Data Business Models Research Articles

On the Willingness to Pay for social media/messenger services taking into account personality and sent/received messages among WhatsApp users

WhatsApp has billions of users worldwide. Instead of paying a subscription fee, users provide their data for the use allowance. This data is used by Meta – the company behind WhatsApp – to obtain insights into user characteristics and monetize those insights. However, this data business model is among others criticized for fostering a loss of privacy that arises when platforms analyze user data, and for the use of design elements to attract users to the platform when they are not online or to extend their online time. Therefore, an increasing number of scientists are discussing whether other payment models are needed to overcome those disadvantages, like a monetary payment model. However, users would probably only pay for improved social media products. This paper provides an empirical basis for understanding the user perspective and, in particular, whether and how much users are willing to pay for improved social media products. For this, 2924 WhatsApp users’ perspectives on this topic were investigated. They were asked whether and how much they are willing to pay money for a messenger/social media service when its quality would be improved. Variables potentially influencing Willingness to Pay (i.e., personality, sent/received messages) were studied as well.47% of the participants were unwilling to pay for a healthier messenger service, and about a quarter were willing or stayed neutral. Further analysis revealed that more agreeable people were more willing to pay. Further: Higher Extraversion was associated with more sent/received messages, but the number of sent/received messages was not linked to Willingness to Pay. The present study shows that many users still are not willing to pay for social media (here messengers), which indicates that the advantages of paying for social media with money instead of with one's own data might need to be better communicated.

Need for Affect, Problematic Social Media Use and the Mediating Role of Fear of Missing Out in European and Arab Samples.

The growing awareness and concern about the excessive use of social media have led to an increasing number of studies investigating the underlying factors contributing to this behavior. In the literature, it is discussed that problematic social media use (PSMU) can impact individuals' mental health and well-being. Drawing on the Interaction of Person-Affect-Cognition-Execution (I-PACE) model, this study aimed to examine the association between the need for affect (affect approach and affect avoidance) and PSMU (operationalized via the social media disorder scale), as well as the mediating role of fear of missing out (FoMO) in that relation. Data were collected via an online survey from 513 participants in European and Arabic countries. Regression and mediation analyses were conducted to explore the relationships between affect approach, affect avoidance, FoMO, and PSMU. Regression analysis results indicated that both affect approach and affect avoidance as part of the need for affect construct significantly predicted PSMU in both cultural contexts. Mediation analysis showed that FoMO partially mediated the relationship between affect approach and PSMU in the Arab sample but not in the European sample. Beyond this, FoMO partially mediated the relationship between affect avoidance and PSMU in both cultural samples. The present study indicates that managing emotions could be an effective strategy to combat PSMU. In line with this and against the background of the data business model behind social media companies, we deem it to be of importance to minimize triggers related to FoMO in the design of social media platforms (for example, push notifications). This might be particularly relevant for individuals with a high inclination towards affect approach and affect avoidance.

Sponsored data: A game-theoretic model with consumer multihoming behaviour

Problem definitionConsumer multihoming behaviour challenges the sponsored data business model initiated by internet service providers (ISPs) associated with content providers (CPs). Academic/practical relevanceDifferent from data subsidization research which focuses on the CPs having no exclusive content and consumer exhibiting only single-homing behaviour, we explore the interplay between a monopolistic ISP and two competing CPs in the presence of consumer multihoming behaviour. We capture the effect of CPs’ differences in operational characteristics (e.g., exclusive content and revenue rate) on the ISP's data plan, CPs’ subsidy regime, and consumer multihoming behaviour. MethodologyWe develop a game-theoretical model with an ISP and two competing content providers (CPs) offering overlapping (i.e., non-exclusive) content and exclusive content to consumers who may have multihoming behaviour. The monopolist ISP designs its data plans by providing the subsidy options for neither, one, or both CPs (represented as strategies N, O, and B), and it announces a usage-based per-packet price. In response, the involved CPs decide the subsidy regime, based on which consumers choose between single-homing and multihoming. ResultsWe find that the CPs’ exclusive content popularity and revenue rate are critical considerations for the ISP in designing the data plans. Only when the revenue rate is high will the ISP allow both CPs to subsidize data; otherwise, it prefers strategy N for high exclusive content (i.e., when a large amount of exclusive content) but strategy O for low exclusive content. For CPs, strategy O induces consumers to choose multihoming when the exclusive content is high, and strategy B further stimulates some consumers to switch from single-homing to multihoming for a high revenue rate. Interestingly, the subsidy amount is not affected by the revenue rate in strategy O after the revenue rate exceeds a threshold. Moreover, we identify conditions under which the ISP's data plan promotes the consumer surplus and social welfare. Managerial implicationsOur study sheds light on the influence of consumer multihoming behaviour on the co-opetition in the streaming industry. It provides insights for ISPs and CPs in the recent debate on the effect of sponsored data on consumer surplus and social welfare.

Enhancing Competitive Capabilities of Healthcare SCM through the Blockchain: Big Data Business Model’s Viewpoint

The reasons why supply chain management (SCM) needs blockchain technology include simplification of transaction procedures, time and cost reductions, and reliability improvement. This study emphasizes the necessity of introducing a blockchain-based joint logistics system to strengthen the competency of medical SCM and proposes a healthcare supply chain management (HSCM) competency measurement item through an analytic hierarchy process. The variables needed for using blockchain-based joint logistics are the performance expectations, effort expectations, promotion conditions, and social impact of the unified theory of acceptance and use of technology (UTAUT) model, as well as the HSCM competency results in increased reliability and transparency, enhanced SCM, and enhanced scalability. By analyzing the importance of securing reliability based on blockchain technology in the establishment of a supply chain network for HSCM competency, we reveal that joint logistics can be achieved, and synergistic effects can be created by implementing the integrated database to secure HSCM competency. Strengthening partnerships, such as joint logistics, will eventually lead to HSCM competency. In particular, HSCM should seek ways to upgrade its competitive capabilities through big data analysis based on the establishment of a joint logistics system.

Understanding open data business models from innovation and knowledge management perspectives

PurposeThis paper aims to analyse the open data business models (ODBMs) as a source of knowledge and innovation to generate economic and social value. A framework for understanding ODBMs is presented. First, the knowledge structure of the ODBMs literature is identified. Second, a conceptual model for analysing the ODBMs is proposed. And finally, the future trends in ODBMs research are discussed.Design/methodology/approachIn this paper, co-word analysis is performed to identify the topics related with ODBMs.FindingsThe ODBMs structure of knowledge is articulated in five themes: business model, smart city, business ecosystem, decision-making and innovation. Based on these results, a five-step model for analysing ODBMs is proposed. Finally, a discussion of the future trends of ODBMs focussed on a knowledge management perspective, open data ecosystems and business intelligence is presented.Originality/valueThe paper presents a picture of what, where, for whom and for what ODBMs have been studied and develops a new model to explain the value creation process of ODBMs. Taking a step further, applying the principles and models of knowledge management and business intelligence to ODBMs is also recommended in order to transfer and transform open data into valuable knowledge that can be used for developing apps. In that context, the importance of encouraging collaboration between different agents in the so-called open data ecosystem is presented.

Plasma processing for advanced microelectronics beyond CMOS

The scientific study of plasma discharges and their material interactions has been crucial to the development of semiconductor process engineering and, by extension, the entire microelectronics industry. In recent years, the proliferation of the big data business model has led to heightened interest in technology candidates with the potential to supplant CMOS architectures in critical metrics such as computational capacity or power consumption. These novel technologies share many common material elements with existing logic and memory devices, but the impact of mass fabrication techniques on their performance is largely unknown due to differences in the underlying physics of their operation. Two components are thus vital to this endeavor: fundamental evaluation of any emerging plasma process interactions and the ability to tailor any aspect of the plasma process necessary to produce the desired specifications. In this article, we review relevant advances in the study of plasma-induced damage mechanisms as well as characterization methods such as diagnostic probes and simulation tools. We also provide an outlook for the application of techniques such as plasma doping, area-selective etch/deposition, and heterogeneous integration. The frontiers of any new computing paradigms can only be explored through a focus on atomic scale engineering, and progress in the field of plasma science supplies the necessary toolset.

Construction of power data products based on STP theory

Under the background of digital revolution, the level of power grid intelligence and automation has been improved, and a large number of data of various types have been gathered. Through the analysis of multi-source data of power grid, the new profit growth point of power grid enterprises can be mined. In this paper, the “STP theory” is introduced into the power data commodity market. Through the in-depth analysis of the three elements of power data market segmentation, target market selection and market positioning, the power data product types facing different data trading objects, such as government, power grid itself, third-party enterprises and ordinary users, are constructed. This study will provide a basis for grid companies to explore new data business model.

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.

Should We Pay for Our Social Media/Messenger Applications? Preliminary Data on the Acceptance of an Alternative to the Current Prevailing Data Business Model.

In the age of surveillance capitalism, the prevailing business model underlying the use of social media applications (“apps”) foresees the exchange of personal data for the allowance to use an online service. Such a data business model comes with many potential negative side effects ranging from violation of privacy issues to election manipulation. Therefore, it is of utmost importance to think of alternatives to the current data business model. The present study investigated how strong the support would be for a monetary payment model among a sample of 210 participants. Participants were asked about their willingness to pay for social media, if in turn their data would be private and other problems concerning social media use would be tackled. Only one-fifth of participants (21.43%) supported such a model. From the Big Five personality traits, Agreeableness was positively associated with support of such a model. Finally, data are also provided on how much participants would be willing to pay for social media on a monthly basis. The present study’s findings are of a preliminary nature and will contribute to the start of an important discussion.

Evaluating Business Models Enabling Organic Additive Manufacturing for Maintenance and Sustainment

This research examines the spare parts data business models allowing the government to produce parts on demand (i.e., only when required versus long-term warehousing) and at the point-of-need using additive manufacturing. The research includes a survey of acquisition and engineering professionals within government and industry, and an analysis using an aviation case study.

RETRACTED ARTICLE: The big data visualization technology based ecosystem cycle on high speed network

Big Data is becoming a key strategy in the business sector, with the increasing number of corporate customer data tracking and collection practices, and the proliferation of multimedia content, such as the proliferation of multimedia and camera modules, and the proliferation of multimedia content. In this paper, we proposed a Big Data ecosystem cycle to create the data utilization strategies and analytic manpower for the success of data utilization with the data uptake. Big Data growth and investment with visualizations are critical to creating a significant role in the development of various Big Data business models. First of all, it is necessary to improve the data for innovation (Releasing Data for Innovation) and data visualization, to minimize the risk of privacy, and to mitigate the risk of privacy leaks to facilitate the promotion of Big Data utilization. It needs to comply with the standardization of Big Data requirements based on ISO/IEC geurigo 1 SC JTC data, and it has necessary to identify the scale of the existing architectures for the sake of accommodating and supporting the architecture that embrace the benefits of legacy architectures.

Re-Allocation of Taxing Rights for Big Data Business Models

The ongoing digitalisation of the economy challenges the international tax system. Specifically, the current rules of source taxation do not fully reflect the paradigm of taxing where value is created. In digital business models, firms often do not need a physical presence to be economically active in a given jurisdiction and, thus, avoid being taxable in this jurisdiction. In this paper, we make three distinct contributions. First, we review all major digital trends and business models for their scope to create value in a given jurisdiction without a physical presence there. Second, we present a concept that integrates non-physical value creation into the current system. The concept is based on sustained user relationships (SURE) that may be operated from abroad and exploited for data collection, advertising and platform services. Third, we discuss the operational aspects of implementing the concept, both in economic and legal terms: nexus rules and profit allocation rules. We conclude that that SURE concept may have important advantages over its main alternatives: Other than proposals based on digital user contributions, it does not require ring-fencing of a small segment of the digital economy. And in contrast to the concept of “marketing intangibles” (and more radical concepts like DBCFT or CCCTB), it does not advocate a general shift towards taxation in the market jurisdiction, which is difficult to justify under the logic of the current system of source taxation.

Analytic surveillance: Big data business models in the time of privacy awareness

Massive data collection and analysis is at the heart of many business models today. New technologies allow for fine-grained recommendation systems that help companies make accurate market predictions while also providing clients with highly personalized services. Because of this, extreme care must be taken when it comes to storing and managing personal (often highly sensitive) information. In this paper we focus on the influence of big data management in media business content platforms, mainly in well-known OTT (Over the Top) services. In addition, we comment on the implications of data management in social networks. We discuss the privacy and security risks associated with this novel scenario, and briefly comment on tools that aid in securing the privacy of business intelligence within this context.

Open data business model: innowacyjne aspekty projektowania modeli biznesu

Problem badawczy opisany w artykule obejmuje identyfikację kluczowych determinant kierunków rozwoju problematyki kształtowania innowacyjnych modeli biznesu opartych na założeniach Open Data. Celem badawczym artykułu jest odpowiedzenie na pytanie, jakie są kluczowe determinanty konceptualizacji i operacjonalizacji procesu projektowania modeli biznesu na podstawie założeń koncepcji Open Data. Metodyka badania obejmuje identyfikację atrybutów jakościowych wybranych modeli biznesu opartych na założeniach Open Data wraz ze wskazaniem kluczowych aspektów projektowania tych modeli biznesu. Rezultatem przeprowadzonych studiów literaturowych, badań i prac konceptualnych jest wskazanie nowoczesnych trendów w zakresie projektowania modeli biznesu opartych na założeniach Big Data. Kluczowe wnioski wskazują, że analiza wybranych modeli biznesu działających z wykorzystaniem OGT (Open Government Data) potwierdza, iż pojawił się dzięki nim nurt nowych innowacyjnych modeli biznesu wspieranych przez rządy bądź bazujących na ich strategii udostępniania dużych zbiorów danych. Rozwijany jest przy tym strumień prywatnych inicjatyw wspierających strategię upowszechniania dużych zbiorów danych.

Big data business models: Challenges and opportunities

AbstractThis paper, based on 28 interviews from a range of business leaders and practitioners, examines the current state of big data use in business, as well as the main opportunities and challenges presented by big data. It begins with an account of the current landscape and what is meant by big data. Next, it draws distinctions between the ways organisations use data and provides a taxonomy of big data business models. We observe a variety of different business models, depending not only on sector, but also on whether the main advantages derive from analytics capabilities or from having ready access to valuable data sources. Some major challenges emerge from this account, including data quality and protectiveness about sharing data. The conclusion discusses these challenges, and points to the tensions and differing perceptions about how data should be governed as between business practitioners, the promoters of open data, and the wider public.

Exploring the economic value of open government data

Business models for open data have emerged in response to the economic opportunities presented by the increasing availability of open data. However, scholarly efforts providing elaborations, rigorous analysis and comparison of open data models are very limited. This could be partly attributed to the fact that most discussions on Open Data Business Models (ODBMs) are predominantly in the practice community. This shortcoming has resulted in a growing list of ODBMs which, on closer examination, are not clearly delineated and lack clear value orientation. This has made the understanding of value creation and exploitation mechanisms in existing open data businesses difficult and challenging to transfer. Following the Design Science Research (DSR) tradition, we developed a 6-Value (6-V) business model framework as a design artifact to facilitate the explication and detailed analysis of existing ODBMs in practice. Based on the results from the analysis, we identify business model patterns and emerging core value disciplines for open data businesses. Our results not only help streamline existing ODBMs and help in linking them to the overall business strategy, but could also guide governments in developing the required capabilities to support and sustain the business models.

Open Innovation, Open Data and New Business Models

Abstract Open government data are the result of as well as the preconditions for open government. Data made publically accessible are an important basis for creating added value for society as a whole. Open data business models support entrepreneurs rein using and combining available open data sources to provide value-added services. Three cases show how open data business models can be realized. 1. Introduction Societies, as well as the business world,are undergoing a digital transformation. Means of information and communication technologies (ICT) are triggering significant and fundamental changes. ICT have had have multiple, diverse effects since their emergenceand continue to do so . From an economic and business perspective, several industries undergone major have transformations over the past four decades; the banking and finance or travel industries are key examples. Many industries are struggling and are searching for new strategies and business models to cope with the effects of digitalization.

Awaiting the Second Big Data Revolution: From Digital Noise to Value Creation

“Big data”—the collection of vast quantities of data about individual behavior via online, mobile, and other data-driven services—has been heralded as the agent of a third industrial revolution—one with raw materials measured in bits, rather than tons of steel or barrels of oil. Yet the industrial revolution transformed not just how firms made things, but the fundamental approach to value creation in industrial economies. To date, big data has not achieved this distinction. Instead, today’s successful big data business models largely use data to scale old modes of value creation, rather than invent new ones altogether. Moreover, today’s big data cannot deliver the promised revolution. In this way, today’s big data landscape resembles the early phases of the first industrial revolution, rather than the culmination of the second a century later. Realizing the second big data revolution will require fundamentally different kinds of data, different innovations, and different business models than those seen to date. That fact has profound consequences for the kinds of investments and innovations firms must seek, and the economic, political, and social consequences that those innovations portend.