Managerial perseptions on corporate social responsbility: A Transatlantic comparison between forest products companies in Europe and North America

Corporate Social Responsibility (CSR) addresses the responsibility of companies for their impacts on society. The concept of strategic CSR is becoming increasingly mainstreamed in the forest industry, but there is, however, little consensus on the definition and implementation of CSR. The objective of this research is to build knowledge on the characteristics of CSR and to provide insights on the emerging trend to increase the credibility and legitimacy of CSR through standardization. The study explores how the sustainability managers of European and North American forest companies perceive CSR and the recently released ISO 26000 guidance standard on social responsibility. The conclusions were drawn from an analysis of two data sets; multivariate survey data based on one subset of 30 European and 13 North American responses, and data obtained through in-depth interviewing of 10 sustainability managers that volunteered for an hour long phone discussion about social responsibility practices at their company. The analysis concluded that there are no major differences in the characteristics of cross-Atlantic CSR. Hence, the results were consistent with previous research that suggests that CSR is a case- and company-specific concept. Regarding the components of CSR, environmental issues and organizational governance were key priorities in both regions. Consumer issues, human rights, and financial issues were among the least addressed categories. The study reveals that there are varying perceptions on the ISO 26000 guidance standard, both positive and negative. Moreover, sustainability managers of European and North American forest companies are still uncertain regarding the applicability of the ISO 26000 guidance standard to the forest industry. This study is among the first to provide a preliminary review of the practical implications of the ISO 26000 standard in the forest sector. The results may be utilized by sustainability managers interested in the best practices on CSR, and also by a variety of forest industrial stakeholders interested in the practical outcomes of the long-lasting CSR debate.

USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS

Sensor networks have been an active research area in the past decade due to the variety of their applications. Many research studies have been conducted to solve the problems underlying the middleware services of sensor networks, such as self-deployment, self-localization, and synchronization. With the provided middleware services, sensor networks have grown into a mature technology to be used as a detection and surveillance paradigm for many real-world applications. The individual sensors are small in size. Thus, they can be deployed in areas with limited space to make unobstructed measurements in locations where the traditional centralized systems would have trouble to reach. However, there are a few physical limitations to sensor networks, which can prevent sensors from performing at their maximum potential. Individual sensors have limited power supply, the wireless band can get very cluttered when multiple sensors try to transmit at the same time. Furthermore, the individual sensors have limited communication range, so the network may not have a 1-hop communication topology and routing can be a problem in many cases. Carefully designed algorithms can alleviate the physical limitations of sensor networks, and allow them to be utilized to their full potential. Graphical models are an intuitive choice for designing sensor network algorithms. This thesis focuses on a classic application in sensor networks, detecting and tracking of targets. It develops feasible inference techniques for sensor networks using statistical graphical model inference, binary sensor detection, events isolation and dynamic clustering. The main strategy is to use only binary data for rough global inferences, and then dynamically form small scale clusters around the target for detailed computations. This framework is then extended to network topology manipulation, so that the framework developed can be applied to tracking in different network topology settings. Finally the system was tested in both simulation and real-world environments. The simulations were performed on various network topologies, from regularly distributed networks to randomly distributed networks. The results show that the algorithm performs well in randomly distributed networks, and hence requires minimum deployment effort. The experiments were carried out in both corridor and open space settings. A in-home falling detection system was simulated with real-world settings, it was setup with 30 bumblebee radars and 30 ultrasonic sensors driven by TI EZ430-RF2500 boards scanning a typical 800 sqft apartment. Bumblebee radars are calibrated to detect the falling of human body, and the two-tier tracking algorithm is used on the ultrasonic sensors to track the location of the elderly people.