Analytics Usage Research Articles

Big Data Analytics and Management Forecasting Behavior

SYNOPSIS This paper investigates whether the use of Big Data analytics by firms has a spillover effect on management forecasting behavior. Insights provided by Big Data could potentially improve firms’ ability to forecast earnings (supply channel) and investor demand for earnings information is likely higher for firms engaging in data analytics (demand channel). Using a text-based measure of firms’ commitments to and usage of Big Data analytics, we find that Big Data analytics usage is positively associated with the propensity to issue management earnings forecasts. Consistent with the “supply channel” explanation, we find that Big Data analytics usage is positively associated with management forecast accuracy as well. Also, supporting the “demand channel” explanation, we find that Big Data analytics usage is associated with greater analyst following. Our findings of improved disclosure following commitments to Big Data analytics highlight a potentially unintended benefit of the Big Data revolution.

Bridging realities into organizations through innovation and productivity: Exploring the intersection of artificial intelligence, internet of things, and big data analytics in the metaverse environment using a multi-method approach

This study investigates how organizations may increase innovation and productivity through the Metaverse environment efficacy (MVEE), Artificial intelligence usage (AIU), Internet of Things usage (IoTU), and Big Data Analytics usage (BDAU). The study gathers responses from the gaming, information technology, and entertainment industries, using a multi-method involving Partial Least Squares Structural Equation Modeling, Fuzzy-set Qualitative Comparative Analysis, and Artificial Neural Networks to investigate how these technologies might be used to improve the linking of disparate realities in a business context. The use of AI in personalized and decision-support applications, IoT for real-time data collecting, and BDAU for an insights-driven strategy all combine to create a dynamic MVEE ecosystem. The research also delves into theoretical implications concerning the viability of using the MVEE to boost innovation and productivity. This research identifies the applications of using AI, IoT, and BDA to drive organizational performance in terms of innovation and productivity. Also, the research lays out the role of AI, IoT, and BDA in creating a dynamic metaverse ecosystem.

Impacts of Innovation and Business Analytics on the Performance of the Service Sector in Nigeria

The study investigated the impacts of innovation, including service innovation and business analytics adoption on the performance of the service sector in Nigeria between 2013 and 2022. The proportion of Nigeria's economy attributable to the service sector, the Innovation Efficiency Index, and the Business Analytics usage index were all calculated using secondary data drawn from metrics such as the number of service firms engaging in R&D, the total amount of R&D expenditures, and the share of total service sector revenue received via online channels. As a result of multicollinearity and heteroscedasticity, the study used quantitative analysis, including regression and correlation analyses. The results show that innovation and its impact on service quality, has a large impact on Nigeria's service sector, but that the sector's current adoption level of business analytics is too low to produce far-reaching changes in performance. It was further revealed from the study that the e-participation rate is very vital for driving both the adoption of business analytics and the performance of the service sector. According to the study, the adoption of business analytics should be given more attention in the Nigerian service sector so that its contribution to the performance of the sector is more significant. Finally, the scope of e-participation in the service sector is expanding by the day, and it is recommended that various organizations become more e-service driven to compete in today's market and maintain good performance.

Big data analytics usage in the banking industry in Tanzania: does perceived risk play a moderating role on the technological factors

PurposeThe main purpose of this paper is to examine the adoption of big data analytics (BDA) in the Tanzania banking industry by investigating the influence of technological, environmental and organizational (TOE) factors while exploring the moderating role of perceived risk (PR).Design/methodology/approachThe study employed a qualitative research design, and the research instrument was developed using per-defined measurement items adopted from prior studies; the items were slightly adjusted to fit the current context. The questionnaires were distributed to top and middle managers in selected banks in Tanzania using the snowball sampling technique. Out of 360 received responses, 302 were considered complete and valid for data analysis. The study employed partial least squares structural equation modeling (PLS-SEM) to examine the developed conceptual framework.FindingsTop management support and financial resources emerged as influential organizational factors, as did competition intensity for the environmental factors. Notably, bank size and perceived trends showed no significant impacts on BDA adoption. The study's novelty lies in revealing PR as a moderating factor, weakening the link between technological readiness, perceived usefulness and the intent to adopt BDA.Originality/valueThis study extends literature by extending the TOE model, through examining the moderating roles of PR on technological factors. Furthermore, the study provides useful managerial support for the adoption of BDA in banking in emerging economies.

Towards an understanding of business intelligence and analytics usage: Evidence from the banking industry

This study investigates the factors influencing the usage of Business Intelligence and Analytics (BIA) in the banking industry sector. Furthermore, it explores the moderating influence of employees' work experience on BIA usage within Jordanian commercial banks. A conceptual model was developed based on the TOE framework to assess the influence of technological, organizational, and environmental factors on BIA utilization in commercial banks. For this reason, this study followed an empirical analysis and developed a questionnaire to collect data from employees in Jordanian commercial banks. A total of 277 responses were analyzed using the partial least squares structural equation modeling (PLS-SEM) to test the hypotheses of this study. The findings reveal a significant positive influence of the TOE factors on BIA usage, which indicates that they play a crucial role in shaping BIA usage in commercial banks. Regarding employees’ work experience, it has been found that it moderates the relationships between technological and organizational factors and the usage of BIA. However, it did not influence the relationships between the environmental factors and BIA usage in commercial banks. These findings highlight the need for banking institutions in Jordan to adopt a holistic approach that aligns advanced technological solutions, a supportive organizational culture, and adaptability to the external environment to optimize their utilization of BIA. This study provides valuable insights for banking institutions seeking to enhance their BIA utilization and make data-driven decisions in the dynamic banking industry.

Global supply chain coordination – the business analytics usage in Industry 4.0 conditions

Global supply chain coordination – the business analytics usage in Industry 4.0 conditions

Risk mitigation – the business analytics usage in Industry 4.0 conditions

Risk mitigation – the business analytics usage in Industry 4.0 conditions

Impacts of Innovation and Business Analytics on the Performance of the Service Sector in Nigeria

The study investigated the impacts of innovation, including service innovation and business analytics adoption on the performance of the service sector in Nigeria between 2013 and 2022. The proportion of Nigeria’s economy attributable to the service sector, the Innovation Efficiency Index, and the Business Analytics usage index were all calculated using secondary data drawn from metrics such as the number of service firms engaging in R&D, the total amount of R&D expenditures, and the share of total service sector revenue received via online channels. As a result of multicollinearity and heteroscedasticity, the study used quantitative analysis, including regression and correlation analyses. The results show that innovation and its impact on service quality, has a large impact on Nigeria’s service sector, but that the sector’s current adoption level of business analytics is too low to produce far-reaching changes in performance. It was further revealed from the study that the e-participation rate is very vital for driving both the adoption of business analytics and the performance of the service sector. According to the study, the adoption of business analytics should be given more attention in the Nigerian service sector so that its contribution to the performance of the sector is more significant. Finally, the scope of e-participation in the service sector is expanding by the day, and it is recommended that various organizations become more e-service driven to compete in today’s market and maintain good performance.

Human Resources Analytics Usage in Turkish Organizations

Since the early 2000s, the use of analytics in Human Resources (HR) has become increasingly widespread. With the spread of analytical applications, it is thought that the Human Resources Department will contribute more to making effective decisions in organizations. This study investigates the fundamental differences between organizations that use and do not use Human Resources Analytics (HRA). The research was carried out by collecting data from organizations operating in different sectors in Turkey. The organizations participating in the survey were compared in terms of the organization's total age, turnover (2022) and number of employees, and it was investigated whether these variables had an impact on the use of HRA. In addition, in organizations that stated that they used HRA, it was also investigated how long these practices have been carried out and by which function they were carried out. The results have been analyzed in the context of descriptive statistics and finalized according to the responses received from a total of 175 organizations, and it was observed that the main difference in the use of HRA emerged in the context of the number of employees and organizational turnover. Moreover, by conducting a t-test for organizational age, it was concluded that the age of organizational age creates a difference in the use of HRA. Additionally, it has been determined that more than half of the organizations that apply analytics have been using HRA for less than 5 years and analytics applications are not carried out by a separate analytics team.

Non-Take-Up of Benefits and Rights: The Universality-Selectivity Debate as Reflected in Online Information and User Patterns

The present study adds valuable perspective to the ongoing theoretical and practical universality-selectivity debate in the context of (non-) take-up of benefits by delving into the online information angle. People with a digital divide may be affected by such differences since they are more likely to need selective benefits. Almost all researchers agree that knowledge is a crucial barrier to benefits take-up. However, most researchers place the information barrier at the client level and do not examine factors contributing to information incomprehensibility. Is the effectiveness of universality- or selectivity-based welfare policies reflected in the online information? Our study examined this question by a computerized text analysis of 2662 online information pages in “Kol Zchut,” a primary Israeli information website about benefits and rights, and its Google Analytics usage pattern data. The results indicated weak but significant differences between universal and selective information pages. Information complexity was higher in selective information pages. The findings were more complicated regarding usage patterns. In order to understand more deeply the information side of rights take-up, future research is needed.

An empirical study on the performance and energy costs of ads and analytics in mobile web apps

Context:As the use of mobile devices has increased immensely through the years, the presence of analytics and advertisements on web and native applications has become prevalent. However, serving ads and analytics comes with costs, as they are associated with additional code and network requests to execute properly. Subsequently, more computing resources are used, having an impact on the energy consumption and the performance of web applications. Previous work has focused only on native Android applications, has used different metrics for performance, or has focused on other aspects of web applications. Goal:This paper aims to investigate the costs of including advertisements and analytics in web applications. This is done in terms of energy consumption and performance. For energy, the consumption is measured in Joules. For performance, the following metrics are used: first contentful paint and full page load time. The results of this study could influence the decisions of web developers and web browser vendors related to ads and analytics usage, while providing the foundation for further research on this topic. Method:To collect reliable and population-representative results, the research focused on 9 popular web applications included in the Tranco list. Energy consumption and performance metrics were gathered for 3 versions of each web application — original version with ads and analytics, without ads, and without analytics. A cross-over paired comparison design is conducted. Multiple executions of each run were performed in random order to ascertain rigorous measures. The experiment is carried out on an Android tablet using two browsers, Google Chrome and Opera. Results:Ads significantly impact the energy consumption of mobile web apps for both browsers, with a large effect size; analytics have a significant impact on the energy consumption of Chrome (with a medium effect size), but not on Opera. In terms of performance, both ads and analytics do not significantly impact the first contentful paint metric on both browsers; differently, both ads and analytics significantly impact the full page load time of the mobile web apps on both browsers, but with a small effect size. Conclusions:This study provides evidence that both ads and analytics can have a significant impact on the energy consumption and performance of mobile web apps loaded either on Opera or Chrome. Depending on the requirements of the mobile web app, it is advisable to limit both ads and analytics in a mobile web app in order to reduce its energy consumption and improve its full page load time. Special attention should be paid to the presence of ads since they resulted to be the most impactful in terms of energy consumption.

Assessing Maturity in Data-Driven Culture

Research on assessing a group's maturity in data-driven culture is rare and fragmented. This article investigates how maturity in data-driven culture can be assessed from a historical perspective. A case study was done on how the Education Council evolved in analytics maturity and as a group during 2014-2023. The assessment showed that the Education Council experienced both successful progression of group development and usage of analytics, as well as regression in group development and analytics usage. The practical implications of the findings are that group leaders need to be aware of the interplay between analytics usage and group development when planning to improve their group's maturity in data-driven culture.

Maintenance resource allocation – the business analytics usage in Industry 4.0 conditions

Purpose: The purpose of this publication is to present the applications of usage of business analytics in maintenance resources allocation. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The integration of business analytics with condition monitoring technologies heralds a transformative shift in maintenance resource allocation. Through the utilization of real-time data from sensors and monitoring devices, organizations can craft dynamic and adaptive maintenance strategies. These strategies, grounded in the current equipment condition, strike a nuanced balance between preventive and corrective actions. Business analytics enriches decision-making by factoring in critical elements such as equipment importance, operational impact, resource availability, and budget constraints. This integration instigates a paradigm shift, fostering proactive, adaptive, and efficient resource allocation, resulting in heightened asset reliability, diminished downtime, and amplified operational performance. The evolving synergy between business analytics and maintenance practices is becoming integral to the future of asset management and industrial operations. The diverse applications of business analytics in maintenance, as outlined in Table 1, underscore its versatility, encompassing predictive maintenance, condition monitoring, asset performance management, work order prioritization, resource optimization, cost-benefit analysis, inventory management, and performance metrics monitoring. Concurrently, the adoption of advanced software solutions in Industry 4.0 conditions, exemplified by IBM Maximo Asset Management, SAP Intelligent Asset Management, and Fiix CMMS, reflects a commitment to efficiency and innovation in maintenance resource allocation. Despite the substantial advantages, addressing challenges outlined in Table 4, including data quality, integration complexities, implementation costs, and skill development, is crucial. These challenges underscore the necessity for a strategic and holistic implementation approach that considers technology, personnel training, and organizational readiness. In essence, the evolution of maintenance resource allocation through business analytics signifies a data-driven revolution poised to optimize current operations and position organizations for sustained success amid rapid technological advancements and the transformations of Industry 4.0. Originality/value: Detailed analysis of business analytics in the case of maintenance resource allocation. Keywords: business analytics, Industry 4.0, digitalization, artificial intelligence, real-time monitoring; maintenance resource allocation. Category of the paper: literature review.

Impact of data analytics on reporting quality of forensic audit: a study focus in Malaysian auditors

The primary objective of this research is to study the impact of data analytics on reporting quality of forensic audit, a study focussed on auditors in Malaysia. In this research, the researcher intends to discover the impact caused by emerging technology, in particular the data analytics, in audit profession, as audit field is the most complex field where tremendous volume of data, and research is conducted to investigate a certain situation, to report findings. The researcher uses the primary method to conduct the data. In this research, four different variables that causes impact to data analytics usage in forensic audit reporting quality, are tested. The data was collected from forensic audit and accounting service providers, operating around Malaysia. To analyse the findings, statistical tool, Statistical Package of the Social Sciences (SPSS) is used, to further analyse the responses, and to derive at a conclusion relating to impact of data analytics to reporting quality of forensic audits. The findings discovered that all the variables have significant relationship with the data analytics and its impact to the forensic audit reporting quality, among forensic auditors and practitioners in Malaysia. The conclusion has been discussed in this research which caters for and provides confidence and data privacy, for users of data analytic tools for their forensic audit work. This research creates the awareness of the use of data analytics in order to assist forensic auditors in their routine data gathering and analysis, in preparing reports, statistics and trend analysis etc, by saving their efforts and time, to focus more on performing further analysis and findings based on case or audit assignment.

Environmental sustainability – the business analytics usage in Industry 4.0 conditions

Environmental sustainability – the business analytics usage in Industry 4.0 conditions

The five stages of business analytics

Purpose: The goal of the paper is to analyze the main features, benefits and problems with the business analytics usage. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The paper explores the main concepts of business analytics, including descriptive, real-time, diagnostic, predictive, and prescriptive analytics. Each stage of development builds upon the previous one, addressing specific needs in data analysis and decision-making. The paper also presents a detailed comparison of the five types of business analytics, showcasing their unique characteristics, techniques, and applications. Understanding these differences helps organizations select the appropriate analytics type to suit their requirements and drive success. As technology and data processing capabilities advance, business analytics continues to evolve. Embracing the power of data and analytics grants organizations a competitive advantage, unlocking opportunities and driving innovation. Integrating analytics into decision-making processes is essential for thriving in a data-driven world, ensuring sustained growth and success in an ever-changing marketplace. Originality/value: Detailed analysis of all subjects related to the problems connected with the prospective analytics. Keywords: Industry 4.0; diagnostic analytics, business analytics, data analysis. Category of the paper: literature review.

Work safety and health – the business analytics usage in Industry 4.0 conditions

Purpose: The purpose of this publication is to present the applications of usage of business analytics in work safety and health. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: In the era of Industry 4.0, characterized by automation and smart technologies, the preservation of workforce health and well-being takes center stage. This paper explores the transformative integration of business analytics into work safety and health practices, signaling a paradigm shift in monitoring, managing, and elevating occupational safety standards. The publication underscores the proactive role of business analytics, especially through wearable device-driven physiological monitoring, in safeguarding worker health without compromising productivity. By aligning seamlessly with regulatory compliance, business analytics becomes a linchpin in mitigating legal and financial risks associated with workplace incidents. The synergy between work safety, health, and business analytics presents a transformative approach, empowering organizations to proactively address safety concerns and cultivate safer work environments. While delving into the software applications, the paper highlights the versatility of analytics in predicting incidents, monitoring compliance, and optimizing workloads. Challenges such as data quality, privacy concerns, and integration issues are acknowledged, yet the multitude of advantages showcased, including incident prevention, data-driven decision-making, and cost savings, affirm the transformative potential of business analytics in shaping a safer, more efficient, and healthier future workplace. Originality/value: Detailed analysis of business analytics in work and safety area. Keywords: business analytics, Industry 4.0, digitalization, artificial intelligence, real-time monitoring; work safety and health. Category of the paper: literature review.

Business Intelligence and Analytics (BIA) Usage in the Banking Industry Sector: An Application of the TOE Framework

This study aims to examine the factors that influence business intelligence and analytics (BIA) usage in the banking sector. Based on a comprehensive literature review, a theoretical model was developed to explore the impact of three key factors on business intelligence and analytics adoption and usage in the banking sector, namely technological, organizational, and environmental factors. The study used the Statistical Package for the Social Sciences (SPSS) to analyze data collected from 120 employees of Jordan Arab bank. The results revealed the critical impact of not only the existence of data and technology infrastructure but also the importance and availability of management and human resources support and capabilities. This study suggests that, more importantly, successful planning for business intelligence and analytics should go beyond the technology aspects to gain the full benefits of such technology, especially in the banking sector. Yet, we argue that more research needs to be conducted, especially in the context of developing countries, to fully understand how banking sectors can successfully implement and utilize business intelligence and analytics.

Analytics applications, limitations, and opportunities in restaurant supply chains

Technology, market, and competitive dynamics are requiring firms in restaurant/food service supply chains to improve their analytics capabilities, which have tended to lag behind other comparable industries. The global COVID‐19 pandemic has further encouraged industrial leaders to evaluate new challenges and opportunities. Our research provides insights into current applications of analytics technologies and organizationally integrates these insights for decision‐makers in restaurant supply chains. The study applies decision theory as a framing perspective to this phenomenon, thereby advancing the academic literature on the interface between data management, analytical techniques, and computing. We combine data drawn from interviews of leading players in U.S. and Chinese‐based restaurant chains with insights from trade publications and social media posts to identify best practices for analytics usage and supporting organizational changes. Our analysis provides examples of ways in which business leaders are applying analytics technologies to structured and unstructured data to address targeted objectives for demand/supply processes and to foster higher order organizational learning. In keeping with the stated objectives of this special issue of Production and Operations Management, this study provides an overview of both current state‐of‐the‐art and next‐generation capabilities for analytics in the restaurant industry. We further identify specific limitations of current processes, opportunities for development and theory‐based research, and challenges to implementation.

How big data analytics enables the alliance relationship stability of contract farming in the age of digital transformation

Notwithstanding the potential of big data analytics technology for alliance management, there is a lack of understanding of how such digital technology influences alliance relationship stability (ARS). Drawing on the information technology-enabled organizational capabilities (IT-enabled OCs) perspective, this study empirically verifies that big data analytics promotes ARS and risk management capability. Moreover, market risk management capability (MRM) enhances ARS, and data quality moderates the relationship between big data analytics usage (BDU) and MRM. This research reveals the impact mechanism of BDU on the ARS. Implications for management and future research are presented as well.