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Predictive analytics is the approach to business analytics that answers the question of what might happen in the future. Although predictive information is critical for making forward-looking decisions, traditional approaches struggle to cope with the increasing uncertainty and complexity that characterise modern supply chains. Simulation is limited by insufficient timeliness, while machine learning is constrained by poor interpretability and data scarcity. Inspired by the complementary nature of simulation and machine learning, an integrated predictive analytics approach is proposed and applied to a humanitarian supply chain. By coupling simulation and machine learning, predictive models can be developed with limited historical data, and pre-crisis performance assessment can be performed to facilitate timely and informed decisions. The proposed approach enables managers to gain valuable insights into the complex evolution of the uncertain future, which also opens up the possibility of further integration with optimisation and digital twins. [ABSTRACT FROM AUTHOR]

Not all relevant data come in structured formats. We created this teaching case to introduce students to an important data analysis technique that can be used on unstructured data: text analytics. This case focuses on why MD&A disclosures may provide auditors, regulators, and investors with decision-relevant information during the global pandemic. There are a wide variety of tools that can be used to introduce accounting students to text analytics. In the Teaching Notes, we provide instructions for Alteryx, a technology that offers a low-code solution to text analytics. Low-code solutions allow faculty and students to focus on critical thinking and applying text analytics to accounting. The learning objectives are (1) expand students' understanding of text analytics in an accounting setting, (2) develop students' skills necessary for performing text analytics, and (3) expand students' critical thinking skills as they apply text analytics in accounting or business contexts. Data Availability: Data are available from public sources cited in the text and materials that are available for download. [ABSTRACT FROM AUTHOR]

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]

The ever-increasing development of digital technologies has brought about significant changes in business performance. The increase in the number of published articles on this topic also shows the special attention of researchers in information systems, business management, and innovation. While digital changes are inevitable in the digital age, previous research has been limited to a specific domain. This research aims to identify key themes and macro topics through a systematic review of 201 articles from 2018 to 2023 through two high-quality databases (Scopus and Web of Science). First, using thematic analysis, the main themes are identified, and their relationships are investigated from the perspective of digital technology development. In the next step, by using topic modeling (Latent Dirichlet Allocation), the major domains of the impact of these technologies will be investigated, and future research trends will be identified using the scientometric approach. The realization of digital transformation leads to value creation for business by enhancing the accuracy of management decision-making, making profit through increasing sales and reducing operational and fixed costs, reducing the time of performing processes and responding to changes, and increasing efficiency, and productivity. On the other hand, it affects customer experience improvement of received services, which leads to long-term relationships between the parties, which is very important in today's competitive market in the digital age. The innovation of this research is to design the thematic network through an in-depth review of texts and text mining analysis, which leads to a better understanding of the relationships between main components. Thematic network analysis and the making of relationship with topic modeling concepts is as an artifact that recommendations are given to researchers and managers to conduct future research. [ABSTRACT FROM AUTHOR]

Purpose: This study investigates the relationships between data governance (DG), business analytics capabilities (BAC), and decision-making performance (DMP), with a focus on the mediating effects of big data literacy (BDL) and data analytics competency (DAC). Design/methodology/approach: The study was conducted with 178 experienced managers in public service organizations, using a quantitative approach. Structural equation modeling (SEM) and mediation tests were employed to analyze the data. Findings: The findings reveal that DG and BDL are critical antecedents for developing analytical capabilities. Big data literacy mediates the relationship between DG and BAC, while BAC mediates the relationship between DG and DMP. Furthermore, DAC mediates the relationship between BA capabilities and DMP, explaining most of the effect of BAC on DMP. Practical implications: These results highlight the importance of DG in fostering BDL and analytical skills for improved decision-making in organizations. Originality/value: By prioritizing DG practices that promote BDL and analytical capabilities, organizations can leverage business analytics to enhance decision-making. [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]

Big data has attracted extensive attention from industries and universities during the past few years. Big data is crucial in many fields, such as business analytics, healthcare, the Internet of Things (IoT), smart home, supply chain, transportation, and fraud detection. Nevertheless, some challenges must be addressed, such as decentralization, integration, data sharing, privacy, and security. On the other hand, blockchain has numerous potential for improving big data services and applications because of its decentralized, verifiable, and anti‐tamper features. We apply the Systematic Literature Review (SLR) approach in this study to investigate blockchain integrity in big data and understand the different topics and significant areas already presented. This paper aims to analyze studies on blockchain integration in big data published between 2017 and 2022. A technical taxonomy is presented for blockchain integration into big data, including data storage, security, and applications based on the field of articles selected by the SLR method. Finally, the achievements and shortcomings of each study are discussed, and future research challenges and open issues related to blockchain integration in big data are highlighted. [ABSTRACT FROM AUTHOR]

Customer data acquisition is an important task in data-driven business analytics. Recently, there has been a growing interest in the effective use of an organization's internal customer data, also known as first-party data. This work studies the acquisition of new data for business analytics based on first-party data resource. We address issues related to both acquisition cost and data quality. To reduce acquisition cost, we consider using auction-based methods, such as the generalized second price (GSP) auction, for acquiring data with differential prices for different customers. We find that the GSP-based data acquisition method incurs a lower cost and/or achieves a higher response rate than fixed price methods. To maximize data quality, we propose novel optimization models for different data acquisition methods and data quality measures. The proposed models maximize the quality of the acquired data while satisfying budget constraints. We derive and discuss the solutions to the optimization models analytically and provide managerial insights from the solutions. The proposed approach is effective in increasing customer responses, reducing selection bias, and enabling more accurate estimation and prediction for business analytics. The results of the experimental evaluation demonstrate the advantage of the proposed approach over existing data acquisition methods. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0037) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0037). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [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]

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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]

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.)

This article aims to comprehensively analyze the crucial role played by data analytics and business intelligence (BI) strategies in propelling digital transformation within diverse industries. Through an extensive literature review and examination of real-world case studies, the study employs a systematic analysis of scholarly works and industry reports. This approach provides a panoramic view of how organizations utilize data-driven insights for competitive advantages, improved customer experiences, and fostering innovation. The findings underscore the pivotal significance of data analytics and BI strategies in influencing strategic decision-making, enhancing operational efficiency, and ensuring long-term sustainability across various industries. The study stands out in its originality by offering a unique synthesis of insights derived from scholarly works and real-world case studies, contributing to a holistic understanding of the transformative impact of data analytics and BI on contemporary business practices. While the study provides valuable insights, limitations include the scope of available literature and case studies. The implications call for further research to explore emerging trends and evolving challenges in the dynamic landscape of data analytics and BI. The practical implications highlight the tangible benefits organizations can derive from integrating data analytics and BI strategies, emphasizing their role in shaping strategic decisions and fostering operational efficiency. In a broader context, the study delves into the social implications of the symbiotic relationship between data analytics, BI, and digital transformation. It explores how these strategies impact broader societal and economic aspects, influencing innovation and sustainability. [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]

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]

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]

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]

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]

Purpose: The purpose of this paper is to manage knowledge pertaining to micro, small and medium enterprise (MSME) actors in the business, agriculture and industry sectors. This study uses text mining techniques, specifically Latent Dirichlet Allocation Mallet, to analyze the data obtained from the in-depth interviews. This analysis helps us identify and understand the issues faced by these actors. Design/methodology/approach: In this study, the authors use big data and business analytics to recalculate the MSME business vulnerability index in 503 districts and 34 provinces across Indonesia. Subsequently, the authors conduct in-depth interviews with MSME actors in Medan, Central Java, Yogyakarta, Bali and Manokwari, West Papua. Through these interviews, the authors explore their strategies for surviving the COVID-19 pandemic and the extent of their digital literacy, and the application of technology to maximize sales and business outcomes. Findings: The findings reveal that, for the sustainable growth of MSMEs during and after the pandemic, collaboration across the Penta-Helix framework is essential. This collaboration enables the development of practical solutions for the challenges posed by COVID-19, particularly in the context of the "new normal." In addition, the authors' survey of MSMEs involved in agriculture, trade and processing sectors demonstrates that 58.33% experienced a decrease in income during the pandemic and 12.66% reported an increase in revenue. In contrast, 25% experienced no change in income before and during the pandemic. Originality/value: This research contributes significantly by offering comprehensive insights obtained from in-depth surveys conducted with MSMEs across multiple sectors. The findings underscore the importance of addressing the challenges MSMEs face and highlight the need for collaboration within the Penta-Helix framework to foster their resilience and success amidst the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

This study examines the impact of data-driven decision-making on innovation within startups. By utilizing a mixed-methods research design, the study integrates quantitative data from surveys and innovation performance metrics with qualitative insights from interviews and focus groups. The findings reveal that startups employing data-driven strategies demonstrate higher innovation outcomes, including the development of new products and revenue growth. The research highlights the importance of data literacy and a data-centric culture as key factors in maximizing the benefits of data-driven decision making. Additionally, leadership commitment is identified as crucial for successful adoption and implementation of data-driven strategies. The study underscores the potential of data-driven decision making to foster innovation within startups and provides recommendations for enhancing data-driven practices. [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.

Purpose: The study explores the extent to which business analytics can address business problems using the task-technology fit theory. Design/methodology/approach: The qualitative research approach of pattern matching was adopted for data analysis and 12 semi-structured interviews were conducted. Four propositions derived from the literature on task-technology fit are compared to emerging core themes from the empirical data. Findings: The study establishes the relationships between various forms of fit, arguing that the iterative application of business analytics improves problem understanding and solutions, and contends that both under-fit and over-fit can be acceptable due to the increasing costs of achieving ideal fit and potential unaffected outcomes, respectively. The study demonstrates that managers should appreciate that there may be a distinction between those who create business analytics solutions and those who apply business analytics solutions to solve problems. Originality/value: Extant studies on business analytics have not focused on how the match between business analytics and tasks affects the level to which problems can be addressed that determines business value. This study enriches the literature on business analytics by linking business analytics and business value through problem resolution demonstrated by task-technology fit. To the authors' knowledge, this study might be the first to apply pattern matching to study the fit between technology and tasks. [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 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: This study examines the mediating roles of the three dimensions of business intelligence (sensing capability, transforming capability and driving capability) in the relationship between the three dimensions of big data analytics capability (big data analytics management, technology and talent capabilities), and radical innovation among Chinese manufacturing enterprises. Design/methodology/approach: A theoretical framework was developed using the resource-based view. The hypothesis was tested using empirical survey data from 326 Chinese manufacturing enterprises. Findings: Empirical results show that, in the Chinese manufacturing context, business intelligence sensing capability, business intelligence transforming capability and business intelligence driving capability positively mediate the impact of big data analytics capability on radical innovation. Practical implications: The results offer managerial guidance for leaders to properly use big data analytics capability, business intelligence and radical innovation as well as offering theoretical insight for future research in the manufacturing industry's radical innovation. Originality/value: This is among the first studies to examine three dimensions of big data analytics capability on the manufacturing industry's radical innovation by considering the mediating role of three dimensions of business intelligence. [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: 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]

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]