Digital maturity variables and their impact on the enterprise architecture layers

This study examines the variables of digital maturity of companies. The framework for enterprise architectures Archimate 3.0 is used to compare the variables. The variables are assigned to the six layers of architecture: Strategy, Business Environment, Applications, Technology, Physical and Implementation and Migration. On the basis of a literature overview, 15 “digital maturity models” with a total of 147 variables are analyzed. The databases Scopus, EBSCO – Business Source Premier and ProQuest are used for this purpose

A Resource-Based Perspective

Geschäftsprozesstechnologien unterstützen dabei, operative Tätigkeiten effizienter und effektiver durchzuführen. In einem sich dynamisch verändernden Umfeld wird es für Organisationen essenziell, diese Technologien gezielt einzusetzen, um durch schnelle Anpassung weiterhin wettbewerbsfähig zu bleiben. Die derzeitige Forschung hat bisher keine Antwort darauf gefunden, wie Organisationen dies trotz ständig wechselnder Umfeldbedingungen und fortschreitender organisationaler Reife durch gezielte Ressourcenallokation erreichen können. Diese Dissertation adressiert diese Forschungslücke, indem untersucht wird, wie organisationale Fähigkeiten mithilfe von Geschäftsprozesstechnologien innerhalb dynamischer Umfelder ausgebildet und erneuert werden können.Business process technologies help to improve the efficiency and effectiveness of day-to-day operations. Organizations face the challenge of leveraging these technologies to quickly adapt business processes accordingly to cope with different levels of environmental turbulence. From prior research, we know how organizations apply business process technologies and how they affect performance. We do not fully understand how organizations orchestrate related resources based on changing environmental conditions and evolving organizational maturity. This dissertation addresses this research problem and presents research on how to develop and renew organizational capabilities with business process technologies through turbulent environments

ERP implementation methodologies and frameworks: a literature review

Enterprise Resource Planning (ERP) implementation is a complex and vibrant process, one that involves a combination of technological and organizational interactions. Often an ERP implementation project is the single largest IT project that an organization has ever launched and requires a mutual fit of system and organization. Also the concept of an ERP implementation supporting business processes across many different departments is not a generic, rigid and uniform concept and depends on variety of factors. As a result, the issues addressing the ERP implementation process have been one of the major concerns in industry. Therefore ERP implementation receives attention from practitioners and scholars and both, business as well as academic literature is abundant and not always very conclusive or coherent. However, research on ERP systems so far has been mainly focused on diffusion, use and impact issues. Less attention has been given to the methods used during the configuration and the implementation of ERP systems, even though they are commonly used in practice, they still remain largely unexplored and undocumented in Information Systems research. So, the academic relevance of this research is the contribution to the existing body of scientific knowledge. An annotated brief literature review is done in order to evaluate the current state of the existing academic literature. The purpose is to present a systematic overview of relevant ERP implementation methodologies and frameworks as a desire for achieving a better taxonomy of ERP implementation methodologies. This paper is useful to researchers who are interested in ERP implementation methodologies and frameworks. Results will serve as an input for a classification of the existing ERP implementation methodologies and frameworks. Also, this paper aims also at the professional ERP community involved in the process of ERP implementation by promoting a better understanding of ERP implementation methodologies and frameworks, its variety and history

Project management maturity in the age of big data

While the area of project management maturity (PMM) is attracting an increased amount of research attention, the approaches to measuring maturity fit within existing social science conventions. This paper aims to examine the potential contribution of new data collection and analytical approaches to develop new insights in PMM. This paper takes the form of a literature review. Findings suggest that the current trends of rapidly growing digital data collection and storage may have the potential to develop approaches to PMM assessment that overcome the limitations of existing qualitative and quantitative approaches

Adaptive Enterprise Resilience Management: Adaptive Action Design Research in Financial Services Case Study

© 2016 IEEE. Resilience is the ability of an enterprise to absorb, recover and adapt from a disruption. Being resilient is a complex undertaking for enterprises operating in a highly dynamic environment and striving for continuous efficiency and innovation. The challenge for enterprises is to offer and run a customer-centric and interdependent large portfolio of resilient services. The fundamental research question is: how to enable service resilience in the practical enterprise resilience context? This paper addresses this important research question, and reports findings from on-going (2014-2016) research on adaptive enterprise resilience management in an Australian financial services organization (FSO). This research is being conducted using the adaptive action-design research (ADR) method to iteratively research, develop and deliver the desired resilience framework in short increments. This paper presents the overall evolved adaptive enterprise resilience management framework and its 'service resilience' element details as one of the key outcomes from the second adaptive ADR increment

Data and Predictive Analytics Use for Logistics and Supply Chain Management

Purpose The purpose of this paper is to explore the social process of Big Data and predictive analytics (BDPA) use for logistics and supply chain management (LSCM), focusing on interactions among technology, human behavior and organizational context that occur at the technology’s post-adoption phases in retail supply chain (RSC) organizations. Design/methodology/approach The authors follow a grounded theory approach for theory building based on interviews with senior managers of 15 organizations positioned across multiple echelons in the RSC. Findings Findings reveal how user involvement shapes BDPA to fit organizational structures and how changes made to the technology retroactively affect its design and institutional properties. Findings also reveal previously unreported aspects of BDPA use for LSCM. These include the presence of temporal and spatial discontinuities in the technology use across RSC organizations. Practical implications This study unveils that it is impossible to design a BDPA technology ready for immediate use. The emergent process framework shows that institutional and social factors require BDPA use specific to the organization, as the technology comes to reflect the properties of the organization and the wider social environment for which its designers originally intended. BDPA is, thus, not easily transferrable among collaborating RSC organizations and requires managerial attention to the institutional context within which its usage takes place. Originality/value The literature describes why organizations will use BDPA but fails to provide adequate insight into how BDPA use occurs. The authors address the “how” and bring a social perspective into a technology-centric area

Survey and Systematization of Prescriptive Analytics Systems: Towards Archetypes from a Human-Machine-Collaboration Perspective

Prescriptive analytics systems (PAS) represent the most mature form of business analytics (BA), offering advanced decision support. However, current research predominantly focuses on technical facets while neglecting social-technical design aspects. A pluralist research methodology was employed to address this gap, starting with a systematic literature review of over 200 papers. We used these papers to derive a concept matrix of fundamental elements that guided the development of a taxonomy conceptualizing the interplay and collaboration between humans and machines in PAS-based decision-making processes. Based on this taxonomy, we identified four recurring PAS archetypes with salient design characteristics: Informative, executive, adaptive, and autonomous PAS. Our findings have important implications for the BA community, including the need to investigate design options for executive, adaptive, and autonomous PAS; the underrepresentation of the human perspective; the missing links to the broader organizational landscape; and the potential for interpretable machine learning and reinforcement learning in PAS