Organizational Capabilities in Data-driven Value Creation:

A Literature Review

Prashanth Madhala, Hongxiu Li and Nina Helander

Information and Knowledge Management, Faculty of Management and Business, Tampere University,

FI-33014 Tampere, Finland

Keywords: Organizational Capabilities, Data-driven Value Creation, Data, Analytics Capabilities.

Abstract: Prior research has shown that organizational capabilities can help enhance competitive advantages. However,

organizational capabilities are also required to develop in order to keep up with the dynamic environment or

changing business environment. Over the last decade, data has been introduced as a new business resource in

organizations, which brings both new opportunities and challenges in data-driven value creation in

organizations. There is a need to understand what organizational capabilities are needed in achieving data-

driven value creation for organizations. This study conducts an exploratory literature review to understand

what organizational capabilities are related to data-driven value creation. Based on the selected 14 articles

from the literature between years 2016 and 2020, the literature review shows that analytics capabilities are

the most important capabilities in data-driven value creation together with other different capabilities. There

is more room for research with regards to organizational capabilities in data-driven value creation, such as to

examining what are the components of analytics capabilities and how organizations should combine different

organizational capabilities for data-driven value creation.

1 INTRODUCTION

According to Barney (1991), a firm contains different

resources which include knowledge, assets,

capabilities, and know-how, resources that can be

used in ways to create value. Resources contain both

assets and capabilities, and capabilities are generated

through organizational processes (Hooley et al.,

1998). Organizational capabilities have been

suggested to affect organizations’ performance and

can help realize value, and different types of

capabilities have been suggested for value creation.

For instance, organizations’ operational capability is

required to convert input into output whereas

dynamic capability of organization is essential for

reconfiguring existing operational capabilities (Lin et

al., 2016). Dynamic capabilities are linked to

competitive advantages or firm performance

(Eriksson, 2014). Information Technology (IT)

capabilities, the ability of a firm to mobilize and use

its IT based resources in combination with other

resources or capabilities, has been found to be

positively linked with firm performance (Bharadwaj,

2000). In recent years, organizational capabilities has

also been posited to lead to data-driven value

creation. For example, big data analytical capability

can be used for creating value for product

development by analysing review data (Zhou et al.,

2018), and business analytics (BA) capabilities can

support decision-making in organizations (Ashrafi &

Zare Ravasan, 2018).

Data is seen as an important resource for creating

value. For instance, it is used for identifying the most

profitable customers or to develop new products and

services (Saarijärvi et al., 2014). In the context of

social networking websites, companies seek to create

value to both

customers and themselves by using

customer data from their online platforms (Jussila &

Madhala, 2019). Automotive industries that are

heavily reliant on physical assets also use data to

identify market value and for creating innovative

products and services (Woźniak et al., 2015). Internet

of Things (IoT) applications also provide possibility

for data-driven value creation. According to Fantana

et al. (2013), IoT applications can create a lot of value

for industry, such as reducing production loss,

reducing energy consumption, improving operation,

safety and sustainability. Universities have also

regarded information technologies as enablers to tacit

knowledge transfer (Chugh, 2018), and data analytics

108

Madhala, P., Li, H. and Helander, N.

Organizational Capabilities in Data-driven Value Creation: A Literature Review.

DOI: 10.5220/0010175601080116

In Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 3: KMIS, pages 108-116

ISBN: 978-989-758-474-9

has been used to improve student retention (de Freitas

et al., 2015).The above examples throw light on the

potential of data-driven value creation in business.

As discussed above capabilities play an important

role in creating value for organizations. But there is a

lack of knowledge on how capabilities are linked with

data-driven value creation. For instance, what types

of capabilities are required in the process of data-

driven value creation. As data-driven value creation

is an emerging research topic in business and

management field, it is necessary to understand the

topic based on prior research, which will provide

scholars on the current scenario of the role of

capabilities on data-driven value creation and the

potential future direction. Therefore, this study

attempts to conduct an exploratory research into the

topic of capabilities in data-driven value creation via

a literature review and will aim to provide an initial

picture of the role of capabilities in data-driven value

creation by proposing answers to following research

question (RQ): “What kind of capabilities are needed

for organizations to use data for value creation?”.

In the next section, a theoretical background of

capabilities and value creation is provided. Then the

research method is presented. Following the research

method, the findings of capabilities in data-driven

value creation are presented. After which the research

results are discussed. Finally, the conclusions and the

limitations of the study are mentioned.

2 THEORETICAL

BACKGROUND

2.1 Capabilities

There are different definitions for capabilities in the

literature. Before defining the term capabilities, it is

important to understand the origin of capabilities and

its link with the competitive advantages of a firm. The

resource-based view (RBV) of the firm proposed by

Barney (1991) states that a firm’s competitive

advantages can be sustained if its resources are

valuable, rare, imperfectly imitable, and non-

substitutable; these resources can be assets,

capabilities, attributes, organizational processes,

information and knowledge which can be used for

enhancing efficiency and effectiveness. Prior

research argued that each firm is unique or

differentiated by the way in which it makes use of its

resources or the possible services based on its

resources (Kor et al., 2016).

A firm’s resources can be classified into tangible

and intangible resources (Wernerfelt, 1984). Both

assets and competencies can be intangible in nature.

For instance, capabilities with intangible nature can

be classified into ‘Having’ capabilities (intangible

assets like patents) and ‘Doing’ capabilities such as

skills, tacit knowledge, and competencies.

Capabilities can either be dependent of people or

independent of people (Hall, 1993). For instance, the

capabilities dependent of people include i) functional

- involving know-how of different stakeholders such

as employees and suppliers, and ii) cultural –

perceptions of quality, perceptions of customer

service, and the ability to change and innovate and in

short, it involves skills (Hall, 1993). According to

Hall (1993), the capabilities independent of people

involve intangible assets namely data bases,

contracts, trade secrets and intellectual property rights

and a capability can be a result of an intangible

resource. For example, a regulatory capability is an

outcome of well-established distribution network or

reputation and the key point is that sustainable

competitive advantages can be a result of possessing

appropriate capability differentials (Hall, 1993).

According to Wójcik (2015), the intangible resource

plays a direct role in supporting the resource-based

view, because possessing intangible resource can act

as an imitation barrier and can help sustain

competitive advantages of a firm. He also argued that

the conceptualization of capabilities was evolved

from the traditional RBV of the firm, but also

criticized the RBV for its lack of clear distinction

between resources and capabilities (Wójcik, 2015).

Prior research has distinguished the differences

between firm resources and firm capabilities.

According to Amit and Schoemaker (1993),

capabilities can be defined as the ability of firm to

deploy or make use of resources, and these processes

are usually tangible or intangible in nature and firm

centric, which are refined and developed over a time

due to the interaction with firm resources. Similarly,

Day (1994) defines capabilities as complex array of

skills and cumulative learning exploited through

organizational processes that establish good

coordination of operations, and firms can take

advantages of assets via making use of capabilities,

which acts like a glue that brings together all firm

assets.

According to Winter (2000), an organization’s

capabilities are a collection of many routines and

when combined with other inputs, the management

can make significant outputs. Likewise, an

organization’s capability relates to the ability or

capacity of an organization to execute balanced set of

Organizational Capabilities in Data-driven Value Creation: A Literature Review

109

tasks by making use of resources to achieve a certain

outcome; a capability irrespective of its characteristic

(operational or dynamic) is the capacity to achieve or

implement a particular task (Helfat et al., 2009). A

firm’s capabilities, if sharpened, would allow the firm

to perform its tasks efficiently (Teece, 2012).

Prior research states that a company’s growth is

steered by the capabilities it possesses and that these

capabilities are subject to change; managing

resources and capabilities are the key drivers to

competitive advantages (Kor & Mahoney, 2000) and

this can be a source of value creation for a firm

(Wójcik, 2015). The process of embedding resources,

capabilities and processes acts as a barrier for

imitation and transferability. Therefore, the higher the

difficulty for a competitor to imitate, the higher the

competitive advantages a firm has. As more

capabilities are intertwined with each other, it creates

a capability embeddedness and therefore becomes a

part of organizational culture and hence becomes a

source of competitive advantages (Grewal &

Slotegraaf, 2007; Wójcik, 2015). For organizations,

they have different capabilities such as leadership

capabilities, human resource capabilities, IT

capabilities, social entrepreneurship capabilities,

digital capabilities, operation capabilities, and

analytics capabilities.

2.2 Value Creation

According to Bowman and Ambrosini (2000),

organizations create value in two ways namely

perceived use value and exchange value; the former

is subjective and is defined by customers based on

how they perceive the usefulness of the offering (e.g.,

product), whereas the latter is created when the

product is sold and the exchange happens between the

buyer and the seller (Bowman & Ambrosini, 2000).

Organizations create value by working with the useful

resources they acquire through people and processes,

and value is created through exchange value, profit

differentials between firms, and labour (generic,

differential, unproductive, and entrepreneurial

labour) (Bowman & Ambrosini, 2000). Value is

created when the end users find a product or service

as useful and when there is a new innovation or when

existing products or technologies can be improved

upon to serve something else (Pagani, 2013).

According to Lepak, Smith and Taylor (2007),

value creation occurs at three levels namely society,

organizations, and individual. Specifically, at

individual level, value is created by an individual

which is perceived as valuable by employer, client, or

customer; at organizational level, value is created

through innovative practices, unique organizational

processes, assets, and capabilities, and at societal

level, value occurs when programs and incentives are

provided to encourage innovation in existing

organizations and entrepreneurial ventures so value

can be created to members of the society.

Data has become a significant resource in creation

of new businesses and thereby promoting value

creation (Alamäki et al., 2018). Data is becoming

important in value creation and has attracted the

attention of scholars to explore how data can be used

to create value in different contexts. For example, in

the context of automotive industry which is a physical

intensive business, huge amounts of data can be

collected to develop maintenance services, to support

driving safety, and to support knowledge-driven

product development, and value is created by

enhancing after-market services with the help of data

in the automotive industry (Johanson et al., 2015).

With regards to companies operating online, they are

extremely profitable from data. Online platform

companies have accumulated huge amounts of

customer data on their platforms, and have monetized

data, which create significant value for them. Such as

in 2017, Booking Holdings, an online travel platform,

attained 98% gross profit margin (Wendy et al.,

2018). This is a good example of business profits and

value creation based on data. Manufacturing

industries are turning into smart manufacturing where

data-driven strategies are being used for creating

value and to become more competitive (Tao et al.,

2018). Obviously, data has been applied in business

for data-driven value creation in different contexts.

3 RESEARCH METHOD

The primary focus of this research is to understand

what type of capabilities can help with data-driven

value creation. To find the right articles related to the

topic, the keywords and phrases “data driven value

creation” OR “data-driven value creation” AND

“capabilities” was applied to search articles in the

Web of Science database. The search was limited to

one database to keep the search results manageable.

In the following process, all the articles are read and

filtered based on the keywords and phrases “value”,

“value creation”, “data based value creation”,

“capabilities” to exclude some articles that are not

really focussed on data-driven value creation and

capabilities. The number of articles that were chosen

for the final analysis was 14 out of 134 articles.

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Table 1: Journal sources of the selected articles.

Table 1 lists the different journals from which the

articles were chosen. All the chosen articles were

published between years 2016 and 2020. The articles

were published in Service Science Journal, European

Journal of Information Systems, Business Process

Management Journal, Journal of Product Innovation

Management, Journal of Services Marketing,

International Journal of Information Management,

Technovation, International Journal of Operations

and Production Management, Journal of Business and

Industrial Marketing, Measuring Business

Excellence, Bottom Line, European Journal of

Operational Research, and Government Information

Quarterly.

4 FINDINGS

Table 2 presents different capabilities that have been

discussed in data-driven value creation in the

literature. Specifically, four different capabilities

were discussed including IT capability, analytics

capabilities (big data capabilities/big data analytics

capabilities., supply chain analytics capabilities,

business intelligence capabilities, business analytics

capabilities, predictive analytics capabilities),

dynamic capabilities, and mobile social media

capabilities.

Different capabilities related to analytics

capabilities have been discussed to explain their roles

in data-driven value creation in different research

contexts, such as big data capabilities/big data

analytics capabilities., supply chain analytics

capabilities, business intelligence capabilities,

business analytics capabilities, and predictive

analytics capabilities.

Prior research have investigated the role of

different organization capabilities in data-driven

value creation in diverse contexts, and focused on the

different specific dimensions of data-driven value

creation. For instance, IT capabilities has been

suggested to be an important organizational

capability closely related to data-driven value

creation.

Borangiu and Polese (2017) highlighted the

importance of IT capabilities on IT service

management, and found that there is a significant

impact of IT capabilities on service innovation.

Nwankpa and Datta (2017) studied the relationship

between IT capabilities and firm performance with a

focus on IT capabilities and the importance of digital

business intensity (DBI). They investigated the

mediating and moderating role of DBI between IT

capabilities and firm performance in the context of

organizational investments in emerging technologies,

and found that DBI promotes firm performance.

Based on the research in the educational context,

Carillo (2017) posited that analytics skills and

capabilities should be developed for future managers

due to the shift towards data-driven business world.

He also highlighted big data analytics and data

science as important skills for future managers.

According to Gantz and Reinsel (2012), big data is

not only about data, but also includes the analysis of

data, and data science is the principle of extracting

information from data using different methods such

as data mining and other algorithms (Provost &

Fawcett, 2013).

In the context of smart services, Töytäri et al.

(2018) emphasized the importance of dynamic

capabilities as an approach to bring about a shift in

organizational mindset and capabilities while

responding to external environment, and value

communication and shared activity system were

identified as specialized and co-specialized

capabilities for smart services. Dynamic capability is

the ability of a firm to combine, build and to

restructure internal and external competences to

tackle rapidly changing environments (Teece et al.,

1997). Kunz et al. (2017) discussed the effect of big

data for both firm and customer in the context of

customer engagement, the results show that big data

analytics capabilities have a significant effect in

understanding customers, the authors also provide a

customer engagement framework using big data.

In the context of service systems, Akter et al.

(2019) provided a framework for big data analytics

based decision making which consists of six steps.

Unlike the previous studies, Urbinati et al.(2019),

focused on how big data solution providers create

value from their solutions, and identified two

innovation startegies for data-driven value creation,

namely use case-driven and process-driven strategies.

Organizational Capabilities in Data-driven Value Creation: A Literature Review

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Table 2: Capabilities in data-driven value creation, research context, method, and findings.

With regards to supply chain context, Fosso Wamba

and Akter (2019) identified three important

antecedents for a dynamic supply chain analytics

capability model which has a positive link to firm

performance, and found that supply chain analytics

capability has an interconnected process that can

produce insights for decision-making in real time

using managerial, technological, and personnel

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capabilities. In the reviewed articles, the capabilities

mentioned related to big data and data analytics can

be grouped under analytics capability, such as big

data analytics and supply chain analytics capability.

Ratia et al. (2019) investigated the role of BI

capability in decision-making. They highlighted the

use of capabilities possessed by individuals for

efficient BI tool utilization for data-driven value

creation, and found that BI capabilities can support

data-driven decision making, customer reporting and

data utilization, and new core businesses emerging

from data. Medeiros, Hoppen, Maçada (2020)

emphasized data science for business as an important

analytics capability, and suggested that analytics

capabilities can help improving data quality and

forging data-driven culture, support agile decision-

making, and dynamic capability enhancement (such

as data sharing, integrating information,

organizational learning and generation of knowledge)

and achieving competitive advantages (such as

increased productivity and business profitability).

Vidgen, Shaw and Grant (2017) defined business

analytics capability as a facilitator between the

generation of organizational data, internal and

external accesses, and the value that can be leveraged

with that data through decision making. They also

identified the management challenges with regards to

business analytics capabilities. In the context of the

healthcare, McBride et al. (2019) explored open-

government-data(OGD)-driven co-created services

and discover six factors that allow for the OGD-

driven services to take place in the form of predictive

analytics or forecasting model to create value. In the

context of on-demand services, big data was found to

enable customer agility for a governmental service

agency, and big data analytics can improve customer

agility and responsiveness; customer agility is an

organization’s dynamic capability that is vital when it

comes to customer-oriented services (Chatfield &

Reddick, 2018). Bolat, Kooli and Wright (2016)

identified mobile social media capability in value-

creation based on social media data, and found that

mobile social media capability are very important for

effective marketing and adverstising.

5 DISCUSSION AND

CONCLUSIONS

This research aims to provide understanding of the

different types of capabilities related to data-driven

value creation based on a literature review.

Prior literature indicates that different types of

capabilities are associated with data-driven value

creation, including IT capabilities, analytics

capability (big data capabilities/big data analytics

capabilities., supply chain analytics capabilities,

business intelligence capabilities, business analytics

capabilities, predictive analytics capabilities),

dynamic capabilities, and mobile social media

capability. Prior studies have investigated the

different dimensions of data-driven value in different

contexts. In the context of service design and

innovation, IT capabilities help with the development

of knowledge intensive services and service

innovation. In the context of private organizational

investments in emerging technologies, IT capabilities

play an important role in achieving organizational

performance. In educational context, analytics

capabilities add value to all businesses by creating a

data-driven mindset for managers. In the context of

smart services, effective coordination of dynamic

capabilities leads to successful adoption of smart

services. In the context of consumer engagement, big

data capabilities provide deeper understanding of

customer and contribute in motivating consumers to

engage with companies. Big data capabilities are also

helpful in decision-making in service systems In the

context of service design big data capabilities play an

important role in decision-making, quality

management and risk management. In the context of

supply chain management, supply chain analytics

capabilities positively affect firm performance. In the

context of social media, mobile social media

capabilities play important roles in creating strategic

value for social media providers by influencing

market sensing, relationship management, branding,

and content development. IT capabilities, analytics

capabilities (such as big data capabilities, supply

chain analytics capability) and dynamic capabilities

seem to be important in data-driven value creation,

and social media capabilities is also mentioned.

Clearly, different capabilities and value propositions

based on data have been validated in different

research contexts though analytics capabilities are

highlighted in data-driven value creation.

In the context of healthcare, BI capabilities are

needed to create value at different levels namely

decision making, combining data sources and

customer reporting, and for future business concept

development. Data science for business helps in

forging a data-driven culture, agile decision making,

organizational learning, increased productivity and

business profitability whereas business analytics

capabilities help create value by increasing revenues,

reducing costs, and improving service quality.

Predictive capabilities create value by predicting food

Organizational Capabilities in Data-driven Value Creation: A Literature Review

113

safety issues in the context of open government data-

driven services. In the context of government on-

demand services, the government agencies improved

service responses through use of big data analytics

capabilities. Analytics capabilities (BI capabilities,

analytical capabilities, business analytics capabilities,

predictive analytics capabilities, and big data

analytics capability) have been identified to be an

important capability in data-driven value creation in

different contexts

Prior literature highlights the importance of

analytics capability in data-driven value creation

though they differ in terms of value dimensions. This

study focuses on organizational capabilities and data-

driven value creation. There are no consistent

findings because different capabilities are related to

different dimensions of value. However, the research

findings in this study show that analytics capabilities

are the most highlighted capabilities in data-driven

value creation. The findings from the literature open

a couple of directions for future research. First, future

research can go deeply into how analytics capabilities

can help with data-driven value creation via exploring

the mechanisms or the strategies that make it work.

Second, future research can investigate what are the

basic and advanced skills needed in analytics

capabilities and the role of these skills in data-driven

value creation. Third, further study can explore

whether analytics capabilities can work with other

organizational capabilities to achieve data-driven

value creation. Fourth, there is a need for further

research on analytics capabilities to investigate the

components of analytics capabilities. Further research

in the above area will provide organizations with

more detailed practical guidelines on how to realize

data-driven value and what they should do.

Different capabilities can help with value

creation in different dimensions. Based on the

literature review we found that the research topic is

not mature, and more research should be conducted to

increase the understanding of the role of capabilities

in data-driven value creation. In addition, analytics

capabilities has been found to be important for data-

driven value creation, which indicates that data as

business resources should be applied in business for

different value based on different data analytics

techniques.

This study has its limitations. First, this study

focuses on capability in data-driven value creation.

However, there are many other factors related to data-

driven value creation, such as digitalization in

organizations, investment in data, and data-driven

organizational culture. Second, this study only found

a small number of articles in the research area, which

might limit the generalization of the research

findings. Future research can also consider expanding

the scope of databases for article search to yield a

larger number of relevant articles.

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