11,847 research outputs found
Pricing Software Development Services
This paper studies the pricing of software development outsourcing. Two pricing techniques – time and material and fixed price – are described and the economic conditions for selecting between them are discussed. Using agency theory and transaction cost economics, it is predicted that risky and specific systems will be priced on time and material basis while other projects will be fixed price. An additional prediction is that confidence in the vendor’s auditing of resources is essential for time and material contracts. The predictions are tested on fourteen external software development projects in two large corporations. Quantitative measures of risk, specificity and confidence are utilised, but the data-set does not support the theoretical predictions. In order to explain this result, interviews with senior managers at the two corporations have been conducted. Both disagree with the theoretical prescriptions: one contracts risky projects on fixed price basis, preferring to pay a risk-premium rather than to rebudget. The second expert allows fixed price only with trusted vendors, preferring time and material with all other vendors
Human Factors Influencing Contractors' Risk Attitudes: A Case Study of the Malaysian Construction Industry
Malaysia is one of the most rapidly developing countries among developing nations. The construction industry has played a major role in Malaysia’s rapid economic growth. Among the major sectors in Malaysia, the importance of the construction industry is unique regardless of the level of the country’s development. However, the attitude of the construction industry in Malaysia towards managing contractors’ risk attitudes is very weak. The introduction of the Occupational Safety and Health Act in 1994 by the Malaysian government made all industries in Malaysia to identify risks, conduct risk assessment and control risk. In addition, the Malaysian construction industry simultaneously implemented an integrated system to ensure consistency and better performance of projects. To identify the factors influencing contractors' risk attitudes, relevant literature was reviewed, and a questionnaire survey was conducted. This study focused on the G7 contractors operating in the Malaysian construction industry. One hundred and nineteen copies of a structured questionnaire were analysed with a response rate of 85%. Structural equation modelling was utilized to test the hypotheses developed for the study. Results showed that government policies played a moderating role in enhancing the relationship between human-related factors affecting contractors’ risk attitudes in the construction industry
Probabilistic Scheduling Based On Hybrid Bayesian Network–Program Evaluation Review Technique,
Project scheduling based on probabilistic methods commonly uses the Program Evaluation Review Technique (PERT). However, practitioners do not widely utilize PERT-based scheduling due to the difficulty in obtaining historical data for similar projects. PERT has several drawbacks, such as the inability to update activity dura- tions in real time. In reality, changes in project conditions related to resources have a highly dynamic nature. The availability of materials, fluctuating labor productiv- ity, and equipment significantly determine the project completion time. This research aims to propose a probabilistic scheduling model based on the Hybrid Bayesian Network-PERT. This model combines PERT with Bayesian Network (BN). BN is used to accommodate real-time changes in resource conditions. The modeling of BN diagrams and variables is obtained through an in-depth literature review, direct field observations, and distributing questionnaires to experts in project scheduling. The model is validated by applying the proposed model to a 60 m concrete bridge construction project in Indonesia. The simulation results of the proposed model are then compared with the case study project to assess the model’s accuracy. The result of the study shows that the proposed hybrid Bayesian-PERT model is accurate and can eliminate the weaknesses of the PERT method. Besides being able to provide an accurate prediction of project completion time (93.4%), this model can also be updated in real-time according to the actual condition of the projec
Innovation races: An experimental study on strategic research activities
In an experimental setting, firms in a duopoly market engage in a patent tournament and compete for profit-enhancing product advancements. The firms generate income by matching exogenously defined demand preferences with an appropriately composed product portfolio of their own. Demand preferences are initially unknown and first need to be revealed by an investigation of the possible product variations. The better firms approximate demand preferences, the higher their profits. In the ensuing innovation race, firms interact through information spillovers resulting from the imperfect appropriability of research successes. In the random period of the experiment, the continuity of the search process is disturbed by an exogenous shock that affects both the supply and demand side and again spurs research competition. Firms may henceforth explore an enlarged product space in attempting to match the equally modified demand preferences. In our analysis, we explore the behavioural regularities of agents who are engaged in innovation activities. As a key element we test to what extend relative economic performance exercises a stimulating effect on the implementation of innovation and imitation strategies.Innovation, Imitation, Patent Tournament, Trial and Error Process
Recommended from our members
Predicting with sparse data
It is well known that effective prediction of project cost related factors is an important aspect of software engineering. Unfortunately, despite extensive research over more than 30 years, this remains a significant problem for many practitioners. A major obstacle is the absence of reliable and systematic historic data, yet this is a sine qua non for almost all proposed methods: statistical, machine learning or calibration of existing models. In this paper we describe our sparse data method (SDM) based upon a pairwise comparison technique and Saaty's Analytic Hierarchy Process (AHP). Our minimum data requirement is a single known point. The technique is supported by a software tool known as DataSalvage. We show, for data from two companies, how our approach — based upon expert judgement — adds value to expert judgement by producing significantly more accurate and less biased results. A sensitivity analysis shows that our approach is robust to pairwise comparison errors. We then describe the results of a small usability trial with a practising project manager. From this empirical work we conclude that the technique is promising and may help overcome some of the present barriers to effective project prediction
The epidemiological impact of antiretroviral use predicted by mathematical models: a review
This review summarises theoretical studies attempting to assess the population impact of antiretroviral therapy (ART) use on mortality and HIV incidence. We describe the key parameters that determine the impact of therapy, and argue that mathematical models of disease transmission are the natural framework within which to explore the interaction between antiviral use and the dynamics of an HIV epidemic. Our review focuses on the potential effects of ART in resource-poor settings. We discuss choice of model type and structure, the potential for risk behaviour change following widespread introduction of ART, the importance of the stage of HIV infection at which treatment is initiated, and the potential for spread of drug resistance. These issues are illustrated with results from models of HIV transmission. We demonstrate that HIV transmission models predicting the impact of ART use should incorporate a realistic progression through stages of HIV infection in order to capture the effect of the timing of treatment initiation on disease spread. The realism of existing models falls short of properly reproducing patterns of diagnosis timing, incorporating heterogeneity in sexual behaviour, and describing the evolution and transmission of drug resistance. The uncertainty surrounding certain effects of ART, such as changes in sexual behaviour and transmission of ART-resistant HIV strains, demands exploration of best and worst case scenarios in modelling, but this must be complemented by surveillance and behavioural surveys to quantify such effects in settings where ART is implemented
Improved risk analysis for large projects: Bayesian networks approach
PhDGenerally risk is seen as an abstract concept which is difficult to measure. In this thesis,
we consider quantification in the broader sense by measuring risk in the context of large projects.
By improved risk measurement, it may be possible to identify and control risks in such a way that
the project is completed successfully in spite of the risks.
This thesis considers the trade-offs that may be made in project risk management,
specifically time, cost and quality. The main objective is to provide a model which addresses the
real problems and questions that project managers encounter, such as:
• If I can afford only minimal resources, how much quality is it possible to achieve?
• What resources do I need in order to achieve the highest quality possible?
• If I have limited resources and I want the highest quality, how much functionality do
I need to lose?
We propose the use of a causal risk framework that is an improvement on the traditional
modelling approaches, such as the risk register approach, and therefore contributes to better
decision making.
The approach is based on Bayesian Networks (BNs). BNs provide a framework for causal
modelling and offer a potential solution to some of the classical modelling problems. Researchers
have recently attempted to build BN models that incorporate relationships between time, cost,
quality, functionality and various process variables. This thesis analyses such BN models and as
part of a new validation study identifies their strengths and weaknesses. BNs have shown
considerable promise in addressing the aforementioned problems, but previous BN models have
not directly solved the trade-off problem. Major weaknesses are that they do not allow sensible
risk event measurement and they do not allow full trade-off analysis. The main hypothesis is that
it is possible to build BN models that overcome these limitations without compromising their
basic philosophy
- …