Integration of performance measurement system and performance – theory

Integration of performance measurement systems is a frequently named success factor for performance measurement. For example, 35% of senior executives surveyed for the recent study by IBM (2010) reported that performance data integration was on the top of their agenda. Other reports by consulting companies indicate the importance of PMS integration for strategy success (Accenture, 2010) and identify the lack of integration as a source of performance management failure (Townsend, Smith, Richards, & Wennekes, 2009).

Peer reviewed articles also identify PMS integration as one of the critical success factors of business management. In fact, claims that the degree of PMS integration, both vertical and horizontal, may have a positive impact on performance are not new (Bititci, 1994). For example in her literature review Langfield-Smith (1997) argues that theoretical and normative research in that area began to appear in the mid-1980s, but still lacked empirical evidence in the mid-1990s. More than a decade later, empirical studies that explicitly model PMS integration as the independent variable and performance as the dependent variabel are still rare.

Since the publications by Kaplan and Norton (1996a, b) integration of PMS has been frequently refered to in academic literature. However, the review by Franco-Santos and Bourne (2005) showed that this concept is not uniformally understood. According to them, two perspectives on integration can be identified (see figure below). The first perspective refers to the integration as the vertical allignment along the reporting chain. This understanding includes the interaction of performance measurement with the organizational strategy, cascading the strategic goals down to operational divisions and the linkage to individual incentives (Bititci, 1994; Chenhall, 2005). The second perspective refers to the horizontal integration, which is the cross functional connection of the performance measurement system along divisions. Horizontal integration includes the interconnection of PMS with other management control systems, such as planning and budgeting (Henri, 2008), strategic accounting systems (Cadez & Guilding, 2008), costing (Anderson, 2006), ERP (Kallunki, Laitinen, & Silvola, forthcoming) and benchmarking (Herzog, Tonchia, & Polajnar, 2009). Both perspectives have been reserached by scholars – with limited results.

For example, the impact of integration (vertical and/or horizontal) on performance is not clearly researched. While some authors state that integration is one of the critical success factors of performance measurement initiatives (Chenhall, 2005; Chenhall & Langfield-Smith, 2007; Franco-Santos & Bourne, 2005), others doubt that integration is important per se (Rom & Rohde, 2007). In fact, it has been argued that companies may need a certain level of PMS integration (Chapman & Kihn, 2009), but may be better off with specialized stand alone solutions instead of creating fully integrated systems (Pike, Tayles, & Abu Mansor, forthcoming). An interesting research question would be therefor, if there is an impact of integrated performance measurement systems on performance. And if there is an effect, through which mechanisms does it take place.


[PDF] Accenture (2010). Swedish match: Enterprise performance management framework. Retrieved from

Anderson, S. W. (2006). Managing costs and cost structure throughout the value chain: Research on strategic cost management. In C. S. Chapman (Ed.), Handbooks of Management Accounting Research (pp. 481–506). Elsevier.

[PDF] Bititci, U. S. (1994). Measuring your way to profit. Management Decision, 32(4), 16–24.

Cadez, S., & Guilding, C. (2008). An exploratory investigation of an integrated contingency model of strategic management accounting. Accounting, Organizations and Society, 33(7/8), 836–863.

Chapman, C. S., & Kihn, L.-A. (2009). Information system integration, enabling control and performance. Accounting, Organizations and Society, 34(2), 151–169.

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

[PDF] Chenhall, R. H., & Langfield-Smith, K. (2007). Multiple perspectives of performance measures. European Management Journal, 25(4), 266–282.

[PDF] Franco-Santos, M., & Bourne, M. (2005). An examination of the literature relating to issues affecting how companies manage through measures. Production Planning & Control, 16(2), 114–124.

Henri, J.-F. (2008). Taxonomy of performance measurement systems. Advances in Management Accounting, 17, 247–288.

Herzog, N. V., Tonchia, S., & Polajnar, A. (2009). Linkages between manufacturing strategy, benchmarking, performance measurement and business process reengineering. Computers & Industrial Engineering, 57(3), 963–975.

IBM (2010). Analytics: The new path to value: How the smartest organizations are embedding analytics to transform insights into action: IBM Institute for Business Value. Retrieved from

Kallunki, J.-P., Laitinen, E. K., & Silvola, H. (forthcoming). Impact of enterprise resource planning systems on management control systems and firm performance. International Journal of Accounting Information Systems,

Kaplan, R. S., & Norton, D. P. (1996a). The balanced scorecard: Translating strategy into action. Boston: Harvard Business School Press.

Kaplan, R. S., & Norton, D. P. (1996b). Using the balanced scorecard as a strategic management system. Harvard Business Review, 74(1), 75–85.

Langfield-Smith, K. (1997). Management control systems and strategy: A critical review. Accounting, Organizations and Society, 22(2), 207–232.

Pike, R. H., Tayles, M. E., & Abu Mansor, N. N. (forthcoming). Activity-based costing user satisfaction and type of system: A research note. The British Accounting Review,

[PDF PhD] Rom, A., & Rohde, C. (2007). Management accounting and integrated information systems: A literature review. International Journal of Accounting Information Systems, 8(1), 40–68.

Townsend, P., Smith, S., Richards, G., & Wennekes, K. (2009). Performance management matters: Sustaining superior results in a global economy: PricewaterhouseCoopers.

How to measure company performance in academic management research

The validity and the comparability of the academic research results depend on the quality of the research methods applied. One of the most important methodological issues in quantitative research is the measurement of the variables used in the conceptual model. For performance management research, company performance is such a variable (see also references for performance measurement of public organizations). Although “performance” may appear to be an easy concept, a unique definition in the literature does not exist. Moreover, academics often use special definitions tailored to fit the individual research purposes (Langfield-Smith, 1997).

Several ways to categorize performance have been presented in the literature (see literature review by Kihn, 2010). One way is to distinguish the outcomes of organizational activities and the means by which these outcomes are reached (Govindarajan & Fisher, 1990). The former is often called “performance” while the latter is commonly referred to as “effectiveness” (Ukko, 2009). Interestingly enough, this distinction appeared only after 1978 (Henri, 2004). Before that time both definitions were used interchangeably.

Another way to characterize performance is to distinguish between financial and non-financial performance (Ittner, 2008). The financial performance is often measured using traditional accounting KPIs such as ROA, ROS, EBIT, EVA® or Sales growth (Ittner & Larcker, 1997; Fraquelli & Vannoni, 2000; Crabtree & DeBusk, 2008). The advantage of these measurements is their general availability, since every profit oriented organization produces these figures for the yearly financial reporting (Chenhall & Langfield-Smith, 2007). However, balance sheet manipulations and choices of accounting methods may also lead to values that allow only limited comparability of the financial strength of companies.

The non-financial performance can be measured using operational KPIs. Market share, innovation rate or customer satisfaction are prominent examples (Hyvönen, 2007). Tangen, (2003) provides an overview of frequently used performance measures. Many researchers also use self reported measures to operationalize performance (Evans, 2004; Chenhall & Morris, 1995; Henri, 2006; Ittner, Lanen, & Larcker, 2002). Others combine both, the accounted financial KPIs and self reported measures in their reports (Cadez & Guilding, 2008). Langfield-Smith, (1997) writes that there are various ways non-financial performance can be measured, however the performance can be hardly assessed without the link to corporate strategy. The consequence for the researcher is simple: it is first to decide what the research question should be, then a performance definition can be created.

Measure Key reference
Sales growth Banker, Potter and Srinivasan, (2000); Davis and Albright, (2004); Eldenburg at al., (2010); Ittner and Larcker, (1997); Ittner, Larcker and Randall, (2003)
ROS Weiner and Mahoney, (1981); Benner and Veloso, (2008); Ittner and Larcker, (1997); Crabtree and DeBusk (2008)
ROA Braam and Nijssen, (2004); Benner and Veloso, (2008); Ittner and Larcker, (1997); Ittner, Larcker and Randall, (2003); Crabtree and DeBusk (2008); Said, HassabElnaby and Wier, (2003)
Share value growth Weiner and Mahoney, (1981); Ittner, Larcker and Randall, (2003); Said, HassabElnaby and Wier, (2003)
Self reported financial perf. Foster and Swenson, (1997);  Ittner, (2002); Evans, (2004); Franco-Santos, (2007); Hyvönen (2007); Widener, (2007); Henri, (2006); Henri, (2010); Grafton, Lillis, Widener, (Forthcomming)
Own vs. Competitors Ittner and Larcker, (1997); Reck, (2001); Braam and Nijssen, (2004); Chapman, Kihn, (2009); Grafton, Lillis, Widener, (Forthcomming)
Table 1: Elements of performance definition (Timur Pasch, 2010)


Cadez, S., & Guilding, C. (2008). An exploratory investigation of an integrated contingency model of strategic management accountingAccounting, Organizations and Society33(7/8), 836–863.

Chenhall, R. H., & Morris, D. (1995). Organic decision and communication processes and management accounting systems in entrepreneurial and conservative business organizationsOmega23(5), 485–497.

Chenhall, R. H., & Langfield-Smith, K. (2007). Multiple perspectives of performance measuresEuropean Management Journal25(4), 266–282.

Crabtree, A. D., & DeBusk, G. K. (2008). The effects of adopting the balanced scorecard on shareholder returns.Advances in Accounting24(1), 8–15.

Evans, J. R. (2004). An exploratory study of performance measurement systems and relationships with performance resultsJournal of Operations Management22(3), 219–232.

Fraquelli, G., & Vannoni, D. (2000). Multidimensional performance in telecommunications, regulation and competition: Analysing the European major playersInformation Economics and Policy12(1), 27–46.

[PDF] Govindarajan, V., & Fisher, J. (1990). Strategy, control systems and ressource sharing: Effects on business-unit performanceAcademy of Management Journal33(2), 259–285.

[PDF] Henri, J.-F. (2004). Performance measurement and organizational effectiveness: Bridging the gapManagerial Finance30(6), 93–123.

Henri, J.-F. (2006). Management control systems and strategy: A resource-based perspectiveAccounting, Organizations and Society31(6), 529–558.

[PDF] Hyvönen, J. (2007). Strategy, performance measurement techniques and information technology of the firm and their links to organizational performanceManagement Accounting Research18(3), 343–366.

Ittner, C. D. (2008). Does measuring intangibles for management purposes improve performance?: A review of the evidence. Accounting & Business Research38(3), 261–272.

Ittner, C. D., Lanen, W., & Larcker, D. (2002). The association between activity-based costing and manufacturing performanceJournal of Accounting Research40(3), 711–726.

Ittner, C. D., & Larcker, D. F. (1997). Quality strategy, strategic control systems, and organizational performance.Accounting, Organizations and Society22(3-4), 293–314.

Kihn, Lili-Anne (2010): Performance outcomes in empirical management accounting research. Recent developments and implications for future research. In International Journal of Productivity and Performance Management 59 (5), pp. 468–492.

Langfield-Smith, K. (1997). Management control systems and strategy: A critical review. Accounting, Organizations and Society22(2), 207–232.

Tangen, S. (2003). An overview of frequently used performance measuresWork Study52(7), 347–354.

[PDF] Ukko, J. (2009). Managing through measurement: A framework for successful operative level performance measurement. PhD thesis at the Lappeenranta University of Technology.

Literature based characteristics of high performing organizations – a review

According to the literature, high performing organizations (HPO) tend to share similar characteristics. Professor de Waal, the academic director at the Center for Organizational Performance based in the Netherlands, proposed a framework to analyse these factors in his latest paper (de Wall 2010). The literature analysis presented here was based on the review of 262 academic and corporate sources (non english and grey literature excluded).

A new method of literature review – the systematic review – was applied here to address the topic of high performance organizations. The insights extracted from the literature are articulated and presented in a framework developed by de Waal ( 2008, 2010a, b). The paper will only focus on the thematic findings of this literature review and its greatest contribution is the overview of academic and practice work aimed at understanding the secrets of top management practices. The identified success factors of organizations are presented below:

These factors include following capabilities (follow the links provided to each marked key word to discover current management thinking):

Organizational design characteristics

  • D1. Stimulate cross-functional and cross-organizational collaboration.
  • D2. Simplify and flatten the organization by reducing boundaries and barriers between and around units.
  • D3. Foster organization-wide sharing of information, knowledge and best practices.
  • D4. Constantly realign the business with changing internal and external circumstances.

Strategy characteristics

Process characteristics

  • P1. Design a good and fair reward and incentive structure.
  • P2. Continuously innovate products, processes and services.
  • P3. Continuously simplify and improve all the organization’s processes.
  • P4. Create highly interactive internal communication.
  • P5. Measure what matters.
  • P6. Report to everyone financial and non-financial information needed to drive improvement.
  • P9. Strive for continuous process optimalization.
  • P8. Strive to be a best practice organization.
  • P9. Deploy resources effectively.

Technology characteristics

  • T1. Implement flexible ICT-systems throughout the organization.
  • T2. Apply user-friendly ICT-tools to increase usage.

Leadership characteristics

  • L1. Maintain and strengthen trust relationships with people on all levels.
  • L2. Live with integrity and lead by example.
  • L3. Apply decisive action-focused decision-making.
  • L4. Coach and facilitate.
  • L5. Stretch yourselves and your people.
  • L6. Develop effective, focused and strong leadership.
  • L7. Allow experiments and mistakes.
  • L8. Inspire the people to accomplish extraordinary results.
  • L9. Grow leaders from within.
  • L10. Stimulate change and improvement.
  • L11. Assemble a diverse and complementary management team and workforce.
  • L12. Be committed to the organization for the long haul.
  • L13. Be confidently humble.
  • L14. Hold people responsible for results and be decisive about nonperformers.

Individuals & Roles characteristics

  • I1. Create a learning organization.
  • I2. Attract exceptional people with a can-do attitude who fit the culture.
  • I3. Engage and involve the workforce.
  • I4. Create a safe and secure workplace.
  • I5. Master the core competencies and be an innovator in them.
  • I6. Develop people to be resilient and flexible.
  • I7. Align employee behaviour and values with company values and direction.

Culture characteristics

  • C1. Empower people and give them freedom to decide and act.
  • C2. Establish strong and meaningful core values.
  • C3. Develop and maintain a performance-driven culture.
  • C4. Create a culture of transparency, openness and trust.
  • C5. Create a shared identity and a sense of community.

External orientation characteristics

  • E1. Continuously strive to enhance customer value creation.
  • E2. Maintain good and long-term relationships with all stakeholders.
  • E3. Monitor the environment consequently and respond adequately.
  • E4. Choose to compete and compare with the best in the market place .
  • E5. Grow through partnerships and be part of a value creating network.
  • E6. Only enter new business that complement the company‟s strengths.

A more recent work around the characteristics of high performing organizations is provided by Kiron et al. (2011). Please read the Analytics: The Widening Divide for more details.


Additional reading:


Research concept updated

The impact of performance management on corporate performance

Why do some companies perform better using corporate performance measurement and management systems than others?

To cope with the increasing complexity of the business environment, management teams must prioritize effectively and focus their attention on activities that lead to higher performance. For that reason many organizations have invested substantial resources in the implementation of various performance measurement and management techniques. They believe, that performance measurement and management might help them to improve the performance somehow (see illustration below).

However, the added value of that investment often remains unclear with only few organizations reporting tangible payoff on the performance measurement systems. Consequently, managers of today need a clear guidance for their efforts to manage performance and on how to get the most value out of the existing performance measurement systems.

Since Kaplan and Norton (1992) first introduced their Balanced Scorecard, a substantial body of research around the design, implementation, and usage of performance measurement systems (PMS), has emerged (see Neely, 2005, for literature review). Despite the growing academic and professional interest, there is currently little knowledge regarding the impact of PMS on organizations (see Bourne, Kennerley, & Franco-Santos, 2005 for literature review). Furthermore, existing research provides contradictory results concerning performance effects of the PMS usage (Martinez, Kennerley, & Neely, 2004). While some authors clearly see positive effects (Davis & Albright, 2004; deWaal, Kourtit, & Nijkamp, 2009; Ukko, Tenhunen, & Rantanen, 2007; de Geuser, Mooraj, & Oyon, 2009), others found no evidence of increasing performance after the implementation of PMS (Meyer, 2007). Therefore, a consensus in the ongoing discussion regarding the effect of PMS on organizations has not yet been achieved.

Based on results from literature, this study is set to identify the success factors leading to a better use of performance management systems. The research problem is to find out how companies can be successfully managed with measures (see illustration below).

The starting point for the research is the believe, that performance management systems (PMS) can lead to a greater performance only when PMS are used by people to make better business decisions (Chenhall, 2003). If this is the case, and other variables can be controlled for, the interesting question is what makes people take better decisions with PMS. Consequently, the conceptual model includes a link between decision making based on PMS information and performance. This link has been often described qualitatively (Goold & Quinn, 1990; Gunasekaran & Kobu, 2007; Laitinen, 2009; Lipe & Salterio, 2000), but quantitative analysis is rare (e.g. Foster, Swenson, 1997). Thus, this study wants to contribute to the research and provide evidence why some companies use PMS better than others (see illustration below).

To provide a solid answer to that question the study is organized as following:

  1. Identify relevant factors leading to an effective performance management from literature.
  2. Define a solid conceptual model.
  3. Create instrument for measuring concepts based on suggestions from literature.
  4. Gather empirical data and analyse the data.
  5. Arrive at conclusions.
  6. Provide final report.

Key characteristics of the research:

  1. Literature based.
  2. Quantitative, positivistic approach.
  3. Company as unit of analysis.
  4. Cross sectional non probability sample of profit oriented firms.


Bourne, M., Kennerley, M., & Franco-Santos, M. (2005). Managing through measures: A study of impact on performance. Journal of Manufacturing Technology Management, 16(4), 373–395.

Chenhall, R. H. (2003). Management control systems design within its organizational context: findings from contingency-based research and directions for the future. Accounting, Organizations and Society, 28(2-3), 127–168.

[PDF] Davis, S., & Albright, T. (2004). An investigation of the effect of Balanced Scorecard implementation on financial performance. Management Accounting Research, 15(2), 135–153.

de Waal, A., Kourtit, K., & Nijkamp, P. (2009). The relationship between the level of completeness of a strategic performance management system and perceived advantages and disadvantages. International Journal of Operations & Production Management, 29(12), 1242–1265.

[Summary] Foster, George; Swenson, Dan W. (1997): Measuring the success of activity-based cost management and its determinants. In Journal of Management Accounting Research 9, pp. 109–141.

Geuser, F. de, Mooraj, S., & Oyon, D. (2009). Does the balanced scorecard add value? Empirical evidence on its effect on performance. European Accounting Review, 18(1), 93–122.

Goold, M., & Quinn, J. J. (1990). The paradox of strategic controls. Strategic Management Journal, 11(1), 43–57.

Gunasekaran, A., & Kobu, B. (2007). Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications. International Journal of Production Research, 45(12), 2819–2840.

Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard: Measures that drive performance. Harvard Business Review, 71–79.

Laitinen, E. K. (2009). Importance of performance information in managerial work. Industrial Management & Data Systems, 109(4), 550–569.

Lipe, M. G., & Salterio, S. E. (2000). The balanced scorecard: Judgmental effects of common and unique performance measures. Accounting Review, 75(3), 283.

Martinez, V., Kennerley, M., & Neely, A. (2004). Impact of PMS on business performance: A methodological approach. Cranfield.

Neely, A. (2005). The evolution of performance measurement research: Developments in the last decade and a research agenda for the next. International Journal of Operations & Production Management, 25(12), 1264–1277.

Ukko, J., Tenhunen, J., & Rantanen, H. (2007). Performance measurement impacts on management and leadership: Perspectives of management and employees. International Journal of Production Economics, 110(1-2), 39–51.