Organizations adopt process automation software to support digital transformation, but leaders managing diverse Information Systems (IS) face challenges in developing automation capabilities that deliver both strategic advantage and operational resilience. This study examines how organizations build and sustain process automation competencies through effective Information Technology (IT) governance, strategic alignment, and systematic capability development. Drawing on Technology-to-Performance Chain (TPC) theory and the Dynamic Capabilities framework, this research analyses IS management practices to demonstrate how process automation initiatives can evolve from tactical implementations into sustainable and resilient sources of competitive advantage. A multi-method research design was employed, combining survey data from 286 IT and business leaders across manufacturing, retail, logistics, financial services, and healthcare organizations in the Nordic region, together with 18 qualitative interviews with Chief Information Officers (CIOs), IT directors, and automation leaders. The study identifies pathways for developing three critical capability dimensions: (1) technical capabilities through platform selection and integration practices, (2) governance capabilities through portfolio management and performance measurement mechanisms, and (3) organisational capabilities through change management and continuous learning. The analysis shows that governance capabilities explain 61% of return on investment variance, while balanced capability development across all three dimensions produces returns 2.5 times higher than single-dimension focus. Organizations with mature capabilities establish governance before scaling these initiatives, adopt hybrid governance models, and invest approximately 35% in governance, 35% in technical, and 30% in organisational capabilities. The study provides a capability development framework, empirical evidence of development and performance outcomes, as well as practical guidance for building automation capabilities.
The convergence of Quantum Computing (QC) and Artificial Intelligence (AI) ushers in a transformative era of computational capability, with profound implications for science and industry alongside significant technical, ethical, and environmental challenges. This article explores quantum-enhanced AI (QAI), focusing on foundational quantum principles-superposition, entanglement, and quantum parallelism-and their applications in molecular simulation, financial optimization, and climate modeling. While quantum algorithms offer exponential speedups for Machine Learning (ML) and optimization, persistent barriers such as qubit decoherence, scalability limitations, and high energy demands hinder progress. Hybrid quantum-classical architectures are identified as critical transitional tools, combining quantum processors for specialized tasks like combinatorial optimization with classical systems for error correction and data preprocessing. Near-term commercial healthcare, finance, and logistics applications are highlighted, including pilot projects using quantum neural networks (QNNs) for drug discovery, portfolio risk analysis, and supply chain optimization. However, dual-use risks-such as quantum-powered surveillance and threats to classical encryption-emphasize the need for post-quantum cryptography (PQC) and ethical frameworks. Environmental concerns, particularly the carbon footprint of superconducting qubits, drive calls for sustainable innovations like photonic qubit designs and renewable-powered quantum data centers. Anticipated milestones include fault-tolerant QNNs, standardized benchmarks for quantum advantage, and democratized cloud access. Achieving these goals requires addressing interdisciplinary talent shortages, algorithmic biases, and global disparities in quantum infrastructure. This work advocates for governance models integrating sustainability metrics and equity-focused policies to steer QAI development responsibly. By harmonizing technical innovation with ethical foresight, the quantum-AI synergy can advance solutions to global challenges while mitigating misuse and ecological harm risks.
Interval-valued Pythagorean fuzzy sets (IVPFSs) serve as a formidable tool for addressing uncertainty in information, resulting in their extensive adoption in the realm of decision making. The challenge of accurately measuring the similarity between IVPFSs remains a pressing issue. In this paper, we present some novel similarity measures based on trigonometric function and their weighted forms considering membership, non-membership and hesitancy degrees, respectively. These similarity measures adhere to key properties, which are demonstrated through numerical experiments. Subsequently, the proposed similarity measures tailored for IVPFSs are employed to address medical diagnosis and multicriteria decision-making (MCDM) problems within the context of interval-valued Pythagorean fuzzy environments. The results conclusively evidence that the proposed similarity measures result in substantially more efficient outcomes.
In real life, we can face many things and services, which are performed regularly. Supply Chain Management (SCM) is the one that does the business. SCM refers to the management of finished products and their distribution to the final consumer. Many types of services relieve people a lot and create a lot more attachment with people. Online services are very easy for those people who cannot afford to buy equipment from cities. From this, they saved a large part of all processing income and time. The use of bipolar complex fuzzy soft sets (BCFSS) in SCM is investigated in this research. The bipolar complex fuzzy soft Hamacher weighted average (BCFSHWA), bipolar complex fuzzy soft Hamacher ordered weighted average (BCFSHOWA), bipolar complex fuzzy soft Hamacher hybrid average (BCFSHHA), bipolar complex fuzzy soft Hamacher weighted geometric (BCFSHWG), bipolar complex fuzzy soft Hamacher ordered weighted geometric (BCFSHOWG), and bipolar complex fuzzy soft Hamacher hybrid geometric (BCFSHHG) are among the operators that we present and illustrate. We utilize these operators to address bipolar complex fuzzy soft Multi-Attribute Border Approximation area Comparison (MABAC) issues and analyze their specific instances. We illustrate the efficacy and superiority of our technique over current methods using a numerical example in SCM, highlighting its potential for attaining optimal performance and solutions in SCM scenarios.
With the rapid development of economic globalization, the political, economic and cultural exchanges between countries in the world are becoming more and more frequent, and the society's demand for Japanese professionals has greatly increased, which also puts higher demands on for college Japanese teaching, especially college Japanese translation teaching (JTT). College JTT, as the important component of the college Japanese teaching system, is affected by the traditional teaching system and model, and there are problems such as lack of teaching materials, outdated teaching approaches, unreasonable teaching curriculum settings, and single teaching implementation approaches. The teaching effect evaluation of JTT courses is multiple-attribute group decision-making (MAGDM) problem. In this work, in order to manage the MAGDM, the interval-valued intuitionistic fuzzy number CoCoSo based on the CRITIC (IVIFN-CRITIC-CoCoSo) approach is constructed under interval-valued intuitionistic fuzzy sets (IVIFSs). Finally, numerical example for teaching effect evaluation of JTT courses has been illustrated and some comparisons is employed to illustrate advantages of IVIFN-CRITIC-CoCoSo approach. This study illustrates four contributions: (1) a novel MAGDM approach based on IVIFN-CRITIC-CoCoSo approach is constructed under IVIFS. (2) The attributes weights are illustrated through CRITIC approach. (3) numerical example for teaching effectiveness evaluation of JTT courses has been illustrated and (4) some comparisons is illustrated advantages of IVIFN-CRITIC-CoCoSo approach.
