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Coronavirus disease 2019 (COVID-19) has had a tremendous negative effect on the economies around the world by infusing uncertainty into supply chains. In this paper, the authors address two important research questions (RQs): (1) did COVID-19 wage subsidies impact small and medium enterprises (SMEs) to become more flexible towards the SMEs' business customers and (2) can such flexibility be a source for greater resilience to the crisis? As a result, the authors investigate the relationship between governmental wage subsidies and SMEs' flexibility norms towards the SMEs' business customers (study 1). The authors further uncover when and how flexibility towards existing customers contributes to SME resilience (study 2).

The authors frame the inquiry under the resource dependence theory (RDT) and behavioural additionality principle. The authors use survey methodology and test the assumptions in study 1 (n = 225) and study 2 (n = 95) on a sample of SMEs from various business-to-business (B2B) industries in Croatia.

Overall, in study 1, the authors find that SMEs that receive governmental wage subsidies have greater flexibility norms. However, this relationship is significantly conditioned by SMEs' competitive profile. SMEs that strongly rely on innovation are more willing to behave flexibly when receiving subsidies, whereas SMEs driven by branding do not. Study 2 sheds light on when flexibility towards existing customers increases SME resilience. Findings show that flexibility norms are negatively related to resilience, but this relationship is becoming less negative amongst SMEs with lower financial dependence on the largest customer.

This study extends RDT in the area of firm–government relationships by showing that wage subsidies became a source of power for the Government and a source of dependency for SMEs. In such cases, the SMEs receiving those subsidies align with the governmental agenda and exhibit higher flexibility towards the SMEs' customers. Drawing arguments from behavioural additionality, the authors show that this effect varies due to SMEs' attention and organisational priorities resulting from different competitive profiles. Ultimately, the authors showcase that higher flexibility norms can contribute to resilience if the SME restructures its dependency by having a less-concentrated customer base.

Green energy as a transportation supply trend is irreversible. In this paper, a highway energy supply system (HESS) evolution model is proposed to provide highway transportation vehicles and service facilities with a clean electricity supply and form a new model of a source-grid-load-storage-charge synergistic highway-PV-WT integrated system (HPWIS). This paper aims to improve the flexibility index of highways and increase CO₂ emission reduction of highways.

To maximize the integration potential, a new energy-generation, storage and information-integration station is established with a dynamic master–slave game model. The flexibility index is defined to evaluate the system ability to manage random fluctuations in power generation and load levels. Moreover, CO₂ emission reduction is also quantified. Finally, the Lianhuo Expressway is taken as an example to calculate emission reduction and flexibility.

The results show that through the application of the scheduling strategy to the HPWIS, the flexibility index of the Lianhuo Expressway increased by 29.17%, promoting a corresponding decrease in CO₂ emissions.

This paper proposed a new model to capture the evolution of the HESS, which provides highway transportation vehicles and service facilities with a clean electricity supply and achieves energy transfer aided by an energy storage system, thus forming a new model of a transportation energy system with source-grid-load-storage-charge synergy. An evaluation method is proposed to improve the air quality index through the coordination of new energy generation and environmental conditions, and dynamic configuration and dispatch are achieved with the master–slave game model.

Strategic flexibility (SF) has become increasingly important for firms because of the fast changes in the external environment. In line with the practical importance of SF, an emerging research field has developed around it that has attempted to understand the nature of SF and the key relationships. The aim of this study is to unveil the semantic structure of the recent literature on SF and to suggest new promising areas for future research.

The authors conduct a systematic literature review with a bibliographic analysis technique, which allows authors to identify the main recent streams in the literature, as well as offer reflections and suggestions for future research.

The authors uncover three main emerging areas in the research on SF, namely SF as a dynamic capability, the role of knowledge management for SF and the relationship between a firm SF and the external environment. The authors put forward three avenues for future research on SF: Avenue 1. SF, business model innovation (BMI) and other dynamic capabilities (DC), Avenue 2. Digital technologies and SF/organizational agility and Avenue 3. SF and sustainability. Articles included in the special issue entitled “A strategic perspective on flexibility, agility and adaptability in the digital era” contribute to Avenue 2, thus paving the way for filling some of the identified gaps regarding the relationship between SF and digitalization.

To the best of authors’ knowledge, this is the first literature review on SF that uses a bibliometric approach to draw conclusions on the findings in the literature. The review contributes to the theoretical understanding of SF by illustrating and explicating core topics that have persisted over time, as well as by presenting three main avenues for further developing authors’ knowledge around SF.

The purpose of this paper is to understand how small and medium enterprises (SMEs) cope with the need for labour flexibility. Most previous studies ignore the labour flexibility practices of SMEs, especially in times of economic growth and tight labour markets.

A multiple case study approach is applied, with ten Dutch SMEs located in one small province with a similar labour market. A survey was executed as an intake, followed by 48 interviews with the entrepreneurs, HR and other managers and employees, and two focus groups in each company. The findings are based on an analysis of the approved case descriptions.

SMEs, like big companies, do not rely on one flexibility practice. Volume fluctuations are countered with all flexibility strategies, the mix fluctuations and the product innovations are mostly countered with flexible functions and flexible production technology. In general, the data suggest that flexibility strategies of SMEs can be characterised as ad hoc, reactive and with a short-term orientation.

Future research should include other sectors and regions enabling to generalise the findings. Future research should have a longitudinal design to include the pathway dependencies of flexibility practices.

This study identifies the need to analyse flexibility demands; reduce flexibility demands before investments in flexibility practices; create production process flexibility; invest in labour flexibility practices only after the first three steps are taken; and develop basic and more advanced levels of flexible contracts, flexible functions and flexible working times.

This study contributes to the authors’ knowledge on the use of labour flexibility practices in SMEs. In addition, it brings empirical data on how these labour flexibility practices relate to the needs for flexibility and how they relate to other sources of organisational flexibility, such as a flexible market approach and flexible production technologies. Dynamic capabilities should include the suggested operationalisation of the flexibility practices.

The purpose of this paper is to determine the factors that affect Industry 4.0 applications, the expected impacts of Industry 4.0 applications in companies and to analyze the importance of these factors and the importance of expected impacts correlatively.

This paper provides an empirical analysis of the factors affecting the adoption of Industry 4.0 transformation and its impacts on the companies. The paper is based on 103 valid answers to a questionnaire-survey distributed among companies in Turkey. The Pearson correlation analysis was conducted to determine the correlation between independent variables and dependent variables. Regression analyses were used to test the proposed hypotheses. A multiple regression analysis was used to investigate the causal relationship between independent and dependent variables. Linear regression method and stepwise regression method was employed for regression analyses. The factors that influence Industry 4.0 applications were determined as company size, technological level of products, budget allocation for R&D department, level of lean applications, level of agility/flexibility and level of automation; and the expected impacts of Industry 4.0 applications were determined as traceability of production processes, traceability of supply chain, flexibility of supply chains, communication between the partners of supply chain, productivity, real-time data analysis, integration between companies and integration in the company according to the literature review

The results of this research study revealed that, there is a stronger relationship between level of Industry 4.0 transformation and level of automation than there is between Industry 4.0 transformation and the other independent variables. From the analyses conducted, it can be stated that budget allocation for R&D and level of lean applications and level of automation had greater impacts on Industry 4.0 transformation than company size has. The independent variables included in the regression analysis had a positive effect on Industry 4.0 transformation of companies. However the effects of company size, technological level of products and level of agility/flexibility on Industry 4.0 transformation was weak. When the impacts of Industry 4.0 on companies were analyzed, it can be stated that there is a stronger relationship between Industry 4.0 transformation and real-time data analysis, traceability of production processes, integration in companies and productivity than there is between Industry 4.0 transformation and integration between companies, traceability of supply chains, flexibility of supply chains and communication between the partners of supply chain. It was determined that Industry 4.0 transformation generally impacts internal factors of company, while Industry 4.0 had limited impacts on the supply chains.

Although there are studies that separately investigated the factors affecting Industry 4.0 transformation and the impacts of Industry 4.0 transformation on companies, the present study provides important contributions to the literature in terms of considering the importance levels of the factors affecting Industry 4.0 transformation and the importance level of impacts of Industry 4.0 transformation on companies as a whole and in relation to each other.

The purpose of this paper is the need to build a novel approach that would allow flexible modeling and solving of food supply chain management (FSCM) problems. The models developed would use the data (data-driven modeling) as early as possible at the modeling phase, which would lead to a better and more realistic representation of the problems being modeled.

An essential feature of the presented approach is its declarativeness. The use of a declarative approach that additionally includes constraint satisfaction problems and provides an opportunity of fast and easy modeling of constrains different in type and character. Implementation of the proposed approach was performed with the use of an original hybrid method in which constraint logic programming (CLP) and mathematical programming (MP) are integrated and transformation of a model is used as a presolving technique.

The proposed constraint-driven approach has proved to be extremely flexible and efficient. The findings obtained during part of experiments dedicated to efficiency were very interesting. The use of the constraint-driven approach has enabled finding a solution depending on the instance data up to 1,000 times faster than using the MP.

Due to the limited use of exact methods for NP-hard problems, the future study should be to integrate the CLP with environments other than the MP. It is also possible, e.g., with metaheuristics like genetic algorithms, ant colony optimization, etc.

There is a possibility of using the approach as a basis to build a decision support system for FSCM, simple integration with databases, enterprise resource planning systems, management information systems, etc.

The new constraint-driven approach to FSCM has been proposed. The proposed approach is an extension of the hybrid approach. Also, a new decision-making model of distribution and logistics for the food supply chain is built. A presolving technique for this model has been presented.

Left atrial appendage occlusion (LAAO) is a structural interventional cardiology procedure that offers several possibilities for the application of additive manufacturing technologies. The literature shows a growing interest in the use of 3D-printed models for LAAO procedure planning and occlusion device choice. This study aims to describe a full workflow to create a 3D-printed LAA model for LAAO procedure planning.

The workflow starts with the patient’s computed tomography diagnostic image selection. Segmentation in a commercial software provides initial geometrical models in standard tessellation language (STL) format that are then preprocessed for print in dedicated software. Models are printed using a commercial stereolithography machine and postprocessing is performed.

Models produced with the described workflow have been used at the Careggi Hospital of Florence as LAAO auxiliary planning tool in 10 cases of interest, demonstrating a good correlation with state-of-the-art software for device selection and improving the surgeon’s understanding of patient anatomy and device positioning.

3D-printed models for the LAAO planning are already described in the literature. The novelty of the article lies in the detailed description of a robust workflow for the creation of these models. The robustness of the method is demonstrated by the coherent results obtained for the 10 different cases studied.

The tourist industry has to adopt a big data-driven foresight approach to enhance decision-making in a post-COVID international landscape still marked by significant uncertainty and in which some megatrends have the potential to reshape society in the next decades. This paper, considering the opportunity offered by the application of the quantitative analysis on internet new data sources, proposes a prediction method using Google Trends data based on an estimated transfer function model.

The paper uses the time-series methods to model and predict Google Trends data. A transfer function model is used to transform the prediction of Google Trends data into predictions of tourist arrivals. It predicts the United States tourism demand in Italy.

The results highlight the potential expressed by the use of big data-driven foresight approach. Applying a transfer function model on internet search data, timely forecasts of tourism flows are obtained. The two scenarios emerged can be used in tourism stakeholders’ decision-making process. In a future perspective, the methodological path could be applied to other tourism origin markets, to other internet search engine or other socioeconomic and environmental contexts.

The study raises awareness of foresight literacy in the tourism sector. Secondly, it complements the research on tourism demand forecasting by evaluating the performance of quantitative forecasting techniques on new data sources. Thirdly, it is the first paper that makes the United States arrival predictions in Italy. Finally, the findings provide immediate valuable information to tourism stakeholders that could be used to make decisions.

Previous studies have found that a proactive market orientation (PMO) has a positive effect on product differentiation and innovation and that the effect is contingent on various factors. However, the influence of logistics on the positive relationship between PMO and product differentiation has received scant attention in marketing research. To fill this research gap, this paper aims to introduce the concept of postponement as a basic logistics strategy, currently used by many firms, and examine the interaction effect of PMO and postponement on new product differentiation.

Drawing on the exploration–exploitation literature, the authors considered PMO and postponement as types of exploration and exploitation, respectively. The authors hypothesized that postponement hampers the positive effect of PMO on product differentiation. The authors tested the hypotheses empirically by applying ordinary least squares regression to a sample of 187 brand managers in the Japanese apparel industry.

PMO is positively related to product differentiation, although the relationship is weakened when design and production systems are postponed, that is, when postponement hinders product differentiation.

Previous studies have examined market orientation and postponement (logistics) separately. However, referring to the exploration–exploitation literature, the authors built a conceptual and empirical bridge between market orientation and logistics management and proposed that this configuration is important for product differentiation.