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There are many studies explaining the innovation activity determinants. Nowadays, the digitalization of sales, the influence of social media, user-driven innovation (UDI) activity might be considered as one of the crucial sources for the development of new products within the research and development activity. Undertaken research is therefore aimed at determining whether the marketing orientation, i.e. the purchasing behavior of customers, affects the innovation activity of R&D departments that work under the usage of UDI concept.

57 R&D departments operating in Poland participated in the study. Correspondence analysis based on the Burt matrix and Cramer's V correlation coefficients was used for the analysis.

The analysis shows that R&D departments in Poland using marketing research and examining consumer purchasing behavior, positively assess the effects of using the UDI concept in R&D departments. They implement it to create or improve products or services offered on the market, especially in the field of customization, while using information from national research and development units in Poland. The motivation for these activities is mainly to increase the assortment level.

The conducted study covers only R&D departments in Poland, thus it is worth extending the generalization of the results. In terms of future research directions, it's worth to analyze the data from R&D departments in other countries. The results of such studies could be used for comparative analyses. The main limitation of the research is that the research sample was 57 R&D departments of enterprises operating in Poland. Therefore, the research results can't be generalized to all the R&D departments in Poland.

The findings could help researchers and practitioners improve their understanding of the determinants of innovation activity, especially its relationship to marketing orientation and UDI practices.

The research regarding marketing orientation of enterprises and its influence on innovation activity is extremely important due to the general change of the conditions for the functioning of enterprises and building their competitive advantage. Knowledge in this area is still insufficient and research gaps are still being exposed. The article presents the correlation between the marketing orientation and customer behavior within the UDI activity and effects of innovation activity of R&D departments being under investigation.

Cross-laminated timber (CLT) is an innovative construction material that provides a balanced mix of structural stiffness, fabrication flexibility and sustainability. CLT development and innovation diffusion require close collaborations between its supply chain architectural, engineering, construction and manufacturing (AECM) stakeholders. As such, the purpose of this study is to provide a preliminary understanding of the knowledge diffusion and innovation process of CLT construction.

The study implemented a longitudinal social network analysis of the AECM companies involved in 100 CLT projects in the UK. The project data were acquired from an industry publication and decoded in the form of a multimode project-company network, which was projected into a single-mode company collaborative network. This complete network was filtered into a four-phase network to allow the longitudinal analysis of the CLT collaborations over time. A set of network and node social network analysis metrics was used to characterize the topology patters of the network and the centrality of the companies.

The study highlighted the scale-free structure of the CLT collaborative network that depends on the influential hubs of timber manufacturers, engineers and contractors to accelerate the innovation diffusion. However, such CLT supply collaborative network structure is more vulnerable to disruptions due to its dependence on these few prominent hubs. Also, the industry collaborative network’s decreased modularity confirms the maturity of the CLT technology and the formation of cohesive clusters of innovation partners. The macro analysis approach of the study highlighted the critical role of supply chain upstream stakeholders due to their higher centralities in the collaborative network. Stronger collaborations were found between the supply chain upstream stakeholders (timber manufacturers) and downstream stakeholders (architects and main contractors).

The study contributes to the field of industrialized and CLT construction by characterizing the collaborative networks between CLT supply chain stakeholders that are critical to propose governmental policies and industry initiatives to advance this sustainable construction material.

The success of construction projects can be indicated by measuring their performance. For effective project performance (PP), the successful execution of a construction projects is very important. A systematic review of the literature on performance areas and performance assessment models was undertaken. The purpose of this paper is to develop a mathematical formulation for construction PP areas to suit the Indian context by modifying the current project quarterback rating (PQR) model.

Based on the literature, the PQR model has not been validated for suitability in the Indian context. To validate the PQR model and modify the same for the Indian context, a survey instrument was used to collect data on performance areas and a multivariate data analysis technique was carried out to develop a modified model. Delphi technique was used to assign the weights for each performance metric in performance areas.

This study concluded the importance of three additional performance areas, namely, productivity, stakeholder satisfaction and environment for assessing PP for Indian construction projects. It also identified the interrelationship between the performance areas and the PP.

The developed modified PQR model (MPQR) will guide the concerned stakeholders to take corrective actions for improving the performance of construction projects.

The MPQR proposed in this paper covers ten areas and is a comprehensive single score that can be used to benchmark and compare performance over different projects to achieve continuous improvement.

COVID-19 continues to spread, and cause increasing deaths. Physicians diagnose COVID-19 using not only real-time polymerase chain reaction but also the computed tomography (CT) and chest x-ray (CXR) modalities, depending on the stage of infection. However, with so many patients and so few doctors, it has become difficult to keep abreast of the disease. Deep learning models have been developed in order to assist in this respect, and vision transformers are currently state-of-the-art methods, but most techniques currently focus only on one modality (CXR).

This work aims to leverage the benefits of both CT and CXR to improve COVID-19 diagnosis. This paper studies the differences between using convolutional MobileNetV2, ViT DeiT and Swin Transformer models when training from scratch and pretraining on the MedNIST medical dataset rather than the ImageNet dataset of natural images. The comparison is made by reporting six performance metrics, the Scott–Knott Effect Size Difference, Wilcoxon statistical test and the Borda Count method. We also use the Grad-CAM algorithm to study the model's interpretability. Finally, the model's robustness is tested by evaluating it on Gaussian noised images.

Although pretrained MobileNetV2 was the best model in terms of performance, the best model in terms of performance, interpretability, and robustness to noise is the trained from scratch Swin Transformer using the CXR (accuracy = 93.21 per cent) and CT (accuracy = 94.14 per cent) modalities.

Models compared are pretrained on MedNIST and leverage both the CT and CXR modalities.

Food manufacturing supply chain systems are the most relevant wheels of the world economy since they provide essential products supporting daily life. Nevertheless, various supply inefficiencies have been reported to compromise food safety in different regions. Sustainable supplier management and digitalization practices have become cornerstone activities in addressing these shortcomings. Therefore, this paper proposes an integrated method for sustainability management in digital manufacturing supply chain systems (DMSCS) from the food industry.

The Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) was used to weigh the criteria and subcriteria under uncertainty. Second, the Intuitionistic Fuzzy Decision-Making Trial and Evaluation Laboratory (IF-DEMATEL) was applied to determine the main DMSCS sustainability drivers whilst incorporating the expert's hesitancy. Finally, the Combined Compromise Solution (CoCoSo) was implemented to pinpoint the weaknesses hindering DMSCS sustainability. A case study from the pork supply chain was presented to validate this method.

The most important criterion for DMSCS sustainability management is “location” while “manufacturing capacity” is the most significant dispatcher.

This paper presents a novel approach integrating IF-AHP, IF-DEMATEL, and CoCoSo methods for sustainability management of DMSCS pillaring the food industry.

Despite the current progress in realizing how Big Data Analytics can considerably enhance the Sustainable Manufacturing Supply Chain (SMSC), there is a major gap in the storyline relating factors of Big Data operations in managing information and trust among several operations of SMSC. This study attempts to fill this gap by studying the key enablers of using Big Data in SMSC operations obtained from the internet of Things (IoT) devices, group behavior parameters, social networks and ecosystem framework.

Adaptive Prospects (Improving SC performance, combating counterfeits, Productivity, Transparency, Security and Safety, Asset Management and Communication) are the constructs that this research first conceptualizes, defines and then evaluates in studying Big Data Analytics based operations in SMSC considering best worst method (BWM) technique.

To begin, two situations are explored one with Big Data Analytics and the other without are addressed using empirical studies. Second, Big Data deployment in addressing MSC barriers and synergistic role in achieving the goals of SMSC is analyzed. The study identifies lesser encounters of barriers and higher benefits of big data analytics in the SMSC scenario.

The research outcome revealed that to handle operations efficiently a 360-degree view of suppliers, distributors and logistics providers' information and trust is essential.

In the Post-COVID scenario, the supply chain practitioners may use the supply chain partner's data to develop resiliency and achieve sustainability.

The unique value that this study adds to the research is, it links the data, trust and sustainability aspects of the Manufacturing Supply Chain (MSC).

This paper aims to investigate the performance of OECD countries' long-term productivity during the period of 1975–2018.

This study employed different approaches to evaluate how efficiency scores vary with changes in inputs and outputs: Data Envelopment Analysis (CRS, VRS and FDH), TOPSIS and TOPSIS of these scores.

The findings suggest that, during the period of this study, countries with higher freedom of religion and with Presidential democracy regimes are positively associated with higher productivity.

To the best of the authors’ knowledge, this is the first study that uses efficiency models to assess the productivity levels of OECD countries based on several contextual variables that can potentially affect it.

The purpose of this research is to provide a framework in which new data quality dimensions are defined. The new dimensions provide new metrics for the assessment of lecture video indexing. As lecture video indexing involves various steps, the proposed framework containing new dimensions, introduces new integrated approach for evaluating an indexing method or algorithm from the beginning to the end.

The emphasis in this study is on the fifth step of design science research methodology (DSRM), known as evaluation. That is, the methods that are developed in the field of lecture video indexing as an artifact, should be evaluated from different aspects. In this research, nine dimensions of data quality including accuracy, value-added, relevancy, completeness, appropriate amount of data, concise, consistency, interpretability and accessibility have been redefined based on previous studies and nominal group technique (NGT).

The proposed dimensions are implemented as new metrics to evaluate a newly developed lecture video indexing algorithm, LVTIA and numerical values have been obtained based on the proposed definitions for each dimension. In addition, the new dimensions are compared with each other in terms of various aspects. The comparison shows that each dimension that is used for assessing lecture video indexing, is able to reflect a different weakness or strength of an indexing method or algorithm.

Despite development of different methods for indexing lecture videos, the issue of data quality and its various dimensions have not been studied. Since data with low quality can affect the process of scientific lecture video indexing, the issue of data quality in this process requires special attention.

This paper aims to measure and unveil the relationship between customer satisfaction and efficiency levels in the most relevant Brazilian airports.

The authors utilize a two-stage network DEA (data envelopment analysis) and AHP (analytic hierarchy process) model as the cornerstones of the study. The first stage of the network productive structure focuses on examining the infrastructure efficiency of the selected airports, while the second stage assesses their business efficiency.

Although the results indicate that infrastructure and business efficiency levels are heterogeneous and widely dispersed across airports, controlling the regression results with different contextual variables suggests that the impact of efficiency levels on customer satisfaction is mediated by a set of socio-economic and demographic (endogenous) and regulatory (exogenous) variables. Furthermore, encouraging investment in airports is necessary to achieve higher infrastructural efficiency and scale efficiency, thereby improving customer satisfaction.

There is a scarcity of studies examining the relationships among customer satisfaction, privatization and airport efficiency, particularly in developing countries like Brazil.

This paper aims to contribute to existing academic work and business practice by presenting original empirical findings and by providing insights into priority setting on Sustainable Development Goals (SDGs) in organizations. From an academic viewpoint, it not only adds to previous work on the topic of SDG materiality (e.g. Van Tulder and Lucht, 2019) but also aims to contribute new insights into the steps that are crucial and influence the adoption of the SDGs in materiality assessments. It may also add to the literature by providing new knowledge on the strategic considerations that organizations may make and institutional dynamics that encourage organizations to implement the SDG materiality method.

By executing a national survey research in Belgium through a collaboration between academics of Antwerp Management School, Louvain School of Management (UCLouvain) and the University of Antwerp, and supported by Belgium’s Federal Institute of Sustainable Development, the authors have obtained several insights into the SDG landscape in Belgium for various types of organizations, including companies, governmental and nongovernmental organizations and educational institutions. This research builds further on a first national survey (SDG Barometer Belgium, 2018) on the adoption and implementation of the SDGs. However, an important aim of this research is to shift the emphasis to more prominent new elements, such as whether or not organizations use the SDGs in materiality assessments. While the main part of the data for this research were collected through an online questionnaire, document analyses were conducted based on the sustainability reports of BEL 20 companies, the benchmark stock market index of Euronext Brussels consisting of 20 companies traded at the Brussels Stock Exchange, and seven interviews were held to obtain additional insights.

A total of 386 organizations across sectors responded to the question “Does your organization perform a materiality analysis”, of which 210 organizations completed the question “Does your organization align the materiality analysis with the SDGs,”after an “exit route” based on a positive answer to the first question. When diving into the survey results, the authors see that no more than 12% of the 210 organizations performing a materiality analysis align their materiality analysis with the SDGs, while 14% indicate that they do not account for the SDGs at all in their materiality analyses. The results show that 41% of the organizations take into account the SDGs to a certain degree when performing their materiality analysis. Speculating on an explanation for these results, it may be the case that organizations do not yet think about coupling the SDGs to their materiality assessment, experience difficulties in practice or generally lack the knowledge for relating the SDGs to the sustainability topics that are relevant to them. This seems in line with other research (e.g. Van Tulder and Lucht, 2019), as the results of this study indicate that it seems to be difficult for organizations to relate the SDGs to the existing sustainability priorities or materiality analyses of companies.

The real contribution of this paper essentially lies in the description of the Janssen Pharmaceuticals case. The company recognized that today’s internally focused approach to goal setting is not enough to address global challenges. Hence, looking at what is needed externally from a global perspective, taking into account sustainability thresholds and setting ambitions accordingly, is needed to bridge the gap between current performance and required performance. From the Janssen Pharmaceuticals case, the authors learned that external stakeholders are an extremely useful source of information to address the required performance by using the SDG framework. For sure, SDG materiality analyses are still in an early phase of development and knowledge on how to conduct such an analysis may be lacking. Future efforts – or the lack thereof – may indicate whether or not companies consider such analyses as sufficiently relevant. Although the uptake of the SDGs is in progress, it remains to be seen which, if any, materiality method will eventually turn out as a new dominant way of defining material issues. The findings presented in this study hopefully serve as a basis for further investigation of the topic.