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Business processes are important knowledge resources of a company. The knowledge contained in business processes impart procedures used to create products and services. However, modelling and application of business processes are affected by problems connected to knowledge transfer. This paper presents and implements a layered model to improve the knowledge transfer. Thus modelling and understanding of business process models is supported. An evaluation of the approach is presented and results and other areas of application are discussed.
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.
A sequence of transactions represents a complex and multi dimensional type of data. Feature construction can be used to reduce the data´s dimensionality to find behavioural patterns within such sequences. The patterns can be expressed using the blue prints of the constructed relevant features. These blue prints can then be used for real time classification on other sequences.
Decision-making in the field of Enterprise Architecture (EA) is a complex task. Many organizations establish a set of complex processes and hierarchical structures to enable strategy-driven development of their EA. This leads to slow and inefficient decision-making entailing bad time-to-market and discontented stakeholders. Collaborative EA delineates a lightweight approach to enable EA decisions but often neglects strategic alignment. In this paper, we present an approach to integrate the concept of collaborative EA and goal-driven decision-making through collaborative modeling of goal-oriented information demands based on ArchiMate’s motivation extension to reach a goal-oriented EA decision support in a collaborative EA environment.
In recent years, the rise of the digital transformation received significant importance in Business-to-Business (B2B) research. Social media applications provide executives with a raft of new options. Consequently, interfaces to social media platforms have also been integrated into B2B salesforce applications, although very little is as yet known about their usage and general impact on B2B sales performance. This paper evaluates 1) the conceptualization of social media usage in a dyadic B2B relationship; 2) the effects of a more differentiated usage construct on customer satisfaction; 3) antecedents of social media usage on multiple levels; and 4) the effectiveness of social media usage for different types of customers. The framework presented here is tested cross-industry against data collected from dyadic buyer seller relationships in the IT service industry. The results elucidate the preconditions and the impact of social media usage strategies in B2B sales relations.
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 stimulation of user engagement has received significant attention in extant research. However, the theory of antecedents for user engagement with an initial electronic word-of-mouth (eWoM) communication is relatively less developed. In an investigation of 576 unique user postings across independent Facebook (FB) communities for two German firms, we contribute to the extant knowledge on user engagement in two different ways. First, we explicate senders’ prior usage experience and the extent of their acquaintance with other community members as the two key drivers of user engagement across a product and a service community. Second, we reveal that these main effects differ according to the type of community. In service communities, experience has a stronger impact on user engagement; whereas, in product communities, acquaintance is more important.
Big Data und Cloud Systeme werden zunehmend von mobilen, benutzerzentrierten und agil veränderbaren Informationssystemen im Kontext von digitalen sozialen Netzwerken genutzt. Metaphern aus der Biologie für lebendige und selbstheilende Systeme und Umgebungen liefern die Basis für intelligente adaptive Informationssysteme und für zugehörige serviceorientierte digitale Unternehmensarchitekturen. Wir berichten über unsere Forschungsarbeiten über Strukturen und Mechanismen adaptiver digitaler Unternehmensarchitekturen für die Entwicklung und Evolution von serviceorientierten Ökosystemen und deren Technologien wie Big Data, Services & Cloud Computing, Web Services und Semantikunterstützung. Für unsere aktuellen Forschungsarbeiten nutzen wir praxisrelevante SmartLife Szenarien für die Entwicklung, Wartung und Evolution zukunftsgerechter serviceorientierter Informationssysteme. Diese Systeme nutzen eine stark wachsende Zahl externer und interner Services und fokussieren auf die Besonderheiten der Weiterentwicklung der Informationssysteme für integrierte Big Data und Cloud Kontexte. Unser Forschungsansatz beschäftigt sich mit der systematischen und ganzheitlichen Modellbildung adaptiver digitaler Unternehmensarchitekturen - gemäß standardisierter Referenzmodelle und auf Standards aufsetzenden Referenzarchitekturen, die für besondere Einsatzszenarien auch bei kleineren Anwendungskontexten oder an neue Kontexte einfacher adaptiert werden können. Um Semantik-gestützte Analysen zur Entscheidungsunterstützung von System- und Unternehmensarchitekten zu ermöglichen, erweitern wir unser bisheriges Referenzmodell für ITUnternehmensarchitekturen ESARC – Enterprise Services Architecture Reference Cube – um agile Mechanismen der Adaption und Konsistenzbehandlung sowie die zugehörigen Metamodelle und Ontologien für Digitale Enterprise Architekturen um neue Aspekte wie Big Data und Cloud Kontexte.
Der lokale Bekleidungseinzelhandel steht unter immer stärkerem Konkurrenzdruck durch Versandunternehmen. Zusätzlich bestehen durch gewachsene Architekturen eine Reihe von Wachstumshemmnissen. Daher sollen hier eine Reihe von Ansätzen zur Gestaltung datenzentrierter Unternehmensarchitekturen für den Bekleidungseinzelhandel vorgestellt werden. Sie basieren auf dem Einsatz von RFID zur Gewinnung von Kundenprofilen in den Niederlassungen und dem Einsatz von Big-Data basierten Auswertungs- und Analysemechanismen. Mit den vorgestellten Konzepten ist es Unternehmen des Bekleidungseinzelhandels möglich, ähnlich wie Versandunternehmen, individuelle Ansprachen des Kunden und Angebote zu entwickeln