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The article investigates the institutional setup of e-lending in public libraries in Denmark, Norway and Sweden. Our point of departure is that e-lending has necessitated new library collaborations between local, regional and national levels, and therefore, institutional e-lending setups have emerged. The study seeks to provide better understanding of how the institutional setups are structured, how governance logics have shaped them and what tensions and dynamics become visible in the key actors’ problematisations of these setups.

The study is situated in the neo-institutional tradition and applies the institutional logics perspective. The research questions are answered by taking a qualitative approach, grounded in an extensive interview study with representatives of libraries, publishers and policy actors in three Scandinavian countries. To provide in-depth insight into e-lending setups, the scope of empirical material is then limited to accounts the central library and policy actors involved in establishing e-lending.

The analysis shows that the e-lending setups are both similar (especially when it comes to financing), and different across Scandinavia, especially when it comes to centralisation and involvement of librarians in this task. The differences are attributed to the influence of different governance logics (question of administrative autonomy, collaboration in the field and existing legal and political frames), and to what extent the digital and market logics are incorporated or rejected in the field.

The study provides new insights into the question of how Scandinavian public libraries face the consequences of the digitalisation of book distribution and consumption by investigating how they organise their e-lending services. This has not been explored before, notably in a comparative perspective.

This study aims to examine foreign direct investment (FDI) factors and develops a rational framework for FDI inflow in Western European countries such as France, Germany, the Netherlands, Switzerland, Belgium and Austria.

Data for this study were collected from the World development indicators (WDI) database from 1995 to 2018. Factors such as economic growth, pollution, trade, domestic capital investment, gross value-added and the financial stability of the country that influence FDI decisions were selected through empirical literature. A framework was developed using interpretable machine learning (IML), decision trees and three-stage least squares simultaneous equation methods for FDI inflow in Western Europe.

The findings of this study show that there is a difference between the most important and trusted factors for FDI inflow. Additionally, this study shows that machine learning (ML) models can perform better than conventional linear regression models.

This research has several limitations. Ideally, classification accuracies should be higher, and the current scope of this research is limited to examining the performance of FDI determinants within Western Europe.

Through this framework, the national government can understand how investors make their capital allocation decisions in their country. The framework developed in this study can help policymakers better understand the rationality of FDI inflows.

An IML framework has not been developed in prior studies to analyze FDI inflows. Additionally, the author demonstrates the applicability of the IML framework for estimating FDI inflows in Western Europe.