EMPOWERING PRACTITIONERS: A CONCEPTUAL FRAMEWORK FOR VALUE CO-CREATION THROUGH SMART SERVICE INNOVATION METHODOLOGIES

Smart services offer great innovation potential by incorporating digital technologies into non-digital value-creation processes. As smart service innovation poses significant challenges to organizations, existing research has contributed to understanding and addressing this phenomenon by developing various methods, tools, and processes. Yet, the academic community often still fails to bridge the “last mile” and help practitioners apply this knowledge in their specific application contexts. This article outlines how research can empower practitioners by systematically providing methodological knowledge for smart service innovation. We review and contrast existing methodologies and present a conceptual framework for value co-creation through smart service innovation methodologies. In addition, we identify six essential resource types required in these methodologies and propose emergent research avenues to guide future contributions to smart service innovation research

Empowering Practitioners: A Conceptual Framework for Value Co-Creation through Smart Service Innovation Methodologies

Smart services offer great innovation potential by incorporating digital technologies into non-digital value-creation processes. As smart service innovation poses significant challenges to organizations, existing research has contributed to understanding and addressing this phenomenon by developing various methods, tools, and processes. Yet, the academic community often still fails to bridge the “last mile” and help practitioners apply this knowledge in their specific application contexts. This article outlines how research can empower practitioners by systematically providing methodological knowledge for smart service innovation. We review and contrast existing methodologies and present a conceptual framework for value co-creation through smart service innovation methodologies. In addition, we identify six essential resource types required in these methodologies and propose emergent research avenues to guide future contributions to smart service innovation research

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Smart Service Innovation: Organization, Design, and Assessment

Background: The emergence of technologies such as the Internet of Things, big data, cloud computing, and wireless communication drives the digital transformation of the entire society. Organizations can exploit these potentials by offering new data-driven services with innovative value propositions, such as carsharing, remote equipment maintenance, and energy management services. These services result from value co-creation enabled by smart service systems, which are configurations of people, processes, and digital technologies. However, developing such systems was found to be challenging in practice. This is mainly due to the difficulties of managing complexity and uncertainty in the innovation process, as contributions of various actors from multiple disciplines must be coordinated. Previous research in service innovation and service systems engineering (SSE) has not shed sufficient light on the specifics of smart services, while research on smart service systems lacks empirical grounding. Purpose: This thesis aims to advance the understanding of the systematic development of smart services in multi-actor settings by investigating how smart service innovation (SSI) is conducted in practice, particularly regarding the participating actors, roles they assume, and methods they apply for designing smart service systems. Furthermore, the existing set of methods is extended by new methods for the design-integrated assessment of smart services and service business models. Approach: Empirical and design science methods were combined to address the research questions. To explore how SSI is conducted in practice, 25 interviews with experts from 13 organizations were conducted in two rounds. Building on service-dominant logic (SDL) as a theoretical foundation and a multi-level framework for SSI, the involvement of actors, their activities, employed means, and experienced challenges were collected. Additionally, a case study was used to evaluate the suitability of the Lifecycle Modelling Language to describe smart service systems. Design science methods were applied to determine a useful combination of service design methods and to build meta-models and tools for assessing smart services. They were evaluated using experiments and the talk aloud method. Results: On the macro-level, service ecosystems consist of various actors that conduct service innovation through the reconfiguration of resources. Collaboration of these actors is facilitated on the meso-level within a project. The structure and dynamics of project configurations can be described through a set of roles, innovation patterns, and ecosystem states. Four main activities have been identified, which actors perform to reduce uncertainty in the project. To guide their work, actors apply a variety of means from different disciplines to develop and document work products. The approach of design-integrated business model assessment is enabled through a meta-model that links qualitative aspects of service architectures and business models with quantitative assessment information. The evaluation of two tool prototypes showed the feasibility and benefit of this approach. Originality / Value: The results reported in this thesis advance the understanding of smart service innovation. They contribute to evidence-based knowledge on service systems engineering and its embedding in service ecosystems. Specifically, the consideration of actors, roles, activities, and methods can enhance existing reference process models. Furthermore, the support of activities in such processes through suitable methods can stimulate discussions on how methods from different disciplines can be applied and combined for developing the various aspects of smart service systems. The underlying results help practitioners to better organize and conduct SSI projects. As potential roles in a service ecosystem depend on organizational capabilities, the presented results can support the analysis of ex¬ternal dependencies and develop strategies for building up internal competencies.:Abstract iii Content Overview iv List of Abbreviations viii List of Tables x List of Figures xii PART A - SYNOPSIS 1 1 Introduction 2 1.1 Motivation 2 1.2 Research Objectives and Research Questions 4 1.3 Thesis Structure 6 2 Research Background 7 2.1 Smart Service Systems 7 2.2 Service-Dominant Logic 8 2.3 Service Innovation in Ecosystems 11 2.4 Systematic Development of Smart Service Systems 13 3 Research Approach 21 3.1 Research Strategy 21 3.2 Applied Research Methods 22 4 Summary of Findings 26 4.1 Overview of Research Results 26 4.2 Organizational Setup of Multi-Actor Smart Service Innovation 27 4.3 Conducting Smart Service Innovation Projects 32 4.4 Approaches for the Design-integrated Assessment of Smart Services 39 5 Discussion 44 5.1 Contributions 44 5.2 Limitations 46 5.3 Managerial Implications 47 5.4 Directions for Future Research 48 6 Conclusion 54 References 55 PART B - PUBLICATIONS 68 7 It Takes More than Two to Tango: Identifying Roles and Patterns in Multi-Actor Smart Service Innovation 69 7.1 Introduction 69 7.2 Research Background 72 7.3 Methodology 76 7.4 Results 79 7.5 Discussion 90 7.6 Conclusions and Outlook 96 7.7 References 97 8 Iterative Uncertainty Reduction in Multi-Actor Smart Service Innovation 100 8.1 Introduction 100 8.2 Research Background 103 8.3 Research Approach 109 8.4 Findings 113 8.5 Discussion 127 8.6 Conclusions and Outlook 131 8.7 References 133 9 How to Tame the Tiger – Exploring the Means, Ends, and Challenges in Smart Service Systems Engineering 139 9.1 Introduction 139 9.2 Research Background 140 9.3 Methodology 143 9.4 Results 145 9.5 Discussion and Conclusions 151 9.6 References 153 10 Combining Methods for the Design of Digital Services in Practice: Experiences from a Predictive Costing Service 156 10.1 Introduction 156 10.2 Conceptual Foundation 157 10.3 Preparing the Action Design Research Project 158 10.4 Application and Evaluation of Methods 160 10.5 Discussion and Formalization of Learning 167 10.6 Conclusion 169 10.7 References 170 11 Modelling of a Smart Service for Consumables Replenishment: A Life Cycle Perspective 171 11.1 Introduction 171 11.2 Life Cycles of Smart Services 173 11.3 Case Study 178 11.4 Discussion of the Modelling Approach 185 11.5 Conclusion and Outlook 187 11.6 References 188 12 Design-integrated Financial Assessment of Smart Services 192 12.1 Introduction 192 12.2 Problem Analysis 195 12.3 Meta-Model Design 200 12.4 Application of the Meta-Model in a Tool Prototype 204 12.5 Evaluation 206 12.6 Discussion 208 12.7 Conclusions 209 12.8 References 211 13 Towards a Cost-Benefit-Analysis of Data-Driven Business Models 215 13.1 Introduction 215 13.2 Conceptual Foundation 216 13.3 Methodology 218 13.4 Case Analysis 220 13.5 A Cost-Benefit-Analysis Model for DDBM 222 13.6 Conclusion and Outlook 225 13.7 References 226 14 Enabling Design-integrated Assessment of Service Business Models Through Factor Refinement 228 14.1 Introduction 228 14.2 Related Work 229 14.3 Research Goal and Method 230 14.4 Solution Design 231 14.5 Demonstration 234 14.6 Discussion 235 14.7 Conclusion 236 14.8 References 23

Un framework para el despliegue y evaluación de procesos software

La Ingeniería de Procesos Software promueve la producción sistemática de software mediante el seguimiento de una serie de procesos bien definidos. Una gestión integral de dichos procesos implica el desarrollo de una serie de actividades como son el diseño de los modelos de procesos, la verificación, la validación, el despliegue y la posterior evaluación. El consorcio OMG publicó el estándar Software Process Engineering Metamodel (SPEM), un lenguaje destinado a facilitar y potenciar el entendimiento, la reutilización y la mejora de los procesos software. Después de realizar una revisión de la literatura con respecto a los usos del lenguaje, se pudieron extraer diversas conclusiones. La más importante es que el estándar ha tenido poca aceptación en la industria, en parte debido a la propia complejidad del lenguaje, a ciertas carencias existentes en aspectos como la gestión de la variabilidad de los procesos y su ejecutabilidad, y la falta de mecanismos para la automatización del despliegue sobre herramientas de soporte. Además, la evaluación de los procesos software es una actividad manual y su automatización requiere mejorar considerablemente la interoperabilidad entre las herramientas de apoyo a la producción y gestión del software. Con los objetivos de minimizar los tiempos requeridos para adaptar las herramientas al comenzar cada nuevo proyecto y disminuir la complejidad técnica existente a la hora de construir mecanismos para automatizar la evaluación, se presenta Software Process Deployment & Evaluation Framework (SPDEF), un marco de trabajo para el despliegue y evaluación de procesos software. Este marco de trabajo se basa en la aplicación de las técnicas de la Ingeniería del Software dirigida por modelos y de la integración de información mediante datos abiertos enlazados. Utilizando las primeras, se consigue la adaptación semi-automática de las herramientas de soporte mediante la transformación sucesiva de modelos, partiendo desde el modelo de procesos. Con los datos abiertos enlazados, se consigue que las herramientas expongan de manera controlada la información que gestionan, para así facilitar la construcción de soluciones de integración destinadas a la evaluación de los procesos. El framework incluye, además de un método sistemático para el despliegue y evaluación, un conjunto de modelos y relaciones, así como una serie de herramientas de apoyo. Para la evaluación del framework se han desarrollado dos casos de estudio consistentes en el despliegue de la metodología OpenUP sobre herramientas de soporte y en la evaluación de competencias en recursos de personal implicados en los procesos software. Además, se presenta un escenario detallado de integración para ilustrar cómo es posible automatizar las revisiones técnicas de calidad sobre los proyectos de desarrollo o mantenimiento de software

Adaptivity engineering : Modeling and quality assurance for self-adaptive software systems

Moderne Softwareentwicklung nutzt Techniken der Selbstadaptation, um Wartung von Softwaresystemen zu automatisieren und diese somit flexibler und robuster zu gestalten. Allerdings führt die Einführung solcher Techniken zu größeren und komplizierten Softwareentwürfen. Die Konsequenz sind Fehler im Entwurf. In der Literatur werden konstruktive Methoden wie MDE oder Patterns und analytische Methoden wie Testen oder Model Checking vorgeschlagen, um das Komplexitätsproblem zu verringern. Allerdings werden die Techniken der Selbstadaption von solchen Methoden bisher noch wenig unterstützt, d.h. dass es wenige integrierte Ansätze für die explizite Modellierung und Qualitätssicherung von Selbstadaptation gibt. In dieser Arbeit schlagen wir einen integrierten Modellierungs- und Qualitätssicherungsansatz für den Entwurf selbstadaptiver Softwaresysteme vor. Es werden sowohl konstruktive Methoden (z.B. Sprachen) als auch analytische Methoden (z.B. Model Checking) für die Unterstützung der Entwicklung solcher Systeme vorgeschlagen. Beide Typen von Methoden sind in Standardtechniken und Werkzeuge integriert. Im Ergebnis wird der Entwickler in der Modellierung selbstadaptiver Softwaresysteme durch den Einsatz von adaptionsspezifischen Sprachen unterstützt. Durch die dazu passenden Qualitätssicherungsverfahren erhält der Entwickler unmittelbare Rückmeldung über die Qualität seiner Modelle. Somit wird die Entwicklung selbstadaptiver Systeme bereits in frühen Phasen des Entwicklungsprozesses unterstützt, Entwurfsfehler werden vermieden und somit bessere Software gebaut.Modern software engineering introduces self-adaptivity features to perform automatic maintenance and make software systems more flexible and resilient. Unfortunately, introducing the additional self-adaptivity features makes software design bloated and complicated. As a consequence, software design models are often prone to errors. The literature proposes constructive approaches such as MDE, patterns, etc. as well as analytical approaches such as testing or model checking to solve the problem of complexity in general. However, there is no sufficient adaptivity-specific support throughout the engineering process, i.e. no approaches that support the creation of self-adaptivity specification models and their quality assurance. In this thesis, we will propose an integrated modeling and quality assurance environment for designing self-adaptive software systems. Therefore, we will propose constructive methods (e.g., languages) and analytical methods (e.g., model-checking) to support the engineering of these systems. Both types of methods are integrated into standard software engineering techniques and tools. As a result, the designer is supported in modeling self-adaptive software systems using concern-specific languages and receives immediate feedback about the quality of his models. This way, software engineering for self-adaptive systems is getting supported starting at the early design phase leading to less errors produced, and thus, to better software, overall.Tag der Verteidigung: 26.09.2013Paderborn, Univ., Diss., 201