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The analysis and interpretation of the data was conducted by both authors. AVL was involved in drafting and coordinating the manuscript, and AS in reviewing it critically. Both authors read and approved the final manuscript. The datasets supporting the conclusions of this article are included within the article and its additional files. This article does not contain any studies with human participants or animals performed by any of the authors.
You can also search for this author in PubMed Google Scholar. Correspondence to Amy Van Looy. See Table 7. See Table 8. See Table 9. Reprints and Permissions. Van Looy, A. Business process performance measurement: a structured literature review of indicators, measures and metrics. SpringerPlus 5, Download citation. Received : 17 June Accepted : 10 October Published : 18 October Anyone you share the following link with will be able to read this content:.
Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search all SpringerOpen articles Search. Download PDF. Abstract Measuring the performance of business processes has become a central issue in both academia and business, since organizations are challenged to achieve effective and efficient results.
Background Since organizations endeavor to measure what they manage, performance measurement is a central issue in both the literature and in practice Heckl and Moormann ; Neely ; Richard et al. What is the current state of the research on business process performance measurement?
Theoretical background This section addresses the concepts of performance measurement models and performance indicators separately in order to be able to differentiate them further in the study. Performance measurement models According to overviews in the performance literature Heckl and Moormann ; Neely ; Richard et al. Full size image. Table 1 An example of translating an organizational strategy into operational terms using the BSC Full size table.
Methods We conducted a structured literature review SLR to find papers dealing with performance measurement in the business process literature. Formulating the research questions and search strategy A comprehensive and unbiased search is one of the fundamental factors that distinguish a systematic review from a traditional literature review Kitchenham Table 2 The structured literature review protocol for this study, based on Boellt and Cecez-Kecmanovic Full size table.
Table 3 The number of papers in the web of science per search query until November Full size table. Exclusion of papers and number of primary studies. Results for RQ1 The final sample of 76 papers consists of 46 journal papers and 30 conference papers Fig. Contact us. Please note you do not have access to teaching notes. Other access options You may be able to access teaching notes by logging in via your Emerald profile. Abstract Asks why business performance measurement has become so topical, so recently.
Join us on our journey Platform update page Visit emeraldpublishing. This effect explains why extraordinary managers may deliver ordinary short-term TRS; conversely, managers of companies with low performance expectations might find it easy to earn high TRS. This predicament illustrates the old saying about the difference between a good company and a good investment: in the short term, good companies may not be good investments, and vice versa. One way of overcoming the limitations of TRS is to employ complementary measures of stock market performance.
One of them is market value added MVA : the difference between the market value of a company's debt and equity and the amount of capital invested. A related metric, expressed as a ratio, is the market-value-to-capital ratio—the ratio of a company's debt and equity to the amount of capital invested.
TRS measures it against the financial markets' expectations and changes in them. Market-value-to-capital ratios and MVA, by contrast, measure the financial markets' view of the future performance of a company relative to the capital invested in it, so they assess expectations about its absolute level of performance.
Let's examine Home Depot and the other large retailers in terms of their stock market performance. Home Depot's market-value-to-capital ratio was in the middle of the pack among large retailers, since the company isn't expected to generate as much value per dollar of capital as did other highfliers such as Best Buy but made up for that with size.
What about TRS? Over the five years ended , Home Depot's—at So the company delivered a strong economic profit, the second-highest MVA, and a strong market-value-to-capital ratio but also had very low TRS.
Evidently, Home Depot's performance over recent years wasn't up to what the market expected at the start of the measurement period By reverse-engineering the current and past share prices of Home Depot, we can develop a perspective on why its TRS was so low. An investor using a DCF model might infer that at the end of the stock market expected the revenue growth of Home Depot to decline gradually, to 5 percent annually, from 12 percent, over the next decade while it maintained its current margins and ROIC.
Given the share price of Home Depot at the end of , an investor would have had to believe that it could grow by 26 percent a year for at least ten years. Such high growth expectations would have required the company to triple its store count over that period—far beyond the estimated saturation level for its markets.
It is tempting to conclude that Home Depot's poor TRS since resulted more from an overly optimistic market value at the start of that year than from ineffective management. Measuring the historical performance of a company is difficult though doable. But coming to grips with its historical performance isn't enough; the assessment must also address the company's health—its ability to sustain and improve its performance in the future—and its share price performance.
Never miss an insight. We'll email you when new articles are published on this topic. Accept Use minimal essential cookies. Skip to main content. Measuring long-term performance. By Richard Dobbs and Timothy Koller. We strive to provide individuals with disabilities equal access to our website. If you would like information about this content we will be happy to work with you. Explore a career with us Search Openings.
Related Articles. Article - McKinsey Quarterly Organizing for the future. This article will reveal how this mistake permeates businesses—probably even yours—driving poor decisions and undermining performance. And it will show you how to choose the best statistics for your business goals.
Moneyball, the best seller by Michael Lewis, describes how the Oakland Athletics used carefully chosen statistics to build a winning baseball team on the cheap. The book was published nearly a decade ago, and its business implications have been thoroughly dissected. Businesses continue to use the wrong statistics. Looks might have nothing to do with the statistics that are actually important: those that reliably predict performance. Baseball managers used to focus on a basic number—team batting average—when they talked about scoring runs.
Moreover, on-base percentage was underpriced relative to other abilities in the market for talent. This allowed the team to recruit winning players without breaking the bank. Many business executives seeking to create shareholder value also rely on intuition in selecting statistics.
The metrics companies use most often to measure, manage, and communicate results—often called key performance indicators—include financial measures such as sales growth and earnings per share EPS growth in addition to nonfinancial measures such as loyalty and product quality.
Most executives continue to lean heavily on poorly chosen statistics, the equivalent of using batting averages to predict runs. Through my work, teaching, and research on these biases, I have identified three that seem particularly relevant in this context: the overconfidence bias, the availability heuristic, and the status quo bias. Most people, for example, regard themselves as better-than-average drivers. The tendency toward overconfidence readily extends to business.
Consider this case from Stanford professors David Larcker and Brian Tayan: The managers of a fast-food chain, recognizing that customer satisfaction was important to profitability, believed that low employee turnover would keep customers happy. Confident in their intuition, the executives focused on reducing turnover as a way to improve customer satisfaction and, presumably, profitability.
As the turnover data rolled in, the executives were surprised to discover that they were wrong: Some stores with high turnover were extremely profitable, while others with low turnover struggled.
Only through proper statistical analysis of a host of factors that could drive customer satisfaction did the company discover that turnover among store managers, not in the overall employee population, made the difference. As a result, the firm shifted its focus to retaining managers, a tactic that ultimately boosted satisfaction and profits.
For example, executives generally believe that EPS is the most important measure of value creation in large part because of vivid examples of companies whose stock rose after they exceeded EPS estimates or fell abruptly after coming up short. To many executives, earnings growth seems like a reliable cause of stock-price increases because there seems to be so much evidence to that effect.
To identify useful statistics, you must have a solid grasp of cause and effect. Consider this: The most common method for teaching business management is to find successful businesses, identify their common practices, and recommend that managers imitate them.
Collins and his team analyzed thousands of companies and isolated 11 whose performance went from good to great. They then identified the practices that they believed had caused those companies to improve—including leadership, people, a fact-based approach, focus, discipline, and the use of technology—and suggested that other companies adopt them to achieve the same great results. This formula is intuitive, includes some compelling narrative, and has sold millions of books.
If causality were clear, this approach would work. The trouble is that the performance of a company almost always depends on both skill and luck, which means that a given strategy will succeed only part of the time. Some companies using the strategy will succeed; others will fail.
The more important question is, How many of the companies that tried the strategy actually succeeded? Say two companies pursue the same strategy, and one succeeds because of luck while the other fails. Since we draw our sample from the outcome, not the strategy, we observe the successful company and assume that the favorable outcome was the result of skill and overlook the influence of luck.
We connect cause and effect where there is no connection. Statistics that are persistent and predictive, and so reliably link cause and effect, are indispensable in that process.
Finally, executives like most people would rather stay the course than face the risks that come with change.
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