Excellent research papers are vital to science and technology advances. Thus, the early identification of potentially excellent research papers and recognizing their value in science and technology is high on the research agenda. This study used a set of 5 static and 8 time-dependent citation features to explore six machine learning methods and identify the method with the best performance to identify potentially excellent papers. The study modelled Random Forest, LightGBM, Naive Bayes, Support Vector Machine, Neural Network, and TabNet to identify PEPs in the artificial intelligence field. The study defined highly cited papers using the threshold of the top 1% and top 5% and collected the data from the Web of Science®. Bibliometric and citation data from 485,041 research articles, proceeding papers, and reviews published in AI between 1990 and 2010 were collected initially. The data was screened and processed, and the final dataset consists of 96,169 papers for the training and test sets. The findings suggest that the time-dependent citation features are more important than the static features, and citation peak features are more significant than the citation features in identifying potentially excellent papers. The findings demonstrate the effect of threshold on machine learning outcomes (e.g., the top 1% and 5%); therefore, the study argues that the decision about threshold selection should be carefully made. LightGBM and Random Forest both performed with the given conditions and achieved the same score in accuracy and recall. Nevertheless, when comparing their performance in other indicators, such as F1 and cross-entropy loss, LightGBM performed better. The study concluded that LightGBM was the best-performing model for identifying potentially excellent papers. The papers identified the contributions and recommended future research.

The home is filled with a rich diversity of sounds from mundane beeps and whirs to dog barks and children's shouts. In this paper, we examine how deaf and hard of hearing (DHH) people think about and relate to sounds in the home, solicit feedback and reactions to initial domestic sound awareness systems, and explore potential concerns. We present findings from two qualitative studies: in Study 1, 12 DHH participants discussed their perceptions of and experiences with sound in the home and provided feedback on initial sound awareness mockups. Informed by Study 1, we designed three tablet-based sound awareness prototypes, which we evaluated with 10 DHH participants using a Wizard-of-Oz approach. Together, our findings suggest a general interest in smarthome-based sound awareness systems particularly for displaying contextually aware, personalized and glanceable visualizations but key concerns arose related to privacy, activity tracking, cognitive overload, and trust.

It has become commonplace for companies operating in a global marketplace to relocate, extend, or establish new production facilities overseas. In this global context it is also normal for organizations to replicate the implementation and use of an established information system in a new location. Such replication is not trivial however and if not properly managed can lead to IS obsolescence. Drawing on a practice lens approach the paper presents a case study of the attempted replication of an e-procurement system within a global notebook manufacturer. The study argues that effective replication needs to take into account not only the technical aspects of the system but also the interpretive schemes of its users and their awareness of organizational routines that take together amount to a technology-in-use. The paper concludes with a discussion of the issues arising from the study and their implications for global information systems management.

It is widely accepted that the successful adoption of an information system depends to a great extent on users' perceptions of the information system. It follows then that an understanding of users' cognitive frames should be a key factor in managing the adoption of information systems. To reach such an understanding is not an easy task as cognitive frames are dynamic phenomena. For example, what an individual perceives as ‘ease of use’ and ‘usefulness’ may depend not only on intrinsic qualities of the information system but also on the changing contexts in which the information system is evaluated. This work argues that the management of information systems' adoption is a social and political process in which stakeholders frame and reframe their perceptions of an information system. A case study carried out in a European bank illustrates how the Bank's technical team influenced users' technological frames, including those of senior management in order to ensure a smooth implementation process. In conclusion, the paper introduces a set of theoretical propositions relating to the social and political processes that occur during information systems adoption.

No abstract available.