The More You Give the More You Get Back: Moderating Effect of Leadership on Knowledge Sharing in Online Programming Communities

Although there is a significant growth of emerging online programming communities, little succeeded in encouraging members to contribute and share their knowledge. The role of leadership to address the under contribution problem is gaining attention among researchers. This study grounded on path-goal theory to Investigates specifically the role of supportive leadership and achievement oriented leadership behaviour toward knowledge sharing in online programming community (OPC). This introduced model is tested empirically using data collected from 20 online programming communities. The findings from the analysis suggests that self-efficacy and outcome expectancy influences knowledge sharing behaviour of members in online programming community. The finding implied that although online communities are informal in nature, the appropriate type of leadership can boost the members’ efficacy and outcome expectancy toward sharing their knowledge, with the suitable level of autonomy and recognition of members contributions can motivate members to continuously contribute to online programming communities and promoting the sustainability in this platform

Development of a model for virtual leadership behavior on knowledge sharing in online programming communities

Despite the significant increase in the number of emerging online programming communities, very few succeed in inspiring members to share their knowledge. Recent studies have focused on personal level factors in encouraging members’ knowledge sharing. However, limited studies emphasis on the role of leader. In addressing this gap, this study aims to develop a model to examine the role of virtual leadership towards knowledge sharing in online programming communities. Then in carrying out the objective, the examination of virtual leadership behaviour moderating members’ personal cognitive factors toward knowledge sharing was conducted. Social Cognitive Theory and Path Goal Theory are used as the basis for the proposed model. The proposed model is tested empirically using data collected from 20 online programming communities. The result suggests that different leadership behaviors significantly moderate the effect of self-efficacy and outcome expectancy on members’ knowledge sharing. This finding implies that although online communities are informal in nature, the appropriate type of leadership can boost members’ efficacy and outcome expectancy to participate in knowledge sharing. Ideally, with the appropriate level of autonomy and recognition of members contributions can motivate members to continuously contribute to online programming communities and promote the sustainability of this platform. The implication of this study will provide meaningful insights for system designers to include several features to facilitate leadership behaviors in online programming communities. In supporting participative-leadership behavior, online poll and online voting need to be accommodated to allow inclusive decisions by members. Additionally, ranking and reputation features can further facilitate the achievement-oriented leadership and increase knowledge sharing among online programming community members

The Design of a Multimedia-Forensic Analysis Tool (M-FAT)

Digital forensics has become a fundamental requirement for law enforcement due to the growing volume of cyber and computer-assisted crime. Whilst existing commercial tools have traditionally focused upon string-based analyses (e.g., regular expressions, keywords), less effort has been placed towards the development of multimedia-based analyses. Within the research community, more focus has been attributed to the analysis of multimedia content; they tend to focus upon highly specialised specific scenarios such as tattoo identification, number plate recognition, suspect face recognition and manual annotation of images. Given the everincreasing volume of multimedia content, it is essential that a holistic Multimedia-Forensic Analysis Tool (M-FAT) is developed to extract, index, analyse the recovered images and provide an investigator with an environment with which to ask more abstract and cognitively challenging questions of the data. This paper proposes such a system, focusing upon a combination of object and facial recognition to provide a robust system. This system will enable investigators to perform a variety of forensic analyses that aid in reducing the time, effort and cognitive load being placed on the investigator to identify relevant evidence

The development of a remote restart services system in production line using PowerShell script

The production line is an important part of most manufacturing industries. However, the services in the devices may experience some issues that disrupt the production line, requiring the assistance of an IT technician to resolve the problem. Therefore, this study seeks to bridge the gap through the design and development of a PowerShell Script to allow the user to remote control the services in a device such as computers and servers. Using this program, the production operator can solve the issue immediately rather than wait for an IT technician, thus increasing productivity and performance. The program design and development followed the Rational Unified Process (RUP) methodology. First, the functional requirements were collected through interviews and content analysis. Later, a prototype called Remote Restart Services System (RRS) is developed using PowerShell programming language on the Windows PowerShell ISE platform. An evaluation was later conducted to assess its usability. The findings suggested that RRS is practical and easy-to-use. The respondents were also pleased with its functions. The field study helps developers to refine the framework by providing direct input from the target audience. It can be a guide to identify areas for improvement and eventually help realise the goals

The construction of a valid and reliable instrument to measure virtual learning environment success

The Virtual Learning Environment (VLE) technology has become an essential element to support 21st-century teaching and learning approaches. To ensure that this technology can be employed effectively by the teachers, the assessment of VLE success is necessary. However, few measures of VLE success amongst teachers exist. Therefore, this article describes the process of the development and validation of a questionnaire to measure the VLE success from the teacher’s point of view. The construction of this questionnaire has been done through a series of procedures, starting by generating the items, conducting face validation and content validation, translating, as well as piloting the questionnaire to a number of selected respondents. As a result, 45 robust items to evaluate VLE success among teachers have been yielded

Forecasting Malaysian stock price using artificial neural networks (ANN)

Predicting a stock price is a very difficult task because it is complex and involves many factors. This has led to drop in the investment level in the Malaysian stock market. It is difficult to predict the stock market because its environments are unstable and dynamic. Recently, the demand for neural network in the business arena is on the increase. It is need to analyze vast data in order to search for information and knowledge that do not exist by using traditional methods. This included stock market prediction that is a very significant research in business area. In regard to Bursa Malaysia, Artificial Neural Network (ANNs). ANNs was only used to predict main index, i.e. Kuala Lumpur Composite Index (KLCI), but no attempt to predict share price and in particular banking sector. Since ANN has potential to predict non-linear behavior, this research attempts the use of ANNs to predict banking sector stock price in FTSE Bursa Saham Malaysia Kuala Lumpur Composite Index (FBM KLCI). One of the interesting topics of stock-market research is stock market prediction. Precise stock forecasting becomes the greatest challenge in the investment industry because stock data distribution changes over time. This paper investigates the use of ANN to predict Malaysian stock price, in particular Maybank Berhad stock price. The feedforward neural back-propagation network with Training Function Gradient Decent Training Algorithm is used in this study. The outcome of selected stocks, namely Maybank, are modeled and simulated and the results show that ANN offers a very accurate stock model and also generates competitive systems using all four trading strategies. The results also show that, neural network is a good tool to predict stock price movement with accuracy higher than 95%. Closing price is a good input for neural network model for stock price prediction

Measurements and Modelling of Radar Signatures of Large Wind Turbine Using Multiple Sensors

This paper presents initial results on the characterization of radar signatures of wind turbines, in particular larger wind turbines (capacity over 7 MW) used for offshore wind farms. Experimental results from simultaneous data collected using a passive DVB-T (Digital Video Broadcasting-Terrestrial) radar sensor and an active radar working at S-band are presented, as well as some comments on the parallel work on the modelling of the turbine and on the development of detection algorithms specific for this type of clutter. The initial results show significant variability of the signatures for different radar sensors used, but also for different parameters (e.g. polarization) for the same radar sensor and operational conditions of the turbine (rotation speed, yaw angle)

Cabaran dalam melaksanakan teknologi maklumat dan komunikasi: analisis kes persekitaran pembelajaran Maya-Frog serta strategi untuk melestarikan penggunaan google classroom dalam kalangan guru

Selari dengan kehendak pendidikan abad ke-21, Malaysia telah menjadi salah sebuah negara yang menerima kesan secara langsung akibat perkembangan ICT di sekolah yang berlaku secara pesat di seluruh dunia. Sejak beberapa tahun yang lalu, beberapa inisiatif telah diambil, termasuklah dengan melaksanakan pelantar VLE sebagai langkah untuk meningkatkan kualiti pengajaran dan pembelajaran. Terkini, pelaksanaan VLE di sekolah-sekolah di Malaysia telah memasuki fasa berikutnya yang mana perkhidmatan Frog VLE telah ditamatkan dan digantikan dengan Google Classroom. Namun begitu, peralihan ini masih gagal menghilangkan kerisauan dalam kalangan guru memandangkan statistik lampau menunjukkan kadar penggunaan pelantar Frog VLE yang sangat rendah, berpunca daripada beberapa kelemahan dan cabaran yang gagal ditangani dengan baik semasa pelaksanaannya. Oleh itu, artikel ini membincangkan cabarancabaran dalam melaksanakan inisiatif ICT, terutamanya dalam konteks pendidikan di peringkat sekolah. Seterusnya, satu draf pelan strategik untuk melaksanakan Google Classroom di sekolah yang dibina berdasarkan Model Kejayaan VLE telah dicadangkan. Pelan strategik ini diharapkan akan membantu pelaksanaan Google Classroom di sekolah, sekaligus memacu penggunaan pelantar VLE yang lestari dalam kalangan guru

Factors Affecting Bubble Size in Ionic Liquids

This study reports on understanding the formation of bubbles in ionic liquids (ILs), with a view to utilising ILs more efficiently in gas capture processes. In particular, the impact of the IL structure on the bubble sizes obtained has been determined in order to obtain design principles for the ionic liquids utilised. 11 ILs were used in this study with a range of physico-chemical properties in order to determine parametrically the impact on bubble size due to the liquid properties and chemical moieties present. The results suggest the bubble size observed is dictated by the strength of interaction between the cation and anion of the IL and, therefore, the mass transport within the system. This bubble size – ILs structure–physical property relationship has been illustrated using a series of QSPR correlations. A predictive model based only on the sigma profiles of the anions and cations has been developed which shows the best correlation without the need to incorporate the physico-chemical properties of the liquids. Depending on the IL, selected mean bubble sizes observed were between 56.1 and 766.9 μm demonstrating that microbubbles can be produced in the IL allowing the potential for enhanced mass transport and absorption kinetics in these systems