The mediating role of innovation in the relationship between high-performance human resource management practices and firm performance.

The study aims to evaluate the impacts of high-performance human resource management practices on firm performance through the mediating variable of innovation. The research model was developed and integrated from high-performance human resource management theory, innovation, and firm performance. The analysis results from 312 enterprises in industrial and service sectors indicated that high-performance human resource management practices had an indirect impact on firm performance and a direct effect on innovation. Furthermore, innovation plays a mediating role in the relationship between high-performance human resource management practices and firm performance. In addition, organizational size is also a characteristic variable that impacts firm performance. Finally, the study provides some theoretical and practical implications for managers to improve enterprises' human resource management systems.

A Variable-Length Chromosome Genetic Algorithm for Time-Based Sensor Network Schedule Optimization.

Scheduling sensor nodes has an important role in real monitoring applications using sensor networks, lowering the power consumption and maximizing the network lifetime, while maintaining the satisfaction to application requirements. Nevertheless, this problem is usually very complex and not easily resolved by analytical methods. In a different manner, genetic algorithms (GAs) are heuristic search strategies that help to find the exact or approximate global optimal solution efficiently with a stochastic approach. Genetic algorithms are advantageous for their robustness to discrete and noisy objective functions, as they are only evaluated at independent points without requirements of continuity or differentiability. However, as explained in this paper, a time-based sensor network schedule cannot be represented by a chromosome with fixed length that is required in traditional genetic algorithms. Therefore, an extended genetic algorithm is introduced with variable-length chromosome (VLC) along with mutation and crossover operations in order to address this problem. Simulation results show that, with help of carefully defined fitness functions, the proposed scheme is able to evolve the individuals in the population effectively and consistently from generation to generation towards optimal ones, and the obtained network schedules are better optimized in comparison with the result of algorithms employing a fixed-length chromosome.

Impact of academic majors on entrepreneurial intentions of Vietnamese students: An extension of the theory of planned behavior.

This article investigates the effect of academic majors on entrepreneurial intentions of engineering and business students. The research model was established based on the extension of the theory of planned behavior (TPB) through combining the TPB model, perceived risks, academic majors and personalities of students. A sample of 1844 students from the four largest universities in engineering and business in Vietnam were surveyed. The main findings indicated that (i) the relationship in the TPB model was accepted except the effect of subjective norms on entrepreneurial intentions; (ii) perceived risks have negative impacts on perceived behavioral control; (iii) male engineering students have a higher entrepreneurial intentions than female students, but this result was not found in business students; (iv) engineering students have a higher entrepreneurial intentions than business students; (vi) there are no differences between the entrepreneurial intention of students coming from rural and urban areas. The study also contributes to some policy discussion to extend the current debate about the role of academic majors that students take in university in the entrepreneurial process as well as the importance of entrepreneurial students to society.

Collaborative Smartphone-Based User Positioning in a Multiple-User Context Using Wireless Technologies.

For the localization of multiple users, Bluetooth data from the smartphone is able to complement Wi-Fi-based methods with additional information, by providing an approximation of the relative distances between users. In practice, both positions provided by Wi-Fi data and relative distance provided by Bluetooth data are subject to a certain degree of noise due to the uncertainty of radio propagation in complex indoor environments. In this study, we propose and evaluate two approaches, namely Non-temporal and Temporal ones, of collaborative positioning to combine these two cohabiting technologies to improve the tracking performance. In the Non-temporal approach, our model establishes an error observation function in a specific interval of the Bluetooth and Wi-Fi output. It is then able to reduce the positioning error by looking for ways to minimize the error function. The Temporal approach employs an extended error model that takes into account the time component between users' movements. For performance evaluation, several multi-user scenarios in an indoor environment are set up. Results show that for certain scenarios, the proposed approaches attain over 40% of improvement in terms of average accuracy.

The Smartphone-Based Offline Indoor Location Competition at IPIN 2016: Analysis and Future Work.

This paper presents the analysis and discussion of the off-site localization competition track, which took place during the Seventh International Conference on Indoor Positioning and Indoor Navigation (IPIN 2016). Five international teams proposed different strategies for smartphone-based indoor positioning using the same reference data. The competitors were provided with several smartphone-collected signal datasets, some of which were used for training (known trajectories), and others for evaluating (unknown trajectories). The competition permits a coherent evaluation method of the competitors' estimations, where inside information to fine-tune their systems is not offered, and thus provides, in our opinion, a good starting point to introduce a fair comparison between the smartphone-based systems found in the literature. The methodology, experience, feedback from competitors and future working lines are described.

User localization in complex environments by multimodal combination of GPS, WiFi, RFID, and pedometer technologies.

Many user localization technologies and methods have been proposed for either indoor or outdoor environments. However, each technology has its own drawbacks. Recently, many researches and designs have been proposed to build a combination of multiple localization technologies system which can provide higher precision results and solve the limitation in each localization technology alone. In this paper, a conceptual design of a general localization platform using combination of multiple localization technologies is introduced. The combination is realized by dividing spaces into grid points. To demonstrate this platform, a system with GPS, RFID, WiFi, and pedometer technologies is established. Experiment results show that the accuracy and availability are improved in comparison with each technology individually.