Search results

The strategic management literature emphasizes the concept of business intelligence (BI) as an essential competitive tool. Yet the sustainability of the firms’ competitive advantage provided by BI capability is not well researched. To fill this gap, this study attempts to develop a model for successful BI deployment and empirically examines the association between BI deployment and sustainable competitive advantage. Taking the telecommunications industry in Malaysia as a case example, the research particularly focuses on the influencing perceptions held by telecommunications decision makers and executives on factors that impact successful BI deployment. The research further investigates the relationship between successful BI deployment and sustainable competitive advantage of the telecommunications organizations. Another important aim of this study is to determine the effect of moderating factors such as organization culture, business strategy, and use of BI tools on BI deployment and the sustainability of firm’s competitive advantage.

This research uses combination of resource-based theory and diffusion of innovation (DOI) theory to examine BI success and its relationship with firm’s sustainability. The research adopts the positivist paradigm and a two-phase sequential mixed method consisting of qualitative and quantitative approaches are employed. A tentative research model is developed first based on extensive literature review. The chapter presents a qualitative field study to fine tune the initial research model. Findings from the qualitative method are also used to develop measures and instruments for the next phase of quantitative method. The study includes a survey study with sample of business analysts and decision makers in telecommunications firms and is analyzed by partial least square-based structural equation modeling.

The findings reveal that some internal resources of the organizations such as BI governance and the perceptions of BI’s characteristics influence the successful deployment of BI. Organizations that practice good BI governance with strong moral and financial support from upper management have an opportunity to realize the dream of having successful BI initiatives in place. The scope of BI governance includes providing sufficient support and commitment in BI funding and implementation, laying out proper BI infrastructure and staffing and establishing a corporate-wide policy and procedures regarding BI. The perceptions about the characteristics of BI such as its relative advantage, complexity, compatibility, and observability are also significant in ensuring BI success. The most important results of this study indicated that with BI successfully deployed, executives would use the knowledge provided for their necessary actions in sustaining the organizations’ competitive advantage in terms of economics, social, and environmental issues.

This study contributes significantly to the existing literature that will assist future BI researchers especially in achieving sustainable competitive advantage. In particular, the model will help practitioners to consider the resources that they are likely to consider when deploying BI. Finally, the applications of this study can be extended through further adaptation in other industries and various geographic contexts.

The purpose of this study was to demonstrate a cloud business intelligence model for industrial SMEs. An initial model was developed to accomplish this, followed by validation and finalization of the cloud business intelligence model. Additionally, this research employs a mixed-techniques approach, including both qualitative and quantitative methods. This paper aims to achieve the following objectives: (1) Recognize the Cloud business intelligence concepts. (2) Identify the role of cloud BI in SMEs. (3) Identify the factors that affect the design and presenting a Cloud business intelligence model based on critical factors affecting SMEs during pandemic COVID-19. (4) Discuss the importance of Cloud BI in pandemic COVID-19 for SMEs. (5) Provide managerial implications for using Cloud BI effectively in Iran’s SMEs.

In the current study, an initial model was first proposed, and the cloud business intelligence model was then validated and finalized. Moreover, this study uses a mixed-methods design in which both qualitative and quantitative methods are used. The fuzzy Delphi Method has been applied for parameter validation purposes, and eventually, the Cloud business intelligence model has been presented through exploiting the interpretive structural modeling. The partial least squares method was also applied to validate the model. Data were also analyzed using the MAXQDA and Smart PLS software package.

In this research, from the elimination of synonym and frequently repeated factors and classification of final factors, six main factors, 24 subfactors and 24 identifiers were discovered from the texts of the relevant papers and interviews conducted with 19 experts in the area of BI and Cloud computing. The main factors of our research include drivers, enablers, competencies, critical success factors, SME characteristics and adoption. The subfactors of included competitors pressure, decision-making time, data access, data analysis and calculations, budget, clear view, clear missions, BI tools, data infrastructure, information merging, business key sector, data owner, business process, data resource, data quality, IT skill, organizational preparedness, innovation orientation, SME characteristics, SME activity, SME structure, BI maturity, standardization, agility, balances between BI systems and business strategies. Then, the quantitative part continued with the fuzzy Delphi technique in which two factors, decision-making time and agility, were deleted in the first round, and the second round was conducted for the rest of the factors. In that step, 24 factors were assessed based on the opinions of 19 experts. In the second round, none of the factors were removed, and thus the Delphi analysis was concluded. Next, data analysis was carried out by building the structural self-interaction matrix to present the model. According to the results, adoptability is a first-level or dependent variable. Regarding the results of interpretive structural modeling (ISM), the variable of critical success factors is a second-level variable. Enablers, competencies and SME characteristics are the third-level and most effective variables of the model. Accordingly, the initial model of Cloud BI for SMEs is presented as follows: The results of ISM revealed the impact of SME characteristics on BI critical success factors and adoptability. Since this category was not an underlying category of BI; thus, it played the role of a moderating variable for the impact of critical success factors on adoptability in the final model.

Since this study is limited to about 100 SMEs in the north of Iran, results should be applied cautiously to SMEs in other countries. Generalizing the study's results to other industries and geographic regions should be done with care since management perceptions, and financial condition of a business vary significantly. Additionally, the topic of business intelligence in SMEs constrained the sample from the start since not all SMEs use business intelligence systems, and others are unaware of their advantages. BI tools enable the effective management of companies of all sizes by providing analytic data and critical performance indicators. In general, SMEs used fewer business intelligence technologies than big companies. According to studies, SMEs understand the value of simplifying their information resources to make critical business choices. Additionally, they are aware of the market's abundance of business intelligence products. However, many SMEs lack the technical knowledge necessary to choose the optimal tool combination. In light of the frequently significant investment required to implement BI approaches, a viable alternative for SMEs may be to adopt cloud computing solutions that enable organizations to strengthen their systems and information technologies on a pay-per-use basis while also providing access to cutting-edge BI technologies at a reasonable price.

Before the implementation of Cloud BI in SMEs, condition of driver, competency and critical success factor of SMEs should also be considered. These will help to define the significant resources and skills that form the strategic edge and lead to the success of Cloud BI projects.

Most of the previous studies have been focused on factors such as critical success factors in cloud business intelligence and cloud computing in small and medium-sized enterprises, cloud business intelligence adoption models, the services used in cloud business intelligence, the factors involved in acceptance of cloud business intelligence, the challenges and advantages of cloud business intelligence, and drivers and barriers to cloud business intelligence. None of the studied resources proposed any comprehensive model for designing and implementing cloud business intelligence in small and medium-sized enterprises; they only investigated some of the aspects of this issue.

The purpose of this paper is to explore the extent to which strategic intelligence is utilised within the South African long‐term insurance industry and whether it could be used to identify opportunities or threats within the global environment to remain competitive, create greater innovation, and corporate advantage.

The approach of this paper is to obtain the qualitative views and opinions of strategic decision makers, on an executive managerial level within the South African long‐term insurance industry, on their organizations' use of strategic intelligence.

There are marked differences in the conformity and usage of strategic intelligence and its components between the organizations surveyed, with a measurable difference between large and small organizations, however, it is generally viewed that the use of a strategic intelligence framework could greatly enhance decision making.

Data collection was limited to the 82 long‐term insurance companies which were registered with the South African Financial Services Board, with a focus on the organizations listed on the Johannesburg Securities Exchange within the Life Assurance Sector, within which a final response rate of 36.1 per cent was achieved, including the 100 per cent response rate from the six listed organizations.

The paper identifies the extent to which strategic intelligence is utilised in the South African long‐term insurance industry, and identifies the benefits or problems that are experienced by implementing and using strategic intelligence as an input to the strategic management process and what value strategic intelligence adds in the decision‐making process.

The identification and utilisation of the most important factors of a strategic intelligence framework will greatly enhance global corporate decision making and result in competitive advantage and constant innovation within the South African business environment.

As the health care sector is changing rapidly, there is a growing need to develop new ways to make data-driven decisions, especially at the organizational level. Data utilization, like business intelligence (BI) activities, benefits health care organizations. The purpose of this paper is to study the potential of Big Data and the utilization of BI tools in creating value in the private health care industry in Finland.

Intellectual capital (IC) components and Möller et al.’s (2005) work on value capabilities are used as a framework to point out the roles of data utilization and BI tools in value creation. Thematic interviews enable understanding of the value creation based on Big Data potential and utilization of BI tools in the Finnish private health care industry.

The findings will provide an understanding of the existing data sources and BI tools used in private health care. In addition, it provides an insight into the future-oriented Big Data potential, which can create new business concepts. The approach provides valuable insights for value identifying the future needs of data utilization and creates an understanding on the current state within the private health care sector.

Data-driven value creation is one of the most discussed topics in private health care sector. By analyzing the current data-source utilization, challenges with data and BI tool utilization and the future vision and development roadmaps, the authors gain a better understanding of the IC components and value creation capabilities.

This article seeks to address the interface between individual learning facilitators that use course management systems (CMS) data to support decision‐making and course design and institutional infrastructure providers that are responsible for institutional business intelligence.

The design of a data warehouse is proposed that draw data from institutional transactional systems to provide decision support to individual action researchers. A prototype data warehouse is built to evaluate by means of a case study the usefulness validity of analyses performed.

Many facilitators of learning draw manually the same type of queries from CMS data for purposes of action research. On the other hand, more and more HEI infrastructure providers create data warehouses to support many kinds of decision‐making. It is possible and valuable to follow a business intelligence approach to facilitate the queries drawn by individual action researchers from course management systems (CMSs).

The expectation exists that as the technology on which CMSs, as well as business intelligence tools are built evolves, the creation of full‐scale business intelligence will become more feasible and scalable.

This article addresses the gap between individual action researchers that use CMS data to support decision making and course design, on the one hand, and institutional infrastructure providers that are responsible for institutional business intelligence on the other hand. Research questions are asked and addressed and processes are designed to manage business measurements consistently.

The purpose of this paper is to explore the effectiveness of business intelligence (BI) tools as enablers of knowledge sharing used by employees in the organisation. This practice‐oriented article on the deployment and impact of BI tools in industry suggests a balanced scorecard (BSC) approach to performance management. More specifically, a suite of web 2.0 tools is used in the practice of BI and their impact measured with a BSC.

The research proposition is that the effectiveness of BI is indeed strategic and relates to its corporate performance. This claim is validated using a global information technology consultancy firm's BI unit as the lead case of an immersive field study. Research engagements with four other firms provide corroborative support.

The BSC approach to deriving targets and ascertaining outcomes was shown to be applicable to good practice. The converse is equally valid. That is, strategic performance management requires the use of BI in order to be sound. Therefore, tools such as web 2.0 and data analytics, must be outcome driven with planned targets identified.

BI is a necessary activity for deriving improved performance. It aids in the identification of a firm's knowledge strengths, as well as gaps with respect to its environment. The key message to executives is that Peter Drucker was right – we cannot manage what we do not measure!

The use of BI as a strategic knowledge management technique is a composite of a host of web 2.0 tools. It does not stand in isolation from other initiatives for exploiting knowledge in order to drive performance.

The nature and amount of data that public organizations have to monitor to counteract corruption lead to a phenomenon called “KPI overload”, consisting of the business analyst feeling overwhelmed by the amount of information and resulting in the absence of appropriate control. The purpose of this study is to develop a solution based on Artificial Intelligence technology to avoid data overloading and, at the same time, under-controlling in business process monitoring.

The authors adopted a design science research approach. The authors started by observing a specific problem in a real context (a healthcare organization); then conceptualized, designed and implemented a solution to the problem with the goal to develop knowledge that can be used to design solutions for similar problems. The proposed solution for business process monitoring integrates databases and self-service business intelligence for outlier detection and artificial intelligence for classification analysis.

The authors found the solution powerful to solve problems related to KPI overload in process monitoring. In the specific case study, the authors found that the combination of Business Intelligence and Artificial Intelligence can provide a significant contribution to the detection of fraud, corruption and/or policy misalignment in public organizations.

The authors provide a big-data-based solution to the problem of data overload in business process monitoring that does not sacrifice any monitored Key Performance Indicators and that also reduces the workload of the business analyst. The authors also developed and implemented this automated solution in a context where data sensitivity and privacy are critical issues.

There is a growing importance of business intelligence systems (BIS) adoption in today’s digital economy age which is characterized by uncertainty and ambiguity considering the magnitude and influence of data-related issues to be solved in contemporary businesses. This study aims to investigate critical success factors that affect business intelligence efficiency based on the DeLone and McLean model in Jordanian banking industry.

A quantitative research method through a questionnaire was used to collect data from actual users who depend on business intelligence tools to make operational and strategic decisions in Jordanian banks. The data obtained were tested using the partial least squares–structural equation modeling approach.

The survey findings attest that system quality, information quality, user quality, user satisfaction and user performance are important factors and contribute to business intelligence efficiency in the Jordanian banking industry.

The findings gained from this work can help policymakers in Jordanian banks to improve the business intelligence success and organizational performance.

To the best of the authors’ knowledge, this study is the first of its kind to propose a theoretical model to assess drivers of BIS efficiency from the Jordanian banks’ perspective.

Higher education institutions (HEIs) face a number of challenges in effectively managing and reporting on sustainability information, such as siloes of data and a limited distribution of information. Business intelligence (BI) can assist in addressing the challenges faced by organisations. The purpose of this study was to propose a BI framework for strategic sustainability information management (the Sustainable BI Framework) that can be used in HEIs.

The research applied the design science research methodology whilst using a South African HEI as a case study. The problems with sustainability information management were identified, and a theoretical framework was proposed. In addition, a practical BI software tool was developed as proof of concept to address these problems and to assist with the management of strategic sustainability information in an HEI.

The proposed sustainability BI tool was evaluated through heuristic and usability evaluations with senior management. The results indicated that the usability of the BI tool was positively rated and that the framework can assist in overcoming the constraints that HEIs face in effectively managing sustainability information.

The research was limited to a single case. However, the theoretical framework was derived from and expanded on existing stakeholder theory, sustainability reporting theory and literature on BI dashboard development. The framework was implemented successfully in the Sustainable BI Tool prototype at the case study, and the results reveal in-depth information regarding information management for sustainability reporting in higher education.

The Sustainable BI Tool is a solution that integrates data from multiple areas of sustainability and provides a single integrated view of the information to stakeholders. The information is provided through performance dashboards, which provide predictive capabilities to enable management to report on sustainability and determine if the institution is meeting its strategic goals. The lessons learnt can also assist other HEIs considering implementing BI for sustainability reporting.

Improved sustainability reporting for HEIs provided by the BI framework can improve the environmental and social impact of the educational community.

This study provides the most comprehensive framework for guiding the design of a BI tool to assist in effectively managing sustainability information in HEIs.

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.