Int. Journal of Business Science and Applied Management, Volume 20, Issue 1, 2025

A taxonomy for data-driven business models

Payam Hanafizadeh

Department of Information Technology and Operations Management, Allameh Tabataba'i University

West End Hemmat Highway, Dehkadeh-ye-Olympic, Tehran 1489684511, Iran

Tel: +982144744372

Email: hanafizadeh@gmail.com

Reza Ashhari

Department of Technology Management and Entrepreneurship, Allameh Tabataba'i University

West End Hemmat Highway, Dehkadeh-ye-Olympic, Tehran 1489684511, Iran

Tel: +982144744372

Email: reza_ashhari@yahoo.com

Abstract

The utilization of data presents significant opportunities for enhancing business models and generating

value. Consequently, the integration of data into value creation processes has given rise to the emergence of

data-driven business models. This study aims to delineate and introduce diverse classes of data-driven

business models. Leveraging the conceptual frameworks of data value networks and ecosystem players, we

propose a theoretical foundation for understanding data-driven business models. The synthesis of these

frameworks elucidates a spectrum of activities within the value network and underscores the diverse roles of

stakeholders in extracting value from data. Employing thematic analysis, we synthesized insights from case

studies and empirical articles to develop profiles for nine distinct data-driven business models. Furthermore,

thematic analysis was employed to analyze empirical studies, facilitating detailed descriptions of each model.

Utilizing the i* ontology, we modelled the nine identified business models, providing a structured taxonomy

for classification. Additionally, we conducted a comparative analysis of our proposed taxonomy with existing

research, thereby validating its robustness and relevance. Real-world evidence was also leveraged to further

validate the applicability and efficacy of various data-driven business models.

Keywords: data-driven business model, taxonomy, data value network, ecosystem players, business

model modeling, i* ontology.

Applied Management under a Creative Commons Attribution 4.0 International Licence. Our journal is an

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Submitted: 2025-03-04 / Accepted: 2025-05-23 / Published: 2025-06-20

Payam Hanafizadeh, Reza Ashhari

1. INTRODUCTION

The rapid advancement of technologies (Barlow et al., 2007) such as the Internet of thing (IoT), cloud

computing, blockchains, social media networks, and artificial intelligence (AI) has transformed business

operations by accelerating network interactions and generating vast amounts of data. These technologies are

increasingly being used by businesses (Coursaris et al., 2008) and promise to disrupt the business models

(Upadhyay, 2024) by altering how data integrity is managed (Cunningham et al., 2025). This surge in data

availability enables organizations to process, store, and analyze information at unprecedented scales. By

leveraging these capabilities, businesses can offer innovative products and services, gaining a competitive

edge in their markets. Sorescu (2017) highlights how big data can create a sustainable competitive advantage,

as it enables organizations to make data-driven decisions that improve business performance (Jensen et al.,

2023; Kurpiela and Teuteberg, 2023). As a result, organizations have moved from intuition-based decision-

making to predictive analytics, using data to guide strategy and enhance value creation (Gokalp et al., 2022;

Kayabay et al., 2022).

One of the key ways in which organizations create value from data is through data monetization, which

involves converting data into marketable products and services (Wixom, 2014). This strategy enables

businesses to adapt to market demands, respond to customer expectations, and generate new revenue streams

(Hanafizadeh & Harati Nik, 2020). More advanced forms, such as insight monetization, combine data

analytics with expert knowledge, allowing companies to extract actionable insights that drive further value

creation (Hanafizadeh et al., 2021). These processes underscore the critical role of data in modern business

models and its potential to foster competitive differentiation.

A business model describes how a business organization creates, delivers, and captures value

(Osterwalder & Pigneur, 2010). It is a framework that explains how a business operates and sustains

profitability. The business model has a direct impact on determining the quality of the product offered (Nimfa

et al., 2021). Today, with the advent of new technologies, data has become a core resource for value creation,

giving rise to data-driven business models. These models integrate data into the value proposition, enhancing

offerings through advanced analytics and supporting better decision-making (Hartmann et al., 2016).

Organizations utilizing such models can personalize products, optimize services, and improve operational

efficiency by leveraging insights derived from data. As noted by Schüritz and Satzger (2016), data-driven

business models facilitate more efficient intra-organizational processes, leading to greater value generation.

This has led to their widespread adoption across industries, as businesses seek to fully capitalize on the

potential of their data resources (Hilbig et al., 2020).

To unlock the full value of data, organizations have to strategically analyze and integrate it into their

business models. While data itself is widely available, its competitive advantage lies in how effectively it is

analyzed and applied (Zeng & Glaister, 2018). Data-driven business models provide a framework for turning

raw data into actionable insights that enhance products, optimize services, and meet customer needs more

effectively (Hartmann et al., 2016). These models continuously extract, analyze, and utilize data, resulting in

innovative value propositions, such as personalized offerings and improved decision-making processes.

Despite the benefits of data-driven business models, many organizations struggle to fully realize their

potential. Studies show that companies often fail to leverage their data resources and analytics capabilities

effectively, resulting in missed opportunities for value creation (Günther et al., 2017). A report by Fortune

Business Insights (2022) projects the global market for data science platforms to grow significantly, yet the

percentage of companies identifying themselves as data-driven has declined (Harvard Business Review,

2019). This suggests that while investment in data-driven technologies is increasing, organizations still face

challenges in utilizing data to create meaningful value (Fruhwirth et al., 2018).

Existing research, such as Hartmann et al. (2016), has provided taxonomies for data-driven business

models, focusing primarily on startups. However, this taxonomy lacks a broader trans-organizational

perspective, limiting its applicability to larger, established companies. Hartmann et al. suggest that future

studies should incorporate data from established firms to provide a more comprehensive understanding of

how data can drive value creation. Furthermore, existing studies have not fully addressed how key activities,

such as data collection, analysis, and distribution, are integrated into business models to maximize value (Lim

et al., 2018).

This study seeks to address these gaps by developing a systematic taxonomy of data-driven business

models. Drawing from both the literature and real-world case studies, the research explores how data can be

strategically utilized to create value. The taxonomy outlines different types of data-driven business models

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and explains how each model generates value through the effective use of data. Specifically, the study

addresses the following research questions:

1. What is the taxonomy for data-driven business models?

2. How is each type of data-driven business model described?

By addressing these questions, this study contributes to a more holistic understanding of data-driven

business models, offering insights for both academia and industry. The taxonomy developed in this research

provides a practical framework for organizations seeking to refine their data strategies and a theoretical

foundation for future research on digital business model innovation.

The paper is organized as follows. Section 2 reviews the literature on data-driven business models.

Section 3 develops the conceptual model used in this research. Section 4 outlines the research methodology.

In Section 5, the study’s findings are presented, followed by theoretical and practical implications in Section

6. Finally, Section 7 concludes the paper, highlighting its contributions, limitations, and suggestions for future

research.

2. LITERATURE REVIEW

The concept of data-driven business models has gained prominence as organizations increasingly

leverage data as a core asset for value creation. Unlike traditional business models, which primarily focus on

tangible assets and predefined value chains, data-driven business models rely on data processing activities

such as aggregation, analytics, and distribution to optimize operations and enhance customer value

(Hartmann et al., 2016). While the role of data in business model transformation is widely acknowledged,

the existing literature presents fragmented perspectives, often emphasizing either industry-specific

applications, innovation potential, or theoretical classifications, rather than offering a holistic view of how

these elements interact.

In industry-specific applications, research highlights how businesses in sectors such as

telecommunications, retail, healthcare, and energy have adopted data-driven business models to improve

efficiency and customer engagement. For example, the food industry leverages multi-sided platforms and

data analytics to monitor market trends and consumer behaviour (Isabelle et al., 2020), yet key activities such

as data aggregation and distribution remain underexplored. Similarly, in the energy sector, data-driven

models emphasize revenue generation through data sales (Chasin et al., 2020) but often overlook the role of

advanced data analysis in creating sustainable value. Facilities management businesses have adopted data-

driven models with a focus on revenue strategies and data management policies (Marcinkowski & Gawin,

2020), yet key operational activities such as data analysis and distribution are inadequately addressed. The

healthcare industry, on the other hand, heavily prioritizes data analysis for predictive diagnostics (Wu &

Wang, 2023) but lacks comprehensive models that integrate upstream (data collection) and downstream (data

dissemination) processes. While these studies underscore the transformative impact of data in different

industries, they tend to isolate specific components rather than examining the full cycle of data utilization

across value networks.

Beyond industry applications, another strand of the literature focuses on the intersection between data-

driven business models and innovation. Big data, artificial intelligence, and real-time analytics are widely

recognized as key enablers of business model innovation, facilitating new forms of value creation through

data monetization, personalization, and automation (Dobni, 2010; Sorescu, 2017). However, much of this

research is firm-centric, focusing on internal innovation processes while neglecting ecosystem-wide

interactions that influence business model evolution. While studies on data-driven innovation highlight the

role of emerging technologies such as 6G for real-time data exchange (Rao, 2021) and blockchains for secure

data transactions, they rarely account for the collaborative roles of ecosystem players. Additionally, ethical

and legal concerns such as data ownership, consumer privacy, and knowledge leaks (Wiśniewski et al., 2021;

Fruhwirth et al., 2024) are often treated as peripheral issues rather than integral components shaping the

adoption and scalability of data-driven business models.

A third body of research focuses on theoretical structuring and taxonomy development, seeking to

categorize data-driven business models based on key attributes such as core activities (e.g., data aggregation,

analytics, and distribution) or data sources (e.g., user-generated, machine-generated, and open data)

(Hartmann et al., 2016). These classifications provide valuable frameworks for analyzing how businesses

create value from data, particularly in startup environments where data monetization strategies are critical.

However, existing taxonomies tend to be limited to an organizational perspective, overlooking the fact that

Payam Hanafizadeh, Reza Ashhari

many data-driven business models operate across multiple entities within digital ecosystems. Moreover,

while some models recognize the role of trans-organizational interactions (Zott & Amit, 2013), they fail to

adequately address how ecosystem players, such as vendors, developers, operators, and cooperation and

standardization fora (Pellinen et al., 2012), coordinate data activities to co-create and distribute value. A

growing body of literature suggests that data-driven business models should not be viewed in isolation but

rather as part of a broader data value network (Attard et al., 2016), where different stakeholders assume

specialized roles in the data economy.

Despite these valuable contributions, the literature still lacks an integrated framework that takes into

account both the complete lifecycle of data-driven activities (from discovery to distribution) and the dynamic

interactions between ecosystem players. While existing studies effectively analyze individual components of

data-driven business models, such as data analytics, monetization, or governance structures, few provide a

comprehensive model that synthesizes these elements into a cohesive business framework. Addressing this

gap, the present study proposes a taxonomy that aligns data value network activities with ecosystem roles,

offering a trans-organizational perspective that better reflects the distributed and networked nature of modern

data-driven business models.

2.1. Theoretical background

Organizations' use of data to create value is increasing day by day. Hence, the issue that gains importance

is how organizations create value by using data. For example, organizations can create value with the help of

data in the form of offering a new product or customizing a service for customers. The concepts of the data

value chain and the data value network explain value creation from data in a step-by-step and networked

manner, respectively. These two concepts consider data as a critical resource in business and address value

and insight creation from data. Accordingly, we examine the studies on the data value chain and data value

network to present the classification and sequence of the stages of this concept in line with the goal of our

research.

Choi et al. (2001) presented the following four activities for the data value chain in business models in

the field of traffic: 1) data collection, 2) data fusion, 3) data dissemination, and 4) end user. The data value

chain concept proposed by Faroukhi et al. (2020a) proposed four activities: 1) generation, 2) collection, 3)

analysis, and 4) exchange for the data value chain. The classifications offered by these two studies are

designed in a step-by-step manner and are sequential. In the business ecosystem, business models have a

network relationship with each other and with different players simultaneously, while the mentioned

classifications lack a network perspective. Choi et al. (2001) consider the end user component in their

classification, although it does not contain any value-creating activity related to the data.

Attard et al. (2016) proposed the concept of the data value network. They introduced the following five

activities for the data value network: 1) data discovery, 2) data curation, 3) data interpretation, 4) data

exploitation, and 5) data distribution. The classification presented by Lindman et al. (2014) for open data

value network components includes: 1) data extraction and transformation; 2) data analyzer; 3) user

experience provider; 4) commercial open data publisher; and 5) support service and consultation.

Lindman et al. (2014) presented the categorized activities for the open data value network. The activities

categorized in this paper are limited to open data. Thus, the results cannot be used for the data value network

in its general sense. On the other hand, the classification presented by Attard et al. (2016) is not limited to a

specific data type. It also involves a networked relationship among the components. The business ecosystem

has a networked nature, and the players in the ecosystem play a role in this ecosystem simultaneously and in

a networked manner. Due to the congruence between the data value network concept and the business

ecosystem concept, we do not choose the classification of activities related to the data value chain. The

classification of Attard et al. (2016) specifically refers to using data to create value. Also, the components of

this classification are activities that are consistent with each other. In this regard, we select the data value

network activities of Attard et al.'s (2016) classification as the theoretical concept.

The business model is at a level above the organization and below the ecosystem (Zott and Amit, 2013).

Most studies have examined the data-driven business model at the organizational level and lack a trans-

organizational perspective. For this reason, to have a trans-organizational view in compiling the taxonomy,

we need a trans-organizational theoretical concept. Not all business model activities are performed by the

organization that owns or designs the business model. Some of these activities, directly or indirectly, depend

upon the business ecosystem players or are even done by them. Hence, we consider players in the data-driven

business ecosystem as one of the theoretical concepts of this research. By ecosystem, we mean all players

who operate in that data-driven business model.

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The classifications related to the players and activists in the business ecosystem are described below.

Pellinen et al. (2012) placed the business ecosystem players in five roles: 1) vendor, 2) developer, 3) operator,

4) end user, and 5) cooperation and standardization fora. This classification placed the operator player in a

separate category and assigned an independent role to it. If the overlap between the two roles of vendor and

developer is considered in this classification, it can be a suitable candidate to consider different types of

players in a data-driven business model. In other words, a business model can simultaneously create value in

both roles. Lipkin and Heinonen (2022) categorized the players in the customer ecosystem as follows: 1)

focal customer, 2) focal provider, 3) other providers, 4) co-customers and peers, family and friends, and 5)

strangers. This classification is presented around the customer. Hence, it does not have roles from the business

ecosystem, such as the vendor. The WallstreetMojo website divides business ecosystem players into the

following 5 roles: 1) producers, 2) suppliers, 3) consumers, 4) competitors, and 5) government agencies. The

role of competitors in this classification can have commonalities with the role of producers. For example, a

business model can have both roles in the ecosystem. Therefore, the distinction between roles is not well

presented in this classification.

From the three classifications of ecosystem players, we have selected the first one, presented by Pellinen

et al. (2012), as our theoretical concept for separating player types in the business ecosystem. This

classification was selected because it distinguishes three ecosystem players: vendor, developer, and operator.

The three distinctive roles in this classification create value differently. In the present research, we consider

the role of players in value creation in data-driven business models to provide a taxonomy.

The end user player plays a role in all data-driven business models. This player uses the services and

products provided by organizations. Our main point in selecting the theoretical concept of business ecosystem

players is to consider the value creation method and the role of the "business model owner" in the ecosystem.

The end user player can play a role in the business ecosystem, though it is not one of the main components

or activities of the data-driven business model. Therefore, the end user player can be removed from the roles

of the selected classification. As a result, the categories of 1) vendor, 2) developer, 3) operator, and 4)

cooperation and standardization fora can be assigned to distinct player types in the business ecosystem.

Using the results of Zott and Amit (2013), which determines the levels of the organization, business

model, and ecosystem, and Pellinen et al. (2012), which categorizes ecosystem players, we can arrive at Fig.

1. The impact of the ecosystem players is not limited to the ecosystem level, and these players also participate

at the business model level, which is a subset of the ecosystem level.

Figure 1: Relation between organization, business model, ecosystem levels and ecosystem players

We synthesized two studies, i.e., Attard et al. (2016) and Pellinen et al. (2012), in order to form the

conceptual model of the study. By combining two theoretical concepts, i.e., four classifications of ecosystem

players and five categories of data value networks, we obtain a matrix with 20 cells. We intend to present a

Payam Hanafizadeh, Reza Ashhari

taxonomy for data-driven business models in this conceptual model. The resulting matrix is shown in Fig. 2.

The taxonomy is built on two axes: data value network activities and ecosystem players. The data value

network activities axis focuses on what activities are performed to extract value from data. These activities

(e.g., data discovery, curation, interpretation, exploitation, distribution) are process-oriented and describe

how data is transformed into value, regardless of who performs them. Therefore, the data value network

theory focuses on outlining the type and structure of the activities that constitute a business model to generate

value—essentially addressing the "what and how." In contrast, the ecosystem players axis identifies who is

responsible for value creation within the business ecosystem. Each player (vendor, developer, operator, etc.)

has a distinct role in the ecosystem, which may involve multiple activities. Therefore, the ecosystem player

perspective takes a deeper look by identifying the role and specific participants within the ecosystem who

engage in these activities. This perspective fundamentally answers the "who" question. According to this

conceptual model, data-driven business models are categorized based on which players are involved in their

value creation and which parts of the value network activities they cover.

Figure 2: The conceptual model for taxonomization of data-driven business models

3. METHODOLOGY

The present research is a taxonomic and descriptive study. This research used thematic analysis to

process secondary data to describe data-driven business models. To this end, first, the literature on data-

driven business models was critically reviewed (Section 2). Based on this critical review, theoretical concepts

were selected and synthesized to develop a conceptual model to classify and describe data-driven business

models. The result involved evaluating and synthesizing theoretical concepts and developing the conceptual

model of the study, as presented in Section 3. Therefore, the deductive-inductive approach was adopted for

inference due to having a conceptual model. Secondary data were analyzed and coded in two steps to develop

a taxonomy and describe data-driven business models. First, data obtained from case studies and,

subsequently, data extracted from empirical studies were used. Since data-driven value creation was realized

in case studies, their data were sufficiently reliable to develop and enhance the taxonomy. Therefore, first,

case studies were coded and thematically analyzed. The results of this analysis formed the initial taxonomy.

Then, empirical studies were coded and thematically analyzed to enhance the initial models obtained in the

previous step. To conduct thematic analysis, the Braun and Clarke (2006) method was used, and the results

obtained were based on a deductive-inductive strategy in coding and achieving themes. Finally, each type

of data-driven business model was modelled using the i* tool. To validate and determine the contribution of

the theory, the proposed taxonomy was compared with the competing study.

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3.1. Search strategy

We conducted a document search using the Scopus and Web of Science (WoS) databases in two phases:

direct and indirect searches.

In the direct search, we used the primary terms "data-driven" and "business model" to retrieve relevant

documents. The search string applied in both Scopus and WoS was:

"({data driven} AND {business model}) OR ({data driven} AND {business models}) OR ({data-driven}

AND {business model}) OR ({data-driven} AND {business models})."

This query was applied to the title, abstract, and keywords, with no time restrictions.

This initial search yielded 1,263 documents. After applying automatic filters to remove non-English

documents and non-article formats, the results were narrowed down to 517 articles. We then manually

reviewed the titles and abstracts to ensure relevance to the data-driven business model definition proposed

by Hartmann et al. (2016), while also eliminating duplicates. This process resulted in 89 articles. A full-text

review was then conducted, focusing on their relevance to value creation and the activities involved in

extracting value from data, ultimately reducing the number to 25 selected articles.

In the indirect search, we focused on technologies related to data collection and data processing, without

directly searching for the term "business model." Instead, we searched for "value creation" along with "data-

driven" and key technologies such as Internet of Things (IoT), blockchain, artificial intelligence (AI), large

language models (LLms), machine learning, deep learning, and cloud computing. The purpose of placing the

term “value creation” next to “data-driven” is to access documents that incorporate value creation with the

help of data. IoT, blockchain, and cloud computing technologies have provided access to various data for

business models and help data-driven business models in collecting data. AI, LLMs, machine learning, and

deep learning technologies also facilitate data analysis for business models and provide access to knowledge

and insight behind the data for data-driven business models. Hence, these terms have been used in the indirect

search.

Additionally, we searched for the terms "data" and "monetization," acknowledging that data-driven

business models often create value through data monetization, because one of the most accessible ways to

use data to create value in a business model is to generate revenue through the sale of data.

The search string used in both Scopus and WoS was:

"({data-driven} AND {value creation} AND ({internet of things} OR {blockchain} OR {artificial

intelligence} OR {large language model} OR {machine learning} OR {deep learning} OR {cloud

computing})) OR ({data} AND {monetization})."

This search retrieved 1,528 documents. Applying the same automatic filters as the direct search

(removing non-English documents and non-article formats), the results were reduced to 744 articles. We then

manually reviewed the titles and abstracts for the relevance to the data-driven business model definition from

Hartmann et al. (2016) and removed duplicates, leaving 202 articles. A full-text review, focused on value

creation and the role of data-driven activities, resulted in 32 selected articles.

Together, the direct and indirect searches yielded 57 articles. By conducting forward and backward

citation searches in the references of these articles, we identified an additional 4 articles, bringing the total to

61 selected articles.

3.2. Collecting case studies

After searching for articles, we searched for case studies among the selected articles in the area of data-

driven business models. These case studies include companies, products, and services that use data-driven

business models. These cases have used data-driven business models appropriately and have created value

with the help of data. Hence, after generating the conceptual model, we started developing the taxonomy

using the data from these case studies.

The result of the search among the selected articles was 10 case studies reported in 8 selected articles.

These case studies are: 1) APST, 2) SESCOM Group, 3) Suning, 4) Haier, 5) Suofeiya, 6) i-BRE, 7) BBVA,

8) DrugCo, 9) Vungle Inc., and 10) L. Vending Intelligence. A brief description of the above 10 cases is

presented in Table 1.

Payam Hanafizadeh, Reza Ashhari

Table 1: Description of cases

Case

number

Case name

Short description

APST

This is a platform that provides services to amyotrophic lateral sclerosis patients

(Fürstenau et al., 2021).

SESCOM

Group

SESCOM operates in the facility management industry and provides building

maintenance, energy efficiency management, and technical service solutions

(Marcinkowski and Gawin, 2020).

Suning

Suning Trading Group is a retailer in China that produces a wide range of

consumer electronics (Cheah and Wang 2017).

Haier

Haier is a Chinese multinational company that manufactures and distributes

home appliances (Cheah and Wang 2017).

Suofeiya

Suofeiya manufactures custom-built wardrobes and other furniture (Cheah and

Wang 2017) .

i-BRE

The i-BRE respiration mask was developed at the University of Auckland and

is a data-driven personalized wearable device (Zheng et al., 2018).

BBVA

BBVA is a financial group that has established a data science centre to

monetize its data (Alfaro et al., 2019) .

DrugCo

DrugCo is a pharmaceutical retailer in the U.S. with several thousand stores in

more than half of the U.S. states (Najjar and Kettinger 2013).

Vungle Inc.

Vungle Inc. is one of the largest mobile advertising networks (De Reyck et al.,

2017) .

L. Vending

Intelligence

L. Vending Intelligence is a Chinese company operating in the Vending

Machine industry (Wang et al., 2023) .

3.3. Analysis of case studies

In this study, the data-driven business model is specified based on the type of value network activities

and the role played by the ecosystem players. In other words, the conceptual model of this study has two

axes: business ecosystem players and data value networks. We used thematic analysis to determine the

location coordinates of each type of data-driven business model on the conceptual model. The axis of the

data value network refers to the key value-creation activities from data in the business model. The axis of the

business ecosystem players represents who the players are and how they contribute to value creation in the

business model. Three layers of thematic analysis, including global themes, organizing themes, and basic

themes for each axis of the conceptual model, are indicated in Table 4 in the Appendix.

The codes were extracted from the case studies by analyzing their text. The underlying themes were

identified by compiling relevant codes. The basic themes obtained determine which of the organizing themes

of Table 4 are related to the case study. Then, the relationship among basic, organizing, and global themes is

determined under each global theme. Finally, a thematic network for ecosystem players and the data value

network can be drawn for each case study. Examples of the codes within the text of the case studies and the

basic codes extracted from them are given in Table 5 of the Appendix. For example, the thematic network

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related to case study 2, including basic, organizing, and global themes and their relationships, is shown in

Figs. 3 and 4. The thematic networks of other case studies are also drawn similarly.

Figure 3: Result of thematic analysis on case 2 for data value network

Figure 4: Result of thematic analysis on case 2 for ecosystem players

The results of thematic analysis on the case studies, including determining the organizing themes for

each global theme, are presented in Table 6 in the Appendix.

Based on the thematic analysis of 10 case studies, the initial taxonomy of data-driven business models

was obtained in 7 types. The location of these 7 types of data-driven business models in the conceptual model

is depicted in Fig. 5. Table 6 in the Appendix also shows the relationship between the 10 case studies and 7

types of data-driven business models.

Payam Hanafizadeh, Reza Ashhari

Figure 5: Initial taxonomy of data-driven business models

3.4. Analysis of empirical studies

In order to complete the development of the taxonomy and offer a detailed description of each type of

data-driven business model, we analyzed the cumulative knowledge of empirical studies on data-driven

business models. By excluding 8 articles that involved case studies from the 61 selected articles, we obtained

53 empirical studies for analysis. The data in the articles helped us complete the initial taxonomy and provide

deep, coherent, and comprehensive narratives for each type of data-driven business model.

We also analyzed empirical articles through the thematic analysis method. This analysis was carried out

through the three layers of theme analysis presented in Table 4. Examples of the codes within the text of the

empirical articles and the basic codes extracted from them are given in Table 7.

The thematic analysis of empirical articles resulted in the classification of these articles into one of the

following two categories: 1) articles referring to one of the seven types of models obtained in the previous

step, and 2) articles pointing to a new model in the conceptual model of the present research. For example,

the thematic network of empirical articles related to type H, including basic, organizing, and global themes

and their relationships, is presented in Figs. 6 and 7. The thematic network of empirical articles related to

other types of data-driven business models was drawn up in the same way. The results obtained from the

thematic analysis of empirical studies show to which type of data-driven business model each empirical study

is related. The matrix consisting of 7 types of data-driven business models and 53 empirical articles is

presented in Fig. 19 in the Appendix.

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Figure 6: Result of thematic analysis on articles that form type H for data value network

Figure 7: Result of thematic analysis on articles that form type H for ecosystem players

The results of the thematic analysis of 53 empirical articles are as follows: 1) 9 empirical articles were

related to type A; 2) 6 empirical articles were related to type B; 3) 6 empirical articles were related to type

C; 4) 4 empirical articles were related to type D; 5) 15 empirical articles were related to type E; 6) 8 empirical

articles were related to type F; 7) 1 empirical article was related to type G; and 8) 4 empirical articles have

addressed new types of data-driven business models.

Empirical articles 1, 2, 14, and 29 did not address any of the data-driven business model types obtained

from the analysis of the case studies (The article numbers and their information are indicated in Table 7 in

the Appendix). The location of new types of data-driven business models obtained from analyzing empirical

articles on the conceptual model is described below. The data-driven business model obtained from the

analysis of two empirical articles (1 and 14) is placed on the business ecosystem players axis in the operator

section. On the other hand, this model includes the activities of data discovery, data curation, data

interpretation, and data exploitation on the data value network axis. We call this new type of data-driven

business model type H. Another data-driven business model obtained from two empirical articles (2 and 29)

Payam Hanafizadeh, Reza Ashhari

is placed on the business ecosystem players axis in the cooperation and standardization fora section. On the

other hand, this model includes the activities of data discovery, data curation, data interpretation, and data

exploitation on the data value network axis. We call this new type of data-driven business model type I. Now,

the final taxonomy of data-driven business models can be developed. By adding these two types to the

previous seven types, nine types of data-driven business models emerged as the final taxonomy. The final

taxonomy of data-driven business models is shown in Fig. 8.

Figure 8: Taxonomy of data-driven business models

3.5. Modelling data-driven business models

To shed more light on the concept of the data-driven business model, we describe each model. These

descriptions are formed by the results of thematic analysis of the case studies and empirical articles. Each

description includes value creation, key activities, and, in some cases, the technologies required to create

value. To give objectivity to the descriptions, examples are provided about the value creation and activities

of the model. Each description presents the terms and expressions taken from the thematic network, the codes

inside the text, the basic theme, the organizing theme, and the global theme in italics.

To better understand how to create and provide data-driven value, we use the i* modelling language in

describing each model. This modelling language is goal-oriented, and by depicting the resources and

activities required to achieve the desired goals, it displays the mechanism of the business model (Yu 1997).

In order to visualize using the i* modelling language, we use the Open Organizational Modeling Environment

(OpenOME) tool provided by the University of Toronto (Horkoff et al., 2011). The guide to symbols used

by the i* tool is shown in Fig. 9. Modelling was carried out using the components specified in the description

of each business model. This modelling language displays the goals, resources, and activities required to

achieve the goal. On the other hand, the agent, its role, and its boundary are determined. Then, the

relationships among the mentioned components are specified. This relationship can be dependency,

decomposition, and role indication.

Figure 9: Legend of i* modelling language

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The data for modelling each data-driven business model type with the i* modelling language is derived

from the relevant narratives. These narratives are, in turn, derived from the networks of themes and basic

codes found within the analyzed literature.

4. FINDINGS

The current research has led to the development of data-driven business models, accompanied by detailed

descriptions and modelling for each model. These descriptions were created using the thematic network

established for each model, outlining the value creation processes and corresponding value propositions.

Essential activities required to realize this value along with their players' roles are also specified. The

descriptions are presented as narratives, with themes from the thematic network highlighted in italics to

illustrate how the narratives were derived. Furthermore, to enhance the understanding of each model's

mechanisms, additional modelling has been conducted based on the descriptions. This involved identifying

goals and key activities, which subsequently facilitated the determination of the necessary key resources for

each model. Ultimately, the components of each model were synthesized and modelled using the i* language.

4.1. Narrative of type A model (Data aggregator & analyst seller & programmer):

In the Type A business model, value is created by delivering goods, services, and applications, with the

core value proposition emerging from the curation and interpretation of data in the development process.

This model emphasizes the generation of value through data-driven services. For instance, offering data-

driven solutions for machinery repair is a notable example of the value proposition within this model. The

results of this data-driven business model are illustrated in Fig. 10.

Figure 10: Type A data-driven business model depicted by the i* language

Fig. 10 depicts the Type A data-driven business model using the i* modelling language, showcasing how

value creation is achieved by providing data-driven goods, services, and applications. A key example is the

provision of data-driven services for machinery repair, aligning with the model's overarching goals.

According to Fig. 10, the following critical resources support these goals:1) Data analysis capabilities, such

as data scientist expertise; 2) Data analysis technologies, like AI; 3) Data curation capabilities, such as SQL

expertise; 4) Data curation technologies, such as cloud infrastructure; and 5) Necessary data, such as

machinery data from production lines.

As outlined in Fig. 10, the model’s key activities include: 1) Data discovery; 2) Data curation; 3) Data

interpretation; and 4) Exploitation of curated and interpreted data to create value.

Payam Hanafizadeh, Reza Ashhari

The data discovery process begins by identifying data sources aligned with the value proposition,

followed by categorizing and collecting the data from both internal and external organizational sources. For

example, external data might include customer feedback or market trends. The literature reports the use of

IoT sensors and Google Trends for data discovery in this model, with one example being the detection of

production line machine performance via sensor data.

In the data curation phase, redundant, duplicate, and outlier data are removed, and relationships between

data points are established, leading to data integration. The use of the SQL programming language and

Microsoft Excel for data curation in this model is well-documented. A practical example includes sorting

production line machinery vibration data over time.

Data interpretation begins with selecting the relevant curated data, followed by analyzing it to extract

tacit knowledge, which transforms the data into information, knowledge, and wisdom. The literature

highlights the use of RapidMiner, Google Analytics, and programming languages such as R and Python in

this phase. Identifying patterns in machinery repair is an example of data interpretation in this model.

As shown in Fig. 10, the results of data curation and interpretation are then applied in the data

exploitation phase, where they are used to create value. Examples include: 1) Delivering data-driven services

for machinery repair; 2) Providing tailored responses to customer inquiries; 3) Offering data visualization

services. Tools like Tableau and Microsoft Power BI are frequently used for visualization in this model.

In the broader business ecosystem, entities leveraging this model position themselves as vendors and

developers. They contribute to the data value network by engaging in key activities: data discovery, data

curation, data interpretation, and data exploitation.

4.2. Narrative of type B model (Data analyst seller & programmer):

In the Type B model, value provision is achieved through the delivery of goods, services, and

applications. The processes of data curation and interpretation involved in developing these offerings

generate value within this business model and establish the core value proposition. Examples of value

provision within this model include: 1) customized advertising services on mobile phones, 2) organizational

applications tailored to the specific characteristics of the organization, and 3) pop-up website advertising

services. The modelling of this data-driven business model is illustrated in Fig. 11.

Figure 11: Type B data-driven business model depicted by the i* language

As described, the primary objective of this model is value creation. As illustrated in Fig. 11, the model

aims to deliver data-driven goods, services, and applications, with pop-up website advertising services

serving as a key example of these objectives. According to Fig. 11, the following essential resources are

utilized to fulfill these goals: 1) data analysis capabilities, including data scientist experts; 2) data

analysis technologies such as artificial intelligence (AI); 3) data curation capabilities, exemplified by SQL

experts; 4) data curation technologies, including cloud solutions; and 5) necessary data, such as search data

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generated from online store websites. The key activities of this model that contribute to achieving these

objectives include: 1) data curation, 2) data interpretation, and 3) the exploitation of the outcomes of data

curation and interpretation to create value.

For effective data curation, the initial step involves the removal of outliers, duplicates, and redundant

data, followed by data processing. The literature highlights the use of cloud technology and Microsoft Excel

as valuable tools for data curation. An example of data curation within this model includes organizing

search terms utilized by customers on the store website based on time.

In the realm of data interpretation, the necessary segments of the curated data are first selected and then

analyzed to extract tacit insights. This analytical process transforms raw data into actionable information,

knowledge, and wisdom. The literature presents experiences involving tools such as RapidMiner, Google

Analytics, and various IoT and AI technologies for effective data interpretation. A salient example of data

interpretation in this model is identifying emerging customer needs based on search statistics for specific

items on the store's website.

As depicted in Fig. 11, the outcomes derived from data curation and interpretation are instrumental in

generating value during the data exploitation phase. Applications of these data

curation and interpretation results include: 1) customized products tailored for customers, and 2)

personalized advertising services. Several platforms that facilitate ad and content customization are 1) Proof,

2) Sender, 3) Clearbit, and 4) Nosto.

In the broader business ecosystem, stakeholders employing this model generate value both as vendors

and developers. These organizations engage in a range of activities to create value within the data value

network, including 1) data curation, 2) data interpretation, and 3) data exploitation.

4.3. Narrative of type C model (Data aggregator & organizer seller & programmer):

The Type C business model focuses on delivering value through the provision of goods, services, and

applications. This value is generated from the processes of data curation and interpretation employed in the

development of these offerings, forming the core value proposition of the model. Notable examples of value

provision within this model include: 1) the delivery of smart goods, and 2) the provision of smart services.

A visual representation of this data-driven business model is presented in Fig. 12.

Figure 12: Type C data-driven business model depicted by the i* language

The primary objective of this model is value creation, as indicated in Fig. 12, which illustrates the goal

of providing data-driven goods, services, and applications. An example of this objective is the development

of smart goods. To achieve these goals, the key resources identified include: 1) data curation capabilities,

exemplified by SQL experts; 2) data curation technologies such as cloud computing; and 3) essential data

inputs, including patient health data. The principal activities integral to realizing these objectives consist of

1) data discovery, 2) data curation, and 3) the exploitation of curated and interpreted data to generate value.

Payam Hanafizadeh, Reza Ashhari

The foundational activity of data discovery may be facilitated through digitization, which can be applied

to both customer and supplier data. The literature reports instances of leveraging IoT tools for this purpose,

such as utilizing IoT sensors to gather temperature and air pressure data.

Data curation encompasses several stages: initially, the data is stored, followed by the removal of

outliers, duplicates, and any redundant information. Subsequently, data processing is conducted. The

literature highlights experiences with using the SQL programming language, Microsoft Excel, and cloud

technologies for effective data curation. In the healthcare domain, specific examples of data curation include

establishing the relationship between decreases in oxygen levels and variations in heart rates.

As depicted in Fig. 12, the outcomes of data curation and interpretation are pivotal to value

creation within the data exploitation phase. Applications derived from these results comprise: 1) health

services provided through smart masks that monitor oxygen levels, 2) smartwatches that assess heart rates,

and 3) electric anklets tracking geographical movement.

Within the broader business ecosystem, stakeholders utilizing this model play dual roles as both vendors

and developers. These organizations engage in various activities to create value within the data value

network, including 1) data discovery, 2) data curation, and 3) data exploitation.

4.4. Narrative of type D model (Data aggregator & analyst programmer):

The Type D model generates value through the provision of services and applications. The results of

data curation and interpretation in developing these services and applications are pivotal in creating value

within this business model and shaping its value proposition. Examples of value provision in this model

include: 1) offering healthcare services to specialized patients; 2) delivering customized accounting

applications for organizations; and 3) providing tailored financial consulting services to Very Important

Person (VIP) clients in the brokerage sector. The modelling outcomes of this data-driven business model are

illustrated in Fig. 13.

Figure 13: Type D data-driven business model depicted by the i* language

As indicated, the primary goal of this model is value creation. Fig. 13 demonstrates that the model aims

to deliver data-driven services and applications, with customized accounting applications for organizations

serving as one illustration of achieving its objectives. According to Fig. 13, several key resources are critical

for realizing these goals: 1) data analysis capabilities, such as data scientists; 2) data analysis technologies,

including artificial intelligence (AI); 3) data curation expertise, such as SQL professionals; 4) data curation

technologies, like cloud infrastructure; and 5) essential data, encompassing the financial and accounting

transactions of both real and legal customers. The key activities that facilitate the accomplishment of these

goals, as depicted in Fig. 13, are 1) data discovery, 2) data curation, 3) data interpretation, and 4) the

exploitation of results from data curation and interpretation for value creation.

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To execute the key activity of data discovery, the necessary data sources aligned with the value

proposition are first identified. These sources are subsequently categorized, followed by a comprehensive

collection of the data. For instance, the data collected within a healthcare provider organization may include:

1) heart rate, 2) blood pressure, 3) blood sugar levels, and 4) cholesterol levels. In this model, IoT

sensors play a significant role in facilitating data discovery.

In the data curation process, initial steps involve removing outliers, duplicates, and redundant data.

Afterwards, the relationships among the data are assessed, followed by the integration of this information.

The literature indicates that proficiency in the SQL programming language and Microsoft Excel is integral to

effective data curation. For example, data curation activities may include creating graphs over time to

visualize a customer’s blood sugar and cholesterol levels.

Regarding data interpretation, the relevant segments of the curated data are selected for analysis. This

analysis aims to derive tacit insights from the data, transforming it into information, knowledge, and

ultimately, wisdom. Reported in the literature as effective tools for data interpretation within this model

are RapidMiner, Google Analytics, and various big data and AI technologies. An example of data

interpretation includes comparing a patient's blood test indicator graphs with those of a healthy individual to

aid in disease diagnosis.

As shown in Fig. 13, the insights obtained from data curation and interpretation are instrumental in

the data exploitation activity for value creation. Examples of the application of these insights include: 1)

delivering customized healthcare services, 2) tailoring organizational applications, and 3) providing

personalized investment packages.

Within the business ecosystem, organizations utilizing this model contribute value as developers. These

entities engage in the following activities to stimulate value creation within the data value network: 1) data

discovery, 2) data curation, 3) data interpretation, and 4) data exploitation.

4.5. Narrative of type E model (Data aggregator, analyst & disseminator seller):

In the Type E model, value provision is achieved through the delivery of goods, services, and data

packages. The processes of data curation and interpretation involved in developing these offerings generate

value, which in turn defines the value proposition of this business model. Examples of value provision within

this model include: 1) the supply of data packages by insurance companies, 2) the provision of financial

consulting services to VIP clients by banks, and 3) the delivery of investment consulting services by financial

firms. The outcomes of this data-driven business model are illustrated in Fig. 14.

Figure 14: Type E data-driven business model depicted by the i* language

As illustrated, value creation is the primary objective of this model. Fig. 14 demonstrates that the model

is designed to deliver data-driven goods, services, and data packages. An example of value creation within

Payam Hanafizadeh, Reza Ashhari

this context is the provision of financial consulting services tailored for VIP clients. According to Fig. 14,

several key resources are essential for achieving the objectives of this business model: 1) data analysis

capabilities, such as data scientists; 2) data analysis technologies like AI; 3) data curation expertise,

including SQL specialists; 4) data curation technologies, such as cloud computing; and 5) requisite data,

including information related to the country's economic indices. The key activities necessary to realize the

objectives of this model include: 1) data discovery; 2) data curation; 3) data interpretation; 4) the

exploitation of data curation and interpretation results to generate value; and 5) data distribution.

To effectively conduct the key activity of data discovery, the relevant data sources corresponding to

the value proposition are first identified. These sources are subsequently categorized, and the data is

collected. Such data may originate from either internal or external sources of the organization, with examples

including external data that captures customer opinions and market trends. Notably, the literature has

documented the use of IoT sensors and tools like Google Trends for data discovery in this context. Specific

instances of data discovered in this model could include financial indicators, such

as unemployment and inflation statistics.

The curation of data involves an initial step of data storage, followed by the removal

of outliers, duplicates, and redundant entries. Finally, data processing occurs. In the context of this model,

the literature highlights the use of SQL programming and Microsoft Excel as pivotal tools for data curation.

Examples of data curation in this model include analyzing the relationship between financial data, such as

the correlation between inflation rates and changes in the national stock market index.

For data interpretation, the relevant portions of the curated data are first selected for analysis. This

analytical process is aimed at extracting tacit knowledge, resulting in the transformation of data into

actionable information, knowledge, and insights. The literature has documented the application

of blockchain, machine learning, and AI technologies in this aspect of data interpretation. An illustrative

example of data interpretation within this model is the analysis of financial data, such as the stock market

index, to forecast its trends.

In the data exploitation phase, as represented in Fig. 14, the results from data

curation and interpretation are utilized to create value. Examples of applying these results include: 1)

tailored consulting services and 2) data visualization services, with tools like Tableau and Microsoft Power

BI being recommended for visualization purposes.

To execute the critical activity of data distribution within this model, the findings from data

curation and interpretation are employed to prepare comprehensive data packages. Significant activities

associated with data distribution in this business model encompass: 1) selling data packages to clients; 2)

offering complimentary data packages for public use; and 3) distributing data packages among supply chain

partners.

In the broader business ecosystem, participants employing this model act as vendors that facilitate value

creation within the data value network. These organizations engage in a series of activities, including 1) data

discovery; 2) data curation; 3) data interpretation; 4) data exploitation; and 5) data distribution.

4.6. Narrative of type F model (Data aggregator & analyst seller):

In Model F, value provision is conceptualized as the delivery of goods and services. The outcomes

of data curation and interpretation in the development of these goods and services generate value within this

business model and establish the value proposition. Notable examples of value provision under this model

include: 1) the provision of smart home appliances, 2) the delivery of customized furniture, and 3) the supply

of smart electronic devices. The modelling outcomes of this data-driven business model are illustrated in Fig.

15.

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Figure 15: Type F data-driven business model depicted by the i* language

As depicted, the primary objective of this model is value creation. Specifically, as illustrated in Fig. 15,

the overarching ambition is to deliver data-driven goods and services. The provision of customized

furniture serves as a pertinent illustration of this objective. According to Fig. 15, several key resources are

essential for achieving these goals: 1) data analysis capabilities, including expertise from data scientists;

2) data analysis technologies, such as AI; 3) data curation capabilities, exemplified by SQL experts; 4) data

curation technologies, such as cloud computing; and 5) pertinent data, including information related to

customer experiences with the products offered.

As indicated in Fig. 15, the key activities integral to this model that contribute to goal realization include:

1) data discovery, 2) data curation, 3) data interpretation, and 4) the exploitation of insights derived from

data curation and interpretation processes for value creation.

To execute the key activity of data discovery, relevant data sources pertaining to the value

proposition are initially identified. These sources are subsequently categorized before collecting the data.

Illustrative examples of collected data within this business model include: 1) customer opinion

data, 2) customer needs data, and 3) customer purchase behaviour data.

In the process of data curation, initial steps involve the removal of outliers, duplicates, and redundant

data. The curated data is then processed and integrated. The literature highlights the use of the SQL

programming language and Microsoft Excel for data curation in this model. One specific instance of data

curation involves organizing customer needs data chronologically.

For data interpretation, the required segments of the organized data are selected and analyzed. The

objective of data analysis is to extract tacit insights from the dataset. The literature documents the use of

blockchain, big data, IoT technologies, and the Python programming language in data interpretation. A

specific example of data interpretation within this model includes identifying patterns in the

evolving customer needs over time.

As outlined in Fig. 15, the outcomes of data curation and interpretation are leveraged in

the exploitation phase to create value. Examples of the application of these results include: 1) the provision

of smart goods, 2) the delivery of customized goods, and 3) the offering of customized services.

Within the business ecosystem, participants employing this model function as vendors. These

organizations engage in several activities to generate value within the data value network: 1) data

discovery, 2) data curation, 3) data interpretation, and 4) data exploitation.

4.7. Narrative of type G model (Data analyst & disseminator seller):

In the Type G model, value creation is achieved through the provision of data-driven goods, services,

and data packages. The outcomes of data curation and interpretation involved in developing these offerings

Payam Hanafizadeh, Reza Ashhari

generate the value that constitutes the core of the value proposition. Illustrative examples of value provision

within this model include: 1) offering data packages to suppliers and 2) selling data packages to

organizations. The modelling results of this data-driven business model are depicted in Fig. 16.

Figure 16: Type G data-driven business model depicted by the i* language

As outlined, value creation is the primary objective of this model. Based on Fig. 16, the model aims to

provide comprehensive data-driven goods, services, and data packages. The sale of data

packages exemplifies one of these objectives. According to Fig. 16, the following key resources are

instrumental in achieving these goals: 1) data analysis capabilities, including data scientists; 2) data

analysis technologies, such as artificial intelligence (AI); 3) data curation capabilities, exemplified by SQL

experts; 4) data curation technologies like cloud computing; and 5) necessary datasets, including warehouse

inventory data. Fig. 16 illustrates how the key activities facilitating the realization of these objectives

encompass: 1) data curation; 2) data interpretation; 3) the exploitation of data

curation and interpretation outcomes to create value; and 4) data distribution.

To effectively curate the data, initial steps include the removal of outliers, duplicates, and redundant

entries. Subsequently, the relationships within the data are identified, and integration occurs. The literature

highlights the practical application of cloud technologies within this model of data curation. For instance, an

example of this process is the creation of time graphs representing warehouse inventory for each type of raw

material.

When interpreting the data, the first step involves selecting the relevant portion of the curated

information, followed by a thorough analysis. The aim of this data analysis is to extract tacit knowledge. The

outcomes of this analysis result in the transformation of data into information, knowledge, and wisdom. The

literature also references the utilization of tools such as RapidMiner, Google Analytics, and programming

languages like R and Python for data interpretation. For example, sales data may be analyzed to assess

income status or patterns of raw materials consumption can be identified.

According to Fig. 16, the results of data curation and interpretation are pivotal in the value creation

process during the data exploitation activities. Examples of how these results are applied include: 1) ensuring

continuous operation of a production line due to adequate warehouse inventory levels, and 2) delivering retail

services without item shortages in stores.

To execute the crucial activity of data distribution within this model, the outcomes of data

curation and interpretation are utilized to prepare data packages. Notable examples of this key activity

include: 1) selling data to governmental organizations and 2) offering various data packages to private

entities.

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Within the broader business ecosystem, players employing this model function as vendors, providing

value. These organizations participate in activities that contribute to value creation within the data value

network, specifically: 1) data curation; 2) data interpretation; 3) data exploitation; and 4) data distribution.

4.8. Narrative of type H model (Data aggregator & analyst operator):

In the Type H model, value creation is realized through the provision of operator services. The outcomes

of data curation and interpretation in the development of these services generate the value that forms the

core value proposition. The provision of mobile network operator services serves as a pertinent example of

value provision within this model. The modelling results of this data-driven business model are illustrated in

Fig. 17.

Figure 17: Type H data-driven business model depicted by the i* language

As described, value creation is the primary objective of this model. According to Fig. 17, the purpose of

this model is to deliver operator services, with the provision of mobile network operator services

exemplifying one of these objectives. The following key resources are essential for achieving these goals:

1) data analysis capabilities, such as data scientists; 2) data analysis technologies, including artificial

intelligence (AI); 3) data curation capabilities, represented by SQL experts; 4) data curation technologies

such as cloud computing; and 5) vital data, including data on the conversation duration of mobile phone

subscribers. Fig. 17 indicates that the key activities driving the realization of these objectives encompass:

1) data discovery; 2) data curation; 3) data interpretation; and 4) the exploitation of data

curation and interpretation results to create value.

To effectively execute the key activity of data discovery, necessary data sources corresponding to the

value proposition are first identified. These sources are subsequently categorized, and the data is collected.

Examples of the data gathered in this model include: 1) the number of households in each district of the city,

and 2) the duration of mobile phone conversations by customers. In this business model, IoT sensor data is

utilized for data discovery.

During the data curation process, outliers, duplicates, and redundant data are removed. Following this,

the relationships among the data are determined, and the data is processed. The literature reports the

application of IT tools, including cloud technologies, in this model's data curation. Among the illustrative

examples of data curation in this model is the creation of geographical maps depicting the density of mobile

phones connected to the operator.

To interpret the data, the relevant portion of the curated dataset is first selected and analyzed. This data

analysis aims to extract tacit knowledge from the data. The use of IoT and machine learning tools for data

interpretation is documented in the literature. For instance, prioritizing city locations for increasing the

number of cell phone operator towers serves as an example of data interpretation in this model.

Payam Hanafizadeh, Reza Ashhari

According to Fig. 17, the results obtained from data curation and interpretation are leveraged to create

value in the data exploitation activities. Examples of applications utilizing the results of data

curation and interpretation include: 1) identifying optimal locations for installing new cell phone network

antenna towers, and 2) offering tailored Internet and conversation packages to customers.

Within the broader business ecosystem, players utilizing this model function as operators, providing

substantial value. These organizations engage in activities that contribute to value creation within the data

value network, specifically: 1) data discovery; 2) data curation; 3) data interpretation; and 4) data

exploitation.

4.9. Narrative of type I model (Data aggregator & analyst regulator):

In the Type I model, value is generated through the provision of management services and the

compilation of regulations. The outcomes of data curation and interpretation involved in delivering these

services contribute to value creation within this business model and define the value proposition. Notable

examples of value delivery under this model include: 1) waste management services and 2) urban wastewater

management services. The modelling results of this data-driven business model are illustrated in Fig. 18.

Figure 18: Type I data-driven business model depicted by the i* language

As highlighted, value creation is the primary objective of this model. Fig. 18 indicates that the core aim

is the provision of management services and the compilation of regulations. Urban wastewater management

services exemplify such objectives. According to Fig. 18, several key resources are essential for achieving

these objectives: 1) data analysis expertise, such as data scientists; 2) data analysis technologies, including

artificial intelligence (AI); 3) data curation proficiency, like SQL experts; 4) data curation technologies,

such as cloud solutions; and 5) essential data, including information on the water consumption of residents

in each city district. The key activities that facilitate the realization of these goals encompass 1) data

discovery, 2) data curation, 3) data interpretation, and 4) the exploitation of data curation and interpretation

results to create value.

To initiate the key activity of data discovery, the required data sources relevant to the value proposition

are first identified. These sources are then categorized, followed by the collection of data. Examples of data

collected under this model include: 1) the volume of water consumed by residents in each city district and 2)

the deterioration of urban wastewater pipes. This business model utilizes data from governmental agencies

and Internet of Things (IoT) sensors for comprehensive data discovery.

In the data curation process, the initial steps include the removal of outliers, duplicates, and redundant

data, followed by the determination of relationships among the data and subsequent processing. The literature

indicates that experience with cloud technology is integral to effective data curation. An illustrative example

in this model involves sorting data related to wastewater pipe pressure over time.

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For data interpretation, the necessary segments of curated data are selected and subjected to detailed

analysis. This analysis aims to extract tacit knowledge from the data, resulting in valuable predictive and

prescriptive analytics. The literature reports the application of IoT and machine learning tools for effective

data interpretation. Notable examples within this model include: 1) identifying patterns for the replacement

and expansion of wastewater pipes and 2) establishing schedules for municipal garbage bin emptying.

During the data exploitation phase, as depicted in Fig. 18, the insights derived from both data curation

and interpretation are utilized to generate value. Examples of applying these insights include: 1) managing

the repair of leaking wastewater pipes and 2) drafting regulations concerning wastewater pipe replacements.

Within this business ecosystem, stakeholders acting as regulators capitalize on this model. These

organizations undertake a series of activities to foster value creation within the data value network, which

includes: 1) data discovery, 2) data curation, 3) data interpretation, and 4) data exploitation.

5. DISCUSSION

5.1. Theoretical implications

This section discusses the theoretical contributions of the present study by juxtaposing its findings with

those of Hartmann et al. (2016), whose taxonomy of data-driven business models in startups serves as a well-

established reference point. The framework developed by Hartmann et al. is structured along two dimensions:

key activity and key data source. In contrast, the taxonomy introduced in this study employs the dimensions

of the data value network and ecosystem players, offering a broader and more integrated view of value

creation in data-driven business models. Table 2 provides a side-by-side comparison of the two taxonomies.

Table 2: Comparison of the taxonomy proposed by the present study with the taxonomy

developed by Hartmann et al. (2016)

Taxonomy

proposed by the

present study

Taxonomy

developed by

Hartmann et al. (2016)

Data axis

Data value

network

Key activity

Components

of the data axis

Data discovery

Data curation

Data

exploitation

Data

interpretation

Data

distribution

Aggregation

Analytics

Data generation

The second

axis

Ecosystem

players

Key data source

Components

of the second axis

Vendor

Developer

Operator

Cooperation

and standardization

fora

Freely available

Customer-provided

Tracked &

generated

Items that

generate

taxonomies

Cases &

Articles

Cases

Payam Hanafizadeh, Reza Ashhari

Limitations

on the items that

generate

taxonomies

No special

limitation

Related to start-up

Number of

final types

Trans-

organizational

view

Yes

Unlike Hartmann et al.’s framework, the present study does not confine its analysis to startups and

incorporates empirical articles in addition to case studies. The use of the data value network as a foundational

concept introduces a networked perspective that more effectively captures the mechanisms of value creation

in modern digital business environments. Furthermore, by incorporating the concept of ecosystem players,

the proposed taxonomy accounts for the distributed nature of organizational roles within broader business

ecosystems, offering a trans-organizational perspective that was absent in earlier work.

The taxonomy developed by Hartmann et al. identifies six types of data-driven business models,

including models focused on aggregation, analytics, and data generation. In comparison, the current study

proposes nine types of data-driven business models, which encompass all six identified by Hartmann et al.

and extend the framework through the addition of five new types. These additional types introduce

combinations of roles and activities not previously addressed, particularly those that integrate data discovery

and interpretation as simultaneous core activities. While four of the nine types align closely with Hartmann

et al.’s categories in terms of key activities, the expanded classification in this study offers greater conceptual

depth and empirical detail.

The present taxonomy captures the full spectrum of the data value network, including data discovery,

curation, exploitation, interpretation, and distribution. It also reflects the roles played by various ecosystem

actors such as vendors, developers, operators, and standardization bodies. This comprehensive view allows

for a richer understanding of the structural and functional characteristics of data-driven business models.

Moreover, the inclusion of empirical articles alongside case studies has enabled the development of deeper

descriptions of each model type, clarifying the specific mechanisms of value creation and the

interrelationships between roles and activities.

5.2. Managerial implications

As discussed in the introduction and literature review sections, limited studies have so far been conducted

to help organizations with data-driven business models. However, in these studies, data-driven value creation

factors and their role in the business models of organizations have not been investigated (Lim et al., 2018).

Companies are prone to failure in using data-driven business models (Forbes, 2022). Finally, it can be stated

that using data-driven business models for organizations is still a challenge (Fruhwirth et al., 2018).

By describing each type of data-driven business model, the results of this research will lead to a better

understanding of these models for organizations. Describing and modelling the 9 types of models and

explaining the difference between them enhances managers’ knowledge of their proper applications.

Organizations need to have an adequate understanding of data-driven business models in order to use them.

Organizations can use the appropriate type of data-driven business models according to their resources,

capabilities, and value-creation methods. Also, according to the description given for each data-driven

business model, managers can strengthen the key activities of the organization in order to create value through

data.

The effectiveness of the nine data-driven business model types is contingent upon the specific context

and organizational objectives. Based on the taxonomy presented, the selection of the most appropriate model

depends on several factors, including the activities encompassed within the value network and the players

involved in the value creation process within the ecosystem. Therefore, it is not possible to provide a general

recommendation for using these 9 types of business models, because each one is designed to meet distinct

needs and suit various environments.

Int. Journal of Business Science and Applied Management / Business-and-Management.org

In the following, we elaborate on some cases of organizations that use data-driven business models.

Then, according to the key activities of the organization and its role in the business ecosystem, we determine

which type of business model is used.

Caterpillar uses data from embedded sensors to develop maintenance schedules. The company uses data

analytics to maximize the lifespan and efficiency of the equipment deployed (Schaefer et al., 2017). This

company sells products and provides services in its business ecosystem. Therefore, it can be said that it

creates value in the role of the vendor. On the other hand, this company discovers, curates, and interprets data

in its key activities and uses the results in developing its products. Therefore, it can be claimed that this

company uses the type F data-driven business model.

BuzzFeed is an American Internet, news, and entertainment media company focused on digital media.

This company has performed successfully by collecting viral content data and using them for content ideation.

Furthermore, it uses content data generated by customers as a key resource in its business (De-Lima-Santos

and Zhou, 2018). It provides its media services to the customer. On the other hand, since it serves on an

Internet platform, it also has the developer role. As a result, BuzzFeed creates value in its business ecosystem

in two roles: vendor and developer. As noted, this company discovers, curates, and interprets data and uses

its results in its products. Thus, it can be said that the data-driven business model used in this company is

type A.

The Italian tyre manufacturer Pirelli has installed sensors on car tyres to collect data. These sensors are

able to record pressure, temperature, and wear statistics to monitor the condition of each tyre. These data are

used to improve the tyre design as well as create value by selling maintenance solutions aimed at minimizing

vehicle downtime (Schaefer et al., 2017). This company sells tyres and provides tyre-related services. It also

creates value by providing data-driven services in the developer role. Therefore, this company performs value

creation in the two roles of vendor and developer in the business ecosystem. The company discovers and

curates data in its key activities and uses the results to develop its products. As a result, Pirelli uses a type C

data-driven business model.

Nettavisen, a Norwegian online newspaper publisher, analyzes a large amount of data on users' web

browsing and purchasing habits, resulting in increased advertising and e-commerce revenues for the company

(Zaki et al., 2016). This company offers its online products to customers. Also, since it serves on an Internet

platform, it plays the developer role. As a result, Nettavisen creates value in its business ecosystem in two

roles: vendor and developer. As noted, this company discovers, curates, and interprets data in its key activity

and uses its results to increase revenue. As a result, the data-driven business model used in this company is

type A.

General Electric, which also operates in the oil and gas sector, embeds sensor technology in all its

equipment and enables them to communicate with the cloud. By curating sensor data, the company increases

the productivity of its equipment and thus creates value (Schaefer et al., 2017). This company sells products

as a vendor in the business ecosystem. It also creates value in the role of developer to deliver data-driven

products. Therefore, it creates value in two roles: vendor and developer. The company discovers and curates

data in its key activities and uses the results to develop its products. As a result, the General Electric company

uses a type C data-driven business model.

All these companies, successful in using data-driven business models, have been able to perform their

key activities appropriately to become data-driven and create value due to their appropriate knowledge and

understanding of these business models.

In this research, 9 data-driven business models have been described. Based on the industry and area in

which the case studies and empirical articles are conducted, a table of industries and fields in which each

model is used was developed in Table 3. This table can help organizations develop a suitable data-driven

business model.

Table 3: Industries and fields where the 9 described business models are used

Type

Industries and fields

• Facility management industry (Marcinkowski and

Gawin, 2020)

• Automotive industry (Kaiser et al., 2021)

• Energy industry (Chasin et al., 2020)

• The healthcare industry (Firouzi et al., 2020)

Payam Hanafizadeh, Reza Ashhari

• Consumer privacy (Wiśniewski et al., 2021)

• Monetizing personal data (Bataineh et al., 2021)

• Healthcare industry (Fürstenau et al., 2021)

• Food industry (Isabelle et al., 2020)

• Automotive industry (Bergman et al., 2022)

• Vending machine industry (Wang et al., 2023)

• Retail industry (Najjar and Kettinger, 2013)

• Mobile network operator (Hmoud et al., 2017)

• 6G (Rao, 2021)

• Drainage monitoring (Maddileti et al., 2019)

6. CONCLUSION

In this research, we have classified data-driven business models with the help of the concepts of data

value networks and ecosystem players. The result of this taxonomy development is 9 types of data-driven

business models, which are: 1) type A (data aggregator & analyst seller & programmer), 2) type B (data

analyst seller & programmer), 3) type C (data aggregator & organizer seller & programmer), 4) type D (data

aggregator & analyst programmer), 5) type E (data aggregator, analyst & disseminator seller), 6) type F (data

aggregator & analyst seller), 7) type G (data analyst & disseminator seller), 8) type H (data aggregator &

analyst operator), 9) type I (data aggregator & analyst regulator).

We then described each of the 9 types of data-driven business models. This description includes the

identification of the main components of the model, the key activities, and resources required by each model.

Technologies used in these business models to perform key activities were also proposed. How to create

value in each type of business model and the role of data in their value proposition was also explained.

Considering that data-driven business models are among the new research topics, the range and diversity

of the case studies are among the limitations of this research. Also, using only published literature data is

another limitation of this research. This study suffered from limitations related to the thematic analysis

method, such as the generic and corpus-based nature of the information obtained in this research. The text

may not accurately express the author's intended meaning. In the thematic analysis, the role of context,

particularly in data aggregation, was not investigated. In other words, socio-cultural factors, values, and the

regulations that affect the formation of data-driven business models were not taken into consideration.

Therefore, it is recommended that future studies consider the human, social, and cultural considerations of

different contexts.

Not all types obtained may be relevant to a specific industry, and the taxonomy may be subject to

modifications during its customization for that sector. Consequently, for a more accurate examination of

various industries, it is advisable for researchers to focus their studies on a single industry and create a tailored

taxonomy for that context. Therefore, future researchers are recommended to examine the proposed

taxonomy in different industries to customize it according to the characteristics and limitations of that

industry. This customization may be done through interviews with industry experts.

List of abbreviations:

AI: Artificial Intelligence

BI: Business Intelligence

DDV: Data-Driven Value

DDBM: Data-Driven Business Model

BICC: Business Intelligence Competency Center

HBR: Harvard Business Review

IoT: Internet of Things

MNO: Mobile Network Operator

OpenOME: Open Organizational Modeling Environment

SQL: Structured Query Language

VIP: Very Important Person

WoS: Web of Science

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Int. Journal of Business Science and Applied Management / Business-and-Management.org

APPENDIX

Table 4: Layers of thematic analysis

Global

theme

Ecosystem players

Data value network

Organizing

theme

• Vendor

• Developer

• Operator

• Cooperation and

Standardization fora

• Data discovery

• Data curation

• Data exploitation

• Data interpretation

• Data distribution

Basic

theme

Retrieved from codes

inside the text

Retrieved from codes

inside the text

Table 5: Examples of codes within the text of case studies and their related basic codes

Case

study

Source

Text codes

Related basic

codes

SESCO

M Group

Marcinkowski

& Gawin,

(2020)

“The transformation of such data into

knowledge and wisdom may constitute a new

source of income.”

Data

transformation

“With over a decade-long experience, the

company focuses on its proficiency in

delivering reliable building maintenance,

energy efficiency Facility management

industry management, and technical service

solutions to its European business partners.”

Service

delivery,

Technical

service

solutions

Vungle

Inc.

De Reyck et

al., (2017)

“The advent of big data has created

opportunities for firms to customize their

products and services to unprecedented levels

of granularity.”

Customization

“It incorporates a user-specific, real-time

procedure that strikes a balance between

sending a large variety of ads and sending ads

of high quality.”

User-specific

i-BRE

Zheng et al.,

(2018)

“The massive sensing data (e.g. PM 2.5) in

the cloud can be composited to generate new

service packages (e.g. air pollution

distribution) as well (online service

innovation)”

Service

package

generation

“The digitalization of users, things,

manufacturers, and service providers in the

cloud-based environment also provides

potential service innovation opportunities.”

Digitization

APST

Fürstenau et

al., (2021)

“This setting calls for digital platforms to

optimize care coordination and research, as

they can facilitate exchanges between these

multiple stakeholders involved in the care for

Data

collection,

Data analysis

Payam Hanafizadeh, Reza Ashhari

ALS patients and add to the current

knowledge about the disease by collecting and

analyzing patient data.”

“In 2011, APST was designed as a multi-

sided platform, linking patients to their

doctors and care providers, as well as to ATD

vendors and then gradually extending into

therapeutic service management (e.g.,

physical therapy, speech therapy,

occupational therapy), pharmacies

(medications), nutritional therapy, and nursing

care services.”

Service

management

BBVA

Alfaro et al.,

(2019)

“It is also a leading digital company that has

achieved great success in the area of data

monetization.”

Selling

information

“Visualization tools (e.g., dashboards,

GISs—geographical information systems).”

Visualization

Creation

Suning

Cheah &

Wang (2017)

“Drawing upon insights from the comments

of the end user community captured on its

open crowdsourcing platform.”

User data

collection

“From their analysis of the data models, it

was apparent that the users did not have

positive experiences from usingthe traditional

cooker hood and side suction products

dominating in the market.”

Data analysis

Haier

Cheah &

Wang (2017)

“Haier’s smart air conditioners are developed

based on a smart air ecosystem that collects

and analyzes data on user behavior and air

conditioner life cycle status.”

Consumer

data, Data

analysis

“Haier Group Corporation is a multinational

company based in Qingdao, Shangdong

province, China that manufactures and

distributes consumer electronics and home

appliances.”

Manufacturer

Suofeiya

Cheah &

Wang (2017)

“The company has invested in the

development of a cloud platform with big data

to integrate its supply chain management

(SCM) and CRM systems.”

Data analysis

“Founded in 2003 and headquartered in

Guangzhou, China, Suofeiya Home

Collection Company Limited designs,

manufactures and distributes custom-built

wardrobes and other furniture.”

Manufacturer

DrugCo

Najjar &

Kettinger

(2013)

“While the improvement in supply-chain

performance might be a good reason for

companies to share data with supply-chain

partners, a more explicit direct dollar value of

the data can be another tempting motivation.”

Data sharing

Int. Journal of Business Science and Applied Management / Business-and-Management.org

“Only a limited number of DrugCo’s major

suppliers were allowed to purchase the

highest Gold level package.”

Package

selling

Vending

Intelligen

Wang et al.,

(2023)

“Mobile payment brought countless valuable

consumer data to L. Vending”

Consumer

data

“According to the data analysis, different

kinds of vending machines could be placed in

a targeted and purposeful manner to

supplement products of different types of

brands.”

Data analysis

Table 6: Result of case thematic analysis

Case number

Case name

Data value network

Ecosystem players

Type

Data discovery, Data

curation, Data

interpretation, Data

exploitation & Data

distribution

Vendor, Developer,

Operator &

Cooperation and

Standardization fora

APST

Data discovery

Data curation

Data interpretation

Data exploitation

Developer

SESCOM

Group

Data discovery

Data curation

Data interpretation

Data exploitation

Vendor

Developer

Suning

Data discovery

Data curation

Data interpretation

Data exploitation

Vendor

Haier

Data discovery

Data curation

Data interpretation

Data exploitation

Vendor

Suofeiya

Data discovery

Data curation

Data interpretation

Data exploitation

Vendor

i-BRE

Data discovery

Data curation

Data exploitation

Vendor

Developer

BBVA

Data discovery

Data curation

Data interpretation

Data exploitation

Data distribution

Vendor

Payam Hanafizadeh, Reza Ashhari

DrugCo

Data curation

Data interpretation

Data exploitation

Data distribution

Vendor

Vungle Inc.

Data curation

Data interpretation

Data exploitation

Vendor

Developer

L. Vending

Intelligence

Data discovery

Data curation

Data interpretation

Data exploitation

Vendor

Table 7: Examples of codes within the text of empirical articles and their related basic codes

Article

number

Source

Text codes

Related basic

codes

Hmoud et al.,

(2017)

“This situation puts a traditional

business model of mobile network

operators (MNOs) under pressure as

they failed to catch up due to lesser

capabilities for providing a new value

proposition and incentives for those

sides of the market to achieve the two-

sided platform compared to dominat

parties.”

Network operator

Rao, (2021)

“The expected features could enable

massive high-speed and low-latency

connectivity across diverse devices,

giving rise to new applications enabling

instant availability of data related to

Smart Cities.”

Instant

availability of

data

Förster et al.,

(2022)

“The data customization perspective

describes a customer Centric view of

value realization from data within

DDBMs, focusing on the primary goal

of offering individualized solutions to

customers in order to achieve a superior

customer

experience.”

Individualized

solutions

Wiśniewski et

al., (2021)

“In digital space, new types of DDBM

are established, which provide

entrepreneurs with added value, based

on the mass use of the consumer’s data

collected often without their knowledge,

on the margins of legality.”

Data collection

Wagner, et al.,

(2021)

“This stream of research is concerned

with the question under which

circumstances Internet users are willing

to have their personal information

gathered and processed by service

providers.”

Processing data

Int. Journal of Business Science and Applied Management / Business-and-Management.org

Isabelle et al.,

(2020)

“The study identifies eight factors that

reveal the role of analytics in those

firms’ DDBM.”

Analytics

Breidbach &

Maglio, (2020)

“And when insurance company State

Farm filed a patent application in 2014

that outlined how to aggregate and

correlate customer data, including

‘home data, vehicle data and personal

health data’ for ‘life management’

purposes and ‘personalized

recommendations,’ few anticipated that

in January 2019, the state of New York

would allow life insurance companies to

use social media data and other ‘non-

traditional’ data sources to set premium

rates.”

Customer data

Zaki, (2019)

“AI – and more specifically machine

learning, and even more specifically

deep learning – is becoming part of our

everyday lives”

Machine learning

Krämer &

Wohlfarth,

(2018)

“The characteristics of digital goods

allow users to fully personalize the

offered services (“mass customization”).

This may not only result in lock-in

effects, but also in difficulties in

defining the relevant market with

respect to the product and time

dimension.”

Personalized

service

Sorescu, (2017)

“I provide examples of how companies

can leverage internal and external data

to generate new business models, and I

propose a few research questions that

can help academics and practitioners

understand the link between big data

and business model innovation.”

Internal data,

External data

Li et al., (2021)

“Blockchain, which is a technology for

building distributed ledgers that provide

an immutable log of transactions

recorded in a distributed network, has

become prominent recently as the

underlying technology of

cryptocurrencies and is revolutionizing

data storage and processing in computer

network systems.”

Data organization

technologies

Kaiser et al.,

(2021)

“In the automotive industry, the data

generated by vehicles during use paves

the way for new types of data-driven

services.”

Data-driven

service

Kunz et al.,

(2017)

“The term used to describe the impact of

the technologies and the nature of the

new digital world is “Big Data.””

Data analysis

technologies

Payam Hanafizadeh, Reza Ashhari

Machchhar, et

al., (2022)

“This paper systematically reviews

scientific literature to underline the kind

of data being collected from the

operational stage, the purposes being

achieved from that data, and how they

lead to value creation.”

Operational stage

data

Faroukhi, et al.,

(2020b)

“This end-to-end framework allows us

to handle Big Data monetization to

make organizations’ processes entirely

data-driven, support decision-making,

and facilitate value co-creation.”

Decision Making

Johnson, et al.,

(2017)

“Big data is transforming the new

product development (NPD) process.”

New product

development

Faroukhi et al.,

(2020a)

“The advances in Big Data and Big Data

Value Chain, using clear processes for

aggregation and exploitation of data,

have given rise to what is called data

monetization.”

Data aggregation,

Data exploitation

Bataineh et al.,

(2021)

“With the unprecedented reliance on

cloud computing as the backbone for

storing today’s big data, we argue in this

paper that the role of the cloud should

be reshaped from being a passive virtual

market to become an active platform for

monetizing the big data through

Artificial Intelligence (AI) services.”

Cloud computing,

Data storage

Ali, et al., (2021)

“This paper presents a framework for

sharing IoT data in a decentralized and

private-by-design manner in exchange

for monetary services.”

Monetary services

Firouzi et al.,

(2020)

“The recent advent of the technological

advances in the fields of Big Data,

Analytics, and Artificial Intelligence

(AI) has opened new avenues of

competition”

Data analysis

technology

Bataineh, et al.,

(2020)

“This exponential growth of the big data

market raises the need to develop an

economic platform that efficiently

monetizes the data on the cloud.”

Big data, Cloud

storage

Hanafizadeh et

al., (2021)

“Companies that use data analytics, and

the derived insights for enriching their

products and services, are wrapping

their offerings with data via an indirect

approach, which is called Data

Wrapping.”

Data analytics,

Product, Service

Hanafizadeh &

Harati Nik,

(2020)

“It provides organizations with

flexibility for using information assets

in response to customer expectations

and environmental pressures.”

Information assets

Lokshina, et al.,

(2018a)

“This article describes ubiquitous

sensing devices, enabled by wireless

Data collection

Int. Journal of Business Science and Applied Management / Business-and-Management.org

sensor network (WSN) technologies,

now cut across every area of modern

day living, affecting individuals and

businesses and offering the ability to

obtain and measure environmental

indicators.”

Lokshina, et al.,

(2018b)

“looking at what to do with data lakes

and turning data through Big Data

analytics into decisions and actions.”

Data analytics

Hashem, (2021)

“Study recommended increasing

investments in developing smart

applications that are able to tackle the

massive amount of data generated by

IoT applications”

Smart

applications

Yu, (2022)

“The improved international business

financial statistics platform consists of

three levels of systems, data collection

system, big data management system”

Data collection

Pflaum, &

Gölzer, (2018)

“Internet of Things (IoT) technologies

have been with us for a while. During

the last two decades, many researchers

have made successful advances in smart

products”

Smart product

Maddileti et al.,

(2019)

“The goal is to achieve an efficient and

optimized drainage analysis system

which is cost-effective and feasible for

conditioning, monitoring and

maintaining drains in the city.”

Monitoring

system

Günther, et al.,

(2022)

“We find that creating data-driven value

propositions involves the performance

of two types of resourcing actions: data

reconstructing and data repurposing.”

data

reconstructing,

data repurposing

Zschech, (2023)

“To this end, we propose a taxonomic

evaluation approach to evaluate and

construct the technical core of analytical

information systems more

systematically”

Information

systems

Bergman, et al.,

(2022)

“Policymakers and analysts are heavily

promoting data marketplaces to foster

data trading between companies.”

Data trade

Acciarini, et al.,

(2023)

“Big data has the potential to be

collected and used by different types of

organizations, processed through

specific capabilities like big data

analytics (BDA)”

Data analysis

capabilities

Taherdoost, &

Madanchian,

(2023)

“It increases the efficacy of transactions

between parties, who may freely

exchange resources and data in an

environment facilitated by blockchain

technology.”

Blockchain

technology

Payam Hanafizadeh, Reza Ashhari

Mendizabal-

Arrieta, et al.,

(2023)

“The cost of sharing, analysing and

collecting trading platform data (C) is

subtracted from the demand price of a

data packet.”

Data sharing

Chasin et al.,

(2020)

“Through connectivity interfaces and

data analytics, the collected data is

processed and analyzed in the cloud and

finally used to create smart energy

services,”

Analytics

Aranda et al.,

(2023)

“This paper offers insights about novel

ESCO business models based on

intensive data-driven Artificial

Intelligence algorithms and analytics

that enable the deployment of smart

energy services in the domestic sector

under a Pay-for-Performance (P4P)

approach.”

Smart service

Troisi et al.,

(2023)

“Moreover, data analysis can have an

impact on value proposition”

Data Analysis

Trzaskowski,

(2022)

“It also includes advertisements which

are individualized based on the

consumers' previous online behavior.”

Personalized

service

Azkan, et al.,

(2022)

“In this article, we focus on using data

and data analytics in product-oriented

industrial companies.”

Data Analytics

Fast, et al.,

(2023)

“Next to these general quality

improvements, big user data is the basis

for the personalization of content and

services”

Personalized

service

Holmes, et al.,

(2023)

“The data collected are aggregated and

sold under a subscription model, along

with several analysis products and

services.”

Data collection,

Selling data

Parvinen, (2020)

“From an organizational perspective,

data may come from internal or external

sources”

Internal data,

External data

Stahl, et al.,

(2023)

“Driven by digital technologies,

manufacturers aim to tap into data-

driven business models, in which value

is generated from data as a complement

to physical products.”

Manufacturer

Younis, et al.,

(2021)

“In addition to supporting secure

telehealth applications, the involvement

of blockchain technology enables billing

for medical services and accountability

of the caregivers.”

Service

Zhang, et al.,

(2023)

“This result reveals that data brokers

with high analytics capabilities can

hinder, rather than facilitate, market

competition.”

Data analysis

capabilities

Int. Journal of Business Science and Applied Management / Business-and-Management.org

100

Fruhwirth, et al.,

(2024)

“Data science methods enable

organisations to discover patterns and

eventually knowledge from data.”

Knowledge

creation

Ji, et al., (2024)

“Regarding banking tenure, individuals

are classified based on their service

duration”

Service provider

Tripathi, et al.,

(2024)

“Interconnectedness and the potential

for synergy lead to many possibilities

for smart products, services, market

accessibility, and business expansion.”

Service provision

Wang, et al.,

(2025)

“By contrast, firm performance grows

much more slowly at the beginning but

has a stronger

acceleration in later stages. Besides,

improving big data analytics cannot

directly increase data value.”

Data analytics

Sterk, et al.,

(2024)

“The vastly increasing amount of data

collected by connected cars grants a

unique driving experience for its users

while providing companies operating in

the automotive industry access

to valuable information and, ultimately,

cost and revenue benefits.”

Data collection

Machado, et al.,

(2024)

“The study yields six design patterns

that address various aspects such as data

pricing, data-driven business models

and best practices for data

monetization.”

Data pricing

Xu, et al., (2024)

“A list of empirical studies has

demonstrated how data generate

revenue through data exchange or direct

selling.”

Selling data

Figure 19: Matrix of types of data-driven business models and empirical articles

Article number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Type A

* * * * *

Type B

* * * * * *

Type C

* * * * *

Type D

Type E

* * * * *

Type F

* * *

Type G

New type

* * *

Article number 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53

Type A

* * * *

Type B

Type C

Type D

* * *

Type E

* * * * * * * * * *

Type F

* * * * *

Type G

New type

Sum