Information Systems Frontiers

Although the broadband market has considerably matured, follow-up research on the continued adoption of broadband is yet to be conducted. The aim of this research was therefore to investigate empirically the influence of service quality and secondary influence on consumers’ behavioural intention to change from their existing internet service provider (ISP) to an alternative service provider. The investigation focuses upon broadband household consumers within the UK. The study was conducted using a postal survey; a self-administered questionnaire was sent to 1600 households and a total of 358 completed replies were obtained. The results suggest that both service quality and secondary influence were significantly correlated to consumers’ behavioural intentions to change ISP. The implications of these findings are presented, followed by a discussion of the limitations of this research and future research directions.

Mitigating misinformation on social media is an unresolved challenge, particularly because of the complexity of information dissemination. To this end, Multivariate Hawkes Processes (MHP) have become a fundamental tool because they model social network dynamics, which facilitates execution and evaluation of mitigation policies. In this paper, we propose a novel light-weight intervention-based misinformation mitigation framework using decentralized Learning Automata (LA) to control the MHP. Each automaton is associated with a single user and learns to what degree that user should be involved in the mitigation strategy by interacting with a corresponding MHP, and performing a joint random walk over the state space. We use three Twitter datasets to evaluate our approach, one of them being a new COVID-19 dataset provided in this paper. Our approach shows fast convergence and increased valid information exposure. These results persisted independently of network structure, including networks with central nodes, where the latter could be the root of misinformation. Further, the LA obtained these results in a decentralized manner, facilitating distributed deployment in real-life scenarios.

Ad hoc interaction between web services and their clients is a worthwhile but seemingly distant goal. At present, most of the interest in web services is focused on pre-planned B2B interaction. Clients interact with services using advance knowledge of the data and sequence requirements of the service and pre-programmed calls to their interfaces. This type of interaction cannot be used for ad hoc interaction between services and their clients such as mobile devices moving in and around rich dynamic environments because clients may not have the necessary knowledge in advance. For unplanned ad hoc interaction an interaction mechanism is required that does not require clients to have advance knowledge of programmatic service interfaces and interaction sequences. The mechanism must ensure clients with different resources and diverse competencies can successfully interact with newly discovered services by providing assistance such as disambiguation of terminology, alternative types of inputs, and context sensitive error reporting when necessary. This paper introduces a service interaction mechanism called guided interaction. Guided interaction is designed to enable clients without prior knowledge of programmatic interfaces to be assisted to a successful outcome. The mechanism is grounded in core computing primitives and based on a dialogue model. Guided interaction has two parts; the first part is a language for the exchange of information between services and their clients. The second part is a language for services to create interaction plans that allow them to gather the data they require from clients in a flexible way with the provision of assistance when necessary. An interpreter uses the plan to generate and interpret messages in the exchange language and to manage the path of the dialogue.

Current research on distributed knowledge processes suggests a critical conflict between knowledge processes in groups and the technologies built to support them. The conflict centers on observations that authentic and efficient knowledge creation and sharing is deeply embedded in an interpersonal face to face context, but that technologies to support distributed knowledge processes rely on the assumption that knowledge can be made mobile outside these specific contexts. This conflict is of growing national importance as work patterns change from same site to separate site collaboration, and millions of government and industrial dollars are invested in establishing academic-industry alliances and building infrastructures to support distributed collaboration and knowledge. In this paper we describe our multi-method approach for studying the tension between embedded and mobile knowledge in a project funded by the National Science Foundation's program on Knowledge and Distributed Intelligence. This project examines knowledge processes and technology in distributed, multidisciplinary scientific teams in the National Computational Science Alliance (Alliance), a prototypical next generation enterprise. First we review evidence for the tension between embedded and mobile knowledge in several research literatures. Then we present our three-factor conceptualization that considers how the interrelationships among characteristics of the knowledge shared, group context, and communications technology contribute to the tension between embedded and mobile knowledge. Based on this conceptualization we suggest that this dichotomy does not fully explain distributed multidisciplinary knowledge processes. Therefore we propose some alternate models of how knowledge is shared. We briefly introduce the setting in which we are studying distributed knowledge processes and finally, we describe the data collection methods and the current status of the project.

Fire evacuation modeling benefits from the application of social science both in terms of accuracy and external validation. This paper describes PrioritEvac, a novel agent-based model which incorporates the social dimension of group loyalty into fire evacuation and responses to fire and smoke. It uses individual priorities, making for a dynamic approach that allows greater agency and nuance. PrioritEvac is programmed in NetLogo and validated using extensive data collected from the Station nightclub fire. The statistical analysis of the results of the model indicate that, compared to historical patterns, it reproduces along multiple metrics including a mean of 114 deaths (std. dev. = 38) over 50 runs, which puts the actual result of the fire within one standard deviation of the mean results of the simulation. Overall, the mean differential along all the metrics is 79, significantly outperforming all published ABM models of the Station nightclub fire that did not incorporate social relationships.

Distributed resource allocation is a very important and complex problem in emerging horizontal dynamic cloud federation (HDCF) platforms, where different cloud providers (CPs) collaborate dynamically to gain economies of scale and enlargements of their virtual machine (VM) infrastructure capabilities in order to meet consumer requirements. HDCF platforms differ from the existing vertical supply chain federation (VSCF) models in terms of establishing federation and dynamic pricing. There is a need to develop algorithms that can capture this complexity and easily solve distributed VM resource allocation problem in a HDCF platform. In this paper, we propose a cooperative game-theoretic solution that is mutually beneficial to the CPs. It is shown that in non-cooperative environment, the optimal aggregated benefit received by the CPs is not guaranteed. We study two utility maximizing cooperative resource allocation games in a HDCF environment. We use price-based resource allocation strategy and present both centralized and distributed algorithms to find optimal solutions to these games. Various simulations were carried out to verify the proposed algorithms. The simulation results demonstrate that the algorithms are effective, showing robust performance for resource allocation and requiring minimal computation time.

Issuing a disaster certificate, which is used to decide the contents of a victim’s support, requires accuracy and rapidity. However, in Japan at large, issuing of damage certificates has taken a long time in past earthquake disasters. Hence, the government needs a more efficient mechanism for issuing damage certificates. This study developed an estimation system of roof-damaged buildings to obtain an overview of earthquake damage based on aero-photo images using deep learning. To provide speedy estimation, this system utilized the trimming algorithm, which automatically generates roof image data using the location information of building polygons on GIS (Geographic Information System). Consequently, the proposed system can estimate, if a house is covered with a blue sheet with 97.57 % accuracy and also detect whether a house is damaged, with 93.51 % accuracy. It would therefore be worth considering the development of an image recognition model and a method of collecting aero-photo data to operate this system during a real earthquake.