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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.

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.

This chapter discussed the role of business intelligence (BI) in healthcare twofold strategic decision making of the organization and the stakeholders. The visualization techniques of data mining are applied for the early and correct diagnosis of the disease, patient’s satisfaction quotient and also helpful for the hospital to know their best commanders.

In this chapter, the usefulness of BI is shown at two levels: at doctor level and at hospital level. As a case study, a hospital is taken which deals with three different kinds of diseases: Breast Cancer, Diabetes, and Liver disorder. BI can be applied for taking better strategic decisions in the context of hospital and its department’s growth. At the doctor level, on the basis of various symptoms of the disease, the doctor can advise the suitable treatment to the patients. At the hospital level, the best department among all can be identified. Also, a patient’s type of admission, continued their treatments with the hospital, patient’s satisfaction quotient, etc., can be calculated. The authors have used different methods like Correlation matrix, decision tree, mosaic plots, etc., to conduct this analysis.

Organizations introduce business intelligence (BI) to increase their performance, but often, this initiative is not aligned with the business process management (BPM) initiative, which also aims to improve organizational performance. Although some findings from the literature indicate that BI implementation has a positive impact on organizational performance, the impact seems to be indirect. Therefore, the purpose of this study is to enhance the understanding of how BI maturity is translated into organizational performance. Alignment of BI and BPM initiatives seems one possible way for creating business value with BI, particularly because BI enables process performance measurement and management, which allows the BI initiative to become more business focused.

A questionnaire was prepared and used to collect data in Croatian and Slovenian organizations with more than 50 employees. A BI–BPM alignment measurement instrument was developed for the purpose of this study using the recommended process of scale development and validation. A total of 185 responses were analyzed by the structural equation modeling technique.

Our results provide evidence that the effect of BI on organizational performance is fully mediated by alignment of BI and BPM initiatives, and therefore, BI business value can be generated through the use of common terminology and methodologies, as well as a strong communication between BI and BPM experts, managers and teams in order to coordinate the two initiatives.

This study has responded to the call for better understanding of how the impact of BI on organization performance is realized. It confirmed that BI and BPM initiatives should be aligned in order to give BI a business value.

The purpose of this paper is to explore the key ingredients that people analytics teams require to contribute to organizational performance. As the information that is currently available is fragmented, it is difficult for organizations to understand what it takes to execute people analytics successfully.

To identify the key ingredients, a narrative literature review was conducted using both traditional people analytics and broader business intelligence literature. The findings were summarized in the People Analytics Effectiveness Wheel.

The People Analytics Effectiveness Wheel identifies four categories of ingredients that a people analytics team requires to be effective. These are enabling resources, products, stakeholder management and governance structure. Under each category, multiple sub-themes are discussed, such as data and infrastructure; senior management support; and knowledge, skills, abilities and other characteristics (KSAOs) (enablers).

Many organizations are still trying to set up their people analytics teams, and many others are struggling to improve decision-making by using people analytics. For these companies, this paper provides a comprehensive overview of the current literature and describes what it takes to contribute to organizational performance using people analytics.

This paper is designed to provide organizations and researchers with a comprehensive understanding of what it takes to execute people analytics successfully. By using the People Analytics Effectiveness Wheel as a guideline, scholars are now better equipped to research the processes that are required for the ingredients to be truly effective.

This paper aims to discuss the effects of COVID-19 on hotel marketing and management practices and outlines a three-pronged research agenda to stimulate knowledge development in the hotel sector.

This paper is based on an overview of the relevant literature on hotel marketing and management and the hotel guest behavior. The authors also investigated hospitality service trends to propose a research agenda.

This paper presents a research agenda from three dimensions – artificial intelligence (AI) and robotics, hygiene and cleanliness and health and health care. First, different types of AI (mechanical, thinking and feeling) might open up distinct research streams at the intersection of health crises and hotel management, in light of the COVID-19 pandemic. Additionally, this paper recommends that researchers move beyond typical perspectives on the antecedents and outcomes of hotel hygiene and cleanliness to delve into guests’ perceptions of the cleanliness of specific hotel surfaces. Furthermore, a more in-depth analysis is warranted about the evolving relationship between hotels and the health-care sector.

The recommended research areas are intended to advance the knowledge base to help hotels recover from the COVID-19 pandemic. The suggested research streams are expected to provide actionable insights to promote the development and sustainability of the hotel sector.

This paper appears to be a frontier study, critically examining possible effects of the COVID-19 pandemic on hotel marketing and management practices and how hoteliers may respond to such challenges to recover after this pandemic.

The construction sector has significantly evolved in recent decades, in parallel with a huge increase in the amount of data generated and exchanged in any construction project. These data need to be managed in order to complete a successful project in terms of quality, cost and schedule in the the context of a safe project environment while appropriately organising many construction documents.

However, the origin of these data is very diverse, mainly due to the sector’s characteristics. Moreover, these data are affected by uncertainty, complexity and diversity due to the imprecise nature of the many factors involved in construction projects. As a result, construction project data are associated with large, irregular and scattered datasets.

The objective of this chapter is to introduce an approach based on a fuzzy multi-dimensional model and on line analytical processing (OLAP) operations in order to manage construction data and support the decision-making process based on previous experiences. On one hand, the proposal allows for the integration of data in a common repository which is accessible to users along the whole project’s life cycle. On the other hand, it allows for the establishment of more flexible structures for representing the data of the main tasks in the construction project management domain. The incorporation of this fuzzy framework allows for the management of imprecision in construction data and provides easy and intuitive access to users so that they can make more reliable decisions.

The role of information and communication technologies (ICTs) and digital platforms in enabling connectivity and collaboration among actors is neglected when dealing with outbound open innovation (OI). Moreover, the outbound OI process is not currently defined in a univocal way. Thus, this paper aims to outline the outbound OI phases and to explore role and capabilities of ICTs in supporting it.

Through a literature review approach, the authors specified the outbound OI process. Hence, the authors leveraged the similarities between the knowledge management process phases and the outbound OI phases for developing a conceptual framework that could match the outbound OI phases with acknowledged categories of ICT tools.

Through a process-view, the authors outlined outbound OI as a three-phase process. The authors developed a matrix-shaped framework in which the columns represent the three outbound OI process phases, while the rows are three ICT categories that could be suitable for supporting the outbound OI process.

The framework is designed to guide a deep understanding of how ICTs could support specific phases of the outbound OI process. In so doing, it could be useful for software developers interested in the preliminary design of an ICT platform for outbound OI.

The conceptual framework proposal as follows: specifies a detailed, process-oriented definition of the outbound OI; allows to identify the main ICT categories supporting the phases of the outbound OI process; and provides guidance for further exploration about the role of ICT in outbound OI.

This study aims to report the design and testing of a maturity model for information and knowledge management in the public sector, intended for use in frequent monitoring, trend analysis and in-depth analysis of the contemporary information and knowledge management practices of an organization.

A design science approach was used to develop the proposed model. Creation of the model was based on an extensive literature review. Testing of the model was implemented as a survey receiving 37 responses from nine organizations organizing and purchasing public services.

The study presents four alternative profiles for an organization’s status, novice, experimenter, facilitator and advanced exploiter, and investigates the differences between these profiles on the basis of the empirical data gathered. The model was found to be both a valid and practical way to determine the state of an organization’s information and knowledge management and identify development needs.

Testing was conducted in the Finnish public sector and further studies applying the model could be implemented in other countries. The model presented was designed specifically for the public sector and more research is needed to test its applicability in the private sector.

Maturity models are useful when evaluating information and knowledge management status in an organization, and beneficial for improving organizational performance. The proposed maturity model combines the fields of knowledge management and information management and contributes to the literature with an overarching maturity model that includes a dimension of satisfaction with the organizational maturity level. While many earlier models originate from the consultancy business, the model presented here was also designed for research purposes and tested in practice.

Foreign direct investment has been extensively recognised as an important resource of economic growth. Governments have been playing an active role in encouraging this type of investment. Despite efforts by governments, only a few countries have been successful in attracting investment for their tourism industry. The purpose of this paper is to detect meaningful relationships between government policies and investment.

This study utilizes statistical and machine learning techniques. A predictive model has been constructed and evaluated using a set of countries, which differ from those putatively used for model generation. Good governance indicators, together with data about investments in the tourism industry, are the main instruments used in the model.

The findings suggest that the formulation and implementation of sound policies, together with regulations, promotes the development of a private sector; and the private sector has a significant role in attracting tourism investment.

The study contributes to research in the tourism industry by using intelligent data analysis techniques.

The availability of comprehensive datasets and a very limited set of empirical studies, related to investment in the tourism industry, has stimulated this research to focus on integrating quantitative resources and assessing the significance of government policies.