Organisations are increasingly striving to become more data-driven by embedding data into decisions, interactions and processes and by leveraging advanced AI technologies to unlock innovative use-cases. However, many remain unprepared to meet the rising demands for data, analytics and AI. A data product mindset—combining, packaging and delivering data as a product—has emerged as a promising approach to meet the needs of an expanding user base. Despite their popularity, data products are often seen as a purely technical concept, with suitable methodologies and tools for designing them still underdeveloped. This paper introduces the data product canvas, a visual and versatile tool that helps cross-functional teams—comprising business, data, analytics and IT experts—collaboratively design new data products and assess existing ones. The canvas ensures that critical themes are addressed: desirability from the customer perspective, feasibility from the technical perspective and viability from the economic perspective. The practical application at SAP illustrates how the data product canvas supports its data democratisation initiative, showcases real-world examples and offers practical insights to guide future adopters: (a) tailoring designs to different data product types, (b) periodically refining data products to increase their value and (c) systematically assessing requests to build a cohesive data product portfolio.
Research into adapting portfolio practices for large-scale agile environments has not kept pace with the growing interest in extending agile practices to the enterprise level. Although several agile scaling frameworks offer portfolio practice recommendations, their longer-term effectiveness is unclear, given the lack of rigorous theoretical foundations. Consequently, it is uncertain how these practices sustain agility and optimise value realisation from their Information Systems investments. This study adopts a systems-theoretic approach to identify causal mechanisms that sustain agility within enterprise IS portfolios. Using a critical realist perspective, we analyse existing IS portfolio practices from three distinct enterprises to evolve an explanatory framework comprising two generative mechanisms and a structural arrangement of portfolio practices that help achieve sustained portfolio agility by sensing, assessing, learning from, and responding to changes in its internal and external environments while ensuring a sustained flow of business value. In addition to being a comprehensive tool for practitioners designing and diagnosing agile IS portfolio capabilities, our results extend existing theoretical approaches to understanding agility at the portfolio level and provide opportunities to refine existing models to address the dynamic complexities of IS portfolio agility.
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.
Most studies on the digital divide have focused on access to digital technology and individual capability to use ICT, thus treating users primarily as consumers of information. In this study, we focus on content production and examine how information technology (IT) can reduce the digital production divide for digitally disadvantaged groups. Despite the literature suggesting that content production skills are more advanced than content consumption, short-video platforms have gained popularity worldwide, offering previously underrepresented communities in digital production an unprecedented medium for self-expression. Using an interpretive case study approach and adaptive structuration theory (AST) as a conceptual framework, we analyse data from platform managers and users of a short-video platform in India. We explore the change in users' practices toward content creation and highlight the role played by technology in more inclusive content production. We find that content consumption and production are interdependent and that various aspects of the IT artefact, such as technical objects, symbolic expressions, functional affordances and spirit, reduce different levels of the digital production divide. We contribute to the research on digital production divide by identifying three mechanisms that illustrate how the appropriation and sense-making of the IT artefacts reduce the skill gap and enable better outcomes for users by minimising the influence of their offline resources.
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.
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