Effectiveness of e-learning systems is a matter of debate in corporate and academics alike, with researchers viewing it from course content and instructional design perspective, learner / teacher perspective or delivery medium and technology perspective. We argue that e-learning systems should not consider either of these perspectives in isolation. We analyze the e-learning systems using the socio-technical systems approach, which treats a work system to be made up of two jointly independent, but correlative interacting systems - the social and the technical. The technical system is concerned with the processes, tasks, and technology needed to transform i nputs to outputs. The social system is concerned with the attributes of people such as attitudes, skills, values, the relationships among people, reward systems, and authority structures. Outputs of the work system are the result of joint interactions between these two systems. We use Leavitt's model to analyze e-learning systems as socio-technical systems. The model suggests that organizations form multivariate systems consisting of four interacting components - task, structure, actor, and technology. We observe that an e-Learning system can fit the definition of a sociotechnical system, as they involve teachers and learners (actor), the organization and the environment (structure), the knowledge and skills being imparted (task) and the technology used for elearning (technology). In this research, we measure variables that represent these four components and understand their significance individually and their combined effect on effectiveness of e- learning systems. The outcome of this research tries to understand factors that influence the effectiveness of e-learning systems.

Knowledge Management (KM) helps organizations adapt, survive, and compete in a discontinuous and ever-changing business environment. Essentially, this involves a set of organizational processes which harness the data and information processing capacity of information technologies, and the creative and innovative capacity of human beings. Communities of practice (CoP) are the social tools to connect, engage, and share knowledge in organizations. The research reported here aims to examine the critical factors for successful implementation of CoPs. Based on a survey of 223 respondents who have participated in successful organizational initiatives related to KM and CoPs, this study uses a regression model to analyze the findings. The results identify four essential factors which lead to an effective CoP implementation—providing proper community support, people factors, alignment of the KM initiative with the business strategy, and key KM processes. The results strongly support the appropriateness of the research model in identifying the success factors. Finally, this study discusses the implications of these findings and offers directions for future research.

Risk management and success in projects are highly intertwined-better approaches to project risk management tend to increase chances of project success in terms of achieving scope & quality, schedule and cost targets. The process of responding to risk factors during a project's life cycle is a crucial aspect of risk management referred to as risk response strategies, in this paper. The current research explores the status of risk response strategies applied in the software development projects in India. India provides a young IT-savvy English-speaking population, which is also cost effective. Other than the workforce, the environment for implementation of software projects in India is different from the matured economies. Risk management process is a commonly discussed theme, though its implementation in practice has a huge scope for improvement in India. The paper talks about four fundamental treatments to risk response-Avoidance, Transference, Mitigation and Acceptance (ATMA). From a primary data of 302 project managers, the paper attempts to address the risk response factors that lead to successful achievement of project scope & quality, schedule and cost targets, by using a series of regressions followed with Seemingly Unrelated Regression Equations (SURE) modelling. Mitigation emerged as the most significant risk response strategy to achieve project targets. Acceptance, transference, and avoidance of risk were mostly manifested in the forms of transparency in communication across stakeholders, careful study of the nature of risks and close coordination between project team, customers/end-users and top management.

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