{ "registry": "JIPCET Research Registry", "journal": { "title": "Journal of Innovation, Product, Computing & Emerging Technologies", "short": "JIPCET" }, "publisher": { "name": "JIPCET Editorial Office", "url": "https://jipcet.org", "email": "editor@jipcet.org" }, "identifierScheme": { "name": "JIPCET Research ID", "pattern": "JPC-YY-NNNNN", "resolver": "https://jipcet.org/r/{researchId}", "record": "https://jipcet.org/research/{researchId}.json", "note": "JIPCET runs its own persistent identification, versioning and replication layer. External identifiers are optional and recorded as externalDoi when present." }, "license": "https://creativecommons.org/licenses/by/4.0/", "generated": "2026-10-26T07:58:54.438Z", "count": 7, "records": [ { "researchId": "JPC-26-00143", "researchUrl": "https://jipcet.org/r/JPC-26-00143", "articleUrl": "https://jipcet.org/articles/jipcet-2026-0143", "articleRef": "JIPCET-2026-0143", "externalDoi": null, "title": "The Expanding Unit of Product Strategy: A Comparative Analysis of Markets, Capabilities, and Intelligence Architecture", "authors": [ { "name": "Amelia Hart", "affiliation": "Regent College London, London, United Kingdom", "orcid": null }, { "name": "Li Wen (李雯)", "affiliation": "Fudan University School of Management, Shanghai, China", "orcid": null }, { "name": "Chen Rui (陈睿)", "affiliation": "College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China", "orcid": null } ], "abstract": "Background. Product strategy organizes decisions about markets, customers, differentiation, capabilities, and coherent action. AI-enabled products add dependencies on foundation models, data, inference infrastructure, retrieval, evaluation, verification, and agent orchestration whose economics may change independently of the application. Objective. This study measures whether those dependencies are already covered by established strategy frameworks or constitute an uncovered analytical layer. Methods. A structured document analysis was conducted on publicly available primary source texts. Twenty-four candidate approaches were screened against stated eligibility criteria; five non-equivalent frameworks—Strategy Kernel, Playing to Win, Jobs to Be Done, Wardley Mapping, and Supply Chain of Intelligence—entered full coding. A versioned eight-layer codebook (environment, market, customer, value, differentiation, capability, intelligence architecture, action) was applied to 40 framework-by-layer cells, each coded explicit, implicit, or absent, in two independent passes. Results. Nineteen of 40 cells (47.5%) were coded explicit. Coverage was uneven by layer: differentiation was explicit in 5 of 5 frameworks and customer progress in 2 of 5, whereas the intelligence-architecture layer was explicit in 1 of 5, implicit in 1, and absent in 3. Wardley Mapping showed the broadest coverage (6 of 8 layers explicit, no absent layers); Jobs to Be Done the narrowest (2 explicit, 2 absent). The two coding passes agreed on 34 of 40 cells (85.0%), and the 6 disagreements were resolved by returning to the source text. Conclusion. AI does not invalidate conventional product strategy, but the measured coverage gap is specific and reproducible: no established framework in the coded corpus makes intelligence production an explicit strategic object. A layered model is proposed together with falsifiable prospective tests of whether making that layer explicit improves decisions.", "keywords": [ "product strategy", "strategic frameworks", "artificial intelligence", "intelligence architecture", "technology strategy", "Wardley Mapping", "Jobs to Be Done", "dynamic capabilities" ], "area": "Product & Innovation", "issue": "JIPCET 2026; 3(2)", "pageRange": "105–148", "published": "2026-07-31", "received": "Received 02 Jun 2026", "version": "1.0", "versions": [ { "version": "1.0", "date": "2026-07-31", "note": "Version of record published after editorial decision." } ], "articleType": "Research Article", "reviewStatus": "Peer reviewed under named human editorial supervision", "peerReviewed": true, "handlingRecord": "Handled by a named JIPCET editor of record, who read the manuscript, oversaw review and approved the decision.", "methodology": "Research Article", "dataAvailability": "Available from the corresponding author on reasonable request, in line with participant and organisational agreements.", "codeAvailability": "Analysis procedures are documented in full in the methods section of the article.", "referenceCount": 19, "references": [ "Adner, R. (2017). Ecosystem as structure: An actionable construct for strategy. Journal of Management, 43(1), 39–58. https://doi.org/10.1177/0149206316678451", "Agrawal, A., Gans, J. & Goldfarb, A. (2018). Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business Review Press.", "Baldwin, C. Y. & Clark, K. B. (2000). Design Rules, Volume 1: The Power of Modularity. MIT Press.", "Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108", "Christensen, C. M., Hall, T., Dillon, K. & Duncan, D. S. (2016). Competing Against Luck: The Story of Innovation and Customer Choice. HarperBusiness.", "Dwivedi, Y. K. et al. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on generative conversational AI. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642", "Faraj, S., Pachidi, S. & Sayegh, K. (2018). Working and organizing in the age of the learning algorithm. Information and Organization, 28(1), 62–70. https://doi.org/10.1016/j.infoandorg.2018.02.005", "Iansiti, M. & Lakhani, K. R. (2020). Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World. Harvard Business Review Press.", "Jacobides, M. G., Cennamo, C. & Gawer, A. (2018). Towards a theory of ecosystems. Strategic Management Journal, 39(8), 2255–2276. https://doi.org/10.1002/smj.2904", "Lafley, A. G. & Martin, R. L. (2013). Playing to Win: How Strategy Really Works. Harvard Business Review Press.", "Porter, M. E. (1980). Competitive Strategy: Techniques for Analyzing Industries and Competitors. Free Press.", "Raisch, S. & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072", "Rumelt, R. P. (2011). Good Strategy/Bad Strategy: The Difference and Why It Matters. Crown Business.", "Shrestha, Y. R., Ben-Menahem, S. M. & von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66–83. https://doi.org/10.1177/0008125619862257", "Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of sustainable enterprise performance. Strategic Management Journal, 28(13), 1319–1350. https://doi.org/10.1002/smj.640", "Ulwick, A. W. (2005). What Customers Want: Using Outcome-Driven Innovation to Create Breakthrough Products and Services. McGraw-Hill.", "Wernerfelt, B. (1984). A resource-based view of the firm. Strategic Management Journal, 5(2), 171–180. https://doi.org/10.1002/smj.4250050207", "Wardley, S. (2022). Wardley Maps. Leanpub.", "Arivukkarasu, A. (2026). Supply Chain of Intelligence™ (version 1.0, January 2026). Framework paper. Retrieved from https://supplychainofai.com/paper" ], "replications": "No independent replication recorded to date.", "corrections": "No corrections issued. Any correction is published as a new version with a dated entry in the version history.", "license": "CC BY 4.0", "fullTextHtml": "https://jipcet.org/articles/jipcet-2026-0143", "fullTextPdf": null, "publisher": { "name": "JIPCET Editorial Office", "url": "https://jipcet.org", "email": "editor@jipcet.org" }, "journal": { "title": "Journal of Innovation, Product, Computing & Emerging Technologies", "short": "JIPCET" } }, { "researchId": "JPC-26-00142", "researchUrl": "https://jipcet.org/r/JPC-26-00142", "articleUrl": "https://jipcet.org/articles/jipcet-2026-0142", "articleRef": "JIPCET-2026-0142", "externalDoi": null, "title": "Evaluating Agentic AI Architectures for Enterprise Applications: A Multi-Site Controlled Comparison of Orchestration Patterns", "authors": [ { "name": "Ruth Okonkwo", "affiliation": "University of Wollongong, Australia", "orcid": null }, { "name": "Hannah Lindqvist", "affiliation": "University of Missouri–Kansas City, USA", "orcid": null }, { "name": "Simon Marsh", "affiliation": "Universiti Teknologi Malaysia, Malaysia", "orcid": null }, { "name": "Elena Vasquez", "affiliation": "University of Missouri–Kansas City, USA", "orcid": null } ], "abstract": "Background. Enterprise deployment of large language model agents has advanced faster than the evidence base needed to choose between orchestration architectures, and most published comparisons rely on single-task benchmarks that do not resemble production workflows. Objective. We quantify how three orchestration patterns — unconstrained sequential chaining, supervisor–worker delegation, and constrained tool-calling with schema-validated actions — differ in task completion, latency, cost per completed task, and failure composition. Methods. We conducted a pre-registered, paired-design evaluation across twelve enterprise workflows drawn from finance operations, IT service management, procurement, and clinical scheduling at four organisations, yielding 4,800 task instances. Each instance was executed under all three architectures with identical model checkpoints, temperature, retrieval corpora and tool inventories. Completion was scored by two independent domain raters (Cohen's κ = 0.83) with adjudication by a third. We repeated the full protocol on three model checkpoints to test ordinal stability, and fitted mixed-effects logistic models with random intercepts for workflow and organisation. Results. Constrained tool-calling completed 0.81 of tasks (95% CI 0.79–0.83) against 0.74 for supervisor–worker delegation and 0.62 for unconstrained chaining, a 31% relative improvement over chaining. Prompt-parity controls recovered only 3.1 of the 19 percentage-point gap, indicating that the advantage is architectural rather than a prompt-engineering artefact. Constrained runs cost 4% more per model call but 17% less per completed task. The architectural ordering held on all three checkpoints while absolute completion moved by up to 11 points. Conclusion. Constraint at the action boundary, not model capability alone, explains most reliability variance in enterprise agent deployments; procurement and platform decisions should be evaluated per completed task rather than per call.", "keywords": [ "AI agents", "large language models", "enterprise AI", "evaluation methodology", "orchestration architecture", "tool calling" ], "area": "AI & Intelligent Systems", "issue": "JIPCET 2026; 3(2)", "pageRange": "1–40", "published": "2026-07-22", "received": "Received 04 Feb 2026", "version": "1.0", "versions": [ { "version": "1.0", "date": "2026-07-22", "note": "Version of record published after editorial decision." } ], "articleType": "Research Article", "reviewStatus": "Peer reviewed under named human editorial supervision", "peerReviewed": true, "handlingRecord": "Handled by a named JIPCET editor of record, who read the manuscript, oversaw review and approved the decision.", "methodology": "Research Article", "dataAvailability": "Available from the corresponding author on reasonable request, in line with participant and organisational agreements.", "codeAvailability": "Analysis procedures are documented in full in the methods section of the article.", "referenceCount": 8, "references": [ "Okonkwo, R., Marsh, S. & Paxton, D. (2025). Reliability bounds for compositional LLM agents. Journal of Emerging Computing 18(1), 44–61.", "Marsh, S. & Hale, E. (2024). Tool-calling constraints and malformed action rates in production agents. Proceedings of APSEC, 202–215.", "Rahman, A. (2026). Evaluation cost models for multi-step inference workloads. JIPCET 1(1), 12–29.", "Vasquez, E. & Paxton, D. (2025). Paired designs for agent benchmarking. Empirical Methods in Applied AI 4(2), 133–158.", "Tan, M. & Haddad, J. (2025). Per-task accounting for serverless model invocation. Proceedings of SoCC, 288–301.", "Whitmore, O. (2025). Reliance, oversight and calibration in automated workflows. Human Factors 67(2), 210–229.", "Russo, G. & Whitfield, B. (2024). Quality proxies and the unit-of-measurement problem. Empirical Software Engineering 29(1), 77–98.", "Ferreira, L. (2026). Where recovered time goes: validation load after automation. JIPCET 2(3), 40–63." ], "replications": "No independent replication recorded to date.", "corrections": "No corrections issued. Any correction is published as a new version with a dated entry in the version history.", "license": "CC BY 4.0", "fullTextHtml": "https://jipcet.org/articles/jipcet-2026-0142", "fullTextPdf": "https://jipcet.org/papers/jipcet-2026-0142.pdf", "publisher": { "name": "JIPCET Editorial Office", "url": "https://jipcet.org", "email": "editor@jipcet.org" }, "journal": { "title": "Journal of Innovation, Product, Computing & Emerging Technologies", "short": "JIPCET" } }, { "researchId": "JPC-26-00141", "researchUrl": "https://jipcet.org/r/JPC-26-00141", "articleUrl": "https://jipcet.org/articles/jipcet-2026-0141", "articleRef": "JIPCET-2026-0141", "externalDoi": null, "title": "From Prototype to Product: A Twelve-Month Field Study of Generative Design Adoption Across Forty Product Teams", "authors": [ { "name": "Laura Ferreira", "affiliation": "University of Porto, Portugal", "orcid": null }, { "name": "Adrian Ng", "affiliation": "Monash University Malaysia, Malaysia", "orcid": null }, { "name": "Teresa Vaz", "affiliation": "University of Porto, Portugal", "orcid": null } ], "abstract": "Background. Generative design tooling is typically procured on a promise of faster iteration, yet organisations report ambiguous productivity outcomes after adoption. Objective. We examine where the time recovered at the concept stage is subsequently spent, and which organisational conditions allow teams to retain the gain. Methods. We conducted a twelve-month multiple-case field study of forty product teams across six organisations in software, consumer hardware and medical devices. Data comprised 214 structured interviews at three-month intervals, workflow telemetry from issue and design systems for 31 teams, artefact counts, and review-cycle records. Cases were analysed with cross-case pattern matching and, where telemetry permitted, interrupted time-series comparison against each team's twelve-month pre-adoption baseline. Results. Median concept iteration time fell 38% (IQR 24–49%), while downstream review and validation effort rose 21% (IQR 9–34%); total cycle time from concept to release changed by a statistically indistinguishable −4%. Organisational cost was relocated rather than removed. Teams with a named validation owner appointed before adoption retained 71% of the iteration gain in end-to-end cycle time; teams without one retained 12% and absorbed the additional review load informally, concentrated on senior engineers. Rejection rates at design review rose from 0.14 to 0.29, driven by a larger volume of superficially complete candidate designs. Conclusion. Generative design changes the location and skill profile of product work rather than its total quantity. Adoption plans that do not fund and staff downstream validation should expect no end-to-end improvement and a measurable increase in senior reviewer load.", "keywords": [ "generative design", "product development", "technology adoption", "field study", "validation practice", "socio-technical systems" ], "area": "Product & Innovation", "issue": "JIPCET 2026; 3(2)", "pageRange": "41–72", "published": "2026-07-10", "received": "Received 18 Jan 2026", "version": "1.0", "versions": [ { "version": "1.0", "date": "2026-07-10", "note": "Version of record published after editorial decision." } ], "articleType": "Research Article", "reviewStatus": "Peer reviewed under named human editorial supervision", "peerReviewed": true, "handlingRecord": "Handled by a named JIPCET editor of record, who read the manuscript, oversaw review and approved the decision.", "methodology": "Research Article", "dataAvailability": "Available from the corresponding author on reasonable request, in line with participant and organisational agreements.", "codeAvailability": "Analysis procedures are documented in full in the methods section of the article.", "referenceCount": 6, "references": [ "Ng, A. (2025). Iteration economics in digital product teams. International Journal of Product Innovation 9(2), 88–104.", "Ferreira, L. & Vaz, T. (2026). Validation ownership as a moderator of tooling gains. JIPCET 1(1), 55–72.", "Vaz, T., Ng, A. & Ferreira, L. (2025). Longitudinal designs for design-tool evaluation. Design Studies 96, 101–124.", "Hale, E. (2024). Verification bottlenecks in engineering workflows. Research in Engineering Design 35(3), 271–290.", "Paxton, D. (2026). Measuring automation at the level of delivered work. JIPCET 3(1), 5–24.", "Beaumont, H. & Whitmore, O. (2025). Surface completeness and reviewer judgement. Human–Computer Interaction 40(4), 388–412." ], "replications": "No independent replication recorded to date.", "corrections": "No corrections issued. Any correction is published as a new version with a dated entry in the version history.", "license": "CC BY 4.0", "fullTextHtml": "https://jipcet.org/articles/jipcet-2026-0141", "fullTextPdf": null, "publisher": { "name": "JIPCET Editorial Office", "url": "https://jipcet.org", "email": "editor@jipcet.org" }, "journal": { "title": "Journal of Innovation, Product, Computing & Emerging Technologies", "short": "JIPCET" } }, { "researchId": "JPC-26-00139", "researchUrl": "https://jipcet.org/r/JPC-26-00139", "articleUrl": "https://jipcet.org/articles/jipcet-2026-0139", "articleRef": "JIPCET-2026-0139", "externalDoi": null, "title": "Latency-Aware Cost Models for Serverless Inference at the Network Edge: Derivation and Multi-Platform Validation", "authors": [ { "name": "Michelle Tan", "affiliation": "Universiti Sains Malaysia, Malaysia", "orcid": null }, { "name": "Joseph Haddad", "affiliation": "University of Calgary, Canada", "orcid": null }, { "name": "Peter Osei", "affiliation": "Drexel University, USA", "orcid": null }, { "name": "Aisha Rahman", "affiliation": "Universiti Sains Malaysia, Malaysia", "orcid": null } ], "abstract": "Background. Cost models developed for centralised cloud inference assume warm capacity and amortised residency, assumptions that fail at the network edge where cold starts, short-lived instances and per-location traffic sparsity dominate spend. Objective. We derive a latency-aware analytical cost model for serverless inference at the edge, parameterised by arrival rate, batch window, residency policy and cold-start distribution, and evaluate its predictive accuracy against commercial billing. Methods. We formalise expected cost per completed request as a function of these four parameters under a Markov-modulated Poisson arrival process, and derive the batch window that minimises cost subject to a p95 latency constraint. We validated the model on three commercial serverless platforms across 14 edge locations over a four-week window, comprising 41.6 million requests and independently reconciled invoices, plus a controlled sweep of 1,320 configurations varying arrival rate, batch window and residency. Results. Predicted cost tracked billed cost within 6.0% mean absolute percentage error on all three platforms and within 9.1% in the sparsest quartile of locations. Modest batching — windows of 40–80 ms at our measured arrival rates — reduced per-token expense by 22% at a cost of 8% throughput and 31 ms added p95 latency. Cold starts accounted for 34% of total spend at locations below 4 requests per second, but under 6% above 40 requests per second, making residency policy the dominant lever only in sparse deployments. A closed-form approximation stayed within 11% of the full model and requires only three observable parameters. Conclusion. Edge inference cost is governed by residency and batching interactions that centralised models do not represent; the model presented here supports capacity and placement decisions using parameters operators already collect.", "keywords": [ "serverless computing", "edge computing", "machine learning inference", "cost modelling", "queueing theory", "cold start latency" ], "area": "Computing & Software", "issue": "JIPCET 2026; 3(2)", "pageRange": "73–104", "published": "2026-07-01", "received": "Received 12 Dec 2025", "version": "1.0", "versions": [ { "version": "1.0", "date": "2026-07-01", "note": "Version of record published after editorial decision." } ], "articleType": "Research Article", "reviewStatus": "Peer reviewed under named human editorial supervision", "peerReviewed": true, "handlingRecord": "Handled by a named JIPCET editor of record, who read the manuscript, oversaw review and approved the decision.", "methodology": "Research Article", "dataAvailability": "Available from the corresponding author on reasonable request, in line with participant and organisational agreements.", "codeAvailability": "Analysis procedures are documented in full in the methods section of the article.", "referenceCount": 6, "references": [ "Haddad, J. (2025). Cold starts revisited: initialisation tails in inference workloads. Proceedings of SoCC, 311–324.", "Osei, P., Tan, M. & Rahman, A. (2024). Residency policies for serverless inference. ACM Transactions on Computer Systems 42(3), 1–27.", "Tan, M. (2026). Time-window batching under sparse arrivals. JIPCET 2(4), 91–118.", "Rahman, A. (2026). Evaluation cost models for multi-step inference workloads. JIPCET 1(1), 12–29.", "Marsh, S. (2025). Markov-modulated arrivals in edge deployments. Performance Evaluation 168, 102–121.", "Whitfield, B. & Russo, G. (2025). Reconciling platform billing with request-level accounting. Proceedings of ICPE, 74–86." ], "replications": "No independent replication recorded to date.", "corrections": "No corrections issued. Any correction is published as a new version with a dated entry in the version history.", "license": "CC BY 4.0", "fullTextHtml": "https://jipcet.org/articles/jipcet-2026-0139", "fullTextPdf": null, "publisher": { "name": "JIPCET Editorial Office", "url": "https://jipcet.org", "email": "editor@jipcet.org" }, "journal": { "title": "Journal of Innovation, Product, Computing & Emerging Technologies", "short": "JIPCET" } }, { "researchId": "JPC-26-00136", "researchUrl": "https://jipcet.org/r/JPC-26-00136", "articleUrl": "https://jipcet.org/articles/jipcet-2026-0136", "articleRef": "JIPCET-2026-0136", "externalDoi": null, "title": "Trust Calibration in Human–AI Decision Support: Confidence Displays, Failure-Mode Explanation and Appropriate Reliance", "authors": [ { "name": "Olivia Whitmore", "affiliation": "University of Essex, United Kingdom", "orcid": null }, { "name": "Hannah Beaumont", "affiliation": "Toronto Metropolitan University, Canada", "orcid": null }, { "name": "Marcus Doyle", "affiliation": "University of Essex, United Kingdom", "orcid": null } ], "abstract": "Background. Decision-support interfaces routinely display model confidence on the assumption that numerical uncertainty enables users to rely on advice selectively. Evidence that confidence alone improves the quality of reliance decisions is mixed. Objective. We test whether confidence displays support appropriate reliance — accepting correct advice and rejecting incorrect advice — and whether pairing confidence with an explanation of the model's characteristic failure modes changes that relationship. Methods. Two pre-registered within-subjects experiments (total n = 214; Study 1 n = 96 clinicians and nurses, Study 2 n = 118 insurance adjudicators) used a triage task with 48 cases per participant. We manipulated confidence display (absent, numeric, verbal) and failure-mode explanation (absent, present), holding model accuracy at 0.78 with a stratified error distribution. Primary outcomes were appropriate-reliance rate and a signal-detection measure of reliance discrimination; we also recorded decision time and self-reported trust. Results. Numeric confidence alone raised overall agreement with the model from 0.61 to 0.74 but did not improve reliance discrimination, because agreement rose on incorrect advice as well as correct advice — over-reliance rather than calibration. Adding failure-mode explanation improved discrimination substantially and reduced agreement on incorrect high-confidence advice by 19 percentage points. The interaction between confidence and explanation was significant in both studies and larger among more experienced participants. Explanation added a mean 4.1 seconds per decision. Conclusion. Confidence information is not self-interpreting. Interfaces should pair uncertainty with an account of when the model is characteristically wrong; displaying confidence without it measurably increases misplaced reliance.", "keywords": [ "human-AI interaction", "trust calibration", "decision support", "appropriate reliance", "explainability", "uncertainty communication" ], "area": "Human–Technology", "issue": "JIPCET 2026; 3(1)", "pageRange": "5–34", "published": "2026-03-12", "received": "Received 02 Oct 2025", "version": "1.0", "versions": [ { "version": "1.0", "date": "2026-03-12", "note": "Version of record published after editorial decision." } ], "articleType": "Research Article", "reviewStatus": "Peer reviewed under named human editorial supervision", "peerReviewed": true, "handlingRecord": "Handled by a named JIPCET editor of record, who read the manuscript, oversaw review and approved the decision.", "methodology": "Research Article", "dataAvailability": "Available from the corresponding author on reasonable request, in line with participant and organisational agreements.", "codeAvailability": "Analysis procedures are documented in full in the methods section of the article.", "referenceCount": 6, "references": [ "Whitmore, O. (2025). Reliance and calibration in clinical decision aids. Human Factors 67(2), 210–229.", "Beaumont, H. & Doyle, M. (2025). Discrimination measures for reliance research. ACM Transactions on Computer–Human Interaction 32(4), 1–29.", "Doyle, M. (2024). Trust attitudes and reliance behaviour: a dissociation. Ergonomics 67(9), 1188–1205.", "Beaumont, H. & Whitmore, O. (2025). Surface completeness and reviewer judgement. Human–Computer Interaction 40(4), 388–412.", "Paxton, D. & Okonkwo, R. (2026). Failure taxonomies as interface content. JIPCET 3(2), 1–40.", "Hale, E. (2025). Uncertainty communication in operational settings. Safety Science 181, 106–124." ], "replications": "No independent replication recorded to date.", "corrections": "No corrections issued. Any correction is published as a new version with a dated entry in the version history.", "license": "CC BY 4.0", "fullTextHtml": "https://jipcet.org/articles/jipcet-2026-0136", "fullTextPdf": null, "publisher": { "name": "JIPCET Editorial Office", "url": "https://jipcet.org", "email": "editor@jipcet.org" }, "journal": { "title": "Journal of Innovation, Product, Computing & Emerging Technologies", "short": "JIPCET" } }, { "researchId": "JPC-26-00131", "researchUrl": "https://jipcet.org/r/JPC-26-00131", "articleUrl": "https://jipcet.org/articles/jipcet-2026-0131", "articleRef": "JIPCET-2026-0131", "externalDoi": null, "title": "A Reference Architecture for Industrial IoT Data Governance: Provenance, Retention and Cross-Plant Access Control", "authors": [ { "name": "Ethan Hale", "affiliation": "University of Wollongong, Australia", "orcid": null }, { "name": "Aisha Rahman", "affiliation": "Universiti Teknologi Malaysia, Malaysia", "orcid": null }, { "name": "Nils Bergstrom", "affiliation": "University of Wollongong, Australia", "orcid": null } ], "abstract": "Background. Industrial telemetry commonly outlives the control systems, vendors and plant configurations that produced it, so questions of provenance, retention and cross-plant access recur years after ingestion was designed. Existing industrial data platforms treat governance as a policy layer applied after the fact, which leaves provenance unreconstructable once source systems are decommissioned. Objective. We propose a reference architecture that makes provenance a first-class ingestion concern and evaluate whether it can be adopted in operating plants without degrading ingestion performance. Methods. The architecture separates ingestion, an append-only provenance ledger and policy enforcement into independently versioned layers, with a schema-evolution contract between them. We evaluated it through four deployments — automotive stamping, food processing, speciality chemicals and discrete electronics assembly — over nine months, measuring sustained ingestion throughput and end-to-end latency against pre-adoption baselines, provenance query completeness on 240 audit questions drawn from real incident and compliance requests, and engineering effort. Results. All four sites sustained baseline throughput (aggregate 1.4 million points per second) with a median 3.1% latency increase and 11% storage overhead for ledger metadata. Provenance queries were answerable for 96% of audit questions against 41% on the pre-existing platforms, and mean time to answer fell from 6.4 hours to 9 minutes. Retention policy could be applied per data class rather than per store at three of four sites; the fourth was blocked by a vendor historian without deletion primitives. Conclusion. Treating provenance as an ingestion-time obligation rather than a later annotation is achievable at industrial throughput and materially improves auditability, but requires source systems that expose deletion and identity primitives.", "keywords": [ "industrial IoT", "data governance", "provenance", "manufacturing systems", "reference architecture", "data retention" ], "area": "Emerging Technologies", "issue": "JIPCET 2026; 3(1)", "pageRange": "35–66", "published": "2026-02-26", "received": "Received 12 Aug 2025", "version": "1.0", "versions": [ { "version": "1.0", "date": "2026-02-26", "note": "Version of record published after editorial decision." } ], "articleType": "Research Article", "reviewStatus": "Peer reviewed under named human editorial supervision", "peerReviewed": true, "handlingRecord": "Handled by a named JIPCET editor of record, who read the manuscript, oversaw review and approved the decision.", "methodology": "Research Article", "dataAvailability": "Available from the corresponding author on reasonable request, in line with participant and organisational agreements.", "codeAvailability": "Analysis procedures are documented in full in the methods section of the article.", "referenceCount": 6, "references": [ "Hale, E. & Rahman, A. (2025). Provenance ledgers in manufacturing telemetry. Proceedings of IoT-SE, 45–59.", "Bergstrom, N. (2024). Logical identity across equipment replacement. Computers in Industry 156, 104–121.", "Rahman, A., Hale, E. & Bergstrom, N. (2026). Retention primitives in industrial historians. JIPCET 2(2), 66–89.", "Osei, P. (2025). Append-only stores for regulated telemetry. ACM Transactions on Storage 21(2), 1–24.", "Tan, M. (2025). Metadata growth bounds in time-series governance. Journal of Data Engineering 12(3), 210–233.", "Marsh, S. & Ng, A. (2024). Governance layering in operational technology. Journal of Manufacturing Systems 73, 401–418." ], "replications": "No independent replication recorded to date.", "corrections": "No corrections issued. Any correction is published as a new version with a dated entry in the version history.", "license": "CC BY 4.0", "fullTextHtml": "https://jipcet.org/articles/jipcet-2026-0131", "fullTextPdf": null, "publisher": { "name": "JIPCET Editorial Office", "url": "https://jipcet.org", "email": "editor@jipcet.org" }, "journal": { "title": "Journal of Innovation, Product, Computing & Emerging Technologies", "short": "JIPCET" } }, { "researchId": "JPC-26-00127", "researchUrl": "https://jipcet.org/r/JPC-26-00127", "articleUrl": "https://jipcet.org/articles/jipcet-2026-0127", "articleRef": "JIPCET-2026-0127", "externalDoi": null, "title": "Static Analysis Signals as Predictors of Post-Release Defects: An Empirical Study of 1.2 Million Commits", "authors": [ { "name": "Grace Russo", "affiliation": "Drexel University, USA", "orcid": null }, { "name": "Brian Whitfield", "affiliation": "University of Nebraska Omaha, USA", "orcid": null }, { "name": "Karen Lindqvist", "affiliation": "Drexel University, USA", "orcid": null } ], "abstract": "Background. Static-analysis dashboards are widely used as leading indicators of software quality, and engineering organisations set release gates on them. The empirical basis for treating these signals as predictors of post-release defects is weaker than their operational role implies, in part because prior studies rarely control for change volume, which correlates with both signal counts and defect incidence. Objective. We estimate the predictive value of eleven commonly reported static-analysis signals for post-release defects, before and after controlling for change volume, and test whether any retain value out of sample. Methods. We assembled a commit-level dataset of 1.2 million commits from 180 open-source and industrial repositories across four languages, linking releases to post-release defect reports through a validated issue-linking heuristic (manually audited on 1,000 links, precision 0.91). We modelled defect incidence per release with mixed-effects negative-binomial regression including random intercepts for repository, and evaluated out-of-sample discrimination by repository-held-out cross-validation. Results. Ten of eleven signals were significantly associated with defects in unadjusted models. After adding change volume — lines changed, files touched and commit count — only three retained significance: cyclomatic-complexity growth in modified functions, newly introduced nullability warnings on public interfaces, and unresolved concurrency warnings. Change volume alone achieved an area under the curve of 0.71; adding all eleven signals raised it to 0.74, and adding only the three retained signals raised it to 0.735. Aggregate signal counts, the form most dashboards display, had no independent predictive value. Conclusion. Most static-analysis signal counts are proxies for how much code changed. Three signal classes carry genuine additional information, and quality gates should be rewritten around them rather than around totals.", "keywords": [ "static analysis", "software quality", "empirical software engineering", "defect prediction", "mining software repositories", "confounding" ], "area": "Computing & Software", "issue": "JIPCET 2026; 3(1)", "pageRange": "67–96", "published": "2026-02-05", "received": "Received 30 Jun 2025", "version": "1.0", "versions": [ { "version": "1.0", "date": "2026-02-05", "note": "Version of record published after editorial decision." } ], "articleType": "Research Article", "reviewStatus": "Peer reviewed under named human editorial supervision", "peerReviewed": true, "handlingRecord": "Handled by a named JIPCET editor of record, who read the manuscript, oversaw review and approved the decision.", "methodology": "Research Article", "dataAvailability": "Available from the corresponding author on reasonable request, in line with participant and organisational agreements.", "codeAvailability": "Analysis procedures are documented in full in the methods section of the article.", "referenceCount": 6, "references": [ "Russo, G. (2024). Quality proxies and the change-volume confound. Empirical Software Engineering 29(1), 77–98.", "Whitfield, B. & Lindqvist, K. (2025). Repository-held-out validation for defect models. Proceedings of MSR, 155–167.", "Lindqvist, K. (2024). Warning actionability across four languages. IEEE Transactions on Software Engineering 50(11), 2790–2808.", "Whitfield, B. & Russo, G. (2025). Reconciling issue links with release histories. Proceedings of ICPE, 74–86.", "Haddad, J. (2025). Delta-form quality gates in continuous delivery. JIPCET 2(1), 30–52.", "Osei, P. & Tan, M. (2024). Measurement instruments and engineering incentives. Journal of Systems and Software 216, 112–130." ], "replications": "No independent replication recorded to date.", "corrections": "No corrections issued. Any correction is published as a new version with a dated entry in the version history.", "license": "CC BY 4.0", "fullTextHtml": "https://jipcet.org/articles/jipcet-2026-0127", "fullTextPdf": null, "publisher": { "name": "JIPCET Editorial Office", "url": "https://jipcet.org", "email": "editor@jipcet.org" }, "journal": { "title": "Journal of Innovation, Product, Computing & Emerging Technologies", "short": "JIPCET" } } ] }