Dataset Information

Project-based maturity assessment model for smart transformation in Taiwanese enterprises.

ABSTRACT: As smart technology proliferates, enterprises must engage not only in the transformation of intelligence but contend with pressure do so as soon as possible. Smart transformation is critical for manufacturing enterprises in the development of smart manufacturing. This study addressed the gap between maturity models and project management by designing an effective assessment framework for smart transformation. It adopts the Smart Industry Readiness Index, created by the Singapore Economic Development Board, as a maturity assessment model to analyze enterprises' smart transformation and formulate project management strategies. Enterprises can use this model to examine the maturity level of their transformation and assess scope for improvement in their project strategies and implementation barriers. This study focuses on Taiwanese enterprises using data collected from 165 valid questionnaires and subjected to a cluster analysis. Enterprises were divided into three categories. The results reveal that, first, most enterprises' smart transformation is at an immature or medium-maturity level, and is therefore amenable to further improvement. Second, inconsistent with research findings, many enterprises invest in transformation projects but fail to advance these projects to maturity. Third, most enterprises' project management plans fail to meet actual transformation needs. Using the thematically oriented maturity model proposed in this study, Taiwanese enterprises can effectively evaluate the maturity of their transformation projects. In conclusion, the study highlights that Taiwanese enterprises must identify more effective external resources to strengthen their competitiveness.

SUBMITTER: Lin TC

PROVIDER: S-EPMC8284801 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BACKGROUND:Digital transformation in health care is being driven by the need to improve quality, reduce costs, and enhance the patient experience of health care delivery. It does this through both the direct intervention of technology to create new diagnostic and treatment opportunities and also through the improved use of information to create more engaging and efficient care processes. OBJECTIVE:In a modern digital hospital, improved clinical and business processes are often driven through enhancing the information flows that support them. To understand an organization's ability to transform their information flows requires a clear understanding of the capabilities of an organization's information technology infrastructure. To date, hospital facilities have been challenged by the absence of uniform ways of describing this infrastructure that would enable them to benchmark where they are and create a vision of where they would like to be. While there is an industry assessment measure for electronic medical record (EMR) adoption using the Healthcare Information and Management Systems Society Analytics EMR Adoption Model, there is no equivalent for assessing the infrastructure and associated technology capabilities for digital hospitals. Our aim is to fill this gap, as hospital administrators and clinicians need to know how and why to invest in information infrastructure to support health information technology that benefits patient safety and care. METHODS:Based on an operational framework for the Capability Maturity Model, devised specifically for health care, we applied information use characteristics to define eight information systems maturity levels and associated technology infrastructure capabilities. These levels are mapped to user experiences to create a linkage between technology infrastructure and experience outcomes. Subsequently, specific technology capabilities are deconstructed to identify the technology features required to meet each maturity level. RESULTS:The resulting assessment framework clearly defines 164 individual capabilities across the five technology domains and eight maturity levels for hospital infrastructure. These level-dependent capabilities characterize the ability of the hospital's information infrastructure to support the business of digital hospitals including clinical and administrative requirements. Further, it allows the addition of a scoring calculation for each capability, domain, and the overall infrastructure, and it identifies critical requirements to meet each of the maturity levels. CONCLUSIONS:This new Infrastructure Maturity Assessment framework will allow digital hospitals to assess the maturity of their infrastructure in terms of their digital transformation aligning to business outcomes and supporting the desired level of clinical and operational competency. It provides the ability to establish an international benchmark of hospital infrastructure performance, while identifying weaknesses in current infrastructure capability. Further, it provides a business case justification through increased functionality and a roadmap for subsequent digital transformation while moving from one maturity level to the next. As such, this framework will encourage and guide information-driven, digital transformation in health care.

Project description:With the rapid development of technologies such as cloud computing and big data, various levels of government departments in the country have successively introduced digital subsidy policies to promote enterprises' digital transformation. However, the effectiveness of these policies and their ability to truly achieve policy objectives have become pressing concerns across society. Against this backdrop, this paper employs a moderated mediation effects model to empirically analyze the incentive effects of financial subsidies on the digital transformation of A-share listed manufacturing companies in the Shanghai and Shenzhen stock markets from 2013 to 2022. The research findings indicate a significant promotion effect of financial subsidies on the digital transformation of manufacturing enterprises, especially demonstrating a notable incentive impact on the digital transformation of large enterprises, non-asset-intensive enterprises, technology-intensive enterprises, and non-labor-intensive enterprises. However, the incentive effect on the digital transformation of small and medium-sized enterprises (SMEs), asset-intensive enterprises, non-technology-intensive enterprises, and labor-intensive enterprises is not significant. Notably, the expansion of financial subsidies positively influences the augmentation of R&D investment within manufacturing enterprises, subsequently providing indirect encouragement for their digital transformation. Additionally, the incorporation of the degree of marketization implies its potential to moderate both the direct and indirect impacts of financial subsidies on enterprise digital transformation. This study enriches the research on the mechanism of the role of financial subsidies in digital transformation and provides empirical evidence on how market participation influences the effects of financial subsidies, thereby assisting policymakers in comprehensively understanding the impact of financial subsidy policies on different types of enterprises.

Dataset Information

Project-based maturity assessment model for smart transformation in Taiwanese enterprises.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets