Data-driven Business Models Research Articles

A method to design Smart Services based on information categorization of industrial use cases

Smart Services can be described as a specific manifestation of Product-Service Systems, which mainly refer to customer individual solutions based on the utilization of data derived from cyber-physical products and production systems. In this context the usage data is allocated and analyzed on a platform in order to extract valuable information, thus enabling the development and operationalization of new data-driven business models. In order to support the initial design of Smart Services companies have to achieve a thorough understanding of their existing or obtainable data as well as the related potential options to utilize these data. Based on the analysis of 51 industrial Smart Service use cases data flows and areas of application have been derived and clustered. Moreover, the defined categories are interlinked and mapped to the use cases. The findings have been comprised into an applicable Smart Service design method addressing early design stages. Finally, the approach (DAU flow method) has been applied and evaluated with two case companies, indicating sound qualitative results and providing valuable feedback.

Uncovering Research Streams in the Data Economy Using Text Mining Algorithms

Data-driven business models arise in different social and industrial sectors, while new sensors and devices are breaking down the barriers for disruptive ideas and digitally transforming established solutions. This paper aims at providing insights about emerging topics in the data economy that are related to companies' innovation potential. The paper uses text mining supported by systematic literature review to automatize the extraction and analysis of beneficial insights for both scientists and practitioners that would not be possible by a manual literature review. By doing so, we were able to analyze 860 scientific publications resulting in an overview of the research field of data economy and innovation. Nine clusters and their key topics are identified, analyzed as well as visualized, as we uncover research streams in the paper.

A Knowledge-Driven Model for Client Relationship Management

Over the past few years, big data analytics trends are shifting the focus to business-driven data approach. Different businesses required to implement an effective and efficient data-driven business model to stay ahead in their competition by making use of the latest agile technologies and advanced analytics. As their key business partner, bank provides different products and services, such as cash management solutions and trade financing, to support their business operations. With the increase in corporate client expectation, new regulatory requirement, and new fintech solution providers, the traditional banking operating model using relationship manager-based sales model may not be a sustainable solution. In this paper, we aim to propose a knowledge-driven model for client relationship management for commercial banking by incorporating the latest machine learning and artificial intelligence applications. Different analytics use cases are discussed to improve the client relationship management process.

Innovation capabilities as a mediator between big data and business model

The digital transformation is forcing organizations to change towards more data-driven business models. In this paper, we propose a conceptual framework that explains the role of innovation capabil...

Datenkapitalismus — eine ökonomische Betrachtung

Companies aggregate, process and monetise data about consumers by leveraging actively shared information as well as the data they glean from tracking offline transactions. Data-driven business models come in two forms: Firms either use the personal data of users to tailor their own offerings to them, or they sell the data to others who use it for their own ends. These models have different implications for the protection of consumers. While they are typically reasonably well-informed about the data they transmit to a particular firm, they have less control about whom their data is sold on to. Consumers are regularly confronted with a lot of privacy-relevant information and choices, but they struggle to make decisions that are in line with their stated privacy preferences. Therefore, regulation should address privacy design concepts that actually foster control. The right to data portability is one of the fundamentally new elements of the General Data Protection Regulation. However, its economic implications are potentially very complex and, under certain circumstances, data portability may not even be in the interest of consumers. Nevertheless, the strengthening of consumer rights may likewise support competition. The monetary value of data is difficult to calculate. But if consumers are interested in a fair profit share of personal data markets, policy makers may disregard their claims. One article highlights the transformation in the academic publishing industry (STM) from a traditional focus on publishing services to a new emphasis on data analytics by reconstructing and illustrating the mechanisms of data capitalism. It offers an explanation for the growing profitability and concentration of the STM industry despite the growing open science movement.

The upside of data privacy – delighting customers by implementing data privacy measures

The targeted analysis of customer data becomes increasingly important for data-driven business models. At the same time, the customers’ concerns regarding data privacy have to be addressed properly. Existing research mostly describes data privacy as a necessary evil for compliance and risk management and does not propose specific data privacy measures which address the customers’ concerns. We therefore aim to shed light on the upside of data privacy. In this paper, we derive specific measures to deal with customers’ data privacy concerns based on academic literature, legislative texts, corporate privacy statements, and expert interviews. Next, we leverage the Kano model and data from two internet-based surveys to analyze the measures’ evaluation by customers. From a customer perspective, the implementation of the majority of measures is obligatory as those measures are considered as basic needs of must-be quality. However, delighting measures of attractive quality do exist and have the potential to create a competitive advantage. In this, we find some variation across different industries suggesting that corporations aiming to improve customer satisfaction by superior privacy protection should elicit the demands of their specific target customers.

Industry 4.0 implementation framework for the producing industry

The competitive pressure induced by progressing globalization has forced companies to find new ways of differentiation. Industry 4.0 as the synonym for the next significant step in value creation advancement offers new technologies and procedures to obtain further improvements in competitiveness. The real-time networking of people, machines and objects is not only an opportunity to leverage productivity potentials; it also enables new data-driven business models to achieve increase in revenues. Although the concept and enablers of industry 4.0 are widely known across the producing industry, companies struggle with its successful and sustainable implementation. Due to the often insufficient embedding of individual solutions in a coherent overall concept, implemented solutions cannot exploit potential synergies within the company. The presented paper introduces a framework to centrally control and synchronize Industry 4.0 activities within a company. By integrating bottom-up and top-down perceptions the framework facilitates the early identification and assessment of potential industry 4.0 applications as well as efficient development of specific solutions up to the implementation in series production. Consisting of an operationalization process and an organizational model optimized for horizontal and vertical exchange within the company, the framework enables a faster and more robust implementation of industry 4.0

Standing on the Shoulders of Web Giants: The Economic Effects of Personal Data Brokers

In several jurisdictions, like the European Union and California, consumers now have a legal right to port the data that a company has collected about them to another service. This has spurred the emergence of personal data brokers (PDBs), which offer consumers financial rewards for access to their personal data collected by large online content and service providers (CSPs). Using a game-theoretic model, we investigate how PDBs affect market outcomes in the data economy and under which conditions users will indeed benefit from them. Across several model variants, we find that in markets with a PDB two main equilibrium outcomes exist. First, a minimum income equilibrium can arise, where the PDB only pays a minimal reward, and the CSP lowers its service quality in the long run, leaving consumers worse off than without a PDB. Second, a positive income equilibrium can arise, where the PDB pays significant rewards to consumers, and the CSP’s quality may be higher or lower depending on the PDB's efficiency on the data market. Whether consumers are better off in this equilibrium depends on the relative strengths of three effects that we characterize formally and denote as competition effect, displacement effect, and appropriation effect. Consumers will only benefit from a PDB if it can sell their data efficiently; but in this case, the CSP will also demand a higher (non-zero) price for using its service. Thus, our results bear important managerial and policy implications for data-driven business models and the regulation of data access and portability.

Market power, regulatory convergence, and the role of data in digital markets

The increased economic importance of digital services has profoundly changed the power structure in telecommunications and media markets. Although these services sometimes directly compete with traditional telecommunications services, the regulatory obligations for both players differ significantly. This article discusses three important areas deemed relevant in order to define a coherent regulatory framework and to account for the specific peculiarities of digital markets: First, challenges associated with assessing market power in digital markets. Second, challenges in harmonizing different regulatory obligations for digital services, and third, the vital role of data and data protection in the context of data-driven business models.

Big data-driven business model innovation by traditional industries in the Chinese economy

PurposeThis study aims to construct mechanisms of big data-driven business model innovation from the market, strategic and economic perspectives and core logic of business model innovation.Design/methodology/approachThe authors applied deductive reasoning and case analysis method on manufacturing firms in China to validate the mechanisms.FindingsThe authors have developed an integrated framework to deduce the elements of big data-driven business model innovation. The framework comprises three elements: perspectives, business model processes and big data-driven business model innovations. As we apply the framework on to three Chinese companies, it is evident that the mechanisms of business model innovation based on big data is a progressive and dynamic process.Research limitations/implicationsThe case sample is relatively small, which is a typical trade-off in qualitative research.Practical implicationsA robust infrastructure that seamlessly integrates internet of things, front-end customer systems and back-end production systems is pivotal for companies. The management has to ensure its organization structure, climate and human resources are well prepared for the transformation.Social implicationsWhen provided with a convenient crowdsourcing platform to provide feedback and witness their suggestions being implemented, users are more likely to share insights about their use experience.Originality/valueExtant studies of big data and business model innovation remain disparate. By adding a new dimension of intellectual and economic resource to the resource-based view, this paper posits an important link between big data and business model innovation. In addition, this study has contributed to the theoretical lens of value by contextualizing the value components of a business model and providing an integrated framework.

Big Data-Driven Value Chains and Digital Platforms: From Value Co-Creation to Monetization

The flow of big data-driven information across the web, known as Internet traffic, is growing in volume, velocity, variety, veracity and value. Scalable data systems connected through the web or other IT platforms as mobile-friendly apps produce a network where information and experiences are shared in real time as never before. Digital value chains are based on sequential steps where big data are captured, stored, processed, and shared. Monetization is the last step, which transforms added value into cash.Big data are present at any stage of the value chain, so network theory is relevant for all steps of its upgrading. Value co-creation is an under-investigated topic, especially considering the hidden potentials of big data, such as networking attitudes of the users that generate data itself, and thus useful for predictive analytics within collaborative communities. Big data-driven business models revolutionize planning with hidden information and extract value from data that are considered a worthy asset. The value of data grows exponentially when it is connected and combined with other heterogeneous sources. This shows the importance of data fusion and big data integration (interoperability).This study proposes a new research avenue with an interdisciplinary approach to big data monetization, generating a shared added value that can boost their exploitation.

Unfair Commercial Practices:

Millions of European internet users access online platforms where their personal data is being collected, processed, analysed or sold. The existence of some of the largest online platforms is entirely based on data driven business models. In the European Union, the protection of personal data is considered a fundamental right. Under Article 8(3) of the EU Charter of Fundamental Rights, compliance with data protection rules should be subject to control by an independent authority. In the EU, enforcement of privacy rules almost solely takes place by the national data protection authorities. They typically apply sector-specific rules, based on the EU Data Protection Directive. In the United States, the Federal Trade Commission is the primary enforcer of consumers’ (online) privacy interests. The agency’s competence is not based on the protection of fundamental rights, but on the basis that maintenance of a competitive, fair marketplace will provide the right choices for consumers to take. In this Article the US legal framework will be discussed and compared to the EU legal framework, which forms our finding that in the EU rules on unfair commercial practices could be enforced in a similar manner to protect people’s privacy. In the EU, the many frictions concerning the market/consumer-oriented use of personal data form a good reason to actually deal with these frictions in a market/consumer legal framework.

Decentralized Data Analytics for Maintenance in Industrie 4.0

Due to the increased digital networking of machines and systems in the production area, large datasets are generated. In addition, more external sensors are installed at production systems to acquire data for production and maintenance optimization purposes. Therefore, data analytics and interpretation is one of the challenges in Industrie 4.0 applications. Reliable analysis of data (e.g. internal and external sensors), such as system-internal alarms and messages produced during the operation, can be used to optimize production and maintenance processes. Furthermore, information and knowledge can be extracted from raw data and used to develop data-driven business models and services, e.g. offer new availability contracts for production systems. This paper illustrates an approach for decentralized data analytics based on smart sensor networks.

Capturing value from big data – a taxonomy of data-driven business models used by start-up firms

Purpose The purpose of this paper is to derive a taxonomy of business models used by start-up firms that rely on data as a key resource for business, namely data-driven business models (DDBMs). By providing a framework to systematically analyse DDBMs, the study provides an introduction to DDBM as a field of study. Design/methodology/approach To develop the taxonomy of DDBMs, business model descriptions of 100 randomly chosen start-up firms were coded using a DDBM framework derived from literature, comprising six dimensions with 35 features. Subsequent application of clustering algorithms produced six different types of DDBM, validated by case studies from the study’s sample. Findings The taxonomy derived from the research consists of six different types of DDBM among start-ups. These types are characterised by a subset of six of nine clustering variables from the DDBM framework. Practical implications A major contribution of the paper is the designed framework, which stimulates thinking about the nature and future of DDBMs. The proposed taxonomy will help organisations to position their activities in the current DDBM landscape. Moreover, framework and taxonomy may lead to a DDBM design toolbox. Originality/value This paper develops a basis for understanding how start-ups build business models capture value from data as a key resource, adding a business perspective to the discussion of big data. By offering the scientific community a specific framework of business model features and a subsequent taxonomy, the paper provides reference points and serves as a foundation for future studies of DDBMs.

Consumers’ Willingness to Share Personal Data: Implications for Newspapers’ Business Models

ABSTRACTAs people’s willingness to pay for digital news remains low, this article investigates whether people would be willing to share personal data as a new currency for accessing news. Increasingly, news organizations collect personal data and track cross-media consumption to build detailed knowledge about and (re)connect with digital news consumers. This article presents the results of an industry-driven big data project that allows news organizations to engage with their audience more deeply by suggesting personalized content recommendations, serving targeted advertising and/or improving the user experience. It presents the concept of the datawall, where the user pays with their data, and delivers new insights into the challenges facing data-driven business models.