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Personnel selection and human resource improvement are characteristically multiple-attribute decision-making (MADM) problems. Previously developed MADM models have principally depended on experts’ judgements as input for the derivation of solutions. However, the subjectivity of the experts’ experience can have a negative influence on this type of decision-making process. With the arrival of today’s data-based decision-making environment, we develop a data-driven MADM model, which integrates machine learning and MADM methods, to help managers select personnel more objectively and to support their competency improvement. First, RST, a machining learning tool, is applied to obtain the initial influential significance-relation matrix from real assessment data. Subsequently, the DANP method is used to derive an influential significance-network relation map and influential weights from the initial matrix. Finally, the PROMETHEE-AS method is applied to assess the gap between the aspiration and current levels for every candidate. An example was carried out using performance data with evaluation attributes obtained from the human resource department of a Chinese food company. The results revealed that the data-driven MADM model could enable human resource managers to resolve the issues of personnel selection and improvement simultaneously, and can actually be applied in the era of big data analytics in the future. First published online 15 May 2020

This empirical study on Industry 5.0 offers verifiable proof of the transformational potential of data-driven decision making. The validation of data-driven choices as a key component of Industry 5.0's performance is shown by a noteworthy 46.15% increase in decision outcomes. The fact that choice criteria are in line with pertinent data sources emphasizes how important data is in forming well-informed decision-making processes. Moreover, the methodical execution and oversight of choices showcase the pragmatic significance of data-driven methodologies. This empirical evidence positions data-driven decision making as a cornerstone for improving operational efficiency, customer happiness, and market share, solidifying its essential role as the industrial environment changes. These results herald in an age when data's revolutionary potential drives industrial progress by providing a compass for companies trying to navigate the complexity of Industry 5.0.

Data-driven school improvement has been proposed to improve and support educational practices, and more studies are emerging describing data-driven practices in schools and the effects of data-driven interventions. This paper reports on a study that has taken place within a national program where 15 schools from 6 different municipalities and organizations are working at classroom, school and municipality levels to improve educational practices using data-driven methods. The study aimed at understanding what educational problems teachers, principals and administrative staff in the project aimed to address through the utilization of data-driven methods and the challenges they face in doing so. Using a mixed-methods design, we identified four thematic areas that reflect the focused problem areas of the participants in the project, namely didactics, democracy, assessment and planning, and mental health. All development groups identified problems that can be solved with data-driven methods. Along with this, we also identified five challenges faced by the participants: time and resources, competence, ethics, digital systems and common language. We conclude that the main challenge faced by the participants is data literacy, and that professional development is needed to support effective and successful data-driven practices in schools.

Background Nigeria has strategically invested in digital health to achieve HIV/AIDS epidemic control, meet SDG health targets, and advance towards UHC. Despite progress, challenges persist. This paper details Nigeria's commitment, in collaboration with PEPFAR, CDC, and other agencies, to address Health Information System (HIS) challenges through participation in the Intergovernmental Learning Exchange to Advance Data-Driven Decision Making (I-LEAD) programme. Methods The I-LEAD programme followed a three-phase approach: 1) conducted an expedited Informatics-Savvy Health Organisation (ISHO) assessment to identify critical national HIS challenges; 2) enhanced informatics capabilities of selected Nigerian delegates, including a purpose-fit session, Bring Your Own Difficult Decision (BYODD), involving SMEs to collaboratively refine, contextualise and guide the localised development of actionable solutions for national HIS challenges; and 3) outlined the nation's approach to implementing the HIS solutions Results The expedited ISHO assessment identified five HIS challenges: governance, interoperability, data security, Electronic Medical Record (EMR) centralisation, and funding. Participating in the I-LEAD programme strengthened Nigeria's leadership technical capacity in informatics, particularly in strategic visioning and planning, with the BYODD sessions resulting in the collaborative development of localised solutions to address the five HIS challenges. In the post-I-LEAD phase, efforts focused on two of the HIS solutions. These activities are 1) improving data quality through harmonisation of value data sets, and 2) decentralising I-LEAD learning and building the capacity of Public Health Informatics (PHI) technical groups through progressive levels of Growing Expertise in E-Health Knowledge and Skills (GEEKS) training. These activities were selected because of their potential to deliver the maximum impact within the HIS ecosystem. Conclusion Nigeria's active participation and commitment through the I-LEAD programme have strengthened its digital health agenda, leveraging health informatics to enhance healthcare delivery and achieve broader health goals. This approach can serve as a model for other developing nations facing similar health informatics hurdles.

We combine ideas from machine learning (ML) and operations research and management science (OR/MS) in developing a framework, along with specific methods, for using data to prescribe optimal decisions in OR/MS problems. In a departure from other work on data-driven optimization, we consider data consisting, not only of observations of quantities with direct effect on costs/revenues, such as demand or returns, but also predominantly of observations of associated auxiliary quantities. The main problem of interest is a conditional stochastic optimization problem, given imperfect observations, where the joint probability distributions that specify the problem are unknown. We demonstrate how our proposed methods are generally applicable to a wide range of decision problems and prove that they are computationally tractable and asymptotically optimal under mild conditions, even when data are not independent and identically distributed and for censored observations. We extend these to the case in which some decision variables, such as price, may affect uncertainty and their causal effects are unknown. We develop the coefficient of prescriptiveness P to measure the prescriptive content of data and the efficacy of a policy from an operations perspective. We demonstrate our approach in an inventory management problem faced by the distribution arm of a large media company, shipping 1 billion units yearly. We leverage both internal data and public data harvested from IMDb, Rotten Tomatoes, and Google to prescribe operational decisions that outperform baseline measures. Specifically, the data we collect, leveraged by our methods, account for an 88% improvement as measured by our coefficient of prescriptiveness. This paper was accepted by Noah Gans, optimization.

PurposeFew studies have conceptualized how companies can build and nurture international dynamic marketing capabilities (IDMCs) by implementing growth hacking strategies. This paper conceptualizes growth hacking, a managerial-born process to embed a data-driven mind-set in marketing decision-making that combines big-data analysis and continuous learning, allowing companies to adapt their dynamic capabilities to the ever-shifting international competitive arenas.Design/methodology/approachGiven the scarcity of studies on growth hacking, this paper conceptualizes this managerial-born concept through the double theoretical lenses of IDMCs and information technology (IT) literature.FindingsThe authors put forward research propositions concerning the four phases of growth hacking and the related capabilities and routines developed by companies to deal with international markets. Additional novel propositions are also developed based on the three critical dimensions of growth hacking: big data analytics, digital marketing and coding and automation.Research limitations/implicationsLack of prior conceptualization as well as the scant literature makes this study liable to some limitations. However, the propositions developed should encourage researchers to develop both empirical and theoretical studies on this managerial-born concept.Practical implicationsThis study develops a detailed compendium for managers who want to implement growth hacking within their companies but have failed to identify the necessary capabilities and resources.Originality/valueThe study presents a theoretical approach and develops a set of propositions on a novel phenomenon, observed mainly in managerial practice. Hence, this study could stimulate researchers to deepen the phenomenon and empirically validate the propositions.

In the current age of the Fourth Industrial Revolution (4 IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated  applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning , which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study’s key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view.

Recently, there has been a growing interest in issues related to maintenance performance management, which is confirmed by a significant number of publications and reports devoted to these problems. However, theoretical and application studies indicate a lack of research on the systematic literature reviews and surveys of studies that would focus on the evolution of Industry 4.0 technologies used in the maintenance area in a cross-sectional manner. Therefore, the paper reviews the existing literature to present an up-to-date and content-relevant analysis in this field. The proposed methodology includes bibliometric performance analysis and a review of the systematic literature. First, the general bibliometric analysis was conducted based on the literature in Scopus and Web of Science databases. Later, the systematic search was performed using the Primo multi-search tool following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The main inclusion criteria included the publication dates (studies published from 2012–2022), studies published in English, and studies found in the selected databases. In addition, the authors focused on research work within the scope of the Maintenance 4.0 study. Therefore, papers within the following research fields were selected: (a) augmented reality, (b) virtual reality, (c) system architecture, (d) data-driven decision, (e) Operator 4.0, and (f) cybersecurity. This resulted in the selection of the 214 most relevant papers in the investigated area. Finally, the selected articles in this review were categorized into five groups: (1) Data-driven decision-making in Maintenance 4.0, (2) Operator 4.0, (3) Virtual and Augmented reality in maintenance, (4) Maintenance system architecture, and (5) Cybersecurity in maintenance. The obtained results have led the authors to specify the main research problems and trends related to the analyzed area and to identify the main research gaps for future investigation from academic and engineering perspectives.

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.

This study examines the effects of the Data-Driven Decision-Making Teacher Training Program (DDDM-TTP), designed to enhance the quality of classroom teachers’ decisions regarding students at risk during the pre-referral process. The research employed a single-group pretest–posttest design. Before and after the implementation of the teacher training program, individual interviews were conducted with the participating teachers. In addition to these interviews, classroom observations were carried out, and teachers were asked to complete the educational assessment request form. The data were analyzed using content analysis, one of the qualitative analysis methods. The findings revealed meaningful changes in the way teachers conducted the pre-referral process. Prior to the training, teachers’ decisions were largely based on intuition and limited observations; however, after the training, teachers began to collect data in a more systematic, planned, and multidimensional manner. They regularly documented indicators related to attention, engagement, performance, and learning processes, and justified their decisions more reliably. Analysis of the educational assessment forms showed that teachers provided more detailed, observation-based explanations in terms of content, clarity, and alignment with student needs. Moreover, one teacher reconsidered the decision to refer a student to the Guidance and Research Center (GRC) and instead concluded that classroom-based interventions were sufficient after the training. Social validity findings indicated that both classroom teachers and school counselors found the program applicable, informative, and supportive of their professional development. In conclusion, DDDM-TTP contributed to teachers’ ability to conduct the pre-referral process more consciously, data-based, and professionally, thereby significantly improving the quality of their evaluation and decision-making practices. This study examines the effects of the Data-Driven Decision-Making Teacher Training Program (DDDM-TTP), designed to enhance the quality of classroom teachers’ decisions regarding students at risk during the pre-referral process. The research employed a single-group pretest–posttest design. Before and after the implementation of the teacher training program, individual interviews were conducted with the participating teachers. In addition to these interviews, classroom observations were carried out, and teachers were asked to complete the educational assessment request form. The data were analyzed using content analysis, one of the qualitative analysis methods. The findings revealed meaningful changes in the way teachers conducted the pre-referral process. Prior to the training, teachers’ decisions were largely based on intuition and limited observations; however, after the training, teachers began to collect data in a more systematic, planned, and multidimensional manner. They regularly documented indicators related to attention, engagement, performance, and learning processes, and justified their decisions more reliably. Analysis of the educational assessment forms showed that teachers provided more detailed, observation-based explanations in terms of content, clarity, and alignment with student needs. Moreover, one teacher reconsidered the decision to refer a student to the Guidance and Research Center (GRC) and instead concluded that classroom-based interventions were sufficient after the training. Social validity findings indicated that both classroom teachers and school counselors found the program applicable, informative, and supportive of their professional development. In conclusion, DDDM-TTP contributed to teachers’ ability to conduct the pre-referral process more consciously, data-based, and professionally, thereby significantly improving the quality of their evaluation and decision-making practices.