Management approaches in the field of smart
Fatemeh Mohammadnezhad Chari; Jahanyar Bamdadsoofi; Iman Raeisi Vanani; Maghsoud Amiri
Abstract
The present paper is conducted through exploratory and inductive approach in order to achieve a business model for data marketplaces. The research paper could be considered as a first attempt in the field of data marketplaces and their business models in Iran. The paper is based on two iterative taxonomy ...
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The present paper is conducted through exploratory and inductive approach in order to achieve a business model for data marketplaces. The research paper could be considered as a first attempt in the field of data marketplaces and their business models in Iran. The paper is based on two iterative taxonomy approach that is first introduced by Nickerson et al.(2013).Mixing of a systematic way on current literatures along with structured interviews by some experts who are involved in this area is applied to gain the main objectives.Our results provide the main bloc of the presented archetype with three sub blocs ،attributes and specifications that is titled value proposition. The sub blocs are named value creation، value capture and value delivery.
Introduction
Recently many online data trading platforms have emerged as a new business paradigm to respond to society’s fundamental needs and rights for specific data. On these data marketplaces, service providers buy raw data from device and application owners or collect it from contributors to offer enriched and value-added data to data consumers such as scientists, businesses, etc. The aim of this study is to develop an architecture of business model for data marketplaces in order to get better a understanding of their business logic.
Hence, the research questions are as follows:
1-What are the attributes of construct blocs of the data marketplace business model?
2- What are the specifications of each attribute in any construct bloc of data marketplace business model?
Literature Review
The concept of business model has evolved during recent years by refining its components. There are different types of business model constructs across the literature, from 9 blocs of Osterwalder and Pigneur (2010) to the business model with 3 blocs proposed by “Hautes Etudes Commerciales de Paris” called Odyssey 3.14. The most famous business model construct includes four components (blocs) with “value proposition” as a core component which refers to the benefits that customers receive and why the company is the best choice for them. (Magretta, 2002; Casadesus et al.,2010). The three sub-constructs include “value creation”, “value delivery”, and “value capture” (Teece, 2010). “Value creation” reflects the products and services offered by the company and also the key activities, resources and processes, and partners. “Value delivery” refers to the corporate interactions with the market and “Value capture” concerns the revenue streams and cost structures which make the profit equation.
Methodology:
The present study is conducted through exploratory and inductive approach to achieve an archetype of a business model for data marketplaces. To the best of our knowledge, this research paper could be considered a first attempt in the field of data marketplaces business model design in Iran.
The methodological orientation of this research is based on two iterative taxonomy approaches that is first introduced by Nickerson et., al (2013). Mixing of a systematic way on current works of literature along with structured interviews by some experts who are involved in this area is applied to gain the main objective and answer the research questions. Through this approach, three following steps are taken in a systematic and repetitive manner.
Systematic literature review of 43 scientific documents and their content analysis
Conducting structured interviews with 5 experts
Visiting 4 online data platforms and data marketplaces websites
Results and discussion:
Findings indicate that the data marketplace business model archetype consists of “value proposition” as a main component with 8 attributes including data goods, technological products, infrastructural services, brokery and curation services, operating services, supporting services, the domain of activities, and proprietary forms. The three sub-components’ attributes concerning the data marketplace business model are figured out as follows:
“Value creation” as a sub-construct with six attributes including key partners, key activities, key processes, key products and services, transaction orientations, data sourcing and data origin, and data time -frame.
“Value delivery” as a second sub-component includes five attributes such as data accessibility, output frames, target audiences, trustworthy mechanisms, and privacy preservation mechanisms.
“Value capture” with five attributes including price discovery mechanisms, payment mechanisms, revenue streams, costing mechanisms, and pricing models.
To sum up, these 24 attributes include more than 100 specifications. All of these specifications are profoundly described in detail across the article. Some attributes have more than 8 specifications such as key partners, key activities, or key processes while others have fewer. Most of the specifications are not exclusive, since a particular platform’s attributes may include one or multiple specifications. For example, a particular data platform could have multiple pricing models such as “pay-per-use”, “freemium” or “flat rate”.
Conclusion
Our taxonomy of the data marketplace business model could be extended by four major concerns of data platforms which are data quality evaluation, data pricing mechanisms, secure data trading and truthfulness, and privacy protection mechanisms. Some aspects of the data marketplace business model are inherently contradictory and a trade-off has to be applied between them. For example, European General Data Protection Regulation (GDPR) tries to make a trade-off between data trading transparency and individual privacy protection. Furthermore, participants’ conflicting interests in order to gain a win-win result have to be considered in all online data platform business models. We suggest future researchers in computer science and IT management science, and data scientists extend our archetype by using methods such as text mining techniques and web crawling.
Keywords: Data Marketplace, Business Model, Archetype, Taxonomy.
