Machine Economies Electronic Markets Springer

Theme Trade sanctions, data sovereignty rules and export-control “kill-switches” are forcing platform operators, digital marketplaces and their... Theme The aim of this special issue is to advance scholarly understanding of Responsible and Trustworthy Artificial Intelligence in tourism and... Theme The economic environment in which companies create value today is characterized by greater vulnerability, uncertainty, complexity and... Congratulations to the authors of these two articles that are the most cited articles of publication year 2024! Category "Research Article": Paula Heeß, Jakob Rockstuhl, Marc-Fabian Körner, and Jens Strüker, "Enhancing trust in global supply chains: Conceptualizing Digital Product Passports for a low-carbon hydrogen market" Working Papers Journal Articles Books and Chapters Software Components

EconPapers FAQ Archive maintainers FAQ Cookies at EconPapers The RePEc blog The RePEc plagiarism page Eduard Hartwich (Obfuscate( 'uni.lu', 'eduard.hartwich' )), Alexander Rieger (Obfuscate( 'uni.lu', 'alexander.rieger' )), Johannes Sedlmeir (Obfuscate( 'uni.lu', 'johannes.sedlmeir' )), Dominik Jurek (Obfuscate( 'berkeley.edu', 'dominik_jurek' )) and Gilbert Fridgen (Obfuscate( 'uni.lu', 'gilbert.fridgen' )) Additional contact information... Electronic Markets, 2023, vol. 33, issue 1, No 36, 13 pages As the access to this document is restricted, you may want to

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Artificial intelligence (AI) was mentioned in Electronic Markets’ last editorial as a key enabling technology that is converging with other technologies such as distributed ledger or extended reality technologies (Alt, 2021). The notion of convergence implies the evolution of a technology and, in fact, AI has been on the table of academics and practitioners for some time. It meanwhile comprises a rather broad methodological and technological spectrum. An analysis of mentions in academic and newspaper sources revealed that AI has seen a steady growth since 1984 and experienced an even stronger rise since 2012 (Katz, 2017). The same source reported that “AI stands for a confused mix of terms—such as “big data”, “machine learning” or “deep learning”—whose common denominator is the use of expensive computing power to analyze massive centralized... 2).

Other attributes like "smart" could be added to this list leading to the legitimate concern as to when an information system qualifies as being “intelligent”. It opens the stage for diverse discussions from various disciplines. To contain the debate at this point, intelligence shall be conceived as closely related to human skills and interactions. It follows the definition of a survey conducted by Lu (2019, p. 1), which defines AI as “any theory, method, and technique that helps machines (especially computers) to analyze, simulate, exploit, and explore human thinking process and behavior.” Along the same lines and based on literature... Other attributes of AI were perceived anthropomorphism, perceived intelligence as well as perceived animacy (Balakrishnan & Dwivedi, 2021).

In particular, the goal to match human intelligence is reflected in levels of AI systems, which range from smart information systems and reactive machines to weak and strong AI until the most “intelligent” form... Since the properties of intelligence are key for decision-making across application domains, AI has been termed a “general purpose technology (GPT)” (Buxmann et al., 2021) with GPTs allegedly having a strong impact for digital... This also entails from the convergence with other GPTs, in particular, digital services and platform technologies such as cloud computing, social media and distributed ledgers. They indicate a close mutual link between AI and digital platforms, which shall be discussed with the triple relationship between AI and digital platforms in the following (see Table 1). The first relationship recognizes digital platforms as vital data sources for AI and follows a prior editorial that introduced a special issue on big data services (Alt & Zimmermann, 2017). Centralized as well as decentralized platforms were described as service systems where data emerges from transactions and interactions on an individual as well as on an aggregated level.

As illustrated in the upper third of Table 1, users leave a large variety of data when acting on digital platforms. First of all, they provide data on the technological performance of the platform itself, for example, the type of devices and operating systems being used. While these are rather application-agnostic and cross-domain in nature, data on content and actors will vary according to the purpose and the participants of the platforms. For example, data from transaction platforms will be more structured than data from innovation or social networking platforms, where content is likely to be unstructured. Since the early days of computer reservation systems and electronic stock exchanges, platform providers are known to leverage their access to this wealth of platform data. This priviledge explains why automotive companies such as Toyota or Volkswagen and banks like BBVA or Citibank are today striving to establish their own platforms.

It allows platform providers to monitor the activites on the platform and to quickly adapt their offerings as well as their strategies. In addition, platform providers are also in a position to sell this data to create additional revenues. A past special issue of Electronic Markets on personal data markets has shown that dedicated electronic market platforms emerged for collecting and trading such data (Agogo, 2020; Spiekermann et al., 2015). However, from the fields of business intelligence (BI), big data (BD) and social media analytics (SMA), two key challenges should be considered. First, the presence of vast amounts of data represents a potential that requires further processing to make it amenable to sense-making in business processes and for decision-making. Much is rooted in the fact that data emerges from various application systems and knowledge bases, which typically feature different conventions in terms of data syntax and semantics.

To derive meaning from this heterogeneous “raw” data, existing technologies foresee tasks for data preprocessing: A. K. M. Bahalul Haque, Najmul Islam, Patrick Mikalef Lukas Gruetzner, David Voss, Michael H Breitner

Frederik Moeller, Ilka Jussen, Virginia Springer, Anna Giess, Julia Christina Schweihoff, Joshua Gelhaar, Tobias Guggenberger, Boris Otto Chia-Ying Li, Chien-Hsiang Liao, Yu-Hui Fang Konrad Degen, Rick Lutzens, Paul Beschorner, Ulrike Lucke

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