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The purpose of this study is to investigate how Industry 4.0 (IR 4.0) operates within the context of the post-COVID-19 environment and its impact on the economic, environmental and social sustainability of the manufacturing industry in a developing economy. By delving into this subject, the authors aim to understand the mechanisms through which IR 4.0 influences various facets of sustainability within the manufacturing sector.

A cross-sectional research design is used using purposive sampling and hypotheses were developed based on seminal theories and literature. Data was collected from 191 Malaysian manufacturing firms that use IR 4.0 technologies. The partial least squares structural equation modeling technique was used to explore how post-COVID uncertainty environment impacts manufacturing sustainability through IR 4.0 adoption.

During the highly uncertain era of the post-COVID-19 pandemic, the implementation of IR 4.0 adoption has had a favorable effect on the manufacturing industry, enhancing process efficiency, automation and data granularity. However, it is worth noting that automation does not significantly contribute to environmental sustainability and data granularity does not have a noteworthy impact on economic sustainability. On the other hand, process efficiency has a positive and significant influence on all three dimensions of sustainability. Moreover, data granularity positively affects environmental sustainability, whereas automation positively impacts both social and economic sustainability.

This study emphasizes the importance of policy interventions to harness the transformative power of IR 4.0 and address the identified concerns. In addition, the findings offer valuable insights for mid-level operational managers, enabling them to actively participate in the creation of sustainable policies in partnership with top management. It is expected that these actions, which involve making necessary adjustments in the manufacturing process, will yield tangible outcomes for sustainable development.

This study offers valuable insights into the relationship between the uncertain post-COVID-19 environment, referred to as hyper-uncertainty and the adoption of IR 4.0 in the context of manufacturing sustainability.

The study aims to identify the extent to which industry 4.0 (IR4.0) adoption impacts the sustainable manufacturing (SM) performance of the manufacturing industry, focusing on the comparative analysis between developed and developing economies amid coronavirus disease 2019 (COVID-19) pandemic.

The study proposes a conceptual model formed on seminal theories and literature using the cross-sectional design. For data collection, a purposive sampling method is used where 154 Malaysian (developing) and Australian (developed) manufacturing firms' data were collected. Partial least square-based structural equation modeling is employed to test the hypothesis and proposed research model.

This study finds that adoption of IR4.0 technologies does not directly influence the sustainability performance of the manufacturing industry, but rather the trajectories of SM (efficiency, flexibility, automation and big data and granularity) fully mediate the relationship between IR4.0 adoption and sustainability manufacturing performance. The comparative analysis between Australia and Malaysia shows no significant difference in the relationships or the framework; hence, the differences between developed and developing countries are not significant in this mechanism.

The study contributes to the insights of the managers regarding COVID-19 and the implementation of IR4.0 in the SM domain. The policymakers would further get better insights since the study pays attention to sustainable development goal, industry, innovation, infrastructure and responsible production.

Lean Six Sigma (LSS) is often perceived as a foundation for implementing Industry 4.0 (I4.0) within an organization, with the two strengthening each other in different ways. The purpose of this article is to examine the evolution, trends and motivation for merging LSS and I4.0 and to gain insights into LSS 4.0 in both manufacturing and service sectors. This article also explores the effective methods for integrating and implementing LSS 4.0 in both manufacturing and service sectors, along with conducting a Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis of LSS 4.0.

By conducting a systematic literature review in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, relevant literature from 2010 to 2023 was reviewed using appropriate keywords and research criteria. The objective was to identify, select and evaluate research articles related to LSS 4.0. Consequently, 159 relevant papers were located in databases, including Elsevier, Taylor and Francis Group, IEEE, Springer, Emerald Insights, Scopus and non-Scopus databases. Additionally, this review aimed to understand the progress of LSS 4.0 from the manufacturing and service sector context and to perform a SWOT analysis of LSS 4.0.

This review reveals a significant surge in the literature on LSS and I4.0 over the past three years. The research articles were categorized based on various themes, including the publisher, journals, types of articles and geographical factors such as country and continent. Additionally, the review examined the progress of LSS 4.0 in both manufacturing and service sectors. A SWOT analysis was also performed to identify the SWOT of LSS 4.0.

This is the first exclusive research work to conduct a SWOT analysis of LSS 4.0. This research article is highly valuable for academicians, researchers, policymakers and practitioners as it helps to identify the SWOT of LSS 4.0. Furthermore, it provides an overview of the progress of LSS 4.0 from both manufacturing and service sector perspectives, and significant case studies are discussed.

The user-oriented reusing of historical heritages is a powerful planning strategy to increase sustainability and provide socioeconomic benefits for cities. The study aims to determine the post-occupancy sustainability of reused historical buildings.

A holistic model was designed by considering expert and user opinions to select the post-occupancy sustainability of reused historical buildings. The proposed model was developed concerning key sustainability indicators (KSI). This proposed model, which integrates expert and user views, is examined in an educational building with significant cultural value in Mardin.

The results of the KSI model indicate that the functional, technical and environmental sustainability of the building are at a medium level, while the aesthetic sustainability is rated as good. An analysis of the differences between importance and performance shows that the largest gap is in functional sustainability, with the smallest gap in technical sustainability. The environmental and aesthetic sustainability indicators show a smaller performance gap than the other two. The results suggest that functional sustainability requires immediate intervention, technical features should be improved based on user needs and aesthetic and environmental sustainability require monitoring but no urgent action.

The originality of the research is that it proposes a model that combines expert opinion and the opinions of building users to monitor the sustainability of the reused buildings throughout the usage process and enables them to be monitored with key sustainability criteria and to develop improvement suggestions in line with the results obtained.

Transitional periods are important for people, such as birth, marriage and death, are important times when ceremonial meals are prepared and served and certain practices are carried out. These periods and the practices constitute our gastronomic cultural heritage. In order to keep our cultural heritage alive and pass it on to future generations, existing values must first be identified and recorded. For this reason, in this study, gastronomic practices of Mentese's transitional periods were examined within the scope of intangible cultural heritage.

In this research, data was collected using ethnographic design, which is one of the qualitative research methods, and document analysis, interviews and focus group studies, as well as participant observation techniques and image/audio materials. The sample was determined using snowball sampling, convenience sampling and maximum diversity sampling methods. In the analysis of the data, themes and codes related to gastronomic practices in transitional periods were created and direct quotations were included with a descriptive approach.

It has been found that traditions are kept more alive in rural areas, while those living in the city centres no longer perform these practices dating back to the ancient times. Participants attribute the main reason for this to the fact that economic conditions are not favourable and that some traditional practices are “unnecessary” today. While wedding meals, which are the main part of the weddings, used to be made by women in the past, they are now mostly made by catering companies.

It was accepted that some of the participants started to give short answers as the duration of the interview increased and that the answers given were limited to what the participants could remember.

This study reveals special day meals and practices, rituals and traditions related to food within the scope of Mentese's culinary culture, which has a very deep-rooted history. In this regard, in addition to making an important contribution to the literature, the study also contributes to the articles about “recording and promoting the culture” mentioned in the Intangible Cultural Heritage convention.

This current research aims to reveal customers’ behaviours who purchased à la carte menu service in a chain hotel operating in Istanbul, Türkiye after the Covid-19 outbreak. In addition to this main objective, customers’ main course preferences, tipping, complaining, maintaining eating and drinking habits, local food preferences, photograph taking and food waste behaviours were determined as the sub-research objectives.

Qualitative research method was adopted, and naturalistic observation was chosen as the data collection tool in this study. Firstly, an observation form was created based on the literature. To ensure the content validity, seven experts (five researchers and two experienced hotel employees) were consulted on 15 July 2021. After revising the form, a pilot study was carried out between 4 August and 29 September 2021. By conducting the pilot study, it was aimed to prevent any unpredictable behaviours of customers. As a result, four new items were added to, and two items were removed from the form. Then, 341 customers who purchased à la carte menu service in the restaurant of the hotel chain were observed between 7 October 2021 and 28 January 2022.

It was revealed that 52% of the customers who purchased à la carte menu services did not make any reservations, while approximately 59% of the customers with reservations did not stay at the hotel in which the research was conducted. In addition, 69% of them started to eat meal together; 56% of them paid in “cash”; 48% of them preferred local food and beverages; 41% left food on the plate; and 43% of them gave tip. In contrast, very few customers (6%) engaged in complaint behaviour.

First, data were collected during the post-pandemic period when international travels were mostly restricted and thus, the researcher observed mostly Turkish customers at the restaurant. Second, only those customers sitting at the six tables close to the guest welcoming were observed. Third, just one observer took part in the data collection process. Fourth, the researchers chose one out of two restaurants of the hotel because only Asian cuisine was served and children under 12 years of age were not allowed to enter the other restaurant. Fifth, focusing on only a hotel and using naturalistic observation as a data collection tool may be shown among the limitations of this study.

This paper presents the customers’ behaviours who preferred à la carte menu service in a chain hotel operating in İstanbul, Türkiye after the Covid-19 outbreak. Although there are some studies focusing on changing of customer preferences during the post-pandemic period, “observation” was not preferred as a data collection tool by most of the researchers; hence, the findings of this study are useful for both researchers and educationists in tourism industry.

This study aims to identify critical determinants of sovereign credit risk by examining the influence of oil prices, gold prices, geopolitical risk, market volatility, exchange rates, inflation and non-performing loans on Türkiye’s credit default swap (CDS) spreads. This analysis provides a comprehensive understanding of how economic uncertainty and political risk impact Türkiye’s financial stability, as reflected in its CDS market. This study investigates the importance of ex ante proxies in explaining changes in CDS spread by financial and economic indicators in Türkiye.

This research explores the connections between critical financial and economic indicators and the credit risk of Türkiye between 2009 and 2022 by using advanced econometric techniques such as ARDL bound tests, fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), Johansen co-integration tests and VECM Granger causality analyses.

ARDL bound test results reveal significant negative impacts of BIST and non-performing loans on CDS, and positive associations with inflation, VIX and geopolitical risk on CDSs. The short-term results show that BIST, INFL, NPL, USD, VIX and GPRT have negative coefficients. Johansen co-integration, FMOLS and DOLS results reinforce the ARDL findings. Moreover, BIST is a significant Granger cause of CDS.

This study is significant, as it jointly considers economic and political risk factors, thereby integrating multiple econometric models to provide more robust, meaningful and comparable results. By examining these factors together, the analysis offers a more comprehensive understanding of risk dynamics, yielding insights relevant to Türkiye. Although the findings are specific to Türkiye, they have broader implications, enriching the understanding of emerging economies. Türkiye’s status as a key representative of emerging markets strengthens the study’s value, as the results can serve as a reference point for other countries with similar economic structures. The importance of this study is also underscored by its potential to inform risk management strategies, guide policy decisions and offer insights to investors and financial analysts. By elucidating the intricate relationships among a broad spectrum of macroeconomic variables, this research contributes to a more comprehensive risk assessment framework. It equips stakeholders with a more informed perspective on the factors influencing credit risk in Türkiye’s economic landscape.

This research aims to investigate the impact of artificial intelligence (AI) adoption on the innovation dynamics of Chinese manufacturing enterprises, with a specific focus on the intricate interplay with the labor structure.

Leveraging panel data of listed companies from 2010 to 2022, this study employs the two-way fixed effects (TWFE) model to examine the influence of AI adoption on Chinese manufacturing companies' innovativeness. Firm-level AI adoption is measured by constructing a three-dimensional attention, application and absorption index.

The results indicate that (1) AI adoption has a positive impact on both internal innovation capability and external innovation interaction, (2) AI adoption has dual effects on the education and skill structure of labor in manufacturing enterprises and (3) enterprises with a highly educated and skilled workforce exhibit a stronger influence of AI adoption on innovativeness.

This research contributes to the academic and practical discourse by unveiling the underlying mechanisms of AI affecting innovation and introducing a new measurement of the AI adoption index. The findings emphasize the need for a highly educated and skilled workforce to navigate the complexities of AI-driven innovation, offering valuable theoretical and practical implications for policymakers and enterprises.

This study aims to assess the feasibility and effectiveness of incorporating predictive maintenance (PdM) into existing practices of spare part inventory management and pinpoint the barriers and identify economic values for such integration within the supply chain (SC).

A two-staged embedded multiple case study with multi-method data collection and a combined discrete/continuous simulation were conducted to diagnose obstacles and recommend a potential solution.

Several major organisational, infrastructure and cultural obstacles were revealed, and an optimum scenario for the integration of spare part inventory management with PdM was recommended.

The proposed solution can significantly decrease the inventory and SC costs as well as machinery downtimes through minimising unplanned maintenance and addressing shortage of spare parts.

This is the first study with the best of our knowledge that offers further insights for practitioners in the Industry 4.0 (I4.0) era looking into embarking on digital integration of PdM and spare part inventory management as an efficient and resilient SC practice for the automotive sector by providing empirical evidence.