Exploring Organisational Approaches to Measuring Knowledge Sharing: Evidence from Business Analysts

Marta Krajewska-Beentjes

Abstract

Keywords: knowledge management, knowledge sharing, knowledge sharing measurement, business analyst, qualitative research

Introduction

Knowledge is inherently difficult to quantify and measure because it is intangible, fluid and embedded in people’s minds (Nakash & Bouhnik, 2024). In addition, knowledge does not conform to formal organisational structures or predefined boundaries (Edwards & Kidd, 2003). However, according to Zeleny (2013), knowledge is a tangible asset whose value can be evaluated, whereas the value of information is more difficult to determine. Information acquires value only when it is applied in action and contributes to the creation or use of knowledge. In such cases, its value is assessed indirectly through the value of the knowledge it helps create.

Knowledge sharing is recognised as a building block for organisational success and competitiveness, serving as a crucial mechanism for survival in the knowledge-based economy (Yeşil & Dereli, 2013). Moreover, within the knowledge management (KM) cycle, it is regarded as one of the most critical processes and forms the foundation of effective knowledge management. Therefore, knowledge sharing has become one of the most extensively researched topics in management (Serenko & Bontis, 2016).

Whether knowledge sharing occurs through formal or informal mechanisms, it follows a process. This knowledge-sharing process is viewed as a structured activity (Chatterjee et al., 2022) that is aligned with organisational strategy, employee skills and competencies, and established guidelines designed to support its implementation (Yeboah, 2023).

As Sveiby (2007) notes, while knowledge sharing is widely recognised as valuable in theory, organisations often face obstacles in translating it into practice, partly because employees perceive it as an activity outside their formally defined job responsibilities. Furthermore, the phenomenon’s complexity stems from the intricate network of interactions and relationships among individuals and within organisations (Yang & Wu, 2008). Moreover, motivating employees to engage in knowledge-sharing activities remains a major challenge for both researchers and practitioners (Martinez, 2015). Lastly, knowledge-sharing goals are often not explicitly integrated into business strategies, partly because the impact of knowledge-sharing practices is challenging to measure (Riege, 2005).

Literature Review

Performance measurement is a critical component of knowledge management, as without measurement, organisations cannot judge what to continue, what to improve, and what to eliminate (Andone, 2009). Knowledge by its nature is a challenging resource to measure (Nakash & Bouhnik, 2024). As a result, measuring knowledge management is often regarded as an attempt to ‘measure the immeasurable’ (Capezzuoli & Jolly, 2019, p. 4). Therefore, it is not surprising that many authors claim that measuring organisational knowledge is a challenging concept to define and measure (Argote, 2011; Hargadon & Fanelli, 2002). According to Sveiby (2010), measuring social phenomena with scientific precision is not feasible. Therefore, all measurement systems, including conventional accounting, rely on proxy measures, such as monetary values (e.g., dollars or euros) and indicators that only indirectly reflect the underlying events or actions responsible for the phenomenon. Moreover, traditionally, knowledge workers have placed greater emphasis on the quality of their outputs than on the process factors involved in their creation. While process variables, including time and cost, can be measured relatively easily, measuring knowledge work outputs remains difficult (Davenport et al., 1996; Pyöriä, 2005).

According to Matášková (2016), methods of measuring knowledge sharing can be divided into three groups: (1) hard data measurement (e.g., contributions to knowledge bases), (2) opinion-based surveys (e.g., which examine knowledge-sharing factors), and (3) a combination of hard and soft indicators. An example of the second approach is provided by Usoro et al. (2007), who measured knowledge sharing in virtual communities of practice using a questionnaire. In their research, they examined three aspects linked to knowledge sharing: how often an employee takes part in a knowledge-sharing process (quantity of sharing), the usefulness of shared knowledge (quality), and the degree to which an individual feels they engage in knowledge sharing (focus). For example, Wang et al. (2014) propose measuring tacit knowledge sharing by assessing whether employees share and seek knowledge derived from their experiences and expertise. In contrast, explicit knowledge sharing can be measured by assessing whether employees share externalised knowledge, such as reports, manuals, and other official documents.

If managers believe knowledge sharing benefits both individuals and the organisation and want to reward knowledge-sharing behaviours, they must measure those behaviours first. A challenge in setting up knowledge-sharing rewards is the difficulty of identifying and evaluating knowledge sharing (especially tacit knowledge) at the individual level (Ipe, 2003). For example, tracking contributions to an online knowledge-sharing system, an example of hard data measurement (Matošková, 2016), is relatively easy; however, monitoring whether knowledge sharing occurs through face-to-face interactions is much more difficult (Yi, 2009).

Despite the recognised importance of knowledge sharing, understanding remains limited of how organisations measure this process in practice, particularly tacit knowledge. Moreover, even when knowledge sharing is measured as one of the knowledge management processes, its assessment remains inconsistent (Fan & Beh, 2024), as no universally accepted framework for measuring knowledge management has been established (Nakash, 2024). This study explores how organisations approach measuring knowledge sharing from the perspective of experienced Business Analysts (BAs). Business Analysts were considered a particularly relevant group because their role typically requires interaction with multiple stakeholders and involves connecting business (e.g., users) with technical staff (e.g. IT staff) (Vashist et al., 2011). This cross-functional role provides BAs with a broad perspective on how knowledge is shared and applied across organisational functions, making them a valuable source of information. The objective of the study is to identify the measurement practices currently used by organisations and to examine how practitioners perceive their effectiveness.

Methodology

A qualitative research approach was adopted to understand the phenomenon under investigation. In simplified terms, qualitative research methods generate non-numerical, non-quantifiable data, such as interview transcripts, observations, expert insights, and other descriptive information (Trutkowski, 2008). The present study adopted a micro-level perspective, with the objective of gaining an in-depth understanding of questions such as ‘what,’ ‘where,’ ‘how,’ and ‘why,’ focusing on the nature and underlying mechanisms of the phenomenon under investigation, rather than quantitative questions concerning ‘how many’ or ‘how much’ (Trocki & Juchniewicz, 2013).

Semi-structured interviews were selected over structured interviews as they provide greater flexibility and allow for deviation from a pre-defined set of questions. Moreover, semi-structured interviews are particularly suitable for exploratory research, as they facilitate the collection of rich, detailed, and in-depth data from participants (Ruslin et al., 2022).

All interviews were conducted online. With the participants’ consent, all interviews were recorded and automatically transcribed using MS Teams. In total, 16 semi-structured interviews were held. While each interview followed a predefined interview script, they varied due to differences in participants’ personalities, communication styles, and other factors. The estimated duration of each interview was 60 minutes.

For the qualitative analysis, data were collected directly from 16 interviewed experts in business analysis and business process analysis. This data-collection method was appropriate because it enabled the collection of in-depth accounts of participants’ experiences and perspectives. The present study draws on a subset of the collected interview material that is related to the research question addressed in this article. The analysis focused on responses to one main open-ended question and two follow-up questions concerning the measurement of knowledge sharing: ‘Does your organisation measure knowledge sharing?’ and ‘If yes, what indicators are used to measure the success of knowledge sharing?’, ‘If not, what indicators would you suggest implementing?’ These questions generated a wide spectrum of responses and provided sufficient empirical material for the analysis.

The interview data were analysed using an inductive thematic analysis, as themes emerged from the data. Interview transcripts were manually coded by a single researcher. Initial codes were generated from the experts’ responses. These codes were then compared in terms of recurring patterns and subsequently grouped into broader clusters based on similarities identified across the interviews. Through this iterative process of familiarisation with the interview transcripts, generating initial codes, identifying recurring patterns and comparing and grouping conceptually related codes, the codes were consolidated into five thematic clusters derived from the empirical material.

Although coding was conducted by one researcher, the coding process followed a consistent analytical procedure throughout the study. To enhance the credibility of the findings, the coding process was conducted iteratively by the researcher. After initial coding, the interview transcripts and assigned codes were reviewed multiple times, with ongoing comparison between the original interview data and emerging categories to ensure the identified categories accurately reflected participants’ responses. This iterative process helped improve the consistency and trustworthiness of the analysis.

Data saturation was achieved at the 16th interview, when no new substantive information emerged, indicating that the collected data were sufficient to address the research objectives and that additional data collection was unnecessary.

A Business Analyst was defined as an individual who performs business analysis activities, regardless of their formal title or position within an organisation. This definition aligns with that of the International Institute of Business Analysis (IIBA), which defines business analysis as ‘the practice of enabling change in an organisational context by defining needs and recommending solutions that deliver value to stakeholders’, and a Business Analyst could be any person who performs business analysis activities, regardless of job title (IIBA®, 2021). Most experts carried out their professional responsibilities in English and collaborated within diverse, international teams. Several interviewees occupied senior positions, such as Senior Business Analyst, while others combined their primary role with additional responsibilities, including Product Owner duties.

As mentioned earlier, the research sample consisted of 16 participants: 5 female and 11 male. The privacy of interview participants was protected, as all personally identifiable information, including names, organisational affiliations, and contact details, was removed or anonymised in accordance with the requirements of the General Data Protection Regulation (GDPR). To further prevent identification, each Business Analyst was assigned a unique identifier and was referenced exclusively by that identifier (Table 1).

Table 1
Demographic and Professional Profile of Interviewed Experts

The experts were recruited through multiple channels, including the International Master of Business Administration (IMBA) Program Network, the Cracow School of Business (CSB) Alumni MBA Club, LinkedIn, professional Business Analyst networks, other professional networks, and professional and private recommendations. The recruitment process was conducted exclusively by the author. Participant selection followed explicitly defined inclusion criteria (Galletta, 2013): (a) participation in cross-functional initiatives, (b) involvement in business process analysis, and (c) at least four years of professional experience in business analysis to ensure informed and conceptually rich data. The four-year experience threshold is consistent with prior research (Ammann et al., 2025) and aligns with the eligibility requirements for the CBPP™ (Kirchmer et al., 2019) and CBAP™ (International Institute of Business Analysis, 2021) professional certifications.

Moreover, the overall professional experience of the experts interviewed ranges from 8 to 35 years, while the majority have over 15 years of professional experience. Experience in business analysis ranges from 4 to 16 years, and most of the experts have over 7 years of experience in business analysis.

The interview process generated three primary data sources. First, all interviews were video-recorded, providing a complete record of the discussions. Second, automatic transcripts were generated, and thirdly, observational notes were documented during the interviews to capture contextual information, non-verbal cues, and additional insights that were not readily captured in the recordings or transcripts.

Results

Based on the data collected through the semi-structured interviews, several thematic clusters emerged regarding the measurement of knowledge sharing in organisations.

• Cluster 1: No formal system to measure knowledge sharing
• Cluster 2: Informal or indirect knowledge-sharing mechanisms
• Cluster 3: Quantitative proxy knowledge-sharing metrics
• Cluster 4. Scepticism towards existing knowledge-sharing metrics
• Cluster 5. Knowledge sharing embedded in organisational culture

The clusters reflect varying approaches to measuring knowledge sharing, including indirect approaches, scepticism towards existing metrics, and scepticism towards the measurement of knowledge sharing more generally.

The first cluster, No formal system to measure knowledge sharing, shows that most interviewed Business Analysts explicitly stated that their organisations did not measure knowledge sharing formally and that they were unaware of any organisational-level indicators, metrics, or key performance indicators (KPIs) designed to evaluate knowledge-sharing practices.

Some BAs made strong statements such as BA12: ‘There are no formal indicators like leading or lagging indicators’. Other typical answers in this cluster were related to the lack of awareness of whether such indicators exist, BA9, ‘I have never heard about it. No idea’. Another interviewed expert with the identifier BA2 noted ‘Honestly, nothing comes to mind’. These statements suggest not only the absence of visible measurement systems but also rather low awareness among employees regarding existing mechanisms for monitoring knowledge-sharing activities. The lack of formal indicators identified by participants might potentially reflect a wider organisational challenge. Although knowledge sharing is frequently acknowledged as a critical factor in organisational learning, innovation, and competitive advantage, organisations seem to struggle to translate it into measurable indicators. The findings could suggest that the Business Analysts were not aware of any formal measurement of knowledge sharing; as a result, it remains underdeveloped in many organisations.

The second cluster, Informal or indirect knowledge-sharing mechanisms, shows that although formal systems for measuring knowledge sharing are not in place in most cases, the interviewed Business Analysts shared that the organisations use a range of indirect indicators, such as onboarding, handovers, or performance observations. Several Business Analysts described onboarding as one mechanism for assessing knowledge sharing. BA12 said, ‘We have the onboarding… once someone is fully onboarded… there is an official document that this person has been through like that.’

This could suggest that successful onboarding is perceived as evidence that knowledge has been transferred from an experienced employee to a new one. Although onboarding completion is not explicitly designed as a knowledge-sharing metric, it functions as an informal indicator. Other participants emphasised recognition and observation rather than formal measurement. As BA11 noted, ‘It’s more in the form of a recognition. It’s not really like measured with certain…’

This could suggest that knowledge-sharing behaviours may be acknowledged informally without being incorporated into structured performance measurement systems. Delegation was identified as another indirect indicator of successful knowledge sharing. According to BA16, ‘Indirectly more the check in how far we are delegating.’ The ability to delegate tasks effectively may indicate that knowledge has been shared and that dependence on specific individuals has been reduced. Similarly, participants highlighted handover processes as critical moments during which knowledge-sharing behaviours become visible. BA12 said, ‘I think handover helps a lot and handover is also a moment that you know if the person knows, if the person is willing to share.’

The findings from the second cluster can suggest that organisations assess knowledge sharing through process outcomes rather than through measurement frameworks. Successful onboarding, effective delegation, smooth handovers, cross-functional collaboration, and supportive employee behaviours are all interpreted as signs that knowledge sharing is taking place. Consequently, knowledge-sharing performance is evaluated indirectly through organisational practices and employee interactions.

The third cluster, Quantitative proxy knowledge sharing metrics (trainings, certifications, platform statistics), shows that, according to the interviewed experts, organisations attempt to assess knowledge sharing through measurable indicators such as training completion rates, professional certifications, employee skills profiles, repository contributions, and usage statistics generated by knowledge management platforms. Unlike the second cluster, in which knowledge sharing was assessed informally or indirectly, this cluster reflects the use of quantitative measures that can be easily collected and monitored.

Several interviewed Business Analysts indicated that organisations create key performance indicators, such as those related to learning and upskilling. For example, BA4 stated: ‘There is definitely a possibility to collect KPIs, for example, how many trainings were delivered over the year, quarter, how many users attended etc.’ This response could suggest that organisations rely on training-related metrics as evidence. Such indicators are relatively easy to collect and compare across dimensions, for example, the number of trainings per year and the average number of trainings received per team member. However, participation in various trainings does not necessarily indicate whether knowledge has been understood or applied.

The use of digital collaboration platforms also emerged as a measurable indicator; some platforms have built-in statistics, and some indicators could be taken directly from them, for example, the number of visited websites. One of the experts, namely, BA6, explained: ‘For example, some stats about contributors in Confluence.’ Similarly, BA3 noted, ‘You can collect stats on how many people access the page, how many people contribute, how many people might download something.’

These observations show that organisations can use platform analytics to monitor user engagement. Metrics such as page views, document downloads, content contributions, and active users provide a quantifiable record of employee interactions with various platforms. From a managerial perspective, such indicators offer some insight into how and how often systems are used. However, these indicators do not present the full picture, as a visit to a page doesn’t directly translate into reading and understanding, let alone knowledge sharing. Participants also identified employee skills profiles and certifications. BA14 shared that employees maintain records of acquired competencies: ‘Every employee has a profile, and when you complete a training or gain a new skill, you have to update it there.’ Similarly, BA15 stated, ‘The only measure…is, for example, which skills, competencies do you have? … or the number of certificates.’ Certifications, completed training programmes, and competency records provide tangible indicators that can be incorporated into various monitoring and evaluation systems.

Building on the third cluster concerning proxy knowledge-sharing metrics, a fourth cluster emerged: scepticism towards existing knowledge-sharing metrics. This cluster was defined independently, as many interview participants showed scepticism towards simplified quantitative metrics and questioned the validity of knowledge-sharing measures.

Many interviewees questioned whether commonly used quantitative indicators, such as the number of completed training courses, were valid measures of knowledge sharing. As BA1 noted, metrics such as ‘how many workshops were done or how many publications were prepared’ are ‘not a real measure,’ while BA15 said that completing a training does not necessarily indicate learning, as participants may not have actively engaged with the content. BA15 said, ‘What kind of measure is that you acquired new competencies like you completed a training? You could have slept during the training.’ Moreover, such measures could create a false impression about knowledge sharing.

Several respondents also emphasised the difficulty of developing meaningful quantitative measures, with BA1 stating that ‘it’s very difficult to propose any quantitative way of assessing effectiveness,’ and BA10 admitting ‘No, actually I couldn’t imagine how we measure it’. BA15 said, ‘I cannot recall an organisation that I would say that they measured knowledge sharing successfully.’ This cluster suggests that Business Analysts perceive knowledge sharing as a complex, qualitative, and context-dependent process that cannot be adequately captured through simplified numerical metrics alone.

The fifth cluster, Knowledge sharing embedded in organisational culture, highlights organisations where knowledge sharing is primarily encouraged through cultural values, leadership, and a focus on collaboration rather than competition. The interview participants described knowledge sharing as established and expected. BA2 said ‘there is this culture of knowledge sharing’ and emphasised that ‘this is something that is being mentioned on a regular basis from the management level.’ Similarly, BA10 explained that knowledge sharing is actively promoted within the organisation, stating ‘We encourage it.’ BA8 pointed to the positive career implications of collaborative behaviour in his organisation, observing that ‘for us knowledge sharing is better for your career than not sharing.’

The emphasis on organisational culture reinforcement indicates that it may play a significant role in motivating knowledge-sharing behaviours; however, it is not recognised as a measure. In such an environment, employees are encouraged to share knowledge as it is perceived as beneficial for both organisational success and individual professional development.

Discussion

The findings of this study demonstrate that knowledge sharing is valued but difficult to measure in the organisational context. Across 16 interviews, a major theme was a lack of formal, organisation-wide systems to measure knowledge sharing. Most participants were either unaware of any organisational indicators related to knowledge sharing or stated that such measures didn’t exist in their organisations. Consequently, organisations rely on organisational practices and employee interactions rather than direct measures of knowledge sharing. Although knowledge sharing is acknowledged as a critical factor in organisational learning, organisations struggle to translate it into measurable indicators.

The findings provide several explanations for why organisations choose particular approaches to measuring knowledge sharing. First, organisations struggle to manage knowledge as a resource because knowledge, particularly expertise-based knowledge, is inherently intangible (Geiger & Schreyögg, 2012). This intangible nature complicates measuring knowledge-sharing activities, making it challenging to track complete knowledge flows and evaluate their outcomes (Danko & Crhová, 2025). This finding also supports the related work of Pérez López-Portillo et al. (2016), who found that knowledge management is not analysed from a market value perspective, but from knowledge quality and quantity, KM processes, KM initiatives, KM outcomes and potential benefits.

Second, the findings suggest that organisations evaluate the outcomes of knowledge sharing rather than the sharing process itself. The emphasis on onboarding, delegation, and handover shows that knowledge sharing is assessed indirectly through operational continuity. Frögéli et al. (2023) claim that formal onboarding facilitates organisational socialisation, with key indicators including role clarity, task mastery, and social acceptance. These include sufficient information about expectations and sufficient knowledge of the behaviours needed to achieve goals. This observation may be explained by the fact that the effectiveness of knowledge-sharing mechanisms may depend on broader organisational factors, including communication quality, trust, teamwork, and managerial support (Balcerzyk, 2021).

The third finding relates to the use of quantitative proxy indicators such as completed training, certifications, skills profiles, and knowledge platform analytics, as these measures offer organisations tangible data. However, the interviewed Business Analysts expressed scepticism regarding their validity. While such indicators prove participation in certain activities, they do not indicate whether knowledge has been understood or shared with others. The findings raise important questions regarding the validity of these measures as proxy indicators of knowledge sharing. Although training attendance, professional certifications, and platform activity are quantifiable, they do not reflect whether meaningful knowledge sharing has actually taken place.

This scepticism is consistent with Wong et al. (2015), who state that knowledge management is difficult to measure and is therefore evaluated indirectly using non-monetary or ‘intermediate’ measures, such as the number of collaborative projects. Similarly, Dzenopoljac et al. (2023) emphasise that previous studies focused primarily on the level of acquired knowledge, not on the role of knowledge quality in the context of knowledge sharing. In practice, it is important to understand whether specific knowledge adds value or has a positive impact. Therefore, the present study questions the validity of proxy indicators as they do not reflect actual outcomes and quality of knowledge sharing.

Finally, the study highlights the importance of organisational culture in facilitating knowledge sharing. Several interview participants described knowledge sharing as an expected and valued behaviour within the organisation, rather than as something enforced through formal measurement systems. Management support, collaboration, and recognition emerged as important mechanisms for encouraging knowledge-sharing behaviour. This finding supports previous research suggesting that organisational factors can act as both enablers and barriers to knowledge sharing (Świgoń, 2015). Sveiby and Simons (2002) propose that the organisational context for knowledge sharing, called collaborative climate, could be perceived as the shared mental space where knowledge sharing takes place.

The findings also reinforce the argument that knowledge sharing is related to ‘soft factors’ such as motivation, communication climate and organisational culture that encourage such behaviours (Ardichvili et al., 2006; Cabrera et al., 2006; Moffett et al., 2003). Moreover, in organisations with a strong knowledge-sharing culture, cultural reinforcement through norms, expectations, and managerial practices seemed to be in place rather than explicit indicators. Therefore, the choice of measurement approach reflects both the difficulty of measuring knowledge and organisations’ preference for observable indicators.

Conclusions

This study explored how organisations measure knowledge sharing from the perspective of experienced Business Analysts. The findings reveal that formal, organisational measurement systems for knowledge sharing are largely absent or not visible to employees, and organisations instead tend to rely on indirect or proxy indicators.

The study identified five thematic clusters: the absence of formal measurement systems, the use of informal and indirect mechanisms, the application of quantitative proxy metrics, scepticism towards existing measures, and the role of organisational culture in knowledge sharing. These findings suggest that organisations acknowledge the importance of knowledge sharing but struggle to define measures.

A managerial implication is to incorporate knowledge sharing into employees’ job descriptions and performance reviews. Clearly defined knowledge-sharing objectives make it easier to assess performance against them at the end of a performance review cycle. Managers should also use a mix of ‘hard’ and ‘soft’ measures. For example, quantitative measures of knowledge asset usage should be complemented by qualitative measures to explain why employees use these assets and the value they bring to the organisation (Janus, 2016).

Finally, recent studies conceptualise knowledge sharing as distinct tacit and explicit dimensions that contribute differently to organisational capabilities (Dzenopoljac et al., 2023). Therefore, tacit indicators should capture the transfer of practical know-how, professional judgement, and contextual understanding (e.g., client workstyle preferences) (Capestro et al., 2024; Nonaka & von Krogh, 2009; Polanyi, 1967). In contrast, indicators of explicit knowledge sharing may include contributions to organisational knowledge repositories, participation in communities of practice, and the preparation of project-closing documentation, including lessons learned (Zamiri & Esmaeili, 2024).

The main limitation of the study is that the findings are based on a relatively small sample of Business Analysts. However, the sample is sufficient from the perspective of a qualitative methodology and the exploratory nature of the approach, with the main objective of gaining in-depth insight. Different perspectives might have emerged if other Business Analysts or additional stakeholder groups had been included. Although the respondents represented different industries and organisational contexts, the data collected were not considered sufficient to support a reliable analysis of how these differences might influence knowledge-sharing practices. Moreover, the transcripts and codes were reviewed iteratively by a single researcher to ensure consistency; the absence of independent verification may have increased the potential for researcher subjectivity in categorising and interpreting the data.

Future research could therefore expand the sample to encompass a broader range of stakeholders, compare practices across industries, and investigate how knowledge-sharing measurement approaches evolve over time. Despite these limitations, the study contributes to the growing body of knowledge management literature by providing empirical evidence that organisations do not have organisation-wide knowledge-sharing measures.

In conclusion, the findings indicate that organisations continue to face challenges in measuring knowledge sharing. While knowledge sharing is recognised as critical, there is still no defined way to measure it. As a result, organisations try to combine various metrics to assess whether knowledge sharing is occurring.

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