Advertisement

Measure and Manage: Intangible Assets Metric Standards for Sustainability

  • William P. FisherJr.

Abstract

To be feasible as components of a business model, social and environmental sustainability practices must be accountable for their returns on investment. Sustainability hinges on the comprehensive, long-term management of all the forms of capital necessary for profitability. Management, in turn, depends heavily on standards—measurement, legal, and financial standards essential to common product definitions, to proving ownership, to pricing, to knowing the quantity and quality of what is traded, and to evaluating where the business stands, where it has been, and where it is going relative to its overall objective. Counts and percentages of events, assessment ratings, or survey responses are often treated as sufficient to the task of measuring sustainability performances and outcomes in business. These kinds of numbers are the obvious and natural place to start in conceiving and designing measures of the intangible assets, performances, and outcomes essential to sustainability. Multiple benefits accrue from building on these intuitively sound beginnings to calibrated tools and universally uniform standards better able to serve the needs of sustainable business practices. Foremost among these benefits is the fact that measures adaptable to the changing needs of business will better support stable profits sustainable over the long term than measures that require business to adapt to their needs.

Keywords

Stakeholder Theory Natural Capital Intangible Asset Item Banking Computerize Adaptive Test 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ackermann, J. R. 1985. Data, Instruments, and Theory: A Dialectical Approach to Understanding Science. Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar
  2. Alonzo, A. C., and Steedle, J. T. 2009. “Developing and Assessing a Force and Motion Learning Progression.” Science Education 93(3): 389–421.CrossRefGoogle Scholar
  3. Andrich, D. 1988. Rasch Models for Measurement. (Vols. series no. 07–068, Sage University Paper Series on Quantitative Applications in the Social Sciences). Beverly Hills, CA: Sage Publications.Google Scholar
  4. —. 1989. “Distinctions between Assumptions and Requirements in Measurement in the Social Sciences.” In J. A. Keats, R. Taft, R. A. Heath, and S. H. Lovibond (eds.), Mathematical and Theoretical Systems: Proceedings of the 24th International Congress of Psychology of the International Union of Psychological Science 4. North-Holland: Elsevier Science Publishers, 7–16.Google Scholar
  5. —. 2002. “Understanding Resistance to the Data-Model Relationship in Rasch’s Paradigm: a Reflection for the Next Generation.” Journal of Applied Measurement 3(3): 325–359.Google Scholar
  6. —. 2010. “Sufficiency and Conditional Estimation of Person Parameters in the Polytomous Rasch Model.” Psychometrika 75(2): 292–308.CrossRefGoogle Scholar
  7. Andrich, D. and Styles, I. M. 1998. “The Structural Relationship between Attitude and Behavior Statements from the Unfolding Perspective.” Psychological Methods 3(4): 454–469.CrossRefGoogle Scholar
  8. Anielski, M. 2007. The Economics of Happiness: Building Genuine Wealth. Gabriola, British Columbia: New Society Publishers.Google Scholar
  9. Ashworth, W. J. 2004. “Metrology and the State: Science, Revenue, and Commerce.” Science 306(5700): 1314–1317.CrossRefGoogle Scholar
  10. Barzel, Y. 1982. “Measurement Costs and the Organization of Markets.” Journal of Law and Economics 25: 27–48.CrossRefGoogle Scholar
  11. Benham, A. and Benham, L. 2000. “Measuring the Costs of Exchange.” In C. Ménard (ed.), Institutions, Contracts and Organizations: Perspectives from New Institutional Economics. Cheltenham, UK: Edward Elgar, 367–375.Google Scholar
  12. Bergstrom, B. A. and Lunz, M. E. 1994. “The Equivalence of Rasch Item Calibrations and Ability Estimates across Modes of Administration.” In M. Wilson (ed.), Objective Measurement: Theory into Practice 2. Norwood, NJ: Ablex Publishing Corp, 122–128.Google Scholar
  13. —. 1999. “CAT for Certification and Licensure.” In F. Drasgow and J. B. Olson-Buchanan (eds.), Innovations in Computerized Assessment. Mahwah, NJ: Lawrence Erlbaum Associates, Inc., Publishers, 67–91.Google Scholar
  14. Berk, J. 2009. “Emerging Issues in Measurement.” Chief Learning Officer. Features section. Available at: http://clomedia.com/articles/view/emerging_issues_in_measurement. Accessed on November 3, 2011.Google Scholar
  15. Bernstein, W. J. 2004. The Birth of Plenty: How the Prosperity of the Modern World was Created. New York: McGraw-Hill.Google Scholar
  16. Bond, T. and Fox, C. 2007. Applying the Rasch Model: Fundamental Measurement in the Human Sciences 2nd edition. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  17. Burdick, D. S., Stone, M. H., and Stenner, A. J. 2006. “The Combined Gas Law and a Rasch Reading Law.” Rasch Measurement Transactions 20(2): 1059–1060.Google Scholar
  18. Chang, W. C. and Chan, C. 1995. “Rasch Analysis for Outcomes Measures: Some Methodological Considerations.” Archives of Physical Medicine and Rehabilitation 76(10): 934–939.CrossRefGoogle Scholar
  19. Cherryholmes, C. 1988. “Construct Validity and the Discourses of Research.” American Journal of Education 96(3): 421–457.CrossRefGoogle Scholar
  20. Choppin, B. 1968. “An Item Bank Using Sample-Free Calibration.” Nature 219: 870–872.CrossRefGoogle Scholar
  21. —. 1976. “Recent Developments in Item Banking.” In D. N. M. DeGruitjer and L. J. van der Kamp (eds.), Advances in Psychological and Educational Measurement. New York: Wiley, 233–245.Google Scholar
  22. Conference note. 2011. “IMEKO Symposium.” August 31–September 2, 2011, Jena, Germany. Rasch Measurement Transactions 25(1): 1318.Google Scholar
  23. De Soto, H. 2000. The Mystery of Capital: Why Capitalism Triumphs in the West and Fails Everywhere Else. New York: Basic Books.Google Scholar
  24. Ekins, P.1992. “a Four-Capital Model of Wealth Creation.” In P. Ekins and M. Max-Neef (eds.), Real-Life Economics: Understanding Wealth Creation. London: Routledge, 147–155.Google Scholar
  25. Ekins, P., Hillman, M., and Hutchison, R. 1992. The Gaia Atlas of Green Economics (Foreword by Robert Heilbroner). New York: Anchor Books.Google Scholar
  26. Ekins, P., Simon, S., Deutsch, L., Folke, C., and De Groot, R. 2003. “a Framework for the Practical Application of the Concepts of Critical Natural Capital and Strong Sustainability.” Ecological Economics 44(2–3): 165–185.CrossRefGoogle Scholar
  27. Ekins, P. and Voituriez, T. 2009. Trade, Globalization and Sustainability Impact Assessment: A Critical Look at Methods and Outcomes. London, England: Earthscan Publications Ltd.Google Scholar
  28. Fisher, W. P., Jr. 2009a. “Invariance and Traceability for Measures of Human, Social, and Natural Capital: Theory and Application.” Measurement 42(9): 1278–1287.CrossRefGoogle Scholar
  29. —. 2009b. “NIST Critical National Need Idea White Paper: Metrological Infrastructure for Human, Social, and Natural Capital.” Washington, DC: National Institute for Standards and Technology. Available at: http://www.nist.gov/tip/wp/pswp/upload/202_metrological_infrastructure_for_human_social_natural.pdf.Accessed on October 28, 2011, from the National Institute for Standards and Technology.Google Scholar
  30. —. 2011a. “Bringing Human, Social, and Natural Capital to Life: Practical Consequences and Opportunities.” In N. Brown, B. Duckor, K. Draney, and M. Wilson (eds.), Advances in Rasch Measurement 2. Maple Grove, MN: JAM Press, 1–27.Google Scholar
  31. —. 2011b. Measuring Genuine Progress by Scaling Economic Indicators to Think Global & Act Local: An Example from the UN Millennium Development Goals Project. Available at: http://ssrn.com/abstract=1739386. Accessed on October 18, 2011 from Social Science Research Network.Google Scholar
  32. —. 2012. “What the World Needs Now: A Bold Plan for New Standards.” Standards Engineering, in press.Google Scholar
  33. Fisher, W. P., Jr. and Stenner, A. J. 2011a. “Metrology for the Social, Behavioral, and Economic Sciences” (Social, Behavioral, and Economic Sciences White Paper Series). Available at: http://www.nsf.gov/sbe/sbe_2020/submission_detail.cfm?upld_id=36. Accessed on October 28, 2011, from National Science Foundation.Google Scholar
  34. —. 2011b. “a Technology Roadmap for Intangible Assets Metrology.” Presented at the International Measurement Confederation (IMEKO), Jena, Germany, August 31 to September 2, 2011.Google Scholar
  35. Goldberg, S. H. 2009. Billions of Drops in Millions of Buckets: Why Philanthropy Doesn’t Advance Social Progress. New York: Wiley.CrossRefGoogle Scholar
  36. Griffin, P. 2007. “The Comfort of Competence and the Uncertainty of Assessment.” Studies in Educational Evaluation 33: 87–99.CrossRefGoogle Scholar
  37. Haley, S. M., Ni, P., Jette, A. M., Tao, W., Moed, R., Meyers, D. et al. 2009. “Replenishing a Computerized Adaptive Test of Patient-Reported Daily Activity Functioning.” Quality of Life Research 18: 461–471.CrossRefGoogle Scholar
  38. Hayek, F. A. 1948. Individualism and Economic Order. Chicago, IL: University of Chicago Press.Google Scholar
  39. Ifrah, G. 1999. The Universal History of Numbers: From Prehistory to the Invention of the Computer (D. Bellos, I. Monk, E. F. Harding, and S. Wood, Trans.). New York: John Wiley and Sons.Google Scholar
  40. IMF Staff. 2002. “The Role of Capacity-Building in Poverty Reduction.” Available at: http://www.imf.org/external/np/exr/ib/2002/031402.htm. from International Monetary Fund. Accessed on March 3, 2012.Google Scholar
  41. Jensen, M. C. 2001. “Value Maximization, Stakeholder Theory, and the Corporate Objective Function.” Journal of Applied Corporate Finance 14(3): 8–21.CrossRefGoogle Scholar
  42. Kennedy, C. A., and Wilson, M. 2007. Using Progress Variables to Interpret Student Achievement and Progress (Berkeley Evaluation and Assessment Research Center No. 2006–12–01). Berkeley, CA: University of California, Berkeley BEAR Center.Google Scholar
  43. Leclercq, D. 1980. “Computerised Tailored Testing: Structured and Calibrated Item Banks for Summative and Formative Evaluation.” European Journal of Education 15(3): 251–260.CrossRefGoogle Scholar
  44. Linacre, J. M. 1999. “Individualized Testing in the Classroom.” In G. N. Masters and J. P. Keeves (eds.), Advances in Measurement in Educational Research and Assessment. New York: Pergamon, 186–94.Google Scholar
  45. Meijer, R. R. and Nering, M. L. 1999. “Computerized Adaptive Testing: Overview and Introduction.” Applied Psychological Measurement 23(3): 187–194.CrossRefGoogle Scholar
  46. Miller, P. and O’Leary, T. 2007. “Mediating Instruments and Making Markets: Capital Budgeting, Science and the Economy.” Accounting, Organizations, and Society 32(7–8): 701–34.CrossRefGoogle Scholar
  47. Moore, G. 1965. “Cramming More Components onto Integrated Circuits.” Electronics 38(8): 114–117.Google Scholar
  48. Okamoto, N. 2011. “Collective Intentionality and Aggressive Earnings Management: Developing Norman Macintosh’s Arguments in the Debate Over Principles-versus Rules-Based Accounting Standards.” Critical Perspectives on Accounting 22(2): 110–117.CrossRefGoogle Scholar
  49. Rasch, G. 1960. Probabilistic Models for Some Intelligence and Attainment Tests (Reprint, with Foreword and Afterword by B. D. Wright, Chicago: University of Chicago Press, 1980). Copenhagen, Denmark: Danmarks Paedogogiske Institut.Google Scholar
  50. Rentz, R. R. and Bashaw, W. L. 1977. “The National Reference Scale for Reading: An Application of the Rasch Model.” Journal of Educational Measurement 14(2): 161–179.CrossRefGoogle Scholar
  51. Riley, B. B., Conrad, K., Bezruczko, N., and Dennis, M. L. 2007. “Relative Precision, Efficiency, and Construct Validity of Different Starting and Stopping Rules for a Computerized Adaptive Test: the gain Substance Problem Scale.” Journal of Applied Measurement 8(1): 48–64.Google Scholar
  52. Robson, K.1992. “Accounting Numbers as ‘Inscription’: Action at a Distance and the Development of Accounting.” Accounting, Organizations and Society 17(7): 658–708.CrossRefGoogle Scholar
  53. Stenner, A. J. 2001. “The Lexile Framework: a Common Metric for Matching Readers and Texts.” California School Library Journal 25(1): 41–2.Google Scholar
  54. Stenner, A. J., Burdick, H., Sanford, E. E., and Burdick, D. S. 2006. “How Accurate Are Lexile Text Measures?” Journal of Applied Measurement 7(3): 307–22.Google Scholar
  55. Velozo, C. A., Wang, Y., Lehman, L., and Wang, J. H. 2008. “Utilizing Rasch Measurement Models to Develop a Computer Adaptive Self-Report of Walking, Climbing, and Running.” Disability & Rehabilitation 30(6): 458–67.CrossRefGoogle Scholar
  56. Vygotsky, L. S. 1978. Mind and Society: The Development of Higher Mental Processes. Cambridge, MA: Harvard University Press.Google Scholar
  57. Wendt, A. and Tatum, D. S. 2005. “Credentialing Health Care Professionals.” In N. Bezruczko (ed.), Rasch Measurement in Health Sciences. Maple Grove, MN: JAM Press, 161–75.Google Scholar
  58. Wilson, M. 2005. Constructing Measures: An Item Response Modeling Approach. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  59. Wouters, H., Zwinderman, A. H., van Gool, W. A., Schmand, B., and Lindeboom, R. 2009. “Adaptive Cognitive Testing in Dementia.” International Journal of Methods in Psychiatric Research 18(2): 118–127.CrossRefGoogle Scholar
  60. Wright, B. D. 1977. “Solving Measurement Problems with the Rasch Model.” Journal of Educational Measurement 14(2): 97–116. Available at: http://www.rasch.org/memo42.htm. Accessed on March 3, 2012.CrossRefGoogle Scholar
  61. —. 1999. “Fundamental Measurement for Psychology.” In S. E. Embretson and S. L. Hershberger (eds.), The New Rules of Measurement: What Every Educator and Psychologist Should Know. Hillsdale, NJ: Lawrence Erlbaum Associates, 65–104.Google Scholar
  62. Wright, B. D. and Bell, S. R. 1984. “Item Banks: What, Why, How.” Journal of Educational Measurement 21(4): 331–345.CrossRefGoogle Scholar
  63. Wright, B. D. and Douglas, G. A. 1975. Best Test Design and Self-Tailored Testing (Tech. Rep. No. 19). Chicago, IL: MESA Laboratory, Department of Education, University of Chicago. Available at: http://www.rasch.org/memo19.pdf. Accessed on March 3, 2012.Google Scholar
  64. Wright, B. D. and Stone, M. H. 1999. Measurement Essentials. Wilmington, DE: Wide Range, Inc. Available at: http://www.rasch.org/measess/me-all.pdf. Accessed on March 3, 2012.Google Scholar

Copyright information

© Joan Marques, Satinder Dhiman, and Svetlana Holt 2012

Authors and Affiliations

  • William P. FisherJr.

There are no affiliations available

Personalised recommendations