scholarly journals Structural Holes in the Multi-Sided Market: A Market Allocation Structure Analysis of China’s Car-Hailing Platform in the Context of Open Innovation

2019 ◽  
Vol 11 (20) ◽  
pp. 5813 ◽  
Author(s):  
Lei Huang ◽  
Yandong Zhao ◽  
Liang Mei ◽  
Peiyi Wu ◽  
Zhihua Zhao ◽  
...  
Keyword(s):  
Open Innovation ◽  
Personal Data ◽  
Data Driven ◽  
Edge Weight ◽  
Data Allocation ◽  
Structural Holes ◽  
Data Resource ◽  
Platform Governance ◽  
Sustainable Innovation ◽  
Market Allocation

Car-hailing platform governance is an emerging topic of research and practice. The governance of the data-driven platform economy is challenging the research paradigm of competition regulation in the context of open innovation. This research is trying to reveal the market allocation structure of China’s online car-hailing industry from the perspective of personal data allocation by the study of Application Programming Interface (API) of sample platforms. On the basis of the networked nature of personal data allocation via APIs, this research constructs a mathematical model of the edge weight of data resource connections between platforms. Furthermore, this research optimises the structural hole analysis of complex networks to discuss the state of personal data resource allocation in China’s car-hailing industry. Results reveal that there are obvious structural holes within the sample network. When compared with related indicators, we found that accessing personal data resources is an essential component of the sample network competition capability and sustainable innovation. Social media platforms and online payment platforms more greatly impact car-hailing platform competition than other types of platforms within the multi-sided market context. This research offers a research perspective of personal data allocation for further study of competition, regulation and sustainable innovation of data-driven platform economies.

Putting trust into antitrust? Competition policy and data-driven platforms

2021 ◽  
pp. 026732312110283
Author(s):  
Stefan Larsson
Keyword(s):  
Competition Policy ◽  
Personal Data ◽  
Competition Law ◽  
Data Driven ◽  
Data Handling ◽  
Special Issue ◽  
Digital Platforms ◽  
Platform Governance ◽  
Consumer Autonomy ◽  
The Relationship

Anti-competitive notions, it seems, are increasingly informing the critical debate on a data-driven economy organised into scalable digital platforms. Issues of market definitions, how to value personal data on multisided platforms, and how to detect and regulate misuses of dominant positions have become key nomenclature on the battlefield of addressing fairness in our contemporary digital societies. This article looks at the central themes for this special issue on governing trust in European platform societies through the lens of contemporary developments in the field of competition law. Three main questions are addressed: (1) To what extent are the platforms’ own abilities to govern their infrastructures, that is, to be de facto regulators over both human behaviour and market circumstances, a challenge for contemporary competition regulation? (2) In what way is the collection, aggregation, or handling of consumers’ data of relevance for competition? (3) How can the particular European challenges of governing US-based digital platforms more broadly be understood in terms of the relationship between transparency and public trust? Of particular relevance – and challenge – here are the platforms’ abilities to govern their infrastructures, albeit through automated moderation, pricing or scalable data handling. It is argued that this aspect of coded, and possibly autonomously adapting, intra-platform governance, poses significant anti-competitive challenges for supervisory authorities, with possible negative implications for consumer autonomy and wellbeing as well as platform-dependent other companies.

scholarly journals Digital Economy, Big Data and Competition Law

2019 ◽  
Vol 3 (1) ◽  
pp. 53-89
Author(s):  
Roberto Augusto Castellanos Pfeiffer
Keyword(s):  
Big Data ◽  
Data Protection ◽  
Personal Data ◽  
Competition Law ◽  
Digital Economy ◽  
Data Driven ◽  
Dominant Position ◽  
Privacy Concerns ◽  
Sense Data ◽  
Abuse Of Dominant Position

Big data has a very important role in the digital economy, because firms have accurate tools to collect, store, analyse, treat, monetise and disseminate voluminous amounts of data. Companies have been improving their revenues with information about the behaviour, preferences, needs, expectations, desires and evaluations of their consumers. In this sense, data could be considered as a productive input. The article focuses on the current discussion regarding the possible use of competition law and policy to address privacy concerns related to big data companies. The most traditional and powerful tool to deal with privacy concerns is personal data protection law. Notwithstanding, the article examines whether competition law should play an important role in data-driven markets where privacy is a key factor. The article suggests a new approach to the following antitrust concepts in cases related to big data platforms: assessment of market power, merger notification thresholds, measurement of merger effects on consumer privacy, and investigation of abuse of dominant position. In this context, the article analyses decisions of competition agencies which reviewed mergers in big data-driven markets, such as Google/DoubleClick, Facebook/ WhatsApp and Microsoft/LinkedIn. It also reviews investigations of alleged abuse of dominant position associated with big data, in particular the proceeding opened by the Bundeskartellamt against Facebook, in which the German antitrust authority prohibited the data processing policy imposed by Facebook on its users. The article concludes that it is important to harmonise the enforcement of competition, consumer and data protection polices in order to choose the proper way to protect the users of dominant platforms, maximising the benefits of the data-driven economy.

A multi-sided market of personal data resource allocation: An empirical study of China’s car-hailing platform

2021 ◽  
pp. 178359172110512
Author(s):  
Lei Huang ◽  
Miltos Ladikas ◽  
Guangxi He ◽  
Julia Hahn ◽  
Jens Schippl
Keyword(s):  
Resource Allocation ◽  
Market Structure ◽  
Market Competition ◽  
Rapid Development ◽  
Personal Data ◽  
Application Programming Interface ◽  
Allocation Mechanism ◽  
Allocation Model ◽  
Data Resource ◽  
Technology And Economy

The current rapid development of online car-hailing services creates a serious challenge to the existing paradigm of market governance and antitrust policy. However, the debate on the market structure of the car-hailing platform requires more empirical evidence to uncover its functions. This research adopts an interdisciplinary methodology based on computer science and economics, and including software reverse engineering tools applied to the interoperability of the terminal application and resource allocation model, to demonstrate the topological market structure of personal data resources allocation in China’s car-hailing industry. Within the discussion of the hybrid nature of technology and economy, the analysis results clearly show that China’s car-hailing platform services present a multi-sided market structure when seen from the perspective of personal data resource allocation. Personal data resource (PDR), that is considered an essential market resource, is applied as an asset transferred unhindered between platforms via the application programming interface, and thus, creating a new market allocation mechanism. The connection between the car-hailing platforms and social media platforms is an essential aspect of the market competition in the domain. As applications of online platforms increase in the global context, this research offers a new perspective in personal data resource allocation with implications for the governance of the platform economy.

scholarly journals GDPR: Is your consent valid?

2020 ◽  
Vol 37 (1) ◽  
pp. 19-24
Author(s):  
Stephen Breen ◽  
Karim Ouazzane ◽  
Preeti Patel
Keyword(s):  
Information Systems ◽  
Data Protection ◽  
Personal Data ◽  
Data Driven ◽  
Privacy Concerns ◽  
General Data Protection Regulation ◽  
Philosophical Background ◽  
General Data ◽  
Compliance With The Law ◽  
Point Of Departure

The General Data Protection Regulation (GDPR) 2018 imposes much greater demands on companies to address the rights of individuals who provide data, that is, Data Subjects. The new law requires a much more transparent approach to gaining consent to process personal data. However, few obvious changes to how consent is gained from Data Subjects to comply with this. Many companies are running the risk of non-compliance with the law if they fail to address how data are obtained and the lack of true consent which Data Subjects currently give to their data being processed. Consent is a complex philosophical principle which relies on the person giving the consent being in full possession of the facts, this article explores the philosophical background of consent and examines the circumstances which were the point of departure for the debate on consent and attempts to develop an understanding of it in the context of the growing influence of information systems and the data-driven economy. The GDPR has gone further than any other regulation or law to date in developing an understanding of consent to address personal data and privacy concerns.

scholarly journals Public perceptions of good data management: Findings from a UK-based survey

2020 ◽  
Vol 7 (1) ◽  
pp. 205395172093561
Author(s):  
Todd Hartman ◽  
Helen Kennedy ◽  
Robin Steedman ◽  
Rhianne Jones
Keyword(s):  
Data Management ◽  
Personal Data ◽  
Current Approach ◽  
Data Driven ◽  
Policy Action ◽  
Driven Systems ◽  
General Data Protection Regulation ◽  
Good Data ◽  
Opt Out ◽  
The Eu

Low levels of public trust in data practices have led to growing calls for changes to data-driven systems, and in the EU, the General Data Protection Regulation provides a legal motivation for such changes. Data management is a vital component of data-driven systems, but what constitutes ‘good’ data management is not straightforward. Academic attention is turning to the question of what ‘good data’ might look like more generally, but public views are absent from these debates. This paper addresses this gap, reporting on a survey of the public on their views of data management approaches, undertaken by the authors and administered in the UK, where departure from the EU makes future data legislation uncertain. The survey found that respondents dislike the current approach in which commercial organizations control their personal data and prefer approaches that give them control over their data, that include oversight from regulatory bodies or that enable them to opt out of data gathering. Variations of data trusts – that is, structures that provide independent stewardship of data – were also preferable to the current approach, but not as widely preferred as control, oversight and opt out options. These features therefore constitute ‘good data management’ for survey respondents. These findings align only in part with principles of good data identified by policy experts and researchers. Our findings nuance understandings of good data as a concept and of good data management as a practice and point to where further research and policy action are needed.

Data access as a big competitive advantage: evidence from China's car-hailing platforms

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lei Huang ◽  
Yandong Zhao ◽  
Guangxi He ◽  
Yangxu Lu ◽  
Juanjuan Zhang ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Personal Data ◽  
Data Access ◽  
Allocation Mechanism ◽  
Content Type ◽  
Data Resource ◽  
Online Platform ◽  
The Social ◽  
Essential Components ◽  
Resource Allocation Mechanism

PurposeThe online platform is one of the essential components of the platform economy that is constructed by a large scale of the personal data resource. However, accurate empirical test of the competition structure of the data-driven online platform is still less. This research is trying to reveal market allocation structure of the personal data resource of China's car-hailing platforms competition by the empirical data analysis.Design/methodology/approachThis research is applying the social network analysis by R packages, which include k-core decomposition and multilevel community detection from the data connectedness via the decompilation and the examination of the application programming interface of terminal applications.FindingsThis research has found that the car-hailing platforms, which establish more constant personal data connectedness and connectivity with social media platforms, are taking the competitive market advantage within the sample network. Data access discrimination is a complementary method of market power in China's car-hailing industry.Research limitations/implicationsThis research offers a new perspective on the analysis of the multi-sided market from the personal data resource allocation mechanism of the car-hailing platform. However, the measurement of the data connectedness requires more empirical industry data.Practical implicationsThis research reveals the competition structure that relies on personal data resource allocation mechanism. It offers empirical evidence for governance, which is considered as the critical issue of big data research, by reviewing the nature of the data network.Social implicationsIt also reveals the data convergence process of the social system and the technological system.Originality/valueThis research offers a new research method for the real-time regulation of the car-hailing platform.

scholarly journals COVID-19 Contact Tracing: Challenges and Future Directions

2020 ◽  
Author(s):  
Mohammad Jabed Morshed Chowdhury ◽  
Md Sadek Ferdous ◽  
Kamanashis Biswas ◽  
Niaz Chowdhury ◽  
Vallipuram Muthukkumarasamy
Keyword(s):  
Personal Data ◽  
Human Memory ◽  
Data Driven ◽  
Contact Tracing ◽  
Future Directions ◽  
Significant Challenge ◽  
Innovative Solution ◽  
Public Health Officials ◽  
Novel Coronavirus ◽  
High Level

Contact tracing has become a vital tool for public health officials to effectively combat the spread of new diseases, such asthe novel coronavirus disease COVID-19. Contact tracing is not new to epidemiologist rather, it used manual or semi-manualapproaches that are incredibly time-consuming, costly and inefficient. It mostly relies on human memory while scalabilityis a significant challenge in tackling pandemics. The unprecedented health and socio-economic impacts led researchersand practitioners around the world to search for technology-based approaches for providing scalable and timely answers.Smartphones and associated digital technologies have the potential to provide a better approach due to their high level ofpenetration, coupled with mobility. While data-driven solutions are extremely powerful, the fear among citizens is thatinformation like location or proximity associated with other personal data and can be weaponised by the states to enforcesurveillance. Low adoption rate of such apps due to the lack of trust questioned the efficacy and demanded researchers tofind innovative solution for building digital-trust, and appropriately balancing privacy and accuracy of data. In this paper,we have critically reviewed such protocols and apps to identify the strength and weakness of each approach. Finally, wehave penned down our recommendations to make the future contact tracing mechanisms more universally inter-operable andprivacy-preserving.

Governance in Digital Open Innovation Platforms

2020 ◽  
Author(s):  
Likoebe Maruping ◽  
Yukun Yang
Keyword(s):  
Open Innovation ◽  
Scale Up ◽  
Innovation Process ◽  
Digital Platforms ◽  
Government Organizations ◽  
Platform Governance ◽  
Innovation Activities ◽  
Innovation Output ◽  
Pass Through ◽  
Innovation Platform

Open innovation is defined as an approach to innovation that encourages a broad range of participants to engage in the process of identifying, creating, and deploying novel products or services. It is open in the sense that there is little to no restriction on who can participate in the innovation process. Open innovation has attracted a substantial amount of research and widespread adoption by individuals and commercial, nonprofit, and government organizations. This is attributable to three main factors. First, open innovation does not restrict who can participate in the innovation process, which broadens the access to participants and expertise. Second, to realize participants’ ideas, open innovation harnesses the power of crowds who are normally users of the product or service, which enhances the quality of innovative output. Third, open innovation often leverages digital platforms as a supporting technology, which helps entities scale up their business. Recent years have witnessed a rise in the emergence of a number of digital platforms to support various open innovation activities. Some platforms achieve notable success in continuously generating innovations (e.g., InnoCentive.com, GitHub), while others fail or experience a mass exodus of participants (e.g., MyStarbucksIdea.com, Sidecar). Prior commentaries have conducted postmortems to diagnose the failures, identifying possible reasons, such as overcharging one side of the market, failing to develop trust with users, and inappropriate timing of market entry. At the root of these and other challenges that digital platforms face in open innovation is the issue of governance. In the article, governance is conceptualized as the structures determining how rigidly authority is exerted and who has authority to make decisions and craft rules for orchestrating key activities. Unfortunately, there is no comprehensive framework for understanding governance as applied to open innovation that takes place on digital platforms. A governance perspective can lend insight on the structure of how open innovation activities on digital platforms are governed in creating and capturing value from these activities, attracting and matching participants with problems or solutions, and monitoring and controlling the innovation process. To unpack the mystery of open innovation governance, we propose a framework by synthesizing and integrating accreted knowledge from the platform governance literature that has been published in prominent journals over the past 10 years. Our framework is built around four key considerations for governance in open innovation: platform model (firm-owned, market, or community), innovation output ownership (platform-owned, pass-through, or shared), innovation engagement model (transactional, collaborative, or embedded), and nature of innovation output (idea or artifact). Further, we reveal promising research avenues on the governance of digital open innovation platforms.

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