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The increasing prevalence and availability of big data represent a potentially revolutionary development for human resource management (HRM) scholars. Despite this, the current literature provides eclectic and often contradictory guidance for scholars attempting to conceptualize big data and subsequently incorporate it into relevant theoretical frameworks. The authors attempt to bridge this gap by discussing key considerations relevant to understanding and integrating big data into the existing theoretical landscape. Building on a novel, integrative definition of big data, the authors propose a parsimonious theoretical framework utilizing the established dimensions of complexity and dynamism as meta-attributes to bring order to the various attributes that have been proposed as central to defining big data (e.g., volume, variety, velocity, and variability). Throughout, the authors highlight numerous theoretical and empirical opportunities and considerations that this perspective holds for future HRM scholarship.

Medical research requires biological material and data collected through biobanks in reliable processes with quality assurance. Medical studies based on data with unknown or questionable quality are useless or even dangerous, as evidenced by recent examples of withdrawn studies. Medical data sets consist of highly sensitive personal data, which has to be protected carefully and is available for research only after the approval of ethics committees. The purpose of this research is to propose an architecture to support researchers to efficiently and effectively identify relevant collections of material and data with documented quality for their research projects while observing strict privacy rules.

Following a design science approach, this paper develops a conceptual model for capturing and relating metadata of medical data in biobanks to support medical research.

This study describes the landscape of biobanks as federated medical data lakes such as the collections of samples and their annotations in the European federation of biobanks (Biobanking and Biomolecular Resources Research Infrastructure – European Research Infrastructure Consortium, BBMRI-ERIC) and develops a conceptual model capturing schema information with quality annotation. This paper discusses the quality dimensions for data sets for medical research in-depth and proposes representations of both the metadata and data quality documentation with the aim to support researchers to effectively and efficiently identify suitable data sets for medical studies.

This novel conceptual model for metadata for medical data lakes has a unique focus on the high privacy requirements of the data sets contained in medical data lakes and also stands out in the detailed representation of data quality and metadata quality of medical data sets.

Survey-based studies suggest that search engines are trusted more than social media or even traditional news, although cases of false information or defamation are known. The purpose of this paper is to analyze query suggestion features of three search engines to see if these features introduce some bias into the query and search process that might compromise this trust. The authors test the approach on person-related search suggestions by querying the names of politicians from the German Bundestag before the German federal election of 2017.

This study introduces a framework to systematically examine and automatically analyze the varieties in different query suggestions for person names offered by major search engines. To test the framework, the authors collected data from the Google, Bing and DuckDuckGo query suggestion APIs over a period of four months for 629 different names of German politicians. The suggestions were clustered and statistically analyzed with regards to different biases, like gender, party or age and with regards to the stability of the suggestions over time.

By using the framework, the authors located three semantic clusters within the data set: suggestions related to politics and economics, location information and personal and other miscellaneous topics. Among other effects, the results of the analysis show a small bias in the form that male politicians receive slightly fewer suggestions on “personal and misc” topics. The stability analysis of the suggested terms over time shows that some suggestions are prevalent most of the time, while other suggestions fluctuate more often.

This study proposes a novel framework to automatically identify biases in web search engine query suggestions for person-related searches. Applying this framework on a set of person-related query suggestions shows first insights into the influence search engines can have on the query process of users that seek out information on politicians.

We provide an overview of how artificial intelligence is transforming the identification, structuring, and prioritization of customer needs – known as the voice of the customer (VOC). First, we summarize how the VOC helps firms gain insights on using user-generated data. Second, we discuss the types of user-generated data and the challenges associated with analyzing each type of data. Third, we describe common methods, matched to the firms' goals and the structure of the data, that are used to analyze the VOC. Fourth, and most importantly, we map the methods to relevant applications, providing guidance to select the appropriate method to address the desired research questions.

The definition of modeling languages is a key‐prerequisite for model‐driven engineering. In this respect, Domain‐Specific Modeling Languages (DSMLs) defined from scratch in terms of metamodels and the extension of Unified Modeling Language (UML) by profiles are the proposed options. For interoperability reasons, however, the need arises to bridge modeling languages originally defined as DSMLs to UML. Therefore, the paper aims to propose a semi‐automatic approach for bridging DSMLs and UML by employing model‐driven techniques.

The paper discusses problems of the ad hoc integration of DSMLs and UML and from this discussion a systematic and semi‐automatic integration approach consisting of two phases is derived. In the first phase, the correspondences between the modeling concepts of the DSML and UML are defined manually. In the second phase, these correspondences are used for automatically producing UML profiles to represent the domain‐specific modeling concepts in UML and model transformations for transforming DSML models to UML models and vice versa. The paper presents the ideas within a case study for bridging ComputerAssociate's DSML of the AllFusion Gen CASE tool with IBM's Rational Software Modeler for UML.

The ad hoc definition of UML profiles and model transformations for achieving interoperability is typically a tedious and error‐prone task. By employing a semi‐automatic approach one gains several advantages. First, the integrator only has to deal with the correspondences between the DSML and UML on a conceptual level. Second, all repetitive integration tasks are automated by using model transformations. Third, well‐defined guidelines support the systematic and comprehensible integration.

The paper focuses on the integrating direction DSMLs to UML, but not on how to derive a DSML defined in terms of a metamodel from a UML profile.

Although, DSMLs defined as metamodels and UML profiles are frequently applied in practice, only few attempts have been made to provide interoperability between these two worlds. The contribution of this paper is to integrate the so far competing worlds of DSMLs and UML by proposing a semi‐automatic approach, which allows exchanging models between these two worlds without loss of information.

This paper aims to amplify the retrieval and utilization of historical newspapers through the application of semantic organization, all from the vantage point of a fine-grained knowledge element perspective. This endeavor seeks to unlock the latent value embedded within newspaper contents while simultaneously furnishing invaluable guidance within methodological paradigms for research in the humanities domain.

According to the semantic organization process and knowledge element concept, this study proposes a holistic framework, including four pivotal stages: knowledge element description, extraction, association and application. Initially, a semantic description model dedicated to knowledge elements is devised. Subsequently, harnessing the advanced deep learning techniques, the study delves into the realm of entity recognition and relationship extraction. These techniques are instrumental in identifying entities within the historical newspaper contents and capturing the interdependencies that exist among them. Finally, an online platform based on Flask is developed to enable the recognition of entities and relationships within historical newspapers.

This article utilized the Shengjing Times·Changchun Compilation as the datasets for describing, extracting, associating and applying newspapers contents. Regarding knowledge element extraction, the BERT + BS consistently outperforms Bi-LSTM, CRF++ and even BERT in terms of Recall and F1 scores, making it a favorable choice for entity recognition in this context. Particularly noteworthy is the Bi-LSTM-Pro model, which stands out with the highest scores across all metrics, notably achieving an exceptional F1 score in knowledge element relationship recognition.

Historical newspapers transcend their status as mere artifacts, evolving into invaluable reservoirs safeguarding the societal and historical memory. Through semantic organization from a fine-grained knowledge element perspective, it can facilitate semantic retrieval, semantic association, information visualization and knowledge discovery services for historical newspapers. In practice, it can empower researchers to unearth profound insights within the historical and cultural context, broadening the landscape of digital humanities research and practical applications.

The purpose of this paper is two‐fold. First, to describe in detail a particular sub‐class of powerful prospective methods based on the method of “morphological analysis”. And second, to extend their use to create a basis for strengthening strategic analysis and policy development.

The paper examines the history and use of morphological methods in foresight work, and briefly describes three main “lineages” currently in use, and proposes some extensions to models of practice.

Recent research in cognitive psychology suggests that requiring a detailed and systematic examination of future possibilities before a decision is made leads to more effective assessments of futures. Morphological methods, by design and construction, are perfectly suited to this, and so can form an exceptionally strong basis for thinking systematically about the future.

The paper also describes how to go about designing a foresighting capacity based on a systematic evaluation of future systemic contexts, as well as discussing what aspects of the external environment to include in robust competitive intelligence, strategic monitoring, environmental scanning, and “horizon scanning” activities.

The paper proposes some extensions to existing practice and describes some ways to tie the development of a strategic meta‐language to clearly‐targeted intelligence scanning. This paper should be of interest to anyone involved in trying to strengthen strategy development, policy planning or intelligence analysis.

The purpose of this paper is to evaluate effectiveness of the graduate development programme that was aimed at the recruitment and professional development of black engineering graduates through the workplace learning method.

The paper adopted qualitative research strategy using in-depth interviews with semi-structured interview guide that was developed after an extensive review of related literature. Data were analysed using thematic analysis technique.

Result of the paper indicates that the strategy provides an effective mechanism for the inclusion and professional development of black engineering graduates. Coaching and mentoring relationships were found to be an effective way for knowledge and skills transfers.

Although this study presented valuable insights into the complexity of the graduate development programme in South Africa, the authors consider it appropriate to draw some limitations to study for in order to provide some guides on the conduct of a similar study by future researchers. It is important to state that qualitative studies inherently lack external validity that limits its generalisability to a wider context. Further, a non-probability sampling method was used in this study thus posing a threat to the scientific representativeness of the participants. At last, but very important is the emotion and tension that is usually associated with social research and discussion regarding the legacies of apartheid in South Africa. This research was not insulated from such sensitivity and social influence. To this extent, while practical efforts were made to mitigate this factor during the interviews, there is no guarantee that the respondents were completely honest, and not influenced by extraneous nuances and considerations in their responses to the questions. In view of the methodological and social limitations to this study, future researchers could consider, for example, the use of a mixed methods wherein a quantitative research component is conducted on trainees of the programme in order to validate or disprove the answers provided by the training managers which were purely from operator/organisational, rather than training participants’ perspective. The mixed method approach could also enhance the external validity or generalisability of the research outcome to a wider context. At last, the administration of structured questionnaire through the use of a web-based survey could potentially eliminate emotions, social tension and response bias since both the researcher and respondents do not engage in a face-to-face contact and personal interaction. This also effectively protects personal identity of both the researcher and respondent.

Not much research has been conducted in the direction of the graduate development programme as an effective strategy for the career advancement, inclusion and affirmation of black engineers within the engineering landscape of South Africa. Corporate and professional skills development managers could integrate the outcome of this paper into a policy framework that shapes corporate social investment, diversity and inclusion management at the workplaces.