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Numerous design methods are available to facilitate digital innovation processes in user interface design. Nonetheless, little guidance exists on their appropriate selection within the design process based on specific situations. Consequently, design novices with limited design knowledge face challenges when determining suitable methods. Thus, this paper aims to support design novices by guiding the situational selection of design methods.

Our research approach includes two phases: i) we adopted a taxonomy development method to identify dimensions of design methods by reviewing 292 potential design methods and interviewing 15 experts; ii) we conducted focus groups with 25 design novices and applied fuzzy-set qualitative comparative analysis to describe the relations between the taxonomy's dimensions.

We developed a novel taxonomy that presents a comprehensive overview of design conditions and their associated design methods in innovation processes. Thus, the taxonomy enables design novices to navigate the complexities of design methods needed to design digital innovation. We also identify configurations of these conditions that support the situational selections of design methods in digital innovation processes of user interface design.

The study’s contribution to the literature lies in the identification of both similarities and differences among design methods, as well as the investigation of sufficient condition configurations within the digital innovation processes of user interface design. The taxonomy helps design novices to navigate the design space by providing an overview of design conditions and the associations between methods and these conditions. By using the developed taxonomy, design novices can narrow down their options when selecting design methods for their specific situations.

The purpose of this study is to provide a systematic literature review on taxonomy alignment methods in information science to explore the common research pipeline and characteristics.

The authors implement a five-step systematic literature review process relating to taxonomy alignment. They take on a knowledge organization system (KOS) perspective, and specifically examining the level of KOS on “taxonomies.”

They synthesize the matching dimensions of 28 taxonomy alignment studies in terms of the taxonomy input, approach and output. In the input dimension, they develop three characteristics: tree shapes, variable names and symmetry; for approach: methodology, unit of matching, comparison type and relation type; for output: the number of merged solutions and whether original taxonomies are preserved in the solutions.

The main research implications of this study are threefold: (1) to enhance the understanding of the characteristics of a taxonomy alignment work; (2) to provide a novel categorization of taxonomy alignment approaches into natural language processing approach, logic-based approach and heuristic-based approach; (3) to provide a methodological guideline on the must-include characteristics for future taxonomy alignment research.

There is no existing comprehensive review on the alignment of “taxonomies”. Further, no other mapping survey research has discussed the comparison from a KOS perspective. Using a KOS lens is critical in understanding the broader picture of what other similar systems of organizations are, and enables us to define taxonomies more precisely.

The purpose of this paper is to review the existing literature on positioning strategies, categorise them as typologies and taxonomies and propose generic positioning strategies for organisations from a theoretical viewpoint.

Typologies and taxonomies are defined and characterised, and then all product or brand positioning strategies are examined. Articles published in reputable marketing and strategic marketing journals from 1969 to 2022 are analysed for this purpose. The analysis was done using qualitative text mining: classification, coding and text analysis.

The review enables the identification of three generic positioning strategies widely accepted in the literature, as well as the distinction between conceptually derived positioning strategies (typology) and empirically derived positioning strategies( taxonomy).

This study provides a comprehensive overview for researchers who wish to get broad-picture research on generic classifications in positioning strategy. Moreover, most notably for academics, to the best of the author’s knowledge, this is the first study to classify positioning strategies into typologies and taxonomies based on their evolution.

Knowledge of positioning typologies and taxonomies can assist managers in developing and implementing a strategy that allows their company to maximise the potential of its product/brand and achieve better results. The literature review contributes to theory development and helps companies understand their positioning strategies.

Despite considerable interest in positioning research, little effort has been made to examine positioning strategies’ current or future development. Some authors use the term taxonomy to describe their conceptually derived classification of positioning strategies, and it was discovered that authors frequently interchangeably use the terms typologies or taxonomies. When attempting to understand and compare the various classifications, this liberal use of the term’s typology and taxonomy creates misunderstanding and confusion. This paper fills that void.

This paper aims to establish a fundamental and comprehensive understanding of non-fungible tokens (NFTs) by identifying and structuring common characteristics within a taxonomy. NFTs are hyped and increasingly marketed as essential building blocks of the Metaverse. However, the dynamic evolution of the NFT space has posed challenges for those seeking to develop a deep and comprehensive understanding of NFTs, their features and their capabilities.

Utilizing common guidelines for the creation of taxonomies, the authors developed (over 3 iterations), a multi-layer taxonomy based on workshops and interviews with 11 academic and 15 industry experts. Through an evaluation of 25 NFTs, the authors demonstrate the usefulness of the taxonomy.

The taxonomy has 4 layers, 14 dimensions and 42 characteristics, which describe NFTs in terms of reference object, token properties, token distribution and realizable value.

The authors' framework is the first to systematically cover the emerging NFT phenomenon. This framework is concise yet extendible and presents many avenues for future research in a plethora of disciplines. The characteristics identified in the authors' taxonomy are useful for NFT- and Metaverse-related research in finance, marketing, law and information systems. Additionally, the taxonomy can serve as an information source for policymakers as they consider NFT regulation.

Several frameworks have been developed to map and document scientific societal interaction and impact, each reflecting the specific forms of impact and interaction that characterize different academic fields. The ReAct taxonomy was developed to register data about “productive interactions” and provide an overview of research activities within the social sciences and humanities (SSH). The purpose of the present research is to examine whether the SSH-oriented taxonomy is relevant to the science, technology, engineering and mathematics (STEM) disciplines when clarifying societal interactions and impact, and whether the taxonomy adds value to the traditional STEM impact indicators such as citation scores and H-index.

The research question was investigated through qualitative interviews with nine STEM researchers. During the interviews, the ReAct taxonomy and visual research profiles based on the ReAct categories were used to encourage and ensure in-depth discussions. The visual research profiles were based on publicly available material on the research activities of the interviewees.

The study provided an insight into how STEM researchers assessed the importance of mapping societal interactions as a background for describing research impact, including which indicators are useful for expressing societal relevance and impact. With regard to the differences between STEM and SSH, the study identified a high degree of cohesion and uniformity in the importance of indicators. Differences were more closely related to the purpose of mapping and impact assessment than between scientific fields. The importance of amalgamation and synergy between academic and societal activities was also emphasised and clarified.

The findings highlight the importance of mapping societal activities and impact, and that societal indicators should be seen as inspiring guidelines depending on purpose and use. A significant contribution is the identification of both uniformity and diversity between the main fields of SSH and STEM, as well as the connection between the choice of indicators and the purpose of mapping, e.g. for impact measurement, profiling, or career development.

The work sheds light on STEM researchers' views on research mapping, visualisation and impact assessment, including similarities and differences between STEM and SSH research.

This study aimed to develop a taxonomy of research areas in open government data (OGD) through a bibliometric mapping tool and a qualitative analysis software.

In this study, the authors extracted metadata of 442 documents from a bibliographic database. The authors used a bibliometric mapping tool for familiarization with the literature. After that, the authors used qualitative analysis software to develop taxonomy.

This paper developed taxonomy of OGD with three research areas: implementation and management, architecture, users and utilization. These research areas are further analyzed into seven topics and twenty-eight subtopics. The present study extends Charalabidis et al. (2016) taxonomy by adding two research topics, namely the adoption factors and barriers of OGD implementations and OGD ecosystems. Also, the authors include artificial intelligence in the taxonomy as an emerging research interest in the literature. The authors suggest four directions for future research: indigenous knowledge in open data, open data at local governments, development of OGD-specific theories and user studies in certain research themes.

Early career researchers and doctoral students can use the taxonomy to familiarize themselves with the literature. Also, established researchers can use the proposed taxonomy to inform future research. Taxonomy-building procedures in this study are applicable to other fields.

This study developed a novel taxonomy of research areas in OGD. Taxonomy building is significant because there is insufficient taxonomy of research areas in this discipline. Also, conceptual knowledge through taxonomy creation is a basis for theorizing and theory-building for future studies.

The reuse of timber building parts, when designing new buildings, has become a topic of increasing discussion as a proposed circular solution in support of sustainable development goals. Designers face the difficulty of identifying and applying different design strategies for reuse due to multiple definitions, which are used interchangeably. The purpose of this study is to propose a taxonomy to define the relationships between various concepts and practices that comprise the relevant strategies for reuse, notably design for disassembly (DfD) and design for adaptability (DfA).

Literature reviews were conducted based on research publications over the previous 12 years and located through the Web of Science and Scopus.

A taxonomy for the design process grounded on two strategies for reuse is presented: DfD and DfA. Based on previous work, the taxonomy aims to build a vocabulary of definitions in DfD and DfA to support other researchers and practitioners working in the field.

The research is limited to the design phase of timber-based buildings. It does not take into account the other phases of the construction process, neither other kind of construction methods.

The application of the taxonomy can facilitate communication between different actors and provide a way for building product manufacturers to demonstrate their reuse credentials, enabling them to produce and promote compliant products and thereby support design for reuse strategies.

This paper could contribute to a closer collaboration of all stakeholders involved in the building process since the very early phases of the conceptual design.

This paper contributes a comprehensive taxonomy to support the deployment of circular reuse strategies and assist designers and other stakeholders from the earliest of phases in the building’s life cycle. The proposed definition framework provided by the taxonomy resolves the longstanding lack of a supporting vocabulary for reuse and can be used as a reference for researchers and practitioners working with the DfD and DfA.

This paper aims to elaborate on current research on fourth party logistics “4PL” by offering a taxonomy that provides a deeper understanding of 4PL service offerings, thus drawing clear frontiers between existing 4PL business models.

The authors collected data using semi-structured interviews conducted with 60 logistics executives working in 44 “4PL” providers located in France. Using automatic analysis of textual data, the authors combined spatial visualisation, clustering analysis and hierarchical descending classification to generate the taxonomy.

Two key dimensions emerged, allowing the authors to clearly identify and distinguish four 4PL business models: the level of reliance on interpersonal relationships and the level of involvement in 4PL service offering. As a result, 4PL providers fall under one of the following business models in the taxonomy: (1) The Metronome, (2) The Architect, (3) The Nostalgic and (4) The Minimalist.

The study focuses on investigating 4PL providers located in France; thus, future studies should explore the classification of 4PL business models across different cultural contexts and social structures.

The findings offer valuable managerial insights for logistics executives and clients of 4PL to better orient their needs, the negotiations and the contracting process with 4PLs.

Using a Lexicometric analysis, the authors develop taxonomy of 4PL service providers based on empirical evidence from logistics executives; the work addresses the existing confusion regarding the conceptualisation of 4PL firms with other types of logistical providers and the role of in/formal interpersonal relationships in the logistical intermediation.

The purpose of this study is to develop a domain independent, cost-effective, time-saving and semi-automated ontology generation framework that could extract taxonomic concepts from unstructured text corpus. In the human disease domain, ontologies are found to be extremely useful for managing the diversity of technical expressions in favour of information retrieval objectives. The boundaries of these domains are expanding so fast that it is essential to continuously develop new ontologies or upgrade available ones.

This paper proposes a semi-automated approach that extracts entities/relations via text mining of scientific publications. Text mining-based ontology (TmbOnt)-named code is generated to assist a user in capturing, processing and establishing ontology elements. This code takes a pile of unstructured text files as input and projects them into high-valued entities or relations as output. As a semi-automated approach, a user supervises the process, filters meaningful predecessor/successor phrases and finalizes the demanded ontology-taxonomy. To verify the practical capabilities of the scheme, a case study was performed to drive glaucoma ontology-taxonomy. For this purpose, text files containing 10,000 records were collected from PubMed.

The proposed approach processed over 3.8 million tokenized terms of those records and yielded the resultant glaucoma ontology-taxonomy. Compared with two famous disease ontologies, TmbOnt-driven taxonomy demonstrated a 60%–100% coverage ratio against famous medical thesauruses and ontology taxonomies, such as Human Disease Ontology, Medical Subject Headings and National Cancer Institute Thesaurus, with an average of 70% additional terms recommended for ontology development.

According to the literature, the proposed scheme demonstrated novel capability in expanding the ontology-taxonomy structure with a semi-automated text mining approach, aiming for future fully-automated approaches.

This paper aims to demonstrate how skills taxonomies can be used in combination with machine learning to integrate diverse online datasets and reveal skills gaps. The purpose of this study is then to show how the skills gaps revealed by the integrated datasets can be used to achieve better labour market alignment, keep educational offerings up to date and assist graduates to communicate the value of their qualifications.

Using the ESCO taxonomy and natural language processing, this study captures skills data from three types of online data (job ads, course descriptions and resumes), allowing us to compare demand for skills and supply of skills for three different occupations.

This study illustrates three practical applications for the integrated data, showing how they can be used to help workers who are disrupted by technology to identify alternative career pathways, assist educators to identify gaps in their course offerings and support students to communicate the value of their training to employers.

This study builds upon existing applications of machine learning (detecting skills from a single dataset) by using the skills taxonomy to integrate three datasets. This study shows how these complementary, big datasets can be integrated to support greater alignment between the needs and offerings of educators, employers and job seekers.