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The private health care sector is seeking to improve their understanding of business processes to be able to improve their performance. The purpose of this paper is to understand the future needs of the private health care sector organizations in terms of business intelligence (BI) and business analytics (BA) to ensure value creation.

The four evolution stages of intellectual capital enriched by managerial data-driven approach are used as a framework to point out the future of BI or BA in the private healthcare sector. The research includes private health care organizations, BI vendors and management consultants in Finland.

Based on the findings, the private health care is stepping towards a new phase of data-driven decision-making, requiring to change the whole set of mind towards use of data and required capabilities. Moreover, it shows that the future factors of BI varied from practical tools and methods such as predictive and prescriptive analytics along with AI, to more conceptual factors such as social BI co-creation and platforms.

As an outcome, this study provides an understanding of the role of IC components in the future BI and use of BA as well as provides a valuable insight into the future potential of BI in the private health care sector.

Data-driven decision-making and seeking for new business opportunities are currently one of the most discussed topics in the private health care sector. By identifying the future opportunities of BI and BA, this study provides a better understanding of the role of IC components and BI in creating potential for new business for private health care.

Big data has raised challenges and opportunities for business, the information technology (IT) industry and research communities. Nowadays, small and medium-sized enterprises (SME) are dealing with big data using their limited resources. The purpose of this paper is to describe the synergistic relationship between big data and knowledge management (KM), analyze the challenges and IT solutions of big data for SME and derives a KM model of big data for SME based on the collected real-world business cases.

The study collects eight well-documented cases of successful big data analytics in SME and conducts a qualitative data analysis of these cases in the context of KM. The qualitative data analysis of the multiple cases reveals a KM model of big data for SME.

The proposed model portrays the synergistic relationship between big data and KM. It indicates that strategic use of data, knowledge guided big data project planning, IT solutions for SME and new knowledge products are the major constructs of KM of big data for SME. These constructs form a loop through the causal relationships between them.

The number of cases used for the derivation of the KM model is not large. The coding of these qualitative data could involve biases and errors. Consequently, the conceptual KM model proposed in this paper is subject to further verification and validation.

The proposed model can guide SME to exploit big data for business by placing emphasis on KM instead of sophisticated IT techniques or the magnitude of data.

The study contributes to the KM literature by developing a theoretical model of KM of big data for SME based on underlying dimensions of strategic use of data, knowledge guided big data project planning, IT solutions for SME and new knowledge products.

This study aims to investigate the Big Social Data (BSD) paradigm, which still lacks a clear and shared definition, and causes a lack of clarity and understanding about its beneficial opportunities for practitioners. In the knowledge management (KM) domain, a clear characterization of the BSD paradigm can lead to more effective and efficient KM strategies, processes and systems that leverage a huge amount of structured and unstructured data sources.

The study adopts a systematic literature review (SLR) methodology based on a mixed analysis approach (unsupervised machine learning and human-based) applied to 199 research articles on BSD topics extracted from Scopus and Web of Science. In particular, machine learning processing has been implemented by using topic extraction and hierarchical clustering techniques.

The paper provides a threefold contribution: a conceptualization and a consensual definition of the BSD paradigm through the identification of four key conceptual pillars (i.e. sources, properties, technology and value exploitation); a characterization of the taxonomy of BSD data type that extends previous works on this topic; a research agenda for future research studies on BSD and its applications along with a KM perspective.

The main limits of the research rely on the list of articles considered for the literature review that could be enlarged by considering further sources (in addition to Scopus and Web of Science) and/or further languages (in addition to English) and/or further years (the review considers papers published until 2018). Research implications concern the development of a research agenda organized along with five thematic issues, which can feed future research to deepen the paradigm of BSD and explore linkages with the KM field.

Practical implications concern the usage of the proposed definition of BSD to purposefully design applications and services based on BSD in knowledge-intensive domains to generate value for citizens, individuals, companies and territories.

The original contribution concerns the definition of the big data social paradigm built through an SLR the combines machine learning processing and human-based processing. Moreover, the research agenda deriving from the study contributes to investigate the BSD paradigm in the wider domain of KM.

Intellectual capital (IC) is essential to the success of new technology-based firms. However, young firms only possess some of the resources and capabilities needed to develop, produce and market their innovative products and services. Hence, many form alliances to access complementary resources. This paper investigates the signaling effect of technology-based start-ups’ stock of IC on alliance formation.

This study analyzes primary data concerning specific classes of IC and the alliances formed. Data were collected from founders of 233 technology-based new ventures in the USA. Hypotheses were tested via hierarchical linear regression.

This study demonstrates that firms' IC, in the form of founders with doctorates and patents, is positively related to the classes of alliances formed. These stocks of IC send signals about credibility to the market for alliance partners, enabling the firms to form alliances and gain access to complementary resources. The number of founders with doctorates was positively related to R&D alliances and alliance partners in a similar place in the value chain as the focal firm. In contrast, the number of patents was positively related to total alliances, production-oriented alliances and alliances considered upstream from the focal firm.

This paper collects retrospective data from founders of technology-based new ventures. The research contributes to the literature with its results that founder human capital and patent portfolios are essential for technology-based firms' innovation and growth. However, little research has investigated how firms' possession of IC facilitates alliance formation. This paper investigates this connection explicitly.

Data science is a relatively new field which has gained considerable attention in recent years. This new field requires a wide range of knowledge and skills from different disciplines including mathematics and statistics, computer science and information science. The purpose of this paper is to present the results of the study that explored the field of data science from the library and information science (LIS) perspective.

Analysis of research publications on data science was made on the basis of papers published in the Web of Science database. The following research questions were proposed: What are the main tendencies in publication years, document types, countries of origin, source titles, authors of publications, affiliations of the article authors and the most cited articles related to data science in the field of LIS? What are the main themes discussed in the publications from the LIS perspective?

The highest contribution to data science comes from the computer science research community. The contribution of information science and library science community is quite small. However, there has been continuous increase in articles from the year 2015. The main document types are journal articles, followed by conference proceedings and editorial material. The top three journals that publish data science papers from the LIS perspective are the Journal of the American Medical Informatics Association, the International Journal of Information Management and the Journal of the Association for Information Science and Technology. The top five countries publishing are USA, China, England, Australia and India. The most cited article has got 112 citations. The analysis revealed that the data science field is quite interdisciplinary by nature. In addition to the field of LIS the papers belonged to several other research areas. The reviewed articles belonged to the six broad categories: data science education and training; knowledge and skills of the data professional; the role of libraries and librarians in the data science movement; tools, techniques and applications of data science; data science from the knowledge management perspective; and data science from the perspective of health sciences.

The limitations of this research are that this study only analyzed research papers in the Web of Science database and therefore only covers a certain amount of scientific papers published in the field of LIS. In addition, only publications with the term “data science” in the topic area of the Web of Science database were analyzed. Therefore, several relevant studies are not discussed in this paper that are not reflected in the Web of Science database or were related to other keywords such as “e-science,” “e-research,” “data service,” “data curation” or “research data management.”

The field of data science has not been explored using bibliographic analysis of publications from the perspective of the LIS. This paper helps to better understand the field of data science and the perspectives for information professionals.

The authors aim to investigate whether ability electronic human resource management (e-HRM) practices, opportunity enhancing e-HRM practices and motivation enhancing e-HRM can possibly lead to development of sustainable e-HRM systems. Finally, the authors also examined if sustainable e-HRM systems can enhance firm performance.

The model was developed using dynamic capability view perspective. The study tests theoretical model and presents findings by analysing data (partial least squares structural equation modelling method) gathered from 151 South African firms.

The findings indicate that ability enhancing e-HRM practices and motivation enhancing e-HRM practices can result in development of sustainable e-HRM systems, and findings also indicate that sustainable e-HRM systems can improve firm performance.

Emphasis is required on ability enhancing e-HRM practices and motivation enhancing e-HRM practices to develop sustainable e-HRM systems. Once workforce understand the complete benefits of e-HRM, they will start using this system on a regular basis for activities including goal setting, and performance measurement. The development of sustainable e-HRM systems will improve firm performance especially from cost control and customer satisfaction perspective.

This study advances the conceptual debate in the e-HRM domain through the development and testing of theoretical model.

The purpose of the study is to investigate the impact of technological pillars of Industry 4.0 based on knowledge to adopt the supply chain performance of Thai small and medium-sized enterprises (SMEs) 4.0. In addition, to increase knowledge and understanding of how to apply knowledge in technology 4.0 to improve the efficiency of supply chains and organizations.

An integrated model was developed from applying knowledge in five technological pillars of Industry 4.0 such as Internet of things (IoTs), cloud computing, big data and analytics, additive manufacturing and cyber-security. The bibliometric analysis was used to find the relationship between the technological pillars of Industry 4.0 and the literature review. The survey questionnaires were sent to Thai SME 4.0 (manufacturing aspect). Of these, 240 useable responses were received, resulting in a response rate of 65.84%, after then, the exploratory factor analysis (EFA), confirmatory factor analysis (CFA), structural equation modeling (SEM) and validity were used to evaluate the model through IBM SPSS 21 and AMOS 22.

EFA showed the four groups of the technological pillars of Industry 4.0, such as support human, automation, real-time and security. These groups positively impact supply chain performance (increase delivery reliability, increase resource efficiency, decrease costs in the supply chain and reduce delivery time). Another important finding is that supply chain performance positively impacts organizational performance in profitability, return on investment (ROI) and sale growth.

This study is a model development to support the supply chain performance and increase understanding related to applying knowledge in technology 4.0 that remains unclear for SME 4.0.

Global virtual teams (GVTs) – composed of members in two or more countries who work together primarily using information and communication technologies – are increasingly prevalent in organizations today. There has been a burgeoning of research on this relatively new organizational unit, spanning various academic disciplines. In this chapter, we review and discuss the major developments in this area of research. Based on our review, we identify areas in need of future research, suggest research directions that have the potential to enhance theory development, and provide practical guidelines on managing and working in GVTs.

The author aims to introduce this themed issue on personal knowledge management.

The author discusses some of the issues surrounding personal and organisational knowledge management, and then introduces the papers in this themed issue.

Personal knowledge management is an under‐researched area. This themed issue contributes both technical and theoretical papers to the field.

The paper provides an introduction to this themed issue.