Exploring design elements of personal data markets

2023 | Electronic Markets | Citations: 0

Authors: Scheider, Simon; Lauf, Florian; Geller, Simon; Möller, Frederik; Otto, Boris

Abstract: Since the emerging information economy relies heavily on data for advancement an ... Expand

Abstract: Since the emerging information economy relies heavily on data for advancement and growth, data markets have gained increasing attention. However, while global data economies are evolving and data are increasingly shared among organizations in various data ecosystems, marketplaces for personal data (PDMs) exhibited considerable start-up difficulties, which doomed their majority to either fail quickly or to operate in legal grey zones. Apparently, in recent times, novel PDMs have arisen which seem economically and technically viable. The study investigates this “new generation” from both an economic and a technological perspective. Adhering to a rigorous methodology for taxonomy building and evaluation, 18 dimensions and 59 characteristics are presented alongside which these new PDMs can be designed. Additionally, archetypes are derived. The findings reveal that PDMs tend to follow certain design commonalities holding for data markets generally but comprise specific design elements distinguishing them both from conventional data markets and among each other. Collapse

Topics: privacy tokenomics blockchain search engine business model

Methods: archival research qualitative interview literature sample personal interview cluster analysis

Towards a reconceptualization of data in organizations: a literature review

2023 | European Conference On Information Systems | Citations: 0

Authors: Xu, Daisy; Someh, Ida Asadi; Indulska, Marta

Abstract: Information Systems researchers have sought to demonstrate the strategic value ... Expand

Abstract: Information Systems researchers have sought to demonstrate the strategic value of data in organizations through robust evidence. Over time, the ways data benefit organizations have evolved and become more diverse, yet definitions of data and their value propositions have not kept up and remain disconnected. The field still lacks clear understanding of various roles data play in organizations and how to define them. This paper presents a comprehensive review of related literature in the Information Systems and Management fields from the past two decades. We first conduct a systematic literature search and organize them into key research themes by the purpose of data use. We then propose a reconceptualization of data that takes into account their distinct features. Our aim is to provide an explanation for the unique nature of data and the diverse sources of their value in organizations. Collapse

Topics: decision making value creation business process management innovation management organizational context

Methods: literature study design artifact literature sample synthesis theory development

How to monetize data: An economic analysis of data monetization strategies under competition

2023 | Decision Support Systems | Citations: 0

Authors: Zhang, Xin; Yue, Wei Thoo; Yu, Yugang; Zhang, Xiong

Abstract: With data being generated at an unprecedented rate, monetizing data has become a ... Expand

Abstract: With data being generated at an unprecedented rate, monetizing data has become a paramount issue in this era of the digital economy. Firms have traditionally leveraged consumer data to improve their product or service of­ ferings and increase their sales revenue, which is indirect data monetization. The recent emergence of online data markets and the data brokerage industry have facilitated direct data monetization, enabling firms to obtain additional revenue by selling their anonymized data directly to data brokers. This study develops a gametheoretic model to investigate the impact of direct data monetization on competition between firms. Our re­ sults show that in equilibrium, high-value firms with competitive advantages in product or service value often sell less data than low-value firms, implying that high-value firms rely less on direct data monetization than lowvalue firms. Surprisingly, as direct data monetization prevails, high-value firms further dominate the market for selling products or services, especially when the data broker’s analytics capability is high. This result reveals that data brokers with high analytics capabilities can hinder, rather than facilitate, market competition. Finally, direct data monetization may benefit both consumer surplus and social welfare because it facilitates data-sharing be­ tween firms, allowing firms to obtain additional insights to improve their product or service offerings. These findings are explored in-depth, along with different model extensions and their relevant managerial implications. Collapse

Topics: data monetization IS economics analytical information system consumer surplus economic surplus

Theories: game theory

Configuring Relationships between Analytics and Business Domain Groups for Knowledge Integration

2023 | Journal of the Association for Information Systems | Citations: 1

Authors: Someh, Ida; Wixom, Barbara; Davern, Michael; Shanks, Graeme

Abstract: To realize value from their wealth of digital data, organizations are investing ... Expand

Abstract: To realize value from their wealth of digital data, organizations are investing in data-driven organizational initiatives—efforts in which they must draw expertise in data, algorithms, and visualization together with knowledge and skills in business domains such as marketing and human resources. However, they face the challenge of crossing the knowledge divide between analytics groups and business groups. Exploring relationships between the two groups in 37 data-driven organizational initiatives, we develop a configuration-based model that explains analytics and businessdomain knowledge integration through the lens of synergy. Our configurational analyses revealed five configurations of relationships between the two, which bring about two distinct change outcomes: “dedicated data groups” and “multidisciplinary teams” lead to the emergence of new datadriven ways to work, and “analytics institutionalization,” “analytics resource optimization,” and “networked communities” produce convergence, through the sharing of data-driven ways to work. Each configuration displays a distinct element of the core processes identified (“developing group connectedness,” “exchanging analytics and business domain knowledge,” and “incentivizing organizational data use”) and yields either an emergence or convergence of data-driven ways of working. The findings demonstrate how data-driven organizational initiatives can benefit from a pervasive form of organizing that entwines analytics groups and business groups such that their members’ tools, mindsets, and behaviors are merged to profoundly change ways of working. Together, these findings and the configurational methodology used provide a nuanced picture of how organizations integrate the requisite specialist knowledge across domains to realize value from data. Collapse

Topics: value creation marketing management social norm fuzzy logic digital dashboard

Methods: case study qualitative interview computational algorithm company material theory development

Theories: fuzzy set theory set theory organizational theory

Data Liquidity: Conceptualization, Measurement and Determinants

2022 | International Conference on Information Systems | Citations: 4

Authors: Piccoli, Gabriele; Rodriguez, Joaquin; Someh, Ida Asadi; Wixom, Barbara

Abstract: Despite the rhetoric that "data is the new oil" organizations continue to face ... Expand

Abstract: Despite the rhetoric that "data is the new oil" organizations continue to face challenges in data monetization, and we don't have a reliable way to measure how easily data assets can be reused and recombined in value creation and appropriation efforts. Data asset liquidity is a critical, yet underexamined, prerequisite for data monetization initiatives. We contribute to the theorizing process by advancing a definition, conceptualization, and measurement of data liquidity as an asset level construct. Based on interviews with 95 Chief Data and Analytics Officers from 67 distinct large global organizations, we identify three determinants of data liquidity: inherent asset characteristics, structural asset characteristics, and asset environment characteristics. We theorize the existence of equifinal configurations that yield liquid data assets, configurations that should prove helpful to academics and practitioners seeking to understand data liquidity and its impact on firms' data monetization efforts as well as society at large. Collapse

Topics: fuzzy logic data monetization organizational value information technology infrastructure value creation

Methods: theory development qualitative interview case study field study computational algorithm

Theories: fuzzy set theory

Understanding Car Data Monetization: A Taxonomy of Data-Driven Business Models in the Connected Car Domain

2022 | International Conference on Business Informatics | Citations: 0

Authors: Sterk, Felix; Peukert, Christian; Hunke, Fabian; Weinhardt, Christof

Abstract: Data monetization has proven to be one of the most viable profit pools across in ... Expand

Abstract: Data monetization has proven to be one of the most viable profit pools across industries. As vehicles become increasingly connected, leveraging their collected data through novel business models is the most promising value driver for automotive enterprises. Despite the increasing practical relevance, theoretical and conceptual insights on connected cars and their associated business models are still scarce. Thus, we develop a taxonomy of data-driven business models in the connected car domain according to four perspectives—value proposition, value architecture, value network, and value finance. Further, we apply the taxonomy to analyze the business model of 70 companies acting under the realm of connected cars. A subsequent evaluation indicates both the robustness and general feasibility of our taxonomy. Our taxonomy contributes to descriptive knowledge in this emerging field and enables researchers and practitioners to analyze, design, and configure data-driven business models for connected cars. Collapse

Topics: business model data monetization website value creation data-driven business model

Methods: literature sample cluster analysis literature study structured literature research design theory

Show me the Money: How to monetize data in data-driven business models?

2022 | International Conference on Business Informatics | Citations: 0

Authors: Woroch, Robert; Strobel, Gero

Abstract: Increasing digitization and the associated tremendous usage of technology have l ... Expand

Abstract: Increasing digitization and the associated tremendous usage of technology have led to data of unprecedented quantity, variety, and speed, which is generated, processed, and required in almost all areas of industry and life. The value creation and capturing from data presents companies with numerous challenges, as they must create or adapt appropriate structures and processes. As a link between corporate strategy and business processes, business models are a suitable instrument for meeting these challenges. However, few research has been conducted focusing on data-based monetization in the context of datadriven business models so far. Based on a systematic literature review the paper identifies five key components and 23 characteristics of data-driven business models having crucial influence on data-based value creation and value capturing and thus on monetization. The components represent key factors for achieving commercial benefits from data and serve as guidance for exploring and designing suitable data-driven business models. Collapse

Topics: data-driven business model value creation business model database system big data

Methods: literature sample structured literature research literature study qualitative content analysis

Data governance: from master data quality to data monetization

2021 | European Conference On Information Systems | Citations: 6

Authors: Fadler, Martin; Lefebvre, Hippolyte; Legner, Christine

Abstract: The rapid increase in data generated by humans and machines has made companies ... Expand

Abstract: The rapid increase in data generated by humans and machines has made companies aware of data's strategic potential. With this changing role of data, enterprises have to effectively implement a set of data governance mechanisms to meet strategic goals and increase organizational performance. However, existing research on data governance tends to emphasize operational aspects and is less applicable to the strategic roles that data plays today. Based on a multiple case study involving companies with substantial data governance experience, we identify three data governance archetypes: (1) Improve master data quality, (2) Enable enterprise-wide data management, and (3) Coordinate the network to enable data monetization. Our findings advance the field of IT governance in general and contribute to data governance research by detailing structural, procedural, and relational governance mechanisms for different strategic contexts. For practitioners, our research provides insights into the priorities of data governance initiatives and outlines pathways to manage data as a strategic asset. Collapse

Topics: data governance data quality data management data monetization IT governance

Methods: case study qualitative interview focus group multiple case study field study

Insight monetization intermediary platform using recommender systems

2021 | Electronic Markets | Citations: 1

Authors: Hanafizadeh, Payam; Barkhordari Firouzabadi, Mahdi; Vu, Khuong Minh

Abstract: Fundamental changes have prepared the grounds for a rapid movement towards becom ... Expand

Abstract: Fundamental changes have prepared the grounds for a rapid movement towards becoming data and insight-driven. Businesses are continually seeking approaches to create more value from data. The main purpose of this article is to propose a model by which experts as Human Intelligence, can participate to share their expectations to orient the data processing towards the generation of insights needed to target industries and consequently, the realization of indirect data monetization. A set of recommendation systems as Artificial Intelligence, facilitate the submission and validation of expectations, access to data, and selling insights. The model also encompasses a direct data monetization strategy, wherein participants access or request their requirements in an Online Insight Marketplace. We have used the design science methodology to develop and validate our proposed model. The model is validated by comparison with competitive models from the literature, and also by bringing evidence from real-world applications which relate to the components of our model. Collapse

Topics: recommender system data monetization artificial intelligence social network implicit knowledge

Methods: literature study conceptual modelling synthesis business process model and notation modeling language

Conceptualising value creation in data-driven services: The case of vehicle data

2021 | International Journal of Information Management | Citations: 0

Authors: Kaiser, Christian; Stocker, Alexander; Viscusi, Gianluigi; Fellmann, Michael; Richter, Alexander

Abstract: The creation of data-driven services generates new value streams, leading to the ... Expand

Abstract: The creation of data-driven services generates new value streams, leading to the emergence of new actors and ultimately to new market configurations. In the automotive industry, the data generated by vehicles during use paves the way for new types of data-driven services. Based on interviews with eleven prominent experts of the Central European automotive industry, we identify key actors in establishing vehicle data-driven services and their data sharing relationships. We illustrate both in a conceptual multi-actor model for value creation in vehicle data-driven services and evaluate it in the context of six real-life cases. Our study adopts an ecosystem perspective and marks an important step towards the systematic design of a conceptual multi-actor model for vehicle data-driven value creation that can help to guide next research endeavours in data-driven service development. Collapse

Topics: value creation business model data monetization cloud computing database system

Methods: conceptual modelling qualitative interview design science design artifact data transformation