Relevant bibliographies by topics / Key performance indicators

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Key performance indicators'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Key performance indicators"

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Chambers, David W. "KEY PERFORMANCE INDICATORS." Journal of the American Dental Association 144, no. 3 (March 2013): 242–44. http://dx.doi.org/10.14219/jada.archive.2013.0105.

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Kowalczyk, Magdalena. "KEY PERFORMANCE INDICATORS IN LOCAL GOVERNMENT IN POLAND." Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, no. 503 (2018): 236–45. http://dx.doi.org/10.15611/pn.2018.503.21.

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Ham, Dennis C., and Alan C. Marshall. "Audited key performance indicators." Tertiary Education and Management 4, no. 4 (December 1998): 255–62. http://dx.doi.org/10.1007/bf02679430.

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Lindberg, Carl-Fredrik, SieTing Tan, JinYue Yan, and Fredrik Starfelt. "Key Performance Indicators Improve Industrial Performance." Energy Procedia 75 (August 2015): 1785–90. http://dx.doi.org/10.1016/j.egypro.2015.07.474.

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Nekrashevych, Olena Vasylivna, and Yuriy Mikhailovich Kovrigo. "Overview of key performance indicators." Modeling, Control and Information Technologies, no. 3 (November 5, 2019): 108–9. http://dx.doi.org/10.31713/mcit.2019.65.

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A set of key performance indicators (KPIs) formanufacturing operation management is introduced in the ISO22400 standart. In this paper, a framework is introduced in orderto solve the problem of dividing into types all the 38 KPIs.
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Gabcanova, Iveta. "Human Resources Key Performance Indicators." Journal of Competitiveness 4, no. 1 (March 30, 2012): 117–28. http://dx.doi.org/10.7441/joc.2012.01.09.

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Grabowska, Sandra. "Key performance indicators – case study." Scientific Papers of Silesian University of Technology. Organization and Management Series 2017, no. 108 (2017): 105–11. http://dx.doi.org/10.29119/1641-3466.2017.108.10.

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Tolmachev, Alexander. "Healthcare department: key performance indicators." Remedium. Journal about the Russian market of medicines and medical equipment, no. 3 (2013): 51–54. http://dx.doi.org/10.21518/1561-5936-2013-3-51-54.

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Shahin, Arash, and M. Ali Mahbod. "Prioritization of key performance indicators." International Journal of Productivity and Performance Management 56, no. 3 (March 20, 2007): 226–40. http://dx.doi.org/10.1108/17410400710731437.

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Pearson, Alan. "Key performance indicators for nursing." International Journal of Nursing Practice 9, no. 6 (October 28, 2003): 337. http://dx.doi.org/10.1046/j.1440-172x.2003.00450.x.

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Dissertations / Theses on the topic "Key performance indicators"

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Arhall, Johanna, and Emmie Cox. "Key Performance Indicators for SAS Flights." Thesis, Linköpings universitet, Kommunikations- och transportsystem, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-104028.

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Revenue management is a thoroughly researched field of study and it is widely used in several different industries. The Revenue Management Department at the airline SAS (Scandinavian Airline System) serves to maximise the profit of the company’s flights. At their disposal they have a number of tools, which use KPIs (Key Performance Indicators) as a measurement. The KPIs are used in prognosis to determine future initiatives, and to analyse and verify results. SAS does not know if the KPIs they focus on to measure performance are the most relevant for optimisation of revenue for their flights. In an attempt to simplify the use of SAS’ optimisation system we have performed a correlation analysis on a set of selected KPIs and assessed the influence relationships between these KPIs, in order to identify pivotal KPIs. Furthermore, we have researched origindestination control and the network effect to broaden the scope of the investigation. We have presented the work and the results in this thesis. The correlation analysis was performed using data, spanning a two-year period, for domestic flights (within Sweden) provided by SAS. The results of the analysis showed that the assessed KPIs could be divided into three groups; within each group the KPIs are closely correlated. As a result, we concluded that to focus on one KPI in each group should be adequate for evaluating performance. The three pivotal KPIs are revenue passenger kilometre, cabin factor and revenue per available seat kilometre. We have recommended SAS to continue working with origin-destination control and that the pivotal KPIs should be used as a measurement for the network, as well as measurements such as net leg revenue. Additionally, we have suggested that SAS conducts equivalent or similar analyses in other suitable areas to identify or confirm pivotal KPIs.
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Davidson, Anne Leslie. "Key performance indicators in humanitarian logistics." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/35540.

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Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2006.
Includes bibliographical references (leaves 86-87).
Non-profit humanitarian relief organizations have typically been unable to measure the performance of their supply chains due to an inability to centrally capture data from operations. With the recent development and implementation of information technology systems that can support the logistics function of these organizations, the data is now available to measure performance, but what is still lacking is a central framework of metrics that measures performance according to the organization's strategic goals. First, this thesis reviews the best practices noted in performance measurement systems of the logistics functions in military and commercial organizations and applies them to the humanitarian sector. Second, the thesis suggests a framework of key performance indicators to be implemented in an international non-profit humanitarian organization based on the unique strategic goals of the sector. The thesis then applies this proposed framework to two actual operations performed by this organization.
(cont.) The analysis performed herein proves that a measurement system would help strengthen the organization's ability to deliver goods to beneficiaries more efficiently and effectively. Finally, the thesis addresses feasibility issues of implementing a measurement system in the non-profit sector and also describes the next steps of opportunities related to measurement systems within humanitarian logistics.
by Anne Leslie Davidson.
M.Eng.in Logistics
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Maier, Philipp. "Website Evaluation Model and Key Performance Indicators /." St. Gallen, 2008. http://www.biblio.unisg.ch/org/biblio/edoc.nsf/wwwDisplayIdentifier/04608352001/$FILE/04608352001.pdf.

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Krishnapillai, Alagesan. "Understanding key performance indicators through driver measures." Thesis, University of Ottawa (Canada), 2009. http://hdl.handle.net/10393/28321.

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Performance management is a vital part of every organization for its success and long term survival in the current competitive global market place. An organization needs a sound performance management framework to analyze its data to make strategic and tactical decisions. The weaknesses in today's performance management frameworks are linked to their inability to integrate strategy formulation with implementation. In our thesis, we introduce D river Measure Models that can define cause-and-effect relationship between d river measures and Key Performance Indicators (KPIs) for effective performance management and strategic alignment. Driver Measure Models make the performance management more dynamic as the operational activities are linked to strategies. Another contribution of the thesis is the identification of mathematical techniques to quantify relationships between KPIs and driver measures. Thesis makes an effort to show how mathematical techniques can be used for planning and forecasting outcomes while changing strategies. After conducting analysis using the mathematical techniques, organization can arrive at a set of operational tasks associated to driver measures which need to be executed to achieve its optimal result. Finally, we identified the essential set of criteria that a performance management framework needs to address through a literature survey and a gap analysis of existing frameworks. We created an extension to the Balanced Scorecard framework based on Driver Measure Models and support for the management of external factors to address these criteria and compared it to existing frameworks using a case study.
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McGill, Susan. "NETWORK PERFORMANCE MANAGEMENT USING APPLICATION-CENTRIC KEY PERFORMANCE INDICATORS." Doctoral diss., University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4055.

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The Internet and intranets are viewed as capable of supplying "Anything, Anywhere, Anytime" and e-commerce, e-government, e-community, and military C4I are now deploying many and varied applications to serve their needs. Network management is currently centralized in operations centers. To assure customer satisfaction with the network performance they typically plan, configure and monitor the network devices to insure an excess of bandwidth, that is overprovision. If this proves uneconomical or if complex and poorly understood interactions of equipment, protocols and application traffic degrade performance creating customer dissatisfaction, another more application-centric, way of managing the network will be needed. This research investigates a new qualitative class of network performance measures derived from the current quantitative metrics known as quality of service (QOS) parameters. The proposed class of qualitative indicators focuses on utilizing current network performance measures (QOS values) to derive abstract quality of experience (QOE) indicators by application class. These measures may provide a more user or application-centric means of assessing network performance even when some individual QOS parameters approach or exceed specified levels. The mathematics of functional analysis suggests treating QOS performance values as a vector, and, by mapping the degradation of the application performance to a characteristic lp-norm curve, a qualitative QOE value (good/poor) can be calculated for each application class. A similar procedure could calculate a QOE node value (satisfactory/unsatisfactory) to represent the service level of the switch or router for the current mix of application traffic. To demonstrate the utility of this approach a discrete event simulation (DES) test-bed, in the OPNET telecommunications simulation environment, was created modeling the topology and traffic of three semi-autonomous networks connected by a backbone. Scenarios, designed to degrade performance by under-provisioning links or nodes, are run to evaluate QOE for an access network. The application classes and traffic load are held constant. Future research would include refinement of the mathematics, many additional simulations and scenarios varying other independent variables. Finally collaboration with researchers in areas as diverse as human computer interaction (HCI), software engineering, teletraffic engineering, and network management will enhance the concepts modeled.
Ph.D.
Other
Engineering and Computer Science
Modeling and Simulation PhD
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Hogg, Sophie Royda. "Using key performance indicators in town centre management." Thesis, Manchester Metropolitan University, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.271269.

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Pais, Fernando José Pinto. "Performance tangível versus performance intangível." Master's thesis, FEUC, 2016. http://hdl.handle.net/10316/32504.

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Relatório de estágio do mestrado em Gestão, apresentado à Faculdade de Economia da Universidade de Coimbra, sob a orientação de Paulo Miguel Marques Gama Gonçalves e Sérgio Daniel Duarte Lopes.
O presente trabalho apresenta-se como um relatório de estágio que permitirá concluir o Mestrado em Gestão da Faculdade de Economia da Universidade de Coimbra. O estágio decorreu na Visabeira Pro – Estudos e Investimentos, SA (empresa pertencente ao Grupo Visabeira, SGPS, SA), mais propriamente no departamento de controlo de gestão e acompanhamento de negócio, entre as datas de 8 de fevereiro e 13 de junho de 2016. Assim, com este relatório pretende-se conjugar a elaboração de um ensaio teórico com a descrição do conjunto de atividades desenvolvidas pelo aluno ao longo do estágio curricular. O objetivo do estágio passou por facultar ao aluno competências de modo a que este seja capaz de produzir reports com periodicidade mensal através de Key Performance Indicators (KPI´s) relevantes à análise de cada negócio. Nos dias de hoje o termo performance assumiu uma importância que há alguns anos atrás não tinha. Vários estudos levaram a que se deixasse de olhar para este como uma simples medição de custos e/ou indicadores financeiros, propondo uma divisão entre indicadores financeiros e não financeiros. Contudo, esta separação não é suficiente, pois não evidencia aspetos como a imagem ou a cultura organizacional, fazendo com que não se tenha uma “imagem” completa da realidade organizacional. Posto isto, o grande propósito deste estudo passa por fornecer ao leitor uma visão alternativa que permita uma melhor compreensão das dimensões da medição de performance, dando assim destaque aos fatores intangíveis, já que estes hoje em dia desempenham um papel fundamental na criação de valor das organizações.
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Samir, Kousay. "Key Performance Indicators in Cyber-Physical Production Systems." Thesis, KTH, Industriell produktion, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217866.

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Kerr, Stephen Gawley. "Key performance indicators as a policy implementation technique." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0013/NQ59609.pdf.

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Kovach, Matthew. "Causal Inference of Human Resources Key Performance Indicators." Bowling Green State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1542361652897175.

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Books on the topic "Key performance indicators"

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Parmenter, David. Key Performance Indicators. Hoboken, NJ, USA: John Wiley & Sons, Inc, 2015. http://dx.doi.org/10.1002/9781119019855.

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Parmenter, David. Key Performance Indicators. New York: John Wiley & Sons, Ltd., 2007.

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Krause, Hans-Ulrich, and Dayanand Arora. Controlling-Kennzahlen - Key Performance Indicators. München: OLDENBOURG WISSENSCHAFTSVERLAG, 2010. http://dx.doi.org/10.1524/9783486712018.

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Georg, Stefan. Key Performance Indicators für junge Unternehmen. Wiesbaden: Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-27546-4.

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Victoria. Department of Infrastructure. Local Government Branch. Victorian public libraries: Key performance indicators. [Melbourne]: Infrastructure, Local Government Branch, 1999.

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Key performance indicators: Developing, implementing, and using winning KPIs. Hoboken, N.J: John Wiley & Sons, 2010.

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Marr, Bernard. Key performance indicators: The 75 measures every manager needs to know. New York: Pearson Financial Times Pub., 2012.

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Savage, D. Identification of key performance indicators for information technology projects. Oxford: Oxford Brookes University, 1996.

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(Zambia), Participatory Assessment Group, ed. Baseline data survey feeder roads, report: Key performance indicators and socio-economic indicators. [Lusaka]: Participatory Assessment Group, 1997.

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Key performance indicators: The 75 measures every manager needs to know. New York: Pearson Financial Times Pub., 2012.

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Book chapters on the topic "Key performance indicators"

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Camilleri, Emanuel. "Performance Measurement." In Key Performance Indicators, 3–25. London: Routledge, 2024. http://dx.doi.org/10.4324/9781032685465-2.

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Panayides, Photis M. "Key performance indicators." In Shipping Performance Management, 43–52. London: Informa Law from Routledge, 2023. http://dx.doi.org/10.4324/9781315717845-5.

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Dorsett, Mark. "Key Performance Indicators." In Das Change Management Workbook, 352–62. München: Carl Hanser Verlag GmbH & Co. KG, 2020. http://dx.doi.org/10.3139/9783446464360.016.

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Durai, P. "Key Performance Indicators." In Quality Control in the Assisted Reproductive Technology Laboratory, 125–31. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781032622736-12.

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Bertagnolli, Frank. "Key Performance Indicators." In Lean Management, 307–17. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-36087-0_23.

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Bergeron, Bryan P. "Key Performance Indicators." In Performance Management in Healthcare, 39–52. Second edition. | Boca Raton : Taylor & Francis, 2018. | “A CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa plc.”: Productivity Press, 2017. http://dx.doi.org/10.4324/9781315102214-5.

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Bannister, Kenneth E. "Key Performance Indicators." In Practical Lubrication for Industrial Facilities, 717–21. 4th ed. New York: River Publishers, 2024. http://dx.doi.org/10.1201/9781032632360-43.

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Houck, Max M., and Paul J. Speaker. "Key Performance Indicators." In Forensic Science Laboratory Benchmarking, 54–58. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.4324/9780429318528-6.

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Camilleri, Emanuel. "Performance Management Framework." In Key Performance Indicators, 104–54. London: Routledge, 2024. http://dx.doi.org/10.4324/9781032685465-5.

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Camilleri, Emanuel. "Application of the Performance Management Tools." In Key Performance Indicators, 480–524. London: Routledge, 2024. http://dx.doi.org/10.4324/9781032685465-14.

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Conference papers on the topic "Key performance indicators"

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Kallab, Chadi, and Marc Ghawi. "Generic key performance indicators." In 2014 Third International Conference on e-Technologies and Networks for Development (ICeND). IEEE, 2014. http://dx.doi.org/10.1109/icend.2014.6991373.

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Sawabini, C. T., and Emmanuel O. Egbogah. "Reservoir Management Key Performance Indicators." In SPE Asia Pacific Oil and Gas Conference and Exhibition. Society of Petroleum Engineers, 1997. http://dx.doi.org/10.2118/38091-ms.

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Garrison, B., and K. Harrington. "47. Developing Key Performance Indicators." In AIHce 2003. AIHA, 2003. http://dx.doi.org/10.3320/1.2757943.

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Mikus, Petr. "KEY PERFORMANCE INDICATORS OF CZECH COOPERATIVES." In SGEM 2014 Scientific SubConference on POLITICAL SCIENCES, LAW, FINANCE, ECONOMICS AND TOURISM. Stef92 Technology, 2014. http://dx.doi.org/10.5593/sgemsocial2014/b22/s6.050.

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Iastrebov, P. V., and A. I. Arhipov. "Key Performance Indicators of Geomechanical Modelling." In Horizontal Wells 2021. European Association of Geoscientists & Engineers, 2021. http://dx.doi.org/10.3997/2214-4609.202154033.

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Scott, R., D. Cooke, P. Lee, and S. Forest. "Quality systems and key performance indicators." In IET Seminar on Medical Device Management - More than Maintenance. IEE, 2007. http://dx.doi.org/10.1049/ic:20070520.

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Paulsen, J. E., A. Saasen, B. Jensen, and M. Grinrød. "Key Environmental Performance Indicators in Drilling Operations." In SPE Offshore Europe Oil and Gas Exhibition and Conference. Society of Petroleum Engineers, 2001. http://dx.doi.org/10.2118/71839-ms.

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Suryadi, Kadarsah. "Key Performance Indicators in Higher Education Institution." In The International Symposium on the Analytic Hierarchy Process. Creative Decisions Foundation, 2007. http://dx.doi.org/10.13033/isahp.y2007.030.

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K. Thorsen, A. "Keynote Address: Key Performance Indicators for Geosteering." In Geosteering and Well Placement Workshop - Geosteering: Balancing Value and Risk. Netherlands: EAGE Publications BV, 2010. http://dx.doi.org/10.3997/2214-4609.20144245.

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"KEY PERFORMANCE INDICATORS IN PLANT-WIDE CONTROL." In 3nd International Conference on Informatics in Control, Automation and Robotics. SciTePress - Science and and Technology Publications, 2006. http://dx.doi.org/10.5220/0001201301790182.

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Reports on the topic "Key performance indicators"

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Sanz, E., P. Alonso, B. Haidar, H. Ghaemi, and L. García. Key performance indicators (KPIs). Scipedia, 2021. http://dx.doi.org/10.23967/prodphd.2021.9.002.

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The project “Social network tools and procedures for developing entrepreneurial skills in PhD programmes” (prodPhD) aims to implement innovative social network-based methodologies for teaching and learning entrepreneurship in PhD programmes. The multidisciplinary teaching and learning methodologies to be developed will enable entrepreneurship education to be introduced into any PhD programme, providing students with the knowledge, skills, and motivation to engage in entrepreneurial activities. However, the use of the output of the project will depend on the nature and profile of the research or scientific field. In this context, key performance indicators (KPIs) form the base on which the quality and scope of the methodologies developed in the project will be quantified and benchmarked. The project’s final product will be an online tool that higher education students can use to learn entrepreneurship from a social network perspective. Performance measurement is one of the first steps of any project and involves the choice and use of indicators to measure the effectiveness and success of the project’s methods and results. All the KPIs have been selected according to criteria of relevance, measurability, reliability, and adequacy, and they cover the process, dissemination methods, and overall quality of the project. In this document, each KPI is defined together with the units and instruments for measuring it. In the case of qualitative KPIs, five-level Likert scales are defined to improve indicator measurability and reliability. The KPIs for prodPhD are divided into three main dimensions, depending on the stage of the project they evaluate. The three main dimensions are performance and development (which are highly related to the project’s process), dissemination and impact (which are more closely correlated with the project’s output), and overall project quality. Different sources (i.e., European projects and papers) have been drawn upon to define a set of 51 KPIs classified into six categories, according to the project phase they aim to evaluate. An Excel tool has been developed that collects all the KPIs analysed in the production of this document. This tool is shared in the Scipedia repository.
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Zenhäusern, Daniel. Key Performance Indicators for PVT Systems. IEA SHC Task 60, November 2020. http://dx.doi.org/10.18777/ieashc-task60-2020-0007.

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Report D1: The aim of this report is to provide precise definitions of useful KPI’s for PVT systems. Where possible, these definitions correspond to those used in the technology fields of solar thermal systems and photovoltaic systems. In particular, the KPI's for the thermal performance of PVT systems are to a considerable extent based on the definitions adopted in IEA SHC Task 44 (Hadorn 2015). The stipulation and use of standardized KPI’s and notations will be essential for the comparability of different research results.
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Zimmerman, Timothy A. Metrics and key performance indicators for robotic cybersecurity performance analysis. Gaithersburg, MD: National Institute of Standards and Technology, April 2017. http://dx.doi.org/10.6028/nist.ir.8177.

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Tang, CheeYee. Key performance indicators for process control system cybersecurity performance analysis. Gaithersburg, MD: National Institute of Standards and Technology, August 2017. http://dx.doi.org/10.6028/nist.ir.8188.

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Serrano, Martin, Edward Griffor, David Wollman, Michael Dunaway, Martin Burns, Sokwoo Rhee, and Christopher Greer. Smart Cities and Communities: A Key Performance Indicators Framework. National Institute of Standards and Technology, February 2022. http://dx.doi.org/10.6028/nist.sp.1900-206.

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Wollman, David. Smart Cities and Communities: A Key Performance Indicators Framework. National Institute of Standards and Technology, June 2022. http://dx.doi.org/10.6028/nist.sp.1900-206-upd1.

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Anilkumar, Rahul, Benjamin Melone, Michael Patsula, Christopher Tran, Christopher Wang, Kevin Dick, Hoda Khalil, and G. A. Wainer. Canadian jobs amid a pandemic : examining the relationship between professional industry and salary to regional key performance indicators. Department of Systems and Computer Engineering, Carleton University, June 2022. http://dx.doi.org/10.22215/dsce/220608.

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The COVID-19 pandemic has contributed to massive rates of unemployment and greater uncertainty in the job market. There is a growing need for data-driven tools and analyses to better inform the public on trends within the job market. In particular, obtaining a “snapshot” of available employment opportunities mid-pandemic promises insights to inform policy and support retraining programs. In this work, we combine data scraped from the Canadian Job Bank and Numbeo globally crowd-sourced repository to explore the relationship between job postings during a global pandemic and Key Performance Indicators (e.g. quality of life index, cost of living) for major cities across Canada. This analysis aims to help Canadians make informed career decisions, collect a “snapshot” of the Canadian employment opportunities amid a pandemic, and inform job seekers in identifying the correct fit between the desired lifestyle of a city and their career. We collected a new high-quality dataset of job postings from jobbank.gc.ca obtained with the use of ethical web scraping and performed exploratory data analysis on this dataset to identify job opportunity trends. When optimizing for average salary of job openings with quality of life, affordability, cost of living, and traffic indices, it was found that Edmonton, AB consistently scores higher than the mean, and is therefore an attractive place to move. Furthermore, we identified optimal provinces to relocate to with respect to individual skill levels. It was determined that Ajax, Marathon, and Chapleau, ON are each attractive cities for IT professionals, construction workers, and healthcare workers respectively when maximizing average salary. Finally, we publicly release our scraped dataset as a mid-pandemic snapshot of Canadian employment opportunities and present a public web application that provides an interactive visual interface that summarizes our findings for the general public and the broader research community.
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Hermann, Ludwig, Ralf Hermann, and Oscar F. Schoumans. Application of economic key performance indicators to five centralised anaerobic digesters : a product from the H2020 project SYSTEMIC. Wageningen: Wageningen Environmental Research, 2021. http://dx.doi.org/10.18174/572619.

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Lee, Minsoo, Joon-Kyung Kim, Myung Ho Park, Longyun Peng, Tetsuji Okazaki, and Susumu Ishii. Developing Modernization Indicators for the People’s Republic of China: Key Considerations and a Proposed Framework. Asian Development Bank, December 2020. http://dx.doi.org/10.22617/wps200417-2.

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Abstract:
The People’s Republic of China (PRC) is in the process of modernization, which it expects to achieve by 2035. This requires a national indicator to gauge the progress and level of the country’s modernization drive. This working paper provides an indicator that assesses the progress in the PRC’s modernization journey, and applies this modernization indicator—structured on thematic categories, subcategories, and intersection—to the PRC’s performance, which is then compared with countries of the Organisation for Economic Co-operation and Development and the G20. The paper also examines four major policy implications of this modernization for the PRC, which will be more durable and successful if coupled with effective division of labor and synergy with stakeholders.
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10

Alves, Pana, Carmen Broto, María Gil, and Matías Lamas. Risk and vulnerability indicators for the spanish housing market. Madrid: Banco de España, July 2023. http://dx.doi.org/10.53479/36275.

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Abstract:
The residential real estate market has a significant weight in the Spanish economy and its performance is closely linked to that of the financial cycle. In addition, as evidenced by the real estate crisis that began in Spain in 2008, the risks generated in this sector have important implications for financial stability. The development of a framework for the early identification of risks in this market is therefore key. This article presents two complementary tools to meet this objective. The first is a heat map that provides a visual interpretation of risk levels in this market for a wide selection of individual indicators. The second is a synthetic indicator that summarizes the information provided by the individual indicators. This index complements the information of the heat map, since it measures both the intensity of the risks in each period and their composition. Both the heat map and the synthetic indicator suggest that, in recent months, the vulnerabilities that had been accumulating in the housing market since 2021 have somewhat reverted.
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