Search results

The paper argues that the classical Marxist theory of capitalist accumulation is inadequate to understand new forms of capitalism and their accumulation processes determined by “platforms” and “big data”. Big data platforms are shaping the processes of production, labour, the price of products and market conditions. “Digital platforms” and “big data” have become an integral part of the processes of production, distribution and exchange relations. These twin pillars are central to the capitalist accumulation processes. The article argues that the classical Marxist theory of capitalist accumulation is inadequate to understand new forms of capitalism and their accumulation processes determined by “platforms” and “big data”.

As a conceptual paper, this paper follows critical methodological lineages and traditions based on non-linear historical narratives around the conceptualisation, construction and transition of the “Marxist theory of capital accumulation” in the age of platform economy. This paper follows a discourse analysis (Fairclough, 2003) to locate the way in which an artificial intelligence (AI)-led platform economy helps identify and conceptualise new forms of capitalist accumulation. It engages with Jørgensen and Phillips' (2002) contextual and empirical discursive traditions to undertake a qualitative comparative analysis by exploring a broad range of complex factors with case studies and examples from leading firms within the platform economy. Finally, it adopts two steps of “Theory Synthesis and Theory Adaptation” as outlined by Jaakkola (2020) to synthesise, adopt and expand the Marxist theory of capital accumulation under platform capitalism.

This article identifies new trends and forms of data driven capitalist accumulation processes within the platform capitalism. The findings suggest that an AI led platform economy creates new forms of capitalist accumulation. The article helps to develop theoretical understanding and conceptual frameworks to understand and explain these new forms of capital accumulation.

This study builds upon the limited theorisation on the AI and new capitalist accumulation processes. This article identifies new trends and forms of data driven capitalist accumulation processes within platform capitalism. The article helps to understand digital and platform capitalisms in the lens of digital labour and expands the theory of capitalist accumulation and its new forms in the age of datafication. While critiquing the Marxist theory of capitalist accumulation, the article offers alternative approaches for the future.

In the open data context, the shared data could come through many transformation processes, originating from many sources, which exposes the risk of non-authentic data. Moreover, each data set has different properties, shared under various licenses, which means the updated data could change its characteristics and related policies. This paper aims to introduce an effective and elastic solution to keep track of data changes and manage their characteristics within the open data platform. These changes have to be immutable to avoid violated modification and could be used as the certified provenance to improve the quality of data.

This paper will propose a pragmatic solution that focuses on the combination of comprehensive knowledge archive network – the broadest used open data platform and hyperledger fabric blockchain to ensure all the changes are immutable and transparent. As using smart contracts plus a standard provenance data format, all processes are running automatically and could be extended to integrate with other provenance systems and so the introduced solution is quite flexible to be used in different open data ecosystems and real-world application domains.

The research involves some related studies about the provenance system. This study finds out that most of the studies are focused on the commercial sector or applicable to a specific domain and not relevant for the open-data section. To show that the proposed solution is a logical and feasible direction, this paper conducts an experimental sample to validate the result. The testing model is running successfully with an elastic system architect and promising overall performance.

Open data is the future of many businesses but still does not receive enough attention from the research community. The paper contributes a novel approach to protect the provenance of open data.

The purpose of this article is to study what platform-related user factors influence the employment potential of a lean platform for self-employed professionals.

The article employs the system data of a Dutch platform firm, which include consumers looking for painters (N = 17,224) and self-employed painters (N = 1,752) who pursue client acquisition by submitting proposals (N = 101,974). This data is analysed using non-parametric tests.

Study of this platform shows that the platform functions as a channel of acquisition for self-employed professionals. This lean platform enables matching of information of supply and demand, thereby facilitating processes of acquisition. The number of competitors, distance to a potential job and non-standard proposals are statistically significant factors that influence whether a consumer is interested in a proposal. Effect sizes are very small.

This platform is a two-way market for information about service jobs, which excludes a price setting mechanism. The findings of this study cannot be generalized to other forms of platforms.

The market for service professionals is very local; therefore, the platform firm may alter the algorithm to accommodate this. Self-employed professionals should approach using the platform in the same way as normal forms of acquisition.

This particular type of two-sided market is an extension of regular forms of acquisition by creating “weak ties” through the platform.

The article uses a unique data set to study the impact and limitations of digitalization of the (labour) market for service professionals.

The purpose of this paper is to describe the process of designing, developing and evaluating a prototype of an open government data (OGD) platform that provided user-centred experiences.

Based on the OGD lifecycle, an OGD prototype was created, which involved the system functionality, user interface, standard specification and security mechanism. The main functionalities of the system included data acquisition, data processing and data management. A usability test was conducted following the prototype implementation.

The usability test indicated that an OGD platform was desired to help the public to find, access, reuse and share government data effectively and efficiently. Functions, such as visualization, local download and digital watermark should be provided and integrated into the platform.

This paper provided a complete case study on the design of an OGD platform and a reference for information system developers to design such system in the future.

The present paper constructed a new framework for government data governance based on the concept of a data middle platform to elicit the detailed requirements and functionalities of a government data governance framework.

Following a three-cycle activity, the design science research (DSR) paradigm was used to develop design propositions. The design propositions are obtained based on a systematic literature review of government data governance and data governance frameworks. Cases and experts further assessed the effectiveness of the implementation of the artifacts.

The study developed an effective framework for government data governance that supported the digital service needs of the government. The results demonstrated the advantages of the framework in adapting to organizational operations and data, realized the value of data assets, improved data auditing and oversight and facilitated communication. From the collection of data to the output of government services, the framework adapted to the new characteristics of digital government.

Knowledge of the “data middle platforms” generated in this study provides new knowledge to the design of government data governance frameworks and helps translate design propositions into concrete capabilities. By reviewing earlier literature, the article identified the core needs and challenges of government data governance to help practitioners approach government data governance in a structured manner.

Online review bias research has predominantly focused on self-selection biases on the user’s side. By collecting online reviews from multiple platforms and examining their biases in the unique digital environment of “Chinanet,” this paper aims to shed new light on the multiple sources of biases embedded in online reviews and potential interactions among users, technical platforms and the broader social–cultural norms.

In the first study, online restaurant reviews were collected from Dianping.com, one of China's largest review platforms. Their distribution and underlying biases were examined via comparisons with offline reviews collected from on-site surveys. In the second study, user and platform ratings were collected from three additional major online review platforms – Koubei, Meituan and Ele.me – and compared for possible indications of biases in platform's review aggregation.

The results revealed a distinct exponential-curved distribution of Chinese users’ online reviews, suggesting a deviation from previous findings based on Western user data. The lack of online “moaning” on Chinese review platforms points to the social–cultural complexity of Chinese consumer behavior and online environment that goes beyond self-selection at the individual user level. The results also documented a prevalent usage of customized aggregation methods by review service providers in China, implicating an additional layer of biases introduced by technical platforms.

Using an online–offline design and multi-platform data sets, this paper elucidates online review biases among Chinese users, the world's largest and understudied (in terms of review biases) online user group. The results provide insights into the unique social–cultural cyber norm in China's digital environment and bring to light the multilayered nature of online review biases at the intersection of users, platforms and culture.

The development of smart cities (SCs) is a costly process as it requires the implementation of physical infrastructure to meet the demand for data collection. On the other hand, there are open government data (OGD) that are open and free, and can be a first step from the current city evolution to SCs at a more affordable cost. However, these data are available in a decentralized way, in different formats and granularities. To date, the authors have not found any literature work that performs the centralization of OGDs on a single platform. The purpose of this paper is provide a centralized OGD platform.

This work investigates the state-of-the-art literature from the OGD, establishes the research question, design and develop the platform for OGD and accomplish the validation of the platform.

Through the validations of the platform, advantages were observed in relation to productivity gain for the development of solutions, in the SC context, using the proposed platform.

The data have been collected in a manual way but for future works, the authors will use the web-crawler for the collection of data.

One town hall (Sorocaba) is interested in using the platform to analyze the data usage in the simple way and compare with the other nearby towns.

It is a recent work in literature using OGD, and there is no work that centralizes the information about several town halls to provide comparison between them and improve the decision-making.

The purpose of this paper is to examine how claims to “ownership” are asserted over publicly accessible platform data and critically assess the nature and scope of rights to reuse these data.

Using Airbnb as a case study, this paper examines the data ecosystem that arises around publicly accessible platform data. It analyzes current statute and case law in order to understand the state of the law around the scraping of such data.

This paper demonstrates that there is considerable uncertainty about the practice of data scraping, and that there are risks in allowing the law to evolve in the context of battles between business competitors without a consideration of the broader public interest in data scraping. It argues for a data ecosystem approach that can keep the public dimension issues more squarely within the frame when data scraping is judicially considered.

The nature of some sharing economy platforms requires that a large subset of their data be publicly accessible. These data can be used to understand how platform companies operate, to assess their compliance with laws and regulations and to evaluate their social and economic impacts. They can also be used in different kinds of data analytics. Such data are therefore sought after by civil society organizations, researchers, entrepreneurs and regulators. This paper considers who has a right to control access to and use of these data, and addresses current uncertainties in how the law will apply to scraping activities, and builds an argument for a consideration of the public interest in data scraping.

The issue of ownership/control over publicly accessible information is of growing importance; this paper offers a framework for approaching these legal questions.

The 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.

This 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.

This 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.

This 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.

This 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.

It also reveals the data convergence process of the social system and the technological system.

This research offers a new research method for the real-time regulation of the car-hailing platform.

This chapter lays out a research agenda in the sociology of work for a type of data and organizational intermediary: work platforms. As an example, the authors employ a case study of the adoption of automated hiring platforms (AHPs) in which the authors distinguish between promises and existing practices. The authors draw on two main methods to do so: critical discourse analysis and affordance critique. The authors collected and examined a mix of trade, popular press, and corporate archives; 135 texts in total. The analysis reveals that work platforms offer five core affordances to management: (1) structured data fields optimized for capture and portability within organizations; (2) increased legibility of activity qua data captured inside and outside the workplace; (3) information asymmetry between labor and management; (4) an “ecosystem” design that supports the development of limited-use applications for specific domains; and (5) the standardization of managerial techniques between workplaces. These combine to create a managerial frame for workers as fungible human capital, available on demand and easily ported between job tasks and organizations. While outlining the origin of platform studies within media and communication studies, the authors demonstrate the specific tools the sociology of work brings to the study of platforms within the workplace. The authors conclude by suggesting avenues for future sociological research not only on hiring platforms, but also on other work platforms such as those supporting automated scheduling and customer relationship management.