nep-ppm New Economics Papers
on Project, Program and Portfolio Management
Issue of 2021‒05‒17
ten papers chosen by
Arvi Kuura
Tartu Ülikool

  1. Applied Algorithmic Machine Learning for Intelligent Project Prediction: Towards an AI Framework of Project Success By Hsu, Ming-Wei; Dacre, Nicholas; Senyo, PK
  2. Reframing Project Management Process Paralysis: An Autoethnographic Study of the UK Fire Service By Barber, Carl; Dacre, Nicholas; Dong, Hao
  3. Fighting Fire with Fire: Optimality of Value Destruction to Mitigate Short-Termism By Corum, Adrian Aycan
  4. Organizational Structure and Investment Strategy By Lóránth, Gyöngyi; Morrison, Alan; Zeng, Jing
  5. Practical Lessons for Government AI Projects By Ramizo, Godofredo Jr
  6. Homeworking Project Management & Agility as the New Normal in a Covid-19 World By Sonjit, Patcharin; Dacre, Nicholas; Baxter, David
  7. Economic analysis of tidal stream turbine arrays: a review By Zoe Goss; Daniel Coles; Matthew Piggott
  8. Cooperation between global and local firms in emerging markets: a coopetition approach The case in Vietnam By Thuy Do; Frédéric Le Roy; Thuy Seran
  9. Do Agrivoltaics Improve Public Support for Solar Photovoltaic Development? Survey Says: Yes! By Pascaris1, Alexis S.; Schelly, Chelsea; Rouleau, Mark; Pearce, Joshua M.
  10. Information Disclosure and the Performance of Public Investment. The Case of Costa Rica By Martín A. Rossi; Antonia Vazquez; Juan Cruz Vieyra

  1. By: Hsu, Ming-Wei; Dacre, Nicholas; Senyo, PK
    Abstract: A growing number of emerging studies have been undertaken to examine the mediating dynamics between intelligent agents, activities, and cost within allocated budgets, in order to predict the outcomes of complex projects in dint of their significant uncertain nature in achieving a successful outcome. For example, prior studies have used machine learning models to calculate and perform predictions. Artificial neural networks are the most frequently used machine learning model with support vector machine, and genetic algorithm and decision trees are sometimes used in several related studies. Furthermore, most machine learning algorithms used in prior studies generally assume that inputs and outputs are independent of each other, which suggests that a project's success is expected to be independent of other projects. As the datasets used to train in prior studies often contain projects from different clients across industries, this theoretical assumption remains tenable. However, in practice projects are often interrelated across several different dimensions, for example through distributed overlapping teams. An ongoing ethnographic study at a leading project management artificial intelligence consultancy, referred to in this research as Company Alpha, suggests that projects within the same portfolio frequently share overlapping characteristics. To capture the emergent inter-project relationships, this study aims to compare two specific types of artificial neural network prediction performances; (i) multilayer perceptron and; (ii) recurrent neural networks. The multilayer perceptron has been found to be one of the most widely used artificial neural networks in the project management literature, and recurrent networks are distinguished by the memory they take from prior inputs to influence input and output. Through this comparison, this research will examine whether recurrent neural networks can capture the potential inter-project relationship towards achieving improved performance in contrast to multilayer perceptron. Our empirical investigation using ethnographic practice-based exploration at Company Alpha will contribute to project management knowledge and support developing an intelligent project prediction AI framework with future applications for project practice.
    Date: 2021–03–15
  2. By: Barber, Carl; Dacre, Nicholas; Dong, Hao
    Abstract: The Covid-19 pandemic has created new social, environmental, and economic challenges for organisational routines, and a multilevel perspective of project management processes and decision making is required to untangle the complex nature of projects and phenomena. This research hence aims to investigate reframing of traditional project failure reasoning in pressurised situations by adopting a wider organisational view of the causation of failure using models from high-risk industries which support good decision-making practices and highlighting the project, programme and organisational structures which inherently position a project manager to fail in conditions with cognitive overload, limitations, and constraints. Through an institutional perspective, both individuals (the project managers) and organisations are considered under the influence of normative and cognitive pressures, and both are sources of change.
    Date: 2021–03–26
  3. By: Corum, Adrian Aycan
    Abstract: I study a model of short-termism where a firm's value is affected by the actions of an agent, who can represent the manager or the board, as well as an entrepreneur, venture capital, private equity, or activist shareholder. The agent either has a project with positive NPV and can further increase the NPV by exerting effort, or has a project that destroys value. The agent has a stake in the company and can liquidate it before the NPV of his actions is realized by the market. This ability to exit creates short-term incentives for the agent to not exert effort, as well as opportunities for him to profit even if he is destroying value. I find that replacing a fraction of agents that have positive NPV projects with value-destroying agents can increase average firm value, because it motivates the value-creating agents to work harder and this effect can dominate the added value destruction. Moreover, this result also holds under endogenous entry of agents: Reducing the entry or operating costs for the agents can increase average firm value and gross value destruction simultaneously, even though the fraction of agents with positive NPV projects decreases. Therefore, regulations that aim mitigating short-termism by curbing value destruction can actually yield opposite results and reduce average firm value instead.
    Date: 2021–03–15
  4. By: Lóránth, Gyöngyi; Morrison, Alan; Zeng, Jing
    Abstract: We show that a firm can use its organizational structure to commit to an investment strategy. The firmdelegates sequential search and project management tasks to a manager. Ex post, the firm turns away projects that generate high project management rent. However, because the expectation of such rent serves to defray the manager's search cost, investment might be optimal ex ante. A leveraged subsidiary mitigates this time-inconsistency problem by creating ex post risk-shifting incentives that counteract underinvestment. Subsidiaries are more valuable for projects with costly search, intermediate management costs, and returns that are uncorrelated with the existing business.
    Keywords: branch; multinational business; Organizational structure; subsidiary
    JEL: G32 G34 L22
    Date: 2021–01
  5. By: Ramizo, Godofredo Jr
    Abstract: Governments around the world are launching projects that embed artificial intelligence (AI) in the delivery of public services. How can government officials navigate the complexities of AI projects and deliver successful outcomes? Using a review of the existing literature and interviews with senior government officials from Hong Kong, Malaysia, and Singapore who have worked on Smart City and similar AI-driven projects, this paper demonstrates the diversity of government AI projects and identifies practical lessons that help safeguard public interest. I make two contributions. First, I show that we can classify government AI projects based on their level of importance to government functions and the level of organisational resources available to them. These two dimensions result in four types of AI projects, each with its own risks and appropriate strategies. Second, I propose five general lessons for government AI projects in any field, and outline specific measures appropriate to each of the aforementioned types of AI projects.
    Date: 2021–04–18
  6. By: Sonjit, Patcharin; Dacre, Nicholas; Baxter, David
    Abstract: The Covid-19 global pandemic crisis has had a deep and profound impact on fundamental elements of society, the economy, and the environment as a whole. Key organisations, businesses, sectors and industries vital for delivering crucial projects have been affected by the relatively fast onset of Covid-19 on a global scale. As a result, organisational routines and project management processes that would have focused on established methods and practices have incurred dramatic changes leading to a greater emphasis on agility as part of a more exhaustive strategic Covid-19 world, where new routines and processes become embedded as the new normal. This research focuses on the increased demand in Homeworking Project Management (HPM) and more significant agility requirements across dispersed virtual project management teams. Initial insights from semi-structured interviews with a cross-section of 12 high-level project professionals suggest that; (i) Transitional homeworking project management processes have a direct impact on collaborative and operational routines; (ii) There is a greater level of demand on agility with HPM teams which do not necessarily have the organisational infrastructure to support these, (iii) Technological resources are becoming a primary concern with inequality of information across HPM teams, and (iv) Increasing critical bottlenecks across dispersed HPM teams is adversely affecting tenable project outcomes.
    Date: 2021–03–23
  7. By: Zoe Goss; Daniel Coles; Matthew Piggott
    Abstract: This tidal stream energy industry has to date been comprised of small demonstrator projects made up of one to a four turbines. However, there are currently plans to expand to commercially sized projects with tens of turbines or more. As the industry moves to large-scale arrays for the first time, there has been a push to develop tools to optimise the array design and help bring down the costs. This review investigates different methods of modelling the economic performance of tidal-stream arrays, for use within these optimisation tools. The different cost reduction pathways are discussed from costs falling as the global installed capacity increases, due to greater experience, improved power curves through larger-diameter higher-rated turbines, to economic efficiencies that can be found by moving to large-scale arrays. A literature review is conducted to establish the most appropriate input values for use in economic models. This includes finding a best case, worst case and typical values for costs and other related parameters. The information collated in this review can provide a useful steering for the many optimisation tools that have been developed, especially when cost information is commercially sensitive and a realistic parameter range is difficult to obtain.
    Date: 2021–05
  8. By: Thuy Do (UM - Université de Montpellier, Groupe Sup de Co Montpellier (GSCM) - Montpellier Business School); Frédéric Le Roy (UM - Université de Montpellier, Groupe Sup de Co Montpellier (GSCM) - Montpellier Business School); Thuy Seran (UM - Université de Montpellier, Groupe Sup de Co Montpellier (GSCM) - Montpellier Business School)
    Abstract: This empirical research investigates the sharing and protecting knowledge mechanism used by global companies while collaborating with a local partner in IT Services sector in an emerging economy. The results reveal the combination and effectiveness of knowledge protection mechanisms used by the global firms when they cooperate with the local firm in the emerging economy. This research contributes, first, to literature on collaboration between global and local firms on emerging economy markets by showing 1) that global companies are able to share and protect their knowledge when they collaborate with local firms, and 2) that global companies and local firms are able to learn from the cooperative project to increase their innovation capabilities. The research contributes, second, to coopetition literature by showing that managing the coopetitive dilemma between sharing and protecting knowledge is also a concern for companies cooperating with potential competitors.
    Keywords: knowledge sharing,knowledge protection,emerging markets,global firms,local firms
    Date: 2021–06–01
  9. By: Pascaris1, Alexis S.; Schelly, Chelsea; Rouleau, Mark; Pearce, Joshua M. (Michigan Technological University)
    Abstract: Agrivoltaic systems allow for the simultaneous production of solar-generated electricity and agriculture. As the climate change related impacts of conventional energy and food production intensify, finding strategies to increase the deployment of solar photovoltaic systems, preserve agricultural land, and minimize competing land uses is urgent. Given the proven technical, economic, and environmental advantages provided by agrivoltaic systems, increased proliferation is anticipated, which necessitates accounting for the nuances of community resistance to solar development on farmland. Minimizing siting conflict and addressing agricultural communities’ concerns will be key in promoting public support for agrivoltaics, as localized acceptance of solar is a critical determinant of project success. This survey study assessed if public support for solar development increases when energy and agricultural production are combined in an agrivoltaic system. Results show that 81.8% of respondents would be more likely to support solar development in their community if it combined the production of both energy and agriculture. This increase in support for solar given the agrivoltaic approach highlights a development strategy that can improve local social acceptance and the deployment rate of solar photovoltaics. Survey respondents prefer agrivoltaic projects that a) are designed to provide economic opportunities for farmers and the local community b) are located on private property or existing agricultural land c) do not threaten local interests and d) ensure fair distribution of economic benefits. Proactively identifying what the public perceives as opportunities and concerns related to agrivoltaic development can help improve the design, business model, and siting of systems in the U.S.
    Date: 2021–05–05
  10. By: Martín A. Rossi; Antonia Vazquez; Juan Cruz Vieyra
    Abstract: This paper provides experimental evidence about the causal impact of publishing information related to public investment projects on the performance of these projects. Specifically, it analyzes the impact of the launch of the website MapaInversiones on the physical and financial progress of public investment projects in Costa Rica. The study finds that published projects (the treatment group) perform better than unpublished projects (the control group). Three months after the release of MapaInversiones, financial progress of public investment projects uploaded onto the platform increased by 18 percentage points, and physical progress increased by 8 percentage points compared to unpublished projects. A year after the intervention, financial progress of treated projects was approximately 15 percentage points higher relative to control projects, while the increase in physical progress after one year of launching the platform was approximately 1 percent.
    Keywords: digital innovation, efficiency, investment projects, monitoring, transparency
    JEL: D73 O31 H50 H83 L78 O54
    Date: 2020–11

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