Lessons learned in aligning data and model evolution in collaborative information systems

Reschenhofer, Thomas; Bhat, M. Seetharama; Hernandez-Mendez, Adrian; Matthes, Florian

doi:10.1145/2889160.2889235

Cited by 12 publications

(5 citation statements)

References 23 publications

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“…Most of the literature in this field can be divided into two clusters. First-generation CIS are considered information systems that support collaborative activities between human actors for tasks like product design [e.g., 54], data management [e.g., 48], and disaster management [e.g., 36,60] and information systems that support the improvement of collaborative behavior [e.g., 5]. With the rise of smart machines, a new second generation of CIS began to emerge.…”

Section: Human-machine Collaborationmentioning

confidence: 99%

Symbiotic Co-Evolution in Collaborative Human-Machine Decision Making: Exploration of a Multi-Year Design Science Research Project in the Air Cargo Industry

Döppner¹,

Derckx²,

Schoder³

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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The work world is set to undergo major changes thanks to advancements in automation and artificial intelligence and is beginning to promote new forms of collaboration. The transition from a technologysupporting environment to a collaborative environment in which people and technology work together to achieve their goals requires a fundamental change in the way we design, build, and ultimately deploy information systems. Most work on information system design focuses on the effective augmentation of humans. However, little is known about constructing a sustainable mutually beneficial collaboration between human and machine. To better understand this relationship, we perform a case study drawing on ethnographic evidence collected during a multi-year design science research project with a major service provider for unit load device management in the air cargo industry that resulted in an artifact for human-machine collaboration (HMC). Our study takes a closer look at the co-evolution that emerges from the collaboration between human and artificial agents over time, in which both parties influence each other, the underlying tasks, and their environment. Our analysis reveals three facets of symbiotic co-evolution: agents' evolution, activity evolution, and structural evolution. The findings contribute to the HMC knowledge base and have implications for future HMC design initiatives.

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Section: Human-machine Collaborationmentioning

confidence: 99%

Symbiotic Co-Evolution in Collaborative Human-Machine Decision Making: Exploration of a Multi-Year Design Science Research Project in the Air Cargo Industry

Döppner¹,

Derckx²,

Schoder³

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…For the design of our Visual Analytic System (VAS) we extend on a software engineering framework that technically resides upon the 'Hybrid Wiki' approach suggested by Reschenhofer et al [56], and which from a use perspective allows to follow an agile process to create and adapt the model that is used by the VAS to represent ecosystem entities and structures (see Section 3.2). This framework addresses the dynamic structure of business ecosystems as it supports the evolution of the model as well as its instances at runtime by stakeholders and ecosystem experts, i.e., users without programming knowledge or skills.…”

Section: Visual Analytic System (Vas): Business Ecosystem Explorer (Bmentioning

confidence: 99%

Modeling and Visualizing Smart City Mobility Business Ecosystems: Insights from a Case Study

et al. 2018

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Smart mobility is a central issue in the recent discourse about urban development policy towards smart cities. The design of innovative and sustainable mobility infrastructures as well as public policies require cooperation and innovations between various stakeholders—businesses as well as policy makers—of the business ecosystems that emerge around smart city initiatives. This poses a challenge for deploying instruments and approaches for the proactive management of such business ecosystems. In this article, we report on findings from a smart city initiative we have used as a case study to inform the development, implementation, and prototypical deployment of a visual analytic system (VAS). As results of our design science research we present an agile framework to collaboratively collect, aggregate and map data about the ecosystem. The VAS and the agile framework are intended to inform and stimulate knowledge flows between ecosystem stakeholders in order to reflect on viable business and policy strategies. Agile processes and roles to collaboratively manage and adapt business ecosystem models and visualizations are defined. We further introduce basic categories for identifying, assessing and selecting Internet data sources that provide the data for ecosystem models and we detail the ecosystem data and view models developed in our case study. Our model represents a first explication of categories for visualizing business ecosystem models in a smart city mobility context.

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“…To address the dynamic structure of business ecosystems, we design an agile framework for modelling ecosystems as integrated, adaptive collaborative work system supporting the evolution of both the model and its instances at runtime by stakeholders and ecosystem experts (i.e., users without programming knowledge or skills). This framework rests on the Hybrid Wiki approach as presented in (Reschenhofer et al 2016) that serves as Knowledge Management System application development platform and contains features for data management as well as collaboration and decision support. All changes can be traced back to the user responsible for the changes, including the time of the change.…”

Section: Business Ecosystem Explorer (Beex)mentioning

confidence: 99%

Visualizing Business Ecosystems: Applying a Collaborative Modelling Process in Two Case Studies

Faber¹,

Hernandez-Mendez²,

Rehm³

et al. 2018

Australasian Conference on Information Systems 2018

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Business ecosystems are increasingly gaining relevance in research and practice. Because business ecosystems progressively change, enterprises are interested in analysing their ecosystem, to identify and address such changes. In order to gain a comprehensive picture of the business ecosystem, various stakeholders of the enterprise should be involved in the analysis process. We propose a collaborative approach to model and visualize the business ecosystem and we validate four central roles in the modelling process. The process consists of six steps, namely the definition of the business ecosystem focus, instantiation of the model, data collection, provision of tailored visualizations, collecting feedback and adapting the models, and using the visualization 'to tell a story'. In this paper, we report case studies of two companies that have instantiated ecosystem models.

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Lessons learned in aligning data and model evolution in collaborative information systems

Cited by 12 publications

References 23 publications

Symbiotic Co-Evolution in Collaborative Human-Machine Decision Making: Exploration of a Multi-Year Design Science Research Project in the Air Cargo Industry

Symbiotic Co-Evolution in Collaborative Human-Machine Decision Making: Exploration of a Multi-Year Design Science Research Project in the Air Cargo Industry

Modeling and Visualizing Smart City Mobility Business Ecosystems: Insights from a Case Study

Visualizing Business Ecosystems: Applying a Collaborative Modelling Process in Two Case Studies

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