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This study aims to examine decision factors for global sourcing, differentiated into transcontinental and continental sourcing to obtain insight into locational aspects of sourcing decisions and global trends. This study analyzed various country perceptions to reveal their influence on sourcing decisions. The country of origin (COO) theory explains why certain country perceptions and images influence purchasing experts in their selection of suppliers.

This study used a two-study approach. In Study 1, the authors conducted discrete choice card experiments with 71 purchasing experts located in Europe and the USA to examine the importance of essential decision factors for global sourcing. Given the clear evidence that location is a factor in sourcing decisions, in Study 2 the authors investigated purchasers’ perceptions and images of countries, adding country ranking experiments on various perceived characteristics such as quality, price and technology.

Study 1 provides evidence that the purchasers’ personal relationship with the supplier plays a decisive role in the supplier selection process. While product quality and location impact sourcing decisions, the attraction of the buying company and cultural barriers are less significant. Interestingly, however, these factors seem as important as price to respondents. This implies that a strong relationship with suppliers and good quality products are essential aspects of a reliable and robust supply chain in the post-COVID-19 era. Examining the locational aspect in detail, Study 2 linked the choice card experiments with country ranking experiments. In this study, the authors found that purchasing experts consider that transcontinental countries such as Japan and China offer significant advantages in terms of price and technology. China has enhanced its quality, which is recognizable in the country ranking experiments. Therefore, decisions on global sourcing are not just based on such high-impact factors as price and availability; country perceptions are also influential. Additionally, the significance of the locational aspect could be linked to certain country images of transcontinental suppliers, as the COO theory describes.

The new approach divides global sourcing into transcontinental and European sourcing to evaluate special decision factors and link these factors to the locational aspect of sourcing decisions. To deepen the clear evidence for the locational aspect and investigate the possible influence of country perceptions, the authors applied the COO theory. This approach enabled authors to show the strong influence of country perception on purchasing departments, which is represented by the locational effect. Hence, the success of transcontinental countries relies not only on factors such as their availability but also on the purchasers’ positive perceptions of these countries in terms of technology and price.

A maturity model for Industry 4.0 (I4.0 MM) with influencing factors is designed to address maturity issues in adopting Industry 4.0. Standardisation in I4.0 supports manufacturing industry transformation, forming reference architecture models (RAMs). This paper aligns key factors and maturity levels in I4.0 MMs with reputable I4.0 RAMs to enhance strategy for I4.0 transformation and implementation.

Three steps of alignment consist of the systematic literature review (SLR) method to study the current published high-quality I4.0 MMs, the taxonomy development of I4.0 influencing factors by adapting and implementing the categorisation of system theories and aligning I4.0 MMs with RAMs.

The study discovered that different I4.0 MMs lead to varied organisational interpretations. Challenges and insights arise when aligning I4.0 MMs with RAMs. Aligning MM levels with RAM stages is a crucial milestone in the journey toward I4.0 transformation. Evidence indicates that I4.0 MMs and RAMs often overlook the cultural domain.

Findings contribute to the literature on aligning capabilities with implementation strategies while employing I4.0 MMs and RAMs. We use five RAMs (RAMI4.0, NIST-SME, IMSA, IVRA and IIRA), and as a common limitation in SLR, there could be a subjective bias in reading and selecting literature.

To fully leverage the capabilities of RAMs as part of the I4.0 implementation strategy, companies should initiate the process by undertaking a thorough needs assessment using I4.0 MMs.

The novelty of this paper lies in being the first to examine the alignment of I4.0 MMs with established RAMs. It offers valuable insights for improving I4.0 implementation strategies, especially for companies using both MMs and RAMs in their transformation efforts.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.