Models are incredibly insidious; they slide undetected into discussions and then dominate the way people think. Since Information Systems (ISs) and particularly Knowledge Management Systems (KMSs) are socio-technical systems, they unconsciously embrace the characteristics of the dominant models of management thinking. Thus, their limitations can often be attributed to the deficiencies of the organizational models they aim to support. Through the case study of a hotel chain, this research suggests that contemporary KMSs in the hospitality sector are still grounded in the assumptions of the mechanistic organizational model which conceives an organization as a rigid hierarchical entity governed from the top. Despite the recent technological advances in terms of supporting dialogue and participation between members, organizational knowledge is still transferred vertically; from the top to the bottom or from the bottom to the top. A number of limitations still exist in terms of supporting effectively the transfer of knowledge horizontally between the geographically distributed units of an organization. Inspired from the key concepts of the more recent complex systems model, referred frequently as complexity theories, a Knowledge Management Architecture (KMA) is proposed aiming to re-conceptualize the existing KMSs towards conceiving an organization as a set self-organizing communities of practice (CoP). In every such CoP, order is created from the dynamic exchange of knowledge between the structurally similar community members. Thus, the focus of the KMA is placed on capturing systematically for reuse the architectural knowledge created upon every initiative for change and share such knowledge with the rest of the members of the CoP. A KMS was also developed to support the dynamic dimensions that the KMA proposes. The KMS was then applied in the case of the hotel chain, where it brought significant benefits which constitute evidence of an improved self-organizing ability. The previously isolated hotel units residing in distant regions could now trace but also reapply easily changes undertaken by the other community members. Top-management’s intervention to promote change was reduced, while the pace of change increased. Moreover, the organizational cohesion, the integration of new members as well as the level of management alertness was enhanced. The case of the hotel chain is indicative. It is believed that the KMA proposed can be applicable to geographically distributed organizations operating in different sectors too. At the same time, this research contributes to the recent discourse between the fields of IS and complexity by demonstrating how fundamental concepts from complexity such as self-organization, emergence and edge-of-chaos can be embraced by contemporary KMSs.

Adaptability and invisibility are hallmarks of modern terrorism, and keeping pace with its dynamic nature presents a serious challenge for societies throughout the world. Innovations in computer science have incorporated applied mathematics to develop a wide array of predictive models to support the variety of approaches to counterterrorism. Predictive models are usually designed to forecast the location of attacks. Although this may protect individual structures or locations, it does not reduce the threat—it merely changes the target. While predictive models dedicated to events or social relationships receive much attention where the mathematical and social science communities intersect, models dedicated to terrorist locations such as safe-houses (rather than their targets or training sites) are rare and possibly nonexistent. At the time of this research, there were no publically available models designed to predict locations where violent extremists are likely to reside. This research uses France as a case study to present a complex systems model that incorporates multiple quantitative, qualitative and geospatial variables that differ in terms of scale, weight, and type. Though many of these variables are recognized by specialists in security studies, there remains controversy with respect to their relative importance, degree of interaction, and interdependence. Additionally, some of the variables proposed in this research are not generally recognized as drivers, yet they warrant examination based on their potential role within a complex system. This research tested multiple regression models and determined that geographically-weighted regression analysis produced the most accurate result to accommodate non-stationary coefficient behavior, demonstrating that geographic variables are critical to understanding and predicting the phenomenon of terrorism. This dissertation presents a flexible prototypical model that can be refined and applied to other regions to inform stakeholders such as policy-makers and law enforcement in their efforts to improve national security and enhance quality-of-life.