Economic growth, population growth, and the drive for sustainability evidenced by the Paris Accords is forcing a radical re-examination of the way electricity is produced, managed, and consumed. Emerging research on sustainable smart electricity markets is facilitating the emergence of sustainable energy systems and a revolution in the eciency and reliability of electricity consumption, production, and distribution. Traditional electricity grids and markets are being disrupted by a range of forces, including the rise of weather-dependent and geographically distributed supply from renewable sources, consumer involvement in managing their power consumption and small-scale production, and the electrification of transport, evidenced by the emergence of electric vehicles. We expect these transformations to bring about increasingly complex and dynamic smart electricity markets that must rely on intelligent analysis of information to continuously inform stakeholders decisions, and on e ective integration of stakeholders actions. We outline how advances in information-intensive processes are fundamental for facilitating these transformations. We describe the roles that such processes will play in the future smart grid and discuss Information Systems research challenges necessary to achieve these goals. The research we discuss spans challenges in public policy, privacy, and security, market mechanisms, and data-driven decision support. Overall, research is necessary to enable information sharing across the grid as well as to develop methods that intelligently exploit rich data to transform the eciency of energy use, production, and distribution. Finally, our commentary underscores that the diverse IS research perspective is instrumental for addressing the complexity and interdisciplinary nature of this research.
Software quality concerns in the banking industry are often addressed by professionals but rarely studied academically. This paper aims to get a deep insight about the most perceived concerns by industry experts. We carried out a Mixed-Method study, performing a Delphi-like study about the Italian banking IT sector. According to our pragmatic epistemological paradigm, we developed a specific research framework to pursue this vertical study, that is domain and country specific. Data collection was drawn in four phases starting with a high level randomly stratified panel of 13 senior managers and then a target-panel of 124 carefully selected and well-informed domain experts. We have identified and dealt with 28 concerns about the present situation; they were discussed in the framework inspired by the ISO/IEC 25010, 42010 and 12207 standards. Moreover, they disclose how a short-term total cost of ownership view of information systems increases the technical debt. After having mapped the concerns within the ISO/IEC standards of Software quality, architecture and process, we discussed such dimensions in relationship with the three relevant ISO standards. Our results show the strong relationship between software quality, software architecture and software process. Thus, we induce and illustrate the novel SQuAP (Software Quality, Architecture, Process) Meta-Model framework, to analyze and assess information systems' quality.
Person-job fit is the process of matching the right talent for the right job by identifying talent competencies that are required for the job. While many qualitative efforts have been made in related fields, it still lacks of quantitative ways of measuring talent competencies as well as the job's talent requirements. To this end, in this paper, we propose a novel end-to-end data-driven model based on Conventional Neural Network (CNN), namely Person-Job Fit Neural Network (PJFNN), for matching a talent qualification to the requirements of a job. To be specific, PJFNN is a bipartite neural network which can project both job postings and candidates' resumes onto a shared latent representation, thus it can effectively learn the joint representation of Person-Job fitness from the successful job applications. In particular, due to the design of a hierarchical representation structure, PJFNN can not only estimate whether a candidate fit a job, but also identify which specific requirement items in the job posting are satisfied by the candidate by measuring the distances between corresponding latent representations. Finally, we evaluate our approach based on a large-scale real-world dataset. The extensive experiments clearly validate the performances of our method in terms of Person-Job Fit prediction. Also, we provide effective data visualization to show some job and talent benchmark insights obtained by PJFNN.
Users play an important role during the design phase of complex information systems. In this study, we develop a repository of best-practice designs, which are encoded as metadata of contemporary web services. We examine if this prototype repository, Web Service Crawler, makes the communication between users and analysts more effective. The development of the repository, which serves as a requirements search and exploration engine, was guided by theories from four different research streams and introduces several new design characteristics. Web Service Crawler serves as a dynamic reference model and reduces the cognitive load of the analyst by sharing that load with the user as the pair explores the repository for best-practice designs. It supports an agile approach to system design through rapid selection of appropriate web services. The evaluation results demonstrate that Web Service Crawler is an effective and efficient tool for supporting designers during initial service design, as well as for supporting users and analysts during system design.