Alireza Nili

Purpose: This study aims to develop a descriptive maturity model for evaluating the integration of IIoT technologies within enterprise environments. Although IIoT enables data-driven and automated operations, integration remains complex because of technological and organisational interdependencies. Existing models lack conceptual consistency and provide limited insight into operational integration. The study therefore proposes a theory-informed maturity model, specifically focused on IIoT integration into business operations. Design/methodology/approach: The study uses a maturity model development methodology guided by design science research. It draws on literature-based insights to develop the initial model components and applies a theoretical foundation to interpret and structure the model dimensions. The model was then evaluated and refined through two design cycles involving expert interviews. Findings: The maturity model comprises four dimensions: physical object, data, process and business service, organised and interpreted using the Semiotic Ladder and five maturity levels: pre-pilot, pilot, operational, organisation-scale and ecosystem-scale. It also identifies the key capabilities required to effectively integrate IIoT into business operations. Originality/value: This study develops a theory-informed maturity model based on the semiotic ladder of organisational semiotics, offering a novel semiotics-based perspective for conceptualising and assessing IIoT maturity. It explicitly focuses on the integration of IIoT into business operations, emphasising the key dimensions required to support this integration.

As digital technology continues to evolve rapidly and get integrated into various aspects of our cities and societies, the alignment of technological advancements with societal values becomes paramount. The evolving socio-technical landscape has prompted an increased focus on responsible innovation and technology (RIT) among technology companies, driven by mounting public scrutiny, regulatory pressure, and concerns about reputation and long-term sustainability. This study contributes to the ongoing discourse on responsible practices by conducting a policy review that delves into insights from the most influential high-tech companies’—so-called tech giants’—RIT guidance. The findings disclose that (a) leading high-tech companies have started to focus on RIT; (b) the main RIT policy focus of the leading high-tech companies is artificial intelligence; (c) trustworthiness and acceptability of technology are the most common policy areas; (d) affordability related to technology outcomes and adoption is almost absent from the policy; and (e) sustainability considerations are rarely part of the RIT policy, but are included in annual corporate reporting. Additionally, this paper proposes a RIT assessment framework that integrates views from the policy community, academia, and the industry and can be used for evaluating how well high-tech companies adhere to RIT practices. The knowledge assembled in this study is instrumental in advancing RIT practices, ultimately contributing to technology-driven cities and societies that prioritise human and social well-being.

In an era in which technological advancements have a profound impact on our cities and societies, it is crucial to ensure that digital technology is not only driven by technological progress with economic goals but that it can also fulfill moral and social responsibilities. Hence, it is needed to advocate for ‘Responsible Innovation and Technology’ (RIT) to ensure cities and societies can harness the potential of technological progress and prosperity while safeguarding the well-being of individuals and communities. This study conducts a PRISMA review to explore and understand RIT concepts and its characteristics. In this study, we emphasize that RIT should deliver acceptable, accessible, trustworthy, and well governed technological outcomes, while ensuring these outcomes are aligned with societal desirability and human values, and should also be responsibly integrated into our cities and societies. The main contribution of this study is to identify and clarify the key characteristics of RIT, which has not been performed in such detail so far. The study, reported in this paper, also broadens the understanding of responsible research and innovation in the technosphere, particularly from a bottom-up perspective. Furthermore, the paper develops an RIT conceptual framework outlining its possible design procedures, which could be used by governments, companies, practitioners, researchers, and other stakeholders as a tool to address the grand challenges that accompany technological and scientific progress. The framework also informs science, technology, and innovation policy.

Most managers lack guidance on how to address uncertainties associated with deploying artificial intelligence (AI) technologies. Drawing on our experiences with projects in the public sector, we describe consistent patterns related to what AI deployment uncertainties are and strategies to successfully tackle them. Synthesizing these patterns, we arrive at five major areas of uncertainties, and relevant solution directions for addressing them.

We discuss the importance of salient design features (which support purposeful action and relate to functional affordances) of customer-to-customer social media platforms that support communities of interest, focussed on open-ended data and user tasks, in contrast to business-to-customer platforms, focussed on relatively fixed data and transactions. We provide an exposition of their design features, shedding light on how they support tasks of searching and decision-making in which users elicit knowledge from their community, for targeted needs to needs which are less known and speculative. We also discuss that how our findings can be useful for designing social media platforms that support other types of online communities such as online communities of practice. Our research contributes to the ongoing discourse in the information systems field about the importance of identifying and articulating the salient design features of an information technology that is useful for theory and practice.

Cognitive Computing Systems (CCSs) are increasing in prominence in the public sector. This paper develops a framework drawing on public value and information technology service management literature to guide the management of CCSs in the public sector. We draw on academic literature, gray literature, legislation and government reports, and examples on CCS initiatives in the public sector to develop insights for research and practice. We then outline the themes and present the insights in the form of guiding principles and specific (detailed) recommendations. These include guiding principles and recommendations for establishing legitimacy, understanding the required capabilities, executing capabilities, creating and measuring public value.

With the increased use of geospatial datasets across heterogeneous user groups and domains, assessing fitness-for-use is emerging as an essential task. Users are presented with an increasing choice of data from various portals, repositories, and clearinghouses. Consequently, comparing the quality and evaluating fitness-for-use of different datasets presents major challenges for spatial data users. While standardization efforts have significantly improved metadata interoperability, the increasing choice of metadata standards and their focus on data production rather than potential data use and application, renders typical metadata documents insufficient for effectively communicating fitness-for-use. Thus, research has focused on the challenge of communicating fitness-for-use of geospatial data, proposing a more “user-centric” approach to geospatial metadata. We present the Geospatial User-Centric Metadata ontology (GUCM) for communicating fitness-for-use of spatial datasets to users in the spatial and other domains, to enable them to make informed data source selection decisions. GUCM enables metadata description for various components of a dataset in the context of different application domains. It captures producer-supplied and user-described metadata in structured format using concepts from domain-independent ontologies. This facilitates interoperability between spatial and nonspatial metadata on open data platforms and provides the means for searching/discovering spatial data based on user-specified quality and fitness-for-use criteria.