMU) are used to determine the 6‐d.o.f. pose of a walking robot. Bundle‐adjustment‐based tracking and structure reconstruction are applied to obtain precise camera poses from the monocular vision data. The inclination of the robot's platform is determined by using IMU. The self‐localization system is used together with the RRT‐based motion planner, which allows to walk autonomously on rough, previously unknown terrain. The presented system operates on‐line on the real hexapod robot. Efficiency and precision of the proposed solution are demonstrated by experimental data.

The PTAM‐based self‐localization system enables the robot to walk autonomously on rough terrain. The software operates on‐line and can be implemented on the robot's on‐board PC. Results of the experiments show that the position error is small enough to allow robust elevation mapping using the laser scanner. In spite of the unavoidable feet slippages, the walking robot which uses PTAM for self‐localization can precisely estimate its position and successfully recover from motion execution errors.

So far the presented self‐localization system was tested in limited‐scale indoor experiments. Experiments with more realistic outdoor scenarios are scheduled as further work.

Precise self‐localization may be one of the most important factors enabling the use of walking robots in practical USAR missions. The results of research on precise self‐localization in 6‐d.o.f. may be also useful for autonomous robots in other application areas: construction, agriculture, military.

The vision‐based self‐localization algorithm used in the presented research is not new, but the contribution lies in its implementation/integration on a walking robot, and experimental evaluation in the demanding problem of precise self‐localization in rough terrain.