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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.
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.
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.
Modern browsers are digital software platforms, as they allow third parties to extend functionality by providing extensions. In a highly competitive environment, differentiation through provided functionality is a key factor for browser platforms. As the development of browsers progress, new functions are constantly being released. Browsers could thus enter complementary markets by adding functionality previously provided by third-party extensions, which is referred to as ‘platform coring’. Previous studies have missed the perspective of the parties involved. To address this gap, we conducted interviews with third-party and core developers in the security and privacy domain from Firefox and Chrome. This study provides three contributions. First, insights into stakeholder-specific issues concerning coring. Second, measures to prevent coring. Third, strategical guidance for developers and owners. Third-party vendors experienced and core developers confirmed that coring occurs on browser platforms. While developers with extrinsic motivations assess coring negatively, developers with intrinsic motivations perceive coring positively.
Industry 4.0, i.e. the connection of cyber-physical systems via the Internet in production and logistics, leads to considerable changes in the socio-technical system of the factory. The effects range from a considerable need for further training, which is exacerbated by the current shortage of skilled workers, to an opening of the previously inaccessible boundaries of the factory to third-party access, an increasing merging of office IT and manufacturing IT, and a new understanding of what machines can do with their data. This results in new requirements for the modeling, analysis and design of information processing and performance mapping business processes.
In the past, procedures were developed under the name of “process-oriented knowledge management” with which the exchange and use of knowledge in business processes could be represented, analyzed and improved. However, these approaches were limited to the office environment. A method that makes it possible to document, analyze and jointly optimize the new possibilities of knowledge processing by using artificial intelligence and machine learning in production and logistics in the same way and in a manner compatible with the approach in the office environment does not exist so far. The extension of the modeling language KMDL, which is described in this paper, will contribute to close this research gap.
This paper describes first approaches for an analysis and design method for a knowledge management integrating man and machine in the age of Industry 4.0.
Faced with the triad of time-cost-quality, the realization of knowledge-intensive tasks at economic conditions is not trivial. Since the number of knowledge-intensive processes is increasing more and more nowadays, the efficient design of knowledge transfers at business processes as well as the target-oriented improvement of them is essential, so that process outcomes satisfy high quality criteria and economic requirements. This particularly challenges knowledge management, aiming for the assignment of ideal manifestations of influence factors on knowledge transfers to a certain task. Faced with first attempts of knowledge transfer-based process improvements [1], this paper continues research about the quantitative examination of knowledge transfers and presents a ready-to-go experiment design that is able to examine quality of knowledge transfers empirically and is suitable to examine knowledge transfers on a quantitative level. Its use is proven by the example of four influence factors, which namely are stickiness, complexity, competence and time pressure.
Since more and more business tasks are enabled by Artificial Intelligence (AI)-based techniques, the number of knowledge-intensive tasks increase as trivial tasks can be automated and non-trivial tasks demand human-machine interactions. With this, challenges regarding the management of knowledge workers and machines rise [9]. Furthermore, knowledge workers experience time pressure, which can lead to a decrease in output quality. Artificial Intelligence-based systems (AIS) have the potential to assist human workers in knowledge-intensive work. By providing a domain-specific language, contextual and situational awareness as well as their process embedding can be specified, which enables the management of human and AIS to ease knowledge transfer in a way that process time, cost and quality are improved significantly. This contribution outlines a framework to designing these systems and accounts for their implementation.
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.
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.
It’s personal
(2021)
The new technologies of the Fourth Industrial Revolution (4IR) are disrupting traditional models of work and learning. While the impact of digitalization on education was already a point of serious deliberation, the COVID-19 pandemic has expedited ongoing transitions. With 90% of the world’s student population having been impacted by national lockdowns—online learning has gone from being a luxury to a necessity, in a context where around 3.6 billion people are offline. As the impacts of the 4IR unfold alongside the current crisis, it is not enough for future policy pathways to prioritize educational attainment in the traditional sense; it is essential to reimagine education itself as well as its delivery entirely. Future policy narratives will need to evaluate the very process of learning and identify the ways in which technology can help reduce existing disparities and enhance digital access, literacy and fluency in a scalable manner. In this context, this chapter analyses the status quo of online learning in India and Germany. Drawing on the experiences of these two economies with distinct trajectories of digitalization, the chapter explores how new technologies intersect with traditional education and local sociocultural conditions. Further, the limitations and opportunities presented by dominant ed-tech models is critically analyzed against the ongoing COVID-19 pandemic.
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.
Yes, we can (?)
(2021)
The COVID-19 crisis has caused an extreme situation for higher education institutions around the world, where exclusively virtual teaching and learning has become obligatory rather than an additional supporting feature. This has created opportunities to explore the potential and limitations of virtual learning formats. This paper presents four theses on virtual classroom teaching and learning that are discussed critically. We use existing theoretical insights extended by empirical evidence from a survey of more than 850 students on acceptance, expectations, and attitudes regarding the positive and negative aspects of virtual teaching. The survey responses were gathered from students at different universities during the first completely digital semester (Spring-Summer 2020) in Germany. We discuss similarities and differences between the subjects being studied and highlight the advantages and disadvantages of virtual teaching and learning. Against the background of existing theory and the gathered data, we emphasize the importance of social interaction, the combination of different learning formats, and thus context-sensitive hybrid learning as the learning form of the future.