Data-driven Business Models Research Articles

Clarifying new urban mobility services based on a threefold business model framework

Market entrants have brought a variety of new urban mobility services over the past years, which are rooted in the sharing economy and have their origin in digitalization. Digital data serve as key resource of a business model and, accordingly, digital technologies are the basis of key activities. Building our analysis on the resource-based view and on the business model debate, we ask: what degrees of digitalization do urban mobility service business models exhibit? We perform a systematic literature review and a qualitative content analysis. As a result, we identify a continuum of highly and lowly digitalized business models. We derive a threefold business model framework, substantiated in conventional mobility, hybrid, and data-driven business models. (1) Conventional mobility business models are dominated by mobility as a key resource, digitalization is low and performed by key partners, (2) hybrid models contain both conventional mobility and data-driven key resources, and (3) data-driven models take digital data as key resources, while conventional mobility is carried out by key partners. As a first main contribution, we conceptualize conventional versus purely data-driven business models along the continuum of data-driven business model components. New urban mobility services are the group of both hybrid and data-driven business models, while conventional urban mobility stands on its own. As a second contribution, we clarify the Mobility-as-a-Service (MaaS) concept by corroborating it as a purely data-driven business model with key partners provisioning mobility.

The Emergence of Data-Driven Business Models in the European Energy Industry

The Emergence of Data-Driven Business Models in the European Energy Industry

Knowledge Leaks in Data-Driven Business Models? Exploring Different Types of Knowledge Risks and Protection Measures

Data-driven business models imply the inter-organisational exchange of data or similar value objects. Data science methods enable organisations to discover patterns and eventually knowledge from data. Further, by training machine learning models, knowledge is materialised in those models. Thus, organisations might risk the exposure of competitive knowledge by sharing data-related value objects, such as data, models or predictions. Although knowledge risks have been studied in traditional business models, little research has been conducted in the direction of data-driven business models. In this explorative qualitative study, we conducted 28 expert interviews in three rounds (two exploratory and one evaluatory) and identified five types of risks along the three basic types of value objects: data, models and predictions. These risks depend on the context, i.e., when competitive knowledge could be discovered from shared value objects. We found that those risks can be mitigated by technology, contractual regulations, trusted relationships, and adjusting the business model design. In this study, we show that the risk of knowledge leakage is a relevant risk factor in data-driven business models. Overall, knowledge risks should be considered already during business model design, and their management requires an interdisciplinary approach via a balanced assessment. The level of knowledge protection from a technology perspective highly depends on computer science innovations and thus is a moving target. As an outlook, we suggest that knowledge risk will become even more relevant with the extensive usage of machine learning and artificial intelligence in data-driven business models.

Is pay-or-consent for privacy justifiable? Evidence from different users' privacy attitudes toward behavioral data collection in South Korea

As regulators began prohibiting online platforms from collecting personal data based on the “take-it-or-leave-it” basis, platform firms must adopt more refined user consent rules such as the pay-or-consent approach. Ensuring sufficient user options could increase the welfare of privacy-sensitive users but reduce the efficiency of data-driven business models. To balance the benefits and costs of enhanced privacy protection, regulators should understand the diversity in users' attitudes toward behavioral data collection in free online platforms. Tradeoffs among privacy, conveniences, and free services based on users' heterogeneous preferences are considered to investigate the user's different privacy attitudes in free online platforms. Three distinct user groups were found: the first one reluctantly accepts the “take-it-or-leave-it” condition because of the lack of alternatives, the second one accepts it for free services, and the third one accepts it because it does not matter. These three user segments constituted 32.9%, 47.0%, and 20.1% of all the respondents, respectively. The pay-or-consent approach can be justifiable in terms of balancing the benefits and costs of the privacy regulations if it properly reflects privacy-sensitive users' willingness to pay for privacy.

Preparedness for Data-Driven Business Model Innovation: A Knowledge Framework for Incumbent Manufacturers

Preparedness for Data-Driven Business Model Innovation: A Knowledge Framework for Incumbent Manufacturers

An Exploratory Analysis of the Current Status and Potential of Service-Oriented and Data-Driven Business Models within the Sheet Metal Working Sector: Insights from Interview-Based Research in Small and Medium-Sized Enterprises

Responding to changing value creation processes in the sheet metal working sector, where the complexity and interchangeability of products challenge traditional differentiation strategies, this exploratory analysis examines the integration of service-oriented and data-driven business models as new paths to ensure competitiveness, especially for small and medium-sized enterprises (SMEs). This study aims to capture the current state and challenges associated with the implementation of these business models in this sector. This research was conducted through semi-structured interviews with SMEs in the industry. The findings indicate that service-oriented and data-driven business models are not yet widely adopted and that manufacturing companies require support in their implementation. Fields of action were identified for the industry. These are “Creating awareness and understanding”, “Recognizing added value”, “Increasing company maturity”, and “Understanding the change process”. Cooperation between science and industry is essential in tackling these fields of action to ensure the successful integration of such business models in manufacturing companies. This paper identifies challenges in the fields of action that companies must address through a structured approach, promoting awareness, recognizing value, improving organizational maturity, and understanding the change process to successfully implement service-oriented and data-driven business models.

Unlocking the value from car data: A taxonomy and archetypes of connected car business models

The automotive industry is relocating from viewing cars as standalone products to an all-encompassing ecosystem built around connected cars enabling data-driven business models. 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. In this article, we develop an empirically and theoretically grounded taxonomy of data-driven business models in the connected car domain to explore the impact of car connectivity and data availability on business models. Building on this, we conduct a cluster analysis revealing seven business model archetypes for the connected car domain: data platforms, location-based services, fleet management, diagnostics and maintenance, driving analytics, cyber-physical protection, and connected infotainment. Our findings advance the theoretical knowledge of data-driven business models, provide researchers with a systematic analysis of connected car-enabled business models, and enable decision-makers to identify strategic opportunities for leveraging connected car technology to enrich their business portfolios.

Design Principles for Industrial Data-Driven Services

The continuously growing availability and volume of data pressure companies to leverage them economically. Subsequently, companies must find strategies to incorporate data sensibly for internal optimization and find new business opportunities in data-driven business models. In this article, we focus on using data and data analytics in product-oriented industrial companies. Although data-driven services are becoming increasingly important, little is known about their systematic design and development in research. Surprisingly, many companies face significant challenges and fail to create these services successfully. Against this background, this article presents findings from a multicase based on qualitative interviews and workshops with experts from different industrial sectors. We propose ten design principles and corresponding design features to successfully design industrial data-driven services in this context. These design principles help practitioners and researchers to understand the peculiarities of creating data-driven services more in-depth on a conceptual, technical, and organizational level.

Data Monopolization by the Tech Industry

Based on Shumaila Shahani's central thesis “that the un­regulated accumulation of data by the tech industry has led to the consolidation of power among a select few, resulting in the erosion of democratic principles” she first explains the data-driven business model of digital platforms providing. She then explains the risks involved in uncontrolled data collection and in data misuse and highlights threats to democracy and national security concerns.

Drafting a pro-antitrust and data protection regulatory framework


 The Digital Personal Data Protection (DPDP) Act 2023 has significant implications for antitrust issues in digital markets. A consensus has emerged among competition regulators across jurisdictions that in markets underpinned by data-driven business models, antitrust concerns intersect with data protection regulatory issues. With the recent enactment of the law, the landscape in India is now ripe to ensure that the two regulatory tools of data protection and competition work in tandem. The paper provides an overview of why a new approach to antitrust law is required in the realm of data-driven digital platforms, and delves into the evolving antitrust cases in India with respect to digital platforms to lay out the jurisprudence on data-related anti-competitive practices. The main objective of this paper is to map the Digital Personal Data Protection Act in the context of India's jurisprudence to theoretically illustrate how regulation of personal data could impact antitrust enforcement. Finally, the paper outlines how the CCI needs to assess digital antitrust cases and how the personal data legislation will need to evolve to achieve the twin goals of data protection and fair competition.

An Integrated Data-Driven Procedure for Product Specification Recommendation Optimization with LDA-LightGBM and QFD

E-commerce and social media have become increasingly essential and influential for sustainable business growth, particularly due to the COVID-19 pandemic, which has permanently altered the business landscape. The vast amount of consumer data available online holds significant potential and value. The strategic utilization of this information can expedite the research and development of new products, leading to shorter product cycles and increased innovation. This study explores the effectiveness of employing the latent Dirichlet allocation (LDA) method and various deep learning technologies to predict Amazon consumer ratings. We propose a product service system that utilizes natural language analyses of online sales data and user reviews, enabling industries to quickly identify and respond to market demands. We present a data-driven procedure for the customer-to-manufacturer (C2M) business model, specifically focusing on sustainable data-driven business models based on knowledge and innovation management. This procedure analyzes user comments on online shopping platforms to match product requirements and features, optimize product values, and address issues related to product specifications and new product development planning. The results of the business verification demonstrate that this procedure accurately evaluates product specifications under different demands, facilitates effective product planning, and enhances research and development decision making. This approach, based on sustainable data-driven business models and knowledge and innovation management, expands market opportunities for the sector and improves overall production efficiency, starting from the research and development stage.

Research on Big Data-driven Business Management Effectiveness Enhancement Methodology

In the digital era, business management effectiveness enhancement is an important way to promote enterprises to gain competitive advantage, and enterprises use big data to drive business model innovation and business risk warning. Based on the progress of existing data-driven business model research, the intrinsic mechanism and methods of big data-driven business model innovation are analysed based on the perspective of business model element restructuring. Meanwhile, in business risk management, especially in the financial banking sector, where risk management is the core of its business management, big data plays a crucial role.

Knowledge management in data-driven business models during the digital transformation of healthcare organisations

ABSTRACT With the rapid development of digital technologies and the outbreak of the COVID‐19, digital transformation (DT) has been accelerated. This appears to pose specific challenges to the medical field, leading to an inevitable trend towards DT in healthcare organisations. Determining how to develop strategies to master the substantial opportunities brought about by DT is a fundamental issue. Knowledge management (KM) is a key vehicle that can drive DT because it provides a solid foundation for organisational strategies and learning, and helping establish operational priorities. Using the operations of healthcare organisations in Taiwan as an example, this study discusses the challenges and opportunities faced by healthcare organisations related to DT based on data-driven business models, where the concept of KM, organisational agility (OA), and business models are integrated to develop a KM-OA-enabled DT conceptual framework intended to support DT implementation in healthcare organisations. This can serve as a foundation for future studies of DT

Is transparency the new green? How business model transparency influences digital service adoption

The proliferation of data-driven business models raises privacy concerns, as users often perceive a lack of transparency in how companies use their data. This study explores how business model transparency (and the absence of it) influences users' decisions to adopt a digital service. We propose a model grounded in rational choice theory, tested through multigroup structural equation modeling. Data were collected through a field experiment that exposed users to two different mock-ups characterized by varying levels of transparency related to data usage. The results show that the level of transparency does not directly influence users' willingness to adopt the service. Switching from a transparent to an opaque data-driven business model negatively affects users' willingness to adopt a service. Such findings remark on the users’ service adoption decision-making process criteria, which are highly influenced by perceived data usage transparency. Consequently, this study introduces a user perspective on data-driven business models. It conveys to managers the importance of transparency when business models are grounded in data-driven strategies and data exploitation.

Data as capital and ethical implications in digital sport business models

Professional sport has entered the digital economy as organisations adopt data-driven business model innovations. The purpose of this article is to highlight the potential ethical vulnerabilities sport organisations and their leaders face when adopting digital sport business models. Here, we treat data as a species of capital that can be converted into economic capital once it undergoes a computational transformation via a data-driven business model innovation. We argue for two advantages in this approach. First, it helps make transparent the mechanisms through which digital sport business models work. Second, it reveals how the extraction and application of big data exacerbates inequitable power relationships between sport organisations and supporters – the big data divide – that leads to ethical vulnerabilities for sport organisations and their consumers. We suggest that sport consumers might be particularly vulnerable to digital data risk as a consequence of their high levels of brand loyalty and involvement, which tend to encourage trust in the sport properties soliciting, analysing, and monetising their data. Platform broadcasting partnerships, e-ticketing in smart stadiums, and cryptocurrency-based fan tokens are used as examples of data-driven business model innovations based on the conversion of data to capital, demonstrating how sport organisations risk violating the trust of supporters when using digital strategies. The article concludes with directions for future research to deliver an ethically informed data-driven sports industry.

Big Data-Driven Business Model Innovation (BMI) From the Perspective of Ambidexterity

This paper explores the dynamic path of business model innovation (BMI) driven by big data and builds a big data-driven BMI model based on ambidexterity perspective. Using the case study approach, this paper examines the ambidexterity theory and employs the structured-pragmatic-situational (SPS) research method to explore the BMI driven by big data. The findings are: 1) Companies could promote business innovation using big data from both exploratory and exploitative activities. 2) It is a dynamic process when the company promotes its BMI based on big data while balancing the ambidexterity activities. The paper illustrates the mechanism of ambidexterity for BMI driven by big data and sums up a theoretical model with general principles.

Digitalizing business models in hospitality ecosystems: toward data-driven innovation

PurposeDigitalization accelerates the need of tourism and hospitality ecosystems to reframe business models in line with a data-driven orientation that can foster value creation and innovation. Since the question of data-driven business models (DDBMs) in hospitality remains underexplored, this paper aims at (1) revealing the key dimensions of the data-driven redefinition of business models in smart hospitality ecosystems and (2) conceptualizing the key drivers underlying the emergence of innovation in these ecosystems.Design/methodology/approachThe empirical research is based on semi-structured interviews collected from a sample of hospitality managers, employed in three different accommodation services, i.e. hotels, bed and breakfast (B&Bs) and guesthouses, to explore data-driven strategies and practices employed on site.FindingsThe findings allow to devise a conceptual framework that classifies the enabling dimensions of DDBMs in smart hospitality ecosystems. Here, the centrality of strategy conducive to the development of data-driven innovation is stressed.Research limitations/implicationsThe study thus developed a conceptual framework that will serve as a tool to examine the impact of digitalization in other service industries. This study will also be useful for small and medium-sized enterprises (SMEs) managers, who seek to understand the possibilities data-driven management strategies offer in view of stimulating innovation in the managers' companies.Originality/valueThe paper reinterprets value creation practices in business models through the lens of data-driven approaches. In this way, this paper offers a new (conceptual and empirical) perspective to investigate how the hospitality sector at large can use the massive amounts of data available to foster innovation in the sector.

Data sharing for business model innovation in platform ecosystems: From private data to public good

Extant research posits that open data could unlock more than $3 trillion in additional value worldwide across various application domains. This paper investigates a data-sharing perspective in business models of platform ecosystems and discusses how platform owners can derive more value using data. We chose a sample of 12 platforms in which data are used as a key resource for service propositions. By contrasting these cases, we identify and analyse four archetypes: data crawler, data marketplace, data aggregator, and data disseminator. We define the key features of these archetypes and demonstrate how they realise value via the platform. These archetypes can guide managers in realising private and public goods via data sharing. Building on our findings, we derive recommendations for data-driven business model innovation for platform ecosystems.

Data or Business First?—Manufacturers’ Transformation Toward Data-driven Business Models

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. However, this transformation can be complex, as different archetypes of data-driven business models require substantially different business and technical capabilities. While there are manifold contributions to research on technical capability development, an integrated and aligned perspective on both business and technology capabilities for distinct data-driven business model archetypes is needed. This perspective promises to enhance research’s understanding of this transformation and offers guidance for practitioners. As maturity models have proven to be valuable tools in capability development, we follow a design science approach to develop a maturity model for the transformation toward archetypal data-driven business models. To provide an integrated perspective on business and technology capabilities, the maturity model leverages a layered enterprise architecture model. By applying and evaluating in use at two manufacturers, we find two different transformation approaches, namely ‘data first’ and ‘business first’. The resulting insights highlight the model’s integrative perspective’s value for research to improve the understanding of this transformation. For practitioners, the maturity model allows a status quo assessment and derives fields of action to develop the capabilities required for the aspired data-driven business model.

Using machine learning to create and capture value in the business models of small and medium-sized enterprises

Start-ups have revolutionised many economic ecosystems, becoming innovation pioneers around the world. Most are based on data-driven business models, particularly relying on machine learning technologies. However, not all start-ups that use machine learning technologies manage to create and capture value. The existing literature on the use value enabled by information technologies does not take into account the unique capabilities of machine learning. The theory of data network effects offers a promising explanation of how to create value using machine learning. However, it does not explicitly describe how to capture value using machine learning. In contrast, business model theory explains how companies use technologies to create and capture value, but not specifically through the use of machine learning technology. Therefore, this study aims to improve the theoretical understanding of the key drivers of value creation and capture in start-ups with business models driven by this kind of technology. Statistical techniques are used in a sample of 122 start-ups to explore the theoretical relationships between these two theories. The analysis reveals the link between specific value creation and capture factors of the two theories, such as efficiency, novelty, and performance expectancy. The study also provides evidence of the need to adopt a co-evolutionary perspective of value creation and capture through the use of machine learning.