Digital technology adoption, digital dynamic capability, and digital transformation performance of textile industry: Moderating role of digital innovation orientation

Lei, Shen; Zhang, Xi; Liu, Hongda

doi:10.1002/mde.3507

Cited by 106 publications

(76 citation statements)

References 58 publications

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“…Digital dynamic capability and digital innovation are of great significance for textile enterprises to achieve digital transformation performance. Shen et al (2022) has developed a conceptual model based on the resource capability performance framework to test how the adoption of digital technology, digital dynamic capabilities and digital innovation orientation affect the digital transformation performance of enterprises. The results show that the positive relationship between digital technology adoption and digital transformation performance is not significant; This path is fully realized through digital dynamic capabilities.…”

Section: Related Workmentioning

confidence: 99%

Analysis on the digital transformation index system of enterprise’s low carbon business performance based on AHP-DEA

Xie

2023

Front. Energy Res.

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With the arrival of the low carbon era, enterprises, as the main body of the market economy, must take the road of low carbon operation whether to fulfill their social responsibilities or to seek enterprise development. However, Chinese enterprises started late to understand the low-carbon economy. So far, except for a few state-owned enterprises, some leading private enterprises and foreign-funded enterprises, they have made remarkable achievements in low-carbon emission reduction. Most enterprises in China have not realized the importance of low carbon emission reduction. In order to step out of the “high carbon” era and coordinate economic development and environmental protection, China must solve the problem of low-carbon transformation of small and medium-sized manufacturing enterprises. How to measure the degree of low carbon transformation, and how the government objectively evaluates the degree of low carbon economic development of enterprises, with a view to formulating corresponding standard incentives and punishment measures for them. It is urgent to establish a sound, reasonable and feasible low carbon operation management indicator system, and comprehensively evaluate the information related to low carbon operation of SMEs through reasonable selection of indicators. According to the connotation of low-carbon enterprises, the economic benefits of enterprises are closely combined with environmental benefits. A set of low carbon operation performance evaluation index system including economy, technology and environment has been constructed. The AHP index weight is calculated by establishing the AHP model and the Data Envelopment Analysis (DEA) index weight is calculated by establishing the DEA model. On this basis, the grey relational evaluation model based on AHP-DEA is established. Then the paper evaluates the performance of representative enterprises’ low carbon operation. Propose corresponding improvement suggestions for the problems reflected in the low carbon operation performance ranking of enterprises and the scoring of various indicators. The research results show that the algorithm has high efficiency and the accuracy of model evaluation is 95.51%. To a certain extent, this study makes up for the research results of the impact of digital transformation on corporate strategic performance, and also provides ideas for the research of corporate financial performance evaluation.

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Section: Related Workmentioning

confidence: 99%

Analysis on the digital transformation index system of enterprise’s low carbon business performance based on AHP-DEA

Xie

2023

Front. Energy Res.

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“…According to Warner and Wäger (2019), digital transformation is defined as the use of new digital technologies to improve business performance, enhance customer experience, simplify operations, or even create new business models. A firm's digital technology adoption and digital transformation performance are mediated through digital DC (Shen et al, 2021). Examples of digital technologies include mobile technology, such as smartphones, artificial intelligence, cloud computing, blockchain technology for online safety, or the internet of things, allowing a business to gain data from the product regarding user behavior.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Survival strategies of the sharing economy from the pandemic to a new normal: A dynamic capabilities approach

Chi

Lin

2022

Manage Decis Econ

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This paper explores the business strategies of car‐sharing platforms between 2020 and 2021, during the COVID‐19 pandemic. The study conducted a systematic review and adopted a dynamic capabilities approach; it analyzes 231 online articles concerning Uber, Ola, and Grab to demonstrate 6S sub‐constructs: SAFETY, SCAN, SYSTEM, SUPPORT, SPREAD, and SPEED. The findings suggest that developing safe riding experiences, identifying threats through technology, realigning culture/structure through financial/community support, and expanding delivery services are necessary strategies to combat the pandemic. The new normal has managerial and policy implications on how businesses in the sharing economy sector adapt to radical and dynamic environments.

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“…Ensuring the current level of economic efficiency dictates the need to introduce end-to-end technologies in various sectors of the economy. These technologies are used not only in traditional science-intensive industries, such as nuclear energy or astronautics, but also in agriculture, textile, food and processing industries [5][6][7][8]. As an example, it can be noted that the large-scale reconstruction of the Sergach Sugar Plant in the Nizhny Novgorod Region, currently underway by decree of the Government of the Russian Federation, provides not only for the modernization of equipment, but also for the optimization of management based on the introduction of artificial intelligence technologies and machine learning, and intelligent data processing.…”

Section: Introductionmentioning

confidence: 99%

Project-based learning in training IT-personnel for the digital economy

2023

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The purpose of the study is to modernize IT personnel training to enhance the development of the digital economy by means of project-based learning methods. The objectives of the study are to harmonize the educational process with the needs of production and to develop appropriate educational and methodological support. The experience for the preparation of IT-bachelors at Lobachevsky State University of Nizhny Novgorod is analyzed on the example of the Sergach sugar factory modernization. The analysis of the experience gained at Lobachevsky State University of Nizhny Novgorod in applying the project approach shows the high potential of project-based training methods for IT specialists to work in the digital economy. In order for the project approach to become an effective means of training personnel to work in line with modern standards, it is necessary that the topics of projects be closely related to the actual problems facing real manufacturers. Therefore, a stable interaction between education and industry must be established. Project-based training methods using the above principles were introduced into the educational process of preparing bachelors in the area “Fundamental Informatics and Information Technology” at Lobachevsky State University of Nizhny Novgorod.

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Digital technology adoption, digital dynamic capability, and digital transformation performance of textile industry: Moderating role of digital innovation orientation

Cited by 106 publications

References 58 publications

Analysis on the digital transformation index system of enterprise’s low carbon business performance based on AHP-DEA

Analysis on the digital transformation index system of enterprise’s low carbon business performance based on AHP-DEA

Survival strategies of the sharing economy from the pandemic to a new normal: A dynamic capabilities approach

Project-based learning in training IT-personnel for the digital economy

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