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Business intelligence (BI) has been applied in various domains to take better decisions and it provides different level of information to its stakeholders according to the information needs. The purpose of this paper is to present a literature review on recent works in BI. The two principal aims in this survey are to identify areas lacking in recent research, thereby offering potential opportunities for investigation.

To simplify the study on BI literature, it is segregated into seven categories according to the usage. Each category of work is analyzed using parameters such as purpose, domain, problem identified, solution applied, benefit and outcome.

The BI contribution in various domains, ongoing research in BI, the convergence of BI domains, problems and solutions, results of congregated domains, core problems and key solutions. It also outlines BI and its components composition, widely applied BI solutions such as algorithm-based, architecture-based and model-based solutions. Finally, it discusses BI implementation issues and outlines the security and privacy policies adopted in BI environment.

In this survey BI has been discussed in theoretical perspective whereas practical contribution has been given less attention.

A comprehensive survey on BI which identifies areas lacking in recent research and providing potential opportunities for investigation.

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.

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.

The purpose of this paper is to obtain evidence concerning the basic dimensions included in cognitive prototypes pertaining to opportunity recognition and decision to launch a new venture; identifying the underlying dimensions of both prototypes – the cognitive frameworks current or nascent entrepreneurs employ in performing these important tasks.

The bi-dimensional models were tested in a sample of 284 founder entrepreneurs, using a 48-item questionnaire. It was used as structural equation confirmatory factor analysis to compare fit indices of uni-dimensional second-order and third-order bi-dimensional models of business opportunity and decision to launch a venture.

Results support the bi-dimensional models and offer support that both prototypes include two basic dimensions. For the business opportunity prototype these are viability and distinctiveness while for the decision to launch a new venture, the basic dimensions are feasibility and motivational aspects.

These results help to further clarify the nature of the cognitive frameworks individuals use to identify potential opportunities and reach an initial decision about whether to pursue their development. Uncovering the cognitive functioning of opportunity recognition and decision to exploit it, allow individuals to recognize opportunities easier and successfully; and to make more accurate and effective decisions.

Knowing the basic dimensions of opportunity and decision-making prototypes contributes to develop effective skills with respect to business opportunity recognition among students enrolled in entrepreneurship programs. These surveys can be used for self-assessment and also for investors, tutors, and entrepreneurship agents in order to help evaluate features of business opportunities and decision to launch a venture.

This study embraces a conceptual contribution, proposing a different model of the business opportunity and decision to exploit prototypes, and it extends Baron and Ensley (2006) previous work, to another important step in the entrepreneurial process – the decision to develop an identified opportunity through the launch of a new venture.

Business intelligence (BI) systems and tools are deemed to be a transformative source with the potential to contribute to reshaping the way different healthcare organizations’ (HCOs) services are offered and managed. However, this emerging field of research still appears underdeveloped and fragmented. Hence, this paper aims to reconciling, analyzing and synthesizing different strands of managerial-oriented literature on BI in HCOs and to enhance both theoretical and applied future contributions.

A literature-based framework was developed to establish and guide a three-stage state-of-the-art systematic literature review (SLR). The SLR was undertaken adopting a hybrid methodology that combines a bibliometric and a content analysis.

In total, 34 peer-review articles were included. Results revealed significant heterogeneity in theoretical basis and methodological strategies. Nonetheless, the knowledge structure of this research’s stream seems to be primarily composed of five clusters of interconnected topics: (1) decision-making, relevant capabilities and value creation; (2) user satisfaction and quality; (3) process management, organizational change and financial effectiveness; (4) decision-support information, dashboard and key performance indicators; and (5) performance management and organizational effectiveness.

To the authors’ knowledge, this is the first SLR providing a business and management-related state-of-the-art on the topic. Besides, the paper offers an original framework disentangling future research directions from each emerged cluster into issues pertaining to BI implementation, utilization and impact in HCOs. The paper also discusses the need of future contributions to explore possible integrations of BI with emerging data-driven technologies (e.g. artificial intelligence) in HCOs, as the role of BI in addressing sustainability challenges.

The purpose of this study is to understand the determinants affecting behavioral intention (BI) to use ePharmacy services. The moderating role of technology discomfort in the relationship between BI and the actual use of ePharmacies in the context of Bangladesh is also examined.

A descriptive, quantitative approach was used to consider the UTAUT-2 model. Using the convenience sampling method, 255 responses were collected. The data were analyzed using Smart-PLS 3.2 software to investigate the hypothesized relationships.

The findings reveal that website information, doctors’ services, performance expectancy, return policy, social influence, perceived reliability and facilitating conditions are significantly related to the BI to use ePharmacy services. Interestingly, the structural equation modeling results also confirmed that technology discomfort has no moderating effect on the relationship between BI and actual usage behavior.

This research provides theoretical contribution by extending the practical knowledge focusing on the relationship of ePharmacy, BI and actual usage behavior by using UTAUT-2 model – a relevant and unexplored issue in the easting literature, offering several research opportunities as the future avenue.

The result highlights the economic and social relevance from the perspective of a developing country. As people are showing their intention toward ePharmacy, managers and decision-makers need to take strategic decision to overcome any difficulties. Policymakers need to improve their services for the expansion of ePharmacy through different development projects.

This study advances past studies on the use of ecommerce in the pharmaceutical industry and provides a general understanding of customers in developing countries.

Research on the Internet of Things (IoT) has gained momentum in various industry contexts. However, the literature lacks broad empirical evidence on the factors that influence users' intention to adopt this cutting-edge technology, especially in the food and beverage industry (F&BI) – a significant yet unexplored setting. Therefore, the authors aim to extend the “Unified Theory of Acceptance and Use of Technology (UTAUT)” model by coupling it with perceived collaborative advantage, organizational inertia and perceived cost and explore the key determinants of IoT adoption for the digital transformation of the F&BI.

This study employs a cross-sectional quantitative approach, where a sample of 307 usable responses was drawn from the senior managers of the Australian F&BI.

The authors have found that performance expectancy, perceived collaborative advantage, effort expectancy, social influence and facilitating conditions have a strong positive influence on the behavioural intention to adopt IoT for the digital transformation of the F&BI. Furthermore, while high perceived costs and organizational inertia are often considered negative factors in adopting new technology, our results reveal the insignificant influence of these factors on the adoption of IoT, which is interesting. The findings also suggest that age and voluntariness significantly moderate most of the relationships, while gender is an insignificant moderator.

The study provides several novel insights into the existing body of knowledge by extending the UTAUT model with three variables and applying it in a unique context.

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 investigate the impact of boundary integration (BI) on innovative work behavior (IWB) of nursing staff. Furthermore, in order to understand the constructive role of BI, this study also examines the mediating role of work-to-family enrichment (WFE) and moderating role of co-worker and supervisor support.

Data were collected from 786 nurses and 144 doctors (nurse supervisors) through self-administered questionnaires from public sector hospitals in Pakistan. Descriptive statistics, correlation, Baron and Kenny approach (Causal steps approach), PROCESS Macro (Normal Test Theory) developed by Hayes and hierarchical regression approaches were used to analyze the collected data that provide several interesting results for the formulated hypotheses.

Results indicated that BI among nursing staff is positively related to doctors’ rating of innovative behaviors. Moreover, WFE mediates the relationship of BI and IWB. Furthermore, the results also confirmed that the relationship between BI and IWB is stronger among those nurses who frequently received support from co-workers and supervisors.

Employees’ involvement in innovative work is of crucial importance for organization’s strength, especially in health care sector. Although researchers have identified various antecedents of nurses’ IWB, however, it is still unclear how BI influences IWB. Moreover, this study focuses on another important element of workplace support and argues that nurses who can successfully manage work and family matters through the integration of boundaries have greater opportunities to achieve enrichment and respond more effectively to demonstrate IWB.

This study aims to explore the impact of “Performance Expectancy” (PE) on the performance of Indian automotive manufacturing employees through their “Behavioural Intention” (BI) in the context of digital learning within the volatile, uncertain, complex and ambiguous (VUCA) world.

A descriptive research design was adopted to gather data from 211 employees of Indian automotive manufacturing companies. Structural equation modelling was applied for data analysis and obtaining results.

Largely, the findings indicate that BI does mediate the relationship between PE and employee performance (EP) in the context of the Indian automotive manufacturing sector. The findings would help in boosting digital learning initiatives, improving EP and fostering organizational success in the VUCA world.

The current study focused on the Indian manufacturing industry. Extending the research beyond the manufacturing sector to other industries in India could possibly help in generalizing the findings, and thereby enhance knowledge of the broader consequences of digital learning on EP.

Digital learning platforms can enhance sustainable industrialization in the manufacturing sector by providing employees with access to digital learning opportunities. Managers must provide employees with access to digital learning opportunities for making them more creative and innovative, so that they can become real change agents, and can handle real-world problems more efficiently, leading the organization to deal with the complexities and unpredictability in this VUCA world.

Within the ambits of Indian automotive manufacturing organizations, there have been limited research studies that have used two constructs of the UTAUT2 framework to see their impact on EP in terms of digital learning. Thus, based on the limited information available, this research stands as a pioneering effort in the realm of digital learning within the Indian automotive manufacturing industry, where the relationship between PE and EP is examined through the mediational role of BI of employees. Moreover, this research possibly would pave the way for further research studies on exploring the role of potential factors influencing the strength and direction of the relationships between these variables (PE and EP).