The Evolution of Performance Management

Historically, the role of performance management evolved over time. At the beginning of the twentieth century, Frederick Taylor was the first to introduce notions of performance and efficiency. He believed that it was management´s responsibility to devise the best method of performing work. Thus, he proposed a mechanistic management approach that became known as “scientific management”. It was based on the analysis of existing

54

work methods through observation and measurement (Martinez et al., 2007). This approach was seen to legitimize management as a control agent. It was enhanced by many other researchers and shaped the work of highly successful managers like Henry Ford or theorists like Henri Fayol. While Ford applied the approach to develop early mass production systems, Fayol derived key managerial functions including planning, organizing and control (Hailey and Sorgenfrei, 2004). At individual level, the scientific management approach laid its focus on the individual output of each worker. Accordingly, on organizational level, managers mostly looked at accounting-based performance measures (Yadav and Sagar, 2013). Thus, performance management was primary volume and cost centered.

During the 1950s and 1960s the perception of performance management shifted. On the one hand, there was criticism on the scientific management approach that evolved from the human relations movement. The supporters of the human relations movement argued that social factors of work were at least as important as the technical ones advocated by the scientific management approach. On the other hand, in the era of low unemployment, management´s focus shifted to concerns on how to attract and retain employees. Thus, a greater emphasis was put on the improvement of working conditions and on the assessment of performance-related inputs factors like competencies (Houldsworth and Jirasinghe, 2006; Martinez et al., 2007).

During the 1970s, the western style management practices were extremely challenged by manufacturers across the world, notably Japanese ones. In this respect, many managers questioned the use of traditional performance measures, since Japanese manufactured goods not only were of greater variety but also of better quality. Thus, they called for better measures that could assess why some factories were better than others. Subsequently, Total Quality Management (TQM) entered the performance management scene in the 1980s and with this, new performance measures like defect rates, response time or delivery commitments came into picture. From the quality management paradigm, different approaches including Six Sigma or Lean Enterprises emerged. Despite the recognition of the quality approaches, the organization´s accounting systems merely included to financial information (Nudurupati et al., 2011).

By the 1990s, quality approaches were well-established and many researchers started to criticize using financial figures only, indicating that qualitative measures like customer satisfaction, competition indexes or innovation should be integrated into performance management (Hailey and Sorgenfrei, 2004). By introducing the Balanced Scorecard, Kaplan and Norton (1992) brought kind of a “performance management revolution” proving that complementary to financial figures operational and strategical measures can

55

be included into one PM framework (Yadav and Sagar, 2013) (see also chapter 3.2.4). Futher, the BSC introduced the involvement of different stakeholders including employees, customers or suppliers. Since then, several integrated PM systems and frameworks emerged and with them the number of performance measures rose (Nudurupati et al., 2011).

In the mid to late 90s, simultaneous to the excessive use of performance measures, the development reached its peak with one book on this subject being published at a rate of one every two weeks in the U.S. alone. Further, between 1994 and 1996, more than 3,600 articles on performance measurement were published (Folan and Browne, 2005; Star et al., 2016). The increasing range of measures led to what Neely and Austin (2000) described as the “first measurement crisis” with organizations becoming more concerned about measuring than actually managing performance.

During the last two decades, the focus of performance management again has shifted from looking across organizations functionally to a rather horizontally (process-oriented) view. Management techniques like Business Process Reengineering (BPR) promoted this view by focusing in particular on processes that crossed departmental boundaries. Both, TQM and BPR shifted the focus of performance management from “producing” to “improvement” (Martinez et al., 2007). In 2006, Davenport noted in times where organizations offer similar products and services by using the same technologies, business processes are among the last remaining points of differentiation. He introduced “Business Analytics” as an organization´s ability to collect, analyze and act on data (Davenport, 2006). Since then, business analytics is an emerging field that has also gained momentum in performance management (Bronzo et al., 2013). The aim of business analytics in the domain of performance management is to supplement this area with sophisticated and analytical decision-making tools, which lie beyond the domain of traditional performance management including mathematics, statistics, econometrics as well as tools for data gathering and analysis. In consequence, business analytics shall provide inside into business dynamics and their related performance in order to use analytical indicators to predict performance (Schläfke et al., 2012).

56

Figure 6: The Evolution of Performance Management

Summarizing, there are three general trends in the evolution of performance management:

1) The broadening of analysis: From a mere financial perspective to an integrated perspective

2) The broadening of performance measures: from cost and output to quality and input.

3) The broadening of the scope: from internal focus towards the inclusion of external focus (stakeholders) and the application of business analytics.