Corporate digital responsibility

Lobschat, Lara; Mueller, Benjamin; Eggers, Felix; Brandimarte, Laura; Diefenbach, Sarah; Kroschke, Mirja; Wirtz, Jochen

doi:10.1016/j.jbusres.2019.10.006

Cited by 266 publications

(225 citation statements)

References 82 publications

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“…SL is distinct from approaches such as CDR (Cooper et al, 2019;Lobschat et al, 2020) in that it places public -or stakeholder -engagement at its heart. Since a SL is granted or refused by external stakeholders (rather than secured internally) it focuses attention at the importance of aligning with public values through public engagement.…”

Section: Discussionmentioning

confidence: 94%

Establishing a social licence for Financial Technology: Reflections on the role of the private sector in pursuing ethical data practices

et al. 2020

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Current attention directed at ethical dimensions of data and Artificial Intelligence have led to increasing recognition of the need to secure and maintain public support for uses (and reuses) of people's data. This is essential to establish a "Social Licence" for current and future practices. The notion of a "Social Licence" recognises that there can be meaningful differences between what is legally permissible and what is socially acceptable. Establishing a Social Licence entails public engagement to build relationships of trust and ensure that practices align with public values. While the concept of the Social Licence is well-established in other sectors-notably in relation to extractive industries-it has only very recently begun to be discussed in relation to digital innovation and data-intensive industries. This article therefore draws on existing literature relating to the Social Licence in extractive industries to explore the potential approaches needed to establish a Social Licence for emerging data-intensive industries. Additionally, it draws on well-established literature relating to trust (from psychology and organisational science) to examine the relevance of trust, and trustworthiness, for emerging practices in data-intensive industries. In doing so the article considers the extent to which pursuing a Social Licence might complement regulation and inform codes of practice to place ethical and social considerations at the heart of industry practice. We focus on one key industry: Financial Technology. We demonstrate the importance of combining technical and social approaches to address ethical challenges in data-intensive innovation (particularly relating to Artificial Intelligence) and to establish relationships of trust to underpin a Social Licence for Financial Technology. Such approaches are needed across all areas and industries of data-intensive innovation to complement regulation and inform the development of ethical codes of practice. This is important to underpin culture change and to move beyond rhetorical commitments to develop best practice putting ethics at the heart of innovation.

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Section: Discussionmentioning

confidence: 94%

Establishing a social licence for Financial Technology: Reflections on the role of the private sector in pursuing ethical data practices

et al. 2020

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“…The latter should be developed by banks, e.g. as part of Corporate Social Responsibility (CSR), which is currently expanded by Corporate Digital Responsibility (CDR) (see Lobschat et al, 2019). The CDR concept is based on the recognition that the organizations acting for technology development and also the ones which use technology to provide services are required to do so in a way that essentially leads to a positive future.…”

Section: Resultsmentioning

confidence: 99%

The digital skills conducive to using the Internet and mobile banking services in light of nationwide own research results

Adamek¹,

Solarz²

2020

Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu

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“…Step 2 Determine the preferences of both parties, it include the HTI and preference, and build Vectors of HTI and preference based on preferences information according to Eqs. (5)(6) and (17)(18).…”

Section: Matching Model For P2p Sharing Accommodation Based On Fair Mmentioning

confidence: 99%

“…Tables 6 and 7 show actual status and expectation data in current preference based on the preference indicators in Table 2 and Eqs. (12)(13)(14)(15)(16)(17).…”

Section: Study Case Design and Model Solvingmentioning

confidence: 99%

“…Besides, in the field of digital and platform economy, price discrimination and unequal between sellers and buyers behind the intelligent model and algorithm caused concern about the safety of machine behavior [ 16 ]. Data unfair use and transaction between different user groups challenging the corporate digital responsibility and impairing the reputation of the enterprise [ 17 ]. User’s perception of unfairness harms user trust and loyalty of the P2P platform [ 18 ], and similar risks are beginning to be noticed, it provides a new perspective for supply and demand matching of P2P platforms, and puts forward new requirements.…”

Section: Introductionmentioning

confidence: 99%

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Supply and demand matching model of P2P sharing accommodation platforms considering fairness

Wang

2020

Electron Commer Res

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Due to the continuous expansion of sharing economy, the diversification of users and the heterogeneity of resources and needs on P2P platform are speeding up, which makes it difficult to match the supply and demand of P2P platform effectively. Therefore, how to achieve effective matching between service providers and customers in an increasingly complex market is a question worthy of study. In order to achieve more effective matching of heterogeneous resources and requirements, this paper focuses on P2P sharing accommodation platform, advances a theoretical framework of fair matching and builds a matching model which considering fairness. First, we analyze the transaction mode of P2P sharing accommodation platform, proposed the framework of fair matching based on preferences consistency and fairness of matching. Second, we build a matching model based on fair matching, to maximize the consistency of preference and minimize the difference between supply and demand, the fair matching framework deals with heterogeneous resources and needs by matching diversified preferences. Finally, the effectiveness and feasibility of the strategy are verified by example and sensitivity analysis. This strategy provides optimization ideas for the matching issue of P2P sharing accommodation.

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Corporate digital responsibility

Cited by 266 publications

References 82 publications

Establishing a social licence for Financial Technology: Reflections on the role of the private sector in pursuing ethical data practices

Establishing a social licence for Financial Technology: Reflections on the role of the private sector in pursuing ethical data practices

The digital skills conducive to using the Internet and mobile banking services in light of nationwide own research results

Supply and demand matching model of P2P sharing accommodation platforms considering fairness

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