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The purpose of this study is to substantiate the matrix approach to digitalization of management objects based on identification of relevant qualitative characteristics of these objects and its dichotomies, which allowing determine the quantity and quality of their main variants, as well as the relationships between them.

Methods of classification and typology are selected as study methods, and binary matrices are used as the tool to determine the main variants of management objects, assign binary codes to it and form codes of more complex management objects on its basis, depending on the content of study tasks.

The main results of study include the classification of organization components; variants for choosing qualitative characteristics of chains components; adjusted content of methodology of qualitative research of management objects; sequences of “up” and “down” digitization of these objects; actual qualitative characteristics of e components of management objects and dichotomies; and variants of forming of ciphers of these objects.

The use of study results allows to reduce the complexity of substantiating and making managerial decisions in organization and supply chains, to structure these decisions by man-agement levels and positions and to reduce costs, time and lost profits for fulfilling orders of end consumers of products and/or services.

The originality of this study is confirmed by the substantiation of choice and use of actual qualitative characteristics of management objects and its dichotomies, which allow obtaining two variants of these objects and assigning them binary codes processed using computer software for management activities.

The tender documents, an essential data source for internet-based logistics tendering platforms, incorporate massive fine-grained data, ranging from information on tenderee, shipping location and shipping items. Automated information extraction in this area is, however, under-researched, making the extraction process a time- and effort-consuming one. For Chinese logistics tender entities, in particular, existing named entity recognition (NER) solutions are mostly unsuitable as they involve domain-specific terminologies and possess different semantic features.

To tackle this problem, a novel lattice long short-term memory (LSTM) model, combining a variant contextual feature representation and a conditional random field (CRF) layer, is proposed in this paper for identifying valuable entities from logistic tender documents. Instead of traditional word embedding, the proposed model uses the pretrained Bidirectional Encoder Representations from Transformers (BERT) model as input to augment the contextual feature representation. Subsequently, with the Lattice-LSTM model, the information of characters and words is effectively utilized to avoid error segmentation.

The proposed model is then verified by the Chinese logistic tender named entity corpus. Moreover, the results suggest that the proposed model excels in the logistics tender corpus over other mainstream NER models. The proposed model underpins the automatic extraction of logistics tender information, enabling logistic companies to perceive the ever-changing market trends and make far-sighted logistic decisions.

(1) A practical model for logistic tender NER is proposed in the manuscript. By employing and fine-tuning BERT into the downstream task with a small amount of data, the experiment results show that the model has a better performance than other existing models. This is the first study, to the best of the authors' knowledge, to extract named entities from Chinese logistic tender documents. (2) A real logistic tender corpus for practical use is constructed and a program of the model for online-processing real logistic tender documents is developed in this work. The authors believe that the model will facilitate logistic companies in converting unstructured documents to structured data and further perceive the ever-changing market trends to make far-sighted logistic decisions.

Critiques of international business (IB) have long pointed to the weaknesses in the understanding of context. This has ignited debate on the understanding of institutions and how they “matter” for IB. Yet how institutions matter ultimately depends on how IB applies institutional theory. It is argued that institutional-based research is dominated by a narrow set of approaches, largely overlooking institutional perspectives that account for institutional diversity. This paper aims to forward the argument that IB research should lend greater attention to comparing the topography of institutional configurations by bringing political economy “back in” to the IB domain.

Using principal components analysis and hierarchical cluster analysis, the authors provide IB with a taxonomy of capitalist institutional diversity which defines the landscape of political economies.

The authors show institutional diversity is characterised by a range of capitalist clusters and configuration arrangements, identifying four clusters with distinct modes of capitalism as well as specifying intra-cluster differences to propose nine varieties of capitalism. This paper allows IB scholars to lend closer attention to the institutional context within which firms operate. If the configurations of institutions “matter” for IB scholarship, then clearly, a quantitative blueprint to assess institutional diversity remains central to the momentum of such “institutional turn.”

This paper provides a comprehensive survey of institutional theory, serving as a valuable resource for the application of context within international business. Further, our taxonomy allows international business scholars to utilise a robust framework to examine the diverse institutional context within which firms operate, whilst extending to support the analysis of broader socioeconomic outcomes. This taxonomy therefore allows international business scholars to utilise a robust framework to examine the institutional context within which firms operate.

Nearly 75% of EU buildings are not energy-efficient enough to meet the international climate goals, which triggers the need to develop sustainable construction techniques with high degree of resilience against climate change. In this context, a promising construction technique is represented by ventilated façades (VFs). This paper aims to propose three different VFs and the authors define a novel machine learning-based approach to evaluate and predict their energy performance under different boundary conditions, without the need for expensive on-site experimentations

The approach is based on the use of machine learning algorithms for the evaluation of different VF configurations and allows for the prediction of the temperatures in the cavities and of the heat fluxes. The authors trained different regression algorithms and obtained low prediction errors, in particular for temperatures. The authors used such models to simulate the thermo-physical behavior of the VFs and determined the most energy-efficient design variant.

The authors found that regression trees allow for an accurate simulation of the thermal behavior of VFs. The authors also studied feature weights to determine the most relevant thermo-physical parameters. Finally, the authors determined the best design variant and the optimal air velocity in the cavity.

This study is unique in four main aspects: the thermo-dynamic analysis is performed under different thermal masses, positions of the cavity and geometries; the VFs are mated with a controlled ventilation system, used to parameterize the thermodynamic behavior under stepwise variations of the air inflow; temperatures and heat fluxes are predicted through machine learning models; the best configuration is determined through simulations, with no onerous in situ experimentations needed.

This study aims to provide insight into Library and Information Science (LIS) research in India using scientometric approaches. Web of Science (WoS) and SCOPUS databases were used for data retrieval. The study examines productivity in terms of source types, gender distribution, document formats, authorship and other factors. In addition, this study sought to identify trends or patterns in the research preferences of LIS scientists through text analysis.

Data were downloaded from the WoS and Scopus databases over 22 years and analysed using VOSviewer, Orange, Biblioshiny and CRExplorer softwares.

The findings reveal that 5,692 out of the 9,384 documents in both databases underwent the final examination. In total, 466 different sources produced all of those papers. Author analysis revealed that 6,603 different authors authored 5,692 documents. There were 4,209 male and 1,063 female authors. Furthermore, India shares maximum collaborations with the USA and England. The spectrogram features nine significant peaks corresponding to Lotka’s, Bradford’s and similar laws. Text analysis revealed that Indian LIS researchers have consistently investigated open access and digital or open libraries.

The findings of this study will provide readers with a better understanding of India’s contribution to LIS. In addition, the study will help academics identify research gaps and undiscovered areas in the Indian context that require further investigation.

Not many studies highlight Indian research trends and international collaboration in LIS. This study highlights research trends, collaboration and gender productivity in LIS. The most cited references and trending topics were also identified using reference publication year spectroscopy and text analysis techniques.

The purpose of this work is to explore the new possibilities enabled by the recent introduction of RDF-star, an extension that allows for statements about statements within the Resource Description Framework (RDF). Alongside Named Graphs, this approach offers opportunities to leverage a meta-level for data modeling and data applications.

In this extended paper, the authors build onto three modeling use cases published in a previous paper: (1) provide provenance information, (2) maintain backwards compatibility for existing models, and (3) reduce the complexity of a data model. The authors present two scenarios where they implement the use of the meta-level to extend a data model with meta-information.

The authors present three abstract patterns for actively using the meta-level in data modeling. The authors showcase the implementation of the meta-level through two scenarios from our research project: (1) the authors introduce a workflow for triple annotation that uses the meta-level to enable users to comment on individual statements, such as for reporting errors or adding supplementary information. (2) The authors demonstrate how adding meta-information to a data model can accommodate highly specialized data while maintaining the simplicity of the underlying model.

Through the formulation of data modeling patterns with RDF-star and the demonstration of their application in two scenarios, the authors advocate for data modelers to embrace the meta-level.

With RDF-star being a very new extension to RDF, to the best of the authors’ knowledge, they are among the first to relate it to other meta-level approaches and demonstrate its application in real-world scenarios.

This study aims to help develop “business principles for stakeholder capitalism” in two steps. First, the study defines internal logic of three theories of capitalism and two variants within each theory. Second, it examines approaches to integration into modern democratic capitalism. Treating the three theories as substitutes identifies relative strengths and weaknesses; complementarity and partial overlap approaches to integration study the institutional settings within which stakeholder capitalism operates. Empirical outcomes reflect competition between market and stakeholder businesses for participants, with institutional conditions determining the scope of collective action.

The approach aligns three typologies in a unique conceptual arrangement defining the three theories of capitalism: forms of capitalism, potential failures of each form and associated types of goods. The first method examines the internal logic of each theory of capitalism. The second draws on traditional narrative review of references documenting each theory of capitalism and variants together with modern Marxist anti-capitalism.

Three typologies align uniquely with the theories of capitalism, each having two variants. Both variants of stakeholder capitalism are compatible with compassionate capitalism, constitutional government or polycentric governance but not with self-interest capitalism, dictatorship or Marxism. A theory of modern democratic capitalism allocates roles for private, club and social goods with empirically variable mixes occurring across countries. Competition among different types of enterprises provides an empirical test for comparative advantages of stakeholder capitalism. Future research should consider approaches for testing the proposed conceptual scheme in practice concerning capacity to deal with grand challenges, wicked problems and black swan events.

Research approach is limited to logical examination of theories and literature documentation without direct empirical confirmation. The study does not address practical implications for managers and public officials or social implications concerning private incentives, stakeholder cooperation or collective action.

Originality lies in shifting terms of debate about stakeholder capitalism from advocacy of substitute theories to understanding of its relationship to market capitalism and collective action capitalism. Value lies in explaining desirability of theoretical integration of three types of capitalism into a comprehensive framework for modern democratic capitalism.

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

When improving business processes, process analysts can use data-driven methods, such as process mining, to identify improvement opportunities. However, despite being supported by data, process analysts decide which changes to implement. Analysts often use process visualisations to assess and determine which changes to pursue. This paper helps explore how process mining visualisations can aid process analysts in their work to identify, prioritise and communicate business process improvement opportunities.

The study follows the design science methodology to create and evaluate an artefact for visualising identified improvement opportunities (IRVIN).

A set of principles to facilitate the visualisation of process mining outputs for analysts to work with improvement opportunities was suggested. Particularly, insights into identifying, prioritising and communicating process improvement opportunities from visual representation are outlined.

Prior work focuses on visualisation from the perspectives – among others – of process exploration, process comparison and performance analysis. This study, however, considers process mining visualisation that aids in analysing process improvement opportunities.

The PICCH research project contributes to opening a dialogue between cultural heritage archives and users. Hence, the users are identified and their information needs, the search strategies they apply and the search challenges they experience are uncovered.

A combination of questionnaires and interviews is used for collection of data. Questionnaire data were collected from users of three different audiovisual archives. Semi-structured interviews were conducted with two user groups: (1) scholars searching information for research projects and (2) archivists who perform their own scholarly work and search information on behalf of others.

The questionnaire results show that the archive users mainly have an academic background. Hence, scholars and archivists constitute the target group for in-depth interviews. The interviews reveal that their information needs are multi-faceted and match the information need typology by Ingwersen. The scholars mainly apply collection-specific search strategies but have in common primarily doing keyword searching, which they typically plan in advance. The archivists do less planning owing to their knowledge of the collections. All interviewees demonstrate domain knowledge, archival intelligence and artefactual literacy in their use and mastering of the archives. The search challenges they experience can be characterised as search system complexity challenges, material challenges and metadata challenges.

The paper provides a rare insight into the complexity of the search situation of cultural heritage archives, and the users’ multi-facetted information needs and hence contributes to the dialogue between the archives and the users.