Semantic metadata in the publishing industry – technological achievements and economic implications

Pellegrini, Tassilo

doi:10.1007/s12525-016-0238-x

Cited by 7 publications

(5 citation statements)

References 54 publications

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“…Therefore, data can be used and re-used within the space where they have been developed fostering a so-called infrastructural innovation based on data (Tempini, 2017), or BD can be used to increase the level of personalization in services, leveraging data as a paradigm (e.g., Ng and Wakenshaw, 2017). On the other, BD may help to extend company's capabilities in a new context and going beyond company's boundaries (Pellegrini, 2017). BD may drive an open innovation strategy to gain new business opportunities (Del Vecchio et al, 2018) or even pursuing business model innovation to enlarge the operating domain of the company (Trabucchi et al, 2018).…”

Section: The Role Of Bd In Innovationmentioning

confidence: 99%

“…On the other, BD may help to extend company's capabilities in a new context and going beyond company's boundaries (Pellegrini, 2017). BD may drive an open innovation strategy to gain new business opportunities (Del Vecchio et al, 2018) or even pursuing business model innovation to enlarge the operating domain of the company (Trabucchi et al, 2018).…”

Section: The Role Of Bd In Innovationmentioning

confidence: 99%

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Data-driven innovation: switching the perspective on Big Data

Trabucchi

Buganza

2019

EJIM

130

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Purpose The pervasive spread of digital technologies brought an incredible boost in data availability. Companies are dealing with massive amount of data that wait to be exploited. At the same time, scholars are providing different strategies and methods to help companies capture the value embedded in their data to foster innovation and improve the efficiency of existing processes. In these research studies, data are the by-product of something else, and they are a silent asset that needs to be exploited. What if data might be considered the final goal? The paper aims to discuss these issues. Design/methodology/approach The research is based on an exploratory multiple case study analysis, on the basis of three cases used as an illustration for new ideas. In particular, the gathered data are analyzed according to models previously presented in the literature review, building on and expanding them. Findings The research proposes a data-driven approach to innovation, offering a peculiar view of the innovation process. The trigger point is the need of data that let begin the entire development process of a complex system. In this perspective, the application that data are a by-product of the entire innovation process and not the primary output is peculiar since the vast majority of the literature consider data as the by-product of the primary product. Research limitations/implications Future research is needed to assess the replicability of the model outside the mobile app industry and to measure its performances. Nevertheless, this paper provides insights both for scholars and managers, enlarging the discussion on digital innovation and digital business models. Practical implications The results provide a development process to foster innovation relying on the need of data as the trigger point, guiding entrepreneurs and managers in the building process of the entire digital system. Originality/value Previous research studies often considered Big Data (BD) in innovation as a way to enlarge the current product offer or to make the innovation process more effective or efficient; this paper changes the perspective by considering BD as the trigger and the enabler of the entire digital innovation process.

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Section: The Role Of Bd In Innovationmentioning

confidence: 99%

Section: The Role Of Bd In Innovationmentioning

confidence: 99%

Data-driven innovation: switching the perspective on Big Data

Trabucchi

Buganza

2019

EJIM

130

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“…2) Metadata are key to the production of recommendations (de Gemmis et al, 2015). For the transformation to data-driven media companies (Pellegrini, 2017), useful semantic metadata are essential, but the metadata produced for broadcasting are not very suitable for algorithmic recommendation. 3) Detailed audience data allow new more detailed key performance indicators.…”

Section: Datafication and Public Service Mediamentioning

confidence: 99%

The datafication of Public Service Media

Sørensen¹

2020

MedieKultur

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Historically, public service broadcasting had no quantifiable knowledge aboutaudiences, nor a great interest in knowing them. Today, the competitive logic of themedia markets encourage public service media (PSM) organizations to increasedatafication. In this paper we examine how a PSM organization interprets the classicpublic service obligations of creating societal cohesion and diversity in the newworld of key performance indicators, business rules and algorithmic parameters.The paper presents a case study of the implementation of a personalization systemfor the video on demand service of the Danish PSM ‘DR’. Our empirical findings,based on longitudinal in-depth interviewing, indicate a long and difficult processof datafication of PSM, shaped by both the organizational path dependencies ofbroadcasting production and the expectations of public service broadcasting.

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“…The mere use of web services does not solve the inflexibility problem: integration artifacts still need to be built, including the mapping of data, serialization and deserialization of messages, interface testing, monitoring and maintenance. A more sophisticated approach to reducing the engineering efforts that are required to connect web service endpoints involves semantic web services, the goal of which is to allow machines to understand the ontological meaning of data, and let them act in a more autonomous fashion (Pellegrini 2017). As yet, semantic web services have not been widely adopted outside of academic studies or prototypical implementations, and hence have not yet solved the agility problem in inter-organizational collaboration.…”

Section: State Of the Artmentioning

confidence: 99%

Platform-based collaboration in digital ecosystems

2019

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Organizations operating in a network require strong information technology support to successfully collaborate. In a business service network, the links between partners must enable quick connect and disconnect relationships, in order to harness market opportunities. Scholars claim that information technology platforms are necessary to enhance this quick connect capability and to transform business networks into digital ecosystems. However, the state of the art in inter-organizational collaboration relies on static collaboration patterns between individual partners, and current systems are engineered without interoperability in mind. In this study, we follow a design science approach to promote the concept of platform-based collaboration. More precisely, we propose an architecture for an inter-organizational platform that facilitates the provisioning of collaboration services. Furthermore, we present a prototype in the context of e-commerce as a means of evaluating the proposed design. We conclude that the platform approach is beneficial in achieving increased flexibility in business-to-business collaboration.

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Semantic metadata in the publishing industry – technological achievements and economic implications

Cited by 7 publications

References 54 publications

Data-driven innovation: switching the perspective on Big Data

Data-driven innovation: switching the perspective on Big Data

The datafication of Public Service Media

Platform-based collaboration in digital ecosystems

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