Thesis (MBA)--Stellenbosch University, 2000. / ENGLISH ABSTRACT: This study was conducted to demonstrate why projects are late and/or ultimately fail, regardless of the fact that project management and project management techniques are being used. A study of literature regarding project failure was done. This was used to illustrate that human, organisational, technical and project type all play a significant role in project successor failure. / AFRIKAANSE OPSOMMING: Hierdie studie is uitgevoer om aan te dui hoekom projekte soms laat is en/of uiteindelik faal, nieteenstaande die feit dat projekbestuur en projekbestuurtegnieke toegepas word. 'n Literatuurstudie aangaande onsuksesvolIe projekte is uitgevoer. Hierdie studie is gebruik om aan te toon dat menslike, organisatoriese, tegniese en projek tipe almal bydra tot projek sukses al dan nie.

Project failures are a reality that most project managers face several times in their careers, but even more significant than the failure itself, is what these individuals learn after experiencing it. Studies do exist within the entrepreneurship literature which analyze failure of entrepreneurial projects. But these are not particularly focused on projects executed within organizations and the experiences of entrepreneurs would be different to those of project managers within firms, after a failure occurs. The authors have made a research from a sample of 6 project managers over such project failures. Using Kolb's experiential Theory to understand the learning that occurs after project failures helped the authors to create a model which the project managers can use in their process of learning after failures. This study also elaborates about the project management literature in association with project failures and also the various aspects of learning that can be achieved in an organization.

We, as authors, have noticed a paradox in today’s society. We often hear inspirational quotes such as “you have to fail in order to reach success” or “you learn from failure”. Thomas Edison himself said “I haven’t failed; I have just identified many ways that donot work”. Yet in society and within many organizations, there is a lack of discussion and acceptance when it comes to mistakes and failures, thus also a lack of the learnings from them. Failure is actually a neglected and feared topic, referred as “the Elephant inthe board room”, where mistakes are manipulated in hope of being forgotten and nevernoticed. Nowadays, we live in a society in which individuals are chasing perfectionism. Therefore, there is a fear of making mistakes which hinders followers to dare and take risk to progress. We believe that employees constantly suffer from this pressure and unhealthy environment within corporations. Therefore, we wished to go beyond this issue as we found ourselves astonished by the large number of quote present out there. In order to explore the concept of failure, we identified two kind of organizations in which the culture of mistakes and failures are different than in corporations. The two fields investigated are the orchestra and football team. Mistakes in these organizationsare not feared rather expected and accepted. We asked ourselves what do these leaders do differently and if managers could learn from the conductors and coaches in order to avoid or prevent. To answer these questions, we analyzed different major componentsof leaders such as their perspective about performances, their role as leaders, the communication and relationship with their followers and last but not the least their perspectives and way of handling mistakes. It appears indeed that organizational leadershave lessons to learn from coaches and conductors regarding the four main area we have analyzed such as considering mistakes as part of the past and source of learning as well as implementing a two-way communication.

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published_or_final_version / abstract / Business / Master / Master of Philosophy

This study compared the estimates of reliability made using one, two, three, four, five, and unlimited consecutive failures as ceiling rules in scoring a mathematics achievement test which is part of the Iowa Tests of Basic Skill (ITBS), Form 8. There were 700 students randomly selected from a population (N=2640) of students enrolled in the eight grades in a large urban school district in the southwestern United States. These 700 students were randomly divided into seven subgroups so that each subgroup had 100 students. The responses of all those students to three subtests of the mathematics achievement battery, which included mathematical concepts (44 items), problem solving (32 items), and computation (45 items), were analyzed to obtain the item difficulties and a total score for each student. The items in each subtest then were rearranged based on the item difficulties from the highest to the lowest value. In each subgroup, the method using one, two, three, four, five, and unlimited consecutive failures as the ceiling rules were applied to score the individual responses. The total score for each individual was the sum of the correct responses prior to the point described by the ceiling rule. The correct responses after the ceiling rule were not part of the total score. The estimate of reliability in each method was computed by alpha coefficient of the SPSS-X. The results of this study indicated that the estimate of reliability using two, three, four, and five consecutive failures as the ceiling rules were an improvement over the methods using one and unlimited consecutive failures.

<div>Space mission-related projects are demanding and risky undertakings because of their complexity and cost. Many missions have failed over the years due to anomalies in either the launch vehicle or the spacecraft. Projects of such magnitude with undetected flaws due to ineffective process controls run into unwarranted cost, schedule overruns and account for huge losses. Such failures continue to occur despite the studies on systems engineering process deficiencies and the best systems engineering practices in place. To understand the reasons behind such failures, this work analyses some of the major contributing factors behind majority of space mission technical failures. To achieve this objective, we analyzed the failure data of space missions that happened over the last decade. Based on that information, we analyzed the launch-related failure events from a design decision-making perspective by employing failure event chain-based framework. By analyzing the failure events with this framework, we identify some dominant cognitive biases that might have impacted the overall system performance leading to unintended catastrophes. </div><div><br></div><div>The ability of any design team to achieve optimal performance is limited by communication and knowledge deficiencies between highly dissimilar subsystems. These inefficiencies work to bias each subsystem engineer to prioritize the utility provided by the subsystem they are responsible for. In order to understand how engineering design decisions are influenced by the presence of cognitive biases, the second part of this study establishes a mathematical framework for utility-based selection based on Cumulative Prospect Theory. This framework captures the effect of cognitive biases on selection of alternatives by a rational decision-maker.</div><div><br></div><div>From the first study, overconfidence and anchoring biases are identified as the two dominant contributing factors that influenced the decisions behind majority of the failures. The theoretical models developed in the second study are employed to depict the influence of biased decision-making on utility-based selection of alternatives for an earth-orbiting satellite's power subsystem. Predictions from these models show a direct correlation between the decision-maker's biased preference structure and local change in utility curve depicting the (negative) influence of cognitive biases on decision-maker's choice(s).</div>

Cette thèse étudie l’impact de la réputation des agences de notation sur la perception de leurs annonces par les investisseurs, ainsi que sur leur propre comportement à divulguer des informations précises et ponctuelles. Elle est constituée de quatre chapitres. Dans les premier et second chapitres, nous étudions comment la perception des investisseurs des notations des agences change suite à l’observation d’une erreur de notation. Les résultats montrent que les investisseurs réagissent peu ou pas aux changements d’annonce après avoir observé des notations erronées, ce qui suggère que la mauvaise performance des agences de notation affecte négativement leur réputation. Dans les troisième et quatrième chapitres, nous étudions les déterminants de la ponctualité et de la précision des annonces de notation financière. Il ressort que la réputation de l’agence de notation affecte la qualité des notations. En particulier, plus la réputation est élevée, et plus la note semble surévaluée et non-ponctuelle. / This thesis studies the impact of the reputation of rating agencies on investors’ perception of ratings, and on rating agencies ability to disclose accurate and timely information. It consists of four chapters. In the first and second chapters, we study changes in investors’ perception of rating agencies’ ratings following the observation of rating failures. The results show that investors either ignore or react less to ratings after such failures, which suggests that rating agencies poor performance affects negatively their reputation. In the third and fourth chapters, we study the determinants of ratings timeliness and accuracy disclosed by credit rating agencies. The results suggest that the reputation of the rating agency affects the quality of ratings. In particular, the higher the rating agency s reputation, the less accurate and timely the rating is.

ABSTRACT WOULD NOT LOAD ON DSpace.

Electricity demand is rapidly increasing with growth of population, development of technologies and electrically intensive industries. Also, emerging climate change concerns compel governments to seek environmentally friendly ways to produce electricity such as wind energy systems. In 2018, the wind energy reached 600 GW total capacity globally. However, this corresponds to only about 6% of global electricity demand and there is a need to increase wind energy penetration in electricity grids. One way to enhance the competitiveness of wind energy is to improve its reliability and availability and reduce associated maintenance costs. This study utilizes a database entitled “Wind Monitor and Evaluation Program (WMEP)” to investigate, model and improve wind turbine reliability and availability. The WMEP database consists of maintenance data of 575 wind turbines in Germany during 1989-2008. It is unique as it includes details of turbine model and size, affected subsystem and component, cause of failure, date and time of maintenance, location, and energy production from the wind turbines. Additional parameters such as climatic regions, geography number of previous failures and mean annual wind speed are added to the database in this study. In this research, two metrics are considered and developed such as time-to-failure or failure rate and time-to-repair or downtime for reliability and availability, respectively. This study investigated failure causes, effects and criticalities of wind turbine subsystems and components, assessed the risk factors impacting wind turbine reliability, modeled the reliability of wind turbines based on assessed risk factors, and predicted the cost of wind turbine failures under various operational and environmental conditions. A well-established reliability assessment technique - Failure Modes, Effects and Criticality Analysis is applied on the WMEP maintenance data from 109 wind turbines and three different climatic regions to understand the impacts of climate and wind turbine design type on wind turbine reliability and availability. First, climatic region impacts on identical wind turbine failures are investigated, then impacts of wind turbine design type are examined for the same climatic region. Furthermore, we compared the results of this investigation with results from previous FMECA studies which neglected impacts of climatic region and turbine design type in section 5.4. Two-step cluster and survival analyses are used to determine risk factors that affect wind turbine reliability. Six operational and environmental factors are considered for this approach, namely capacity factor (CF), wind turbine design type, number of previous failures (NOPF), geographical location, climatic region and mean annual wind speed (MAWS). Data are classified as frequent (time-to-failure<40 days) and non-frequent (time-to-failure>80 days) failures and we identified 615 operations listing all these factor and energy production from 21 wind turbines in the WMEP data base. These factors are examined for their impact on wind turbine reliability and results are compared. In addition, wind turbine reliability is modeled by machine learning methods, namely logistic regression (LR) and artificial neural network (ANN), using the considered 615 operations. The objective of this investigation is to model and predict probability of frequently-failing wind turbines based on wind turbines’ known operational and environmental conditions. The models are evaluated and cross validated with 10-fold cross validation and prediction performances and compared with other algorithms such as k-nearest neighbor and support vector machines. Also, prediction performances of LR and ANN are discussed along with their easiness to interpret and share with others. Lastly, using data from 753 operations, a decision support tool for predicting cost of wind turbine failures is developed. The tool development includes machine learning application for estimating probability of failures in 60 days of operation and time-to-repair probabilities for divisions of 0-8hrs, 8-16hrs, 16-24hrs and more than 1 day based on operational and environmental conditions of wind turbines. Prediction for cost of wind turbine failures for 60 days of operation is calculated using assumed costs from time-to-repair divisions. The decision support tool can be updated by the user’s discretion on the cost of failures. This study provides a better understanding of wind turbine failures by investigating associated risk factors, modeling wind turbine reliability and predicting the future cost of failures by applying state-of-the art reliability and data analysis techniques. Wind energy developers and operators can be guided by this study in improving the reliability of wind turbines. Also, wind energy investors, operators and maintenance service managers can predict the cost of wind turbine failures with the decision support tool provided in this study.