Despite the recent interest in the Industry 4.0 applications for sustainability, little is known on the processes through which digital transformation and Industry 4.0 technologies enable sustainable innovation in manufacturing. The present study addresses this knowledge gap by developing a strategic roadmap that explains how businesses can leverage Industry 4.0 technologies to introduce sustainability into innovative practices. For this purpose, the study conducts a systematic review of extant literature to identify Industry 4.0 functions for sustainable innovation and applies interpretive structural modeling to devise the promised roadmap. The results offer interesting insights into Industry 4.0 applications for sustainable innovation.The strategic roadmap developed reveals that Industry 4.0 enables sustainable innovation through 11 functions. Industry 4.0 and the underlying digital technologies and principles allow businesses to improve interfunctional collaboration and better integrate with internal and external stakeholders. Industry 4.0 further improves the knowledge base and advanced manufacturing competency and promotes organizational capabilities valuable to sustainable innovation such as green absorptive capacity, sustainable partnership, and sustainable innovation orientation. Through these functions, Industry 4.0 subsequently enhances green process innovation capacity and the ability to develop or reintroduce eco-friendly products economically and competitively. Overall, the roadmap explains the complex precedence relationships among the 11 sustainable innovation functions of Industry 4.0, offering important implications for businesses that seek to leverage Industry 4.0 sustainability implications and manage sustainable development.
PurposeThe present study offers a holistic but detailed understanding of the factors that might affect small and medium-sized enterprises (SMEs) adoption of Industry 4.0 technologies to empower smaller businesses to embrace Industry 4.0.Design/methodology/approachThe study conducted a systematic review of the literature and drew on the technology-organization-environment framework to identify various technological, organizational and environmental determinants of Industry 4.0 technology adoption and their underlying components. The study applied the textual narrative synthesis to extract findings from the eligible articles and interpret them into the Industry 4.0 technology adoption roadmap.FindingsIndustry 4.0 is a vital strategic option to SMEs, enabling them to keep up with the digitalization race. SMEs significantly lag behind large organizations in benefiting from disruptive Industry 4.0 technologies. SMEs are still struggling with the initial adoption decisions regarding the digital transformation under Industry 4.0. Results identified various determinants that might explain this condition. The study developed a digitalization roadmap that describes the necessary conditions for facilitating SMEs’ digitalization under Industry 4.0.Practical implicationsVarious technological, organizational and environmental factors might determine the current positioning of SMEs against Industry 4.0. These determinants can act as barriers or drivers depending on their properties. The roadmap describes determinants indispensable to promoting Industry 4.0 technology adoption among SMEs, such as knowledge competencies or value chain digitalization readiness.Originality/valueExclusively focusing on empirical research that reported applied insights into Industry 4.0 technology adoption, the study offers unique implications for promoting Industry 4.0 digital transformation among SMEs.
As the COVID-19 pandemic came unexpectedly, many real estate experts claimed that the property values would fall like the 2007 crash. However, this study raises the question of what attributes of an apartment are most likely to influence a price revision during the pandemic. The findings in prior studies have lacked consensus, especially regarding the time-on-the-market variable, which exhibits an omnidirectional effect. However, with the rise of Big Data, this study used a web-scraping algorithm and collected a total of 18,992 property listings in the city of Vilnius during the first wave of the COVID-19 pandemic. Afterwards, 15 different machine learning models were applied to forecast apartment revisions, and the SHAP values for interpretability were used. The findings in this study coincide with the previous literature results, affirming that real estate is quite resilient to pandemics, as the price drops were not as dramatic as first believed. Out of the 15 different models tested, extreme gradient boosting was the most accurate, although the difference was negligible. The retrieved SHAP values conclude that the time-on-the-market variable was by far the most dominant and consistent variable for price revision forecasting. Additionally, the time-on-the-market variable exhibited an inverse U-shaped behaviour.
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