Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities

Technological Forecasting and Social Change, Journal Year: 2020, Volume and Issue: 163, P. 120420 - 120420

Published: Nov. 1, 2020

Language: Английский

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Internationalization, digitalization, and sustainability: Are SMEs ready? A survey on synergies and substituting effects among growth paths

Technological Forecasting and Social Change, Journal Year: 2021, Volume and Issue: 166, P. 120650 - 120650

Published: Feb. 8, 2021

Language: Английский

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Digital economy, technological innovation, and green economic efficiency—Empirical evidence from 277 cities in China

Managerial and Decision Economics, Journal Year: 2021, Volume and Issue: 43(3), P. 616 - 629

Published: June 29, 2021

Based on the panel data of 277 cities in China from 2011 to 2018, this paper constructs digital economy index and green efficiency index. The research found following: first, has significantly improved region. Second, a greater impact eastern region large than central western regions small cities. Third, technological innovation is an important way for improve level economy.

Language: Английский

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How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops

Journal of Business Research, Journal Year: 2021, Volume and Issue: 134, P. 574 - 587

Published: June 12, 2021

Artificial intelligence (AI) is predicted to radically transform the ways manufacturing firms create, deliver, and capture value. However, many manufacturers struggle successfully assimilate AI capabilities into their business models operations at scale. In this paper, we explore how can develop innovate scale in digital servitization. We present empirical insights from a case study of six leading engaged AI. The findings reveal three sets critical capabilities: data pipeline, algorithm development, democratization. To these capabilities, need by focusing on agile customer co-creation, data-driven delivery operations, scalable ecosystem integration. combine co-evolutionary framework for scaling through model innovation underscoring mechanisms feedback loops. offer AI, with important implications management.

Language: Английский

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Sustainable industrial and operation engineering trends and challenges Toward Industry 4.0: a data driven analysis

Journal of Industrial and Production Engineering, Journal Year: 2021, Volume and Issue: 38(8), P. 581 - 598

Published: July 11, 2021

This study supplies contributions to the existing literature with a state-of-the-art bibliometric review of sustainable industrial and operation engineering as field moves toward Industry 4.0, guidance for future studies practical achievements. Although is being promoted forward sustainability, systematization knowledge that forms firms' manufacturing operations encompasses their wide concepts abundant complementary elements still absent. aims analyze contemporary in 4.0 context. The analysis fuzzy Delphi method are proposed. Resulting total 30 indicators criticized clustered into eight groups, including lean cyber-physical production system, big data-driven smart communications, safety security, artificial intelligence circular economy digital environment, business virtual reality, environmental sustainability.

Language: Английский

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Does industrial robot application promote green technology innovation in the manufacturing industry?

Technological Forecasting and Social Change, Journal Year: 2022, Volume and Issue: 183, P. 121893 - 121893

Published: July 28, 2022

Language: Английский

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Investigating the Influence of Artificial Intelligence on Business Value in the Digital Era of Strategy: A Literature Review

Information, Journal Year: 2023, Volume and Issue: 14(2), P. 85 - 85

Published: Feb. 2, 2023

For organizations, the development of new business models and competitive advantages through integration artificial intelligence (AI) in IT strategies holds considerable promise. The majority businesses are finding it difficult to take advantage opportunities for value creation while other pioneers successfully utilizing AI. On basis research methodology Webster Watson (2020), 139 peer-reviewed articles were discussed. According literature, performance advantages, success criteria, difficulties adopting AI have been emphasized prior research. results this review revealed open issues topics that call further research/examination order develop capabilities integrate them into business/IT enhance various streams. Organizations will only succeed digital transformation alignment present era by precisely implementing these new, cutting-edge technologies. Despite revolutionary potential may promote, resource orchestration, along with governance dynamic environment, is still complex enough early stages regarding strategic implementation which issue aims address and, as a result, assist future organizations effectively outcomes.

Language: Английский

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Assessing the impact of industrial robots on manufacturing energy intensity in 38 countries

Energy Economics, Journal Year: 2021, Volume and Issue: 105, P. 105748 - 105748

Published: Dec. 2, 2021

Language: Английский

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The effect of manufacturing intelligence on green innovation performance in China

Technological Forecasting and Social Change, Journal Year: 2022, Volume and Issue: 178, P. 121569 - 121569

Published: Feb. 17, 2022

Language: Английский

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Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review

Expert Systems with Applications, Journal Year: 2021, Volume and Issue: 185, P. 115695 - 115695

Published: Aug. 4, 2021

Language: Английский

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