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Productivity of knowledge workers. How can we analyze and measure it?

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Presentation from the 12th International Conference in Accounting and Management Information Systems

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Productivity of knowledge workers. How can we analyze and measure it?

  1. 1. Productivity of knowledge workers. How can we analyze and measure it? Costin Ciora Ion Anghel Vasile Robu Department of Financial Analysis and Valuation (AEEF) The Bucharest University of Economic Studies (ASE) Prepared for AMIS 2017. WORK IN PROGRESS
  2. 2. 2 1. Motivation 2. Theoretical background 3. Hypothesis 4. The challenge: input, activities and output 5. Indicators to measure productivity: a critical analysis 6. A quantitative view on productivity Agenda Ciora | Anghel | Robu
  3. 3. 3 1.Motivation Ciora | Anghel | Robu Research questions: • How can we measure knowledge-worker productivity? • What are the challenges from the methodological point of measuring productivity?
  4. 4. 4 1.Motivation Ciora | Anghel | Robu The importance of assessing motions, effort and time in order to increase the efficiency of repetitive tasks. (Taylor, 1911) => So, in today’s knowledge economy, managers often think in terms of standardizing the activities and derive them to a more repetitive ones.
  5. 5. 5 1.Motivation Ciora | Anghel | Robu knowledge worker = “the most valuable asset of a 21st- century institution, whether business or non-business, will be its knowledge workers and their productivity” (Druker, 1959) To achieve higher productivity, knowledge workers should be seen a capital asset, with the purpose of growing and acquiring new knowledge. (Druker, 1999)
  6. 6. 6 1.Motivation Ciora | Anghel | Robu Industrial production Eight-fold increase in 2015 compared to 1997. (The World Bank, 2016).
  7. 7. 7 1.Motivation Ciora | Anghel | Robu Figure 1. Evolution of employment worldwide, by sectors (Source: International Labour Organization, 2014)
  8. 8. 8 1.Motivation Ciora | Anghel | Robu (Source: International Labour Organization, 2014) Services weight more than 46% in the total employment, with more than 74% in developed economies. The industry employment weights 24% globally and less than 22.5% in developed economies (International Labour Organization, 2014). The same organization states that more than 1.56 billion people are estimated to work in services until 2018.
  9. 9. 9 1.Motivation Ciora | Anghel | Robu (Tully & Rapp, 2016) Fortune 500 financial data> cumulated profits FinancialHealthcare Technology 37.9% FinancialHealthcare Technology 57.2%
  10. 10. 10 2.Theoretical background Ciora | Anghel | Robu Druker, 1995: One of the most important challenges for managers today is to increase productivity of knowledge and service workers. Maruta, 2012: • in the production activities, there is a skilled work where front-line workers yield uniform outputs according to given standard procedures. • in the non-production activities, the first level are front-line workers that yield individually unique outputs according to self judgements. This difference is crucial related to outputs in the knowledge economy.
  11. 11. 11 2.Theoretical background Ciora | Anghel | Robu This difference is known in the literature as WHITE-COLLAR versus BLUE-COLLAR work. Davis, 1991: • the blue-collar work which is more standardized, related to manufacturing, can be planned and scheduled, and create the traditional model of productivity for increasing output and lowering input. • the white-collar productivity becomes a broader concept because of the involvement of efficiency, effectiveness and transformations. Robu et al., 2014: there are two methods of analyzing productivity: extensive, through working time and intensive through work productivity and profit per employee
  12. 12. 12 3. Hypothesis Ciora | Anghel | Robu Hypothesis 1 Traditional measures should be understood in a larger context (revenues workers vs. non revenues workers that provide important information for revenues workers) Hypothesis 2 The need of adding other key performance indicators as KPIs for the effect in the Effect/Effort formula of calculating productivity. Hypothesis 3 Rethinking the "Effort" not only related to time not only in the time spent but in the idea of "time well spent" (e.g. a knowledge worker could work on a project for 6 hours instead of average 4 hours of a team).
  13. 13. 13 4. The challenge: input, activities and output Ciora | Anghel | Robu The data related to manual workers is so vast that there are a lot of studies on the impact of night shifts on workers’ productivity and influence on health. (Folkard & Tucker, 2003) INPUT In production, this type of schedule is common as there is a strong need to continue the production seven days a week.
  14. 14. 14 4. The challenge: input, activities and output Ciora | Anghel | Robu For manual workers, the activities consisted in standardized procedures and movements set in order to increase the productivity and reach the target production. ACTIVITIES Knowledge workers are expected to produce better results than before, by innovating simple tasks and being more creative than ever. In this sense, the actual activities are not a measure of quantity and uniformity, but rather a challenge of innovation and discovering unique individuals
  15. 15. 15 4. The challenge: input, activities and output Ciora | Anghel | Robu As there are many attempts to standardize the knowledge worker output through managerial systems or human resources procedures, this could only lead to poorer outputs. For example, if a knowledge worker has to prepare a monthly report, this will be completely different depending on the experience in the field, amount of information and time for the analysis. Even if, the report would have standard fields, we cannot consider the output the report itself, but the quality of the date in the report. OUTPUT
  16. 16. 16 5. Indicators to measure productivity: a critical analysis Ciora | Anghel | Robu Effect = Gross Value Added (GVA) and Value of industrial production (Q). Effor = number of employees (Ne) and the total time expressed by number of hours worked in that sector (T) Effect/ Effort or Effort/Effect. Annual work productivity based on • gross value added per employee (GVA/Ne) and • gross value added per hour (GVA/T) and • indicators of productivity based on industrial production (Q/Ne and Q/T). For the traditional sectors of the economy
  17. 17. 17 5. Indicators to measure productivity: a critical analysis Ciora | Anghel | Robu Effect = revenues (R), profit (Pr), gross value added (GVA) Effor = number of employees (Ne) Effect/ Effort or Effort/Effect. Efficiency of workforce, through productivity based on • revenues (R/Ne); • productivity based on gross value added (GVA/Ne) and • profit per employee (Pr/Ne). In the knowledge economy, at the sector level
  18. 18. 18 5. Indicators to measure productivity: a critical analysis Ciora | Anghel | Robu Efficiency of human potential, can be measured through productivity per employee • revenues (R/Ne), • gross value added (GVA/Ne), • earnings before interest and taxes per employee (EBIT/Ne) and • economic value added per employee (EVA/Ne).
  19. 19. 19 5. Indicators to measure productivity: a critical analysis Ciora | Anghel | Robu (R/Ne) has the advantage of comparison between companies from the same sector, and for average of the sector, but has the disadvantage of lack of comparison between different sectors. (GVA/Ne) has a higher informational value than Revenues/employee because it takes into consideration the higher degree of integration of activities and allows comparison between companies from the same industries but also between different sectors. The analysis of work productivity in the knowledge economy must be correlated with the analysis of the evolution of salary expenses per employee (Sal.exp/Ne), resulting in the efficiency of such expenses.
  20. 20. 20 6. A quantitative view on productivity Ciora | Anghel | Robu • For presenting the difference in terms of measuring productivity, we chose two sectors related to the Romanian economy. • Food industry – a traditional sector with mainly blue collar workforce; • IT services sector – a recent developed industry, with mainly white collar workforce. • We identified two samples based on an initial sample at the economy level of 1102 companies with good performance, with a COFACE rating good and very good, revenues higher than 1 million euro.
  21. 21. 21 6. A quantitative view on productivity Ciora | Anghel | Robu • The final sample included 44 companies in the food sector and 22 companies in IT, analyzed between 2008 and 2015. • Our analysis included 31 indicators. The result shows significant difference between the two sectors in terms of profitability ratios, as presented in the table below.
  22. 22. 22 6. A quantitative view on productivity Ciora | Anghel | Robu Food industry IT services sector Median Average Median Average Difference in average EBITDA 7.70% 9.00% 13.10% 14.30% 58.30% Profit margin 4.70% 6.10% 11.10% 10.80% 77.60% Net profit 232,450 1,044,921 494,583 1,919,296 83.70% Equity 3,025,504 9,093,138 1,116,997 6,869,331 -24.50% ROE 11.60% 12.30% 38.00% 48.00% 290.80% ROA 4.00% 5.50% 15.70% 21.00% 278.20% OROA 7.50% 10.50% 26.60% 40.60% 286.10% ROIC 14.20% 19.50% 42.70% 49.50% 154.30% Sal. Exp 1,348 1,792 3,928 4,558 154.40% Value added (%) 20.70% 21.10% 40.30% 40.10% 90.30% Sal.exp/ VA 48.00% 47.40% 54.80% 63.60% 34.20%
  23. 23. 23 6. A quantitative view on productivity Ciora | Anghel | Robu • One sector, the food industry, is considered to be a blue collar sector, in which the productivity is measured through the number of units made by each employee. • In the IT sector, characterized as a white collar sector, measuring productivity is correlated with the actual work of employees involved in different projects. • This difference is almost 300% for return on equity. By looking at the salary expenses on value added we calculated a difference of 34.20% between the two sectors in favor of the IT sector.
  24. 24. 24 Conclusions Ciora | Anghel | Robu • A company working with many sub-contractors will be interested to measure, as pointed before, the partial productivity of different stages of the product development or service delivery. • The partial productivity could be analysis as cost per suppliers per hours. • Because of increase in outsourcing activities, this could become another important indicator. • Working from home might affect productivity through the time needed for performing a task.
  25. 25. 25 Conclusions Ciora | Anghel | Robu • Measuring productivity becomes an important challenge for companies in the knowledge economy, also because of frequent interruption (email, social networks) of employees, which reduce the focus on the actual tasks and projects. • As new type of business will be created in the next years, new jobs will provide the need of measuring productivity • And because of this complexity of the business world, the quantitative measures will be correlated with other factors like behavior, expectations or overall impact of the work on the value of the business.
  26. 26. Corresponding author: Costin Ciora: costin.ciora@cig.ase.ro Thank you! Costin Ciora Ion Anghel Vasile Robu Department of Financial Analysis and Valuation (AEEF) The Bucharest University of Economic Studies (ASE)

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