Another Take on the Emerging Paradigm Shift

Over the course of human history, people have usually been able to rely on some stable source of authority and control in their lives, be it religion, the king or queen, or the social order itself. However benevolent or malevolent a regime might be, usually there have been clear lines along which blame or credit can be assigned.

So, even though the complexity and scale of success and failure in today’s world provide ample evidence that no one exerts centralized control over events, it is not surprising that many people today still find it comforting to think some individuals or groups must be manipulating others to their own ends. There is, however, an alternative point of view that may provide a more productive path toward effective action.

After all, efforts to date that have focused on the removal and replacement of any given group that appears to be in control have simply resulted in an alteration of the system, and not the institution of a fundamentally new system. Thus, socialist and communist governments have failed in large part because they were unable to manage resources as effectively as capitalist systems do (which is, of course, not all that well). That is, despite the appearance of having put in place a radically different system of priorities, the constraints of socioeconomics themselves did not change in the context of socialist and communist regimes.

The individual incumbents of social and economic positions have nothing whatsoever to do with the creation of the socioeconomic system’s likelihoods of success and failure, and if they had not accepted their roles in that system, others would have. Changing the system is much more difficult, both conceptually and practically, than merely assigning blame and replacing an individual or group with another individual or group. To the extent the system remains the same, changing the occupants within it makes little difference.

The idea is much the same as was realized in industry when it shifted from quality control’s “tail-chopping” methods to continuous quality improvement’s “curve-shifting” methods. In the former, a certain ratio of acceptable to malformed parts is dictated by the system’s materials and processes. Quality control simply removes the bad parts from the production line and does nothing to change the system. Since quality is often normally distributed, taking the statistical shape of a bell curve, it is accordingly inevitable that cutting off the bad end of that distribution (tail-chopping) only results in it being filled in again in the next production cycle.

Continuous quality improvement methods, in contrast, focus on changing the system and on reducing the likelihood of producing bad parts. Efforts of these kind move the entire quality distribution up the scale so that no parts fall in the previous distribution’s bad tail at all. Of course, the outcomes of our socioeconomic system’s processes are very different from the manufacturing of machine parts. The point of this simple illustration is only that there is remarkable value in thinking less about removing undesired individuals from a process and in thinking more about changing the process itself.

There is no denying that those who seem to be in control benefit disproportionately from others’ efforts. But even though they have had little or nothing to do with creating the system that confers these benefits on them, they certainly do have a vested interest in maintaining that system. This fact reveals another important aspect of any solution that will prove truly viable: the new system must provide benefits not available under the old one. The shift from old to new cannot be a matter of mere will power or organizational efficiency. It must come about as a result of the attractions offered by the new system, which motivate behavior changes universally with little or no persuasion. Qualitatively different classes of opportunities and rewards can come about only by integrating into the system features of the environment that were excluded from the previous system. The central problem of life today is how to provoke this kind of shift and its new integrations.

We can begin to frame this problem in its proper context when we situate it horizontally as an ecological problem and vertically as an evolutionary one. In the same way that ecological niches define the evolutionary opportunities available to species of plants and animals, historical and cultural factors set up varying circumstances to which human societies must adapt. Biological and social adaptations both become increasingly complex over time, systematically exhibiting characteristic patterns in the ways matter, energy, and information are functionally integrated.

The present form of contemporary global society has evolved largely in terms of the Western European principles of modern science, capitalism, and democracy. These principles hinge on the distinction between a concrete, solid, and objective world and an impressionistic, intuitive, and subjective mind. For instance, science and economics focus traditionally on measuring and managing material things and processes, like volts, meters, kilograms, barrels, degrees Celsius, liters, speed, flows, etc. Human, social, and environmental issues are treated statistically, not in terms of standardized metric units, and they are economically regarded as “externalities” excluded from profit and loss calculations.

So, if qualitatively different classes of opportunities and rewards can come about only by integrating into the system features of the environment that were excluded from the previous system, what can we do to integrate the subjective with the objective, and to also then incorporate standardized metric units for the externalities of human, social, and environmental capital into science and economics? The question demands recognition of a) a new system of ecological niches with their own unique configurations of horizontal relationships, and b) the evolution of new species capable of adapting to life in these niches.

The problem is compounded by the complexity of seeing the new system of niches as emerging from the existing system of ecological relationships. Economically speaking, today’s cost centers will be tomorrow’s profit drivers. Scientifically speaking, sources of new repeatable and stable phenomena will have to be identified in what are today assumed to be unrepeatable and unstable phenomena, and will then have to be embodied in instrumental ensembles.

The immediate assumption, which we will have to strive to overcome, is that any such possibilities for new economic and scientific opportunities could hardly be present in the world today and not be widely known and understood. A culturally ingrained presupposition we all share to some extent is that objective facts are immediately accessible and become universally adopted for their advantages as soon as they are recognized. Claims to the contrary can safely be ignored, even if, or perhaps especially if, they represent a truly original potential for system change.

This assumption is an instance of what behavioral economists like Simon and Kahnemann refer to as bounded rationality, which is the idea that language and culture prethink things for us in ways we are usually unaware of. Research has shown that many decisions in daily life are tinged with emotion, such that a certain kind of irrationality takes an irrefutable place in how we think. Examples include choices involving various combinations of favorable and unfavorable odds of profiting from some exchange. Small but sure profits are often ignored in favor of larger and less sure profits, or mistaken calculations are assumed correct, to the disadvantage of the decision maker. There is surely method in the madness, but the pure rationality of an ideal thought process can no longer be accommodated.

Given the phenomenon of bounded rationality, and the complexity of the metasystematic shift that’s needed, how is change to be effected? As Einstein put it, problems of a certain kind cannot be solved from within the same framework that gave rise to them. As long as we continue to think in terms of marshalling resources to apply to the solution of a problem we have failed in conceiving the proper magnitude and scope of the problem we face.

We must instead think in terms of problem-solution units that themselves embody a new evolutionary species functioning within a new system of ecological niches. And these species-niche combinations must be born fully functional and viable, like birds from lizard eggs, caught up in the flow and play of their matter, energy and information streams from the moment of their arrival.

A vitally important aspect of this evolutionary leap is that the new system emerge of its own accord, seemingly with a will of its own. But it will not take shape as a result of individuals or groups deliberately executing a comprehensive design. There will be no grand master architect, though the co-incidence of multiple coordinations and alignments will seem so well planned that many may assume one exists.

It may be, however, that a new spontaneously self-organizing culture might be grown from a few well-placed spores or seeds. The seeds themselves need to be viable in terms of their growth potential and the characteristics of the particular species involved. But equally important are the characteristics of the environment in which the seeds are planted. Bernstein (2004) describes four conditions necessary to the birth of plenty in the modern world:

1. Property rights: those who might create new forms of value need to own the fruits of their labors.
2. Scientific rationalism: innovation requires a particular set of conceptual tools and a moral environment in which change agents need not fear retribution.
3. Capital markets: investors must be able to identify entrepreneurs and provide them with the funds they need to pursue their visions.
4. Transportation/communications: new products and the information needed to produce and market them must have efficient channels in which to move.

If we take the new emerging culture as unmodern, nonmodern, or amodern, might a new paradigm of plenty similarly take shape as these four conditions are applied not just to manufactured capital, land, and labor, but to human capital (abilities, health, performance), social capital (trust, honesty, dependability, etc.), and natural capital (the environmental services of watersheds, fisheries, estuaries, forests, etc.)? Should not we own legal title to defined shares of each form of capital? Should not science be systematically employed in research on each form of capital? Should not investments in each form of capital be accountable? Should not each form of capital be mobile and fungible within established networks? Should not there be common languages serving as common currencies for the exchange of each form of capital? Instead of assuming the answers to these questions are uniformly “No,” should not we at least entertain them long enough to firmly establish why they cannot be “Yes”?

Parameterizing Perfection: Practical Applications of a Mathematical Model of the Lean Ideal

To properly pursue perfection, we need to parameterize it. That is, taking perfection as the ideal, unattainable standard against which we judge our performance is equivalent to thinking of it as a mathematical model. Organizations are intended to realize their missions independent of the particular employees, customers, suppliers, challenges, products, etc. they happen to engage with at any particular time. Organizational performance measurement (Spitzer, 2007) ought to then be designed in terms of a model that posits, tests for, and capitalizes on the always imperfectly realized independence of those parameters.

Lean thinking (Womack & Jones, 1996) focuses on minimizing waste and maximizing value. At every point at which resources are invested in processes, services, or products, the question is asked, “What value is added here?” Resources are wasted when no value is added, when they can be removed with no detrimental effect on the value of the end product. In their book, Natural Capitalism: Creating the Next Industrial Revolution, Hawken, Lovins, and Lovins (1999, p. 133) say

“Lean thinking … changes the standard for measuring corporate success. … As they [Womack and Jones] express it: ‘Our earnest advice to lean firms today is simple. To hell with your competitors; compete against perfection by identifying all activities that are muda [the Japanese term for waste used in Toyota’s landmark quality programs] and eliminating them. This is an absolute rather than a relative standard which can provide the essential North Star for any organization.”

Further, every input should “be presumed waste until shown otherwise.” A constant, ongoing, persistent pressure for removing waste is the basic characteristic of lean thinking. Perfection is never achieved, but it aptly serves as the ideal against which progress is measured.

Lean thinking sounds a lot like a mathematical model, though it does not seem to have been written out in a mathematical form, or used as the basis for calibrating instruments, estimating measures, evaluating data quality, or for practical assessments of lean organizational performance. The closest anyone seems to have come to parameterizing perfection is in the work of Genichi Taguchi (Ealey, 1988), which has several close parallels with Rasch measurement (Linacre, 1993).  But meaningful and objective quantification, as required and achieved in the theory and practice of fundamental measurement (Andrich, 2004; Bezruczko, 2005; Bond & Fox 2007; Smith & Smith, 2004; Wilson, 2005; Wright, 1999), in fact asserts abstract ideals of perfection as models of organizational, social, and psychological processes in education, health care, marketing, etc. These models test the extent to which outcomes remain invariant across examination or survey questions, across teachers, students, schools, and curricula, or across treatment methods, business processes, or policies.

Though as yet implemented only to a limited extent in business (Drehmer, Belohlav, James, & Coye, 2000; Drehmer & Deklava, 2001;  Lunz & Linacre, 1998; Salzberger, 2009), advanced measurement’s potential rewards are great. Fundamental measurement theory has been successfully applied in research and practice thousands of times over the last 40 years and more, including in very large scale assessments and licensure/certification applications (Adams, Wu, & Macaskill, 1997; Masters, 2007; Smith, Julian, Lunz, et al., 1994). These successes speak to an opportunity for making broad improvements in outcome measurement that could provide more coherent product definition, and significant associated opportunities for improving product quality and the efficiency with which it is produced, in the manner that has followed from the use of fundamental measures in other industries.

Of course, processes and outcomes are never implemented or obtained with perfect consistency. This would be perfectly true only in a perfect world. But to pursue perfection, we need to parameterize it. In other words, to raise the bar in any area of performance assessment, we have to know not only what direction is up, but we also need to know when we have raised the bar far enough. But we cannot tell up from down, we do not know how much to raise the bar, and we cannot properly evaluate the effects of lean experiments when we have no way of locating measures on a number line that embodies the lean ideal.

To think together collectively in ways that lead to significant new innovations, to rise above what Jaron Lanier calls the “global mush” of confused and self-confirming hive thinking, we need the common languages of widely accepted fundamental measures of the relevant processes and outcomes, measures that remain constant across samples of customers, patients, employees, students, etc., and across products, sales techniques, curricula, treatment processes, assessment methods, and brands of instrument.

We are all well aware that the consequences of not knowing where the bar is, of not having product definitions, can be disastrous. In many respects, as I’ve said previously in this blog, the success or failure of health care reform hinges on getting measurement right. The Institute of Medicine report, To Err is Human, of several years ago stresses the fact that system failures pose the greatest threat to safety in health care because they lead to human errors. When a system as complex as health care lacks a standard product definition, and product delivery is fragmented across multiple providers with different amounts and kinds of information in different settings, the system becomes dangerously cumbersome and over-complicated, with unacceptably wide variations and errors in its processes and outcomes, not to even speak of its economic inefficiency.

In contrast with the widespread use of fundamental measures in the product definitions of other industries, health care researchers typically implement neither the longstanding, repeatedly proven, and mathematically rigorous models of fundamental measurement theory nor the metrological networks through which reference standard metrics are engineered. Most industries carefully define, isolate, and estimate the parameters of their products, doing so in ways 1) that ensure industry-wide comparability and standardization, and 2) that facilitate continuous product improvement by revealing multiple opportunities for enhancement. Where organizations in other industries manage by metrics and thereby keep their eyes on the ball of product quality, health care organizations often manage only their own internal processes and cannot in fact bring the product quality ball into view.

In his message concerning the Institute for Healthcare Improvement’s Pursuing Perfection project a few years ago, Don Berwick, like others (Coye, 2001; Coye & Detmer, 1998), observed that health care does not yet have an organization setting new standards in the way that Toyota did for the auto industry in the 1970s. It still doesn’t, of course. Given the differences between the auto and health care industries uses of fundamental measures of product quality and associated abilities to keep their eyes on the quality ball, is it any wonder then, that no one in health care has yet hit a home run? It may well be that no one will hit a home run in health care until reference standard measures of product quality are devised.

The need for reference standard measures in uniform data systems is crucial, and the methods for obtaining them are widely available and well-known. So what is preventing the health care industry from adopting and deploying them? Part of the answer is the cost of the initial investment required. In 1980, metrology comprised about six percent of the U.S. gross national product (Hunter, 1980). In the period from 1981 to 1994, annual expenditures on research and development in the U.S. were less than three percent of the GNP, and non-defense R&D was about two percent (NIST Subcommittee on Research, National Science and Technology Council, 1996). These costs, however, must be viewed as investments from which high rates of return can be obtained (Barber, 1987; Gallaher, Rowe, Rogozhin, et al., 2007; Swann, 2005).

For instance, the U.S. National Institute of Standards and Technology estimated the economic impact of 12 areas of research in metrology, in four broad areas including semiconductors, electrical calibration and testing, optical industries, and computer systems (NIST, 1996, Appendix C; also see NIST, 2003). The median rate of return in these 12 areas was 147 percent, and returns ranged from 41 to 428 percent. The report notes that these results compare favorably with those obtained in similar studies of return rates from other public and private research and development efforts. Even if health care metrology produces only a small fraction of the return rate produced in physical metrology, its economic impact could still amount to billions of dollars annually. The proposed pilot projects therefore focus on determining what an effective health care outcomes metrology system should look like. What should its primary functions be? What should it cost? What rates of return could be expected from it?

Metrology, the science of measurement (Pennella, 1997), requires 1) that instruments be calibrated within individual laboratories so as to isolate and estimate the values of the required parameters (Wernimont, 1978); and 2) that individual instruments’ capacities to provide the same measure for the same amount, and so be traceable to a reference standard, be established and monitored via interlaboratory round-robin trials (Mandel, 1978).

Fundamental measurement has already succeeded in demonstrating the viability of reference standard measures of health outcomes, measures whose meaningfulness does not depend on the particular samples of items employed or patients measured. Though this work succeeds as far as it goes, it being done in a context that lacks any sense of the need for metrological infrastructure. Health care needs networks of scientists and technicians collaborating not only in the first, intralaboratory phase of metrological work, but also in the interlaboratory trials through which different brands or configurations of instruments intended to measure the same variable would be tuned to harmoniously produce the same measure for the same amount.

Implementation of the two phases of metrological innovation in health care would then begin with the intralaboratory calibration of existing and new instruments for measuring overall organizational performance, quality of care, and patients’ health status, quality of life, functionality, etc.  The second phase takes up the interlaboratory equating of these instruments, and the concomitant deployment of reference standard units of measurement throughout a health care system and the industry as a whole. To answer questions concerning health care metrology’s potential returns on investment, the costs for, and the savings accrued from, accomplishing each phase of each pilot will be tracked or estimated.

When instruments measuring in universally uniform, meaningful units are put in the hands of clinicians, a new scientific revolution will occur in medicine. It will be analogous to previous ones associated with the introduction of the thermometer and the instruments of optometry and the clinical laboratory. Such tools will multiply many times over the quality improvement methods used by Brent James, touted as holding the key to health care reform in a recent New York Times profile. Instead of implicitly hypothesizing models of perfection and assessing performance relative to them informally, what we need is a new science that systematically implements the lean ideal on industry-wide scales. The future belongs to those who master these techniques.

References

Adams, R. J., Wu, M. L., & Macaskill, G. (1997). Scaling methodology and procedures for the mathematics and science scales. In M. O. Martin & D. L. Kelly (Eds.), Third International Mathematics and Science Study Technical Report: Vol. 2: Implementation and Analysis – Primary and Middle School Years (pp. 111-145). Chestnut Hill, MA: Boston College.

Andrich, D. (2004, January). Controversy and the Rasch model: A characteristic of incompatible paradigms? Medical Care, 42(1), I-7–I-16.

Barber, J. M. (1987). Economic rationale for government funding of work on measurement standards. In R. Dobbie, J. Darrell, K. Poulter & R. Hobbs (Eds.), Review of DTI work on measurement standards (p. Annex 5). London: Department of Trade and Industry.

Berwick, D. M., James, B., & Coye, M. J. (2003, January). Connections between quality measurement and improvement. Medical Care, 41(1 (Suppl)), I30-38.

Bezruczko, N. (Ed.). (2005). Rasch measurement in health sciences. Maple Grove, MN: JAM Press.

Bond, T., & Fox, C. (2007). Applying the Rasch model: Fundamental measurement in the human sciences, 2d edition. Mahwah, New Jersey: Lawrence Erlbaum Associates.

Coye, M. J. (2001, November/December). No Toyotas in health care: Why medical care has not evolved to meet patients’ needs. Health Affairs, 20(6), 44-56.

Coye, M. J., & Detmer, D. E. (1998). Quality at a crossroads. The Milbank Quarterly, 76(4), 759-68.

Drehmer, D. E., Belohlav, J. A., & Coye, R. W. (2000, Dec). A exploration of employee participation using a scaling approach. Group & Organization Management, 25(4), 397-418.

Drehmer, D. E., & Deklava, S. M. (2001, April). A note on the evolution of software engineering practices. Journal of Systems and Software, 57(1), 1-7.

Ealey, L. A. (1988). Quality by design: Taguchi methods and U.S. industry. Dearborn MI: ASI Press.

Gallaher, M. P., Rowe, B. R., Rogozhin, A. V., Houghton, S. A., Davis, J. L., Lamvik, M. K., et al. (2007). Economic impact of measurement in the semiconductor industry (Tech. Rep. No. 07-2). Gaithersburg, MD: National Institute for Standards and Technology.

Hawken, P., Lovins, A., & Lovins, H. L. (1999). Natural capitalism: Creating the next industrial revolution. New York: Little, Brown, and Co.

Hunter, J. S. (1980, November). The national system of scientific measurement. Science, 210(21), 869-874.

Linacre, J. M. (1993). Quality by design: Taguchi and Rasch. Rasch Measurement Transactions, 7(2), 292.

Lunz, M. E., & Linacre, J. M. (1998). Measurement designs using multifacet Rasch modeling. In G. A. Marcoulides (Ed.), Modern methods for business research. Methodology for business and management (pp. 47-77). Mahwah, New Jersey: Lawrence Erlbaum Associates, Inc.

Mandel, J. (1978, December). Interlaboratory testing. ASTM Standardization News, 6, 11-12.

Masters, G. N. (2007). Special issue: Programme for International Student Assessment (PISA). Journal of Applied Measurement, 8(3), 235-335.

National Institute for Standards and Technology (NIST). (1996). Appendix C: Assessment examples. Economic impacts of research in metrology. In C. o. F. S. Subcommittee on Research (Ed.), Assessing fundamental science: A report from the Subcommittee on Research, Committee on Fundamental Science. Washington, DC: National Standards and Technology Council [http://www.nsf.gov/statistics/ostp/assess/nstcafsk.htm#Topic%207; last accessed 18 February 2008].

National Institute for Standards and Technology (NIST). (2003, 15 January). Outputs and outcomes of NIST laboratory research. Retrieved 12 July 2009, from http://www.nist.gov/director/planning/studies.htm#measures.

Pennella, C. R. (1997). Managing the metrology system. Milwaukee, WI: ASQ Quality Press.\

Salzberger, T. (2009). Measurement in marketing research: An alternative framework. Northampton, MA: Edward Elgar.

Smith, R. M., Julian, E., Lunz, M., Stahl, J., Schulz, M., & Wright, B. D. (1994). Applications of conjoint measurement in admission and professional certification programs. International Journal of Educational Research, 21(6), 653-664.

Smith, E. V., Jr., & Smith, R. M. (2004). Introduction to Rasch measurement. Maple Grove, MN: JAM Press.

Spitzer, D. (2007). Transforming performance measurement: Rethinking the way we measure and drive organizational success. New York: AMACOM.

Swann, G. M. P. (2005, 2 December). John Barber’s pioneering work on the economics of measurement standards [Electronic version]. Retrieved http://www.cric.ac.uk/cric/events/jbarber/swann.pdf from Notes for Workshop in Honor of John Barber held at University of Manchester.

Wernimont, G. (1978, December). Careful intralaboratory study must come first. ASTM Standardization News, 6, 11-12.

Wilson, M. (2005). Constructing measures: An item response modeling approach. Mahwah, New Jersey: Lawrence Erlbaum Associates.

Womack, J. P., & Jones, D. T. (1996, Sept./Oct.). Beyond Toyota: How to root out waste and pursue perfection. Harvard Business Review, 74, 140-58.

Wright, B. D. (1999). Fundamental measurement for psychology. In S. E. Embretson & S. L. Hershberger (Eds.), The new rules of measurement: What every educator and psychologist should know (pp. 65-104 [http://www.rasch.org/memo64.htm]). Hillsdale, New Jersey: Lawrence Erlbaum Associates.

LivingCapitalMetrics Blog by William P. Fisher, Jr., Ph.D. is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Based on a work at livingcapitalmetrics.wordpress.com.
Permissions beyond the scope of this license may be available at http://www.livingcapitalmetrics.com.

Review of Spitzer’s Transforming Performance Measurement

Everyone interested in practical measurement applications needs to read Dean R. Spitzer’s 2007 book, Transforming performance measurement: Rethinking the way we measure and drive organizational success (New York, AMACOM). Spitzer describes how measurement, properly understood and implemented, can transform organizational performance by empowering and motivating individuals. Measurement understood in this way moves beyond quick fixes and fads to sustainable processes based on a measurement infrastructure that coordinates decisions and actions uniformly throughout the organization.

Measurement leadership, Spitzer says, is essential. He advocates, and many organizations have instituted, the C-suite position of Chief Measurement Officer (Chapter 9). This person is responsible for instituting and managing the four keys to transformational performance measurement (Chapters 5-8):

• Context sets the tone by presenting the purpose of measurement as either negative (to inspect, control, report, manipulate) or positive (to give feedback, learn, improve).
• Focus concentrates attention on what’s important, aligning measures with the mission, strategy, and with what needs to be managed, relative to the opportunities, capacities, and skills at hand.
• Integration addresses the flow of measured information throughout the organization so that the covariations of different measures can be observed relative to the overall value created.
• Interactivity speaks to the inherently social nature of the purposes of measurement, so that it embodies an alignment with the business model, strategy, and operational imperatives.

Spitzer takes a developmental approach to measurement improvement, providing a Measurement Maturity Assessment in Chapter 12, and also speaking to the issues of the “living company” raised by Arie de Geus’ classic book of that title. Plainly, the transformative potential of performance measurement is dependent on the maturational complexity of the context in which it is implemented.

Spitzer clearly outlines the ways in which each of the four keys and measurement leadership play into or hinder transformation and maturation. He also provides practical action plans and detailed guidelines, stresses the essential need for an experimental attitude toward evaluating change, speaks directly to the difficulty of measuring intangible assets like partnership, trust, skills, etc., and shows appreciation for the value of qualitative data.

Transforming Performance Measurement is not an academic treatise, though all sources are documented, with the endnotes and bibliography running to 25 pages. It was written for executives, managers, and entrepreneurs who need practical advice expressed in direct, simple terms. Further, the book does not include any awareness of the technical capacities of measurement as these have been realized in numerous commercial applications in high stakes and licensure/certification testing over the last 50 years (Andrich, 2005; Bezruczko, 2005; Bond & Fox, 2007; Masters, 2007; Wilson, 2005). This can hardly be counted as a major criticism, since no books of this kind have yet to date been able to incorporate the often highly technical and mathematical presentations of advanced psychometrics.

That said, the sophistication of Spitzer’s conceptual framework and recommendations make them remarkably ready to incorporate insights from measurement theory, testing practice, developmental psychology, and the history of science. Doing so will propel the strategies recommended in this book into widespread adoption and will be a catalyst for the emerging re-invention of capitalism. In this coming cultural revolution, intangible forms of capital will be brought to life in common currencies for the exchange of value that perform the same function performed by kilowatts, bushels, barrels, and hours for tangible forms of capital (Fisher, 2009, 2010).

Pretty big claim, you say? Yes, it is. Here’s how it’s going to work.

• First, measurement leadership within organizations that implements policies and procedures that are context-sensitive, focused, integrated, and interactive (i.e., that have Spitzer’s keys in hand) will benefit from instruments calibrated to facilitate:
  • meaningful mapping of substantive, additive amounts of things measured on number lines;
  • data volume reductions on the order of 80-95% and more, with no loss of information;
  • organizational and individual learning trajectories defined by hierarchies of calibrated items;
  • measures that retain their meaning and values across changes in item content;
  • adapting instruments to people and organizations, instead of vice versa;
  • estimating the consistency, and the leniency or harshness, of ratings assigned by judges evaluating performance quality, with the ability to remove those effects from the performance measures made;
  • adjusting measurement precision to the needs of the task at hand, so that time and resources are not wasted in gathering too much or too little data; and
  • providing the high quality and uniform information needed for networked collective thinking able to keep pace with the demand for innovation.
• Second, measurement leadership sensitive to the four keys across organizations, both within and across industries, will find value in:
  • establishing industry-wide metrological standards defining common metrics for the expression of the primary human, social, and natural capital constructs of interest;
  • lubricating the flow of human, social, and natural capital in efficient markets broadly defined so as to inform competitive pricing of intangible assets, products, and services; and
  • new opportunities for determining returns on investments in human, community, and environmental resource management.
• Third, living companies need to be able to mature in a manner akin to human development over the lifespan. Theories of hierarchical complexity and developmental stage transitions that inform the rigorous measurement of cognitive and moral transformations (Dawson & Gabrielian, 2003) will increasingly find highly practical applications in organizational contexts.

Leadership of the kind described by Spitzer is needed not just to make measurement contextualized, focused, integrated, and interactive—and so productive at new levels of effectiveness—but to apply systematically the technical, financial, and social resources needed to realize the rich potentials he describes for the transformation of organizations and empowerment of individuals. Spitzer’s program surpasses the usual focus on centralized statistical analyses and reports to demand the organization-wide dissemination of calibrated instruments that measure in common metrics. The flexibility, convenience, and scientific rigor of instruments calibrated to measure in units that really add up fit the bill exactly. Here’s to putting tools that work in the hands of those who know what to do with them!

References

Andrich, D. (2005). Georg Rasch: Mathematician and statistician. In K. Kempf-Leonard (Ed.), Encyclopedia of Social Measurement (Vol. 3, pp. 299-306). Amsterdam: Academic Press, Inc.

Bezruczko, N. (Ed.). (2005). Rasch measurement in health sciences. Maple Grove, MN: JAM Press.

Bond, T., & Fox, C. (2007). Applying the Rasch model: Fundamental measurement in the human sciences, 2d edition. Mahwah, New Jersey: Lawrence Erlbaum Associates.

Dawson, T. L., & Gabrielian, S. (2003, June). Developing conceptions of authority and contract across the life-span: Two perspectives. Developmental Review, 23(2), 162-218.

Fisher, W. P., Jr. (2009, November). Invariance and traceability for measures of human, social, and natural capital: Theory and application. Measurement (Elsevier), 42(9), 1278-1287.

Fisher, W. P., Jr. (2010). Bringing human, social, and natural capital to life: Practical consequences and opportunities. Journal of Applied Measurement, 11, in press [Pre-press version available at http://www.livingcapitalmetrics.com/images/BringingHSN_FisherARMII.pdf%5D.

Masters, G. N. (2007). Special issue: Programme for International Student Assessment (PISA). Journal of Applied Measurement, 8(3), 235-335.

Spitzer, D. (2007). Transforming performance measurement: Rethinking the way we measure and drive organizational success. New York: AMACOM.

Wilson, M. (2005). Constructing measures: An item response modeling approach. Mahwah, New Jersey: Lawrence Erlbaum Associates.

LivingCapitalMetrics Blog by William P. Fisher, Jr., Ph.D. is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Based on a work at livingcapitalmetrics.wordpress.com.
Permissions beyond the scope of this license may be available at http://www.livingcapitalmetrics.com.