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Using performance measures conceptually in innovation control

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Abstract

In recent years, companies have been changing their innovation strategies, as they have realized that original new products can offer a major competitive advantage. Therefore, many companies are focusing on a more closely managed product-innovation process, and have consequently increased their use of performance-management frameworks. By doing so, such companies hope to increase the effectiveness and efficiency of their new-product-development activities. Within the performance-management framework, the use of innovation metrics plays an important and beneficial role.

For this reason, the present paper investigates the relationship between the design of innovation performance management frameworks and the actual utilization of information obtained from the implemented innovation metrics. We collected data from 133 technology-intensive companies and employed structural equation modeling for empirical analysis. This method allowed us to determine which design factors positively affect the extent to which managers conceptually use innovation metrics. In particular, we investigated how the balance, coherence, adaption, and user know-how of innovation metrics relate to their conceptual use.

Our results suggest that balance and user know-how of the metrics improve conceptual use of the performance measures, whereas no effect can be observed regarding coherence and adaption of the metrics. Thus, it seems highly advisable for firms to implement a simple, comprehensible performance management framework, consisting of financial and nonfinancial performance measures.

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References

  • Adams, R., Bessant, J., & Phelps, R. (2006). Innovation management measurement: a review. International Journal of Management Reviews, 8(1), 21–47.

    Article  Google Scholar 

  • Ansari, S., & Euske, K. J. (1987). Rational, rationalizing, and reifying uses of accounting data in organizations. Accounting, Organizations and Society, 12(6), 549–570.

    Article  Google Scholar 

  • Arbuckle, J. L. (2006). AMOS 7.0 user’s guide. Chicago.

  • Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14, 396–402.

    Article  Google Scholar 

  • Aschhoff, B., Doherr, T., Köhler, C., Peters, B., Rammer, C., Schubert, T., & Schwiebacher, F. (2008). Innovation in Germany. Centre for European Economic Research. ftp://ftp.zew.de/pub/zew-docs/mip/07/MIP_2007_engl.pdf.

  • Bagozzi, R., & Baumgartner, H. (1994). The evaluation of structural equation models and hypothesis testing. In R. Bagozzi (Ed.), Principles of marketing research (pp. 386–422), Cambridge, MA.

    Google Scholar 

  • Bagozzi, R. P., & Yi, Y. (1988). On the evaluation on structural equation models. Journal of the Academy of Marketing Science, 16, 74–94.

    Article  Google Scholar 

  • Balachandra, R., & Friar, J. H. (1997). Factors for success in R&D projects and new product innovation: a contextual framework. IEEE Transactions on Engineering Management, 44(3), 276–288.

    Article  Google Scholar 

  • Baumgartner, H., & Homburg, C. (1996). Applications of structural equation modeling in marketing and consumer research: a review. International Journal of Research in Marketing, 13, 139–161.

    Article  Google Scholar 

  • Bernaards, C. A., & Sijtsma, K. (1999). Factor analysis of multidimensional polytomous item response data suffering from ignorable item nonresponse. Multivariate Behavioral Research, 34, 277–313.

    Article  Google Scholar 

  • Bollen, K. A. (1989). Structural equations with latent variables. New York.

  • Boomsma, A. (2000). Reporting analyses of covariance structures. Structural Equation Modeling, 7, 461–483.

    Article  Google Scholar 

  • Bosnjak, M., Tuten, T. L., & Wittmann, W. W. (2005). Unit (non)response in web-based access panel surveys: an extended planned-behavior approach. Psychology & Marketing, 22(6), 489–505.

    Article  Google Scholar 

  • Bourne, M., Mills, J., Wilcox, M., Neely, A., & Platts, K. (2000). Designing, implementing and updating performance measurement systems. International Journal of Operations & Production Management, 20(7), 754–771.

    Article  Google Scholar 

  • Bremser, W. G., & Barsky, N. P. (2004). Utilizing the balanced scorecard for R&D performance measurement. R&D Management, 34(3), 229–238.

    Article  Google Scholar 

  • Broadbent, J., & Laughlin, R. (2009). Performance management systems: a conceptual model. Management Accounting Research, 20(4), 283–295.

    Article  Google Scholar 

  • Brown, M. G., & Svenson, R. A. (1988). Measuring R&D productivity. Research Technology Management, 31(4), 11–15.

    Google Scholar 

  • Bruns, W. J., & DeCoster, D. T. (1969). Accounting and its behavioral implications. New York.

  • Burchell, S., Clubb, C., Hopwood, A., Hughes, J., & Nahapiet, J. (1980). The role of accounting in organization and society. Accounting, Organizations & Society, 5(1), 5–27.

    Article  Google Scholar 

  • Burney, L., & Widener, S. K. (2007). Strategic performance measurement systems, job-relevant information, and managerial behavioral responses—role stress and performance. Behavioral Research in Accounting, 19, 43–69.

    Article  Google Scholar 

  • Byrne, B. M. (2001). Structural equation modeling with AMOS, basic concepts, applications, and programming. New Jersey.

  • Cardinal, L. B. (2001). Technological innovation in the pharmaceutical industry: the use of organizational control in managing research and development. Organization Science, 12(1), 19–36.

    Article  Google Scholar 

  • Chenhall, R. H. (2005). Integrative strategic performance measurement systems, strategic alignment of manufacturing, learning and strategic outcomes: an exploratory study. Accounting, Organizations & Society, 30(5), 395–422.

    Article  Google Scholar 

  • Chiesa, V., & Frattini, F. (2007). Exploring the differences in performance measurement between research and development: evidence from a multiple case study. R&D Management, 37(4), 283–301.

    Article  Google Scholar 

  • Chiesa, V., Frattini, F., Lazzarotti, V., & Manzini, R. (2008). Designing a performance measurement system for the research activities: a reference framework and an empirical study. Journal of Engineering & Technology Management, 25(3), 213–226.

    Article  Google Scholar 

  • Chiesa, V., Frattini, F., Lazzarotti, V., & Manzini, R. (2009). Performance measurement in R&D: exploring the interplay between measurement objectives, dimensions of performance and contextual factors. R&D Management, 39(5), 487–519.

    Article  Google Scholar 

  • Codero, R. (1990). The measurement of innovation performance in the firm: an overview. Research Policy, 19, 185–192.

    Article  Google Scholar 

  • Collins, J., & Smith, D. (1999). Innovation metrics: a framework to accelerate growth. In Prism (pp. 33–47).

    Google Scholar 

  • Cooper, R. G., & Edgett, S. J. (2008). Maximizing productivity in product innovation. Research Technology Management, 51(2), 47–58.

    Google Scholar 

  • Cooper, R. G., Edgett, S. J., & Kleinschmidt, E. J. (2004). Benchmarking best NPD practices—I. Research Technology Management, 47(1), 31–43.

    Google Scholar 

  • Dabholkar, P. A., & Bagozzi, R. P. (2002). An attitudinal model of technology—based self—service: moderating effects of consumer traits and situational factors. Journal of the Academy of Marketing Science, 30, 184–201.

    Google Scholar 

  • Davila, T. (2000). An empirical study on the drivers of management control systems’ design in new product development. Accounting, Organizations & Society, 25(4/5), 383–409.

    Article  Google Scholar 

  • Davila, T., Epstein, M. J., & Matusik, F. (2004). Innovation strategy and the use of performance measures. Advances in Management Accounting, 13, 27–58.

    Article  Google Scholar 

  • Davila, T., Epstein, M. J., & Shelton, R. (2005). Making innovation work: how to manage it, measure it, and profit from it. Philadelphia, PA.

  • De Vaus, D. (2002). Surveys in social research, 5th ed. London.

  • Diamantopoulos, A., & Souchon, A. L. (1999). Measuring export information use: scale development and validation. Journal of Business Research, 46(1), 1–14.

    Article  Google Scholar 

  • Dillman, D. A. (2007). Mail and Internet surveys: the Tailored design method, 2nd ed. Hoboken, NJ.

  • Donnelly, G. (2000). A P&L for R&D. CFO, 16(2), 44–50.

    Google Scholar 

  • Driva, H., Pawar, K. S., & Menon, U. (2000). Measuring product development performance in manufacturing organisations. International Journal of Production Economics, 63(2), 147–159.

    Article  Google Scholar 

  • Eccles, R. G., & Pyburn, P. J. (1992). Creating a comprehensive system to measure performance. Management Accounting, 74(4), 41–44.

    Google Scholar 

  • Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. New York.

  • Ernst, H. (2002). Success factors of new product development: a review of the empirical literature. International Journal of Management Reviews, 4(1), 1.

    Article  Google Scholar 

  • Feldman, M. S., & March, J. G. (1981). Information in organizations as signal and symbol. Administrative Science Quarterly, 26(2), 171–186.

    Article  Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Article  Google Scholar 

  • Foster, R. N., Linden, L. H., Whiteley, R. L., & Kantrow, A. M. (1985). Improving the return on R&D—II. Research Management, 28(2), 13–22.

    Google Scholar 

  • Frank, S. (2000). Erfolgreiche Gestaltung und Kostenrechnung: Determinanten und Wirkungen am Beispiel mittelständischer Unternehmen. Wiesbaden.

  • Frattini, F., Lazzarotti, V., & Manzini, R. (2006). Towards a system of performance measures for research activities: Nikem research case study. International Journal of Innovation Management, 10(4), 425–454.

    Article  Google Scholar 

  • Garengo, P., Biazzo, S., & Bititci, U. S. (2005). Performance measurement systems in SMEs: a review for a research agenda. International Journal of Management Reviews, 7(1), 25–47.

    Article  Google Scholar 

  • Godener, A., & Söderquist, K. E. (2004). Use and impact of performance measurement results in R&D and NPD: an exploratory study. R&D Management, 34(2), 191–219.

    Article  Google Scholar 

  • Griffin, A., & Page, A. L. (1993). An interim report on measuring product development success and failure. Journal of Product Innovation Management, 10(4), 291–308.

    Article  Google Scholar 

  • Hague, P. N. (1993). Questionnaire design. London.

  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: a global perspective, 7th ed. Upper Saddle River, NJ.

  • Hall, M. (2008). The effect of comprehensive performance measurement systems on role clarity, psychological empowerment and managerial performance. Accounting, Organizations & Society, 33(2/3), 141–163.

    Article  Google Scholar 

  • Hauser, J. R., & Zettelmeyer, F. (1997). Metrics to evaluate R&D. Research Technology Management, 40(4), 32–38.

    Google Scholar 

  • Henard, D. H., & Szymanski, D. M. (2001). Why some new products are more successful than others. Journal of Marketing Research, 38(3), 362–375.

    Article  Google Scholar 

  • Henri, J.-F. (2010). The periodic review of performance indicators: an empirical investigation of the dynamism of performance measurement systems. European Accounting Review, 19(1), 73–96.

    Article  Google Scholar 

  • Hirst, M. K., & Baxter, J. A. (1993). A capital budgeting case study: an analysis of a choice process and roles of information. Behavioral Research in Accounting, 5, 187.

    Google Scholar 

  • Ittner, C. D. (2008). Does measuring intangibles for management purposes improve performance? A review of the evidence. Accounting & Business Research, 38(3), 261–272.

    Article  Google Scholar 

  • Janssen, S. (2011). Innovationssteuerung mit Kennzahlen – Erfolgswirkungen und Determinanten einer konzeptionellen Kennzahlennutzung. Göttingen.

  • Janssen, S., & Möller, K. (2011). Erfolgreiche Steuerung von Innovationsprozessen und – projekten – Ergebnisse einer empirischen Studie. Zeitschrift für Controlling und Management, 55(2), 96–103.

    Google Scholar 

  • Jöreskog, K. G. (1971). Simultaneous factor analysis in several populations. Psychometrika, 36, 409–426.

    Article  Google Scholar 

  • Kerssens-van Drongelen, I. C., & Bilderbeek, J. (1999). R&D performance measurement: more than choosing a set of metrics. R&D Management, 29(1), 35.

    Article  Google Scholar 

  • Kerssens-van Drongelen, I. C., & Cook, A. (1997). Design principles for the development of measurement systems for research and development. R&D Management, 27(4), 345–357.

    Article  Google Scholar 

  • Kerssens-van Drongelen, I. C., Pearson, A., & Nixon, B. (2000). Performance measurement in industrial R&D. International Journal of Management Reviews, 2(2), 111–143.

    Article  Google Scholar 

  • Kline, R. B. (2005). Principles and practice of structural equation modeling, 2nd ed. New York.

  • Knorr, K. D. (1977). Policymakers’ use of social science knowledge: symbolic or instrumental. In C. H. Weiss (Ed.), Using social research in public policy making (pp. 165–182), Lexington.

    Google Scholar 

  • Kumar, N., Stern, L. W., & Anderson, J. C. (1993). Conducting interorganizational research using key informants. Academy of Management Journal, 36, 1633–1651.

    Article  Google Scholar 

  • Lee, M., & Son, B. (1996). Measuring R&D effectiveness in Korean companies. Research Technology Management, 39(6), 28–31.

    Google Scholar 

  • Loch, C. H., & Tapper, U. A. S. (2002). Implementing a strategy-driven performance measurement system for an applied research group. Journal of Product Innovation Management, 19(3), 185–198.

    Article  Google Scholar 

  • Loch, C., Stein, L., & Terwiesch, C. (1996). Measuring development performance in the electronics industry. Journal of Product Innovation Management, 13(1), 3–20.

    Article  Google Scholar 

  • Malina, M. A., & Selto, F. H. (2004). Choice and change of measures in performance measurement models. Management Accounting Research, 15(4), 441–469.

    Article  Google Scholar 

  • Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57, 519–530.

    Article  Google Scholar 

  • Menon, A., & Varadarajan, P. R. (1992). A model of marketing knowledge use within firms. Journal of Marketing, 56(4), 53–71.

    Article  Google Scholar 

  • Meyer, M. H., Tertzakian, P., & Utterback, J. M. (1997). Metrics for managing research and development in the context of the product family. Management Science, 43(1), 88–111.

    Article  Google Scholar 

  • Möller, K., & Janssen, S. (2009). Performance Measurement von Produktinnovationen: Konzepte, Instrumente und Kennzahlen des Innovationscontrollings. Controlling, 21(2), 89–96.

    Google Scholar 

  • Möller, K., Menninger, J., & Robers, D. (2011a). Innovationscontrolling. Stuttgart.

  • Möller, K., Schläfke, M., & Schönefeld, C. (2011b). Wirkungsorientiertes Performance Management. Controlling, 23(7), 372–378.

    Google Scholar 

  • Montoya-Weiss, M. M., & Calantone, R. (1994). Determinants of new product performance: a review and meta-analysis. Journal of Product Innovation Management, 11(5), 397–417.

    Article  Google Scholar 

  • Moorman, C. (1995). Organizational market information processes: cultural antecedents and new product outcomes. Journal of Marketing Research, 32(3), 318–335.

    Article  Google Scholar 

  • Muller, A., Valikangas, L., & Merlyn, P. (2005). Metrics for innovation: guidelines for developing a customized suite of innovation metrics. Strategy & Leadership, 33(1), 37–45.

    Article  Google Scholar 

  • Neely, A., Kennerley, M., & Adams, C. (2007). Performance measurement frameworks: a review. In A. Neely (Ed.), Business performance measurement: unifying theory and integrating practice (pp. 143–162), Cambridge.

    Chapter  Google Scholar 

  • Ojanen, V., & Vuola, O. (2006). Coping with the multiple dimensions of R&D performance analysis. International Journal of Technology Management, 33(2/3), 279–290.

    Article  Google Scholar 

  • Page, A. L., & Schirr, G. R. (2008). Growth and development of a body of knowledge: 16 years of new product development research. Journal of Product Innovation Management, 25(3), 233–248.

    Article  Google Scholar 

  • Pelz, D. C. (1978). Some expanded perspectives on use of social science in public policy. In M. Yinger & S. Cutler (Eds.), Major social issues: a multidisciplinary view (pp. 346–357), New York.

    Google Scholar 

  • Pillai, A. S., Joshi, A., & Rao, K. S. (2002). Performance measurement of R&D projects in a multi-project, concurrent engineering environment. International Journal of Project Management, 20(2), 165–177.

    Article  Google Scholar 

  • Rich, R. F. (1977). Use of social science information by federal bureaucrats: knowledge for actions vs. knowledge for understanding. In C. H. Weiss (Ed.), Using social research in public policy making (pp. 199–211), Lexington.

    Google Scholar 

  • Roussel, P. A., Saad, K. N., & Erickson, T. J. (1991). Third generation R&D: managing the link to corporate strategy. Boston.

  • Sandstrom, J., & Toivanen, J. (2002). The problem of managing product development engineers: can the balanced scorecard be an answer? International Journal of Production Economics, 78, 79–90.

    Article  Google Scholar 

  • Sandt, J. (2004), Management mit Kennzahlen und Kennzahlensystemen. Wiesbaden.

  • Schäffer, U. (2007). Management accounting & control scales handbook. Wiesbaden.

  • Schäffer, U., & Steiners, D. (2004). Zur Nutzung von Controllinginformationen. Zeitschrift für Planung & Unternehmenssteuerung, 15, 377–404.

    Google Scholar 

  • Schumann, P. A., & Ransley, D. L. (1995). Measuring R&D performance. Research Technology Management, 38(3), 45.

    Google Scholar 

  • Sharma, S., Mukherjee, S., Kumar, A., & Dillon, W. (2005). A simulation study to investigate the use of cutoff values for assessing model fit in covariance structure models. Journal of Business Research, 58(7), 935–943.

    Article  Google Scholar 

  • Simon, H. A., Guetzkow, H., Kozmetsky, G., & Tyndall, G. (1954). Centralization vs. decentralization in organizing the controller’s department: a research study and report. New York, NY.

  • Simons, R. (1994). How new top managers use control systems as levers of strategic renewal. Strategic Management Journal, 15(3), 169–189.

    Article  Google Scholar 

  • Souchon, A. L., & Diamantopoulos, A. (1996). A conceptual framework of export marketing information use: key issues and research propositions. Journal of International Marketing, 4(3), 49–71.

    Google Scholar 

  • Souchon, A. L., Diamantopoulos, A., Holzmuller, H. H., Axinn, C. N., Sinkula, J. M., Simmet, H., & Durden, G. R. (2003). Export information use: a five-country investigation of key determinants. Journal of International Marketing, 11(3), 106–127.

    Article  Google Scholar 

  • Stivers, B. P., Covin, T. J., Hall, N. G., & Smalt, S. W. (1998). How nonfinancial performance measures are used. Management Accounting, 79(8), 44–48.

    Google Scholar 

  • Toften, K., & Olsen, S. O. (2004). The relationships among quality, cost, value, and use of export market information: an empirical study. Journal of International Marketing, 12(2), 104–131.

    Article  Google Scholar 

  • Ullrich, M. J., & Tuttle, B. M. (2004). The effects of comprehensive information reporting systems and economic incentives on managers’ time-planning decisions. Behavioral Research in Accounting, 16, 89–105.

    Article  Google Scholar 

  • Utterback, J. A. (1994). Mastering the dynamics of innovation. Boston.

  • Van der Stede, W. A., Young, S. M., & Chen, C. X. (2005). Assessing the quality of evidence in empirical management accounting research: the case of survey studies. Accounting, Organizations & Society, 30(7/8), 655–684.

    Google Scholar 

  • Vandenbosch, B. (1999). An empirical analysis of the association between the use of executive support systems and perceived organizational competitiveness. Accounting, Organizations & Society, 24(1), 77–92.

    Article  Google Scholar 

  • Werner, B. M., & Souder, W. E. (1997). Measuring R&D performance: state of the art. Research Technology Management, 40(2), 34–42.

    Google Scholar 

  • West, S. G., Finch, J. F., & Curran, P. J. (1995). Structural equation models with no-normal variables: problems and remedies. In R. H. Hoyle (Ed.), Structural equation modeling (pp. 56–75), Thousand Oaks, CA.

    Google Scholar 

  • Wheelwright, S., & Clark, K. (1992). Revolutionizing product development: quantum leaps in speed, efficiency, and quality. New York.

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  1. Institute of Accounting, Control and Auditing, University of St. Gallen, Rosenbergstrasse 52, 9000, St. Gallen, Switzerland

    Sebastian Janssen, Klaus Moeller & Marten Schlaefke

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  1. Sebastian Janssen
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  2. Klaus Moeller
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  3. Marten Schlaefke
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Correspondence to Klaus Moeller.

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Dr. Sebastian Janssen is a former doctoral candidate of Prof. Dr. Klaus Moeller.

Prof. Dr. Klaus Moeller is Professor of Performance Management/Management Control and director of the Institute of Accounting, Controlling and Auditing at the University of St. Gallen, Switzerland and chairs the CEPRA—Center for Performance Research & Analytics, Augsburg.

Marten Schlaefke is research assistant at the chair of Performance Management/Management Control at the University of St. Gallen and a doctoral candidate of Prof. Dr. Klaus Moeller.

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Janssen, S., Moeller, K. & Schlaefke, M. Using performance measures conceptually in innovation control. J Manag Control 22, 107–128 (2011). https://doi.org/10.1007/s00187-011-0130-y

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