A Conceptual Model to Assess KM and Innovation Projects: A Need for an Unified Framework

Firm performance required numerous projects like total quality, reengineering of innovation and knowledge processes, rationalization projects. Their respective results and impacts are assessed through performance models or frameworks which are rarely combined although managers could benefit from integrated and coherent models, mainly for innovation and KM (Knowledge Management). Models for measuring innovation and KM performance are new and concern mainly large companies. They have almost all been developed relying on input/output frameworks. The processes generating performance are not thoroughly taking in account. Drawing upon a literature review and a theoretical study, this paper contribution is based on an integrated conceptual model combining the value innovation chain of Hansen and Birkinshaw (2007) [1], and the SECI KM model of Nonaka and Takeuchi (1995) [2], to build an integrated KM-innovation framework which can help to assess KM projects and innovation projects in different types of organizations.


1-Introduction
In order to improve their performance, most organizations put in place different types of projects namely BPR (Business Process Reengineering), KM and innovation projects. For these various projects, managers need to measure impacts and outcomes on organizational performance. Scholars had developed several models with different perspectives to measure the outcomes of these projects (Andreeva & Kianto, 2012 [3]). But each of these models concerns specifically one project type at a time. However, organizations manage limited resources (financial, human, informational, etc) and must recognize that many organizational projects are integrated and combined to fulfill the same final mission, to improve organizational performance. The scope of this paper is based on KM and innovation projects. KM projects are a key solution to build a competitive advantage and enhance business performance (Bontis and al., 2001 [4]; Bose, 2004 [5]; Carlucci & Schiuma, 2006 [6]). Innovation projects also contribute to the same result. To be successful, innovations projects need to develop new knowledge. According to Nelson & Winter (1982) [7], the firm process of acquisition, storage, maintenance and renewal of technological and organizational knowledge is the cornerstone of the firm innovation performance. The process of knowledge management (creation, exploitation, sharing, transfer) is achieved by various strategies. Nonaka and Takeuchi (1997) [2] underline four strategies, namely socialization (tacit to tacit), externalization (tacit to explicit), combination (explicit to explicit) and internalization (explicit to tacit). Both KM an innovation projects contribute to improve productivity, consumer satisfaction, and new products and services. They are intertwined but available frameworks in the literature evaluate the nature and value of their impacts separately. For managers and from a strategic point of view, it would be useful to have an integrated framework to assess KM and innovation projects. This paper is structured as follows: the first section is a review of the different KM assessment models, the second section is a review of innovation measurement frameworks and the third section proposes an integrated conceptual framework based on input-ouput model combined with the balanced scorecard model.

2-1 Knowledge management assessment models: options and limits
Knowledge is intangible ( Nonaka and Takeuchi (1997) [2]) and its management cannot be assessed with conventional methods, as financial or accounting ones (Bontis and al., 2001 [4]). Furthermore, financial resources are necessary to put in place KM projects and managers are looking for return on investment. Measurement is thus necessary to justify these investments although it remains difficult to establish the link between investment in knowledge management and organisational performance.
The literature about KM addresses the measurement issues with numerous different approaches. These differences are mostly due to the profile, experience and disciplinary field of the scholar. Thus, all the KM measurement frameworks, within an organization, can be grouped into three main approaches. The first one focuses on metrics, the second one focuses on methodological aspects and the third one prioritizes measurement models. In the first approach, various authors propose "metrics" of the level of knowledge within an organization. Those metrics are related to a characteristic or a condition of the organization. No processing measure is proposed between an initial and a final state. Table 1 below illustrated the parameters of all the three approaches.  Hanley and Malafsky (2003) [8] present a systemic approach based on input-output model ( Table 2) where they identify process metrics, output metrics and outcome metrics for KM measurement. They outline the link between the knowledge project and the organizational performance. But there is no organizational level underlined, nor any specific human resource, namely individual, group or service related to the performance achieved by the KM project. However, Hanley and Malafsky [8] approach presents parameters to consider when assessing knowledge management project influence on organizational performance. The Balanced Scorecard is a framework which offers many advantages in terms of measurement of the performance. First of all, it takes into account several dimensions, namely: customer, finances, internal processes, training and improvement. This integration of the 4 distinct, but complementary prospects makes it possible to ensure the multi-factor approach of measurement. Secondly, it is non-prescriptive and therefore can be adapted to various contexts and situations. With that in mind, it becomes relevant to see under which conditions it will be applicable in a context of knowledge management. Chen and al. (2005) [9] adapted the BSC for KM purposes. Drawing on the work of various authors (Kaplan & Norton , 1996 [10]; Nonaka and Takeuchi, 1997 [2]; Alavi, 1997 [11]; Liebowitz, 1999 [12]), Chen and al. (2005) [9] established that the process of KM can be divided into 4 core activities, namely: 1creation, 2-conversion, 3-circulation, and 4 -completion. These processes are used as substitutes for the four initial ones proposed in the primary Norton and Kaplan model. Conceptually, Chen and al. (2005) [9] framework summarised in Table 3 adapts the BSC in response to the specific needs of KM performance measurement. Another adaptation of the BSC to KM performance assessment has been proposed by Wu (2005) [13].
Here, a more qualitative and integrated approach is adopted by associating the dimensions related to the organization (human capital, customer capital, organisational capital) to the operational dimensions of the BSC (finance, process, learning, etc). This combination makes it possible to distinguish elements related to KM as a stock (organizational capital) from the dynamic aspects related to the transformation from stock into flow. Table 4 below summarises the adaptation developed by Wu (2005) [13], which proves to be very relevant in a non-commercial organisational context, where results are not necessarily financial or quantitative. Drawing on the BSC architecture, we can underline that the financial results are only one consequence of the improvement of the competencies of the employees, the control of the processes and the capability to adequately meet needs and customer requirements. Moreover, the BSC integrates internal and external dimensions, as well as qualitative and quantitative indicators. In particular, measurements related to the customer are mainly qualitative (example: satisfaction, time, etc) whereas those related to financial results are mainly quantitative. Incidentally, the BSC is applicable as well as within business unit as to the level of a project or to the whole of the organization. The BSC represents a viable option to evaluate the impact of KM projects on organization.
The flexibility and adaptability of the balanced scorecard enable its use in different contexts. Although they are all relevant, these categorizations of KM models remain difficult to operationalize and the innovation dimensions are not included.

2-2 Innovation performance measurement
The evolutionary theory of economic populated by Nelson & Winter (1982) [7] gave some foundations to innovation research. It states that firms evolve not only through optimization but also through learning and exploration. It put also an emphasis on the firm process of acquisition, storage, maintenance and renewal of technological and organizational knowledge. According to the authors, that process is the cornerstone of the firm innovation performance. The stakeholder theory (Freeman & al., 2010[14],) also contributed to the current stream of innovation research based on networks and ecosystem. In concordance with that theory, the knowledge required for the building and management of disruptive change lies increasingly outside the boundaries of the firm and the innovation performance is related to an efficient management of the firm relevant stakeholders through partnership and alliances.
Drivers for successful innovation are well documented, specifically for large firms but their metrics are still unsatisfactory (Adam & al., 2006) [15]. Four drivers for successful innovation were identified by Tidd & al. (2006) [16]: an appropriate strategy, internal and external effective links, creative mechanisms to promote change, the existence of an organizing framework wearer.
Models of innovation performance has been developed drawing on different methodologies including empirical ones like firms survey (OECD, 2005 [17]; Alegre & al., 2006[18]), case study (Lazzarotti & al., 2011[19]) and theoretical approaches (Adams & al., 2006[15]; Schentler & al., 2010[20]; Edison & al., 2013[21]). The OCDE methodology is well spread and validated among the OCDE thirty members and its main focus is the national innovation system performance and less the firm performance. The following Table 5 illustrated different methodologies from quantitative to qualitative ones that are involved in innovation measurement studies.  [19] to develop a five perspectives R&D model based on the soft measurement theory and a case study. The five perspectives comprise financial, customer, innovation and learning, internal business, alliances and networks. The following Table 6 illustrated the diverse innovation frameworks and their respective scope or limit.
Emerging models of innovation performance measurement are built with operations research tools such as Data Envelopment Analysis (DEA) or multicriteria analysis tools such as Analytic Hierarchy Process (AHP). By developing a function whose form is determined by the most efficient producers, DEA is well suited for innovation efficiency calculation and for benchmark (Cruz-Cazares & al., 2013 [23],). As a multicriteria analysis tool, AHP can be well-suited for innovation portfolio management.  [15] proposed this framework to innovation managers in their attempt to construct a comprehensive measure of innovation performance. They stated: « the measures proposed in the literature often seem to be proposed abstractly, with little consideration given to the use of measures as a management tool in the day to day context of managing innovation».
Drawing on a survey among CEO of large companies, Mankin (2007) [30] observed a diversity of approaches that companies uses to measure innovation performance. He states: «The challenge in effectively measuring innovation performance is one of abundance, rather of scarcity-there are so many approaches and no one of them is perfect…». The following Table 7 illustrated that diversity. Traditional and recent models of innovation performance measurement are still input/output oriented and the innovation process between is neglected (Adams & al., 2006) [15]. Their indicators focus on past innovation performance, stressing more on control rather than management purpose. One of the consequences of the lack of process-oriented innovation performance measurement framework is that the innovation dilemma is still not managed properly in the enterprises, particularly in the SMEs (Chang & Hughes, 2012) [31]. Furthermore, different models and frameworks are used to measure innovation performance projects but they don't take in account the global dimension or process of knowledge management. This can be considered as a gap because value creation is driven by knowledge management and only a purposeful management of knowledge base at every stage of project innovation process can deliver the enterprise expected results.

3-1 Joining innovation and knowledge management projects: a process-driven and effective organization
Knowledge creation and evaluation are considered today as drivers of value creation in every organization.
In the same vein, innovation projects are a solution to ensure the effectiveness of knowledge management projects. Therefore, measuring impacts or performance of knowledge management and innovation projects becomes an interesting challenge for both executives and scholars. It helps executives to determine impacts at different levels of the organization namely, productivity improvement, customer and employee satisfaction, new products and services development. It helps them also to use enterprise available knowledge as a multiplying effect of value creation.
Today, organizations must devote numerous resources to innovation management and for the effectiveness of that investment; they must consider innovation management as in line with knowledge management. In putting forward innovation projects, organizations bring creative solutions to their problems and identify new products and services which contribute to improve customer satisfaction, anticipate future needs; they also build synergy with the available knowledge and the needed one created through R&D activities. After all, whatever the nature of the innovation project, organizations deal with every activity of the knowledge management process, namely: a-knowledge identification -audit (cartography), b-codification -storage c-exploitation -transformation, d-acquisition -conservation, e-diffusion -disposition, f-transferexchange, g-use -re-use, h-integration -renewal. Therefore, taking into account those activities in a process approach helps to generate the results and outcomes expected in innovation and knowledge management projects.

3-2 Challenges related to innovation and knowledge management projects
The joined management of innovation and knowledge projects generate specific challenges at the organizational and operational level, impacts and outcomes measurement level. Three particular challenges need to be addressed with a specific measurement framework.
First of all, innovation projects required extensive human, financial, informational and material resources without certainty of results. Furthermore, executives reported a high percentage of project innovations failure (Schentler & al., 2010) [20]. Secondly, innovation projects investments are competing with available but limited resources required also for traditional products and services portfolio which must be adequately managed in order to generate cash flow for the survival of the business. Consequently, innovation projects viability must be reinforced through the knowledge management projects so that the knowledge capital already available in the enterprise is used genuinely and generates synergy across units. Thirdly, small and medium enterprises face more severe human, informational and financial resource limitation (Hudson, 2001) [32]. Furthermore, they have poor marketing and strategic capacities and could gain benefits from a performance measurement framework for better decision analysis. Almost all performance measurement models are designed for large companies and not for SMEs.
Finally, innovation projects are an imperative for enterprises and knowledge management can be a strategy to strengthen their viability by improving the executive decision skills and favouring positive results through new knowledge creation, productivity improvement, solutions to customer needs, new and customized products and services.

3-3 A conceptual model to assess KM and Innovation projects
We notice earlier an abundant literature on the need of measurement of knowledge management projects and on innovation projects. Frameworks for both measurements remain separated despite similarities and the fact that they share the same purpose of organizational performance. They also share a similar logic and mutual influence. An innovation project can be strengthened and consolidated by knowledge management activities as innovation requires mainly generating knowledge in order to produce new solutions embedded in enterprise new products and services.
We advocate a new performance measurement framework to combine knowledge management and innovation projects to fill a gap in the literature, as the two actual generic measurement models consider them separately despite similarities and complementarities. First of all, the input/output model emphasizes the production function related to the process from the input to the output. It identifies the results and the impacts. Secondly, the balance scorecard model emphasizes the dimensions and criteria measuring the performance. It helps to put a holistic view on the organization and recognizes that performance must be tailored at different levels of the organization with transformative projects such as innovation and knowledge management projects. The following Table 8 illustrated the two performance models for both innovation and knowledge management.  [19] Joining innovation and knowledge management projects can be achieved through a process-based approach that allows the measurement of results of activities involved in the input-process-output-outcome cycle, at every stage of the innovation process. Our unified framework is built from structural concept of the balance scorecard as it takes in account multiple dimensions of the performance measurement. It links innovation and knowledge as a continuum. In fact, innovation consists in the production of new knowledge which is embedded in new products and services. Furthermore, the unified framework established that innovation and knowledge projects are convergent.
Our unified framework is based on the renowned Nonaka & Takeuchi Table 9 identifies the questions related to the decision process and Table 10 identifies the relevant financial and non-financial criteria and indicators.

4-Conclusion
The challenge addressed by this paper is that innovation and KM initiatives must be considered as intertwined projects. But the literature measurements frameworks evaluate them separately. The unified framework we proposed is process-based and an integrated conceptual model combining the value innovation chain (ideation, conversion and diffusion) and the SECI KM model (socialization, externalization, combination and internalization). Our next challenge is to test this model on an empirical basis on different business context.