The emergence of big data analytics (BDA) has posed opportunities as well as multiple challenges to business practitioners, who have called for research on the behavioural factors underlying BDA adoption at the individual level. The purpose of this study is to extend the information systems (IS) research on storytelling and to explore the role and characteristics of deliberate storytelling in individual‐level BDA adoption. This case study used the grounded theory approach to extract qualitative data from 24 interviews, field notes, and documentary data. The explicit contributions of the study to the literature include (a) increasing our understanding of the facilitating role of deliberate storytelling in individual‐level BDA adoption, (b) identifying four deliberate storytelling patterns and seven underlying corporate stories disseminated by organizations to influence individual behaviour, and (c) defining the core characteristics of effective deliberate storytelling. This study has multiple implications for business practitioners and demonstrates how deliberate storytelling can be used as a facilitating mechanism in daily business practice.

Purpose 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). Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Practical implications 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. Originality/value 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 manufacturing industry is facing the impact of a dynamic market and intensive competition. Many companies are looking for a new approach to improve their business activities in a collaborat ive business ecosystem with other stakeholders. Cloud computing enables the sharing of manufacturing resources and capabilities between different stakeholders to support business and physical production. The purpose of this resea rch is to explore approaches moving towards a cloud manufacturing ecosystem and present possible implications for practice. To fulfil the research objectives, a multiple-case study was conducted within sheet metal manufacturing companies. Business and technology related requirements for cloud-based collaborative manufacturing porta ls were collected through interviews with industrial practitioners from the sheet metal manufacturing perspective. Based on analysis a prototype model of a CloudEcosystem was presented to demonstrate the essential features of the portals. This research found that there are three different porta l types for cloud manufacturing ecosystems depending on the value chain configuration. Close to real-time information provided by cloud-based platforms can create manufacturing ecosystems where machine owners, product designers and customers may collaborate and compete simultaneously. The CloudEcosystem designed in this research can provide a practical tool and technical solution to help manufacturers think about moving towards cloud manufacturing ecosystem s.Ecosystems, web porta l, cloud platform, business collaboration, cloud manufacturing.

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With the emerging role of digitalization in the industrial sector, more and more companies attempt to increase asset availability, improve product quality and reduce maintenance costs. Manufacturing companies are faced with the need to transform traditional services into remote factory monitoring solutions using big data and advanced analytics. Kone is a global leader in the elevator and escalator production industry, which is continuously looking for new ways of improving production efficiency and reducing machine downtime in order to run unmanned 24/7 production. However, the process of collecting data from equipment and utilizing it for predictive analytics can be challenging and time consuming. Therefore, during Serena project Kone cooperated with VTT and Prima Power, which provided necessary capabilities and competencies in the areas of data collection, analysis and utilization for developing and testing predictive maintenance solutions in the elevator manufacturing industry. As a result of this collaboration, VTT integrated sensors into Prima Power production line used at Kone and developed algorithms for measuring the remaining useful life of conveyor bearings. As a machine tool builder, Prima Power contributed to the project with a cloud environment for remote collection of vibration measurement data and Serena Customer Web analytics for condition-based maintenance.