Measures of organizational performance

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Many consultants maintain that various initiatives and programs improve the performance of organizations. Nevertheless, many of these assertions have not been assessed. Indeed, even the optimal definitions or measures of performance remain controversial.

Fortunately, when these propositions are assessed, the results are often encouraging. That is, practices that improve the commitment and attitudes of employees do indeed enhance many financial indicators of workplace performance (e.g., Gong, Law, Chang, & Xin, 2009).

Measures of organizational performance


Many researchers utilize traditional accounting measures of profit. One the most common indices, for example, is return on assets (Staw & Epstein, 2000& Wan & Hoskisson, 2003). Roughly, return on assets is the annual profit or net income divided by the average assets over the year. More precisely, to compute the numerator, researchers usually subtract the interest expense and the interest tax savings from the annual profit. As van Dyck, Frese, Baer, and Sonnentag (2005) highlight, return on assets is a measure of operating efficiency, reflecting the long term financial strength of organizations.

Although a ubiquitous measure, return on assets is not always an optimal measure. For example, return on assets should not be used to compare organizations in different industries. The peculiarites of any industry will bias this index. Because of the massive reserves in the insurance and banking industries, for example, return on assets will tend to underestimate the profitability of these organizations.

Return on assets differs from return on investment, which is also called the rate of return. The return on investment is usually calculated to examine the efficiency of a specific investment or initiative--or to compare the efficiency of several investments of initiatives. Return on investment is merely the return of an investment--that is, the gain minus the costs--divided by the costs of this investment.


Although related to profit, some researchers instead compute the productivity of employees. Roughly, productivity is the revenue divided by the total number of employees. Many researchers, however, prefer to compute the natural log of revenue divided by the total number of employees (e.g., Huselid, 1995& Subramony, Krause, Norton, & Burns, 2008).

Subramony, Krause, Norton, and Burns (2008), for example, showed that perceptions of pay affected productivity. That is, productivity, as measured by the natural log of revenue divided by the total number of employees, increased if employees, one year earlier, had reported they felt their pay was competitive.

Sales and market share

Sales is often used to gauge the performance of organizations. Nevertheless, several variants of sales have been utilized.

In one study, for example, conducted by Salamon and Robinson (2008), sales relative to targets was calculated. That is, senior management had estimated the sales target of each site, depending on the product lines, characteristics of the clientele, and other factors. To compute sales performance, actual sales was divided by target sales, and then multiplied by 100. This study showed that sites in which employees felt trusted by management experienced a sense of responsibility and accountability, which translated into improvements in this sales index (Salamon & Robinson, 2008).

Many related measures of sales are also used. Typical examples are total sales growth, rather than merely sales, as well as market share (Gong, Law, Chang, & Xin, 2009).

Studies indicate that HR systems that relate to productivity have been shown to enhance performance, as measured by similar measures. These systems include extensive training, competitive pay that is contingent upon performance, career planning, performance appraisal, and participation in decision making (Gong, Law, Chang, & Xin, 2009).

Customer service

In lieu of more objective measures of workplace performance, some researchers also assess subjective indices. One of the most common subjective indices is customer service.

To illustrate, in the study undertaken by Salamon and Robinson (2008), customer surveys were conducted. This particular survey comprised 10 questions, such as whether customers were assisted or greeted appropriately. Customer service was rated more favorably if employees felt trusted by management.

Subjective estimates of financial performance

Some researchers utilize a measure that, in essence, combines the benefits of objectives indices with the merits of subjective indices. Specifically, participants are asked to complete a series of subjective questions, which are intended to gauge objective indices (Delaney & Huselid, 1996& Rhodes, Hung, Lok, Ya-Hui Lien, & Wu, 2008). These measures have been shown to correlate appreciably with objective measures (Delaney & Huselid, 1996).

Achievement of goals

Many indices of workplace performance disregard the goals of organizations. In one year, for example, organizations might want to invest in expensive technology, to enhance productivity in the future. The profit in this year might be negligible even if the workplace fulfills its objectives. Accordingly, profit might not be a suitable measure of performance in this context.

Therefore, to gauge workplace performance, some researchers examine the extent to which the organization has fulfilled its goals. In one study, for example, conducted by van Dyck, Frese, Baer, and Sonnentag (2005), participants rated the extent to which their "...organization achieved its most important goal in the last year" and the degree to which the organization was successful " comparison to other companies in the same line of industry and of (about) the same size?".

As the authors predicted, organizations that manage errors effectively were more likely to fulfill their goals (van Dyck, Frese, Baer, & Sonnentag, 2005). That is, organizations were more likely to satisfy their goals, as measured by these two items, is employees communicated knowledge about errors, collaborated to resolve errors, and introduced practices that detect and manage errors expeditiously.

Measures specific to the industry

Some studies utilize measures that are specific to particular industries. In a study that examined the determinants of performance at schools, five domains were measured: academic achievement, student behavior, student satisfaction, teacher turnover, and administrative performance--which was a subjective measure, as rated by principals (see Ostraff, 1992).

Holistic measures

Many consultancies now measure workplace performance with tools that are intended to assess all key domains of a business. Examples include the concept of balanced scorecards as well as measures of quality, applied in the Six Sigma and Total Quality Management paradigms.

Six Sigma, a trademark of Motorola, is a paradigm in which defects--that is, anything that could undermine customer satisfaction--is detected and then eliminated. The term has derived from a simple proposition: if the number of standard deviations, designated as sigmas, between the process mean and specification limit is six, almost all items will comply with these specifications.

Like other quality management programs, Six Sigma assumes that process variations need to be minimized. In addition, this strategy assumes that everyone in the organization, especially senior management, need to be involved the eradication of defects or errors. Furthermore, decisions need to be derived from observable data

From the perspective of Six Sigma, individuals must define projects goals and existing processes. Next, they must measure key dimensions of these processes. Third, they need to identify the factors that affect these dimensions. Fourth, they need to optimize or refine these processes. Finally, control mechanisms and monitoring needs to be implemented to ensure that deviations from targets are corrected in a timely fashion. These sequence of steps is conducted to improve existing processes.

To create novel products or services, however, a different, but related, sequence of phases need to be considered. First, individuals need to define goals that align with both the demands of customers and the strategy or objectives of the firm. Next, they need to measure the key determinants of quality as well as existing capabilities, processes, and risks. Third, individuals need to develop, design, and compare various alternatives. Fourth, they need to optimize the design, focussing on details, often through simulations. Finally, the design is piloted and the production process is implemented.

Six Sigma differentiates several key roles--in contrast to other quality paradigms, in which the objectives are confined only to relevant experts. First, executive leadership, which entails senior management and the CEO, promulgate the vision and empower employees to assume relevant roles. Second, champions, usually senior managers, implement and integrate the institution of this Six Sigma strategy across the organization. Master Black Belts, identified by champions, offer coaching to the remaining participants of this strategy& usually, their entire role is dedicated to this function. Black Belts focus on executing, rather than identifying, Six Sigma projects. Green Belts are also involved in execution, but only as a limited part of their job, usually under the guidance of Black Belts.

Related topics

How to calculate the return on investment, or ROI, of programs

Consultants often want to demonstrate their programs, such as training or facilitation workshops, are actually valuable. Managers often want to assess the value of these programs. Phillips and Stone (2002) provide a comprehensive account of how to calculate the return on investment of training programs and similar initiatives (see also Phillips, 1997).

First, Phillips and Stone (2002) enumerate a range of indices or sources of data that should be measured. They recommended that many of these indices be measured before and after the program at various times. Some of the data are more objective and revolve around:

  • Outputs, such as units produced, units sold, revenue collected, loans approved, and new accounts generated
  • Costs, such as unit costs, sales expenses, program costs, accident costs, operating costs, and overhead costs
  • Time, such as equipment downtime, overtime, lost days because of injury, and work stoppages
  • Quality, such as level of waste, rejections from customers, error rates, reworks, product defects, and deviations from some standard

Other data, in contrast, are more subjective and revolve around:

  • Workplace climate or satisfaction, such as number of grievances and complaints, turnover of employees, litigation, and organizational commitment, satisfaction, or engagement
  • Work behaviors, such as absenteeism, tardiness, and violation of rules
  • Customer service, such as loyalty, retention, impressions, complaints, and satisfaction
  • Employee development and advancement, such as performance ratings or number of learning programs attended
  • Innovation, such as number of patent, copyrights, new services, new products, new goals, and suggestions implemented

Second, practitioners need to determine the extent to which any improvements can be ascribed to the program rather than other changes. In particular, Phillips and Stone (2002) recommend a variety of strategies:

  • Practitioners should identify all the other changes or circumstances that could have improved the data, from biases such as the Hawthorn effect, to other initiatives in the organization. They could derive this information from executives, participants, subject matter experts, and other individuals. Next, executives or subject matter experts could estimate the relative impact of all these changes including the targeted program& they might, for example, be asked "To what extent do you feel improvements in productivity can be ascribed to this initiative, on a scale from 0 to 100%"
  • Practitioners could include a control group who do not receive the program initially
  • Practitioners could utilize various statistical techniques, such as trend line analysis and regression, to predict what the indices would probably have been had the program not been implemented

Third, practitioners need to convert numerical data to monetary estimates. For example:

  • Most objective improvements can be readily converted to monetary estimates. For example, if the number of units sold has increased, practitioners can calculate increases in marginal profit. If time to complete some task has diminished, practitioners can convert this time to decreases in labor costs. If number of defects has decreased, practitioners can convert this improvement to decreases in time, and thus labor costs, as well as decreases in material costs.
  • Sometimes, historical data can also be used to estimate the costs of some improvements. For example, the practitioner might calculate that $100 000 was spent each year in response to accidents, including medical costs, workers compensation, insurance premiums, legal expenses, safety programs, and lost productivity. The practitioner can then estimate the costs that would be spent if accidents diminished by 50%.
  • For some subjective improvements, such as customer satisfaction, experts and managers could be asked the money they would be willing to spend to generate these improvements--an estimate of the value of this change.
  • Databases could be used to estimate the relationships between subjective improvements and financial benefits. For example, practitioners might be able to uncover studies that relate increases in engagement or employee retention to increases in profit.

Fourth, practitioners need to compute the costs of this program. Some costs, such as needs analysis, design and development, evaluation, and overhead costs such as clerical support, are prorated because they facilitate other initiatives as well. Other costs, such as salaries and benefits of participants, trainers, and coordinators as well as travel, lodging meals, facilities, and program materials, are not prorated.

Finally, practitioners need to calculate the return on investment, or ROI. The formula is ROI = (monetary benefits of the program - costs of the program) / costs of the program X 100. Practitioners should also record benefits that were not incorporated into this formula, such as improvements in work climate, community image, teamwork, and communication--and then communicate these benefits to the stakeholders.


Delaney, J., & Huselid, M. (1996). The impact of human resource management practices on perceptions of organisational performance. Academy of Management Journal, 39, 949-969.

Dollinger, M., & Golden, P. (1992). Interorganizational and collective strategies in small firms: Environmental effects and performance. Journal of Management, 18, 695-715.

Fulmer, I. S., Gerhart, B., & Scott, K. S. (2003). Are the 100 best better? An empirical investigation of the relationship between being a "great place to work" and firm performance. Personnel Psychology, 56, 965-993.

Gong, Y., Law, K. S., Chang, S., & Xin, K. R. (2009).Human resources management and firm performance: The differential role of managerial affective and continuance commitment. Journal of Applied Psychology, 94, 263-275.

Harel, G. H., & Tzafrir, S. S. (1999). The effect of human resource management practices on the perceptions of organizational and market performance of the firm. Human Resource Management, 38, 185-200.

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Phillips, J. J., (1997). Return on investment in training and performance improvement programs. Houston: Gulf publishing.

Phillips, J. J., & Stone, R. D. (2002). How to measure training results. New York: McGraw Hill.

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Salamon, S. D., & Robinson, S. L. (2008). Trust that binds: The impact of collective felt trust on organizational performance. Journal of Applied Psychology, 93, 593-601.

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van Dyck, C., Frese, M., Baer, M., & Sonnentag, S. (2005). Organizational error management culture and its impact on performance: A two-study replication. Journal of Applied Psychology, 90, 1228-1240.

Wan, W. P., & Hoskisson, R. E. (2003). Home country environments, corporate diversification strategies, and firm performance. Academy of Management Journal, 46, 27-45.

Youndt, M. A., Snell, S. A., Dean, J. E., & Lepak, D. P. (1996). Human resource management, manufacturing strategy, and firm performance. Academy of Management Journal, 39, 836-866.

Created by Dr Simon Moss on 5/19/2009

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