The evolution of performance measurement research: Developments in the last decade and a research agenda for the next
By Andy Neely. 2005
1,014 citations recorded by the source corpus.
Subfields: Management Information Systems, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/882bab703d65b380dda178ada68639d01360c163
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Management Information Systems
- Andy Neely
- Flavio Tonelli
- Paolo Taticchi
- Luca Cagnazzo
- Federico Caniato
- Davide Luzzini
- Vieri Maestrini
- Paolo Maccarrone
In Management Science and Operations Research
- Flavio Tonelli
- Paolo Taticchi
- Luca Cagnazzo
- Hajar Fatorachian
- Hadi Kazemi
- Benita M. Beamon
- Burcu Balcik
- Oscar F. Bustinza
Citations in the corpus
- Performance measurement in humanitarian relief chains (2008, background): “Review studies in performance measurement research particularly addressing the corporate sector include Neely et al. (1995), Folan and Browne (2005), and Neely (2005).”
- Contemporary Performance Measurement Systems: A Review of Their Consequences and a Framework for Research (2012, background): “42 how well it fits the context in which it operates (e.g., Otley, 1999; Neely, 2005; FrancoSantos and Bourne, 2005).”
- An overview of the literature on technology roadmapping (TRM): Contributions and trends (2013, background): “They also focus on the identification of the most important academic works and authors based on an analysis of citations [11].”
- Impact of Industry 4.0 on supply chain performance (2020, background): “Supply chain performance is one of the challenging themes in supply chain management literature (Neely 2005; Bhagwat and Sharma 2009; Martinez, Pavlov and Bourne 2010; Bititci, Firat and Garengo 2012).”
- Performance measurement and management: a literature review and a research agenda (2010, methodology): “This paper provides an update of Neely’s work ‘‘The evolution of performance measurement research’’ The literature review has been carried out by using two different methodologies.”
- Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009, neutral): “Second, few measurement systems have a systematic method for prioritizing the measures [19] and, therefore, many companies have difficulties in figuring out ways of adapting their continuously changing strategic objectives and meeting the requirements of the dynamic decision-making environment.”
- Towards a definition of a business performance measurement system (2007, background): “This last criterion was included because of the change in perspective that took place in the 1980s, by which performance measurement moved away from having a pure financial focus to include more comprehensive business characteristics (Kaplan, 1983; Neely, 2005).”
- Servitisation and value co-production in the UK music industry: An empirical study of Consumer Attitudes (2012, background): “Servitization is seen as one of the best ways for manufacturing firms in developed economies to address the five forces that influence an industry’s dynamics and inherent profitability (Porter and Ketels, 2003; Neely, 2005).”
- Supply chain performance measurement systems: A systematic review and research agenda (2017, background): “The literature on internal PMSs historically unveils a tortuous path between system adoption and actual performance improvement (Neely, 2005; Melnyk et al., 2014).”
- Outsourcing, competitive capabilities and performance: an empirical study in service firms (2010, neutral): “In the process of monitoring the success of organizations, performance and the measurement of performance are the most commonly used tools for assessing whether or not organizations have made the right decisions (Dixon et al., 1990; Neely, 2002, 2005).”
- A bibliometric analysis of leading universities in innovation research (2017, background): “…bibliometric studies, to either help us understand the data previously analyzed or show us possible hidden patterns that could be very interesting to address (Bonilla, Merigó, & Torres-Abad, 2015; Carvalho, Fleury, & Lopes, 2013; Chun-Hao & Jian-Min, 2012; Neely, 2005; Wagstaff & Culyer, 2012).”