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Performance Measures for Managerial Decision Making: Performance Measurement Synergies in Multi-Attribute Performance Measurement Systems

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Performance Measures for Managerial Decision Making: Performance Measurement Synergies in Multi-Attribute Performance Measurement Systems Portland State University PDXScholar Dissertations and Theses Dissertations and Theses 1-1-2010 Performance Measures for Managerial Decision Making: Performance Measurement Synergies in Multi-Attribute Performance Measurement Systems Robert Andrew Fowke Portland State University Follow this and additional works at: https://pdxscholar.library.pdx.edu/open_access_etds Let us know how access to this document benefits ou.y Recommended Citation Fowke, Robert Andrew, "Performance Measures for Managerial Decision Making: Performance Measurement Synergies in Multi-Attribute Performance Measurement Systems" (2010). Dissertations and Theses. Paper 164. https://doi.org/10.15760/etd.164 This Dissertation is brought to you for free and open access. It has been accepted for inclusion in Dissertations and Theses by an authorized administrator of PDXScholar. Please contact us if we can make this document more accessible: [email protected]. Performance Measures for Managerial Decision Making: Performance Measurement Synergies in Multi-Attribute Performance Measurement Systems by Robert Andrew Fowke A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Systems Science: Business Administration Dissertation Committee: Beverly Fuller, Chair Alan Raedels Richard Sapp Wayne W. Wakeland Timothy Anderson Portland State University ©2010 ABSTRACT This research tests for correlation between corporate performance and use of financial measures, nonfinancial measures, and number of balanced scorecard categories used. Literature notes a preference for managing by nonfinancial measures because financial measures are lagging indicators, but little empirical evidence is available on the relationship between nonfinancial measures and financial performance, and few companies are found to realize the benefits of nonfinancial measurements. The balanced scorecard has been studied to find the impact of diversity of performance measures, and anecdotal improvements have been reported, but there is a paucity of empirical evidence regarding how the use of a balanced scorecard impacts organizational performance. These issues are investigated in this research with a web based survey distributed to a sample of publicly traded companies using a systematic selection process based on randomly selected numbers generated for each 3-digit NAICS category. The dependent variable is a rank of high, medium or low performance based on 12-month rolling average stock price comparisons from January 2005 to January 2009. These averages are analyzed as a percent change for each company, with performance standardized by 3- digit NAICS category to eliminate cross industry variance in performance ranking. Kruskal-Wallis one-way ANOVA is used to test for correlation. High performers show greatest utilization of both financial and nonfinancial measures, followed by medium performers, with low performers utilizing both measures i the least. Nonfinancial performance measures are more correlated to firm value than financial measures with the high performers’ mean score for nonfinancial measures being higher than for financial measures. By contrast, medium and low performers exhibit the opposite: higher mean scores for financial measures than for nonfinancial measures [p ≤ 0.05 for nonfinancial measures and p ≤ 0.1 for financial measures]. Correlation is found to be borderline significant (p = 0.06) for the number of balanced scorecard categories used with high performers utilizing the highest number of categories and low performers utilizing the lowest number of categories [p = 0.009 with inclusion of two respondents reporting no usage of balanced scorecard categories]. ii AKNOWLEDGEMENTS I am grateful to Beverly Fuller, my chair, for her guidance in completing this dissertation and to the remaining members of my committee: Alan Raedels, Richard Sapp, Wayne W. Wakeland, Timothy Anderson; and Tom Gillpatrick who served as proxy member. Collectively they have provided me with intellectual guidance and have been role models as researchers. I am also grateful to Sam Adams. Without his encouragement I would not have started this project. Though he did not live to see the culmination of this research he was confident that it would be completed. He was both a friend and a mentor and is missed. Finally, I am most grateful to my family for their support: my wife, Kelli; my children Sarah, Chelsea, Steven and Lacy; and my parents for their belief in the value of education. With so much help from many sources, I have only myself to blame for flaws that remain. iii TABLE OF CONTENTS PAGE ABSTRACT ………………………………………………………………… i AKNOWLEDGEMENTS …………………………………………………… iii LIST OF TABLES …………………………………………………………. vi LIST OF FIGURES ………………………………………………………… viii PREFACE ………………………………………………………………….. ix CHAPTER I INTRODUCTION …………………………………………………. 1 The Firm as a Complex Dynamic System …………………………. 1 II REVIEW OF THE LITERATURE ………………………………… 7 Traditional Cost Accounting ………………………………………. 7 Contemporary Techniques ………………………………………..... 11 Agency Based Theory ……………………………………………… 16 Contingency Based Theory ………………………………………… 20 Activity Based Costing and Economic Value Added Measures ..….. 27 Benchmarking and Key Performance Indicators …………………… 30 Financial, Nonfinancial Objective – Subjective Measures: Balanced Scorecards ……………………………………………….. 32 Performance Measurement Review Conclusions …….……………. 35 Clockspeeed ………………………………………………………… 35 Systems Definitions and Concepts …………………………………. 41 Conclusions …………………………………………………………. 53 III DESCRIPTION OF THE PROBLEM …………………………….. 55 Questions and Hypotheses ……………………………………….…. 55 IV METHODS AND TECHNIQUES …………………………………. 68 Web Based Survey …………………………………………………. 70 NAICS Company Definition ……………………………………….. 72 Stock Price Histories ………………………………………….…….. 73 Testing Procedures ………………………………..………………… 79 Conclusions …………………………………………………………. 117 V RESULTS ……………………………………….………….………. 118 Sample Definition and Survey Setup ……………………………….. 118 Pilot Test Observations / Conclusions / Plan ……………………….. 120 Dissertation Survey Results ………………………………………… 128 Hypothesis Testing …………………………………………………. 132 iv VI DISCUSSION ……………………………………………………… 155 Results and Implications …………………………………………… 155 Limitations …………………………………………………………. 161 Strengths …………………………………………………………… 177 Suggestions for Further Research ………………………………….. 180 BIBLIOGRAPY ……………………………………………………………. 182 APPENDICES …………………………………………………………….… 191 A The Firm—A Systems Perspective (Figure 1) Detailed Description with Citations ………………………………………….. 191 B Proposed Dissertation Timeline …………………………………….. 194 C Hypotheses ………………………………………………………….. 195 D Consent Form Web Based Survey ………………………………….. 196 E Questionnaire ……………………………………………………….. 197 F NAICS Company Listings ………………………………………….. 201 G NAICS Company Comparisons ……………………………………. 207 H NAICS Codes and Companies per Code …………………………… 222 I Random NAICS Category Start Number for Company Selection …. 229 J Pilot Test Email Campaign ………………………………………… 237 K Dissertation Survey Email Campaign ….………………………….. 242 L Survey Results ….…………………………………………………. 247 M NAICS Response Categories Data and Dependent Variable Definition ………………………………………………… 256 N Dependent Variable by Responding Companies Only ………..…… 281 O Process Ranking Conversion H 1 …………………………………… 283 P Financial Nonfinancial H2 Kruskal-Wallis Ranked Means by Clockspeed …………………………………………………….. 285 Q H3 Cramer’s Phi …………………………………………………… 289 R Number of Balanced Scorecard Categories Used …………………. 290 S Dissertation Survey ………………………………………………... 292 v LIST OF TABLES TABLE PAGE 1 Sample Industry Clockspeeds ……………………………………….. 39 2 Performance Measures Integrated Perspective .……………………… 67 3 NAICS 31621 Company Listings ……………………………………. 73 4 Foot Locker Stock Price Changes …………………………………… 75 5 Monthly Average Annual Stock Price Change by Company …..…… 77 6 Company Performance Comparisons .……………………………… 78 7 Test Variables and Type of Measure …………………………….… 79 8 Statistical Tests of Association for Each Hypothesis …..…….…..… 80 9 Statistical Tests of Strength of Association for Each Hypothesis ..… 81 10 Hypothetical Responses Survey Question 1 …………………………. 83 11 Hypothetical Case – Responses by Performance Level and Response Rank (Rate) …………………………………………….…. 84 12 Kruskal-Wallis Example Steps 1 and 2 (Identity) ……………………. 86 13 Kruskal-Wallis Example Steps 3 and 4 (Identity) …………………… 88 14 Kruskal-Wallis Example Steps 1 and 2 (Priority) ……………………. 90 15 Kruskal-Wallis Example Steps 3 and 4 (Priority) ……………………. 91 16 Kruskal-Wallis Example Steps 1 and 2 (Background) ………………… 92 17 Kruskal-Wallis Example Steps 3 and 4 (Background) ………………… 93 18 Kruskal-Wallis Example Results (Identity, Priority, Background) ……………………………………….. 94 19 Chi-Square Contingency Test Example H 1a ………………………….. 95 20 Chi-Square Contingency Test Example H 1b …………………………. 96 21 Kruskal-Wallis one-way ANOVA Test Ranked Data H 2 Example 1 ... 99 22 Kruskal-Wallis Test Results H 2 Example 1 ….……………………… 100 23 Kruskal-Wallis Test Results H 2 Example 2 …………………………. 101 24 Hypothetical Survey Response for H 2 ………………………………. 102 25 Hypothetical Responses Ranking Conversions H 2 ..………………… 103 26 Ranked Survey Response Data H 2 ………………………………….. 104 27 High, Middle and Low Performer Financial and Nonfinancial Ranked Means H 2 ……………………………………………………. 105 28 Kruskal-Wallis Test of Ranked Means H 2 ………………………….. 106 29 High Clockspeed Financial and Nonfinancial Ranked Means H 3 ……. 109 30 Medium Clockspeed Financial and Nonfinancial Ranked Means H 3 ……………………………………………………. 110
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