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It may be a cliché of modern business for a company to say that “people are our greatest asset,” but this is one truism that should be taken seriously. Up to 70 percent of a company's value is tied up in the skills and experience of its employees. All too often, however, business executives and Human Resource (HR) departments have very little insight into how to use this asset for better business outcomes. This paper aims to look at the importance of effective talent metrics and to examine the problems organizations face when trying to develop talent intelligence.

The paper discusses the findings of Talent Intelligence: Key to Business Success, an independent research report examining business and HR attitudes to talent metrics and analytics.

Despite the business value that accurate, accessible talent intelligence can provide, the research finds that there are significant differences between those talent metrics that organizations consider important and the data to which they have access. A legacy of disparate technology systems and a focus on measuring efficiency rather than effectiveness are the primary reasons for the lack of talent intelligence among many businesses.

The paper examines the findings of a comprehensive international survey of HR and business managers, identifying the barriers to collecting and analyzing useful talent metrics and laying out the key steps towards generating talent intelligence.

Using analytics for firms' competitiveness is a vital component of a company's strategic planning and management process. In recent years, organizations have started to capitalize on the significant use of big data for analyses to gain valuable insights to improve decision-making processes. In this regard, leveraging and unleashing the potential of big data has become a significant success factor for steering firms' competitiveness, and the related literature is increasing at a very high pace. Thus, the authors propose a bibliometric study to understand the most important insights from these studies and enrich existing conceptual models.

In this study, the authors use a bibliometric review on articles related to the use of big data for firms' competitiveness. The authors examine the contributions of research constituents (authors, institutions, countries and journals) and their structural and thematic relationships (collaborations, co-citations networks, co-word networks, thematic trends and thematic map). The most important insights are used to enrich a conceptual model.

Based on the performance analysis results, the authors found that China is by far the most productive country in this research field. However, in terms of influence (by the number of citations per article), the most influential countries are the UK, Australia and the USA, respectively. Based on the science mapping analysis results, the most important findings are projected in the common phases of competitive intelligence processes and include planning and directions concepts, data collection concepts, data analysis concepts, dissemination concepts and feedback concepts. This projection is supplemented by cross-cutting themes such as digital transformation, cloud computing, privacy, data science and competition law. Three main future research directions are identified: the broadening of the scope of application fields, the specific case of managing or anticipating the consequences of pandemics or high disruptive events such as COVID-19 and the improvement of connection between firms' competitiveness and innovation practices in a big data context.

The findings of this study show that the most important research axis in the existing literature on big data and firms' competitiveness are mostly related to common phases of competitive intelligence processes. However, concepts in these phases are strongly related to the most important dimensions intrinsic to big data. The use of a single database (Scopus) or the selected keywords can lead to bias in this study. Therefore, to address these limitations, future studies could combine different databases (i.e. Web of Science and Scopus) or different sets of keywords.

This study can provide to practitioners the most important concepts and future directions to deal with for using big data analytics to improve their competitiveness.

This study can help researchers or practitioners to identify potential research collaborators or identify suitable sources of publications in the context of big data for firms' competitiveness.

The authors propose a conceptual model related to big data and firms' competitiveness from the outputs of a bibliometric study.

This chapter presents an overarching overview of how the rather recent technological phenomena, like data mining, machine learning, and artificial intelligence, are applied in the field of education. The author provides examples of how technological developments associated with the so-called Fourth Industrial Revolution are applied in education and considers the benefits and challenges they may bring regarding the economic system, as education (at least in the higher education sector) tends to be monetized and commercialized. The framework for education is perceived in the context of the economic intelligence of states, which is instrumental in ensuring their economic security. It is further expanded to the global scale, as Digital Education is crossing national borders and is being implemented in broader national processes.

This paper aims to bring together the existing theory from knowledge management (KM), competitive intelligence (CI) and big data analytics to develop a more comprehensive view of the full range of intangible assets (data, information, knowledge and intelligence). By doing so, the interactions of the intangibles are better understood and recommendations can be made for the appropriate structure of big data systems in different circumstances. Metrics are also applied to illustrate how one can identify and understand what these different circumstances might look like.

The approach is chiefly conceptual, combining theory from multiple disciplines enhanced with practical applications. Illustrative data drawn from other empirical work are applied to illustrate some concepts.

Theory suggests that the KM theory is particularly useful in guiding big data system installations that focus primarily on the transfer of data/information. For big data systems focused on analytical insights, the CI theory might be a better match, as the system structures are actually quite similar.

Though the guidelines are general, practitioners should be able to evaluate their own situations and perhaps make better decisions about the direction of their big data systems. One can make the case that all the disciplines have something to add to improving how intangibles are deployed and applied and that improving coordination between KM and analytics/intelligence functions will help all intangibles systems to work more effectively.

To the authors’ knowledge, very few scholars work in this area, at the intersection of multiple types of intangible assets. The metrics are unique, especially in their scale and attachment to theory, allowing insights that provide more clarity to scholars and practical direction to industry.

Organizations and their members operate in increasingly complex, dynamic and even disruptive environments, with risk and uncertainty being major challenges. To that effect, data, information, knowledge, and respective competences are increasingly instrumental in enabling and sustaining organizational intelligence that translates into resilience in the shorter and sustainable excellence in the longer term. Therefore, the purpose of this paper is to explore the role of the artifacts and routines in a sustainable organizational excellence context.

An extensive literature review was used to develop the context of the paper, focusing on big data and organizational intelligence for enterprise excellence and resilience. In addition, a thematic literature review method was used to study the role and impacts of routines and artifacts in organizational change, policies, structure and performance.

Although many traditional management practices retain their validity, knowledge management must give a clearer view of the existing connection between firm-level competitive advantage in open economies flows and difficult-to-use knowledge assets. The proposed framework studies knowledge exploration and knowledge exploitation as organizational phenomena opposed and mutually incompatible.

The paper presents a first attempt to study the linkages of organizational routines and artifacts as a cycle wherein knowledge acquisition and learning competencies form and enhance a firm’s organizational intelligence, leading to robust competitiveness and sustainable entrepreneurship.

This paper aims to examine how competitive intelligence (CI) relates to the strategy formulation process of firms.

Due to the novelty of the phenomenon and to the depth of the investigation required to grasp the mechanisms and logics of CI, a multiple case study has been performed related to four companies located in Brazil that adopted CI practices within dedicated business units to inform and support strategic decision-making.

The authors provide detailed empirical evidence on the connection and use of CI practices throughout each stage of the strategy formulation process. Moreover, the study suggests that CI practices, despite their strategic relevance and diffusion, are still extensively adopted for tactical use.

This study sheds light on how CI practices may inform, support, and be integrated in the strategy formulation process, as few studies have done before.

Despite the increasing role of the data warehouse as a supportive decision-making tool in today's business world, academic research for measuring its effectiveness has been lacking. This paucity of academic interest stimulated us to evaluate data warehousing effectiveness in the organizational context of Jordanian banks.

This paper develops a theoretical model specific to the data warehouse system domain that builds on the DeLone and McLean model. The model is empirically tested by means of structural equation modelling applying the partial least squares approach and using data collected in a survey questionnaire from 127 respondents at Jordanian banks.

Empirical data analysis supported that data quality, system quality, user satisfaction, individual benefits and organizational benefits have made strong contributions to data warehousing effectiveness in our organizational data context.

The results provide a better understanding of the data warehouse effectiveness and its importance in enabling the Jordanian banks to be competitive.

This study is indeed one of the first empirical attempts to measure data warehouse system effectiveness and the first of its kind in an emerging country such as Jordan.

Competitive intelligence failures have devastating effects in marketplaces. They are attributed to various factors but seldom explicitly to information behaviour. This paper addresses causes of competitive intelligence failures from an information behaviour lens focussing on problems with key intelligence and information needs. The exploratory study was conducted in 2016/2017. Managers (end-users) identify key intelligence needs on which information is needed, and often other staff members seek the information (proxy information seeking). The purpose of this paper is to analyse problems related to key intelligence and information needs, and make recommendations to address the problems.

The study is placed in a post-positivism research paradigm, using qualitative and limited quantitative research approaches. In total, 15 participants (competitive intelligence professionals and educators/trainers originating from South Africa and the USA) contributed rich data through in-depth individual interviews.

Problems associated with articulation of information needs (key intelligence needs is the competitive intelligence term – with a broader scope) include inadequate communication between the person in need of information and the proxy information searcher; awareness and recognition of information needs; difficulty in articulation, incomplete and partial sharing of details of needs.

Participant recruitment was difficult, representing mostly from South Africa. The findings from this exploratory study can, however, direct further studies with a very understudied group.

However, revealed valuable findings that can guide research.

Little has been published on competitive intelligence from an information behaviour perspective. Frameworks guiding the study (a combination of Leckie et al.’s 1996 and Wilson’s, 1981 models and a competitive intelligence life cycle), however, revealed valuable findings that can guide research.

This chapter examines firm strengths and weaknesses from the standpoint of intangible assets. These are compared within and across industry sectors in order to better understand who might be a potential collaborator (or competitor) in different contexts. Establishing the conceptual basis of a range of intangibles, including data, explicit knowledge, tacit knowledge, and intelligence, the chapter moves to metrics for assessing industry averages and individual firm capabilities. Finally, several sectors in healthcare are examined, specifically identifying what kinds of collaborators would best fit with a technology-driven start-up like Theranos.