Monitoring and diagnosis of energy consumption in wastewater treatment plants. A state of the art and proposals for improvement

Theoretical “necessary but not sufficient” statements are common in the organizational sciences. Traditional data analyses approaches (e.g., correlation or multiple regression) are not appropriate for testing or inducing such statements. This article proposes necessary condition analysis (NCA) as a general and straightforward methodology for identifying necessary conditions in data sets. The article presents the logic and methodology of necessary but not sufficient contributions of organizational determinants (e.g., events, characteristics, resources, efforts) to a desired outcome (e.g., good performance). A necessary determinant must be present for achieving an outcome, but its presence is not sufficient to obtain that outcome. Without the necessary condition, there is guaranteed failure, which cannot be compensated by other determinants of the outcome. This logic and its related methodology are fundamentally different from the traditional sufficiency-based logic and methodology. Practical recommendation...

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Necessary Condition Analysis (NCA) Logic and Methodology of “Necessary but Not Sufficient” Causality

This paper provides an extensive overview of the literature on efficiency in education. It summarizes the earlier applied inputs, outputs and contextual variables, as well as the used data sources of papers in the field of efficiency in education. Moreover, it reviews the papers on education that applied methodologies as data envelopment analysis, Malmquist index, Bootstrapping, robust frontiers, metafrontier or stochastic frontier analysis. Based on the insights of the literature review, a second part of the paper provides some ways forward. It attempts to establish a link between the parametric ‘economics of education’ literature and the (semi-parametric) ‘efficiency in education literature’. We point to the similarities between matching and conditional efficiency; difference-in-differences and metafrontiers; and quantile regressions and partial frontiers. The paper concludes with some practical directions for prospective researchers in the field.

/pdf/efficiency-in-education-a-review-of-literature-and-a-way-jq65h8tz6o.pdf

Efficiency in education: a review of literature and a way forward

International comparisons of the technical efficiency of the hospital sector: Panel data analysis of OECD countries using parametric and non-parametric approaches ☆☆

The measurement of corporate performance is central to strategic management research. A common objective of this research is to identify top performers in an industry and their sources of competitive advantage. Despite this focus on best firms and practices, most researchers utilize statistical methods that identify average effects in a sample, and they assess a single performance dimension while ignoring other relevant dimensions. Emphasis on purely financial measures can overlook the fact that a firm's efficiency in transforming resources has been shown as a major source of competitive advantage. In this article we demonstrate how frontier methodologies, such as Data Envelopment Analysis and the Stochastic Frontier approach, can address these challenges. We provide an illustration based on longitudinal data from U.S. and Japanese automobile producers.

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Production Frontier Methodologies and Efficiency as a Performance Measure in Strategic Management Research

1. Introduction to Benchmarking.- 2. Efficiency Measures.- 3. Production Models and Technology.- 4. Data Envelopment Analysis DEA.- 5. Additional Topics in DEA.- 6. Statistical Analysis in DEA.- 7. Stochastic Frontier Analysis SFA.- 8. Additional Topics in SFA.- 9. Merger Analysis.- 10. Regulation and Contracting.- Appendix. Getting Started with R: A Quick Introduction.

Benchmarking with DEA, SFA, and R

Benchmarking: Benchmark and Frontier Analysis Using DEA and SFA

As explained in Chap. 1, there are two dominant approaches to modern benchmarking. One is the non-parametric, deterministic DEA approach discussed in some detail in the last three chapters; other is the stochastic frontier analysis (SFA), which we will cover in this and the next chapter. In the subsequent chapters, we will discuss a series of major applications of both approaches and examine the underlying benchmarking problem.

Stochastic Frontier Analysis SFA

We covered the basics of DEA in the previous chapter. In this chapter, we will cover some additional topics that we find particularly relevant both to applications and to our understanding of DEA.

Lars Otto

Papers

Benchmarking with DEA, SFA, and R

Benchmarking: Benchmark and Frontier Analysis Using DEA and SFA

Benchmarking with DEA, SFA, and R

Stochastic Frontier Analysis SFA

Additional Topics in DEA