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As AI technology is increasingly used in production systems, different approaches have emerged from highly decentralized small-scale AI at the edge level to centralized, cloud-based services used for higher-order optimizations. Each direction has disadvantages ranging from the lack of computational power at the edge level to the reliance on stable network connections with the centralized approach. Thus, a hybrid approach with centralized and decentralized components that possess specific abilities and interact is preferred. However, the distribution of AI capabilities leads to problems in self-adapting learning systems, as knowledgebases can diverge when no central coordination is present. Edge components will specialize in distinctive patterns (overlearn), which hampers their adaptability for different cases. Therefore, this paper aims to present a concept for a distributed interchangeable knowledge base in CPPS. The approach is based on various AI components and concepts for each participating node. A service-oriented infrastructure allows a decentralized, loosely coupled architecture of the CPPS. By exchanging knowledge bases between nodes, the overall system should become more adaptive, as each node can “forget” their present specialization.
The chapter analyses recent reforms in the multilevel system of the Länder, specifically territorial, functional and structural reforms, which represent three of the most crucial and closely interconnected reform trajectories at the subnational level. It sheds light on the variety of reform approaches pursued in the different Länder and also highlights some factors that account for these differences. The transfer of state functions to local governments is addressed as well as the restructuring of Länder administrations (e.g. abolishment of the meso level of the Länder administration and of single-purpose state agencies) and the rescaling of territorial boundaries at county and municipal levels, including a brief review of the recently failed (territorial) reforms in Eastern Germany.
Since more and more production tasks are enabled by Industry 4.0 techniques, the number of knowledge-intensive production tasks increases as trivial tasks can be automated and only non-trivial tasks demand human-machine interactions. With this, challenges regarding the competence of production workers, the complexity of tasks and stickiness of required knowledge occur [1]. Furthermore, workers experience time pressure which can lead to a decrease in output quality. Cyber-Physical Systems (CPS) have the potential to assist workers in knowledge-intensive work grounded on quantitative insights about knowledge transfer activities [2]. By providing contextual and situational awareness as well as complex classification and selection algorithms, CPS are able to ease knowledge transfer in a way that production time and quality is improved significantly. CPS have only been used for direct production and process optimization, knowledge transfers have only been regarded in assistance systems with little contextual awareness. Embedding production and knowledge transfer optimization thus show potential for further improvements. This contribution outlines the requirements and a framework to design these systems. It accounts for the relevant factors.
Helping overcome distance, the use of videoconferencing tools has surged during the pandemic. To shed light on the consequences of videoconferencing at work, this study takes a granular look at the implications of the self-view feature for meeting outcomes. Building on self-awareness research and self-regulation theory, we argue that by heightening the state of self-awareness, self-view engagement depletes participants’ mental resources and thereby can undermine online meeting outcomes. Evaluation of our theoretical model on a sample of 179 employees reveals a nuanced picture. Self-view engagement while speaking and while listening is positively associated with self-awareness, which, in turn, is negatively associated with satisfaction with meeting process, perceived productivity, and meeting enjoyment. The criticality of the communication role is put forward: looking at self while listening to other attendees has a negative direct and indirect effect on meeting outcomes; however, looking at self while speaking produces equivocal effects.
Der nutzbringenden Einsatz einer Datenbrille besteht nicht nur aus der Brille selbst. Die potenzielle ressourcenschonende Assistenz bei der Abarbeitung von komplexen Workflows bedarf eine ausreichende Integration in die Anwendungssystemlandschaft. Implikation sind demnach zwei Hauptelemente: die Brille selbst und die Integrationssoftware. Beide Komponenten sind in geeigneter Form auszulegen und auf die intendierten Anwendungsfälle zu konfigurieren. Dieser Beitrag fasst die Erfahrungen aus zahlreichen Projekten zusammen und liefert einen Überblick über die Herausforderungen bei AR-Einführungen.
Auf Basis einer Umfrage unter 300 Beschäftigten im öffentlichen Dienst untersucht dieser Beitrag, welche möglichen Auswirkungen die Digitale Transformation auf das Tätigkeitsprofil von Mitarbeiterinnen und Mitarbeitern im öffentlichen Sektor haben kann. Zum einen finden sich erste Hinweise auf signifikante Effizienzpotenziale durch Automatisierung im öffentlichen Sektor. Zum anderen wird deutlich, dass die Mitarbeiterinnen und Mitarbeiter dieser Entwicklung mehrheitlich positiv gegenüberstehen und sie aktiv an der Verbesserung von Dienstleistungen mitwirken wollen. Aus diesen Erkenntnissen können zahlreiche Handlungsimplikationen für Veränderungsprojekte in der Praxis abgeleitet werden. Gleichzeitig ruft dieser Beitrag dazu auf, die Folgen der Digitalen Transformation für Mitarbeiterinnen und Mitarbeiter noch besser zu erforschen.
Im Zuge der Digitalisierung bietet Business Analytics das Potenzial, die Budgetierung insbesondere durch eine Automatisierung von Prozessschritten der Budgetierung maßgeblich weiterzuentwickeln. Dieser Beitrag zeigt mittels einer empirischen Untersuchung den Status quo des Einsatzes von Business Analytics im Rahmen der Budgetierung in Deutschland und geht auf die Beurteilung einer Automatisierung der Budgetierung durch Unternehmen ein.
The design of qualitative, excellent teaching requires collaboration between teachers and learners. For this purpose, face-to-face teaching benefits from a long-standing tradition, while digital teaching is comparatively still at the beginning of its dissemination. A major developmental step toward the digitization of teaching was achieved in the context of university teaching during the Covid 19 pandemic in spring 2020, when face-to-face teaching was interrupted for months. During this time, important insights into the opportunities and limitations of digital teaching were gained. This paper presents selected results of a study conducted at four German universities and with 875 responses in spring 2020. The study uncovers opportunities and limitations of digital teaching from the students’ perspective and against the background of their experience in the completely digital semester. The results are used as a basis for deriving design guidelines for digital teaching and learning offerings. Based on a model for analyzing the design of teaching and learning formats, these indications are structured according to the elements learners, teachers, teaching content, environment and teaching style.
Chinas neuer langer Marsch
(2021)
Choice-Based Conjointanalyse
(2021)
Die auswahlbasierte oder auch Choice-Based Conjointanalyse (CBC) ist die derzeit wohl beliebteste Variante der Conjointanalyse. Gründe dafür bestehen einerseits in der leichten Verfügbarkeit benutzerfreundlicher Software (z.B. R, Sawtooth Software), andererseits weist das Verfahren aufgrund seiner Sonderstellung auch aus methodischer sowie praktischer Sicht Stärken auf. So werden bei einer CBC im Gegensatz zur bewertungsbasierten Conjointanalyse keine Präferenzurteile, sondern diskrete Entscheidungen der Auskunftspersonen erhoben und ausgewertet. Bei der CBC handelt es sich also genau genommen um eine Discrete Choice Analyse (DCA), die auf ein conjointanalytisches Erhebungsdesign angewandt wird. Beide Bezeichnungen werden nach wie vor verwendet, die Methodik wird in diesem Kapitel grundlegend und anhand eines Anwendungsbeispiels diskutiert.
The German system of public sector employment (including civil servants and public employees) qualifies as a classical European continental civil service model moulded in traditional forms of a Weberian bureaucracy. Its features include a career-based employment system with entry based on levels of formal qualification. Coordinated by legal frames and centralised collective bargaining, the civil service is, at the same time, decentralised and flexible enough to accommodate regional differences and societal changes. In comparison, the civil service system stands out for its high degrees of professionalism and legal fairness with low levels of corruption or cronyism.
ControlCenter 4.0
(2021)
Today’s mobile devices are part of powerful business ecosystems, which usually involve digital platforms. To better understand the complex phenomenon of coring and related dynamics, this paper presents a case study comparing iMessage as part of Apple’s iOS and WhatsApp. Specifically, it investigates activities regarding platform coring, as the integration of several functionalities provided by third-party applications in the platform core. The paper makes three contributions. First, a systematization of coring activities is developed. Coring modes are differentiated by the amount of coring and application maintenance. Second, the case study revealed that the phenomenon of platform coring is present on digital platforms for mobile devices. Third, the fundamentals of coring are discussed as a first step towards theoretical development. Even though coring constitutes a potential threat for third-party developers regarding their functional differentiation, an idea of what a beneficial partnership incorporating coring activities could look like is developed here.
Die Digitalisierung des deutschen Mittelstandes schreitet weiterhin schleppend voran. So verfügt zwar ein wachsender Teil dieser Unternehmen über vereinzelte Informations- und Kommunikationssysteme, die zielführende Vernetzung und Integration dieser Systeme stellt jedoch weiterhin eine große Aufgabe dar [1]. Besonders vor dem Hintergrund wachsender Bedürfnisse für Informationen und Transparenz sehen sich Unternehmen zunehmend mit der analyseorientierten Nutzbarmachung der Unternehmensdaten konfrontiert [2].
The digitalization of value networks holds out the prospect of many advantages for the participating compa- nies. Utilizing information platforms, cross-company data exchange enables increased efficiency of collab- oration and offers space for new business models and services. In addition to the technological challenges, the fear of know-how leakage appears to be a significant roadblock that hinders the beneficial realization of new business models in digital ecosystems. This paper provides the necessary building blocks of digital participation and, in particular, classifies the issue of trust creation within it as a significant success factor. Based on these findings, it presents a solution concept that, by linking the identified building blocks, offers the individual actors of the digital value network the opportunity to retain sovereignty over their data and know-how and to use the potential of extensive networking. In particular, the presented concept takes into account the relevant dilemma, that every actor (e. g. the machine users) has to be able to control his commu- nicated data at any time and have sufficient possibilities for intervention that, on the one hand, satisfy the need for protection of his knowledge and, on the other hand, do not excessively diminish the benefits of the system or the business. Taking up this perspective, this paper introduces dedicated data sovereignty and shows a possible implementation concept.
Increasingly fast development cycles and individualized products pose major challenges for today's smart production systems in times of industry 4.0. The systems must be flexible and continuously adapt to changing conditions while still guaranteeing high throughputs and robustness against external disruptions. Deep reinforcement learning (RL) algorithms, which already reached impressive success with Google DeepMind's AlphaGo, are increasingly transferred to production systems to meet related requirements. Unlike supervised and unsupervised machine learning techniques, deep RL algorithms learn based on recently collected sensorand process-data in direct interaction with the environment and are able to perform decisions in real-time. As such, deep RL algorithms seem promising given their potential to provide decision support in complex environments, as production systems, and simultaneously adapt to changing circumstances. While different use-cases for deep RL emerged, a structured overview and integration of findings on their application are missing. To address this gap, this contribution provides a systematic literature review of existing deep RL applications in the field of production planning and control as well as production logistics. From a performance perspective, it became evident that deep RL can beat heuristics significantly in their overall performance and provides superior solutions to various industrial use-cases. Nevertheless, safety and reliability concerns must be overcome before the widespread use of deep RL is possible which presumes more intensive testing of deep RL in real world applications besides the already ongoing intensive simulations.
Increasingly fast development cycles and individualized products pose major challenges for today's smart production systems in times of industry 4.0. The systems must be flexible and continuously adapt to changing conditions while still guaranteeing high throughputs and robustness against external disruptions. Deep rein- forcement learning (RL) algorithms, which already reached impressive success with Google DeepMind's AlphaGo, are increasingly transferred to production systems to meet related requirements. Unlike supervised and unsupervised machine learning techniques, deep RL algorithms learn based on recently collected sensor- and process-data in direct interaction with the environment and are able to perform decisions in real-time. As such, deep RL algorithms seem promising given their potential to provide decision support in complex environments, as production systems, and simultaneously adapt to changing circumstances. While different use-cases for deep RL emerged, a structured overview and integration of findings on their application are missing. To address this gap, this contribution provides a systematic literature review of existing deep RL applications in the field of production planning and control as well as production logistics. From a performance perspective, it became evident that deep RL can beat heuristics significantly in their overall performance and provides superior solutions to various industrial use-cases. Nevertheless, safety and reliability concerns must be overcome before the widespread use of deep RL is possible which presumes more intensive testing of deep RL in real world applications besides the already ongoing intensive simulations.
Shortening product development cycles and fully customizable products pose major challenges for production systems. These not only have to cope with an increased product diversity but also enable high throughputs and provide a high adaptability and robustness to process variations and unforeseen incidents. To overcome these challenges, deep Reinforcement Learning (RL) has been increasingly applied for the optimization of production systems. Unlike other machine learning methods, deep RL operates on recently collected sensor-data in direct interaction with its environment and enables real-time responses to system changes. Although deep RL is already being deployed in production systems, a systematic review of the results has not yet been established. The main contribution of this paper is to provide researchers and practitioners an overview of applications and to motivate further implementations and research of deep RL supported production systems. Findings reveal that deep RL is applied in a variety of production domains, contributing to data-driven and flexible processes. In most applications, conventional methods were outperformed and implementation efforts or dependence on human experience were reduced. Nevertheless, future research must focus more on transferring the findings to real-world systems to analyze safety aspects and demonstrate reliability under prevailing conditions.
Den Austausch fördern
(2021)
While a growing body of literature finds positive impacts of Start-Up Subsidies (SUS) on labor market outcomes of participants, little is known about how the design of these programs shapes their effectiveness and hence how to improve policy. As experimental variation in program design is unavailable, we exploit the 2011 reform of the current German SUS program for the unemployed which strengthened caseworkers' discretionary power, increased entry requirements and reduced monetary support. We estimate the impact of the reform on the program's effectiveness using samples of participants and non-participants from before and after the reform. To control for time-constant unobserved heterogeneity as well as differential selection patterns based on observable characteristics over time, we combine Difference-in-Differences with inverse probability weighting using covariate balancing propensity scores. Holding participants' observed characteristics as well as macroeconomic conditions constant, the results suggest that the reform was successful in raising employment effects on average. As these findings may be contaminated by changes in selection patterns based on unobserved characteristics, we assess our results using simulation-based sensitivity analyses and find that our estimates are highly robust to changes in unobserved characteristics. Hence, the reform most likely had a positive impact on the effectiveness of the program, suggesting that increasing entry requirements and reducing support increased the program's impacts while reducing the cost per participant. (C) 2021 Economic Society of Australia, Queensland. Published by Elsevier B.V. All rights reserved.
As the complexity of learning task requirements, computer infrastruc- tures and knowledge acquisition for artificial neuronal networks (ANN) is in- creasing, it is challenging to talk about ANN without creating misunderstandings. An efficient, transparent and failure-free design of learning tasks by models is not supported by any tool at all. For this purpose, particular the consideration of data, information and knowledge on the base of an integration with knowledge- intensive business process models and a process-oriented knowledge manage- ment are attractive. With the aim of making the design of learning tasks express- ible by models, this paper proposes a graphical modeling language called Neu- ronal Training Modeling Language (NTML), which allows the repetitive use of learning designs. An example ANN project of AI-based dynamic GUI adaptation exemplifies its use as a first demonstration.
As the focus on digital transformation and its unexplored opportunities is prospering, consulting firms have also turned their attention to it. Despite this increased attention, digital transformation project failure rate remains high, thereby reaffirming the “IT productivity paradox” or “AI productivity paradox. The purpose of this paper is to holistically scrutinize the current approach of digital transformation consulting. A deductive qualitative study draws upon the Human Technology Performance model to elucidate papers on digital transformation published by four major management consulting firms in 2014-2019. Whereas the focus on customer-centricity and some measures of corporate performance is prominent in the consulting approaches, the study results have revealed several “blind spots” that are still either neglected or poorly covered. Three main blind spots are particularly prominent from the analysis. First of all, consulting firms often apply a “one size fits all” approach, thereby neglecting contextual factors, such as age, size, or industry. Secondly, they neglect process level in the return on investment of IT. Thirdly, the suitability of IS development methods remains poorly considered. Hence, the paper argues that, while consulting firms purport to support digital transformation exigences and efforts, they, in fact, adhere to traditional approaches to business consulting.
Digitizing grocery retailing
(2021)
Multiple emerging technologies both threaten grocers and offer them attractive opportunities to enhance their value propositions, improve processes, reduce costs, and therefore generate competitive advantages. Among the variety of technological innovations and considering the scarcity of resources, it is unclear which technologies to focus on and where to implement them in the value chain. To develop the most probable technology forecast that addresses the application of emerging technologies in the grocery value chain within the current decade, we conduct a two-stage Delphi study. Our results suggest a high relevance of almost all technologies. The panel is only skeptical about three specific projections. As a consequence, grocers are advised to build up knowledge regarding the application of these technologies in the most promising areas of their value chain.
Um in der digitalisierten Wirtschaft mitzuspielen, müssen Unternehmen, Markt und insbesondere Kunden detailliert verstanden werden. Neben den „Big Playern“ aus dem Silicon Valley sieht der deutsche Mittelstand, der zu großen Teilen noch auf gewachsenen IT-Infrastrukturen und Prozessen agiert, oft alt aus. Um in den nächsten Jahren nicht gänzlich abgehängt zu werden, ist ein Umbruch notwendig. Sowohl Leistungserstellungsprozesse als auch Leistungsangebot müssen transparent und datenbasiert ausgerichtet werden. Nur so können Geschäftsvorfälle, das Marktgeschehen sowie Handeln der Akteure integrativ bewertet und fundierte Entscheidungen getroffen werden. In diesem Beitrag wird das Konzept der Data-Driven Organization vorgestellt und aufgezeigt, wie Unternehmen den eigenen Analyticsreifegrad ermitteln und in einem iterativen Transformationsprozess steigern können.
Software platforms regularly introduce new features to remain competitive. While platform innovation is considered to be a critical success factor, adding certain features could hurt the ecosystem. If platform owners provide functionality that was previously provided by a contributor, the owners enter complementary product spaces. Complementary market entry frequently occurs on software platforms and is a major concern for third-party developers.
Divergent findings on the impact of complementary market entry call for the consideration of additional factors. As prior research neglected the third-party perspective, this contribution aims to address this gap. We explore the use of measures to prevent complementary market entry using a survey approach on browser platforms. The research model is tested with 655 responses among developer from Mozilla Firefox and Google Chrome. To explain countermeasures employment, developer’s attitude and perceived likelihood are important. The results reveal that developers employ countermeasures if complementary market entry is assessed negatively and perceived as likely for their extension. Differences among browser platforms concerning complementary market entry are identified. Product spaces of extensions being available on multiple platforms are less likely to be entered and more heavily protected. Implications for research and stakeholders, i.e. platform owners and contributors are discussed.
ERP-Systeme
(2021)
Perfectionism is a personality disposition characterized by setting extremely high performance-standards coupled with critical self-evaluations. Often conceived as positive, perfectionism can yield not only beneficial but also deleterious outcomes ranging from anxiety to burnout. In this proposal, we set out to investigate the role of the technology and, particularly, social media in individuals’ strivings for perfection. We lay down theoretical bases for the possibility that social media plays a role in the development of perfectionism. To empirically test the hypothesized relationship, we propose a comprehensive study design based on the experience sampling method. Lastly, we provide an overview of the planned analysis and future steps.
Already successfully used products or designs, past projects or our own experiences can be the basis for the development of new products. As reference products or existing knowledge, it is reused in the development process and across generations of products. Since further, products are developed in cooperation, the development of new product generations is characterized by knowledge-intensive processes in which information and knowledge are exchanged between different kinds of knowledge carriers. The particular knowledge transfer here describes the identification of knowledge, its transmission from the knowledge carrier to the knowledge receiver, and its application by the knowledge receiver, which includes embodied knowledge of physical products. Initial empirical findings of the quantitative effects regarding the speed of knowledge transfers already have been examined. However, the factors influencing the quality of knowledge transfer to increase the efficiency and effectiveness of knowledge transfer in product development have not yet been examined empirically. Therefore, this paper prepares an experimental setting for the empirical investigation of the quality of knowledge transfers.
Digitization and demographic change are enormous challenges for companies. Learning factories as innovative learning places can help prepare older employees for the digital change but must be designed and configured based on their specific learning requirements. To date, however, there are no particular recommendations to ensure effective age-appropriate training of bluecollar workers in learning factories. Therefore, based on a literature review, design characteristics and attributes of learning factories and learning requirements of older employees are presented. Furthermore, didactical recommendations for realizing age-appropriate learning designs in learning factories and a conceptualized scenario are outlined by synthesizing the findings.
The increasing demand for software engineers cannot completely be fulfilled by university education and conventional training approaches due to limited capacities. Accordingly, an alternative approach is necessary where potential software engineers are being educated in software engineering skills using new methods. We suggest micro tasks combined with theoretical lessons to overcome existing skill deficits and acquire fast trainable capabilities. This paper addresses the gap between demand and supply of software engineers by introducing an actionoriented and scenario-based didactical approach, which enables non-computer scientists to code. Therein, the learning content is provided in small tasks and embedded in learning factory scenarios. Therefore, different requirements for software engineers from the market side and from an academic viewpoint are analyzed and synthesized into an integrated, yet condensed skills catalogue. This enables the development of training and education units that focus on the most important skills demanded on the market. To achieve this objective, individual learning scenarios are developed. Of course, proper basic skills in coding cannot be learned over night but software programming is also no sorcery.
Software platforms allow for the extension of features by third-party contributors. Thereby, platform innovation is an important aspects of platforms attractiveness for users and complementors. While previous research focused the introduction of new features, the aspect of feature removal and discontinued features on software platforms has been disregarded. To explore the phenomenon and motivations for feature removal on software platforms, a review of recent literature is provided. To illustrate the existence of and motivations for feature removal, a case study of the browser platform Mozilla Firefox is presented. The results reveal feature removal to regularly occur on browser platforms for user- and developer-related features. Frequent reasons for feature removal involve unused features, security concerns, and bugs. Related motivations for feature removal are discussed from the platform owner's perspective. Implications for complementors and users are highlighted.
Federal Administration
(2021)
The federal administration is significantly small (around 10 percent of all public employees). This speciality of the German administrative system is based on the division of responsibilities: the central (federal) level drafts and adopts most of the laws and public programmes, and the state level (together with the municipal level) implements them. The administration of the federal level comprises the ministries, subordinated agencies for special and selected operational tasks (e.g. the authorisation of drugs, information security and registration of refugees) in distinct administrative sectors (e.g. foreign service, armed forces and federal police). The capacity for preparing and monitoring government bills and statutory instruments is well developed. Moreover, the instruments and tools of coordination are exemplary compared with other countries, although the recent digital turn has been adopted less advanced than elsewhere.
Research on corporate entrepreneurship—venturing activities by established corporations—has received increasing scholarly attention. We employ bibliometric methods to analyze the literature on corporate entrepreneurship published over the last five decades. Based on the results of citation and co-citation analyses, we reveal central works in the field and how they are interconnected. We investigate the underlying intellectual structure of the field. Our findings provide evidence of the growing maturity and interdisciplinarity of corporate entrepreneurship and provide insight into research themes. We find that resource-based view and its extensions still remain the predominant theoretical perspectives in the field. Drawing on these findings, we suggest directions for future research.
Research on corporate entrepreneurship—venturing activities by established corporations—has received increasing scholarly attention. We employ bibliometric methods to analyze the literature on corporate entrepreneurship published over the last five decades. Based on the results of citation and co-citation analyses, we reveal central works in the field and how they are interconnected. We investigate the underlying intellectual structure of the field. Our findings provide evidence of the growing maturity and interdisciplinarity of corporate entrepreneurship and provide insight into research themes. We find that resource-based view and its extensions still remain the predominant theoretical perspectives in the field. Drawing on these findings, we suggest directions for future research.
Damit die EU ihre ambitionierten Klimaschutzziele erreichen kann, werden die Preise für Treibhausgasemissionen in den nächsten Jahren spürbar steigen. Das hat ökonomische Auswirkungen für die EU-Mitgliedsländer, aber auch den Rest der Welt. Einzelne Sektoren und auch Volkswirtschaften werden davon unterschiedlich stark getroffen.
From employee to expert
(2021)
In the context of the collaborative project Ageing-appropriate, process-oriented and interactive further training in SME (API-KMU), innovative solutions for the challenges of demographic change and digitalisation are being developed for SMEs. To this end, an approach to age-appropriate training will be designed with the help of AR technology. In times of the corona pandemic, a special research design is necessary for the initial survey of the current state in the companies, which will be systematically elaborated in this paper. The results of the previous methodological considerations illustrate the necessity of a mix of methods to generate a deeper insight into the work processes. Video-based retrospective interviews seem to be a suitable instrument to adequately capture the employees' interpretative perspectives on their work activities. In conclusion, the paper identifies specific challenges, such as creating acceptance among employees, open questions, e.g., how a transfer or generalization of the results can succeed, and hypotheses that will have to be tested in the further course of the research process.
Future ERP Systems
(2021)
This paper presents a research agenda on the current generation of ERP systems which was developed based on a literature review on current problems of ERP systems. The problems are presented following the ERP life cycle. In the next step, the identified problems are mapped on a reference architecture model of ERP systems that is an extension of the three-tier architecture model that is widely used in practice. The research agenda is structured according to the reference architecture model and addresses the problems identified regarding data, infrastructure, adaptation, processes, and user interface layer.
The idea of the continuous improvement process (CIP) helps companies to continuously improve their operation and thereby contributes to their competitiveness. Through digi tization, new potentials emerge to solve known CIP issues. This contribution specifically addresses the individual motivation of employees to contribute to the CIP. Typically, related initiatives lack contributions over time. The use of gamification is a promising way to achieve continuous participation by addressing the individual needs of participants. While the use of extrinsic motivation elements is common in practice, the idea of this approach is to specifically address intrinsic motivations which serve as a long-term motivator. This article contributes to a gam-ification concept for the continuous improvement process. The main results include an adapted CIP, a gamification concept, and a market mechanism. Furthermore, the concept is implemented and demonstrated as a prototype in an online platform.
German Public Administration
(2021)
The international community of public administration and administrative sciences shows a great interest in the basic features of the German administrative system. The German public administration with its formative decentralisation (called: administrative federalism) is regarded as a prime example of multilevel governance and strong local self-government. Furthermore, over the past decades, the traditional profile of the German administrative system has significantly been reshaped and remoulded through reforms, processes of modernisation and the transformation process in East Germany. Studies on the German administrative system should focus especially on
key institutional features of public administration;
changing relationships between public administration, society and the private sector;
administrative reforms at different levels of the federal system; and
new challenges and modernisation approaches, such as digitalisation, open government and better regulation.
Government as a platform?
(2021)
Digital platforms, by their design, allow the coordination of multiple entities to achieve a common goal. Motivated by the success of platforms in the private sector, they increasingly receive attention in the public sector. However, different understandings of the platform concept prevail. To guide the development and further research a coherent understanding is required. To address this gap, we identify the constitutive elements of platforms in the public sector. Moreover, their potential to coordinate partially autonomous entities as typical for federal organized states is highlighted.
This study contributes through a uniform understanding of public service platforms. Despite constitutive elements, the proposed framework for platforms in the public sector may guide future analysis. The analysis framework is applied to platforms of federal states in the European Union.
Human resource management (HRM) reform has not been the focus of attention in Germany despite its obvious relevance for effective policy implementation. Although there is a general trend worldwide towards convergence between public and private HRM strategies and practices, management of the workforce in German public administration still remains largely traditional and bureaucratic. This chapter describes and analyses German practices regarding the central functions and elements of HRM such as planning, recruitment, training and leadership. Furthermore, it explores the importance and contribution of public service motivation, performance-related pay and diversity management in the context of German practices. The chapter concludes by highlighting some of the major paradoxes of German public HRM in light of current challenges, such as demographic change, digital transformation and organisational development capabilities.
Firms increasingly use ideas from online innovation communities to solve problems or to better address customer needs. However, in many cases the number of submitted ideas has exploded, it leads to an information overload that firms hardly can handle considering their limited cognitive resources. Therefore, we use the Elaboration Likelihood Model to distinguish between the quick and lean idea preselection process as a peripheral route of information processing and the subsequent idea review process as a central route of information processing. In our empirical study with a sample of more than 163,000 ideas collected from the Xiaomi MIUI community, we analyse influencing factors that increase the likelihood of ideas being preselected or reviewed. Results show that user status, user initiative contribution, and community recognition have a significantly positive influence on idea preselction, whereas user response contribution has no influence. Idea presentation characteristics have an inverted U-curve relationship with idea adoption. Community absorptive capacity has a moderate effect on the curvilinear relationship between idea description length and idea adoption.
Indirect rebound effects on the consumer level occur when potential greenhouse gas emission savings from the usage of more efficient technologies or more sufficient consumption in one consumption area are partially or fully offset through the consumers’ adverse behavioral responses in other areas. As both economic (e.g., price effects) and psychological (e.g., moral licensing) mechanisms can stimulate these indirect rebound effects, they have been studied in different fields, including economics, industrial ecology, psychology, and consumer research. Consequently, the literature is highly fragmented and disordered. To integrate the body of knowledge for an interdisciplinary audience, we review and summarize the previous literature, covering the microeconomic quantification of indirect rebounds based on observed expenditure behavior and the psychological processes underlying indirect rebounds. The literature review reveals that economic quantifications and psychological processes of indirect rebound effects have not yet been jointly analyzed. We derive directions for future studies, calling for a holistic research agenda that integrates economic and psychological mechanisms.
Das Angebot digitaler Plattformen ist mittlerweile auch im Maschinen- und Anlagenbau weit verbreitet. Dabei konnte in den letzten Jahren der Trend verzeichnet werden, dass die Herstellerunternehmen von Maschinen und Anlagen nicht mehr ausschließlich physische Produkte veräußern, sondern zusätzliche auf das Produkt abgestimmte Dienstleistungen, wie bspw. digitale Services. Dieser Wandel kann einen großen Einfluss auf die Veränderung des Geschäftsmodells haben und je nach Komplexität der digitalen Plattformen unterschiedliche Ausmaße annehmen, die auch strategische Entscheidungen bestimmen können. In diesem Beitrag wird eine Klassifizierung der digitalen Plattformen im deutschen Maschinen- und Anlagenbau vorgenommen, mithilfe derer unterschiedliche Plattformtypen auf Grundlage ihrer Funktionszusammensetzung identifiziert werden. Demnach können bspw. Plattformen, über die lediglich grundlegende Funktionen wie die Verwaltung von Maschinen angeboten werden, von umfangreicheren Plattformen unterschieden werden, die eine höhere Komplexität aufweisen und somit einen größeren Einfluss auf die Veränderung des Geschäftsmodells haben. Diese Einteilung unterschiedlicher Plattformtypen kann Unternehmen im Maschinen- und Anlagenbau dabei unterstützen, strategische Entscheidungen bezüglich der Entwicklung und des Angebots digitaler Plattformen zu treffen und eine Einordnung ihrer digitalen Plattform im Wettbewerb vorzunehmen.
Das Angebot digitaler Plattformen ist mittlerweile auch im Maschinen- und Anlagenbau weit verbreitet. Dabei konnte in den letzten Jahren der Trend verzeichnet werden, dass die Herstellerunternehmen von Maschinen und An- lagen nicht mehr ausschließlich physische Produkte veräußern, sondern zusätzliche auf das Produkt abgestimmte Dienstleistungen, wie bspw. digitale Services. Dieser Wandel kann einen großen Einfluss auf die Veränderung des Geschäftsmodells ha- ben und je nach Komplexität der digitalen Plattformen unterschiedliche Ausmaße annehmen, die auch strategische Entscheidungen bestimmen können. In diesem Bei- trag wird eine Klassifizierung der digitalen Plattformen im deutschen Maschinen- und Anlagenbau vorgenommen, mithilfe derer unterschiedliche Plattformtypen auf Grundlage ihrer Funktionszusammensetzung identifiziert werden. Demnach können bspw. Plattformen, über die lediglich grundlegende Funktionen wie die Verwaltung von Maschinen angeboten werden, von umfangreicheren Plattformen unterschieden werden, die eine höhere Komplexität aufweisen und somit einen größeren Einfluss auf die Veränderung des Geschäftsmodells haben. Diese Einteilung unterschiedli- cher Plattformtypen kann Unternehmen im Maschinen- und Anlagenbau dabei unter- stützen, strategische Entscheidungen bezüglich der Entwicklung und des Angebots digitaler Plattformen zu treffen und eine Einordnung ihrer digitalen Plattform im Wettbewerb vorzunehmen.
In coherence with the progressive digitalization of all areas of life, the Internet of Things (IoT) is a flourishing concept in both research and practice. Due to the increasing scholarly attention, the literature landscape has become scattered and fragmented. With a focus on the commercial application of the IoT and corresponding research, we employ a co-citation analysis and literature review to structure the field. We find and describe 19 research themes. To consolidate the extant research, we propose a research framework, which is based on a theoretical implementation process of IoT as a concept, specific IoT applications, or architectures integrated in an adapted input–process–output model. The main variables of the model are an initial definition and conceptualization of an IoT concept (input), which goes through an evaluation process (process), before it is implemented and can have an impact in practice (output). The paper contributes to interdisciplinary research relating to a business and management perspective on IoT by providing a holistic overview of predominant research themes and an integrative research framework.
Introduction
(2021)