Linking Uses Of Management Control Systems With Strategic Capabilities And Business Level Strategies For Organizational Performance Evidence From The Sri Lankan Textile And Apparel Industry,
2010
Edith Cowan University
Linking Uses Of Management Control Systems With Strategic Capabilities And Business Level Strategies For Organizational Performance Evidence From The Sri Lankan Textile And Apparel Industry, Nirosha D. Kapu Arachchilage
Theses: Doctorates and Masters
This research investigates how the uses of Management Control Systems (MCS), namely, diagnostic use and interactive use, as moderating variables, influence the relationship between business (competitive) strategies (which are determined by strategic capabilities) and organizational performance. Simons’ levers of control model, Porter’s theory of generic competitive strategy and the Resource Based View (RBV) of strategy have been considered as the underpinning theories and models of the study in developing the theoretical framework and hypotheses. The theoretical framework consists of two strategic capabilities (low cost competency and uniqueness competency), two generic business strategies (cost leadership and differentiation), two uses of MCS …
Ontology-Based Information Extraction For Analyzing It Services,
2009
Dakota State University
Ontology-Based Information Extraction For Analyzing It Services, Amit Deokar, Sagnika Sen
Amit Deokar
Service Level Agreements (SLA) for multi-service Information Technology (IT) outsourcing contracts contain vast amounts of textual information. The SLAs provide details about a specific service, Key Performance Indicators (KPI) to measure its performance; as well as process elements, such as activities, events, and resources that are integral in achieving performance goals. However, KPIs and the process elements may be interrelated. The knowledge of such interrelationships is often tacitly present in the SLAs. The aim of our research is to extract this hidden information from IT service contracts and analyze them to empower customers of IT services to make better performance …
Hospital Capacity Planning For Efficient Disaster Mitigation During An Earthquake,
2009
Kennesaw State University
Hospital Capacity Planning For Efficient Disaster Mitigation During An Earthquake, Pengfei Yi, Santosh George, Jomon Aliyas Paul, Li Lin
Jomon Aliyas Paul
Hospitals are an integral part of a society’s critical functions designed to respond to man-made and natural disasters. Effective hospital capacity planning can significantly enhance the capability and effectiveness of treatment for emergency patients with injuries resulting from a disaster. Such information can be used for patient/ambulance routing, resource planning, and emergency operations management. In the current paper, we develop a generic simulation model that is capable of representing the operations of a wide range of hospitals given an earthquake disaster situation. Using results from our simulations, generalized regression equations are fitted to obtain steady-state hospital capacities. A parametric metamodel …
Models For Solving Emergency Room Crisis.,
2009
Kennesaw State University
Models For Solving Emergency Room Crisis., Jomon Aliyas Paul, Kedar Sambhoos
Jomon Aliyas Paul
Emergency Departments (ED) across United States are distraught with issues like overcrowding, ambulance diversion, medical errors, patient left without being seen etc. The primary cause for all these interrelated problems is artificial variability that results mainly because of inaccurate severity estimation leading to inappropriate bed allocation and final disposition. To this effect, we propose Bayesian decision support tools that accurately classify new incoming patients into different severity types based on their chief complaints and at the same time assist doctors in subsequent diagnosis and disposition of patients. These tools are developed based on the decision making principles of attending physicians …
Dynamic Pickup And Delivery Problems,
2009
Melbourne Business School
Dynamic Pickup And Delivery Problems, Gerardo Berbeglia
Gerardo Berbeglia
No abstract provided.
Business Failure Prediction Using Decision Trees,
2009
Bond University
Business Failure Prediction Using Decision Trees, Adrian Gepp, Kumar Kuldeep, Sukanto Bhattacharya
Adrian Gepp
Accurate business failure prediction models would be extremely valuable to many industry sectors, particularly financial investment and lending. The potential value of such models is emphasised by the extremely costly failure of high-profile companies in the recent past. Consequently, a significant interest has been generated in business failure prediction within academia as well as in the finance industry. Statistical business failure prediction models attempt to predict the failure or success of a business. Discriminant and logit analyses have traditionally been the most popular approaches, but there are also a range of promising non-parametric techniques that can alternatively be applied. In …
Roads Or Radar: The Tradeoff Between Investments In Infrastructure And Forecasting When Facing Hurricane Risk,
2009
University of Texas at Austin
Roads Or Radar: The Tradeoff Between Investments In Infrastructure And Forecasting When Facing Hurricane Risk, Eric Bickel, Seong Dae Kim
Eric Bickel
When faced with a significant risk, society must decide howmuch to invest in prediction and response. For example, in the face of hurricane risk how much should we invest in better forecasting versus increased evacuation speed? To address this need, we develop a Markov decision processes model to analyze the interaction between the emergency response system and the emergency forecasting system. The model shows the tradeoff between the two investments given a budget limit. In addition, the research indicates that the superiority of the investment changes sharply by the lead time.
Bayesian Inference For A Periodic Stochastic Volatility Model Of Intraday Electricity Prices,
2009
Melbourne Business School
Bayesian Inference For A Periodic Stochastic Volatility Model Of Intraday Electricity Prices, Michael S. Smith
Michael Stanley Smith
The Gaussian stochastic volatility model is extended to allow for periodic autoregressions (PAR) in both the level and log-volatility process. Each PAR is represented as a first order vector autoregression for a longitudinal vector of length equal to the period. The periodic stochastic volatility model is therefore expressed as a multivariate stochastic volatility model. Bayesian posterior inference is computed using a Markov chain Monte Carlo scheme for the multivariate representation. A circular prior that exploits the periodicity is suggested for the log-variance of the log-volatilities. The approach is applied to estimate a periodic stochastic volatility model for half-hourly electricity prices …
