PurposeThe space industry has experienced rapid development over the last few years. Activities such as building things in space, learning about our Earth and exploring outer space are satisfying people's fantasies and achieving humanity's ambitions. Such activities have also generated many issues that belong to several information systems (IS) research domains. In this article, the authors discuss the challenges and opportunities associated with the space economy.Design/methodology/approachThe authors discuss why the emerging space economy opens a new frontier of e-commerce and data analytics. Linking three important IS research areas (i.e. digital commerce, data analytics and information security) to the space economy, this study motivates scholars to pay close attention to this promising new frontier for IS research.FindingsThe authors identify new research opportunities within several IS research contexts (digital commerce, data analytics and information security). The authors highlight the potential for opening a robust, interdisciplinary field in the IS domain that could provide valuable insights for practitioners and academics.Originality/valueBecause of the unique characteristics of the space economy, this article presents some promising avenues, research opportunities and implications for several IS fields (digital commerce, data analytics, decision science, information sharing and information security and new business models). Indeed, many opportunities are interdisciplinary in scope, with overlaps occurring between IS and other disciplines.

As generative AI increasingly enables conversational interaction, fintech platforms are rapidly adopting generative AI–enabled conversational advisors (GAICs) to support complex financial decisions. Despite growing practical interest and rapid deployment, existing research provides limited insight into how and why users respond to conversational generative AI in highstakes decision contexts, how such responses unfold through concrete decision processes, and whether such systems fundamentally reshape users’ underlying decision processes rather than merely improving observable economic outcomes. Drawing on a large-scale field study conducted in collaboration with Ant Fortune, China’s leading fintech platform, we examine how the use of GAICs reshapes users’ decision processes. Integrating the exploration–exploitation framework with behavioral bias theory, we develop a mechanism-based perspective that conceptualizes GAIC usage as a decision-support technology that alters how users search for information, reassess existing choices, and act under uncertainty. Empirically, we show that GAICs broaden investors’ exploration of alternatives, deepen the reassessment and exploitation of existing holdings, and mitigate disposition-driven misallocation in both gain and loss domains. Importantly, these process-level effects exhibit systematic heterogeneity closely linked to investors’ preexisting behavioral tendencies. GAICs generate the largest decision-process improvements among investors with stronger baseline disposition-effect tendencies, reflected in more pronounced changes in gain–loss realization behavior. Beyond baseline biases, experienced investors benefit more from GAICs, consistent with their greater capacity to interpret and operationalize AI-generated information in decision making. GAICs are also particularly effective in promoting more deliberate, disciplined, and goal-consistent decision processes among risk-averse investors. To further deepen the interpretation of these mechanisms, we complement the field evidence with structured survey data that probe users’ self-reported decision strategies, cognitive responses, and perceived decision support when interacting with conversational AI. The survey evidence corroborates the same mechanism-based pathways identified in the field data, strengthening the interpretability of our findings without substituting for real-world behavioral evidence. Our results advance information systems research by shifting attention from outcome validation to mechanism-based explanations of technology-supported decision making, and by clarifying when and for whom GAICs reshape decision processes in high-stakes financial decision environments.

Purpose A smart city is a potential solution to the problems caused by the unprecedented speed of urbanization. However, the increasing availability of big data is a challenge for transforming a city into a smart one. Conventional statistics and econometric methods may not work well with big data. One promising direction is to leverage advanced machine learning tools in analyzing big data about cities. In this paper, the authors propose a model to learn region embedding. The learned embedding can be used for more accurate prediction by representing discrete variables as continuous vectors that encode the meaning of a region. Design/methodology/approach The authors use the random walk and skip-gram methods to learn embedding and update the preliminary embedding generated by graph convolutional network (GCN). The authors apply this model to a real-world dataset from Manhattan, New York, and use the learned embedding for crime event prediction. Findings This study's results show that the proposed model can learn multi-dimensional city data more accurately. Thus, it facilitates cities to transform themselves into smarter ones that are more sustainable and efficient. Originality/value The authors propose an embedding model that can learn multi-dimensional city data for improving predictive analytics and urban operations. This model can learn more dimensions of city data, reduce the amount of computation and leverage distributed computing for smart city development and transformation.

Hospital emergency management is an important issue because of the characteristics of hospital constructions, patients, and environment. In this study, a BIM-based integrated multiple level emergency management is proposed in the large hospital context. The framework includes the seamless integration of BIM operation and management models, the evaluation of risk level and the analysis of corresponding emergency response plans. The framework should provide a significant guidance for smart hospital emergent management both in academic and practice.

E2.0 facilitates the efficient collaboration of employers and workers across departmental boundaries. The exponential growth of nascent enterprise-level social network platforms implies important impacts on employees’ daily working styles and the implementation decisions made regarding these platforms represent the significant digital innovation. Despite this importance, limited effort has been devoted to understanding whether company senior managers’ leadership influences employees’ commitment to E2.0-driven change. Using a novel proprietary dataset from a leading E2.0 platform, we investigate the impact of change leadership perceived by employees on the implementation of E2.0. The sample includes information on 575 paid customers (i.e. firms) with 65,407 individual users and 2,286 previous customers with 99,807 individual users from 2011-2016. Our research will provide key insights for several groups of stakeholders, including platform developers, company senior managers, and workers. The expected contribution and practical implications are discussed.

Traffic congestion has become a serious problem in large hospital located in the areas with the high-density population. To mitigate the serious traffic congestion situation so that patients can timely obtain diagnosis and treatment, by accessing real-time monitoring traffic data from a provincial Maternal and Child Health Care Hospital, we propose and simulate three proposed traffic management strategies based on hospital traffic characteristics and the estimated traffic volume through a model developed based on the queueing theory. The results show that the three proposed strategies can relieve the traffic congestion faced by the large hospital.