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In modern times markets are very dynamic. This situation requires agile enterprises to have the ability to react fast on market influences. Thereby an enterprise’ IT is especially affected, because new or changed business models have to be realized. However, enterprise architectures (EA) are complex structures consisting of many artifacts and relationships between them. Thus analyzing an EA becomes to a complex task for stakeholders. In addition, many stakeholders are involved in decision-making processes, because Enterprise Architecture Management (EAM) targets providing a holistic view of the enterprise. In this article we use concepts of Adaptive Case Management (ACM) to design a decision-making case consisting of a combination of different analysis techniques to support stakeholders in decision-making. We exemplify the case with a scenario of a fictive enterprise.
The acquisition of data for reality mining applications is a critical factor, since many mobile devices, e.g. smartphones, must be capable of capturing the required data. Otherwise, only a small target group would be able to use the reality mining application. In the course of a survey, we have identified smartphone features which might be relevant for various reality mining applications. The survey classifies these features and shows how the support of each feature has changed over the years by analyzing 143 smartphones released between 2004 and 2015. All analyzed devices can be ranked by their number of provided features. Furthermore, this paper deals with quality issues which have occurred during carrying out the survey.
Many organizations identified the opportunities of big data analytics to support the business with problem-specific insights through the exploitation of generated data. Socio-technical solutions are developed in big data projects to reach competitive advantage. Although these projects are aligned to specific business needs, common architectural challenges are not addressed in a comprehensive manner. Enterprise architecture management is a holistic approach to tackle the complex business and IT architecture. The transformation of an organization's EA is influenced by big data projects and their data-driven approach on all layers. To enable strategy oriented development of the EA it is essential to synchronize these projects supported by EA management. In
this paper, we conduct a systematic review of big data literature to analyze which requirements for the EA management discipline are proposed. Thereby, a broad overview about existing research is presented to facilitate a more detailed exploration and to foster the evolution o the EA management discipline.
There is a growing consensus in research and practice that value-creating networks and ecosystems are supplementing the traditional distinction between the internal firm and market perspectives. To achieve joint value in ecosystems, it is crucial to align the various interests of independently acting ecosystem actors and create a common vision. In this paper, we argue that the ecosystem-wide use of product roadmaps may help with this. To get a better understanding of how roadmapping is conducted in the dynamic ecosystem environment, we systematize the main characteristics of product roadmaps and perform a conceptual comparison with the known challenges of ecosystem management. Comparing the two concepts of ecosystems and product roadmaps, we highlight the fit between the characteristics and objectives of the roadmaps and the challenges of ecosystem management. Hence, we propose to experiment with the ecosystem-wide use of product roadmaps as well as the empirical study of the challenges emerging in the process and the associated redesign of the roadmaps.
Intelligent Tutoring Systems (ITSs) are increasingly used in modern education to automatically give students individual feedback on their performance. The advantage for students is fast individual feedback on their answers to asked questions, while lecturers benefit from considerable time savings and easy delivery of educational material. Of course, it is important that the provided feedback is as effective as direct feedback from the lecturer. However, in digital teaching, lecturers cannot assess the student’s knowledge precisely but can only provide information on which questions were answered correctly and incorrectly. Therefore, this paper presents a concept for integrating ITS elements into the gamified e-learning platform IT-REX so that the feedback quality can be improved to support students in the best possible way.
The character of knowledge-intense processes is that participants decide the next process activities on base of the present information and their expert knowledge. The decisions of these knowledge workers are in general non-deterministic. It is not possible to model these processes in advance and to automate them using a process engine of a BPM system. Hence, in this context a process instance is called a case, because there is no predefined model that could be instantiated. Domain-specific or general case management systems are used to support the knowledge workers. These systems provide all case information and enable users to define the next activities, but they have no or only limited activity recommendation capabilities. In the following paper, we present a general concept for a self-learning system based on process mining that suggests the next best activity on quantitative and qualitative data for a given case. As a proof of concept, it was applied to the area of insurance claims settlement.
Enterprise Architecture (EA) management is an activity that seeks to foster the alignment of business and IT, and pursues various goals further operationalizing this alignment. Key to effective EA management is a framework that defines the roles, activities, and viewpoints used for EA management in accordance to the concerns that the stakeholders aim to address. Consensus holds that such frameworks are organization-specific and hence they are designed in governance activities for EA management. As of today, top-down approaches for governance are used to derive organization-specific frameworks. These usually lack systematic mechanisms for improving the framework based on the feedback of the responsible stakeholders. We outline a bottom-up approach for EA management governance that systematically observes the behavior of the actors to learn user concerns and recommend appropriate viewpoints. With this approach, we complement traditional top-down governance activities.
This paper investigates the impact of dynamic capabilities (DC) on brand love. From a resource-based view, there is little clarity vis-à-vis the specific capabilities that drive the ability to create brand love. This paper focuses on three research questions: Firstly, which dynamic capabilities are relevant for brand love? Secondly, how strong is the impact of certain dynamic capabilities on brand love? Thirdly, which conditions mediate and moderate the impact of specific dynamic capabilities on brand love? Data from a multi-method research approach have been used to itentify the specific capabilities that corporations need, to enhance brand love. Furthermore, a standardized online survey was conducted on marketing executives and evaluated by structural equation modeling. The results indicate, that customer expertise plays a major role in the relationship between dynamic capabilities and brand love. Furthermore, this relationship is more important in markets that have a low competitive differentiation in products and services.
With the Internet of Things being one of the most discussed trends in the computer world lately, many organizations find themselves struggling with the great paradigm shift and thus the implementation of IoT on a strategic level. The Ignite methodoogy as a part of the Enterprise-IoT project promises to support organizations with these strategic issues as it combines best practices with expert knowledge from diverse industries helping to create a better understanding of how to transform into an IoT driven business. A framework that is introduced within the context of IoT business model development is the Bosch IoT Business Model Builder. In this study the provided framework is compared to the Osterwalder Business Model Canvas and the St. Gallen Business Model Navigator, the most commonly used and referenced frameworks according to a quantitative literature analysis.
Converting users into customers : the role of user profile information and customer journey analysis
(2016)
Due to the digital transformation, the importance of web analysis and user profiling for enterprises is increasing rapidly as customers focus on digital channels to obtain information about products and brands. While there exists a lot research on these topics, only a minority of firms use them to their advantage. This study aims to tighten the link between research and business such that experimental methods can be used for the improvement of communication strategies in practice. Therefore, a systematic literature analysis is conducted, workshops are observed and documented and an empirical study is used to integrate single steps into a framework for the
practical usage of user profiling and customer journey analysis.
Customer services in the digital transformation: social media versus hotline channel performance
(2015)
Due to the digital transformation online service strategies have gained prominence in practice as well as in the theory of service management. This study examines the efficacy of different types of service channels in customer complaint handling. The theoretical framework, developed using complaint handling and social media literature, is tested against data collected from two different channels (hotline and social media) of a German telecommunication service provider. We contribute to the understanding of firm’s multichannel distribution strategy in two ways: a) by conceptualizing and evaluating complaint handling quality across traditional and social media channels, and b) by testing the impact of complaint handling quality on key performance outcomes like customer loyalty, positive word-of-mouth, and cross purchase intentions.
The increasing number of connected mobile devices such as fitness trackers and smartphones define new data for health insurances, enabling them to gain deeper insights into the health of their customers. These additional data sources plus the trend towards an interconnected health community, including doctors, hospitals and insurers, lead to challenges regarding data filtering, organization and dissemination. First, we analyze what kind of information is relevant for a digital health insurance. Second, functional and non-functional requirements for storing and managing health data in an interconnected environment are defined. Third, we propose a data architecture for a digitized health insurance, consisting of a data model and an application architecture.
Selecting a suitable development method for a specific project context is one of the most challenging activities in process design. To extend the so far statistical construction of hybrid development methods, we analyze 829 data points to investigate which context factors influence the choice of methods or practices. Using exploratory factor analysis, we derive five base clusters consisting of up to 10 methods. Logistic regression analysis then reveals which context factors have an influence on the integration of methods from these clusters in the development process. Our results indicate that only a few context factors including project/product size and target application domain significantly influence the choice. This summary refers to the paper “Determining Context Factors for Hybrid Development Methods with Trained Models”. This paper was published in the proceedings of the International Conference on Software and System Process in 2020.
The second Digital Enterprise Computing Conference DEC 16 at the Herman Hollerith Center in Böblingen brings together students, researchers, and practitioners to discuss solutions, experiences, and future developments for the digital transformation. Digitization of business and IT defines the conference agenda: technology acceptance, digital transformation, digital business & administration, digital process challenges, analytics, and big data & data processing.
The third Digital Enterprise Computing Conference DEC 17 at the Herman Hollerith Center in Böblingen brings together students, researchers, and practitioners to discuss solutions, experiences, and future developments for the digital transformation. Digitization of business and IT defines the conference agenda: digital models & architecture, digital marketing, agility & innovation.
Digitization in the energy sector is a necessity to enable energy savings and energy efficiency potentials. Managing decentralized corporate energy systems is hindered by a non-existence. The required integration of energy objectives into business strategy creates difficulties resulting in inefficient decisions. To improve this, practice-proven methods such as Balanced Scorecard, Enterprise Architecture Management and the Value Network approach are transferred to the energy domain. The methods are evaluated based on a case study. Managing multi-dimensionality, high complexity and multiple actors are the main drivers for an effective and efficient energy management system. The underlying basis to gain the positive impacts of these methods on decentralized corporate energy systems is digitization of energy data and processes.
In this paper we build on our research in data management on native Flash storage. In particular we demonstrate the advantages of intelligent data placement strategies. To effectively manage phsical Flash space and organize the data on it, we utilize novel storage structures such as regions and groups. These are coupled to common DBMS logical structures, thus require no extra overhead for the DBA. The experimental results indicate an improvement of up to 2x, which doubles the longevity of Flash SSD. During the demonstration the audience can experience the advantages of the proposed approach on real Flash hardware.
The Internet of Things (IoT) fundamentally influences today’s digital strategies with disruptive business operating models and fast changing markets. New business information systems are integrating emerging Internet of Things infrastructures and components. With the huge diversity of Internet of Things technologies and products organizations have to leverage and extend previous enterprise architecture efforts to enable business value by integrating the Internet of Things into their evolving Enterprise Architecture Management environments. Both architecture engineering and management of current enterprise architectures is complex and has to integrate beside the Internet of Things synergistic disciplines like EAM - Enterprise Architecture and Management with disciplines like: services & cloud computing, semantic-based decision support through ontologies and knowledge-based systems, big data management, as well as mobility and collaboration networks. To provide adequate decision support for complex business/IT environments, it is necessary to identify affected changes of Internet of Things environments and their related fast adapting architecture. We have to make transparent the impact of these changes over the integral landscape of affected EAM-capabilities, like directly and transitively impacted IoT-objects, business categories, processes, applications, services, platforms and infrastructures. The paper describes a new metamodel-based approach for integrating partial Internet of Things objects, which are semi-automatically federated into a holistic Enterprise Architecture Management environment.
The digital transformation of our society changes the way we live, work, learn, communicate, and collaborate. This disruptive change interacts with all information processes and systems that are important business enablers for the digital transformation since years. The Internet of Things, social collaboration systems for adaptive case management, mobility systems and services for Big Data in cloud services environments are emerging to support intelligent user-centered and social community systems. They will shape future trends of business innovation and the next wave of information and communication technology. Biological metaphors of living and adaptable ecosystems provide the logical foundation for self-optimizing and resilient run-time environments for intelligent business services and related distributed information systems with service-oriented enterprise architectures. The present research investigates mechanisms for flexible adaptation and evolution of digital enterprise architectures in the context of integrated synergistic disciplines like distributed service-oriented architectures and information systems, EAM - Enterprise Architecture and Management, metamodeling, semantic echnologies, web services, cloud computing and Big Data technology. Our aim is to support flexibility and agile transformations for both business domains and related enterprise systems through adaptation and evolution of digital enterprise architectures. The present research paper investigates digital transformations of business and IT and integrates fundamental mappings between adaptable digital enterprise architectures and service-oriented information systems.
In a world with rapidly changing customer requirements and the increased role of technology, companies need more flexible systems to adapt their processes and react dynamically to changes. Adaptive Case Management (ACM) comes into consideration by providing a concept to adapt to changing business conditions. Within our research project we did a first foundational evaluation of the potential of ACM in supporting unpredictable sales processes. Based on a set of criteria we tested the concept of ACM with the open source tool Cognoscenti. The evaluation gave us the possibility to experience the concept of ACM. Hence we were able to provide a statement about the potential of ACM within the context of an unpredictable sales process, setting the path to further research and discussion of ACM in the area of sales processes.