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Focusing on ethical issues when making organizational decisions should encourage a variety of positive outcomes for companies and their employees. The purpose of this paper is to determine the degree to which data-based ethical decision making, lateral relations and organizational commitment are interrelated in organizations.

Data were collected from business professionals employed at multiple locations of a financial services firm operating in the USA. Mediation analysis (based on structural equation modeling) was used to test the proposed relationships.

Results indicated that employees’ perceptions of data-based ethical decision making were positively related to perceived lateral relations, and that perceived lateral relations were positively related to organizational commitment.

Given that information was collected using only a self-report questionnaire, common method bias could be an issue. In addition, the study’s cross-sectional design limits conclusions about causality. Another limitation involves the study’s homogenous sample, which decreases the generalizability of the findings. Finally, variable responses could have been impacted by individual frames of reference and other perceptual differences.

Results suggest that information flow enhancements should support or be consistent with horizontal information flow enhancements, and that together these factors should increase employee commitment.

Given the dearth of existing research, this interdisciplinary investigation is important because it fills gaps in the management literature. This study is also important because the results could inform decisions regarding the use of data analysis in ethical decisions and lateral forms of organizational structuring to improve work attitudes.

Progress monitoring and data-based intervention are unique special education developments stemming from efforts to find an effective alternative to diagnostic/prescriptive instruction. Springing from research on Curriculum-based Measurement (CBM) in the late 1970s and early 1980s at the Minnesota Institute for Research on Learning Disabilities, the approach has generated a large body of empirical research and development. While the original work demonstrated that teachers could be more effective using progress monitoring in data-based intervention, most research and development activity has focused on development and extensions of the CBM model with less attention to data-based intervention. While research on progress monitoring has occurred at a high rate, widespread implementation of progress monitoring has been spurred by both federal funding and commercial development. As might be expected, all of this activity has resulted in a large set of successes and disappointments that are described here. For better or worse, as progress monitoring and data-based intervention have been incorporated into Response to Intervention (RTI) models it seems likely that the future of progress monitoring and data-based intervention is tied to the future of RTI. The question is whether this linking will result in adding to the set of successes or to that of disappointments for this unique special education innovation.

This paper aims to present a dual-perspective framework for maintenance service delivery that should be used by manufacturing companies to structure and manage their maintenance service delivery process, using aggregated historical and real-time data to improve operational decision-making. The framework, built for continuous improvement, allows the exploitation of maintenance data to improve the knowledge of service processes and machines.

The Dual-perspective, data-based decision-making process for maintenance delivery (D3M) framework development and test followed a qualitative approach based on literature reviews and semi-structured interviews. The pool of companies interviewed was expanded from the development to the test stage to increase its applicability and present additional perspectives.

The interviews confirmed that manufacturing companies are interested in exploiting the data generated in the use phase to improve operational decision-making in maintenance service delivery. Feedback to improve the framework methods and tools was collected, as well as suggestions for the introduction of new ones according to the companies' necessities.

The paper presents a novel framework addressing the data-based decision-making process for maintenance service delivery. The D3M framework can be used by manufacturing companies to structure their maintenance service delivery process and improve their knowledge of machines and service processes.

The purpose of this paper is to provide a theoretical framework of how knowledge management (KM) systems can facilitate the incorporation of big data into strategic decisions. Advanced analytics are becoming increasingly critical in making strategic decisions in any organization from the private to public sectors and from for-profit companies to not-for-profit organizations. Despite the growing importance of capturing, sharing and implementing people’s knowledge in organizations, it is still unclear how big data and the need for advanced analytics can inform and, if necessary, reform the design and implementation of KM systems.

To address this gap, a combined approach has been applied. The KM and data analysis systems implemented by companies were analyzed, and the analysis was complemented by a review of the extant literature.

Four types of data-based decisions and a set of ground rules are identified toward enabling KM systems to handle big data and advanced analytics.

The paper proposes a practical framework that takes into account the diverse combinations of data-based decisions. Suggestions are provided about how KM systems can be reformed to facilitate the incorporation of big data and advanced analytics into organizations’ strategic decision-making.

This is the first typology of data-based decision-making considering advanced analytics.

The Ethiopian educational system has witnessed considerable structural and curricular changes aimed to address access, equity and relevance. At the same time, there are serious concerns about educational quality as a consequence of these changes. Data use can be an important approach for changing the planning, execution, monitoring and evaluation of activities having the purpose of improving teaching and learning. The purpose of this paper is, therefore, to investigate data use in primary education in Ethiopia.

Using a mixed methods approach, surveys and semi-structured interviews were conducted to collect data from a cluster random sample of eight primary schools representing four different levels of effectiveness in implementing a mandated school improvement program in Ethiopia.

The availability of wider ranges of input, process, outcome and context data per se does not ensure actual use. A complex combination of data, user and organizational factors influences data use in schools, with organizational factors appearing to be most influential. Unrealistic accountability pressures and lack of targeted supervision support seemed to cause unintended data use, such as abuse of data.

Schools need more systematic professional development in data use, with explicit attention to school leadership. Moreover, it is important to make educational inspection processes more responsive to the demands of the school improvement process by adding aspects of the school improvement tradition, such as data-based decision making.

This study contributes to understanding of the nature, characteristics and processes of data use in a developing country context, in which competing accountability mandates often shape policy and practice.

The advancements in internet technologies and the use of sophisticated digital devices in supply chain operations incessantly generate enormous amounts of data, which is termed as big data (BD). The BD technologies have brought about a paradigm shift in the supply chain decision-making towards profitability and sustainability. The aim of this work is to address the issue of implementation of the big data analytics (BDA) in sustainable supply chain management (SSCM) by identifying the relevant factors and developing a structural model for this purpose.

Through a comprehensive literature review and experts’ opinion, the crucial factors are found using the PESTEL framework, which covers political, economic, social, technological, environmental and legal factors. The structural model is developed based on the results of the total interpretive structural modelling (TISM) procedure and MICMAC analysis.

The policy support regarding IT, culture of data-based decision-making, inappropriate selection of BDA technologies and the laws related to data security and privacy are found to affect most of the other factors. Also, the company’s vision towards environmental performance and willingness for material and energy optimization are found to be crucial for the environmental and social sustainability of the supply chain.

The study is focused on the manufacturing supply chain in emerging economies. It may be extended to other industry sectors and geographical areas. Also, additional factors may be included to make the model more robust.

The proposed model imparts an understanding of the relative importance and interrelationship of factors. This may be useful to managers to assess their strengths and weaknesses and ascertain their priorities in the context of their organization for developing a suitable investment plan.

The study establishes the importance of BDA for conservation and management of energy and material. This is crucial to develop strategies for enhancing eco-efficiency of the supply chain, which in turn enhances the economic returns for the society.

This study addresses the implementation of BDA in SSCM in the context of emerging economies. It uses the PESTEL framework for identifying the factors, which is a comprehensive framework for strategic planning and decision-making. This study makes use of the TISM methodology for model development and deliberates on the social and environmental implications too, apart from theoretical and managerial implications.

Big data and related technologies are expected to drastically change the way industrial maintenance is managed. However, at the moment, many companies are collecting large amounts of data without knowing how to systematically exploit it. It is therefore important to find new ways of evaluating and quantifying the value of data. This paper addresses the value of data-based profitability of maintenance investments.

An analytical wasted value of data model (WVD-model) is presented to quantify how the value of data can be increased through eliminating waste. The use of the model is demonstrated with a case example of a maintenance investment appraisal of an automotive parts manufacturer.

The presented model contributes to the gap between the academic research and the solutions implemented in practice in the area of value optimization. The model provides a systematic way of evaluating if the benefits of investing in maintenance data exceed the additional costs incurred. Applying the model to a case study revealed that even though the case company would need to spend more time in analyzing and processing the increased data, the investment would be profitable if even a modest share of the current asset failures could be prevented through improved data analysis.

The model is designed and developed on the principle of eliminating waste to increase value, which has not been previously extensively discussed in the context of data management.

Digital twins (DTs) and other data-based solutions are gaining an increasing foothold in manufacturing business, whereas a mere physical product is often insufficient to satisfy all customers’ expectations. As a result, companies are seeking novel ways of value creation, and one exciting opportunity is the use of DTs in new business creation, where they can offer diverse possibilities for innovative businesses. This paper aims to examine the impacts and challenges of DTs on new business creation in the manufacturing industry.

This study used a qualitative research approach, which combined semistructured interviews and an iterative Delphi study as research methods. The participants for the interviews and Delphi study were from different sectors and roles in the manufacturing industry. Altogether, 10 interviewees from eight companies took part in the interviews, and the expert panel of the Delphi method contained 12 professionals.

The results of the study indicated that DT can significantly impact the business models of manufacturing companies. DT can enhance operations, offer cost savings and business growth and allow stakeholders to focus on core competencies while developing their businesses. Several challenges for leveraging DT were identified, such as data ownership, resource allocation, internal bureaucracy and the difficulty of demonstrating the actual value of data-based services to potential customers.

This paper provides a structured expert-led assessment of the potential impacts of DT utilization in the creation of new business opportunities.

This contribution to the special issue integrates findings addressed by the other papers. In order to structure the insights delivered by the studies and to address the perspectives with the objectives of the special issue, the purpose of this paper is to identify two major components: system characteristics and culture.

These are discussed and subsequently the studies are positioned according to this framework.

The authors further discuss elements involved in increasing the power of clients of the educational system, needs for capacity building, and the need for horizontal and vertical accountability.

Discussion paper to a special issue on data-based decision making.

The shift from data-informed to data-driven educational policymaking is conceptually framed by institutional and transhumanist perspectives. Examples of the shift to large-scale quantitative data driving educational decision-making suggest that data-driven educational policy will not adjust for context to the degree as done by the data-informed or data-based policymaking. Instead, the algorithmization of educational decision-making is both increasingly realizable and necessary in light of the overwhelmingly big data on education produced annually around the world. Evidence suggests that the isomorphic shift from localized data and individual decision-making about education to large-scale assessment data has changed the nature of educational decision-making and national educational policy. Big data are increasingly legitimized in educational policy communities at national and international levels, which means that algorithms are assumed to be the best way to analyze and make decisions about large volumes of complex data. There is a conceptual concern, however, that decontextualized or de-humanized educational policies may have the effect of increasing student achievement, but not necessarily the translation of knowledge into economically, socially, or politically productive behavior.