This article presents literature across four linked domains — Agile practices, AI adoption, Digital Transformation, and Environmental Sustainability Performance — alongside the moderating role of Digital Leadership. The nexus of digital innovation and environmental responsibility is one of the most strategic issues facing organizations in the 21st century. Pressured by mounting pressures related to climate change, resource exhaustion, and increasing regulations, global organizations are shifting from merely 'symbolic sustainability commitments' to tangible and operational green performance [1,2]. However, there has always been and will continue to be a notorious and consistent "intent-practice" gap: the gap between green strategies that are designed at the organizational level, and the actual environmental outcomes at the operational level. Three forces that appear especially salient include Agile practice, Artificial Intelligence (AI) capabilities, and Digital Transformation (DT). Agile practices, stemming from iterative development, incremental delivery, learning from retrospective events and self-organizing teams, are becoming increasingly ubiquitous outside of software development [3,4]. AI capabilities offer predictive analysis, intelligent automation, and data-supported decision making [5,6], whereas Digital Transformation is the organizational environment where AI capabilities will become more prevalent and sustainble [7]. In this regard, we develop and test an integrative model by PLS-SEM as shown in [8].
This article presents literature across four linked domains — Agile practices, AI adoption, Digital Transformation, and Environmental Sustainability Performance — alongside the moderating role of Digital Leadership. The nexus of digital innovation and environmental responsibility is one of the most strategic issues facing organizations in the 21st century. Pressured by mounting pressures related to climate change, resource exhaustion, and increasing regulations, global organizations are shifting from merely 'symbolic sustainability commitments' to tangible and operational green performance [1,2]. However, there has always been and will continue to be a notorious and consistent "intent-practice" gap: the gap between green strategies that are designed at the organizational level, and the actual environmental outcomes at the operational level. Three forces that appear especially salient include Agile practice, Artificial Intelligence (AI) capabilities, and Digital Transformation (DT). Agile practices, stemming from iterative development, incremental delivery, learning from retrospective events and self-organizing teams, are becoming increasingly ubiquitous outside of software development [3,4]. AI capabilities offer predictive analysis, intelligent automation, and data-supported decision making [5,6], whereas Digital Transformation is the organizational environment where AI capabilities will become more prevalent and sustainble [7]. In this regard, we develop and test an integrative model by PLS-SEM as shown in [8].
This article develops a practical, theoretical, methodological, and legal framework for the attribution and attributive analysis of artworks in museum collections. It preserves the core professional focus of museum attribution while reorganizing it into an IMRAD research structure and strengthening the relationship between connoisseurship, provenance research, technical examination, digital humanities, and museum documentation. The study is intended primarily for new and practicing research staff in museum art departments. Classical approaches associated with Giovanni Morelli, Heinrich Wölfflin, and Erwin Panofsky are considered alongside infrared and ultraviolet imaging, radiography, microscopy, spectroscopy, X-ray fluorescence, digital image analysis, and carefully bounded applications of artificial intelligence. Particular attention is given to provenance, evidentiary hierarchy, legal due diligence, and the scientific passport of a museum object. The legal context is discussed with reference to the Law of the Republic of Uzbekistan “On Museums,” the ICOM Code of Ethics for Museums, and the UNESCO 1970 Convention. As a principal result, a five-stage integrated attribution protocol is proposed: preliminary documentation and provenance screening; visual-stylistic and iconographic analysis; technical and material examination; digital/computational assistance; and collegial synthesis with formal documentation. An illustrative training case based on a Fergana Valley painting is used to demonstrate how a museum researcher should distinguish hypotheses from verified evidence.
D. Nusratullaeva, Worldly Knowledge Publishing Centre· Zenodo (CERN European Organi...· 0 citations
This article develops a practical, theoretical, methodological, and legal framework for the attribution and attributive analysis of artworks in museum collections. It preserves the core professional focus of museum attribution while reorganizing it into an IMRAD research structure and strengthening the relationship between connoisseurship, provenance research, technical examination, digital humanities, and museum documentation. The study is intended primarily for new and practicing research staff in museum art departments. Classical approaches associated with Giovanni Morelli, Heinrich Wölfflin, and Erwin Panofsky are considered alongside infrared and ultraviolet imaging, radiography, microscopy, spectroscopy, X-ray fluorescence, digital image analysis, and carefully bounded applications of artificial intelligence. Particular attention is given to provenance, evidentiary hierarchy, legal due diligence, and the scientific passport of a museum object. The legal context is discussed with reference to the Law of the Republic of Uzbekistan “On Museums,” the ICOM Code of Ethics for Museums, and the UNESCO 1970 Convention. As a principal result, a five-stage integrated attribution protocol is proposed: preliminary documentation and provenance screening; visual-stylistic and iconographic analysis; technical and material examination; digital/computational assistance; and collegial synthesis with formal documentation. An illustrative training case based on a Fergana Valley painting is used to demonstrate how a museum researcher should distinguish hypotheses from verified evidence.
D. Nusratullaeva, Worldly Knowledge Publishing Centre· Zenodo (CERN European Organi...· 0 citations
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