Business Intelligence, Business Analytics, and Intellectual Capital:

An Opportunity for Innovation Potential in the Private Healthcare?

Pasi Hellsten

, Jussi Myllärniemi

and Milla Ratia

Unit for Knowledge Management, Tampere University, PO Box 541, FI-33014 Tampere, Finland

Keywords: Knowledge Management, Business Intelligence, Business Analytics, Intellectual Capital, Innovation.

Abstract: This paper looks upon the role of business intelligence (BI), business analytics (BA), and intellectual capital

(IC) in managerial decision-making in the private healthcare sector in Finland, and scrutinizes the potential

for innovation, enabled by BI/BA as a function and think it’s value creation in organizations. The study was

conducted by qualitative research methods with inductive approach using semi-structured, thematic

interviews. The study scrutinizes the managerial insight of BI and BA and the tools’ use in data-driven value

creation, also contemplating the potential for organizational operation, both from private healthcare and

consulting companies’ point of view, enabling the management of the private healthcare sector to utilize the

whole potential and best practices. Two practical outcomes of the study are: it will provide information and

understanding on the managerial aspect of BI/BA area in the Finnish private healthcare sector companies and

show its potential for innovation.

1 INTRODUCTION

Finnish private health care sector has been changing

during recent years; a need for data-driven

management and decision-making especially at the

organizational level has emerged (Ratia and

Myllärniemi 2017; Bates et al. 2014, Stewart et al.

2016). The phrase ‘data-driven’ is seen in various

contexts, from strategy to concrete operational

approach. The decision-making may generally be

divided into strategic decisions, conducted by the top

management, and operational level, where the

decision-making concerns the everyday operations.

The latter is done by operational business managers

and alike. The role of business intelligence (BI) and

business analytics (BA) in decision-making logically

varies between the organizations case-specifically.

Value of BI/BA can be generated on different value

creation levels, where the significance of BI/BA is

linked to the value created with these actions (Ratia

and Myllärniemi 2017; Ratia et al. 2017). At its best,

value creation may mean innovating, e.g. new

business openings based on the findings and

interpretations of the data.

https://orcid.org/0000-0001-7602-1690

https://orcid.org/0000-0002-2846-0426

https://orcid.org/0000-0002-3360-9555

According to the study the healthcare sector

benefits from innovations, not only clinical ones but

also regarding the cost effectivity and efficiency of

the entire healthcare system, with technology playing

a vital role (Omachonu and Einspruch 2010).

Although the utilization of BI/BA and tools may be

considered as IT-infrastructure change, a successful

BI/BA renewal requires managerial commitment and

absorptive capacity (Foshay and Kuziemsky 2014;

Isik et al. 2011). The data driven approach is strongly

related to the intellectual capital’s (IC) role in value

creation, its acknowledgement is one crucial driver

for organizational performance (Ratia 2018; Hussinki

et al. 2017). Continuous development of operational

approaches, technologies, and infrastructures, such as

BI/BA, can be considered as one of the roles of

managing IC. Using the IC enables continuous data

creation in the organizational systems, which requires

management of human, social, and structural capital

within this ecosystem (Secundo et al. 2017; Ratia and

Myllärniemi 2018). Similarly, the well-functioning

infrastructures and managerial approach are essential

(Helander et al. 2022).

278

Hellsten, P., Myllärniemi, J. and Ratia, M.

Business Intelligence, Business Analytics, and Intellectual Capital: An Opportunity for Innovation Potential in the Private Healthcare?.

DOI: 10.5220/0012234200003598

In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2023) - Volume 3: KMIS, pages 278-285

ISBN: 978-989-758-671-2; ISSN: 2184-3228

The purpose of this paper is to point out the role

of BI/BA and IC in managerial decision-making with

an example of the private healthcare sector

organizations. Secondary goal is to show their

connection to organizational value creation and

innovation. IC may be regarded as a driver of BI/BA

utilization, and thus data-driven value creation (Ratia

2018). Moreover, the notion is close to claim that the

more IC-components are involved, the more value

can be generated. Combined BI/BA and IC may

contribute to creating new business opportunities

(Ratia et al. 2018).

This study combines IC and BI/BA used in the

organizational data-driven decision-making and

researching the potential of innovation. By analysing

the value creation factors in terms of BI/BA and IC in

the private healthcare industry sector, the study brings

understanding of the factors affecting the value

creation potential of innovations. The practical

outcome will provide information for practitioners of

BI/BA and IC in organizational decision-making, also

showing the potential of them in creating innovations.

The generalizability of the findings needs closer

scrutiny.

First the paper presents conceptual basis for

BI/BA and IC. The Walter et al.’s (2001) model for

value creation along with a theoretical discussion on

knowledge-based innovation in the private healthcare

is introduced. Followed by empirical setting, the

methodology and the empirical material showing the

results of knowledge-based innovation potential and

data-driven value creation in the Finnish private

healthcare sector. Future avenues for research are

proposed and the conclusions and discussion are

presented in the end.

2 BI CREATING VALUE AND

INNOVATION POTENTIAL

2.1 Business Intelligence?

A manager needs data. Organizational decision-

making is to be based on data and knowledge of the

organizations operation and environment. For this,

the operation requires decision-making support tools,

i. e. information systems, such as business

intelligence, to enable data-driven decision-making in

the organization (Bolloju et al. 2002). This includes

e. g. financial information, cost evaluation and

performance evaluation (Bose 2003). Even if

decision-making is based on data, there is an

interactive knowledge management process,

requiring knowledge-sharing between decision-

makers and data providers (Wang and Wang 2008).

The effects of such initiatives are different for various

groups, which requires recognizing the issues

(Hellsten and Pekkola 2020).

To understand business analytics (BA), one needs

to understand the concept of business intelligence

(BI). In research literature, BI is often described being

multidimensional having several definitions with

similar features. There are some differences in the

description of BI. For instance, Turban et al. (2008)

identifies BI to be a concept combining different

tools, applications, and methods. A review by

Nykänen et al. (2016) introduces the technological

and the processual approaches to BI. Lönnqvist et. al

(2006) suggest that BI has many similar concepts, e.g.

competitive intelligence, market intelligence,

customer intelligence, strategic intelligence, and data

analytics. These may have minor discrepancies

between them. In summary, it can be identified as

being a selection of techniques, practices,

methodologies, and applications to analyse critical

business data to enable better business decisions

(Côrte-Real et al. 2014; Nykänen et al. 2016).

However, the traditional concept of BI is facing

changes as new methods and concepts, e.g. big data

and machine learning, emerge. In research as well as

among practitioners. Business intelligence, business

analytics and big data have been recently used

interchangeably (Ratia and Myllärniemi 2018; Trieu

2017; Wang et al. 2016). BI and related concepts,

such as business analytics, may effect positively on

business performance (Ratia and Myllärniemi 2018;

Kakhki and Palvia 2016). Data processing

capabilities require tools to enable knowledge and

value creation to the organization (Jinpon et al. 2011).

As of the most novel approach of BA, the value

generating difference to their previous versions is

being accumulated by timely access to data, real time

analysis and visual or storytelling presentation of

required information (Ratia and Myllärniemi 2018;

Popovič 2017). BA-tools are ranked among the most

important technologies by chief information officers

(Yeoh and Popovič 2015, Visinescu et al. 2016). The

predominant approach in the healthcare BA research

is concentrating on the clinical side and less on

managerial aspects.

2.2 Intellectual Capital in Value

Creation

Seems that BA utilization can generate more value in

organization the more IC components are used in the

value production (Ratia et al. 2018). IC components

Business Intelligence, Business Analytics, and Intellectual Capital: An Opportunity for Innovation Potential in the Private Healthcare?

279

are important in data-driven decision-making (Ratia

2018). The literature identifies Intellectual Capital

(IC) as a multilateral concept, hard to define, as there

are several perspectives on the topic and no precise

definition. To understand the role of IC in discussion,

we introduce Secundo et al’s (2017) four stages of IC

evolution. The first two stages of IC focus on the

awareness of IC and acknowledging its potential in

creating competitive advantage in organizations and

its meaning for strategic management and

measurement of its efficiency (Secundo et al. 2017;

Petty and Guthrie 2000; Ratia and Myllärniemi

2018). As the third stage of IC evolution, IC is

introduced as a dynamic system of intangible assets,

where the focus is on the interactions between IC

components and managerial activities (Secundo et al.

2017; Guthrie et al. 2012; Silvestri and Veltri 2011;

Ratia and Myllärniemi 2018). The fourth stage of IC

evolution introduces a broader perspective of IC,

focusing on the ecosystems, where knowledge can be

created on a wider scale (Secundo et al. 2017; Dumay

and Garanina 2013; Ratia and Myllärniemi).

Focusing on the new social aspects, where human,

relational, and structural capital are being combined

into a new view of IC. A view with focus on

performance of IC in networks. The knowledge flow

goes beyond traditional boundaries of relational

capital and where there exists a knowledge flow

between networks (Secundo et al. 2017; Guthrie et al.

2012; Dumay and Garanina 2013; Borin and Donato

2015; Edvinsson and Lin 2012; Edvinsson and Lin

2009). Intellectual capital is considered to be an

essential part of organizational value creation (e.g.

Secundo et al. 2017; Moustaghfir and Schiuma 2013;

Ratia et al. 2018).

The concepts of value and value creation are

brought up in business discussions (Ojala and

Helander 2014; Ratia and Myllärniemi 2017). The

extended concept of value is a trade-off between

benefits and sacrifices (e.g. Ojala and Helander 2014;

Hugos et al. 2011; Ratia and Myllärniemi 2017),

which can be tangibles or primary, for example

enhanced performance and resource utilization, or

being intangible or secondary, such as competence,

market position, social rewards, time, effort and

energy (e.g. Walter et al. 2001; Ojala et al. 2014;

Myllärniemi and Helander 2012; Hagen et. al 2006;

Nordgren 2009; Ratia and Myllärniemi 2017).

Modified Walter et al.'s (2001) function-oriented

value analysis is used to point out the BA-tools’ role

in value creation as a basis of our analysis (Ratia and

Myllärniemi 2017; Myllärniemi and Helander 2012;

Walter et al. 2001). IC and value functions

frameworks support the suggestion of Secundo et al.

(2017), that value creation is the main objective for

incorporating BA approach into organisational IC

strategy. Furthermore, the role of IC is to enable

continuous development of approaches, technologies,

and infrastructures to enable data creation in the

organizational IC ecosystem, requiring management

of human, social, and structural capital, or IC

components within this ecosystem (Secundo et al.

2017; Ratia and Myllärniemi 2018).

2.3 Knowledge-Based Innovation

An ongoing competition and growth of information

has forced organizations to reconsider their

competitive edge and value creation capacity. (Lerro

et al. 2014). As a concept, innovation within

economic and managerial fields can be considered

multilateral. Even though innovation is a complex

combination of different conceptualizations, the

literature agrees about innovation having an origin in

newness and change. There has been an ongoing

debate about the meaning, location and nature of

newness and change (e.g. Lerro et al. 2014;

Damanpour, 1992; Becker and Whisler, 1967). One

of the first definitions of innovation was a new

combination of productive resources or recombining

existing capabilities and resources (Moustaghfir and

Schiuma 2013; Schumpeter 1934; Pennings and

Hariato 1992). Innovation can be seen as

organizations searching out for new resources or

discovering on how to utilize the existing resources in

a new way. One perspective is that innovation is

formed of organizations enhancing knowledge

sharing and generating economic value through co-

creation (Lerro et al. 2014; Galunic and Rodan 1998;

Miles et al. 2009). IC components in BA utilization

can provide organization an opportunity to grow its’

ability to create value and potential for innovation

(Ratia et al. 2018).

When it comes to technology, technical resources

and knowledge may also have a positive impact on

technical innovation (Lerro et al. 2014; Damanpour

1991). Companies are developing their technological

capabilities to be able to improve their efficiency and

innovativeness through new methods of knowledge

flow and data gathering (Santoro et al. 2017; Del

Giudice and Straub 2011; Del Giudice and Della

Peruta 2016). For example, the private healthcare

organizations are implementing BA-tools, to enable

increased efficiency in their organizational

performance (Ratia and Myllärniemi 2017). Research

is often concentrating on the internal resources, rarely

combining internal and external knowledge.

Combination of internal and external data was

KMIS 2023 - 15th International Conference on Knowledge Management and Information Systems

280

considered to be an element enhancing organizational

decision-making and creating potential for innovation

(Ratia 2018). Knowledge-based view is often

suggested to explain innovation processes, especially

open innovations, where internal and external

resources are combined to create new products and

services (Santoro et al. 2017; Vanhaverbeke and

Cloodt 2014; Ferreras-Méndez et al. 2016).

3 RESEARCH SETTING

The aim of this research is to understand the role of

IC and BA in managerial decision-making in the

Finnish private healthcare sector organizations, and to

examine, what is the potential of BA-tools in creating

potential for innovations, and thus value. The

research was conducted by using qualitative research

methods and a case study research strategy which is

suitable for studying complex and context-dependent

research topics as it provides better explanations and

deeper understanding on the research questions as

well as enables the adjusted questions and gather

more information (Yin, 2003). In addition, flexible

semi-structured interview allows information

gathering to be conducted effectively and

conveniently (Qu and Dumay 2011).

The private healthcare case companies, that were

participating in the research, have business activities

in the dental, social, and health care. Furthermore,

among companies involved were Finnish and

international companies having an office in Finland.

To identify the relevant companies and to analyze of

their suitability for this study both the private health

care and the consulting, open-source documentation

about the companies’ background was gathered. The

semi-structured thematic interviews were conducted

as face-to-face interviews, skype-interviews, and

phone interviews. The interview discussions were

recorded and transcribed to enable systematic

organizing and analysing the gathered data (McLellan

et al. 2003).

The interviewees were executives and top

managers mainly from ICT (information and

communication technology) or financial

organizational functions, chosen on a basis of their

area of responsibility for Business Intelligence within

their organization. Ten thematic semi-structured

interviews were conducted. The thematic interviews

included issues e.g. what are the benefits of BA-tools

utilization, how they use BA in decision-making and

how BA is used in management.

In addition, twenty technology and management

consultants were chosen to be interviewed. The

approach was semi-structured, thematic interviews.

The discussive interviews included issues e.g. what

value do BA tools bring to the private healthcare and

whether is BA being a part of the strategy

One private healthcare company was chosen for

deeper case study. The approach was to study

different organizational levels and BA from their

perspective. The study was conducted with semi-

structured, thematic interviews. The four

interviewees of private healthcare organization were

business and finance directors as well as

representative of controlling function. The gathered

data was analysed and classified according to the

interview themes in the first round. In the second

analysis round the identified classes were further

integrated by using the theoretical framework of

value creation as the analysis lens. In chapter 4.1. we

present our results by answering to the interview

themes based on the analysis round one. In chapter

4.2. we analyse the result with Walter et al’s

framework, based on the second round of the analysis

round.

4 RESULTS AND IMPLICATIONS

4.1 The Role of BA-Tools in

Data-Driven Decision-Making

The thematic interviews among the private healthcare

companies’ were centred around the benefits of BA

tools utilization, how they use BA in decision-making

and how BA is used in management. All the private

healthcare companies participating in the research

shared that there were significant benefits of BA. The

benefits varied from seeking for efficiency and

enhancing business operations to data-driven

decision-making and creating new products.

Competitive advantage was also mentioned as a value

creating factor. Utilization of BA on decision-making

was considered to be divided into operative decision-

making and higher-level decision-making. BA was

clearly the tool for both operative and managerial

decision-making. Operative BA concentrates mainly

on following specific KPI’s (key performance

indicator) and actions based on the data. On

managerial level, the focus was more on monthly

reporting and as a strategic management tool. The

operative daily or weekly data-driven decision

making was considered more tangible and data-

driven management was clearly still in a development

stage in many of the organizations, even though there

is a strong will to continue towards an enhanced data-

driven management.

Business Intelligence, Business Analytics, and Intellectual Capital: An Opportunity for Innovation Potential in the Private Healthcare?

281

The interviews among technology and

management consultants discussed the value BA tools

bring to the private healthcare and whether BA is a part

of the management strategy. Among the consultants,

efficiency was considered an important value adding

factor, e.g. optimizing business and operational

processes, and forecasting. Data-driven, or evidence-

based decision-making, timely access to business-

critical information and efficient utilization of

organizational intellectual capital (IC) were considered

to bring value. The actual value was a larger question

than just BA-tools, more as a whole combination of IC

dimensions. Modern BA solutions are able to reduce

the dependency of ICT function and heavy

specifications in advance, e.g. different information for

ad-hoc decision-making can be gathered in real-time.

Sometimes the value is created together with other

angles of digitalization, e.g. business process

automation or RPA (robotic process automation).

Social co-creation of BA was also considered valuable,

as they create the potential for innovation. Utilization

of external data and combining it to existing data was

seen as an asset, as it could create new business

opportunities. Some criticism appeared, mainly

focusing on the fact that BA-tools themselves do not

bring value, but the actual BA and understanding data,

business and KPI’s that are being measured. BA is not

always included as a part of the management strategy,

sometimes it is a part of a larger digitalization strategy.

Sometimes the link to the strategic level is weak, even

though a strong link between strategy and BA are

strengthening both.

The interviewed healthcare company reported

seeking for operational excellence. Understanding

customers’ needs and understanding own resources

were considered to bring value. Utilization of BA in

business-critical decision-making was considered

valuable. BA as a process and concept was seen to

bring value to customers by creating new products,

services, and business concepts, and enhancing

efficiency in current ones. Also data-based product

and service innovations are being created, especially

in digital services and platforms. These innovations

are being co-created together with customers. The

aim is to bring value by providing overall care

relationship and health management rather than

single healthcare services, again aiming for more

holistic solutions. The value of being able to predict

the future (as good as it goes), planning for new

actions, and looking forward was clearly stated. BA

is an important part of operational management

throughout the organization, also in forecasting and

new business development. All the operative

management is based on either actual reporting or

forecasting. Finding anomalies in the business

processes was considered as part of data-driven

management. BA is a ground for all decision-making.

4.2 Data-Driven Value Creation:

An Innovation Potential

More knowledge about managerial practices is still

needed. Healthcare organizations could use BA to

create value. The identified value creation functions

of BA-tool utilization from the empirical data is

shown by applying modified framework of Walter et

al. (2001) (below). The modifications to the findings

are based on earlier research of the healthcare sector

(Myllärniemi and Helander 2012) and private

healthcare sector (Ratia and Myllärniemi 2017). The

benefits of BA-tool utilization were collected,

analysed, and used in Table 1. to point out the direct

and indirect value functions of BA utilization.

Table 1: Direct and indirect BA-tool utilization value

functions and their measurement in the private healthcare

sector (based on Walter et al. 2001, Myllärniemi et al. 2012

and Ratia and Myllärniemi 2017).

The function-oriented value analysis enables the

identification the kind of value that can be co-created

in the private healthcare organizations by utilizing

BA-tools. The analysis of the value functions

revealed that organizations aim for efficiency and

improving their business. When commercialized,

new products and services could bring direct value.

Utilization of organizational IC can be direct value

creating factor in some cases.

Value

function

Description of the function Measurement examples for private

healthcare sector

DIRECT

Profit

Performance and efficiency

- Seeking for efficiency

- Enhancing business operations

- New products and services when

commercialized

- Utilization o

anizational I

Volume

Scalabilit

- Scalability of decision-making

- Resource optimization

Safeguard

Reliability of data

- Data-driven decision-making

- Timely and accurate data

- Reducing dependency of ICT functions

- Spottin

anomalies in business performance

INDIRECT

Market

Market position

- Competitive advantage on the market

- Understanding customer needs and the

market

Innovation

Creating new business

opportunities

- Creating new products and services

- Social co-creation of BA

- Efficient utilization of external data sources

and combining with organizational data

- Overall care relationship, co-created with the

customers, health management

Scout

Creating value through data

- External data sources creating new market

knowledge

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Also other direct value bringing functions were in

the key role, e.g. scalability of decision-making, data-

driven decision-making, quality of data, reducing

dependencies and quality of business process or

performance. As indirect value creation, the main

examples were position on the market, and social co-

creation of BA together with stakeholders as well as

co-creation of new products and services together

with customers to achieve overall care relationship or

to enable health management as a service.

Furthermore, it is not surprising, that a business

organization is seeking for improved performance

and optimization of resources etc. thus more

profitable business. Through indirect value functions

private healthcare organizations aim to find new and

innovative ways to create value. For example,

creating new products and services together with

customers, and health management as a service.

Nevertheless, this value function model helps us to

understand the activities and functions that create

value in the private healthcare sector.

The value creation, utilizing BA-tools in the

private healthcare sector, can be seen multifunctional.

As a classification for BA-tools value creation, we

used modified direct and indirect value functions and

their measurement -model (Walter et al. 2001;

Myllärniemi et al. 2012). The value creation

generated by efficient BA utilization and data-driven

approach in decision-making and creation of new

products and services are the key factors when

building competitive advantage on the private

healthcare sector. The most significant elements in

value creation in the private healthcare sector can be

considered to be co-creation of new products and

services and health management as a service.

5 DISCUSSION

This paper introduces a novel approach to discussion

of the impact of business analytics (BA) derived from

business intelligence and BA-tools to the role of BA

and IC in managerial decision-making in the private

healthcare sector organizations, and the potential of

BA and BA-tools in creating innovation potential and

value. The healthcare is facing changes and

challenges all over the world, not only having a

pressure for improving performance, but also in

utilizing their data more efficiently (Ratia and

Myllärniemi 2017). The utilization of BA and tools

could be considered as one of the ways to improve the

efficiency (Ratia and Myllärniemi 2017; Nykänen et

al. 2016; Malmi 1999). We analysed the direct and

indirect value functions of BA utilization, to gain

better understanding of the managerial decision-

making and value brought by innovation potential in

the context of the private healthcare in Finland.

The study showed several significant benefits of

BA utilization. The benefits varied from efficiency

and enhancing business operations to data-driven

decision-making and creating new products.

Utilization of BA in decision-making was divided

into operative decision-making and higher-level

management decision-making. BA created value for

the organizations, optimizing business and

operational processes, data-driven or evidence-based

decision-making, timely access to business-critical

information and efficient utilization of organizational

IC. Social co-creation of BA and new products and

services together with customers and health

management as a service were clear value adding

functions. The actual value was seen to be a larger

question than just BA-tools or the direct benefits.

Some criticism appeared pointing out that BA-tools

themselves do not bring value, but rather

understanding of the data and business. They may or

may not bring actual value. As a result, the target is

to bring value not only by data-driven decision-

making, but also by providing better understanding of

the big picture and whole care relationship and health

care instead of individual healthcare services.

There are two practical outcomes of this study.

Firstly, this study will provide deep understanding on

the managerial aspect of BA tool utilization in the

Finnish private healthcare sector companies.

Secondly, this study will provide information on what

are the value creating factors that BA and tools

provide to create potential for innovation. This study

will help the consulting companies to understand how

they can support the business and managerial

decision-making in the private healthcare sector

organizations. This also helps the private healthcare

companies to discover their potential of value

creation in utilizing BA and tools. As to the

generalizability of the findings, we assume that

similar findings will appear in other environments as

well, but this needs more attention before any

conclusions are made. To get deeper view on this

issue, we need to gather more empirical data from the

private healthcare organizations, from different

organizational levels. We need to research more of

the required capabilities for BA-tool, especially from

the vendors perspective, to be able to point out

specific tool requirements and functional features that

are essential for the private healthcare sector to gain

deeper understanding of factors having impact on

value creation (Ratia et al. 2017; Brandão et al. 2016).

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283

REFERENCES

Bates, D., Saria, S., Ohno-Machado, L., Shah, A., Escobar,

G. (2014). Big Data In Health Care: Using Analytics

To Identify And Manage High-Risk And High-Cost

Patients. Health Affairs 33.7, Jul2014, pp.1123-31.

Becker, S.W. and Whisler, T.L. (1967). The innovative

organization: a selective view of current

theory and research. The Journal of Business, Vol. 40

No. 4, pp. 462-469.

Bolloju, N., Khalifa, M. and Turban, E. (2002). Integrating

knowledge management into enterprise environments

for the next generation decision support. Decision

Support Systems 33, 2002, pp. 163 – 176.

Bose, R. (2003). Knowledge management-enabled health

care management systems: capabilities, infrastructure,

and decision-support. Expert Systems with

Applications 24, 2003, pp. 59–71

Côrte-Real, N., Ruivo, P. and Oliveira, T. (2014). The

diffusion stages of business intelligence & analytics

(BI&A): A systematic mapping study. Procedia

Technology. Vol.16, pp.172–179

Del Giudice, M. and Della Peruta, M.R. (2016). The impact

of IT-based knowledge management systems on

internal venturing and innovation: a structural equation

modeling approach to corporate performance. Journal

of Knowledge Mgmt, 20 (3).

Del Giudice, M. and Straub, D. (2011). IT and

entrepreneurism: an on-again, off-again love affair or a

marriage? MIS Q. 35 (4), pp. 3–11.

Damanpour, F. (1991). Organizational innovation: a meta-

analysis of effects of determinants and moderators.

Academy of Management Journal, Vol. 34 No. 5, pp.

555-590.

Damanpour, F. (1992). Organizational size and innovation.

Organization Studies, Vol. 13 No. 3, pp. 375-402.

Ferreras-Méndez, J.L., Fernández-Mesa, A. and Alegre, J.

(2016). The Relationship Between Knowledge Search

Strategies and Absorptive Capacity: a Deeper Look

Technovation.

Foshay, N. and Kuziemsky, C. (2014). Towards an

implementation framework for business intelligence in

healthcare. International Journal of Information

Management 34 (2014). pp. 20–27

Galunic, C.D. and Rodan, S. (1998). Resource

recombination in the firm: knowledge structures

and the potential for Schumpeterian innovation.

Strategic Management Journal, Vol. 19 No. 12, pp.

1193-1201.

Hagen, H.-O. and Hagsten, E. (2006), “Därför behöver

produktiviteten i den offentliga sektorn mätas”. Rapport

till Expertgruppen för Studier i Samhällsekonomi

2006:2, Fritzes, Stockholm.

Helander, N., Paunu, A., Hellsten, P., 2022. Knowledge

management processes in practice: Empirical insights

from the public sector. 55

Hawaii International

Conference on System Sciences (HICSS-55). Maui

(virtual), USA. 03

- 07

January 2022.

Hellsten, P. & Pekkola, S. 2020. Impacts of Digitalization:

Many Agendas on Different Levels. HICSS 2020: 1-10.

Hawaii International Conference on System

Sciences (HICSS-53). Kauai, USA. 07

– 10

January

2020.

Hugos, M.H. and Hulitzky, D. (2011). Business in the

Cloud: What Every Business Needs to Know About

Cloud Computing. John Wiley & Sons, Inc.

Hussinki, H., Ritala, P., Vanhala, M. and Kianto, A. (2017).

Intellectual capital, knowledge management practices

and firm performance. Journal of Intellectual Capital,

Vol. 18 Issue: 4, pp.904-922

Isik, O., Jones, M. C. and Sidorova, A. (2011). Business

Intelligence (BI) Success And The Role Of BI

Capabilities. International Journal of Intelligent

Systems in Accounting and Finance Management.

Vol.18, n.4, October 2011. pp.161-176

Jinpon P., Jaroensutasinee M. and Jaroensutasinee K.

(2011). Business Intelligence and its Applications in the

Public Healthcare System. Walailak Journal of Science

and Technology (WJST). 2011. Vol8, Issue 2, pp.97-

110

Kakhki, M. D. and Palvia, P. (2016). Effect of Business

Intelligence and Analytics on Business Performance.

Business Intelligence and Analytics and Performance.

Twenty-second Americas Conference on Information

Systems, San Diego, 2016.

Lerro, A., Linzalone, R. and Schiuma, G. (2014). Managing

intellectual capital dimensions for organizational value

creation, Journal of Intellectual Capital, Vol. 15 Issue:

3, pp.350-361.

Lönnqvist A. and Pirttimäki V. (2006). The measurement

of business intelligence. Information Systems

Management , Vol.23, 2006. Issue 1, pp.32-40

McLellan, E., MacQueen, K. M. and Neidig, J L. (2003).

Beyond the Qualitative Interview: Data Preparation and

Transcription. Sage Journals, Feb2003, pp.63-84

Miles, R.E., Miles, G., Snow, C.C., Blomqvist, K. and

Rocha, H. (2009). The I-form organization. California

Management Review, Vol. 51 No. 4, pp. 61-76.

Moustaghfir, K. and Schiuma, G. (2013). Knowledge,

learning, and innovation: research and perspectives.

Journal of Knowledge Management, Vol. 17 Issue: 4,

pp.495-510.

Murdoch, T.B. and Detsky, A.S. (2013). The inevitable

application of big data to health care. J. Am. Med.

Assoc. 309 (13), pp. 1351–1352

Myllärniemi, J. and Helander, N. (2012). Healthcare

System as a Value Network. World Review of

Entrepreneurship, Management and Sustainable

Development. 2012, Vol.8, Issue 2.

Nordgren, L. (2009). Value creation in health care services

– developing service productivity. Experiences from

Sweden. International Journal of Public Sector

Management Vol.22 No. 2, 2009 pp.114-127

Nykänen, E., Järvenpää, M., and Teittinen, H. (2016).

Business intelligence in decision making in Finnish

enterprises. Nordic J. of Business, 65 (2), pp.24-44.

Ojala, A. and Helander, N. (2014). Value Creation and

Evolution of a Value Network: A Longitudinal Case

Study on a Platform-as-a-Service Provider. System

KMIS 2023 - 15th International Conference on Knowledge Management and Information Systems

284

Sciences (HICSS), 2014 47th Hawaii International

Conference on, Issue Date: 6-9 Jan. 2014.

Omachonu, V. K. and Einspruch, N. G. (2010). Innovation

in Healthcare Delivery Systems: A Conceptual

Framework. The Innovation Journal: The Public Sector

Innovation Journal, Volume 15(1), 2010, Article 2.

Pennings, J.M. and Harianto, F. (1992). The diffusion of

technological innovation in the commercial banking

industry. Strategic Management Journal, Vol. 13 No. 1,

pp. 29-46.

Qu, S.Q. and Dumay, J., (2011). The qualitative research

interview. Qualitative Research in Accounting &

Management, 8(3), pp. 238-264.

Ratia, M. and Myllärniemi, J. (2017). Business Intelligence

Tools for Private Healthcare Data-Driven Value

Creation. Proceedings IFKAD 2017. St. Petersburg,

Russia, 7-9 June 2017. pp. 408-419.

Ratia, M., Myllärniemi, J. and Helander, N. (2017).

Benefits and Required Capabilities of BI-tools in the

Private Healthcare. Proceedings of the 21st

International Academic Mindtrek Conference. ACM.

Tampere, Finland. Sept. 20 - 21, 2017. pp. 103-110

Ratia, M. (2018). Intellectual Capital and BI-tools in

Private Healthcare Value Creation. The Electronic

Journal of Knowledge Management, Vol 16, Issue 2,

pp. 143-154

Santoro, G., Vrontis, D., Thrassou, A. and Dezi, L. (2017).

The Internet of Things: Building a knowledge

management system for open innovation and

knowledge management capacity. Technological

Forecasting & Social Change 2017.

Schumpeter, J. (1934). The Theory of Economic

Development – An inquiry into Profits, Capital, Credit,

Interest, and the Business Cycle, Harvard University

Press, Cambridge, MA.

Secundo, G., Del Vecchio, P., Dumay, J. and Passiante, G.

(2017). Intellectual capital in the age of Big Data:

establishing a research agenda. Journal of Intellectual

Capital, Vol. 18 Issue: 2. pp. 242-261.

Stewart, R. and Davis K. (2016). ‘Big data’ in mental health

research: current status and emerging possibilities.

Social Psychiatry and Psychiatric Epidemiology.

Aug2016, Vol.51, Issue 8, pp.1055–1072

Trieu, V. (2017). Getting value from Business Intelligence

systems: A review and research agenda. Decision

Support Systems. Vol. 93, January 2017, pp. 111-124

Turban, E., Aronson, J. E., Liang, T-P. and Sharda, R.

(2008): Business intelligence: a managerial approach.

Pearson Prentice Hall, New Jersey.

Visinescu, L. L., Jones, M.C. and Sidorova A. (2016).

Improving Decision Quality: The Role of Business

Intelligence. The Journal of Computer Information

Systems. Vol. 57. Issue 1. Fall 2016, pp. 58-66

Vanhaverbeke, W. and Cloodt, M. (2014). Theories of the

firm and open innovation. New Frontiers in Open

Innovation. Oxford University Press, Oxford.

Walter, A., Ritter, T. and Gemünden, H.G. (2001). Value

creation in buyer-seller relationships. Industrial

Marketing Management 30(4), pp.365-377.

Wang, H. and Wang, S. (2008). A knowledge management

approach to data mining process for business

intelligence. Industrial Management & Data Systems

Vol. 108 No. 5, 2008, pp. 622-634

Wang, Y., Kung, LA., Byrd., T. A. (2016). Big data

analytics: Understanding its capabilities and potential

benefits for healthcare organizations. Technological

Forecasting and Social Change Vol. 126, January 2018,

pp. 3-13

Yeoh, W. and Popovič, A. (2015). Extending the

Understanding of Critical Success Factors for

Implementing Business Intelligence Systems. Journal

of Association for Information Science and

Technology. Vol. 67, Iss., January 2016, pp. 134-147

Yin, R.K. (2003). Case Study Research: Design and

Methods. 3rd ed., Sage, Thousand Oaks.

Business Intelligence, Business Analytics, and Intellectual Capital: An Opportunity for Innovation Potential in the Private Healthcare?

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