Modeling of Business Intelligence Systems Using the Potential Determinants and Theories with the Lens of Individual, Technological, Organizational, and Environmental Contexts-A Systematic Literature Review

Mitigate cross-market competition caused by the risk of uncertainty and improve firm performance through business intelligence

In the face of various risks and uncertainties including political instability, technological advancements, and natural disasters, businesses involved in cross-market activities are encountering a more competitive environment. This study investigates the relationship between competitive intensity, business intelligence, internal process efficiency, and the performance of small and medium-sized manufacturing enterprises in China. By incorporating the Technology-Organization-Environment (TOE) framework and Dynamic Capabilities Theory, a research model is developed to demonstrate that competitive intensity improves firms' performance through the utilization of business intelligence capabilities and internal process efficiency. Through the use of a structural equation model, data collected from 15 industrial parks in Henan province, China, involving 429 participants, was analyzed. The findings show a positive correlation between competitive intensity and business intelligence sensing capability (both internal and external). The impact of business intelligence sensing capability on the performance of small and medium-sized manufacturing enterprises is shown to be mediated through internal process efficiency. Our study reveals the mediating roles of business intelligence capability and internal process efficiency in improving organizational performance among Chinese small and medium-sized manufacturing enterprises. This research not only fills gaps in business intelligence research from a management perspective but also contributes to the literature on the interactions among competitive intensity, business intelligence, internal processes, and organizational performance. It also sheds light on the relationship between competitive intensity and organizational performance, offering insights for companies involved in cross-market activities. By highlighting the importance of business intelligence capabilities in adapting to environmental influences, this study offers practical guidance for enterprise digital transformation efforts.

Understanding the User-Generated Geographic Information by Utilizing Big Data Analytics for Health Care

There are two main ways to achieve an active lifestyle, the first is to make an effort to exercise and second is to have the activity as part of your daily routine. The study's major purpose is to examine the influence of various kinds of physical engagements on density dispersion of participants in Shanghai, China, and even prototype check-in data from a Location-Based Social Network (LBSN) utilizing a mix of spatial, temporal, and visualization methodologies. This paper evaluates Weibo used for big data evaluation and its dependability in some types rather than physically collected proofs by investigating the relationship between time, class, place, frequency, and place of check-in built on geographic features and related consequences. Kernel density estimation has been used for geographical assessment. Physical activities and frequency allocation are formed as a result of hour-to-day consumption habits. Our observations are based on customer check-in activities in physical venues such as gyms, parks, and playing fields, the prevalence of check-ins, peak times for visiting fun parks, and gender disparities, and we applied relative difference formulation to reveal the gender difference in a much better way. The purpose of this research is to investigate the influence of physical activity and health-related standard of living on well-being in a selection of Shanghai inhabitants.

Business Intelligence: Business Evolution after Industry 4.0

Industry 4.0 is a set of technologies that companies require to promote innovation strategies and obtain a rapid response in dynamic markets. It focuses mainly on interconnectivity, digital technology, predictive analytics and machine learning to revolutionize the way companies operate and develop. Therefore, this article proposes and motivates the implementation of Industry 4.0 in organizations. Studying the state of the art and reviewing the current situation of business intelligence (BI) technology, the way it has positively impacted organizations at the economic and business level in terms of decision-making and some success stories implemented in different business, academic, social and governmental environments. Moreover, it addresses the future expected for Industry 4.0 primarily in BI and how companies should face this revolution. This article provides knowledge contribution about the current state and positive consequences of Industry 4.0, and high development in technology when implemented in the organization and the harmonization between production and intelligent digital technology.

Exploration of Influential Determinants for the Adoption of Business Intelligence System in the Textile and Apparel Industry

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.

Sustainability of Commercial Banks Supported by Business Intelligence System

This article was focused on establishing whether Business Intelligence (BI) systems provide sustainability to commercial banks by influencing their financial condition. As part of the search for a solution to the research problem, a hypothesis was formulated which assumes that the use of the Business Intelligence management system improves the financial condition of commercial banks. To assess this impact, a novel comparative method was used, which assumed comparing financial condition indicators in three aspects: before and after the implementation of the Business Intelligence system (comparison over time), with average indicators of a group of banks (comparison to the industry), with reference to changes in the overall economic situation. As a result of the method used, a synthetic indicator of the impact of using Business Intelligence (ABI) was calculated. This study was conducted in relation to six out of the thirteen largest commercial banks listed on the Warsaw Stock Exchange in 2020, which have implemented the Business Intelligence system since 2001. The assets of the examined banks cover 60% of the assets of commercial banks in Poland. As a result of the study, a positive impact of using the BI system on selected areas of the financial condition of commercial banks was identified. In particular, this impact relates to areas of productivity, the quality of assets and liabilities, profitability and debt. The generalized results of this study allow for the determination of cause and effect relationships between the use of the BI system in commercial banks and the improvement of the financial condition indicators as well as sustainability banking.