Measuring the Maturity of Knowledge-Based Management of Human

Resources

Sini Tenhovuori

and Nina Helander

Department of Management and Business, Tampere University, Korkeakoulunkatu 7, Tampere, Finland

Keywords: Knowledge-Based Management, Human Resources, Knowledge Management, HR-Analytics, Maturity of

HR-Analytics, Maturity of Knowledge-Based Management of Human Resources.

Abstract: In today’s volatile and demanding environments, organizations face increasing pressure to balance employee

well-being (Kim & Cho, 2024). Given that personnel expenses often constitute the largest share of

organizational budgets (Kuntaliitto, 2024), knowledge-based human resource management (KHRM) becomes

essential for organizations to survive and remain competitive. The purpose of this research was to investigate

the factors influencing the development of knowledge-based management of human resources and to develop

an assessment model for evaluating the maturity level of knowledge-based management of human resources.

The developed assessment model was tested in Finnish public sector context. The model complements

existing maturity frameworks by operationalizing maturity dimensions into measurable statements, enabling

organizations to assess not only structural readiness but also perceived satisfaction and cultural alignments.

Measuring employee satisfaction as part of determining maturity levels is important, as previous studies have

shown that employee satisfaction supports the adoption of new ways of working in organizations. In addition,

this model can be used to measure the realization of the benefits achieved with KHRM in the organization.

This model has dual focus on objective capability and subjective experience, and it offers a novel contribution

to the maturity model literature and supports more holistic HR development strategies.

1 INTRODUCTION

The advancement of technology has increased the

amount of information within organizations, thereby

making the role of knowledge-based management

(KBM) increasingly important (Jääskeläinen et al.,

2022). According to Helander et al. (2020) KBM can

be defined as a holistic and systematic process that

integrates technology and human aspects to enable

dialogic management in organizations. Many

organizations are heavily investing in the

development of KMB by allocating both employee

work effort and financial resources (Sen, 2024). It is

not uncommon for organizations to utilize separate

information systems to manage customer, personnel,

and financial data and processes (Chuma, 2020).

From the perspective of KBM this presents a

significant development challenge, as supporting

architectures and data governance must be

established for each system. As a result, organizations

https://orcid.org/0009-0003-4258-7241

https://orcid.org/0000-0003-2201-6444

often must prioritize which internal domains receive

focus in terms of KBM.

Many maturity models assess the overall maturity

level of KBM (see e.g. Hsieh et al., 2009;

Jääskeläinen et al., 2022; Khatibian et al., 2010; Pee

& Kankanhalli, 2009). This can lead to uneven

development across different organizational domains.

According of Finnish Local and Regional Authorities,

in Finnish public organizations, personnel expenses

often account for approximately 40–50% of the cost

structure (Kuntaliitto, 2024). At the same time, the

provision of public services relies on a skilled and

sufficient workforce. By measuring the maturity of

KBM of human resources separately, a more

comprehensive picture of its implementation can be

obtained, as it is a vital area for the functioning of the

organization

The purpose of this research is to investigate the

factors influencing the development of knowledge-

based management of human resources (KHRM) and

484

Tenhovuori, S. and Helander, N.

Measuring the Maturity of Knowledge-Based Management of Human Resources.

DOI: 10.5220/0013746100004000

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2025) - Volume 2: KEOD and KMIS, pages

484-491

ISBN: 978-989-758-769-6; ISSN: 2184-3228

to develop a maturity model for evaluating the

maturity level of KHRM. The research question is:

How the maturity level of KHRM can be measured?

In developing the model, the process phases for

designing maturity models suggested by de Bruin et

al. (2005) were followed.

2 THEORETICAL

BACKGROUND

2.1

Knowledge-Based Human Resource

Management

(KHRM)

The core components of human resource

management (HRM) are considered the most critical

elements in driving organizational effectiveness

(Delaney & Huselid, 1996). KHRM is related to

every element of HRM. It leverages analytics to

transform employee-related data into actionable

insights and organizational knowledge. This

approach enables decision-makers to uncover

patterns and understand the underlying causes and

effects of employee-related phenomena based on

systematically collected HR data (Kaboor et al.,

2012). KHRM seeks to promote decision-making that

is efficient, impartial, and grounded in rational

analysis (Margherita, 2022).

Ferreira et al. (2022) show in their literature

review that KMB processes – such as the creating,

sharing and application of knowledge – support the

different dimensions of HRM. In particular, the

individual and professional development of

employees benefits from both creation and sharing of

knowledge. The preservation and application of

knowledge, on the other hand, strengthen

organizational and strategic development, while the

acquisition and evaluation of knowledge can support

technological and cultural renewal. (Ferreira et al.,

2022)

KHRM processes must be designed so that they

strengthen the flow of information in the

organization. This requires the acquisition,

assimilation, transformation and sharing of

information. (Donate & de Pablo, 2015). KHRM is

most effective, when it is part of the organization’s

strategy and organizational culture (Le & Ha, 2024).

It enables the development of a significant

competitive advantage for the organizations by

supporting organizational learning and innovations

(Al-Qaralleh & Atan, 2022) and it can also contribute

to the growth of internal trust, mutual respect,

employee dedication and a sense of belonging

(Soliman & Spooner, 2000).

In essence, KHRM aligns closely with the

evolution of human resource analytics (HRA), which

has matured from basic administrative reporting to

advanced diagnostic and predictive capabilities. As

highlighted by Margherita (2022), HR analytics now

encompasses a wide range of enablers, applications,

and value drivers that support strategic decision-

making. When embedded within organizational

processes and supported by digital technologies and

artificial intelligence, KHRM can transform HR

functions into powerful engines of organizational

agility and innovation. This integration not only

enhances employee development but also strengthens

the organization’s ability to adapt and thrive in

dynamic environments. (Margherita, 2022.)

2.2 Maturity Models (MM)

According to Kucińska-Landwójtowicz (2019),

maturity refers to “the extent to which a specific

process is defined, managed, measured, controlled,

and effective”, and maturity models help

organizations to identify their current state and guide

their progression toward higher levels. In other

words, maturity models refer to structured

frameworks for assessing the different development

levels of organizational capabilities, processes, or

systems. They provide structured description of

different stages of maturity. (Kucińska-

Landwójtowicz, 2019.) Several maturity models have

been developed to measure the maturity level of

KBM, but almost none of them are suitable for

measuring the maturity level of KHRM (Hsieh et al.,

2009; Jääskeläinen et al., 2022; Khatibian et al., 2010;

Pee & Kankanhalli, 2009; Serrat, 2023).

In the context of HRM and HRA, these models

help organizations evaluate current state, identify

gaps, and define pathways for strategic

advancements. Traditional maturity models, such as

the Capability Maturity Model (CMM), have been

adapted to HR contexts to assess areas like strategic

alignment, data governance and analytics

capabilities. (Marler & Boudreau, 2017; Ulrich &

Dulebohn, 2015.) A recent influential framework in

the field of HR analytics is the HR Analytics Maturity

Model (HRAMM), developed by Rigamonti et al.

(2024). This model outlines four key domains –

strategy, data, technology, and people – each of which

is further divided into specific dimensions that

together describe an organization’s overall capability

to utilize HRA effectively. HRAMM has broader and

more integrated perspective than earlier maturity

models and it incorporates not only technical and

process-related aspects but also highlights the

importance of organizational culture, leadership

commitment, and employee engagement as essential

enablers of maturity.

Measuring the Maturity of Knowledge-Based Management of Human Resources

485

3 THE PROPOSED MATURITY

MODEL FOR KHRM

The development of this maturity model is illustrated

in Figure 1, which visualizes the six phases of

designing a maturity model following the framework

by de Bruin et al (2005). Each phase is described

below in the context of developing a maturity model

for KHRM. The lighter-coloured phases in the figure-

Deploy and Maintain- represent steps that will guide

the future refinement and implementation of the

model (Figure 1).

Figure 1: Developing KHRM maturity model (modified

from de Bruin et al. 2005).

In the first phase the scope of this model was

determined. The purpose of this model is to provide a

description of the maturity level of KHRM. This

model is not tied only to organizations in a specific

industry, but it is suitable only for the measurement

of KHRM maturity level. The first phase is followed

by design phase, which includes designing the

model’s architecture and audience, choosing

application methods and defining maturity levels (de

Bruin et al., 2005). The audience of this model is

primarily HR professionals, organizational

developers and strategic decision-makers. The

suggested model has staged maturity structure,

consisting of five cumulative levels. Each level builds

upon the capabilities of the previous one. The levels

are initial, emerging, defined, advanced and strategic.

In the third phase the content of the maturity

model was defined. The goal was to ensure that the

model captures all the essential capabilities and

practices required for effective knowledge-based

management of human resources, while also enabling

meaningful assessment and improvement. To do so, a

systematic literature review was made. The results

provided a theoretical foundation of the model, and

they have been published in Tenhovuori (2024).

Based on the results of the review a survey instrument

consisting of 55 questions was designed. The

questions were divided into five sub-areas:

organizational culture and strategy, resources,

information needs, information acquisition and

storage, HR analytics, HR metrics and information

products, and the use and benefits of HR information.

All the questions were evaluated on a Likert scale of

1 to 5, where 1 = strongly disagree and 5 = completely

agree. In addition, it was possible to answer the

statements "I don't know". The survey was designed

to be distributed either electronically or through

structured interviews.

In the testing phase, the validity, reliability and

usability of the HR knowledge maturity model was

evaluated. The developed maturity model was first

assessed by a steering group of 9 experts in field of

knowledge-based human resources management.

The pre-testers for the questionnaire were selected

based on their work tasks and they worked in several

different organisations. The purpose of the pre-testing

was to ensure that the content of the maturity model

is easy to understand and that there are no

contradictions in the separate statements. The pre-

testers were also asked whether they thought the

model was missing anything essential. Based on the

pre-testing phase, the maturity model was redefined

by harmonizing the terminology used. In addition,

several statements were clarified-particularly to

better anchor them in the context of HR management

and HR data.

After the pre-testing, the actual testing phase of

the maturity model was carried out. Choice of

research method has a significant impact on the

reliability and validity of the research (Anders, 2012).

The data for knowledge-based management maturity

measurements are often collected with the help of

electronic questionnaires (Becker et al., 2009;

Khabatian et al., 2010). However, when using an

electronic questionnaire, there is a risk that

respondents do not understand the questions and

therefore answer "I don't know" or, in the worst case,

do not answer at all. Although the maturity models

of knowledge management are often surveys; in this

study the researcher ended up conducting interviews.

The choice of interview method was particularly

supported by the desire to ensure that the respondents

understood the questions correctly and could give

also their development ideas to specific questions.

Interviews can be divided into structured, semi-

structured and unstructured (Doody & Noonan, 2013;

Rowley, 2012). In a structured interview, all

participants are asked the questions in the same order.

This reduces the subjectivity of the researcher but

does not allow for additional questions (Doody &

Noonan, 2013). As the aim was to develop a tool that

can be used to determine the maturity level of

KHRM, the interviews were conducted in a structured

manner and the answers were collected in numerical

form.

The data was analysed using quantitative

methods using SPSS for MAC. The data is described

KMIS 2025 - 17th International Conference on Knowledge Management and Information Systems

486

using frequencies, percentages, averages, standard

deviation, medians and quartiles. Average sum

variables were formed from the maturity of

knowledge-based management of human resources,

its five dimensions and the satisfaction with the

knowledge-based management of human resources.

The reliability of the sum variables was examined

using Cronbach's alpha value. The results of this

testing phase analysis are presented in detail in

section four.

The main purpose of this maturity model is to

provide a structured description of a current state of

KHRM within an organization. It enables

organizations to identify the maturity level of

different KHRM dimensions and to target

development actions where they are most needet.

Furthermore, the model allows organizations to

assess how the benefits of KHRM are realized in

practice and to evaluate the impact of development

efforts over time. In this paper the model has been

tested in a limited setting and these results are opened

in next section four. However, further development is

required to enhance its generalizability and precision.

In the next phase, the model will be tested using an

electronic questionnaire to reach a broader group of

respondents. This will allow for more robust

statistical validation and a more detailed description

of maturity test.

The qualitative data collected through transcribed

interviews will also be used to refine the model.

Special attention will be given to statements that

required clarification during interviews. A more

granular and validated description of the maturity

levels will require a larger sample size. This will

support the model’s continued evolution and its

practical utility in diverse organizational contexts.

4 RESULTS OF TESTING THE

MODEL

4.1 Demographics of Respondents

Testing of the model was carried out in a Finnish

public sector organisation with a total of 755

employees. The interviewees were selected based on

the following admission criteria: work in knowledge

management positions, work as a manager or

manager, or work in human resources management

tasks. A total of 20 employees were interviewed. It

was considered more important than the large number

of interviewees that the interviewees work in

knowledge-based management, management,

managerial or human resources management

positions. The interviews were conducted remotely

using Microsoft Teams, and the interviews were

transcribed using the same application. The

interviews were recorded and transcribed.

Most of the interviewees worked in managerial

positions (n=9) or in leadership positions (n=5) and

most of them had worked for the organization for five

years or more (n=12). Only a quarter of the

interviewees did not work as a supervisor (n=5). Most

of the interviewees had a master’s degree (n=18). The

interviewees were on average 46 years old (MD 45,5,

Q1 43, Q3 53.75).

Composite score variables (Cheung et al., 2024)

were formed of the organizational culture and

strategy, resources and employee capabilities, HR-

information needs, HR information products and HR

analytics, knowledge usage and benefits achieved and

satisfaction with knowledge-based management of

human resources. The reliability of the variables was

examined using Cronbach's alpha value (Gagnon et

al., 2017). Table 1 summarizes the results of

statistical evaluation of the model. Cronbach’s alphas

varied between 0,492 and 0,889. Therefore,

composite score variables were not formed among

those variables with lowest Cronbach’s alphas

(availability and collection of HR data, organization

and storage of HR data and integration of HR data).

Those variables were analysed as independent

variables for this measurement.

Table 1: Statistical testing of the survey instrument.

Perspective N of

items

Cronbach’

s Alpha

Organizational culture and

strateg

12 0,753

Resources and employee

abilities

6 0,677

Availability and collection of

HR data

3 0,508

Organization and storage of HR

data

2 0,492

Integration of HR data

3 0,648

HR information needs

4 0,766

HR analytics

3 0,738

Level of HR-analytics

3 0,741

HR-metrics

4 0,766

Utilisation of HR-data and

enefits achieve

10 0,889

Satisfaction with knowledge-

based management of human

resources

5 0,778

4.2 Organizational Culture and

Strategy

Organizational culture and strategy were moderately

realised in the organization (Md= 3,4). Of the areas of

organisational culture and strategy, the best

Measuring the Maturity of Knowledge-Based Management of Human Resources

487

realisation was the positive attitude towards the

KHRM and the support of executives for the

development of KHRM and the assigned roles of

KHRM (Table 2).

Table 2: Organizational culture and strategy.

Perspective Md Q

-Q

KHRM and strategy

4 2.00-4.00

KHRM and management system

4 2.00-4.00

KHRM objectives

4 2.75-4.00

KHRM roles

5 4.00-5.00

KHRM development plan

4 3.75-4

KHRM and ICT architecture

3 2.00-4.00

Executive commitment

4 4.00-5.00

Supervisors encourage

2 2.00-4.00

Executive support for KHRM

development

5 4.00-5.00

Employees participate in KHRM

development

4 2.00-4.00

End-user participation in KHRM

develo

ment

4 4.00-5.00

Attitude towards KHRM

5 4.00-5.00

4.3 Resources

The interviewees estimated the resources of KHRM

to be rather inadequate (MD 2,5, Q1 2,33, Q3 3,13).

The interviewees estimated that competence related

to KHRM is particularly poor. The employees do not

understand what knowledge management of human

resources means, and the organization does not offer

training related to knowledge management of human

resources. The interviewees also estimated that the

knowledge management tools of human resources are

poorly used. Although the organization has a team

responsible for the implementation and development

of KHRM, the resources were estimated to be

sufficient to process data but not to utilize it (Table 3).

Table 3: Resources.

Perspective Md Q

-Q

Employees understanding

ardin

KHRM

2 1.75-2.00

Capability to utilize KHRM

tools

2 2.00-3.00

Existence of KHRM training

1 1.00-4.00

Sufficient resources for data

rocessing

4 2.25-4.00

Sufficient resources for data

utilization

2 2.00-4.00

KHRM team in organization 5 4.25-5.00

4.4 HR Data

The interviewees estimated that the availability of HR

data is quite poor (Md 2, Q

1, Q

3,5) and the HR

data collection processes are not automated (Md 1,

1, Q

=2) or efficient (Md 1, Q

1, Q

=2). HR data

has not been integrated to the organization's data lake

(Md 1, Q

1, Q

=), but common basic data for the

most important HR entities is available (Md 4, Q

=4).

The interviewees estimated that the integration of

HR data is not systematic or controlled, and that HR

data is not consistent between different systems. The

interviewees estimated that HR data does not enable

real-time reporting (Table 4). Overall, the integration

of HR data was estimated to be unavoidable (Md

1.33).

Table 4: Integration of HR Data.

Perspective

-Q

Integration of HR data 2 1.00-2.00

HR data consistency 1 1.00-2.00

Real-time reporting capability 2 1.00-2.00

4.5 HR Information Needs and HR

Analytics

According to the interviewees the organization

mainly utilizes electronic reports (Md 4, Q

=4, Q

=5)

and visual analytics (Md 4, Q

=2, Q

=4) in HR

reporting. The organization's practices enable a good

level of granularity when examining HR data (Md 4,

=2, Q

=4) However, drilling down into the HR data

is only moderately well supported (Md 3,5, Q

=1,75,

=4). There is no dashboard solution that includes

HR data in the organization (Md 2, Q

=1, Q

=2).

Overall, the level of implementation of HR analytics

in the organization was quite low (M 2,58, Sd 1,18)

(Table 6). The interviewees estimated that all levels

of HR analytics are implemented poorly or fairly

poorly. However, based on the responses, it can be

assumed that the organization uses some descriptive

analytics (Md 2,50, Q

=2, Q

=4). Overall, the level

of HR analytics was relatively low (M 2, Sd 0,84)

(Table 5).

Table 5: HR analytics and the level of HR-analytics.

Perspective Md Q

-Q

Descriptive HR analytics is

utilized

2,50 2,00-4,00

Explanatory HR analytics is

utilized

2 1,00-2,00

Viewing HR data at different

levels of accuracy

4 2,00-4,00

KMIS 2025 - 17th International Conference on Knowledge Management and Information Systems

488

Drilling down into HR data 3,50 1,75-4,00

The existence of dashboard that

contains HR information

2 1,00-2,00

The interviewees assessed that the definition of

HR metrics based on strategic objectives was

implemented at a moderate level in the organization

(Md 3, Q

=2,00, Q

=3,00). Similarly, the

requirements of different stakeholder groups and

management levels were moderately considered in

the design of the metrics (Md 3,00, Q

=2,00,

=4,00). Respondents indicated that the analysis and

modelling of relationships between measurable HR

aspects were carried out rather weakly (Md 2,

=1,00, Q

=3,00), and that the examination of HR

metrics alongside other indicators, such as those

describing customer data, was also implemented

rather weakly (Md 3, Q

=2,00, Q

=4,00. Overall, the

framework describing HR information needs and

metrics was realized rather weakly (Md 2,5, Q

=1,25,

=2,75) (Table 6).

Table 6: HR metrics.

Perspective Md Q

-Q

HR metrics and strategic objectives

3 2,00-3,00

HR metrics and requirements of

stakeholders and management

levels

3 2,00-4,00

Analysis and relationships between

HR as

ects

2 1,00-3,00

Examination of HR-metrics

alongside other indicators

3 2,00-4,00

4.6 Utilization of HR Data and Benefits

Achieved

Overall, the benefits and utilization of KHRM were

realized to a rather limited extent (M 2,41, Sd 0,88).

None of the areas of the utilization of HR data and the

benefits achieved were realized even moderately. Of

all the areas, the interviewees estimated the

improvement in capability management through

KHRM to be the weakest (Table 7).

Table 7: Utilization of HR Data and Benefits Achieved.

Perspective Md Q

-Q

Decisions are guided by KHRM 2 1.00-2.00

Information can reveal

operational issues

2,50 2,00-4,00

HR metrics are used to monitor

resource utilization

2 1,50-4,00

Resource allocation is justified

with data

2,50 2,00-4,00

KHRM supports the quality of

decision-making

2 1,75-3,00

KHRM increases employee

well-being

2 2,00-4,00

KHRM improves resource

allocation

2 2,00-4,00

KHRM improves capability

management

1 1,00-2,00

KHRM improves performance

management

2 1,00-4,00

KHRM enhances employee

retention and attractiveness

2 1,00-4,00

4.7 Satisfaction with Knowledge-Based

Management of Human Resources

Overall, the interviewees were quite dissatisfied with

the current level of HR knowledge management in the

organization (M 2,46, Sd 0,80). The interviewees

estimated that they were most satisfied with the

organisation and resources of KHRM (Md 4,

=2,25, Q

=4,00). Satisfaction with how HR data is

used to support decision-making, and management

was the weakest (Md 2, Q

=2,00, Q

=2,80) (Table 8).

Table 8: Satisfaction with KHRM.

Perspective Md Q

-Q

Satisfaction with KHRM

strategy and organizational

culture

2 2,00-4,00

Satisfaction with KHRM

resources

4 2,25-4,00

Satisfaction with HR data 2 2.00-3.00

Satisfaction with HR analytics

and HR metrics

2 2.00-4.00

Satisfaction with utilization of

HR data

2 2,00-2,80

5 DISCUSSION AND

CONCLUSIONS

This study contributes to the field of KHRM by

developing and testing a maturity model tailored to

the needs of public sector organizations. The model

provides a structured framework for assessing the

maturity level of KHRM across five dimensions:

organizational culture and strategy, resources, HR

data, HR analytics and the utilization and benefits of

HR knowledge. The results indicate that while

strategic alignment and executive support for KHRM

are relatively well established, significant gaps

remain in data integration, analytics capabilities and

employee competence.

Although various KM maturity models have been

presented in literature, only a few of those are suitable

Measuring the Maturity of Knowledge-Based Management of Human Resources

489

for measuring the maturity level of KHRM (Hsieh et

al., 2009; Jääskeläinen et al., 2022; Khatibian et al.,

2010; Pee & Kankanhalli, 2009; Serrat, 2023). The

aim of the new model is to create a tool that allows

public sector organisations to measure the maturity

level of KHRM in a practical way while assessing the

realisation of the benefits achieved by KHRM in the

organisation. Based on previous literature, employee

satisfaction strengthens the rooting of new operating

models in organizations (Vieira et al., 2023; Voordt

& Jensen, 2023). With the help of this maturity

model, it is possible for organizations to examine the

level of maturity of the KHRM, but also the

satisfaction experienced by the employees. Thanks to

this feature, organizations can, in addition to

developing HR systems, for example, identify areas

where organizational culture needs to be supported

and developed.

In this study, a practical self-assessment model

was tailored primarily in the context of the Finnish

public sector. However, based on the comments

received during the preliminary testing, the model can

also be used in organizations operating in other

sectors, and this is one interesting idea for further

development. The model complements existing

frameworks by operationalizing maturity dimensions

into measurable statements, enabling organizations to

assess not only structural readiness but also perceived

satisfaction and cultural alignments. Measuring

employee satisfaction as part of determining maturity

levels is important, as previous studies have shown

that employee satisfaction supports the adoption of

new ways of working in organizations (Vieira et al.,

2023; Voordt & Jensen, 2023). In addition, this model

can be used to measure the realization of the benefits

achieved with KHRM in the organization. This model

has dual focus on objective capability and subjective

experience, and it offers a novel contribution to the

maturity model literature and supports more holistic

HR development strategies.

Despite its contributions, this study has

limitations. The maturity model developed in this

study needs further development, especially in those

areas where Cronbach's alpha does not reach the

target value of 0.7. The qualitative data collected in

the interviews will be utilised in the development of

these areas. Due the rather small sample of

interviewees further testing with larger sample should

also be done in the future. It is also relevant to test

how the model works when the data is collected using

an electronic questionnaire instead of interviews.

Additionally, longitudinal studies could explore how

maturity levels evolve over time and how they

correlate with organizational performance indicators.

ACKNOWLEDGEMENTS

The first author of this paper gratefully acknowledges

the financial support received from Tietojohtamisen

verkosto ry for participation in the KMIS conference,

including travel and accommodation expenses. This

support made it possible to present and discuss the

findings of this study with an international academic

audience.

REFERENCES

Anders, A. In: Lesley, A. (2012). “Designing and doing

survey research”. London: Sage

Becker, J., Knackstedt, R. and Pöppelbuß, J., (2009)

Developing maturity models for IT management – a

procedure model and its application, Business &

Information Systems Engineering, s. 213–222.

Cheung, G. W., Wang, C., & Chan, D. (2024).

Measurement reliability and validity in organizational

research: Advances in composite score modeling.

Journal of Applied Psychology, 109(2), 215–230.

https://doi.org/10.1037/apl0001056

Chuma. L. (2020) The Role of Marketing Information

Systems on Business Firms Competitiveness:

Integrated Review Paper from Business Perspective.

Journal of Marketing and Consumer Research. Vol. 72.

de Bruin, T., Rosemann, M., Freeze, R. & Kulkarni, U.

(2005), “Understanding the main phases of developing

a maturity assessment model”, Proceedings of 16

Australasian Conference on Information Systems

(ACIS), Sydney, November 30-December 2.

Delaney, J. T., & Huselid, M. A. (1996). The impact of

human resource management practices on perceptions

of organizational performance. Academy of

Management Journal, 39(4), 949–969,

https://www.jstor.org/stable/256718

Donate, M.J. & de Pablo, J.D.S. (2015), “The role of

knowledge-oriented leadership in knowledge

management practices and innovation”, Journal of

Business Research, Vol. 68 No. 2, pp. 360-370.

Doody, O., & Noonan, M. (2013). Preparing and

conducting interviews to collect data. Nurse researcher,

20(5).

Ferreira, B., Curado, C., & Oliveira, M. (2022). The

contribution of knowledge management to human

resource development: A systematic and integrative

literature review. Journal of the Knowledge Economy,

13, 2319–2347. https://doi.org/10.1007/s13132-021-

00815-9

Gagnon, R. J., Stone, G. A., & Garst, B. A. (2017). Beyond

composite scores and Cronbach’s alpha: Advancing

methodological rigor in recreation research. Journal of

Outdoor Recreation, Education, and Leadership, 9(4),

323–327. https://js.sagamorepub.com/jorel/article/

view/7959

KMIS 2025 - 17th International Conference on Knowledge Management and Information Systems

490

Helander, N., Ahonen, O., Houhala, K., & Jääskeläinen, A.

(2020). Tiedolla johtaminen julkisella sektorilla:

käytännön tapauksia eri hallinnon aloilta. Focus localis,

48(3), 22–42. (In Finnish)

Hsieh, P.J., Lin, B. & Lin, C., (2009). The construction and

application of knowledge navigator model (KNMTM):

An evaluation of knowledge management maturity.

Expert Systems with Applications 36, 4087–4100.

Jääskeläinen, A., Sillanpää, V., Helander, N., Leskelä, R.-

L., Haavisto, I., Laasonen, V. & Torkki, P., (2022).

Designing a maturity model for analyzing information

and knowledge management in the public sector. VINE

Journal of information and knowledge management

systems 52, 120–140.

Kaboor, B. & Sherif, J. (2012). Human resources in an

enriched environment of business intelligence.

Kybernetes. 41(10), 1625–1637.

Khatibian, N., Hasan, T. & Jafari, H. A. (2010),

Measurement of knowledge management maturity level

within organizations, Business Strategy Series, Vol. 11,

No. 1, s. 54-70.

Kim T. & Cho W. (2024) Employee Voice Opportunities

Enhance Organizational Performance When Faced with

Competing Demands. Review of Public Personnel

Administration. Vol. 44, No. 4, pp 713-739.

https://doi.org/10.1177/0734371X231190327

Kucińska-Landwójtowicz, A. (2019). Organizational

maturity models – Review and classification. Central

Bohemia University Proceedings, 7, 258–265.

https://doi.org/10.12955/cbup.v7.1360

Kuntaliitto (2024). Kuntatalouden tilastot. Retrieved from

https://www.kuntaliitto.fi/talouselinvoima/kuntataloud

en-tilastot

Margherita A. (2022). Human resources analytics: a

systematization of research topics and directions for

future research. Human Resource Management

Review, 32 (2022), 100795.

Marler, J. H., & Boudreau, J. W. (2017). An evidence-based

review of HR Analytics. The International Journal of

Human Resource Management, 28(1), 3–26.

https://doi.org/10.1080/09585192.2016.1244699

Pee, L.G. & Kankanhalli, A., (2009). A Model of

Organisational Knowledge Management Maturity

Based on People, Process, and Technology. Journal of

Information and Knowledge Management, 08, 79–99.

Presser, S., Couper, M. P., Lessler, J. T., Martin, E., Martin,

J., Rothgeb, J. M., & Singer, E. (2004) Methods for

testing and evaluating survey questions. Public Opinion

Quarterly. https://doi.org/10.1093/poq/nfh008

Rigamonti, E., Gastaldi, L., & Corso, M. (2024). Measuring

HR analytics maturity: Supporting the development of

a roadmap for data-driven human resources

management. Management Decision, 62(13), 243–266.

https://doi.org/10.1108/MD-11-2023-2087

Rowley, J. (2012). Conducting research interviews.

Management Research Review, 35(3/4), 260–271.

Sen A. (2024). Organizational Knowledge and Knowledge

Management – A New Framework. American Journal

of Management Science and Engineering, Vol. 9, No.

1, pp.1-12. https://doi.org/10.11648/j.ajmse.20240

901.11

Soliman, F. & Spooner, K. (2000) Strategies for

implementing knowledge management: of human

resources management, Journal of Knowledge

Management, Vol. 4, No. 4, pp. 337-345.

https://doi.org.libproxy.tuni.fi/10.1108/136732700103

79894

Tenhovuori, S. (2024). The Maturity Level and Benefits of

Human Resource Analytics in a Public Organization

Providing Social and Health Services. In G. Morales-

Alonso & G. Schiuma (Eds.), Proceedings of the 19th

International Forum on Knowledge Asset Dynamics

(IFKAD 2024): Translating Knowledge into Innovation

Dynamics (pp. 1081–1093). Madrid, Spain: Institute of

Knowledge Asset Management. ISBN 978-88-96687-

17-8.

Ulrich, D., & Dulebohn, J. H. (2015). Are we there yet?

What’s next for HR? Human Resource Management

Review, 25(2), 188–204. https://doi.org/10.101

6/j.hrmr.2015.01.00

Vieira J., Silva F., Teixeira J., Menezes A. & de Azevedo

S. (2023) Climbing the ladders of job satisfaction and

employee organizational commitment: cross-country

evidence using a semi-nonparametric approach, Journal

of Applied Economics, 26:1, https://doi.org/1

0.1080/15140326.2022.2163581

Voordt, T.v.d. and Jensen, P.A. (2023), "The impact of

healthy workplaces on employee satisfaction,

productivity and costs", Journal of Corporate Real

Estate, Vol. 25 No. 1, pp. 29-49. https://doi.

org/10.1108/JCRE-03-2021-0012

Measuring the Maturity of Knowledge-Based Management of Human Resources

491