Business Analytics Research Articles

Setting the Grounds for the Transition from Business Analytics to Artificial Intelligence in Solving Supply Chain Risk

As supply chains (SCs) become more complex globally, businesses are looking for efficient business analytics (BA), business intelligence (BI), and artificial intelligence (AI) tools for managing supply-chain risk. The tools and methodologies proposed by the supply-chain risk management (SCRM) literature are mostly based on experts’ judgments, their knowledge, and past data. The expert evaluation-based approach could be partly or fully replaced by AI solutions, increasing objectivity, impartiality, and impersonality, reducing sources of human mistakes, biases, and inefficiencies in SCRM. However, the transition from BA to AI in SCRM is not a self-contained process; though attractive as a vision, it is not straightforward as a management or implementation process. The purpose of this research is to explore and define the conceptual grounds for transitioning from BA to AI in SCRM. The conceptual SCRM structure, its AI suitability, and implementation terms are defined theoretically based on a literature review. A single, in-depth business case study is employed to explore the theoretically defined terms of AI-based SCRM implementation. The proposed conceptual AI-suitable SCRM structure is defined by five principal building blocks: risk events, risk-event indicators, data-processing rules and algorithms, analytical techniques, and risk event probability forecasts. The study concludes that the business environment meets AI-based SCRM-implementation terms of data existence and access. Since data on risk events and negative outcomes are limited for machine learning, experts’ experience and knowledge might be utilised to build initial rules and data-processing algorithms for AI.

Business analytics competencies in stabilizing firms’ agility and digital innovation amid COVID-19

Digital innovation is not a technology in itself but businesses’ ability to exploit digital technology to resolve outmoded problems. Digital innovation is leveraging improvements or innovating technology to reimagine business practice. The Coronavirus disease 2019 exerted enormous effects on people's physical and psychological health. In addition, this pandemic adversely affected the global economy, from sole proprietors to multinational firms. However, such an effect did not hinder versatile products, services, and upgraded versions of technologies. Modern firms rely heavily on available data sets to make decisions through analytics. Manufacturing is one of the most dynamic industries due to market pressures and continually changing customer demands. This study examines the relationship between business analytics competencies and digital innovation and explores the mediating role of absorptive capacity and firm agility. Data are collected from 493 managers of manufacturing firms and analyzed by using structural equation modeling through smart-PLS. Results reveal a positive relationship between business analytics competence and digital innovation mediated by absorptive capacity and firm agility. With its theoretical contributions, this study can initiate practical research outcomes in manufacturing firms.

Preventing Reverse Engineering of Critical Industrial Data with DIOD

Business analytics augmented by artificial intelligence and machine learning (AI/ML) have revolutionized the role of data in the modern world. In recent years, businesses have incorporated data into their decision-making process for better prediction, risk assessment, content creation, etc. While such businesses often seek to leverage the full use of their data through third-party AI/ML services, they are often hampered by the risks of data leaks, reverse engineering, stolen technology, etc., that often have disastrous consequences for businesses and their stakeholders alike. This is especially relevant to the nuclear industry where proprietors are reluctant to share nuclear data for fear of misuse despite their willingness to integrate the additional insight provided by AI/ML applications and remain competitive. Thus, there arises a need for data masking prior to its transmission that obfuscates proprietary information while preserving the information relevant for AI/ML applications. In order to meet the needs of industrial data that are significantly different from those of data warehouses, previous work proposed an efficient time and space-scalable data masking paradigm known as the deceptive infusion of data (DIOD) methodology. The present work expands upon this work by leveraging existing reverse-engineering capabilities to facilitate the decomposition of industrial data into its proprietary and AI/ML-relevant parts, referred to as fundamental and inference metadata, respectively. Both sets of metadata are further obfuscated in accordance with the DIOD methodology to create the DIOD rendition of the industrial data, which is rendered immune to reverse engineering by discarding proprietary information and preserving only AI/ML–relevant information. Additionally, constraints of the original DIOD paper are relaxed using mutual information by configuring the methodology to the target AI/ML application to unlock the full potential of the DIOD methodology. Since the present work focuses on the nuclear industry, data from a nuclear reactor is transformed into that from a nonlinear spring-mass system with different levels of data masking as required by the generic system and the target AI/ML application.

Predictive Analytics for Real-time Auction Bidding Support: a Case on Fantasy Football

This work reports about an end-to-end business analytics experiment, applying predictive and prescriptive analytics to real-time bidding support for fantasy football draft auctions. Forecast methods are used to quantify the expected return of each investment alternative, while subgradient optimization is used to provide adaptive online recommendations on the allocation of scarce budget resources. A distributed front-end implementation of the prescriptive modules and the rankings of simulated leagues testify the viability of this architecture for actual support.

Understanding the Processes of how Small and Medium Enterprises derive Value from Business Intelligence and Analytics

This paper provides an in-depth study of how small and medium enterprises (SMEs) use business intelligence and analytics (BI&A) to derive business value and why so many SMEs fail to do so. A qualitative research approach based on semi-structured interviews with five SMEs in Australia was applied with the goal is to understand the process in which SMEs adopt BI&A to derive business value. This involved exploring how owners and managers lead their employees in using data and analytical processes to derive insights to make business decisions. The findings suggest that SMEs which adopt BI&A use a short and simple six-step iterative BI&A process to derive insights for business process application. In addition to the short process, a longer three phase process has been identified which progresses SMEs from solving operational issues to strategic challenges. The resulting short and long BI&A implementation process framework provides a progressive pathway for SME owners and managers to initiate and lead BI&A transformation in their SMEs to derive greater business value. The process model considers dimensions of data, analysis, business process change, social influence, level of information use and financial returns.

Knowledge Integration and Organisational Performance of Data Analytics in the Family Business

This study addressed the connection between the organisational performance of data analytics and the elements that contribute to knowledge integration (KI) in family businesses under the scope of the knowledge-based view theory. The conceptual model was validated based on the responses of 135 directors of Chilean family firms by using a partial least squares–structural equation modeling (PLS-SEM) methodology. The results show a relationship between the three components of KI and their impact on the organisational performance of data analytics, in addition to the moderating effects of both the quality of information and the information alignment within the proposed model. Our findings contribute data analytics to the family business literature and support organisational management by encouraging cooperation and the sharing of information and expertise among family members.

Business Analytics: A Simplified Review

Abstract: Business analytics is primarily about getting the most out of data. Data has lately been dubbed "the new oil" rather than the "sludge of the information era." While data can be used to develop new products and services, identify market niches, and spot new opportunities, it is also notoriously amorphous and difficult to extract value from. It involves different steps to get the insights from the data present majorly involving approaches like Aligning strategy, desired behaviors, and business performance management with analytical activities and capabilities is necessary to derive value from data. This article uses both conventional and qualitative research methods to examine the expanding body of work on business analytics (BA).In this paper, an attempt is being made to review several viewpoints on how business analytics is defined and how it relates to business intelligence. Additionally, we highlight business education and demonstrate how business analytics are applied in both company and industrial sectors

A Business Analytics Framework for Primary Care.

In this paper, we present a Business Analytics (BA) framework, which addresses the challenge of analysing primary care outcomes for both patients and clinicians from multiple data sources in an accurate manner. A review of the process monitoring literature has been conducted in the context of healthcare management and decision making and its findings have informed the formulation of a BA conceptual framework for process monitoring and decision support in primary care. Furthermore, a real case study is conducted to demonstrate the application of the BA framework to implement a BA dashboard tool within one of the largest primary care providers in England. Findings: The main contributions of the presented work are the development of a conceptual BA framework and a BA dashboard tool to support management and decision making in primary care. This was evaluated through a case study of the implementation of the BA dashboard tool in London's largest primary care provider. This BA tool provides real-time information to enable simpler decision-making processes and to inform business transformation in a number of areas. The resulting increased efficiency has led to significant cost savings and improved delivery of patient care.

Potential of Composition Market: Public acceptance in translated musical works on YouTube platform

Due to the worldwide demand for music consumption, more songs are translated into several languages. However, it remains a question whether the public i) prefers a loosely translated version, or ii) prefers a word-by-word translated version. Inspired by online data mining in business analytics, this quantitative research studied the public’s sentiments on two translated versions of Disney’s “We Don’t Talk About Bruno” song on the YouTube platform. Through semantic analysis, the researchers had found that the public prefers loosely, poetically, translated songs whilst preferring the harmony, melody, and musical sense of the songs to be retained. Keywords: translation techniques; compositional market; public acceptance; online data mining. eISSN: 2398-4287© 2022. The Authors. Published for AMER ABRA CE-Bs by E-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians), and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v7iSI7%20(Special%20Issue).3765

Smart Classroom Teaching Strategy to Enhance Higher Order Thinking Skills (HOTS)—An Agile Approach for Education 4.0

The development of Industry 4.0 revolutionising the concept of automation and digitisation in an organisation poses a huge challenge in employee knowledge and skills to cope with the huge leap from Industry 3.0. The high-level digitisation of an organisation requires the workforce to possess higher order thinking skills (HOTS) for the changing job roles matching the rapid technological advancements. The Education 4.0 framework is aimed at supporting the Industry 4.0 skills requirement not only in digital technologies but more towards soft skill development such as collaboration and lifelong learning. However, the education sector is also facing challenges in its transition from Education 3.0 to Education 4.0. The main purpose of the paper is to propose an Agile approach for developing smart classroom teaching strategies that foster employee adaptability with the new learning paradigm of upskilling in line with Industry 4.0. By adopting an exploratory research methodology, the pilot study investigates the implementation of the proposed Agile approach in a higher education setting for graduates to achieve HOTS using smart classroom teaching strategies. This study uses learning theories such as experiential learning in smart classroom environments to enhance students’ HOTS individually as well as collaboratively in an Agile iterative manner. This is the first empirical study carried out for graduates specialising in the Business Analytics skillset required for Industry 4.0. The findings of the pilot study show promising results that pave the way for further exploration and pedagogical insights in this research direction.

Business Analytics and Firm Performance: A Literature Review

Technological advancements and increasingly available intra- and extra-organizational data have attracted firms to invest in business analytics. Despite the growing number of firms that have invested in business analytics, only some of them have been able to turn their investments into tangible business benefits. This has raised questions among researchers and practitioners about the causal ambiguity between business analytics adoption and firm performance outcomes. Consequently, academic research community has recently launched an endeavor to produce empirical evidence on the relationship between business analytics and firm performance, considering different complementary resources, capabilities, and mechanisms that could better explain what firms need to gain business benefits from business analytics (e.g., Brynjolfsson and McElheran, 2016; Gupta and George, 2016).

Antecedents of Organizational Agility During Business Uncertainty in Noninformation Technology Sectors

The prolonged COVID-19 pandemic, economic stress, and geopolitical tensions have caused market disruptions and other forces that have likely increased organizational agility. This article focuses on the antecedents of organizational agility under such business uncertainty in the noninformation technology (IT) sectors. The research model stems from the uncertainty reduction theory and the following three frameworks: (1) dynamic capabilities; (2) decision making; and (3) business intelligence and analytics (BI&A) competitive advantage maturity model. It considers intelligence (risk and opportunity) and aligned decision making as agility predictors. It lists employee capability and IT flexibility as antecedents of intelligence, aligned decision making, and organizational agility. The results indicate that employee capability affects agility through the mediating variables of intelligence and aligned decision making. IT flexibility impacts agility only through intelligence. Both intelligence and aligned decision making have significant direct effects on agility.

Sales Forecasting, Market Analysis, and Performance Assessment for US Retail Firms: A Business Analytics Perspective

Retail firms are the best representatives of a developed country’s economic condition because they sell many of the necessary goods used for daily consumption, including food, clothes, shoes, electric appliances, and office supplies. This study presents a novel framework to help retail practitioners achieve the following goals: (1) predict sales revenues by identifying significant economic indicators, (2) estimate stable equilibriums by capturing interactive dynamics between competing firms, and (3) derive operational efficiencies and indicate required improvements by conducting performance assessments. To verify the validity of the research, data pertaining to Walmart, Costco, and Kroger are collected. Specifically, the least absolute shrinkage and selection operator (Lasso) is adopted in order to identify significant economic indicators. Consumer price index and regular wage are two common indicators that affect the the three firms’ sales numbers. In sales forecasting, support vector regression (SVR) and multivariate adaptive regression splines (MARS), respectively, perform the best in the training set and the testing set. Finally, the Lotka–Volterra model (LVM) and data envelopment analysis (DEA) are used for competitive analysis and performance assessment. A relationship of economic mutualism has been identified between the three firms. Furthermore, research findings show that Kroger performs inefficiently, though it can expect to increase sales more than the others in stable equilibriums.

NDPD: an improved initial centroid method of partitional clustering for big data mining

PurposeThe K-means (KM) clustering algorithm is extremely responsive to the selection of initial centroids since the initial centroid of clusters determines computational effectiveness, efficiency and local optima issues. Numerous initialization strategies are to overcome these problems through the random and deterministic selection of initial centroids. The random initialization strategy suffers from local optimization issues with the worst clustering performance, while the deterministic initialization strategy achieves high computational cost. Big data clustering aims to reduce computation costs and improve cluster efficiency. The objective of this study is to achieve a better initial centroid for big data clustering on business management data without using random and deterministic initialization that avoids local optima and improves clustering efficiency with effectiveness in terms of cluster quality, computation cost, data comparisons and iterations on a single machine.Design/methodology/approachThis study presents the Normal Distribution Probability Density (NDPD) algorithm for big data clustering on a single machine to solve business management-related clustering issues. The NDPDKM algorithm resolves the KM clustering problem by probability density of each data point. The NDPDKM algorithm first identifies the most probable density data points by using the mean and standard deviation of the datasets through normal probability density. Thereafter, the NDPDKM determines K initial centroid by using sorting and linear systematic sampling heuristics.FindingsThe performance of the proposed algorithm is compared with KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms through Davies Bouldin score, Silhouette coefficient, SD Validity, S_Dbw Validity, Number of Iterations and CPU time validation indices on eight real business datasets. The experimental evaluation demonstrates that the NDPDKM algorithm reduces iterations, local optima, computing costs, and improves cluster performance, effectiveness, efficiency with stable convergence as compared to other algorithms. The NDPDKM algorithm minimizes the average computing time up to 34.83%, 90.28%, 71.83%, 92.67%, 69.53% and 76.03%, and reduces the average iterations up to 40.32%, 44.06%, 32.02%, 62.78%, 19.07% and 36.74% with reference to KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms.Originality/valueThe KM algorithm is the most widely used partitional clustering approach in data mining techniques that extract hidden knowledge, patterns and trends for decision-making strategies in business data. Business analytics is one of the applications of big data clustering where KM clustering is useful for the various subcategories of business analytics such as customer segmentation analysis, employee salary and performance analysis, document searching, delivery optimization, discount and offer analysis, chaplain management, manufacturing analysis, productivity analysis, specialized employee and investor searching and other decision-making strategies in business.

Innovation Crowdsourcing Mechanisms and Innovation Performance: An Empirical Study of a Business Intelligence Community

Purpose: This study aims to develop a research model to investigate how the structure and mechanisms of innovation crowdsourcing influence knowledge management and innovation performance, based on the perspectives of open innovation theory and the knowledge-based view (KBV) of the firm. Design/Methodology/Approach: The research model and associated hypotheses were tested using partial least squares structural equation modelling (PLS-SEM), based on a dataset from the Microsoft Power BI community of business intelligence (BI) and analytics tools. Findings: The results show that both organisational and technical mechanisms of the community positively influence the community structure. The community structure has a positive impact on knowledge acquisition, knowledge transformation and the size and diversity of crowd participation. The mechanisms of innovation crowdsourcing and knowledge transformation in turn have a strong influence on innovation performance. Originality: This study is among the first to provide analytical insights into the mechanisms of innovation crowdsourcing and their underlying impact on innovation performance in the context of BI and analytics tools that exhibit a multiplicity and complexity of functions and capabilities. It therefore provides strategic guidance on how to effectively stimulate crowd intelligence and maximise the collaborative and synergistic effectiveness of innovation crowdsourcing communities, focusing on knowledge management practices and user innovation behaviour and performance.

A methodological framework for the implementation of an interdisciplinary business analytics program using ordinal game theory

The authors’ aim was to present a practical decision-making framework for decision makers (DMs) to use in evaluating interdisciplinary strategic approaches to degree program development. A framework based on ordinal game theory was applied using a recent decision to create an interdisciplinary business analytics program at a small, public university. The optimal Nash, in which each decision maker receives the most desirable outcome, was determined. The proposed methodological framework can be used for the design, evaluation, and postcommitment support of any interdisciplinary degree program.

Teaching binary logistic regression modeling in an introductory business analytics course

AbstractThere is an increasing demand to introduce Introductory Business Analytics (IBA) courses into undergraduate business education. Many real‐world business contexts require predictive analytics to understand the determinants of a dichotomous outcome; hence, IBA courses should include binary logistic regression analysis. This article provides our reflective discussions on the design of learning activities and assessments to assist business students in learning binary logistic regression in an IBA course. Data on student engagement and learning outcomes are used to shed light on the impacts of teaching logistic regression on student learning and experience. Notably, students opt to focus their assessment work more on logistic regression than on multiple regression analysis, showing the potential attraction of students toward binary logistic regression analysis. We also observed several challenges, mainly related to the use of Excel, that require special attention from instructors.

Sales Prediction Based on Machine Learning Scenarios

With the development of technology, business analysis plays a crucial role among companies. Companies prefer to manage their operation by using high-tech rather than the traditional method. Sale is one of the important parts of the operation of a company which also determines the company’s profit and management behavior. On this basis, business analytics becomes a new area suitable for promoting the progress of companies. The sale prediction based on machine learning gets popular among them. The article is trying to introduce the mainstream algorithm and models of machine learning that are used for prediction and the process of how to analyze a certain part of a company using the machine learning method. Decision tree and Neural networks are two main algorithms that will be mentioned in the following article, each algorithm will present a basic mathematics approach that is easy to read. In addition, the application of sales prediction of how to apply the machine learning method to real-world examples will be the last part of the article. These results give a brief and plain understanding of sales prediction based on machine learning to the people who first contact this field, which shed light on guiding further exploration of sales prediction.

Preface: Business analytics and operations research

Preface: Business analytics and operations research

Building Adaptive Capacity: The Role of Advanced Analytics in Risk Management

In this study, we theoretically argue for a potential trade-off between risk information integrated into the strategy and the use of advanced analytics for risk management purposes with regard to the adaptive capacity of organizations. Furthermore, we investigate whether the adoption of an advanced analytics strategy helps overcome this suggested substitutive interrelationship by giving direction and communicating a clear strategic focus to employees, including senior management. To test our hypotheses, we collected survey data in summer 2020 from 174 non-financial companies that allows us to exploit the challenging setting of the Covid-19 pandemic for organizations and their risk management. Our results support both the substitutive relationship between strategy integration of risk management and the use of advanced analytics for risk management purposes, as well as the three-way complementarity between the two risk management design choices and advanced analytics strategy with regard to adaptive capacity. Thus, we contribute to the literature with new insights into the potential benefits of linking risk management practices to business intelligence and analytics in light of better decision-making and control.