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Sustainability
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Digital Economy Transformation: Driving Sustainability Through Innovative Management
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Digital Economy Transformation: Driving Sustainability Through Innovative Management

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Economic and Business Aspects of Sustainability".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 5292

Special Issue Editors

Prof. Dr. Alfonso Marino

E-Mail Website
Guest Editor
Department of Engineering, Università degli Studi della Campania “L. Vanvitelli”, 81031 Aversa, CE, Italy
Interests: SSD: IEGE-01/A–circular economy; strategic management; digital innovation
Dr. Paolo Pariso

E-Mail Website
Guest Editor
Department of Engineering, Università degli Studi della Campania “L. Vanvitelli”, 81031 Aversa, CE, Italy
Interests: SSD: IEGE-01/A–sustainability cycles; digital innovation

Special Issue Information

Dear Colleagues,

The Special Issue, titled "Digital Economy Transformation: Driving Sustainability Through Innovative Management", aims to explore the intersection of digital innovation and strategic management in fostering sustainable business practices. As companies face increasing pressure to align their operations with sustainability goals, this Issue will provide a comprehensive analysis of how digital technologies, such as AI, the IoT, and blockchain, can enhance strategic decision making, drive eco-friendly innovations, and create long-term value for businesses.

The focus will be on identifying the ways digital tools can support sustainability by improving efficiency, reducing waste, and enabling better resource management. This Issue will also address the socio-economic implications of digital transformation in various sectors and its role in promoting sustainable development.

By bridging gaps in the existing literature, this Special Issue will offer fresh insights into how digital innovations can complement strategic management frameworks, and how businesses can integrate sustainability into their core strategies to thrive in an increasingly environmentally conscious marketplace.

Prof. Dr. Alfonso Marino
Dr. Paolo Pariso
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • digital innovation
  • strategic management
  • sustainability
  • digital transformation
  • circular economy
  • business strategy
  • socio-economic impact
  • technological integration

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Published Papers (4 papers)

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Research

35 pages, 2829 KiB  
Article
Research on the Coupling and Coordination Between New-Quality Productivity and Digital Transformation in China’s Provinces
by Debao Dai, Shali Cao and Min Zhao
Sustainability 2025, 17(9), 3806; https://doi.org/10.3390/su17093806 - 23 Apr 2025
Viewed by 620
Abstract
Against the backdrop of the global digital wave and the “dual carbon” goals, the coordinated development of new-quality productivity and digital transformation has emerged as a critical engine for high-quality economic growth. This study constructs an evaluation system for new-quality productivity incorporating 20 [...] Read more.
Against the backdrop of the global digital wave and the “dual carbon” goals, the coordinated development of new-quality productivity and digital transformation has emerged as a critical engine for high-quality economic growth. This study constructs an evaluation system for new-quality productivity incorporating 20 indicators, including green innovation and digital infrastructure, and a 17-indicator system for digital transformation. Using the entropy method, modified coupling coordination model, and Dagum Gini coefficient, the spatiotemporal coupling characteristics of 31 provinces in China from 2011 to 2023 are systematically analyzed. The findings reveal a gradient distribution of coupling coordination degree, with higher levels in the East and lower in the West. The Eastern region forms a “technology-ecology” dual-driven model through digital innovation and environmental protection investment, while Central and Western regions face dual constraints of lagging digital infrastructure and insufficient pollution control capacity. Difference decomposition shows that inter-regional disparities contribute 64–70% to overall differences, with green technology innovation and digital infrastructure efficiency identified as core influencing factors. This study proposes a differentiated coordination mechanism of “eastern leadership-central rise-western revitalization”, aiming to promote a sustainable development pattern integrating digital empowerment and ecological protection through improving cross-regional digital infrastructure networks, innovating market-based factor allocation, and establishing green technology sharing platforms. Full article
(This article belongs to the Special Issue Digital Economy Transformation: Driving Sustainability Through Innovative Management)
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34 pages, 629 KiB  
Article
Driving Innovation Through Customer Relationship Management—A Data-Driven Approach
by Jung-Yi (Capacity) Lin and Chien-Cheng Chen
Sustainability 2025, 17(8), 3663; https://doi.org/10.3390/su17083663 - 18 Apr 2025
Cited by 1 | Viewed by 1452
Abstract
Customer relationship management (CRM) is a key factor driving innovation and organizational growth. The present study investigated the relationship between data-driven CRM (DDCRM) and innovation in Taiwan. We developed a research model involving CRM theory, innovation theory, and the technology adoption model (TAM) [...] Read more.
Customer relationship management (CRM) is a key factor driving innovation and organizational growth. The present study investigated the relationship between data-driven CRM (DDCRM) and innovation in Taiwan. We developed a research model involving CRM theory, innovation theory, and the technology adoption model (TAM) theory to account for the cultural and organizational contexts of Taiwan and investigate this relationship. The study distributed questionnaires to employees and stakeholders within Taiwanese firms to understand their firms’ innovation and CRM practices. The results indicate that technology adoption and organizational culture have mediating effects and industry dynamics and organizational size have moderating effects on the relationship between DDCRM and innovation. That is, adopting new technology and having an organizational culture that supports innovation and company-wide collaboration can enhance the effects of implementing DDCRM practices. In addition, certain industries (e.g., the technology industry) are more likely to effectively leverage DDCRM practices to drive innovation, and although large organizations have more resources and can therefore more easily implement CRM systems, small and medium-sized enterprises (SMEs) can more quickly adapt and innovate on the basis of CRM insights. These findings highlight the importance of DDCRM in driving innovation and reveal key factors influencing the effectiveness of CRM in doing so. The study features comprehensive suggestions of operable strategies and measures for Taiwanese SMEs, hopefully assisting them in gaining a market advantage and elevating their innovation capabilities by leveraging DDCRM practices. Full article
(This article belongs to the Special Issue Digital Economy Transformation: Driving Sustainability Through Innovative Management)
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30 pages, 689 KiB  
Article
Factors Influencing Consumer Buying Behavior for Smart Home Technologies
by Jung-Yi (Capacity) Lin and Chien-Cheng Chen
Sustainability 2025, 17(7), 2992; https://doi.org/10.3390/su17072992 - 27 Mar 2025
Viewed by 1617
Abstract
Smart home technologies (SHT) offer numerous benefits to consumers. This study explored the relationship between the perceived benefits of and the likelihood of subsequently purchasing SHT among Taiwanese consumers. The study conducted a survey in May 2024 and collected data from 424 respondents [...] Read more.
Smart home technologies (SHT) offer numerous benefits to consumers. This study explored the relationship between the perceived benefits of and the likelihood of subsequently purchasing SHT among Taiwanese consumers. The study conducted a survey in May 2024 and collected data from 424 respondents of various ages, educational backgrounds, and income levels. Data on the perceived benefits of SHT, the perceived challenges of adopting these technologies, current methods for managing household tasks and energy consumption, and the likelihood of purchasing SHT were collected. The perceived benefits of SHT include enhanced comfort, security, and energy efficiency. Comfort and energy efficiency but not enhanced security were significant predictors of adoption. Proficiency in online research but not general technical proficiency also significantly predicted adoption. Consumers dissatisfied with current home energy management methods were more likely to adopt SHT. Positive perceptions of benefits and dissatisfaction with current methods drive the adoption of SHT. As there is increasing environmental awareness in Taiwan, this study verifies that environmentally conscious consumers affect their buying decisions positively. This study highlights how SHT can improve quality of life while promoting sustainable development. The study offers valuable insights into consumer buying behaviors and contributes to the SHT industry with actionable suggestions for improving product design, incorporating more green technology into their products, enhancing user interfaces, strengthening security protocols, and upgrading interoperability between different smart home devices that may facilitate users to embrace SHT. Full article
(This article belongs to the Special Issue Digital Economy Transformation: Driving Sustainability Through Innovative Management)
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17 pages, 3758 KiB  
Article
Application of Interpretable Artificial Intelligence for Sustainable Tax Management in the Manufacturing Industry
by Ning Han, Wen Xu, Qian Song, Kai Zhao and Yao Xu
Sustainability 2025, 17(3), 1121; https://doi.org/10.3390/su17031121 - 30 Jan 2025
Cited by 1 | Viewed by 1133
Abstract
The long-term development of the manufacturing industry relies on sustainable tax management, which plays a key role in optimizing production costs. While artificial intelligence models have been applied to tax-related predictions, research on their application for predicting tax management levels is quite limited, [...] Read more.
The long-term development of the manufacturing industry relies on sustainable tax management, which plays a key role in optimizing production costs. While artificial intelligence models have been applied to tax-related predictions, research on their application for predicting tax management levels is quite limited, with no studies focused on the manufacturing industry in China. To enhance digital innovation in corporate management, this study applies interpretable artificial intelligence models to predict the tax management level, which helps decision-makers maintain it within a sustainable range. The ratio of total tax expense to total profits (ETR) is used to represent the tax management level, which is predicted using decision trees, random forests, linear regression, support vector regression, and artificial neural networks with eight input features. Comparisons among the developed models indicate that the random forest model exhibits the best performance in terms of prediction accuracy and generalization capability. Additionally, the Shapley additive explanations (SHAP) technique is integrated with the developed model to enhance the interpretability of its predictions. The SHAP results reveal the importance of the input features and also highlight the dominance of certain features. The results show that the ETR from the previous year holds the greatest importance, being more than twice as significant as the second most important factor, whereas the effect of board size is negligible. Moreover, benefiting from the local interpretations using SHAP values, this approach aids managers in making rational tax management decisions. Full article
(This article belongs to the Special Issue Digital Economy Transformation: Driving Sustainability Through Innovative Management)
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