customer success management

To address interdisciplinary interoperability, variant configuration governance, end-to-end traceability, and cross-organizational collaboration challenges arising from the networked evolution of Systems of Systems (SoS), this paper proposes a lifecycle management framework for Network-Centric Development (NCD). Methodologically, it grounds the framework in Model-Based Systems Engineering (MBSE) semantics and integrates Product Lifecycle Management (PLM) governance, CAD-CAE model synchronization, and closed-loop digital thread/digital twin capabilities. Its core contributions are four foundational principles: (1) reference architecture with a unified data model; (2) end-to-end configuration sovereignty; (3) review-driven model gating; and (4) quantifiable value contribution assessment. Empirical validation across transportation, healthcare, and public-sector domains demonstrates significant improvements in change robustness and model reuse rate, reduced delivery cycles, and enhanced support for sustainability-oriented decision-making.

Rigid activity implementation binding in digital business processes hinders adaptation to heterogeneous organizational requirements. Method: This paper proposes a three-level dynamic binding mechanism—operating at compile time, launch time, and runtime—that enables concurrent execution of multiple implementations for the same activity and supports context-aware, dynamic customization of input/output data contracts. Integrating Software Product Line (SPL) engineering with Process-Aware Information Systems (PAIS), we develop a variability modeling and runtime feature configuration framework. Contribution/Results: Our approach achieves, for the first time, end-to-end flexible activity binding across the full process lifecycle. It overcomes the limitations of conventional single-version, static binding by enabling on-demand composition of diverse activity implementations and data interfaces within a unified process model. This significantly enhances the adaptability and configurability of process systems in multi-organizational settings.

This study addresses the persistent paradox of engineering managers in agile software development: despite agile’s emphasis on decentralization and team autonomy, the engineering manager role remains salient. Adopting a mixed-methods approach—integrating systematic literature review with multi-case empirical investigation—the research identifies three interlocking drivers of this persistence: historical path dependence, theoretical tensions (e.g., balancing autonomy with cross-team coordination), and organizational constraints (e.g., technical debt governance, inter-team alignment, and talent development). Based on these findings, the paper proposes the “adaptive leadership” conceptual model, positioning engineering managers not as control-oriented supervisors but as enablers and architects who focus on technical strategy alignment, capability co-development, and systemic resilience. The model offers a theoretically grounded framework for rethinking leadership in agile organizations, informing management tool design and guiding future empirical inquiry. (149 words)

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.

This study addresses occupational burnout among Security Operations Center (SOC) practitioners, often stemming from misalignment between job demands and individual capabilities. Drawing on flow theory, the authors conduct an inductive content analysis of 106 global SOC job postings to systematically map the prevalence of certifications (e.g., CISSP), technical skills (e.g., Python, Splunk), and soft skills—particularly communication skills, mentioned in 50.9% of listings. The research reveals, for the first time, a structured pattern in the skill and certification requirements of SOC roles. These findings provide empirical grounding for achieving challenge–skill balance, refining recruitment practices, and guiding professional development. Furthermore, the study advances the discourse on flow-aligned person–job fit and sets the stage for future investigations into the impact of artificial intelligence on SOC workforce dynamics.

This study addresses a critical gap in geriatric care research, which has predominantly emphasized operational efficiency while lacking robust connections to clinical health outcomes. Through a systematic review of 30 interdisciplinary studies at the intersection of industrial engineering and operations research applied to elderly care, the work categorizes existing literature into three thematic domains: home-based medical care, polypharmacy management, and chronotherapeutic clinical scheduling. It proposes a novel conceptual framework that explicitly links operational optimization with clinical outcomes, advocating a paradigm shift from isolated task-level improvements toward integrated, multi-layer decision-making. By incorporating human-centered design, systems analysis, and emerging technologies such as digital twins and large language models, the study highlights the field’s current overemphasis on workforce scheduling at the expense of health impact, thereby laying a theoretical and technical foundation for intelligent care decision systems spanning hospital and community settings.

This work addresses the limited goal-directed execution capability of large language models in long-horizon tasks by introducing a Goal-Directed Execution (GDE) behavioral framework. The authors conduct post-training on the Qwen3.5-122B-A10B model using 363 long-horizon, multi-tool agent tasks from office scenarios, without relying on software engineering data. This approach yields a notable improvement on SWE-Bench Pro, increasing pass@1 by 5.8 percentage points. Experimental results demonstrate significant enhancements across four core GDE capabilities: goal selection, state construction, goal consistency maintenance, and environment validation. Furthermore, the model exhibits effective cross-domain transfer between office and software engineering tasks, confirming that long-horizon post-training can successfully drive the transfer of behavioral mechanisms.

This study addresses the profound transformations in user roles, workflows, and collaboration patterns within enterprise software platforms driven by artificial intelligence, which existing role frameworks—such as the BTP user type matrix—struggle to accommodate. Through 20 expert interviews and a participatory design workshop involving 24 participants, the research employs qualitative methods to investigate structural shifts in developer roles on the SAP Business Technology Platform. Findings reveal three key trends: automation of operational tasks, expanded human-AI collaboration, and increased reliance on agent-based systems. In response, the study argues for a necessary reconfiguration of role taxonomies and governance mechanisms, offering both theoretical grounding and practical guidance for designing and governing AI-native enterprise software.

This study addresses the challenge of automating workflows in complex industries—such as logistics, healthcare, and construction—where processes are fragmented across heterogeneous tools and involve multi-party collaboration. The work proposes orchestration as a core abstraction to enable effective automation by dynamically coordinating multi-step tasks, enforcing domain-specific constraints, managing human approvals, and integrating legacy systems. It introduces the novel concept of “orchestration bottlenecks” and develops a theoretical framework that unifies multi-agent systems, workflow modeling, constraint reasoning, and human–AI collaboration, while exposing critical gaps in current multi-agent approaches at the orchestration level. Based on distinct sources of operational friction across domains, the paper advocates for targeted architectural safeguards—such as constraint enforcement or explainability—and phased implementation strategies to provide actionable pathways for automation in complex operational environments.

This study addresses the limited understanding of how practitioners actually develop software engineering (SE) agents, particularly the lack of systematic investigation into the evolution of development workflows and core challenges. Through semi-structured interviews with 20 practitioners complemented by a survey of 80 respondents, this work proposes the first seven-stage workflow for SE agent development, revealing a paradigm shift toward “evaluation-driven iteration.” The research identifies that bottlenecks have moved beyond coding to non-coding tasks such as requirement specification, cross-role coordination, review, and deployment. It systematically characterizes six key challenges—including unreliable evaluation signals, accumulating comprehension debt, and behavioral drift induced by model updates—and synthesizes corresponding practical mitigation strategies.