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Industry 4.0 (I40) is an open window of opportunity for Turkey, a developed country, to eliminate technological dependence and produce with maximum productivity. However, I40, which corresponds to the fourth wave of industrial revolutions, brings both opportunities and challenges. In this context, this study aims to reveal the foresight of managers in the Turkish white goods industry (TWGI) regarding the advantages and challenges of I40 and compare them with the literature.

The Delphi method was used for the study. Data were collected from managers of companies that are members of the White Goods Suppliers Association (BEYSAD). Seventy managers from 55 companies participated in the first round, and 19 managers participated in the second round of Delphi.

The results show that the most frequently cited advantages are productivity/resource efficiency, data and information-enabled effectiveness/productivity, quality 4.0 and competitiveness/strategy. The most frequently mentioned challenges are financial resources/investment, employee qualification/training, technical/processual challenges and organizational transformation/leadership.

The sample was limited to the managers of the TWGI.

Players in similar ecosystems and policymakers should consider the advantages and respond to potential challenges when creating roadmaps, taking the necessary steps and positioning themselves in the marketplace. In particular, the TWGI – Turkey’s showcase in international markets – should consider the undeniable benefits of the I40 transition to increase innovation.

The findings for the first time highlight the advantages and challenges of I40 in an industry in Turkey, and they will benefit the TWGI, which is among the leaders in Turkey in terms of digital maturity and innovation in its journey to I40.

Managers play a critical role in shaping the development of firms due to the risky and long-term nature of innovation. Although the managerial effect on strategic change has long been factored into organizational theories, scholars still lack a complete understanding of the specific managerial capabilities that drive innovation in today's digital economy. The present study builds on dynamic managerial capabilities theory to close this research gap. The paper proposes managers' dynamic capabilities and their three underlying drivers – managerial human capital, social capital, and cognition – as a direct antecedent to digital firms' innovativeness.

The study draws on survey data from German Industry 4.0 manufacturing firms, which were analyzed using regression analysis.

The results confirm managers' dynamic capabilities as facilitators of innovation. In contrast to previous research on nondigital industries, the findings demonstrate that only the complete portfolio of managers' dynamic capabilities promotes innovativeness in digital firms. The study provides evidence for the importance of dynamic managerial capabilities in the digital economy yet contradicts previous research on nondigital industries related to the advantageousness of managers' human capital, social capital, and cognition for innovation.

The study contributes to the literature by being the first to holistically test the effects of dynamic managerial capabilities on innovation in digital firms. The results offer a nuanced account of managers' dynamic capabilities, thereby expanding dynamic managerial capabilities theory to the digital economy.

This study aims to investigate the motivations for the adoption of Industry 4.0 technologies among manufacturing firms in developing economies. Specifically, the effects of relative advantage of the technologies, competitive pressure, and government support on the adoption are explored. Moreover, the mediating role of top management support between environmental factors (government support and competitive pressure) and the adoption of Industry 4.0 technologies is examined.

A research model is developed based on the technology-organization-environment (TOE) framework strengthened by institutional theory. Structural equation modeling (SEM) approach is employed to evaluate the model using data obtained from 215 manufacturing firms through a cross-industry survey. Additionally, a post-hoc analysis is conducted using cluster analysis and ANOVA.

The results show that competitive pressure and government support significantly promote top management support, which in turn contributes to the adoption of Industry 4.0 technologies. Relative advantage of the technologies is not significantly related to the adoption.

This study does not explore the relationship between technology type and the specific needs of manufacturing firms. Future researchers can conduct a more comprehensive analysis by examining how different technology types align with the unique needs of individual companies.

The findings of this study have implications for both policymakers and managers. Policymakers can leverage these insights to understand the underlying motivations behind manufacturing firms' adoption of Industry 4.0 technologies and develop promoting policies. In turn, managers should keep an eye on government policies and utilize government support to facilitate technology adoption.

This study uncovers the underlying motivations—government support and competitive pressure—for the adoption of Industry 4.0 technologies among manufacturing firms in developing economies. Meanwhile, it complements previous research by showing the mediating role of top management support between environmental factors (government support and competitive pressure) and the adoption of Industry 4.0 technologies.

The purpose of this paper is to discover how Hungarian manufacturing companies interpret technology and human resources as driving forces and barriers in terms of Industry 4.0 implementation.

The authors conducted 23 semi-structured interviews with corporate leaders and applied qualitative content analysis using Atlas.ti software.

The authors formulated a new definition of Industry 4.0 which emphasises the role of human factors. The authors identified driving forces (efficiency with speed/information flow/precision) and barriers (technology compatibility, human fears and lack of digital skills) in terms of Industry 4.0 implementation and developed the DIGI-TEcH performance management dimensions.

Comparison with other countries is limited. Given the exploratory and qualitative nature, further quantitative research would be needed to generalise results. Finally, only manufacturing companies are examined.

It provides empirical evidence to practitioners to understand concerns about technology and human resource in terms of Industry 4.0 implementation. In addition, corporate performance management can be extended by the developed DIGI-TEcH dimensions.

This paper reveals key evidence for the uptake of technology and human factors in terms of Industry 4.0 implementation and their impacts on corporate operation and performance. It also provides an insight into a specific country context, which can be a useful benchmark for other Central and Eastern European countries.

The purpose of this study is to ensure productive, robust and sustainable production systems and realise digitalised manufacturing trough implementation of Smart Maintenance – “an organizational design for managing maintenance of manufacturing plants in environments with pervasive digital technologies”. This paper aims to support industry practitioners in selecting performance indicators (PIs) to measure the effects of Smart Maintenance, and thus facilitate its implementation.

Intercoder reliability and negotiated agreement were used to analyse 170 maintenance PIs. The PIs were structurally categorised according to the anticipated effects of Smart Maintenance.

Companies need to revise their set of PIs when changing manufacturing and/or maintenance strategy (e.g. reshape the maintenance organisation towards Smart Maintenance). This paper suggests 13 categories of PIs to facilitate the selection of PIs for Smart Maintenance. The categories are based on 170 PIs, which were analysed according to the anticipated effects of Smart Maintenance.

The 13 suggested categories bring clarity to the measuring potential of the PIs and their relation to the Smart Maintenance concept. Thereby, this paper serves as a guide for industry practitioners to select PIs for measuring the effects of Smart Maintenance.

This is the first study evaluating how maintenance PIs measure the anticipated effects of maintenance in digitalised manufacturing. The methods intercoder reliability and negotiated agreement were used to ensure the trustworthiness of the categorisation of PIs. Such methods are rare in maintenance research.

In the past decade, in the space industry, many initiatives intended at offering open access to big data from space multiplied. Therefore, firms started adopting business models (BMs) which lever on digital technologies (e.g. cloud computing, high-performance computing and artificial intelligence), to seize these opportunities. Within this scenario, this article aims at answering the following research question: which digital technologies do impact which components the BM is made of?

An exploratory multiple case study approach was used. Three cases operating in the space industry that lever on digital technologies to implement their business were analyzed. Despite concerns regarding reliability and validity, multiple case studies allow greater understanding of causality, and show superiority respect to quantitative studies for theory building.

Big data, system integration (artificial intelligence, high-performance computing) and cloud computing seem to be pivotal in the space industry. It emerges that digital technologies involve all the different areas and components of the BM.

This paper sheds light on the impact that digital technologies have on the different BM components. It is only understanding which technologies can support the value proposition, which technologies make the infrastructural part able to support this proposition, which technologies may be helpful for delivering and communicating this value to customers and which technologies may help firms to appropriate the value that it is possible to seize the impact of digital technologies on BM.