Factors impacting knowledge transfer success in information systems outsourcing

Despite increased research interest on knowledge transfer in information systems (IS) outsourcing, the field still lacks sound and holistic understanding of the key factors influencing knowledge transfer success. The present paper attempts to provide a synthesis of existing theoretical perspectives and empirical findings related to the factors that facilitate or hamper knowledge transfer success in IS outsourcing. The data collection method is discussed and the key findings are presented. Conclusion is drawn and further research is suggested

Critical success factors in ERP implementation: A review

ERP systems have become vital strategic tools in today’s competitive business environment. This ongoing research study presents a review of recent research work in ERP systems. It attempts to identify the main benefits of ERP systems, the drawbacks and the critical success factors for implementation discussed in the relevant literature. The findings revealed that despite some organizations have faced challenges undertaking ERP implementations, many others have enjoyed the benefits that the systems have brought to the organizations. ERP system facilitates the smooth flow of common functional information and practices across the entire organization. In addition, it improves the performance of the supply chain and reduces the cycle times. However, without top management support, having appropriate business plan and vision, re-engineering business process, effective project management, user involvement and education and training, organizations can not embrace the full benefits of such complex system and the risk of failure might be at high level

The successful implementation of e-government transformation: A case study in Oman

The aim of this paper is to investigate, and to discuss the key critical factors that facilitate the successful implementation of E-government projects. The nature of this research is mainly qualitative. This investigation uses a single case study and data was mainly collected by means of semi-structured interviews and organisational documents from the Ministry of Higher Education in Oman. The research findings suggest that there are three paradigms which each include a set of factors that impacts the success of E-government success namely, organisational paradigm, technology paradigm and end-users paradigm. The authors believe that, this paper demonstrates an added value to the current literature on transformation of E-government and to E-government projects success, within the wider context of E-government implementation projects. Also the research will benefit organisations in the public sector, as it has identified main key success factors in E-government transformations and implementations

Mobile wallet inhibitors: Developing a comprehensive theory using an integrated model

© 2018 Elsevier Ltd The concept of the mobile wallet is increasingly adopted in developed and developing countries for improving the scale, productivity, and excellence of banking services. Oman is one of the most growing countries of the Middle Eastern economies. Acceptance of mobile wallets in Oman is being hindered by various inhibitors. There is no study in the Middle Eastern countries that addressed the concerns of probable inhibitors influencing mobile wallet acceptance from expert's perspective. In this study, eleven key inhibitors to mobile wallet adoption are identified from the literature and expert's feedback. This study employed Interpretive Structural Modelling (ISM) in conjunction with fuzzy MICMAC to reveal the intricate relationship among inhibitors to mobile wallet acceptance. To the end, an integrated hierarchical model is developed to understand the influence of a particular inhibitor on others. ‘Anxiety towards new technology’ ‘Lack of new technology skills’ ‘Lack of awareness of mobile wallet benefits’ and ‘Complexity of new technology’ have been reported as key inhibitors to promote mobile wallets in Oman. This study also suggests several recommendations for banking organizations and policymakers in developing the effective model to popularize mobile wallets in Oman

Boosting Object Recognition in Point Clouds by Saliency Detection

Object recognition in 3D point clouds is a challenging task, mainly when time is an important factor to deal with, such as in industrial applications. Local descriptors are an amenable choice whenever the 6 DoF pose of recognized objects should also be estimated. However, the pipeline for this kind of descriptors is highly time-consuming. In this work, we propose an update to the traditional pipeline, by adding a preliminary filtering stage referred to as saliency boost. We perform tests on a standard object recognition benchmark by considering four keypoint detectors and four local descriptors, in order to compare time and recognition performance between the traditional pipeline and the boosted one. Results on time show that the boosted pipeline could turn out up to 5 times faster, with the recognition rate improving in most of the cases and exhibiting only a slight decrease in the others. These results suggest that the boosted pipeline can speed-up processing time substantially with limited impacts or even benefits in recognition accuracy.Comment: International Conference on Image Analysis and Processing (ICIAP) 201

Deep regression tracking with shrinkage loss

Regression trackers directly learn a mapping from regularly dense samples of target objects to soft labels, which are usually generated by a Gaussian function, to estimate target positions. Due to the potential for fast-tracking and easy implementation, regression trackers have recently received increasing attention. However, state-of-the-art deep regression trackers do not perform as well as discriminative correlation filters (DCFs) trackers. We identify the main bottleneck of training regression networks as extreme foreground-background data imbalance. To balance training data, we propose a novel shrinkage loss to penalize the importance of easy training data. Additionally, we apply residual connections to fuse multiple convolutional layers as well as their output response maps. Without bells and whistles, the proposed deep regression tracking method performs favorably against state-of-the-art trackers, especially in comparison with DCFs trackers, on five benchmark datasets including OTB-2013, OTB-2015, Temple-128, UAV-123 and VOT-2016.Xiankai Lu, Chao Ma, Bingbing Ni, Xiaokang Yang, Ian Reid and Ming-Hsuan Yan