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Although knowledge based on business intelligence (BI) is crucial, few studies have explored the core of BI knowledge; this study explores this topic.

The authors collected 1,306 articles and 54,020 references from the Web of Science (WoS) database and performed co-citation analysis to explore the core knowledge of BI; 52 highly cited articles were identified. The authors also performed factor and cluster analyses to organize this core knowledge and compared the results of these analyses.

The factor analysis based on the co-citation matrix revealed seven key factors of the core knowledge of BI: big data analytics, BI benefits and success, organizational capabilities and performance, information technology (IT) acceptance and measurement, information and business analytics, social media text analytics, and the development of BI. The cluster analysis revealed six categories: IT acceptance and measurement, BI success and measurement, organizational capabilities and performance, big data-enabled business value, social media text analytics, and BI system (BIS) and analytics. These results suggest that numerous research topics related to big data are emerging.

The core knowledge of BI revealed in this study can help researchers understand BI, save time, and explore new problems. The study has three limitations that researchers should consider: the time lag of co-citation analysis, the difference between two analytical methods, and the changing nature of research over time. Researchers should consider these limitations in future studies.

This study systematically explores the extent to which scholars of business have researched and understand BI. To the best of the authors’ knowledge, this is one of the first studies to outline the core knowledge of BI and identify emerging opportunities for research in the field.

This paper aims to examine the extent to which Business Intelligence and Big Data feature within academic research in hospitality and tourism published until 2016, by identifying research gaps and future developments and designing an agenda for future research.

The study consists of a systematic quantitative literature review of academic articles indexed on the Scopus and Web of Science databases. The articles were reviewed based on the following features: research topic; conceptual and theoretical characterization; sources of data; type of data and size; data collection methods; data analysis techniques; and data reporting and visualization.

Findings indicate an increase in hospitality and tourism management literature applying analytical techniques to large quantities of data. However, this research field is fairly fragmented in scope and limited in methodologies and displays several gaps. A conceptual framework that helps to identify critical business problems and links the domains of business intelligence and big data to tourism and hospitality management and development is missing. Moreover, epistemological dilemmas and consequences for theory development of big data-driven knowledge are still a terra incognita. Last, despite calls for more integration of management and data science, cross-disciplinary collaborations with computer and data scientists are rather episodic and related to specific types of work and research.

This work is based on academic articles published before 2017; hence, scientific outputs published after the moment of writing have not been included. A rich research agenda is designed.

This study contributes to explore in depth and systematically to what extent hospitality and tourism scholars are aware of and working intendedly on business intelligence and big data. To the best of the authors’ knowledge, it is the first systematic literature review within hospitality and tourism research dealing with business intelligence and big data.

The purpose of this paper is to examine a range of metrics concerning knowledge and related intangible assets in financial service industries. The metrics are then analyzed according to theory from several disciplines so as to better understand intangible dynamics, the relationship between different intangibles. Guidance is provided in terms of valuing knowledge and pursuing competitive intelligence (CI) based on the unique characteristics of financial services intangibles.

Data are drawn from a large database and supplemented with other sources. The primary database includes a considerable collection of publicly available financial results combined with proprietary data from a CI consultancy. Results on knowledge asset-levels and CI activity, by industry sector, are presented as well as the degree to which big data is employed.

Financial services show high levels of big data, low levels of knowledge assets, and high levels of CI activity. In some ways, these differing valuations of intangibles by different parties are counterintuitive, but they can be explained with reference to theory and a deeper understanding of the intangible dynamics.

The results allow a deeper understanding of the relationship between data, information, knowledge (explicit and tacit), and intelligence in a specific industry. Given the uniqueness of the financial services results, these findings provide considerable insight into how intangibles strategies and applications can differ by industry.

These results provide direction to financial services decision-makers concerning investment in knowledge management systems (limited), CI initiatives (aggressive), and big data (aggressive).

The paper brings unique data to the table and brings together theory from a number of disparate fields of study, providing a different perspective on the interplay of knowledge, intelligence, and data in financial services.

This paper aims to examine the history of data leaks and investigative journalism, the techniques and technology that enable them and their influence in Australia and abroad. It explores the ethical and professional considerations of investigative journalists, how they approach privacy and information-sharing and how this differs from intelligence practice in government and industry. The paper assesses the strengths and limitations of Collaborative Investigative Reporting based on Information Leaks (CIRIL) as a kind of public-facing intelligence practice.

This study draws on academic literature, source material from investigations by the International Consortium of Investigative Journalists and the Organised Crime and Corruption Reporting Project, and a survey of financial crime compliance professionals conducted in 2022.

The paper identifies three key causal factors that have enabled the rise of CIRIL even as traditional journalism has declined: the digital storage of information; increasing public interest in offshore finance and tax evasion; and “virtual newsrooms” enabled by internet communications. It concludes that the primary strength of CIRIL is its creation of complex global narratives to inform the public about corruption and tax evasion, while its key weakness is that the scale and breadth of the data released makes it difficult to focus on likely criminal activity. Results of a survey of industry and government professionals indicate that CIRIL is generally more effective as public information than as an investigative resource, owing to the volume, age and quality of information released. However, the trends enabling CIRIL are likely to continue, and this means that governments and financial institutions need to become more effective at using leaked information.

Over the past decade, large-scale, data-driven investigative journalism projects such as the Pandora Papers and the Russian Laundromat have had a significant public impact by exposing money laundering, financial crime and corruption. These projects share certain hallmarks: the use of human intelligence, often sourced from anonymous leaks; inventive fusion of this intelligence with data from open sources; and collaboration among a global collective of investigative journalists to build a narrative. These projects prioritise informing the public. They are also an important information source for government and private sector organisations working to investigate and disrupt financial crime.

Companies are currently facing the challenge of understanding how their business is affected by the large volume of opinions continually generated by their stakeholders in social media regarding their intangible assets (experiences, emotions and attitudes). With this in mind, the purpose of this paper is to present an innovative management model, named E2AB, to measure and analyse reputational intangibles from digital ecosystems and their impacts on tangible assets.

The methodology applied was big data and business intelligence techniques. These methods were used in the computing process to obtain daily data from every asset guarantees that the model is validated with robust data. This model has been corroborated using data from the banking sector, specifically 402,383 net data inputs from the digital ecosystems.

This study illustrates the existence of a holistic influence of intangible assets over tangible assets. The findings demonstrate complex relationships between tangible and intangible assets, determined not only by the type of variable but also by its valence and intensity.

These findings may help chief communication officers and general managers a better understanding of how intangible assets extracted from online users’ opinions are related to their organisation’s tangible assets plus a chance to find out about their impact and how to manage them for a practical and agile decision making in real time.

It is a pioneering work in establishing a model, which demonstrates transversal and holistic relationships between relational intangible and tangible assets of firms from digital ecosystems, using business intelligence techniques.

The benefits of artificial intelligence (AI) related technologies for manufacturing firms are well recognized, however, there is a lack of industrial AI (I-AI) maturity models to enable companies to understand where they are and plan where they should go. The purpose of this study is to propose a comprehensive maturity model in order to help manufacturing firms assess their performance in the I-AI journey, shed lights on future improvement, and eventually realize their smart manufacturing visions.

This study is based on (1) a systematic review of literature on assessing I-AI-related technologies to identify relevant measured indicators in the maturity model, and (2) semi-structured interviews with domain experts to determine maturity levels of the established model.

The I-AI maturity model developed in this study includes two main dimensions, namely “Industry” and “Artificial Intelligence”, together with 12 first-level indicators and 35 second-level indicators under these dimensions. The maturity levels are divided into five types: planning level, specification level, integration level, optimization level, and leading level.

The maturity model integrates indicators that can be used to assess AI-related technologies and extend the existing maturity models of smart manufacturing by adding specific technical and nontechnical capabilities of these technologies applied in the industrial context. The integration of the industry and artificial intelligence dimensions with the maturity levels shows a road map to improve the capability of applying AI-related technologies throughout the product lifecycle for achieving smart manufacturing.

This paper aims to illustrate how integrating competitive intelligence (CI) into a US health-care firm can aid in information sharing and building knowledge for the organization.

This study is exploratory using a systematic literature review to develop a conceptual model applied to the US health-care industry.

This research presents key propositions of CI’s role in the CI process along with the C-suite’s role in supporting a process and culture to ultimately, gain competitive advantage through the knowledge-based view.

With the growing volume of data, a unified system and culture within a firm is paramount. The US health-care system is a privatized industry that has become more competitive stifling information sharing. The need for prompt and accurate decision-making has become an imperative. Crises, like the current COVID-19 pandemic, only exacerbate the issue. This model offers a blue print for executives to build a CI function and encourage information sharing.

Previous research has focused on the CI process and its value. Yet, little research is found on how to integrate CI into a firm and its role through the CI process. This study builds a conceptual model addressing integration and the flow of information to knowledge along with key firm dynamics to nurture the function. Although the model is applied specifically to US health care, it offers application to most any industry.

The purpose of this study is to explore the latest approaches in integrating artificial intelligence and analytics (AIA) in energy smart grid projects. Empirical results are synthesized to highlight their relevance from a technology and project management standpoint, identifying several lessons learned that can be used for planning highly integrated and automated smart grid projects.

A systematic literature review leads to selecting 108 research articles dealing with smart grids and AIA applications. Keywords are based on the following research questions: What is the growth trend in Smart Grid projects using intelligent systems and data analytics? What business value is offered when AI-based methods are applied? How do applications of intelligent systems combine with data analytics? What lessons can be learned for Smart Grid and AIA projects?

The 108 selected articles are classified according to the following four research issues in smart grids project management: AIA integrated applications; AI-focused technologies; analytics-focused technologies; architecture and design methods. A broad set of smart grid functionality is reviewed, seeking to find commonality among several applications, including as follows: dynamic energy management; automation of extract, transform and load for Supervisory Control And Data Acquisition (SCADA) systems data; multi-level representations of data; the relationship between the standard three-phase transforms and modern data analytics; real-time or short-time voltage stability assessment; smart city architecture; home energy management system; building energy consumption; automated fault and disturbance analysis; and power quality control.

Given the diversity of issues reviewed, a more capability-focused research agenda is needed to further synthesize empirical findings for AI-based smart grids. Research may converge toward more focus on business rules systems, that may best support smart grid design, proof development, governance and effectiveness. These AIA technologies must be further integrated with smart grid project management methodologies and enable a greater diversity of renewable and non-renewable production sources.

Accurate evaluation of the consequences of new technologies in various industries is of great significance. So, it will be essential to examine the impact of this technology on the banking industry, representing how to create, deliver and gain value in this industry. This study aims to investigate whether blockchain can affect the business intelligence efficiency of banks. This study also aims to examine the impact of security, fraud reduction and privacy of blockchain, equal and anonymous access to the blockchain, decentralization and sustainability of blockchain, accountability and transparency of blockchain, quality, speed and efficiency of blockchain on business intelligence efficiency.

Technological changes are creating new challenges and opportunities for various industries. The inability of organizations to adapt to these changes may even lead to their deletion from the market. Blockchain is one of the most critical technologies in recent years. One of the sectors that will undergo significant changes in blockchain technology is the banking industry. According to the reviewed literature in this study, a comprehensive model has been proposed to examine the impact of security, fraud reduction and privacy of blockchain, equal and anonymous access to the blockchain, the decentralization and sustainability of blockchain, accountability and transparency of blockchain and quality, speed and efficiency of blockchain on business intelligence efficiency. A survey method was used to collect data from banks of the Nanjing city. The partial least square technique was used for data analysis.

The results showed that the fit of the proposed model was very good. Also, all assumptions except one were confirmed. It means that security, fraud reduction and privacy of blockchain factor have a remarkable and positive impact on all aspects of business intelligence efficiency, namely information technology, employees, competitors and customers. Also, equal and anonymous access to the blockchain factor has a positive and significant effect on all aspects of business intelligence efficiency. The decentralization and sustainability of blockchain factors have an impact on business intelligence efficiency. Also, blockchain's accountability and transparency as a fourth factor have a positive and significant impact on all aspects of business intelligence efficiency. Finally, the last factor (quality, speed and efficiency of blockchain) has a positive and significant effect on information technology, employees and customers' dimensions. But, it does not affect the competitors' dimension, and this hypothesis has not been confirmed.

This paper offers valuable insight for business intelligence practitioners into how blockchain technology has the potential to disrupt existing business intelligence provisions.

This paper is one of the first studies to examine the impact of blockchain on IT dimension, organizational employees' dimension, customer dimension and competitors' dimension. It lays a firm foundation for future research.

The purpose of this paper is to see how critical and vital artificial intelligence (AI) and big data are in today’s world. Besides this, this paper also seeks to explore qualitative and theoretical perspectives to underscore the importance of AI and big data applications in multi-sectoral scenarios of businesses across the world. Moreover, this paper also aims at working out the scope of ontological communicative perspectives based on AI alongside emphasizing their relevance in business organizations that need to survive and sustain with a view to achieve their strategic goals.

This paper attempts to explore the qualitative perspectives to build a direction for strategic management via addressing the following research questions concerned with assessing the scope of ontological communicative perspectives in AI relevant to business organizations; exploring benefits of big data combined with AI in modern businesses; and underscoring the importance of AI and big data applications in multi-sectoral scenarios of businesses in today’s world. Employing bibliometric analysis along with NVivo software to do sentiment analysis, this paper attempts to develop an understanding of what happens when AI and big data are combined in businesses.

AI and big data have tremendous bearing on modern businesses. Because big data comprises enormous information of diverse sorts, AI-assisted machines, tools and devices help modern businesses process it quickly, efficiently and meaningfully. Therefore, business leaders and entrepreneurs need to focus heavily on ontological and communicative perspectives to deal with diverse range of challenges and problems particularly in the context of recent crises caused by COVID-19 pandemic.

There is hardly any arena of human activity wherein AI and big data are not relevant. The implication of this paper is that of combining both well so that we may find answers to the difficult and challenging multi-sectoral scenarios concerning not just businesses but life at large. Moreover, automated tools based on AI such as natural language processing and speech to text also facilitate meaningful communication at various levels not just in business organizations but other fields of human activities as well.

This paper has layered social implications, as it conceptually works out as to how strategically we may combine AI and big data to benefit modern business scenarios dealing with service providers, manufacturers, entrepreneurs, business leaders, customers and consumers. All the stakeholders are socio-culturally and contextually rooted/situated, and that is how this study becomes socially relevant.

This paper is an original piece of research and has been envisioned in view of the challenging business scenarios across the world today. This paper underscores the importance of strategically combining AI and big data, as they have enormous bearing on modern businesses. The insights arrived at in this paper have implications for business leaders and entrepreneurs across the globe who could focus more on ontological and communicative perspectives of AI combined with Big Data to deal with diverse range of challenges and problems that modern businesses have been facing particularly in recent times.