Information Systems Journal最新文献

Low-code/no-code (LCNC) platforms, such as ServiceNow and Microsoft Power Platform, enable employees without formal IT training to build applications and automate workflows, thus driving agility and reducing dependence on traditional IT teams. However, LCNC platforms also pose a persistent challenge for organisations: while they offer flexibility and freedom by enabling decentralised development, they also require standardisation and control to manage risks that can be exacerbated by these platforms, such as shadow IT and technical debt. Striking the right balance is difficult—too much flexibility can compromise stability, while too much standardisation can stifle the autonomy and creativity that make LCNC platforms valuable in the first place. This study explores flexibility–standardisation tensions in LCNC development through an investigation of two multinational technology firms with differing LCNC maturity levels, both using ServiceNow. Drawing from 57 interviews, we identify three types of flexibility-standardisation tensions shaped by three key elements of LCNC development: the platform itself, the people using the platform and the organisational processes targeted for improvement. We derive six guidelines used to navigate flexibility–standardisation tensions and demonstrate how these are applied across different stages of LCNC maturity. Building on these insights, we provide concrete, context-sensitive recommendations to help organisations adapt the guidelines to their specific environments. We conclude with forward-looking reflections on how firms can dynamically make sense of these tensions as LCNC platforms and practices evolve. Overall, our findings show that effective LCNC governance requires a dynamic approach—one that balances flexibility and standardisation simultaneously rather than treating them as opposing choices.

Organisations across various industries are still exploring the potential of Generative Artificial Intelligence (GenAI) to automate a variety of knowledge work processes, including managing innovation. While innovation is often viewed as a product of individual creativity, it more commonly unfolds through a collaborative process where creativity intertwines with knowledge. However, the extent and effectiveness of GenAI in supporting this process remain open questions. Our study investigates this issue using a collaborative practice research approach focused on three GenAI-enabled innovation projects conducted within different organisations. We explored how, why, and when GenAI could effectively be integrated into design sprints—a highly structured, collaborative process enabling human-centred innovation. Our research identified challenges and opportunities in synchronising AI capabilities with human intelligence and creativity. To translate these insights into practical strategies, we propose four recommendations for organisations eager to leverage GenAI to both streamline and bring more value to their innovation processes: (1) establish a collaborative intelligence value loop with GenAI; (2) build trust in GenAI; (3) develop robust data collection and curation workflows; and (4) embrace a craftsman's discipline.

Practically exercising responsibility when developing algorithms is a non-trivial activity. The plethora of perspectives on responsible AI often leaves practitioners overwhelmed and confused about how to start and how to ensure efforts are embedded in ongoing practice, not just one-off activities. Further, leaders who are concerned about the role of human-centred values, like dignity, are often disappointed about a lack of guidance to practically make this prioritisation a reality in technology development. This paper presents a framework—the Dignity Lens—for reflecting on how technologies developed and/or implemented impact (human) relationships. The Dignity Lens is the outcome of multi-year design science research undertaken in three organisational contexts. In this article, we describe the journey of the Dignity Lens from inception to now and an in-depth application of the Dignity Lens in the most recent organisational context within which it was developed. We illustrate how this organisation has begun to embed the Dignity Lens into their everyday practices and the benefits of doing so. We argue that the Dignity Lens offers a unique way for practitioners to exercise protective and proactive responsibility and tangibly engage with concepts of dignity while building algorithms, offering space for constructive reflection, innovation and accountability in line with human-centred commitments.

This paper addresses the challenge posed by the high cost and extensive time commitment required for Metaverse platform development by proposing an innovative, process-centric methodology. Traditional approaches to creating such platforms are hindered by both limited expertise and significant resource investments, exposing substantial development risks. Here we use Design Fictions to construct a low-cost, low-effort Minimum Viable Product for archives, record labels, publishers, and private collections. In identifying our key learnings, our research advances a coherent four-stage process model comprising of conceptualisation, realisation, materialisation, and evaluation. The model delineates the critical activities required at each stage, offering practitioners a structured yet flexible framework that bridges the gap between initial concept development and full-scale system implementation. By prioritising Design Fictions as a means of early-stage prototyping, we encourage a more agile and responsive exploration of ‘provocations of the future’, thus minimising risks while refining the understanding of business and user needs alongside technological possibilities and constraints. In doing so, we expedite the identification of vital design insights and provide practical guidance for those striving to harness the potential of emerging Metaverse technologies within a sustainable and economically viable framework.

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.

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.

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.