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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]

Risk and uncertainty are crucial factors in decision-making processes, especially when integrating emerging technologies into essential systems like supply chains. Failing to adequately consider significant risks can disrupt supply chain operations, leading to a loss of competitive edge and causing financial and reputational damage. On the other hand, the complex nature of new technology environments, differing viewpoints among stakeholders, and the challenges of interpreting data introduce a variety of uncertainties in decision-making. In this study, we conduct a thorough examination of how blockchain strategies can be applied within supply chain frameworks. Our analysis utilizes data-driven network decision-making models that are refined to effectively manage uncertainty and risk. These models take into account aspects such as supply chain dynamics and technological factors. Importantly, we meld risk considerations with our models to tackle efficiency shortfalls, while also accounting for uncertainty caused by ambiguous and stochastic data environments. By applying and assessing these models in a real-world case study of the oil and gas industry, our research uncovers insightful observations. Specifically, we find that adopting a localization strategy presents specific risks, while a single-use strategy yields significant efficiency improvements. [ABSTRACT FROM AUTHOR]

Data analytics (DA) use has been frequently considered a transformational practice in the public sector, particularly in terms of its potential for data-driven decision making by local governments. Despite growing interest from academics and practitioners, empirical research on what cities are actually doing regarding DA and how is still relatively scarce, particularly in terms of a focus on specific activities and processes. Based on a socio-technical view, this paper examines a local government’s experience with DA use and how DA can be seen as a process and transformational practice. Results reveal that DA practices go beyond the use of specific technologies and involve collaborative efforts to formulate meaningful problems and prepare data for specific uses. Indeed, problem formulation is an essential step where collective discussions and assessment by technical and nontechnical DA practitioners occurs. Our findings suggest that (1) data analytics viewed as a socio-technical process involves data management and analysis, but also collaborative processes between multiple city agencies, (2) collaborative efforts go beyond data analytics and include data collection, data representation, and problem formulation; and (3) formulating the problem in a collaborative manner could be considered an important first step when using data analytics for decision-making in local governments. Furthermore, collaborative problem formulation in DA seems to be affected by specific variables related to the collaborative effort such as leadership, governance, and technology. [ABSTRACT FROM AUTHOR]

In the evolving landscape of airport management, leveraging public data emerges as a transformative strategy to enhance operational efficiency and customer satisfaction. This paper explores the integration of data analytics in airport operations, illustrating through case studies how airports can utilise open data to optimise performance, improve passenger experience and streamline security processes. We highlight the significant benefits of adopting data-driven approaches, including increased operational transparency, improved decision making and enhanced service quality. Our findings underscore the pivotal role of public data in driving innovation and sustainability in airport management, offering actionable insights for airport authorities and stakeholders. This paper serves as a stimulus on how to benefit from the use of open data. [ABSTRACT FROM AUTHOR]

Our paper proposes an effective way to make decisions based on data for uncertain situations. In simple terms, a data-driven decision is just a choice we make by looking at the available data. We express this choice as the best one according to a model we create from the data. The quality of this decision is judged by how well it performs in situations not seen during training. We also consider how often it disappoints in those situations. The challenge is that we do not know the exact probability of generating the data. An ideal data-driven decision should work well for any possible probability. However, such ideal decisions are usually not possible. Therefore, we look for decisions that work well on unseen data, considering the chances of disappointment. We prove that such effective decisions exist under certain conditions, allowing for practical applications. This approach holds regardless of whether the original problem is simple or complex, and it works even when the data are not uniformly collected. Our study also uncovers how the characteristics of the data-generating process influence the optimal decision-making model. We propose a statistically optimal approach to construct data-driven decisions for stochastic optimization problems. Fundamentally, a data-driven decision is simply a function that maps the available training data to a feasible action. It can always be expressed as the minimizer of a surrogate optimization model constructed from the data. The quality of a data-driven decision is measured by its out-of-sample risk. An additional quality measure is its out-of-sample disappointment, which we define as the probability that the out-of-sample risk exceeds the optimal value of the surrogate optimization model. The crux of data-driven optimization is that the data-generating probability measure is unknown. An ideal data-driven decision should therefore minimize the out-of-sample risk simultaneously with respect to every conceivable probability measure (and thus in particular with respect to the unknown true measure). Unfortunately, such ideal data-driven decisions are generally unavailable. This prompts us to seek data-driven decisions that minimize the in-sample risk subject to an upper bound on the out-of-sample disappointment—again simultaneously with respect to every conceivable probability measure. We prove that such Pareto dominant data-driven decisions exist under conditions that allow for interesting applications: The unknown data-generating probability measure must belong to a parametric ambiguity set, and the corresponding parameters must admit a sufficient statistic that satisfies a large deviation principle. If these conditions hold, we can further prove that the surrogate optimization model generating the optimal data-driven decision must be a distributionally robust optimization problem constructed from the sufficient statistic and the rate function of its large deviation principle. This shows that the optimal method for mapping data to decisions is, in a rigorous statistical sense, to solve a distributionally robust optimization model. Maybe surprisingly, this result holds irrespective of whether the original stochastic optimization problem is convex or not and holds even when the training data are not independent and identically distributed. As a byproduct, our analysis reveals how the structural properties of the data-generating stochastic process impact the shape of the ambiguity set underlying the optimal distributionally robust optimization model. Funding: This research was supported by the Swiss National Science Foundation under the NCCR Automation [Grant Agreement 51NF40_180545]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/opre.2021.0609. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Professional Business Review (JPBReview) is the property of Open Access Publications LLC 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.)

Although coronavirus disease 2019 (COVID-19) vaccines have been introduced, their allocation is a challenging problem. We propose a data-driven, spatially-specific vaccine allocation framework that aims to minimize the number of COVID-19-related deaths or infections. The framework combines a regional risk-level classification model solved by a self-organizing map neural network, a spatially-specific disease progression model, and a vaccine allocation model that considers vaccine production capacity. We use data obtained from Wuhan and 35 other cities in China from January 26 to February 11, 2020, to avoid the effects of intervention. Our results suggest that, in region-wise distribution of vaccines, they should be allocated first to the source region of the outbreak and then to the other regions in order of decreasing risk whether the outcome measure is the number of deaths or infections. This spatially-specific vaccine allocation policy significantly outperforms some current allocation policies. [ABSTRACT FROM AUTHOR]

Risk and uncertainty are crucial factors in decision-making processes, especially when integrating emerging technologies into essential systems like supply chains. Failing to adequately consider significant risks can disrupt supply chain operations, leading to a loss of competitive edge and causing financial and reputational damage. On the other hand, the complex nature of new technology environments, differing viewpoints among stakeholders, and the challenges of interpreting data introduce a variety of uncertainties in decision-making. In this study, we conduct a thorough examination of how blockchain strategies can be applied within supply chain frameworks. Our analysis utilizes data-driven network decision-making models that are refined to effectively manage uncertainty and risk. These models take into account aspects such as supply chain dynamics and technological factors. Importantly, we meld risk considerations with our models to tackle efficiency shortfalls, while also accounting for uncertainty caused by ambiguous and stochastic data environments. By applying and assessing these models in a real-world case study of the oil and gas industry, our research uncovers insightful observations. Specifically, we find that adopting a localization strategy presents specific risks, while a single-use strategy yields significant efficiency improvements.

Purpose: This study addresses the lack of objective player-based metrics in men’s rugby league by introducing a comprehensive set of novel performance metrics designed to quantify a player’s overall contribution to team success. Methods: Player match performance data were captured by Stats Perform for every National Rugby League season from 2018 until 2022; a total of five seasons. The dataset was divided into offensive and defensive variables and further split according to player position. Five machine learning algorithms (Principal Component Regression, Lasso Regression, Random Forest, Regression Tree, and Extreme Gradient Boost) were considered in the analysis, which ultimately generated Wins Created and Losses Created for offensive and defensive performance, respectively. These two metrics were combined to create a final metric of Net Wins Added. The validity of these player performance metrics against traditional objective and subjective measures of performance in rugby league were evaluated. Results: The metrics correctly predicted the winner of 80.9% of matches, as well as predicting the number of team wins per season with an RMSE of 1.9. The metrics displayed moderate agreement (Gwet AC1 = 0.505) when predicting team of the year award recipients. When predicting State of Origin selection, the metrics displayed moderate agreement for New South Wales (0.450) and substantial agreement for Queensland (0.652). Conclusion: The development and validation of these objective player performance metrics represent significant potential to enhance talent evaluation and player recruitment. [ABSTRACT FROM AUTHOR]

This study delves into the correlation between economic outcomes and the competitiveness of innovative management practices and sports organisations. In today's rapidly evolving sports industry, it is crucial to recognise the importance of innovation in reaching organisational objectives. As new opportunities and challenges arise in the digital and data-driven landscape, staying ahead of the curve is essential. This study utilises empirical and quantitative economic analyses, as well as insights from industry experts and practitioners, to investigate the adoption patterns of innovative practices and their effects on different performance indicators. The financial statements of major European football clubs were examined in this study to showcase the feasibility and future prospects of organisations that prioritise innovation in response to the COVID-19 crisis. The research primarily concentrates on the influence of innovations like smart stadiums and digital strategies on market capitalisation and revenues, achieved through enhanced fan engagement. The findings indicate that incorporating innovative approaches to resource management can have a positive impact on economic performance. In addition, the investigation uncovers a clear link between the adoption of innovative strategies and improved performance in sports and finances, establishing a mutually beneficial cycle. Nevertheless, this study highlights concerns regarding employee turnover and job satisfaction, underscoring the importance of incorporating personnel culture and well-being into a company's innovation system. Important factors to consider involve ensuring that strategic innovation is aligned with business objectives, consistently investing in digital assets and data insights, and making infrastructure advancements to create new avenues for profitability. This study further highlights the significance of fostering innovation in organisational processes as a means of preparing for future crises. This study provides valuable insights to the literature on sports management innovation and offers practical implications for industry practitioners. It is evident that the successful adoption of cutting-edge management strategies will play a vital role in maintaining a competitive edge as the sports industry continues to develop. [ABSTRACT FROM AUTHOR]

This paper examines how data-driven personalized decisions can be made while preserving consumer privacy. Our setting is one in which the firm chooses a personalized price based on each new customer's vector of individual features; the true set of individual demand-generating parameters is unknown to the firm and so must be estimated from historical data. We extend the existing personalized pricing framework by requiring also that the firm's pricing policy preserve consumer privacy, or (formally) that it be differentially private: an industry standard for privacy preservation. We develop privacy-preserving personalized pricing algorithms and show that they achieve near-optimal revenue by deriving theoretical (upper and lower) performance bounds. Our analyses further suggest that, if the firm possesses a sufficient amount of historical data, then it can achieve a certain level of differential privacy almost "for free." That is, the revenue loss due to privacy preservation is of smaller order than that due to estimation. We confirm our theoretical findings in a series of numerical experiments based on synthetically generated and online auto lending (CPRM-12-001) data sets. Finally, motivated by practical considerations, we also extend our algorithms and findings to a variety of alternative settings, including multiproduct pricing with substitution effect, discrete feasible price set, categorical sensitive features, and personalized assortment optimization. This paper was accepted by Vishal Gaur, operations management. Funding: R. Momot acknowledges financial support from the HEC Paris Foundation and the Agence Nationale de la Recherche (French National Research Agency) "Investissements d'Avenir" [Grant LabEx Ecodec/ANR-11-LABX-0047] during the initial stages of this project. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4925. [ABSTRACT FROM AUTHOR]

To make the last mile of parcel delivery more efficient, service providers offer an increasing number of modes of delivery as alternatives to the traditional and often cost-intensive home delivery service. Parcel lockers and pickup stations can be utilized to reduce the number of stops and avoid costly detours. To design smart delivery networks, service providers must evaluate different business models. In this context, a multitrip vehicle routing problem with delivery options and location-dependent costs arises. We present a data-driven framework to evaluate alternative delivery strategies, formulate a corresponding model and solve the problem heuristically using adaptive large neighborhood search. By examining large, real-life instances from a major European parcel service, we determine the potential and benefits of different delivery options. Specifically, we show that delivery costs can be mitigated by consolidating orders in pickup stations and illustrate how pricing can be applied to steer customer demand toward profitable, eco-friendly products. [ABSTRACT FROM AUTHOR]

This paper is a conceptual exploration and diffractive reading of refugee/(im)migrant education through multiple lenses, including data-driven decision making, critical refugee studies, new materialism and critical feminist and posthumanist studies, and trans theorizations such as Black trans feminism. After a brief introduction to "the field" of refugee/(im)migrant education, the paper turns to diffractive readings of refugee/(im)migrant education as means of exploring what is the matter, as in the material and discursive substance, in refugee/(im)migrant education, and why and how (including when, where, and by whom) does that matter come to matter? The paper concludes with discoveries, or findings, from this diffractive, transdisciplinary exploration and considerations for educators, policymakers, researchers, activists, and other actors (co)constituting and "becoming with" refugee/(im)migrant education. [ABSTRACT FROM AUTHOR]

Stochastic optimization models always assume known probability distributions about uncertain parameters. However, it is unrealistic to know the true distributions. In the era of big data, with the knowledge of informative features related to uncertain parameters, this study aims to estimate the conditional distributions of uncertain parameters directly and solve the resulting contextual stochastic optimization problem by using a set of realizations drawn from estimated distributions, which is called the contextual distribution estimation method. We use an energy scheduling problem as the case study and conduct numerical experiments with real-world data. The results demonstrate that the proposed contextual distribution estimation method offers specific benefits in particular scenarios, resulting in improved decisions. This study contributes to the literature on contextual stochastic optimization problems by introducing the contextual distribution estimation method, which holds practical significance for addressing data-driven uncertain decision problems. [ABSTRACT FROM AUTHOR]

Transport-on-demand services, such as demand-responsive transport (DRT), involve a flexible transportation service that offers convenient and personalised mobility choices for public transport users. Integrating DRT with mobile data and data analytics enhances understanding of travel patterns and allows the development of improved algorithms to support design-optimised services. This study introduces a replicable framework for DRT that employs an on-demand transport simulator and routing algorithm. This framework is supported by a mobile data set, enabling a more accurate service design grounded on actual demand data. Decision-makers can use this framework to understand traffic patterns better and test a DRT solution before implementing it in the actual world. A case study was conducted in Porto, Portugal, to demonstrate its practicality and proof of concept. Results show that the DRT solution required 135% fewer stops and travelled 81% fewer kilometres than the existing fixed-line service. Findings highlight the potential of this data-driven framework for urban public transportation systems to improve key performance metrics in required buses, energy consumption, travelled distance, and stop frequency, all while maintaining the number of served passengers. Under specific circumstances, embracing this approach can offer a more efficient, user-centric, and environmentally sustainable urban transportation service. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of data-driven business, marketing intelligence has become a cornerstone for organizational success. This entails the systematic collection, analysis, and interpretation of vast data sets, empowering businesses to make strategic marketing decisions. Employing techniques like data mining, machine learning, and sentiment analysis, marketing intelligence uncovers hidden patterns, identifies opportunities, and predicts challenges. A pivotal outcome is enhanced customer understanding through detailed profiles derived from sources like social media, website interactions, and purchase history. This depth of insight allows for the creation of personalized, precisely targeted campaigns, fostering stronger customer relationships and loyalty. Moreover, marketing intelligence facilitates data-driven decisions aligned with overarching business goals, optimizing resource allocation, budgeting, and strategic planning. Utilizing software solutions such as RapidMiner, organizations gain efficiency in data analysis, machine learning, and predictive analytics. Net Promoter Score (NPS) acts as a key metric, gauging customer sentiment and likelihood to recommend products or services. This paper focuses on evaluating customer satisfaction in the insurance sector, aiming to propose enhancements for service quality based on existing research results. Through a comprehensive examination of customer satisfaction levels, the study seeks to provide actionable insights for continual improvement. [ABSTRACT FROM AUTHOR]

There is a greater awareness of safety issues, emerging risks, and challenges in dairy products. Lean philosophy is one of the strategies that significant corporations worldwide have tried to adopt to stay competitive in an increasingly global market. This paper presents the relationship between different critical success factors for the Information and Communication Technology (ICT) enabled lean dairy supply chain. This study will help to bring leanness in the perishable supply chain by showing the interrelationship of digitalization and emerging Information and Communication Technologies like automation, cloud computing, big data, digital twins, metaverse, etc., with other critical success factors of the supply chain. This paper proposes a twelve-level hierarchical model to illustrate the inter-relationships among the critical success factors of the ICT-enabled lean dairy supply chain.

An increasingly common practice for policy-makers is to leverage e-participation to collect citizens’ opinions and improve their decision-making processes. This practice, however, is hindered by the large quantity of collected opinions which are often overloading and hard to value. This is referred to as information overload. As a way to mitigate this challenge for policy-makers, this article develops a prioritization framework for citizens’ ideas collected through e-participation. The framework builds on Design Science Research and is validated on a real-world case in collaboration with the European Commission. The resulting contributions are threefold. First, theoretical criteria, popularity and polarization, are developed to prioritize citizens’ proposals. Then, automated and quantitative metrics are proposed to measure these criteria. Finally, a prioritization matrix is developed to visually assess the relative priority of these citizens’ proposals.

Human-machine collaboration has potentially led to higher quality and more informed data-driven decisions. However, evaluating these decisions is necessary to measure the benefits, as well as enable experiential learning and posterior rationalization of the results and consequences. Nevertheless, the multiplicity of human-machine collaboration modes, as well as the multi-faceted nature of data-driven decisions complicates evaluation, and evaluation solutions are lacking both in research and in practice. This is further reflected in the complexity of incorporating evaluation in the design of such data-driven decision making systems, since developers are left without theoretically grounded and practically feasible principles to guide implementation. In this paper, we propose a set of five design principles, explicated from theory and practice, for systems implementing data-driven decision evaluation as the output of design science research cycles. The design principles are: 1) multi-faceted evaluation criteria, 2) unified viewpoint, 3) collaborative rationality, 4) processual ex-post evaluation, and 5) adaptive feedback and learning loops. They are further contextualized in the case of AI-enabled menu design at Antell, an innovative pioneer in the restaurant business in Finland, and consequently evaluated by the development managers of the project. Accordingly, the design principles contribute to the knowledge base on metahuman systems and data-driven decision evaluation, by concretizing existing normative concepts into prescriptive knowledge, also guiding future research and generalizing towards a design theory. Furthermore, they provide implementable statements for designing and developing such systems in practice and can be used as a checklist to compare and evaluate existing systems. [ABSTRACT FROM AUTHOR]

Currently, there is a national level debate on security-based school safety policies and practices, particularly in relation to the presence of police in schools. Decisions about school safety are discussed at the district level by administrators and board of education members who may not be aware of the importance of utilizing local data to ensure these decisions are data driven and beneficial to all the stakeholders they represent in diverse, urban districts. Research on security, policing, and social-emotional and school climate-based strategies are discussed, as is research on district-level decision-makers (boards of education and administrators) and decision making. This paper provides local school stakeholders, policy makers, and school safety researchers with a framework for collaborative work to ensure that evidence-based violence prevention programming and local data drive district-level decision making about school safety. [ABSTRACT FROM AUTHOR]

Copyright of Informatica (03505596) is the property of Slovene Society Informatika 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.)

Higher education institutions are pressured to use data for university rankings, accreditation, decision making, and technical support of students and faculty. However, these organizations also experience barriers and resistance to such data-driven aspirations. Rather than simply focusing on the whole organization, this research attends to variations in organizational subunits and asks what processes help and hinder data use. It pays attention to the puzzle of how data use variation can lead to effective subunits just as data fragmentation contributes to ineffective systems. Although universities may create centralized coordinating bodies, the structural and cultural bases of loosely coupled university subunits may upset these efforts. Using the case of a university attempting to centralize its data systems, we employ in-depth interviews of senior and mid-level administrators to understand why technical changes happen within specific subunits even as only ceremonial changes happen for the larger organization. This suggests new insights for interrogating how loose and tight coupling can co-exist in the same organization. [ABSTRACT FROM AUTHOR]

We take a unifying approach to single selection optimal stopping problems with random arrival order and independent sampling of items. In the problem we consider, a decision maker (DM) initially gets to sample each of N items independently with probability p, and can observe the relative rankings of these sampled items. Then, the DM faces the remaining items in an online fashion, observing the relative rankings of all revealed items. While scanning the sequence the DM makes irrevocable stop/continue decisions and her reward for stopping the sequence facing the item with rank i is Yi. The goal of the DM is to maximize her reward. We start by studying the case in which the values Yi are known to the DM, and then move to the case in which these values are adversarial. For the former case we are able to recover several classic results in the area, thus giving a unifying framework for single selection optimal stopping. For the latter, we pin down the optimal algorithm, obtaining the optimal competitive ratios for all values of p. Funding: This work was partially supported by The Center for Mathematical Modeling at the University of Chile (ANID FB210005), Grant Anillo Information and Computation in Market Design (ANID ACT210005), FONDECYT 1220054 and 1181180, and a Meta Research PhD Fellowship. [ABSTRACT FROM AUTHOR]

In the rapidly evolving telecommunications landscape, the shift towards advanced communication technologies marks a critical milestone. This transition promises to revolutionize connectivity by enabling seamless data downloads, high-quality video streaming, and instant access to applications. However, adapting to these advanced technologies poses significant challenges for infrastructure expansion, requiring innovative investment and deployment strategies. These strategies aim not only to enhance service quality but also to ensure extensive network coverage. To address the need for systematic planning in infrastructure investment, this paper presents a novel methodology that combines the Full Consistency Method (FUCOM) with cosine similarity analysis. This integrated approach effectively prioritizes service areas for the deployment of 5G technology, emphasizing the importance of detailed planning in mobile strategy development. By leveraging FUCOM to determine the weights of various criteria and employing cosine similarity analysis to rank service areas, the methodology facilitates efficient resource allocation and service quality enhancements. Empirical validation using real data from a Turkish telecommunications company confirmed the effectiveness of the proposed algorithm. The results indicate that this integrated approach can significantly advance the telecommunications industry by providing essential insights for companies seeking to improve service quality amidst the transition to 5G and beyond. The successful implementation of the proposed algorithm demonstrates its effectiveness in addressing the challenges faced by telecommunications companies and underscores the importance of a data-driven approach in strategic decision-making and resource allocation. Furthermore, the findings suggest that the integrated FUCOM and cosine similarity analysis approach can offer a valuable tool for telecommunications companies worldwide, offering a systematic method for prioritizing infrastructure investments and enhancing network performance.

Precision irrigation systems have become indispensable tools in modern agriculture due to their ability to address water management challenges and optimize bamboo productivity. This paper presents a novel innovation called AquaBamboo, a next-generation precision irrigation system designed to revolutionize sustainable bamboo water management. The paper outlines the shortcomings of traditional irrigation methods, highlighting their inefficient water management for bamboo, lack of real-time data integration, and limited precision. This suggested approach overcomes these challenges by integrating IT sensors, advanced analytics, and predictive modelling to deliver water precisely tailored to each bamboo species' requirements. The system's dynamic adaptability to changing environmental factors ensures optimal water delivery, enhancing bamboo health and yield while minimizing water waste. AquaBamboo's holistic approach not only improves resource efficiency but also contributes to sustainable agriculture practices, aligning with the principles of responsible water management. The significance of this approach lies in its potential to transform conventional irrigation practices, optimize water utilization, promote bamboo species' health, and advance the sustainability of all bamboo agricultural operations. This paper offers a comprehensive understanding of AquaBamboo's features, advantages, and innovative contributions to precision bamboo agriculture management.

Data from production environments is now available in unprecedented volumes, making the problem-solving of incidents through root cause analysis straightforward. However, the root cause analysis process remains time-consuming. This study employs the Kitchenham standard systematic literature review methodology to explore how information models and deep learning can streamline this process. By conducting a comprehensive search across four major databases, we evaluate the current technological advancements and their application in root cause analysis. The aim of this study is to assesses the impact of information models for root cause analysis in a production environment. Our findings reveal that integrating knowledge graphs, association rule mining, and deep learning algorithms significantly improves the speed and depth of root cause analysis compared to traditional methods. Specifically, the use of neural networks in recent literature shows substantial advancements in analyzing complex datasets, facilitating large-scale data integration, and enabling automated learning capabilities. Comparing our findings with other recent studies highlights the advantages of using information modeling and deep learning technologies in root cause analysis. This comparison underscores the superior accuracy and efficiency of these advanced methodologies over traditional manual interpretation methods. The effective implementation of these technologies requires a robust foundation of clean, standardized data, giving rise to the concept of “Production IT.” Furthermore, it is crucial for this data to be openly available to facilitate academic research, thereby enabling the development of new methods for more efficient and effective root cause analysis.

The era of digitization is radically transforming the manufacturing sector. The advancement of sensors, communication networks, and internet-based systems enables the collection of vast amounts of data, acquired at a high speed that can be used to support managerial decision making. However, the full use of this technological potential often encounters barriers in missing resources and competences, especially in small and medium-sized enterprises that do not have sufficient funds for the acquisition of tailor-made solutions and the systematic training of employees in Data-Driven Decision Making. We are trying to address this deficiency through a newly designed learning path that allows employees of SMEs to acquire competencies that will enable them to create their own low-cost tools to support managerial decision-making in their companies. In this article, we want to present both the structure and content of the newly designed learning path, but also the expected educational and evaluation strategy. [ABSTRACT FROM AUTHOR]

In this paper, we consider a data-driven distributionally robust two-stage stochastic linear optimization problem over 1-Wasserstein ball centered at a discrete empirical distribution. Differently from the traditional two-stage stochastic programming which involves the expected recourse function as the preference criterion and hence is risk-neutral, we take the conditional value-at-risk (CVaR) as the risk measure in order to model its effects on decision making problems. We mainly explore tractable reformulations for the proposed robust two-stage stochastic programming with mean-CVaR criterion by analyzing the first case where uncertainties are only in the objective function and then the second case where uncertainties are only in the constraints. We demonstrate that the first model can be exactly reformulated as a deterministic convex programming. Furthermore, it is shown that under several different support sets, the resulting convex optimization problems can be converted into computationally tractable conic programmings. Besides, the second model is generally NP-hard since checking constraint feasibility can be reduced to a norm maximization problem over a polytope. However, even with the case of uncertainty in constraints, tractable conic reformulations can be established when the extreme points of the polytope are known. Finally, we present numerical results to discuss how to control the risk for the best decisions and illustrate the computational effectiveness and superiority of the proposed models. [ABSTRACT FROM AUTHOR]

Health systems around the world are facing challenges in achieving their goals. In the wake of the coronavirus disease pandemic, the need for resilient health systems has become even more apparent. This article argues that embedding resilience into health system performance assessment (HSPA) frameworks can be a valuable approach for improving health system performance. This perspective examines key challenges threatening health systems and makes a case for the continued relevance of HSPA by embedding resilience-related performance intelligence. [ABSTRACT FROM AUTHOR]

With the rapid growth of urbanization and the proliferation of mobile phone usage, smart city initiatives have gained momentum in leveraging data-driven insights to enhance urban planning and resource allocation. This paper proposes a novel approach for predicting people's concentration and movements within a smart city environment using mobile phone data provided by telecommunication operators. By harnessing the vast amount of anonymized and aggregated mobile phone data, we present a predictive framework that offers valuable insights into urban dynamics. The methodology involves collecting and processing location-based data obtained from telecommunication operators. Using machine learning techniques, including clustering and spatiotemporal analysis, we developed models to identify patterns in people's movements and concentration across various city regions. Our proposed approach considers factors such as time of day, day of the week, and special events to capture the intricate dynamics of urban activities. The predictive models presented in this paper demonstrate the ability to predict areas of high concentration of people, such as commercial districts during peak hours, as well as the people flow during the time. These insights have significant implications for urban planning, traffic management, and resource allocation. Our approach respects user privacy by working with aggregated and anonymized data, ensuring compliance with privacy regulations and ethical considerations. The proposed models were evaluated using real-world mobile phone data collected from a smart city environment in Lisbon, Portugal. The experimental results demonstrate the accuracy and effectiveness of our approach in predicting people's movements and concentration. This paper contributes to the growing field of smart city research by providing a data-driven solution for enhancing urban planning and resource allocation strategies. As cities continue to evolve, leveraging mobile phone data from telecommunication operators can lead to more efficient and sustainable urban environments. [ABSTRACT FROM AUTHOR]

We present a historical case study of "data-driven" general education policy reform at the City University of New York, where within-system transfer issues prompted the need for curricular reform that was debated and eventually implemented from 2011 to 2017. Through an empirical examination of artifacts such as meeting minutes, internal memoranda, institutional reports, speeches, testimonies and position statements, and recordings of public meetings, we trace the emergence of a policy problem, contests over its framing, and the development of a policy solution for a curricular crisis across competing strands of collaborative governance and conflict over curriculum-making. We illustrate how administrators and their allies engage informatic power to unify the means and ends of curriculum reform- producing curricular policy and new language practices for discussing curriculum that facilitate increased managerialism and the rise of audit culture. When curricular conversation primarily focuses on the use of data, normative questions about the purpose and organization of undergraduate curricula are elided. In this case, policy proponents and opponents focused on a narrow definition of what kind of data "counts" for policy making. We argue that governance actors need to allow for and incorporate an array of data resources into their curricular conversation. [ABSTRACT FROM AUTHOR]

Powered by educational policy recommendations around the world, studies on different aspects of data-driven decision making have soared both in number and scope in different educational contexts. However, despite the availability of diverse data, second or foreign language research has been quite tardy in the study of language teachers' application of data-driven decision making. On this basis and with the aim of exploring the conditions that can lead to the teachers' optimum use of data, this qualitative study investigated English as a Foreign Language (EFL) teachers' data use for instructional decision making purposes. Hence, different forms of data, purposes, challenges, enablers, and barriers of data use were studied from EFL teachers' perspective. To collect the required data, semi-structured interviews were run with 30 EFL teachers and the collected data were analyzed following the grounded-theory approach and qualitative content analysis procedure. The findings indicated that EFL teachers mainly use both formative and summative assessment results type of data. In addition, they use data for accountability and instructional purposes. Furthermore, integrating data use across the curriculum was mentioned the most among the challenges faced by EFL teachers in data-driven decision making. Considering promoting and inhibiting factors, data use for instructional purposes appears to be affected by language school organizational characteristics, EFL teachers' characteristics, and data characteristics. The findings underscore the significance of the provision of the conditions that support data-driven decision making for second or foreign language teachers. Moreover, teacher education programs are recommended to include in-service data-driven decision making courses in their curricula. [ABSTRACT FROM AUTHOR]

This paper is a conceptual exploration and diffractive reading of refugee/(im)migrant education through multiple lenses, including data-driven decision making, critical refugee studies, new materialism and critical feminist and posthumanist studies, and trans theorizations such as Black trans feminism. After a brief introduction to “the field” of refugee/(im)migrant education, the paper turns to diffractive readings of refugee/(im)migrant education as means of exploring what is the matter, as in the material and discursive substance, in refugee/(im)migrant education, and why and how (including when, where, and by whom) does that matter come to matter? The paper concludes with discoveries, or findings, from this diffractive, transdisciplinary exploration and considerations for educators, policymakers, researchers, activists, and other actors (co)constituting and “becoming with” refugee/(im)migrant education.

Stochastic optimization models always assume known probability distributions about uncertain parameters. However, it is unrealistic to know the true distributions. In the era of big data, with the knowledge of informative features related to uncertain parameters, this study aims to estimate the conditional distributions of uncertain parameters directly and solve the resulting contextual stochastic optimization problem by using a set of realizations drawn from estimated distributions, which is called the contextual distribution estimation method. We use an energy scheduling problem as the case study and conduct numerical experiments with real-world data. The results demonstrate that the proposed contextual distribution estimation method offers specific benefits in particular scenarios, resulting in improved decisions. This study contributes to the literature on contextual stochastic optimization problems by introducing the contextual distribution estimation method, which holds practical significance for addressing data-driven uncertain decision problems.

This paper focuses on quantifying the economic and financial viability of NB-IoT and LoRaWAN technologies, two low-power wide-area network (LPWAN) technologies with unique characteristics that make them suitable for IoT applications. The purpose of this study is to propose a “pragmatic” artifact for performing life cycle cost analysis and demonstrate its application to these technologies. The methodology uses pragmatic computational tools to facilitate the analysis and considers all relevant economic and financial factors, such as operating costs, equipment costs, and revenue potential. The main finding of this study is that Narrow Band-Internet of Things (NB-IoT) and Long Range Wide Area Network (LoRaWAN) technologies have different cost structures and revenue potentials, which may affect their economic and financial viability for different IoT applications. Ultimately, the study concludes that a comprehensive life cycle cost analysis is critical to making informed decisions about technology adoption, and that the proposed methodology can be applied to other IoT technologies to gain insight into their economic and financial viability.

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