The 2021 impact factor for ISJ was 7.767, for 2022 it was 6.4. These are some of the highest impact factors of any IS Journals. See past ISJ impact factors and the Editor’s comment on impact factors here. The next impact factor (2023) will not be available until around mid June 2024.

ABSTRACT

Covariance-based structural equation modelling (CB-SEM) is a robust analytical technique for validating complex measurements and theoretical models. Despite criticisms regarding overfitting, misspecification and sample size limitations, SEM remains invaluable for rigorous theoretical model testing when applied correctly. This Methods Article aims to streamline the extensive SEM criteria into essential considerations segmented across three critical stages: data preparation, measurement validation and structural modelling. This provides scholars with a comprehensive guide tailored to meet the stringent requirements of top-tier scientific journals. We outline data design considerations, progress through key SEM processes, and conclude with guidelines for testing specific hypotheses. We also illuminate relevant validation criteria for each stage, forming a foundational framework for rigorous SEM analysis. Neglecting any of these criteria can trigger irreversible analytical errors. We provide examples of how missing some criteria can drastically change results. We also demonstrate an ongoing issue with inadequate reporting of these criteria in IS journals, exacerbating these issues. Currently, SEM instruction is dispersed across numerous books and articles across different fields and decades, often with complex explanations. Our principal contribution is consolidating a comprehensive set of validation criteria into an articulated guide for scholars not yet proficient in SEM. However, this is not a step-by-step walkthrough for advanced SEM users. We advocate for a structured, transparent reporting system for these criteria, shifting the responsibility for methodological clarity onto the author and facilitating a more precise understanding for readers. Our recommendations aim to enhance the integrity of SEM applications in research by elevating reporting standards.

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ABSTRACT

To remain competitive, many organisations are undertaking agile transformations in pursuit of agility. Information technology (IT) plays a pivotal role in supporting organisational agility; thus, it is essential that organisations select the right IT projects in a timely manner to deliver. In practice, though, organisations have struggled with effective decision-making in the IT planning process, especially in competitive environments where there is a need for agile decisions. To guide organisations on how they can transform their IT project selection (ITPS) process to become more agile, we examine two large and well-established Australian organisations and their digital-only subsidiaries launched as agile organisations in start-up style. We explain how the parent companies conduct ITPS and contrast this with the digital-only subsidiaries, highlighting the strengths and challenges each approach presents for agility. We provide an ITPS agility framework that identifies five dimensions that can enable or inhibit agility. These are: ITPS funding approach, number of ITPS decision-makers, granularity of ITPS work-packages, frequency of ITPS process and duration of ITPS process. Our findings indicate that the traditional approach that the parent organisations have taken with these ITPS dimensions has inhibited agility, whereas the ITPS dimensions have been configured to enable agility in their digital-only subsidiaries. We recommend that those responsible for agile transformations of ITPS within their organisations fund teams instead of projects, delegate ITPS decision-making authority, make faster and more frequent ITPS decisions about work-packages that are smaller in scope, and use agility in the right places, as ITPS does not always need to be agile.

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ABSTRACT

Innovation is crucial for small businesses to remain competitive and adaptable in dynamic markets. Recent advancements in AI, particularly machine learning and natural language processing, offer promising tools for enhancing product innovation. However, small businesses often face significant challenges in adopting AI due to limited financial resources, data infrastructure, technical expertise, operational and cultural barriers. This paper presents a novel and holistic human–AI-assisted product innovation (HAIAPI) framework designed to address these challenges by integrating an advanced large language model approach across four key stages of the product innovation process: (1) AI-augmented problem articulation, (2) human expert problem selection, (3) AI-augmented solution generation and (4) human expert solution selection. Through an in-depth case study of an Australian e-retailer, this paper provides practical insights into how AI can enhance problem articulation and solution generation, while human expertise ensures relevant problem and solution selection. The detailed instructions on implementing this framework, including Generative Pre-Trained Transformers prompts, for small businesses are supported by a comprehensive resource toolkit and checklist detailing necessary financial, technical and human resources. Last, three key principles of human–AI collaboration are synthesised, offering further actionable strategies for small business managers/owners looking to effectively integrate AI into their product innovation processes.

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ABSTRACT

Healthcare organisations stand at a critical juncture, facing pressing challenges such as constrained budgets, growing demands, workforce shortages and heightened public expectations. Now, more than ever, there is a dire need for innovative solutions. Digital service innovation holds immense promise, offering the potential to improve health outcomes, reduce costs, enhance service quality and elevate patient experiences. However, the adoption of these innovations has been slow. The COVID-19 pandemic served as a catalyst, dramatically accelerating the implementation of digital innovations in healthcare organisations. This rapid transformation was a necessary response to unprecedented conditions. However, there are concerns that the momentum gained during the pandemic is waning, with innovation rates slipping back to pre-pandemic levels. This paper argues that we must harness the lessons learned from the pandemic to sustain and increase the pace of innovation, addressing healthcare organisations’ urgent challenges. It aims to provide practical insights for healthcare managers at various organisational levels, drawing from a compelling case study of digital service innovation during the COVID-19 pandemic at a Norwegian hospital. Here, practitioners will find six actionable recommendations designed to inspire and empower them to drive innovation in a post-pandemic era. By embracing these insights, healthcare managers can lead their organisations toward a more resilient, efficient and patient-centric future. Now is the time to build on the strides made during the pandemic and transform the healthcare landscape for the better.

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ABSTRACT

The relationship between digital transformation and regulation is complex and bidirectional: regulation both drives and responds to changes in the technology landscape. Moreover, regulatory efforts to shape industry-level digital transformation often produce unwanted outcomes. Existing theories are insufficient for examining this complex relationship between regulation and digital transformation. Our case study of the Finnish taxi industry illustrates these complexities. The industry underwent a legal reform intended to legalise Uber-type solutions while restricting certain other solutions. By drawing on the notion of regulatory ambiguity and mechanism-based explanation, we show how ambiguity arises from the imprecise regulation in connection with conflicting regulation and technological uncertainties. We model the regulatory ambiguity mechanism consisting of the interconnected elements that, by affecting each other and working together, drive unintended changes in the technology landscape. We theorise regulatory ambiguity as a condition that emerges when regulations are imprecise, inconsistent, or evolving. This ambiguity shapes the technology landscape and related industry-specific practices, impacting digital transformation. Our research contributes to the literature on digital transformation and on the regulation of technology. We identify and analyse the regulatory ambiguity mechanism, providing information systems (IS) researchers with a novel framework to examine the role of regulation in digital transformation. We also conceptualise regulatory impact as a lens for future IS research.

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ABSTRACT

The widespread applications of generative AI (GenAI) have sparked significant interest, with many organisations eager to leverage its transformative potential. Rather than focusing on individual organisations, this study examines GenAI integration within enterprise platforms, which are extensively adopted by many organisations and thus amplify both the benefits and risks of GenAI. We offer targeted recommendations for enterprise platform owners and their complementors, addressing challenges they face when integrating GenAI into these platforms. Drawing on a case study of Salesforce’s experience, we recommend actions in three foundational areas – platform capability, architecture and governance – ensuring that our guidance is broadly applicable across enterprise platforms. In platform capability, we advise developing a unified GenAI stack built on existing platform services, offering generic and industry-specific GenAI use cases to accelerate customer adoption and providing tools for customisation and creation of new use cases to enhance GenAI’s transformational impact. For platform architecture, we recommend adding new layers for accommodating diverse GenAI foundation models and creating a trusted environment for secure data access, privacy and content monitoring. We also recommend implementing a prompt architecture to improve content relevance and accuracy. In platform governance, we recommend establishing new mechanisms to mitigate GenAI risks. Partnerships with GenAI providers and proactive investments in GenAI are essential to retain critical GenAI technologies. Personalised consultancy and training along with joint design and implementation with platform customers are also recommended. These combined actions, pursued in parallel across capability, architecture and governance, form a sustainable roadmap for GenAI integration in enterprise platforms.

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ABSTRACT

To embark on a digital transformation journey, organisations should prepare and adapt their workforce to meet the continuous need for skill adjustments. This paper reports insights from the journey of one organisation—Johnson & Johnson—that developed an employee skills inference platform based on artificial intelligence with the objective of creating a digital-first workforce capable of thriving amid the new reality of continuous digital innovation. We describe the challenges J&J faced during the deployment of the platform and the activities they undertook in response to these challenges. Based on that, we identify three organisational practices critical for the successful deployment of AI: blueprinting the future workforce, managing ethical data work across borders, and compensating for AI blind spots. From Johnson & Johnson’s experience, we derive several important lessons for other organisations interested in using AI to develop a digital-first workforce.

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ABSTRACT

With the emergence of data ecosystems organisations are not only analysing their own data but also utilising data outside of their boundaries. Interorganizational data exchange comes with new challenges in terms of data governance. Inspired by organisational information processing theory, this paper follows an action design research (ADR) approach to create a robust data governance framework. Four design principles, namely flexible digital platform, corporate data broker, mutual trust, and multi-level monetisation, are introduced. Based on these principles, it is possible to develop a lean and robust business process for interorganizational data exchange. The evaluation of this process is based on exemplary projects coming from the Catena-X network. The cross-functional observations from these projects provide practical insights that guide practitioners and managers within networked organisations in identifying best practice principles and processes for effective interorganizational data governance. Thereby, this framework enables organisations to promote a standardised approach to data exchange that can be replicated across different organisational networks.

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Information Systems Journal, EarlyView. Source

ABSTRACT

Digital transformation (DT) is a complex, lengthy and risky process that can disrupt habituated operations. Thus, the board of directors can and should play a crucial role in strategically steering DT initiatives. However, interviews (n?=?21) with and survey responses (n?=?19) from board members and IT leaders of large enterprises revealed that boards often lack digital awareness, which makes them insufficiently equipped to understand the risks and opportunities presented by new digital technologies (e.g., AI). We identified four ways in which the digital awareness deficit manifested in practice, as well as specific impacts of such deficits on the DT process. Our data also revealed several practices that can be used for increasing the digital awareness of board members and translating this awareness into board DT actions.

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