Search results

1 – 3 of 3
To view the access options for this content please click here
Article
Publication date: 20 February 2007

Zhang Yunsheng, Zeng Deming, Zhang Lifei and Lucy Yang Lu

With the clarification of three effective methods (share holding, decision participation and promotion of technical grade) to govern R&D teams in software enterprises, the…

Abstract

Purpose

With the clarification of three effective methods (share holding, decision participation and promotion of technical grade) to govern R&D teams in software enterprises, the purpose of this paper is to provide an empirical investigation of the application of these methods in Chinese software enterprises.

Design/methodology/approach

The research is based on a quantitative approach with 34 items designed in the questionnaire to measure the indicators. All items were rated by respondents on a five‐point Likert‐type scale. In order to testify the validity of the three methods of R&D team governance, a correlation analysis and a linear regression were made to examine the relationship between these methods of R&D team governance and R&D performance.

Findings

The evidence shows that decision participation and promotion of technical grades are the two most effective methods to govern R&D teams in Chinese software enterprises, while share holding is not conducive to R&D performance. The share holding level of R&D staffs is fairly low; even “no share.” What is more important is that many software enterprises regard it as a welfare institution but not an incentive method. Therefore, the shareholding mechanism cannot enhance R&D performance. In addition, relevant regulations have not been established completely. There are many deficiencies in the process of intellectual property management of Chinese enterprises. These factors have hindered the effective performance of R&D staff.

Practical implications

The research findings emphasize the importance of governance of R&D teams in the Chinese software industry and highlight the critical issues that need to be addressed in order to enhance the performance of R&D staff.

Originality/value

The concept of R&D team governance is examined and elaborated within the context of China, which points to the need of developing new direction of R&D team management.

Details

Journal of Technology Management in China, vol. 2 no. 1
Type: Research Article
ISSN: 1746-8779

Keywords

To view the access options for this content please click here
Article
Publication date: 24 January 2020

Meishan Jiang, Krishna P. Paudel, Donghui Peng and Yunsheng Mi

The purpose of this paper is to study land title’s credit effect from a financial inclusion perspective in China. The focus is both small land holding and poor farmers…

Abstract

Purpose

The purpose of this paper is to study land title’s credit effect from a financial inclusion perspective in China. The focus is both small land holding and poor farmers. Formal and informal finances are considered to test their differences in land title’s credit effect.

Design/methodology/approach

The authors use augmented inverse-probability weights of the doubly robust method to test the effect of land titling on the rural credit market by addressing self-selection, endogeneity and heterogeneity concerns.

Findings

Results show that the poor, non-poor and small land holders with land titles are willing to borrow more from formal financial institutions. Land titling increases loan accessibility for non-poor and small land holding farmers. As for informal financing, large land holding and non-poor farmers show a decrease in informal lending. Land titling has a financial inclusion effect for some farmers, but poor farmers’ credit restrictions are not entirely solved by land titling.

Originality/value

This is the first study that focuses on the financial inclusion effect of farm land titling in China.

Details

China Agricultural Economic Review, vol. 12 no. 2
Type: Research Article
ISSN: 1756-137X

Keywords

To view the access options for this content please click here
Article
Publication date: 3 July 2020

Azra Nazir, Roohie Naaz Mir and Shaima Qureshi

The trend of “Deep Learning for Internet of Things (IoT)” has gained fresh momentum with enormous upcoming applications employing these models as their processing engine…

Abstract

Purpose

The trend of “Deep Learning for Internet of Things (IoT)” has gained fresh momentum with enormous upcoming applications employing these models as their processing engine and Cloud as their resource giant. But this picture leads to underutilization of ever-increasing device pool of IoT that has already passed 15 billion mark in 2015. Thus, it is high time to explore a different approach to tackle this issue, keeping in view the characteristics and needs of the two fields. Processing at the Edge can boost applications with real-time deadlines while complementing security.

Design/methodology/approach

This review paper contributes towards three cardinal directions of research in the field of DL for IoT. The first section covers the categories of IoT devices and how Fog can aid in overcoming the underutilization of millions of devices, forming the realm of the things for IoT. The second direction handles the issue of immense computational requirements of DL models by uncovering specific compression techniques. An appropriate combination of these techniques, including regularization, quantization, and pruning, can aid in building an effective compression pipeline for establishing DL models for IoT use-cases. The third direction incorporates both these views and introduces a novel approach of parallelization for setting up a distributed systems view of DL for IoT.

Findings

DL models are growing deeper with every passing year. Well-coordinated distributed execution of such models using Fog displays a promising future for the IoT application realm. It is realized that a vertically partitioned compressed deep model can handle the trade-off between size, accuracy, communication overhead, bandwidth utilization, and latency but at the expense of an additionally considerable memory footprint. To reduce the memory budget, we propose to exploit Hashed Nets as potentially favorable candidates for distributed frameworks. However, the critical point between accuracy and size for such models needs further investigation.

Originality/value

To the best of our knowledge, no study has explored the inherent parallelism in deep neural network architectures for their efficient distribution over the Edge-Fog continuum. Besides covering techniques and frameworks that have tried to bring inference to the Edge, the review uncovers significant issues and possible future directions for endorsing deep models as processing engines for real-time IoT. The study is directed to both researchers and industrialists to take on various applications to the Edge for better user experience.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 13 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

1 – 3 of 3