Industrial data ecosystems and data spaces

The evolution of seaports to smart ports is the most important revolution they must face in order to meet the high requirements of efficiency, economy, and security. Achieving this evolution is a task full of challenges. One of these challenges is the interoperability of the stakeholders' information systems. Interoperability has been widely studied but there is still some reluctance to adopt the solutions because the stakeholders are afraid of sharing their data. Recently, the Industrial Data Space (IDS) reference architecture proposes several recommendations and specifications to solve this issue based on data sovereignty concept. This architecture has not been studied nor implemented for the seaport use case yet. For this reason, this paper presents a seaport data space based on IDS architecture to share information in a secure and interoperable manner. Along with this, a big data architecture is added to exploit the shared data. The seaport data space is implemented using the FIWARE IoT platform. The seaport data space is tested by sharing the data of the port authority to the port terminal and analyzing the large shared data through descriptive analysis. The results show that by using the seaport data space, the shared data allow improving the decision making in the planning operations of the seaport.

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This minitrack, for the second time, deepens understanding of data sharing, data ecosystems, data platforms, data spaces, data hubs, and repositories.It emphasizes diverse perspectives, covering technical, organizational, social, and regulatory aspects, and examines multiple levels, including system, individual, community, and societal dimensions.This minitrack fosters discussions on emerging technologies and is open to insights on repurposing existing technologies for data-driven care coordination and research, involving increasing data over extended periods.

The rapid evolution of health information systems (Health IS) research has led to many significantcontributions. However, while the Health IS subset of information systems (IS) scholarship hasconsiderably grown over the past two decades, this growth hasled to questions regarding the currentintellectual structure of this area of inquiry. In an effort to more fully understand how Health ISresearchhas contributed to the IS discipline, and what this may mean for future Health IS researchin the IS domain, we conduct an in-depth evaluation of Health IS research published in mainstreamIS journals. We apply citation analysis, latent semantic analysis (LSA), and social network analysis(SNA) to ourdata setof Health IS articles in order to: (1) identify Health IS research themes andthematic shifts, (2)determine which Health IS research themes are cohesive (versus disparate), (3)identify which Health IS research themes are central (versus peripheral), (4) clarify networks ofresearchers (i.e., thought leaders) contributing to these research themes, and(5) provide insights intothe connection of Health ISresearchto its reference disciplines. Overall, we contribute a systematicdescription and explanation of the intellectual structure ofHealth ISresearchand highlight how theexisting intellectual structure of Health ISresearchprovides opportunities for future research.

While ethics are recognized as an integral part of information systems (IS) research, many questions about the role of ethics in research practice remain unanswered. Our report responds to this emerging set of concerns with a broad and integrative account of five perspectives on ethics in IS research and design science research (DSR) in particular. Our report is informed by a broad literature review, a panel discussion at DESRIST 2020, and substantial personal experience from wrestling with ethical considerations in the field. The report provides a comprehensive discussion of prevailing perspectives on ethics and the implications for IS research. Together, we hope the report will inspire more ethics-conscious and responsible IS research. © 2022 by the Association for Information Systems.

A literature analysis of the use of Absorptive Capacity construct in IS research

This study examines information systems (IS) research in Chinese Mainland in recent years and identifies the similarities and the differences between international and Chinese IS research in terms of research topics and research methods. The data consists of a total of 410 research papers published in ten leading Chinese academic journals related to IS over the five-year period from 1999 to 2004. Compared to IS research abroad, the following may be said of Chinese IS research: (1) Systems-related issues, especially system design issues, system implementation, analytical models and tools, DSS/ESS/EIS are emphasized in research topics, whereas human-related and research methodologies issues are scarce. (2) Empirical studies, including case study, survey and experiment, are few and far between. (3) The majority of non-empirical studies focus on applications and conceptual description of IS, mainly in different applied fields and introducing or illustrating hot topics abroad. The "general shift" from traditional issues such as system design to non-technical problems is still continuing. There is a need to go beyond mere description of IS related phenomenon and to move away from non-empirical to empirical studies.

The exponential growth of digital content has generated massive textual datasets, necessitating the use of advanced analytical approaches. Large Language Models (LLMs) have emerged as tools that are capable of processing and extracting insights from massive unstructured textual datasets. However, how to leverage LLMs for text analytics Information Systems (IS) research is currently unclear. To assist the IS community in understanding how to operationalize LLMs, we propose a Text Analytics for Information Systems Research (TAISR) framework. Our proposed framework provides detailed recommendations grounded in IS and LLM literature on how to conduct meaningful text analytics IS research for design science, behavioral, and econometric streams. We conducted three business intelligence case studies using our TAISR framework to demonstrate its application in several IS research contexts. We also outline the potential challenges and limitations of adopting LLMs for IS. By offering a systematic approach and evidence of its utility, our TAISR framework contributes to future IS research streams looking to incorporate powerful LLMs for text analytics.

In today's Computing world Cloud enrolling is one of the best advancement which utilizes advanced computational power and it improves data sharing and data securing limits. Key inconvenience in dispersed processing was issues of data reliability, data security and data access by unapproved customers. TTA (Trusted Third Party) is used to store and share data in dispersed registering. Change and sharing of data is totally essential as a social event. To check uprightness of the shared data, people in the social affair needs to figure blemishes on all common data squares. Various pieces in shared data are all things considered set apart by differing customers in view of data changes performed by particular customers. Customer repudiation is one of the best security perils in data sharing in gettogethers. In the midst of customer foreswearing shared data piece set apart by repudiated customer needs to download and re-sign by existing customer. This errand is particularly inefficacious due to the broad size of shared data pieces on cloud. PANDA Plus is the new open assessing instrument for the keeping up respectability of conferred data to successful customer revocation in the cloud. This segment relies on upon middle person re-marks thought which allows the cloud to re-sign squares on advantage of existing customers in the midst of customer repudiation so that downloading of shared data squares is not required. PANDA other than is the overall public controller which surveys the reliability of shared data without recouping the entire data from the cloud. It also screen bunch to affirm different surveying endeavors at the same time. For user revocation or data revocation system does not relies on group manager it does automatically over public cloud.

Sharing data produced through health research projects has been increasingly recognized as a way to advance science more rapidly by facilitating discovery and increasing rigor and reproducibility. Much of the data collected from human subjects includes sufficient sociodemographic detail and/or covers sensitive topics, and thus requires restricted data management and sharing practices. Over the last two decades, scientific organizations, presidential memoranda, and other sources have all called for increasing opportunities to share data. Recognizing the value of shared data, the National Institutes of Health issued a new Data Management and Sharing Policy, effective January 25, 2023. Prior to this updated policy, in 2009, the National Institute on Drug Abuse recognized the value of sharing data and established an archive, the National Addiction and HIV Data Archive Program. This program focused on sharing data, often highly sensitive, generated from social and behavioral addiction research, including quantitative and qualitative assessments as well as biomarker and imaging data. NAHDAP has developed practices and curation standards to ensure datasets are improved and usable, and provides technical assistance for both data depositors and users. We share three key lessons learned working to disseminate sensitive data over the last 13 years, including (1) protecting the confidentiality of human subjects; (2) ensuring careful consideration of costs for archiving data requiring protection ; and (3) providing support to facilitate the discovery and use of the data.

This paper reports on a panel held during the 2006 International Conference on Information Systems (ICIS). The panel titled, “Social Activism in IS Research: Making the World a Better Place,” was organized to question whether and how Information System (IS) research is making tangible impacts to our society. More specifically, each panelist was asked to address: (1) How can IS research, and researchers, make contributions to underdeveloped societies and underserved communities?; and (2) How can IS researchers learn from the particularities of these communities to inform better research, teaching, and service? While each panel member had different perspectives to offer in relation to these two questions, all agreed that IS academe needs to raise its awareness and efforts considerably with a view to address the needs of underserved communities.

Information Systems (IS) research strives for the design of innovative as well as the investigation of existing methods and techniques for information management in organizations. IS methods and techniques can support the automation of operational tasks as well as management tasks in organizations. Furthermore, they offer considerable potential for the automation of research processes in IS research. This paper examines the potential and boundaries of automated analyses of fads and trends in the field of IS research based on a design-oriented research approach. A concept for automated trend analysis is developed and implemented in an innovative software tool. The proof of concept software is exemplarily applied for the investigation of fads and trends in Business Process Management (BPM) research as an important field in IS research. Moreover, the potential and boundaries of the presented approach are discussed.

This essay discusses the use of big data analytics (BDA) as a strategy of enquiry for advancing information systems (IS) research. In broad terms, we understand BDA as the statistical modelling of large, diverse, and dynamic data sets of user-generated content and digital traces. BDA, as a new paradigm for utilising big data sources and advanced analytics, has already found its way into some social science disciplines. Sociology and economics are two examples that have successfully harnessed BDA for scientific enquiry. Often, BDA draws on methodologies and tools that are unfamiliar for some IS researchers (e.g., predictive modelling, natural language processing). Following the phases of a typical research process, this article is set out to dissect BDA’s challenges and promises for IS research, and illustrates them by means of an exemplary study about predicting the helpfulness of 1.3 million online customer reviews. In order to assist IS researchers in planning, executing, and interpreting their own studies, and evaluating the studies of others, we propose an initial set of guidelines for conducting rigorous BDA studies in IS.

Artificial intelligence (AI) is quickly transforming business operations and society, as AI capabilities are incorporated into applications ranging from mobile personal assistants to self-driving cars. The potentially disruptive nature of AI calls for an extensive investigation into all aspects of AI-human interactions at individual, group, organizational and market levels. However, there is paucity of academic information systems (IS) research in this area that goes beyond the development and testing of specific narrow AI capabilities. AI represents an important opportunity for organizational and behavioral IS researchers, but also presents challenges associated with the underlying complexity of AI technologies and the diversity of AI applications. Understanding how existing AI research and business practice relate to traditional areas of IS research is an important step towards creating a comprehensive behavioral and organizational AI research agenda. This dissertation seeks to achieve a dual purpose in a series of three essays. Essay 1 seeks to understand the current state of business AI research and practice in business through a quantitative literature review, relate the findings to traditional IS research areas, and identify potentially fruitful research areas for AI-focused IS research. Essays 2 and 3 seek to address specific research questions related to one of such research areas, namely, human interactions with AI enabled applications. Essay 2 focus on user experience with a chatbot, a popular AI application, and Essay 3 explores how user experiences with AI assistant apps differ from their interactions with more traditional IT artifacts.

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.