Facilitating Emergence: Complex, Adaptive Systems Theory and the Shape of Change

This study used Principal Component Analysis to examine factors that facilitate emergent change in an organization. As organizational life becomes more complex, today’s dominant management paradigms no longer suffice. This is particularly true in a health care setting where multiple sources of disease interacting with each other meet with often-competing organizational priorities and accountabilities in a highly complex world. This study identifies new ways of approaching complexity by embracing the capacity of complex systems to find their own form of order and coherence. Based on a review of the literature, interviews with hospital CEOs, and my organization development practice experience in the health care sector, I identified nine constructs of interest: a strategic framework; organizational culture; work structures; CEO and executive team; leadership culture; quality control systems; accountability framework; learning structures; and feedback processes. One hundred and sixty-two senior leaders, managers, and staff at a hospital in Toronto, Canada, who had completed an eight-week leadership program, completed an Emergence Survey© based on the nine constructs of interest. The survey included Likert items representing the nine constructs, as well as opportunities to provide narrative feedback. In the initial analysis of the survey results, the items taken as a whole would not converge on a clear set of components. It was also clear that the mean for most of the items was very high. I theorized that the size of the sample and possibility that they were a favorably biased convenience sample because they had self-selected as leaders may have contributed to the lack of convergence and high mean. I then theorized three clusters of constructs, based on what appeared to be natural affinities. At that point I facilitated two focus groups with people who were among the survey group. Both focus groups affirmed the importance of each of the factors in improving organizational performance indicators such as patient satisfaction, staff engagement, and quality. I then completed a principal component analysis of each of the three clusters of constructs. From this analysis, seven components emerged. Five of these, executive engagement, safe-fail culture, collaborative decision-processes, a collaborative quality, and intentional learning processes had reliability \u3e.70; culture of experimentation and purposeful orientation had reliability \u3c .70. The electronic version of this Dissertation is at OhioLink ETD Center, www.ohiolink.edu/et

A service accountability framework for QoS service management and engineering

Service science, management and engineering (SSME) research is to study the methodology and technology for service innovation, design, development and delivery. Since service industry is very quality-sensitive and trust-dependent, we propose a service accountability management framework to detect, diagnose, defuse and disclose the root cause for any problematic service process. The accountability support is important for SSME since service processes often rely on external service providers to deliver part of the service functionalities. A service system must have effective yet efficient mechanisms to ensure that every external service is delivering a consistent and acceptable level of performance to meet the end-to-end quality of service (QoS) of the whole service process. In this paper, we present the accountability framework, identify the components in an accountable service architecture, and design an accountability diagnosis methodology. We also briefly present the inteLLigent Accountability Management Architecture (LLAMA) project which implements the accountability service bus (ASB), an agent-based middleware to support the monitoring, diagnosis, and reconfiguration of e-services. LLAMA ASB interacts with accountability agents to monitor services and the Accountability Authority to automatically diagnose faulty situations. The LLAMA technology is useful to ensure the QoS in SSME-based systems