Dinesh R. Pai

Bio: Dinesh R. Pai is an academic researcher from Penn State Harrisburg. The author has contributed to research in topics: Medicine & Revenue. The author has an hindex of 6, co-authored 26 publications receiving 113 citations. Previous affiliations of Dinesh R. Pai include Pennsylvania State University & Rutgers University.

Benchmarking software development productivity of CMMI level 5 projects

Abstract: In this paper, data envelopment analysis variable returns to scale (DEA VRS) model is applied to data collected on 79 software development projects from a leading CMMI level 5 organization. We divide overall software effort into software development effort, software quality conformance effort (EoC), and software maintenance non-conformance (EoNC) effort due to poor software quality at delivery time. Partitioning effort into software development and software quality metrics provides us a comprehensive model to measure productivity of software projects and to identify best practice projects. Some of positive productivity drivers from the DEA best practice efficient projects point to good customer rapport and application familiarity. Inefficient projects had problems such as customer requirements volatility, and the use of unfamiliar technology. The DEA results identify 12 "best practice" projects that can be emulated for software process improvement. Additionally, our results point to approximately 50 % potential for productivity improvement in software projects to get to the level of "best practice" projects. This study shows that including EoC and EoNC as inputs has a positive impact on the best practice frontier.

Does efficiency and quality of care affect hospital closures

Abstract: In recent decades, a large number of hospitals in Pennsylvania and across the United States have been forced to close entirely, or to transform their beds for alternative uses including outpatient ...

Software Effort Estimation Using a Neural Network Ensemble

Abstract: Accurate software effort estimation is crucial for software consulting organizations to stay competitive in their software development costs and retain customers. Artificial Neural Network (ANN) is an effective tool to obtain accurate effort estimates. In this paper, software effort estimation models using Artificial Neural Network (ANN) ensembles and regression analysis are developed based on data collected from 163 software development projects. The main emphasis of the paper is in developing an effective experimental design to achieve superior effort estimation results. In addition, we compare the software effort estimation of ANNs and multiple regression analysis. We found two interesting results. First, variables other than size (function points) are not especially helpful in predicting software development effort. Second, a properly designed ANN ensemble significantly outperforms estimation using regression analysis and can achieve better effort estimate predictions.

Enterprise Information System and Data Mining

Abstract: The advent of information technology and the consequent proliferation of information systems have lead to generation of vast amounts of data, both within the organization and across its supply chain. Enterprise information systems (EIS) have added to organizational complexity, and at the same time, created opportunities for enhancing its competitive advantage by utilizing this data for business intelligence purposes. Various data mining tools have been used to gain a competitive edge through these large data bases. In this paper, the authors discuss EIS-aided business intelligence and data mining as applicable to organizational functions, such as supply chain management (SCM), marketing, and customer relationship management (CRM) in the context of EIS.

Systems analysis and design

Abstract: Dr. Ashenhurst set the tone of the session by saying that the panel would discuss systems analysis in the industrial management sense, "With a view toward coordinating educational programs with management needs".

Software engineering economics

Abstract: This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

The Impact of Working Capital Management on Firm Profitability in Different Business Cycles: Evidence from Finland

Abstract: The recent economic downturn of 2007-2008 has brought renewed focus on working capital policies. In this paper we examine the role of business cycles on the working capital-profitability relationship using a sample of Finnish listed companies over an 18 year period. We find the impact of business cycle on the working capital-profitability relationship is more pronounced in economic downturns relative to economic booms. We further show that the significance of efficient inventory management and accounts receivables conversion periods increase during periods of economic downturns. Our results demonstrate that active working capital management matters and, thus, should be included in firms’ financial planning.

High-Powered Incentives in Hierarchies: The Determinants of Incentive Intensity in Group-Based Rewards

Abstract: Highly incentive intensive rewards have been linked both theoretically and empirically to higher effort. Nonetheless, historically the incentive intensity of individual rewards has been quite modest in most hierarchies. In an effort to escalate the incentive intensity of rewards, managers have increasingly implemented pay systems which reward individuals for group performance. While the determinants of incentive intensity for individual rewards have been widely examined, the determinants of incentive intensity for group- based rewards remain unexplored. In this paper, we draw upon literature in economics and social psychology to develop a theory of the determinants of incentive intensity in group rewards. Our derived hypotheses are tested using data from a large sample of 663 group pay plans in the US private sector.