Keywords

1 Introduction

Knowledge is recognized as an important organizational resource. The main concern of knowledge management (KM) is to ensure an effective knowledge flow, while furthering the organization’s performance [1]. Thus, companies are looking continuously on promoting KM and developing needed KM projects that structure knowledge content, people and technology in order to achieve an improved organizational performance [2].

Within the increased investment imputed to KM projects costs, companies should evaluate these projects performance and assess their ability to achieve the organizational performance [3]. KM project performance measurement is thus important [4].

In fact, many KM performance measurement approaches has been proposed in the literature. Each approach provides a significant insight toward the understanding of the KM environment. [5,6,7] assume that KM can be evaluated from a process-based view while KM process models proposed to this end are so diverse. [8,9,10] investigate the KM outcomes, the KM enablers and their relationships similar to the success factors studies.

Drawing on the literature, developing an enterprise KM performance measurement framework is challenging in many aspects: Firstly, what to measure in KM is still subject to controversy. KM models reported, which are mainly based on knowledge activities, are so diverse that the whole model design should be reviewed. Secondly, the attributes of KM outcomes related to company performance vary and needs empirical verification. And lastly, influencing factors that affect the KM success should be identified and validated as well.

Moreover, assessing KM performance in the KM project level brings an additional difficulty. In fact, technological advancement drives the progress of KM initiatives; accordingly, KM solutions are so diverse and the design of a unified and up-to-date performance measurement model is more constraining. This explains the lack of such model in the literature.

To address these issues, this study presents a KM project performance measurement model that is composed of KM drivers, KM outcomes and KM activities as the model constructs. The model claims to be generic and applicable to the assessment of all kinds of KM projects.

The article is structured as follows: Sect. 2 sheds light on the literature of KM. Section 3 presents the constructs of the KM project performance measurement model. The methods of constructs validation are discussed in Sect. 4. Finally, we conclude in Sect. 5.

2 Background

2.1 About KM

Although KM understanding varies in scope and focus depending on the target perspective, knowledge remains a core concept of KM. In fact, there are many aspects around which knowledge can be described, namely, knowledge dimensions and knowledge types. As stated in a previous study [11], knowledge is commonly defined as the information which has been processed in some meaningful ways [12]. Regarding knowledge types, there is a large consensus on Polanyi typology [13] that divided knowledge into two types: explicit and tacit knowledge. The former is easily captured and codified, while the latter is difficult to codify and verbalize, it refers to skills, experience and mental models. [14, 15, 27] state that knowledge resides within individuals or groups. Individual knowledge is the set of knowledge, skills and experience owned by a person. Collective knowledge indeed refers to all kinds of knowledge that exists within an entity like a group or even and organization. They result from the interactions and combination of organizational member’s knowledge. Accordingly, knowledge exists in the organization in four forms: (1) Explicit and individual, (2) Tacit and individual (3) Explicit in group and (4) Tacit in group.

The diversity of knowledge forms determines the knowledge flow and activities [11, 15]. In fact, building on a previous work on the KM flow [11], we might suppose that KM activities are knowledge conversions from the above identified knowledge forms in accordance with the SECI model of [14]. They are also the responses to earlier knowledge problems as stated by [16].

In this vein, we assume that knowledge flow may be represented according to the four knowledge problems categories which are knowledge identification, preservation, valuing and update. Under each category, relevant knowledge conversions are listed.

In the organization, the collection of technologies, social practices and mechanisms that enable knowledge to flow is called knowledge management system (KMS).

2.2 KM Project

A KM project also called KM initiative is an attempt to accomplish organizational objectives through structuring people knowledge, technology and knowledge content [2]. It is closely related to KMS. Accordingly, it ranges from social practice minimally relying on technology (After action review projects) to IT systems largely driven by technology (Knowledge portal).

[16] state that KM projects consist on implementing the whole or a part of the KMS. A KM project is in turn concerned with insuring knowledge flow in some extent, it is impacted by some contextual factors, and it is undertaken to achieve some benefits. Figure 1 illustrates this KM project view.

Fig. 1.
figure 1

KM project view [2]

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In this vein, KM project performance is measured against implemented knowledge flow effectiveness, degree of achievement of KM outcomes and adherence of KM contextual factors.

3 Project Performance Measurement Constructs

As stated previously, KM project deal with three main themes: Knowledge flow, KM factors and KM outcomes. When it comes to performance measurement, indicators should be designed based on these themes.

This section presents the constructs used to measure KM project performance with their respective indicators retrieved from the literature and tailored to our need (cf. Fig. 2). Relevance of the presented constructs and variables is also discussed.

Fig. 2.
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KM project measurement model

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3.1 KM Activities (a)

KM activities are the building block of KM measurement. As stated further, KM activities can be classified into four sequential categories that carry out nine knowledge sub-activities: knowledge identification that responds to the knowledge location problem, knowledge preservation category that deals with retention of knowledge by its acquisition, codification and storing. Knowledge valuing category that deals with how to benefit from available knowledge by accessing it, applying it, combining it and eventually transferring it. And lastly, the knowledge update category that deals with knowledge actualization.

Knowledge Identification (a1).

It relies on the analysis of tacit organizational knowledge in order to locate crucial knowledge and knowledge sources and to identify competencies [7]. Such analysis may help to determine the crucial knowledge and knowledge gaps. Following knowledge identification, employees should know where knowledge resides. They should also locate available knowledge within enterprise. These considerations led to the following two items to measure c1:

  • Contributors know from each other who know what (a1,1).

  • We know how to find the knowledge that is available (a1,2).

Knowledge Acquisition (a2).

Knowledge and experience are globally embedded in knowledge worker’s mind. It is of significant importance to capture this knowledge from its sources. Obviously, an enterprise enhance knowledge acquisition when knowledge collection is organized frequently and when process of knowledge collection is well defined [17]. Hence, following items represent knowledge acquisition:

  • Knowledge is collected from employee on regular basis (a2,1).

  • Knowledge acquisition process is provided by the enterprise (a2,2).

Knowledge Modelling (a3).

Following the knowledge acquisition step, acquired knowledge needs to be represented through a formal models in order to make it usable [18]. Knowledge modelling usually uses methods from knowledge engineering and produces a knowledge book per knowledge area. The resulting knowledge book should be a living object in order to achieve re-usability purpose. Measure items selected are:

  • We have a knowledge book for the knowledge area related to our project (a3,1).

  • Knowledge book is a living object subject to regular update (a3,2).

Knowledge Storing (a4).

Codified knowledge needs to be stored in order to provide further access to all organizational members [3]. Globally, knowledge is stored in knowledge repositories. Ultimately, not all knowledge is relevant for store, enterprise should define and diffuse clear policy for knowledge storing [17].

  • We all agree on what knowledge should be stored (a4,1).

  • We know how and where we can store our knowledge (a4,2).

  • The stored knowledge is quite relevant (a4,3).

Knowledge Retrieval (a5).

Consists of making individual explicit knowledge available to all organizational knowledge users by providing appropriate search mechanisms and available knowledge sources [1].

  • Knowledge sources are available (a5,1).

  • We have search mechanisms that facilitate access to available knowledge (a5,2).

  • Employees use search mechanisms administered by the organization (a5,3).

  • The provided search mechanisms are relevant (a5,4).

Knowledge Utilization (a6).

Refers to the application of explicit knowledge without acquiring or learning it. Examples are: solving problems, troubleshooting… In fact, knowledge is only valuable when it is putted in practical use [19]. In practice, knowledge application is mainly supported by technologies qualified as intelligent technologies [3].

  • Employees apply frequently the accessed knowledge (a6,1).

  • We have systems that make it easier to use available knowledge (a6,2).

Knowledge Internalization (a7).

Is the process of embodying explicit knowledge into its own tacit knowledge. Within this activity, individual use various cognitive mechanisms to convert back explicit knowledge [20]. This task requires a high degree of self management skills acquired by knowledge worker [21]; also organization should provide conducive conditions for knowledge internalization. Measures of this construct are:

  • Collaborators have the mental capability to internalize task-related knowledge (a7,1).

  • Organization arrange conditions for knowledge internalization (a7,2).

Knowledge transfer (a8).

It is based on sharing knowledge with the target group [1]. Transfer approaches may vary from systematic (structured and formal) to organic (informal and unstructured) depending on both knowledge sender and receiver’s nature. Hence, enterprise should provide needed mechanisms for knowledge dissemination. Additionally, as transfer is basically verbal, knowledge workers should possess needed communication capability [3]. Items measuring a8 are:

  • Organization possesses formal mechanisms ensuring knowledge transfer (a8,1).

  • Organization possesses informal mechanisms ensuring knowledge transfer (a8,2).

  • Collaborators possess needed communication capability (a8,3).

Knowledge Update (a9).

Knowledge is not static, it evolves constantly. Accordingly, maintenance of knowledge sources should be performed on a regular basis by incorporating new knowledge, removing obsolete one and maintaining remaining knowledge [5]. Accordingly, two items may evaluate knowledge update:

  • Organization-wide knowledge resources are updated regularly (a9,1).

  • We have assigned roles and responsibilities for maintenance of knowledge (a9,2).

3.2 KM Factors (b)

KM success factors are defined as contextual elements that, when addressed, KM activities are enhanced and when neglected or poorly dealt with, they would cause some real obstacles to KM efficiency [8].

Literature on KM success factors is very rich. Based on the literature review and particularly prior empirical research for which a measurement scales was validated, factors can be broadly identified as cultural, structural and technological [19, 22]. Selected items for each constructs are presented in the following:

  • Culture (b1): is the set of values, beliefs and assumptions shared within a community. These soft aspects are considered as a key element to a successful KM [2]. Collaboration (b1,1), professionalism (b1,2), and transparency (b1,3) are three basic values indicative of a friendly culture that promotes knowledge preservation and valuing [9].

  • KM structure (b2): have the role to plan, decide, follow and act on KM activities. A dedicated KM structure (b2,1) along with a clear aligned KM strategy (b2,2) are crucial elements to provide continual support and sustain KM activities [19].

  • Technology (b3): refers to systems, infrastructures, platforms and solutions that facilitate the knowledge flow. It is identified as an important factor for KM enhancement that gains more and more importance thanks to the technological edges. The following three items are indicative of an efficient technology infrastructure [22]: reliability (b3,1), responsiveness(b3,2) and flexibility (b3,3).

3.3 KM Outcomes (c)

KM is deployed for the benefits and values it brings to organization. These benefits are considered differently depending on contexts and on implied stakeholders [23].

In fact, KM performance was initially measured against hard financial outcomes; it is the management trends that evaluate organization success from a financial perspective [24]. Progressively, a holistic view of KM benefits is adopted. Soft not-financial outcomes such as innovation, competency development, and customer satisfaction are integrated to the KM success dimensions [23,24,25]. Expectations of different stakeholders are considered as well. Analyzing prior studies on KM outcomes leads to the identification of following measurement scales:

  • Business performance (c1): finance is the direct and tangible observed result of KM success. In fact a successful KM streamlines KM activities. Consequently, the customer needs are better addressed and the competitive advantage is enhanced which is reflected positively in organization benefits [7]. Items that measure financial performance are: Growth in sales revenue (c1,1), Cost reduction (c1,2), increased productivity (c1,3) [7, 17, 21].

  • Competency development (c2): employees are a critical force of organization since knowledge resides in their head. To sustain its KM, organization should leverage and develop employee’s knowledge. Skills and learning improvement are a good indicator of effective KM benefits [21, 23]. Items that measure this specific construct are: level of employee satisfaction (c2,1), skills increase (c2,2), improvement in staff retention (c2,3).

  • Customer satisfaction (c3): a better handling of customer knowledge through effective knowledge processes enhance client interaction and increase customer satisfaction. a single item construct was selected for this construct: level of customer satisfaction (c3,1) [21].

  • Innovation (c4): innovation is an abstract human process that generates and implements new or modified results (product, process or service). It is much tied to knowledge creation and can be measured by two items: technological innovation (product and process) (c4,1), non-technological innovation (organizational or marketing method) (c4,2) [26].

4 Model Constructs Validation

To conform the measurement scale of the research constructs, the confirmatory factor analysis (CFA) method is conducted (using SmartPLS software). Data used to perform CFA are responses to KM project model constructs assessment using a survey measurement instrument. Following measurement analysis are performed [19].

4.1 Unidimensionality Analysis

Prior to analyzing validity for the research constructs, unidimensionality test should be performed to prevent a deceptive artificial correlation between constructs. Uni-dimensionality is checked via the application of the principal component analysis to extract significant unidimensional factors.

4.2 Convergent Validity

Shows the strength of the relationship between items that represent a single construct, by looking on their correlation coefficients. A high inter-correlation provides evidence of items converging to the same construct. Validity can also be determined based on average variance extracted (AVE) while a value of 0.5 or higher indicates that the construct explains more than the half of its indicators variance.

4.3 Internal Consistency Reliability

Internal consistency reliability provides for a construct an estimate of the reliability based on the inter-correlation of its observed indicator variables. The traditional used criterion is Cronbash’s alpha. The construct is considered reliable if its cronbach alpha value is greater than 0.7.

4.4 Discriminant Validity

The discriminant validity contributes to evidence that each pair of constructs stand in for theoretically different concept. Concretely, a construct should share more variance with its manifest variable than other constructs. It can be examined by checking correlation matrix. Fornell-Larcker criterion is also informative about discriminant validity. It checks that each factor’s AVE is higher than its correlation with remaining factors in the model.

5 Conclusion

For past decades, the field of knowledge management performance measurement was largely studied with the aim of assisting organization in managing their knowledge asset through measurement. However, studies were focused on the KM assessment in the enterprise level and KM project assessment received a limited attention.

This paper aims to overcome the barrier by proposing a model for KM project performance measurement. The proposed model is based on three constructs: KM activities, KM outcomes and KM factors.

KM outcomes and KM factors constructs were operationalized based on the literature review of related theoretical and empirical work. However, literature on KM activities does not allow an accurate representation of KM projects - thus justifying the development of a KM activity model based on KM project requirements

Designed model is believed to be comprehensive. It covers the major determinants of KM project performance. Constructs validation were proposed using CFA.

In terms of limitation, the developed model would be improved by including relationships between constructs and considering path analysis and regression. Also the developed model target medium and large enterprise where KM is somehow structured. Findings may not be generalized to small enterprises.