The literature on information systems emphasizes the positive impact of information from business intelligence systems (BIS) on decision-making, especially in highly regulated environments. Assessing ...BIS effectiveness is vital to understanding its value and significance in improving operational performance and management. However, deploying BIS and understanding how BIS dimensions are interrelated and how they affect the decision-making process in organizations in the nuclear field still need to be explored. In order to address this research gap, this article investigates the process of implementing BIS in a Brazilian company from the nuclear industry using an action research methodology. Results suggest that the use of BIS in decision-making routines allowed company managers to expand their perception of previously neglected information, significantly helping in decision-making and prioritizing actions and/or solutions.
Data-centric approaches such as big data and related approaches from business intelligence and analytics (BI&A) have recently attracted major attention due to their promises of huge improvements in ...organizational performance based on new business insights and improved decision making. Incorporating data-centric approaches into organizational decision processes is challenging, even more so with big data, and it is not self-evident that the expected benefits will be realized. Previous studies have identified the lack of a research focus on the context of decision processes in data-centric approaches. By using a multiple case study approach, the paper investigates different types of BI&A-supported decision processes, and makes three major contributions. First, it shows how different facets of big data and information processing mechanism compositions are utilized in different types of BI&A-supported decision processes. Second, the paper contributes to information processing theory by providing new insights about organizational information processing mechanisms and their complementary relationship to data-centric mechanisms. Third, it demonstrates how information processing theory can be applied to assess the dynamics of mechanism composition across different types of decisions. Finally, the study’s implications for theory and practice are discussed.
In the last decade, social media platforms have become important communication channels between businesses and consumers. As a result, a lot of consumer‐generated data are available online. ...Unfortunately, they are not fully utilized, partly because of their nature: they are unstructured, subjective, and exist in massive databases. To make use of these data, more than one research method is needed. This study proposes a new, multiple approach to social media data analysis, which counteracts the aforementioned characteristics of social media data. In this new approach the data are first extracted systematically and coded following the principles of content analysis, after a comprehensive literature review has been conducted to guide the coding strategy. Next, the relationships between codes are identified by statistical cluster analysis. These relationships are used in the next step of the analysis, where evaluation criteria weights are derived on the basis of the social media data through probability weighting function. A case study is employed to test the proposed approach.
Purpose
The recent progress of digital accounting has significantly affected businesses’ sustainable production process. Businesses generally use digital accounting applications to automate their ...operational procedures and increase their corporate efficiencies through improved output quality and sustainability. Consequently, the purpose of this study is to look into the antecedent factors that directly and indirectly influence blockchain technology adoption in the context of digital accounting systems.
Design/methodology/approach
The data of the current study were obtained from 346 accountants working in information technology companies. Partial least squares structural equation modeling was used to test the research proposal model.
Findings
The empirical results confirmed that the adoption of blockchain technology is most considerably impacted by perceived usefulness, whereby it was also revealed that perceived ease of use has a direct and indirect effect on blockchain technology adoption.
Originality/value
According to the researchers’ knowledge, this study addresses a vital research gap in the literature by suggesting a comprehensive research model that can help garner enhanced usage of blockchain technology and its implications in digital accounting systems in the Jordanian context.
The cross-disciplinary virtually simulated platform for enterprise management in universities' new business courses is an initiative practical framework based on scenario-driven tasks. However, there ...is a prominent conflict between the rapid operating cycle of simulation enterprises plus their fierce competitions and the strategic demand for real-time analysis for operational data. Based on such demand, this study takes the development method of the simulated enterprise management cockpit from Guangzhou Huashang College as an example. It adopts the combined weighting method based on cloud models to determine indicator weights, then qualitative and quantitative data analyses are conducted from five aspects: "business, finance and operation", "customer management and marketing", "internal operational objectives", "product development strategy", along with "team building and management". This approach achieves a comprehensive evaluation and early warning of the enterprise management process. Specifically, the subjective weights are determined by the Analytic Hierarchy Process, while the objective weights by the entropy weight method, finally verified by cloud model evaluation of its overall indicator performance. The design can evaluate the comprehensive performance of enterprise management indicators and students' activity participation through the cloud-based application and the digital cockpit, so as to fully presents the enterprise's overall management level, along with judgement of whether it is reasonable through pointers in different colors. In addition, apparent indicator-related characteristics are also utilized to assess future decision-making directions. Finally, this comprehensive approach can timely optimize operation strategies and facilitate budget allocation for future development.
Higher education institutions (HEIs) are investing in business intelligence (BI) to meet the increasing demand for information stemming from their operations. Information technology (IT) managers in ...higher education may turn to BI maturity models to evaluate the current state of HEIs’ BI operation capabilities and evaluate the readiness for future improvements. However, generic BI maturity models do not have domain-specific attributes that ensure a high degree of compatibility with HEIs. This study’s objective is to survey maturity models that could be used in HEIs and identify those used for BI to perform an analysis of their qualities and identify future avenues for research into HEI-specific BI maturity models. A systemic mapping was undertaken via both a keyword and snowball search of five indexing services, 6037 articles were processed using inclusion and exclusion criteria resulting in the identification of forty-one academic works regarding maturity model uses which were mapped to ten categories. The mapping reveals an increasing number of publications featuring maturity models for HEI, particularly since 2018, focused on e-learning and ICT. A single instance of a BI maturity model for HEI emerged in 2022 within the European HEI context. The HE-BIA MM has more dimensions than most other models identified, yet only a single co-occurrence of dimensions was identified in name only. We conclude that BI maturity models for HEI are emerging as a field of research with future directions for research including exploring co-occurrence of dimensions with existing maturity models, performing case studies, and validation of HE-BIA MM outside the European HEI context.
Pipelines combining SQL-style business intelligence (BI) queries and linear algebra (LA) are becoming increasingly common in industry. As a result, there is a growing need to unify these workloads in ...a single framework. Unfortunately, existing solutions either sacrifice the inherent benefits of ex-clusively using a relational database (e.g. logical and physical independence) or incur orders of magnitude performance gaps compared to specialized engines (or both). In this work, we study applying a new type of query processing architecture to standard BI and LA benchmarks. To do this, we present a new in-memory query processing engine called LevelHeaded. LevelHeaded uses worst-case optimal joins as its core execution mechanism for both BI and LA queries. With LevelHeaded, we show how crucial optimizations for BI and LA queries can be captured in a worst-case optimal query architecture. Using these optimizations, LevelHeaded outperforms other relational database engines (LogicBlox, MonetDB, and HyPer) by orders of magnitude on standard LA benchmarks, while performing on average within 31% of the best-of-breed BI (HyPer) and LA (Intel MKL) solutions on their own benchmarks. Our results show that such a single query processing architecture can be efficient on both BI and LA queries.
With chronic labor shortages in STEM-related industries, much research has focused on how to get more women and minorities interested in STEM careers. The most recent studies seem to indicate that ...the actual gap in user performance between genders has narrowed, although women tend to have less self-efficacy. However, all these previous studies involved student subjects in an educational context. This research uses a quasi-experimental field study with actual managers to test whether there are differences between genders when performing a variety of tasks of differing complexity on different computing devices and whether there are differences in self-efficacy. Subjects' performance was measured by question accuracy and time taken to complete a task, while self-efficacy was measured by self-assessed confidence. The results support recent studies indicating that the gender gap in performance is minimal in accuracy with no differences in time spent, while the gap in self-efficacy has remained.
Purpose
The emergence of the knowledge economy and Industry 4.0 has prompted many firms to invest considerable resources into knowledge-based human resource management (HRM) practices and business ...analytics capabilities aimed at enhancing competitiveness. This paper aims to propose a conceptual model that examines the collective effects of knowledge-based HRM practices, business analytics capabilities and organizational agility on innovative performance using knowledge-based theory as a theoretical framework.
Design/methodology/approach
The present study empirically tested the above-said idea by surveying (n = 182) individuals with supervisory capacity in Jordanian 4- and 5-star hotels. The obtained data was analyzed using linear modeling and fuzzy sets (fsQCA) techniques.
Findings
Results from linear modeling revealed that knowledge-based HRM practices, business analytics and organizational agility are important antecedents for innovative performance. Conversely, findings from fsQCA revealed that organizational agility is a necessary and sufficient condition to achieve high innovative performance. While business analytics is a sufficient condition to achieve high innovative performance.
Originality/value
This study is among the first to unveil the linear and complimentary effects of knowledge-based HRM practices, business analytics capabilities and organizational agility on innovative performance. Implications for theory and practice are discussed.
•A “BI factor” is qualitative evidence that influences market reactions on a company.•We developed an intelligent system to extract and categorize BI factors from news.•We examined the system's ...capability in profiling four IT companies’ BI factors.•The system outperformed a benchmark technique in profiling BI factors.•Our research addresses an important yet under-explored concern of BI research.
Company movements and market changes often are headlines of the news, providing managers with important business intelligence (BI). While existing corporate analyses are often based on numerical financial figures, relatively little work has been done to reveal from textual news articles factors that represent BI. In this research, we developed BizPro, an intelligent system for extracting and categorizing BI factors from news articles. BizPro consists of novel text mining procedures and BI factor modeling and categorization. Expert guidance and human knowledge (with high inter-rater reliability) were used to inform system development and profiling of BI factors. We conducted a case study of using the system to profile BI factors of four major IT companies based on 6859 sentences extracted from 231 news articles published in major news sources. The results show that the chosen techniques used in BizPro – Naïve Bayes (NB) and Logistic Regression (LR) – significantly outperformed a benchmark technique. NB was found to outperform LR in terms of precision, recall, F-measure, and area under ROC curve. This research contributes to developing a new system for profiling company BI factors from news articles, to providing new empirical findings to enhance understanding in BI factor extraction and categorization, and to addressing an important yet under-explored concern of BI analysis.
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