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In this article, the author discusses works from the French Documentation Movement in the 1940s and 1950s with regard to how it formulates bibliographic classification systems as documents. Significant writings by Suzanne Briet, Éric de Grolier and Robert Pagès are analyzed in the light of current document-theoretical concepts and discussions.

Conceptual analysis.

The French Documentation Movement provided a rich intellectual environment in the late 1940s and early 1950s, resulting in original works on documents and the ways these may be represented bibliographically. These works display a variety of approaches from object-oriented description to notational concept-synthesis, and definitions of classification systems as isomorph documents at the center of politically informed critique of modern society.

The article brings together historical and conceptual elements in the analysis which have not previously been combined in Library and Information Science literature. In the analysis, the article discusses significant contributions to classification and document theory that hitherto have eluded attention from the wider international Library and Information Science research community. Through this, the article contributes to the currently ongoing conceptual discussion on documents and documentality.

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.

This study aims to reach a perception of the advance of research on deviant organisational behaviours.

This research has been done through the text mining method. By reviewing, the papers were selected 360 papers between 1984 and 2020. Based on the Davis–Boldin index, 11 optimal clusters were gained. Then the roots were ranked in any group, using the Simple Additive Weighting technique. Data were analysed by RapidMiner and MATLAB software.

According to the results obtained, clusters are included leadership styles, job attitudes, spirituality in the workplace, work psychology, personality characteristics, classification and management of deviant workplace behaviours, service and customer orientation, deviation in sales, psychological contracts, group dynamics and inappropriate supervision.

This study provides a landscape and roadmap for future investigation on deviant organisational behaviours.

This paper defines and assesses student learning patterns under the influence of problem-based learning (PBL) and their classification into a reasonable minimum number of classes. Study utilizes PBL implemented in an undergraduate Statistics and Operations Research course for techno-management students at a private university in India.

Study employs an in situ experiment using a conceptual model based on learning theory. The participant's end-of-semester GPA is Performance Indicator. Integrating PBL with classroom teaching is unique instructional approach to this study. An unsupervised and supervised data mining approach to analyse PBL impact establishes research conclusions.

The administration of PBL results in improved learning patterns (above-average) for students with medium attendance. PBL, Gender, Math background, Board and discipline are contributing factors to students' performance in the decision tree. PBL benefits a student of any gender with lower attendance.

This study is limited to course students from one institute and does not consider external factors.

Researchers can apply learning patterns obtained in this paper highlighting PBL impact to study effect of every innovative pedagogical study. Classification of students based on learning behaviours can help facilitators plan remedial actions.

1. Clustering is used to extract student learning patterns considering dynamics of student performances over time. Then decision tree is utilized to elicit a simple process of classifying students. 2. Data mining approach overcomes limitations of statistical techniques to provide knowledge impact in presence of demographic characteristics and student attendance.

This study aims to analyze and synthesize the design features of existing digital transformation maturity models with a developed classification scheme and propose a generic maturity model development wireframe based on design science research.

A systematic literature review is conducted on digital transformation maturity models in peer-reviewed journals, including the Emerald Insight, Science Direct, Scopus, Taylor & Francis and Web of Science databases, which resulted in 21 studies. A concept-centric tabular approach is used to analyze the studies, and intersectional demonstrations are used to synthesize the findings regarding the design features.

The classification scheme derived from the tabular concept-centric approach and iteratively evolved results in three main and 25 subcategories related to the design features. Analysis and synthesis of the studies reveal the granularity of the existing digital transformation maturity models concerning the design features. Furthermore, considering the design features in the classification scheme, a generic maturity model development wireframe is proposed to guide the researchers.

The generic maturity model development wireframe and the classification scheme that represents the design features of existing maturity models guide the researchers for the maturity model development roadmap.

The existing literature review studies do not focus on the design feature of digital transformation maturity models within a systematic literature review perspective. A unique classification scheme derived from the tabular concept-centric approach aims to analyze the granularity level of the existing models. Furthermore, the generic maturity model development wireframe includes the guidelines and recommendations of design science studies and presents a roadmap for maturity model researchers.

Spam emails classification using data mining and machine learning approaches has enticed the researchers' attention duo to its obvious positive impact in protecting internet users. Several features can be used for creating data mining and machine learning based spam classification models. Yet, spammers know that the longer they will use the same set of features for tricking email users the more probably the anti-spam parties might develop tools for combating this kind of annoying email messages. Spammers, so, adapt by continuously reforming the group of features utilized for composing spam emails. For that reason, even though traditional classification methods possess sound classification results, they were ineffective for lifelong classification of spam emails duo to the fact that they might be prone to the so-called “Concept Drift”. In the current study, an enhanced model is proposed for ensuring lifelong spam classification model. For the evaluation purposes, the overall performance of the suggested model is contrasted against various other stream mining classification techniques. The results proved the success of the suggested model as a lifelong spam emails classification method.

Background: This article presents a methodology to categorize scientific publications according to the targets of the 17 Sustainable Development Goals (SDGs) of the United Nations. For the above, a dataset with bibliographic and descriptive attributes of 2,379 articles from 2017 by co-authors affiliated to the Pontificia Universidad Católica de Chile, indexed in the Web of Science and Scopus databases, was used.

Methods: The methodology considered three relevant and consecutive milestones: establishment of the reading level that was applied for each publication record, which considers a proportional amount of information; assignment of one of the 18 categories identified for the analysis of the information, which include the 17 SDGs and the option “unclassified” and one of the 169 subcategories corresponding to the specific goals; and, finally, recording the status of the review process carried out, which allowed control of the progress and quality of the cross-review.

Results: The results show that 58.6% of the articles contribute to a primary target, of these 233 contribute to a secondary target; goals 3, 4, 9, and 11 are the most frequent in the process of assigning SDGs. There is an 81% increase in the use of alphanumeric targets when they are assigned as secondary targets. At the same time, cross-checking is shown to be beneficial when allowing the reclassification of 190 articles to some of the targets. Finally, it is established that levels 2 and 3 enabled better classification, given that the contents considered provide more information; however, it is significant that through level 1, 355 articles were categorized as “unclassified”.

Conclusions: It is concluded that the methodology allows for a conclusive, exhaustive, rigorous, extensive, and varied classification through the different milestones and actions carried out, providing strategic information for decision making and research management in the academy-society relationship.

This study aims to examine the impact of national culture on classification shifting in Eastern European Member States of EU Eastern European countries (EEU) vis-à-vis the Western Member States of EU (WEU). The EEU provides a unique sample to study the quality of financial reporting that the authors measure with classification shifting given that for more than five decades they were following the model of a centrally planned economy, where market-based financial reporting was absent. Yet, the EEU transitioned to a market-based economy and completed its accession to the EU.

This study uses a panel data set of firm year observations from 1996 and 2020 that covers the full transition of EEU. This empirical analysis is based on fixed effects panel regression analysis where the authors report a plethora of identifications.

This study finds classification shifting in the EEU countries since their transition to the market-based economy, though they have no long record of market-based financial reporting. This study also notices that cultural factors are associated with classification shifting across all Member States of the EU. This study further examines the impact of interactions between cultural characteristics and special items and reveal variability between WEU and EEU. As part of the robustness analysis, this study also tests the impact of culture on real earnings management measures for both WEU vs EEU, confirming the variability of the impact of culture on earnings management.

Future research could explore the role of religion differences in WEU vis-à-vis EEU states, as they are also subject to cultural differences.

The findings are important for regulators, external monitors and investors, as they show that cultural factors affect earnings management with some variability across countries in the EU, and they should be acknowledged in policymaking.

The findings show that cultural differences between EEU and the “old” Member States of the EU could explain classification shifting.

To the best of the authors’ knowledge, this is the first study that sheds light on the impact of national culture on classification shifting in EEU of EU vis-à-vis the “old” WEU of EU.

The Accounting Information Systems (AIS) research field emerged around 30 years ago as a subfield of accounting but is at risk to develop further as an isolated discipline. However, given the importance of digitalization and its relevance for accounting, an amalgamation of the parent research field of accounting and the subfield of accounting information systems is pivotal for continuing relevant research that is of high quality. This study empirically investigates the distance between AIS research that is included in accounting literature and AIS research that prevails in dedicated AIS research outlets.

To understand which topics define AIS research, all articles published in the two leading AIS journals since 2000 were analyzed. Based on this topical inventory, all AIS studies that were published in the top 16 accounting journals, also since 2000, are identified and categorized in terms of topic, subtopic and research methodology. Next, AIS studies published in the general accounting field and AIS studies published in the AIS field were compared in terms of topics and research methodology to gain insights into the distance between the two fields.

The coverage of AIS topics in accounting journals is, to no small extent, concentrated around the topics “information disclosure”, “network technologies” and “audit and control”. Other AIS topics remain underrepresented. A possible explanation might be the focus on archival studies in accounting outlets, but other elements might play a role. The findings suggest that there is only a partial overlap between the parent accounting research field and the AIS subfield, in terms of both topic and research methodology diversity. These findings suggest a considerable distance between both fields, which might hold detrimental consequences in the long run, if no corrective actions are taken.

This is the first in-depth investigation of the distance between the AIS research field and its parent field of accounting. This study helped develop an AIS classification scheme, which can be used in other research endeavors. This study creates awareness of the divergence between the general accounting research field and the AIS subfield. Given the latter's relevance to the accounting profession, isolation or deterioration of the AIS research must be avoided. Some actionable suggestions are provided in the paper.

The purpose of this study is to explore to which extent data mining research would be associated with the library and information science (LIS) discipline. This study aims to identify data mining related subject terms and topics in representative LIS scholarly publications.

A large set of bibliographic records over 38,000 was collected from a scholarly database representing the fields of LIS and the data mining, respectively. A multitude of text mining techniques were applied to investigate prevailing subject terms and research topics, such as influential term analysis and Dirichlet multinomial regression topic modeling.

The findings of this study revealed the relationship between the LIS and data mining research domains. Various data mining method terms were observed in recent LIS publications, such as machine learning, artificial intelligence and neural networks. The topic modeling result identified prevailing data mining related research topics in LIS, such as machine learning, deep learning, big data and among others. In addition, this study investigated the trends of popular topics in LIS over time in the recent decade.

This investigation is one of a few studies that empirically investigated the relationships between the LIS and data mining research domains. Multiple text mining techniques were employed to delineate to which extent the two research domains would be associated with each other based on both at the term-level and topic-level analysis. Methodologically, the study identified influential terms in each domain using multiple feature selection indices. In addition, Dirichlet multinomial regression was applied to explore LIS topics in relation to data mining.