product adoption

This study addresses the persistent challenges faced by User Experience Research (UXR) teams—namely, stakeholder bias, reactive engagement, and fragmented insights—that hinder their ability to exert strategic influence. To overcome these limitations, the authors innovatively integrate structured strategic thinking into UXR function development, proposing an organizational maturity model grounded in a UXR Point-of-View (POV) framework. Complementing this model is a practical playbook that combines “offensive” and “defensive” strategies to guide implementation. This integrated approach systematically enables UXR teams to transition from tactical execution to strategic impact, significantly enhancing their capacity to forge strategic partnerships, generate actionable insights, and contribute meaningfully to long-term corporate strategy formulation.

The rapid adoption of AI in software engineering introduces significant human factors challenges, yet existing research remains predominantly technology-centric, lacking systematic investigation into team adaptability and trust mechanisms. Method: Integrating organizational change theory and behavioral software engineering, we propose the first human-centered, nine-dimensional AI transformation framework—covering strategic design, collaboration, governance, and other critical dimensions—and derive corresponding design principles. Using a mixed-methods approach, we developed the initial framework via literature review, refined it through thematic analysis of 24 practitioner interviews, and validated and optimized it via a survey (N=105) and expert workshops (N=4). Contribution/Results: Findings indicate that skill development and AI strategic design are most prioritized (each consuming >15% of allocated resources), whereas socio-technical safeguards remain consistently under-resourced. This framework bridges a critical gap in human factors integration within technology-dominated AI adoption paradigms and provides an actionable, socio-technical pathway for responsible AI implementation in software engineering.

New software engineers often struggle to comprehend large legacy systems, leading to prolonged onboarding periods. Method: This study introduces a systems-thinking training program grounded in Labelled Transition System (LTS) modeling and a structured understanding template—the first application of LTS modeling in software engineering onboarding education—featuring differentiated learning pathways across five sessions, integrating pedagogical best practices and pre-/post-assessment design. Contribution/Results: While overall comprehension gains were not statistically significant, learners with low initial proficiency showed a robust 15-percentage-point improvement (p < 0.05). Qualitative feedback indicated high engagement and perceived practical relevance. The framework offers a scalable, low-cost, reusable instructional model for cultivating software comprehension skills, addressing a critical gap in industry onboarding programs by introducing formal modeling techniques into foundational training.

Enterprises face a critical gap in systematic guidance for large language model (LLM) adoption, manifested as heightened data security risks, ambiguous development paradigms, infrastructure integration challenges, and unclear deployment strategies. To address this, we propose the first structured, six-step decision-making framework tailored to enterprise-scale LLM adoption. Grounded in in-depth interviews and empirical analysis across healthcare, finance, and software development domains—and validated against real-world B2B and B2C use cases—the framework integrates strategic decision modeling, use-case-driven design, security and regulatory compliance assessment, and deployment-path optimization. It significantly enhances implementation safety and operational efficiency of LLMs in high-impact scenarios including customer service automation, content generation, and advanced analytics. By aligning technical capabilities with business objectives, the framework delivers an actionable, cross-industry decision-support tool for responsible and effective LLM integration.

This study addresses the persistent “pilot purgatory” that hinders the large-scale deployment of industrial extended reality (XR) applications. Through in-depth interviews with 17 industry experts and an ecosystem analysis framework, it reveals that the primary barriers have shifted from technological maturity to organizational readiness and stakeholder coordination. The work proposes a “great reversal” perspective, arguing that systemic factors—such as organizational change resistance, misaligned performance metrics, and internal political dynamics—now constitute the core challenges, rather than technical limitations. Emphasizing a problem-driven, ecosystem-coordinated transition pathway, the study identifies incentive misalignment as a critical friction point, offering both theoretical grounding and practical guidance for scaling industrial XR beyond isolated pilots.

This study addresses the lack of empirical evidence on the real-world impact of Continuous Integration (CI) in mobile application development, particularly concerning app store performance. Leveraging a large-scale dataset of open-source Android projects, we systematically compare CI adopters and non-adopters through time-series analysis to assess CI’s effects on development activity, bug-fixing efficiency, release frequency, and user engagement metrics on Google Play—specifically downloads and review counts. Our findings reveal, for the first time, distinct adoption patterns of CI in the mobile ecosystem: CI is predominantly adopted by larger, more active projects in finance and productivity categories, which exhibit higher release frequencies and significantly greater downloads and reviews, while maintaining stable ratings. This work fills a critical gap in empirical research on CI effectiveness within mobile development contexts.

This work proposes a systematic approach to derive task effectiveness requirements in the absence of explicit user needs. The method deconstructs task intent into context, functionality, constraints, critical dimensions, performance attributes, and architectural solutions, and introduces a task complexity factor to quantify the impact of external challenges and technology maturity. By integrating Best-Worst Scaling, it prioritizes critical dimensions based on stakeholder judgments. Through task decomposition modeling and quantitative complexity analysis, the framework supports integration with UAF/SysML artifacts and establishes a traceable mechanism for generating Tier 1 and Tier 2 requirements. The approach is validated using a close air support mission case study, effectively addressing a critical gap in requirements engineering when clear initial inputs are unavailable.

Existing Digital Maturity Models (DMMs) exhibit significant inconsistencies and semantic ambiguities in their dimensional definitions and structural formulations, which hinder effective assessment of digital transformation. This study addresses these limitations through a systematic literature mapping approach, combining automated retrieval with snowballing techniques to conduct a multi-source comparative and content analysis of 76 DMMs. For the first time, it integrates and harmonizes the definitions and constituent elements of ten frequently occurring dimensions—such as organization, strategy, and technology—thereby resolving the prevailing inconsistencies and structural ambiguities across existing models. The work establishes a coherent theoretical foundation and offers practical guidance for developing more consistent, actionable frameworks for evaluating digital maturity.

本文提出了一种通过生产使用信号直接计算变更管理阶段进展的方法来衡量企业AI采用情况，并提供了一个统一框架、具体操作化模型及开源实现。

This study addresses the persistent challenges impeding the effective integration of Agile and DevOps practices, which are often constrained by cultural, organizational, procedural, and technological barriers that undermine software delivery performance. Through semi-structured interviews with six senior practitioners from Brazil and Germany, the research employs qualitative thematic analysis to systematically identify—within a cross-national context—four core integration challenges and proposes a corresponding solution framework. The findings underscore the pivotal roles of cultural alignment, team autonomy, process coordination, and infrastructure automation, highlighting that organizational and cultural factors are critical enablers of successful technical integration. By elucidating these interdependencies, the study offers actionable, cross-cultural guidance for software organizations seeking to enhance their Agile–DevOps convergence and overall delivery effectiveness.