Search

Drawing on the story of Aginic and its educational analytics platform edPortal, this teaching case study examines how applying agile methods and design thinking to analytics has helped unlock significant value for Aginic, its clients and the education sector overall. It describes key factors driving the successful integration of agile values and design approaches, allowing students to gain a deep understanding on how such integration can facilitate the development of innovative data analytics products. [ABSTRACT FROM AUTHOR]

This paper introduces a novel statistical testing technique known as segmented detrended multifractal fluctuation analysis (SMF‐DFA) to analyze the structured scaling properties of financial returns and predict the long‐term memory of financial markets. The proposed methodology is applied to assess the efficiency of major cryptocurrencies, expanding upon conventional approaches by incorporating different fluctuation regimes identified through a change‐point detection test. A single‐factor model is employed to characterize the endogenous factors influencing scaling behavior, leading to the development of a self‐explanatory machine learning approach for price forecasting. The proposed method is evaluated using daily data from three major cryptocurrencies spanning from April 2017 to December 2022. The analysis aims to determine whether the digital market has experienced significant changes in recent years and assess whether this has resulted in structured multifractal behavior. The study identifies common periods of local scaling among the three prices, with a noticeable decrease in multifractality observed after 2018. Furthermore, complementary tests on shuffled and surrogate data are conducted to explore the distribution, linear correlation, and nonlinear structure, shedding light on the explanation of structured multifractality to some extent. Additionally, prediction experiments based on neural networks fed with multi‐fractionally differentiated data demonstrate the utility of this new self‐explanatory algorithm for decision‐makers and investors seeking more accurate and interpretable forecasts. [ABSTRACT FROM AUTHOR]

In this study, we present a novel cost-sensitive approach for uplift modeling in the context of cross-selling and workforce analytics. We leverage referrals from sales agents across business units to estimate the individual treatment effects of incentives on the cross-selling outcomes within a company. Uplift modeling is employed to predict relationships between salespeople that should be encouraged based on the probability of successful cross-selling - defined when a customer accepts the product suggested by sales agents. We conducted experiments on data from a Chilean financial group, evaluating both statistical and profit metrics. Exploring various machine learning classifiers for predictive purposes, we observed a significant improvement over the current approach, which exhibits an uplift below 0.01. Finally, we show that selecting the best classifier with profit metrics results in a 31.6% improvement in terms of average customer profit. This emphasizes the importance of defining an adequate compensation scheme and integrating it into the modeling process. [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]

Purpose: The purpose of this publication is to present the applications of usage of business analytics in continuous improvement. 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 pivotal role of business analytics in driving continuous improvement within Industry 4.0 environments. It examines how the integration of advanced analytics tools, such as predictive modeling and real-time data visualization, transforms operational efficiency, quality management, and strategic decision-making. By leveraging vast datasets generated by interconnected systems, organizations can identify inefficiencies, anticipate potential issues, and enhance customer experiences. The paper highlights both the advantages, including improved decision-making, increased efficiency, and data-driven innovation, as well as the challenges, such as data quality concerns and integration difficulties. Ultimately, it underscores the significance of business analytics in fostering a culture of ongoing refinement and adaptability, crucial for sustaining competitive advantage and achieving longterm success in the evolving industrial landscape. Originality/Value: Detailed analysis of all subjects related to the problems connected with the usage of business analytics in the case of continuous improvement. [ABSTRACT FROM AUTHOR]

In the era of Industry 4.0, where disruption and uncertainty reign, the strategic adoption of business analytics emerges as a vital force in driving competitive advantage through value creation. This study investigates the pivotal role of business analytics adoption in fostering value creation to achieve a sustainable edge in Industry 4.0. By analyzing data from 237 E-commerce firms operating in fiercely competitive markets, the research explores the strategic imperatives for maximizing the utilization of data and information assets. It highlights the importance of integrating business analytics into organizational processes and fostering collaboration and dynamic capability to optimize decision-making and spur innovation. The findings underscore the necessity for firms to develop competencies in leveraging data-driven insights for competitive advantage. Moreover, this study contributes to bridging gaps in both Resource-Based Theory and dynamic capability literature, offering insights into practical actions for navigating the complexities of Industry 4.0. Ultimately, the research provides a comprehensive framework for understanding and implementing business analytics adoption as a strategic imperative for organizational success in the digital era, with implications for enhancing competitive advantage and driving sustained growth. [ABSTRACT FROM AUTHOR]

The Industrial Revolution 4.0 is characterized by the integration of digital technologies such as big data analytics, the Internet of Things, artificial intelligence, and robotics into traditional business processes. This transformation has triggered a huge increase in the use of the internet and gave birth to innovations that change the way we live and work. To stay ahead in this digital era, companies must implement a digital transformation strategy that involves the use of digital tools and platforms to optimize operations, lower costs, and improve efficiency. The adoption of digital transformation allows companies to respond more quickly and appropriately to market changes, gain insights into customer behavior and preferences, and drive growth and profitability. In today's fast-changing business environment, digital transformation is an essential necessity for companies looking to thrive. The study, which involves analyzing data from 327 e-commerce companies, proposes a model that offers valuable insights into practical actions as well as deepens understanding of the importance of digital transformation strategies in the era of big data, taking into account the role of the TOE Framework (Technology-Organization-Environment), the adoption of business analytics, knowledge retention, and dynamic capabilities to achieve competitive advantage. [ABSTRACT FROM AUTHOR]

The proliferation of big data and the growth of e-commerce have intensified the challenges associated with extracting actionable data for personalised recommendations and decision-making. With data-driven marketing strategies, understanding and predicting customer behaviour has become paramount for maintaining competitive advantage. This study leverages business analytics tools, focusing on Recency, Frequency, and Monetary (RFM) Analysis, alongside K-Means and Hierarchical (Agglomerative) Clustering algorithms, to segment customer transactional data. Data normalisation, a critical step for accurate clustering, was performed using log transformation and the Power Transformer technique with the Yeo-Johnson parameter, the latter proving more effective for handling both positively and negatively skewed data, enhancing data normalisation and suitability for analysis. This study reveals that RFM Analysis with Hierarchical Clustering outperforms K-Means Clustering, achieving a Silhouette Score of 0.47 and a Calinski-Harabasz Index of 3787.1, indicating a more accurate identification of customer segments. RFM Analysis alone generated eight clusters, while integrating RFM Analysis with both Hierarchical Clustering and K-Means generated three similar-sized clusters with interchanged labels. These metrics highlight the proficiency of Hierarchical Clustering in identifying unique customer segments and customising marketing strategies. The findings indicate that the RFM-Hierarchical Clustering approach enhances segmentation precision and facilitates more refined and effective marketing strategies. [ABSTRACT FROM AUTHOR]

Although Business Intelligence (BI) and Business Analytics (BA) have been widely adopted in the tourism sector, comparative research using BI and BA remains scarce. To fill this gap in the literature, the present study explores how BI and BA contribute to strategic innovation, address operational challenges, and enhance customer engagement. To this end, using a dual-method approach that incorporates both quantitative and qualitative methodologies, we first conduct a bibliometric analysis using SciMAT. This sets the stage for the subsequent application of the Gioia methodology. Specifically, we perform an in-depth qualitative examination of a total of 12 scholarly articles on the tourism sector, evenly split between BI and BA. Upon synthesizing the findings on the roles of BI and BA, we outline distinct pathways through which they influence tourism sector management solutions. Based on the obtained evidence, we argue that, while BI focuses on technological advancement and operational integration, BA is more aligned with predictive analytics and data-driven customer engagement. These insights provide managers with a better understanding of the roles of BI and BA, serving as a guide for their strategic applications, from improving service quality to innovating in customer engagement. The novelty of this approach lies in its use of the Gioia methodology, in a comparative analysis to evaluate the separate yet complementarily roles of BI and BA, and in enhancing tourism industry practices. [ABSTRACT FROM AUTHOR]

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]

Data-driven business models imply the inter-organisational exchange of data or similar value objects. Data science methods enable organisations to discover patterns and eventually knowledge from data. Further, by training machine learning models, knowledge is materialised in those models. Thus, organisations might risk the exposure of competitive knowledge by sharing data-related value objects, such as data, models or predictions. Although knowledge risks have been studied in traditional business models, little research has been conducted in the direction of data-driven business models. In this explorative qualitative study, we conducted 28 expert interviews in three rounds (two exploratory and one evaluatory) and identified five types of risks along the three basic types of value objects: data, models and predictions. These risks depend on the context, i.e., when competitive knowledge could be discovered from shared value objects. We found that those risks can be mitigated by technology, contractual regulations, trusted relationships, and adjusting the business model design. In this study, we show that the risk of knowledge leakage is a relevant risk factor in data-driven business models. Overall, knowledge risks should be considered already during business model design, and their management requires an interdisciplinary approach via a balanced assessment. The level of knowledge protection from a technology perspective highly depends on computer science innovations and thus is a moving target. As an outlook, we suggest that knowledge risk will become even more relevant with the extensive usage of machine learning and artificial intelligence in data-driven business models. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Social & Technology / Jurnal Sosial dan Teknologi (SOSTECH) is the property of Green Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: As businesses keep investing substantial resources in developing business analytics (BA) capabilities, it is unclear how the performance improvement transpires as BA affects performance in many different ways. This paper aims to analyze how BA capabilities affect firms' agility through resources like information quality and innovative capacity considering industry dynamism and the resulting impact on firm performance. Design/methodology/approach: This paper tested the research hypothesis using primary data collected from 192 companies operating in Bangladesh. The data were analyzed using partial least squares-based structural equation modeling. Findings: The results indicate that BA capabilities improve business resources like information quality and innovative capacity, which, in turn, significantly impact a firm's agility. This paper also found out that industry dynamism moderates the firms' agility and, ultimately, firms' performance. Practical implications: The contribution of this work provides insight regarding the role of business analytics capabilities in increasing organizational agility and performance under the moderating effects of industry dynamism. Originality/value: The present research is to the best of the authors' knowledge among the first studies considering a firm's agility to explore the impact of BA on a firm's performance in a dynamic environment. While previous researchers discussed resources like information quality and innovative capability, current research theoretically argues that these items are a leveraging point in a BA context to increase firm agility. [ABSTRACT FROM AUTHOR]

Bus rapid transit systems (BRT) have been an indispensable public transportation pillar, especially in densely populated regions. Accurate insight into the BRT network's utilization is vital in BRT resource allocation planning contexts. This research focuses on how operators can utilize passengers' smart card data to develop origin-destination (OD) matrix-based business analytics. This research proposes a hybrid approach combining trip chaining, direct pairing, mode estimation methods, and visual analytics development. The novel approach is robust in handling incomplete smart card data transactions to generate origin-destination matrices and corresponding visual analytics as decision support systems for the BRT operators. As a case study, we applied and validated the proposed analytics to more than 20.6 million smart card transactions in one of the largest global BRT systems in Jakarta, Indonesia. [ABSTRACT FROM AUTHOR]

This article introduces four case studies to integrate healthcare analytics topics into classroom learning. Each case study employs distinct analytical methods, including optimization with Excel Solver, multiple linear regression, Monte Carlo simulation, and time series forecasting models, providing diverse practical applications in healthcare analytics. The cases offer students hands-on experience in practical healthcare challenges, enhancing their analytical and decision-making skills. For each case, we provide a detailed background, an in-depth data description, and comprehensive teaching notes. These elements are structured to facilitate understanding and teaching analytics concepts. The paper also summarizes student feedback collected from various courses where these case studies were implemented. This feedback consistently indicates that the cases significantly contributed to the student’s perceived learning, particularly in understanding and applying healthcare analytics in concrete scenarios. These case studies bridge the gap between theoretical knowledge and practical application and serve as a valuable resource for instructors seeking to enrich their healthcare analytics curriculum.

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 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.

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Se presentan los resultados obtenidos del diseño e implementación de un sistema de indicadores, el cual analiza la información de una plataforma responsiva que muestra el potencial tecnológico, de investigación y formación de recursos humanos con que cuentan los 493 laboratorios nacionales, universitarios y unidades de apoyo de la Universidad Nacional Autónoma de México (UNAM). El sistema de indicadores de desempeño utiliza la base de datos de la plataforma para monitorear, analizar y revelar las ventajas conseguidas en el tiempo, para lo que incorpora una herramienta de vanguardia en business intelligence y analytics: Microsoft Power BI®, con el que se logró la visualización avanzada, segmentación, KPI’s, gráficos interactivos con nivel de profundidad y agregación, estadísticas clave y optimización. El sistema es responsivo para cinco categorías: capacidades, equipamiento, personal, comunicación y pronósticos, así como 45 KPI’s simples y una sección de pronósticos (machine learning), para aprovechar la información generada los últimos siete años.