In this study, we draw on the structurational model of technology in an institutional setting to investigate how top management affects the development of a firm's business intelligence (BI) ...capability. We propose a multiple mediator model in which organizational factors, such as user participation and analytical decision making orientation, act as mediating mechanisms that transmit the positive effects of top management championship to advance a firm's BI capability. BI capability has two distinct aspects: information capability and BI system capability. Drawing on data collected from 486 firms from six different countries, we found support for the mediating effects of top management championship through user participation and analytical decision making orientation. These findings contribute to a nuanced understanding of how firms can develop BI capability. This study is one of the first to comprehensively investigate the antecedents of BI capability.
Due to the uncertainty of the market and the intensity of rivalry, business owners and managers are often compelled to experiment with a wide variety of strategies for enhancing their company's ...performance. By enhancing the timeliness and quality of inputs to the decision-making process, Business Intelligence (BI) is one such idea and tool that combines operational data with analytical tools to show complex and competitive information to planners and decision-makers. Business intelligence (BI) tools help companies rapidly generate insights that guide managers toward operational efficiencies, lead them to new opportunities, and set them apart from the competition. The literature study shows that there is a debate about whether BI tools have an effect on the quality of decisions and the development of businesses. The present research explores the varied empirical facets of BI application through ML models. This study concluded with a discussion of how Machine Learning models can be used to assess the value of BI tools. Machine learning models, fed with historical data and a wealth of input features, can foresee the effect of new systems on metrics like revenue development, customer behavior, and inventory management. Using these models, businesses will be able to better evaluate potential investments in new tools and systems.
Small scale enterprises can improve their operations by implementing business intelligence systems. The business intelligence systems are complex and require expertise to ensure successful ...implementation, hence the need for small scale enterprises to determine their readiness before undertaking the project. To improve chances for successful implementation, this study proposed a framework to guide small scale enterprises on the requirements for business intelligence systems. The design steps defined by Edwards and Goodrich & Tamassia were followed to design the framework. The framework components were informed by the Diffusion of Innovation and Technology Organization and Environment theories, the Information Evaluation Model, and the critical success factors for BIS implementation. A small business may evaluate its resources against the framework components to determine whether to implement a business intelligence system. In future, the framework may be extended to include weights and other criteria to calculate a business’s status.
The information systems (IS) literature has long emphasized the positive impact of information provided by business intelligence systems (BIS) on decision-making, particularly when organizations ...operate in highly competitive environments. Evaluating the effectiveness of BIS is vital to our understanding of the value and efficacy of management actions and investments. Yet, while IS success has been well-researched, our understanding of how BIS dimensions are interrelated and how they affect BIS use is limited. In response, we conduct a quantitative survey-based study to examine the relationships between maturity, information quality, analytical decision-making culture, and the use of information for decision-making as significant elements of the success of BIS. Statistical analysis of data collected from 181 medium and large organizations is combined with the use of descriptive statistics and structural equation modeling. Empirical results link BIS maturity to two segments of information quality, namely content and access quality. We therefore propose a model that contributes to understanding of the interrelationships between BIS success dimensions. Specifically, we find that BIS maturity has a stronger impact on information access quality. In addition, only information content quality is relevant for the use of information while the impact of the information access quality is non-significant. We find that an analytical decision-making culture necessarily improves the use of information but it may suppress the direct impact of the quality of the information content.
► Analyzing the impacts of information content and access quality on information use ► Analytical decision-making culture is introduced as the moderator variable. ► Related to the use of information, only information content quality is relevant. ► Nevertheless, the impact of BIS maturity on information access quality is stronger. ► Focus on analytical decision making culture improves the use of information.
The implementation of a business intelligence (BI) system is a complex undertaking requiring considerable resources. Yet there is a limited authoritative set of critical success factors (CSFs) for ...management reference because the BI market has been driven mainly by the IT industry and vendors. This research seeks to bridge the gap that exists between academia and practitioners by investigating the CSFs influencing BI systems success. The study followed a two-stage qualitative approach. Firstly, the authors utilised the Delphi method to conduct three rounds of studies. The study develops a CSFs framework crucial for BI systems implementation. Next, the framework and the associated CSFs are delineated through a series of case studies. The empirical findings substantiate the construct and applicability of the framework. More significantly, the research further reveals that those organisations which address the CSFs from a business orientation approach will be more likely to achieve better results. PUBLICATION ABSTRACT
The textile and apparel industry is prone to digitization with business intelligence systems (BIS) and big data concepts to contribute the global sustainability. BIS, an impactful and leading ...technology, is being implemented in many industrial sectors but almost 80% of BIS fail to give expected results due to unknown reasons. Although many scholars put effort into finding the influential determinants for the BIS implementation, they neglect the BIS adoption context, especially in the textile and apparel industry. A purposive and proportionate choice of potential determinants in the context of adoption would contribute significantly to the success of BIS. Multi-stage research is employed to identify and prioritize the significant determinants. In the first stage, twenty-two semi-structured in-depth interviews are conducted with seventeen textile and apparel companies. Ten significant determinants emerged after thematic analysis of interview data. The determinants are sustainability, competitive pressure, market trends, compatibility, technology maturity, leadership commitment and support, satisfaction with existing systems, sustainable data quality and integrity, users’ traits, and interpersonal communications that influence the adoption of BIS. In the second stage, the Best Worst Method (BWM) is used to calculate the weights for prioritizing the determinants based on experts’ opinion. These weights are then used to evaluate and rank the determinants. The findings of this research show that the leadership commitment and support, sustainability, users’ traits, and technology maturity, are the top-ranked determinants that influence the practitioners’ choice to adopt the BIS in the textile and apparel industry. The results of this study enable the BIS stakeholders to holistically comprehend the significant determinants that would drive or impede the success of BIS projects in the sustainable textile and apparel industry.
Big Data analysis is the process that can help organizations to make better business decisions. Organizations use data warehouses and business intelligence systems, i.e. enterprise information ...systems (EISs), to support and improve their decision-making processes. Since the ultimate goal of using EISs and Big Data analytics is the same, a logical task is to enable these systems to work together. In this paper we propose a framework of cooperation of these systems, based on the schema on read modeling approach and data virtualization. The goal of data virtualization process is to hide technical details related to data storage from applications and to display heterogeneous data sources as one integrated data source. We have tested the proposed model in a case study in the transportation domain. The study has shown that the proposed integration model responds flexibly and efficiently to the requirements related to adding new data sources, new data models and new data storage technologies.
PurposeAlthough much is understood about Business Intelligence (BI) technology adoption, less is known about the complementary organisational resources that drive the actual use of BI systems and the ...impacts of BI systems at an individual employee level. This study aims to develop and test a model of the impact of key complementary organisational resources on employees' actual BI systems’ use behaviours and their decision-making performance.Design/methodology/approachTo test the research model, a cross-sectional survey of 437 North American employees, who described themselves as using a BI system to make decisions, was conducted. The partial least square (PLS), a structural equational modelling (SEM) technique, was employed to analyse the survey data.FindingsThe survey findings attest to the influence of key complementary organisational resources (i.e. data-based culture (DBC), quality of data in source systems and decision-making autonomy) on employees' actual BI use (comprising BI system dependence and BI system infusion) and on their decision-making performance. Specifically, a DBC and the quality of data in source systems are found to significantly enhance BI system dependence and BI system infusion. Decision-making autonomy, DBC, BI system dependence and BI system infusion are significant contributors to achieving decision-making performance.Originality/valueThis study proposes a theoretical model of actual BI systems’ use from an individual user perspective that increases our understanding of both the complexity of BI usage and the complementary organisational resources that drive both actual BI systems’ use and the impacts of BI systems.
PDF
