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With businesses increasingly prioritizing data-driven decision making, the demand for business analysts is high and expected to grow. In response, many universities and institutions have developed courses and programs related to business analytics to prepare more graduates for careers in this field. Business analytics programs and educators consistently strive to achieve a high level of student learning success, ensuring competence in working in the business analytics field after graduation. In this study, we aim to examine key factors influencing student learning in business analytics, focusing on performance expectancy and satisfaction. We examined specific factors, including personal interest, career relevance expectancy, learning effort, and perceived course structure effectiveness, from perspectives related to both students and instructors. A research model was developed and empirically tested. The results showed that all factors significantly influenced both perceived academic performance and learning satisfaction. Additionally, personal interest and career relevance expectancy could significantly impact learning effort. [ABSTRACT FROM AUTHOR]

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In the present digital environment, a data-driven organizational culture has become a vital emerging driver of organizational growth. This data-driven culture has assumed an advanced shape due to adoption of artificial intelligence (AI) integrated business analytics tools in the organization. Data-driven culture in the organization could considerably impact product innovation strategy as well as organizational process alteration. In this context, the aim of this study is to investigate how an organization's data-driven culture impacts process performance and product innovation that led to enhanced organizational overall performance and higher business value. Methodologically, supported by relevant extant literature and inputs from the resource-based view and dynamic capability theories (organizational context), a conceptual model and a set of hypotheses are initially developed. These are subsequently statistically validated through a survey involving 513 usable responses from employees of different organizations using business analytics tools embedded with AI capability. The findings demonstrate that an organizational data-driven culture has considerable moderating impact on product innovation and process improvement, which ultimately enhance business value through improved organizational overall performance. [ABSTRACT FROM AUTHOR]

Purpose: The exact process of construction projects performance assessment and benchmarking still remains subjective relying on qualitative techniques, which does not allow stakeholders to address the issues and the drawbacks of their respective projects as effectively as possible for performance improvement purposes. Hence, this research aims to establish a unified project performance score (PPS) for assessing and comparing projects performance. Design/methodology/approach: Data were collected from Construction Industry Institute (CII) members and through University of Wisconsin active research projects. Exploratory data analysis was done to investigate the calculated performance metrics and the collected data characteristics. Data were converted into six performance metrics which were used as the independent variables in creating the PPS model. Logistic regression model was developed to generate the unified PPS equation in order to explain the variables that significantly affect construction projects successful post-completion performance. The PPS model was then applied on the collected dataset to benchmark projects in terms of project delivery systems, compensation types and project types in order to showcase the PPS capabilities and possible applications. Findings: The model revealed that construction cost and schedule growth are the most important metrics in assessing projects performance, while RFIs' processing time and change orders per million dollars were the features with the least effect on the PPS value. The authors found that integrated project delivery (IPD) and target value (TV) projects outperformed all other project delivery and compensation types. While, industrial projects showed the worst performance, as compared to commercial or institutional projects. Originality/value: The PPS model can be used to assess the performance of any pool of executed projects, and introducing a novel addition to the field of construction business analytics which is a supplementary tool to successful decision making and performance improvement. Additionally, the bidding selection system can be revolutionized from a cost-based to a performance based one using the PPS model to improve the outcomes of the buyout process. [ABSTRACT FROM AUTHOR]

We introduce taxi ridership between the Federal Reserve (Fed) Bank of New York and large financial institutions headquartered in New York City as a novel proxy for Fed–bank face-to-face interactions. We document a negative relation between past Fed–bank interactions and future stock market returns, particularly on days around the Fed's public announcements. We also find significantly elevated Fed–bank interactions immediately following the lifting of the Federal Open Market Committee blackout. Our findings suggest that the Fed increases its information gathering via face-to-face interactions when it possesses negative private information about the condition of the economy. This paper was accepted by Agostino Capponi, finance. Funding: This work was supported by the Muma College of Business Center for Analytics and Creativity at the University South Florida, the George Stigler Center for the Study of the Economy and the State, the Lynde and Harry Bradley Foundation, the University of Chicago Booth School of Business, the Liew Fama-Miller Fellowship, and the Fischer Black Fellowship. Supplemental Material: Data and the online appendix are available at https://doi.org/10.1287/mnsc.2023.4885. [ABSTRACT FROM AUTHOR]

This research examines the data analytics job market, focusing on prominent tools in job advertisements and their salary implications. Analyzing a diverse range of postings for business analysts, data analysts, and other analytics roles, the most sought-after tools were identified: SQL, Tableau, Python, R and Power BI. The study reveals SQL's critical importance for business analysts, data analysts, and business intelligence analysts. Additionally, Tableau surpasses Power BI in popularity, while Python is in higher demand compared to R. The findings also indicate distinct salary trends across specializations. Data analysts witness salary increments for all top five tools. However, for system analysts, these tools do not tend to impact salaries. Data scientist roles prioritize programming, with SQL and Python leading to salary increases. By understanding the current tool trends and their salary implications, stakeholders can strategically position themselves in the data-driven landscape. [ABSTRACT FROM AUTHOR]

The paper explores the potential role of Machine learning (ML) in supporting the development of a company's Performance Management System (PMS). In more details, it investigates the capability of ML to moderate the complexity related to the identification of the business value drivers (methodological complexity) and the related measures (analytical complexity). A second objective is the analysis of the main issues arising in applying ML to performance management. The research, developed through an action research design, shows that ML can moderate complexity by (1) reducing the subjectivity in the identification of the business value drivers; (2) accounting for cause-effect relationships between business value drivers and performance; (3) balancing managerial interpretability vs. predictivity of the approach. It also shows that the realisation of such benefits requires a combined understanding of the ML techniques and of the performance management model of the company to frame and validate the algorithm in light of the context in which the organisation operates. The paper contributes to the literature analysing the role of business analytics in the field of performance management and it provides new insights into the potential benefits of introducing an ML-based PMS and the issues to consider to increase its effectiveness. [ABSTRACT FROM AUTHOR]

In the process of transitioning to digital businesses, managers are faced with numerous decision-making challenges across various domains. This complexity poses a significant hurdle for traditional businesses seeking to embrace digital transformation. To address this challenge, the Preference Selection Index (PSI) and Additive Ratio Assessment (ARAS) methods are utilized for selecting Big Data Analytics (BDA) software, employing multi-criteria decision-making (MCDM) approaches. With a scenario involving 8 alternatives and 7 criteria, the PSI method is employed to establish the weights of the criteria. Subsequently, the ARAS method is utilized to rank the alternatives. The analysis identifies "Ease of Use" as the criterion with the highest importance weight (0.1464), while "Data Workflow" emerges as the least significant criterion (0.1378). Based on the highest utility degree (0.9548), the fifth alternative was identified as the most suitable big data analytics software for this scenario. Furthermore, the proposed method's applicability is validated through comparative analysis with five different MCDM methods, reinforcing the reliability of the obtained results. [ABSTRACT FROM AUTHOR]

Recent advancements in Enterprise Information Systems (EISs) have transitioned from primarily supporting operational and tactical processes to enabling strategic decision-making through integrated analytics. This systematic review critically examines global literature from 2010 to 2023, focusing on the factors influencing the adoption of analytical components in EISs and assessing their impact on strategic decision-making in organizations. Following the PRISMA 2020 guidelines, we meticulously selected and reviewed articles from the Scopus database, employing a robust taxonomy based on the technology–organization–environment (TOE) framework to categorize findings. Our methodology involved a thorough screening of 234 studies, leading to a final analysis of 45 peer-reviewed articles that met our stringent criteria. These studies collectively underscore a significant gap in organizational capabilities, notably in the business ecosystems surrounding EISs, which hampers the effective adoption and utilization of advanced analytics. The results highlight a distinct need for improved understanding and implementation strategies for integrated analytics within EISs to enhance strategic decision-making processes. This review identifies critical factors for integrating analytics into Enterprise Information Systems (EISs), emphasizing technological, organizational, and environmental dimensions. It highlights a significant gap in models guiding ERP systems with Business Intelligence (BI) capabilities and underscores the need for robust research to enhance strategic decision-making through analytics. [ABSTRACT FROM AUTHOR]

This empirical study evaluates the impact of faculty training in online teaching on perceived comfort, perceived effectiveness, and stress during the Emergency Transition to Online Learning (ETOL) caused by COVID‐19. Survey data revealed a positive relationship between training in online teaching and perceived effectiveness during the ETOL. However, this relationship is fully mediated by perceived comfort in teaching online, meaning training in online teaching significantly increased faculty perceived comfort, which in turn increased perceived effectiveness. Relative to their counterparts, faculty who agreed that the ETOL was stressful were significantly more likely to cite working from home distractions and a lack of physical resources as the greatest challenges. Going forward, our results suggest faculty should be trained in best practices in online teaching as a regular part of their development. Doing so would not only benefit online courses, but the tools used in online courses can also benefit faculty teaching in‐person courses. The emerging tools used in online courses can also serve to enhance teaching in emerging, technology‐based disciplines in business, such as digital marketing or business analytics. In addition to ongoing training, another best practice to prepare for a future ETOL would be to allow business school faculty to share what they have learned with other business faculty. [ABSTRACT FROM AUTHOR]

The purpose of this study was to analyze the diffusion of one sport innovation to forecast a second. Contextualized within the diffusion of innovations theory, this study investigated cumulative business analytics diffusion as an analog for cumulative natural language processing (NLP) diffusion in professional sport. A total of 89 teams of the 123 teams in the Big Four North American men's professional sport leagues contributed: 21 from the National Football League, 23 from the National Basketball Association, 22 from Major League Baseball, and 23 from the National Hockey League. Utilizing an analogous forecasting approach, a discrete derivation of the Bass model was applied to cumulative BA adoption data. Parameters were then extended to predict cumulative NLP adoption. Resulting BA-estimated parameters (p =.0072, q =.3644) determined a close fit to NLP diffusion (root mean square error of approximation = 3.51, mean absolute error = 2.98), thereby validating BA to predict the takeoff and full adoption of NLP. This study illuminates an ongoing and isomorphic process for diffusion of innovations in the professional sport social system and generates a novel application of diffusion of innovations theory to the sport industry. [ABSTRACT FROM AUTHOR]

The surge in data-related investments has drawn the attention of both managers and academia to the question of whether and how this (re)shapes decision making routines. Drawing on the information processing theory of the organization and the agency theory, this paper addresses how putting a strategic emphasis on business analytics supports an analytical decision making culture that makes enhanced use of data in each phase of the decision making process, along with a potential change in authorities resulting from shifts in information asymmetry. Based on a survey of 305 medium-sized and large companies, we propose a multiple-mediator model. We provide support for our hypothesis that top management support for business analytics and perceived data quality are good predictors of an analytical culture. Furthermore, we argue that the analytical culture increases the centralization of data use, but interestingly, we found that this centralization is not associated with data-driven decision making. Our paper positions a long-running debate about information technology-related centralization of authorities in the new context of business analytics. [ABSTRACT FROM AUTHOR]

The Internet has become essential in increasing output between corporate and country levels. However, the impact of the Internet on trading potential still needs to be known. On the other hand, Indonesia has great potential to export agricultural food products in global trade. Therefore, the relationship between the Internet development and agri-food export potential could be explored. This study aims to investigate the effect of Internet development on Indonesia’s export potential in the global market. The export potential estimation was measured using gravity estimation in 124 destination countries from 2010 to 2020. Furthermore, the panel regression was employed to determine the three indicators of Internet development: Internet users, secure Internet servers, and fixed broadband subscriptions on Indonesia’s agricultural export potential. This study also utilized simulation due to the possibility of rising the number of Internet indicators. The results revealed several positive factors of Indonesia’s agricultural exports, such as importers’ gross domestic bruto (GDP), contagious border, and colonial relationship. Otherwise, geographical distance, exchange rate, and being a landlocked country negatively affected Indonesia’s agricultural exports. Indonesia possessed a greater potential for agricultural exports in Europe, especially in the conditions of emerging and developing economies. There were 85 destination countries with higher potential for Indonesia’s agri-food export. Additionally, Internet users and secure Internet servers positively influenced the agricultural export potential to target countries. The simulation revealed that improving Internet indicators boosted the new market rather than raising the export value to target countries.

Purpose: The purpose of this publication is to present the applications of usage of business analytics in cybersecurity analytics. 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 into cybersecurity practices within Industry 4.0 signifies a pivotal advancement in safeguarding organizational assets against the evolving cyber threat landscape. As industrial systems grow more complex and interconnected, traditional security methods focused on perimeter defenses are increasingly inadequate. Modern cybersecurity strategies must therefore incorporate advanced analytics to manage and mitigate risks effectively. Business analytics enhances cybersecurity through sophisticated machine learning algorithms, predictive capabilities for anticipating future threats, and improved incident response via real-time monitoring and automated alerts. These innovations foster a proactive and efficient security approach, enabling swift detection, response, and informed decision-making based on thorough risk assessments. Despite these advantages, challenges such as data overload, false positives, integration hurdles, and the need for specialized expertise persist. Additionally, concerns about data privacy, costs, and analytical complexity must be managed. Embracing business analytics while addressing these challenges will enable organizations to fortify their security posture, optimize resource use, and adapt to the demands of Industry 4.0, thereby shaping the future of cybersecurity in a rapidly evolving digital landscape. Originality/Value: Detailed analysis of all subjects related to the problems connected with the usage of business analytics in the case of cybersecurity analytics. [ABSTRACT FROM AUTHOR]

With the emerging era of E‐commerce and online shopping, people are also in a habit to receive default product recommendations on the web pages that they access. Google is already providing such suggestions. Till now recommendations were made only based on previous sentiments or feedback or ratings, but this research has improved the product recommendation method by including one more parameter for the same. This article represents two parameters for making predictions of product allocation to a new customer. These parameters are ratings given by the existing users for that particular product and the region to which the new customer belongs. Following these parameters, a prediction model and an algorithm, Improved_Collab_Similarity, have been implemented. The dataset has been developed where India as a country along with all its States has been considered for products which are popular for their creation based on regional and ancient skills of the people belonging to that area. Results for the mentioned prediction model have been discussed in this article where generally precision increases with the increase in a number of products but at some points, it does not increase when a smaller number of that product was purchased by the customers. [ABSTRACT FROM AUTHOR]

Sustainability has become a prominent goal in tourism and hospitality. While green practices can benefit the environment and society, they can also lead to customer dissatisfaction. Therefore, the current study aims to identify different hotel green practices and examine their impact on customer satisfaction and dissatisfaction. This study uses a mixed method of business analytics and regression analysis on a dataset of 813,791 online reviews of 450 hotels in New York City derived from TripAdvisor.com. The research findings guide hotel operators to build strategies for green practices while reducing customers' discomfort and improving the overall customer experience. [ABSTRACT FROM AUTHOR]

Analytics services provided by marketplace platforms have become increasingly important for sellers seeking market insights. In this paper, we examine a scenario in which an analytics service plays a vital role in enhancing sellers' understanding of market size and improving their decision-making. Using a game-theoretic model, we analyze the pricing strategies of the platform and the adoption strategies of sellers for the analytics service. Our study identifies two distinct effects of analytics services: the competition effect and the accuracy effect. Specifically, the competition effect manifests in opposing ways across different market scenarios, with a competition-intensifying effect in lowdemand markets and a competition-weakening effect in high-demand markets. Consequently, sellers using an analytics service command lower prices in low-demand markets and higher prices in highdemand markets. More interestingly, our results reveal that offering an analytics service could potentially hurt the total market demand, subsequently impacting the platform's revenue from the marketplace service and potentially leaving the platform worse off. Additionally, driven by both the accuracy and competition effects, adopting an analytics service may adversely affect seller profitability and consumer surplus without necessarily improving overall welfare. Moreover, the transaction fee for the marketplace service plays a crucial role in the interplay between the analytics and marketplace services. Specifically, in low-demand (high-demand) markets, as the transaction fee increases, platforms should consider reducing (increasing) the subscription fee to encourage more (fewer) sellers to adopt the analytics service, thereby enhancing overall market demand and increasing revenue from the marketplace service. Our findings also suggest that platforms should refrain from offering analytics services in high-demand markets when the transaction fee is relatively high. Furthermore, policymakers (sellers) should be mindful of the potential negative consequences associated with the adoption of analytics services in high-demand (low-demand) markets. [ABSTRACT FROM AUTHOR]

Forty micro and small steel products manufacturing enterprises in Salem District of Tamil Nadu, South India faced problems in value addition of the steel products like windows, grill gates, truss work and panel boards manufactured by them. They formed M/s Salem Steel Cluster Pvt Ltd; Salem, a special purpose vehicle, in 2012 by getting funds from the Government of Tamil Nadu and the Government of India through the Tamil Nadu Small Industries Development Corporation under Micro Small Enterprises Cluster Development Programme of Ministry of Micro, Small and Medium Enterprises, Government of India. The objective is to find the physical and financial performance of the Steel Product Fabrication Cluster (SPFC) before and after Cluster Development Approach (CDA) to find the productivity of the cluster by taking independent variables like number of units, employment and production and dependent variable like turnover, and to find the performance of SPFC before and after CDA. To find business analytics models like Diagnostic Analytics, Descriptive Analytics, Inferential Analytics, Predictive Analytics, Prescriptive Analytics and Decision Analytics. The methodology adopted is by collecting primary data like number of units, employment in numbers, production in crores and turnover in crores before and after CDA and analysing using Compound Annual Growth Rate, Descriptive Analysis, Correlation Analysis, Trend Analysis, Regression Analysis, Structural Equation Modelling and T-Test. There is a Difference in Difference between controlled units which have not adopted CDA and experimental units which have adopted CDA, where there is an increase in number of units, employment, profit and turnover. To conclude, there is an increase in number of units, employment, production and turnover after CDA when compared to before CDA, which leads to an increase in productivity thereby Sustainable Development Goals of 1, 4, 5, 8 and 9 are achieved. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the applications of usage of business analytics in human resource analytics. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: This paper explores the transformative potential of business analytics within human resource (HR) analytics, particularly in the context of Industry 4.0. It highlights how the integration of advanced analytics tools and methodologies enables organizations to gain deep insights into workforce behaviors, trends, and patterns, ultimately facilitating more informed decision-making and strategic workforce management. Through applications such as talent acquisition and retention, performance management, workforce planning, and employee wellbeing, business analytics empowers HR professionals to optimize HR processes, enhance employee satisfaction, and drive organizational success. However, challenges such as data quality issues, privacy concerns, and skills gaps among HR professionals underscore the need for a strategic approach and investment in technology and talent to fully realize the benefits of business analytics in HR analytics. Originality/Value: Detailed analysis of all subjects related to the problems connected with the usage of business analytics in the case of human resource analytics [ABSTRACT FROM AUTHOR]

Machine learning and artificial intelligence (ML/AI) promise higher degrees of personalization and enhanced efficiency in marketing communication. The paper focuses on causal ML/AI models for campaign targeting. Such models estimate the change in customer behavior due to a marketing action known as the individual treatment effect (ITE) or uplift. ITE estimates capture the value of a marketing action when applied to a specific customer and facilitate effective and efficient targeting. We consolidate uplift models for multiple treatments and continuous outcomes and perform a benchmarking study to demonstrate their potential to target promotional monetary campaigns. In this use case, the new models facilitate selecting the optimal discount amount to offer to a customer. Large-scale analysis based on eight marketing data sets from leading B2C retailers confirms the significant gains in the campaign return on marketing when using the new models compared to relevant model benchmarks and conventional marketing practices. [ABSTRACT FROM AUTHOR]

In the increasingly knowledge-based world economy, the multinational firm's success often hinges on its business intelligence capability nurtured by business analytics (BA). Despite the growing recognition of BA's role in enhancing the firm's intellectual capital and subsequent competitiveness, it is still unknown what truly motivates and inhibits BA adoption. This study aims to identify key influencing factors for BA adoption such as organizational characteristics, information security/privacy, and information technology maturity (knowledge level). In so doing, this study employed data mining and data visualization techniques to develop specific patterns of BA adoption practices based on a combined sample of 224 Korean firms and 106 U.S. firms representing various industry sectors. This study is one of the first attempts to develop practical guidelines for the successful implementation of BA based on the cross-national study of BA practices among both Korean and U.S. firms. [ABSTRACT FROM AUTHOR]

Purpose: This research investigates the impact of business analytics (BA) on corporate entrepreneurship (CE) and open innovation (OI), considering the moderated mediation analysis in the context of Iran as a developing country. The study was conducted in various industries, including food, chemicals, agriculture, automobile, and service industries, with 207 observations. Design/methodology/approach: Through an in-depth review of the extant literature a conceptual model was developed and the proposed hypotheses were tested using Structural Equation Modeling technique (PLS-SEM). Findings: The results indicate that business analytics has significant effects on corporate entrepreneurship and open innovation. Open innovation has a significant effect on corporate entrepreneurship, with open innovation serving as a suitable mediator. Furthermore, the moderated mediation analysis shows the positive impact of Business Analytics on Open Innovation-Corporate Entrepreneurship relationship. Research limitations/implications: As this study was conducted in Iran, one of the main limitations can be attributed to the specific characteristics of the country which may affect how and how much the variables influence each other. Practical implications: The study highlights the importance of promoting Open Innovation in organizations and utilizing Business Analytics to make strategic decisions and foster innovation in entrepreneurial activities. Originality/value: This study fills the gap in the literature by exploring how BA contributes to corporate entrepreneurship of the Iranian organizations in various industries, given open innovation as a mediator under dynamic market conditions. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the applications of usage of business analytics in demand forecasting. 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 rise of Industry 4.0 has revolutionized the business landscape by creating a data-rich environment fueled by interconnected devices and digital systems throughout the supply chain. In this era, business analytics emerges as a crucial tool, leveraging the abundance of data to uncover intricate patterns and trends in consumer behavior, market dynamics, and product demand. By analyzing historical data and integrating external factors, business analytics enables more accurate demand forecasts, essential for effective inventory management, production planning, and overall business success. Advanced analytics techniques such as machine learning and predictive modeling thrive in Industry 4.0 environments, enabling businesses to process large datasets and predict future demand with precision. Moreover, business analytics facilitates the integration of demand forecasting with other supply chain components, optimizing resource allocation and enhancing efficiency. Industry 4.0 also fosters greater customization and personalization of products and services through market segmentation analysis and tailored forecasting, driving competitive advantage. This publication has explored the applications of business analytics in demand forecasting, emphasizing its importance, aspects, software applications, advantages, and challenges within the Industry 4.0 context. By addressing these aspects, organizations can harness the power of business analytics to optimize operations, foster growth, and stay competitive in today's dynamic business environment. Originality/Value: Detailed analysis of all subjects related to the problems connected with the usage of business analytics in the case of smart manufacturing. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the applications of usage of business analytics in risk mitigation. 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 incorporation of business analytics into risk mitigation strategies amid the landscape of Industry 4.0 signifies a pivotal paradigm shift in response to the multifaceted challenges posed by technological disruptions, supply chain vulnerabilities, regulatory changes, and cybersecurity threats. As industries undergo unprecedented digital transformations, traditional risk management approaches are rendered obsolete, prompting the integration of more data-driven and analytical methodologies. Business analytics plays a crucial role in this transformative shift, leveraging vast volumes of data generated in Industry 4.0 environments. Through advanced analytics techniques, organizations can proactively anticipate and respond to potential risks in real-time, with predictive analytics enabling the forecast of disruptions and the implementation of preemptive measures. Beyond immediate operational concerns, the applications of business analytics extend to supply chain optimization, cybersecurity monitoring, and regulatory compliance. The symbiotic relationship between business analytics and risk mitigation emerges as a cornerstone for sustainable and resilient business practices in the evolving landscape of Industry 4.0, emphasizing the necessity of addressing associated challenges and leveraging a diverse array of software applications for comprehensive risk management. Originality/Value: Detailed analysis of all subjects related to the problems connected with the usage of business analytics in the case of risk mitigation. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the applications of usage of business analytics in global supply chain coordination. 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 in global supply chain coordination within the framework of Industry 4.0 is a transformative strategy that propels organizations toward enhanced efficiency, adaptability, and competitiveness. This approach is particularly crucial as industries undergo significant technological transformations. Business analytics strategically deployed in the modern supply chain serves as a vital tool, addressing the complexities inherent in Industry 4.0. The diverse applications of business analytics, including demand forecasting, supply chain visibility, predictive maintenance, collaboration, and risk management, highlight its multifaceted benefits. These applications empower organizations to anticipate market demands, optimize inventory, ensure reliable production flows, foster collaboration, and proactively address risks, thereby bolstering overall supply chain resilience and responsiveness. The use of sophisticated software tools like SAP Integrated Business Planning, Oracle SCM Cloud, and IBM Watson Supply Chain underscores the pivotal role of technology in overcoming the intricate challenges of managing global supply chain processes. Despite the numerous advantages, challenges such as data quality, integration issues, implementation costs, and skill shortages necessitate careful consideration and strategic planning. Organizations must address these challenges, along with security and privacy concerns, resistance to change, and the complexity of analytics tools, through investments in training and change management strategies to fully unlock the potential of business analytics. The presented tables further illustrate the advantages and challenges, emphasizing the positive impact of business analytics on efficiency, risk management, and strategic alignment with organizational goals. In conclusion, the judicious use of business analytics offers substantial opportunities for optimizing global supply chain coordination in Industry 4.0, requiring organizations to navigate both advantages and challenges proactively to position themselves at the forefront of innovation and competitiveness in the dynamic landscape of modern industry. Originality/Value: Detailed analysis of all subjects related to the problems connected with the usage of business analytics in the case of global supply chain coordination. [ABSTRACT FROM AUTHOR]

The article explores the integration of Artificial Intelligence (AI) and predictive analytics in business growth, emphasizing their transformative impact on decision-making. Topics discussed include the advanced data processing and continuous learning capabilities of AI, real-world applications across diverse industries such as retail, healthcare, and finance, and the ethical considerations associated with these technologies.

Purpose: The purpose of this publication is to present the applications of usage of business analytics in customer behaviour analysis. 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 customer behavior analysis in Industry 4.0 environments offers businesses a transformative opportunity to gain profound insights into customer preferences, trends, and behaviors. Through the utilization of state-of-the-art technologies and data-driven methodologies, organizations can attain unprecedented levels of precision and detail in understanding customer behavior. Real-time data collection and analysis facilitate agile responses to evolving market dynamics, enabling personalized customer experiences across various channels. Additionally, advanced analytics tools such as predictive modeling and sentiment analysis empower businesses to forecast future trends, address churn, and enhance customer satisfaction. However, businesses may encounter challenges like data quality issues, privacy concerns, and resource limitations. Overcoming these obstacles necessitates a comprehensive approach, involving investments in data governance, talent acquisition, and technology infrastructure. By surmounting these challenges, businesses can harness the full potential of business analytics to drive strategic decisions, refine marketing strategies, and elevate overall business performance within Industry 4.0 environments. Originality/Value: Detailed analysis of all subjects related to the problems connected with the usage of business analytics in the case of smart manufacturing. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the applications of usage of business analytics in smart manufacturing. 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 in smart manufacturing within the framework of Industry 4.0 marks a significant stride in industrial processes, offering manifold advantages alongside notable challenges. Throughout this study, we delve into the expansive realm of business analytics applications, encompassing predictive maintenance, quality control, supply chain optimization, and real-time decision-making. Leveraging business analytics yields palpable benefits in smart manufacturing, exemplified by proactive equipment maintenance, stringent quality standards adherence, and streamlined supply chain operations. Additionally, analytics-driven enhancements in production optimization, energy management, demand forecasting, and asset performance management contribute to heightened productivity, cost reduction, and sustainability improvement. Challenges including data integration complexities, implementation intricacies, security concerns, scalability limitations, model interpretability issues, and skill gaps necessitate concerted efforts for effective resolution. Collaboration among stakeholders—manufacturers, software developers, policymakers, and educational institutions—is imperative. Joint initiatives aimed at bolstering data integration capabilities, providing specialized training, fortifying cybersecurity measures, and fostering a culture of continuous improvement are crucial for successful business analytics deployment. Originality/Value: Detailed analysis of all subjects related to the problems connected with the usage of business analytics in the case of smart manufacturing. [ABSTRACT FROM AUTHOR]

Simultaneous exploration and exploitation (i.e., exploration–exploitation) can help a firm address short‐term environmental requirements and ensure long‐term environmental viability. Exploration–exploitation, however, challenges organisational practices because they compete for resources and time. While business analytics (BA) offers the potential to overcome these challenges, research to date offers very limited insights into how BA capabilities interact with ambidextrous capabilities to realise environmental value. We address this issue by conducting a comparative case study at a bank and at a real‐estate trust through the theoretical lens of dynamic capabilities. We develop a process model to explain how BA powers ambidextrous practices to achieve sustainability outcomes over time. We uncover two mechanisms: a BA‐powered context shaping mechanism by which BA powers contextual ambidexterity at the employee level using data availability, timeliness, and analytics culture; and a BA‐powered resource linking mechanism by which BA powers structural ambidexterity at intra‐ and inter‐organisational levels using holistic insights and analytics leadership. Our model highlights the contextual factors that condition the extent to which a firm moves along the continuum of exploration–exploitation. We also define a new dimension of sustainability outcomes which we label eco‐awareness to explain how BA shapes employees' environmental alertness and enables the paradigm shift in an organisation's sustainability mindset. [ABSTRACT FROM AUTHOR]

Infrastructure construction companies encounter numerous challenges in managing large and dispersed teams, along with fragmented or non-existent operational data. These challenges often result in delays, inefficiencies, and over-budget projects. Road pavement works are an example of this, as they heavily rely on expensive heavy construction equipment that requires detailed planning and real-time adjustments. Also, pavement quality is closely linked to the quality of the asphalt mixture in terms of viscosity and compactability, which is significantly influenced by temperature. This paper describes the features, challenges and results of a road paving real-time management system that was conceived in a co-creation environment with a construction company. Such a partnership has allowed to specify the requirements of such an application aligned with the identified needs of a real-world development. According with the state-of-the-art, this innovative system is unique in the way it is manufacturer-agnostic and designed to be compatible with most situations. It is also data production-oriented to allow future developments that may provide business analytics or scientific research in the road paving area. This work also presents the development of sensors such as a high precision geolocalized infrared matricial temperature sensor for the application of the bituminous mixture, the data and communication structure, and a web-based interface that manages the construction projects for different stakeholders. [ABSTRACT FROM AUTHOR]

Background: Models for structuring big-data and data-analytics projects typically start with a definition of the project's goals and the business value they are expected to create. The literature identifies proper project definition as crucial for a project's success, and also recognizes that the translation of business objectives into data-analytic problems is a difficult task. Unfortunately, common project structures, such as CRISP-DM, provide little guidance for this crucial stage when compared to subsequent project stages such as data preparation and modeling. Contribution: This paper contributes structure to the project-definition stage of data-analytic projects by proposing the Data-Analytic Problem Structure (DAPS). The diagrammatic technique facilitates the collaborative development of a consistent and precise definition of a data-analytic problem, and the articulation of how it contributes to the organization's goals. In addition, the technique helps to identify important assumptions, and to break down large ambitions in manageable subprojects. Methods: The semi-formal specification technique took other models for problem structuring — common in fields such as operations research and business analytics — as a point of departure. The proposed technique was applied in 47 real data-analytic projects and refined based on the results, following a design-science approach. [ABSTRACT FROM AUTHOR]

The Secured Independent Intelligent Transport System (SIITS) is poised to revolutionize traditional transport management systems, leveraging autonomous vehicles (AVs) connected through an open-channel Internet to link Traffic Command Centers (TCCs), Road Side Units (RSUs), and AVs within the SIITS framework. However, this reliance on the Internet exposes users to various security risks, safety vulnerabilities, and other challenges that impede the progress of SIITS applications. In this method, ensuring robust security management and trustworthiness is paramount for the long-term adoption of this innovative trend. While previous efforts have focused on integrating security methods from multiple disciplines into a unified reference design, this article presents a reference architecture primarily centered around ITS safety. Additionally, the article introduces a proposed framework for enhancing ITS safety, addressing the confidence issues. To further address these challenges, the article offers categorization of goods, Big Data methods and services, and validates the utility of ITS business analytics for corporate applications through a groundbreaking multi-tier ITS security architecture. [ABSTRACT FROM AUTHOR]

Businesses use data to enhance operational performance, creating demand for professionally trained data/business analytics graduates. Universities offer a variety of academic programs in response to the increasing labor market demands. Yet, research on how well educational offerings adapt to market demand is scant. This paper aims to evaluate how well university curricula match Saudi Arabia's marketplace needs. A content analysis was conducted to compare universities' analytics programs with a sample of internet job postings over a 12-month period. The results were evaluated quantitatively to assess the alignment between academia and industry. This information is of value to industry, universities, and learners. [ABSTRACT FROM AUTHOR]