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With production systems become more digitized, data-driven maintenance decisions can improve the performance of production systems. While manufacturers are introducing predictive maintenance and maintenance reporting to increase maintenance operation efficiency, operational data may also be used to improve maintenance management. Research on the value of data-driven decision support to foster increased internal integration of maintenance with related functions is less explored. This paper explores the potential for further development of solutions for cross-functional responsibilities that maintenance shares with production and logistics through data-driven approaches.

Fifteen maintenance experts were interviewed in semi-structured interviews. The interview questions were derived based on topics identified through a structured literature analysis of 126 papers.

The main findings show that data-driven decision-making can support maintenance, asset, production and material planning to coordinate and collaborate on cross-functional responsibilities. While solutions for maintenance planning and scheduling have been explored for various operational conditions, collaborative solutions for maintenance, production and logistics offer the potential for further development. Enablers for data-driven collaboration are the internal synchronization and central definition of goals, harmonization of information systems and information visualization for decision-making.

This paper outlines future research directions for data-driven decision-making in maintenance management as well as the practical requirements for implementation.

With the advent of the intelligent environment, as novice researchers, graduate students face digital challenges in their research topic selection (RTS). The purpose of this paper is to explore their cognitive processes during data-driven decision making (DDDM) in RTS, thus developing technical and instructional strategies to facilitate their research tasks.

This study developes a theoretical model that considers data-driven RTS as a second-order factor comprising both rational and experiential modes. Additionally, data literacy and visual data presentation were proposed as an antecedent and a consequence of data-driven RTS, respectively. The proposed model was examined by employing structural equation modeling based on a sample of 931 graduate students.

The results indicate that data-driven RTS is a second-order factor that positively affects the level of support of visual data presentation and that data literacy has a positive impact on DDDM in RTS. Furthermore, data literacy indirectly affects the level of support of visual data presentation.

These findings provide support for developers of knowledge discovery systems, data scientists, universities and libraries on the optimization of data visualization and data literacy instruction that conform to students’ cognitive styles to inform RTS.

This paper reveals the cognitive mechanisms underlying the effects of data literacy and data-driven RTS under rational and experiential modes on the level of support of the tabular or graphical presentations. It provides insights into the match between the visualization formats and cognitive modes.

The growing turbulence of the external environment has progressively led to the necessity by organizations of exploiting new opportunities provided by data-driven approaches for supporting the even more complex decision-making processes. The new digital environment has led to the development and adoption of innovative approaches; also in the urban context which has always been characterized by different, interconnected, and dynamic dimensions. Urban governance models have been enhanced by smart technologies, which act as enablers of advanced services and foster connections between citizens, public and private organizations, and decision-makers. In this context, the objective of this chapter is to examine the role of data-driven approaches in the urban context during the chaotic and high variable circumstances related to the diffusion of the Coronavirus disease 2019 (Covid-19). Thanks to the adoption of the co-evolutionary perspective, a cycle in urban governance decision-making approach based on digital technologies is depicted and its contribution for managing the ongoing Covid-19 is traced. The results of the analysis highlight how the data-driven approach supports urban decision-making process and shed light on the co-evolutionary perspective as heuristic device to map the interactions settled in the networks between local governments, data-driven technologies, and citizens. In this sense, this chapter offers interesting insights, potentially capable of generating useful implications for both researchers and professionals in the public sector.

The purpose of this paper is to analyze the inadequacies of current business education in the tackling of the educational challenges inherent to the advent of a data-driven business world. It presents an analysis of the implications of digitization and more specifically big data analytics (BDA) and data science (DS) on organizations with a special emphasis on decision-making processes and the function of managers. It argues that business schools and other educational institutions have well responded to the need to train future data scientists but have rather disregarded the question of effectively preparing future managers for the new data-driven business era.

The approach involves analysis and review of the literature.

The development of analytics skills shall not pertain to data scientists only, it must rather become an organizational cultural component shared among all employees and more specifically among decision makers: managers. In the data-driven business era, managers turn into manager-scientists who shall possess skills at the crossroad of data management, analytical/modeling techniques and tools, and business. However, the multidisciplinary nature of big data analytics and data science (BDADS) seems to collide with the dominant “functional silo design” that characterizes business schools. The scope and breadth of the radical digitally enabled change, the author are facing, may necessitate a global questioning about the nature and structure of business education.

For the sake of transparency and clarity, academia and the industry must join forces to standardize the meaning of the terms surrounding big data. BDA/DS training programs, courses, and curricula shall be organized in such a way that students shall interact with an array of specialists providing them a broad enough picture of the big data landscape. The multidisciplinary nature of analytics and DS necessitates to revisit pedagogical models by developing experiential learning and implementing a spiral-shaped pedagogical approach. The attention of scholars is needed as there exists an array of unexplored research territories. This investigation will help bridge the gap between education and the industry.

The findings will help practitioners understand the educational challenges triggered by the advent of the data-driven business era. The implications will also help develop effective trainings and pedagogical strategies that are better suited to prepare future professionals for the new data-driven business world.

By demonstrating how the advent of a data-driven business era is impacting the function and role of managers, the paper initiates a debate revolving around the question about how business schools and higher education shall evolve to better tackle the educational challenges associated with BDADS training. Elements of response and recommendations are then provided.

The purpose of this paper is to explore the role of storytelling in data interpretation, decision-making and individual-level adoption of business analytics (BA).

Existing theory is extended by introducing the concept of BA data-driven storytelling and by synthesizing insights from BA, storytelling, behavioral research, linguistics, psychology and neuroscience. Using theory-building methodology, a model with propositions is introduced to demonstrate the relationship between storytelling, data interpretation quality, decision-making quality, intention to use BA and actual BA use.

BA data-driven storytelling is a narrative sensemaking heuristic positively influencing human behavior towards BA use. Organizations deliberately disseminating BA data-driven stories can improve the quality of individual data interpretation and decision-making, resulting in increased individual utilization of BA on a daily basis.

To acquire a deeper understanding of BA data-driven storytelling in behavioral operational research (BOR), future studies should test the theoretical model of this study and focus on exploring the complexity and diversity in individual attitudes toward BA.

This study provides practical guidance for business practitioners who struggle with interpreting vast amounts of complex data, making data-driven decisions and incorporating BA into daily operations.

This cross-disciplinary study develops existing BOR, storytelling and BA literature by showing how a novel BA data-driven storytelling approach can facilitate BA adoption in organizations.

The study queries the drivers of innovation management in contemporary data-driven organizations/companies. It is argued that data-driven organizations that integrate a strategic orientation grounded in data, human abilities and proactive management are more effective in triggering innovation.

Research reported in this paper employs constructivist grounded theory, Gioia methodology, and the abductive approach. The data collected through semi-structured interviews administered to 20 Italian start-up founders are then examined.

The paper identifies the key enablers of innovation development in data-driven companies and reveals that data-driven companies may generate different innovation patterns depending on the kind of capabilities activated.

The study provides evidence of how the combination of data-driven culture, skills' enhancement and the promotion of human resources may boost the emergence of innovation.

Because of the use of digital technologies in smart cities, municipalities are increasingly facing issues related to urban data management and are seeking ways to exploit these huge amounts of data for the actualization of data driven services. However, only few studies discuss challenges related to data driven strategies in smart cities. Accordingly, the purpose of this study is to present data driven approaches (architecture and model), for urban data management needed to improve smart city planning and design. The developed approaches depict how data can underpin sustainable urban development.

Design science research is adopted following a qualitative method to evaluate the architecture developed based on top-level design using a case data from workshops and interviews with experts involved in a smart city project.

The findings of this study from the evaluations indicate that the identified enablers are useful to support data driven services in smart cities and the developed architecture can be used to promote urban data management. More importantly, findings from this study provide guidelines to municipalities to improve data driven services for smart city planning and design.

Feedback as qualitative data from practitioners provided evidence on how data driven strategies can be achieved in smart cities. However, the model is not validated. Hence, quantitative data is needed to further validate the enablers that influence data driven services in smart city planning and design.

Findings from this study offer practical insights and real-life evidence to define data driven enablers in smart cities and suggest research propositions for future studies. Additionally, this study develops a real conceptualization of data driven method for municipalities to foster open data and digital service innovation for smart city development.

The main findings of this study suggest that data governance, interoperability, data security and risk assessment influence data driven services in smart cities. This study derives propositions based on the developed model that identifies enablers for actualization of data driven services for smart cities planning and design.

This study explores the enablers of data driven strategies in smart city and further developed an architecture and model that can be adopted by municipalities to structure their urban data initiatives for improving data driven services to make cities smarter. The developed model supports municipalities to manage data used from different sources to support the design of data driven services provided by different enterprises that collaborate in urban environment.

Business analytics (BA) has attracted growing attention mainly due to the phenomena of big data. While studies suggest that BA positively affects organizational performance, there is a lack of academic research. The purpose of this paper, therefore, is to examine the extent to which top- and bottom-performing companies differ regarding their use and organizational facilitation of BA.

Hypotheses are developed drawing on the information processing view and contingency theory, and tested using multivariate analysis of variance to analyze data collected from 117 UK manufacture companies.

Top- and bottom-performing companies differ significantly in their use of BA, data-driven environment, and level of fit between BA and data-drain environment.

Extensive use of BA and data-driven decisions will lead to superior firm performance. Companies wishing to use BA to improve decision making and performance need to develop relevant analytical strategy to guide BA activities and design its structure and business processes to embed BA activities.

This study provides useful management insights into the effective use of BA for improving organizational performance.

In today's business setting, the business analytic capability, data-driven culture and product development features are highly pronounced in light of the firm's competitive advantage. Though widespread attention has been given to the above concepts, there hasn't been much research done on how it could support achieving competitive advantage.

This research strongly lies on the theoretical background and empirically tests the hypothesized relationships. The primary survey of 272 responses was analysed by using the partial least squares structural equation modelling (PLS-SEM).

The findings of this study show a significant relationship for the constructs in the research model except for the third hypothesis. Accordingly, the firm's data-driven culture does not have a significant impact on new product newness.

This study empirically tests the business analytics capability, data-driven culture, and new product development features in the context of a firm's competitive advantage. The findings of this study contribute to the theoretical, practical and managerial aspects of this field.

Based on organizational information processing theory, this research explores how big data analytics capability (BDAC) contributes to green supply chain integration (GSCI) and the contingency role that data-driven decision culture plays.

Using the two-wave survey data collected from 317 Chinese manufacturing firms, the authors validate the hypotheses.

The results show that big data managerial capability has positive impacts on three dimensions of GSCI, while big data technical capability has positive impacts on green internal and customer integration. Moreover, green internal integration mediates the impacts of big data technical capability and managerial capability on green supplier and customer integration. Finally, data-driven decision culture alleviates the positive impacts of big data technical and managerial capability on green internal integration.

The findings suggest that firms can leverage big data technical and managerial capability to enhance information processing capability for achieving a higher degree of GSCI. Further, the critical role of data-driven decision culture in affecting the link between BDAC and GSCI should not be overlooked.

This research contributes to literature on green supply chain management by revealing the role of BDAC in improving GSCI.