TOWARDS SUSTAINABLE QUALITY OF SERVICE IN INTERCONNECTION

This paper analyses the structure of the Internet marketplace and the business relationships of key players involved in network services provision. A brief overview of existing pricing policies and research work in this area is presented and some new issues are introduced. We believe that the role of information asymmetry is critical when considering agreements for Internet access and interconnection. In negotiation and contract preparation, information asymmetry gives rise to adverse selection. The current structure of connectivity agreements does not address information asymmetries thus allowing the possibility of opportunistic behaviour in the form of moral hazard. Inasmuch as interconnection agreements involve sharing and/or exchanging network resources, either party will tend to exploit the agreement to its own advantage (i.e. conserving its own resources) and, possibly, to the detriment of the other (i.e. overutilising the other’s resources). The discussion focuses on interconnection agreements between Internet Service Providers, namely peering and transit. The paper concludes with an outline of an incentive compatible mechanism that can sustain quality of service requirements in interconnection agreements.interconnection information asymmetry

Machine Learning in Transaction Monitoring: The Prospect of xAI

Banks hold a societal responsibility and regulatory requirements to mitigate the risk of financial crimes. Risk mitigation primarily happens through monitoring customer activity through Transaction Monitoring (TM). Recently, Machine Learning (ML) has been proposed to identify suspicious customer behavior, which raises complex socio-technical implications around trust and explainability of ML models and their outputs. However, little research is available due to its sensitivity. We aim to fill this gap by presenting empirical research exploring how ML supported automation and augmentation affects the TM process and stakeholders' requirements for building eXplainable Artificial Intelligence (xAI). Our study finds that xAI requirements depend on the liable party in the TM process which changes depending on augmentation or automation of TM. Context-relatable explanations can provide much-needed support for auditing and may diminish bias in the investigator's judgement. These results suggest a use case-specific approach for xAI to adequately foster the adoption of ML in TM.Comment: Proceedings of the 56th Hawaii International Conference on System Sciences, 2023 https://hdl.handle.net/10125/10305

Is there a viable future for residential IP-Telephony users? Exploring providers’ and end-users’ perspectives

The emergence of IP telephony as a lower-priced alternative to traditional phone services has raised questions of what users want and what vendors have to offer. In this article we investigate the views of both users and vendors on critical issues for IP telephony diffusion based on diffusion of innovation theory. We draw our conclusions from the study of a dynamic IP-telephony market where we interviewed IP telephony providers and surveyed residential users. Our results indicate that vendors’ main concern is users’ lack of technical skills, while users are comfortable with technical complexity, and are interested in price reductions

Bundle Pricing for Location Based Mobile Services

Locations-based services are a promising revenue source for the market players in mobile business. A critical parameter for accruing revenue is the applied pricing strategy. This paper proposes the introduction of bundle pricing in the market of location-based services and describes a research model that will be used to test its applicability and indicate its robustness. To motivate the proposed research, the key players of location-based services’ market are identified and the main theoretical results on bundle pricing are briefly presented. Then, the proposed research model is discussed to indicate how economic theory’s results may be applied in a real market case

A European study of e-business maturity and ICT-benefits: Is there a conditional relationship?

The present study explores the relationship between the e-business maturity and the perceived benefits from (Information and Communication Technologies) ICT at the firm level. We aim to debunk this relationship in terms of its strength and stability, and to explore the conditions which may influence it. Taking an economic approach, we hypothesise that the relation between e-business maturity and perceived benefits from ICT adoption will be influenced the costs of intra-organisational adaptations due to ICT. Using data from an European survey on ICT adoption and e-business maturity of the firms (N=7,072) we show that the correlation between the e-business maturity and the perceived benefits from ICT adoption is indeed positive, significant and stable over countries, industries, firm size and age. Further, the findings confirm the hypothesis that intra-organisational adaptations due to ICT moderate the positive correlation between a firms’ e-business maturity and perceived benefits from ICT

Big data revisited: a rejoinder

We elaborate on key issues of our paper New games, new rules: big data and the changing context of strategy as a means of addressing some of the concerns raised by the paper’s commentators. We initially deal with the issue of social data and the role it plays in the current data revolution. The massive involvement of lay publics as instrumented by social media breaks with the strong expert cultures that have underlain the production and use of data in modern organizations. It also sets apart the interactive and communicative processes by which social data is produced from sensor data and the technological recording of facts. We further discuss the significance of the very mechanisms by which big data is produced as distinct from the very attributes of big data, often discussed in the literature. In the final section of the paper, we qualify the alleged importance of algorithms and claim that the structures of data capture and the architectures in which data generation is embedded are fundamental to the phenomenon of big data

New games, new rules: big data and the changing context of strategy

Big data and the mechanisms by which it is produced and disseminated introduce important changes in the ways information is generated and made relevant for organizations. Big data often represents miscellaneous records of the whereabouts of large and shifting online crowds. It is frequently agnostic, in the sense of being produced for generic purposes or purposes different from those sought by big data crunching. It is based on varying formats and modes of communication (e.g., texts, image and sound), raising severe problems of semiotic translation and meaning compatibility. Crucially, the usefulness of big data rests on their steady updatability, a condition that reduces the time span within which this data is useful or relevant. Jointly, these attributes challenge established rules of strategy making as these are manifested in the canons of procuring structured information of lasting value that addresses specific and long-term organizational objectives. The developments underlying big data thus seem to carry important implications for strategy making, and the data and information practices with which strategy has been associated. We conclude by placing the understanding of these changes within the wider social and institutional context of longstanding data practices and the significance they carry for management and organizations

Machine Learning in Transaction Monitoring: The Prospect of xAI

Banks hold a societal responsibility and regulatory requirements to mitigate the risk of financial crimes. Risk mitigation primarily happens through monitoring customer activity through Transaction Monitoring (TM). Recently, Machine Learning (ML) has been proposed to identify suspicious customer behavior, which raises complex socio-technical implications around trust and explainability of ML models and their outputs. However, little research is available due to its sensitivity. We aim to fill this gap by presenting empirical research exploring how ML supported automation and augmentation affects the TM process and stakeholders’ requirements for building eXplainable Artificial Intelligence (xAI). Our study finds that xAI requirements depend on the liable party in the TM process which changes depending on augmentation or automation of TM. Context-relatable explanations can provide much-needed support for auditing and may diminish bias in the investigator’s judgement. These results suggest a use case-specific approach for xAI to adequately foster the adoption of ML in TM