Innovation performance measurement

Performance management literature has noticed the importance of taking account of innovation performance measurement as a part of performance measurement and management. Kaplan and Atkinson (1998) regarded innovation as one of the main business processes of an organization. Adams et al. (2006) observed that innovation performance measurement does not appear to take place routinely within management practice in organizations. Innovation in organizations can be non-linear, fuzzy, or ill defined, rather than being dependent on cause and effect rationale (Ford, 2000). This is amplified in SMEs where

complexity of innovation is determined by issues such as scarcity of resources, lack of skills, skepticism towards formal training, the need for flexibility and lack of systematic innovation performance measurement (Lee et al., 2000; McAdam et al., 2010). Many authors have suggested that the competitiveness of SMEs can be increased through innovation by defining innovation more consistently and paying attention to innovation performance measurement (Gorton, 2000; McAdam and Keogh, 2004). As pointed out by Neely et al. (2000), performance measurement must not be seen as obtrusive and contradictory within innovation.

When performance measurement has been conducted in a proper way, it can boost innovation.

According to Skarzynski and Gibson (2008), measures of innovation can help managers in two ways: first, to make informed decisions based on objective data; and second, to help align goals and daily endeavors for near- and long-term innovation goals. Especially in the context of innovation, the measures should be dynamic and changeable and be continually reviewed and developed during the transitional process of developing the innovation capability (Neely et al., 2000; McAdam and Keogh, 2004). It is also emphasized that measuring innovation must be given more strategic and operational importance and a wide range of measures of innovation should be adopted, reflecting the diversity within innovation (McAdam and Keogh, 2004). After all, the management of innovation demands appropriate controlling and performance measurement approaches (Schentler et al., 2010).

Appropriate performance measures can contribute to a significantly better understanding of innovation. To be effective, any measure should focus attention on the critical success factors in the particular business and its sector of activity (Birchall et al., 2011). Similarly, Tidd et al.

(2005) argue that innovation performance measurement must relate the firm’s innovation to its success in the marketplace. Thus, it is significant to evaluate the innovation accurately, and to find the key factors influencing innovation (Shan and Zhang, 2009). If the prime aim of innovation is to create new, better value for the customer or end user so as to gain improved return on investment, then the factors likely to provide that success are key areas for innovation performance measurement (Birchall et al., 2004). Literature on innovation performance measurement stress the importance to measure a wide number of determinants including areas like innovation strategy; ideas and ideation; customer and market;

organizational learning and knowledge management tools; and organizational culture and leadership (Adams et al., 2006; Crossan and Apaydin, 2010).

However, the development of comprehensive measures of innovation to support innovation in SMEs is often limited within production-oriented measures (Freel, 2000). According to Birchall et al. (2011) a number of perspectives have been presented on the topic. At the firm level, these include: the effectiveness of R&D investment, the effectiveness of the new

product development process, the effectiveness of the management of change, and the degree to which enablers of innovation are present and hence the future secured.

Generally, four types of innovation performance measurement can be subsumed: input, process, output, and outcome. Input measurement represents the resources provided for innovation, for example, personnel, funds, equipment, and ideas (c.f., Skarzynski and Gibson, 2008; Janssen et al., 2011). Process measurement indicates how the mechanism between the inputs and outputs of innovation occur (Carayannis and Provance, 2008). Process measures include the achievement of time, cost, and quality objectives as well as the project progress.

Output measurement reflects the direct results of innovation activities (i.e., new products or generated knowledge) and helps identify trends and developments over time. Outcome measurement demonstrates innovation success in the market and thus focuses on revenue, profit, market share, and customer satisfaction (Janssen et al., 2011). Thus, outcomes represent the performance implications of innovation. Adams et al. (2006) reviewed current measures of innovation and found that measurement tends to focus on output measurement.

Carayannis and Provance (2008) discussed how coherent measurement of the performance implications of innovation requires the consideration of input, process, and output measurement simultaneously. A wide range of innovation measures should be adopted, because single or more limited measures do not offer information comprehensive enough that managing innovation requires (McAdam and Keogh, 2004; Carayannis and Provance, 2008).

Results from Janssen et al. (2011) underline the importance of a balanced set of innovation measures since a balanced framework increases the extent to which innovation performance measurement is used. Moreover, innovation performance measurement should cover financial as well as nonfinancial aspects (Janssen et al., 2011). Measurement should make the relationships among objectives explicit so that they can be managed and validated (Kaplan and Atkinson, 1998). Linkages to both cause-and-effect relationships and mixtures of performance implications and performance drivers (innovation inputs, process, and outputs) by considering dependencies, time lags, etc. should be incorporated (c.f., Kaplan and Atkinson, 1998; Janssen et al., 2011). Performance implications without performance drivers do not communicate how the implications are to be achieved (Kaplan and Atkinson, 1998).

It is necessary to understand the nature of the innovation in order to align innovation performance measurement and enable more actionable outcomes from measurement. It is important to define the areas in which innovation performance measurement is most needed in order to support decision-makers in the design of measurement and the selection of appropriate measures (Birchall et al., 2011). Neely et al. (2000) argue that measurement

frameworks often fail to account for the diversity and requirements within individual organizations. To avoid this they suggest that the frameworks should be “a set of design guidelines designed to inform the development of a process for performance measurement system design” (Neely, 2000, p. 1120). Innovation performance measurement differs depending on the characteristics of the dominant determinants in each market and technological environment, and thus measurement has to be adapted to its specific needs (Perez-Freije and Enkel, 2007). This would imply that the firm should design innovation performance measurement appropriate to their own particular situation. It can be dependent upon the outcomes being pursued from innovation (Birchall et al., 2011). Innovation measures are not an end point, rather dynamic phenomena that must be continually reviewed and developed during the transitional period when innovation is developed (McAdam and Keogh, 2004).