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This exploratory study aims to help contribute to the understanding of online information search behaviour of PhD students from different humanities fields, with a focus on subject searching.

The methodology is based on a semi-structured interview within which the participants are asked to conduct both a controlled search task and a free search task. The sample comprises eight PhD students in several humanities disciplines at Linnaeus University, a medium-sized Swedish university from 2020.

Most humanities PhD students in the study have received training in information searching, but it has been too basic. Most rely on web search engines like Google and Google Scholar for publications' search, and university's discovery system for known-item searching. As these systems do not rely on controlled vocabularies, the participants often struggle with too many retrieved documents that are not relevant. Most only rarely or never use disciplinary bibliographic databases. The controlled search task has shown some benefits of using controlled vocabularies in the disciplinary databases, but incomplete synonym or concept coverage as well as user unfriendly search interface present hindrances.

The paper illuminates an often-forgotten but pervasive challenge of subject searching, especially for humanities researchers. It demonstrates difficulties and shows how most PhD students have missed finding an important resource in their research. It calls for the need to reconsider training in information searching and the need to make use of controlled vocabularies implemented in various search systems with usable search and browse user interfaces.

This paper aims to compare the nature of three different business and management education (BME) research streams (online/blended learning, entrepreneurship education and experiential learning), along with their citation sources to draw insights on their support and legitimacy bases, with lessons on improving such support and legitimacy for the streams and the wider BME research field.

The authors analyze the nature of three BME research streams and their citation sources through tests of differences across streams.

The three streams differ in research foci and approaches such as the use of managerial samples in experiential learning, quantitative studies in online/blended education and literature reviews in entrepreneurship education. They also differ in sources of legitimacy recognition and avenues for mobilization of support. The underlying literature development pattern of the experiential learning stream indicates a need for BME scholars to identify and build on each other’s work.

Identification of different research bases and key supporting literature in the different streams shows important core articles that are useful to build research in each stream.

Readers will understand the different research bases supporting the three research streams, along with their targeted audience and practice implications.

The discovery of different support bases for the three different streams helps identify the network of authors and relationships that have been built in each stream.

According to the authors’ knowledge, this paper is the first to uncover differences in nature and citation sources of the three continuously growing BME research streams with recommendations on ways to improve the support of the three streams.

Predatory publishing is a growing and global issue infecting all scientific domains. Predatory publishers create counterfeit, not (properly) peer-reviewed journals to exploit the open access (OA) model in which the author pays. The plethora of predatory marketing journals along with the sophisticated deceptive practices of their publishers may create total confusion. One of the many highly likely risks of that bewilderment is when peer-reviewed, prestigious marketing journals cite these pseudo-marketing journals. This phenomenon is called citation contamination. This study aims to investigate the extent of citation contamination in the peer-reviewed marketing literature.

Using Google Scholar as a citation gathering tool, this study investigates references to four predatory marketing journals in 68 peer-reviewed marketing journals listed in the 2018 version of the Academic Journal Guide by the Chartered Association of Business Schools (CABSs).

Results indicate that 59 of the 68 CABS-ranked peer-reviewed marketing journals were, up to late January 2021, contaminated by at least one of the four sampled predatory journals. Together, these four pseudo-journals received (at least) 605 citations. Findings from nonparametric statistical procedures show that citation contamination occurred irrespective of the age of a journal or its 2019 Journal Impact Factor (JIF). They also point out that citation contamination happened independently from the fact that a journal is recognized by Clarivate Analytics or not.

This study investigated citations to only four predatory marketing journals in only 68 CABS-listed peer-reviewed marketing journals.

These findings should sound an alarm to the entire marketing community (including academics and practitioners). To counteract citation contamination, recommendations are provided for researchers, practitioners, journal editors and academic and professional associations.

This study is the first to offer a systematic assessment of references to predatory journals in the peer-reviewed marketing literature.

The purpose of this paper is to critically analyze the influence of the algorithm used on scholarly search engines (Garfield’s algorithm) and propose metrics to improve it so that science could be based on a more democratic way.

This paper used a snow-ball approach to collect data that allowed identifying the history and the logic behind the Garfield’s algorithm. It follows on excerpting the foundation of existing algorithm and databases of major scholarly search engine. It concluded proposing new metrics so as to surpass restraints and to democratize the scientific discourse.

This paper finds that the studied algorithm currently biases the scientific discourse toward a narrow perspective, while it should take into consideration several researchers’ characteristics. It proposes the substitution of the h-index by the number of times the scholar’s most cited work has been cited. Finally, it proposes that works in languages different than English should be included.

The broad comprehension of any phenomena should be based on multiple perspectives; therefore, the inclusion of diverse metrics will extend the scientific discourse.

The improvement of the existing algorithm will increase the chances of contact among different cultures, which stimulate rapid progress on the development of knowledge.

The value of this paper resides in demonstrating that the algorithm used in scholarly search engines biases the development of science. If updated as proposed here, science will be unbiased and bias aware.

Identifying the frontiers of a specific research field is one of the most basic tasks in bibliometrics and research published in leading conferences is crucial to the data mining research community, whereas few research studies have focused on it. The purpose of this study is to detect the intellectual structure of data mining based on conference papers.

This study takes the authoritative conference papers of the ranking 9 in the data mining field provided by Google Scholar Metrics as a sample. According to paper amount, this paper first detects the annual situation of the published documents and the distribution of the published conferences. Furthermore, from the research perspective of keywords, CiteSpace was used to dig into the conference papers to identify the frontiers of data mining, which focus on keywords term frequency, keywords betweenness centrality, keywords clustering and burst keywords.

Research showed that the research heat of data mining had experienced a linear upward trend during 2007 and 2016. The frontier identification based on the conference papers showed that there were five research hotspots in data mining, including clustering, classification, recommendation, social network analysis and community detection. The research contents embodied in the conference papers were also very rich.

This study detected the research frontier from leading data mining conference papers. Based on the keyword co-occurrence network, from four dimensions of keyword term frequency, betweeness centrality, clustering analysis and burst analysis, this paper identified and analyzed the research frontiers of data mining discipline from 2007 to 2016.

The purpose of this paper is to conduct systematic reviews on Indonesian papers, to examine the most recent evidence of the efficacy of the combination of cognitive training and physical exercise, and to make recommendations in order to improve prevention, care and treatment services in elderly patients with mild cognitive impairment (MCI).

The databases of Cochrane, Medline, NIH (US National Library Medicine), ProQuest, EbscoHost, Clinical Key, EMBASE, Medical Librarian (TWE) in Ovid, Science Direct, Scopus, The Lancet Global Health, PubMed, Emerald, Indonesian National Library, Google Scholar, Google Indonesia, and Garuda Portal were systematically searched using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to obtain empirical papers published between June 1976 and January 2018.

Out of the 3,293 articles collected, 10 were included in this analysis. The result of this combined meta-analysis compares the combination therapy group (cognitive therapy and physical exercise) with a control group. It shows that the control group was likely to experience MCI 1.65 times more often than the combination therapy group. According to the result acquired from the synthesized meta-analysis, the control group experienced MCI 1.65 times higher than the combination therapy. The finding is proven to be statistically significant (95% CI= 1.42–1.93).

The research considers only English and Indonesian articles.

It is important to explore the most effective training characteristics in a special combined intervention differentiated by the duration, frequency, intervention, type and combination mode. There is a need for further investigation that focuses on the physiological mechanisms underlying the positive effects, by inserting a more comprehensive neuro-imaging measurement to assess specifically the domain that benefits in terms of cognitive functions and molecular markers. Finally, exploratory studies are definitely required, which will specifically examine maintenance and treatment effects as well as derive theoretical explanations related to the interventions and predictors.

A combination of cognitive training and physical exercise intervention may improve the global health or cognitive functions.

A combination of cognitive training and physical exercise has been found to improve prevention, care and treatment services in elderly patients with MCI. There is an increase in value in comparison to the study of Karssemeijer, which considered five Indonesian articles.

The innovation of business model (BM) is a strategic process for many firms, from which depends competitiveness and sustainability. Despite its theoretical relevance in management sciences, research on business model innovation is in its infancy and lacks of research consistency and theoretical connections to the theme of “performance”. With the aim to contribute in bridging this gap, this paper aims to identify and analyse drivers of business model innovation performance.

This research is based on an integrative literature review methodology.

BMI performance drivers are conditions related to various dimensions (i.e. processes, resources, market, BM structure, etc). that, when fulfilled, allow the BMI to have higher performance. BMI performance drivers are antecedents of BMI performance, and their identification is of both theoretical and practical value. The authors find and report a set of 35 BMI performance drivers.

The value of this research is both theoretical and practical. From a theoretical point of view, the identified “Business Model Innovation performance drivers” define and identify a variable of BMI performance, from a practical perspective, and they provide a comprehensive set of key conditions whose attainment should be planned, pursued and monitored by managers.

This paper aims to unpack the sets of policy ideas underpinning the use of social innovation, thus permeating the allegedly politically neutral language of the concept.

Drawing on Daigneualt (2014), this paper adapts a four-dimensional approach to investigate the sets of ideas underpinning different conceptualisations of social innovation, particularly in relation to who the actors driving social change are, the nature of the problems addressed, the objectives pursued and the means used to achieve these objectives.

Applying the four-dimensional approach to a corpus of literature, this paper found evidence of two different perspectives along each dimension, namely, a radical empowerment approach and an incremental market-oriented one.

A limitation of the study is the focus on academic literature, whereas a broader focus on policy discourse may give further insights. However, this paper argues that this study can be the ground for future research to investigate whether and how the two approaches identified have been adopted in different institutional and policy contexts.

The paper contributes to the development of social innovation research by boosting and encouraging further investigation on how different sets of ideas underpin social innovation discourse and its use as a policy concept.

Through a comprehensive review of the literature on open innovation (OI), this study aimed to achieve two objectives: (1) to identify the main thematic areas discussed in the past and track their evolution over time; and (2) to provide recommendations for future research avenues.

To achieve the first objective, a method based on text mining was implemented, with the analysis focusing on 1,772 journal articles published between 2003 and 2018. For the second objective, a review based on recent and relevant papers was conducted for each thematic area.

The paper identified nine thematic areas explored in existing research: (1) context-dependency of OI, (2) collaborative frameworks, (3) organizational dimensions of OI, (4) performance and OI, (5) external search for OI, (6) OI in small and medium-sized enterprises, (7) OI in the pharmaceutical industry, (8) OI and intellectual property rights, and (9) technology. The analysis of the most recent papers belonging to the more investigated areas offers suitable suggestions for future research avenues.

To the best of the authors’ knowledge, no review has yet been undertaken to reorganize the OI literature.

Several online services offer functionalities to access information from “big research graphs” (e.g. Google Scholar, OpenAIRE, Microsoft Academic Graph), which correlate scholarly/scientific communication entities such as publications, authors, datasets, organizations, projects, funders, etc. Depending on the target users, access can vary from search and browse content to the consumption of statistics for monitoring and provision of feedback. Such graphs are populated over time as aggregations of multiple sources and therefore suffer from major entity-duplication problems. Although deduplication of graphs is a known and actual problem, existing solutions are dedicated to specific scenarios, operate on flat collections, local topology-drive challenges and cannot therefore be re-used in other contexts.

This work presents GDup, an integrated, scalable, general-purpose system that can be customized to address deduplication over arbitrary large information graphs. The paper presents its high-level architecture, its implementation as a service used within the OpenAIRE infrastructure system and reports numbers of real-case experiments.

GDup provides the functionalities required to deliver a fully-fledged entity deduplication workflow over a generic input graph. The system offers out-of-the-box Ground Truth management, acquisition of feedback from data curators and algorithms for identifying and merging duplicates, to obtain an output disambiguated graph.

To our knowledge GDup is the only system in the literature that offers an integrated and general-purpose solution for the deduplication graphs, while targeting big data scalability issues. GDup is today one of the key modules of the OpenAIRE infrastructure production system, which monitors Open Science trends on behalf of the European Commission, National funders and institutions.