Data-driven Business Models Research Articles

The rise of Baidu, Alibaba and Tencent (BAT) and their role in China’s Belt and Road Initiative (BRI)

The Chinese digital technology giants, Baidu, Alibaba and Tencent (BAT), dominate over their competitors in China across platforms that include e-commerce, digital entertainment, e-finance and artificial intelligence (AI). To understand BAT’s corporate power and their strategic role working with the government – in this case, their involvement in the Belt and Road Initiative (BRI) – this paper unveils the capabilities of these three oligopolies and discusses their international expansion in relation to the BRI. The BRI is being constructed on two layers, the physical and digital infrastructure, and the BAT are contributing to the latter. This paper examines the interrelations between BAT and the state through case studies, observing the tensions and potential contradictions arising from the reliance of the Chinese state on the BAT to build digital infrastructure, while the BAT seek to minimize direct state regulation for their data-driven business models.

Research Paper on Data-Driven Business Models: Why the Asia-Pacific Region Serves as an Example for the Western Companies? How Might they Improve Their Business Models in 5 Years?

This article is an expansion of Anastasov, T.’s master’s thesis (2019) and attempts to give a clear definition and taxonomy of the Data-Driven Business Models (DDBMs) as well as illustrate data challenges and opportunities that come along with this. These definitions were cross-analyzed with 3 cases from the Asia-Pacific region to deliver concrete insights and inspiration for Western companies to reinvent their businesses in the next 5 years. A comparison between Data-Driven and Data-Centric models was given as well, not previously analyzed in the thesis, as a view on the current state-of-the-art data business models.

Creating Value From Energy Data: A\xa0Practitioner\u2019s Perspective on Data-Driven Smart Energy Business Models

Along the energy value chain where produced energy is delivered for consumption within individual households, physical devices are being replaced by smart and connected products referred to as the Internet of Things. These smart products generate large volumes of data that can enable new data-driven business models. In the energy sector, consumers produce data by consuming energy, which is monitored and controlled by different smart energy products like microgeneration units or home automation devices. Although smart energy business models have been subject to academic research, the business value of data, which is created from smart energy products, remains unclear. Against this background, the paper presents a practitioners’ perspective on the data-driven potential of smart energy technologies for new business models, the constituting elements of these business models, and the challenges associated with their implementation. By doing this, we provide trajectories for the future of the energy industry and draw guiding implications for developing data-driven smart energy business models.

Contract Law in the Age of Big Data

Abstract In data-driven business models, users’ personal data is collected in order to determine the preferences of consumers and to the tailor production and advertising to these preferences. In these business models, consumers do not pay a price but provide their data, such as IP numbers, locations, and email addresses to benefit from the digital service or content. Contracts facilitate interactions between these providers and users. Their transactions are regulated by contracts in which their agreement on data use and data processing are stipulated. Data is always collected and processed through a contractual relationship and in this paper, I will argue that there are problems arising from contracts involving data to which contract law applies and that contract law can map these problems and offer insights. The scope of this study will be limited to issues where data is provided as counter-performance and where data is provided in addition to a monetary payment.

The Role of Analytics in Data-Driven Business Models of Multi-Sided Platforms: An exploration in the food industry

IntroductionData-driven business models (DDBMs) are either emergent or new multi-layered, multi-dimensional business models enabled by big data (Hartmann et al., 2016). Several highly diverse industries are moving towards DDBM to survive and compete. Such industries include especially those in which understanding user-buying patterns in an in-depth manner is becoming

Managing Digital Transformation: Comprehending Digitalization Tensions for Driving Disruptive Change

Recent studies have shown the significance of disruptive innovation activities in the context of digital transformation for incumbent organizations. These companies strive to move towards data-driven business models through applying digital technologies which were attested a disruptive nature. However, it is unclear why many of those innovation projects cannot seize the full potential of digital technologies and rather result in an incremental adaptation of existing products or processes. Applying paradox as a meta-theory in this context, we seek to examine the mechanisms of digital innovation management projects in established, industrial companies. By conducting a qualitative study based on a collaborative research project and additional interviews with experts from industrial companies we were able to identify and categorize emerging tensions in digital innovation projects. Based on those results, we derived underlying paradoxes, notably regarding the openness in collaborations, the determination of the digital innovation process and integration of the innovation outcome in the existing business. Based on paradox theory, we discuss that those subliminal paradoxes may become salient in form of tensions in digitalization projects. An awareness for existing paradoxes can reduce the impact of tensions in digital innovation management in order to create disruptive innovations through digital transformation.

Opportunity complementarity in data-driven business models

Business model literature has been focused on value co-creation and co-capture logic in understanding business models in the ecosystem. To understand the “Ex Ante” source of ecosystem-based value creation/capture, this paper proposes opportunity complementarity as a key antecedent and construct for the ecosystem-based value creation and capture in data-driven business ecosystems.

Information asymmetries: recognizing the limits of the GDPR on the data-driven market

Online search engines, social media platforms, and targeted advertising services often employ a “data-driven” business model based on the large-scale collection, analysis, and monetization of personal data. When providing such services significant information asymmetries arise: data-driven companies collect much more personal data than the consumer knows or can reasonably oversee, and data-driven companies have much more (technical) information about how this data is processed than consumers would be able to understand. This article demonstrates the vulnerable position consumers continue to find themselves in as a result of information asymmetries between them and data-driven companies. The GDPR, by itself, is in practice unable to mitigate these information asymmetries, nor would it be able to provide for effective transparency, since it does not account for the unique characteristics of the data-driven business model. Consumers are thus faced with an insurmountable lack of transparency which is inherent in, as well as the inevitable consequence of, the magnitude of the information asymmetries present on the data-driven market.

Accountable algorithms? The ethical implications of data-driven business models

PurposeThe purpose of this study is to identify, analyze and explain the ethical implications that can result from the datafication of service.Design/methodology/approachThis study uses a midrange theorizing approach to integrate currently disconnected perspectives on technology-enabled service, data-driven business models, data ethics and business ethics to introduce a novel analytical framework centered on data-driven business models as the general metatheoretical unit of analysis. The authors then contextualize the framework using data-intensive insurance services.FindingsThe resulting midrange theory offers new insights into how using machine learning, AI and big data sets can lead to unethical implications. Centered around 13 ethical challenges, this work outlines how data-driven business models redefine the value network, alter the roles of individual actors as cocreators of value, lead to the emergence of new data-driven value propositions, as well as novel revenue and cost models.Practical implicationsFuture research based on the framework can help guide practitioners to implement and use advanced analytics more effectively and ethically.Originality/valueAt a time when future technological developments related to AI, machine learning or other forms of advanced data analytics are unpredictable, this study instigates a critical and timely discourse within the service research community about the ethical implications that can arise from the datafication of service by introducing much-needed theory and terminology.

Platformisation of Mobile Operators Business Model: A Proposition Using Design Science Approach and Grounded Theory Principles

Mobile network operators (MNOs) business models (BM) are under pressure due to their lesser capability of introducing superior values to their customers in the mobile ecosystem. However, recent research efforts in developing new BM advocate two-sided BM that encompasses the diversity of MNO’s activities and capabilities. As a result, a new multidisciplinary service-based two-sided BM needs to be developed that incorporates these activities and skills. The small body of the extant literature on the subject suggests that it may be possible to enhance MNOs' BM by combining contemporary information technology tools with managerial design principles and concepts. This study designs a two-sided mobile advertising BM to investigate the application of a big data-driven BM to transform MNOs' current one-sided BMs to two-sided ones. To accomplish this, it combines the design science research methodology (DSR), which aims to create a problem-solving artifact for real-world problems, and the grounded theory approach, which aims to develop substantive theory and increase the rigor of the design process. The initial BM was proposed using deductive and adductive reasoning from academic and grey literature. The results of this study have shown that the new BM can enhance their revenue streams and competitive edge. This study identified that successful BM should be built on established MNOs core competencies and business activities. This study showed the applicability of two-sided theory and big data-driven tools and technologies to create new superior value propositions to both advertisers and end-users and thus innovative BMs for MNOs. The paper concludes with the fundamental requirements to build a data-driven two-sided BM.

Explaining the Technology Use Behavior of Privacy-Enhancing Technologies: The Case of Tor and JonDonym

Abstract Today’s environment of data-driven business models relies heavily on collecting as much personal data as possible. Besides being protected by governmental regulation, internet users can also try to protect their privacy on an individual basis. One of the most famous ways to accomplish this, is to use privacy-enhancing technologies (PETs). However, the number of users is particularly important for the anonymity set of the service. The more users use the service, the more difficult it will be to trace an individual user. There is a lot of research determining the technical properties of PETs like Tor or JonDonym, but the use behavior of the users is rarely considered, although it is a decisive factor for the acceptance of a PET. Therefore, it is an important driver for increasing the user base. We undertake a first step towards understanding the use behavior of PETs employing a mixed-method approach. We conducted an online survey with 265 users of the anonymity services Tor and JonDonym (124 users of Tor and 141 users of JonDonym). We use the technology acceptance model as a theoretical starting point and extend it with the constructs perceived anonymity and trust in the service in order to take account for the specific nature of PETs. Our model explains almost half of the variance of the behavioral intention to use the two PETs. The results indicate that both newly added variables are highly relevant factors in the path model. We augment these insights with a qualitative analysis of answers to open questions about the users’ concerns, the circumstances under which they would pay money and choose a paid premium tariff (only for JonDonym), features they would like to have and why they would or would not recommend Tor/JonDonym. Thereby, we provide additional insights about the users’ attitudes and perceptions of the services and propose new use factors not covered by our model for future research.

Demystification of Artificial Intelligence in Education – How much AI is really in the Educational Technology?

<span lang="EN-US">The data-driven development of education through Learning Analytics in combination with Artificial Intelligence is an emerging field in the education sector. In the field of Artificial Intelligence in Education, numerous studies and research have been carried out over the past 60 years, and since then drastic changes have taken place. In the first part of this paper we present a brief overview of the current status of Learning Analytics and Artificial Intelligence in education. In order to develop a better understanding of the relationship between Learning Analytics and Artificial Intelligence in education, we outline the relationship between the two phenomena. The results show that the previous studies only vaguely distinguish between them: the terms are often used synonymously. In the second part of the paper we focus on the question why the European market currently has hardly any real applications for Artificial Intelligence in education. The research is based on a meta-investigation of data-driven business models, in particular the so-called Educational Technology providers. The core of the analysis is the question of how data-driven these companies really are, how much Learning Analytics and Artificial Intelligence is applied and whether there is a causal connection between the growth of the Educational Technology market and the application relevance of Artificial Intelligence in Education. In the scientific and public discourse, we can observe a distortion between the theoretical-conjunctive understanding of the application of Artificial Intelligence in Education and the current practical relevance.</span>

Virtual Reality enriched Business Model Canvas Building Blocks for enhancing Customer Retention

The upcoming digital transformation is a present key challenge for companies. Nevertheless, this key challenge is also a megatrend, which goes along with great opportunities like production- and process optimization, possible new data-driven business models and new perspectives for interaction with customers. Osterwalder’s Business Model Canvas provides companies a template and guideline for documenting the existing and developing new business models. Two of the key questions when describing the existing business model as well as when developing a new one is: “Who are our most important customers and for whom are we creating value?” These questions regarding the customer segments go along with the channels and the relationship between producer and customer. Due to everywhere available connectivity to the internet, customer segments may change and new customer segments expect new communication channels and a different producer/customer relationship. Virtual Reality is a cutting-edge technology with potential in various areas like product development, product presentation, and remote support. However, remote support can be more than an expert/expert communication for guided maintenance work, it has the potential for reaching new customer segments, setting up new communication channels and thus creating new customer relationships. All this together bears the potential for a Virtual Reality driven Customer Retention.

The Economics of the German Investigation of Facebook’s Data Collection

The importance of digital platforms and related data-driven business models is ever in-creasing and poses challenges for the workability of competition in the respective markets (tendencies towards dominant platforms, paying-with-data instead of traditional money, privacy concerns, etc.). Due to such challenges, investigations of such markets are of high interest. One of recent cases is the investigation of Facebook’s data collection practices by German competition authorities. Our paper, in contrast to the wide stream of legal stud-ies on this case, aims to analyze whether Facebook’s practices regarding data collection could constitute an abuse of market power from an economic perspective, more specifi-cally against the background of modern data economics. In doing so we summarize the state of the advanced theories, including influences from behavioral economics, address-ing such markets, and discuss four potential theories of harm.

Evaluating the impact of AI on insurance: the four emerging AI- and data-driven business models

The increasing capabilities of artificial intelligence (AI) are changing the way organizations operate and interact with users both internally and externally. The insurance sector is currently using AI in several ways but its potential to disrupt insurance is not clear. This research evaluated the implementation of AI-led automation in 20 insurance companies. The findings indicate four business models (BM) emerging: In the first model the insurer takes a smaller part of the value chain allowing others with superior AI and data to take a larger part. In the second model the insurer keeps the same model and value chain but uses AI to improve effectiveness. In the third model the insurer adapts their model to fully utilize AI and seek new sources of data and customers. Lastly in the fourth model a technology focused company uses their existing AI prowess, superior data and extensive customer base, and adds insurance provision.

Evaluating the impact of AI on insurance: The four emerging AI- and data-driven business models

The increasing capabilities of artificial intelligence (AI) are changing the way organizations operate and interact with users both internally and externally. The insurance sector is currently using AI in several ways but its potential to disrupt insurance is not clear. This research evaluated the implementation of AI-led automation in 20 insurance companies. The findings indicate four business models (BM) emerging: In the first model the insurer takes a smaller part of the value chain allowing others with superior AI and data to take a larger part. In the second model the insurer keeps the same model and value chain but uses AI to improve effectiveness. In the third model the insurer adapts their model to fully utilize AI and seek new sources of data and customers. Lastly in the fourth model a technology focused company uses their existing AI prowess, superior data and extensive customer base, and adds insurance provision.

Understanding the Role of Data in Data-Driven Business Models from a Transfer Pricing Perspective

The importance of data for (digital) business models is also from a transfer pricing perspective of great interest. In this article, the authors provide established frameworks to understand the individual role and quality of data for (digital) business models.

Cloud Computing, Cross-Border Data Flows and New Challenges for Measurement in Economics

When economists talk about ‘measurement’ they tend to refer to metrics that can capture changes in quantity, quality and distribution of goods and services. In this paper we argue that the digital transformation of the economy, particularly the rise of cloud computing as a general-purpose technology, can pose serious challenges to traditional concepts and practices of economic measurement. In the first part we show how quality-adjusted prices of cloud services have been falling rapidly over the past decade, which is currently not captured by the deflators used in official statistics. We then discuss how this enabled the spread of data-driven business models, while also lowering entry barriers to advanced production techniques such as artificial intelligence or robotic-process-automation. It is likely that these process innovations are not fully measured at present. A final challenge to measurement arises from the fragmentation of value chains across borders and increasing use of intangible intermediate inputs such as intellectual property and data. While digital technologies make it very easy for these types of inputs to be transferred within or between companies, existing economic statistics often fail to capture them at all.

Big Data for the Sustainability of Healthcare Project Financing

This study aims to detect if and how big data can improve the quality and timeliness of information in infrastructural healthcare Project Finance (PF) investments, making them more sustainable, and increasing their overall efficiency. Interactions with telemedicine or disease management and prediction are promising but are still underexploited. However, given rising health expenditure and shrinking budgets, data-driven cost-cutting is inevitably required. An interdisciplinary approach combines complementary aspects concerning big data, healthcare information technology, and PF investments. The methodology is based on a business plan of a standard healthcare Public-Private Partnership (PPP) investment, compared with a big data-driven business model that incorporates predictive analytics in different scenarios. When Public and Private Partners interact through networking big data and interoperable databases, they boost value co-creation, improving Value for Money and reducing risk. Big data can also help by shortening supply chain steps, expanding economic marginality and easing the sustainable planning of smart healthcare investments. Flexibility, driven by timely big data feedbacks, contributes to reducing the intrinsic rigidity of long-termed PF healthcare investments. Healthcare is a highly networked and systemic industry, that can benefit from interacting with big data that provide timely feedbacks for continuous business model re-engineering, reducing the distance between forecasts and actual occurrences. Risk shrinks and sustainability is fostered, together with the bankability of the infrastructural investment.

Digital transformation: harnessing digital technologies for the next generation of services

PurposeThe purpose of this paper is to discuss digital transformation and its four trajectories – digital technology, digital strategy, customer experience and data-driven business models – that could shape the next generation of services. This includes a discussion on whether both the market and organizations are all ready for the digital change and what are the opportunities that will enable firms to create and capture value though new business models.Design/methodology/approachProviding services is a proven and effective way to secure a competitive position, deliver long-term stable revenues and open up new market opportunities. However, it is also clear that some organisations are struggling to digitally transform. Therefore, the commentary provides a brief insight into how firms explore the possibilities of digital transformation and navigate these uncharted waters.FindingsToday’s digital technologies affect the organisation outside and in, enabling the creation of new business models and transforming the customer experience. The incumbents are acutely aware that they need to transform strategically – to build new networks and value chains.Originality/valueThis commentary extends earlier work exploring the digital disruption within services to highlight a number of connected areas: the challenges and opportunities of digital transformation at a strategic level, as well as understanding and enhancing the customer experience and seeing how new data-driven business models can underpin service transformation.